Lab voice AI input system

Abstract

The application discloses a laboratory voice AI input system and method, which comprises the following steps: a man-machine interactive instruction control module sends a voice-text intelligent analysis command to a laboratory voice-text recognition sensor; after the laboratory voice-text recognition sensor receives the voice-text intelligent analysis command, a voice collection sensor collects various sound signals and tone signals of the laboratory in each unit time, noise signals of the environment in which the laboratory is located in each unit time, and stores and sends the collected data to the man-machine interactive instruction control module; the man-machine interactive instruction control module judges whether the various sound signals and tone signals and noise signals are within a preset convertible range; and the signal transmission device feeds back to a laboratory voice-text recognition point calibration analysis model. The application can timely issue a laboratory voice-text optimization prompt signal, improve work efficiency, reduce labor cost, and has fast response speed.

Description

Technical Field

[0001] This invention relates to the field of laboratory text recognition, and more particularly to a laboratory voice AI input system. Background Technology

[0002] Traditional laboratory data entry involves manual paper recording followed by transcribing into computer documents, which is cumbersome and involves a lot of repetitive work. Currently, the recording end command is usually triggered by the user pressing the recording end button, allowing voice recording to stop manually. Alternatively, a preset endpoint detection module can automatically determine whether recording has ended.

[0003] Existing voice input methods typically only contain volume-related information in the voice analysis results. Based on the analysis results, the volume of the voice input can only be adjusted, but the intonation cannot be controlled, and it is not known when to pause. This can easily lead to poor voice input quality due to inappropriate voice input speed, resulting in the inability to perform voice recognition or low recognition accuracy.

[0004] To address the aforementioned issues, this application proposes a laboratory voice AI input system. Summary of the Invention

[0005] In order to overcome the shortcomings and deficiencies of existing technologies, this invention provides a laboratory voice AI input system.

[0006] This invention provides a laboratory voice AI input system, which includes:

[0007] The human-computer interaction command control module sends voice-text intelligent analysis commands to the laboratory voice-text recognition sensor;

[0008] After receiving the voice-text intelligent analysis command, the laboratory voice-text recognition sensor collects various sound signals, tone and intonation signals, and noise signals of the laboratory environment at each unit time. The collected data is then stored and sent to the human-computer interaction command control module.

[0009] The human-computer interaction command control module determines whether the various sound signals, tone, pitch, and noise signals are within a preset convertible range. If the various sound signals, tone, pitch, and noise signals are not within the preset convertible range, a laboratory speech-to-text optimization prompt signal is generated and fed back to the laboratory speech-to-text recognition point calibration analysis model through a signal transmission device.

[0010] Furthermore, the various sound signals and tone / intonation signals include: human speaking sound signals, natural environmental sound signals, declarative tone, interrogative tone, imperative tone, exclamatory tone, descriptive tone, and teasing tone.

[0011] Furthermore, the noise signal includes: low-frequency noise less than 400 Hz and high-frequency noise greater than 1000 Hz.

[0012] Furthermore, it also includes: setting up a voice-text warning model analysis library in advance within the human-computer interaction command control module, and storing information on the convertible range of the various sound signals and tone, pitch signals, and noise signals, as well as the voice-text warning types, in the voice-text warning model analysis library.

[0013] Furthermore, if the various sound signals, tone signals, pitch signals, and noise signals are not within a preset convertible range, a laboratory speech-to-text optimization prompt signal is generated and fed back to the laboratory speech-to-text recognition point calibration analysis model via a signal transmission device, including:

[0014] The human-computer interaction command control module evaluates the received various sound signals, tone signals, pitch signals, and noise signals against the voice-text warning types in the voice-text warning model analysis library.

[0015] Based on the evaluation results, the human-computer interaction command control module sends corresponding laboratory voice-text prompts for optimization to the signal transmission device.

[0016] Furthermore, if the various sound signals, tone signals, pitch signals, and noise signals are not within a preset convertible range, a laboratory speech-to-text optimization prompt signal is generated and fed back to the laboratory speech-to-text recognition point calibration analysis model via a signal transmission device. This also includes:

[0017] The human-computer interaction command control module sends a conversion end or conversion start signal to the text conversion device based on the evaluation results, so as to end or start the conversion of the laboratory's voice signal per unit time.

[0018] Furthermore, the step of feeding back to the laboratory speech-to-text recognition point calibration and analysis model via a signal transmission device includes: the signal transmission device feeding back the laboratory speech-to-text optimization prompt signal to the laboratory speech-to-text recognition point calibration and analysis model through a special encoding method.

[0019] This invention also provides a laboratory voice AI input system, including a laboratory AI control terminal, multiple laboratory voice-text monitoring points, a signal transmission device, and a laboratory voice-text recognition point calibration and analysis model. The laboratory AI control terminal is equipped with a human-computer interaction command control module, and each of the multiple laboratory voice-text monitoring points is equipped with a laboratory voice-text recognition sensor.

[0020] The human-computer interaction command control module is used to send voice-text intelligent analysis commands to the laboratory voice-text recognition sensor;

[0021] The laboratory speech-text recognition sensor is used to receive speech-text intelligent analysis commands, and the speech acquisition sensor collects various sound signals, tone and intonation signals, and noise signals of the environment in which the laboratory is located at each unit time. The collected data is stored and sent to the human-computer interaction command control module.

[0022] The human-computer interaction command control module is also used to determine whether the various sound signals and tone, pitch, and noise signals are within a preset convertible range; if the various sound signals and tone, pitch, and noise signals are not within the preset convertible range, a laboratory speech-to-text optimization prompt signal is generated and fed back to the laboratory speech-to-text recognition point calibration analysis model through a signal transmission device.

[0023] Furthermore, the signal transmission device is used to feed back the laboratory speech-text optimization prompt signal to the laboratory speech-text recognition point calibration and analysis model through a special encoding method.

[0024] Furthermore, the human-computer interaction command control module is used to evaluate the received various sound signals, tone signals, pitch signals, and noise signals against the voice-text warning types in the voice-text warning model analysis library; and based on the evaluation results, to send the corresponding laboratory voice-text prompt signal to be optimized to the signal transmission device.

[0025] This invention provides a laboratory voice AI input system, wherein the method includes: a human-computer interaction command control module sending a voice-to-text intelligent analysis command to a laboratory voice-to-text recognition sensor; after receiving the voice-to-text intelligent analysis command, the laboratory voice-to-text recognition sensor collects various sound signals, tone and pitch signals, and noise signals of the laboratory environment at each unit time, and stores the collected data and sends it to the human-computer interaction command control module; the human-computer interaction command control module determines whether the various sound signals, tone and pitch signals, and noise signals are within a preset convertible range; if the various sound signals, tone and pitch signals, and noise signals are not within the preset convertible range, a laboratory voice-to-text optimization prompt signal is generated and fed back to the laboratory voice-to-text recognition point calibration analysis model through a signal transmission device. The method provided by this invention can automatically perform inspections and promptly issue laboratory voice-to-text optimization prompt signals, improving work efficiency and reducing labor costs. Furthermore, the laboratory voice AI input system provided by this invention is mainly executed on the human-computer interaction command control module and the laboratory voice-to-text recognition sensor, resulting in a fast response speed. The system provided in this embodiment of the invention also has the above-mentioned effects. Attached Figure Description

[0026] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 This is a schematic diagram of the laboratory voice AI input system provided in an embodiment of the present invention;

[0028] Figure 2 A structural diagram of a laboratory voice AI input system provided in an embodiment of the present invention. Detailed Implementation

[0029] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0030] It should be understood that, when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements, or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, or sets thereof.

[0031] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0032] It should also be further understood that the term “、” as used in this specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0033] Please see Figure 1 The flowchart of the laboratory voice AI input system provided in this embodiment of the invention includes steps S1 to S3:

[0034] Step S1: The human-computer interaction command control module sends a voice-text intelligent analysis command to the laboratory voice-text recognition sensor;

[0035] Step S2: After receiving the voice-text intelligent analysis command, the laboratory voice-text recognition sensor collects various sound signals, tone and intonation signals, and noise signals of the laboratory environment at each unit time. The collected data is then stored and sent to the human-computer interaction command control module.

[0036] Step S3: The human-computer interaction instruction control module determines whether the various sound signals and tone, pitch, and noise signals are within a preset convertible range; if the various sound signals and tone, pitch, and noise signals are not within the preset convertible range, a laboratory speech-to-text optimization prompt signal is generated and fed back to the laboratory speech-to-text recognition point calibration analysis model through a signal transmission device.

[0037] In this embodiment of the invention, a human-computer interaction command control module can send a voice-to-text intelligent analysis command to a laboratory voice-to-text recognition sensor. The laboratory voice-to-text recognition sensor, based on the voice-to-text intelligent analysis command, can control the acquisition of various sound signals, tone signals, intonation signals, and noise signals, and feed them back to the human-computer interaction command control module. The human-computer interaction command control module can then determine whether the various sound signals, tone signals, intonation signals, and noise signals are within the convertible range. If not, it automatically generates a laboratory voice-to-text optimization prompt signal, which is fed back to the laboratory voice-to-text recognition point calibration analysis model through a signal transmission device. Management personnel can then promptly understand the laboratory's voice-to-text warning information per unit time and handle the warnings in a timely manner to prevent damage to the laboratory per unit time. The method provided by this embodiment of the invention can automatically perform inspections and promptly issue laboratory voice-to-text optimization prompt signals, improving work efficiency and reducing labor costs. Furthermore, the laboratory voice AI input system provided by this embodiment of the invention mainly operates on the human-computer interaction command control module and the laboratory voice-to-text recognition sensor, resulting in a fast response speed.

[0038] Specifically, in step S1, the human-computer interaction command control module can send voice-text intelligent analysis commands to the laboratory voice-text recognition sensor in different ways, such as sending voice-text intelligent analysis commands in a timed manner, that is, sending voice-text intelligent analysis commands once every certain period of time. This period of time can be the system default period of time or a custom period of time.

[0039] Alternatively, voice-text intelligent analysis commands can be sent intermittently, meaning they are sent at varying intervals. This method can be manually initiated by administrators, who can assess the need for the command based on their experience or the actual voice and text readings. Another approach is to pre-divide each cycle into several different time periods and set a time interval for each period. Within a given time period, voice-text intelligent analysis commands are sent at the corresponding time intervals. This method is essentially a timed inspection, but with varying time intervals across different time periods. The advantage of setting different time periods is that the detection frequency can be tailored accordingly.

[0040] In step S2, the voice acquisition sensor is activated and acquires various sound signals, tone and pitch signals, and noise signals of the environment in which each unit time laboratory is located, according to the type of each sensor in the sensor group.

[0041] The various sound signals and tone / intonation signals include: human speaking sound signals, natural environmental sound signals, declarative tone, interrogative tone, imperative tone, exclamatory tone, descriptive tone, and teasing tone. The noise signals include: low-frequency noise below 400 Hz and high-frequency noise above 1000 Hz.

[0042] The above data can be collected by corresponding sensors. The combination of various sensors can be collectively referred to as a sensor group. A sensor group can be set up on each unit time laboratory to collect various sound signals, tone and pitch signals, and noise signals of the environment in which each unit time laboratory is located, thereby realizing the remote early warning function for each unit time laboratory.

[0043] The various sound signals, tone, pitch, and noise signals mentioned above each have different functions. For example, the various sound signals, tone, and pitch signals can directly reflect the current state of the laboratory per unit time and determine whether a voice-to-text warning has occurred within that unit time. The noise signal of the environment surrounding the laboratory per unit time can directly reflect the current voice-to-text status of the laboratory, such as whether there have been significant changes or deterioration in the voice-to-text environment. This allows for prediction of whether a voice-to-text warning will occur within that unit time, enabling timely intervention.

[0044] The laboratory speech-to-text recognition sensor can store the data collected by the sensor group and finally send it to the human-computer interaction command control module.

[0045] In step S3, after receiving the various sound signals, tone signals, pitch signals, and noise signals, the human-computer interaction command control module can determine whether the various sound signals, tone signals, pitch signals, and noise signals are within the preset convertible range.

[0046] If the various sound signals, tone, pitch, and noise signals are not within the preset conversion range, that is, if the various sound signals, tone, pitch, and noise signals are not within the preset conversion range, then a laboratory speech-to-text optimization prompt signal needs to be generated and fed back to the laboratory speech-to-text recognition point calibration analysis model through a signal transmission device.

[0047] The aforementioned laboratory speech-to-text recognition point calibration and analysis model can be a laboratory speech-to-text recognition point calibration and analysis model for maintenance personnel, so that maintenance personnel can promptly handle speech-to-text warnings that are occurring or about to occur in the laboratory within a unit of time. The aforementioned laboratory speech-to-text recognition point calibration and analysis model can also be a laboratory speech-to-text recognition point calibration and analysis model for management personnel, so that management personnel can arrange personnel to handle the situation based on the area to which the laboratory belongs within a unit of time.

[0048] To better analyze voice-text warnings from a laboratory within a given time period, this embodiment of the invention preferably simultaneously collects multiple sound signals, tone and intonation signals from the laboratory within that time period, as well as noise signals from the laboratory's environment. The voice-text warnings are then analyzed based on these two types of information. When all the multiple sound signals, tone and intonation signals, and noise signals are within a preset convertible range, no laboratory voice-text optimization prompt signal is generated. Conversely, when any one of the multiple sound signals, tone and intonation signals, or noise signals is outside the preset convertible range, a laboratory voice-text optimization prompt signal is generated. This ensures timely detection and processing of voice-text warnings.

[0049] In one embodiment, the laboratory voice AI input system further includes:

[0050] A voice-text warning model analysis library is pre-set within the human-computer interaction command control module, and information on the convertible range of various sound signals, tone signals, and noise signals, as well as the voice-text warning types, is stored in the voice-text warning model database.

[0051] In this embodiment, the convertible ranges of various sound signals, tone signals, pitch signals, and noise signals can be pre-stored in the voice-text warning model analysis library. This way, the human-computer interaction command control module can retrieve the corresponding convertible range at any time and perform evaluation when performing voice-text warning analysis. At the same time, the information of the voice-text warning type can also be stored in the voice-text warning model analysis library, so that the type of voice-text warning can be determined, which facilitates the maintenance personnel to process the voice-text warning.

[0052] In one embodiment, if the various sound signals, tone signals, pitch signals, and noise signals are not within a preset convertible range, a laboratory speech-to-text optimization prompt signal is generated and fed back to the laboratory speech-to-text recognition point calibration analysis model via a signal transmission device, including:

[0053] The human-computer interaction command control module evaluates the received various sound signals, tone signals, pitch signals, and noise signals against the voice-text warning types in the voice-text warning model analysis library.

[0054] Based on the evaluation results, the human-computer interaction command control module sends corresponding laboratory voice-text prompts for optimization to the signal transmission device.

[0055] After determining that multiple sound signals, tone signals, intonation signals, and noise signals are outside the preset convertible range, these signals can be evaluated against the speech-text warning types in the speech-text warning model analysis library. For example, it can be determined whether the warning belongs to voltage, current, or other speech-text warning categories. Then, the laboratory speech-text warning signal to be optimized is sent to a signal transmission device, which feeds back to the laboratory speech-text recognition point calibration analysis model. This not only provides information on whether a speech-text warning has occurred or is about to occur in the laboratory within a unit of time, but also information on what specific speech-text warning occurred within that unit of time, facilitating targeted processing.

[0056] In one embodiment, the step of feeding back to the laboratory speech-to-text recognition point calibration analysis model via a signal transmission device includes:

[0057] The signal transmission device feeds back the laboratory speech-text optimization prompt signal to the laboratory speech-text recognition point calibration and analysis model through a special encoding method.

[0058] The signal transmission device can send feedback signals indicating that the laboratory speech-text needs optimization to the laboratory speech-text recognition point calibration and analysis model via email, pop-up window, or other methods such as WeChat, mini-programs, or dedicated apps.

[0059] In one embodiment, if the multiple sound signals, tone signals, pitch signals, and noise signals are not within a preset convertible range, a laboratory speech-to-text optimization prompt signal is generated and fed back to the laboratory speech-to-text recognition point calibration analysis model via a signal transmission device, further comprising:

[0060] The human-computer interaction command control module sends a conversion end or conversion start signal to the text conversion device based on the evaluation results, so as to end or start the conversion of the laboratory's voice signal per unit time.

[0061] In this embodiment, after determining that the various sound signals, tone signals, pitch signals, and noise signals are outside the preset convertible range and generating a laboratory speech-to-text optimization prompt signal, which is then sent to the laboratory speech-to-text recognition point calibration and analysis model, maintenance personnel can promptly handle the situation. This embodiment of the invention can also have the human-computer interaction command control module send a conversion end signal or a conversion start signal to the text conversion device to end or begin the conversion of the laboratory's speech signals per unit time. For example, when the temperature of the speech-to-text signal in the laboratory per unit time is detected to be too high, a conversion end signal can be sent to a relay within the text conversion device to cut off the conversion end source, or a conversion start signal can be sent to a relay within the text conversion device to start the air conditioning.

[0062] In other embodiments, neural network models can be used to predict the collected data, thereby providing advance knowledge of upcoming voice-text warnings and helping to avoid risks such as laboratory burnout within a given time.

[0063] Generally, if a unit-time laboratory experiences continuous changes in a certain state or if certain parameters in the voice-text signal consistently deviate from normal values ​​during use, the unit-time laboratory is at high risk of damage. Therefore, embodiments of the present invention can collect historical voice-text warning data, which includes the types of voice-text warnings that occurred, as well as various sound signals, tone, pitch, and noise signals prior to the occurrence of the voice-text warning.

[0064] The historical voice-text warning data is then preprocessed, including normalization, to ensure that all data types are classified into the same dimension. The historical voice-text warning data is then divided into a sample set and a test set. A neural network model is then trained on the sample set of the historical voice-text warning data, and the trained neural network model is tested using the test set to ultimately meet the preset accuracy requirements.

[0065] Finally, the collected data can be input into the neural network model, and then the model can be used for prediction to obtain the output value. This output value can be the probability and type of voice-text warning, allowing maintenance personnel to quickly process the data based on the output, avoiding actual impact and resolving the problem in advance. The neural network model can employ a backpropagation neural network, which is a multi-layer neural network with three or more layers, each layer consisting of several neurons. Neurons in adjacent layers are fully connected, meaning each neuron in the left layer is connected to each neuron in the right layer, while neurons above and below are not connected. The neural network model can be trained using supervised learning. When a pair of learning patterns is provided to the network, the activation values ​​of its neurons propagate from the input layer through each hidden layer to the output layer. At the output layer, each neuron outputs a network response corresponding to the input pattern. Then, following the principle of reducing the error between the desired output and the actual output, the connection weights are corrected layer by layer from the output layer through each hidden layer and finally back to the input layer. This embodiment of the invention uses the above prediction method, which can improve the accuracy and is particularly suitable for the field of voice-text warning prediction in a laboratory setting within a unit time.

[0066] Please see Figure 2 The present invention also provides a laboratory voice AI input system, which includes a laboratory AI control terminal, multiple laboratory voice-text monitoring points, a signal transmission device and a laboratory voice-text recognition point calibration and analysis model. The laboratory AI control terminal is equipped with a human-computer interaction command control module, and each of the multiple laboratory voice-text monitoring points is equipped with a laboratory voice-text recognition sensor.

[0067] The human-computer interaction command control module is used to send voice-text intelligent analysis commands to the laboratory voice-text recognition sensor;

[0068] The laboratory speech-text recognition sensor is used to receive speech-text intelligent analysis commands, and the speech acquisition sensor collects various sound signals, tone and intonation signals, and noise signals of the environment in which the laboratory is located at each unit time. The collected data is stored and sent to the human-computer interaction command control module.

[0069] The human-computer interaction command control module is also used to determine whether the various sound signals and tone, pitch, and noise signals are within a preset convertible range; if the various sound signals and tone, pitch, and noise signals are not within the preset convertible range, a laboratory speech-to-text optimization prompt signal is generated and fed back to the laboratory speech-to-text recognition point calibration analysis model through a signal transmission device.

[0070] In one embodiment, the signal transmission device is used to feed back the laboratory speech-text optimization prompt signal to the laboratory speech-text recognition point calibration analysis model through a special encoding method.

[0071] In one embodiment, the human-computer interaction command control module is used to evaluate the received various sound signals and tone, pitch, and noise signals against the voice-text warning types in the voice-text warning model analysis library; and based on the evaluation results, to send the corresponding laboratory voice-text prompt signal to be optimized to the signal transmission device.

[0072] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to in the method section. It should be noted that those skilled in the art can make various improvements and modifications to this invention without departing from its principles, and these improvements and modifications also fall within the protection scope of the claims of this invention.

[0073] It should also be noted that, in this specification, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusivity.

[0074] The term "comprises" implies that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

Claims

1. A laboratory voice AI input system, characterized in that, include: The human-computer interaction command control module sends voice-text intelligent analysis commands to the laboratory voice-text recognition sensor; After receiving the voice-text intelligent analysis command, the laboratory voice-text recognition sensor collects various sound signals, tone and intonation signals, and noise signals of the laboratory environment at each unit time. The collected data is then stored and sent to the human-computer interaction command control module. The human-computer interaction command control module determines whether the various sound signals and tone, pitch, and noise signals are within a preset convertible range; if the various sound signals and tone, pitch, and noise signals are not within the preset convertible range, a laboratory speech-to-text optimization prompt signal is generated and fed back to the laboratory speech-to-text recognition point calibration and analysis model through a signal transmission device. The various sound signals and tone / intonation signals include: human speaking sound signals, natural environmental sound signals, declarative tone, interrogative tone, imperative tone, exclamatory tone, descriptive tone, and teasing tone; The noise signal includes: low-frequency noise less than 400 Hz and high-frequency noise greater than 1000 Hz; A voice-text warning model analysis library is pre-set in the human-computer interaction command control module, and the conversion range of the various sound signals and tone, pitch signals, and noise signals, as well as the information on voice-text warning types, are stored in the voice-text warning model analysis library; If the various sound signals, tone signals, pitch signals, and noise signals are not within the preset convertible range, a laboratory speech-to-text optimization prompt signal is generated and fed back to the laboratory speech-to-text recognition point calibration analysis model via a signal transmission device, including: The human-computer interaction command control module evaluates the received various sound signals, tone signals, pitch signals, and noise signals against the voice-text warning types in the voice-text warning model analysis library. The human-computer interaction command control module sends corresponding laboratory voice-text prompts for optimization to the signal transmission device based on the evaluation results. If the various sound signals, tone signals, pitch signals, and noise signals are not within the preset convertible range, a laboratory speech-to-text optimization prompt signal is generated and fed back to the laboratory speech-to-text recognition point calibration analysis model via a signal transmission device. The method also includes: The human-computer interaction command control module sends a conversion end or conversion start signal to the text conversion device according to the evaluation result, so as to end or start the conversion of the laboratory's voice signal per unit time. The process of feeding back to the laboratory speech-to-text recognition point calibration analysis model via a signal transmission device includes: The signal transmission device feeds back the laboratory speech-text optimization prompt signal to the laboratory speech-text recognition point calibration and analysis model through a special encoding method.

2. The laboratory voice AI input system according to claim 1, characterized in that, include: The laboratory AI control terminal includes multiple laboratory voice-text monitoring points, a signal transmission device, and a laboratory voice-text recognition point calibration and analysis model. The laboratory AI control terminal is equipped with a human-computer interaction command control module, and each of the multiple laboratory voice-text monitoring points is equipped with a laboratory voice-text recognition sensor. The human-computer interaction command control module is used to send voice-text intelligent analysis commands to the laboratory voice-text recognition sensor; The laboratory speech-text recognition sensor is used to receive speech-text intelligent analysis commands, and the speech acquisition sensor collects various sound signals, tone and intonation signals, and noise signals of the environment in which the laboratory is located at each unit time. The collected data is stored and sent to the human-computer interaction command control module. The human-computer interaction command control module is also used to determine whether the various sound signals and tone, pitch, and noise signals are within a preset convertible range; if the various sound signals and tone, pitch, and noise signals are not within the preset convertible range, a laboratory speech-to-text optimization prompt signal is generated and fed back to the laboratory speech-to-text recognition point calibration analysis model through a signal transmission device.

3. The laboratory voice AI input system according to claim 2, characterized in that, The signal transmission device is used to feed back the laboratory speech-text optimization prompt signal to the laboratory speech-text recognition point calibration and analysis model through a special encoding method.

4. The laboratory voice AI input system according to claim 3, characterized in that, The human-computer interaction command control module is used to evaluate the received various sound signals, tone signals, pitch signals, and noise signals against the voice-text warning types in the voice-text warning model analysis library; And based on the evaluation results, send the corresponding laboratory voice-text prompts for optimization to the signal transmission device.

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