Data acquisition method and device of spectrum analyzer, user terminal and storage medium

Abstract

This application belongs to the field of spectrum analyzer technology, and particularly relates to a data acquisition method, device, user terminal, and storage medium for a spectrum analyzer. The data acquisition method includes: acquiring first acquisition data and second acquisition data; generating stitched data based on the first acquisition data and second acquisition data; generating video processing data based on the stitched data; reading pre-stored data (data stored in a storage unit); comparing the video processing data and the pre-stored data; extracting dissimilar data when the video processing data and the pre-stored data are inconsistent; and sending the dissimilar data to the storage unit, combining the pre-stored data with the dissimilar data to form new pre-stored data. This data acquisition method effectively relieves the data transmission pressure of the spectrum analyzer, thereby providing the ability to process effective data in real time.

Description

Technical Field

[0001] This application relates to the field of spectrum analyzer technology, and in particular to a data acquisition method, device, user terminal and storage medium for a spectrum analyzer. Background Technology

[0002] In related technologies, there are many types of spectrum analyzers, but the signal link and data acquisition are all implemented using a fully digital intermediate frequency (IF) spectrum analyzer architecture. This involves using a medium-speed or low-speed analog-to-digital converter (ADC) to acquire data from a programmable logic device (PLD), followed by down-conversion, an IF bandwidth filter, a detector, and a video filter, before finally displaying the spectrum trace on a screen. However, spectrum analyzers with high bandwidth requirements necessitate faster ADCs, larger-scale PLDs, and higher-performance processors. This results in high hardware costs, a large data volume at the interface between the PLD and the processor, and difficulty in controlling stability.

[0003] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art.

[0004] Application content

[0005] In view of at least one of the above technical problems, this application provides a data acquisition method, device, user terminal and storage medium for a spectrum analyzer, which solves the problem that spectrum analyzers with high bandwidth requirements need higher speed analog-to-digital converters, larger-scale programmable logic devices and higher performance processors, which leads to high hardware costs, large data volume of the exchange interface between programmable logic devices and processors and difficulty in controlling stability.

[0006] A first aspect of this application provides a data acquisition method for a spectrum analyzer. The spectrum analyzer includes a dual-channel analog-to-digital converter and a storage unit. The dual-channel analog-to-digital converter has a first acquisition channel and a second acquisition channel. The data acquisition method includes:

[0007] Acquire first and second data. The first data is acquired by the first acquisition channel of the dual-channel analog-to-digital converter, and the second data is acquired by the second acquisition channel of the dual-channel analog-to-digital converter.

[0008] Based on the first and second collected data, spliced ​​data is generated;

[0009] Based on the spliced ​​data, generate video processing data;

[0010] Read the preceding data, which is data pre-stored in the storage unit;

[0011] Compare video processing data with pre-processed data;

[0012] When video processing data differs from pre-processed data, dissimilar data is extracted.

[0013] Dissimilar data is sent to the storage unit, which combines the previous data with the dissimilar data to form new previous data.

[0014] This application has the following technical effects: This data acquisition method acquires first acquisition data and second acquisition data and generates spliced ​​data, then processes the spliced ​​data to generate video processing data, and performs corresponding operations on the video processing data by comparing whether the video processing data is consistent with the previous data. This effectively relieves the data transmission pressure of the spectrum analyzer and provides the ability to process effective data in real time.

[0015] In some possible implementations, the sampling clocks of the first acquisition channel and the second acquisition channel are 180° out of phase.

[0016] In some possible implementations, video processing data is generated based on the stitched data, including:

[0017] Using the resolution bandwidth parameter, the spliced ​​data is converted into low-frequency data, where the resolution bandwidth parameter is pre-stored in the spectrum analyzer;

[0018] Low-frequency data is filtered using resolution bandwidth parameters to generate low-frequency filtered data.

[0019] Convert low-frequency filtered data into video voltage data;

[0020] The video voltage data is filtered to generate video processing data.

[0021] In some possible implementations, video processing data and pre-processed data are compared, including:

[0022] The data similarity parameters, high-level trigger range values, and low-level trigger range values ​​are used to compare whether the video processing data and the pre-processed data are the same. The data similarity parameters, high-level trigger range values, and low-level trigger range values ​​are pre-stored in the spectrum analyzer.

[0023] If the video processing data is consistent with the previous data, discard the video processing data;

[0024] When the video processing data is inconsistent with the pre-processed data, the dissimilar data is extracted.

[0025] In some possible implementations, the video processing data has a first high-level data segment and a first low-level data segment, and the pre-processing data has a second high-level data segment and a second low-level data segment;

[0026] When the first high-level data segment is higher than the high-level trigger range value and the first high-level data segment does not overlap with the second high-level data segment, and the non-overlapping portion of the first high-level data segment and the second high-level data segment satisfies the data similarity parameter, the non-overlapping portion of the first high-level data segment and the second high-level data segment is extracted to generate the first difference data; and / or

[0027] When the first low-level data segment is lower than the low-level trigger range value and the first low-level data segment does not overlap with the second low-level data segment, and the non-overlapping part of the first low-level data segment and the second low-level data segment satisfies the data similarity parameter, the non-overlapping part of the first low-level data segment and the second low-level data segment is extracted to generate the second difference data.

[0028] Dissimilar data are generated based on the first difference data and / or the second difference data.

[0029] A second aspect of this application provides a data acquisition device for a spectrum analyzer. The spectrum analyzer includes a dual-channel analog-to-digital converter and a storage unit. The dual-channel analog-to-digital converter has a first acquisition channel and a second acquisition channel. The data acquisition device includes:

[0030] The acquisition module is used to acquire first acquisition data and second acquisition data. The first acquisition data is acquired by the first acquisition channel of the dual-channel analog-to-digital converter, and the second acquisition data is acquired by the second acquisition channel of the dual-channel analog-to-digital converter.

[0031] The data splicing module is used to generate spliced ​​data based on the first collected data and the second collected data;

[0032] The data processing module is used to generate video processing data based on the spliced ​​data;

[0033] The read module is used to read the pre-stored data, which is data that has been stored in the storage unit in advance.

[0034] The comparison module is used to compare video processing data with pre-processed data;

[0035] The comparison result processing module is used to extract dissimilar data when the video processing data is inconsistent with the previous data;

[0036] The storage module is used to send dissimilar data to the storage unit, so that the previous data is combined with the dissimilar data to form new previous data.

[0037] In some possible implementations, the data processing module includes:

[0038] The downconversion module is used to convert spliced ​​data into low-frequency data using resolution bandwidth parameters, where the resolution bandwidth parameters are pre-stored in the spectrum analyzer.

[0039] The first filtering module is used to filter low-frequency data using resolution bandwidth parameters to generate low-frequency filtered data.

[0040] The detection module is used to convert low-frequency filtered data into video voltage data;

[0041] The second filtering module is used to filter the video voltage data and generate video processing data.

[0042] In some possible implementations, the comparison result processing module is also used to discard the video processing data if the video processing data is consistent with the preceding data.

[0043] A third aspect of this application provides a user terminal, including a processor and a memory, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the data acquisition method of the first aspect of this application.

[0044] A fourth aspect of the present application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the data acquisition method of the first aspect of the present application.

[0045] The present application will be further described below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a schematic diagram of the spectrum analyzer shown in the embodiment of this application;

[0048] Figure 2 This is a first flowchart of the data acquisition method shown in the embodiments of this application;

[0049] Figure 3 This is a second flowchart of the data acquisition method shown in the embodiments of this application;

[0050] Figure 4 This is a third flowchart of the data acquisition method shown in the embodiments of this application;

[0051] Figure 5 This is a schematic diagram of the data acquisition device shown in the embodiments of this application;

[0052] Figure 6 This is a schematic diagram of the user terminal structure shown in the embodiments of this application; Detailed Implementation

[0053] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0054] In related technologies, there are many types of spectrum analyzers, but the signal link and data acquisition are all implemented using a fully digital intermediate frequency (IF) spectrum analyzer architecture. This involves using a medium-speed or low-speed analog-to-digital converter (ADC) to acquire data from a programmable logic device (PLD), followed by down-conversion, an IF bandwidth filter, a detector, and a video filter, finally displaying the spectrum trace on a screen. However, for spectrum analyzers with high bandwidth requirements, even faster ADCs, larger-scale PLDs, and higher-performance processors are needed. This results in high hardware costs, large data volumes at the interface between the PLD and the processor, and difficulty in controlling stability. This data acquisition method obtains first and second acquisition data and generates stitched data. The stitched data is then processed to generate video processing data. By comparing the video processing data with the preceding data, corresponding operations are performed on the video processing data. This effectively relieves the data transmission pressure on the spectrum analyzer, thereby providing the ability to process effective data in real time.

[0055] This application provides a data acquisition method for a spectrum analyzer, which is applied to a spectrum analyzer. See also... Figure 1 The spectrum analyzer includes a display unit. Specifically, the display unit is the screen of the spectrum analyzer, which has a display interface. In addition to displaying waveforms, it can also display menus, waveform background colors, grids, etc. Therefore, the display interface is affected not only by waveform changes but also by menu status, menu background color settings, grid settings, etc. For the specific internal structure of the spectrum analyzer, please refer to [reference needed]. Figure 1 The system includes a dual-channel analog-to-digital converter for signal acquisition, a processing chip, and a storage unit. The processing chip is used to implement the steps of the data acquisition method for a spectrum analyzer provided in this application embodiment. Specifically, the processing chip is an MPSOC with a built-in quad-core application processor, a dual-core real-time processor, a single-core display processor, and an FPGA.

[0056] A first aspect of this application provides a data acquisition method for a spectrum analyzer. The spectrum analyzer includes a dual-channel analog-to-digital converter and a storage unit. The dual-channel analog-to-digital converter has a first acquisition channel and a second acquisition channel. (See also...) Figure 2 The data acquisition method includes steps 100 to 600.

[0057] Step 100: Acquire the first acquisition data and the second acquisition data. The first acquisition data is acquired by the first acquisition channel of the dual-channel analog-to-digital converter, and the second acquisition data is acquired by the second acquisition channel of the dual-channel analog-to-digital converter.

[0058] In step 100, the sampling clock phases of the first acquisition channel and the second acquisition channel are 180° out of phase, which can double the data sampling rate.

[0059] Step 200: Generate spliced ​​data based on the first and second collected data;

[0060] In step 200, the processing chip splices the first acquired data and the second acquired data into a single data stream, that is, spliced ​​data.

[0061] Step 300: Generate video processing data based on the spliced ​​data;

[0062] In step 300, the processing chip processes and transforms the spliced ​​data and generates video processing data.

[0063] Step 400: Read the preceding data, which is data pre-stored in the storage unit;

[0064] Step 500: Compare the video processing data with the pre-processed data; if the video processing data and the pre-processed data are inconsistent, extract the dissimilar data;

[0065] Step 600: Send dissimilar data to the storage unit to combine the previous data with the dissimilar data to form new previous data.

[0066] See also some possible implementations. Figure 3 Based on the spliced ​​data, video processing data is generated, including steps 310 to 340.

[0067] Step 310: Using the resolution bandwidth parameter, the spliced ​​data is converted into low-frequency data, where the resolution bandwidth parameter is pre-stored in the spectrum analyzer;

[0068] Step 320: Filter the low-frequency data using the resolution bandwidth parameter to generate low-frequency filtered data;

[0069] Step 330: Convert the low-frequency filtered data into video voltage data;

[0070] Step 340: Filter the video voltage data to generate video processing data.

[0071] In step 340, the video voltage data is filtered to reduce the range of display jitter.

[0072] Among some possible implementations, compare the video processing data with the pre-processed data; see [reference needed]. Figure 4 This includes steps 510 to 520.

[0073] Step 510: Use data similarity parameters, high-level trigger range values, and low-level trigger range values ​​to compare whether the video processing data and the pre-processed data are the same. The data similarity parameters, high-level trigger range values, and low-level trigger range values ​​are pre-stored in the spectrum analyzer.

[0074] Step 520: If the video processing data is consistent with the previous data, discard the video processing data; or if the video processing data is inconsistent with the previous data, extract the dissimilar data.

[0075] In some possible implementations, the video processing data has a first high-level data segment and a first low-level data segment, and the pre-processing data has a second high-level data segment and a second low-level data segment;

[0076] When the first high-level data segment is higher than the high-level trigger range value and the first high-level data segment does not overlap with the second high-level data segment, and the non-overlapping portion of the first high-level data segment and the second high-level data segment satisfies the data similarity parameter, the non-overlapping portion of the first high-level data segment and the second high-level data segment is extracted to generate the first difference data; and / or

[0077] When the first low-level data segment is lower than the low-level trigger range value and the first low-level data segment does not overlap with the second low-level data segment, and the non-overlapping part of the first low-level data segment and the second low-level data segment satisfies the data similarity parameter, the non-overlapping part of the first low-level data segment and the second low-level data segment is extracted to generate the second difference data.

[0078] Dissimilar data are generated based on the first difference data and / or the second difference data.

[0079] A second aspect of this application provides a data acquisition device for a spectrum analyzer, see below. Figure 5 The spectrum analyzer includes a dual-channel analog-to-digital converter and a storage unit. The dual-channel analog-to-digital converter has a first acquisition channel and a second acquisition channel. The data acquisition device includes:

[0080] The acquisition module 100 is used to acquire first acquired data and second acquired data. The first acquired data is acquired by the first acquisition channel of the dual-channel analog-to-digital converter, and the second acquired data is acquired by the second acquisition channel of the dual-channel analog-to-digital converter.

[0081] The data splicing module 200 is used to generate spliced ​​data based on the first collected data and the second collected data;

[0082] The data processing module 300 is used to generate video processing data based on the spliced ​​data;

[0083] The reading module 400 is used to read the pre-stored data, which is data that has been stored in the storage unit in advance.

[0084] Comparison module 500 is used to compare video processing data and pre-processed data;

[0085] The comparison result processing module 600 is used to extract dissimilar data when the video processing data is inconsistent with the previous data;

[0086] Storage module 700 is used to send dissimilar data to the storage unit, so that the previous data is combined with the dissimilar data to form new previous data.

[0087] See also some possible implementations. Figure 5 The data processing module includes:

[0088] The downconversion module 310 is used to convert spliced ​​data into low-frequency data using resolution bandwidth parameters, wherein the resolution bandwidth parameters are pre-stored in the spectrum analyzer;

[0089] The first filtering module 320 is used to filter low-frequency data using resolution bandwidth parameters to generate low-frequency filtered data.

[0090] Detector module 330 is used to convert low-frequency filtered data into video voltage data;

[0091] The second filtering module 340 is used to filter the video voltage data and generate video processing data.

[0092] In some possible implementations, the comparison result processing module is also used to discard the video processing data if the video processing data is consistent with the preceding data.

[0093] A third aspect of the embodiments of this application is described in [reference]. Figure 6 A user terminal is provided, including a processor and a memory. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the data acquisition method of the first aspect of the embodiments of this application.

[0094] The user terminal of the embodiment of the third aspect of this application can be implemented with reference to the content specifically described in the embodiment of the first aspect of this application, and has similar beneficial effects as the data acquisition method according to the embodiment of the first aspect of this application, which will not be repeated here.

[0095] User terminal 10 can be implemented as a general-purpose computing device. The components of user terminal 10 may include, but are not limited to: one or more processors or processing units 11, system memory 12, and bus 13 connecting different system components (including system memory 12 and processing unit 11).

[0096] Bus 13 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0097] User terminal 10 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by user terminal 10, including volatile and non-volatile media, removable and non-removable media.

[0098] Memory 12 may include computer-readable media in the form of volatile memory, such as Random Access Memory (RAM) 14 and / or cache memory 15. User terminal 10 may further include other removable / non-removable, volatile / non-volatile computer-readable storage media. By way of example only, storage system 16 may be used to read and write non-removable, non-volatile magnetic media (not shown, commonly referred to as a "hard disk drive"). Although Figure 5Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disc drive for reading and writing to a removable non-volatile optical disc (e.g., a compact disc read-only memory (CD-ROM), a digital video disc read-only memory (DVD-ROM), or other optical media). In these cases, each drive may be connected to bus 13 via one or more data media interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of this disclosure.

[0099] A program / utility 18 having a set (at least one) of program modules 17 may be stored, for example, in memory. Such program modules 17 include, but are not limited to, an operating system, one or more application programs, other program modules 17, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 17 typically perform the functions and / or methods described in the embodiments of this disclosure.

[0100] User terminal 10 can also communicate with one or more external devices 19 (e.g., keyboard, pointing device, display 20, etc.), one or more devices that enable the user to interact with the computer system / server, and / or any device that enables the computer system / server to communicate with one or more other user terminals 10 (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 21. Furthermore, user terminal 10 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 22. As shown, network adapter 22 communicates with other modules of user terminal 10 via bus 13. It should be noted that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with user terminal 10, including but not limited to: microcode, device drivers, redundant processing unit 11, external disk drive array, RAID system, tape drive, and data backup storage system 16, etc.

[0101] The processing unit 11 executes various functional applications and data processing by running programs stored in the system memory 12, such as implementing the methods mentioned in the foregoing embodiments.

[0102] The user terminal 10 in this embodiment can be a server or a terminal device with limited computing power.

[0103] A fourth aspect of the present application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the data acquisition method of the first aspect of the present application.

[0104] Generally, computer instructions for implementing the methods of the present invention can be carried on any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media can include any computer-readable medium except for signals themselves that are temporarily propagating.

[0105] Computer-readable storage media can be, for example—but not limited to—electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM14), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.

[0106] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. In particular, Python, suitable for neural network computation, and platform frameworks based on TensorFlow, PyTorch, etc., can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0107] It should be understood that, in the various embodiments of this application, the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. Furthermore, in some possible implementations, each step in the above embodiments may be selectively executed according to actual circumstances; it may be partially or fully executed, without limitation here. All or part of any feature of any embodiment of this application can be freely and arbitrarily combined without contradiction. The combined technical solutions are also within the scope of this application.

[0108] It should also be understood that, in the embodiments of this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0109] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this application.

[0110] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0111] In the embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, apparatuses, or units, or they may be electrical, mechanical, or other forms of connection.

[0112] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of this application, depending on actual needs.

[0113] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0114] The above are merely preferred embodiments of this application and do not constitute any limitation on this application. Any person skilled in the art can make many possible variations and modifications to the technical solution of this application, or modify it into equivalent embodiments, without departing from the scope of the technical solution of this application. Therefore, all equivalent changes made based on the shape, structure, and principle of this application without departing from the content of the technical solution of this application should be covered within the protection scope of this application.

Claims

1. A method of data acquisition for a spectrum analyzer, characterized by, The spectrum analyzer includes a dual-channel analog-to-digital converter and a storage unit. The dual-channel analog-to-digital converter has a first acquisition channel and a second acquisition channel. The data acquisition method includes: Acquire first and second data. The first data is acquired by the first acquisition channel of the dual-channel analog-to-digital converter, and the second data is acquired by the second acquisition channel of the dual-channel analog-to-digital converter. The sampling clock phases of the first and second acquisition channels are 180° apart. Based on the first and second collected data, spliced ​​data is generated; Based on the spliced ​​data, video processing data is generated; Read the preceding data, which is data pre-stored in the storage unit; Compare the video processing data with the pre-processed data; In cases where the video processing data is inconsistent with the preceding data, dissimilar data is extracted. The dissimilar data is sent to the storage unit, so that the preceding data is combined with the dissimilar data to form new preceding data.

2. The method of data acquisition for a spectrum analyzer of claim 1, wherein, The step of generating video processing data based on the spliced ​​data includes: The spliced ​​data is converted into low-frequency data using resolution bandwidth parameters, wherein the resolution bandwidth parameters are pre-stored in the spectrum analyzer; Low-frequency data is filtered using resolution bandwidth parameters to generate low-frequency filtered data. Convert low-frequency filtered data into video voltage data; The video voltage data is filtered to generate the video processing data.

3. The method of data acquisition for a spectrum analyzer of claim 1, wherein, The comparison of the video processing data and the pre-processed data includes: The video processing data and the pre-processed data are compared using data similarity parameters, high-level trigger range values, and low-level trigger range values, wherein the data similarity parameters, high-level trigger range values, and low-level trigger range values ​​are pre-stored in the spectrum analyzer. If the video processing data is consistent with the preceding data, the video processing data is discarded. If the video processing data is inconsistent with the preceding data, the dissimilar data is extracted.

4. The method of data acquisition for a spectrum analyzer of claim 3 wherein, The video processing data has a first high-level data segment and a first low-level data segment, and the pre-processing data has a second high-level data segment and a second low-level data segment; When the first high-level data segment is higher than the high-level trigger range value and the first high-level data segment does not overlap with the second high-level data segment, and the non-overlapping portion of the first high-level data segment and the second high-level data segment satisfies the data similarity parameter, the non-overlapping portion of the first high-level data segment and the second high-level data segment is extracted to generate first difference data; and / or When the first low-level data segment is lower than the low-level trigger range value and the first low-level data segment does not overlap with the second low-level data segment, and the non-overlapping part of the first low-level data segment and the second low-level data segment satisfies the data similarity parameter, the non-overlapping part of the first low-level data segment and the second low-level data segment is extracted to generate the second difference data. The dissimilar data is generated based on the first difference data and / or the second difference data.

5. A data acquisition device for a spectrum analyzer, characterized by The spectrum analyzer includes a dual-channel analog-to-digital converter and a storage unit. The dual-channel analog-to-digital converter has a first acquisition channel and a second acquisition channel. The data acquisition device includes: The acquisition module is used to acquire first acquisition data and second acquisition data. The first acquisition data is acquired by the first acquisition channel of the dual-channel analog-to-digital converter, and the second acquisition data is acquired by the second acquisition channel of the dual-channel analog-to-digital converter. The sampling clock phases of the first acquisition channel and the second acquisition channel are 180° apart. The data splicing module is used to generate spliced ​​data based on the first collected data and the second collected data; The data processing module is used to generate video processing data based on the spliced ​​data; A reading module is used to read pre-stored data, which is data that has been stored in advance in the storage unit; The comparison module is used to compare the video processing data with the pre-processed data; The comparison result processing module is used to extract dissimilar data when the video processing data is inconsistent with the preceding data; A storage module is used to send the dissimilar data to the storage unit, so that the preceding data is combined with the dissimilar data to form new preceding data.

6. The data acquisition apparatus of a spectrum analyzer according to claim 5, wherein, The data processing module includes: The downconversion module is used to convert the spliced ​​data into low-frequency data using resolution bandwidth parameters, wherein the resolution bandwidth parameters are pre-stored in the spectrum analyzer. The first filtering module is used to filter low-frequency data using resolution bandwidth parameters to generate low-frequency filtered data. The detection module is used to convert low-frequency filtered data into video voltage data; The second filtering module is used to filter the video voltage data to generate the video processing data.

7. The data acquisition apparatus of a spectrum analyzer according to claim 5, wherein, The comparison result processing module is further configured to discard the video processing data if the video processing data is consistent with the preceding data.

8. A user terminal comprising a processor and a memory, said memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the data acquisition method according to any one of claims 1 to 4.

9. A storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the data acquisition method according to any one of claims 1 to 4.

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