A radio station fault diagnosis method, system and device

Abstract

The present invention discloses a radio station fault diagnosis method, system and device, and relates to the field of radio station testing. The method comprises collecting radio station test data, performing median near average interpolation on the radio station test data, generating interpolated radio station test data; determining amplitude information of the interpolated radio station test data; converting the interpolated radio station test data into the frequency domain using a virtual frequency conversion algorithm, generating frequency domain radio station test data; determining frequency data of the frequency domain radio station test data; and determining the fault type of the radio station according to the amplitude information and the frequency data. The present invention can normally extract frequency features without reducing the data sampling rate.

Description

Technical Field

[0001] The invention relates to the field of radio station testing, and in particular to a radio station fault diagnosis method, system and equipment. Background Art

[0002] By extracting the features of radio station test data to perform radio station fault diagnosis, the amplitude feature extraction of test data is currently completed through the time domain hard threshold comparison method. In order to reduce the total amount of data after sampling, a relatively low data sampling rate is generally selected. When the sampling frequency is low, the frequency resolution accuracy after converting the data to frequency is low, resulting in the inability to extract frequency features normally. Therefore, due to the limitation of sampling frequency, the frequency features of the test data cannot be extracted, resulting in the subsequent application of data features for fault diagnosis being relatively limited, and it is impossible to complete efficient and accurate fault location. Summary of the invention

[0003] The purpose of the present invention is to provide a radio station fault diagnosis method, system and device to solve the problem that the existing feature extraction method reduces the data sampling rate in order to reduce the total amount of sampled data, and cannot extract frequency features normally.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A radio station fault diagnosis method, comprising:

[0006] Collecting radio station test data, and performing median near average interpolation on the radio station test data to generate interpolated radio station test data;

[0007] Determining amplitude information of the interpolated radio station test data;

[0008] Converting the interpolated radio station test data into the frequency domain using a virtual frequency conversion algorithm to generate frequency domain radio station test data;

[0009] determining frequency data of the frequency domain radio station test data;

[0010] The fault type of the radio station is determined based on the amplitude information and the frequency data.

[0011] Optionally, collecting radio station test data and performing median near average interpolation on the radio station test data to generate interpolated radio station test data specifically includes:

[0012] Performing median neighbor average interpolation on the radio station test data using the formula z_ci=(z(i)+z(i+1)) / 2+(1 / i)×z(i)+(1 / (i+1))×z(i+1) to generate interpolated radio station test data;

[0013] Among them, z_ci is the interpolated radio station test data; z(i) is the i-th sampling point of the radio station test data, i is the sampling point sequence number; z(i+1) is the i+1-th sampling point of the radio station test data.

[0014] Optionally, the converting the interpolated radio station test data into the frequency domain by using a virtual frequency conversion algorithm to generate frequency domain radio station test data specifically includes:

[0015] Generate frequency domain radio station test data using the formula z_c_fi=z_c1×exp(-j×2×pi×(1 / f2)×(i / N)×1)+z_c2×exp(-j×2×pi×(1 / f2)×(i / N)×2)+…+z_cn×exp(-j×2×pi×(1 / f2)×(i / N)×n);

[0016] Among them, z_c_fi is the frequency domain radio station test data; z_cn is the interpolated radio station test data, n = 1, 2, 3...N; n is the serial number of the interpolated radio station test data; N is the total number of interpolated radio station test data; pi is pi; j is the symbol representing the imaginary part of the complex number; f2 is the sampling frequency after interpolation.

[0017] Optionally, determining the fault type of the radio station according to the amplitude information and the frequency data specifically includes:

[0018] When the amplitude information is between 0 and 1.5, it is determined that the fault type of the radio station is a radio station receiving module fault;

[0019] When the amplitude information is between 1.5 and 2, it is determined that the fault type of the radio station is a fault of a radio station transmission module;

[0020] When the frequency data is between 3 and 4.5, it is determined that the fault type of the radio station is a fault of the encryption and decryption module of the radio station;

[0021] When the amplitude information is between 2 and 2.5, and the frequency data is between 2.5 and 3, it is determined that the fault type of the radio station is a radio station data processing module fault.

[0022] A radio station fault diagnosis system, comprising:

[0023] An interpolation processing module, used for collecting radio station test data, and performing median near average interpolation on the radio station test data to generate interpolated radio station test data;

[0024] An amplitude information determination module, used to determine the amplitude information of the interpolated radio station test data;

[0025] A frequency domain radio station test data generation module, used to convert the interpolated radio station test data into the frequency domain using a virtual frequency conversion algorithm to generate frequency domain radio station test data;

[0026] A frequency domain data determination module, used to determine the frequency data of the frequency domain radio station test data;

[0027] The fault type determination module is used to determine the fault type of the radio station according to the amplitude information and the frequency data.

[0028] Optionally, the interpolation processing module specifically includes:

[0029] an interpolation processing unit, used for performing median proximity average interpolation on the radio station test data using a formula z_ci=(z(i)+z(i+1)) / 2+(1 / i)×z(i)+(1 / (i+1))×z(i+1) to generate interpolated radio station test data;

[0030] Among them, z_ci is the interpolated radio station test data; z(i) is the i-th sampling point of the radio station test data, i is the sampling point sequence number; z(i+1) is the i+1-th sampling point of the radio station test data.

[0031] Optionally, the frequency domain radio station test data generation module specifically includes:

[0032] A frequency domain radio station test data generating unit, used for generating frequency domain radio station test data by using a formula z_c_fi=z_c1×exp(-j×2×pi×(1 / f2)×(i / N)×1)+z_c2×exp(-j×2×pi×(1 / f2)×(i / N)×2)+…+z_cn×exp(-j×2×pi×(1 / f2)×(i / N)×n);

[0033] Among them, z_c_fi is the frequency domain radio station test data; z_cn is the interpolated radio station test data, n = 1, 2, 3...N; n is the serial number of the interpolated radio station test data; N is the total number of interpolated radio station test data; pi is pi; j is the symbol representing the imaginary part of the complex number; f2 is the sampling frequency after interpolation.

[0034] Optionally, the fault type determination module specifically includes:

[0035] A radio station receiving module fault determination unit, configured to determine that the fault type of the radio station is a radio station receiving module fault when the amplitude information is between 0 and 1.5;

[0036] A radio station transmission module fault determination unit, configured to determine, when the amplitude information is between 1.5 and 2, that the fault type of the radio station is a radio station transmission module fault;

[0037] A radio station encryption and decryption module fault determination unit, configured to determine that the fault type of the radio station is a radio station encryption and decryption module fault when the frequency data is between 3 and 4.5;

[0038] The radio station data processing module fault determination unit is used to determine that the fault type of the radio station is a radio station data processing module fault when the amplitude information is between 2 and 2.5 and the frequency data is between 2.5 and 3.

[0039] An electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the above-mentioned radio station fault diagnosis method.

[0040] A computer-readable storage medium stores a computer program, wherein the computer program implements the above-mentioned radio station fault diagnosis method when executed by a processor.

[0041] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the present invention performs interpolation processing on the sampled data, and through the interpolation processing, the data sampling rate is relatively improved without reducing the amount of sampled data, thereby improving the frequency accuracy of the data after conversion to the frequency domain, and then the frequency characteristics can be extracted normally. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. 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 paying creative labor.

[0043] Figure 1 A flow chart of the radio station fault diagnosis method provided by the present invention;

[0044] Figure 2 This is a structural diagram of the radio station fault diagnosis system provided by the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] The object of the present invention is to provide a radio station fault diagnosis method, system and device, which can normally extract frequency characteristics without reducing the data sampling rate.

[0047] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Embodiment 1

[0049] Figure 1 The flowchart of the radio station fault diagnosis method provided by the present invention is as follows: Figure 1 As shown, the present invention provides a radio station fault diagnosis method, comprising:

[0050] Step 101: Collect radio station test data, and perform median nearest average interpolation on the radio station test data to generate interpolated radio station test data.

[0051] The step 101 specifically includes: using the formula z_ci=(z(i)+z(i+1)) / 2+(1 / i)×z(i)+(1 / (i+1))×z(i+1) to perform median nearest average interpolation on the radio station test data to generate interpolated radio station test data; wherein z_ci is the interpolated radio station test data; z(i) is the i-th sampling point of the radio station test data, i is the sampling point sequence number; z(i+1) is the i+1-th sampling point of the radio station test data.

[0052] In practical applications, the collected radio station test data is interpolated by the median nearest average. Assume that the collected radio station data is z = [z1, z2, …, zn], and its sampling frequency is f1. The data interpolation is completed using the following formula: z_ci = (z(i) + z(i+1)) / 2 + (1 / i) × z(i) + (1 / (i+1)) × z(i+1). Using the above formula to interpolate the original sampled data, the sampling frequency can be increased by 2 times to 2*f1. Assume that the interpolated radio station test data is represented by z_c, then z_c = [z1, z1_c, z2, z2_c, z3, z3_c, …zn] = [z_c1, z_c2, …, z_cn].

[0053] Step 102: Determine the amplitude information of the interpolated radio station test data.

[0054] In practical applications, the amplitude information of the interpolated radio station test data is obtained by hard threshold comparison. Among all the sampling points, the maximum value is taken as the amplitude information A of the signal.

[0055] Step 103: convert the interpolated radio station test data into the frequency domain using a virtual frequency conversion algorithm to generate frequency domain radio station test data.

[0056] The step 103 specifically includes: generating frequency domain radio station test data using the formula z_c_fi=z_c1×exp(-j×2×pi×(1 / f2)×(i / N)×1)+z_c2×exp(-j×2×pi×(1 / f2)×(i / N)×2)+…+z_cn×exp(-j×2×pi×(1 / f2)×(i / N)×n);

[0057] Among them, z_c_fi is the frequency domain radio station test data; z_cn is the interpolated radio station test data, n = 1, 2, 3...N; n is the serial number of the interpolated radio station test data; N is the total number of interpolated radio station test data; pi is pi; j is the symbol representing the imaginary part of the complex number; f2 is the sampling frequency after interpolation.

[0058] Step 104: Determine frequency data of the frequency domain radio station test data.

[0059] In practical applications, after the frequency domain data z_c_f is obtained, based on the hard threshold comparison method, the maximum value of the frequency domain radio station test data corresponding to the interpolated radio station test data is used as the frequency data P of the test data.

[0060] Step 105: Determine the fault type of the radio station according to the amplitude information and the frequency data.

[0061] The step 105 specifically includes: when the amplitude information is between 0 and 1.5, determining that the fault type of the radio station is a radio station receiving module fault; when the amplitude information is between 1.5 and 2, determining that the fault type of the radio station is a radio station transmitting module fault; when the frequency data is between 3 and 4.5, determining that the fault type of the radio station is a radio station encryption and decryption module fault; when the amplitude information is between 2 and 2.5 and the frequency data is between 2.5 and 3, determining that the fault type of the radio station is a radio station data processing module fault; when the amplitude information and the frequency data are other values, determining that the radio station has no fault.

[0062] The present invention interpolates the test data of the radio station by using the median adjacent average interpolation method, which equivalently increases the sampling frequency of the signal. After the signal is transformed into the frequency domain, a wider range of spectrum information can be observed.

[0063] Compared with the existing technology, it not only extracts the frequency domain characteristics of the data, but also expands the signal spectrum to obtain richer spectrum information. Using the above information, efficient and accurate fault location can be completed.

[0064] Embodiment 2

[0065] In order to execute the method corresponding to the above-mentioned embodiment 1 to achieve the corresponding functions and technical effects, a radio station fault diagnosis system is provided below.

[0066] Figure 2 The structure diagram of the radio station fault diagnosis system provided by the present invention is as follows: Figure 2 As shown, the present invention provides a radio station fault diagnosis system, comprising:

[0067] The interpolation processing module 201 is used to collect radio station test data and perform median near average interpolation on the radio station test data to generate interpolated radio station test data.

[0068] The interpolation processing module 201 specifically includes: an interpolation processing unit, which is used to perform median nearest average interpolation on the radio station test data using the formula z_ci=(z(i)+z(i+1)) / 2+(1 / i)×z(i)+(1 / (i+1))×z(i+1) to generate interpolated radio station test data; wherein z_ci is the interpolated radio station test data; z(i) is the i-th sampling point of the radio station test data, i is the sampling point sequence number; z(i+1) is the i+1-th sampling point of the radio station test data.

[0069] The amplitude information determination module 202 is used to determine the amplitude information of the interpolated radio station test data.

[0070] The frequency domain radio station test data generating module 203 is used to convert the interpolated radio station test data into the frequency domain by using a virtual frequency conversion algorithm to generate frequency domain radio station test data.

[0071] The frequency domain radio station test data generation module 203 specifically includes: a frequency domain radio station test data generation unit, which is used to generate frequency domain radio station test data using the formula z_c_fi=z_c1×exp(-j×2×pi×(1 / f2)×(i / N)×1)+z_c2×exp(-j×2×pi×(1 / f2)×(i / N)×2)+…+z_cn×exp(-j×2×pi×(1 / f2)×(i / N)×n); wherein z_c_fi is the frequency domain radio station test data; z_cn is the interpolated radio station test data, n=1,2,3...N; n is the serial number of the interpolated radio station test data; N is the total number of the interpolated radio station test data; pi is the ratio of pi; j is the symbol representing the imaginary part of the complex number; f2 is the sampling frequency after interpolation.

[0072] The frequency domain data determination module 204 is used to determine the frequency data of the frequency domain radio station test data.

[0073] The fault type determination module 205 is used to determine the fault type of the radio station according to the amplitude information and the frequency data.

[0074] The fault type determination module 205 specifically includes: a radio station receiving module fault determination unit, which is used to determine that the fault type of the radio station is a radio station receiving module fault when the amplitude information is between 0 and 1.5; a radio station transmitting module fault determination unit, which is used to determine that the fault type of the radio station is a radio station transmitting module fault when the amplitude information is between 1.5 and 2; a radio station encryption and decryption module fault determination unit, which is used to determine that the fault type of the radio station is a radio station encryption and decryption module fault when the frequency data is between 3 and 4.5; and a radio station data processing module fault determination unit, which is used to determine that the fault type of the radio station is a radio station data processing module fault when the amplitude information is between 2 and 2.5 and the frequency data is between 2.5 and 3.

[0075] Embodiment 3

[0076] An embodiment of the present invention provides an electronic device including a memory and a processor. The memory is used to store a computer program. The processor runs the computer program to enable the electronic device to execute the method for supporting radio station fault diagnosis provided in the first embodiment.

[0077] In practical applications, the above electronic device may be a server.

[0078] In practical applications, an electronic device includes: at least one processor, a memory, a bus and a communications interface.

[0079] Wherein: the processor, the communication interface, and the memory communicate with each other via a communication bus.

[0080] Communication interface, used to communicate with other devices.

[0081] The processor is used to execute the program, and specifically can execute the method described in the above embodiment.

[0082] Specifically, the program may include program codes including computer operation instructions.

[0083] The processor may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the electronic device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0084] The memory is used to store programs. The memory may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0085] Based on the description of the above embodiments, the embodiments of the present application provide a storage medium on which computer program instructions are stored. The computer program instructions can be executed by a processor to implement the method described in any embodiment.

[0086] The radio station fault diagnosis support system provided in the embodiments of the present application exists in various forms, including but not limited to:

[0087] (1) Mobile communication devices: These devices are characterized by having mobile communication functions and their main purpose is to provide voice and data communications. These terminals include: smart phones (such as iPhone), multimedia phones, functional phones, and low-end phones.

[0088] (2) Ultra-mobile personal computer devices: These devices fall into the category of personal computers, have computing and processing capabilities, and generally also have mobile Internet access capabilities. These terminals include: PDA, MID and UMPC devices, such as iPad.

[0089] (3) Portable entertainment devices: These devices can display and play multimedia content. They include audio and video players (such as iPods), handheld game consoles, e-books, smart toys, and portable car navigation devices.

[0090] (4) Other electronic devices with data interaction functions.

[0091] Thus far, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired results. Additionally, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing may be advantageous.

[0092] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0093] For the convenience of description, the above device is described by being divided into various units according to their functions and described separately. Of course, when implementing the present application, the functions of each unit can be implemented in the same one or more software and / or hardware. It should be understood by those skilled in the art that the embodiments of the present application can be provided as methods, systems or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0094] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0097] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0098] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0099] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM),

[0100] Digital Versatile Disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, disk storage or other magnetic storage devices

[0101] Or any other non-transmission medium that can be used to store information that can be accessed by a computing device. According to the definition in this article, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0102] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0103] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific transactions or implement specific abstract data types. The present application may also be practiced in distributed computing environments where transactions are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0104] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0105] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A radio station fault diagnosis method, characterized in that: include: Collecting radio station test data, and performing median near average interpolation on the radio station test data to generate interpolated radio station test data; Determining amplitude information of the interpolated radio station test data; Converting the interpolated radio station test data into the frequency domain using a virtual frequency conversion algorithm to generate frequency domain radio station test data; determining frequency data of the frequency domain radio station test data; Determining the fault type of the radio station according to the amplitude information and the frequency data specifically includes: When the amplitude information is between 0 and 1.5, it is determined that the fault type of the radio station is a radio station receiving module fault; When the amplitude information is between 1.5 and 2, it is determined that the fault type of the radio station is a fault of a radio station transmission module; When the frequency data is between 3 and 4.5, it is determined that the fault type of the radio station is a fault of the encryption and decryption module of the radio station; When the amplitude information is between 2 and 2.5, and the frequency data is between 2.5 and 3, it is determined that the fault type of the radio station is a radio station data processing module fault.

2. The radio station fault diagnosis method according to claim 1, characterized in that: The collecting of radio station test data and performing median near average interpolation on the radio station test data to generate interpolated radio station test data specifically includes: ×z(i+1) performs median neighbor average interpolation on the radio station test data to generate interpolated radio station test data; Among them, z_ci is the interpolated radio station test data; z(i) is the i-th sampling point of the radio station test data, i is the sampling point sequence number; z(i+1) is the i+1-th sampling point of the radio station test data.

3. The radio station fault diagnosis method according to claim 2, characterized in that: The method of converting the interpolated radio station test data into the frequency domain by using a virtual frequency conversion algorithm to generate frequency domain radio station test data specifically includes: Generate frequency domain radio station test data using the formula z_c_fi=z_c1×exp(-j×2×pi×(1 / f2)×(i / N)×1)+z_c2×exp(-j×2×pi×(1 / f2)×(i / N)×2)+…+z_cn×exp(-j×2×pi×(1 / f2)×(i / N)×n); Among them, z_c_fi is the frequency domain radio station test data; z_cn is the interpolated radio station test data, n = 1, 2, 3...N; n is the serial number of the interpolated radio station test data; N is the total number of interpolated radio station test data; pi is pi; j is the symbol representing the imaginary part of the complex number; f2 is the sampling frequency after interpolation.

4. A radio station fault diagnosis system, characterized in that: include: An interpolation processing module, used for collecting radio station test data, and performing median near average interpolation on the radio station test data to generate interpolated radio station test data; An amplitude information determination module, used to determine the amplitude information of the interpolated radio station test data; A frequency domain radio station test data generation module, used to convert the interpolated radio station test data into the frequency domain using a virtual frequency conversion algorithm to generate frequency domain radio station test data; A frequency domain data determination module, used to determine the frequency data of the frequency domain radio station test data; A fault type determination module is used to determine the fault type of the radio station according to the amplitude information and the frequency data, specifically comprising: A radio station receiving module fault determination unit, configured to determine that the fault type of the radio station is a radio station receiving module fault when the amplitude information is between 0 and 1.5; A radio station transmission module fault determination unit, configured to determine, when the amplitude information is between 1.5 and 2, that the fault type of the radio station is a radio station transmission module fault; A radio station encryption and decryption module fault determination unit, configured to determine that the fault type of the radio station is a radio station encryption and decryption module fault when the frequency data is between 3 and 4.5; The radio station data processing module fault determination unit is used to determine that the fault type of the radio station is a radio station data processing module fault when the amplitude information is between 2 and 2.5 and the frequency data is between 2.5 and 3.

5. The radio station fault diagnosis system according to claim 4, characterized in that: The interpolation processing module specifically includes: an interpolation processing unit, used for performing median proximity average interpolation on the radio station test data using a formula z_ci=(z(i)+z(i+1)) / 2+(1 / i)×z(i)+(1 / (i+1))×z(i+1) to generate interpolated radio station test data; Among them, z_ci is the interpolated radio station test data; z(i) is the i-th sampling point of the radio station test data, i is the sampling point sequence number; z(i+1) is the i+1-th sampling point of the radio station test data.

6. The radio station fault diagnosis system according to claim 5, characterized in that: The frequency domain radio station test data generation module specifically includes: A frequency domain radio station test data generating unit, used for generating frequency domain radio station test data by using a formula z_c_fi=z_c1×exp(-j×2×pi×(1 / f2)×(i / N)×1)+z_c2×exp(-j×2×pi×(1 / f2)×(i / N)×2)+…+z_cn×exp(-j×2×pi×(1 / f2)×(i / N)×n); Among them, z_c_fi is the frequency domain radio station test data; z_cn is the interpolated radio station test data, n = 1, 2, 3...N; n is the serial number of the interpolated radio station test data; N is the total number of interpolated radio station test data; pi is pi; j is the symbol representing the imaginary part of the complex number; f2 is the sampling frequency after interpolation.

7. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the radio station fault diagnosis method according to any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that: The device stores a computer program, which, when executed by a processor, implements the radio station fault diagnosis method according to any one of claims 1 to 3.

Images