KR20200031355A - Method for assessing and alerting workers on the effectiveness of their work at large scale - Google Patents

Abstract

Provided are a method for determining task efficiency of a worker and a notification method thereof. The method for determining task efficiency comprises the steps of: receiving information on a sensor value sensed by a wearable device worn by a worker from the wearable device; calculating workload by inputting information on the received sensor value into a workload calculation process; and outputting an alarm according to the calculated workload.

Description

Method for evaluating and notifying work efficiency of workers performing large-scale work {METHOD FOR ASSESSING AND ALERTING WORKERS ON THE EFFECTIVENESS OF THEIR WORK AT LARGE SCALE}

The technical field is related to a method for evaluating work efficiency and determining a notification by monitoring a worker's condition. More specifically, the present invention relates to a method for evaluating work efficiency and evaluating a work efficiency based on the condition of a worker performing work on a large-scale farm or an orchard.

In modern times, smartphones and wearable devices are evolving as part of an extended computing environment, and there is an increasing need to respond to the needs of mobile users in various situations and environments. As a result, interest in user experience design is increasing, and it is important to accurately provide necessary information to a necessary person at a required time.

Devices that can be computed anytime, anywhere can only be wearable devices that are 'attached to, worn on, or worn on the body'. Wearable device is a compound word of wearable and device, and refers to an electronic device that can be worn on a user's body, such as clothes, glasses, and watches. With this device, detailed information about changes in the user's body and surroundings can be continuously collected in real time. Early wearable devices formed a market for accessory / clothing / fabric types, but are now gradually developing into bio-grafting.

Early wearable devices were limited in growth due to limitations such as size, accuracy, and duration, but have recently become a core device for information collection through low power and miniaturization. In particular, thanks to the growth of the smart phone market, the market is getting larger by grafting with various objects.

However, only 1% of IoT technologies using various sensors are being serviced in the field of smart agriculture. Moreover, various fields of IoT grafting of smart farming are also concentrated on "agricultural products, livestock, and environment management," and the system for agricultural workers is vulnerable. In particular, when the size of agricultural work is large, several workers perform the same work within a certain period of time, and at this time, a difference in work load occurs between workers by comparing the results with respect to "total production" regardless of individual work proficiency.

Therefore, the "average above, below average" degree between the same workers using the work frequency (work load, work time) between workers. That is, a method for minimizing the difference between workers and improving work efficiency is provided by determining the work efficiency evaluation and notifying the work manager.

The technical problem to be solved by the present invention is the degree of "average or above, below average" between workers by using the frequency (work volume, work time) between workers. In other words, it is to provide a method for determining work efficiency evaluation and notifying it to the work manager to minimize the difference between workers and increase work efficiency.

The technical problems of the present invention are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description.

In order to solve the above technical problem, a method for determining work efficiency and a notification method according to an embodiment of the present invention include receiving information on a sensor value detected by a wearable device worn by an operator from the wearable device, the The method may include inputting information on the received sensor value into a workload calculation process to calculate an average workload, and outputting a notification regarding work efficiency according to the calculated average workload.

In the method for determining work efficiency according to some embodiments of the present invention and the notification method, information on the sensor value may include a temperature value, an acceleration value, and bio information.

In the method of determining work efficiency according to some embodiments of the present invention and calculating the average amount of work in the notification method, the maximum number of operations corresponding to the body information for each worker is retrieved from a matching table, and the accumulation of the workers Correcting the retrieved maximum number of jobs according to the work time, rest time and the temperature value, determining the number of work of the operator using the acceleration value, and of the corrected maximum work number and the number of work It may include the step of determining the average workload according to the ratio.

At this time, the step of determining the number of operations may include extracting a pattern that is repeated in the trend of change in the acceleration value and determining the number of times the pattern is repeated as the number of operations.

In the method for determining work efficiency according to some embodiments of the present invention and the notification method, the step of correcting the maximum number of tasks may include correcting the maximum number of tasks to decrease as the cumulative work time increases. .

1 is a configuration diagram of a worker condition monitoring system according to an embodiment of the present invention.
FIG. 2 is a view for explaining various wearing positions of a wearable device that may be included in the operator condition monitoring system described with reference to FIG. 1.
FIG. 3 is a diagram for describing various wearable sensors that may be included in a wearable device that may be included in the operator condition monitoring system described with reference to FIG. 1.
4 is a view for explaining an example of a screen that can be used in the operator's terminal described with reference to FIG. 1.
5 is a hardware configuration diagram of a worker status monitoring server according to another embodiment of the present invention.
6 is a flowchart of a worker monitoring method according to another embodiment of the present invention.
7 is a flowchart of a method for evaluating work efficiency of an operator according to another embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. Advantages and features of the present invention, and methods for achieving them will be clarified with reference to embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only the embodiments allow the publication of the present invention to be complete, and general knowledge in the technical field to which the present invention pertains. It is provided to fully inform the holder of the scope of the invention, and the invention is only defined by the scope of the claims. The same reference numerals refer to the same components throughout the specification.

Unless otherwise defined, all terms (including technical and scientific terms) used in this specification may be used in a sense that can be commonly understood by those skilled in the art to which the present invention pertains. In addition, terms defined in the commonly used dictionary are not ideally or excessively interpreted unless specifically defined. The terminology used herein is for describing the embodiments and is not intended to limit the present invention. In this specification, the singular form also includes the plural form unless otherwise specified in the phrase.

Hereinafter, some embodiments of the present invention will be described with reference to the drawings.

1 is a configuration diagram of a worker condition monitoring system according to an embodiment of the present invention.

The worker status monitoring system according to an embodiment may include a wearable device 10 and a worker status monitoring server 100. In some cases, the worker condition monitoring system may further include a mobile terminal 11.

The wearable device 10 may be worn on the body of the worker 1, and may sense a sensor value using one or more sensors. According to some embodiments, the wearable device 10 may detect an acceleration value using an inertial sensor such as an accelerometer or a gyro sensor. Alternatively, the wearable device 10 may detect a temperature around the wearable device 10 using a temperature sensor. Alternatively, the wearable device 10 uses a heart rate sensor, an electromyogram (EMG) sensor, an electrocardiogram sensor, or a biosensor such as an oxygen saturation (Sp02) sensor to measure the heart rate, electromyogram value, cardiac severity value, or oxygen saturation value. Biometric information such as can be detected. This will be described in detail in FIG. 3.

According to some embodiments, the wearable device 10 may output the sensed sensor value to the outside using the sensor. For example, the wearable device 10 may include a mobile communication module, and transmit a sensor value to the worker status monitoring server 100 through the mobile communication network using the mobile communication module.

Further, according to some other embodiments, the wearable device 10 may include a short-range wireless communication module, and transmit a sensor value to the mobile terminal 11 using the short-range wireless communication module. In this case, the mobile terminal 11 with the mobile communication module may transmit sensor values collected from one or more devices including the wearable device 10 to the worker status monitoring server 100.

However, the structure of the worker condition monitoring system shown in FIG. 1 is for describing some embodiments, and the detailed structure of the worker condition monitoring system may be changed according to the embodiment. For example, the mobile terminal 11 may perform the function of the worker status monitoring server 100.

The worker condition monitoring server 100 directly receives information about the sensor value detected by the wearable device 10 worn by the worker 1 from the wearable device 10 or receives the sensor value from the wearable device 10 It can be received through other devices.

The worker status monitoring server 100 may calculate the average amount of work for the work status of the worker 1 based on the received sensor value. Here, the worker status monitoring server 100 may calculate the average workload using the worker status monitoring program 131. The worker status monitoring program 131 may be a program that allows the worker status monitoring server 100 to perform a workload calculation process that outputs work efficiency by calculating an average workload when a sensor value is input.

According to some embodiments, the worker status monitoring server 100 may determine the number of operations per unit time or the total number of operations based on the received sensor value. For example, the worker status monitoring server 100 may generate a change trend of the acceleration value among the received sensor values, and if there is a repeating pattern in the change trend, it may be determined that one operation is performed for each pattern. The worker condition monitoring server 100 may calculate an average amount of work according to the number of work of the worker. For example, the average amount of work may be determined according to the ratio of the determined number of tasks to the maximum number of tasks set for the worker 1.

The worker status monitoring server 100 may output an alarm to the manager according to the calculated average workload. For example, the worker status monitoring server 100 may output an alarm by extracting the top 10% of workers and the bottom 20% of workers compared to the average workload. Here, for example, the alarm may be a warning sound output through a speaker or an alarm message image output through a display device. The worker status monitoring server 100 may output an alarm through a peripheral device connected to the worker status monitoring server 100 or may transmit a command for outputting an alarm to another device.

The worker status monitoring server 100 is not necessarily implemented in the form of a server, and may be implemented as another type of computing device capable of executing the worker status monitoring program 131.

The wearable device 10 includes a device including a sensor, a user, and a terminal serving as a server such as a smartphone. When the wearable sensor device collects data from a user and transmits it to a smart device (terminal), the smart device analyzes the data and provides feedback to the user according to the result. These smart devices also play a role in controlling the user according to the user's context (situation).

Wearable sensors are made of MEMS (Micro Electro Mechanical Systems) capable of high performance, low power, low cost, and low area manufacturing. Wearable devices are mainly worn on the wrist, head, and waist. Devices worn on the head are in the form of helmets, hats, glasses, augmented reality, etc. that measure momentum. Devices worn on the legs or waist measure muscle condition, heart rate, balance, etc. This will be described in detail below with reference to FIG. 2.

FIG. 2 is a view for explaining various wearing positions of the wearable device and various sensor values measured thereby, which may be included in the operator condition monitoring system described with reference to FIG. 1.

For example, referring to FIG. 2, a sensor worn on the forehead may measure whether sleep is sleeping. It may be used to measure whether workers 1 rest.

As another example, the total activity and heart rate may be measured through a sensor worn on the wrist. These sensor values of the workers 1 act as an important factor in measuring the workload. This will be described in detail below. In addition, blood pressure, stress, and skin temp will be measured.

The wearable sensors illustrated in FIG. 2 are exemplary and need not be used together, and it is needless to say that some of them may be omitted or new sensors may be added.

FIG. 3 is a diagram for describing various wearable sensors that may be included in a wearable device that may be included in the operator condition monitoring system described with reference to FIG. 1. Hereinafter, some sensors will be described with reference to FIG. 3.

First, the gyro sensor is a sensor that recognizes motion, and senses a rotation angle of a rotating object through a sensor. Recently, it has been introduced into healthcare wearables to create various markets. In the present invention, since the role of sensing the movement of the human body 1, that is, the human body will be important, the gyro sensor will be an essential technology for various wearable devices.

A temperature sensor is a device that senses and measures temperature and converts it into an electrical signal, and can be divided into a contact type that contacts a measurement object and reacts to the temperature change, and a non-contact temperature sensor that senses energy emitted by the measurement object. Currently, it is used in various household appliances, office equipment, automobiles, medical equipment, etc., and is used in agricultural products dryers and greenhouse cultivation in rural areas. In particular, summer room. It can be used to prevent the disease in advance by measuring the temperature inside and outside in real time and predicting and warning the situation that a heat disease can occur due to heat waves.

The Bluetooth module is a technology made for the purpose of transferring low-speed data between devices. Technology is continuously being improved. Currently, technologies such as authentication, location tracking, and low power consumption have been added. In addition, the price of the Bluetooth chip is low and there is an advantage that the current technology can secure a long life even with a coin-type battery.

Each sensor may be used together, but it is not necessarily the case, and the illustrated sensors are exemplary and are not limited thereto.

4 is a view for explaining an example of a screen that can be used in the operator's terminal described with reference to FIG. 1. 4 shows an example of worker monitoring using an existing platform. Referring to FIG. 4, the manager can check the sensor value monitored for each worker in real time. In addition, by deriving the average workload of team work using the basic data collected for each team worker, it is possible to receive notifications by extracting the "top 10% workers" and "bottom 20% workers" compared to the average workload. will be.

5 is a hardware configuration diagram of a worker status monitoring server according to another embodiment of the present invention.

Referring to FIG. 5, the worker status monitoring server 100 includes one or more processors 120, a bus 150, a memory 140 that loads computer programs executed by the processor 120, and worker status monitoring The storage 130 storing the program 131 and the network interface 160 may be included. However, since FIG. 2 illustrates components related to some embodiments, other general-purpose components other than the components illustrated in FIG. 5 are further included, or other functions for performing the functions of the operator status monitoring server 100 It may be replaced by a component. Alternatively, some components may be omitted according to embodiments.

The processor 120 may control the overall operation of each component of the worker status monitoring server 100. The processor 120 may include a CPU (Central Processing Unit), an MPU (Micro Processor Unit), an MCU (Micro Controller Unit), a GPU (Graphic Processing Unit), or any known type of processor. Also, the processor 120 may perform operations on at least one application or program for executing a method according to some embodiments.

The memory 140 may store various data, commands, and / or information. The memory 140 may load one or more programs from the storage 130 in order to execute a worker status monitoring method according to some embodiments. For example, the memory 140 may include RAM. When one or more programs are loaded on the memory 140, a module for executing a worker status monitoring method may be implemented in the form of logic.

The bus 150 may provide a communication function between components of the worker status monitoring server 100. The bus 150 may be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

The network interface 160 may support wired or wireless communication of the worker status monitoring server 100. The network interface 160 may include a known communication module.

The storage 130 may be read by a non-volatile memory such as a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EPMROM), a flash memory, a hard disk, a removable disk, or any known computer. It can be configured to include a recording medium that can.

The worker condition monitoring program 131 sets an operation of body information for each worker, receives information about sensor values sensed by a wearable device worn by an operator from a wearable device, and calculates the amount of work for the received sensor values It may include instructions for inputting a process to calculate an amount of work and outputting an alarm according to the calculated amount of work. The operator condition monitoring program 131 may further include instructions for performing methods according to some embodiments.

According to some embodiments, the storage 130 may further store the matching table 132. The matching table 132 may be a table that stores a worker's workload so that the maximum number of tasks for each worker can be matched. In addition, according to an embodiment, the matching table 132 may further include a table stored by matching a correction condition and a correction ratio to correct the maximum number of operations.

Hereinafter, a worker monitoring method according to another embodiment of the present invention will be described with reference to FIG. 6. The worker monitoring method according to the present embodiment may be performed by a computing device. For example, the computing device may be an operator condition monitoring server described with reference to FIGS. 1 and 5. Hereinafter, each operation of this embodiment will be described. When the subject of each operation is omitted, the subject may be the computer device.

According to some embodiments, body information for each operator may be set in step S310. Here, the body information for each worker means information related to the body of the worker 1 wearing the wearable device 10. For example, body information for each worker may include information on the gender, age, and strength level of the worker. According to some embodiments, in order to set body information for each worker, the worker status monitoring server 100 may match and store the body information input through the input device with identification information for identifying the wearable device 10. Here, the identification information for identifying the wearable device 10 may be information received with the sensor value to identify which device is the device that transmitted the sensor value when the sensor value is received from the wearable device 10. .

Thereafter, information on the sensor value may be received from the wearable device 10 in step S320. According to some embodiments, information about the sensor value may include a sensor value sensed by the wearable device 10.

Thereafter, the average workload may be calculated based on the sensor value received in step S330. That is, the amount of motion of the worker 1 is determined based on the information on the sensor value received in step S330, and the average amount of work is analyzed by analyzing the amount of motion and pattern based on the sensor value of each device received by the worker 1 Can be calculated. According to some embodiments, information on the received sensor value may be input to a workload derivation process to calculate an average workload.

Thereafter, it is determined in step S340 whether the work of the worker 1 has ended. For example, when the worker 1 removes the wearable device 10 from the body, or when the work completion time has elapsed, the worker status monitoring server 100 may determine that the work of the worker 1 has ended. Alternatively, when the unit time has elapsed, but the work of the worker 1 has not ended, or when the worker 1 resumes the work after the rest time, step S320 for the worker 1 continues to be performed so that the worker 1 Status can be continuously monitored.

Thereafter, an alarm may be output by extracting the top 10% of workers and the bottom 20% of workers compared to the average workload calculated according to the average workload calculated in step S350. Alternatively, the list of the extracted workers may be transmitted to the manager device.

7 is a flowchart of a method for evaluating work efficiency of an operator according to another embodiment of the present invention.

The worker monitoring method according to the present embodiment may be performed by a computing device. For example, the computing device may be an operator condition monitoring server described with reference to FIGS. 1 and 5. Hereinafter, each operation of this embodiment will be described. When the subject of each operation is omitted, the subject may be the computer device.

First, in step S410, the maximum number of operations for each worker may be searched from the matching table. Here, the maximum number of operations can be retrieved from the matching table 132 in which the workload is stored for the worker 1 wearing the wearable device 10.

Thereafter, in step S420, the maximum number of jobs retrieved may be corrected.

According to some embodiments, the maximum number of tasks may be corrected according to the accumulated work time, which is the accumulated time from the time when the worker 1 starts work. Here, the maximum number of jobs retrieved may be corrected so that the maximum number of jobs decreases as the cumulative work time increases. For example, referring to the table below, the worker status monitoring server 100 decreases the maximum number of detected jobs by 3% when the cumulative work time is 2 hours or more and less than 3 hours, and the cumulative work time is 3 hours or more and 4 If less than hours, the maximum number of jobs retrieved is reduced by 6%, if the cumulative working time is 4 hours or more, and if it is less than 5 hours, the maximum number of retrieved jobs is reduced by 9%, and if the cumulative working time is 5 hours or more and 6 hours or more, the maximum detected jobs Decrease the number of times by 12%, and if the cumulative work time is more than 6 hours and less than 7 hours, decrease the maximum number of searched tasks by 15%, and if the cumulative work time is 7 hours or more and less than 8 hours, decrease the maximum number of searched tasks by 18%. You can.

Cumulative work time Adjustment rate ~ 2 hours 0% 2-3 hours -3% 3-4 hours -6% 4-5 hours -9% 5-6 hours -12% 6-7 hours -15%

<Table 1> In addition, according to some other embodiments, the maximum number of operations may be corrected according to the resting time occurring for the operator 1. For example, if a rest time occurs for the worker 1 during the morning, the maximum number of tasks may be increased.

Further, according to some other embodiments, the maximum number of operations may be corrected according to the temperature value included in the sensor value received from the wearable device 10. For example, referring to Table 2, the worker condition monitoring server 100 decreases the maximum number of operations by 10% when the temperature value exceeds 0 degrees Celsius and 5 degrees Celsius or less, and the temperature value exceeds 5 degrees Celsius. If the temperature is below 10 degrees Celsius, the maximum number of operations is reduced by 5%, and if the temperature value exceeds 25 degrees Celsius and below 30 degrees, the maximum number of operations is reduced by 5%, and if the temperature value exceeds 30 degrees Celsius and below 35 degrees, maximum The number of operations can be reduced by 10%.

Temperature value Adjustment rate 0-5 degrees -10% 6-10 degrees -5% 11-25 degrees 0 26-30 degrees -5% 31-35 degrees -10%

<Table 2> In step S430, the actual number of operations of the worker 1 may be determined. According to some embodiments, a pattern that is repeated in a trend of a change in the acceleration value received from the wearable device 10 is extracted, and the number of times the extracted pattern is repeated may be determined as the number of operations of the worker 1.

Then, in step S440, the amount of work may be determined according to the ratio of the corrected maximum number of tasks and the determined actual number of tasks. For example, when the corrected maximum number of operations is 100 and the actual number of operations performed by an operator for 30 minutes is 80, it may be determined that the amount of work is 80%.

The average workload can be determined by comparing the determined workload.

Further, according to some embodiments, a workload may be calculated based on a heart rate value.

According to some embodiments, the average value of the heart rate while the acceleration value included in the sensor value received from the wearable device 10 indicates that the worker 1 is in the resting state may be determined as the resting heart rate value. In addition, the heart rate value during the operation of the worker 1 may be monitored by receiving the heart rate value from the wearable device 1 during the period in which the worker 1 is working. Thereafter, the workload may be calculated according to the ratio of the heart rate value during work to the resting heart rate value.

The methods according to the embodiments of the present invention described so far can be performed by executing a computer program embodied in computer readable code. The computer program may be transmitted from a first computing device to a second computing device through a network such as the Internet and installed in the second computing device, and thus used in the second computing device. The first computing device and the second computing device include both server devices, physical servers belonging to a server pool for cloud services, and fixed computing devices such as desktop PCs.

The computer program may be stored in a recording medium such as a DVD-ROM or flash memory device.

Although the embodiments of the present invention have been described with reference to the accompanying drawings, a person skilled in the art to which the present invention pertains may be implemented in other specific forms without changing the technical concept or essential features of the present invention. Can understand. Therefore, it should be understood that the above-described embodiments are illustrative in all respects and not restrictive.

Claims (5)

A server receiving information on a sensor value sensed by a wearable device worn by an operator from the wearable device;
Calculating, by the server, an average workload by inputting information about the received sensor value into a workload calculation process; And
And outputting, by the server, a notification regarding work efficiency according to the calculated average work amount,
Method for determining work efficiency and notification method. According to claim 1,
Information about the sensor value,
Including temperature values, acceleration values and biometric information,
Method for determining work efficiency and notification method. According to claim 2,
The step of calculating the average workload,
Searching a matching table for the maximum number of tasks corresponding to the body information for each worker;
Correcting the maximum number of retrieved jobs according to the cumulative work time, rest time, and temperature value of the worker;
Determining the number of operations of the worker using the acceleration value; And
Determining the average amount of work according to the ratio of the corrected maximum number of work and the number of work,
Method for determining work efficiency and notification method. According to claim 3,
The step of determining the number of operations,
Extracting a repeating pattern from the trend of change in the acceleration value; And
And determining the number of times the pattern is repeated as the number of operations,
Method for determining work efficiency and notification method. According to claim 3,
Compensating the maximum number of operations,
Compensating to decrease the maximum number of tasks as the cumulative work time increases,
Method for determining work efficiency and notification method.

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