A method for generating autism family intervention plans based on generative artificial intelligence

Abstract

The present invention belongs to the field of artificial intelligence technology, and discloses a method for generating autism family intervention programs based on generative artificial intelligence. The method includes: using a Transformer model to conduct multiple rounds of questions and answers with objects of different intimacy levels, extracting key behaviors and frequency information, evaluating the level of social communication ability, and then combining the evaluation results of the social interaction video with the CNN model to comprehensively obtain the ability level and correspond it to the development milestones, and generate an intervention program containing elements such as the execution object and time schedule. At the same time, the ability level is adjusted according to the patient's identity information such as age. During execution, real-time behavioral data is collected, and the real-time social communication ability level is evaluated by the first neural network model. Then, according to the deviation value from the intervention target, the intervention program is adjusted and updated by the second neural network model to achieve accurate and scientific autism behavior correction.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a method for generating an autism family intervention plan based on generative artificial intelligence. Background Art

[0002] Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by difficulties with social interaction, language expression, and repetitive behaviors. For preschool children with ASD, social communication impairments significantly impact their long-term development, making it difficult for them to establish normal social relationships with peers and impacting their social adaptability.

[0003] Traditional methods of generating autism intervention programs rarely refer to the natural development laws of children in setting goals, and rarely follow the natural development sequence of social communication skills to conduct interventions. As a result, they face problems such as difficulty tracking intervention progress, difficulty quantifying intervention effects, and difficulty applying intervention results in the real world. At the same time, it is often difficult to customize programs based on the child's situation and the needs of each family, and it is also difficult to update intervention programs in a timely and efficient manner. Summary of the Invention

[0004] The purpose of the present invention is to provide a method, medium and device for generating autism family intervention plan based on generative artificial intelligence, so as to at least partially solve the above problems.

[0005] According to one aspect of the present application, a method for generating autism family intervention plans based on generative artificial intelligence is proposed, including: conducting multiple rounds of questions and answers with a first subject through a Transformer model, extracting first key behaviors of children with autism and their frequency information based on the first subject's answers, and evaluating a first ability level of the social communication ability of children with autism based on at least the first key behaviors and their frequency information; matching the first ability level with milestones in the development of social communication abilities, and generating corresponding intervention goals, and generating a corrective intervention plan at least based on the intervention goals.

[0006] Optionally, the method also includes conducting multiple rounds of questions and answers with the second object through the Transformer model, extracting the second key behavior of the autistic child and its frequency information based on the second object's answer, and evaluating the second ability level of the social communication ability of the autistic child based on at least the second key behavior and its frequency information; wherein the first object and the second object are objects with different degrees of intimacy with the autistic child respectively; taking a weighted average of the first ability level and the second ability level to obtain a third ability level; wherein, the higher the intimacy between the object and the child, the higher the weight corresponding to the ability level obtained based on the object's evaluation; the third ability level is matched with the social communication ability development milestone, and a corresponding intervention target is generated, and a corrective intervention plan is generated at least based on the intervention target.

[0007] Optionally, the method also includes obtaining video information of social interaction behavior of children with autism, and evaluating the fourth ability level of the social communication ability of children with autism through a CNN model, fusing the fourth ability level with the first ability level or the third ability level to obtain a fifth ability level, corresponding the fifth ability level with the social communication ability development milestone, and generating corresponding intervention goals, and at least generating a corrective intervention plan based on the intervention goals; the intervention plan at least includes the implementation object of the intervention plan, the implementation schedule of the intervention plan, the intervention goal of the child, the intervention scenario, and the intervention dose.

[0008] Optionally, the method further includes obtaining identity information of the autistic patient, the identity information including at least age; and adjusting the ability level based on the identity information, wherein age is negatively correlated with the ability level.

[0009] Optionally, the method also includes recording real-time behavioral data of the implementation of the intervention plan, and based on the real-time behavioral data, obtaining a real-time social communication ability level through evaluation by a first neural network model, wherein the real-time behavioral data includes at least the child's behavioral data, the actual effective delivery dose, the intervention video clips, and the actual self-assessment data.

[0010] Optionally, the method further includes adjusting and updating the intervention plan through a second neural network model based on the deviation value between the real-time social communication ability level and the intervention target and the intervention plan.

[0011] The present application also provides a computer-readable storage medium storing a computer program, including the steps of the above method executed when the computer program is executed by a processor.

[0012] An embodiment of the present application further provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the steps in the above method by calling the computer program stored in the memory.

[0013] The present application discloses a method for generating autism family intervention programs based on generative artificial intelligence. The method includes: using a Transformer model to conduct multiple rounds of questions and answers with objects of different intimacy levels, extracting key behaviors and frequency information, evaluating the level of social communication ability, and then combining the evaluation results of the social interaction video with the CNN model to comprehensively obtain the ability level and correspond it with the development milestones, and generate an intervention program containing elements such as the execution object and time schedule. At the same time, the ability level is adjusted according to the patient's identity information such as age. During execution, real-time behavioral data is collected, and the real-time social communication ability level is evaluated by the first neural network model. Then, according to the deviation value from the intervention target, the intervention program is adjusted and updated by the second neural network model to achieve accurate and scientific autism behavior correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A schematic diagram of a method for generating an autism family intervention plan based on generative artificial intelligence provided in an embodiment of the present application.

[0015] Figure 2 A schematic diagram of a system for generating autism family intervention plans based on generative artificial intelligence provided in an embodiment of the present application.

[0016] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The following is a further description of specific embodiments of the present invention in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is intended to facilitate understanding of the present invention and does not constitute a limitation of the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0018] It should be noted that, in this application, the terms "first," "second," and various numerical references are used to distinguish between different categories for ease of description and are not intended to limit the scope of the embodiments of this application. For example, they are used to distinguish between different classification results, rather than to describe a specific order or precedence. It should be understood that the terms described in this manner are interchangeable, where appropriate, to enable description of solutions beyond the embodiments of this application.

[0019] Specifically, Figure 1 The following is a flowchart showing a specific implementation of a method for generating autism family intervention plan based on generative artificial intelligence provided by an embodiment of the present application. Figure 1, this application provides a method for generating autism family intervention plans based on generative artificial intelligence, and the specific steps are as follows: S101, conducting multiple rounds of questions and answers with a first subject through a Transformer model, extracting the first key behavior and its frequency information of the autistic child based on the first subject's answer, and evaluating the first ability level of the autistic child's social communication ability based on at least the first key behavior and its frequency information.

[0020] In this embodiment, the first subject can be the parent of a child with autism, such as the mother. The Transformer model conducts multiple rounds of question-and-answer sessions with the mother, asking questions such as, "If your child wants to eat the cake on the table but can't reach it, will he look at you? What is the probability that he will look at you in similar situations every day?" (i.e., eye contact probability) and "Does your child initiate conversations when he sees something he's interested in? What is the probability that he will approach you in similar situations?" (i.e., proactive conversation probability). For example, the mother's response might be, "Such situations occur an average of 5 times a day. On average, the child looks at me for help about 3 times and initiates conversations 2 times." Based on the mother's response, the model extracts that the child makes eye contact 3 times when in need and initiates conversations 2 times. The Transformer model can generate new questions based at least on the mother's response, such as, "When you call your child's name, does he respond verbally or nonverbally? How often?" The mother's response is, "There is a 50% probability that he turns his head when his name is called." This continues until sufficient features are extracted. It is understandable that the key behaviors may be a variety of behaviors related to the assessment of social communication ability, which is not limited in this embodiment. The first ability level of the social communication ability of the autistic child is obtained by at least evaluating the information through a pre-set scoring rule.

[0021] S102, matching the first ability level with a social communication ability development milestone, generating a corresponding intervention target, and generating a correction intervention plan based at least on the intervention target.

[0022] In one embodiment, an example of predefined scoring rules is as follows: Table 1 Predefined scoring rules:

[0023]

[0024] In this embodiment, the total score is 3 points, which is mapped to the milestones of social communication ability development. The milestones are divided into: L1 (0-1 points): basic social awakening intervention is required.

[0025] L2 (2-3 points): Needs reinforcement of non-verbal communication.

[0026] L3 (4 points): Requires training in complex social scenarios.

[0027] Evaluation result: The first ability level is L2.

[0028] Based on the L2 level, for example, a goal is generated: "Increase the probability of non-verbal response to 70%", and a plan is output: Execution target: primary caregiver, such as a professional rehabilitation therapist, parent of the child, etc.

[0029] Time arrangement: Monday to Friday, 30 minutes of one-on-one interaction after lunch every day, and family interaction activities are arranged on Saturdays and Sundays.

[0030] Intervention settings: structured games (such as asking for help with a puzzle), or activities in outdoor settings, etc.

[0031] Intervention dose: 7 times per week, 10 trigger opportunities each time.

[0032] In some possible embodiments, to avoid overly subjective judgments caused by family members, especially parents, with their particular emotional biases toward their children, which could distort the assessment of the child's ability level, it may be necessary to additionally reference question-and-answer data from others, such as kindergarten teachers, for evaluation. Therefore, this embodiment, based on the previous embodiment, also includes multiple rounds of question-and-answer sessions with a second subject. Assume the second subject is the child's kindergarten teacher, who is less intimate with the child than the mother. Similarly, the Transformer model conducts multiple rounds of question-and-answer sessions with the teacher, such as "In class, does the child proactively raise needs with others when they have them? What is the probability of raising needs in this situation?" and "Does the child share things they are interested in with other children? What is the probability of sharing interests?" Based on the teacher's responses, information about the second key behavior of the autistic child and its frequency is extracted. For example, the probability of the child proactively raising needs every day of the week is 20%, and the probability of proactively sharing things they are interested in with other children is 50%. Assuming the second ability level is L1 based on predefined scoring rules, the scoring rules in this embodiment can be predefined in the same manner as in the previous embodiment, and are not limited in this embodiment. In addition, due to the high degree of intimacy between the mother and the child, the weight of the ability level obtained based on the mother's conversation is relatively high, and the weight of the ability level obtained based on the teacher's conversation is relatively low. For example, the level weight corresponding to the mother's conversation is 0.7, and the level weight corresponding to the teacher's conversation is 0.3. The first ability level and the second ability level are weighted averaged to obtain the third ability level, that is, the third ability level = mother data weight (0.7) × L2 + teacher data weight (0.3) × L1 = 0.7 × 2 + 0.3 × 1 = 1.7 → L1.7 (rounded down to L1). Generate corresponding intervention targets based on the L1 level, and at least generate corrective intervention plans based on the intervention targets. For specific intervention targets and corresponding corrective intervention plans, please refer to the previous embodiment for settings, and will not be repeated here.

[0033] By collecting longitudinal behavioral data of children provided by parents and / or primary caregivers, the limitations of traditional one-on-one assessments, which only collect cross-sectional data and are easily affected by the current state of the children, are avoided, ensuring the accuracy of behavioral data and assessments of children's social communication abilities.

[0034] In another embodiment, in order to further improve the accuracy of evaluating the social ability level of children, in addition to the subjective question-and-answer data of the caregivers of the children in the aforementioned embodiment, this embodiment also introduces the behavioral video information data of the children. The behavioral video information data of the children is relatively objective and can eliminate the deviation caused by some subjective factors. Video information of social interaction behaviors of children with autism in family gatherings and / or kindergartens is obtained. The video is analyzed by a trained CNN model, for example, the duration and interaction method of the interaction between the children and others in the video are detected. For example, the frequency of non-verbal communication behaviors (such as pointing to objects) is 2 times / 10 minutes, and the duration of shared attention is 8 seconds on average. The fourth ability level of the social communication ability of children with autism is evaluated, for example, level L2. The fourth capability level is fused with the third capability level. The fusion method can adopt existing technical methods, which are not limited in this embodiment. For example, it can be linear fusion. The weight of the third capability level is 0.4, and the weight of the fourth capability level is 0.6. After fusion, the fifth capability level is obtained, that is, the fifth capability level = the weight of the third capability level (0.4) × L1 + the weight of the fourth capability level (0.6) × L2 = 0.4 × 1 + 0.6 × 2 = 1.6 → L1.6 (rounded down to L1). Based on the L1 level, a corresponding intervention target is generated, and at least a corrective intervention plan is generated based on the intervention target. The specific intervention target and the corresponding corrective intervention plan can be set with reference to the previous embodiment and will not be repeated here.

[0035] By collecting longitudinal behavioral data of children provided by parents and / or primary caregivers, as well as cross-sectional behavioral data of children provided by video information of children, the accuracy of behavioral data and assessment of children's social communication abilities can be further improved.

[0036] In some possible embodiments, it also includes obtaining the identity information of the autistic patient, where the identity information can be age, for example, the patient Xiao Ming is 8 years old. Based on age, since the older the age, the stronger the social communication ability should be in theory, and 8 years old is relatively old in the group of autistic children, it has a certain negative impact on the ability level. For example, for an 8-year-old patient, it is assumed that the original assessment is L2. According to the adjustment rule: age adjustment coefficient : 0 (age < 5 years), -0.3 (age between 5 and 7 years), -0.5 (age > 7 years).

[0037] The calibration formula is: ,in, Indicates the level after calibration, For the original level, is the age adjustment factor.

[0038] Based on the calibration formula, the calibrated level (rounded down to L1), generate corresponding intervention targets based on the L1 level, and at least generate a corrective intervention plan based on the intervention targets. Specific intervention targets and corresponding corrective intervention plans can be set with reference to the previous embodiment and will not be repeated here.

[0039] In some embodiments, in addition to age, identity information also includes the patient's gender and personality. Generally speaking, male patients are more proactive than female patients, and patients with strong social motivation are more willing to socialize than those with weak social motivation. Therefore, male patients or patients with strong social motivation need to adjust their ability level downward. For example, the patient Xiao Xia is 5 years old, male, and has a strong social motivation personality. Assume that the original assessment is L2. According to the adjustment rule: Age adjustment coefficient : 0 (age < 5 years), -0.3 (age between 5 and 7 years), -0.5 (age > 7 years).

[0040] Gender adjustment factor :-0.2.

[0041] Personality Adjustment Factor :-0.1.

[0042] The calibration formula is: ,in, Indicates the level after calibration, For the original level, is the age adjustment factor, is the gender adjustment coefficient, It is the personality adjustment coefficient.

[0043] Based on the calibration formula, the calibrated level (rounded down to L1), generate corresponding intervention targets based on the L1 level, and at least generate a corrective intervention plan based on the intervention targets. Specific intervention targets and corresponding corrective intervention plans can be set with reference to the previous embodiment and will not be repeated here.

[0044] Some possible embodiments also include real-time tracking of intervention effectiveness. For example, during the implementation of the intervention plan, IoT devices combined with manual recording can collect the following data in real time: Child behavior data: Nonverbal response latency (e.g., the time from instruction to eye contact), for example, an average latency of 5 seconds (standard deviation ±2 seconds).

[0045] Completion rate of target behavior (such as the number of times a child actively shares toys). Example: 3 times / hour (the preset target is 5 times / hour).

[0046] Practical effective delivery dose: The number of intervention triggers actually completed each day (i.e., the effective delivery dose, such as the number of times parents successfully guide their children to make requests). Example: 12 times / day planned, 9 times actually completed.

[0047] Duration of a single intervention, example: average 8 minutes (target 10 minutes).

[0048] Intervention video clip analysis: Key scenes (such as social game interactions) are captured using edge computing devices, and a lightweight CNN model is used to extract: the child's facial orientation angle (an angle >45° with the interactor is defined as distraction), for example: a distraction frequency of 40%.

[0049] Practical self-assessment data: The intervention implementer fills out the execution log every day. Quantitative indicators include but are not limited to: the accuracy rate of intervention strategy use (based on self-assessment), example: 78%.

[0050] This rich real-time behavioral data is fed into a trained first neural network model. The model comprises multiple hidden layers, each of which extracts and transforms features from the input data using a specific activation function. After layer-by-layer calculations, the output layer ultimately evaluates the user's real-time social communication ability as L2. The first neural network model can be an existing model, and this is not a limitation in this embodiment.

[0051] Calculate the deviation between the real-time social communication ability level L1 and the ability level corresponding to the intervention target (assuming the intervention target's ability level is L3). Input this deviation value and the current intervention plan into a trained second neural network model. The second neural network model comprehensively adjusts the intervention plan based on the deviation, the current intervention plan, and the intervention target. The second neural network model can use an existing model, and this embodiment does not impose any restrictions.

[0052] The adjusted plan, for example, includes, but is not limited to, increasing the daily intervention time from 30 to 40 minutes. Furthermore, the training sessions will be allocated more appropriately based on the child's fatigue level and attention span. For example, a 40-minute game session could be divided into two 20-minute sessions, with a 5-minute break in between, to improve training effectiveness.

[0053] As interactive games increase in difficulty, they are carefully designed based on the child's current ability level. For example, the original interactive game was a simple building block game, but now it has been upgraded to a building block game with imaginary attributes. This requires children not only to build blocks but also to give them real-world meaning and describe them, thereby better promoting the improvement of children's social communication skills.

[0054] Expand the scenarios, such as adding a community supermarket shopping scenario, and increase participation in more novel environmental stimuli.

[0055] Furthermore, training content is adjusted based on the child's interests and weaknesses as revealed by real-time behavioral data. If a child expresses a keen interest in animal-related topics, further content on animal knowledge and habits will be added to subsequent interventions. If a child has difficulty expressing complex sentences, specialized training sessions will be designed to address this, such as demonstrating more complex sentences and providing different types of support for different types of complex sentences.

[0056] Corresponding to the above embodiment of the method for generating autism family intervention plan based on generative artificial intelligence, Figure 2 A structural block diagram of an autism behavior correction intervention program generation system based on generative artificial intelligence provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0057] See also Figure 2 As shown, an autism behavior correction intervention program generation system 200 based on generative artificial intelligence provided by an embodiment of the present application includes: a first evaluation unit, which is used to conduct multiple rounds of questions and answers with a first object through a Transformer model, extract the first key behavior of the autistic child and its frequency information based on the answer of the first object, and evaluate the first ability level of the social communication ability of the autistic child based on at least the first key behavior and its frequency information; an intervention program generation unit, which is used to correspond the first ability level with the social communication ability development milestone, and generate corresponding intervention goals, and generate a correction intervention program at least based on the intervention goals.

[0058] Optionally, the system also includes a second evaluation unit, which is used to conduct multiple rounds of questions and answers with the second object through the Transformer model, extract the second key behavior of the autistic child and its frequency information based on the second object's answer, and evaluate the second ability level of the social communication ability of the autistic child based on at least the second key behavior and its frequency information; wherein the first object and the second object are objects with different degrees of intimacy with the autistic child respectively; a third unit, which is used to take a weighted average of the first ability level and the second ability level to obtain a third ability level; wherein, the higher the intimacy between the object and the child, the higher the weight corresponding to the ability level obtained based on the evaluation of the object; a second intervention plan generation unit, which is used to correspond the third ability level with the social communication ability development milestone, and generate corresponding intervention goals, and at least generate a corrective intervention plan based on the intervention goals.

[0059] Accordingly, an embodiment of the present application further provides an electronic device, which may be a terminal or a server. Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0060] The electronic device 300 includes a processor 301 having one or more processing cores, a memory 302 having one or more computer-readable storage media, and a computer program stored in the memory 302 and executable on the processor. The processor 301 is electrically connected to the memory 302. Those skilled in the art will appreciate that the electronic device structure shown in the figures does not limit the electronic device and may include more or fewer components than shown, or combine certain components, or arrange the components differently.

[0061] The processor 301 is the control center of the electronic device 300. It uses various interfaces and lines to connect various parts of the entire electronic device 300. By running or loading software programs (computer programs) and / or units stored in the memory 302 and calling data stored in the memory 302, it executes various functions of the electronic device 300 and processes data, thereby monitoring the electronic device 300 as a whole.

[0062] In an embodiment of the present application, the processor 301 in the electronic device 300 will load the instructions corresponding to the processes of one or more applications into the memory 302 according to the following steps, and the processor 301 will run the applications stored in the memory 302 to achieve various functions: conduct multiple rounds of questions and answers with the first object through the Transformer model, extract the first key behavior and frequency information of the autistic child based on the answer of the first object, and evaluate the first ability level of the social communication ability of the autistic child based on at least the first key behavior and frequency information; correspond the first ability level with the development milestone of social communication ability, and generate corresponding intervention goals, and generate a corrective intervention plan at least based on the intervention goals.

[0063] The specific implementation of the above operations can be found in the aforementioned embodiments and will not be described again here.

[0064] Optional, such as Figure 3 As shown, the electronic device 300 further includes: an intervention plan generation module 303, a communication module 304, an input unit 305, and a power supply 306. Among them, the processor 301 is electrically connected to the intervention plan generation module 303, the communication module 304, the input unit 305, and the power supply 306 respectively. It can be understood by those skilled in the art that Figure 3 The electronic device structure shown in the figure does not constitute a limitation to the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0065] The intervention plan generation module 303 can be used to generate an autism behavior correction intervention plan based on generative artificial intelligence.

[0066] The communication module 304 can be used to communicate with other devices.

[0067] The input unit 305 may be configured to receive input digital, character information, or user feature information (such as fingerprint, iris, or facial information), and generate keyboard, mouse, joystick, optical, or trackball signal input related to user settings and function control.

[0068] Power supply 306 is used to supply power to various components of electronic device 300. Optionally, power supply 306 can be logically connected to processor 301 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. Power supply 306 can also include one or more DC or AC power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0069] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0070] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0071] To this end, an embodiment of the present application provides a computer-readable storage medium, which stores multiple computer programs. The computer programs can be loaded by a processor to execute the steps of a method for generating an autism family intervention plan based on generative artificial intelligence provided by an embodiment of the present application.

[0072] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0073] The computer-readable storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0074] Since the computer program stored in the storage medium can execute the steps of any of the methods for generating autism family intervention plans based on generative artificial intelligence provided in the embodiments of the present application, the beneficial effects of any of the methods for generating autism family intervention plans based on generative artificial intelligence provided in the embodiments of the present application can be achieved. For details, please refer to the previous embodiments and will not be repeated here.

[0075] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present application. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the flowchart. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0076] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0078] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. It is apparent to those skilled in the art that various changes, modifications, substitutions, and variations to these embodiments may be made without departing from the principles and spirit of the present invention, and these changes and modifications still fall within the scope of protection of the present invention.

Claims

1. A method for generating autism family intervention plans based on generative artificial intelligence, characterized in that: include: Conducting multiple rounds of question-and-answer sessions with a first subject using a Transformer model, extracting first key behaviors and frequency information of the child with autism based on the first subject's responses, and assessing a first ability level of the child with autism's social communication ability based at least on the first key behaviors and frequency information; Corresponding the first ability level to a social communication ability development milestone and generating a corresponding intervention goal, and generating a corrective intervention plan based at least on the intervention goal; Obtaining identity information of autistic individuals, which includes at least age; Adjusting the ability level based on the identity information, wherein age is negatively correlated with ability level; The intervention plan at least includes the target of the intervention plan, the time schedule for the implementation of the intervention plan, the intervention target for the child, the intervention scenario, and the intervention dose; The intervention implementer records the real-time behavioral data of the intervention plan, and based on the real-time behavioral data, the first neural network model is used to evaluate the real-time social communication ability level. The real-time behavioral data at least includes patient behavioral data, effective delivery dose, intervention video clips, and self-assessment data of the actual operation; Based on the deviation value between the real-time social communication ability level and the intervention target and the intervention plan, the intervention plan is adjusted and updated through the second neural network model.

2. The method for generating autism family intervention plans based on generative artificial intelligence according to claim 1, characterized in that: The method further comprises: Conducting multiple rounds of question-and-answer sessions with a second subject using the Transformer model, extracting second key behaviors and frequency information of the child with autism based on the second subject's responses, and assessing a second ability level of the child with autism's social communication ability based at least on the second key behaviors and frequency information; The first object and the second object are objects with different degrees of intimacy with the autistic child; Taking a weighted average of the first capability level and the second capability level to obtain a third capability level; The higher the intimacy between the subject and the patient, the higher the weight corresponding to the ability level obtained based on the subject's assessment; The third ability level is matched with a social communication ability development milestone, and a corresponding intervention target is generated, and the corrective intervention plan is generated at least based on the intervention target.

3. The method for generating autism family intervention plans based on generative artificial intelligence according to claim 2, characterized in that: The method further comprises: Obtaining video information of social interaction behaviors of children with autism, and evaluating the social communication ability of the children with autism using a CNN model to obtain the fourth ability level, The fourth capability level is integrated with the first capability level or the third capability level to obtain a fifth capability level, The fifth ability level is matched with a social communication ability development milestone, and a corresponding intervention target is generated, and the corrective intervention plan is generated at least based on the intervention target.

4. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 3 is executed.

5. An electronic device, characterized in that: The method comprises a memory storing executable program code and a processor coupled to the memory; wherein the processor calls the executable program code stored in the memory to execute the method according to any one of claims 1 to 3.

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