CN111160697A - Method for judging on duty of bus driver based on pattern recognition and related equipment - Google Patents
Method for judging on duty of bus driver based on pattern recognition and related equipment Download PDFInfo
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Abstract
A method for judging on duty of a bus driver based on pattern recognition comprises the following steps: acquiring a health detection result of a bus driver; if the health detection result shows that various indexes of the body of the bus driver meet the standard requirements, a verification-type question bank is constructed for the bus driver; outputting intelligent questions in the verification-type question bank; calculating the answer score of each intelligent question answered by the bus driver through an answer calculation model; recording face micro-expression information and sound information of a bus driver; inputting the face micro-expression information and the voice information into an emotion risk detection model to obtain emotion risk scores of bus drivers for each intelligent problem; and inputting the answer scores and the emotion risk scores of all the intelligent questions into the comprehensive judgment model to obtain the on-duty judgment result of the bus driver. The invention also provides a device for judging whether the bus driver is on duty, electronic equipment and a storage medium. The invention can carry out on-duty test on the bus driver.
Description
Technical Field
The invention relates to the technical field of intelligent terminals, in particular to a method for judging whether a bus driver is on duty based on pattern recognition and related equipment.
Background
With the rapid development of economy, vehicles such as private cars, buses, taxis and the like are increasing. Due to the limitation of roads, the number of vehicles and pedestrians is increased, so that traffic accidents frequently occur. The traffic accident occurs due to various causes, generally, human factors, vehicle factors, road and environmental factors, etc., in which the human factors are the top. The human factor is most important, and the driver factor is most important.
At present, for buses (such as buses, school buses and buses) capable of carrying multiple persons, bus drivers usually take duty in turn according to a schedule, and the requirement of whether the bus drivers have duty is not strictly limited. If a certain unqualified bus driver is on duty, the life safety of a plurality of people can be threatened once a traffic accident occurs.
Therefore, how to carry out on duty test on bus drivers is a technical problem to be solved urgently.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method and related apparatus for determining the on duty of a bus driver based on pattern recognition, which can perform an on duty test on the bus driver.
The invention provides a method for judging whether a bus driver is on duty based on pattern recognition, which comprises the following steps:
when a post-on-duty test request for a bus driver is received, acquiring a health detection result of the bus driver;
if the health detection result shows that all physical indexes of the bus driver meet the standard requirements, a verification-type question bank is established for the bus driver;
outputting intelligent questions in the verification-type question bank;
in the process that the bus driver answers the intelligent questions, calculating answer scores of the bus driver for answering each intelligent question through an answer calculation model;
recording the face micro-expression information and the sound information of the bus driver;
inputting the human face micro-expression information and the voice information into an emotion risk detection model to obtain emotion risk scores of the bus driver for each intelligent problem;
and inputting the answer scores and the emotion risk scores of all the intelligent questions into a comprehensive judgment model to obtain a duty-on judgment result of the bus driver.
In one possible implementation manner, the acquiring the health detection result of the bus driver includes:
sending a health detection instruction to portable equipment carried by the bus driver, and receiving a health detection result returned by the portable equipment in response to the health detection instruction; or
Carrying out health detection on the bus driver through a health detection device on electronic equipment to obtain a health detection result; or
And inquiring whether a health detection result of the bus driver exists in a health detection center, and if the health detection result of the bus driver exists and the difference value between the detection time of the health detection result and the current time is less than a preset time threshold, acquiring the health detection result.
In one possible implementation, the constructing a verification-like question bank for the bus driver includes:
acquiring a historical post-going test question library of the bus driver;
classifying and analyzing the problems in the historical on-duty test question bank according to a preset classification algorithm to obtain a plurality of categories;
calculating the accuracy rate of answering questions of the bus drivers in each category;
determining the category with the accuracy rate lower than a preset accuracy rate threshold value as a target category;
acquiring a plurality of problems matched with the target category from a total problem library, and removing the problems in the historical on-duty test problem library from the plurality of problems;
and constructing a verification-type problem library according to the plurality of rejected problems.
In one possible implementation, the constructing a verification-like question bank for the bus driver includes:
constructing a problem knowledge graph for a plurality of problems in a general question bank by adopting a knowledge graph technology;
acquiring a historical post-going test question library of the bus driver;
determining a target question to be answered currently by the bus driver according to the questions in the historical on-duty test question bank and the question knowledge graph;
and constructing a verification-type question bank according to the target question.
In one possible implementation, the outputting the intelligent questions in the verification-type question bank includes:
if the target types of the intelligent questions in the verification type question bank are multiple, dividing the intelligent questions in the verification type question bank into multiple question sets according to the types;
randomly extracting a first problem set from the plurality of problem sets;
sequentially outputting first intelligent questions in the first question set;
accumulating the number of the first intelligent questions with correct answers and the answer duration;
and if the number reaches the preset number and the answer duration is less than the preset duration, switching to a second question set and outputting a second intelligent question in the second question set.
In one possible implementation, the calculating, by the answer calculation model, the answer score for the bus driver to answer each of the intelligent questions comprises:
for each intelligent question, acquiring the time length for the bus driver to answer the intelligent question, a selected answer and a correct answer;
calculating the matching degree of the selected answer and the correct answer;
and inputting the duration and the matching degree into an answer calculation model to obtain the answer score of the intelligent question.
In one possible implementation, the method further includes:
obtaining a plurality of audio and video samples needing model training;
for each audio and video sample, a plurality of face image samples and a plurality of sound samples matched with the face image samples are captured from the audio and video sample;
extracting a plurality of face micro-expression features from the plurality of face image samples and extracting a plurality of sound features from the plurality of sound samples;
fusing the human face micro expression characteristics and the sound characteristics to obtain fused characteristics;
and inputting the fusion characteristics into a model to be trained for training to obtain an emotion risk detection model.
A second aspect of the present invention provides a bus driver on duty determination device, the device comprising:
the system comprises an acquisition module, a monitoring module and a control module, wherein the acquisition module is used for acquiring a health detection result of a bus driver when receiving a post-on test request aiming at the bus driver;
the construction module is used for constructing a verification-type question bank for the bus driver if the health detection result shows that various indexes of the body of the bus driver meet standard requirements;
the output module is used for outputting the intelligent questions in the verification question bank;
the calculation module is used for calculating answer scores of the bus driver for answering each intelligent question through an answer calculation model in the process of answering the intelligent questions by the bus driver;
the recording module is used for recording the face micro-expression information and the voice information of the bus driver;
the input module is used for inputting the face micro-expression information and the voice information into an emotion risk detection model to obtain emotion risk scores of the bus driver for each intelligent problem;
the input module is further used for inputting the answer scores and the emotion risk scores of all the intelligent questions into a comprehensive judgment model to obtain a duty-on judgment result of the bus driver. A third aspect of the present invention provides an electronic device comprising a processor and a memory, wherein the processor is configured to implement the method for determining that a bus driver is on duty when executing a computer program stored in the memory.
A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method for determining on duty of a bus driver.
According to the technical scheme, when a post-on-duty test request for a bus driver is received, a health detection result of the bus driver is obtained, if the health detection result shows that various indexes of the body of the bus driver meet standard requirements, a verification-type question bank is constructed for the bus driver, intelligent questions in the verification-type question bank are output, furthermore, in the process that the bus driver answers the intelligent questions, answer scores of answering each intelligent question by the bus driver are calculated through an answer calculation model, the face micro-form situation information and the voice information of the bus driver are recorded, meanwhile, the face micro-form situation information and the voice information are input into an emotion risk detection model, the emotion risk score of the bus driver for each intelligent question is obtained, and finally, the answer scores and the emotion risk score of all the intelligent questions are input into a comprehensive judgment model, and obtaining the on duty judgment result of the bus driver. Therefore, the method and the system can comprehensively form a comprehensive risk analysis report by integrating the health detection, the micro-expression and voice emotion analysis and the question answer score, and further obtain the on duty judgment result of the bus driver, so that the on duty judgment of the bus driver can be more accurately carried out, and the reliability is higher.
Drawings
FIG. 1 is a flow chart of a method for determining the on duty of a bus driver based on pattern recognition according to a preferred embodiment of the present invention.
Fig. 2 is a functional block diagram of a preferred embodiment of a post-on-duty judgment device for a bus driver disclosed by the invention.
FIG. 3 is a schematic structural diagram of an electronic device for implementing a method for determining a shift-on duty of a bus driver based on pattern recognition according to a preferred embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, 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.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
The electronic device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware thereof includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like. The electronic device may also include a network device and/or a user device. The network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of hosts or network servers. The user device includes, but is not limited to, any electronic product that can interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device, for example, a personal computer, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), or the like.
Referring to fig. 1, fig. 1 is a flow chart of a method for determining the on duty of a bus driver based on pattern recognition according to a preferred embodiment of the present invention. The order of the steps in the flowchart may be changed, and some steps may be omitted.
S11, when the on duty test request aiming at the bus driver is received, the electronic equipment obtains the health detection result of the bus driver.
The bus mainly refers to a large-sized vehicle capable of bearing dozens of people or even hundreds of people, and can include but is not limited to short-distance urban buses, school buses, long-distance passenger vehicles, buses and the like.
The bus driver needs to be subjected to the on-duty test before going on duty, and only the driver passing the on-duty test can be subjected to the on duty test.
The health detection result of the bus driver can be obtained in various ways, such as on-site detection or directly obtaining the health detection result detected in a short time ago.
Specifically, the acquiring the health detection result of the bus driver includes:
sending a health detection instruction to portable equipment carried by the bus driver, and receiving a health detection result returned by the portable equipment in response to the health detection instruction; or
Carrying out health detection on the bus driver through a health detection device on electronic equipment to obtain a health detection result; or
And inquiring whether a health detection result of the bus driver exists in a health detection center, and if the health detection result of the bus driver exists and the difference value between the detection time of the health detection result and the current time is less than a preset time threshold, acquiring the health detection result.
The health test result may include, but is not limited to, an alcohol content test result, a blood pressure measurement result, a heart rate test result, an ophthalmic test result, a respiratory test result, a nervous system test result, and the like of the bus driver.
And S12, if the health detection result shows that all indexes of the body of the bus driver meet the standard requirements, the electronic equipment constructs a verification-type question bank for the bus driver.
In medicine, each examination item of a body has a corresponding standard range, if a certain body index of the examination is in the standard range, the standard requirement is considered to be met, and if the certain body index of the examination is out of the standard range, the standard requirement is considered to be not met. Because the identity particularity of the bus driver generally has strict requirements on the physical health of the bus driver, when the physical health of the bus driver is judged, all indexes of the body of the bus driver are required to meet standard requirements, the bus driver is considered to have basic physical quality requirements for working on duty, and then the bus driver is subjected to next emotion risk detection.
On the contrary, if a certain physical index of the bus driver does not meet the standard requirement, the bus driver is considered not to have the basic physical quality requirement of working on duty, and the working on duty is not allowed.
Optionally, the constructing a verification-type question bank for the bus driver includes:
acquiring a historical post-going test question library of the bus driver;
classifying and analyzing the problems in the historical on-duty test question bank according to a preset classification algorithm to obtain a plurality of categories;
calculating the accuracy rate of answering questions of the bus drivers in each category;
determining the category with the accuracy rate lower than a preset accuracy rate threshold value as a target category;
acquiring a plurality of problems matched with the target category from a total problem library, and removing the problems in the historical on-duty test problem library from the plurality of problems;
and constructing a verification-type problem library according to the plurality of rejected problems.
Wherein the preset classification algorithm may include: a K-means cluster analysis algorithm, a text classification method based on feature voting and the like. The problems existing in the text form can be subjected to cluster analysis according to a preset classification algorithm, so that the problems in a plurality of problem lists are classified, the problems with the same or similar meanings are classified into the same class, and the problems with different meanings are classified into different classes. The K-means cluster analysis algorithm and the text classification method based on feature voting are all the prior art, and are not described in detail herein.
In this alternative embodiment, the number of correct answers of the bus drivers in each category may be counted, the accuracy rate of the answer of the bus drivers is calculated according to the number of correct answers and the total number of correct answers, a preset accuracy rate threshold may be preset, the preset accuracy rate threshold is a minimum limit value, for example, 60%, if the accuracy rate is lower than the preset accuracy rate threshold, it indicates that the problem in the category is stranger for the bus drivers, the theoretical knowledge is not sufficiently mastered, and the learning needs to be strengthened. Therefore, the category with the accuracy lower than the preset accuracy threshold can be determined as the target category, and the problem of the target category needs to be emphasized. Specifically, a plurality of questions matched with the target categories can be obtained from a general question bank, the questions in the historical on duty test question bank are removed from the questions, and a verification-type question bank is constructed according to the removed questions, so that the bus driver can be subjected to key assessment aiming at the questions of the target categories, and meanwhile, the bus driver is prevented from repeatedly answering the same questions. Therefore, the method can be used for examining the driving related theoretical knowledge unfamiliar to the bus driver, and is beneficial to the bus driver to learn the unfamiliar driving related theoretical knowledge, and the driving safety is improved.
Optionally, the constructing a verification-type question bank for the bus driver includes:
constructing a problem knowledge graph for a plurality of problems in a general question bank by adopting a knowledge graph technology;
acquiring a historical post-going test question library of the bus driver;
determining a target question to be answered currently by the bus driver according to the questions in the historical on-duty test question bank and the question knowledge graph;
and constructing a verification-type question bank according to the target question.
The knowledge Graph is essentially a knowledge base of a Semantic Network (Semantic Network), and from the perspective of practical application, the knowledge Graph can be simply understood as a Multi-relational Graph (Multi-relational Graph). A multi-relationship graph generally contains multiple types of nodes and multiple types of edges.
The general library comprises all the problems, automatic processing is carried out according to the problem types and keywords, and the general library is mainly divided into three categories: safety education, driving technique and emergency problem disposal; for example, safety education problems include traffic safety laws, professional moral education and the like, driving technology problems include defensive driving skills, driving operations and the like, and application problem disposal problems include critical emergency disposal measures, post-accident emergency disposal measures and the like.
Wherein, the verification question bank automatically matches correct answers according to the answers of bus drivers.
In this optional implementation, a problem knowledge graph is constructed for a plurality of problems in the topic library by using a knowledge graph technology, and relevant information among various problems can be visually seen from the problem knowledge graph, such as: the number of questions per category, the use of each question, etc. The problems in the historical on duty test question bank can be deleted from the total question bank according to the problems in the historical on duty test question bank and the problem knowledge graph to obtain residual problems, then the problems meeting the preset number are randomly selected from the residual problems to serve as target problems, and a verification type question bank is constructed according to the target problems.
By the method, the bus driver can be prevented from repeatedly answering the same questions every day when arriving at the post, so that the driving related theoretical knowledge of the bus driver can be examined pertinently, the bus driver can master the driving related theoretical knowledge, and the driving safety is improved.
And S13, the electronic equipment outputs the intelligent questions in the verification question bank.
Wherein, can also export on this test APP and do intelligent question in the verification class question bank that the bus driver found, simultaneously, trigger each camera on the start terminal the bus driver answers every the in-process of intelligent question is monitored and is recorded the bus driver and answers every through the camera the answer score of intelligent question to and record bus driver's little expression information of people's face and sound information.
Optionally, the outputting the intelligent questions in the verification-type question bank includes:
if the target types of the intelligent questions in the verification type question bank are multiple, dividing the intelligent questions in the verification type question bank into multiple question sets according to the types;
randomly extracting a first problem set from the plurality of problem sets;
sequentially outputting first intelligent questions in the first question set;
accumulating the number of the first intelligent questions with correct answers and the answer duration;
and if the number reaches the preset number and the answer duration is less than the preset duration, switching to a second question set and outputting a second intelligent question in the second question set.
In this alternative embodiment, if there are multiple target categories of the intelligent questions in the verification-type question bank, the intelligent questions in the verification-type question bank may be divided into multiple question sets, for example, 2 target categories, according to the categories, the intelligent questions in the verification-type question bank may be divided into 2 question sets, further, any one of the question sets, for example, the first question set, may be randomly extracted, and the first intelligent questions in the first question set may be sequentially output, the bus driver may answer the first intelligent questions that are output, the electronic device may record the answering conditions of the bus driver, for example, the answering time length, whether the answer is correct, and accumulate the number of the first intelligent questions that are correctly answered and the answering time length, if the number reaches the preset number and the answering time length is less than the preset time length, the method has the advantages that the number of the first intelligent questions answered correctly by the bus driver is large, the answering time is short, the fact that the bus driver is skilled in mastering the types of the questions is further reflected, in order to improve the testing efficiency, the bus driver can be directly switched to the second question set to conduct examination, the second intelligent questions in the second question set are output in the same similar mode, if a third question set exists, the analogy is carried out until all the intelligent questions of all the target types are examined completely.
By the method, the intelligent problem to be output can be dynamically adjusted according to the current problem answering condition of the bus driver, so that the efficiency of theoretical knowledge assessment can be improved, the theoretical knowledge unfamiliar to the bus driver can be mainly assessed, and the effect of strengthening and improving the theoretical knowledge is achieved.
S14, in the process that the bus driver answers the intelligent questions, the electronic equipment calculates the answer scores of the bus driver for answering each intelligent question through an answer calculation model.
The answer score of the intelligent question is related to a plurality of factors, such as the time length for the bus driver to answer the intelligent question, the answer selection and the correct answer, and also related to the number of times of answer modification, such as for the same question, the final answer is obtained by repeatedly modifying the answer selection due to uncertainty.
Specifically, the calculating, by the answer calculation model, the answer score for the bus driver to answer each of the intelligent questions includes:
for each intelligent question, acquiring the time length for the bus driver to answer the intelligent question, a selected answer and a correct answer;
calculating the matching degree of the selected answer and the correct answer;
and inputting the duration and the matching degree into an answer calculation model to obtain the answer score of the intelligent question.
Generally, in the process of answering a question, the time for answering the question by a bus driver is long, and the matching degree of the selected answer and the correct answer is more critical. In this alternative embodiment, an answer calculation model may be trained in advance, and the weights of the individual answer factors may be set in the answer calculation model. After the duration, the selected answers and the correct answers of the bus drivers for answering the intelligent questions are obtained, the matching degree of the selected answers and the correct answers can be determined, the duration and the matching degree are further input into an answer calculation model, and the answer scores of the intelligent questions are calculated and obtained through the answer calculation model according to the respective weights of the matching degree and the duration.
The answer score determined by the answer calculation model can better reflect the degree of the bus driver mastering the driving related theoretical knowledge.
And S15, recording the micro expression information and the sound information of the face of the bus driver by the electronic equipment.
In the embodiment of the invention, in the process of answering the intelligent question by the bus driver, the camera can be triggered to shoot the bus driver to obtain a shot video, and further the face micro-expression information and the sound information of the bus driver are obtained from the shot video.
The facial micro expression information includes expression actions of each part of the face, such as mouth corner rising, mouth closing, lip separation and the like, and the sound information includes but is not limited to tone, loudness, sound frequency and information carried by sound.
It should be noted that, when the bus driver answers each intelligent question, the face micro-expression information and the voice information of the bus driver are required to be recorded.
S16, the electronic equipment inputs the face micro-expression information and the voice information into an emotion risk detection model, and emotion risk scores of the bus drivers for each intelligent problem are obtained.
The emotion risk detection model is a pre-trained model, can perform emotion recognition on input human face micro-expression information and voice information, determines the emotion state of the bus driver, such as recognition of 7 kinds of emotions including happy emotion, calm emotion, anger, sadness, impatience, panic and vague emotion, and further determines the emotion risk score of the bus driver for each intelligent problem by combining the recognized emotion.
Wherein, during training, the corresponding relation between the emotion and the risk score can be preset.
As an optional implementation, the method further comprises:
obtaining a plurality of audio and video samples needing model training;
for each audio and video sample, a plurality of face image samples and a plurality of sound samples matched with the face image samples are captured from the audio and video sample;
extracting a plurality of face micro-expression features from the plurality of face image samples and extracting a plurality of sound features from the plurality of sound samples;
fusing the human face micro expression characteristics and the sound characteristics to obtain fused characteristics;
and inputting the fusion characteristics into a model to be trained for training to obtain an emotion risk detection model.
The audio/video samples include audio information and video information, and may include but are not limited to audio/video samples extracted from multimedia files, other public audio/video data sets, or audio/video marked by user acquisition. Wherein the audio-video samples comprise audio-video samples with positive emotions and audio-video samples with negative emotions.
And splicing the plurality of individually obtained human face micro-expression features and the plurality of sound features on a splicing layer, namely, fusing to obtain a fusion feature. Before training the fused features, each fused feature can be identified by a different label, each different label represents a different risk score, the higher the risk score is, the more unstable the representation emotion is, the greater the risk of the bus driver on duty is, and conversely, the lower the risk score is, the more stable the representation emotion is, the less the risk of the bus driver on duty is.
S17, inputting the answer scores and the emotion risk scores of all the intelligent questions into a comprehensive judgment model by the electronic equipment, and obtaining the on duty judgment result of the bus driver.
The comprehensive judgment model may be configured to calculate a comprehensive score of the bus driver, weights of the answer score and the emotional risk score may be preset in the comprehensive judgment model, and after the answer scores and the emotional risk scores of all the intelligent questions are input to the comprehensive judgment model, the comprehensive judgment model may calculate the comprehensive score of the bus driver according to the preset weights of the answer score and the emotional risk score, and further, may determine an on duty suggestion corresponding to the calculated comprehensive score according to a preset correspondence between a score interval and the on duty suggestion, for example: 71-100 points suggest going on duty, 51-70 suggests resting, and 0-50 suggests not going on duty.
In the method flow described in fig. 1, when a post-on-duty test request for a bus driver is received, a health detection result of the bus driver is obtained, if the health detection result indicates that various indexes of the body of the bus driver meet standard requirements, a verification-type question bank is constructed for the bus driver, and intelligent questions in the verification-type question bank are output, further, in the process of answering the intelligent questions by the bus driver, a question answering score of the bus driver answering each intelligent question is calculated through an answer calculation model, face micro-expression information and voice information of the bus driver are recorded, meanwhile, the face micro-expression information and the voice information are input into an emotion risk detection model, an emotion risk score of the bus driver aiming at each intelligent question is obtained, and finally, the answer scores and the emotion risk scores of all the intelligent questions are input into a comprehensive judgment model, and obtaining the on duty judgment result of the bus driver. Therefore, the method and the system can comprehensively form a comprehensive risk analysis report by integrating the health detection, the micro-expression and voice emotion analysis and the question answer score, and further obtain the on duty judgment result of the bus driver, so that the on duty judgment of the bus driver can be more accurately carried out, and the reliability is higher.
The above description is only a specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and it will be apparent to those skilled in the art that modifications may be made without departing from the inventive concept of the present invention, and these modifications are within the scope of the present invention.
Referring to fig. 2, fig. 2 is a functional block diagram of a preferred embodiment of a post-on-duty determination device for a bus driver according to the present invention.
In some embodiments, the bus driver on duty determination device operates in an electronic device. The bus driver on duty determination device may include a plurality of function modules composed of program code segments. The program codes of the program segments in the bus driver on duty determination device may be stored in the memory and executed by at least one processor to perform part or all of the steps of the bus driver on duty determination method based on pattern recognition described in fig. 1, which is described with reference to fig. 1 for details and is not repeated herein.
In this embodiment, the on duty determination device for the bus driver may be divided into a plurality of functional modules according to the functions performed by the device. The functional module may include: the device comprises an acquisition module 201, a construction module 202, an output module 203, a calculation module 204, a recording module 205 and an input module 206. The module referred to herein is a series of computer program segments capable of being executed by at least one processor and capable of performing a fixed function and is stored in memory.
The obtaining module 201 is configured to obtain a health detection result of a bus driver when a post-entry test request for the bus driver is received.
Specifically, the acquiring module 201 acquires the health detection result of the bus driver, including:
sending a health detection instruction to portable equipment carried by the bus driver, and receiving a health detection result returned by the portable equipment in response to the health detection instruction; or
Carrying out health detection on the bus driver through a health detection device on electronic equipment to obtain a health detection result; or
And inquiring whether a health detection result of the bus driver exists in a health detection center, and if the health detection result of the bus driver exists and the difference value between the detection time of the health detection result and the current time is less than a preset time threshold, acquiring the health detection result.
The construction module 202 is configured to construct a verification-type question bank for the bus driver if the health detection result indicates that various body indexes of the bus driver meet standard requirements.
Specifically, the constructing module 202 constructs a verification-type problem database for the bus driver, including:
acquiring a historical post-going test question library of the bus driver;
classifying and analyzing the problems in the historical on-duty test question bank according to a preset classification algorithm to obtain a plurality of categories;
calculating the accuracy rate of answering questions of the bus drivers in each category;
determining the category with the accuracy rate lower than a preset accuracy rate threshold value as a target category;
acquiring a plurality of problems matched with the target category from a total problem library, and removing the problems in the historical on-duty test problem library from the plurality of problems;
and constructing a verification-type problem library according to the plurality of rejected problems.
Specifically, the constructing module 202 constructs a verification-type problem database for the bus driver, including:
constructing a problem knowledge graph for a plurality of problems in a general question bank by adopting a knowledge graph technology;
acquiring a historical post-going test question library of the bus driver;
determining a target question to be answered currently by the bus driver according to the questions in the historical on-duty test question bank and the question knowledge graph;
and constructing a verification-type question bank according to the target question.
And the output module 203 is used for outputting the intelligent questions in the verification-type question bank.
Specifically, the outputting module 203 outputs the intelligent questions in the verification-type question bank, including:
if the target types of the intelligent questions in the verification type question bank are multiple, dividing the intelligent questions in the verification type question bank into multiple question sets according to the types;
randomly extracting a first problem set from the plurality of problem sets;
sequentially outputting first intelligent questions in the first question set;
accumulating the number of the first intelligent questions with correct answers and the answer duration;
and if the number reaches the preset number and the answer duration is less than the preset duration, switching to a second question set and outputting a second intelligent question in the second question set.
And the calculating module 204 is used for calculating the answer scores of the intelligent questions answered by the bus driver through an answer calculating model in the process of answering the intelligent questions by the bus driver.
Specifically, the calculating module 204 calculates the answer score of the bus driver for answering each intelligent question through the answer calculation model, including:
for each intelligent question, acquiring the time length for the bus driver to answer the intelligent question, a selected answer and a correct answer;
calculating the matching degree of the selected answer and the correct answer;
and inputting the duration and the matching degree into an answer calculation model to obtain the answer score of the intelligent question.
And the recording module 205 is used for recording the face micro-expression information and the voice information of the bus driver.
An input module 206, configured to input the facial micro-expression information and the voice information into an emotion risk detection model, and obtain an emotion risk score of the bus driver for each intelligent question.
The input module 206 is further configured to input the answer scores and the emotional risk scores of all the intelligent questions into a comprehensive judgment model, so as to obtain a result of the on duty judgment of the bus driver.
Optionally, the obtaining module 201 is further configured to obtain a plurality of audio/video samples that need to be subjected to model training;
the bus driver on duty determination device further comprises:
the capturing module is used for capturing a plurality of face image samples and a plurality of sound samples matched with the face image samples from the audio and video samples aiming at each audio and video sample;
the extraction module is used for extracting a plurality of face micro-expression characteristics from the plurality of face image samples and extracting a plurality of sound characteristics from the plurality of sound samples;
the fusion module is used for fusing the human face micro expression characteristics and the sound characteristics to obtain fusion characteristics;
and the training module is used for inputting the fusion characteristics into a model to be trained for training to obtain an emotion risk detection model.
In the post-on-duty judgment device of the bus driver described in fig. 2, when a post-on-duty test request for the bus driver is received, a health detection result of the bus driver is obtained, if the health detection result indicates that various indexes of the body of the bus driver meet standard requirements, a verification-type question bank is constructed for the bus driver, and intelligent questions in the verification-type question bank are output, further, in the process of answering the intelligent questions by the bus driver, the answering score of the bus driver answering each intelligent question is calculated through an answering calculation model, the face micro-expression information and the sound information of the bus driver are recorded, meanwhile, the face micro-expression information and the sound information are input into an emotion risk detection model, the emotion risk score of the bus driver for each intelligent question is obtained, and finally, and inputting the answer scores and the emotion risk scores of all the intelligent questions into a comprehensive judgment model, so that the on duty judgment result of the bus driver can be obtained. Therefore, the method and the system can comprehensively form a comprehensive risk analysis report by integrating the health detection, the micro-expression and voice emotion analysis and the question answer score, and further obtain the on duty judgment result of the bus driver, so that the on duty judgment of the bus driver can be more accurately carried out, and the reliability is higher.
Fig. 3 is a schematic structural diagram of an electronic device for implementing the method for determining the duty-on of a bus driver based on pattern recognition according to the preferred embodiment of the present invention. The electronic device 3 comprises a memory 31, at least one processor 32, a computer program 33 stored in the memory 31 and executable on the at least one processor 32, and at least one communication bus 34.
Those skilled in the art will appreciate that the schematic diagram shown in fig. 3 is merely an example of the electronic device 3, and does not constitute a limitation of the electronic device 3, and may include more or less components than those shown, or combine some components, or different components, for example, the electronic device 3 may further include an input/output device, a network access device, and the like.
The at least one Processor 32 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. The processor 32 may be a microprocessor or the processor 32 may be any conventional processor or the like, and the processor 32 is a control center of the electronic device 3 and connects various parts of the whole electronic device 3 by various interfaces and lines.
The memory 31 may be used to store the computer program 33 and/or the module/unit, and the processor 32 may implement various functions of the electronic device 3 by running or executing the computer program and/or the module/unit stored in the memory 31 and calling data stored in the memory 31. The memory 31 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data) created according to the use of the electronic device 3, and the like. Further, the memory 31 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other non-volatile solid state storage device.
With reference to fig. 1, the memory 31 of the electronic device 3 stores a plurality of instructions to implement a method for determining a shift-on-duty of a bus driver based on pattern recognition, and the processor 32 can execute the plurality of instructions to implement:
when a post-on-duty test request for a bus driver is received, acquiring a health detection result of the bus driver;
if the health detection result shows that all physical indexes of the bus driver meet the standard requirements, a verification-type question bank is established for the bus driver;
outputting intelligent questions in the verification-type question bank;
in the process that the bus driver answers the intelligent questions, calculating answer scores of the bus driver for answering each intelligent question through an answer calculation model;
recording the face micro-expression information and the sound information of the bus driver;
inputting the human face micro-expression information and the voice information into an emotion risk detection model to obtain emotion risk scores of the bus driver for each intelligent problem;
and inputting the answer scores and the emotion risk scores of all the intelligent questions into a comprehensive judgment model to obtain a duty-on judgment result of the bus driver.
Specifically, the processor 32 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the instruction, which is not described herein again.
In the electronic device 3 depicted in fig. 3, when a post-on-duty test request for a bus driver is received, a health detection result of the bus driver is obtained, if the health detection result indicates that various indexes of the bus driver's body meet standard requirements, a verification-type question bank is established for the bus driver, and intelligent questions in the verification-type question bank are output, further, in the process of answering the intelligent questions by the bus driver, answer scores of answering each intelligent question by the bus driver are calculated through an answer calculation model, face micro-expression information and voice information of the bus driver are recorded, meanwhile, the face micro-expression information and the voice information are input into an emotion risk detection model, emotion risk scores of the bus driver for each intelligent question are obtained, and finally, the answer scores and the emotion risk scores of all the intelligent questions are input into a comprehensive judgment model And obtaining the on duty judgment result of the bus driver. Therefore, the method and the system can comprehensively form a comprehensive risk analysis report by integrating the health detection, the micro-expression and voice emotion analysis and the question answer score, and further obtain the on duty judgment result of the bus driver, so that the on duty judgment of the bus driver can be more accurately carried out, and the reliability is higher.
The integrated modules/units of the electronic device 3 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A method for judging on duty of a bus driver based on pattern recognition is characterized by comprising the following steps:
when a post-on-duty test request for a bus driver is received, acquiring a health detection result of the bus driver;
if the health detection result shows that all physical indexes of the bus driver meet the standard requirements, a verification-type question bank is established for the bus driver;
outputting intelligent questions in the verification-type question bank;
in the process that the bus driver answers the intelligent questions, calculating answer scores of the bus driver for answering each intelligent question through an answer calculation model;
recording the face micro-expression information and the sound information of the bus driver;
inputting the human face micro-expression information and the voice information into an emotion risk detection model to obtain emotion risk scores of the bus driver for each intelligent problem;
and inputting the answer scores and the emotion risk scores of all the intelligent questions into a comprehensive judgment model to obtain a duty-on judgment result of the bus driver.
2. The method of claim 1, wherein the obtaining the health detection of the bus driver comprises:
sending a health detection instruction to portable equipment carried by the bus driver, and receiving a health detection result returned by the portable equipment in response to the health detection instruction; or
Carrying out health detection on the bus driver through a health detection device on electronic equipment to obtain a health detection result; or
And inquiring whether a health detection result of the bus driver exists in a health detection center, and if the health detection result of the bus driver exists and the difference value between the detection time of the health detection result and the current time is less than a preset time threshold, acquiring the health detection result.
3. The method of claim 1, wherein the constructing a verification-like question bank for the bus driver comprises:
acquiring a historical post-going test question library of the bus driver;
classifying and analyzing the problems in the historical on-duty test question bank according to a preset classification algorithm to obtain a plurality of categories;
calculating the accuracy rate of answering questions of the bus drivers in each category;
determining the category with the accuracy rate lower than a preset accuracy rate threshold value as a target category;
acquiring a plurality of problems matched with the target category from a total problem library, and removing the problems in the historical on-duty test problem library from the plurality of problems;
and constructing a verification-type problem library according to the plurality of rejected problems.
4. The method of claim 1, wherein the constructing a verification-like question bank for the bus driver comprises:
constructing a problem knowledge graph for a plurality of problems in a general question bank by adopting a knowledge graph technology;
acquiring a historical post-going test question library of the bus driver;
determining a target question to be answered currently by the bus driver according to the questions in the historical on-duty test question bank and the question knowledge graph;
and constructing a verification-type question bank according to the target question.
5. The method of claim 1, wherein outputting the intelligent questions in the verification-like question bank comprises:
if the target types of the intelligent questions in the verification type question bank are multiple, dividing the intelligent questions in the verification type question bank into multiple question sets according to the types;
randomly extracting a first problem set from the plurality of problem sets;
sequentially outputting first intelligent questions in the first question set;
accumulating the number of the first intelligent questions with correct answers and the answer duration;
and if the number reaches the preset number and the answer duration is less than the preset duration, switching to a second question set and outputting a second intelligent question in the second question set.
6. The method of claim 1, wherein said calculating, via an answer calculation model, an answer score for said bus driver to answer each of said intelligent questions comprises:
for each intelligent question, acquiring the time length for the bus driver to answer the intelligent question, a selected answer and a correct answer;
calculating the matching degree of the selected answer and the correct answer;
and inputting the duration and the matching degree into an answer calculation model to obtain the answer score of the intelligent question.
7. The method according to any one of claims 1 to 6, further comprising:
obtaining a plurality of audio and video samples needing model training;
for each audio and video sample, a plurality of face image samples and a plurality of sound samples matched with the face image samples are captured from the audio and video sample;
extracting a plurality of face micro-expression features from the plurality of face image samples and extracting a plurality of sound features from the plurality of sound samples;
fusing the human face micro expression characteristics and the sound characteristics to obtain fused characteristics;
and inputting the fusion characteristics into a model to be trained for training to obtain an emotion risk detection model.
8. A bus driver on duty determination device, said device comprising:
the system comprises an acquisition module, a monitoring module and a control module, wherein the acquisition module is used for acquiring a health detection result of a bus driver when receiving a post-on test request aiming at the bus driver;
the construction module is used for constructing a verification-type question bank for the bus driver if the health detection result shows that various indexes of the body of the bus driver meet standard requirements;
the output module is used for outputting the intelligent questions in the verification question bank;
the calculation module is used for calculating answer scores of the bus driver for answering each intelligent question through an answer calculation model in the process of answering the intelligent questions by the bus driver;
the recording module is used for recording the face micro-expression information and the voice information of the bus driver;
the input module is used for inputting the face micro-expression information and the voice information into an emotion risk detection model to obtain emotion risk scores of the bus driver for each intelligent problem;
the input module is further used for inputting the answer scores and the emotion risk scores of all the intelligent questions into a comprehensive judgment model to obtain a duty-on judgment result of the bus driver.
9. An electronic device, comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the pattern recognition based on-duty decision method of a bus driver as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium storing at least one instruction which, when executed by a processor, implements the pattern recognition based highway driver on duty decision method of any of claims 1-7.
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