Man-machine interaction intelligent judgment system

Abstract

The invention relates to a man-machine interaction intelligent judgment system which comprises a region analysis mechanism, a target acquisition mechanism, an information acquisition mechanism, an object remodeling mechanism, a collision prediction device and an interaction judgment device. The man-machine interaction intelligent judgment system is intelligent in operation and convenient to control.

Description

Technical Field

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

[0002] Human-Computer Interaction or Human-Machine Interaction (HCI or HMI) is a discipline that studies the interactive relationship between systems and users. Systems can be various machines or computerized systems and software. The human-computer interaction interface usually refers to the part visible to the user. Users communicate with the system and operate it through the human-computer interaction interface. It can be as small as the play button on a radio, as large as the dashboard on an airplane, or the control room of a power plant. The design of the human-computer interaction interface must include the user's understanding of the system (i.e., mental model), which is for the usability or user-friendliness of the system.

[0003] CN119173835A discloses a multimodal interaction system. The multimodal interaction system receives a selection of an augmented reality (AR) experience within an application on a computer device, displays a collection of AR objects associated with the AR experience on a graphical user interface (GUI) of the computer device, displays text prompts associated with the collection of augmented reality objects on the GUI, receives gestures and voice commands, modifies a subset of the collection of augmented reality objects based on the gestures and voice commands, and displays the modified subset of augmented reality objects on the GUI.

[0004] CN119170008A discloses a method, system, device and medium for intelligent scheduling of system function pages based on voice recognition, which belongs to the field of human-computer interaction technology. The technical problem to be solved by the present invention is how to accurately recognize the user's voice instructions and intelligently schedule the corresponding system function pages to improve the convenience of operation and user experience. The technical scheme adopted is: voice acquisition: collecting the user's voice instructions and obtaining the voice signal; voice signal preprocessing: performing noise reduction and enhancement preprocessing operations on the collected voice signal; voice recognition: using a recurrent neural network to build a deep learning model, after training, the voice signal is interfaced, and the acoustic features are converted into a text sequence, and then the text sequence is corrected for grammar and punctuation, and the recognized text data is output; natural language understanding: parsing the semantic content in the text data, and extracting the user's intention by a classification method; intention mapping; page scheduling. Summary of the invention

[0005] In order to solve the technical problems in the prior art, the present invention provides a human-computer interactive intelligent judgment system, which can use an artificial intelligence mechanism to perform joint intelligent judgment on the positions of multiple target vehicles around an electric wheelchair at multiple past moments and the driving speed of the electric wheelchair, so as to determine whether the electric wheelchair will collide with more than one of the multiple target vehicles at the next moment of the current moment, thereby completing the intelligent identification of whether the driving speed of the electric wheelchair is a safe driving speed, wherein the BP neural network after each learning is output as a BP neural network model, in the BP neural network model, each learning is performed on the BP neural network to obtain the BP neural network after each learning, the number of learnings performed by the BP neural network is monotonically positively correlated with the value of the set radius, thereby completing the structural customization of the BP neural network model for implementing the artificial intelligence mechanism, and also introduces an interactive judgment device set on the electric wheelchair, which is used to display a request to adjust the driving speed of the electric wheelchair to the driver of the electric wheelchair when the received state prediction mark indicates that the electric wheelchair will collide with the multiple target vehicles at the next moment of the current moment, otherwise, display a notification to the driver of the electric wheelchair that the driving speed of the electric wheelchair is a safe driving speed, thereby ensuring the driving safety of the electric wheelchair at future moments.

[0006] According to the present invention, a human-computer interaction intelligent judgment system is provided, the system comprising: The area analysis mechanism is arranged on the electric wheelchair, and is used to provide the current positioning data of the electric wheelchair in real time, and determine the surrounding area of ​​the electric wheelchair with the current positioning data as the center and the set radius as the radius of the circle; A target acquisition mechanism is arranged on the electric wheelchair, connected to the area resolution mechanism and connected to a remote vehicle positioning server, and is used to take a vehicle whose current positioning position is within the surrounding area of ​​the electric wheelchair as a target vehicle, so as to obtain a plurality of target vehicles; An information collection mechanism, connected to the target acquisition mechanism, for obtaining a plurality of positioning data corresponding to a plurality of past moments before the current moment for each target vehicle as output of the positioning-related data of the target vehicle, wherein the number of the plurality of past moments is monotonically positively correlated with the value of the set radius; An object reshaping mechanism, used for outputting the BP neural network after each learning as a BP neural network model, in which each learning is performed on the BP neural network to obtain the BP neural network after each learning; a collision prediction device, which is arranged on the electric wheelchair and is respectively connected to the object reshaping mechanism, the information collection mechanism and the area analysis mechanism, and is used to synchronously input the positioning data of the electric wheelchair at a current moment, the driving speed of the electric wheelchair, a plurality of positioning-related data respectively corresponding to the plurality of target vehicles and the set radius into the BP neural network model, and run the BP neural network model to obtain a state prediction identifier output by the BP neural network model as to whether the electric wheelchair will collide with the plurality of target vehicles at the next moment after the current moment; an interactive judgment device, arranged on the electric wheelchair and connected to the collision prediction device, for displaying a request to adjust the driving speed of the electric wheelchair to the driver of the electric wheelchair when the received state prediction identifier indicates that the electric wheelchair will collide with the multiple target vehicles at the next moment after the current moment, and otherwise, displaying a notification to the driver of the electric wheelchair that the driving speed of the electric wheelchair is a safe driving speed; The positioning data of the electric wheelchair at the current moment, the driving speed of the electric wheelchair, the multiple positioning-related data corresponding to the multiple target vehicles, and the set radius are synchronously input into the BP neural network model, and the BP neural network model is run to obtain the state prediction identification of whether the electric wheelchair will collide with the multiple target vehicles at the next moment after the current moment output by the BP neural network model, including: multiple past moments before the current moment include the current moment, and the multiple past moments before the current moment and the next moment after the current moment are evenly distributed on the time axis; Among them, the positioning data of the electric wheelchair at the current moment, the driving speed of the electric wheelchair, the multiple positioning-related data corresponding to the multiple target vehicles and the set radius are synchronously input into the BP neural network model, and the BP neural network model is run to obtain the state prediction identification of whether the electric wheelchair will collide with the multiple target vehicles at the next moment after the current moment output by the BP neural network model, which also includes: if the electric wheelchair collides with any target vehicle at the next moment after the current moment, it is determined that the electric wheelchair collides with the multiple target vehicles at the next moment after the current moment.

[0007] Therefore, the present invention has the following three outstanding technical effects: First: an artificial intelligence mechanism is used to make a joint intelligent judgment on the positions of multiple target vehicles around the electric wheelchair at multiple past moments and the driving speed of the electric wheelchair, so as to determine whether the electric wheelchair will collide with one or more of the multiple target vehicles at the next moment of the current moment, thereby completing the intelligent identification of whether the driving speed of the electric wheelchair is a safe driving speed; Second, the BP neural network after each learning is output as a BP neural network model, in which each learning is performed on the BP neural network to obtain the BP neural network after each learning, and the number of learnings performed by the BP neural network is monotonically positively correlated with the value of the set radius, thereby completing the structural customization of the BP neural network model for implementing the artificial intelligence mechanism; Third: introducing an interactive judgment device arranged on the electric wheelchair, which is used to display a request to adjust the driving speed of the electric wheelchair to the driver of the electric wheelchair when the received state prediction mark indicates that the electric wheelchair will collide with the multiple target vehicles at the next moment after the current moment; otherwise, displaying a notification to the driver of the electric wheelchair that the driving speed of the electric wheelchair is a safe driving speed, thereby ensuring the driving safety of the electric wheelchair in the future.

[0008] The human-machine interactive intelligent judgment system of the present invention is intelligent in operation and easy to operate. Since an artificial intelligence mechanism can be used to perform joint intelligent judgment on the positions of multiple target vehicles around the electric wheelchair at multiple past moments and the driving speed of the electric wheelchair, it is determined whether the electric wheelchair will collide with more than one of the multiple target vehicles at the next moment of the current moment, thereby completing the intelligent identification of whether the driving speed of the electric wheelchair is a safe driving speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] The embodiments of the present invention will be described below with reference to the accompanying drawings, wherein:

[0010] Figure 1 1 is a structural block diagram of a human-computer interaction intelligent judgment system according to Embodiment 1 of the present invention.

[0011] Figure 2 1 is a structural block diagram of a human-computer interaction intelligent judgment system according to Embodiment 2 of the present invention.

[0012] Figure 3 4 is a structural block diagram of a human-computer interaction intelligent judgment system according to Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0013] Human-computer interaction has multiple application areas. For example, a human-computer interaction system is set on an electric wheelchair to help people with limited mobility in the electric wheelchair to interact with the electric wheelchair, for example, to adjust the driving speed, etc. However, the driving speed of the electric wheelchair is lagging, and it is difficult for people to control the driving speed of the electric wheelchair in the future through the human-computer interaction system. The main reason is that it is difficult to predict the driving scenario of the electric wheelchair in the future. For example, whether the electric wheelchair will collide with the vehicle in front at the next moment from the current moment, so as to configure the electric wheelchair to slow down in the future at the current moment.

[0014] The embodiments of the human-computer interaction intelligent judgment system of the present invention will be described in detail below with reference to the accompanying drawings.

[0015] Figure 1 The structure block diagram of the human-computer interaction intelligent judgment system according to the first embodiment of the present invention is shown, and the system includes: The area analysis mechanism is arranged on the electric wheelchair, and is used to provide the current positioning data of the electric wheelchair in real time, and determine the surrounding area of ​​the electric wheelchair with the current positioning data as the center and the set radius as the radius of the circle; Specifically, the area resolution mechanism is arranged on the electric wheelchair, and is used to provide the current positioning data of the electric wheelchair in real time, and determine the surrounding area of ​​the electric wheelchair with the current positioning data as the center and the set radius as the radius of the circle, including: the area resolution mechanism has a built-in real-time positioning component; A target acquisition mechanism is arranged on the electric wheelchair, connected to the area resolution mechanism and connected to a remote vehicle positioning server, and is used to take a vehicle whose current positioning position is within the surrounding area of ​​the electric wheelchair as a target vehicle, so as to obtain a plurality of target vehicles; An information collection mechanism, connected to the target acquisition mechanism, for obtaining a plurality of positioning data corresponding to a plurality of past moments before the current moment for each target vehicle as output of the positioning-related data of the target vehicle, wherein the number of the plurality of past moments is monotonically positively correlated with the value of the set radius; An object reshaping mechanism, used for outputting the BP neural network after each learning as a BP neural network model, in which each learning is performed on the BP neural network to obtain the BP neural network after each learning; a collision prediction device, which is arranged on the electric wheelchair and is respectively connected to the object reshaping mechanism, the information collection mechanism and the area analysis mechanism, and is used to synchronously input the positioning data of the electric wheelchair at a current moment, the driving speed of the electric wheelchair, a plurality of positioning-related data respectively corresponding to the plurality of target vehicles and the set radius into the BP neural network model, and run the BP neural network model to obtain a state prediction identifier output by the BP neural network model as to whether the electric wheelchair will collide with the plurality of target vehicles at the next moment after the current moment; an interactive judgment device, arranged on the electric wheelchair and connected to the collision prediction device, for displaying a request to adjust the driving speed of the electric wheelchair to the driver of the electric wheelchair when the received state prediction identifier indicates that the electric wheelchair will collide with the multiple target vehicles at the next moment after the current moment, and otherwise, displaying a notification to the driver of the electric wheelchair that the driving speed of the electric wheelchair is a safe driving speed; The positioning data of the electric wheelchair at the current moment, the driving speed of the electric wheelchair, the multiple positioning-related data corresponding to the multiple target vehicles, and the set radius are synchronously input into the BP neural network model, and the BP neural network model is run to obtain the state prediction identification of whether the electric wheelchair will collide with the multiple target vehicles at the next moment after the current moment output by the BP neural network model, including: multiple past moments before the current moment include the current moment, and the multiple past moments before the current moment and the next moment after the current moment are evenly distributed on the time axis; The positioning data of the electric wheelchair at the current moment, the driving speed of the electric wheelchair, the multiple positioning-related data corresponding to the multiple target vehicles and the set radius are synchronously input into the BP neural network model, and the BP neural network model is run to obtain the state prediction mark of whether the electric wheelchair will collide with the multiple target vehicles at the next moment after the current moment output by the BP neural network model, which also includes: the collision of the electric wheelchair with any one of the target vehicles at the next moment after the current moment is determined as the collision of the electric wheelchair with the multiple target vehicles at the next moment after the current moment; The BP neural network after each learning is output as a BP neural network model, wherein in the BP neural network model, each learning is performed on the BP neural network to obtain the BP neural network after each learning, including: the number of times of learning performed by the BP neural network is monotonically positively correlated with the value of the set radius; Among them, a plurality of positioning data corresponding to a plurality of past moments before the current moment of each target vehicle are obtained as the positioning-related data output of the target vehicle, and the number of moments of the plurality of past moments is monotonically positively correlated with the numerical value of the set radius, including: using an information conversion formula to represent the information conversion relationship of the monotonically positive correlation between the number of moments of the plurality of past moments and the numerical value of the set radius.

[0016] Figure 2 1 is a structural block diagram of a human-computer interaction intelligent judgment system according to Embodiment 2 of the present invention.

[0017] Compared to Figure 1 , Figure 2 The human-computer interaction intelligent judgment system may also include: a data service component, connected to the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism, respectively, for providing the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism with their respective required state configuration services; The data service component is connected to the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism respectively, and is used to provide the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism with the state configuration services required by each of them, including: the data service component uses an IIC configuration bus to provide the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism with the state configuration services required by each of them; The data service component uses the IIC configuration bus to provide the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism with their respective required state configuration services, including: the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism have different IIC configuration addresses respectively; The collision prediction device, the object reshaping mechanism, the information collection mechanism, and the area analysis mechanism respectively have different IIC configuration addresses, including: the collision prediction device, the object reshaping mechanism, the information collection mechanism, and the area analysis mechanism respectively have different IIC configuration addresses in a binary value representation mode; Among them, the different IIC configuration addresses respectively possessed by the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism are in a binary numerical representation mode, including: the byte lengths of the different IIC configuration addresses respectively possessed by the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism are equal.

[0018] Figure 3 4 is a structural block diagram of a human-computer interaction intelligent judgment system according to Embodiment 3 of the present invention.

[0019] Compared to Figure 1 , Figure 3 The human-computer interaction intelligent judgment system may also include: A field positioning component, connected to the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism, respectively, for providing positioning services for the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism respectively; Wherein, the on-site positioning component is connected to the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional resolution mechanism respectively, and is used to provide positioning services for the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional resolution mechanism respectively, including: the on-site positioning component uses the GPS positioning mechanism to provide positioning services based on Galileo navigation data for the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional resolution mechanism respectively; And wherein, the on-site positioning component is respectively connected to the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional resolution mechanism, and is used to provide positioning services for the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional resolution mechanism respectively, including: the on-site positioning component adopts the Beidou positioning mechanism to provide Beidou navigation-based positioning services for the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional resolution mechanism respectively.

[0020] In addition, in the human-computer interaction intelligent judgment system, the current positioning data of the electric wheelchair, the driving speed of the electric wheelchair, the multiple positioning-related data corresponding to the multiple target vehicles, and the set radius are synchronously input into the BP neural network model, and the BP neural network model is run to obtain the state prediction identification of whether the electric wheelchair will collide with the multiple target vehicles at the next moment after the current moment output by the BP neural network model, including: the current positioning data of the electric wheelchair, the driving speed of the electric wheelchair, the multiple positioning-related data corresponding to the multiple target vehicles, and the set radius are respectively converted into binary values ​​and then synchronously input into the BP neural network model; And wherein, the current positioning data of the electric wheelchair, the driving speed of the electric wheelchair, the multiple positioning-related data corresponding to the multiple target vehicles and the set radius are synchronously input into the BP neural network model, and the BP neural network model is run to obtain the state prediction identifier of whether the electric wheelchair will collide with the multiple target vehicles at the next moment after the current moment output by the BP neural network model, which also includes: the state prediction identifier of whether the electric wheelchair will collide with the multiple target vehicles at the next moment after the current moment output by the BP neural network model is a binary value.

[0021] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A human-computer interaction intelligent judgment system, characterized in that: The system comprises: The area analysis mechanism is arranged on the electric wheelchair, and is used to provide the current positioning data of the electric wheelchair in real time, and determine the surrounding area of ​​the electric wheelchair with the current positioning data as the center and the set radius as the radius of the circle; A target acquisition mechanism is arranged on the electric wheelchair, connected to the area resolution mechanism and connected to a remote vehicle positioning server, and is used to take a vehicle whose current positioning position is within the surrounding area of ​​the electric wheelchair as a target vehicle, so as to obtain a plurality of target vehicles; An information collection mechanism, connected to the target acquisition mechanism, for obtaining a plurality of positioning data corresponding to a plurality of past moments before the current moment for each target vehicle as output of the positioning-related data of the target vehicle, wherein the number of the plurality of past moments is monotonically positively correlated with the value of the set radius; An object reshaping mechanism, used for outputting the BP neural network after each learning as a BP neural network model, in which each learning is performed on the BP neural network to obtain the BP neural network after each learning; a collision prediction device, which is arranged on the electric wheelchair and is respectively connected to the object reshaping mechanism, the information collection mechanism and the area analysis mechanism, and is used to synchronously input the positioning data of the electric wheelchair at a current moment, the driving speed of the electric wheelchair, a plurality of positioning-related data respectively corresponding to the plurality of target vehicles and the set radius into the BP neural network model, and run the BP neural network model to obtain a state prediction identifier output by the BP neural network model as to whether the electric wheelchair will collide with the plurality of target vehicles at the next moment after the current moment; an interactive judgment device, arranged on the electric wheelchair and connected to the collision prediction device, for displaying a request to adjust the driving speed of the electric wheelchair to the driver of the electric wheelchair when the received state prediction identifier indicates that the electric wheelchair will collide with the multiple target vehicles at the next moment after the current moment, and otherwise, displaying a notification to the driver of the electric wheelchair that the driving speed of the electric wheelchair is a safe driving speed; The positioning data of the electric wheelchair at the current moment, the driving speed of the electric wheelchair, the multiple positioning-related data corresponding to the multiple target vehicles, and the set radius are synchronously input into the BP neural network model, and the BP neural network model is run to obtain the state prediction identification of whether the electric wheelchair will collide with the multiple target vehicles at the next moment after the current moment output by the BP neural network model, including: multiple past moments before the current moment include the current moment, and the multiple past moments before the current moment and the next moment after the current moment are evenly distributed on the time axis; Among them, the positioning data of the electric wheelchair at the current moment, the driving speed of the electric wheelchair, the multiple positioning-related data corresponding to the multiple target vehicles and the set radius are synchronously input into the BP neural network model, and the BP neural network model is run to obtain the state prediction identification of whether the electric wheelchair will collide with the multiple target vehicles at the next moment after the current moment output by the BP neural network model, which also includes: if the electric wheelchair collides with any target vehicle at the next moment after the current moment, it is determined that the electric wheelchair collides with the multiple target vehicles at the next moment after the current moment.

2. The human-computer interaction intelligent judgment system according to claim 1, characterized in that: Outputting the BP neural network after each learning as a BP neural network model, wherein the BP neural network is subjected to each learning to obtain the BP neural network after each learning, comprising: the number of times of learning performed by the BP neural network is monotonically positively correlated with the value of the set radius; Among them, a plurality of positioning data corresponding to a plurality of past moments before the current moment of each target vehicle are obtained as the positioning-related data output of the target vehicle, and the number of moments of the plurality of past moments is monotonically positively correlated with the numerical value of the set radius, including: using an information conversion formula to represent the information conversion relationship of the monotonically positive correlation between the number of moments of the plurality of past moments and the numerical value of the set radius.

3. The human-computer interaction intelligent judgment system according to claim 2, characterized in that: The system further comprises: a data service component, connected to the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism, respectively, for providing the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism with their respective required state configuration services; Among them, the data service component is connected to the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism, respectively, and is used to provide the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism with their respective required state configuration services, including: the data service component uses the IIC configuration bus to provide the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism with their respective required state configuration services.

4. The human-computer interaction intelligent judgment system according to claim 3, characterized in that: The data service component uses the IIC configuration bus to provide the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism with their respective required status configuration services, including: the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism have different IIC configuration addresses respectively.

5. The human-computer interaction intelligent judgment system according to claim 4, characterized in that: The collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism respectively have different IIC configuration addresses, including: the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism respectively have different IIC configuration addresses in binary value representation mode.

6. The human-computer interaction intelligent judgment system according to claim 5, characterized in that: The different IIC configuration addresses respectively possessed by the collision prediction device, the object reshaping mechanism, the information collection mechanism and the area analysis mechanism are represented in a binary numerical mode, including: the byte lengths of the different IIC configuration addresses respectively possessed by the collision prediction device, the object reshaping mechanism, the information collection mechanism and the area analysis mechanism are equal.

7. The human-computer interaction intelligent judgment system according to claim 2, characterized in that: The system further comprises: The on-site positioning component is respectively connected to the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism, and is used to provide positioning services for the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional analysis mechanism respectively.

8. The human-computer interaction intelligent judgment system according to claim 7, characterized in that: The on-site positioning component is respectively connected to the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional resolution mechanism, and is used to provide positioning services for the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional resolution mechanism respectively, including: the on-site positioning component adopts the GPS positioning mechanism to provide positioning services based on Galileo navigation data for the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional resolution mechanism respectively.

9. The human-computer interaction intelligent judgment system according to claim 7, characterized in that: An on-site positioning component is respectively connected to the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional resolution mechanism, and is used to provide positioning services for the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional resolution mechanism respectively, including: the on-site positioning component adopts the Beidou positioning mechanism to provide Beidou navigation-based positioning services for the collision prediction device, the object reshaping mechanism, the information collection mechanism and the regional resolution mechanism respectively.

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