Signal analyzer semantic recognition control method, signal analyzer and medium

Abstract

The present invention provides a signal analyzer semantic recognition control method, a signal analyzer and a medium, comprising the steps of acquiring user speech; extracting semantic information of characters, numbers and English in the user speech using a pre-built speech recognition model; pre-processing the semantic information to acquire standardized text; extracting keywords in the standardized text according to preset extraction rules and dictionaries, matching program control statements based on the keywords and building an instruction mapping library; querying program control instructions in a preset scenario database, and classifying the instruction mapping library into scenario instructions or non-scenario instructions; judging the instruction type, and when the instruction mapping library is a scenario instruction, querying to acquire a program control instruction set, and controlling the instrument step by step based on the preset steps in the instruction set; and when the instruction mapping library is a non-scenario instruction, acquiring parameters from the standardized control statement, combining the acquired parameters with the program control statement, and controlling the instrument based on the preset steps of the program control instruction without setting a large number of complex parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of signal analyzers, and in particular to a semantic recognition control method for a signal analyzer. Background Art

[0002] In recent years, with the rapid development of artificial intelligence technology, voice recognition technology has become more mature. Due to its convenience and efficiency, it provides efficient and safe product experience for application scenarios such as smart home, robot system, and car system. Signal analyzers also face such needs during use. Since the DUT cannot modify parameters through the panel due to its distance, sending commands through voice has great application value. Secondly, with the increase in complexity and operation frequency, users are in urgent need of simpler and more efficient testing methods. The intelligent control method based on semantic recognition provides scenario-based testing functions, so that users do not need to set a large number of complex parameters when facing the DUT. Taking the measurement of phase noise as an example, the user only needs to say the "measure phase noise" command, and the signal analyzer will automatically switch the phase noise function and search the carrier after receiving the command, and display the phase noise curve and the decade frequency table, etc., which previously required step-by-step cumbersome settings to obtain results. Therefore, it is of great significance to study the intelligent control method of signal analyzers based on semantic recognition.

[0003] At present, because the existing technologies mostly use the method of command word parsing, it is not only difficult to maintain, but also has a low recognition rate when facing multiple expressions under a single command; on the other hand, with the development of the field of signal analysis, in order to adapt to signal measurements in different scenarios, the complexity of instrument functions is gradually increasing, so that when measuring complex signals, a large number of parameters are usually required, which requires repeated voice interactions, and the convenience of voice interaction is inevitably lost. These two shortcomings make the measurement of complex signals by the previous voice control method very inefficient. The voice control method for signal analyzers only supports simple single parameter modification control, and does not have intelligent control functions based on semantic recognition. The present invention aims to provide a solution to this demand. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the first object of the present invention is to provide a signal analyzer semantic recognition control method, comprising:

[0005] Get user voice;

[0006] Use the pre-built speech recognition model to extract the semantic information of characters, numbers and English in the user's speech;

[0007] Preprocess the semantic information to obtain standardized text;

[0008] Extract keywords from the standardized text according to preset extraction rules and dictionaries, match program-controlled statements based on keywords, and build an instruction mapping library containing keywords, text instruction information, and program-controlled statements;

[0009] Searching for program control instructions in a preset scene database, and classifying the instruction mapping library into scene instructions or non-scene instructions;

[0010] Determine the instruction type. When the instruction mapping library is a scene instruction, query to obtain the program-controlled instruction set, and control the instrument step by step based on the preset steps in the instruction set. When the instruction mapping library is a non-scene instruction, obtain parameters from the standardized control statement, combine the obtained parameters with the program-controlled statement, and control the instrument based on the preset steps of the program-controlled instruction.

[0011] Specifically, the preprocessing of the semantic information is to use a word segmentation tool to segment the semantic information into a word sequence consisting of multiple words, input the word sequence into a pre-trained FastCorrect model, and perform standardized error correction on the semantic information.

[0012] Specifically, the dictionary includes a first keyword dictionary and a second keyword dictionary, the first keyword dictionary and the second keyword dictionary are divided according to a threshold value of the weight of the keyword in the instruction, the first keyword dictionary contains keywords in the instruction whose weight is greater than the threshold value, and the second keyword dictionary contains keywords in the instruction whose weight is less than the threshold value, the first keyword dictionary and the second keyword dictionary contain several categories, each category contains several keywords, and the extraction rule of the keywords in the standardized text is:

[0013] The standardized texts are searched one by one using the keywords in the first keyword dictionary to filter out the standardized texts containing the keywords in the first keyword dictionary, and the standardized texts that do not contain the keywords in the first keyword dictionary are searched one by one using the keywords in the second keyword dictionary to filter out the standardized texts containing the keywords in the second keyword dictionary.

[0014] Specifically, a semantic similarity matching algorithm is used in the category to which the keyword belongs to traverse and calculate the similarity between each program-controlled sentence and the standardized text, and the program-controlled sentence with the highest similarity and a similarity greater than a threshold is matched with the standard text.

[0015] Specifically, the speech recognition model is an improved Transformer speech recognition model, which divides the feedforward operation of the model into two times and adds a convolution operation.

[0016] Specifically, a parameter matching algorithm is used to obtain parameters in the standardized text.

[0017] Specifically, the audio corresponding to the wake-up word in the user's voice is obtained, and semantic recognition is activated within a preset time; the system automatically exits after failing to obtain the user's voice within the preset time, or exits after obtaining the audio corresponding to the sleep word in the user's voice.

[0018] Specifically, according to the content of the programming statement, some instruction mapping libraries also include a parameter dictionary, in which the units covered by the corresponding programming statement are recorded. The parameter dictionary identifies the text instruction information and selects matching units from the units covered by the programming statement.

[0019] A second object of the present invention is to provide a signal analyzer, comprising:

[0020] Audio acquisition module, used to receive user voice;

[0021] A semantic recognition module, which obtains user speech, extracts semantic information of Chinese characters, numbers and English in the user speech using a pre-built speech recognition model, pre-processes the semantic information, and obtains standardized text;

[0022] An instruction matching module, which extracts keywords from the standardized text according to preset extraction rules and dictionaries, matches program-controlled statements based on the keywords, builds an instruction mapping library containing keywords, text instruction information, and program-controlled statements, and searches for program-controlled statements in the category to which the keywords belong;

[0023] A scene control module, wherein the scene control module searches for program control instructions in a preset scene database, classifies the instruction mapping library into scene instructions or non-scene instructions, and when the instruction mapping library is a scene instruction, queries to obtain a program control instruction set; when the instruction mapping library is a non-scene instruction, obtains parameters from a standardized control statement, combines the obtained parameters with the program control statement, and obtains the program control instruction;

[0024] An execution module, wherein the execution module obtains program control instructions and executes instruction operations.

[0025] The third object of the present invention is to provide a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the signal analyzer semantic recognition control method is implemented.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The present invention adopts semantic recognition to train and recognize the instructions of the signal analyzer. After capturing the voice input, the semantic recognition module will first perform voice recognition to obtain the text control instructions corresponding to the voice signal, and then perform word segmentation and error correction on the text control instructions, and output standardized corrected text for instruction matching. After the text is standardized, it is classified based on keywords and related program-controlled instructions are queried in the category to which the keywords belong. Finally, scene control or non-scene control is performed through program-controlled instructions. When facing the device under test, the user does not need to set a large number of complex parameters. After matching the relevant scene instructions, the parameters that previously required multiple steps to set are automatically executed. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a flow chart of the present invention;

[0029] Figure 2 Designed for the framework of the present invention;

[0030] Figure 3 This is a flow chart of voice wake-up and exit of the present invention;

[0031] Figure 4 This is a flow chart of semantic recognition of the present invention;

[0032] Figure 5 This is a flow chart of instruction mapping and scene control of the present invention. DETAILED DESCRIPTION

[0033] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0034] The terms "first", "second", etc. in the specification and claims of the present invention are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "comprising" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, or device that includes a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules that are not listed, or may optionally include other steps or modules that are inherent to these processes, methods, products, or devices.

[0035] The present invention provides a signal analyzer semantic recognition control method, which includes the following four parts: voice wake-up and exit, semantic recognition, command matching and scene control. The present invention is described below in conjunction with preferred implementation steps. Figure 1-Figure 4, shows the process of some embodiments of the identification control method according to the present application. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

[0036] S1, obtain the audio corresponding to the wake-up word in the user's voice and activate semantic recognition within a preset time.

[0037] The audio acquisition module receives the user's voice, which can be a microphone, etc. The voice wake-up module captures the audio corresponding to the preset wake-up word (such as "Hello, XX") in the user's language. The voice wake-up module activates the semantic recognition module to change the semantic recognition module from a static state to an active state, and at the same time gives the user voice feedback (such as "Are you there"). By setting the voice wake-up module, the resource usage of the semantic recognition module during idle time can be reduced.

[0038] An activation time is preset in the voice wake-up module. If the audio collection module does not collect the user's voice within the preset activation time, it will automatically exit.

[0039] When activated, the voice wake-up module captures the audio corresponding to the preset sleep word in the user's language, which can actively trigger the exit mechanism. The voice wake-up module changes the semantic recognition module from an active state to a static state.

[0040] In addition to preset wake-up words and sleep words, the semantic recognition module can also be activated or put into sleep mode by, for example, key operations.

[0041] S2, obtaining user voice.

[0042] S3 uses a pre-built speech recognition model to extract semantic information of characters, numbers, and English in the user's speech.

[0043] The speech recognition model based on the Transformer structure is used to extract the semantic information of characters, numbers and English in the user's speech. Based on the traditional Transformer, the model's feedforward operation is divided into two times and the convolution operation is added to improve the ability to analyze speech signals.

[0044] The speech recognition model is trained by combining public datasets and project-built datasets. In order to improve the coverage of the dataset, multiple samples containing different common phrases are added for each control instruction, as shown in the following table.

[0045]

[0046] By controlling the weight ratio of the self-built data set, the speech recognition accuracy of the signal analyzer is improved, and the user's speech is output as a character string through the speech recognition model.

[0047] S4, preprocess the semantic information to obtain standardized text.

[0048] The preprocessing of semantic information includes word segmentation and standardization error correction of the character string output by speech recognition, and converting the character string into standardized text that conforms to the standard control instructions of the signal analyzer, so as to improve the accuracy of subsequent instruction mapping.

[0049] The word segmentation tool can use Jieba word segmentation, LTP word segmentation, IR word segmentation and other word segmentation tools. First, use the word segmentation tool to take the speech recognition string as input and segment the string into a word sequence consisting of multiple words. If the speech recognition result is "turn on the automatic level function", the ideal result after word segmentation is "turn on / automatic level / function / ". For example, if "query the offset frequency of offset A", the ideal result after word segmentation is "query / offset / A / the / offset frequency / is / ".

[0050] The correction model takes the word sequence after word segmentation as input and performs standardized correction on the word sequence. The correction model preferably uses the FastCorrect model. During the training process, the FastCorrect model constructs a training set by randomly deleting, inserting and replacing words in the text data to make the training set close to the pattern of the error rate generated by the real speech recognition. The FastCorrect model calculates the minimum editing distance between the input sequence and the output text, that is, the minimum number of editing operations required. When replacing text, homophones are preferred for replacement. The general errors in speech recognition are homophone errors. The training set produced in this way can effectively improve the error correction accuracy of the FastCorrect model.

[0051] S5, extracting keywords from the standardized text according to preset extraction rules and dictionaries, matching program control statements based on keywords and building an instruction mapping library.

[0052] First, with the help of the program instructions and instruction parameters in the "Signal Analyzer Program Control Manual", an instruction mapping library containing text instructions and corresponding program instructions is constructed. The data in the instruction mapping library includes keywords, text instruction information and program control statements. According to the text instruction information and its corresponding program control statements, the instruction mapping library may also include a program control parameter dictionary. The signal analyzer obtains the corresponding program control instructions according to the information recorded in the instruction mapping library and controls the signal analyzer to execute instruction operations.

[0053] The signal analyzer has a large number of functions and parameters, and it is inefficient to directly establish a separate library to match. Therefore, the core of the instruction mapping library is to classify all program-controlled statements according to the keywords in the standardized text. This application takes into account the retrieval speed and sets a weight threshold. According to the weight ratio of the keywords, it is divided into the first keyword dictionary and the second keyword dictionary, where the group with a weight greater than the threshold is the first keyword dictionary. The first keyword dictionary is mainly based on the core content of the first-level menu of the signal analyzer, including the following categories: {frequency, bandwidth, amplitude, scanning, trajectory, marking, measurement, triggering, peak, automatic, display, input and output}; the keywords with a parameter function group weight less than the threshold are classified into the second keyword dictionary. The number of parameters in this function group is not as large as the volume under the first-level menu such as frequency and amplitude, such as "limit line", "transmission factor", etc., including the following categories {limit, transmission, signal, noise, gain, field strength, antenna, phase noise, direct sampling, spurious, harmonics, Ndb, mark count, carrier-to-noise ratio}. In the first keyword dictionary and the second keyword dictionary, the major categories are sorted according to the weight of each category.

[0054] The first keyword dictionary and the second keyword dictionary include the above categories respectively, and each category also includes several keywords. For example, the recognized text instructions are "set center frequency to 100MHz", "set start frequency to 20MHz", and "set bandwidth to 1GHz". The keywords "center frequency", "start frequency", and "bandwidth" in the above three text instructions all belong to the "frequency" category, so the above text instructions are instructions of the "frequency" category; for example, the recognized text instructions are "set resolution bandwidth to 100KHz", "set video bandwidth to 1MHz", and "resolution bandwidth automatic". The keywords "resolution bandwidth" and "video bandwidth" in the above three text instructions all belong to the "bandwidth" category, so the above text instructions are instructions of the "bandwidth" category; for another example, the recognized text instruction is "open limit line", and the keyword "limit line" of the text instruction belongs to the "limit" category, so the above text instruction is an instruction of the "limit" category; for another example, the recognized text instruction is "open transmission factor" and "select transmission factor". The keyword "transmission factor" of the instruction all belongs to the "transmission" category, so the above text instruction is a "transmission" category.

[0055] The rule for extracting keywords is as follows: using keywords in the first keyword dictionary, according to the weight of the category to which the keywords belong, the standardized texts are searched one by one in order, the standardized texts containing the keywords in the first keyword dictionary are screened out, and the texts are classified into the categories of the keywords they contain, and an instruction mapping library is established. For standardized texts that do not contain keywords in the first keyword dictionary, the keywords in the second keyword dictionary are used, according to the weight of the category to which the keywords belong, the standardized texts containing the keywords in the second keyword dictionary are searched one by one in order, and an instruction mapping library is established. Standardized texts that do not contain keywords in either the first keyword dictionary or the second keyword dictionary are excluded as invalid texts.

[0056] According to the content of the program control statement, some instruction mapping libraries also include a parameter dictionary, which records the units that can be covered in the corresponding program control statement, specifically including the following parameters: <hz> , <khz> , <mhz> , <ghz> , <thz> , <db> , <dbm> , <w> , <v> , <s>, , <us> , <ns>…}. By identifying the text instruction information, a matching unit is selected from the units covered by the program control statement.

[0057] The instruction mapping library established is as follows:

[0058]

[0059] The semantic similarity matching algorithm is used to match the text instruction information in the instruction mapping library with the program-controlled statements. The semantic similarity matching algorithm traverses and calculates the similarity between each program-controlled statement and the standardized text in the category to which the keyword belongs, and matches the program-controlled statement with the highest similarity and a similarity greater than a threshold with the standard text. If a similar instruction cannot be matched, the broadcast informs the user that the match failed.

[0060] S6, querying the program control instructions in the preset scene database, and classifying the instruction mapping library into scene instructions or non-scene instructions.

[0061] After matching the standardized text with the program-controlled statements, first determine whether the instruction is a scenario instruction or a non-scenario instruction. The single subdivision parameter function item of the signal analyzer is called a non-scenario instruction, such as "set center frequency", "set resolution bandwidth", etc., and the scenario instruction is generally a set of serialized control instructions for the purpose of facilitating user measurements and improving user measurement efficiency. When the user uses a signal analyzer to measure a certain measurement scenario, the text instruction obtained is "measure power", and the power measurement includes the following five steps: 1. Create a new swept frequency analysis; 2. Set the center frequency; 3. Set the bandwidth; 4. Peak search; 5. Read the mark Y-axis 3 value. Although each of the above five steps is a non-scenario instruction, this instruction that is implemented by multiple non-scenario instructions or scenario instructions to achieve the measurement purpose is a scenario instruction.

[0062] The program control statement in the instruction mapping is queried in the scene database. If the program control instruction is found in the scene database, the instruction is a scene instruction. The queried control instruction set (instruction sequence) is used to control the instrument step by step to achieve the effect of scene control. If the instruction is not found in the scene database, the instruction is a non-scene instruction. The parameter matching algorithm is used to obtain parameters from the standardized text and combined with the program control statement to obtain the program control instruction. The instrument is controlled according to the program control instruction. After matching the corresponding program control instruction or program control instruction set, the voice broadcast informs the user.

[0063] The present invention also provides a signal analyzer, comprising:

[0064] Audio acquisition module, used to receive user voice;

[0065] Audio output module, used to broadcast the processing results to the user;

[0066] The voice wake-up module is used to capture the audio corresponding to the preset wake-up word in the user's language and activate the semantic recognition module. When the semantic recognition module is activated, the audio corresponding to the preset sleep word in the user's language is captured, which can actively trigger the exit mechanism.

[0067] A semantic recognition module, which obtains user speech, uses a pre-built speech recognition model to extract semantic information of Chinese characters, numbers and English in the user's speech, and pre-processes the semantic information through a word segmentation tool and a correction model to obtain a standardized text;

[0068] An instruction matching module, which extracts keywords from the standardized text according to preset extraction rules and dictionaries, matches program-controlled statements based on the keywords, builds an instruction mapping library containing keywords, text instruction information, and program-controlled statements, and searches for program-controlled statements in the category to which the keywords belong;

[0069] A scene control module, wherein the scene control module searches for program control instructions in a preset scene database, classifies the instruction mapping library into scene instructions or non-scene instructions, and when the instruction mapping library is a scene instruction, queries to obtain a program control instruction set; when the instruction mapping library is a non-scene instruction, obtains parameters from a standardized control statement, combines the obtained parameters with the program control statement, and obtains the program control instruction;

[0070] An execution module, wherein the execution module obtains program control instructions and executes instruction operations.

[0071] It is understandable that the modules recorded in the signal analyzer correspond to the steps in the signal analyzer semantic recognition control method. The operations, features and beneficial effects described above for the method are also applicable to the signal analyzer and the modules contained therein, and will not be repeated here.

[0072] Another embodiment of the present invention is a storage medium, which is a computer-readable storage medium. The computer-readable storage medium stores computer instructions. The computer instructions can be stored in a memory and, when executed by one or more processors, thereby implementing the semantic recognition control method described in the above method embodiment.

[0073] If the module is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a signal analyzer to perform all or part of the steps of the method described in each embodiment of the present invention.

[0074] The memory is used to store program code. The memory can be a circuit with storage function in an integrated circuit without a physical form, such as RAM (Random-Access Memory), FIFO (First In First Out), etc. Alternatively, the memory can also be a memory in a physical form, such as a memory stick, TF card (Trans-flash Card), smart media card (smart media card), secure digital card (secure digital card), flash memory card (flash card) and other storage devices. The memory can communicate data with the processor through a communication bus. The memory may include an operating system, a network communication module and a semantic recognition control program. The operating system is a program for managing and controlling signal analyzers and software resources, and supports the operation of semantic recognition control programs and other software and / or programs. The network communication module is used to realize communication between the components inside the memory, as well as communication with other hardware and software in the semantic recognition control program.

[0075] The processor may include one or more microprocessors and digital processors. The processor may call the program code stored in the memory to execute related functions. The processor is also called a central processing unit (CPU), which is a large-scale integrated circuit and a computing core (Core) and a control core (Control Unit).

[0076] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention.< / ns> < / us> < / s> < / v> < / w> < / dbm> < / db> < / thz> < / ghz> < / mhz> < / khz> < / hz>

Claims

1. A signal analyzer semantic recognition control method, characterized in that: include: Get user voice; Use the pre-built speech recognition model to extract the semantic information of characters, numbers and English in the user's speech; Preprocess the semantic information to obtain standardized text; Extract keywords from the standardized text according to preset extraction rules and dictionaries, match program-controlled statements based on keywords, and build an instruction mapping library containing keywords, text instruction information, and program-controlled statements; Searching for program control instructions in a preset scene database, and classifying the instruction mapping library into scene instructions or non-scene instructions; Determine the instruction type. When the instruction mapping library is a scene instruction, query to obtain the program-controlled instruction set, and control the instrument step by step based on the preset steps in the instruction set. When the instruction mapping library is a non-scene instruction, obtain parameters from the standardized control statement, combine the obtained parameters with the program-controlled statement, and control the instrument based on the preset steps of the program-controlled instruction.

2. The signal analyzer semantic recognition control method according to claim 1, characterized in that: The preprocessing of the semantic information is to use a word segmentation tool to segment the semantic information into a word sequence consisting of multiple words, input the word sequence into a pre-trained FastCorrect model, and perform standardized error correction on the semantic information.

3. The signal analyzer semantic recognition control method according to claim 1, characterized in that: The dictionary includes a first keyword dictionary and a second keyword dictionary. The first keyword dictionary and the second keyword dictionary are divided according to a threshold value of a weight of a keyword in an instruction. The first keyword dictionary contains keywords in the instruction whose weight is greater than the threshold value, and the second keyword dictionary contains keywords in the instruction whose weight is less than the threshold value. The first keyword dictionary and the second keyword dictionary contain several categories, and each category contains several keywords. The extraction rule of keywords in the standardized text is: The standardized texts are searched one by one using the keywords in the first keyword dictionary to filter out the standardized texts containing the keywords in the first keyword dictionary, and the standardized texts that do not contain the keywords in the first keyword dictionary are searched one by one using the keywords in the second keyword dictionary to filter out the standardized texts containing the keywords in the second keyword dictionary.

4. The signal analyzer semantic recognition control method according to claim 3, characterized in that: A semantic similarity matching algorithm is used in the category to which the keyword belongs to traverse and calculate the similarity between each program-controlled sentence and the standardized text, and the program-controlled sentence with the highest similarity and a similarity greater than a threshold is matched with the standard text.

5. The signal analyzer semantic recognition control method according to claim 1, characterized in that: The speech recognition model is an improved Transformer speech recognition model, which divides the feedforward operation of the model into two times and adds a convolution operation.

6. The signal analyzer semantic recognition control method according to claim 1, characterized in that: Use the parameter matching algorithm to obtain the parameters in the normalized text.

7. The signal analyzer semantic recognition control method according to claim 1, characterized in that: Acquire the audio corresponding to the wake-up word in the user's voice and activate semantic recognition within a preset time; automatically exit if the user's voice is not acquired within the preset time, or exit after acquiring the audio corresponding to the sleep word in the user's voice.

8. The signal analyzer semantic recognition control method according to claim 1, characterized in that: According to the content of the program control statement, part of the instruction mapping library also includes a parameter dictionary, in which the units covered by the corresponding program control statement are recorded. The parameter dictionary identifies the text instruction information and selects the matching unit from the units covered by the program control statement.

9. A signal analyzer, characterized in that: include: Audio acquisition module, used to receive user voice; A semantic recognition module, which obtains user speech, extracts semantic information of Chinese characters, numbers and English in the user speech using a pre-built speech recognition model, pre-processes the semantic information, and obtains standardized text; An instruction matching module, which extracts keywords from the standardized text according to preset extraction rules and dictionaries, matches program-controlled statements based on the keywords, builds an instruction mapping library containing keywords, text instruction information, and program-controlled statements, and searches for program-controlled statements in the category to which the keywords belong; A scene control module, wherein the scene control module searches for program control instructions in a preset scene database, classifies the instruction mapping library into scene instructions or non-scene instructions, and when the instruction mapping library is a scene instruction, queries to obtain a program control instruction set; when the instruction mapping library is a non-scene instruction, obtains parameters from a standardized control statement, combines the obtained parameters with the program control statement, and obtains the program control instruction; An execution module, wherein the execution module obtains program control instructions and executes instruction operations.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the signal analyzer semantic recognition control method according to any one of claims 1 to 8 is implemented.

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