Intelligent dialogue control system of spaceflight measurement and control equipment

Abstract

The invention discloses an intelligent dialogue control system for spaceflight measurement and control equipment, which uses a voice interaction mode to more quickly and conveniently control the spaceflight measurement and control equipment and carry out state check and auxiliary decision making. The system comprises a master control AI model with spaceflight measurement and control equipment knowledge, an instruction conversion program and a measurement and control equipment action execution subsystem. The main control AI model with spaceflight measurement and control equipment knowledge is used for acquiring voice input of an operator and carrying out natural language understanding on a voice text to obtain specific intention semantics; the main control AI model with spaceflight measurement and control equipment knowledge is also used for performing task decomposition and autonomous processing according to intention semantics obtained by natural language understanding processing or issuing an instruction to a corresponding instruction conversion program; the instruction conversion program is used for sending a control instruction to the corresponding measurement and control equipment action execution subsystem according to the issued instruction so as to complete a task; and the measurement and control equipment action execution subsystem is used for completing a specific action task according to the received control instruction. According to the method, the task is split into specific main models or subsystem sub-tasks for processing in a layer-by-layer refining mode, data information interaction between systems is supported, the efficiency and accuracy of intelligent assistance and control in the field of spaceflight measurement and control equipment are improved, and higher-quality intelligent services are provided for users.

Description

Technical Field

[0001] The present invention relates to the fields of aerospace measurement and control and artificial intelligence, and in particular to an intelligent dialogue control system for aerospace measurement and control equipment. Background Art

[0002] Existing aerospace measurement and control equipment usually uses manual operation according to procedures or automation according to predetermined procedures. The means of human-computer interaction are single, and the interactive information between systems is not fully extracted and uniformly summarized. At the same time, the efficiency of manually checking information, checking status and operating equipment through clicking and browsing is not high, and there are insufficient means to assist decision-making when encountering problems.

[0003] In addition, the existing general artificial intelligence technology lacks knowledge in the vertical field of aerospace measurement and control equipment, and it is difficult to solve problems in this field. Therefore, the main control AI model with knowledge of aerospace measurement and control equipment can better solve problems in the vertical field and achieve more flexible assistance and control.

[0004] At present, it is very important to open up the information flow and control flow between artificial intelligence and aerospace measurement and control equipment entities, and to make the internal data interface of the equipment visible and controllable, which can effectively improve work efficiency and the ability to complete tasks. Summary of the invention

[0005] In view of the defects in the prior art, the present invention provides an intelligent dialogue control system for aerospace measurement and control equipment, which uses voice interaction to more quickly and conveniently control aerospace measurement and control equipment, conduct status checks and assist decision-making.

[0006] In the first aspect, an intelligent dialogue control system for aerospace measurement and control equipment includes a main control AI model with knowledge of aerospace measurement and control equipment, an instruction conversion program, and a measurement and control equipment action execution subsystem, wherein the main control AI model is connected to the measurement and control equipment action execution subsystem through the instruction conversion program;

[0007] The master control AI model with knowledge of aerospace measurement and control equipment is used to: have the ability to obtain the operator's voice input and perform natural language understanding on the voice text to obtain specific intention semantics;

[0008] The master control AI model with knowledge of aerospace measurement and control equipment is also used to: decompose tasks according to the intention semantics obtained by natural language understanding and processing, and autonomously process or issue instructions to the corresponding instruction conversion program;

[0009] The command conversion program is used to: send control instructions to the corresponding measurement and control equipment action execution subsystem according to the command conversion program for issuing instructions to complete the task;

[0010] The measurement and control equipment action execution subsystem is used to complete specific action tasks according to the received control instructions.

[0011] Furthermore, the main control AI model with knowledge of aerospace measurement and control equipment includes a voice intelligent recognition and correction module, which is used to:

[0012] Collect user audio, and determine whether to trigger the system wake-up word "Xiaozhi Xiaozhi" according to the user audio;

[0013] If not, then remain in standby mode;

[0014] If so, the system enters the working mode and intelligently converts the user voice information into voice text. The intelligent conversion mainly refers to the use of neural networks to achieve end-to-end input and output, while using the context to correct the output text to further improve the accuracy of the voice-to-text conversion.

[0015] Furthermore, the master control AI model with knowledge of aerospace measurement and control equipment includes natural language understanding, which is used to:

[0016] The text converted from the speech is combined with the AI ​​model for natural language processing and understanding to obtain the user's true intention and content.

[0017] The master control AI model with knowledge of aerospace measurement and control equipment also includes a task splitting function for:

[0018] According to the obtained real intention and content of the user, it is converted into a specific task function, so as to autonomously process or call different functions to realize the splitting of subtasks.

[0019] The instruction conversion program is specifically used for:

[0020] Receive subtasks, parse the subtasks, obtain the measurement and control equipment action execution subsystem and extract the associated execution actions and action times; call a specific programmed execution process or measurement and control equipment data query command according to the measurement and control equipment action execution subsystem.

[0021] The measurement and control equipment action execution subsystem is used to:

[0022] Receive the programmed execution process, automatically control the measurement and control equipment antenna rotation, working mode switching and channel switching index test, and independently summarize the inspection status and comparison parameter results.

[0023] The measurement and control equipment action execution subsystem is also used for:

[0024] Receive the measurement and control equipment data query command, convert the query command into an SQL query statement or a log query statement, query the corresponding result, and return the main control AI model with aerospace measurement and control equipment knowledge.

[0025] The measurement and control equipment action execution subsystem is also used for:

[0026] During the programmed execution process, the progress and results of the action execution are returned to the main control AI model that has knowledge of aerospace measurement and control equipment, and further monitoring is performed to determine whether there are interruptions and other command operations.

[0027] The master control AI model with aerospace measurement and control equipment knowledge includes aerospace measurement and control knowledge dialogue function:

[0028] Based on the user's questions, large model calculations are performed to obtain the calculated answers to the conversations, which are then output in real time for the user to view and understand.

[0029] The master control AI model with aerospace measurement and control equipment knowledge includes aerospace measurement and control knowledge recommendation function:

[0030] According to the user's question, the recommendation algorithm is calculated to obtain the most likely Figure 1 The three questions are output in real time for the user to choose as the final conversation content.

[0031] The beneficial effects of the present invention are as follows: the user's voice text is acquired and natural language understanding is performed through a main control AI model with knowledge of aerospace measurement and control equipment to obtain a specific intention semantic result, and at the same time, instructions are issued to the corresponding instruction conversion program according to the processing results, and the task conversion is assigned to the corresponding measurement and control equipment. The subsystem executes the corresponding actions of the control instructions according to the task, and the task is decomposed to the end of the measurement and control equipment for processing in a multi-layer task refinement and integrated linkage manner, which supports the ability of the AI ​​model to directly control equipment, test indicators and compare status, thereby improving the efficiency and accuracy of intelligent completion of measurement and control tasks, and at the same time can provide users with intelligent services to assist decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0033] Figure 1 A module block diagram of an intelligent dialogue control system for aerospace measurement and control equipment provided in Example 1 of the present invention.

[0034] Figure 2 A method flow chart of an intelligent dialogue control system for aerospace measurement and control equipment provided in Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0035] The embodiments of the technical solution of the present invention will be described in detail below in conjunction with the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and are therefore only used as examples of the present invention, and cannot be used to limit the scope of protection of the present invention. It should be noted that, unless otherwise specified, the technical terms or scientific terms used in this application should be the usual meanings understood by those skilled in the art to which the present invention belongs.

[0036] Embodiment 1

[0037] like Figure 1 As shown, an intelligent dialogue control system for aerospace measurement and control equipment includes a main control AI model with knowledge of aerospace measurement and control equipment, an instruction conversion program and a measurement and control equipment action execution subsystem. The main control AI model is connected to the measurement and control equipment action execution subsystem through the instruction conversion program.

[0038] The master control AI model with knowledge of aerospace measurement and control equipment is used to: have the ability to obtain the operator's voice input and perform natural language understanding on the voice text to obtain specific intention semantics;

[0039] The master control AI model with knowledge of aerospace measurement and control equipment is also used to: decompose tasks according to the intention semantics obtained by natural language understanding and processing, autonomously perform computational answering or perform decomposed tasks according to a predetermined process, and issue instructions to the corresponding instruction conversion program;

[0040] The command conversion program is used to: send control instructions to the corresponding measurement and control equipment action execution subsystem according to the command conversion program for issuing instructions to complete the task;

[0041] The measurement and control equipment action execution subsystem is used to complete specific action tasks according to the received control instructions.

[0042] The master control AI model with aerospace measurement and control equipment knowledge and the instruction conversion program are deployed on the same hardware device. The master control AI model with aerospace measurement and control equipment knowledge and the instruction conversion program communicate with each other through an internal interface to achieve instruction conversion.

[0043] Furthermore, the main control AI model with knowledge of aerospace measurement and control equipment includes an intelligent speech recognition correction module, and the speech recognition correction module includes three parts: speech endpoint detection, speech recognition model, and text post-processing.

[0044] The speech endpoint detection part uses a feedforward sequence memory neural network. The main difference between it and the feedforward fully connected network is that a memory module is added to its hidden layer. The function of the memory module is to encode the previous and next units of each hidden state together, so as to capture the previous and next relationship of the sequence. The network model can consider contextual information when building and training, and the training and inference speeds are fast, and the latency is controllable. At the same time, according to the size of the endpoint detection model and the requirements of low latency, the network structure and the number of frames calculated to the right are adapted.

[0045] The speech recognition model consists of an encoder, a predictor, a sampler, a decoder, and a loss function. The encoder and decoder use a Transformer structure to encode and extract features of speech. The predictor uses a feedforward neural network to predict the number of target characters and extract the acoustic vectors corresponding to the target characters. The sampler enhances the model's modeling of contextual semantics based on the browsing language model to improve the accuracy of speech recognition. The loss functions include cross entropy and mean absolute error.

[0046] The text post-processing part adopts the Transformer model with controllable delay, and uses the prior knowledge in text error correction, that is, the information of addition, deletion and modification operations. It first aligns the erroneous text and the correct text, and uses the editing distance of the text to guide the alignment logic. Through alignment, we can know which words to delete, which words to add, and which words to replace.

[0047] The speech recognition and correction module uses the audio input device to collect the audio signal input by the user in real time, and determines whether the keyword that triggers the system wake-up, namely the wake-up word "Xiaozhi Xiaozhi", is detected based on the user audio. If the wake-up keyword is not recognized, the system remains in standby mode and only receives but does not output the user's current voice information; if so, it enters the working mode, further recognizes and corrects the user audio to obtain the final text data, and converts the user's voice information into text information.

[0048] The wake-up keyword system is preset according to actual needs, and the word "Xiaozhi Xiaozhi" is weighted at multiple levels.

[0049] Preferably, the speech recognition correction module described in this embodiment has aerospace measurement and control field and user expansion and enhancement processing capabilities, including:

[0050] (1) Space tracking and control domain enhancement processing: In order to adapt to the speech vocabulary sentences of the specialized knowledge in the field of space tracking and control equipment, the domain enhancement processing technology can be used. In the initial stage, the documents and materials in the field of space tracking and control equipment are organized into audio data, and the speech training of the recognition correction model is carried out to enhance the speech training verification samples in the specialized field. Through the analysis and training of the specialized field samples, a specific speech recognition correction model can be generated, which can capture some characteristics of the vertical field proper nouns, thereby improving the recognition accuracy of the field speech.

[0051] (2) User enhancement processing: In order to adapt to the needs of speech recognition correction for users with different pronunciation characteristics and operator personnel, user enhancement processing technology can be used. In the initial training and modeling stage, the voice signals of people from different regions, pronunciations and specific operators in daily and system use are collected and recorded to collect specific voice samples with different audio pronunciations. Through the training and modeling of this part of the voice samples, a personalized voice model can be generated. The model can capture the personalized characteristics of common and different pronunciation habits, speaking speed, tone, etc., thereby further improving the recognition accuracy of such enhanced user voices.

[0052] (3) Noise environment enhancement processing: In order to meet the accuracy requirements of speech recognition correction in different noise environments, noise signal enhancement processing technology is used. In the actual training process, noise, equipment environment sound and other factors are added to the collected normal training speech signal samples. The method of random sampling training iteration after fusion of noise is adopted to analyze the recognition difference between normal speech signals and speech signals after noise addition, and obtain the encoding and decoding module of the signal under the noise state. According to the characteristics of the noise, a model that can effectively suppress the recognition noise is obtained to reduce the impact of the noise. Then, the speech digital signal can be processed according to the characteristics of the noise to achieve the recognition correction adaptive ability under noise conditions. For example, according to the real-time environmental noise conditions, some parameters of the noise suppression model can be expanded and fine-tuned to adapt to the characteristics and intensity of different environmental noises to achieve better recognition accuracy.

[0053] (4) Transfer learning capability: In order to achieve the dynamic adaptation and functional expansion capabilities of the speech recognition model, model transfer learning and external expansion mechanisms can be introduced. Transfer learning can further change the model parameter status based on offline audio data, correct the model errors based on the user's error correction feedback, or fine-tune the model parameters based on the user's voice training feedback to achieve overall functional adjustment and adaptability. At the same time, a partially adjustable fine-tuning channel is added to the model branch to dynamically adjust the parameters and status of this part of the speech recognition model based on the real-time voice input of some users and the feedback on the satisfaction of the results. For example, the neural network parameters of the external branch model can be adjusted according to the user's pronunciation habits and speaking speed, or the parameters of the noise encoding and decoding model can be adjusted according to the intensity and characteristics of the environmental noise. Through dynamic migration and expansion adjustment, the speech recognition model can be better adapted to different voice and scene conditions.

[0054] Furthermore, the main control AI model with knowledge of aerospace measurement and control equipment includes a natural language understanding module, which adopts a deep neural network model commonly used in natural language processing, including but not limited to one-dimensional convolutional neural network, recurrent neural network, long short-term memory network and attention mechanism network model (Transformer). The deep neural network model is used to achieve rapid understanding of complex user input text and semantics. These models can learn the structure and semantics of text sentence content, so as to better and more accurately understand user intentions and needs.

[0055] The natural language understanding module analyzes the speech text converted by the speech recognition module based on the features automatically calculated and extracted from the text to understand the content and semantics of the speech text, and effectively segments the speech text according to the content and semantics of the text itself to decompose the speech text into smaller language units, such as meaningful entity nouns or phrases, which facilitates subsequent semantic analysis and understanding.

[0056] The natural language understanding module performs semantic analysis on the text after word segmentation processing, realizes the questioning method, tone and scenario understanding of the sentence, realizes preliminary analysis of user emotional needs and auxiliary understanding, and parses some proper nouns, such as "Can a certain device start a status check" and "Can a certain device start a status check?" The marking of modal particles can effectively distinguish the specific instructions of the function, and through effective analysis of the tone, intonation, and speaking speed in the text after analysis, the tone expressed by the voice text is judged, and the tone of affirmation, negation, question and other tones are distinguished. At the same time, the specific application scenarios of the text can be effectively understood, such as device control, auxiliary question-answering and other scenarios.

[0057] For example, the text output by the model speech recognition module is "Xiaozhi Xiaozhi, the measurement and control equipment needs to go to work at 2 o'clock this afternoon." The natural language understanding module will understand the speech text and analyze the specific content. First, it is analyzed as the equipment control category, and then the text is decomposed into "the measurement and control equipment must be started before 2 o'clock" and "check the equipment startup status."

[0058] Furthermore, the main control AI model with knowledge of aerospace measurement and control equipment also includes a task splitting module, which is used to combine the text processing results to disassemble the main task content of text analysis and the auxiliary task content of semantic understanding. Among them, the main task content of text analysis is the action to be performed first, including but not limited to power on, testing, switching, control, answering questions, etc. The auxiliary task content must be performed after the main task content is completed. The execution subject is the main control AI model with knowledge of aerospace measurement and control equipment or the aerospace measurement and control equipment connected through the instruction conversion program. The location is the location where the equipment is deployed, and the action time is the specified time point for the execution action obtained by intelligent analysis. The task decomposition process combines the content and semantics of the text, associates the main keywords of the task with the corresponding secondary keywords of the task, and generates subtasks based on the association results. For example, the text processing results output by the natural language understanding module are "the measurement and control equipment completes the startup before two o'clock" and "starts up the equipment". The task decomposition module will identify the two execution actions of "complete startup" and "status check", and at the same time identify "measurement and control equipment" as the execution subject. Combined with the voice text structure and content, it will associate "startup" with "status check" for execution, distinguish them in terms of order, and complete the association between the execution action and the action time.

[0059] The task splitting module determines the instruction conversion program according to the obtained task category and assigns the corresponding subtasks, thereby controlling the specific subsystem of the aerospace measurement and control equipment to be responsible for an execution action. For example: when the task splitting module identifies the main content of the task as "equipment two-point power-on" based on the text processing results, it means that the subtask needs to be processed by the instruction conversion program responsible for the power-on function of the measurement and control equipment, and then determines the instruction conversion process and assigns the corresponding subtasks. The instruction conversion program sets the timer to "two points", and further controls the corresponding measurement and control equipment to perform the corresponding power-on operation after the time is reached, so as to complete the action within the agreed time and feedback the results.

[0060] In order to further improve the accuracy of task splitting, the task splitting module has the functions of semantic vector clustering and classification. It uses the good generalization classification results of deep neural networks to specifically allocate them to several subcategories such as equipment control, signal testing, and intelligent question answering, so as to more accurately determine the type of task and assign it to the corresponding instruction conversion program for processing, thereby improving the intelligence level and response accuracy of the artificial intelligence system, making it more efficient and accurate in processing different tasks, and able to better understand user intentions and provide intelligent services.

[0061] Furthermore, the instruction conversion program receives subtasks sent by the main control AI model with knowledge of aerospace measurement and control equipment, identifies the measurement and control equipment subsystem that performs specific actions, and extracts the associated execution actions and action times. According to the execution actions and action times, control instructions are generated, and the control instructions are sent to the subsystem corresponding to the measurement and control equipment to be controlled, thereby controlling the equipment to perform the corresponding actions at the specified time point and complete the subtask.

[0062] Furthermore, among the modules included in the main control AI model that has knowledge of aerospace measurement and control equipment, the natural language understanding module also has the function of directly performing generative question answering. By using a large-scale pre-trained language model, for text understanding classified as question-answering needs, a large generative model calculation is directly performed to obtain the answer and strategy corresponding to the input question text, and the output is provided to personnel to assist in answering questions.

[0063] Furthermore, the main control AI model with knowledge of aerospace measurement and control equipment also includes aerospace measurement and control knowledge recommendation function. The system can automatically collect process usage data and historical question control processes belonging to the user account, including but not limited to historical interaction data, historical question and answer data, knowledge focus points, and opinion data directly fed back to the model by the user using the question and answer module. Attribute analysis is performed based on the historical data recorded by the system to analyze the user's usage habits and personnel weaknesses, and intelligent recommendations are made to the user based on the analysis results and sub-tasks. For example, in the historical data of a user, multiple questions were asked about the noise coefficient in the indicator test. When the user mentions the noise coefficient in the questioning process again, several possible questions and knowledge questions will be intelligently recommended, such as the test method of the noise coefficient, the impact of the noise coefficient on the system function, and the impact of the noise coefficient on the accuracy of the measurement and control process data.

[0064] Furthermore, the measurement and control equipment action execution subsystem receives control instructions issued by the main control AI model with knowledge of aerospace measurement and control equipment through the instruction conversion program, and executes specific action processes according to the instructions to obtain corresponding result parameters. After the execution is completed, the result of the action execution is fed back to the main control AI model with knowledge of aerospace measurement and control equipment to complete the specific process task.

[0065] Furthermore, the measurement and control equipment action execution subsystem has the measurement and control equipment control function, and can receive control instructions from the remote command conversion program through the network interface multicast protocol to accurately and timely control the equipment, such as controlling the rotation of the antenna, switching different channel links, and automatically capturing the target.

[0066] The measurement and control equipment action execution subsystem also has the measurement and control equipment status query function. It can independently summarize the inspection status and comparison parameter results, and perform one-click query on the full status or part of the key status of the equipment. For example, it can query certain key parameters of the subsystem, compare them with the agreed correct results, generate a comparison result report and provide feedback.

[0067] The measurement and control equipment action execution subsystem also has a log quick review and query function, which can quickly summarize log information through instructions, such as querying certain operation processes and execution logs of the subsystem, generating log reports and providing feedback.

[0068] The measurement and control equipment action execution subsystem also has automatic calibration and index testing functions. It can quickly perform system calibration through instructions, such as phase correction and adjustment, and load result values; test certain system indicators of the measurement and control equipment, such as testing power spectrum density, and the test results are fed back in a timely manner through the network multicast protocol.

[0069] Embodiment 2

[0070] An intelligent dialogue control system for aerospace measurement and control equipment, the method is based on the system described in the first aspect, and the steps include:

[0071] S1: The main control AI model with knowledge of aerospace measurement and control equipment obtains the voice signal input by the user, uses the model to perform feature extraction, encoding and decoding, analysis and correction, and recognizes and corrects the voice signal to obtain the final output result of the processing;

[0072] S2: The main control AI model with knowledge of aerospace measurement and control equipment performs natural language understanding and task splitting based on the text output results after the recognition and correction processing, combined with the semantics contained therein, and assigns the specific subtasks obtained by splitting to the corresponding instruction conversion program;

[0073] S3: the instruction conversion program identifies the subsystem executed by the measurement and control equipment according to the disassembled subtask, and sends a control instruction to the corresponding device according to the identified execution subsystem to complete the subtask;

[0074] S4: The subsystem executed by the measurement and control equipment performs specific actions, such as control, testing, query, etc. according to the control instructions sent.

[0075] It should be noted that for a more specific workflow of an intelligent dialogue control system for aerospace measurement and control equipment, please refer to the aforementioned system embodiment section, which will not be repeated here.

[0076] The present invention obtains the voice input signal of the system user through the main control AI model with knowledge of aerospace measurement and control equipment, performs voice recognition and correction, and conducts natural language processing and understanding analysis to obtain text processing and analysis results with attached semantic results. At the same time, the task issued by the user is split according to the processing results, and the subtasks are assigned to the corresponding instruction conversion program for processing. The instruction conversion program sends control instructions to its own AI model or the corresponding measurement and control equipment subsystem according to the subtask, and then performs real-time feedback processing on the task in a predetermined parameter state working manner, supports the issuance of multiple tasks and the collaborative operation of permutations and combinations, and has the ability of multiple subsystems to jointly respond to and execute commands, thereby improving the efficiency and accuracy of intelligently solving user problems and controlling equipment, and providing higher quality intelligent services for the mastery and control of aerospace measurement and control equipment.

[0077] Finally, it should be noted that 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 they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein by equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. An intelligent dialogue control system for aerospace measurement and control equipment, characterized in that: It includes a main control AI model with knowledge of aerospace measurement and control equipment, an instruction conversion program and a measurement and control equipment action execution subsystem. The main control AI model is connected to the measurement and control equipment action execution subsystem through the instruction conversion program.

2. The master control AI model with knowledge of aerospace measurement and control equipment is used to: have the ability to obtain the operator's voice input and perform natural language understanding on the voice text to obtain specific intent semantics; The master control AI model with knowledge of aerospace measurement and control equipment is also used to: decompose tasks according to the intention semantics obtained by natural language understanding and processing, and issue instructions to the corresponding instruction conversion program; The command conversion program is used to: send control instructions to the corresponding measurement and control equipment action execution subsystem according to the command conversion program for issuing instructions to complete the task; The measurement and control equipment action execution subsystem is used to complete specific action tasks according to the received control instructions.

3. The intelligent dialogue control system for aerospace measurement and control equipment according to claim 1, characterized in that: The master control AI model with knowledge of aerospace measurement and control equipment includes a voice intelligent recognition and correction module for: Collect user audio, and determine whether to trigger the system wake-up word "Xiaozhi Xiaozhi" based on the user audio; If not, then remain in standby mode; If so, the system enters the working mode and intelligently converts the user voice information into voice text. The intelligent conversion mainly refers to the use of neural networks to achieve end-to-end input and output, while using the context to correct the output text to further improve the accuracy of the voice-to-text conversion.

4. The intelligent dialogue control system for aerospace measurement and control equipment according to claim 2, characterized in that: The master control AI model with knowledge of aerospace measurement and control equipment includes natural language understanding, which is used to: The text converted from the speech is combined with the AI ​​model for natural language processing and understanding to obtain the user's true intention and content.

5. The intelligent dialogue control system for aerospace measurement and control equipment according to claim 3, characterized in that: The master control AI model with knowledge of aerospace measurement and control equipment also includes a task splitting function for: According to the obtained real intention and content of the user, it is converted into a specific task function, so as to call different functions and realize the splitting of subtasks.

6. The intelligent dialogue control system for aerospace measurement and control equipment according to claim 4, characterized in that: The instruction conversion program is specifically used for: Receive subtasks, parse the subtasks, obtain the measurement and control equipment action execution subsystem and extract the associated execution actions and action times; call a specific programmed execution process or measurement and control equipment data query command according to the measurement and control equipment action execution subsystem.

7. The intelligent dialogue control system for aerospace measurement and control equipment according to claim 5, characterized in that: The measurement and control equipment action execution subsystem is used to: Receive the programmed execution process, automatically control the measurement and control equipment antenna rotation, working mode switching and channel switching index test, and independently summarize the inspection status and comparison parameter results.

8. The intelligent dialogue control system for aerospace measurement and control equipment according to claim 6, characterized in that: The measurement and control equipment action execution subsystem is also used for: Receive the measurement and control equipment data query command, convert the query command into an SQL query statement or a log query statement, query the corresponding result, and return the main control AI model with aerospace measurement and control equipment knowledge.

9. The intelligent dialogue control system for aerospace measurement and control equipment according to claim 7, characterized in that: The measurement and control equipment action execution subsystem is also used for: During the programmed execution process, the progress and results of the action execution are returned to the main control AI model that has knowledge of aerospace measurement and control equipment, and further monitoring is performed to determine whether there are interruptions and other command operations.

10. The intelligent dialogue control system for aerospace measurement and control equipment according to claim 1, characterized in that: The master control AI model with aerospace measurement and control equipment knowledge includes aerospace measurement and control knowledge dialogue function: Based on the user's questions, large model calculations are performed to obtain the calculated answers to the conversations, which are then output in real time for the user to view and understand.

11. The intelligent dialogue control system for aerospace measurement and control equipment according to claim 1, characterized in that: The master control AI model with aerospace measurement and control equipment knowledge includes aerospace measurement and control knowledge recommendation function: Based on the user's question, a recommendation algorithm is run to obtain the three questions that are most likely to be consistent with the user's intention, and these are output in real time for the user to choose as the final conversation content.

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