A RRU channel fault diagnosis and processing method and system

Abstract

The present invention discloses a RRU channel fault diagnosis and processing method and system, the method comprising: collecting RRU operation data, performance index data, environmental monitoring data and historical fault information to build a database; establishing a fault prediction model and training it; monitoring RRU channel data in real time through the fault prediction model, and immediately triggering an early warning when exceeding a set threshold; locating and processing the fault according to the early warning information. The present invention monitors the system status in real time and uses a fault prediction model to analyze data, and immediately triggers an early warning once a potential fault risk is detected, which not only greatly shortens the time for fault discovery, but also reduces the reliance on manual judgment through automated early warning.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a RRU channel fault diagnosis and processing method and system. Background Art

[0002] With the rapid development of communication technology and the widespread deployment of communication equipment, users have higher expectations for the service quality of communication networks. As an indispensable network element node device in modern communication networks, the Radio Remote Unit (RRU) undertakes important data transmission responsibilities. However, with the increase in network complexity and the expansion of the number of devices, RRU channel fault diagnosis and processing has become one of the key challenges to ensure stable network operation and high-quality services.

[0003] The existing technical processing flow mainly relies on manual operations, from remote reset, checking the bit error rate of the IR link, to replacing the optical module or optical fiber, to software fault analysis and hardware replacement. This series of operations is not only time-consuming but also inefficient. In addition, the complexity and uncertainty of software fault analysis further prolongs the fault handling cycle, resulting in delayed network service recovery time and affecting user experience.

[0004] Especially when a fault occurs, the first manual remote reset operation often cannot solve the problem immediately. It takes multiple attempts and a combination of multiple methods to locate the cause of the fault. This process is not only inefficient, but also requires high technical capabilities of the operation and maintenance personnel. If the fault cannot be eliminated in time, the operation and maintenance personnel must be dispatched to handle it on site, which increases the manpower and material costs, and also exposes the shortcomings of existing technologies in automated fault diagnosis and processing. Summary of the invention

[0005] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0006] In view of the above existing problems, the present invention is proposed. Therefore, the present invention provides an RRU channel fault diagnosis and processing method to solve the problem that when a fault occurs, the manual remote reset operation often cannot solve the problem immediately, and multiple attempts and a combination of multiple methods are required to locate the cause of the fault. This process is not only inefficient, but also requires high technical capabilities of the operation and maintenance personnel. In the case that the fault cannot be eliminated in time, the operation and maintenance personnel need to be dispatched for on-site processing, which increases the manpower and material costs, and also exposes the problem of the shortcomings of the existing technology in automated fault diagnosis and processing.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, the present invention provides a method for diagnosing and processing RRU channel faults, including: collecting RRU operation data, performance indicator data, environmental monitoring data and historical fault information to build a database; establishing a fault prediction model and training it; monitoring the RRU channel data in real time through the fault prediction model, and immediately triggering an early warning when the set threshold is exceeded; locating and processing the fault according to the early warning information.

[0009] As a preferred solution of the RRU channel fault diagnosis and processing method of the present invention, wherein: the operation data includes uplink signal strength, downlink signal strength, signal-to-noise ratio, RRU transmit power and RRU receive power;

[0010] The performance indicator data includes bit error rate, throughput and delay time;

[0011] The environmental monitoring data includes the temperature of the working environment, the humidity of the working environment and the degree of electromagnetic interference;

[0012] The historical fault information includes fault type, fault handling measures, fault handling time and RRU configuration records.

[0013] As a preferred solution of the RRU channel fault diagnosis and processing method of the present invention, wherein: the construction of the database includes preprocessing the collected data, including data cleaning, data standardization and feature selection;

[0014] The data cleaning includes removing or filling missing values, identifying and processing outliers, and deduplication;

[0015] Data standardization refers to converting data from different sources and scopes into a unified format.

[0016] As a preferred solution of the RRU channel fault diagnosis and processing method of the present invention, the fault prediction model expression is:

[0017]

[0018] Among them, P(f) represents the probability of fault occurrence, ranging from 0 to 1, f1(SSI, SNR) is a comprehensive signal quality function that takes into account the uplink and downlink signal strengths and the signal-to-noise ratio, f2(ENV, t)dt is an environmental factor influence function that represents the time-varying environmental factors, f3(PERF) is a weighted average function of performance indicators, H is a scoring function based on historical fault information, α, β, γ, δ are model parameters, and T represents the length of time for the investigation.

[0019] As a preferred solution of the RRU channel fault diagnosis and processing method of the present invention, the signal quality comprehensive function expression is:

[0020]

[0021] Among them, SSI up Indicates uplink signal strength, SSI down Indicates downlink signal strength, SNR is signal-to-noise ratio;

[0022] The environmental factor influence function expression is:

[0023] (f2(ENV,t)=e -(t-T) / T ·(0.5·Temp t +0.3 Humidity t +0.2 EMI t )

[0024] Among them, Temp t It represents the ambient temperature at time t, Humidity t is the relative humidity, EMI t Indicates the electromagnetic interference intensity, e -(t-T) / T The decay factor over time is taken into account.

[0025] As a preferred solution of the RRU channel fault diagnosis and processing method of the present invention, the performance index weighted average function expression is:

[0026]

[0027] Among them, BER represents bit error rate, Throughput represents network throughput, and Delay represents data transmission delay time;

[0028] The historical fault information scoring function expression is:

[0029]

[0030] Where N is the number of historical failures considered, Severity i is the severity score of the ith fault, Recency i is a decay factor with respect to time.

[0031] As a preferred solution of the RRU channel fault diagnosis and processing method of the present invention, wherein: the immediate triggering of an early warning when exceeding a set threshold is specifically:

[0032] Set two basic fault warning thresholds T1 and T2, where T1 < T2;

[0033] The thresholds T1 and T2 are dynamically adjusted according to real-time environmental data. The expression is: T′1=T1-δ(T env ), T′2=T2-δ(T env ), where δ(T env ) is an adjustment factor based on environmental conditions;

[0034] T′1 and T′2 are further adjusted based on historical fault information and RRU configuration records. The expression is: T″1=T′1-δ(T hist ), T″2=T′2-δ(T hist ), where δ(T hist ) is an adjustment factor based on historical failures;

[0035] When P(f)<T″2, the system is in normal state;

[0036] When T″1<P(f)<T″2, the primary warning is triggered and mild intervention measures are taken;

[0037] When P(f)≥T″2, an emergency warning is triggered and emergency intervention measures are taken.

[0038] In a second aspect, the present invention provides an RRU channel fault diagnosis and processing system, comprising:

[0039] Data collection module, which collects RRU operation data, performance indicator data, environmental monitoring data and historical fault information;

[0040] The data processing module pre-processes the collected data, including data cleaning, data standardization, and feature selection;

[0041] The fault prediction module monitors the RRU channel data in real time and triggers an early warning immediately when the set threshold is exceeded;

[0042] Fault location module, for fault location and processing.

[0043] In a third aspect, the present invention provides a computing device, comprising:

[0044] Memory and processor;

[0045] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the RRU channel fault diagnosis and processing method are implemented.

[0046] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the RRU channel fault diagnosis and processing method.

[0047] Compared with the prior art, the present invention has the following beneficial effects: the present invention monitors the system status in real time and uses a fault prediction model to analyze data. Once a potential fault risk is detected, an early warning is immediately triggered. This not only greatly shortens the time for fault discovery, but also reduces reliance on manual judgment through automated early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0049] Figure 1 The present invention is a schematic diagram of the overall process of an RRU channel fault diagnosis and processing method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0051] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0052] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0053] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0054] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0055] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0056] Example 1

[0057] Reference Figure 1 , as an embodiment of the present invention, provides a RRU channel fault diagnosis and processing method, comprising:

[0058] S1. Collect RRU operation data, performance indicator data, environmental monitoring data and historical fault information to build a database.

[0059] The operation data includes uplink signal strength, downlink signal strength, signal-to-noise ratio, RRU transmit power and RRU receive power.

[0060] Among them, the signal-to-noise ratio is a key indicator that directly affects communication efficiency and quality.

[0061] Performance indicator data includes bit error rate, throughput and latency.

[0062] Bit error rate, the ratio of erroneous bits during data transmission, a high bit error rate usually indicates potential problems.

[0063] Throughput, the amount of data successfully transmitted per unit time, reflects network performance.

[0064] Environmental monitoring data includes the temperature of the working environment, the humidity of the working environment and the degree of electromagnetic interference.

[0065] Electromagnetic interference level, the electromagnetic interference level of the surrounding environment may affect the RRU performance.

[0066] Historical fault information includes fault type, fault handling measures, fault handling time and RRU configuration records.

[0067] Building a database involves preprocessing the collected data, including data cleaning, data standardization, and feature selection.

[0068] Data cleaning includes removing or filling missing values, identifying and handling outliers, and deduplication. Specifically, using tools such as Pandas (a Python data analysis library) can easily check for missing values ​​and handle them. Common methods include filling missing values ​​(for example, using the mean, median, or mode) or deleting records with missing values;

[0069] Outliers may be caused by incorrect data entry or measurement errors. Statistical methods (such as box plots, Z-score methods) can be used to identify outliers and decide whether to delete, replace, or retain these values;

[0070] Remove duplicate records to ensure that each sample in the dataset is unique. Libraries such as Pandas provide convenient functions to detect and remove duplicates.

[0071] Data standardization refers to converting data from different sources and ranges into a unified format. Specifically, it involves scaling the data to the [0,1] interval. Common methods include minimum-maximum scaling. Data is converted to a distribution with a mean of 0 and a standard deviation of 1, so that the data follows a standard normal distribution. Common tools include StandardScaler in Python's Scikit-learn library.

[0072] The purpose of feature selection is to identify and select the most useful features for model prediction in order to improve the performance of the model and reduce training time; filtering methods can be used to select features based on statistical tests (such as correlation coefficients, chi-square tests), embedding methods, and automatic feature selection using machine learning algorithms (such as decision trees, regularized linear models). These methods select features during the training process.

[0073] S2. Establish a fault prediction model and conduct training.

[0074] The fault prediction model expression is:

[0075]

[0076] Among them, P(f) represents the probability of fault occurrence, which ranges from 0 to 1. f1(SSI, SNR) is a comprehensive signal quality function that considers the uplink and downlink signal strengths and the signal-to-noise ratio. f2(ENV, t)dt is an influence function that represents the time-varying environmental factors. f3(PERF) is a weighted average function of performance indicators. H is a scoring function based on historical fault information. α, β, γ, and δ are model parameters. T represents the length of time for the investigation.

[0077] The signal quality comprehensive function expression is:

[0078]

[0079] Among them, SSI up Indicates the measurable uplink and downlink signal strength in dBm, SSI down Indicates the downlink signal strength. SNR is the signal-to-noise ratio, measured in dB, taking into account the impact of the signal-to-noise ratio on communication quality.

[0080] The expression of environmental factors affecting the function is:

[0081] (f2(ENV,t)=e -(t-T) / T ·(0.5·Temp t +0.3 Humidity t +0.2 EMI t )

[0082] Among them, Temp t It represents the ambient temperature at time t in °C. Humidity t is the relative humidity in %, EMI t It indicates the electromagnetic interference intensity, which can be measured by special equipment in V / m. -(t-T) / T The decay factor over time is considered, indicating that the most recent environmental data has a greater impact on the failure prediction.

[0083] It should be noted that the coefficient 0.8 in the signal quality comprehensive function is used to adjust the downlink signal strength (SSI down ) in calculating the comprehensive signal strength index. A value less than 1 means that the impact of downlink signal strength on the overall signal quality is considered to be slightly lower than that of uplink signal strength; 10 is the coefficient used when calculating the logarithm of the signal-to-noise ratio (SNR). This coefficient is used to amplify the logarithm of the signal-to-noise ratio to a more appropriate magnitude so that it has a similar order of magnitude when added to the comprehensive value of the signal strength, ensuring that the impact of different types of data on the result is balanced; 0.5 in the environmental factor impact function means that the impact of temperature on device performance is considered to be the greatest, because too high or too low temperature may directly affect the operating efficiency and stability of electronic equipment; 0.3 refers to the degree of influence of humidity. Changes in humidity may affect the physical state of circuit boards and other electronic components, thereby affecting performance; 0.2 is the impact weight of electromagnetic interference. Although electromagnetic interference is also very important, it is considered to have a smaller impact weight than temperature and humidity in this model.

[0084] The performance index weighted average function expression is:

[0085]

[0086] Among them, BER stands for bit error rate, which is the ratio of data transmission errors. Throughput stands for network throughput, which is measured in Mbps. The reciprocal is taken to convert it into the same impact direction as other indicators (i.e., the larger the value, the worse the performance). Delay is the data transmission delay time, which is measured in ms.

[0087] The historical fault information scoring function expression is:

[0088]

[0089] Where N is the number of historical failures considered, Severity i is the severity score of the i-th fault, which is determined by the impact of the fault on the system, for example, from 1 to 10. i is a decay factor related to time. The most recent fault has a greater impact on the current state, which can be expressed as To express, where Δt i is the number of months from the ith failure to the current time.

[0090] During training, the model is trained using the collected historical data, where the failure data is used to teach the model to recognize different types of failure modes.

[0091] Evaluate the performance of the model through cross-validation and other methods, including indicators such as accuracy and recall, and adjust the model parameters according to the evaluation results for optimization

[0092] S3, monitor the RRU channel data in real time through the fault prediction model, and trigger an early warning immediately when the set threshold is exceeded;

[0093] Exceeding the set threshold immediately triggers an early warning:

[0094] Two basic fault warning thresholds T1 and T2 are set, where T1<T2; for example, T1=0.5 is used for primary warning, and T2=0.8 is used for emergency warning.

[0095] The thresholds T1 and T2 are dynamically adjusted according to real-time environmental data. The expression is: T′1=T1-δ(T env ), T′2=T2-δ(T env ), where δ(T env ) is an adjustment factor based on environmental conditions; for example, if environmental conditions deteriorate (temperature or humidity exceeds certain limits, or the level of electromagnetic interference increases significantly), thresholds T1 and T2 are appropriately lowered to provide early warning.

[0096] T′1 and T′2 are further adjusted based on historical fault information and RRU configuration records. The expression is: T″1=T′1-δ(T hist), T′2=T′2-δ(T hist ), where δ(T hist ) is an adjustment factor based on historical failures;

[0097] When P(f)<T″2, the system is in normal state;

[0098] When T″1<P(f)<T″2, a primary warning is triggered and mild intervention measures are taken, such as parameter adjustment and remote inspection.

[0099] When P(f)≥T″2, an emergency warning is triggered and emergency intervention measures are taken, such as dispatching a maintenance team or immediately shutting down for maintenance.

[0100] The above method will automatically adjust the model parameters and threshold adjustment strategies according to the actual warning effect (such as false alarm rate, missed alarm rate) and fault handling feedback to achieve continuous optimization. In addition to direct operation data and performance indicators, the system will also integrate data from other related systems (such as weather forecasts, local area network status) to predict faults and adjust warning thresholds to improve the accuracy of warnings.

[0101] S4. Fault location and processing based on early warning information. Specifically, use specialized diagnostic tools and methods to locate possible fault locations and causes based on early warning information, which may involve on-site inspections, system log analysis, etc. Take appropriate processing measures based on the results of fault location, including but not limited to software upgrades, parameter adjustments, hardware replacement, etc. After completing fault processing, perform necessary testing and verification to ensure that the fault has been correctly processed. At the same time, feed back the processing results to the monitoring system to optimize the fault prediction model and processing flow.

[0102] The present invention monitors the system status in real time and uses a fault prediction model to analyze data. Once a potential fault risk is detected, an early warning is immediately triggered, which not only greatly shortens the time for fault discovery, but also reduces reliance on manual judgment through automated early warning. The fault early warning threshold is dynamically adjusted according to real-time environmental data and historical fault information. This innovative design enables the fault diagnosis and processing method to respond more flexibly and accurately to different operating conditions and historical experience, further improving the accuracy of fault diagnosis.

[0103] The above is a schematic scheme of an RRU channel fault diagnosis and processing method of this embodiment. It should be noted that the technical scheme of the RRU channel fault diagnosis and processing system and the technical scheme of the above RRU channel fault diagnosis and processing method belong to the same concept, and the details not described in detail in the technical scheme of the RRU channel fault diagnosis and processing system in this embodiment can all be referred to the description of the technical scheme of the above RRU channel fault diagnosis and processing method.

[0104] The RRU channel fault diagnosis and processing system in this embodiment includes:

[0105] Data collection module, which collects RRU operation data, performance indicator data, environmental monitoring data and historical fault information;

[0106] The data processing module pre-processes the collected data, including data cleaning, data standardization, and feature selection;

[0107] The fault prediction module monitors the RRU channel data in real time and triggers an early warning immediately when the set threshold is exceeded;

[0108] Fault location module, for fault location and processing.

[0109] This embodiment further provides a calculation device, which is applicable to the case of cumulative climbing height calculation, and includes:

[0110] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the RRU channel fault diagnosis and processing method proposed in the above embodiment.

[0111] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the RRU channel fault diagnosis and processing method proposed in the above embodiment is implemented.

[0112] The storage medium proposed in this embodiment and the method for implementing RRU channel fault diagnosis and processing proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0113] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0114] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A RRU channel fault diagnosis and processing method, characterized in that: include: Collect RRU operation data, performance indicator data, environmental monitoring data and historical fault information to build a database; Establish and train fault prediction models; Real-time monitoring of RRU channel data is performed through the fault prediction model, and an alarm is triggered immediately when the set threshold is exceeded; Locate and handle faults based on early warning information; The fault prediction model expression is: Among them, P(f) represents the probability of fault occurrence, ranging from 0 to 1, f1(SSI, SNR) is a comprehensive signal quality function that considers uplink and downlink signal strength and signal-to-noise ratio, f2(ENV, t)dt is an environmental factor influence function that represents the change of environmental factors over time, f3(PERF) is a weighted average function of performance indicators, H is a scoring function based on historical fault information, α, β, γ, δ are model parameters, and T represents the length of time for investigation; The signal quality comprehensive function expression is: Among them, SSI up Indicates uplink signal strength, SSI down Indicates downlink signal strength, SNR is signal-to-noise ratio; The environmental factor influence function expression is: f2(ENV,t)=e -(t-T) / T ·(0.5·Temp t +0.3·Humidity t +0.2·EMI t ) Among them, Temp t represents the ambient temperature at time t, Humidity t is the relative humidity, EMI t Indicates the electromagnetic interference intensity, e -(t-T) / T The decay factor over time is taken into account; The performance index weighted average function expression is: Among them, BER represents bit error rate, Throughput represents network throughput, and Delay represents data transmission delay time; The historical fault information scoring function expression is: Where N is the number of historical failures considered, Severity i is the severity score of the ith fault, Recency i is a decay factor with respect to time; The specific method of immediately triggering an early warning when the set threshold is exceeded is: Set two basic fault warning thresholds T1 and T2, where T1 < T2; Dynamically adjust thresholds T1 and T2 according to real-time environmental data. The expression is: T1 ′ =T1-δ(T env ), T2 ′ =T2-δ(T env ), where δ(T env ) is an adjustment factor based on environmental conditions; Further adjust T1 based on historical fault information and RRU configuration records ′ and T2 ′ , the expression is: T1 ″ =T1 ′ -δ(T hist ), T2 ″ =T2 ′ -δ(T hist ), where δ(T hist ) is an adjustment factor based on historical failures; When P(f)<T2 ″ When , the system is in normal state; When T1 ″ <P(f)<T2 ″ When the situation becomes serious, a primary warning is triggered and mild intervention measures are taken; When P(f)≥T2 ″ When an emergency occurs, an emergency warning is triggered and emergency intervention measures are taken.

2. The RRU channel fault diagnosis and processing method according to claim 1, characterized in that: The operation data includes uplink signal strength, downlink signal strength, signal-to-noise ratio, RRU transmit power and RRU receive power; The performance indicator data includes bit error rate, throughput and delay time; The environmental monitoring data includes the temperature of the working environment, the humidity of the working environment and the degree of electromagnetic interference; The historical fault information includes fault type, fault handling measures, fault handling time and RRU configuration records.

3. The RRU channel fault diagnosis and processing method according to claim 1 or 2, characterized in that: The said building of database includes preprocessing the collected data, including data cleaning, data standardization and feature selection; The data cleaning includes removing or filling missing values, identifying and processing outliers, and deduplication; Data standardization refers to converting data from different sources and scopes into a unified format.

4. An RRU channel fault diagnosis and processing system, based on the RRU channel fault diagnosis and processing method according to any one of claims 1 to 3, characterized in that: include, Data collection module, which collects RRU operation data, performance indicator data, environmental monitoring data and historical fault information; The data processing module pre-processes the collected data, including data cleaning, data standardization, and feature selection; The fault prediction module monitors the RRU channel data in real time and triggers an early warning immediately when the set threshold is exceeded; Fault location module, for fault location and processing.

5. An electronic device comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the RRU channel fault diagnosis and processing method according to any one of claims 1 to 3 are implemented.

6. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the RRU channel fault diagnosis and processing method described in any one of claims 1 to 3.

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