CN112435515B - Artificial intelligence education robot - Google Patents
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Abstract
The invention discloses an artificial intelligent education robot, relates to the technical field of intelligent education, and solves the technical problems that the existing education robot cannot update learning materials in time and cannot customize a learning plan according to the learning records of children; the learning planning module is arranged, the result mean value is obtained according to the learning curve and the learning record of the learning materials, the learning efficiency of the children is analyzed according to the historical learning record of the children, and the teaching efficiency of the education robot is improved; the cloud server is arranged, and the firmware version and the learning data of the educational robot are updated by the cloud server, so that the working efficiency of the educational robot is ensured, and the learning quality of children is improved; the intelligent terminal is provided with the fault detection module, and the fault detection module is used for respectively carrying out fault analysis on the hardware equipment and the built-in sensor, generating an early warning signal and sending the early warning signal to the intelligent terminal of the user, so that the user can find out fault information of the educational robot in time, and the influence on the learning of children is avoided.
Description
Technical Field
The invention belongs to the field of intelligent education, relates to an artificial intelligence technology, and particularly relates to an artificial intelligence education robot.
Background
The development of the robot industry in the scientific and technological age is vigorous, and the market scale of the global service type robot is continuously expanded. Educational robots are gradually replacing traditional products such as point-to-read machines, story tellers, tablet machines, and the like. At present, with the release of the two-child policy, the population quantity of children will continuously increase in the future, and the education and teaching of children are very important. Therefore, the educational robot is used for accompanying, educating and teaching children, so that the children can learn and grow in a happy environment.
The invention patent with publication number CN110322737A provides an artificial intelligence education robot, which comprises a head, a camera, a light supplement lamp, an array microphone, a loudspeaker, a high-resolution touch screen, a human body pyroelectric sensor, an infrared ray receiving sensor, an obstacle avoidance sensor, a mechanical arm and a machine base, wherein the camera, the light supplement lamp and the high-resolution touch screen are embedded and installed on the front of the head, the array microphone is embedded and installed right above the head, the loudspeaker is installed on the left side and the right side of the head, the human body pyroelectric sensor, the infrared ray receiving sensor and the obstacle avoidance sensor are embedded in a trunk, and the mechanical arm is installed on the left side and the right side of the trunk.
The education robot has the intelligent voice interaction function, including education course resources, entertainment interaction, voice chat encyclopedia question answering, remote video call and the like, and a user can select corresponding function items through voice or interface operation; however, the scheme does not specially analyze the learning condition of the children and does not provide targeted teaching; therefore, the above solution still needs further improvement.
Disclosure of Invention
In order to solve the problems of the scheme, the invention provides an artificial intelligence education robot.
The purpose of the invention can be realized by the following technical scheme: an artificial intelligence education robot comprises a robot body and a control system; the robot body comprises a camera, a loudspeaker, a robot base, a pyroelectric sensor, an obstacle avoidance sensor and a high-resolution touch screen; the control system comprises a processor, a user management module, a data storage module, a cloud server, a learning planning module and a fault detection module;
the pyroelectric sensor and the obstacle avoidance sensor are in communication connection with the processor; the processor is respectively in linear connection with the camera, the robot base, the loudspeaker and the high-resolution touch screen;
the processor is respectively in linear connection with the data storage module, the fault detection module and the learning planning module; the processor is respectively in communication connection with the cloud server and the user management module; the learning planning module is in communication connection with the data storage module;
the user management module is in communication connection with the data storage module; the user management module is used for managing the robot body through an intelligent terminal by a user, and the intelligent terminal comprises an intelligent mobile phone, a tablet personal computer and a notebook computer;
the fault detection module is used for early warning the fault of the robot body;
the data storage module stores an identity of the robot body;
the cloud server is used for updating resources; the resources comprise learning materials and firmware versions of the robot body; the learning materials comprise education courses and encyclopedia questions and answers.
Preferably, the obstacle avoidance sensor is automatically started when the robot body moves and automatically sleeps when the robot body is forbidden; the robot body moves through the robot base and is combined with the camera to control the distance between the robot body and the child.
Preferably, the speaker is used for playing sound; the pyroelectric sensor is used for detecting the information of people around the robot body.
Preferably, the learning planning module is configured to schedule learning of the child, and includes:
acquiring learning materials stored in a data storage module and marking the learning materials as k, k being 1, 2, … …, m;
acquiring a learning curve and a completion time of learning data k stored in a data storage module through a processor;
obtaining a difference value between the system time and the completion time, taking the difference value as an independent variable to be brought into a learning curve to obtain a calculation result, obtaining a mean value of T3 calculation results, and marking the mean value as a result mean value PFkt; wherein T3 is a preset proportionality coefficient;
when the result mean value PFkt meets the condition that PFkt is not less than 0 and not more than L2, judging that the content of the learning material k is not deeply memorized by the children, sending the learning material k to a high-resolution touch screen through a processor, and sending a review signal to a user management module; when the result mean value PFkt meets the condition that PFkt is larger than L2, judging that the content of the learning material k is deeply memorized by the children, and sending a learning completion signal to the user management module through the processor; wherein L2 is a preset calculation result threshold;
and sending the review signal sending record, the learning completion signal sending record and the result mean value to the data storage module for storage.
Preferably, the step of acquiring the learning curve includes:
when the child finishes learning the learning data k, sorting the knowledge main points of the learning data k, asking questions of the knowledge main points through a high-resolution touch screen at the moment t, grading according to the answer result of the child and marking the grade as PFt; wherein t is the time difference between the questioning time and the time when the children finish learning the learning data k;
taking t as an independent variable and PFt as a dependent variable to carry out polynomial fitting to obtain a learning curve;
and sending the learning curve to a data storage module for storage.
Preferably, the specific step of updating the robot body resource by the cloud server includes:
the maintenance personnel send the firmware version to the cloud server, the cloud server obtains the identity of the robot body matched with the firmware version and marks the identity matched with the robot body as a target identity;
the cloud server sends the firmware version to a processor of the robot body corresponding to the target identification; after receiving the firmware version, the processor of the robot body updates the firmware of the robot body according to a preset firmware updating scheme; deleting the firmware version after the updating is finished;
the maintenance personnel upload the learning data to the cloud server periodically, and the user downloads the learning data from the cloud server through the intelligent terminal and stores the learning data in the data storage module;
the cloud server periodically filters and updates the learning data of the cloud server.
Preferably, the filtering update includes:
the user scores the learning data through the intelligent terminal and sends the scores to the cloud server through the processor;
the learning materials of the cloud server are marked as j, j is 1, 2, … …, n;
acquiring the total score of the learning material j and marking the total score as ZPFj;
acquiring the downloading times of the learning material j and marking as XCj;
by the formula
Obtaining a screening evaluation coefficient SPXj; wherein beta 1 and beta 2 are preset proportionality coefficientsAnd β 1 and β 2 are both real numbers greater than 0;
sorting the learning materials in a descending order according to the screening evaluation coefficient SPXj to obtain a learning material grading table;
marking the learning materials of H1 before ranking in the learning material scoring table as first learning materials; marking the learning material ranked at the last H2 in the learning material scoring table as a second learning material; wherein H1 and H2 are both preset proportionality coefficients;
deleting the second learning data from the cloud server; and periodically recommending the first learning data to the robot body.
Preferably, the fault detection module includes the following steps:
sequentially marking hardware devices as i, i is 1, 2, 3 and 4; the hardware equipment comprises a camera, a loudspeaker, a robot base and a high-resolution touch screen;
detecting the connection state of the processor and the hardware equipment, and marking the detection result as YLTi; the detection result YLTi takes values of 0 and 1; when the YLTI is equal to 1, indicating that the connection state of the hardware device corresponding to the mark i and the processor is normal, and when the YLTI is equal to 0, indicating that the connection state of the hardware device corresponding to the mark i and the processor is abnormal;
acquiring the power consumption of hardware equipment within a preset time T1, and marking the power consumption as HDi; wherein T1 is a preset proportionality coefficient;
by the formula
Acquiring a hardware fault evaluation coefficient YGPX; wherein alpha 1 and alpha 2 are preset proportionality coefficients, and both alpha 1 and alpha 2 are real numbers larger than 0;
when the hardware fault evaluation coefficient YGPX satisfies YGPX 0 or YGPX > L1, determining that the hardware device is abnormal, and sending a hardware device abnormal signal to the user management module through the processor; when the hardware fault evaluation coefficient YGPX meets 0< YGPX ≤ L1, determining that the hardware device is normal; wherein L1 is a preset hardware evaluation coefficient threshold;
starting the built-in sensor through the processor, and judging that the built-in sensor is normal when the built-in sensor has a signal returned at preset time T2; when no signal returns from the built-in sensor within the preset time T2, judging that the built-in sensor is abnormal, and sending a sensor abnormal signal to the user management module through the processor; wherein T2 is a preset proportionality coefficient; the built-in sensors comprise pyroelectric sensors and obstacle avoidance sensors;
acquiring an identity of a robot body in a data storage module; sending the abnormal signal sending record of the hardware equipment, the abnormal signal sending record of the sensor, the hardware fault evaluation coefficient and the identity identification of the robot body to a cloud server through a processor; meanwhile, the processor sends the abnormal signal sending record of the hardware equipment, the abnormal signal sending record of the sensor and the hardware fault evaluation coefficient to the data storage module for storage.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention provides a learning planning module, which is used for arranging the learning of children; acquiring learning materials k stored in a data storage module; acquiring a learning curve and a completion time of learning data k stored in a data storage module through a processor; obtaining a difference value between the system time and the completion time, taking the difference value as an independent variable to be brought into a learning curve to obtain a calculation result, obtaining a mean value of T3 calculation results, and marking the mean value as a result mean value PFkt; when the result mean value PFkt meets the condition that PFkt is not less than 0 and not more than L2, judging that the content of the learning material k is not deeply memorized by the children, sending the learning material k to a high-resolution touch screen through a processor, and sending a review signal to a user management module; when the result mean value PFkt meets the condition that PFkt is larger than L2, judging that the content of the learning material k is deeply memorized by the children, and sending a learning completion signal to the user management module through the processor; the learning planning module acquires a result mean value according to the learning curve and the learning record of the learning material, and analyzes the learning efficiency of the children according to the learning history record of the children, so that the teaching efficiency of the educational robot is improved;
2. the invention is provided with a cloud server, and the cloud server is used for updating the robot body resources; the maintenance personnel send the firmware version to the cloud server, and the cloud server acquires the identity of the robot body matched with the firmware version and marks the identity matched with the robot body as a target identity; the cloud server sends the firmware version to a processor of the robot body corresponding to the target identification; after receiving the firmware version, the processor of the robot body updates the firmware of the robot body according to a preset firmware updating scheme; deleting the firmware version after the updating is finished; the maintenance personnel upload the learning data to the cloud server periodically, and the user downloads the learning data from the cloud server through the intelligent terminal and stores the learning data in the data storage module; the cloud server is in communication connection with the education robot, the work efficiency of the education robot is guaranteed by updating the firmware version and the learning data of the education robot, and meanwhile, the best learning data is provided for the education robot, so that the learning quality of children is improved;
3. the invention is provided with a fault detection module, which is used for early warning of the fault of the robot body; sequentially marking hardware equipment as i; detecting the connection state YLTi of the processor and the hardware equipment; acquiring the power consumption HDi of hardware equipment within preset time T1; acquiring a hardware fault evaluation coefficient YGPX; when the hardware fault evaluation coefficient YGPX satisfies YGPX 0 or YGPX > L1, determining that the hardware device is abnormal, and sending a hardware device abnormal signal to the user management module through the processor; when the hardware fault evaluation coefficient YGPX meets 0< YGPX ≦ L1, the hardware device is determined to be normal; starting the built-in sensor through the processor, and judging that the built-in sensor is normal when the built-in sensor has a signal returned at preset time T2; when no signal returns from the built-in sensor within the preset time T2, judging that the built-in sensor is abnormal, and sending a sensor abnormal signal to the user management module through the processor; acquiring an identity of a robot body in a data storage module; sending the abnormal signal sending record of the hardware equipment, the abnormal signal sending record of the sensor, the hardware fault evaluation coefficient and the identity identification of the robot body to a cloud server through a processor; meanwhile, sending the abnormal signal sending record of the hardware equipment, the abnormal signal sending record of the sensor and the hardware fault evaluation coefficient to a data storage module for storage through a processor; the fault detection module carries out fault analysis to hardware equipment and built-in sensor respectively, and generate early warning signal and send to user's intelligent terminal for the user can in time discover education robot's fault information, avoids influencing children's study.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a schematic diagram of the control principle of the present invention.
Detailed Description
The technical solutions of the present invention will be described below clearly and completely in conjunction with the embodiments, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an artificial intelligence educational robot includes a robot body and a control system; the robot body comprises a camera, a loudspeaker, a robot base, a pyroelectric sensor, an obstacle avoidance sensor and a high-resolution touch screen; the control system comprises a processor, an analysis reporting module, a data storage module, a user management module, a cloud server, a learning planning module and a fault detection module;
the pyroelectric sensor and the obstacle avoidance sensor are in communication connection with the processor; the processor is respectively in linear connection with the camera, the robot base, the loudspeaker and the high-resolution touch screen; the processor controls the robot base to realize the movement of the robot body;
the processor is respectively in linear connection with the data storage module, the fault detection module, the learning and planning module and the analysis and report module; the processor is respectively in communication connection with the cloud server and the user management module; the learning planning module is in communication connection with the data storage module;
the user management module is in communication connection with the analysis reporting module and the data storage module; the user management module is used for managing the robot body through an intelligent terminal by a user, and the intelligent terminal comprises an intelligent mobile phone, a tablet computer and a notebook computer;
the fault detection module is used for early warning the fault of the robot body;
the data storage module stores the identity of the robot body;
the cloud server is used for updating resources; the resources comprise learning materials and the firmware version of the robot body; the learning materials include educational courses and encyclopedia questions and answers.
Further, the learning planning module is used for arranging the learning of the children, and comprises:
acquiring learning materials stored in a data storage module and marking the learning materials as k, k being 1, 2, … …, m;
acquiring a learning curve and a completion moment of learning data k stored in a data storage module through a processor;
obtaining a difference value between the system time and the completion time, taking the difference value as an independent variable to be brought into a learning curve to obtain a calculation result, obtaining a mean value of T3 calculation results, and marking the mean value as a result mean value PFkt; wherein T3 is a preset proportionality coefficient;
when the result mean value PFkt meets the condition that PFkt is not less than 0 and not more than L2, judging that the content of the learning material k is not deeply memorized by the children, sending the learning material k to a high-resolution touch screen through a processor, and sending a review signal to a user management module; when the result mean value PFkt meets the condition that PFkt is larger than L2, judging that the content of the learning material k is deeply memorized by the children, and sending a learning completion signal to the user management module through the processor; wherein L2 is a preset calculation result threshold;
and sending the review signal sending record, the learning completion signal sending record and the result mean value to the data storage module for storage.
Further, the learning curve acquiring step includes:
when the child finishes learning the learning data k, sorting the knowledge main points of the learning data k, asking questions of the knowledge main points through a high-resolution touch screen at the moment t, grading according to the answer result of the child and marking the grade as PFt; wherein t is the time difference between the questioning time and the time when the children finish learning the learning data k;
taking t as an independent variable and PFt as a dependent variable to carry out polynomial fitting to obtain a learning curve;
and sending the learning curve to a data storage module for storage.
Further, the specific steps of the cloud server for updating the robot body resources comprise:
the maintenance personnel send the firmware version to the cloud server, the cloud server obtains the identity of the robot body matched with the firmware version and marks the identity matched with the robot body as a target identity;
the cloud server sends the firmware version to a processor of the robot body corresponding to the target identification; after receiving the firmware version, the processor of the robot body updates the firmware of the robot body according to a preset firmware updating scheme; deleting the firmware version after the updating is finished;
the maintenance personnel upload the learning data to the cloud server periodically, and the user downloads the learning data from the cloud server through the intelligent terminal and stores the learning data in the data storage module;
the cloud server periodically filters and updates the learning data of the cloud server.
Further, the filtering update includes:
the user scores the learning data through the intelligent terminal and sends the scores to the cloud server through the processor;
the learning material of the cloud server is marked as j, j equals to 1, 2, … …, n;
acquiring the total score of the learning material j and marking the total score as ZPFj;
acquiring the downloading times of the learning material j and marking as XCj;
by the formula
Obtaining a screening evaluation coefficient SPXj; wherein beta 1 and beta 2 are preset proportionality coefficients, and both beta 1 and beta 2 are real numbers larger than 0;
sorting the learning materials in a descending order according to the screening evaluation coefficient SPXj to obtain a learning material grading table;
marking the learning materials of H1 before ranking in the learning material scoring table as first learning materials; marking the learning material with the last rank H2 in the learning material grading table as a second learning material; wherein H1 and H2 are both preset proportionality coefficients;
deleting the second learning materials from the cloud server; and periodically recommending the first learning data to the robot body.
Further, the preset firmware update scheme includes:
a user sets a firmware updating mode through an intelligent terminal, wherein the firmware updating mode comprises an automatic updating mode and an active updating mode;
the active updating mode comprises the following steps:
the processor sends an update reminding signal to the intelligent terminal after receiving the firmware version, the intelligent terminal sends a feedback signal to the processor after receiving the update reminding signal, and the processor updates the firmware of the robot body according to the feedback signal; the feedback signals comprise an immediate update signal, a later update signal and a temporary non-update signal; sending the feedback signal to a data storage module for storage in real time through a processor;
the automatic updating mode comprises the following steps:
the processor acquires system time after receiving the firmware version, and immediately updates the firmware of the robot body when the system time is within a preset time zone range; when the system time is not in the preset time zone range, temporarily not updating the firmware of the robot body, and updating the firmware of the robot body until the system time is in the preset time zone range; the preset time zone is a time zone preset by a user through the intelligent terminal.
Further, the early warning step of fault detection module to robot body trouble includes:
sequentially marking hardware devices as i, i is 1, 2, 3 and 4; the hardware equipment comprises a camera, a loudspeaker, a robot base and a high-resolution touch screen;
detecting the connection state of the processor and the hardware equipment, and marking the detection result as YLTi; the detection result YLTi takes values of 0 and 1; when the YLTI is equal to 1, indicating that the connection state of the hardware device corresponding to the mark i and the processor is normal, and when the YLTI is equal to 0, indicating that the connection state of the hardware device corresponding to the mark i and the processor is abnormal;
acquiring the power consumption of hardware equipment within a preset time T1, and marking the power consumption as HDi; wherein T1 is a preset proportionality coefficient;
by the formula
Acquiring a hardware fault evaluation coefficient YGPX; wherein alpha 1 and alpha 2 are preset proportionality coefficients, and both alpha 1 and alpha 2 are real numbers larger than 0;
when the hardware fault evaluation coefficient YGPX satisfies YGPX 0 or YGPX > L1, determining that the hardware device is abnormal, and sending a hardware device abnormal signal to the user management module through the processor; when the hardware fault evaluation coefficient YGPX meets 0< YGPX ≤ L1, determining that the hardware device is normal; wherein L1 is a preset hardware evaluation coefficient threshold;
starting the built-in sensor through the processor, and judging that the built-in sensor is normal when the built-in sensor has a signal returned at preset time T2; when no signal returns from the built-in sensor within the preset time T2, judging that the built-in sensor is abnormal, and sending a sensor abnormal signal to the user management module through the processor; wherein T2 is a preset proportionality coefficient;
acquiring an identity of a robot body in a data storage module; sending the abnormal signal sending record of the hardware equipment, the abnormal signal sending record of the sensor, the hardware fault evaluation coefficient and the identity identification of the robot body to a cloud server through a processor; meanwhile, sending the abnormal signal sending record of the hardware equipment, the abnormal signal sending record of the sensor and the hardware fault evaluation coefficient to the data storage module for storage through the processor.
Further, the analysis reporting module is used for analyzing the learning record of the learning material k, and comprises:
acquiring the learning times XCk and the result mean value PFkt of the learning material k through a data storage module;
the preference coefficient PXk is obtained by the formula PXk ═ γ 1 × XCk × PFkt + γ 2; wherein gamma 1 and gamma 2 are preset proportionality coefficients, and both gamma 1 and gamma 2 are real numbers larger than 0;
when the preference coefficient PXk meets the condition that PXk is more than K2, the children are judged to have excessive preference on the learning material K, and a partiality signal is sent to the user management module through the processor; when the preference coefficient PXk meets the condition that K1 is more than PXk and less than or equal to K2, judging that the children's liking degree of the learning material K is normal; when the preference coefficient PXk satisfies 0< PXk < K1, the children are judged to dislike learning data K, and a data deleting signal is sent to the user management module through the processor;
and sending the preference coefficient, the partial department signal sending record and the data deleting signal sending record to the data rough-out module for storage through the processor.
The above formulas are all calculated by removing dimensions and taking values thereof, the formula is one closest to the real situation obtained by collecting a large amount of data and performing software simulation, and the preset parameters in the formula are set by the technical personnel in the field according to the actual situation.
The working principle of the invention is as follows:
when the child finishes learning the learning data k, sorting the knowledge main points of the learning data k, asking questions of the knowledge main points through a high-resolution touch screen at the moment t, grading according to the answer result of the child and marking the grade as PFt; wherein t is the time difference between the questioning time and the time when the children finish learning the learning data k; taking t as an independent variable and PFt as a dependent variable to carry out polynomial fitting to obtain a learning curve;
acquiring learning materials stored in a data storage module and marking the learning materials as k; acquiring a learning curve and a completion moment of learning data k stored in a data storage module through a processor; obtaining a difference value between the system time and the completion time, taking the difference value as an independent variable to be brought into a learning curve to obtain a calculation result, obtaining a mean value of T3 calculation results, and marking the mean value as a result mean value PFkt; when the result mean value PFkt meets the condition that PFkt is not less than 0 and not more than L2, judging that the content of the learning material k is not deeply memorized by the children, sending the learning material k to a high-resolution touch screen through a processor, and sending a review signal to a user management module; when the result mean value PFkt meets the condition that PFkt is larger than L2, judging that the content of the learning material k is deeply memorized by the children, and sending a learning completion signal to the user management module through the processor;
the maintenance personnel send the firmware version to the cloud server, the cloud server obtains the identity of the robot body matched with the firmware version and marks the identity matched with the robot body as a target identity; the cloud server sends the firmware version to a processor of the robot body corresponding to the target identification; after receiving the firmware version, the processor of the robot body updates the firmware of the robot body according to a preset firmware updating scheme; deleting the firmware version after the updating is finished; the maintenance personnel upload the learning data to the cloud server periodically, and the user downloads the learning data from the cloud server through the intelligent terminal and stores the learning data in the data storage module;
sequentially marking hardware equipment as i; detecting the connection state of the processor and the hardware equipment, and marking the detection result as YLTi; acquiring the power consumption of hardware equipment within a preset time T1, and marking the power consumption as HDi; acquiring a hardware fault evaluation coefficient YGPX; when the hardware failure evaluation coefficient YGPX is YGPX of 0 or YGPX > L1, determining that the hardware device is abnormal, and sending a hardware device abnormal signal to the user management module through the processor; when the hardware fault evaluation coefficient YGPX meets 0< YGPX ≤ L1, determining that the hardware device is normal; starting the built-in sensor through the processor, and judging that the built-in sensor is normal when the built-in sensor has a signal returned at preset time T2; when no signal returns from the built-in sensor within the preset time T2, judging that the built-in sensor is abnormal, and sending a sensor abnormal signal to the user management module through the processor; acquiring an identity of a robot body in a data storage module; sending the abnormal signal sending record of the hardware equipment, the abnormal signal sending record of the sensor, the hardware fault evaluation coefficient and the identity identification of the robot body to a cloud server through a processor; meanwhile, the processor sends the abnormal signal sending record of the hardware equipment, the abnormal signal sending record of the sensor and the hardware fault evaluation coefficient to the data storage module for storage.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.
Claims (5)
1. An artificial intelligence education robot is characterized by comprising a robot body and a control system; the robot body comprises a camera, a loudspeaker, a robot base, a pyroelectric sensor, an obstacle avoidance sensor and a high-resolution touch screen; the control system comprises a processor, a data storage module, a user management module, a cloud server, a learning planning module and a fault detection module;
the pyroelectric sensor and the obstacle avoidance sensor are in communication connection with the processor; the processor is respectively in linear connection with the camera, the robot base, the loudspeaker and the high-resolution touch screen;
the processor is respectively in linear connection with the data storage module, the fault detection module and the learning planning module; the processor is respectively in communication connection with the cloud server and the user management module; the learning planning module is in communication connection with the data storage module;
the user management module is in communication connection with the data storage module; the user management module is used for managing the robot body through an intelligent terminal by a user, and the intelligent terminal comprises an intelligent mobile phone, a tablet computer and a notebook computer;
the fault detection module is used for early warning the fault of the robot body;
the data storage module stores the identity of the robot body;
the cloud server is used for updating resources; the resources comprise learning materials and the firmware version of the robot body; the learning materials comprise education courses and encyclopedic questions and answers;
the learning planning module is used for arranging the learning of children, and comprises:
acquiring learning materials stored in a data storage module and marking the learning materials as k, k being 1, 2, … …, m;
acquiring a learning curve and a completion time of learning data k stored in a data storage module through a processor;
obtaining a difference value between the system time and the completion time, taking the difference value as an independent variable to be brought into a learning curve to obtain a calculation result, obtaining a mean value of T3 calculation results, and marking the mean value as a result mean value PFkt; wherein T3 is a preset proportionality coefficient;
when the result mean value PFkt meets the condition that PFkt is more than or equal to 0 and less than or equal to L2, judging that the content of the learning material k is not deeply memorized by the children, sending the learning material k to a high-resolution touch screen through a processor, and sending a review signal to a user management module; when the result mean value PFkt meets the condition that PFkt is greater than L2, determining that the content of the learning material k is deeply memorized by the children, and sending a learning completion signal to the user management module through the processor; wherein L2 is a preset calculation result threshold;
and sending the review signal sending record, the learning completion signal sending record and the result mean value to the data storage module for storage.
2. An artificial intelligence educational robot, according to claim 1, wherein the learning curve obtaining step comprises:
when the child finishes learning the learning data k, sorting the knowledge key points of the learning data k, asking questions of the knowledge key points through the high-resolution touch screen at the moment t, grading according to the answer result of the child and marking the grade as PFt; wherein t is the time difference between the questioning time and the time when the children finish learning the learning data k;
taking t as an independent variable and PFt as a dependent variable to carry out polynomial fitting to obtain a learning curve;
and sending the learning curve to a data storage module for storage.
3. The artificial intelligence educational robot of claim 1, wherein the specific step of the cloud server updating the robot ontology resource comprises:
the maintenance personnel send the firmware version to the cloud server, the cloud server obtains the identity of the robot body matched with the firmware version and marks the identity matched with the robot body as a target identity;
the cloud server sends the firmware version to a processor of the robot body corresponding to the target identification; after receiving the firmware version, the processor of the robot body updates the firmware of the robot body according to a preset firmware updating scheme; deleting the firmware version after the updating is finished;
the maintenance personnel upload the learning data to the cloud server periodically, and the user downloads the learning data from the cloud server through the intelligent terminal and stores the learning data in the data storage module;
the cloud server periodically filters and updates the learning data of the cloud server.
4. The artificial intelligence educational robot of claim 1, wherein the early warning step of the fault detection module to the robot body fault comprises:
sequentially marking hardware devices as i, i is 1, 2, 3 and 4; the hardware equipment comprises a camera, a loudspeaker, a robot base and a high-resolution touch screen;
detecting the connection state of the processor and the hardware equipment, and marking the detection result as YLTi; the detection result YLTi takes values of 0 and 1; when the YLTi is equal to 1, indicating that the connection state of the hardware device corresponding to the mark i and the processor is normal, and when the YLTi is equal to 0, indicating that the connection state of the hardware device corresponding to the mark i and the processor is abnormal;
acquiring the power consumption of hardware equipment within a preset time T1, and marking the power consumption as HDi; wherein T1 is a preset proportionality coefficient;
by the formula
Acquiring a hardware fault evaluation coefficient YGPX; wherein alpha 1 and alpha 2 are preset proportionality coefficients, and both alpha 1 and alpha 2 are real numbers larger than 0;
when the hardware fault evaluation coefficient YGPX satisfies YGPX 0 or YGPX > L1, determining that the hardware device is abnormal, and sending a hardware device abnormal signal to the user management module through the processor; when the hardware fault evaluation coefficient YGPX meets 0< YGPX ≦ L1, determining that the hardware device is normal; wherein L1 is a preset hardware evaluation coefficient threshold;
starting the built-in sensor through the processor, and judging that the built-in sensor is normal when the built-in sensor has a signal returned at preset time T2; when no signal returns from the built-in sensor within the preset time T2, judging that the built-in sensor is abnormal, and sending a sensor abnormal signal to the user management module through the processor; wherein T2 is a preset proportionality coefficient;
acquiring an identity of a robot body in a data storage module; sending the abnormal signal sending record of the hardware equipment, the abnormal signal sending record of the sensor, the hardware fault evaluation coefficient and the identity identification of the robot body to a cloud server through a processor; meanwhile, the processor sends the abnormal signal sending record of the hardware equipment, the abnormal signal sending record of the sensor and the hardware fault evaluation coefficient to the data storage module for storage.
5. An artificial intelligence educational robot, according to claim 3, wherein the filtering update comprises:
the user scores the learning data through the intelligent terminal and sends the scores to the cloud server through the processor;
the learning material of the cloud server is marked as j, j equals to 1, 2, … …, n;
acquiring the total score of the learning material j and marking the total score as ZPFj;
acquiring the downloading times of the learning material j and marking as XCj;
by the formula
Obtaining a screening evaluation coefficient SPXj; wherein β 1 and β 2 are preset proportionality coefficients, and both β 1 and β 2 are real numbers greater than 0;
sorting the learning materials in a descending order according to the screening evaluation coefficient SPXj to obtain a learning material grading table;
marking the learning materials of H1 before ranking in the learning material scoring table as first learning materials; marking the learning material with the last rank H2 in the learning material grading table as a second learning material; wherein H1 and H2 are both preset proportionality coefficients;
deleting the second learning materials from the cloud server; and periodically recommending the first learning data to the robot body.
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