Man-machine interaction computer system

Abstract

The invention relates to a man-machine interaction computer system which comprises a multi-training device, a visual monitor, an object detection device, an intelligent judgment mechanism and a man-machine interaction interface. According to the method, the AI judgment model with the customized structural design can be introduced for intelligent judgment.

Description

Technical Field

[0001] The present invention relates to the field of human-computer interaction, and in particular to a human-computer interaction computer system. Background Art

[0002] Human-computer interaction technology refers to the technology that realizes the dialogue between people and computers in an effective way through computer input and output devices. It includes the machine providing a large amount of relevant information and prompts to people through output or display devices, people inputting relevant information and prompts to machines through input devices, people inputting relevant information to machines through input devices, answering questions, etc. Human-computer interaction technology is one of the important contents in computer user interface design. It is closely related to cognitive science, ergonomics, psychology and other disciplines. The literature related to human-computer interaction is as follows: CN119169700A discloses a gesture recognition system based on human-computer interaction, including a gesture information acquisition module, a recognition processing module and an instruction execution module, wherein the gesture information acquisition module, the recognition processing module and the instruction execution module are sequentially communicatively connected, and the recognition processing module is also communicatively connected to a database; the present invention can perform recognition processing on a recognition video of a user's gesture, and the processing modes include a standard splitting mode and a recognition splitting mode, wherein the two splitting modes have a characteristic of a reference deviation in terms of processing means, and when one splitting mode cannot complete recognition, another splitting mode is directly used for recognition processing, thereby reducing the probability of system recognition failure and preventing users from frequently performing gestures and affecting user experience.

[0003] CN119165957A discloses an intelligent human-computer interaction method and system for dual-mode data processing, which relates to the field of data processing technology. It includes obtaining an effective area for collecting user behavior data in an identification area; based on the user behavior data type, it is divided into first user behavior data and second user behavior data. The present invention classifies user behavior data into two different types of behavior data, and constructs a priority processing queue that grants priority authority to meet the behavior data standard, so that when processing two different types of behavior data, it can first analyze and process the behavior data with high priority authority, and based on the behavior data with high priority authority, retrieve the second user behavior data that is linked to the behavior data with high priority authority in the historical user behavior data, so that when interactively processing data, it can quickly obtain another related data through one data type, which guarantees the processing speed to a certain extent.

[0004] CN119105685A discloses a human-computer interaction method, a terminal device, a readable storage medium and a chip, which relate to the field of artificial intelligence AI technology. The method is applied to a terminal device provided with an AI assistant, and specifically includes: displaying a first interface, the first interface including a target object; in response to a drag operation on the target object, starting the AI ​​assistant, and displaying a first AI control at the bottom of the first interface; after the target object is dragged to the area where the first AI control is located and the drag operation is released, controlling the AI ​​assistant to store the target object. In an embodiment of the present application, the AI ​​assistant can provide a richer human-computer interaction method, including helping users store target objects for subsequent use by users, and having a better user experience. Summary of the invention

[0005] In order to solve the technical problems in the related fields, the present invention provides a human-computer interaction computer system, which introduces a multiple training device set on the current electronic wheelchair to output a deep neural network that has been trained multiple times as an AI judgment model, wherein the deep neural network is trained multiple times to obtain a deep neural network that has been trained multiple times, and the number of times the deep neural network is trained is positively correlated with the driving speed of the current electronic wheelchair, thereby completing the targeted design of the artificial intelligence model that performs intelligent judgment of the safe distance, and also uses an adaptive shooting frame rate to perform frame-by-frame picture acquisition on the front driving scene of the current electronic wheelchair to obtain each frame of driving scene image corresponding to each acquisition moment, wherein the value of the adaptive shooting frame rate is positively correlated with the driving speed of the current electronic wheelchair, and also detects each imaging area occupied by each vehicle object in each frame of the driving scene image, and uses each depth of field value, each vertical coordinate value and each horizontal coordinate value corresponding to each constituent pixel point of each imaging area as the The pixel point related data of the imaging area is obtained to obtain the pixel point related data of each imaging area respectively occupied by each vehicle object in each frame of the driving scene image and output it as the targeted visual data of the frame of the driving scene image, so as to provide reliable and comprehensive basic information for the subsequent intelligent judgment of the safe distance, and use the AI ​​judgment model to intelligently judge the various physical distances of each vehicle object relative to the current electronic wheelchair at the next moment of the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment, and introduce a human-computer interaction interface set on the current electronic wheelchair, which is used to perform an alarm action of abnormal safety distance at the next moment of the current moment to the user of the current electronic wheelchair when there is a physical distance less than or equal to a preset distance limit in the various physical distances of each vehicle object relative to the position of the current electronic wheelchair at the next moment of the current moment, thereby improving the safety and reliability of the current electronic wheelchair.

[0006] According to the present invention, a human-computer interaction computer system is provided, the system comprising: A multiple training device is provided on the current electronic wheelchair, and is used to output a deep neural network that has been trained multiple times as an AI judgment model, wherein the deep neural network is trained multiple times to obtain a deep neural network that has been trained multiple times, and the number of times the deep neural network is trained is positively correlated with the current driving speed of the electronic wheelchair; A visual monitor is arranged at the seat position of the current electronic wheelchair, and is used to perform frame-by-frame image acquisition of the front driving scene of the current electronic wheelchair using an adaptive shooting frame rate, so as to obtain driving scene images of each frame corresponding to each acquisition moment; An object detection device is connected to the visual monitor and is used to detect each imaging area respectively occupied by each vehicle object in each frame of the driving scene image, and use each depth of field value, each vertical coordinate value and each horizontal coordinate value corresponding to each pixel point of each imaging area as pixel point related data of the imaging area, so as to obtain the pixel point related data of each imaging area respectively occupied by each vehicle object in each frame of the driving scene image and output it as the targeted visual data of the frame of the driving scene image; An intelligent judgment mechanism is connected to the multiple training devices and the object detection device, respectively, and is used to use an AI judgment model to intelligently judge the physical distances of each vehicle object relative to the current electronic wheelchair at the next moment of the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment; A human-machine interaction interface is provided on the current electronic wheelchair, and is used to perform an alarm action of abnormal safety distance at the next moment from the current moment to the user of the current electronic wheelchair when there is a physical distance less than or equal to a preset distance limit among the physical distances of each vehicle object at the next moment from the current moment to the position of the current electronic wheelchair at the next moment from the current moment; The human-computer interaction interface is further used to temporarily suspend the execution of an alarm action of abnormal safety distance at the next moment from the current moment to the user of the current electronic wheelchair when there is a physical distance greater than the preset distance limit in each physical distance between each vehicle object at the next moment from the current moment and the position of the current electronic wheelchair at the next moment from the current moment; Among them, when there is a physical distance less than or equal to a preset distance limit in each physical distance of each vehicle object relative to the position of the current electronic wheelchair at the next moment after the current moment, an alarm action of abnormal safety distance at the next moment after the current moment is performed to the user of the current electronic wheelchair, including: determining the time difference between the current moment and the next moment after the current moment based on the adaptive shooting frame rate of the visual monitor, and calculating the position of the current electronic wheelchair at the next moment after the current moment based on the driving speed of the current electronic wheelchair and the position of the current electronic wheelchair at the current moment.

[0007] It can be seen that the present invention has at least the following main inventive concepts: Inventive concept 1: Introducing a multiple training device set on the current electronic wheelchair, used to output a deep neural network that has been trained multiple times as an AI judgment model, wherein the deep neural network is trained multiple times to obtain a deep neural network that has been trained multiple times, and the number of times the deep neural network is trained is positively correlated with the current driving speed of the electronic wheelchair, thereby completing the targeted design of an artificial intelligence model that performs intelligent judgment of a safe distance; Inventive concept 2: Adopting an adaptive shooting frame rate to perform frame-by-frame picture acquisition of the current electronic wheelchair's front driving scene, so as to obtain each frame of driving scene image corresponding to each acquisition moment, wherein the value of the adaptive shooting frame rate is positively correlated with the current driving speed of the electronic wheelchair, and also detecting each imaging area occupied by each vehicle object in each frame of the driving scene image, and taking each depth of field value, each vertical coordinate value and each horizontal coordinate value corresponding to each pixel point of each imaging area as pixel point related data of the imaging area, so as to obtain pixel point related data of each imaging area occupied by each vehicle object in each frame of the driving scene image and output it as targeted visual data of the frame of the driving scene image, thereby providing reliable and comprehensive basic information for subsequent intelligent judgment of safe distance; Invention concept three: using an AI judgment model to intelligently judge the physical distances of each vehicle object at the next moment of the current moment relative to the current electronic wheelchair according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment, and introducing a human-computer interaction interface set on the current electronic wheelchair, for performing an alarm action of abnormal safety distance at the next moment of the current moment to the user of the current electronic wheelchair when there is a physical distance less than or equal to a preset distance limit among the physical distances of each vehicle object at the next moment of the current moment relative to the position of the current electronic wheelchair at the next moment of the current moment, thereby improving the safety and reliability of the current electronic wheelchair; Inventive conception four: When using an AI judgment model to intelligently judge the physical distances of each vehicle object relative to the current electronic wheelchair at the next moment of the current moment based on the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment, the number of multiple acquisition moments is proportional to the number of each vehicle object. DETAILED DESCRIPTION

[0008] Electronic wheelchairs are an important application and implementation carrier of human-computer interaction. Patients with various mobility impairments can use intelligent human-computer interaction systems to interact with electronic wheelchairs. However, the human-computer interaction systems of electronic wheelchairs in the prior art cannot effectively determine the physical distances of various vehicle objects in front of the electronic wheelchair at the next moment from the current moment, resulting in the difficulty in accurately grasping and controlling the safety and reliability of the electronic wheelchair at future moments.

[0009] The following is a detailed description of an embodiment of the human-computer interaction computer system of the present invention.

[0010] The human-computer interaction computer system shown according to the primary embodiment of the present invention includes: A multiple training device is provided on the current electronic wheelchair, and is used to output a deep neural network that has been trained multiple times as an AI judgment model, wherein the deep neural network is trained multiple times to obtain a deep neural network that has been trained multiple times, and the number of times the deep neural network is trained is positively correlated with the current driving speed of the electronic wheelchair; Specifically, a multiple training device is provided on the current electronic wheelchair, and is used to output a deep neural network that has been trained multiple times as an AI judgment model, wherein the deep neural network is trained multiple times to obtain a deep neural network that has been trained multiple times, and the number of times the deep neural network is trained is positively correlated with the current driving speed of the electronic wheelchair, including: selecting and using a MATLAB toolbox to implement testing and simulation of a model construction process of outputting a deep neural network that has been trained multiple times as an AI judgment model; A visual monitor is arranged at the seat position of the current electronic wheelchair, and is used to perform frame-by-frame image acquisition of the front driving scene of the current electronic wheelchair using an adaptive shooting frame rate, so as to obtain driving scene images of each frame corresponding to each acquisition moment; An object detection device is connected to the visual monitor and is used to detect each imaging area respectively occupied by each vehicle object in each frame of the driving scene image, and use each depth of field value, each vertical coordinate value and each horizontal coordinate value corresponding to each pixel point of each imaging area as pixel point related data of the imaging area, so as to obtain the pixel point related data of each imaging area respectively occupied by each vehicle object in each frame of the driving scene image and output it as the targeted visual data of the frame of the driving scene image; An intelligent judgment mechanism is connected to the multiple training devices and the object detection device, respectively, and is used to use an AI judgment model to intelligently judge the physical distances of each vehicle object relative to the current electronic wheelchair at the next moment of the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment; A human-machine interaction interface is provided on the current electronic wheelchair, and is used to perform an alarm action of abnormal safety distance at the next moment from the current moment to the user of the current electronic wheelchair when there is a physical distance less than or equal to a preset distance limit among the physical distances of each vehicle object at the next moment from the current moment to the position of the current electronic wheelchair at the next moment from the current moment; The human-computer interaction interface is further used to temporarily suspend the execution of an alarm action of abnormal safety distance at the next moment from the current moment to the user of the current electronic wheelchair when there is a physical distance greater than the preset distance limit in each physical distance between each vehicle object at the next moment from the current moment and the position of the current electronic wheelchair at the next moment from the current moment; Wherein, when there is a physical distance less than or equal to a preset distance limit in each physical distance of each vehicle object at the next moment after the current moment relative to the position of the current electronic wheelchair at the next moment after the current moment, performing an alarm action of abnormal safety distance at the next moment after the current moment to the user of the current electronic wheelchair includes: determining a time difference between the current moment and the next moment after the current moment based on an adaptive shooting frame rate of a visual monitor, and calculating the position of the current electronic wheelchair at the next moment after the current moment based on the driving speed of the current electronic wheelchair and the position of the current electronic wheelchair at the current moment; Among them, using the AI ​​judgment model to intelligently judge the physical distances of each vehicle object relative to the current electronic wheelchair at the next moment of the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment, includes: the number of the multiple acquisition moments is proportional to the number of each vehicle object; The adaptive shooting frame rate is used to perform frame-by-frame image acquisition of the front driving scene of the current electronic wheelchair to obtain the driving scene images of each frame corresponding to each acquisition moment, including: the value of the adaptive shooting frame rate is positively correlated with the driving speed of the current electronic wheelchair; And wherein, the input data of the step of intelligently judging the respective physical distances of each vehicle object relative to the current electronic wheelchair at the next moment of the current moment using the AI ​​judgment model according to the adaptive shooting frame rate, the imaging focal length of the visual monitor and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is numerically normalized to obtain the input data after the numerical normalization is performed; And wherein, the input data of the step of using the AI ​​judgment model to intelligently judge the respective physical distances of each vehicle object at the next moment from the current moment to the current electronic wheelchair according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is numerically normalized to obtain the input data after the numerical normalization is performed, including: the input data of the step of using the AI ​​judgment model to intelligently judge the respective physical distances of each vehicle object at the next moment from the current moment to the current electronic wheelchair according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is numerically normalized based on hexadecimal numerical conversion to obtain the input data after the numerical normalization is performed; And wherein, the input data of the step of using the AI ​​judgment model to intelligently judge the respective physical distances of each vehicle object relative to the current electronic wheelchair at the next moment of the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is numerically normalized to obtain the input data after the numerical normalization processing is performed, including: the input data of the step of using the AI ​​judgment model to intelligently judge the respective physical distances of each vehicle object relative to the current electronic wheelchair at the next moment of the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is numerically normalized based on octal numerical conversion to obtain the input data after the numerical normalization processing is performed; And wherein, the input data of the step of using the AI ​​judgment model to intelligently judge the respective physical distances of each vehicle object relative to the current electronic wheelchair at the next moment of the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is numerically normalized to obtain the input data after the numerical normalization processing is performed, including: the input data of the step of using the AI ​​judgment model to intelligently judge the respective physical distances of each vehicle object relative to the current electronic wheelchair at the next moment of the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is numerically normalized based on binary numerical conversion to obtain the input data after the numerical normalization processing is performed; And wherein, the input data of the step of using the AI ​​judgment model to intelligently judge the respective physical distances of each vehicle object relative to the current electronic wheelchair at the next moment of the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is numerically normalized to obtain the input data after the numerical normalization processing is performed, including: the input data of the step of using the AI ​​judgment model to intelligently judge the respective physical distances of each vehicle object relative to the current electronic wheelchair at the next moment of the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is numerically normalized based on decimal value conversion to obtain the input data after the numerical normalization processing is performed; And wherein, the output data of the step of using the AI ​​judgment model to intelligently judge the physical distances of each vehicle object relative to the current electronic wheelchair at the next moment of the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is in the form of numerical representation after numerical normalization; And wherein, the output data of the step of using an AI judgment model to intelligently judge the respective physical distances of each vehicle object at the next moment from the current moment to the current electronic wheelchair according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is a numerical representation after numerical normalization, including: the output data of the step of using an AI judgment model to intelligently judge the respective physical distances of each vehicle object at the next moment from the current moment to the current electronic wheelchair according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is a numerical representation after numerical normalization based on hexadecimal numerical conversion, a numerical representation after numerical normalization based on octal numerical conversion, a numerical representation after numerical normalization based on binary numerical conversion, and a numerical representation after numerical normalization based on decimal numerical conversion.

[0011] In addition, in the human-computer interaction computer system, using an adaptive shooting frame rate to perform frame-by-frame image acquisition of the front driving scene of the current electronic wheelchair to obtain each frame of the driving scene image corresponding to each acquisition moment also includes: using a numerical mapping function to represent a numerical mapping relationship in which the value of the adaptive shooting frame rate is positively correlated with the driving speed of the current electronic wheelchair, in which the driving speed of the current electronic wheelchair is the input value, and the value of the adaptive shooting frame rate corresponding to the driving speed of the current electronic wheelchair is the output value.

[0012] The human-computer interaction computer system of the present invention is used to address the technical problem in the prior art that the interactive system of the electronic wheelchair cannot warn of dangerous scenes in the future. By introducing an AI judgment model with a customized structure design to intelligently judge the physical distances of each vehicle object relative to the electronic wheelchair at the next moment from the current moment, and introducing a human-computer interaction interface set on the electronic wheelchair to execute safety alarm actions at future moments based on each physical distance, the safety and reliability of the current electronic wheelchair are improved, and the above-mentioned technical problems are solved.

[0013] Although the present invention has been disclosed in detail with various preferred embodiments as above, the present invention should not be interpreted as only covering the above embodiments, which are only exemplary embodiments. Any person skilled in the art may make some changes and improvements without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be determined by the claims and their equivalents.

Claims

1. A human-computer interaction computer system, characterized in that: The system comprises: A multiple training device is provided on the current electronic wheelchair, and is used to output a deep neural network that has been trained multiple times as an AI judgment model, wherein the deep neural network is trained multiple times to obtain a deep neural network that has been trained multiple times, and the number of times the deep neural network is trained is positively correlated with the current driving speed of the electronic wheelchair; A visual monitor is arranged at the seat position of the current electronic wheelchair, and is used to perform frame-by-frame image acquisition of the front driving scene of the current electronic wheelchair using an adaptive shooting frame rate, so as to obtain driving scene images of each frame corresponding to each acquisition moment; An object detection device is connected to the visual monitor and is used to detect each imaging area respectively occupied by each vehicle object in each frame of the driving scene image, and use each depth of field value, each vertical coordinate value and each horizontal coordinate value corresponding to each pixel point of each imaging area as pixel point related data of the imaging area, so as to obtain the pixel point related data of each imaging area respectively occupied by each vehicle object in each frame of the driving scene image and output it as the targeted visual data of the frame of the driving scene image; An intelligent judgment mechanism is connected to the multiple training devices and the object detection device, respectively, and is used to use an AI judgment model to intelligently judge the physical distances of each vehicle object relative to the current electronic wheelchair at the next moment of the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment; A human-machine interaction interface is provided on the current electronic wheelchair, and is used to perform an alarm action of abnormal safety distance at the next moment from the current moment to the user of the current electronic wheelchair when there is a physical distance less than or equal to a preset distance limit among the physical distances of each vehicle object at the next moment from the current moment to the position of the current electronic wheelchair at the next moment from the current moment; The human-computer interaction interface is further used to temporarily suspend the execution of an alarm action of abnormal safety distance at the next moment from the current moment to the user of the current electronic wheelchair when there is a physical distance greater than the preset distance limit in each physical distance between each vehicle object at the next moment from the current moment and the position of the current electronic wheelchair at the next moment from the current moment; Among them, when there is a physical distance less than or equal to a preset distance limit in each physical distance of each vehicle object relative to the position of the current electronic wheelchair at the next moment after the current moment, an alarm action of abnormal safety distance at the next moment after the current moment is performed to the user of the current electronic wheelchair, including: determining the time difference between the current moment and the next moment after the current moment based on the adaptive shooting frame rate of the visual monitor, and calculating the position of the current electronic wheelchair at the next moment after the current moment based on the driving speed of the current electronic wheelchair and the position of the current electronic wheelchair at the current moment.

2. The human-computer interaction computer system according to claim 1, characterized in that: Using an AI judgment model to intelligently judge the physical distances of each vehicle object at the next moment of the current moment relative to the current electronic wheelchair according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment, including: the number of the multiple acquisition moments is proportional to the number of each vehicle object; Among them, the adaptive shooting frame rate is used to perform frame-by-frame image acquisition on the front driving scene of the current electronic wheelchair to obtain each frame of the driving scene image corresponding to each acquisition moment, including: the value of the adaptive shooting frame rate is positively correlated with the driving speed of the current electronic wheelchair.

3. The human-computer interaction computer system according to claim 2, characterized in that: An AI judgment model is used to intelligently judge the input data of the steps of respectively determining the physical distances of each vehicle object relative to the current electronic wheelchair at the next moment based on the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of driving scene image corresponding to multiple acquisition moments before the current moment. The input data of the step of performing numerical normalization processing is processed to obtain the input data after the numerical normalization processing is performed.

4. The human-computer interaction computer system according to claim 3, characterized in that: The step of using an AI judgment model to intelligently judge the physical distances of each vehicle object relative to the current electronic wheelchair at the next moment from the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is numerically normalized to obtain the input data after the numerical normalization processing is performed, including: using an AI judgment model to intelligently judge the physical distances of each vehicle object relative to the current electronic wheelchair at the next moment from the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is numerically normalized based on hexadecimal numerical conversion to obtain the input data after the numerical normalization processing is performed.

5. The human-computer interaction computer system according to claim 3, characterized in that: The input data of the step of using an AI judgment model to intelligently judge the physical distances of each vehicle object relative to the current electronic wheelchair at the next moment from the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is numerically normalized to obtain the input data after the numerical normalization processing is performed, including: the input data of the step of using an AI judgment model to intelligently judge the physical distances of each vehicle object relative to the current electronic wheelchair at the next moment from the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is numerically normalized based on octal numerical conversion to obtain the input data after the numerical normalization processing is performed.

6. The human-computer interaction computer system according to claim 3, characterized in that: The input data of the step of using an AI judgment model to intelligently judge the physical distances of each vehicle object relative to the current electronic wheelchair at the next moment from the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is numerically normalized to obtain the input data after the numerical normalization processing is performed, including: the input data of the step of using an AI judgment model to intelligently judge the physical distances of each vehicle object relative to the current electronic wheelchair at the next moment from the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is numerically normalized based on binary numerical conversion to obtain the input data after the numerical normalization processing is performed.

7. The human-computer interaction computer system according to claim 3, characterized in that: The input data of the step of using an AI judgment model to intelligently judge the physical distances of each vehicle object relative to the current electronic wheelchair at the next moment from the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is numerically normalized to obtain the input data after the numerical normalization processing is performed, including: the input data of the step of using an AI judgment model to intelligently judge the physical distances of each vehicle object relative to the current electronic wheelchair at the next moment from the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment is numerically normalized based on decimal value conversion to obtain the input data after the numerical normalization processing is performed.

8. The human-computer interaction computer system according to claim 2, wherein: The AI ​​judgment model is used to intelligently judge the physical distances of each vehicle object relative to the current electronic wheelchair at the next moment based on the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of the driving scene image corresponding to multiple acquisition moments before the current moment. The output data of the step is a numerical representation after numerical normalization.

9. The human-computer interaction computer system according to claim 8, characterized in that: The output data of the step of using an AI judgment model to intelligently judge the respective physical distances of each vehicle object relative to the current electronic wheelchair at the next moment from the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of driving scene image corresponding to multiple acquisition moments before the current moment is a numerical representation after numerical normalization processing, including: the output data of the step of using an AI judgment model to intelligently judge the respective physical distances of each vehicle object relative to the current electronic wheelchair at the next moment from the current moment according to the adaptive shooting frame rate, the imaging focal length of the visual monitor, and the targeted visual data of each frame of driving scene image corresponding to multiple acquisition moments before the current moment is a numerical representation after numerical normalization processing based on hexadecimal numerical conversion, a numerical representation after numerical normalization processing based on octal numerical conversion, a numerical representation after numerical normalization processing based on binary numerical conversion, and a numerical representation after numerical normalization processing based on decimal numerical conversion.