Work evaluation device and work evaluation method

Abstract

The present invention comprises a subject data acquisition unit (111) that acquires time-series subject data, a normative data acquisition unit (112) that acquires a plurality of sets of time-series normative data, a preprocessing unit (12) that generates subject feature data relating to features extracted from the subject data, and a plurality of sets of normative feature data relating to features extracted from the plurality of sets of normative data, respectively, and a similarity level calculation unit (13) that compares the subject feature data with each of the plurality of sets of normative feature data to calculate a similarity level between the subject feature data and each of the plurality of sets of normative feature data.

Description

[Technical field]

[0001] The present disclosure relates to an activity evaluation device and an activity evaluation method. [Background technology]

[0002] Conventionally, techniques for evaluating the quality of human work have been known. For example, Patent Document 1 discloses an evaluation device that judges the skill level of a new welding operator based on original data for judgment, including behavior data representing the behavior of the welding operator, environmental data representing the working environment when the behavior data was measured, work state data representing the state of the work when the behavior data was measured, and work state data representing the state of the work when the behavior data was measured, and a learning model. The skill level indicates an expert, intermediate, or beginner. The learning model is generated by having welding operators with various skill levels perform welding work in advance, and performing machine learning using learning data based on data collected during the welding work as input data and the skill level as output data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-1959 Summary of the Invention [Problem to be solved by the invention]

[0004] In conventional work evaluation techniques as described above, in order to evaluate the quality of a person's work, it was necessary to first classify the work performed by the person to be evaluated into a range of skill levels, such as whether the skill level for performing the work is that of an expert, an intermediate level, or a beginner's level.

[0005] The present disclosure has been made to solve the above-mentioned problems, and aims to provide a work evaluation device that is capable of evaluating the quality of a person's work without requiring that the range of skill required to perform the work be defined in advance in correspondence with the content of the work of the person to be evaluated. [Means for solving the problem]

[0006] The task evaluation device according to the present disclosure includes a target data acquisition unit that acquires time-series target data related to a task that is generated when a task subject is performing the task, a norm data acquisition unit that acquires a plurality of time-series norm data related to the task that is generated when a plurality of norm workers perform the task, a pre-processing unit that generates target feature data related to features extracted from the target data acquired by the target data acquisition unit and a plurality of norm feature data related to features extracted respectively from the plurality of norm data acquired by the norm data acquisition unit, and a similarity calculation unit that compares the target feature data with each of the plurality of norm feature data and calculates a similarity between the target feature data and each of the norm feature data. and a score calculation unit that calculates an activity score indicating how much the level of the activity performed by the activity evaluation subject has reached the level of the activity performed by the plurality of standard workers, based on similarity data in which the target feature data is associated with the plurality of similarities calculated by the similarity calculation unit, and the score calculation unit calculates the activity score from the largest value of the similarities for each of the standard feature data. It is something. Effect of the Invention

[0007] According to the present disclosure, a work evaluation device can evaluate the quality of a person's work without requiring that the range of skill required to perform the work be defined in advance in accordance with the content of the work of the person to be evaluated. [Brief description of the drawings]

[0008] [Figure 1] 1 is a diagram illustrating an example of the configuration of an activity evaluation device according to a first embodiment. [Diagram 2] FIG. 4 is a diagram showing an example of an evaluation result screen displayed by the task evaluation device according to the first embodiment. [Diagram 3] FIG. 11 is a diagram showing another example of the evaluation result screen displayed by the task evaluation device according to the first embodiment. [Figure 4]4 is a flowchart for explaining the operation of the task evaluation device according to the first embodiment. [Diagram 5] 5A and 5B are diagrams illustrating an example of a hardware configuration of the task evaluation device according to the first embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. Embodiment 1 The task evaluation device according to the first embodiment evaluates tasks performed by a worker whose task is to be evaluated (hereinafter referred to as the "task evaluation subject"). In detail, the task evaluation device according to the first embodiment evaluates the task performed by the task evaluation subject by determining to what extent the level of the task performed by the task evaluation subject (hereinafter referred to as the "task level") reaches the task levels of tasks performed by a plurality of model workers (hereinafter referred to as the "model workers"). Note that in the first embodiment, the model workers are assumed to be workers who are skilled in the task, so-called veterans. In the first embodiment, the task level is indicated by, for example, a task score. In the first embodiment, the task evaluation device evaluates the task performed by the task evaluation subject, which means calculating a task score indicating the task level of the task performed by the task evaluation subject. In the following first embodiment, as an example, the task score is expressed by a numerical value between "0" and "1". For example, the task score calculated by the task evaluation device becomes larger as the task level of the task performed by the task evaluation subject is estimated to reach the task levels of tasks performed by a plurality of model workers. In other words, the closer the task score calculated by the task evaluation device is to "1", the closer the task score calculated by the task evaluation device is to the task of the task evaluation subject to which the task score has been assigned is estimated to be closer to the task of model workers, in other words, it is estimated to be a skilled task.

[0010] In the first embodiment, as an example, it is assumed that the operation evaluated by the operation evaluation device is a brazing operation. The work evaluation device evaluates the brazing work performed by the work evaluation subject by determining to what extent the work level of the brazing work performed by the work evaluation subject on the object to be brazed (hereinafter referred to as the "work") reaches the work level of the brazing work performed by a standard worker who is skilled in the brazing work.

[0011] The work processes until the brazing of the workpiece is completed include a heating process for heating the workpiece and a brazing insert process for supplying the brazing material to the workpiece after the heating process. The worker who performs the brazing completes the brazing work on the workpiece by performing the work required in the heating process and the work required in the brazing insert process. In this way, the series of tasks performed by the worker until the brazing of the workpiece is completed includes a number of different types of tasks corresponding to each work process. In the first embodiment, the types of tasks corresponding to each work process are referred to as work types. In the first embodiment, the brazing work includes two work types: the task type "heating" (task required in the heating process) and the task type "brazing" (task required in the brazing process).

[0012] FIG. 1 is a diagram showing an example of a configuration of an activity evaluation device 1 according to the first embodiment. The activity evaluation device 1 is connected to a sensor 2, a norm data storage device 3, and an output device 4. The activity evaluation device 1, the sensor 2, the norm data storage device 3, and the output device 4 constitute an activity evaluation system.

[0013] Sensor 2 is placed around the work evaluation subject or the workpiece, such as in a brazing device (not shown), and acquires time-series data related to the work (here, brazing work) that is generated as the work evaluation subject performs the work (hereinafter referred to as "target data"). The target data includes, for example, data showing a change in the state of the workpiece due to the brazing work performed by the work evaluation subject. More specifically, the target data includes data showing a fire position on the workpiece, data showing a brazing insert position, data showing a surface temperature of the workpiece, etc. In the first embodiment, the sensor 2 includes a camera. The camera, for example, three-dimensionally measures the movement of a gas burner (not shown) or a flame and the movement of a brazing material when the person to be evaluated brazes a workpiece, and acquires the measured results as digital data. The sensor 2 outputs the acquired target data to the task evaluation device 1.

[0014] The reference data storage device 3 stores time-series data (hereinafter referred to as "reference data") related to a task (here, brazing task) that is generated when a reference worker performs the task. The content of the data included in the normative data is the same as the content of the data included in the target data. That is, the normative data is data showing changes in the state of the workpiece caused by the brazing work performed by the normative worker, and includes data showing the fire position on the workpiece, data showing the brazing insert position, data showing the surface temperature of the workpiece, etc. For example, a manager or the like who evaluates the work evaluation subjects may have a number of standard workers perform the same work (here, brazing work) as the work to be evaluated by the work evaluation subjects in advance, to collect a number of standard data, and store the collected standard data in the standard data storage device 3. In this embodiment, the normative data is stored in the normative data storage device 3, but this is merely an example. For example, the normative data may be stored in a database (not shown). For example, an administrator or the like collects a plurality of normative data in advance and creates a database in which the normative data is stored.

[0015] The output device 4 is, for example, a display device. The output device 4 may be, for example, a storage device such as a server. In the first embodiment, as an example, the output device 4 is a display device.

[0016] The operation evaluation device 1 is mounted on, for example, a brazing device. The work evaluation device 1 evaluates the brazing work performed by the work evaluation subject based on the target data acquired from the sensor 2 and the normative data stored in the normative data storage device 3. Then, the work evaluation device 1 outputs the evaluation result of the brazing work performed by the work evaluation subject to the output device 4. In detail, the work evaluation device 1 displays the evaluation result of the brazing work performed by the work evaluation subject on the display device.

[0017] The task evaluation device 1 includes a data acquisition unit 11, a pre-processing unit 12, a similarity calculation unit 13, a score calculation unit 14, a task type estimation unit 15, a representative normative data selection unit 16, and an output control unit 17. The data acquisition unit 11 includes a target data acquisition unit 111 and a normative data acquisition unit 112.

[0018] The data acquisition unit 11 acquires the target data and the normative data.

[0019] The target data acquisition unit 111 of the data acquisition unit 11 acquires time-series target data related to a task, which is generated when a task evaluation subject performs the task, from the sensor 2. In detail, the target data acquisition unit 111 acquires time-series target data related to the brazing task, which is generated when a task evaluation subject performs the brazing task, from the sensor 2. The data length of the target data may be any length. In addition, the target data acquired by the target data acquisition unit 111 from the sensor 2 may be multidimensional data including multiple different types of data, such as data indicating the fire location, data indicating the solder insertion location, and data indicating the surface temperature, or it may be one-dimensional data including only one type of data, such as only data indicating the surface temperature. For example, the target data acquiring unit 111 may acquire multi-dimensional target data from the sensor 2, and extract one type of data from the multi-dimensional target data to set it as one-dimensional target data. In the following first embodiment, as an example, it is assumed that the target data acquired by the target data acquisition unit 111 from the sensor 2 is one-dimensional data indicating a surface temperature. The target data acquisition unit 111 outputs the acquired target data to the pre-processing unit 12.

[0020] The normative data acquiring unit 112 of the data acquiring unit 11 acquires a plurality of time-series normative data related to the work, which is generated by a plurality of model workers performing the work, from the normative data storage device 3. In detail, the normative data acquiring unit 112 acquires a plurality of time-series normative data related to the brazing work, which is generated by a plurality of model workers performing the brazing work, from the normative data storage device 3. Note that the normative data acquired by the normative data acquiring unit 112 from the normative data storage device 3 may be multidimensional data including a plurality of different types of data, or one-dimensional data including only one type of data, like the target data. For example, the normative data acquiring unit 112 may acquire multidimensional target data from the normative data storage device 3, and extract one type of data from the multidimensional target data to set it as one-dimensional normative data. It is sufficient that the contents of the target data and the normative data are the same. In the following first embodiment, as an example, it is assumed that the normative data acquired by the normative data acquiring unit 112 from the normative data storage device 3 is one-dimensional data indicating a surface temperature. The normative data acquisition unit 112 outputs the acquired normative data to the pre-processing unit 12.

[0021] The pre-processing unit 12 performs pre-processing to generate data on features extracted from the target data acquired by the target data acquisition unit 111 (hereinafter referred to as "target feature data"), and multiple data on features extracted from multiple normative data acquired by the normative data acquisition unit 112 (hereinafter referred to as "normative feature data"). In detail, the pre-processing unit 12 first divides the target data of any length into a plurality of fixed-length data (hereinafter referred to as "divided target data"). The pre-processing unit 12 then extracts features from each of the plurality of divided target data, and generates target feature data for each of the extracted features. The target feature data is, for example, data in the form of a vector. The pre-processing unit 12 also classifies the multiple pieces of normative data of arbitrary length into multiple pieces of fixed length data (hereinafter referred to as "classified normative data"). The pre-processing unit 12 then extracts features from each of the multiple pieces of classified normative data, and generates normative feature data for each of the extracted features. The normative feature data is, for example, data in the form of a vector. When the plurality of reference data are data that have already been divided into fixed lengths, the pre-processing unit 12 may simply treat the plurality of reference data as is as the plurality of divided reference data. The pre-processing unit 12 outputs the generated plurality of target feature data and the plurality of norm feature data to the similarity calculation unit 13. The pre-processing unit 12 outputs the generated plurality of target feature data to the similarity calculation unit 13 in association with the target data from which the features were extracted. The pre-processing unit 12 also outputs the generated plurality of norm feature data to the similarity calculation unit 13 in association with the norm data from which the features were extracted.

[0022] Based on the multiple target feature data and the multiple normative feature data output from the pre-processing unit 12, the similarity calculation unit 13 compares the target feature data with each of the multiple normative feature data, and calculates the similarity of the target feature data with each of the normative feature data. The similarity calculation unit 13 performs the above comparison for each of the target feature data generated by the pre-processing unit 12, and calculates the similarity of the target feature data with each of the norm feature data.

[0023] The similarity calculation unit 13 may use a known method to compare the target feature data with each normative feature data and calculate the similarity of the target feature data with each normative feature data. For example, the similarity calculation unit 13 may input the target feature data and normative feature data output from the pre-processing unit 12 into a trained model (hereinafter referred to as a "machine learning model") that receives the target feature data and normative feature data as input and outputs the similarity, thereby obtaining the similarity, and may calculate the similarity. For example, the similarity calculation unit 13 may use an Attention method to calculate the attention_weight between the waveform of the target feature data and the waveform of the normative feature data as the similarity. For example, the similarity calculation unit 13 may calculate the similarity according to a rule that is generated in advance by an administrator or the like and stored in a location that the similarity calculation unit 13 can refer to.

[0024] The similarity calculation unit 13 outputs multiple pieces of data relating to similarities (hereinafter referred to as “similarity data”) calculated for each piece of target feature amount data to the score calculation unit 14, the task type estimation unit 15, and the representative normative data selection unit 16. The similarity data is data in which the target feature data, the target data, and a plurality of similarities are associated with each other for each target feature data. Note that the similarity calculation unit 13 may associate the target data output from the pre-processing unit 12 in association with the target feature data with the target feature data in the similarity data. The similarity data may be data in which, for each of the target feature data, the target data, a plurality of similarities, the norm feature data used to calculate the similarities, and the norm data are associated with each other. Note that the similarity calculation unit 13 may associate the norm data output from the pre-processing unit 12 in association with the norm feature data with the norm feature data in the similarity data.

[0025] Based on the similarity data output from the similarity calculation unit 13, the score calculation unit 14 calculates a work score indicating to what extent the work level of the work performed by the work evaluation subject has reached the work levels of the work performed by multiple standard workers.

[0026] The method of calculating the task score by the score calculation unit 14 will be described below with some specific examples. In the following concrete examples (<Concrete Example 1> and <Concrete Example 2>), it is assumed that the similarity calculation unit 13 calculates the similarity of a certain object feature data (first object feature data) with five normative feature data (first normative feature data, second normative feature data, third normative feature data, fourth normative feature data, and fifth normative feature data) such that the similarity with the first normative feature data is "0.8", the similarity with the second normative feature data is "0.8", the similarity with the third normative feature data is "0.9", the similarity with the fourth normative feature data is "0.7", and the similarity with the fifth normative feature data is "0.3". In other words, it is assumed that the similarity calculation unit 13 outputs to the score calculation unit 14 similarity data in which the first object feature data, the object data corresponding to the first object feature data, and "0.8", "0.8", "0.9", "0.7", and "0.3" are associated with each other. The score calculation unit 14 calculates an activity score corresponding to the first object feature data based on the similarity data as described above. In detail, the score calculation unit 14 calculates an activity score of an activity indicated by the first object feature data based on the similarity data as described above. The activity indicated by the first object feature data is an activity performed by the person to be evaluated from which the object data from which the first object feature data is extracted was obtained.

[0027] Example 1 For example, the score calculation unit 14 calculates the task score for the first object feature amount data from the largest value among the similarities associated with the first object feature amount data. In this case, the score calculation unit 14 first selects the largest value "0.9" from among the similarities associated with the first object feature amount data. Then, the score calculation unit 14 calculates the task score of the first object feature amount data from the selected "0.9". In detail, the score calculation unit 14 calculates the task score of the task indicated by the first object feature amount data from the selected "0.9". The score calculation unit 14 converts the selected similarity into an action score to calculate the action score. For example, the score calculation unit 14 multiplies the selected similarity by 100. Specifically, the score calculation unit 14 calculates 0.9×100=90 as the action score. Note that this is merely an example, and the score calculation unit 14 may, for example, use the selected similarity as the operation score as is. That is, the score calculation unit 14 may use "0.9" as the operation score. A conversion formula for converting the selected similarity into an activity score is preset and stored in a location that the score calculation unit 14 can refer to.

[0028] Example 2 For example, the score calculation unit 14 may calculate the operation score for the first object feature amount data from an average value of the similarities associated with the first object feature amount data. In this case, the score calculation unit 14 first calculates the average value of the similarities "0.8", "0.8", "0.9", "0.7", and "0.3" associated with the first target feature data. Here, the score calculation unit 14 calculates the average value "0.7". The score calculation unit 14 calculates the task score by converting the calculated average value of the similarities into a task score. For example, the score calculation unit 14 multiplies the calculated average value of the similarities by 100. Specifically, the score calculation unit 14 calculates 0.7×100=70 as the task score. Note that this is merely an example, and the score calculation unit 14 may set "0.7" as the activity score. A conversion formula for converting the calculated average similarity into an activity score is set in advance and stored in a location that the score calculation unit 14 can refer to.

[0029] The above Specific Examples 1 and 2 are merely examples. The score calculation unit 14 may calculate the task score by a method other than the above-mentioned <Specific Example 1> and <Specific Example 2>. For example, the score calculation unit 14 may calculate the task score by a method as shown in the following <Specific Example 3>.

[0030] Example 3 For example, the score calculation unit 14 may calculate the task score by weighting the similarity associated with a certain object feature amount data (second object feature amount data). The weighting here is assumed to be, for example, a padding. In detail, for example, if all similarities associated with the second target feature data are smaller than a preset target similarity (hereinafter referred to as "target similarity") but are close to the target similarity, the score calculation unit 14 determines the target similarity as the similarity corresponding to the second target feature data. If the difference between the similarity and the target similarity is within a preset threshold (hereinafter referred to as "difference determination threshold"), the score calculation unit 14 determines that the similarity is close to the target similarity. For example, suppose that the similarity calculation unit 13 calculates the similarities of the second object feature data with the first normative feature data, the second normative feature data, the third normative feature data, the fourth normative feature data, and the fifth normative feature data, and the similarity with the first normative feature data is "0.8", the similarity with the second normative feature data is "0.8", the similarity with the third normative feature data is "0.8", the similarity with the fourth normative feature data is "0.8", and the similarity with the fifth normative feature data is "0.8". In other words, suppose that the similarity calculation unit 13 outputs to the score calculation unit 14 similarity data in which the second object feature data, the object data corresponding to the second object feature data, and "0.8", "0.8", "0.8", "0.8", and "0.8" are associated with each other. Also, suppose that the target similarity is "0.9", and the difference determination threshold is "0.1". In this case, all similarities ("0.8") are smaller than "0.9", but the difference from "0.9" is within "0.1". Therefore, the score calculation unit 14 sets the similarity corresponding to the second target feature data to "0.9". In other words, the score calculation unit 14 weights the similarity associated with the second target feature data, and sets the similarity corresponding to the second target feature data to "0.9". Then, the score calculation unit 14 converts the weighted similarity into an action score to calculate the action score. For example, the score calculation unit 14 multiplies the weighted similarity by 100. Specifically, the score calculation unit 14 calculates 0.9×100=90 as the action score. Note that this is merely an example, and the score calculation unit 14 may set "0.9" as the activity score. A conversion formula for converting the weighted similarity into an operation score is set in advance and stored in a location that the score calculation unit 14 can refer to.

[0031] The rules for how the score calculation unit 14 calculates the task score based on the similarity data output from the similarity calculation unit 13 can be appropriately set by an administrator or the like.

[0032] The score calculation unit 14 outputs data relating to the calculated task score (hereinafter referred to as “task score data”) to the output control unit 17. The task score data is data in which the target feature data, the target data, and the task score are associated with each other for each piece of target feature data. The task score data may be data in which, for each target feature data, the target data, the task score, and normative feature data and normative data corresponding to the similarity used to calculate the task score are associated with each other. Note that, when the feature data is data in which the target feature data, a plurality of similarities, the target data, the normative feature data, and normative data are associated with each other, the score calculation unit 14 can identify, from the feature data, the normative feature data and normative data corresponding to the similarity used to calculate the task score.

[0033] The task type estimation unit 15 estimates the task type of the task performed by the task evaluation target person. The task type estimation unit 15 estimates, for each piece of target feature data, the task type of the task of the person to be evaluated that corresponds to the target feature data. In the first embodiment, the task type estimation unit 15 estimates whether the task of the person to be evaluated that corresponds to the target feature data is “heating” or “insertion of wax”. For example, the task type estimation unit 15 estimates the task type of the task performed by the task evaluation subject based on the similarity data output from the similarity calculation unit 13.

[0034] For example, data indicating the task type is added to the norm data stored in the norm data storage device 3 so that it is possible to know which task type the norm data was generated from by the norm worker. When the pre-processing unit 12 generates the norm feature data, it outputs the norm feature data and data indicating the task type in association with each other to the similarity calculation unit 13. The similarity calculation unit 13 generates similarity data in which, for each target feature data, the target feature data, multiple similarities, the norm feature data used to calculate the similarities, and data indicating the task type are associated with each other. For example, based on the similarity data output from the similarity calculation unit 13, the task type estimation unit 15 estimates the task type associated with the largest similarity among the multiple similarities associated with the target feature data as the task type of the task performed by the person being evaluated that corresponds to the target feature data.

[0035] Also, for example, the task type estimation unit 15 may input target feature amount data included in the similarity data into a machine learning model (hereinafter referred to as the "task type estimation model") that receives feature amount data as input and outputs information indicating the task type, thereby obtaining information indicating the task type, thereby estimating the task type of the task performed by the task evaluation subject. The task type estimation model is generated in advance and stored in a location that the task type estimation unit 15 can refer to. For example, the task type estimation unit 15 may directly acquire target feature amount data to be input to the task type estimation model from the pre-processing unit 12. In this case, the pre-processing unit 12 associates each of the generated target feature amount data with the target data from which the features were extracted, and outputs the data to the task type estimation unit 15. Note that the arrow from the pre-processing unit 12 to the task type estimation unit 15 is omitted in FIG. 1.

[0036] Furthermore, for example, when estimating the task type of the task performed by the task evaluation subject, the task type estimation unit 15 may take into consideration the task score corresponding to the target feature amount data calculated by the score calculation unit 14. In this case, the task type estimation unit 15 may, for example, acquire task score data from the score calculation unit 14 and identify the task score corresponding to the target feature amount data from the acquired task score data. Note that the arrow from the score calculation unit 14 to the task type estimation unit 15 is omitted in FIG. 1. For example, if the task score corresponding to the target feature amount data does not satisfy a certain value, the task type estimation unit 15 determines that the task is “unclassifiable.”

[0037] The task type estimation unit 15 outputs data related to the estimated task type (hereinafter referred to as “task type post-estimation data”) to the output control unit 17. The post-task type estimation data is data in which the target feature amount data is associated with data indicating the task type. The task type estimation data may be data in which the target feature data, the target data corresponding to the target feature data, and data indicating the task type are associated with each other. Note that the task type estimation unit 15 can determine the target data corresponding to the target feature data from the similarity data.

[0038] Based on the similarity data output by the similarity calculation unit 13, the representative normative data selection unit 16 selects, from among the multiple normative data, normative data that is deemed to be similar to the target data (hereinafter referred to as "representative normative data"). The representative normative data selecting unit 16 may select one or more representative normative data.

[0039] For example, the representative normative data selection unit 16 selects the largest similarity among the similarities associated with the target feature amount data in the similarity data, and sets the normative data associated with that similarity as the representative normative data. In this case, the similarity data is data in which, for each target feature data, the target data, multiple similarities, the normative feature data used to calculate the similarities, and the normative data are associated with each other. For example, in the case where there are multiple similarities in the similarity data that correspond to the target feature data and each of the multiple similarities has the largest similarity, the representative normative data selection unit 16 may select any one of them and set the normative data that corresponds to the selected similarity as the representative normative data, or may set all of the multiple normative data that correspond to the multiple similarities as the representative normative data.

[0040] Also, for example, the representative normative data selection unit 16 may select multiple (e.g., three) similarities in descending order from the largest similarities associated with the target feature data in the similarity data, and set the multiple (e.g., three) normative data associated with the similarities as representative normative data.

[0041] Furthermore, for example, when selecting representative normative data, the representative normative data selection unit 16 may take into consideration the task score corresponding to the target feature amount data calculated by the score calculation unit 14. In this case, the representative normative data selection unit 16 may, for example, acquire task score data from the score calculation unit 14 and identify the task score corresponding to the target feature amount data from the acquired task score data. Note that the arrow from the score calculation unit 14 to the representative normative data selection unit 16 is omitted in FIG. 1. For example, if the task score corresponding to the target feature amount data does not satisfy a certain value, the representative normative data selection unit 16 determines that there is "no representative normative data."

[0042] The representative normative data selection unit 16 outputs data relating to the selected representative normative data (hereinafter referred to as “selected normative data”) to the output control unit 17. The selected normative data is data in which the target feature amount data is associated with representative normative data selected by the representative normative data selection unit 16. The selection normative data may be data in which the target feature data, the target data corresponding to the target feature data, and the selection normative data are associated with each other. Note that the representative normative data selection unit 16 can determine the target data corresponding to the target feature data from the similarity data.

[0043] The output control unit 17 outputs data indicating the task score to the output device 4 based on the task score data output from the score calculation unit 14. In detail, the output control unit 17 causes the display device to display data indicating the task score.

[0044] At this time, the output control unit 17 may output the data indicating the task score to the output device 4 in association with the target data. In other words, the output control unit 17 may cause the display device to display the data indicating the task score in association with the target data.

[0045] Furthermore, the output control unit 17 may cause the display device to display data indicating the task score in association with data indicating the task type. In addition, the output control unit 17 can match the task score and task type, for example, by matching the task score data output from the score calculation unit 14 with the task type estimation data output from the task type estimation unit 15 using the target feature data.

[0046] Furthermore, the output control unit 17 may cause the display device to display data indicating the task score in association with the representative normative data. In addition, the output control unit 17 can match the task score and the representative normative data, for example, by matching the task score data output from the score calculation unit 14 with the selected normative data output from the representative normative data selection unit 16 using the target feature data.

[0047] The output control unit 17 may cause the display device to display the data indicating the task score in association with the target data, the data indicating the task type, and the representative norm data.

[0048] Here, Figure 2 is a diagram for explaining an example of a screen (hereinafter referred to as the "evaluation result display screen") that the output control unit 17 displays on the display device based on the task score data output from the score calculation unit 14, the task type estimation data output from the task type estimation unit 15, and the selected normative data output from the representative normative data selection unit 16 in embodiment 1. For example, as shown in FIG. 2, the output control unit 17 displays an evaluation result display screen that displays the task score in association with the task type, the target data, and the representative normative data. The output control unit 17 can display a plurality of target data in chronological order on the evaluation result display screen. In the evaluation result display screen shown in FIG. 2, the vertical axis indicates the surface temperature, and the horizontal axis indicates the time. 2, the time-series target data is indicated by "T," and the time-series representative normative data is indicated by "N." Note that, here, it is assumed that there is one representative normative data. The evaluation result display screen shown in Fig. 2 shows target data and representative normative data for the task type "heating" up until time t, and indicates that the task score for the "heating" task is "0.8". Furthermore, the evaluation result display screen shown in Fig. 2 shows target data and representative normative data for the task type "wax insertion" after time t, and indicates that the task score for the "wax insertion" task is "0.2".

[0049] In other words, the evaluation result display screen shown in Figure 2 indicates that the work level of the heating work in the brazing work performed by the work evaluation subject is "0.8," which means that the heating work performed by the work evaluation subject is close to the heating work performed by a representative standard worker, and that the work evaluation subject can be said to be skilled in heating work. In addition, the evaluation result display screen shown in Figure 2 indicates that the work level of the brazing work performed by the work evaluation subject is "0.2," which indicates that the level of the brazing work performed by the work evaluation subject is still far from reaching the level of brazing work performed by a representative standard worker. By checking the evaluation result display screen, a manager or the like can see that the person being evaluated has reached a skilled level in heating work, but has not yet reached a skilled level in soldering work. In addition, by checking the deviation between the target data and the representative norm data on the evaluation result display screen, the manager or the like can grasp, for example, which part of the brazing work of the work evaluation subject is far removed from that of the normative worker, that is, which part of the work should be focused on in training so that the work evaluation subject can be said to have reached a level of proficiency in brazing work. The manager or the like can improve the skills of the work evaluation subject by, for example, providing guidance to the work evaluation subject based on the grasped content. In this way, by displaying data indicating the work score, the output control unit 17 can provide data that will help the work evaluation subject to understand the current work level and improve the skills of the work evaluation subject.

[0050] FIG. 3 is a diagram illustrating another example of the evaluation result display screen displayed on the display device by the output control unit 17 based on the task score data output from the score calculation unit 14, the task type estimation data output from the task type estimation unit 15, and the selected normative data output from the representative normative data selection unit 16 in embodiment 1. The evaluation result display screen shown in FIG. 3 differs from the evaluation result display screen shown in FIG. 2 in that a plurality of representative normative data are included. Here, it is assumed that there are three pieces of representative normative data, and on the evaluation result display screen shown in FIG. 3, the time-series representative normative data are indicated by "N1", "N2", and "N3". For example, as shown in Fig. 3, the output control unit 17 can display the ranges of the representative normative data on the evaluation result display screen so that the ranges can be seen. In Fig. 3, the output control unit 17 indicates the ranges of the representative normative data by diagonal lines on the evaluation result display screen.

[0051] Even so-called veteran model workers have various work habits. By selecting multiple representative model workers and displaying the target data together with multiple model data on the evaluation result display screen, for example, a manager or the like can understand which tasks of the work evaluation subjects particularly require training. Tasks corresponding to target data that deviate from any of the multiple model data can be said to be tasks that require special training to be closer to the work performed by model workers.

[0052] It should be noted that the evaluation result display screens shown in FIG. 2 and FIG. 3 are merely examples. The output control unit 17 is required to at least display the task score. For example, the output control unit 17 may display an evaluation result display screen that displays only the task score, or may display an evaluation result display screen that displays the task score and the task type. The output control unit 17 may also display an evaluation result display screen that displays the task score and time-series target data, or may display an evaluation result display screen that displays the task score and the representative norm data. The output control unit 17 can display an evaluation result display screen in which the task score and the task type, the target data, or the representative normative data are appropriately combined.

[0053] The operation of the task evaluation device 1 according to the first embodiment will be described. FIG. 4 is a flowchart for explaining the operation of the task evaluation device 1 according to the first embodiment. For example, when the work evaluation device 1 is powered on or receives a start instruction from an administrator or the like, the work evaluation device 1 executes the operation as shown in the flowchart of Fig. 4. For example, the administrator or the like operates a touch panel display equipped in the brazing device to input the start instruction. A control unit (not shown) of the work evaluation device 1 receives the start instruction input by the administrator or the like and causes the data acquisition unit 11, the pre-processing unit 12, the similarity calculation unit 13, the score calculation unit 14, the work type estimation unit 15, the representative normative data selection unit 16, and the output control unit 17 to start their operations.

[0054] The target data acquisition unit 111 of the data acquisition unit 11 acquires, from the sensor 2, time-series target data related to the work, which is generated when the work evaluation subject performs the work (step ST1). In detail, the target data acquisition unit 111 acquires, from the sensor 2, time-series target data related to the brazing work, which is generated when the work evaluation subject performs the brazing work. The target data acquisition unit 111 outputs the acquired target data to the pre-processing unit 12.

[0055] The normative data acquiring unit 112 of the data acquiring unit 11 acquires, from the normative data storage device 3, a plurality of time-series normative data related to the work, which is generated by a plurality of model workers performing the work (step ST2). In detail, the normative data acquiring unit 112 acquires, from the normative data storage device 3, a plurality of time-series normative data related to the brazing work, which is generated by a plurality of model workers performing the brazing work. The normative data acquisition unit 112 outputs the acquired normative data to the pre-processing unit 12.

[0056] The pre-processing unit 12 performs pre-processing to generate target feature data relating to features extracted from the target data acquired by the target data acquisition unit 111 in step ST1, and multiple normative feature data relating to features extracted from each of the multiple normative data acquired by the normative data acquisition unit 112 in step ST2 (step ST3). The pre-processing unit 12 outputs the generated plurality of target feature data and the plurality of norm feature data to the similarity calculation unit 13. The pre-processing unit 12 outputs the generated plurality of target feature data to the similarity calculation unit 13 in association with the target data from which the features were extracted. The pre-processing unit 12 also outputs the generated plurality of norm feature data to the similarity calculation unit 13 in association with the norm data from which the features were extracted.

[0057] Based on the multiple target feature data and the multiple normative feature data output from the pre-processing unit 12 in step ST3, the similarity calculation unit 13 compares the target feature data with each of the multiple normative feature data, and calculates the similarity of the target feature data with each of the normative feature data (step ST4). The similarity calculation unit 13 performs the above comparison for each of the target feature data generated by the pre-processing unit 12, and calculates the similarity of the target feature data with each of the norm feature data. The similarity calculation unit 13 outputs similarity data relating to the multiple similarities calculated for each piece of target feature amount data to the score calculation unit 14, the task type estimation unit 15, and the representative normative data selection unit 16.

[0058] Based on the similarity data output from the similarity calculation unit 13 in step ST4, the score calculation unit 14 calculates a work score indicating how well the work level of the work performed by the work evaluation subject has reached the work levels of the work performed by multiple standard workers (step ST5). The score calculation unit 14 outputs task score data relating to the calculated task score to the output control unit 17.

[0059] The work type estimation unit 15 estimates the work type of the work performed by the person being evaluated based on the similarity data output from the similarity calculation unit 13 in step ST4 or the target feature data output from the pre-processing unit 12 in step ST3 (step ST6). The task type estimation unit 15 estimates the task type for each piece of target feature amount data. The task type estimation unit 15 outputs post-task type estimation data relating to the estimated task type to the output control unit 17.

[0060] The representative normative data selection unit 16 selects representative normative data from among the plurality of normative data based on the similarity data output by the similarity calculation unit 13 in step ST4 (step ST7). The representative normative data selection unit 16 outputs selected normative data relating to the selected representative normative data to the output control unit 17.

[0061] The output control unit 17 outputs data indicating the task score to the output device 4 based on the task score data output from the score calculation unit 14 in step ST5 (step ST8). In detail, the output control unit 17 causes the display device to display data indicating the task score.

[0062] 4, the process is performed in the order of step ST1 and step ST2, but this is merely an example. The process of step ST1 and the process of step ST2 may be performed in the reverse order, or the process of step ST1 and the process of step ST2 may be performed in parallel. 4, the processing is performed in the order of step ST5, step ST6, and step ST7, but this is merely an example. The order of the processing of step ST5, step ST6, and step ST7 may be reversed, or the processing of step ST5, step ST6, and step ST7 may be performed in parallel. However, when the task type estimation unit 15 estimates the task type of the task performed by the task evaluation subject based on the similarity data and the task score in step ST6, the task evaluation device 1 performs the processing of step ST5 before performing the processing of step ST6.

[0063] In this way, the task evaluation device 1 acquires time-series target data and acquires multiple time-series normative data. After performing pre-processing to generate target feature data and multiple normative feature data, the task evaluation device 1 compares the target feature data with each of the multiple normative feature data and calculates the similarity of the target feature data with each normative feature data. Then, the task evaluation device 1 calculates an task score based on similarity data in which the target feature data is associated with multiple similarities. As a result, the work evaluation device 1 can evaluate the quality of the work of a person (work evaluation subject) without requiring that the range of skill required to perform the work be defined in advance in correspondence with the content of the work of the person (work evaluation subject) to be evaluated.

[0064] Even so-called veteran model workers have various work habits. When evaluating the work of a work evaluation subject (here, brazing work), it is very difficult to determine how to reflect and evaluate the work characteristics of the model worker's work. In other words, even if one attempts to evaluate the work of a person being evaluated by comparing target data generated based on the work of the person being evaluated with normative data generated based on the work of a norm worker, it is difficult to determine, by comparing the target data with a single piece of normative data, how closely the work level of the work performed by the person being evaluated approaches the level of work performed by a norm worker, because the features of the normative data are not uniquely determined. In contrast, the task evaluation device 1 according to the first embodiment acquires multiple pieces of normative data and compares target data with the multiple pieces of normative data, more specifically, compares target feature data based on the target data with multiple pieces of normative feature data based on the multiple pieces of normative data, thereby calculating the similarity between the target feature data and each piece of normative feature data.The task evaluation device 1 then calculates the task score based on similarity data in which the target feature data is associated with the multiple similarities. This allows the task evaluation device 1 to determine the task level of the task performed by the task evaluation subject, reflecting various task characteristics of the model worker. In other words, the task evaluation device 1 can evaluate the task performed by the task evaluation subject, taking into account the task characteristics of the model worker.

[0065] The task evaluation device 1 also classifies the target data into a plurality of classified target data, and generates target feature data for each of a plurality of feature amounts extracted from each of the plurality of classified target data. Then, the task evaluation device 1 calculates the similarity of the target feature data with each of the normative feature data for each of the plurality of generated target feature data. This allows the task evaluation device 1 to prevent a decrease in learning efficiency due to an increase in the length of the target data. Also, the task evaluation device 1 prevents the administrator or the like from having to prepare a huge amount of normative data in preparation for a decrease in the frequency of occurrence of normative data determined to be similar due to an increase in the length of the target data.

[0066] Furthermore, the task evaluation device 1 estimates the task type of the task (here, brazing) performed by the task evaluation subject based on the target feature data or similarity data. The task evaluation device 1 can provide the task score together with the task type, thereby making it possible to provide the evaluation of the task performed by the task evaluation subject, more specifically, the task score, in a form that is easier to understand for evaluating the task performed by the task evaluation subject. For example, when a task performed by the task evaluation subject in an incorrect procedure is evaluated, the task evaluation device 1 can inform the subject that the task itself was not performed well by providing information on the task type. As a specific example, for example, in the case of brazing, the correct task procedure is heating → brazing, but if the task evaluation subject performs the task in a procedure such as heating → brazing → heating, the task can be said to be insufficiently mastered.

[0067] Furthermore, the task evaluation device 1 may estimate the task type of the task performed by the task evaluation subject, based on the object feature amount data or similarity data, and the task score. For example, when the task evaluation device 1 estimates a task type using a task type estimation model, by taking the task score into consideration, it becomes possible to externalize the estimation of the task type to a certain extent, and this is expected to reduce the burden of estimating the task type using the task type estimation model; in other words, it is expected to improve the accuracy of the task type estimation, improve the efficiency of the task type estimation, or reduce the learning data for generating the task type estimation model.

[0068] Furthermore, the task evaluation device 1 selects representative normative data that is similar to the target data from among the multiple normative data based on the similarity data. By making it possible to provide the task score together with the representative normative data, the task evaluation device 1 can provide, for example, tasks that the task evaluation subject should train and that have not reached the task level of the normative worker in an identifiable form.

[0069] Furthermore, the task evaluation device 1 may select representative normative data based on the similarity data and the task score. The task evaluation device 1 selects representative normative data in consideration of the task score, thereby improving the accuracy of the selection of representative normative data.

[0070] In the above first embodiment, the activity level is indicated by the activity score, but this is merely an example. For example, the activity level may be indicated by the degree of similarity. Even in this way, the work evaluation device 1 can evaluate the quality of the work of the person being evaluated without requiring that the range of skill required to perform the work be defined in advance in accordance with the content of the work performed by the person being evaluated. It should be noted that the closer the activity level of the activity performed by the activity evaluation subject is estimated to be to the activity levels of a plurality of standard workers, the larger the similarity value calculated by the activity evaluation device will be. In this case, the task evaluation device 1 may be configured not to include the score calculation unit 14. The output control unit 17 outputs data indicating the similarity to the output device 4, based on the similarity data output from the similarity calculation unit 13, for example. In this case, the process of step ST5 in the operation of the task evaluation device 1 described using the flowchart in FIG. 4 can be omitted. Regarding the operation of the work evaluation device 1 described with reference to FIG. 4, the work evaluation device 1 can omit the process of step ST5.

[0071] In the above-described first embodiment, as shown in Fig. 1, the task evaluation device 1 includes the task type estimation unit 15 and the representative normative data selection unit 16, but this is merely an example. The task evaluation device 1 may be configured not to include the task type estimation unit 15 and the representative normative data selection unit 16. In this case, in the operation of the task evaluation device 1 described using the flowchart in Fig. 4, the processing of steps ST6 and ST7 can be omitted.

[0072] Furthermore, in the above-described first embodiment, the task evaluation device 1 may be configured not to include the output control unit 17. For example, the function of the output control unit 17 may be borne by a device external to the task evaluation device 1 and connected to the task evaluation device 1 and the output device 4. For example, the task evaluation device 1 may hold the task score data in an internal memory, and a device external to the task evaluation device 1 may read the task score data from the internal memory and output it to the output device 4. In this case, the task evaluation device 1 does not necessarily need to be connected to the output device 4.

[0073] In the above-described first embodiment, in the task evaluation device 1, the pre-processing unit 12 divides the target data into time series, but this is merely an example. The pre-processing unit 12 may not have the function of dividing the target data into time series. The pre-processing unit 12 may extract features from the target data acquired by the target data acquiring unit 111, and generate target feature data. In this case, the pre-processing unit 12 does not need to classify the multiple normative data acquired by the normative data acquiring unit 112. The pre-processing unit 12 extracts features from each normative data acquired by the normative data acquiring unit 112, and generates normative feature data.

[0074] In addition, in the above-mentioned first embodiment, the output device 4 is a display device, but this is merely one example. For example, the output device 4 may be a storage device, and the output control unit 17 of the task evaluation device 1 may output the task score data, post-task type estimation data, and selection norm data to the storage device. For example, the output control unit 17 may output data indicating the task score to the storage device based on the task score data, or may output the data indicating the task score to the storage device in association with the target data. Furthermore, the output control unit 17 may output data indicating the task score to the storage device in association with data indicating the task type based on the task score data and the task type estimated data. Furthermore, the output control unit 17 may output data indicating the task score to the storage device in association with the representative norm data based on the task score data and the selection norm data. Furthermore, the output control unit 17 may output data indicating the task score to the storage device in association with the target data, the data indicating the task type, and the representative norm data based on the task score data, the task type estimated data, and the selection norm data.

[0075] In the above-described first embodiment, it is assumed that the work evaluation device 1 is mounted on a brazing device, but this is merely one example. For example, the work evaluation device 1 may be mounted on a server. Also, for example, some of the components constituting the work evaluation device 1, that is, the data acquisition unit 11, the pre-processing unit 12, the similarity calculation unit 13, the score calculation unit 14, the work type estimation unit 15, the representative normative data selection unit 16, and the output control unit 17, may be provided on the work device and the rest may be provided on the server, so that the work device and the server constitute a system.

[0076] In the above-described first embodiment, the work evaluated by the work evaluation device 1 is assumed to be brazing work, but this is merely an example. The work evaluated by the work evaluation device 1 may be work that can be performed in a factory other than brazing work. For example, the work evaluated by the work evaluation device 1 may be soldering work, cutting work, screwing work on wood, screwing work on wood, or metal welding work. The work evaluated by the work evaluation device 1 may be operating a vehicle such as an automobile, an airplane, a train, or a ship. The work evaluation device 1 according to the first embodiment can be applied to a work evaluation device that evaluates various works (so-called instinct and skill work) that require measuring the level of skill.

[0077] 5A and 5B are diagrams illustrating an example of a hardware configuration of the task evaluation device 1 according to the first embodiment. In the first embodiment, the functions of the data acquisition unit 11, the pre-processing unit 12, the similarity calculation unit 13, the score calculation unit 14, the task type estimation unit 15, the representative normative data selection unit 16, the output control unit 17, and a control unit (not shown) are realized by the processing circuit 1001. That is, the task evaluation device 1 includes the processing circuit 1001 for performing control for evaluating tasks performed by a task evaluation subject. The processing circuit 1001 may be dedicated hardware as shown in FIG. 5A, or may be a processor 1004 executing a program stored in a memory as shown in FIG. 5B.

[0078] When the processing circuit 1001 is dedicated hardware, the processing circuit 1001 may be, for example, a single circuit, a composite circuit, a programmed processor, a parallel programmed processor, an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a combination thereof.

[0079] 0 When the processing circuit is the processor 1004, the functions of the data acquisition unit 11, the pre-processing unit 12, the similarity calculation unit 13, the score calculation unit 14, the task type estimation unit 15, the representative norm data selection unit 16, the output control unit 17, and a control unit (not shown) are realized by software, firmware, or a combination of software and firmware. The software or firmware is described as a program and stored in the memory 1005. The processor 1004 reads out and executes the program stored in the memory 1005, thereby executing the functions of the data acquisition unit 11, the pre-processing unit 12, the similarity calculation unit 13, the score calculation unit 14, the task type estimation unit 15, the representative norm data selection unit 16, the output control unit 17, and a control unit (not shown). That is, the task evaluation device 1 includes a memory 1005 for storing a program that, when executed by the processor 1004, results in the execution of steps ST1 to ST8 in FIG. 4 described above. The program stored in the memory 1005 can be said to cause the computer to execute the procedures or methods of the data acquisition unit 11, the pre-processing unit 12, the similarity calculation unit 13, the score calculation unit 14, the task type estimation unit 15, the representative normative data selection unit 16, the output control unit 17, and a control unit (not shown). Here, the memory 1005 corresponds to, for example, a non-volatile or volatile semiconductor memory such as a RAM, a ROM (Read Only Memory), a flash memory, an EPROM (Erasable Programmable Read Only Memory), or an EEPROM (Electrically Erasable Programmable Read-Only Memory), a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, a DVD (Digital Versatile Disc), and the like.

[0080] The functions of the data acquisition unit 11, pre-processing unit 12, similarity calculation unit 13, score calculation unit 14, task type estimation unit 15, representative norm data selection unit 16, output control unit 17, and a control unit (not shown) may be partially realized by dedicated hardware and partially realized by software or firmware. For example, the function of the data acquisition unit 11 may be realized by a processing circuit 1001 as dedicated hardware, and the functions of the pre-processing unit 12, similarity calculation unit 13, score calculation unit 14, task type estimation unit 15, representative norm data selection unit 16, output control unit 17, and a control unit (not shown) may be realized by a processor 1004 reading and executing a program stored in a memory 1005. The task evaluation device 1 also includes an input interface device 1002 and an output interface device 1003 that perform wired or wireless communication with devices such as a sensor 2, a normative data storage device 3, and an output device 4.

[0081] As described above, according to the first embodiment, the task evaluation device 1 is configured to include a target data acquisition unit 111 that acquires time-series target data related to the task, which is generated when a person to be evaluated performs the task, a norm data acquisition unit 112 that acquires multiple time-series norm data related to the task, which is generated when multiple norm workers perform the task, a pre-processing unit 12 that generates target feature data regarding features extracted from the target data acquired by the target data acquisition unit 111 and multiple norm feature data regarding features extracted respectively from the multiple norm data acquired by the norm data acquisition unit 112, and a similarity calculation unit 13 that compares the target feature data with each of the multiple norm feature data and calculates the similarity of the target feature data with each norm feature data. Therefore, the work evaluation device 1 can evaluate the quality of the work of a person (work evaluation subject) without requiring that the range of skill required to perform the work be defined in advance in correspondence with the content of the work of the person (work evaluation subject) to be evaluated.

[0082] Furthermore, according to embodiment 1, in the work evaluation device 1, the pre-processing unit 12 classifies the target data into a plurality of classified target data and generates target feature data for each of a plurality of features extracted from each of the plurality of classified target data, and the similarity calculation unit 13 calculates the similarity of the target feature data with each of the norm feature data for each of the plurality of target feature data generated by the pre-processing unit 12. This allows the task evaluation device 1 to prevent a decrease in learning efficiency due to an increase in the length of the target data. Also, the task evaluation device 1 prevents the administrator or the like from having to prepare a huge amount of normative data in preparation for a decrease in the frequency of occurrence of normative data determined to be similar due to an increase in the length of the target data.

[0083] Furthermore, according to embodiment 1, the work evaluation device 1 can be configured to include a score calculation unit 14 that calculates a work score indicating to what extent the work level (work level) of the work performed by the work evaluation subject has reached the work levels (work levels) of multiple standard workers, based on similarity data in which the target feature data is associated with multiple similarities calculated by the similarity calculation unit 13. Therefore, the work evaluation device 1 can evaluate the quality of the work of a person (work evaluation subject) without requiring that the range of skill required to perform the work be defined in advance in correspondence with the content of the work of the person (work evaluation subject) to be evaluated.

[0084] Furthermore, according to the first embodiment, the task evaluation device 1 can be configured to include a task type estimation unit 15 that estimates the task type of the task performed by the task evaluation subject, based on the object feature amount data or the similarity data. Therefore, the task evaluation device 1 can provide the task score together with the task type, and can provide the evaluation of the task performed by the task evaluation subject, more specifically, the task score, in a form that is easier to understand for evaluating the task performed by the task evaluation subject. For example, if the task performed by the task evaluation subject using an incorrect procedure is evaluated, the task evaluation device 1 can inform the subject that the task itself was not performed well by providing information on the task type. As a specific example, for example, in brazing, the correct task procedure is heating → brazing insertion, but if the task evaluation subject performs the task using a task procedure such as heating → brazing insertion → heating, the task can be said to be insufficiently mastered.

[0085] Furthermore, according to embodiment 1, the task evaluation device 1 includes a task type estimation unit 15 that estimates the task type of the task performed by the task evaluation subject based on the target feature data or similarity data in which the target feature data is associated with multiple similarities calculated by the similarity calculation unit 13, and the task type estimation unit 15 can be configured to estimate the task type of the task performed by the task evaluation subject based on the target feature data or the similarity data, and the task score calculated by the score calculation unit 14. Therefore, the task evaluation device 1 can improve the accuracy of task type estimation, improve the efficiency of task type estimation, or reduce the amount of learning data required for generating a task type estimation model.

[0086] Furthermore, according to the first embodiment, the task evaluation device 1 can be configured to include a representative normative data selection unit 16 that selects representative normative data that is similar to the target data from among the multiple normative data, based on similarity data in which the target feature data is associated with the multiple similarities calculated by the similarity calculation unit 13. Therefore, the task evaluation device 1 can provide the task score together with the representative norm data, and can provide, for example, the task for which the task evaluation subject has not reached the task level of the norm worker and for which he or she should be trained, in a form that enables the task to be identified.

[0087] Furthermore, according to embodiment 1, the task evaluation device 1 includes a representative normative data selection unit 16 that selects, from the multiple normative data, representative normative data that is deemed to be similar to the target data, based on similarity data in which the target feature data is associated with the multiple similarities calculated by the similarity calculation unit 13, and the representative normative data selection unit 16 can be configured to select, from the multiple normative data, representative normative data that is deemed to be similar to the target data, based on the similarity data and the task score calculated by the score calculation unit 14. Therefore, the task evaluation device 1 can improve the accuracy of selecting representative normative data by selecting representative normative data in consideration of the task score.

[0088] Furthermore, according to the first embodiment, the task evaluation device 1 can be configured to include an output control unit 17 that outputs data indicating the task score calculated by the score calculation unit 14 to the output device 4. Therefore, the task evaluation device 1 can provide data that helps to grasp the current task level of the task evaluation subject and to help the task evaluation subject improve their skills.

[0089] Furthermore, according to embodiment 1, the task evaluation device 1 can be configured to include an output control unit 17 that associates data indicating the task score calculated by the score calculation unit 14 with data indicating the task type estimated by the task type estimation unit 15 and outputs the data to the output device 4. Therefore, the task evaluation device 1 can provide data that helps to grasp the current task level of the task evaluation subject and to help the task evaluation subject improve their skills.

[0090] Furthermore, according to embodiment 1, the task evaluation device 1 can be configured to include an output control unit 17 that outputs data indicating the task score calculated by the score calculation unit 14 to the output device 4 in association with the representative normative data selected by the representative normative data selection unit 16. Therefore, the task evaluation device 1 can provide data that helps to grasp the current task level of the task evaluation subject and to help the task evaluation subject improve their skills.

[0091] Furthermore, according to the first embodiment, in the task evaluation device 1, the output control unit 17 can be configured to output data indicating the task score to the output device 4 in association with the target data. Therefore, the task evaluation device 1 can provide data that helps to grasp the current task level of the task evaluation subject and to help the task evaluation subject improve their skills.

[0092] Furthermore, according to the first embodiment, in the task evaluation device 1, the output control unit 17 can be configured to cause the display device, which is the output device 4, to display data indicating the task score. Therefore, the task evaluation device 1 can provide data that helps to grasp the current task level of the task evaluation subject and to help the task evaluation subject improve their skills.

[0093] Furthermore, according to embodiment 1, in the task evaluation device 1, the output control unit 17 can be configured to associate data indicating the task score with data indicating the task type estimated by the task type estimation unit 15 and display it on the display device, which is the output device 4. Therefore, the task evaluation device 1 can provide data that helps to grasp the current task level of the task evaluation subject and to help the task evaluation subject improve their skills.

[0094] Furthermore, according to embodiment 1, in the task evaluation device 1, the output control unit 17 can be configured to associate data indicating the task score with the representative normative data selected by the representative normative data selection unit 16 and display the data on the display device, which is the output device 4. Therefore, the task evaluation device 1 can provide data that helps to grasp the current task level of the task evaluation subject and to help the task evaluation subject improve their skills.

[0095] Furthermore, according to the first embodiment, in the task evaluation device 1, the output control unit 17 can be configured to cause the data indicating the task score to be displayed on the display device, which is the output device 4, in association with the target data. Therefore, the task evaluation device 1 can provide data that helps to grasp the current task level of the task evaluation subject and to help the task evaluation subject improve their skills.

[0096] Any of the components of the embodiments may be modified or omitted. [Industrial Applicability]

[0097] The work evaluation device according to the present disclosure can evaluate the quality of a person's work without requiring that the range of skill required to perform the work be defined in advance in accordance with the content of the work performed by the person to be evaluated. [Explanation of symbols]

[0098] 1 work evaluation device, 2 sensor, 3 normative data storage device, 4 output device, 11 data acquisition unit, 111 target data acquisition unit, 112 normative data acquisition unit, 12 pre-processing unit, 13 similarity calculation unit, 14 score calculation unit, 15 work type estimation unit, 16 representative normative data selection unit, 17 output control unit, 1001 processing circuit, 1002 input interface device, 1003 output interface device, 1004 processor, 1005 memory.

Claims

1. a target data acquisition unit that acquires time-series target data related to a task, the time-series target data being generated when a task evaluation subject performs the task; a normative data acquiring unit that acquires a plurality of time-series normative data related to the work, the normative data being generated by a plurality of norm workers performing the work; a pre-processing unit that generates target feature data relating to features extracted from the target data acquired by the target data acquiring unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquiring unit; a similarity calculation unit that compares the target feature data with each of the plurality of reference feature data and calculates a similarity between the target feature data and each of the reference feature data; a score calculation unit that calculates a task score indicating how much a level of the task performed by the task evaluation subject has reached the task levels of the tasks performed by the multiple reference workers, based on similarity data in which the target feature data is associated with the multiple similarities calculated by the similarity calculation unit; The score calculation unit calculates the task score from a maximum value of the similarity for each of the normative feature data. A task evaluation device comprising:

2. a target data acquisition unit that acquires time-series target data related to a task, the time-series target data being generated when a task evaluation subject performs the task; a normative data acquiring unit that acquires a plurality of time-series normative data related to the work, the normative data being generated by a plurality of norm workers performing the work; a pre-processing unit that generates target feature data relating to features extracted from the target data acquired by the target data acquiring unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquiring unit; a similarity calculation unit that compares the target feature data with each of the plurality of reference feature data and calculates a similarity between the target feature data and each of the reference feature data; a score calculation unit that calculates a task score indicating how much a level of the task performed by the task evaluation subject has reached the task levels of the tasks performed by the multiple reference workers, based on similarity data in which the target feature data is associated with the multiple similarities calculated by the similarity calculation unit; The score calculation unit calculates the task score from an average value of the similarity for each of the normative feature data. A task evaluation device comprising:

3. a target data acquisition unit that acquires time-series target data related to a task, the time-series target data being generated when a task evaluation subject performs the task; a normative data acquiring unit that acquires a plurality of time-series normative data related to the work, the normative data being generated by a plurality of norm workers performing the work; a pre-processing unit that generates target feature data relating to features extracted from the target data acquired by the target data acquiring unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquiring unit; a similarity calculation unit that compares the target feature data with each of the plurality of reference feature data and calculates a similarity between the target feature data and each of the reference feature data; a score calculation unit that calculates a task score indicating how much a level of the task performed by the task evaluation subject has reached the task levels of the tasks performed by the multiple reference workers, based on similarity data in which the target feature data is associated with the multiple similarities calculated by the similarity calculation unit; The score calculation unit calculates the task score by weighting the similarity for each of the normative feature data. A task evaluation device comprising:

4. a target data acquisition unit that acquires time-series target data related to a task, the time-series target data being generated when a task evaluation subject performs the task; a normative data acquiring unit that acquires a plurality of time-series normative data related to the work, the normative data being generated by a plurality of norm workers performing the work; a pre-processing unit that generates target feature data relating to features extracted from the target data acquired by the target data acquiring unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquiring unit; a similarity calculation unit that compares the target feature data with each of the plurality of reference feature data and calculates a similarity between the target feature data and each of the reference feature data; a score calculation unit that calculates an activity score indicating to what extent a level of the activity performed by the activity evaluation subject has reached the levels of the activities performed by the multiple reference workers, based on similarity data in which the object feature data is associated with the multiple similarities calculated by the similarity calculation unit; a task type estimation unit that estimates a task type of the task performed by the task evaluation subject based on the target feature amount data or based on the similarity data in which the target feature amount data is associated with a plurality of the similarities calculated by the similarity calculation unit, The task type estimation unit is The task type of the task performed by the task evaluation subject is estimated based on the object feature amount data or the similarity data and the task score calculated by the score calculation unit. A task evaluation device comprising:

5. a target data acquisition unit that acquires time-series target data related to a task, the time-series target data being generated when a task evaluation subject performs the task; a normative data acquiring unit that acquires a plurality of time-series normative data related to the work, the normative data being generated by a plurality of norm workers performing the work; a pre-processing unit that generates target feature data relating to features extracted from the target data acquired by the target data acquisition unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquisition unit; a similarity calculation unit that compares the target feature data with each of the plurality of reference feature data and calculates a similarity between the target feature data and each of the reference feature data; a score calculation unit that calculates an activity score indicating to what extent a level of the activity performed by the activity evaluation subject has reached the levels of the activities performed by the multiple reference workers, based on similarity data in which the object feature data is associated with the multiple similarities calculated by the similarity calculation unit; a representative normative data selection unit that selects representative normative data that is similar to the target data from among the plurality of normative data based on the similarity data in which the target feature data is associated with the plurality of similarities calculated by the similarity calculation unit, The representative normative data selection unit The representative normative data that is similar to the target data is selected from among the plurality of normative data based on the similarity data and the task score calculated by the score calculation unit. A task evaluation device comprising:

6. a target data acquisition unit that acquires time-series target data related to a task, the time-series target data being generated when a task evaluation subject performs the task; a normative data acquiring unit that acquires a plurality of time-series normative data related to the work, the normative data being generated by a plurality of norm workers performing the work; a pre-processing unit that generates target feature data relating to features extracted from the target data acquired by the target data acquisition unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquisition unit; a similarity calculation unit that compares the target feature data with each of the plurality of reference feature data and calculates a similarity between the target feature data and each of the reference feature data; a score calculation unit that calculates an activity score indicating to what extent a level of the activity performed by the activity evaluation subject has reached the levels of the activities performed by the multiple reference workers, based on similarity data in which the object feature data is associated with the multiple similarities calculated by the similarity calculation unit; a task type estimation unit that estimates a task type of the task performed by the task evaluation subject based on the target feature amount data or based on similarity data in which the target feature amount data is associated with a plurality of the similarities calculated by the similarity calculation unit; an output control unit that outputs, to an output device, data indicating the task score calculated by the score calculation unit in association with data indicating the task type estimated by the task type estimation unit. A work evaluation device comprising:

7. a target data acquisition unit that acquires time-series target data related to a task, the time-series target data being generated when a task evaluation subject performs the task; a normative data acquiring unit that acquires a plurality of time-series normative data related to the work, the normative data being generated by a plurality of norm workers performing the work; a pre-processing unit that generates target feature data relating to features extracted from the target data acquired by the target data acquisition unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquisition unit; a similarity calculation unit that compares the target feature data with each of the plurality of reference feature data and calculates a similarity between the target feature data and each of the reference feature data; a score calculation unit that calculates an activity score indicating to what extent a level of the activity performed by the activity evaluation subject has reached the levels of the activities performed by the multiple reference workers, based on similarity data in which the object feature data is associated with the multiple similarities calculated by the similarity calculation unit; a representative normative data selection unit that selects representative normative data that is similar to the target data from among the plurality of normative data based on the similarity data in which the target feature data is associated with the plurality of similarities calculated by the similarity calculation unit; an output control unit that outputs data indicating the task score calculated by the score calculation unit to an output device in association with the representative normative data selected by the representative normative data selection unit; A work evaluation device comprising:

8. a target data acquisition unit that acquires time-series target data related to a task, the time-series target data being generated when a task evaluation subject performs the task; a normative data acquiring unit that acquires a plurality of time-series normative data related to the work, the normative data being generated by a plurality of norm workers performing the work; a pre-processing unit that generates target feature data relating to features extracted from the target data acquired by the target data acquisition unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquisition unit; a similarity calculation unit that compares the target feature data with each of the plurality of reference feature data and calculates a similarity between the target feature data and each of the reference feature data; a task type estimation unit that estimates a task type of the task performed by the task evaluation subject based on the target feature amount data or similarity data in which the target feature amount data is associated with the multiple similarities calculated by the similarity calculation unit. A work evaluation device comprising:

9. a target data acquisition unit that acquires time-series target data related to a task, the time-series target data being generated when a task evaluation subject performs the task; a normative data acquiring unit that acquires a plurality of time-series normative data related to the work, the normative data being generated by a plurality of norm workers performing the work; a pre-processing unit that generates target feature data relating to features extracted from the target data acquired by the target data acquisition unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquisition unit; a similarity calculation unit that compares the target feature data with each of the plurality of reference feature data and calculates a similarity between the target feature data and each of the reference feature data; a representative normative data selection unit that selects representative normative data that is similar to the target data from among the plurality of normative data based on similarity data in which the target feature data and the plurality of similarities calculated by the similarity calculation unit are associated with each other. A work evaluation device comprising:

10. The pre-processing unit divides the target data into a plurality of divided target data, and generates the target feature amount data for each of the plurality of feature amounts extracted from each of the plurality of divided target data, The similarity calculation unit calculates the similarity between the target feature data and each of the norm feature data for each of the plurality of target feature data generated by the pre-processing unit.

10. The task evaluation device according to claim 1, wherein the task evaluation device is a task evaluation device that performs a task evaluation process.

11. a score calculation unit that calculates a task score indicating how much a level of the task performed by the task evaluation subject has reached the task levels of the tasks performed by the plurality of reference workers, based on the similarity data in which the target feature data and the plurality of similarities calculated by the similarity calculation unit are associated with each other.

9. The task evaluation device according to claim 8.

12. a score calculation unit that calculates a task score indicating how much a level of the task performed by the task evaluation subject has reached the task levels of the tasks performed by the plurality of reference workers, based on the similarity data in which the target feature data and the plurality of similarities calculated by the similarity calculation unit are associated with each other.

10. The task evaluation device according to claim 9.

13. The score calculation unit calculates the task score for each of the plurality of object feature amount data generated by the pre-processing unit based on the object feature amount data and the similarity data.

8. The task evaluation device according to claim 1, wherein the task evaluation device is a task evaluation device that performs a task evaluation process.

14. The score calculation unit calculates the task score for each of the plurality of object feature amount data generated by the pre-processing unit based on the object feature amount data and the similarity data.

13. The task evaluation device according to claim 11 or 12.

15. an output control unit that outputs data indicating the task score calculated by the score calculation unit to an output device The task evaluation device according to claim 1 , further comprising:

16. The output control unit outputs data indicating the task score to the output device in association with the target data.

16. The task evaluation device according to claim 15.

17. The output control unit outputs data indicating the task score to the output device in association with the target data.

7. The task evaluation device according to claim 6.

18. The output control unit outputs data indicating the task score to the output device in association with the target data.

8. The task evaluation device according to claim 7.

19. the output device is a display device; The output control unit causes the display device to display data indicating the task score.

16. The task evaluation device according to claim 15.

20. the output device is a display device; The output control unit causes the display device to display data indicating the task score in association with data indicating the task type estimated by the task type estimation unit.

7. The task evaluation device according to claim 6.

21. the output device is a display device; The output control unit causes the display device to display data indicating the task score in association with the representative normative data selected by the representative normative data selection unit.

8. The task evaluation device according to claim 7.

22. The output control unit causes the display device to display data indicating the task score in association with the target data.

20. The task evaluation device according to claim 19.

23. The output control unit causes the display device to display data indicating the task score in association with the target data.

21. The task evaluation device according to claim 20.

24. The output control unit causes the display device to display data indicating the task score in association with the target data.

22. The task evaluation device according to claim 21.

25. a step of a target data acquisition unit acquiring time-series target data related to a task, the time-series target data being generated when a task evaluation subject performs the task; a reference data acquiring unit acquiring a plurality of time-series reference data related to the work, the reference data being generated by a plurality of reference workers performing the work; a pre-processing unit generating object feature data relating to features extracted from the object data acquired by the object data acquiring unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquiring unit; a similarity calculation unit comparing the target feature data with each of a plurality of the normative feature data and calculating a similarity between the target feature data and each of the normative feature data; a score calculation unit calculating an activity score indicating to what extent a level of the activity performed by the activity evaluation subject has reached the levels of the activities performed by the multiple reference workers, based on similarity data in which the object feature data is associated with the multiple similarities calculated by the similarity calculation unit; The score calculation unit calculates the task score from a maximum value of the similarity for each of the normative feature data. A work evaluation method comprising:

26. A step of a target data acquisition unit acquiring time-series target data related to a task, the time-series target data being generated by a task evaluation subject performing the task; a reference data acquiring unit acquiring a plurality of time-series reference data related to the work, the reference data being generated by a plurality of reference workers performing the work; a pre-processing unit generating object feature data relating to features extracted from the object data acquired by the object data acquiring unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquiring unit; a similarity calculation unit comparing the target feature data with each of a plurality of the normative feature data and calculating a similarity between the target feature data and each of the normative feature data; a score calculation unit calculating an activity score indicating to what extent a level of the activity performed by the activity evaluation subject has reached the levels of the activities performed by the multiple reference workers, based on similarity data in which the object feature data is associated with the multiple similarities calculated by the similarity calculation unit; The score calculation unit calculates the task score from an average value of the similarity for each of the normative feature data. A work evaluation method comprising:

27. ​​A step of a target data acquisition unit acquiring time-series target data related to a task, the time-series target data being generated by a task subject performing the task; a reference data acquiring unit acquiring a plurality of time-series reference data related to the work, the reference data being generated by a plurality of reference workers performing the work; a pre-processing unit generating object feature data relating to features extracted from the object data acquired by the object data acquiring unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquiring unit; a similarity calculation unit comparing the target feature data with each of a plurality of the normative feature data and calculating a similarity between the target feature data and each of the normative feature data; a score calculation unit calculating an activity score indicating to what extent a level of the activity performed by the activity evaluation subject has reached the levels of the activities performed by the plurality of reference workers, based on similarity data in which the object feature data is associated with the plurality of similarities calculated by the similarity calculation unit; The score calculation unit calculates the task score by weighting the similarity for each of the normative feature data. A work evaluation method comprising:

28. A step of a target data acquisition unit acquiring time-series target data related to a task, the time-series target data being generated by a task evaluation subject performing the task; a reference data acquiring unit acquiring a plurality of time-series reference data related to the work, the reference data being generated by a plurality of reference workers performing the work; a pre-processing unit generating object feature data relating to features extracted from the object data acquired by the object data acquiring unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquiring unit; a similarity calculation unit comparing the target feature data with each of a plurality of the normative feature data and calculating a similarity between the target feature data and each of the normative feature data; a score calculation unit calculating an activity score indicating to what extent a level of the activity performed by the activity evaluation subject has reached the levels of the activity performed by the multiple reference workers, based on similarity data in which the target feature data is associated with the multiple similarities calculated by the similarity calculation unit; a task type estimation unit estimating a task type of the task performed by the task evaluation subject based on the target feature amount data or the similarity data in which the target feature amount data is associated with a plurality of the similarities calculated by the similarity calculation unit, The task type estimation unit is The task type of the task performed by the task evaluation subject is estimated based on the object feature amount data or the similarity data and the task score calculated by the score calculation unit. A work evaluation method comprising:

29. A step of a target data acquisition unit acquiring time-series target data related to a task, the time-series target data being generated by a task evaluation subject performing the task; a reference data acquiring unit acquiring a plurality of time-series reference data related to the work, the reference data being generated by a plurality of reference workers performing the work; a pre-processing unit generating object feature data relating to features extracted from the object data acquired by the object data acquiring unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquiring unit; a similarity calculation unit comparing the target feature data with each of a plurality of the normative feature data and calculating a similarity between the target feature data and each of the normative feature data; a score calculation unit calculating an activity score indicating to what extent a level of the activity performed by the activity evaluation subject has reached the levels of the activity performed by the multiple reference workers, based on similarity data in which the target feature data is associated with the multiple similarities calculated by the similarity calculation unit; a representative normative data selection unit selecting representative normative data that is similar to the target data from among the plurality of normative data based on the similarity data in which the target feature data is associated with the plurality of similarities calculated by the similarity calculation unit; The representative normative data selection unit The representative normative data that is similar to the target data is selected from among the plurality of normative data based on the similarity data and the task score calculated by the score calculation unit. A work evaluation method comprising:

30. A step of a target data acquisition unit acquiring time-series target data related to a task, the time-series target data being generated by a task subject performing the task; a reference data acquiring unit acquiring a plurality of time-series reference data related to the work, the reference data being generated by a plurality of reference workers performing the work; a pre-processing unit generating object feature data relating to features extracted from the object data acquired by the object data acquiring unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquiring unit; a similarity calculation unit comparing the target feature data with each of a plurality of the normative feature data and calculating a similarity between the target feature data and each of the normative feature data; a score calculation unit calculating an activity score indicating to what extent a level of the activity performed by the activity evaluation subject has reached the levels of the activity performed by the multiple reference workers, based on similarity data in which the target feature data is associated with the multiple similarities calculated by the similarity calculation unit; a task type estimation unit estimating a task type of the task performed by the task evaluation subject based on the target feature amount data or based on the similarity data in which the target feature amount data is associated with a plurality of the similarities calculated by the similarity calculation unit; an output control unit that outputs data indicating the task score calculated by the score calculation unit to an output device in association with data indicating the task type estimated by the task type estimation unit; A work evaluation method that includes:

31. A step of a target data acquisition unit acquiring time-series target data related to a task, the time-series target data being generated by a task evaluation subject performing the task; a reference data acquiring unit acquiring a plurality of time-series reference data related to the work, the reference data being generated by a plurality of reference workers performing the work; a pre-processing unit generating object feature data relating to features extracted from the object data acquired by the object data acquiring unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquiring unit; a similarity calculation unit comparing the target feature data with each of a plurality of the normative feature data and calculating a similarity between the target feature data and each of the normative feature data; a score calculation unit calculating an activity score indicating to what extent a level of the activity performed by the activity evaluation subject has reached the levels of the activity performed by the multiple reference workers, based on similarity data in which the target feature data is associated with the multiple similarities calculated by the similarity calculation unit; a representative normative data selection unit selecting representative normative data that is similar to the target data from among the plurality of normative data based on the similarity data in which the target feature data is associated with the plurality of similarities calculated by the similarity calculation unit; an output control unit outputting data indicating the task score calculated by the score calculation unit to an output device in association with the representative normative data selected by the representative normative data selection unit; A work evaluation method that includes:

32. A step of a target data acquisition unit acquiring time-series target data related to a task, the time-series target data being generated by a task evaluation subject performing the task; a reference data acquiring unit acquiring a plurality of time-series reference data related to the work, the reference data being generated by a plurality of reference workers performing the work; a pre-processing unit generating object feature data relating to features extracted from the object data acquired by the object data acquiring unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquiring unit; a similarity calculation unit comparing the target feature data with each of a plurality of the normative feature data and calculating a similarity between the target feature data and each of the normative feature data; A step in which a task type estimation unit estimates a task type of the task performed by the task evaluation subject based on the target feature amount data or similarity data in which the target feature amount data is associated with the multiple similarities calculated by the similarity calculation unit. A work evaluation method that includes:

33. A step of a target data acquisition unit acquiring time-series target data related to a task, the time-series target data being generated by a task evaluation subject performing the task; a reference data acquiring unit acquiring a plurality of time-series reference data related to the work, the reference data being generated by a plurality of reference workers performing the work; a pre-processing unit generating object feature data relating to features extracted from the object data acquired by the object data acquiring unit, and a plurality of normative feature data relating to the features extracted from each of the plurality of normative data acquired by the normative data acquiring unit; a similarity calculation unit comparing the target feature data with each of a plurality of the normative feature data and calculating a similarity between the target feature data and each of the normative feature data; A step in which a representative normative data selection unit selects representative normative data that is similar to the target data from among the plurality of normative data based on similarity data in which the target feature data is associated with the plurality of similarities calculated by the similarity calculation unit. A work evaluation method that includes:

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