JP2020184303A - Fatigue level determination program - Google Patents

Abstract

To allow for highly accurately determining fatigue levels of employees using a relatively simple method.SOLUTION: A fatigue level determination program for determining a fatigue level of an employee is provided, the program being configured to make a computer perform: an information inputting step for acquiring a working condition of an employee and employment environment information pertaining to an external employment environment; and a fatigue level determination step for using three or more levels of association between fatigue levels and combinations of reference working conditions and reference employment environment information to determine a fatigue level based on three or more levels of association between fatigue levels and a combination of a reference working condition and reference employment environment information corresponding to the working condition and the employment environment information entered in the information inputting step.SELECTED DRAWING: Figure 3

Description

The present invention relates to a fatigue level determination program capable of determining the fatigue level of an employee due to excessive work in the workplace and determining in advance that the employee will fall into an overworked state.

Excessive workload is a problem in modern workplaces. If the amount of work is excessive, it may lead to death from overwork, so it is necessary to prevent this as much as possible. For this purpose, it is desirable to monitor the degree of fatigue of employees and confirm in advance whether there is a risk of overwork. However, in the past, the current situation is that a system capable of determining the degree of fatigue of an employee with high accuracy by a relatively simple method has not yet been proposed.

Therefore, the present invention has been devised in view of the above-mentioned problems, and the purpose of the present invention is to determine the degree of fatigue of an employee with high accuracy by a relatively simple method. The purpose is to provide a discrimination program.

The fatigue degree determination program according to the present invention is an information input step for acquiring the work state of an employee and the employment environment information regarding the external employment environment in the fatigue degree determination program for determining the fatigue degree of an employee, and a reference work state. The reference work status and reference according to the work status and employment environment information entered in the above information input step, using the combination of the reference employment environment information and the fatigue level of three or more levels. It is characterized in that a computer executes a fatigue degree determination step for determining the fatigue degree based on a combination with employment environment information and the degree of association between the fatigue degree and the fatigue degree in three or more stages.

Even if you do not have any special skills or experience, it is possible to determine the degree of fatigue of an employee with high accuracy using a relatively simple method.

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the fatigue degree determination program to which the present invention is applied will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing an overall configuration of a fatigue degree determination system 1 in which a fatigue degree determination program to which the present invention is applied is implemented. The fatigue degree discrimination system 1 includes an information acquisition unit 9, a discrimination device 2 connected to the information acquisition unit 9, and a database 3 connected to the discrimination device 2.

The information acquisition unit 9 is a device for a person who uses this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an image pickup device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the discrimination device 2 described later. The information acquisition unit 9 outputs the detected information to the determination device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. The information acquisition unit 9 also includes a temperature sensor, a humidity sensor, a flow rate sensor, and other sensors capable of identifying substances and physical properties.

The database 3 stores various information necessary for determining the degree of fatigue. Information necessary for determining the degree of fatigue includes reference work status information indicating employee work status information, reference attribute information such as employee family structure, age, and gender, and previously given to employees. A data set of improvement measures for reference regarding improvement measures, the degree of fatigue for which judgments are actually made, and improvement measures for improving the degree of fatigue is stored.

That is, in the database 3, in addition to such reference work status information, any one or more of reference attribute information, reference employee number information, reference employment environment information, reference work volume information, and reference improvement measures And, the degree of fatigue or improvement measures are memorized in association with each other.

The discrimination device 2 is composed of, for example, an electronic device such as a personal computer (PC), but is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc., in addition to the PC. It may be converted. The user can obtain a search solution by the discrimination device 2.

FIG. 2 shows a specific configuration example of the discrimination device 2. The discrimination device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire discrimination device 2 and an operation unit 25 for inputting various control commands via operation buttons, a keyboard, or the like. A communication unit 26 for the purpose, an estimation unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the discrimination device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 in response to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the estimation unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The estimation unit 27 estimates the search solution. The estimation unit 27 reads out various information stored in the storage unit 28 and various information stored in the database 3 as necessary information when executing the estimation operation. The estimation unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any well-known artificial intelligence technique.

The display unit 23 is composed of a graphic controller that creates a display image based on the control by the control unit 24. The display unit 23 is realized by, for example, a liquid crystal display (LCD) or the like.

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program is read by the control unit 24 and executed.

The operation in the fatigue degree determination system 1 having the above-described configuration will be described.

In the fatigue degree determination system 1, for example, as shown in FIG. 3, it is premised that the reference work state information and the degree of association between the fatigue degree and the fatigue degree are set and acquired in advance. The reference work status information includes all data regarding the work status of the employee, for example, the time of arrival and departure, working hours, vacation days, late days, early leaving days, and the like.

Fatigue is the degree of fatigue of an individual employee evaluated subjectively or objectively. As an example of this fatigue level, for example, it may be based on evaluation data, medical care data, medical examination results, etc. in which the fatigue level is objectively evaluated by a doctor or a specialist having specialized knowledge. Further, the evaluator of the degree of fatigue is not limited to those who have specialized knowledge about medicine, and includes those who do not have such specialized knowledge. That is, the evaluator of the degree of fatigue may be any evaluation as long as it is evaluated by the person himself / herself or a third party (for example, a boss). Further, the degree of fatigue may be obtained only from objective data (for example, lactic acid level, blood glucose level, sweat amount, walking speed), etc., without human intervention.

As an example of evaluation of the degree of fatigue, it may be evaluated between 0 and 100%, where 0% is the state where there is no fatigue and 100% is the most tired case.

In the example of FIG. 3, it is assumed that the input data is, for example, reference work status information P01 to P03. The reference work status information P01 to P03 as such input data is linked to the output. In this output, the degree of fatigue as the output solution is displayed.

The reference work status information is associated with each other through three or more levels of association with the fatigue level as the output solution. The reference work status information is arranged on the left side via this degree of association, and each fatigue degree is arranged on the right side via this degree of association. The degree of association indicates which degree of fatigue is highly related to the degree of reference precautionary health arranged on the left side. In other words, this degree of association is an index showing what kind of fatigue degree each reference work condition information is likely to be associated with, and is used to select the most probable fatigue degree from the reference work condition information. It shows the accuracy in. In the example of FIG. 3, w13 to w19 are shown as the degree of association. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of association between each combination as an intermediate node with the degree of fatigue as an output. On the contrary, the closer to one point, the lower the degree of relevance of each combination as an intermediate node to the price as an output.

The discriminating device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data sets, which of the reference work state information and the fatigue degree in that case are adopted and evaluated in discriminating the actual search solution, and stores these. By analyzing and analyzing, the degree of association shown in FIG. 3 is created.

For example, it is assumed that the reference work status information is "early leave" for four consecutive days. It is assumed that the fatigue level of 75% is highly evaluated as the fatigue level for such reference work status information. By collecting and analyzing such a data set, the degree of association between the reference work status information (“early leave” for 4 consecutive days) and the degree of fatigue (75%) becomes stronger.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference work status information P01, analysis is performed from various data as a result of evaluating the past fatigue level. For example, the data set may be extracted by performing a text mining analysis from the electronic data of the diagnosis result or the evaluation result in the workplace. In the case of reference work status information P01, if there are many cases of fatigue level 75%, the degree of association that leads to the evaluation of this fatigue level is set higher, and if there are many cases of fatigue level 0%, The degree of association that leads to the evaluation of this degree of fatigue is set higher. For example, in the example of the reference work status information P01, the fatigue level is 75% and the fatigue level is 25%. However, the degree of association of w13, which leads to the fatigue level of 75% from the previous case, is set to 7 points, and the fatigue level is 25. The degree of association of w14 that leads to% is set to 2 points.

Further, the degree of association shown in FIG. 3 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, the fatigue level will be searched for by using the above-mentioned learned data when actually determining the fatigue level for a new employee. In such a case, new work status information regarding the employee to be discriminated is newly acquired. The newly acquired work status information is input by the above-mentioned information acquisition unit 9. The work status information may be fetched directly from, for example, a database that records the time of attendance and departure.

Based on the newly acquired work status information in this way, the degree of fatigue of the employee is determined. In such a case, the degree of association shown in FIG. 3 (Table 1) acquired in advance is referred to. For example, when the newly acquired work status information is the same as or similar to P02, the fatigue degree 0% is associated with w15 and the fatigue degree 25% is associated with the association degree w16 through the association degree. .. In such a case, the fatigue degree of 0%, which has the highest degree of association, is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and 25% of the fatigue degree in which the degree of association itself is recognized although the degree of association is low may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In this way, the most suitable degree of fatigue can be searched for and displayed to the user from the newly acquired work status information. By looking at the search results, the user can obtain approach guidelines for various improvement measures for caring for employees, that is, for recovering work style and health level, based on the searched fatigue level. By the way, this degree of fatigue is not limited to the case of mere evaluation of the condition, and it also mentions how to care for employees, that is, to propose improvement measures for working styles for the degree of fatigue. It may be the one that is.

In the example of FIG. 4, the reference attribute information on the premise that a combination of the reference work status information and the reference attribute information is formed is the employee's age, gender, family structure, academic background, and work history. , Property, address, value of house owned, years of service, conditions at the time of joining, position, qualification, special skill, skill, etc., resume information, work history information, etc., all information about the employee is included. .. This reference attribute information may be acquired from the data of employees managed in the company, or may be acquired as text data from a resume or resume through character recognition technology.

In the example of FIG. 4, it is assumed that the input data is, for example, reference work status information P01 to P03 and reference attribute information P14 to 17. The intermediate node shown in FIG. 4 is a combination of reference attribute information and reference work status information as such input data. Each intermediate node is further linked to the output. In this output, the degree of fatigue as the output solution is displayed.

Each combination (intermediate node) of the reference work state information and the reference attribute information is associated with each other through three or more levels of association with the fatigue degree as this output solution. The reference work status information and the reference attribute information are arranged on the left side via the degree of association, and the fatigue degree is arranged on the right side via the degree of association. The degree of association indicates the degree of high relevance to the degree of fatigue with respect to the reference work status information and the reference attribute information arranged on the left side. In other words, this degree of association is an index indicating what kind of fatigue degree each reference work status information and reference attribute information is likely to be associated with, and is a reference work status information and reference attribute information. It shows the accuracy in selecting the most probable fatigue level from. In addition to what the actual attendance / leaving situation is, employee attribute information (employee age, gender, family structure, educational background, work history, property, address, value of housing owned, years of service, conditions at the time of joining the company, etc. Depending on the position, qualifications, special skills, skills, etc., resume information, work history information, etc., all information about the employee), the degree of fatigue to be evaluated will differ. Therefore, the optimum fatigue level is searched for by combining the reference work status information and the reference attribute information.

In the example of FIG. 4, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data as to which of the reference working state information, the reference attribute information, and the degree of fatigue in that case was suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 4 is created.

For example, it is assumed that the reference work status information in the actual case in the past has an average working time of 16 hours per day. In addition, the reference attribute information is 40's, male, family composition is wife (double-income), child (5 years old, 2 years old), value of the house owned is 0.00 million yen, years of service is 12 years, position is manager, educational background 〇 〇University graduates, qualifications to hold ■■. In such a case, the degree of fatigue evaluated for the employee is learned as a data set and defined in the form of the degree of association described above. In addition, such attribute information may be extracted from the employee management database managed in the company.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, when the reference work status information P01 and the reference attribute information P16, the degree of fatigue is analyzed from the past data. If there are many cases with a fatigue level of 90%, the degree of association that leads to this 90% is set higher, and if there are many cases with a fatigue level of 25% and there are few cases with a fatigue level of 90%, the fatigue level is 25. The degree of association that leads to% is set high, and the degree of association that leads to 90% of fatigue is set low. For example, in the example of the intermediate node 61a, it is linked to the output of fatigue degree 75% and fatigue degree 0%, but from the previous case, the degree of association of w13, which leads to fatigue degree 75%, is reduced to 7 points and fatigue degree 0%. The degree of association of the connected w14 is set to 2 points.

Further, the degree of association shown in FIG. 4 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 4, the node 61b is a node in which the reference work state information P01 is combined with the reference attribute information P14, and the degree of association of 25% fatigue is w15 and the degree of fatigue is 50%. The degree of association is w16. The node 61c is a node in which the reference attribute information P15 and P17 are combined with the reference work status information P02, and the degree of association of 0% fatigue is w17 and the degree of association of 90% fatigue is w18. There is.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually determining the degree of fatigue of an employee from now on, the above-mentioned trained data will be used. In such a case, the work status information and the attribute information are actually acquired from the employee to be discriminated. The work status information may be fetched directly from, for example, a database that records the time of attendance and departure. For the attribute information, the attribute information related to the employee may be extracted from the employee database managed by the company.

Based on the newly acquired work status information and attribute information in this way, the optimum fatigue level is searched for. In such a case, the degree of association shown in FIG. 4 (Table 1) acquired in advance is referred to. For example, when the newly acquired work status information is the same as or similar to P02 and the attribute information is P17, the node 61d is associated via the degree of association. The node 61d is associated with a fatigue degree of 25% by w19 and a fatigue degree of 90% by an association degree w20. In such a case, the fatigue degree of 25%, which has the highest degree of association, is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and 90% of the fatigue degree in which the degree of association itself is recognized although the degree of association is low may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

In addition, Table 2 below shows examples of the degrees of association w1 to w12 extending from the input.

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the association degrees w1 to w12 may all have the same value, and the weightings in the selection of the intermediate node 61 may all be the same.

FIG. 5 shows an example in which the combination of the above-mentioned reference work status information, the reference employee number information, and the degree of fatigue with respect to the combination are set to three or more levels of association.

Reference employee number information is any information regarding the number of employees in the organizational unit to which the reference belongs. This reference employee number information may be shown by data such as the number of employees and the ratio between competing companies in the same industry, the transition of the number of employees in the organization, and the annual increase / decrease rate of the number of employees. And so on. This reference employee number information may be obtained from external data such as the company's quarterly report. The affiliated organization referred to here is not limited to the affiliated organization to which the employee to be discriminated belongs, and may be the transition or increase / decrease rate of employees focusing on one affiliated organization.

In the example of FIG. 5, it is assumed that the input data is, for example, reference work status information P01 to P03 and reference employee number information P18 to 21. The intermediate node shown in FIG. 5 is a combination of reference work status information and reference employee number information as such input data. Each intermediate node is further linked to the output. In this output, the degree of fatigue as the output solution is displayed.

Each combination (intermediate node) of the reference work status information and the reference employee number information is associated with each other through three or more levels of association with the fatigue degree as this output solution. The reference work status information and the reference employee number information are arranged on the left side via this degree of association, and the degree of fatigue is arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the degree of fatigue with respect to the reference work status information and the reference employee number information arranged on the left side. In other words, this degree of association is an index indicating what degree of fatigue each reference work status information and reference employee number information is likely to be associated with, and is a reference work status information and reference employee. It shows the accuracy in selecting the most probable fatigue level from the number information. In addition to the working conditions of employees, the rate of increase or decrease in the number of employees can be used to estimate to some extent whether or not each employee is overworked, and as a result, the degree of fatigue to be evaluated differs. Therefore, the optimum degree of fatigue is searched for by combining the reference work status information and the reference employee number information.

In the example of FIG. 5, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of each combination as an intermediate node is related to the degree of fatigue as an output, and vice versa. The closer it is to one point, the lower the degree of association between each combination as an intermediate node with the degree of fatigue as an output.

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data as to which of the reference work status information, the reference employee number information, and the degree of fatigue in that case was suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 5 is created.

For example, at the time of evaluating the actual fatigue level in the past, the reference work status information shows that the average working hours per day is 16 hours, and the reference employee number information shows that the number of employees has decreased by 10% for the second consecutive year. It is assumed that there was. In such a case, if there are many cases where the degree of fatigue is determined to be 90%, these are trained as a data set and defined in the form of the degree of association described above.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, when the reference work status information P01 and the reference employee number information P20, the degree of fatigue is analyzed from the past data. When there are many cases where the degree of fatigue is 75%, the degree of association that leads to this degree of fatigue is set higher, and when there are many cases where the degree of fatigue is 25% and there are few cases where the degree of fatigue is 75%. Sets the degree of association that leads to a degree of fatigue of 25% to be high, and the degree of association that leads to a degree of fatigue of 75% to be low. For example, in the example of the intermediate node 61a, it is linked to the output of fatigue degree 75% and fatigue degree 0%, but from the previous case, the degree of association of w13, which leads to fatigue degree 75%, is reduced to 7 points and fatigue degree 0%. The degree of association of the connected w14 is set to 2 points.

Further, the degree of association shown in FIG. 5 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 5, the node 61b is a node in which the reference work status information P01 is combined with the reference employee number information P18, and the degree of association of 25% fatigue is w15 and the degree of fatigue is 50. The degree of association of% is w16. The node 61c is a node in which the reference work status information P02 is combined with the reference employee number information P19 and P21, and the degree of association with 0% fatigue is w17 and the degree of association with 90% fatigue is w18. ing.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the degree of fatigue from now on, the above-mentioned learned data will be used. In such a case, the work status information of the employee whose fatigue level is to be determined and the employee number information are actually acquired. The number of employees information referred to here is information on the increase / decrease rate of employees, the number of employees, etc. in the organization to which the employee to be discriminated belongs.

Based on the newly acquired work status information and the number of employees information in this way, the optimum degree of fatigue is searched for. In such a case, the degree of association shown in FIG. 5 (Table 1) acquired in advance is referred to. For example, when the newly acquired work status information is the same as or similar to P02 and the number of employees information is P21, the node 61d is associated via the degree of association. The node 61d is associated with a fatigue degree of 25% by w19 and a fatigue degree of 90% by an association degree w20. In such a case, the fatigue degree of 25%, which has the highest degree of association, is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and 90% of the fatigue degree in which the degree of association itself is recognized although the degree of association is low may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 6 shows an example in which the combination of the above-mentioned reference work status information, the reference employment environment information, and the degree of fatigue with respect to the combination are set to three or more levels of association.

The reference employment environment information includes all information regarding the employment environment of the entire society beyond the organization to which the employee to be discriminated belongs. For example, this includes information on employment statistics. This reference employment environment information may be obtained from employment statistics data published by the government.

In the example of FIG. 6, it is assumed that the input data is, for example, reference work status information P01 to P03 and reference employment environment information P22 to 25. The intermediate node shown in FIG. 6 is a combination of reference work status information and reference employment environment information as such input data. Each intermediate node is further linked to the output. In this output, the degree of fatigue as the output solution is displayed.

Each combination (intermediate node) of the reference work status information and the reference employment environment information is associated with each other through three or more levels of association with the fatigue level as this output solution. The reference work status information and the reference employment environment information are arranged on the left side via this degree of association, and the degree of fatigue is arranged on the right side via this degree of association. The degree of association indicates the degree of relevance to the degree of fatigue with respect to the reference work status information and the reference employment environment information arranged on the left side. In other words, this degree of association is an index showing what kind of fatigue degree each reference work status information and reference employment environment information is likely to be associated with, and is a reference work status information and reference employment. It shows the accuracy in selecting the most probable fatigue level from environmental information. In addition to the reference work status information, depending on what kind of reference employment environment information is obtained from the employment statistics at the time of the determination, it is possible to know whether or not the world is in a labor shortage situation as a whole, and the labor shortage Under the circumstances, the degree of fatigue of individual employees in each company is expected to increase accordingly. Therefore, the optimum degree of fatigue will be searched for by combining the reference work status information and the reference employment environment information.

In the example of FIG. 6, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of each combination as an intermediate node is related to the degree of fatigue as an output, and vice versa. The closer it is to one point, the lower the degree of association between each combination as an intermediate node with the degree of fatigue as an output.

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data as to which of the reference working state information, the reference employment environment information, and the degree of fatigue in that case was more suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 6 is created.

For example, it is assumed that the reference work status information is working for 15 hours for 3 consecutive days at the time of evaluating the actual fatigue level in the past. It is assumed that the unemployment rate is 1.5% in the reference employment environment information. In such a case, if there are many cases where the degree of fatigue is determined to be 25%, these are trained as a data set and defined in the form of the degree of association described above.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, when the reference work status information P01 and the reference employment environment information P24, the degree of fatigue is analyzed from the past data. When there are many cases of fatigue level 75%, the degree of association leading to this fatigue level 75% is set higher, and when there are many cases of fatigue level 25% and there are few cases of fatigue level 75%, the degree of fatigue level is set. The degree of association leading to 25% is set high, and the degree of association leading to fatigue of 75% is set low. For example, in the example of the intermediate node 61a, it is linked to the output of fatigue degree 75% and fatigue degree 25%, but from the previous case, the degree of association of w13, which leads to fatigue degree 75%, is reduced to 7 points and fatigue degree 25%. The degree of association of the connected w14 is set to 2 points.

Further, the degree of association shown in FIG. 6 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 6, the node 61b is a node in which the reference employment environment information P22 is combined with the reference work status information P01, and the degree of association of 25% fatigue is w15 and the degree of fatigue is 50. The degree of association of% is w16. The node 61c is a node in which the reference employment environment information P23 and P25 are combined with the reference work status information P02, and the degree of association with 0% fatigue is w17 and the degree of association with 90% fatigue is w18. ing.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the degree of fatigue from now on, the above-mentioned learned data will be used. In such a case, the work status information of the employee whose fatigue level is to be determined and the employment environment information are actually acquired. The employment environment information may be obtained directly from the employment statistics data or the like at the time of the determination.

Based on the newly acquired work status information and employment environment information in this way, the optimum degree of fatigue is searched for. In such a case, the degree of association shown in FIG. 6 (Table 1) acquired in advance is referred to. For example, when the newly acquired work status information is the same as or similar to P02 and the employment environment information is P25, the node 61d is associated via the degree of association. The node 61d is associated with a fatigue degree of 25% by w19 and a fatigue degree of 90% by an association degree w20. In such a case, the fatigue degree of 25%, which has the highest degree of association, is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and 90% of the fatigue degree in which the degree of association itself is recognized although the degree of association is low may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 7 shows an example in which a combination of the above-mentioned reference work status information, reference work amount information, and a degree of fatigue with respect to the combination are set to three or more levels of association.

Reference workload information includes all information regarding the workload of individual employees. For example, if you are a sales person, the amount of work that can be measured may be something like a sales quota, if it is a tele appointment business, the number of calls per day, and if it is a restaurant, the food you cook in a day. In the case of the translation industry, it is information on all the measurable workloads of employees, such as the number of words to be translated per unit time. This reference work volume information may be acquired from the employee work volume data actually measured here in the company. In addition, the reference work volume information includes the time zone and time when the personal computer used by the employee is turned on, the power consumption, the CPU operating rate, the mail transmission / reception history, the packet communication volume, and the like. May be good.

In the example of FIG. 7, it is assumed that the input data is, for example, reference work status information P01 to P03 and reference work amount information P26 to 29. The intermediate node shown in FIG. 7 is a combination of the reference work status information and the reference work amount information as such input data. Each intermediate node is further linked to the output. In this output, the degree of fatigue as the output solution is displayed.

Each combination (intermediate node) of the reference work status information and the reference work amount information is associated with each other through three or more levels of association with the fatigue degree as this output solution. The reference work status information and the reference work volume information are arranged on the left side via this degree of association, and the degree of fatigue is arranged on the right side via the degree of association. The degree of association indicates the degree of relevance to the degree of fatigue with respect to the reference work status information and the reference work amount information arranged on the left side. In other words, this degree of association is an index indicating what kind of fatigue degree each reference work status information and reference work volume information is likely to be associated with, and is a reference work status information and reference work. It shows the accuracy in selecting the most probable fatigue level from the quantitative information. In addition to reference work status information, work volume information is also a factor that controls the degree of fatigue. Therefore, the optimum fatigue level is searched for by combining the reference work status information and the reference work volume information.

In the example of FIG. 7, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of each combination as an intermediate node is related to the degree of fatigue as an output, and vice versa. The closer it is to one point, the lower the degree of association between each combination as an intermediate node with the degree of fatigue as an output.

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 7. That is, the discriminating device 2 accumulates past data as to which of the reference work state information, the reference work amount information, and the degree of fatigue in that case was more suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 7 is created.

For example, it is assumed that the reference work status information is working for 15 hours for 3 consecutive days at the time of evaluating the actual fatigue level in the past. It is assumed that the number of translation words per day at the translation company is 5000 words in the reference work volume information. In such a case, if there are many cases where the degree of fatigue is determined to be 25%, these are trained as a data set and defined in the form of the degree of association described above.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference work state information P01 and the reference work amount information P28, the degree of fatigue is analyzed from the past data. When there are many cases of fatigue level 75%, the degree of association leading to this fatigue level 75% is set higher, and when there are many cases of fatigue level 25% and there are few cases of fatigue level 75%, the degree of fatigue level is set. The degree of association that leads to 25% is set high, and the degree of association that leads to a degree of fatigue of 75% is set low. For example, in the example of the intermediate node 61a, it is linked to the output of fatigue degree 75% and fatigue degree 25%, but from the previous case, the degree of association of w13, which leads to fatigue degree 75%, is reduced to 7 points and fatigue degree 25%. The degree of association of the connected w14 is set to 2 points.

Further, the degree of association shown in FIG. 7 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 7, the node 61b is a node in which the reference work amount information P26 is combined with the reference work status information P01, and the degree of association of 25% fatigue is w15 and the degree of fatigue is 50. The degree of association of% is w16. The node 61c is a node in which the reference work amount information P27 and P29 are combined with the reference work status information P02, and the degree of association with 0% fatigue is w17 and the degree of association with 90% fatigue is w18. ing.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the degree of fatigue from now on, the above-mentioned learned data will be used. In such a case, the work status information of the employee whose fatigue level is to be determined and the work amount information are actually acquired. The work volume information is the content corresponding to the above-mentioned reference work volume information. The work amount information may be manually input by counting the work amount of the employee to be discriminated, or may be obtained directly from the database if it is recorded.

Based on the newly acquired work status information and work volume information in this way, the optimum fatigue level is searched for. In such a case, the degree of association shown in FIG. 7 (Table 1) acquired in advance is referred to. For example, when the newly acquired work status information is the same as or similar to P02 and the workload information is P29, the node 61d is associated through the degree of association. The node 61d is associated with a fatigue degree of 25% by w19 and a fatigue degree of 90% by an association degree w20. In such a case, the fatigue degree of 25%, which has the highest degree of association, is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and 90% of the fatigue degree in which the degree of association itself is recognized although the degree of association is low may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

FIG. 8 shows an example in which the combination of the above-mentioned reference work status information, the reference improvement measure, and the degree of fatigue for the combination are set to three or more levels of association.

Reference improvement measures are improvement measures that have been previously taken for employees with a high degree of fatigue. This improvement measure includes various well-known measures such as intake of drugs and nutrients, exercise, mood change, mental training, and the amount of sleep. This reference improvement measure may be acquired as text data through character recognition technology from a database recording the improvement measures taken according to the degree of fatigue previously determined, or other medical records or documents in which the improvement measures are recorded.

In the example of FIG. 8, it is assumed that the input data is, for example, reference work status information P01 to P03 and reference improvement measures P30 to 33. The intermediate node shown in FIG. 8 is a combination of reference work status information and reference improvement measures as such input data. Each intermediate node is further linked to the output. In this output, the degree of fatigue as the output solution is displayed.

Each combination (intermediate node) of the reference work status information and the reference improvement measure is associated with each other through three or more levels of association with the fatigue level as this output solution. The reference work status information and the reference improvement measures are arranged on the left side via this degree of association, and the degree of fatigue is arranged on the right side via the degree of association. The degree of association indicates the degree of relevance to the degree of fatigue with respect to the reference work status information and the reference improvement measures arranged on the left side. In other words, this degree of association is an index showing what kind of fatigue degree each reference work status information and reference improvement measure is likely to be associated with, and is a reference work status information and reference improvement measure. It shows the accuracy in selecting the most probable fatigue level from. In addition to the reference work status information, improvement measures and confirmation of the effects of previously implemented measures will be a factor that controls the degree of fatigue. Therefore, the optimum degree of fatigue will be searched for by combining these reference work status information and reference improvement measures.

In the example of FIG. 8, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of each combination as an intermediate node is related to the degree of fatigue as an output, and vice versa. The closer it is to one point, the lower the degree of association between each combination as an intermediate node with the degree of fatigue as an output.

The discriminating device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the discriminating device 2 accumulates past data as to which of the reference work state information, the reference improvement measure, and the degree of fatigue in that case was more suitable for discriminating the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 8 is created.

For example, it is assumed that the reference work status information is working for 15 hours for 3 consecutive days at the time of evaluating the actual fatigue level in the past. The reference improvement measure shall take the drug 〇〇 daily to the employee and complete the exercise menu □□. In such a case, if there are many cases where the degree of fatigue is determined to be 25%, these are trained as a data set and defined in the form of the degree of association described above.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference work status information P01 and reference improvement measure P32, the degree of fatigue is analyzed from past data. When there are many cases of fatigue level 75%, the degree of association leading to this fatigue level 75% is set higher, and when there are many cases of fatigue level 25% and there are few cases of fatigue level 75%, the degree of fatigue level is set. The degree of association that leads to 25% is set high, and the degree of association that leads to a degree of fatigue of 75% is set low. For example, in the example of the intermediate node 61a, it is linked to the output of fatigue degree 75% and fatigue degree 25%, but from the previous case, the degree of association of w13, which leads to fatigue degree 75%, is reduced to 7 points and fatigue degree 25%. The degree of association of the connected w14 is set to 2 points.

Further, the degree of association shown in FIG. 8 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 8, the node 61b is a node in which the reference work state information P01 is combined with the reference improvement measure P30, and the degree of association of 25% fatigue is w15 and the degree of fatigue is 50%. The degree of association is w16. The node 61c is a node that is a combination of the reference improvement measures P31 and P33 with respect to the reference work status information P02, and the degree of association with 0% fatigue is w17 and the degree of association with 90% fatigue is w18. There is.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, when actually searching for the degree of fatigue from now on, the above-mentioned learned data will be used. In such a case, the work status information of the employee whose fatigue level is to be determined and the improvement measures are actually acquired. The improvement measures may be obtained manually or directly from the data of the improvement measures for each employee managed by the company.

Based on the newly acquired work status information and improvement measures in this way, the optimum degree of fatigue is searched for. In such a case, the degree of association shown in FIG. 8 (Table 1) acquired in advance is referred to. For example, when the newly acquired work status information is the same as or similar to P02 and the improvement measure is P33, the node 61d is associated via the degree of association. Node 61d is associated with a fatigue level of 25% by w19 and a fatigue level of 90% by a degree of association w20. In such a case, the fatigue degree of 25%, which has the highest degree of association, is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and 90% of the fatigue degree in which the degree of association itself is recognized although the degree of association is low may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and as long as it is based on the degree of association, it may be selected in any other priority.

When the degree of fatigue is evaluated in this way and improvement measures are taken according to the degree of fatigue, the improvement measures may be included in the learning data set as the above-mentioned reference improvement measures. Then, when newly determining the degree of fatigue of the employee, the improvement measures derived from the previous determination result may be newly input as input data.

In FIG. 9, in addition to the above-mentioned reference work status information and reference attribute information, a combination of reference employee number information and a degree of fatigue with respect to the combination are set to three or more levels of association. An example is shown.

In such a case, as shown in FIG. 9, the degree of association is such that the set of combinations of the reference work status information, the reference attribute information, and the reference employee number information is the nodes 61a to 61e of the intermediate node as described above. Will be expressed as.

For example, in FIG. 9, in the node 61c, the reference work state information P02 is associated with the association degree w3, the reference attribute information P15 is associated with the association degree w7, and the reference employee number information P19 is associated with the association degree w11. Similarly, in the node 61e, the reference work status information P03 is associated with the association degree w5, the reference attribute information P15 is associated with the association degree w8, and the reference employee number information P18 is associated with the association degree w10.

Similarly, when such a degree of association is set, the degree of fatigue is determined based on the newly acquired reference work status information, reference attribute information, and reference employee number information.

In determining the degree of fatigue, the degree of association shown in FIG. 9 acquired in advance is referred to. For example, the acquired work status information is the same as or similar to the reference work status information P02, the acquired attribute information corresponds to the reference attribute information P15, and the acquired employee number information corresponds to the reference employee number information P19. In this case, the combination is associated with a node 61c, which has a fatigue degree of 0% associated with a degree of association w17 and a fatigue degree of 90% with an association degree w18. As a result of such a degree of association, a search solution is actually obtained based on w17 and w18.

When combining three or more types of such input parameters, in addition to reference work status information, reference attribute information, reference employee number information, reference employment environment information, reference workload information, and reference It is applicable even if the combination is composed of any two or more of the improvement measures.

According to the present invention, as an alternative to the above-mentioned fatigue level, an improvement measure may be searched for as shown in FIG. Similarly, in addition to the reference work status information or the reference work status information, any of the reference attribute information, the reference employee number information, the reference employment environment information, the reference work volume information, and the reference improvement measures. Create a data set from past cases in combination with 1 or more. In other words, the degree of association is formed by collecting past cases of what kind of input data and what kind of improvement measures are being proposed and learning this. An example in which this output data is used as an improvement measure will be explained by quoting the above explanation and replacing the fatigue level with the improvement measure, and the following description will be omitted.

In the above-mentioned degree of association, the degree of association is expressed by a 10-step evaluation, but it is not limited to this, and it may be expressed by a degree of association of 3 or more levels, and conversely, it may be expressed by 3 or more levels. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are related to each other, either 1 or 0.

According to the present invention having the above-described configuration, anyone can easily search for the degree of fatigue and improvement measures without any special skill or experience. Further, according to the present invention, it is possible to determine the search solution with higher accuracy than that performed by a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Further, according to the present invention, it is characterized in that an optimum solution search is performed through the degree of association set in three or more stages. The degree of association can be described by, for example, a numerical value from 0 to 100% in addition to the above-mentioned 10 steps, but the degree of association is not limited to this, and any step can be described as long as it can be described by a numerical value of 3 or more steps. It may be configured.

By discriminating the most probable fatigue level and improvement measures based on the degree of association expressed by the numerical values of three or more levels, the degree of association can be determined in a situation where there are multiple possible candidates for the search solution. It is also possible to search and display in descending order. If the user can be displayed in descending order of the degree of association in this way, it is possible to preferentially display more probable search solutions.

In addition to this, according to the present invention, it is possible to judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. Remind the user that even a judgment result with an extremely low degree of association is connected as a slight sign, and may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. is there. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with high probability, but the degree of association is usually low and it is passed through, but it is suitable to appear once in tens or hundreds of times. Sometimes the solution is overlooked. It is possible to decide which one should be emphasized based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided via a public communication network such as the Internet. In addition, reference work status information, reference attribute information, reference employee number information, reference employment environment information, reference work volume information, reference improvement measures are acquired, and the degree of fatigue for these, knowledge and information on improvement measures, When data is acquired, the degree of association is increased or decreased according to these.

In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is not based on information that can be obtained from the public communication network, but is also updated by the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts. It may be updated artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating the trained model and the above-mentioned update may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of reading and learning the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

1 Fatigue discrimination system 2 Discrimination device 21 Internal bus 23 Display unit 24 Control unit 25 Operation unit 26 Communication unit 27 Estimate unit 28 Storage unit 61 Node

The fatigue degree determination program according to the present invention is a fatigue degree determination program for determining the degree of fatigue of an employee, and acquires employment environment information regarding the working status and employment statistics of the employee and information on the number of employees in the organization to which the employee belongs. The work status entered in the above information input step using the information input step, the reference work status, the combination of the reference employment environment information and the reference employee number information, and the degree of association of three or more levels of fatigue. based on the employment environment information and employees information and three or more levels of association degree between the reference work state reference employment environment information in combination with the degree of fatigue with reference employees information according to, determine the fatigue It is characterized in that a computer is made to perform a fatigue degree determination step.

Claims (9)

In the fatigue level determination program that determines the fatigue level of employees
An information input step to obtain employment environment information about the employee's working status and external employment environment,
Using the combination of the reference work status and the reference employment environment information and the degree of linkage of three or more levels of fatigue, the reference work according to the work status and employment environment information entered in the above information input step. A fatigue level determination program characterized by having a computer execute a fatigue level determination step for determining the fatigue level based on a combination of a state and reference employment environment information and the above-mentioned fatigue level in three or more stages. In the above information input step, the attribute information of the employee is acquired and
In the fatigue degree determination step, the combination of the reference work state, the reference employment environment information, the reference attribute information, and the degree of association between the fatigue degree and the fatigue degree are used in the information input step. Fatigue based on the combination of reference work status, reference employment environment information and reference attribute information according to the input work status, employment environment information and attribute information, and the degree of association with the above fatigue level of three or more levels. The fatigue degree determination program according to claim 1, wherein the degree is determined. In the above information input step, the number of employees in the organization to which the employee belongs is acquired.
In the fatigue degree determination step, the information input step is performed by using the combination of the reference work state, the reference employment environment information, the reference employee number information, and the degree of association between the fatigue degree and the fatigue degree. The combination of the reference work status, the reference employment environment information, and the reference employee number information according to the work status, the employment environment information, and the number of employees input in the above, and the degree of association of the above fatigue level with three or more levels. The fatigue degree determination program according to claim 1, wherein the degree of fatigue is determined based on the method. In the fatigue degree determination step, as an alternative to the fatigue degree, the information input in the information input step is used by using three or more levels of association with the improvement measures to be taken for the employee based on the fatigue degree. The fatigue degree determination program according to any one of claims 1 to 3, wherein the improvement measure is determined based on the degree of association with the improvement measure in three or more stages. The fatigue degree determination according to any one of claims 1 to 4, wherein in the fatigue degree determination step, the association degree corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence is used. program. In the fatigue degree determination system that determines the fatigue degree of employees
Information input means for acquiring employee work status and employment environment information regarding the external employment environment,
Using the combination of the reference work status and the reference employment environment information and the degree of association of three or more levels of fatigue, the reference work according to the work status and employment environment information entered in the above information input means. A fatigue degree determination system characterized in that it is provided with a fatigue degree determination means for determining the fatigue degree based on a combination of a state and reference employment environment information and the degree of association of the above fatigue degree with three or more levels. The above information input means acquires information on the number of employees in the organization to which the employee belongs, and obtains information on the number of employees.
The fatigue degree determination means uses the combination of the reference work state, the reference employment environment information, the reference employee number information, and the degree of association of the fatigue degree with three or more levels, and the information input means. The combination of the reference work status, the reference employment environment information, and the reference employee number information according to the work status, the employment environment information, and the number of employees input in the above, and the degree of association of the above fatigue level with three or more levels. The fatigue degree determination system according to claim 6, wherein the degree of fatigue is determined based on the method. The fatigue degree determining means utilizes the degree of association with the improvement measures to be taken for the employee based on the fatigue degree as an alternative to the fatigue degree, and the information input in the information input means. The fatigue degree determination system according to claim 6 or 7, wherein the improvement measure is determined based on the degree of association with the improvement measure in three or more stages. The fatigue degree determination system according to any one of claims 6 to 8, wherein the fatigue degree determination uses the association degree corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence. ..

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