JP2021179981A - Information processing system, information processing device, and program - Google Patents

Abstract

To provide a technique capable of providing a user involved in an article manufacturing process with more useful information.SOLUTION: A sensor information acquisition unit 60 acquires sensor information transmitted from an on-site device 2. An analysis unit 61 analyzes sensor information acquired by the sensor information acquisition unit 60 and stores the analysis results in an analysis result DB 400. An analysis report generation unit 62 generates an analysis report for a user on the basis of the analysis results stored in the analysis result DB 400. A cost calculation unit 100 calculates cost information on the basis of the analysis results and user information. A statistical processing unit 101 executes various statistical processing for the analysis results. A graph generation unit 102 generates a graph and the like on the basis of the analysis results. An advice generation unit 103 generates a concrete advice on manufacturing efficiency of a manufacturing process.SELECTED DRAWING: Figure 3

Description

The present invention relates to an information processing system, an information processing device and a program.

Traditionally, the so-called manufacturing industry has been one of the most important industries in Japan.
However, the manufacturing industry is known as one of the industries whose efficiency is difficult to improve because it handles old-fashioned machines and the like.
In this regard, for example, as a technique for improving the efficiency of the manufacturing industry, a technique for calculating the operating status and manufacturing efficiency of a manufacturing line by a simple device has been proposed (see Patent Document 1).

Japanese Unexamined Patent Publication No. 2018-323396

However, conventional techniques such as the technique described in Patent Document 1 described above can merely grasp the operating status and calculate the manufacturing efficiency, and specific methods and suggestions for improving the manufacturing efficiency. Did not give.
Further, in particular, the technique described in Patent Document 1 described above can only process the entire information of the production line in the post-process of the production line. However, the manufacture of articles and the like can only be realized by combining a plurality of processes.

The present invention has been made in view of such a situation, and provides a technique capable of providing more useful information to a user involved in a manufacturing process of an article.

In order to achieve the above object, the information processing system of one aspect of the present invention is
An information processing system used to manage or inspect the manufacturing process of goods.
A primary information acquisition means for acquiring work time information regarding the work time of a worker in the manufacturing process and primary information including image information regarding the manufacturing process.
An image information analysis means for analyzing whether or not the worker is involved in the work of the manufacturing process at a predetermined time based on the image information.
An actual working time calculating means for calculating the actual working time in which the worker actually performed the work for each of the manufacturing processes from the working time information and the result of the analysis by the image information analysis means.
A cost information generating means for generating cost information regarding the cost of the goods from the calculated actual working hours, and
To prepare for.

The information processing device and program according to one aspect of the present invention are also provided as the information processing device and program corresponding to the information processing system according to one aspect of the present invention.

According to the present invention, it is possible to provide a technique capable of providing more useful information to a user involved in a manufacturing process of an article.

It is a block diagram which shows the structure of the information processing system which concerns on one Embodiment of this invention. It is a block diagram which shows the hardware configuration of the server as one Embodiment of this invention among the information processing system of FIG. It is a functional block diagram which shows an example of the functional configuration of the server of FIG. It is a figure for demonstrating an example of the analysis method by the analysis part of the server of FIG. It is a figure which shows an example of the analysis report generated by the server of FIG. It is a figure for demonstrating the detail of the analysis report of FIG. It is a figure which shows an example of the analysis report generated by the server of FIG. 3, and is the figure which shows the example different from the example of FIG. It is a figure which shows an example of the analysis report generated by the server of FIG. 3, and is the figure which shows the example different from the example of FIGS. 5 and 7. It is a figure which shows an example of the analysis report generated by the server of FIG. 3, and is the figure which shows the example different from the example of FIGS. 5, 7, and 8. It is a flowchart explaining an example of the flow of analysis report generation processing among the processing of the server having the functional configuration of FIG. It is a figure which shows an example of the image which can be included in the analysis report generated by the server of FIG. 3, and is an example of the figure which is used for confirming the progress state of a manufacturing process. It is a figure which shows an example of the cost calculation method in consideration of the actual working time and the absence time. It is a figure which shows an example of the image which can be included in the analysis report generated by the server of FIG. 3, and is an example of the figure which is used for confirmation of the image of a manufacturing site.

[Embodiment]
Hereinafter, embodiments of the present invention will be described with reference to the drawings.

<Explanation of outline>
FIG. 1 is a block diagram showing a configuration of an information processing system (hereinafter, referred to as “the present system”) according to an embodiment of the present invention.
As shown in FIG. 1, this system includes a server 1 and a field device 2. The server 1 and the field device 2 are connected to each other via a predetermined network N such as the Internet.
The network N is not an essential component, and may be arbitrarily communicated according to a predetermined communication standard such as LAN (Local Area Network) and various short-range wireless communications.

Further, the field device 2 includes a PC unit 11 and a sensor unit 12.
The PC unit 11 is composed of a general-purpose mobile device or the like, and is used for simple arithmetic processing, transmission / reception of information acquired via the sensor unit 12, and the like. Specifically, for example, the PC unit 11 transmits various sensing data (hereinafter referred to as “sensor information”) acquired via the sensor unit 12 to the server 1.
The sensor unit 12 is composed of, for example, a general-purpose weight sensor, an acceleration sensor, a camera sensor, or the like.
The sensor unit 12 may be configured by a sensor or the like in any mode depending on the situation of the factory in which it is installed and the type of manufacturing process.

Here, the outline of this system will be briefly described.
This system is an information processing system used for management or inspection of the manufacturing process of products. Specifically, for example, grasping the operating status of the process of manufacturing products (hereinafter referred to as "manufacturing process") and work. Used to improve efficiency.
The on-site device 2 is typically installed in a factory or the like where products are manufactured, and is used for acquiring various information.
That is, the on-site device 2 acquires various information in the factory via the sensor unit 12 and the like, and transmits the information to the server 1. Then, the server 1 performs an analysis based on the acquired information, and finally generates an output report (hereinafter, referred to as an "analysis report") for the user.
The user is a person who desires to provide a service related to this system, and is typically a factory manager or the like.

Subsequently, a specific embodiment of the sensor unit 12 and a place where the sensor unit 12 is installed will be briefly described.
The weight sensor is installed at the bottom of the product or part, for example, in the process of putting the product or part in a case or the like.
When the sensor unit 12 is composed of a weight sensor, the time for manufacturing a product per predetermined unit (hereinafter referred to as "cycle time") or the time for suspending product manufacturing (hereinafter referred to as "cycle time") based on a change in the weight of a case or the like (hereinafter referred to as "cycle time"). Hereinafter referred to as "suspension time") is acquired.

The accelerometer is installed in an automatic device such as a press machine.
When the sensor unit 12 is composed of an acceleration sensor, the cycle time and the interruption time are acquired according to the movement of the shot of each device.

Camera sensors (cameras) are installed in all processes, including, for example, changes in movement involving humans and machines.
When the sensor unit 12 is a camera sensor, the cycle time and the interruption time are acquired by using a technique such as image recognition for an image including the movement of a person or a machine.
In the following description, a case where a weight sensor is adopted for the sensor unit 12 will be described.

<Hardware configuration>
FIG. 2 is a block diagram showing a hardware configuration of a server as an embodiment of the present invention in the information processing system of FIG.
The server 1 is composed of a personal computer or the like. As shown in FIG. 2, the server 1 has a control unit 21, a ROM (Read Only Memory) 22, a RAM (Random Access Memory) 23, a bus 24, an input / output interface 25, an output unit 26, and an input. A unit 27, a storage unit 28, a communication unit 29, and a drive 30 are provided.

The control unit 21 is composed of a CPU, a GPU, a microcomputer including a semiconductor memory, and the like, and executes various processes according to a program recorded in the ROM 22 or a program loaded from the storage unit 28 into the RAM 23.
Information and the like necessary for the control unit 21 to execute various processes are also appropriately stored in the RAM 23.

The control unit 21, ROM 22 and RAM 23 are connected to each other via the bus 24. An input / output interface 25 is also connected to the bus 24. An output unit 26, an input unit 27, a storage unit 28, a communication unit 29, and a drive 30 are connected to the input / output interface 25.

The output unit 26 is composed of various liquid crystal displays and the like, and outputs various information.

The input unit 27 is composed of various hardware and the like, and inputs various information.

The storage unit 28 is composed of an HDD (Hard Disk Drive), an SSD (Solid State Drive), or the like, and stores various data. For example, various data including various programs and databases are stored.

The communication unit 29 controls communication with another device (for example, a field device 2) via a network N including the Internet.

The drive 30 is provided as needed. A removable media 41 made of a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is appropriately mounted on the drive 30. The program read from the removable media 41 by the drive 30 is installed in the storage unit 28 as needed. Further, the removable media 41 can also store various data stored in the storage unit 28 in the same manner as the storage unit 28.

Since the hardware configuration of the PC unit 11 of the field device 2 can be basically the same as the hardware configuration of the server 1, the description thereof will be omitted.

<Functional configuration>
FIG. 3 is a functional block diagram showing an example of the functional configuration of the server of FIG.
As shown in FIG. 3, the control unit 21 of the server 1 functions as a sensor information acquisition unit 60, an analysis unit 61, and an analysis report generation unit 62 by executing various programs and the like.
Further, a user information DB 300, an analysis result DB 400, and a template DB 500 are provided in one area of the storage unit 28 of the server 1.

Here, the user information DB 300 stores various information (hereinafter, referred to as “user information”) regarding the user and the products manufactured by the user, which are acquired in advance from the field apparatus 2 and the like.
The user information includes, for example, the product name of the product, the selling price of the product, the process configuration of the product, the process name of the manufacturing process, the machine information of the manufacturing process (scheduled operating time, depreciation, etc.), the labor wage rate, the number of people, and so on. Process estimation (estimated time) etc. are included. In this system, cost calculation can be realized by making good use of this information.
Further, the template DB 500 stores a template of a text document and the like that can be used when generating the advice described later.

The PC unit 11 of the field device 2 transmits the sensor information acquired via the sensor unit 12 installed in each manufacturing process to the server 1. Therefore, the sensor information acquisition unit 60 acquires the sensor information transmitted from the field device 2.
Here, the sensor information of the present embodiment is, for example, information on the weight acquired by the weight sensor (hereinafter referred to as "weight information") and information on the time when the weight information is acquired (hereinafter referred to as "time information"). ) Is included. The details will be described later with reference to FIG. 4 and the like, but it is presumed at what timing and how many products were manufactured based on the weight information and the time information. When acquiring the above-mentioned time information, for example, falsification prevention measures such as a time stamp may be taken together.

The analysis unit 61 analyzes the sensor information acquired by the sensor information acquisition unit 60, and stores the analysis result in the analysis result DB 400.
Here, a method of calculating the cycle time in the cycle time calculation unit 80 of the analysis unit 61 will be briefly described.
FIG. 4 is a diagram for explaining an example of an analysis method by the analysis unit of the server of FIG. In the example of FIG. 4, each point (circle) shows the raw data acquired by the weight sensor. As a specific method for processing such raw data, for example, when the preceding and following data are included in a certain range, the earliest value among consecutive values with a certain value (first value, for example, for example). The black dots in FIG. 4) are extracted. Then, by comparing the difference between the extracted values and the value registered as the weight of the product, it is possible to know the timing when the product is manufactured.
For example, in the example of FIG. 4, values having a weight of about 50 (g) appear continuously before and after time = t1 to time = t2. Here, the value (first value) that appears first among the continuous values is the value (black dot) at the time point of time = t1. Similarly, in the example of FIG. 4, it can be estimated that the product was manufactured at the timing of time = t2 and time = t3.
By such a method, it is possible to know the number of manufactured products, the cycle time, and the like.

The analysis report generation unit 62 generates an analysis report for the user based on the analysis result stored in the analysis result DB 400.
Here, the analysis report generation unit 62 includes a cost calculation unit 100, a statistical processing unit 101, a graph generation unit 102, and an advice generation unit 103.
The cost calculation unit 100 calculates the cost of the product based on the analysis result (cycle time, interruption time) stored in the analysis result DB 400 and the user information stored in the user information DB 300, and the information (hereinafter, """Costinformation") is acquired.

The statistical processing unit 101 executes various statistical processes on the analysis results (cycle time, interruption time) stored in the analysis result DB 400. The specific contents of the statistical processing executed by the statistical processing unit 101 and the method of using the same will be described later with reference to FIGS. 5 to 9.

The graph generation unit 102 generates a graph or the like based on the analysis result (cycle time, interruption time) stored in the analysis result DB 400. A specific graph generated by the graph generation unit 102 and a method of using the graph will be described later with reference to FIGS. 5 to 9.

The advice generation unit 103 adds the analysis result (cycle time, interruption time) stored in the analysis result DB, the cost information, the result of statistical processing by the statistical processing unit 101, and the graph generated by the graph generation unit 102. , Based on the text information stored in the template DB 500, generate specific advice on the manufacturing efficiency of the manufacturing process. Specific advice generated by the advice generation unit 103 will be described later with reference to FIGS. 5 to 9 and the like.

FIG. 5 is a diagram showing an example of an analysis report generated by the server of FIG.
In the graph of the example of FIG. 5, the x-axis represents the cycle time (the number of seconds per production), and the y-axis represents the number of times the task is completed in that number of seconds. According to this graph, it is possible to confirm how many times the product is manufactured in about "how many seconds".
Here, the example of FIG. 5 is not a graph of the entire manufacturing process, but a graph of “process A”. That is, the user presented with such an analysis report can not only confirm the efficiency of the entire manufacturing process, but can confirm the efficiency of each process in the manufacturing process in detail. Then, if there is room for improvement in each of the processes, the user can improve the process and finally flexibly improve the efficiency of the entire manufacturing process.
In addition, process A is, for example, manufacturing of products such as "visual inspection", "packing", "cleaning", "drilling", "visual inspection of all products", "bulge", "deburring", and "cutting / bending". It is an arbitrary manufacturing process among the processes.

Further, "points of analysis" are displayed on the right side of FIG. The "points of analysis" are points for the user to interpret the contents of the analysis report and advice for improving the efficiency of a specific manufacturing process.
The details of the analysis points will be described with reference to FIG. For example, looking at the graph of (A) in FIG. 6, there is a peak of the number of appearances (y-axis) near the cycle time (x-axis value) of 40 to 50 seconds, and most of the other cycle times. There is no peak in the number of occurrences. In this way, when the peak peak is relatively narrow and there is no particular peak in the region to the left of the cycle time at which the peak occurs, the most efficient cycle time of the process appears frequently. Therefore, it is shown that high production efficiency is realized at present.
Next, looking at the graph of FIG. 6 (B), a peak exists in the vicinity of the cycle time (x-axis value) of 40 to 50 seconds as in FIG. 6 (A), but the value is lower than that. There are also many small peaks (on the left side of the graph). The example of (B) in FIG. 6 shows that the ideal cycle time is not necessarily 40 to 50 seconds, and depending on the improvement, the cycle time may be further increased.
Similarly, looking at the graph of FIG. 6 (C), there is a peak near the cycle time (x-axis value) of 40 to 50 seconds as in FIG. 6 (A) and FIG. 6 (B). However, there are many small peaks in the vicinity of the cycle time (value of the x-axis) of 20 to 30 seconds and in the vicinity of 70 to 80 seconds. In the example of FIG. 6C, there are few cases where the work is completed in a time close to the ideal cycle time, indicating that the work efficiency is low. In such a case, for example, in the manufacturing process, there is a possibility that the difference in work efficiency has widened among the workers.
That is, the examples of FIGS. 6A to 6C are, for example, information that assists in interpreting the meaning of the graph of FIG. The information presented to the user as an analysis report including FIG. 5 includes statistically advanced information, and it may be difficult to understand at first glance what the presented information means.
Summarizing the above, for example, at the point of analysis in FIG. 5, advice and simple information for the user to interpret the analysis report as shown in the graphs (A) to (C) of FIG. 6 and the above explanation are given. Explanation is displayed. This allows the user to easily interpret the analysis report.

FIG. 7 is a diagram showing an example of an analysis report generated by the server of FIG. 3, and is a diagram showing an example different from the example of FIG.
FIG. 7 shows the coefficient of variation of the actual unit work time and the normal unit work time.
The coefficient of variation is the standard deviation divided by the average value. The coefficient of variation is used when you want to compare the variability in groups with different mean values.
The normal working time is, for example, a value excluding a value three times or more the median value of the actual unit working time. Since it excludes cases where the distribution deviates significantly, for example, when the working time is significantly extended, the variation is not affected by interruption.
The solid line shows the degree of variation per unit time, and the dotted line shows the degree of variation in unit work excluding interruptions. That is, the example of FIG. 7 shows the difference in the degree of variation between the actual working time and the ideal working time excluding interruptions.

As the points of analysis in the example of FIG. 7, for example, the following contents are described.
For example, when the value of the coefficient of variation is small and the difference between the unit work time and the normal unit work time is small, it indicates that the state is in a good state with less interruption and less normal variation.
On the other hand, when the coefficient of variation is large and the difference between the unit work time and the normal unit work time is small, there is little interruption, but there is a lot of normal variation, so caution is required. In addition, when the coefficient of variation is large and the difference between the unit work time and the normal unit work time is large, it is shown that the interruption has a large effect and caution is required.

Here, although not shown, the points of analysis including the example of FIG. 7 can include specific advice for improving efficiency to the user.
Specifically, for example, the point of analysis is that "process A and process B have relatively large variations. In addition, in process C and process D, data exists far to the right of the peak of the histogram. It is thought that the cause is that the work was interrupted because there are several. On the other hand, in process E, there is almost no such data and it is considered that there is almost no interruption. Therefore, for example, the process. Regarding C and process E, although they are similar work processes, there is room for consideration regarding the large difference in the number of interruptions. " This is, for example, specific advice that the user can take to improve efficiency on the premise of the result of interpreting various information.
It should be noted that such advice is generated by the advice generation unit 103. Then, the advice generation unit 103 can use various text sentences stored in the template DB 500 in generating such specific advice.

FIG. 8 is a diagram showing an example of an analysis report generated by the server of FIG. 3, and is a diagram showing an example different from the examples of FIGS. 5 and 7.
In FIG. 8, the unit working time in "process B" is plotted. The x-axis shows the date and the y-axis shows the cycle time (seconds). The solid line shows the raw data of the unit work time, and the dotted line shows the moving average (number of sections = n) based on the raw data.
Then, as in the examples of FIGS. 5 and 7, "points of analysis" are displayed on the right side of FIG.

Specifically, for example, in the example of FIG. 8, "even when the raw data fluctuates drastically and the tendency is difficult to grasp, by checking the moving average, the tendency of the graph (work efficiency is improved or decreased) is clearly shown. You can understand what you are doing, etc.) ”, and other points for analyzing the graph and other specific advice for improving manufacturing efficiency are described. In this way, by analyzing the data of each process in time series, it is possible to perform a more detailed analysis on the manufacturing efficiency of the product.

9 is a diagram showing an example of an analysis report generated by the server of FIG. 3, and is a diagram showing an example different from the examples of FIGS. 5, 7, and 8.
FIG. 9 shows a comparison between the actual cost and the estimated amount in each process. Here, the actual cost is the cost calculated by the cost calculation unit 100 based on the actual sensor information, and the estimated amount is the cost estimated in advance by the user. The estimated amount is, for example, acquired in advance by the server 1 as a part of the user information and stored in the user information DB 300.

Specifically, for example, in the example of FIG. 9, "It is important to see not only the total value but also the ratio of the cost of the product. Compare the actual cost with the estimated amount, and the accuracy of which process with respect to the estimated amount. Highly accurate cost analysis is possible by seeing whether the cost is high. Comparing the actual cost with the estimated amount, it is about 15% lower. However, the cost is not low overall. In process C, there is no change between the estimated amount and the actual cost. "
In the cost calculation, the cost can be calculated by adding various statistical methods as in the examples of FIGS. 6 to 8.
For example, the median cycle time can be used. Since the median can reduce the effects of outliers, it is likely that only the effects of normal work can be extracted.
Further, for example, the average value of the cycle time can be used. When using the average value, there is a high possibility that it will be affected by the interruption, but as long as the manufacturing efficiency is actually affected by the interruption, it may be considered that the value closer to the true value is rather the average value of the cycle time. can.
Also, for example, the difference between the median value and the average value can be used. As described above, the median is the normal cycle time with little influence of the abnormal value, and the average value is the total cycle time including the abnormal value. Therefore, the difference between the median and the average value is the degree of being affected by interruption or the like. Is considered to mean.

For example, in step D (not shown), it is assumed that the median value and the average value greatly deviate from each other. In such a case, it is considered that the process D is greatly affected by the interruption. In this respect, interruptions are likely to occur in processes that generally involve many people. Therefore, for example, the server 1 "is difficult to improve if process D is a process involving many people, because it is unavoidable that interruption occurs. On the other hand, if process D is a process involving not many people, improvement is possible. There is room for analysis. "

Furthermore, such an analysis of costs can be calculated monthly and the time-series fluctuations can be analyzed. Further, for example, "variation" can be analyzed in the same manner as in the example of FIG.

FIG. 10 is a flowchart illustrating an example of the flow of the analysis report generation process among the processes of the server having the functional configuration of FIG.
In step S1, the sensor information acquisition unit 60 acquires the sensor information transmitted from the field device 2.

In step S2, the analysis unit 61 analyzes the sensor information acquired in step S1 and stores the analysis result in the analysis result DB 400.

In step S3, the analysis report generation unit 62 generates an analysis report for the user based on the analysis result stored in the analysis result DB 400.
Specifically, for example, in step S3, the cost calculation unit 100 acquires the cost information calculated based on the analysis result stored in the analysis result DB 400 and the user information stored in the user information DB 300.
Further, for example, in step S3, the statistical processing unit 101 executes various statistical processes on the analysis result stored in the analysis result DB 400.
Further, for example, in step S3, the graph generation unit 102 generates a graph or the like based on the analysis result stored in the analysis result DB 400.
Further, for example, in step S3, the advice generation unit 103 takes into account the analysis result, cost information, statistical processing result, generated graph, etc. stored in the analysis result DB 400, and gives specific advice regarding the manufacturing efficiency of the manufacturing process. To generate. This completes the analysis report generation process.

Although one embodiment of the present invention has been described above, the present invention is not limited to the above-described embodiment, and modifications, improvements, and the like to the extent that the object of the present invention can be achieved are included in the present invention. be.

Here, although the reference is omitted in the above-described embodiment, the cost calculation method will be supplemented. Generally, the representative value of the cost is calculated by the average value. Here, when the average value is used in the above-mentioned cost calculation, the average value in one day follows the central limit theorem, so that it generally has a normal distribution.
However, as a result of calculation based on the actual measured value, the average value in units of one day or one hour is different from the normal distribution, and may have a distribution distorted to the left. Therefore, the above-mentioned embodiment is included, and in such a case, the representative value of the cost is calculated by the median value.

Further, for example, in the above-described embodiment, the sensor unit 12 has been described as being composed of a weight sensor, an acceleration sensor, a camera sensor, and the like, but the present invention is not particularly limited thereto.
The sensor unit 12 may be configured by any sensing data acquisition device including any sensor. Further, each of the sensors constituting the sensor unit 12 does not necessarily have to be used alone, and a plurality of sensors may be used in combination.

Further, for example, in the above-described embodiment, the generated graph and the generated advice are merely examples, and the present invention is not particularly limited thereto.
The analysis report in the above embodiments may include graphs that can be generated based on the acquired sensor information and broadly include advice according to the sensor information and the statistical properties extracted from the analysis results. obtain.

Here, as an example of a specific configuration of the sensor unit 12, an example different from the above-described embodiment will be described. Specifically, for example, the sensor unit 12 may be configured by, for example, the following sensors and the like.
(1) RFID sensor An RFID (Radio Frequency Identification) sensor exchanges information acquired by a tag in which ID information or the like is embedded by short-range wireless communication or the like. The server 1 installs an RFID sensor in each manufacturing process as a specific configuration of the sensor unit 12, for example, the start (time) of the work in each manufacturing process, the end (time) of the work, information on the product to be manufactured, and the like. Can be acquired as sensor information.
In this embodiment, for example, the tag is displayed on an instruction sheet in which information such as product specifications is described, and the RFID sensor (reader) is arranged at the end of each manufacturing process. Then, the worker obtains various information by having the RFID sensor (reader) read the tag while moving while holding the work instruction sheet including the tag.
(2) Camera sensor (camera)
The camera sensor is composed of a digital camera or the like and is used for acquiring image information in each manufacturing process. The server 1 installs a camera sensor in each manufacturing process as a specific configuration of the sensor unit 12, and for example, information on human recognition (stay of worker, detection of absence) in each manufacturing process is used as sensor information. Can be obtained.
It should be noted that the detection of a person by a camera sensor is not limited to simply detecting a human skeleton or the like from image information by a technique such as image recognition. Specifically, the detection of a person by a camera sensor may be realized by combining, for example, the result of image recognition and the detection of the movement of an imaged object. As a result, the camera sensor can prevent erroneous detection of a person due to a structure or the like in a factory, and can accurately detect the stay or absence of a worker.

Further, the various sensor information acquired by the plurality of methods in this way may be used in combination. Specifically, an example in which an RFID sensor and a camera sensor are combined will be described.
As described above, when the server 1 adopts the RFID sensor, the server 1 can acquire information about the start (time) of the work of each manufacturing process, the end (time) of the work, the product to be manufactured, and the like.
However, it is difficult to obtain such information only with these information, for example, when the worker takes a break in the middle or when the work is stopped due to some trouble or the like.
On the other hand, when the server 1 adopts a camera sensor, the server 1 can detect the stay, absence, etc. of the worker by human recognition. Therefore, for example, when the worker takes a break in the middle, or something. Even if the work is stopped due to a trouble or the like, the information can be accurately grasped.
Therefore, since the server 1 can accurately distinguish between the actual working time and the absent time of the worker and perform various analyzes, the manufacturing efficiency and cost of the product can be calculated more accurately.

The information regarding the actual working time and the absence time of the worker acquired in this way can also be generated as output information (for example, the analysis report of the above-described embodiment). Specifically, it will be described with reference to FIG.
In the example of FIG. 11, the progress of each manufacturing process is displayed in chronological order for each of the product number “AF-0001” and the product number “AF-0002”.
For example, looking at the manufacturing process H of the product number "AF-0001", out of the three hours from 12:00 to 15:00, 30 minutes from 14:00 to 14:30 is the absence time. .. This indicates that the worker's stay could not be confirmed for 30 minutes from 14:00 to 14:30 based on the image information acquired by the above-mentioned camera sensor or the like. Therefore, out of the operating time of the entire manufacturing process H of 3 hours, the actual working time that actually contributed to the production of the product is only 2 hours and 30 minutes. In this service, the actual working time and the absence time in each manufacturing process can be combined and presented to the user or the like as output information for each product or manufacturing process.
For example, information on the time required for each of the above-mentioned manufacturing processes is acquired by an RFID sensor or the like.

In addition, as described above, information on the actual working hours and absentee hours of workers is also important in calculating costs and the like. With reference to FIG. 12, a method of calculating the cost using information on the actual working time and the absent time of the worker will be described.
In the example of FIG. 12, the process of the actual cost calculation method of the product number “AF-0001” is shown. For example, in each manufacturing process, the actual working time per product calculated based on the actual working time calculated by the method of FIG. 11 or the like is shown. In the example of FIG. 12, the actual working time of the manufacturing process G (per piece) is "60 seconds", the actual working time of the manufacturing process H (per piece) is "80 seconds", and the actual working time of the manufacturing process I (per piece). ) Is "90 seconds". On the other hand, the hourly wage unit price of each worker in the manufacturing process is, for example, "3000 yen" for the manufacturing process G, "3000 yen" for the manufacturing process H, and "2000 yen" for the manufacturing process I.
The cost required for each product calculated on the premise of these is about 166.7 yen. Here, in the example of FIG. 12, the estimated cost is displayed as "150 yen" together with the cost calculated based on the actual working time calculated by such a procedure (hereinafter referred to as "actual cost"). ing. The estimated cost is the cost estimated at the time of the contract, etc. acquired in advance of the provision of this service.
One of the purposes of this service is to present to users, etc. the highly accurate actual cost calculated based on the actual working hours, etc. in the manufacturing industry, etc., and the estimated cost at the time of estimation, so that manufacturing in the manufacturing industry, etc. This is to contribute to the improvement of efficiency and the efficiency of staffing.

Here, the actual cost shown in FIG. 12 is calculated based only on the labor cost, but is not particularly limited. The server 1 may calculate the actual cost by utilizing the information on the actual working time and the absent time of the worker and adding other expenses and the like. In addition, when the actual cost is calculated including the manufacturing equipment cost such as the depreciation cost of the equipment, the server 1 extracts the occupied time of the equipment etc. instead of simply using the actual working time of the worker. Then, the actual cost may be calculated. The occupancy time of the equipment or the like is the time for occupying the equipment for manufacturing the target product regardless of the stay or absence of the worker. In the above example, the work measured by the RFID sensor starts and ends. It will be time to.

Further, the image information acquired by the camera sensor or the like may be presented to the user or the like by any method. An example of a specific method of presenting image information to a user or the like will be described with reference to FIG.
In the histogram shown in FIG. 13, the horizontal axis shows the cycle time (seconds) per product to be manufactured, and the vertical axis shows the number of appearances for each cycle time.
That is, FIG. 13 is a graph showing the cycle time in a specific manufacturing process within a predetermined period (day, week, month) and the number of appearances of the cycle time.
Specifically, in the example of FIG. 13, the number of appearances of the cycle time peaks near P2 (cycle time is about 2 to 3 seconds), and many samples are distributed near P1 (cycle time is about 1 to 2 seconds). doing. On the other hand, almost no samples are distributed near P3 (cycle time is about 7 to 8 seconds).
In such a case, the product manufactured at the cycle time near P1 or P2 can be manufactured efficiently, while the product manufactured at the cycle time near P3 may have some trouble. Highly sex.
In this regard, in the example of FIG. 13, image information (moving image, still image) in each manufacturing process is associated with each cycle time in the histogram. The user can refer to the image information and the like associated with each other by selecting (clicking or the like) a predetermined position on the histogram. For example, looking at the image information associated with the cycle time near P1 and P2, it seems that the product is manufactured without causing any particular problem. On the other hand, looking at the image information associated with the cycle time near P3, the work site where the worker is absent is shown. That is, in the example of FIG. 13, since the worker was absent at least in a part of the manufacturing process, it is highly probable that inefficient manufacturing was performed.

Summarizing the above, the server 1 can generate display information for displaying information on the work time for each manufacturing process with respect to the output information (for example, an analysis report) to be output. Further, the display information may include a link to image information about each work process indicated by the work time information included in the display information.
The working time is the time during which the worker is widely involved in the work, including the actual working time and the absence time of the worker. In this embodiment, the working time is acquired by an RFID sensor or the like.
As a result, the user could not work efficiently (cycle time is slow) by checking the image information associated with each cycle time, and conversely, the user was able to work efficiently (cycle). You can consider the reason (the time is fast).

Further, although the description is omitted in the above-described embodiment, the actual working time may broadly include the time when the worker is present in the image of the work site. Further, as the actual working time, it may be determined from the image of the work site whether the worker is actually performing the work, and only the time determined to be actually performing the work may be set as the actual working time.

Further, in the above-described embodiment, the description of the method of setting the hourly wage unit price of the worker is omitted, but the hourly wage unit price of the worker may be set by any method.
For example, the hourly wage unit price may be set in advance by the user or the like, or may be automatically determined by the server 1 or the like. When the server 1 automatically determines the hourly wage unit price, the server 1 determines the hourly wage unit price of the worker based on the annual budget in manufacturing and the work schedule of the worker.

Further, although the description is omitted in the above-described embodiment, the definition and calculation method of various costs will be supplemented.
The depreciation cost can be calculated by, for example, the following method.
(1) Method by planned allocation Depreciation cost can be calculated based on the planned operating time of the equipment. Specifically, the planned operating time of each facility can be calculated by the past operating time, for example, the average of the operating time of each process in the immediately preceding month. (2) Method by actual operation allocation Depreciation The depreciation cost can be calculated based on the operating time when the equipment is actually operated.
For example, (1) the method by scheduled allocation is useful in that the depreciation cost can be calculated in advance of manufacturing. On the other hand, the method (2) based on the actual operation allocation is useful in that an accurate depreciation cost can be calculated based on the actual operation time. That is, the server 1 calculates the depreciation cost by appropriately combining arbitrary methods or those methods based on the purpose of use of the depreciation cost and the like.
Depending on the situation of the factory, equipment, etc., the server 1 may not be able to acquire information on the actual allocation of equipment operation. In that case, for example, the server 1 may determine whether or not the equipment is in operation based on the occupancy rate of the equipment in the manufacturing process.

Furthermore, the concepts of labor costs and depreciation costs in the above embodiments are merely examples and are not limited.
In particular, the concept of depreciation is used in cost (1) material cost: cost of consumed goods (material), (2) labor cost: cost incurred by labor consumption, (3) cost: other than the above. It is certain that it corresponds to the cost of (3) in the three types of costs incurred in the above, but the method of allocating to each product is not particularly limited. For example, the depreciation cost and labor cost of all equipment may be combined as a processing cost.

Further, the term estimated cost in the above-described embodiment is based on a method such as statistics such as standard cost as well as a cost estimated based on a non-scientific method such as cost estimation and empirical rules such as estimated cost. It can be interpreted as a wide range of terms including the meaning of estimated cost.

Further, for example, the series of processes described above can be executed by hardware or software.
In other words, the functional configuration of FIG. 3 is merely an example and is not particularly limited.
That is, it suffices if the information processing system is provided with a function capable of executing the above-mentioned series of processes as a whole, and what kind of functional block is used to realize this function is not particularly limited to the example of FIG. Further, the location of the functional block is not particularly limited to FIG. 3, and may be arbitrary.
Further, one functional block may be configured by a single piece of hardware, a single piece of software, or a combination thereof.

Further, for example, the number and users of various hardware (server 1 and field device 2) constituting this system are arbitrary, and may be configured including other hardware and the like.

Further, for example, when a series of processes are executed by software, a program constituting the software is installed in a computer or the like from a network or a recording medium.
The computer may be a computer embedded in dedicated hardware.
Further, the computer may be a computer capable of executing various functions by installing various programs, for example, a general-purpose smartphone or a personal computer in addition to a server.

Further, for example, the recording medium including such a program is not only composed of a removable medium (not shown) provided separately from the device main body for providing the program to the player, but also in a state of being preliminarily incorporated in the device main body. It is composed of a recording medium or the like provided to the player.

In the present specification, the steps for describing a program recorded on a recording medium are not only processed in chronological order but also in parallel or individually, even if they are not necessarily processed in chronological order. It also includes the processing to be executed.
Further, in the present specification, the term of the system means an overall device composed of a plurality of devices, a plurality of means, and the like.

Summarizing the above, the information processing system to which the present invention is applied can take various embodiments having the following configurations.
That is, the information processing system to which the present invention can be applied is
An information processing system used to manage or inspect the manufacturing process of goods.
A primary information acquisition means (for example, a sensor information acquisition unit 60) for acquiring primary information including image information related to a worker in the manufacturing process and image information related to the manufacturing process.
An image information analysis means (for example, analysis unit 61) that analyzes whether or not the worker is involved in the work of the manufacturing process at a predetermined time based on the image information.
An actual working time calculation means (for example, analysis unit 61) that calculates the actual working time in which the worker actually performed the work for each of the manufacturing processes from the work time information and the result of the analysis by the image information analysis means.
A cost information generation means (for example, cost calculation unit 100) that generates cost information regarding the cost of the goods from the calculated actual working hours, and
Information processing system equipped with.

The cost information generating means can generate cost information for calculating the labor cost included in the cost based on the actual working time and calculating the manufacturing equipment cost included in the cost based on the working time.

The image information analysis means can perform analysis based on the presence or absence of movement of the worker that can be included in the image information.

The image information analysis means and the actual working time calculation means can perform the analysis or the calculation based on statistical properties.

An output information generation means for generating output information including at least the cost information can be further provided.

Further provided with an absence time calculation unit for calculating the absence time of the worker within the working time based on the working time and the actual working time.
The output information generation means can generate output information that outputs the working time separately from the actual working time and the absent time.

The output information generation means can generate output information for outputting the cost information in each of the manufacturing processes.

The output information generating means may generate output information for comparing the actual cost based on the cost information generated by the cost information generating means with the pre-acquired estimated cost from which the cost information is generated. can.

The output information generation means generates display information as the output information, which displays the working time information in each of the manufacturing processes.
The display information may include a link to the image information regarding the work process indicated by the work time information included in the display information.

The output information generation means can generate output information including suggestions for improving the manufacturing efficiency of the article.

Further, another aspect of the information processing system to which the present invention is applied is
An information processing system used to manage or inspect the manufacturing process of goods.
Primary information acquisition means (for example, sensor information acquisition unit 60) for acquiring primary information about a sensor arranged in a predetermined process in the manufacturing process, and
An analysis means (for example, analysis unit 61) that analyzes the manufacturing time of the article based on the primary information.
An output information generation means (for example, an analysis report generation unit 62) that generates output information including at least information on the manufacturing efficiency of the article based on the analysis result by the analysis means.
Can be provided.

1 Server 21 Control unit 60 Sensor information acquisition unit 61 Analysis unit 80 Cycle time calculation unit 62 Analysis report generation unit 100 Cost calculation unit 101 Statistical processing unit 102 Graph generation unit 103 Advice generation unit 300 User information DB
400 Analysis result DB
500 template DB
2 Field equipment 11 PC unit 12 Sensor unit

Claims (11)

An information processing system used to manage or inspect the manufacturing process of goods.
A primary information acquisition means for acquiring work time information regarding the work time of a worker in the manufacturing process and primary information including image information regarding the manufacturing process.
An image information analysis means for analyzing whether or not the worker is involved in the work of the manufacturing process at a predetermined time based on the image information.
An actual working time calculating means for calculating the actual working time in which the worker actually performed the work for each of the manufacturing processes from the working time information and the result of the analysis by the image information analysis means.
A cost information generating means for generating cost information regarding the cost of the goods from the calculated actual working hours, and
Information processing system equipped with. The cost information generating means calculates the labor cost included in the cost based on the actual working time, and generates the cost information for calculating the manufacturing equipment cost included in the cost based on the working time.
The information processing system according to claim 1. The image information analysis means performs analysis based on the presence or absence of movement of the worker that may be included in the image information.
The information processing system according to claim 1 or 2. The image information analysis means and the actual working time calculation means execute the analysis or the calculation based on statistical properties.
The information processing system according to any one of claims 1 to 3. Further comprising an output information generating means for generating output information including at least the cost information.
The information processing system according to any one of claims 1 to 4. Further provided with an absence time calculation unit for calculating the absence time of the worker within the working time based on the working time and the actual working time.
The output information generation means generates output information for outputting the working time by distinguishing between the actual working time and the absent time.
The information processing system according to claim 5. The output information generation means generates output information for outputting the cost information in each of the manufacturing processes.
The information processing system according to claim 5 or 6. The output information generation means generates output information for comparing the actual cost based on the cost information generated by the cost information generation means with the pre-acquired estimated cost from which the cost information is generated.
The information processing system according to any one of claims 5 to 7. The output information generation means generates display information as the output information, which displays the working time information in each of the manufacturing processes.
The display information includes a link to the image information regarding the work process indicated by the work time information included in the display information.
The information processing system according to any one of claims 5 to 8. The output information generating means generates output information including suggestions for improving the manufacturing efficiency of the article.
The information processing system according to any one of claims 5 to 9. For information processing equipment used for management or inspection of the manufacturing process of goods
A primary information acquisition means for acquiring work time information regarding the work time of a worker in the manufacturing process and primary information including image information regarding the manufacturing process.
An image information analysis means for analyzing whether or not the worker is involved in the work of the manufacturing process at a predetermined time based on the image information.
An actual working time calculating means for calculating the actual working time in which the worker actually performed the work for each of the manufacturing processes from the working time information and the result of the analysis by the image information analysis means.
A cost information generating means for generating cost information regarding the cost of the goods from the calculated actual working hours, and
A program that executes processing including.

Images