Artificial intelligence-based pre-cry sound recognition and care teaching system and method for infants

Abstract

The present application relates to a kind of pre-cry sound recognition and nursing teaching system and method based on artificial intelligence, it is related to nursing education technical field, system includes: pre-cry stage sound acquisition module, for obtaining original pre-cry stage audio data;Audio pre-processing module, for obtaining standard audio feature data of stabilization;Demand feature identification module, for obtaining corresponding nursing demand identification result;Teaching situation mapping module, for obtaining the teaching guide information of adaptation learner level;Virtual and training module, for guiding learner to select virtual nursing operation based on teaching guide information, and the simulation evaluation of learner's pacification communication behavior is carried out simultaneously, and learner operation performance data and interactive response result are obtained;Teaching feedback and learning record module, for obtaining the quantitative teaching effect evaluation information.This system can improve the newborn demand assessment ability and communication ability of nursing staff.

Description

Technical Field

[0001] This invention relates to the field of nursing education technology, and in particular to an artificial intelligence-based system and method for recognizing and teaching nursing care of infant pre-cry sounds. Background Technology

[0002] Neonatal care education is an important component of nursing and midwifery education. Its core objective is to cultivate nurses' ability to identify, assess, and respond to the physiological states and care needs of newborns. Since newborns lack language expression abilities, their needs are primarily conveyed through crying and related vocalizations. In current nursing teaching practices, instruction on infant crying typically relies on the teacher's clinical experience, using methods such as classroom lectures, verbal descriptions, live demonstrations, or playing a limited number of audio samples.

[0003] However, the existing practices described above still have significant shortcomings in nursing education. On the one hand, the process of judging cries relies heavily on the teacher's personal experience and subjective understanding, lacking unified teaching standards, which leads to significant differences in learning outcomes among different teachers and learners. On the other hand, the existing teaching scenarios lack structured and standardized design, making it difficult to support nurses in conducting repeatable training and objective assessments. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing an artificial intelligence-based infant pre-cry sound recognition and nursing teaching system and method that can improve nursing staff's ability to assess newborn needs and their communication skills.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: This invention provides an artificial intelligence-based infant pre-cry sound recognition and care teaching system, the system comprising: The pre-crying stage sound acquisition module is used to acquire audio signals during a preset time period before the baby enters continuous crying after the environment initialization is completed, and obtain the original pre-crying stage audio data. The audio preprocessing module is used to perform noise reduction, normalization, and frame segmentation on the original pre-crying stage audio data to obtain stable standard audio feature data. The demand feature recognition module is used to extract acoustic prosodic features reflecting infant care needs based on the standard audio feature data, and to classify the acoustic prosodic features to obtain the corresponding care need recognition results. The teaching scenario mapping module is used to generate corresponding nursing teaching prompts based on the nursing needs identification results, and to match and adjust the presentation difficulty of the prompts according to the teaching objectives to obtain teaching guidance information that is suitable for the learners' level. The virtual and practical training module is used to guide learners to select virtual nursing operations based on the teaching guidance information, and to simulate and evaluate the learners' soothing and communication behaviors. At the same time, it simulates changes in infant vocalization based on the learners' operational correctness and communication adaptability to obtain learners' operational performance data and interactive response results. The teaching feedback and learning record module is used to summarize the multi-dimensional data generated by the audio acquisition module, the audio preprocessing module, the demand feature recognition module, and the virtual and training module, to comprehensively evaluate the learning process, and generate structured teaching feedback results and learning records, thereby obtaining quantitative teaching effectiveness evaluation information.

[0006] Optionally, the pre-crying stage sound acquisition module is further used for: Configure the audio acquisition device's acquisition mode according to the environmental initialization parameters to obtain the acquisition preparation status; Based on the monitoring of the infant's vocalization behavior during the acquisition preparation state, the acquisition trigger conditions are obtained; Based on the acquisition trigger conditions, audio signals within a preset time period before the infant enters a continuous crying phase are extracted to obtain the original pre-crying stage audio data.

[0007] Optionally, the audio preprocessing module is further used for: Environmental noise suppression processing is performed on the original pre-crying stage audio data to obtain noise-reduced audio data; The noise-reduced audio data is subjected to amplitude normalization processing to obtain audio data with stable amplitude; The amplitude-stable audio data is subjected to time-frame processing to obtain the standard audio feature data.

[0008] Optionally, the demand feature recognition module is further used for: Based on the standard audio feature data, acoustic feature parameters reflecting the infant's vocal rhythm and pitch changes are extracted; Based on the acoustic feature parameters, a corresponding set of acoustic prosodic features is constructed; The nursing needs identification results are obtained by performing category matching analysis based on the set of acoustic prosodic features.

[0009] Optionally, the teaching context mapping module is also used for: Based on the nursing needs identification results, the corresponding nursing situation type is determined; Based on the described nursing scenario type, corresponding nursing teaching prompts are generated; Based on the nursing teaching prompts, structured nursing teaching prompts are generated.

[0010] Optionally, the teaching context mapping module is also used for: The teaching guidance level is determined based on the learner's teaching stage or historical learning record; Based on the teaching guidance level, the information content of the nursing teaching prompts is adjusted to obtain the adjusted nursing teaching prompts. Based on the adjusted nursing teaching prompts, output the teaching guidance information that is appropriate for the learner's level.

[0011] Optionally, the virtual and training module is also used for: Based on the teaching guidance information, a set of virtual nursing operations is generated for the learner to choose from; Record the corresponding operation process based on the learner's selection of the set of virtual nursing operations; The learner's performance data is generated based on the described operation process.

[0012] Optionally, the virtual and training module is also used for: Based on the reassurance and communication information input or recorded by the learner, extract the corresponding communication features; Based on the aforementioned communication characteristics, analyze the learner's communication adaptability; The interactive response results are obtained by simulating changes in an infant's vocalization state based on the aforementioned communication adaptability.

[0013] Optionally, the teaching feedback and learning record module is also used for: The data is summarized based on the original pre-crying stage audio data, the nursing needs identification results, and the learner's operational performance data to obtain the summarized data. Based on the summarized data, multidimensional evaluation parameters reflecting the learning process are generated. Based on the multidimensional evaluation parameters, the quantitative teaching effectiveness assessment information is output and the learning record is formed.

[0014] This invention also provides an artificial intelligence-based method for recognizing and teaching care of infant pre-cry sounds, the method comprising: After completing the environmental initialization, the audio signal of the baby during the preset time period before the baby enters continuous crying is obtained to obtain the original pre-crying stage audio data. The original pre-crying stage audio data is subjected to noise reduction, normalization, and frame segmentation to obtain stable standard audio feature data. Based on the standard audio feature data, acoustic prosodic features reflecting infant care needs are extracted, and the acoustic prosodic features are classified to obtain the corresponding care need identification results. The teaching scenario mapping module is used to generate corresponding nursing teaching prompts based on the nursing needs identification results, and to match and adjust the presentation difficulty of the prompts according to the teaching objectives to obtain teaching guidance information that is suitable for the learners' level. Based on the teaching guidance information, learners are guided to select virtual nursing operations, and their soothing and communication behaviors are simulated and evaluated. At the same time, the changes in infant vocalization are simulated based on the correctness of the learners' operations and their communication adaptability, so as to obtain learners' operation performance data and interactive response results. By summarizing the multidimensional data generated by the audio acquisition module, the audio preprocessing module, the demand feature recognition module, and the virtual and training module, a comprehensive evaluation of the learning process is conducted, and structured teaching feedback results and learning records are generated to obtain quantitative teaching effectiveness evaluation information.

[0015] Furthermore, to achieve the above objectives, the present invention also proposes an electronic device, comprising: a memory for storing computer software programs; and a processor for reading and executing the computer software programs, thereby realizing the artificial intelligence-based infant pre-cry sound recognition and nursing teaching method described above.

[0016] Furthermore, to achieve the above objectives, the present invention also proposes a non-transitory computer-readable storage medium storing a computer software program, which, when executed by a processor, implements the artificial intelligence-based infant pre-crying sound recognition and nursing teaching method described above.

[0017] The beneficial effects of this invention are: (1) This invention collects and analyzes the audio signals of an infant before he enters the stage of continuous crying, and identifies and processes the vocal characteristics of the pre-crying stage. Compared with the traditional method of analysis based on continuous crying sounds, it can provide a basis for judging nursing needs before the infant's emotions are significantly agitated, thereby improving the timeliness and foresight of needs identification in nursing education.

[0018] (2) The present invention introduces preprocessing steps such as noise reduction, normalization and framing after audio acquisition, which effectively reduces the impact of environmental noise, equipment differences and changes in acquisition conditions on audio analysis results, so that subsequent nursing needs identification is based on stable and consistent audio features, and improves the applicability and reliability of the system in the teaching environment.

[0019] (3) The present invention uses a teaching context mapping module to further convert the identified nursing needs results into corresponding nursing teaching prompts, so that the abstract identification conclusions can directly serve the teaching process, avoid the problem that learners only obtain judgment results but lack specific nursing operation guidance, and improve the practicality of nursing teaching. Attached Figure Description

[0020] Figure 1 A scene diagram illustrating an artificial intelligence-based method for recognizing and teaching care of infant pre-cry sounds, provided by this invention. Figure 2 A schematic diagram of the structure of an artificial intelligence-based infant pre-crying sound recognition and nursing teaching system provided by the present invention; Figure 3 A flowchart of an artificial intelligence-based infant pre-crying sound recognition and nursing teaching method provided by the present invention; Figure 4 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention; Figure 5 This is a schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. Detailed Implementation

[0021] 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 embodiments of the present invention, and not all embodiments. 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.

[0022] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0023] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0024] Please see Figure 1 , Figure 1This is a scene illustration illustrating an artificial intelligence-based method for recognizing and teaching care of infant pre-cry sounds, as provided by this invention. Figure 1 As shown, the terminal and server are connected via a network, such as a wired or wireless network. The terminal can include, but is not limited to, portable devices such as mobile phones and tablets with various network platform applications installed, as well as fixed terminals such as computers, kiosks, and advertising machines. The server provides users with various business services, including service push servers and user recommendation servers.

[0025] It should be noted that, Figure 1 The scenario diagram shown is merely an example of an artificial intelligence-based method for recognizing and teaching care of infant pre-cry sounds. The terminals, servers, and application scenarios described in this embodiment are for the purpose of more clearly illustrating the technical solutions of this embodiment and do not constitute a limitation on the technical solutions provided by this embodiment. As those skilled in the art will know, with the evolution of systems and the emergence of new business scenarios, the technical solutions provided by this embodiment are also applicable to similar technical problems.

[0026] The terminal can be used for: After completing the environmental initialization, the audio signal of the baby during the preset time period before the baby enters continuous crying is obtained to obtain the original pre-crying stage audio data. The original pre-crying stage audio data is subjected to noise reduction, normalization, and frame segmentation to obtain stable standard audio feature data. Based on standard audio feature data, acoustic prosodic features reflecting infant care needs are extracted, and the acoustic prosodic features are classified to obtain the corresponding care needs identification results. The teaching scenario mapping module is used to generate corresponding nursing teaching prompts based on the nursing needs identification results, and to match and adjust the presentation difficulty of the prompts according to the teaching objectives to obtain teaching guidance information that is suitable for the learners' level. Based on the teaching guidance information, learners are guided to select virtual nursing operations, and the learners' soothing and communication behaviors are simulated and evaluated. At the same time, the changes in infant vocalization are simulated based on the learners' operational correctness and communication adaptability to obtain learners' operational performance data and interactive response results. By summarizing the multidimensional data generated by the audio acquisition module, audio preprocessing module, demand feature recognition module, and virtual and practical training module, the learning process is comprehensively evaluated, and structured teaching feedback results and learning records are generated to obtain quantitative teaching effectiveness evaluation information.

[0027] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of an artificial intelligence-based infant pre-cry sound recognition and nursing teaching system provided by the present invention.

[0028] like Figure 2 As shown in the embodiment of the present invention, an artificial intelligence-based infant pre-cry sound recognition and nursing teaching system includes: The pre-crying stage sound acquisition module 201 is used to acquire audio signals within a preset time period before the baby enters continuous crying after the environment initialization is completed, and obtain the original pre-crying stage audio data.

[0029] In some embodiments, the pre-crying stage sound acquisition module 201 can first initialize the audio acquisition environment, including setting the working status and acquisition parameters of the sound pickup device. After completing the environment initialization, the module 201 continuously monitors the infant's vocalization state. When it detects that the infant has entered the vocalization stage before continuous crying, it extracts the audio signal of that stage according to a preset time window, thereby acquiring the audio signal within a preset time period before the infant enters continuous crying, and outputs the acquired audio signal as the raw pre-crying stage audio data.

[0030] In some embodiments, the pre-crying stage sound acquisition module 201 can also be used for: Configure the audio acquisition device's acquisition mode according to the environmental initialization parameters to obtain the acquisition preparation status; Based on monitoring the infant's vocalization behavior during the data acquisition preparation state, the data acquisition trigger conditions are obtained; Based on the acquisition trigger conditions, audio signals within a preset time period before the infant enters continuous crying are extracted to obtain the original pre-crying stage audio data.

[0031] In some embodiments, before performing audio acquisition, the pre-acquisition audio acquisition module first configures the audio acquisition device according to preset environmental initialization parameters. These environmental initialization parameters include at least information such as the ambient noise level, microphone sensitivity, and sampling frequency. By reading and configuring these parameters, the audio acquisition device operates in an acquisition mode adapted to the current environmental conditions, thus forming a ready-to-acquire state for subsequent audio acquisition.

[0032] After establishing the acquisition preparation state, the pre-crying sound acquisition module continuously monitors the infant's vocalization behavior in this state. During monitoring, the intensity and duration of the acquired real-time audio signals are analyzed to determine whether the infant has entered the preset vocalization state. When the detection result meets the preset triggering conditions, the current vocalization behavior is determined to meet the audio acquisition triggering requirements, thus obtaining the acquisition triggering conditions.

[0033] After the acquisition trigger conditions are met, the pre-crying stage sound acquisition module extracts audio from the infant's vocalization stage before they enter a continuous crying state, according to a preset time window. The time window corresponds to the period before the infant transitions from a non-continuous crying state to a continuous crying state. By extracting and storing the audio signal within this time period, raw pre-crying stage audio data is obtained, providing basic data support for subsequent audio preprocessing and feature identification.

[0034] The audio preprocessing module 202 is used to perform noise reduction, normalization, and frame segmentation on the original pre-crying stage audio data to obtain stable standard audio feature data.

[0035] In some embodiments, the audio preprocessing module 202 can receive the raw pre-crying stage audio data output by the pre-crying stage sound acquisition module 201, and first perform noise reduction processing on the audio data to reduce interference from environmental noise and non-target sounds. Subsequently, module 202 performs amplitude normalization processing on the noise-reduced audio data to reduce the impact of differences in audio amplitude under different acquisition conditions. Finally, the normalized audio data undergoes time-frame processing to form stable standard audio feature data suitable for subsequent analysis.

[0036] In some embodiments, the audio preprocessing module 202 can also be used for: Environmental noise suppression processing was performed on the original pre-crying audio data to obtain noise-reduced audio data; Amplitude normalization is performed on the noise-reduced audio data to obtain audio data with stable amplitude. Time-frame processing is performed on audio data with stable amplitude to obtain standard audio feature data.

[0037] In this embodiment, after receiving the original pre-crying stage audio data, the audio preprocessing module first performs environmental noise suppression processing on the original pre-crying stage audio data. Specifically, according to preset noise reference parameters, background noise components in the audio signal that are unrelated to the baby's vocalization are weakened to reduce the impact of environmental interference on the audio signal, thereby obtaining noise-reduced audio data.

[0038] After completing the environmental noise suppression process, the audio preprocessing module performs amplitude normalization on the noise-reduced audio data. By uniformly adjusting the amplitude range of the audio signal, the audio signal under different acquisition conditions is kept consistent on the amplitude scale, thereby reducing amplitude fluctuations caused by factors such as pickup distance and differences in device sensitivity, and thus obtaining audio data with stable amplitude.

[0039] After obtaining stable audio data, the audio preprocessing module further performs time-frame segmentation on the audio data. By segmenting the audio signal according to a preset time length, the continuous audio signal is divided into multiple time frame units, thereby forming standard audio feature data suitable for subsequent acoustic feature extraction and analysis.

[0040] The demand feature recognition module 203 is used to extract acoustic prosodic features that reflect infant care needs based on standard audio feature data, and to classify the acoustic prosodic features to obtain the corresponding care need recognition results.

[0041] In some embodiments, the demand feature recognition module 203 can extract acoustic prosodic features reflecting the infant's vocal rhythm, pitch changes, and energy distribution based on the standard audio feature data output by the audio preprocessing module 202. Subsequently, the module 203 performs matching analysis between the acoustic prosodic features and preset nursing demand feature categories, determines the category of the current vocal state, and thus obtains the corresponding infant nursing demand recognition result.

[0042] In some embodiments, the demand feature recognition module 203 can also be used for: Based on standard audio feature data, acoustic feature parameters reflecting infant vocal rhythm and pitch changes are extracted; Based on the acoustic feature parameters, construct the corresponding acoustic prosodic feature set; Based on the set of acoustic prosodic features, category matching analysis was performed to obtain the nursing needs identification results.

[0043] Specifically, after receiving the standard audio feature data output by the audio preprocessing module, the demand feature recognition module first analyzes and processes the standard audio feature data to extract acoustic feature parameters that reflect the changes in the infant's vocal rhythm and pitch. These acoustic feature parameters are used to characterize the vocal patterns and changes in different time periods during the infant's vocalization process.

[0044] After obtaining the acoustic feature parameters, the demand feature recognition module organizes and combines the acoustic feature parameters based on preset feature combination rules to construct an acoustic prosodic feature set corresponding to the current vocalization state. The acoustic prosodic feature set is used to comprehensively reflect the overall vocalization characteristics of the infant in the pre-crying stage.

[0045] After constructing the acoustic prosodic feature set, the demand feature recognition module matches and analyzes the acoustic prosodic feature set with preset nursing demand category features. By judging the matching results, the nursing demand type corresponding to the current acoustic prosodic feature set is determined, thereby obtaining the nursing demand recognition result, which is then output to subsequent modules for use.

[0046] The teaching context mapping module 204 is used to generate corresponding nursing teaching prompts based on the nursing needs identification results, and to match and adjust the presentation difficulty of the prompts according to the teaching objectives to obtain teaching guidance information that is suitable for the learners' level.

[0047] In some embodiments, the teaching scenario mapping module 204 can determine the type of nursing teaching scenario corresponding to the nursing need based on the nursing need identification results output by the need feature identification module 203. Subsequently, the module 204 generates nursing teaching prompts that match the nursing teaching scenario, and adjusts the presentation difficulty and information content of the nursing teaching prompts according to the preset teaching objectives or the learner's teaching stage, thereby outputting teaching guidance information adapted to the learner's level.

[0048] In some embodiments, the teaching context mapping module 204 can also be used for: Based on the nursing needs identification results, determine the corresponding nursing situation type; Generate corresponding nursing teaching prompts based on the type of nursing scenario; Based on the nursing teaching guidelines, structured nursing teaching guidelines information is generated.

[0049] Specifically, after receiving the nursing need identification results output by the need feature identification module, the teaching scenario mapping module first analyzes and processes the nursing need identification results to determine the nursing scenario type corresponding to the nursing need. The nursing scenario type is used to characterize the category of nursing scenarios that require attention in the current infant state.

[0050] After determining the nursing scenario type, the teaching scenario mapping module calls upon a pre-set nursing teaching content library to select nursing teaching prompts that match the scenario type. These prompts must include at least the key points and precautions for the specific nursing procedure required for that scenario.

[0051] After obtaining the nursing teaching prompts, the teaching scenario mapping module organizes and formats the nursing teaching prompts, organizing them according to the preset information structure to form structured nursing teaching prompts. The structured nursing teaching prompts are then output to subsequent modules for use.

[0052] In some embodiments, the teaching context mapping module 204 can also be used for: The teaching guidance level is determined based on the learner's teaching stage or historical learning record; Based on the teaching guidance level, the amount of information in the nursing teaching prompts is adjusted to obtain the adjusted nursing teaching prompts. Based on the revised nursing teaching prompts, output teaching guidance information that is appropriate for the learners' levels.

[0053] Specifically, after generating nursing teaching prompts, the teaching scenario mapping module further adapts these prompts based on the learner's teaching stage or historical learning records. Specifically, the module first reads the learning stage information or historical learning record information associated with the learner, and then analyzes and processes the information according to preset guidance level classification rules to determine the corresponding teaching guidance level for the current learner.

[0054] After determining the teaching guidance level, the teaching scenario mapping module adjusts the information content of the nursing teaching prompts according to the teaching guidance level. The adjustment includes adjusting the level of detail in the explanation of nursing operation steps, the number of prompts, or the order of presentation, to form adjusted nursing teaching prompts that match the teaching guidance level.

[0055] After adjusting the information content of the nursing teaching prompts, the teaching scenario mapping module outputs the adjusted nursing teaching prompts as teaching guidance information adapted to the current learner's level, for subsequent use by the virtual and practical training modules.

[0056] The virtual and practical training module 205 is used to guide learners to select virtual nursing operations based on teaching guidance information, and to simulate and evaluate learners' soothing and communication behaviors. At the same time, it simulates changes in infant vocalization based on the learners' operational correctness and communication adaptability, and obtains learners' operational performance data and interactive response results.

[0057] In some embodiments, the virtual and practical training module 205 can provide learners with selectable virtual nursing operation options based on the teaching guidance information output by the teaching scenario mapping module 204, and record the learner's nursing operation process in the virtual environment. Simultaneously, module 205 simulates and evaluates the learner's input or recorded soothing communication behaviors, and, combined with the learner's nursing operation correctness and communication adaptability, simulates the changes in the infant's vocalization state, thereby obtaining learner performance data and corresponding interactive response results.

[0058] In some embodiments, the virtual and training module 205 can also be used for: Based on the teaching guidance information, a set of virtual nursing operations is generated for learners to choose from; Record the corresponding operation process based on the learner's selection of the set of virtual nursing operations; Learner performance data is generated based on the operational process.

[0059] Specifically, after receiving the teaching guidance information output by the teaching scenario mapping module, the virtual and practical training module first generates a corresponding set of virtual nursing operations based on the teaching guidance information. The set of virtual nursing operations includes multiple nursing operation options that match the current nursing scenario, allowing learners to select them in the virtual environment.

[0060] After generating the set of virtual nursing operations, the virtual and practical training module records the learner's selection behavior of the set of virtual nursing operations in the virtual environment. The recorded content includes at least the type of nursing operation selected by the learner and the corresponding operation execution process, thus forming an operation process record related to the learner's nursing operations.

[0061] After the operation process is recorded, the virtual and training module organizes and summarizes the learner's nursing operation behavior based on the operation process record, forming learner operation performance data that reflects the learner's nursing operation situation, and outputs the learner operation performance data to subsequent modules for use.

[0062] In some embodiments, the virtual and training module 205 can also be used for: Extract corresponding communication features based on the reassurance communication information input or entered by the learner; Analyze learners' communication adaptability based on communication characteristics; The interactive response results were obtained by simulating changes in an infant's vocalization state based on communication adaptability.

[0063] Specifically, the virtual and practical training module records the learner's nursing procedures while also receiving reassurance and communication information input or entered by the learner. This reassurance and communication information can include voice or text input. The virtual and practical training module first processes this information to extract communication features that characterize the learner's communication style.

[0064] After obtaining the communication characteristics, the virtual and practical training module analyzes and processes these characteristics based on preset communication adaptability analysis rules to determine the learner's communication adaptability in the current nursing situation. Communication adaptability reflects the degree of match between the learner's reassuring communication behavior and the current nursing situation.

[0065] After determining the communication adaptability, the virtual and training module simulates the infant's vocalization state based on the communication adaptability. By simulating the process of the infant's vocalization state changing with communication behavior, corresponding interactive response results are generated and output to subsequent modules or presented to the learner.

[0066] The teaching feedback and learning record module 206 is used to summarize the multi-dimensional data generated by the audio acquisition module, audio preprocessing module, demand feature recognition module, and virtual and practical training module, to comprehensively evaluate the learning process, and generate structured teaching feedback results and learning records, thereby obtaining quantitative teaching effectiveness evaluation information.

[0067] In some embodiments, the teaching feedback and learning record module 206 can uniformly summarize multidimensional data from the pre-crying stage sound acquisition module 201, the audio preprocessing module 202, the demand feature recognition module 203, and the virtual and practical training module 205. Based on this, module 206 comprehensively evaluates the learner's judgment, operation, and interaction performance throughout the teaching process, and generates structured teaching feedback results and learning records, thereby outputting quantitative teaching effectiveness evaluation information.

[0068] In some embodiments, the teaching feedback and learning record module 206 can also be used for: The data were summarized based on the original pre-crying stage audio data, nursing needs identification results, and learner performance data to obtain the summarized data. Based on the aggregated data, generate multidimensional evaluation parameters that reflect the learning process; Based on multidimensional evaluation parameters, quantitative teaching effectiveness assessment information is output and learning records are generated.

[0069] Specifically, after acquiring relevant data from the aforementioned modules, the teaching feedback and learning record module first summarizes and processes the data. Specifically, the module uniformly organizes the original pre-crying stage audio data, nursing needs identification results, and learner performance data, and integrates them according to a preset data association method to obtain the summarized data.

[0070] After data aggregation, the teaching feedback and learning record module generates multidimensional evaluation parameters based on the aggregated data to reflect the learning process status. These multidimensional evaluation parameters characterize learners' overall performance in nursing judgment, nursing procedures, and interactions, and serve as the basis for subsequent teaching effectiveness evaluation.

[0071] After obtaining the multidimensional evaluation parameters, the teaching feedback and learning record module outputs quantitative teaching effectiveness evaluation information based on the multidimensional evaluation parameters, and stores the quantitative teaching effectiveness evaluation information in association with the learner, thereby forming a corresponding learning record for subsequent teaching analysis or learning process review.

[0072] In some embodiments, the quantitative teaching effectiveness evaluation information can be a comprehensive composite index of the teaching context, specifically represented as: ; in, This is the comprehensive synthesis index of the teaching context. The higher the value, the higher the standardization and teaching value of the current teaching case. It is the first Acoustic feature weights. It is the standard deviation of the eigenvalues, which reflects the stability of the signal. It refers to the amplitude intensity of the sound in the pre-crying stage. It is the environmental noise base. It is the complexity coefficient of nursing operation steps. It is the learner's historical competence assessment score . The sound of crying in the early stages varies with time. Evolved acoustic envelope function (during acquisition time) (Inner integral).

[0073] This formula is obtained through integration. Obtain the complete acoustic energy distribution during the audio acquisition phase. Utilize The signal-to-noise ratio data is processed to reflect the complexity of the environment. Finally, it is combined with human-computer interaction data (operational complexity). ) and learners' historical competence The final teaching value index is obtained by weighting the results.

[0074] Specifically, This index is used to comprehensively characterize the standardization and teaching value of current nursing teaching scenarios. It is calculated based on multiple factors, including audio feature stability, environmental complexity, nursing procedure complexity, and learners' historical performance. A higher value indicates better performance in terms of teaching organization, training effectiveness, and assessment completeness for the corresponding teaching case.

[0075] This indicates the output of the demand feature recognition module. The weighting coefficients of acoustic features in nursing needs identification are used to reflect the importance of different acoustic features in characterizing the vocal state of an infant in the pre-crying stage.

[0076] Indicates the corresponding number The degree of numerical dispersion of the acoustic feature within the acquisition time range is used to reflect the stability level of the acoustic feature in the pre-crying stage audio. The smaller the value, the more stable the corresponding acoustic characteristics.

[0077] This indicates the overall amplitude intensity of the audio signal acquired in the pre-crying stage sound acquisition module, used to reflect the vocal energy level of the infant before entering the sustained crying stage.

[0078] This represents the baseline background noise value measured during the environmental initialization or audio preprocessing stage. It is used to characterize the basic noise level in the current acquisition environment, thereby normalizing the data under different environmental conditions in the exponent calculation.

[0079] This indicates the complexity of the nursing procedures performed by learners in the virtual and practical training modules, reflecting the operational workload level of nursing procedures during the teaching process. The higher the value, the more complex the nursing procedure.

[0080] This represents a comprehensive competency parameter derived from learners' historical learning records and past teaching performance, with a value range of [missing value]. This parameter reflects the impact of learners' prior nursing competence on the assessment of the current teaching context.

[0081] This indicates the audio signal during the pre-crying stage over time. The varying acoustic envelope is used to describe the pre-crying audio over a period of time. The dynamic evolutionary characteristics within. Through analysis of... Collection duration Integrating within the audio stream can reflect the overall acoustic energy distribution of the audio in the pre-crying stage.

[0082] This indicates the duration of audio signal acquisition during the pre-crying stage, corresponding to the time interval before the infant transitions from a non-continuous crying state to a continuous crying state.

[0083] Please see Figure 3 The present invention provides a flowchart of an artificial intelligence-based infant pre-cry sound recognition and nursing teaching method, including the following steps: Step 301: After completing the environment initialization, acquire the audio signal of the baby during the preset time period before the baby enters continuous crying, and obtain the original pre-crying stage audio data.

[0084] Step 302: Perform noise reduction, normalization, and frame segmentation on the original pre-crying stage audio data to obtain stable standard audio feature data.

[0085] Step 303: Extract acoustic prosodic features that reflect infant care needs based on standard audio feature data, and classify the acoustic prosodic features to obtain the corresponding care need identification results.

[0086] Step 304: The teaching scenario mapping module is used to generate corresponding nursing teaching prompts based on the nursing needs identification results, and to adjust the difficulty of the prompts according to the teaching objectives to obtain teaching guidance information that is suitable for the learners' level.

[0087] Step 305: Based on the teaching guidance information, guide learners to select virtual nursing operations, and conduct a simulation assessment of learners' soothing and communication behaviors. At the same time, simulate changes in infant vocalization based on the correctness of learners' operations and communication adaptability to obtain learners' operation performance data and interactive response results.

[0088] Step 306: Summarize the multi-dimensional data generated by the audio acquisition module, audio preprocessing module, demand feature identification module, and virtual and practical training module, conduct a comprehensive evaluation of the learning process, and generate structured teaching feedback results and learning records to obtain quantitative teaching effectiveness evaluation information.

[0089] Please see Figure 4 , Figure 4 This is a schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... Figure 4 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, it performs the following steps: After completing the environmental initialization, the audio signal of the baby during the preset time period before the baby enters continuous crying is obtained to obtain the original pre-crying stage audio data. The original pre-crying stage audio data is subjected to noise reduction, normalization, and frame segmentation to obtain stable standard audio feature data. Based on standard audio feature data, acoustic prosodic features reflecting infant care needs are extracted, and the acoustic prosodic features are classified to obtain the corresponding care needs identification results. The teaching scenario mapping module is used to generate corresponding nursing teaching prompts based on the nursing needs identification results, and to match and adjust the presentation difficulty of the prompts according to the teaching objectives to obtain teaching guidance information that is suitable for the learners' level. Based on the teaching guidance information, learners are guided to select virtual nursing operations, and the learners' soothing and communication behaviors are simulated and evaluated. At the same time, the changes in infant vocalization are simulated based on the learners' operational correctness and communication adaptability to obtain learners' operational performance data and interactive response results. By summarizing the multidimensional data generated by the audio acquisition module, audio preprocessing module, demand feature recognition module, and virtual and practical training module, the learning process is comprehensively evaluated, and structured teaching feedback results and learning records are generated to obtain quantitative teaching effectiveness evaluation information.

[0090] Please see Figure 5 , Figure 5 This is a schematic diagram illustrating an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. For example... Figure 5As shown, this embodiment provides a computer-readable storage medium 500 on which a computer program 411 is stored. When the computer program 411 is executed by a processor, it performs the following steps: After completing the environmental initialization, the audio signal of the baby during the preset time period before the baby enters continuous crying is obtained to obtain the original pre-crying stage audio data. The original pre-crying stage audio data is subjected to noise reduction, normalization, and frame segmentation to obtain stable standard audio feature data. Based on standard audio feature data, acoustic prosodic features reflecting infant care needs are extracted, and the acoustic prosodic features are classified to obtain the corresponding care needs identification results. Based on the nursing needs identification results, corresponding nursing teaching prompts are generated, and the difficulty of the prompts is adjusted according to the teaching objectives to obtain teaching guidance information that is suitable for the learners' level. Based on the teaching guidance information, learners are guided to select virtual nursing operations, and the learners' soothing and communication behaviors are simulated and evaluated. At the same time, the changes in infant vocalization are simulated based on the learners' operational correctness and communication adaptability to obtain learners' operational performance data and interactive response results. By summarizing the multidimensional data generated by the audio acquisition module, audio preprocessing module, demand feature recognition module, and virtual and practical training module, the learning process is comprehensively evaluated, and structured teaching feedback results and learning records are generated to obtain quantitative teaching effectiveness evaluation information.

[0091] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0092] Those skilled in the art will understand that embodiments of the present invention can be provided as systems, methods, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0093] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A system that specifies functions in one or more boxes.

[0094] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0095] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0096] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0097] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. An artificial intelligence-based infant pre-cry sound recognition and care teaching system, characterized in that, The system includes: The pre-crying stage sound acquisition module is used to acquire audio signals during a preset time period before the baby enters continuous crying after the environment initialization is completed, and obtain the original pre-crying stage audio data. The audio preprocessing module is used to perform noise reduction, normalization, and frame segmentation on the original pre-crying stage audio data to obtain stable standard audio feature data. The demand feature recognition module is used to extract acoustic prosodic features reflecting infant care needs based on the standard audio feature data, and to classify the acoustic prosodic features to obtain the corresponding care need recognition results. The teaching scenario mapping module is used to generate corresponding nursing teaching prompts based on the nursing needs identification results, and to match and adjust the presentation difficulty of the prompts according to the teaching objectives to obtain teaching guidance information that is suitable for the learners' level. The virtual and practical training module is used to guide learners to select virtual nursing operations based on the teaching guidance information, and to simulate and evaluate the learners' soothing and communication behaviors. At the same time, it simulates changes in infant vocalization based on the learners' operational correctness and communication adaptability to obtain learners' operational performance data and interactive response results. The teaching feedback and learning record module is used to summarize the multi-dimensional data generated by the audio acquisition module, the audio preprocessing module, the demand feature recognition module, and the virtual and training module, to comprehensively evaluate the learning process, and generate structured teaching feedback results and learning records, thereby obtaining quantitative teaching effectiveness evaluation information.

2. The artificial intelligence-based infant pre-cry sound recognition and nursing teaching system according to claim 1, characterized in that, The pre-crying stage sound acquisition module is also used for: Configure the audio acquisition device's acquisition mode according to the environmental initialization parameters to obtain the acquisition preparation status; Based on the monitoring of the infant's vocalization behavior during the acquisition preparation state, the acquisition trigger conditions are obtained; Based on the acquisition trigger conditions, audio signals within a preset time period before the infant enters a continuous crying phase are extracted to obtain the original pre-crying stage audio data.

3. The artificial intelligence-based infant pre-cry sound recognition and nursing teaching system according to claim 2, characterized in that, The audio preprocessing module is also used for: Environmental noise suppression processing is performed on the original pre-crying stage audio data to obtain noise-reduced audio data; The noise-reduced audio data is subjected to amplitude normalization processing to obtain audio data with stable amplitude; The amplitude-stable audio data is subjected to time-frame processing to obtain the standard audio feature data.

4. The artificial intelligence-based infant pre-cry sound recognition and nursing teaching system according to claim 3, characterized in that, The demand feature identification module is also used for: Based on the standard audio feature data, acoustic feature parameters reflecting the infant's vocal rhythm and pitch changes are extracted; Based on the acoustic feature parameters, a corresponding set of acoustic prosodic features is constructed; The nursing needs identification results are obtained by performing category matching analysis based on the set of acoustic prosodic features.

5. The artificial intelligence-based infant pre-cry sound recognition and nursing teaching system according to claim 4, characterized in that, The teaching context mapping module is also used for: Based on the nursing needs identification results, the corresponding nursing situation type is determined; Based on the described nursing scenario type, corresponding nursing teaching prompts are generated; Based on the nursing teaching prompts, structured nursing teaching prompts are generated.

6. The artificial intelligence-based infant pre-cry sound recognition and nursing teaching system according to claim 5, characterized in that, The teaching context mapping module is also used for: The teaching guidance level is determined based on the learner's teaching stage or historical learning record; Based on the teaching guidance level, the information content of the nursing teaching prompts is adjusted to obtain the adjusted nursing teaching prompts. Based on the adjusted nursing teaching prompts, output the teaching guidance information that is appropriate for the learner's level.

7. The artificial intelligence-based infant pre-cry sound recognition and nursing teaching system according to claim 6, characterized in that, The virtual and training module is also used for: Based on the teaching guidance information, a set of virtual nursing operations is generated for the learner to choose from; Record the corresponding operation process based on the learner's selection of the set of virtual nursing operations; The learner's performance data is generated based on the described operation process.

8. The artificial intelligence-based infant pre-cry sound recognition and nursing teaching system according to claim 7, characterized in that, The virtual and training module is also used for: Based on the reassurance and communication information input or recorded by the learner, extract the corresponding communication features; Based on the aforementioned communication characteristics, analyze the learner's communication adaptability; The interactive response results are obtained by simulating changes in an infant's vocalization state based on the aforementioned communication adaptability.

9. The artificial intelligence-based infant pre-cry sound recognition and nursing teaching system according to claim 1, characterized in that, The teaching feedback and learning record module is also used for: The data is summarized based on the original pre-crying stage audio data, the nursing needs identification results, and the learner's operational performance data to obtain the summarized data. Based on the summarized data, multidimensional evaluation parameters reflecting the learning process are generated. Based on the multidimensional evaluation parameters, the quantitative teaching effectiveness assessment information is output and the learning record is formed.

10. A teaching method for infant pre-cry sound recognition and care based on artificial intelligence, characterized in that, The method includes: After completing the environmental initialization, the audio signal of the baby during the preset time period before the baby enters continuous crying is obtained to obtain the original pre-crying stage audio data. The original pre-crying stage audio data is subjected to noise reduction, normalization, and frame segmentation to obtain stable standard audio feature data. Based on the standard audio feature data, acoustic prosodic features reflecting infant care needs are extracted, and the acoustic prosodic features are classified to obtain the corresponding care need identification results. The teaching scenario mapping module is used to generate corresponding nursing teaching prompts based on the nursing needs identification results, and to match and adjust the presentation difficulty of the prompts according to the teaching objectives to obtain teaching guidance information that is suitable for the learners' level. Based on the teaching guidance information, learners are guided to select virtual nursing operations, and their soothing and communication behaviors are simulated and evaluated. At the same time, the changes in infant vocalization are simulated based on the correctness of the learners' operations and their communication adaptability, so as to obtain learners' operation performance data and interactive response results. By summarizing the multidimensional data generated by the audio acquisition module, the audio preprocessing module, the demand feature recognition module, and the virtual and training module, a comprehensive evaluation of the learning process is conducted, and structured teaching feedback results and learning records are generated to obtain quantitative teaching effectiveness evaluation information.

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