CN211749663U

CN211749663U - Staff emotion prediction system

Info

Publication number: CN211749663U
Application number: CN201921856483.XU
Authority: CN
Inventors: 薛宏全; 官淦悦; 申文; 王小刚; 麻堃
Original assignee: Xian University of Technology
Current assignee: Xian University of Technology
Priority date: 2019-10-31
Filing date: 2019-10-31
Publication date: 2020-10-27
Anticipated expiration: 2029-10-31

Abstract

The utility model discloses a staff emotion prediction system, including data acquisition module, data acquisition module is used for gathering workshop staff emotion information, and data acquisition module and emotion recognition module communication connection, emotion recognition module are used for drawing workshop staff emotion information and classify, and emotion recognition module and emotion prediction module communication connection, emotion prediction module are used for establishing emotion prediction model with prediction workshop staff's emotion. The utility model relates to a staff emotion prediction system can in time accurately predict out staff's emotion, provides the reference for staff administrative work.

Description

Staff emotion prediction system

Technical Field

The utility model belongs to the technical field of emotion prediction equipment, concretely relates to staff emotion prediction system.

Background

In the production and manufacturing process, the subjective emotion of staff has great influence on the product quality and the production efficiency. Therefore, how to predict the emotional changes of the employees in the workshop becomes an urgent problem to be solved in the workshop management. The novel workshop employee emotion prediction system requires that the emotion of employees is predicted and rated according to real-time behavior, physiology and environment data of the employees and historical data, source employee emotion information support from a production field is provided for workshop production scheduling and daily management, and the aim of improving workshop production management efficiency is fulfilled.

Cognitive computing is an artificial intelligence derived from a computer system that simulates the human brain. While traditional computing techniques are quantitative and focus on accuracy and sequence ranking, cognitive computing attempts to solve the problems of inaccuracy, uncertainty, and partial trueness in biological systems to achieve varying degrees of perception, memory, learning, language, thinking, and problem solving. With the development of scientific technology and the arrival of the big data era, it is important how to realize the cognition and judgment similar to human brain and find new association and mode so as to make correct decision. The development of cognitive computing technology also brings possibility for staff emotion prediction and processing in workshop production management.

However, no production workshop employee emotion prediction system which is combined with cognitive calculation and meets the requirements of workshop production management is available in the market at present, and the emotion prediction system is a hot trend of current research.

SUMMERY OF THE UTILITY MODEL

The utility model aims at providing a staff's mood prediction system can in time accurately predict out staff's mood, provides the reference for leading instruction work.

The utility model provides a technical scheme who adopts is, a staff emotion prediction system, including data acquisition module, data acquisition module is used for gathering workshop staff emotion information, and data acquisition module and emotion recognition module communication connection, emotion recognition module are used for drawing workshop staff emotion information and classify, and emotion recognition module and emotion prediction module communication connection, emotion prediction module are used for establishing emotion prediction model in order to predict workshop staff's emotion.

The utility model is also characterized in that,

the data acquisition module comprises a behavior detection unit, an environment temperature detection unit, a respiratory frequency monitoring unit, a heartbeat monitoring unit, a body temperature monitoring unit and a wireless data transmission unit, and the wireless data transmission unit transmits data acquired by the behavior detection unit, the respiratory frequency monitoring unit, the heartbeat monitoring unit and the body temperature monitoring unit to the emotion recognition module.

The action detecting element is for installing the camera in the workshop, and ambient temperature detecting element is for setting up the temperature sensor in the workshop, and respiratory frequency monitoring unit, heartbeat monitoring unit, body temperature monitoring unit concentrate the gathering on a bracelet, wear on staff's wrist.

The camera module adopts an OV2640 camera.

Respiratory rate monitoring unit adopts respiratory rate sensor to breathe the monitoring to the staff, and heartbeat monitoring unit adopts heart rate sensor to carry out the rhythm of the heart monitoring to the staff, and body temperature monitoring unit adopts digital temperature sensor to carry out the body temperature monitoring to the staff.

The emotion recognition module comprises a feature extraction module, a database server and a matching module, wherein the matching module is respectively in communication connection with the feature extraction module and the database server, the feature extraction module is used for receiving workshop staff emotion information sent by the data acquisition module and performing feature extraction on the workshop staff emotion information, historical emotion information of staff individuals is stored in the database server, the matching module is used for matching the features extracted by the feature extraction module with the historical emotion information of the staff individuals in the database server to complete extraction and classification, and the classified workshop staff emotion information is sent to the emotion prediction module in a communication mode.

The wireless data transmission unit employs an NRF24L01 chip.

The emotion prediction module comprises a training module and a prediction module, the training module is in communication connection with the database server, the training module is used for training and classifying the personal historical emotion information of the employees stored in the database server, the prediction module is in communication connection with the matching module and the training module respectively, and the prediction module is combined with the classified result of the training module and the emotion information of the employees in the workshop classified by the matching module to establish an emotion prediction model so as to predict the emotion of the employees in the workshop.

The beneficial effects of the utility model are that, a staff emotion prediction system, through the automatic acquisition to staff emotion data in the workshop production process, with the help of cognitive technology analysis and prediction mood, can accurate make the best of mood, so that managers in time dredge the mood of staff, can improve the production efficiency in workshop, can further expand to use the school to the aspect such as the emotion detection to people in student's psychological management, the public safety equally, avoid because of the huge influence that the mood suddenly out of control brought.

Drawings

Fig. 1 is the staff emotion prediction system structure schematic diagram of the utility model.

Detailed Description

The present invention will be described in detail with reference to the accompanying drawings and specific embodiments.

The utility model discloses a staff emotion prediction system, as shown in figure 1, including data acquisition module, data acquisition module is used for gathering workshop staff emotion information, and data acquisition module and emotion recognition module communication connection, emotion recognition module are used for drawing workshop staff emotion information and classify, and emotion recognition module and emotion prediction module communication connection, emotion prediction module are used for establishing emotion prediction model with prediction workshop staff's emotion.

Furthermore, the data acquisition module comprises a behavior detection unit, an environment temperature detection unit, a respiratory rate monitoring unit, a heartbeat monitoring unit, a body temperature monitoring unit and a wireless data transmission unit, and the wireless data transmission unit transmits data acquired by the behavior detection unit, the respiratory rate monitoring unit, the heartbeat monitoring unit and the body temperature monitoring unit to the emotion recognition module.

Further, the action detecting unit is the camera of installing in the workshop, and ambient temperature detecting unit is the temperature sensor who sets up in the workshop, and respiratory frequency monitoring unit, heartbeat monitoring unit, body temperature monitoring unit concentrate the gathering on a bracelet, wear on staff's wrist.

Further, the camera module employs an OV2640 camera.

Further, respiratory rate monitoring unit adopts respiratory rate sensor to carry out respiratory monitoring to the staff, and heartbeat monitoring unit adopts heart rate sensor to carry out heart rate monitoring to the staff, and body temperature monitoring unit adopts digital temperature sensor to carry out body temperature monitoring to the staff.

Furthermore, the emotion recognition module comprises a feature extraction module, a database server and a matching module, wherein the matching module is respectively in communication connection with the feature extraction module and the database server, the feature extraction module is used for receiving the workshop staff emotion information sent by the data acquisition module and extracting features of the workshop staff emotion information, historical emotion information of staff individuals is stored in the database server, and the matching module is used for matching the features extracted by the feature extraction module with the historical emotion information of the staff individuals in the database server, completing extraction and classification, and sending the classified workshop staff emotion information to the emotion prediction module in a communication mode.

Further, the wireless data transmission unit employs an NRF24L01 chip.

Furthermore, the emotion prediction module comprises a training module and a prediction module, the training module is in communication connection with the database server, the training module is used for training and classifying the personal historical emotion information of the employees stored in the database server, the prediction module is in communication connection with the matching module and the training module respectively, and the prediction module is combined with the results classified by the training module and the emotion information of the employees classified by the matching module to establish an emotion prediction model so as to predict the emotion of the employees in the workshop.

Furthermore, the emotion prediction module is connected with the display screen and reflects the emotion prediction result on the display screen.

The utility model relates to a staff emotion prediction system principle explains: the staff emotion prediction system adopts a distributed network structure, and all modules work independently and can cooperate with each other; the emotion recognition module comprises a feature extraction module, a database server and a matching module, wherein the feature extraction module adopts an open source algorithm of an OpenCV vision library to extract features in images of collected emotion information of workshop employees, the database server stores past emotion information of the workshop employees, and the matching module also adopts the open source algorithm of the OpenCV vision library to match and classify the extracted features and the past emotion information of the workshop employees so as to finish emotion recognition; the emotion prediction module comprises a training module which trains and classifies the past emotion information of the workshop staff by adopting a Markov chain algorithm, and a prediction module which predicts the emotion state of the staff which possibly appears in the future by establishing an emotion model by adopting an svm support vector product method and utilizing the classification result and the currently recognized emotion state of the staff.

The utility model relates to a staff emotion prediction system working process: when an employee enters a workshop, the camera and the bracelet start to acquire information of the employee, the emotion recognition module recognizes emotion of the employee by using an open source algorithm of an OpenCV vision library of a cognitive computing technology while acquiring data, the emotion prediction module combines real-time emotion data of the employee with historical emotion data along with accumulation of the data and the emotion information, an emotion prediction model is established by using a Markov chain and an SVM method, emotion prediction of the employee is achieved, and a result is finally displayed on a display screen, so that workshop managers can take a persuasion measure for the employee with a problem in emotion or large fluctuation, and production efficiency of the workshop is maximized.

Claims

1. The employee emotion prediction system is characterized by comprising a data acquisition module, wherein the data acquisition module is used for acquiring emotion information of employees in a workshop, the data acquisition module is in communication connection with an emotion recognition module, the emotion recognition module is used for extracting and classifying the emotion information of the employees in the workshop, the emotion recognition module is in communication connection with an emotion prediction module, and the emotion prediction module is used for establishing an emotion prediction model to predict the emotion of the employees in the workshop;

2. The system for predicting the emotion of an employee according to claim 1, wherein the behavior detection unit is a camera installed in a workshop, the environment temperature detection unit is a temperature sensor arranged in the workshop, and the respiratory frequency monitoring unit, the heartbeat monitoring unit and the body temperature monitoring unit are gathered together on a bracelet and worn on the wrist of the employee.

3. The system for predicting emotion of staff as recited in claim 2, wherein said camera module employs an OV2640 camera.

4. The system for predicting the emotion of an employee according to claim 2, wherein the respiratory rate monitoring unit monitors the respiration of the employee by using a respiratory rate sensor, the heartbeat monitoring unit monitors the heart rate of the employee by using a heart rate sensor, and the body temperature monitoring unit monitors the body temperature of the employee by using a digital temperature sensor.

5. The staff emotion prediction system of claim 1, wherein the emotion recognition module comprises a feature extraction module, a database server and a matching module, the matching module is in communication connection with the feature extraction module and the database server, the feature extraction module is used for receiving workshop staff emotion information sent by the data acquisition module and performing feature extraction on the workshop staff emotion information, historical emotion information of staff individuals is stored in the database server, and the matching module is used for matching the features extracted by the feature extraction module with the historical emotion information of the staff individuals in the database server, completing extraction and classification, and sending the classified workshop staff emotion information to the emotion prediction module in a communication manner.

6. The system for predicting emotion of staff as recited in claim 2, wherein said wireless data transmission unit employs NRF24L01 chip.

7. The staff emotion prediction system of claim 5, wherein the emotion prediction module comprises a training module and a prediction module, the training module is in communication connection with the database server, the training module is used for training and classifying the personal historical emotion information of staff stored in the database server, the prediction module is in communication connection with the matching module and the training module respectively, and the prediction module is combined with the result of classification by the training module and the emotion information of staff in the workshop classified by the matching module to establish an emotion prediction model so as to predict the emotion of the staff in the workshop.