JP7335159B2 - Information processing device and program - Google Patents

Description

The present invention relates to a technology for grasping the production status of products.

In the manufacturing industry, one of the major challenges is to improve productivity and increase profit margins. Efforts to visualize and analyze the production situation are continuing as a means of improving productivity. Companies that are able to make large-scale capital investments introduce cutting-edge production equipment, and efficiently record information on production status (hereinafter referred to as production information) using, for example, PLCs (Programmable Logic Controllers) and production control systems. There are many cases where However, this is limited to a small number of companies, such as large companies and cutting-edge fields. In contrast, SMEs, which account for 95% of all companies in the manufacturing industry in Japan, collect production information manually while using production equipment for a long period of time. The reality is that the vast majority of people make improvements by relying on

Regarding technology related to this type of productivity improvement, for example, Patent Document 1 discloses that a sensor unit of a facility operation rate monitor attached to a production facility is composed of an optical sensor, a sound sensor, and an object recognition sensor. A mechanism for determining the operation status of equipment is disclosed.

Japanese Patent Application Laid-Open No. 2001-100820

Similar to the mechanism disclosed in Patent Document 1, even if a method of attaching an external sensor to a production device and automatically determining the operating state of the production device is adopted at the manufacturing site of a small or medium-sized company, it is possible to produce large quantities of small quantities. In a manufacturing site where many product types are produced, it is necessary to finely manage the operation status for each product type. However, the mechanism using the external sensor as described above has a problem of poor usability, for example, the type classification must be manually switched.

SUMMARY OF THE INVENTION Accordingly, it is an object of the present invention to grasp the production status of a production apparatus that does not have a function of recording the production status for each product type.

In order to solve the above problems, the present invention provides an acquisition unit that acquires detection data representing a waveform of acceleration detected by the acceleration sensor from an acceleration sensor attached to a production apparatus, and the acquired detection data and a threshold value. is compared to generate binary data indicating an operating state in which the production device is operating or a non-operating state in which the production device is not operating, and the operating state indicated by the binary data is continuous an identification unit that identifies the product type and the number of products produced by the production equipment by comparing the current production period and the production cycle for each product type; and information on the results identified by the identification unit . and an output unit for outputting, when the type of the product identified as having been produced by the production apparatus is changed from the first type to the second type, the identification unit outputs the first type of product. The period existing between the production period in which the product of the second variety is produced and the production period in which the product of the second variety is produced is changed from the product of the first variety to the second variety in the production apparatus. learning the pattern of the detection data acquired by the acquisition unit during the period during which parts or devices for producing products of the type are replaced, and acquired Also provided is an information processing device that compares the detection data with the learned detection data to specify a parts replacement period during which parts are replaced for producing products in the production apparatus.

When the probability of identifying the product type based on the pattern of the binary data is equal to or less than a threshold value, the identification unit uses the acquired detection data and the sensor when the known product is being produced. The type of product produced by the production apparatus may be identified by comparing the detected data.

When the binary data corresponding to the acquired detection data does not match the binary data corresponding to the detection data detected by the sensor when producing a known product, the identification unit may identify that a new variety of product is being produced.

When the binary data corresponding to the acquired detection data does not match the binary data corresponding to the detection data detected by the sensor when producing a known product, the identification unit When the binary data corresponding to the acquired detection data are not continuous, it may not be specified that a new type of product is being produced.

When identifying that a new type of product is being produced, the identifying unit compares detection data detected by the sensor when the new type of product is being produced with a threshold, A pattern of the binary data for identifying the new product may be learned.

a registering unit that prompts a user to select whether or not to register the learned pattern when the pattern of the binary data for specifying the new product type is learned by the specifying unit. may

Further, according to the present invention, a computer includes an acquisition unit that acquires detection data representing a waveform of acceleration detected by the acceleration sensor from an acceleration sensor attached to a production apparatus, and the acquired detection data and a threshold value. By comparison, binary data indicating an operating state in which the production device is operating or a non-operating state in which the production device is not operating is generated, and production in which the operating state indicated by the binary data is continuous an identification unit that identifies the product type and the number of the products produced by the production apparatus by comparing the period and the production cycle for each product type, wherein it is specified that the product was produced by the production apparatus ; When the product type is changed from the first type to the second type, the production period during which the product of the first type is produced and the product of the second type is produced. The period that exists between the production period and the production period is defined as a period during which parts or devices for producing the first type of product to the second type of product are exchanged in the production equipment. A pattern of the detection data generated by the replacement work and acquired by the acquisition unit is learned, the acquired detection data is compared with the learned detection data, and the production apparatus produces a product. and an output unit for outputting information about the result specified by the specifying unit .

According to the present invention, it is possible to ascertain the production status of a production apparatus that does not have a function of recording the production status for each product type.

It is a figure showing an example of composition of production control system 1 concerning one embodiment of the present invention. 3 is a block diagram showing an example of a hardware configuration of an information processing device 30; FIG. 3 is a block diagram showing an example of a functional configuration of an information processing device 30; FIG. 4 is a flowchart showing an example of basic operation of the information processing device 30; 4 is a flowchart showing an example of a product type determination processing operation of the information processing device 30; 4 is a flowchart showing an example of a new product type estimation processing operation of the information processing device 30;

[composition]
FIG. 1 is a diagram showing an example of a production management system 1 of this embodiment. The production management system 1 includes a production device 10 that produces products, a so-called external sensor 20 attached to the production device 10, and an information processing device 30 that functions as an information processing device according to the present invention. The network 2 connects the sensor 20 and the information processing device 30 so that they can communicate with each other. The network 2 is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network), or a combination thereof, and includes wired sections and wireless sections. The network 2 is preferably a network conforming to, for example, the 5th generation mobile communication system, but is not necessarily limited to this.

The production apparatus 10 is an apparatus capable of producing multiple types of products by exchanging parts corresponding to the types of products. The production device 10 itself does not have a function of recording its own production status. The sensor 20 is detecting means for detecting the physical state of the production apparatus 10 and outputting the detected data, and is an acceleration sensor in this embodiment. The sensor 20 is attached to a predetermined portion of the production apparatus 10 by, for example, a method using adhesion, fixtures, or the like. The information processing device 30 is, for example, a computer such as a personal computer or a server device. Based on the detection data transmitted from the sensor 20 via the network 2, the information processing device 30 determines the production status of the product (for example, the number or amount of production of the product, the production period, the operation period of the production apparatus, and the period of operation). calculate. An analysis using this calculation result verifies, for example, a bottleneck in the production process of a product, and improves productivity.

FIG. 2 is a diagram showing an example of the hardware configuration of the information processing device 30. As shown in FIG. The information processing device 30 is physically configured as a computer device including a processor 3001, a memory 3002, a storage 3003, a communication device 3004, an input device 3005, an output device 3006, and a bus connecting them. Note that in the following description, the term "apparatus" can be read as a circuit, device, unit, or the like. The hardware configuration of the information processing device 30 may be configured to include one or more of each device shown in the figure, or may be configured without including some of the devices.

Each function of the information processing device 30 is performed by causing the processor 3001 to perform calculations, controlling communication by the communication device 3004, and controlling the It is realized by controlling at least one of data reading and writing in 3002 and storage 3003 .

The processor 3001, for example, operates an operating system and controls the entire computer. The processor 3001 may be configured by a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic device, registers, and the like. Also, for example, a baseband signal processing unit, a call processing unit, and the like may be implemented by the processor 3001 .

The processor 3001 reads programs (program codes), software modules, data, etc. from at least one of the storage 3003 and the communication device 3004 to the memory 3002, and executes various processes according to them. As the program, a program that causes a computer to execute at least part of the operations described later is used. The functional blocks of information processing device 30 may be implemented by a control program stored in memory 3002 and running on processor 3001 . Various processes may be executed by one processor 3001, but may also be executed by two or more processors 3001 simultaneously or sequentially. Processor 3001 may be implemented by one or more chips. Note that the program may be transmitted from the network 2 to the information processing device 30 via an electric communication line.

The memory 3002 is a computer-readable recording medium, and is composed of at least one of, for example, ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), and RAM (Random Access Memory). may be The memory 3002 may also be called a register, cache, main memory (main storage device), or the like. The memory 3002 can store executable programs (program code), software modules, etc. to perform the methods of the present invention.

The storage 3003 is a computer-readable recording medium, for example, an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (for example, a compact disk, a digital versatile disk, a Blu-ray disk), smart card, flash memory (eg, card, stick, key drive), floppy disk, magnetic strip, and/or the like. Storage 3003 may also be called an auxiliary storage device. The storage 3003 stores a program describing a procedure for executing operations to be described later, and data (various threshold values, etc.) used in these operations.

The communication device 3004 is hardware (transmitting/receiving device) for communicating between computers via at least one of a wired network and a wireless network, and is also called a network device, network controller, network card, communication module, or the like. The communication device 3004 includes a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc., in order to realize at least one of frequency division duplex (FDD) and time division duplex (TDD). may consist of For example, a transmitting/receiving antenna, an amplifier section, a transmitting/receiving section, a transmission path interface, etc. may be implemented by the communication device 3004 . The transceiver may be physically or logically separate implementations for the transmitter and receiver.

The input device 3005 is an input device (for example, a key, a microphone, a switch, a button, etc.) that receives input from the outside. The output device 3006 is an output device (for example, a display, a drive for a storage medium, a speaker, an LED lamp, etc.) that outputs to the outside. Note that the input device 3005 and the output device 3006 may be integrated (for example, a touch screen).

Each device such as processor 3001 and memory 3002 is connected by a bus for communicating information. The bus may be configured using a single bus, or may be configured using different buses between devices.

The information processing device 30 includes hardware such as a microprocessor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), and an FPGA (Field Programmable Gate Array). , and part or all of each functional block may be implemented by the hardware. For example, processor 3001 may be implemented using at least one of these pieces of hardware.

FIG. 3 is a block diagram showing an example of the functional configuration of the information processing device 30. As shown in FIG. As shown in FIG. 5, in the information processing device 30, functions of an acquisition unit 31, a specification unit 32, an output unit 33, and a registration unit 34 are realized. The acquisition unit 31 acquires detection data detected by the sensor 20 attached to the production apparatus 10 . The specifying unit 32 uses the detection data acquired by the acquiring unit 31 to specify the product type produced by the production apparatus 10, and specifies the production status of the product for each product type. The identification unit 32 includes a learning unit 321 that performs machine learning using, for example, supervised data for identifying product types and production conditions. The output unit 33 outputs information about the production status specified by the specifying unit 32 . When the learning unit 321 of the specifying unit 32 has learned to specify a new breed, the registration unit 34 prompts the user to select whether or not to register the learning result, and the user selects whether to register. If so, the identification unit 32 registers the learning result.

[motion]
Next, the operation of this embodiment will be described with reference to the flow charts illustrated in FIGS. In FIG. 4 , first, the acquisition unit 31 acquires detection data detected by the sensor 20 attached to the production apparatus 10 . This detection data is temporally continuous waveform data output from the sensor 20, which means the acceleration occurring in the production apparatus 10. FIG. The identification unit 32 determines whether or not the production apparatus 10 is operating based on the detection data (step S11). Specifically, the specifying unit 32 compares the value of the detection data with a predetermined threshold value, and determines whether the production apparatus 10 is in an operating state or non-operating state for a predetermined unit period (for example, in units of one second). Binary data (hereinafter referred to as operation data) indicating the state is generated. For example, if the value of the detected data is greater than or equal to the threshold value during the predetermined unit period, operation data "1" is generated, which means that the production apparatus 10 is in operation, and the value of the detected data increases during the predetermined unit period. If it is less than the threshold, operation data of "0" is generated, which means that it is in a non-operating state. The threshold used at this time is smaller than the acceleration that occurs when the production apparatus 10 is operating to actually produce the product, and is smaller than the minute acceleration that occurs as noise when the production apparatus 10 is not operating. is also a large value. This threshold is obtained by, for example, experiments, simulations, or calculations.

When the production apparatus 10 is in operation, that is, when the operation data is "1" (step S11; YES), the specifying unit 32 executes the product type determination process illustrated in FIG. 5 (step S12). On the other hand, if the production apparatus 10 is not in operation, that is, if the operation data is "0" (step S11; NO), the identification unit 32 determines that the period during which the operation data is "0" is N (N is the threshold value). ) minutes or more (step S13). If the period in which the operation data is "0" continues for N minutes or more (step S13; YES), the specifying unit 32 determines that the period is a period in which the production apparatus 10 is not producing products, It is specified that it is a rest period during which the device 10 is not in operation (step S14). If the period in which the operation data is "0" does not continue for N minutes or more (step S13; NO), the processing of the information processing device 30 returns to step S11.

Next, details of the product type determination process corresponding to step S12 in FIG. 4 will be described with reference to FIG. The specifying unit 32 determines whether the period of the generated operation data is longer than the production cycle of each product (step S121). Here, the identifying unit 32 stores in advance the length of the production cycle when producing each product by learning processing, which will be described later, manual setting in advance, or the like. When the identification unit 32 determines that the period of the operation data is longer than the production cycle of any product (step S121; YES), the product type identified from the operation data is processed in steps S121 to S126 as one cycle. It is determined whether or not the probability of matching with a certain product type (for example, product type X1) specified in the previous cycle (that is, immediately before) is greater than n% (where n is a threshold value) (step S122). Specifically, the learning unit 321 of the identifying unit 32 compares the detection data acquired as described above with a threshold to generate operation data. and "0", and the probability of being the product type X1 is calculated by comparing the learning result with the generated operation data. This learning process is performed by well-known machine learning using operation data generated from detection data detected by the sensor 20 as teacher data, for example, when the types of products being produced are known. there is Further, when the probability of identifying the product type based on the pattern of the operation data is equal to or less than the threshold value (n%), the identifying unit 32 detects data (that is, waveform data) acquired from the sensor 20, and The type of product is identified by comparing the detection data detected by the sensor 20 when producing a known product with the result of learning. This learning process is also executed by well-known machine learning using detection data detected by the sensor 20 as teacher data when the types of products being produced are known.

In this way, the identifying unit 32 attempts to identify the product type based on the operation data, which is binary data, and if the probability that the identification is correct is less than the threshold, attempts to identify the product type based on the detected data, which is waveform data. Then, if the probability of matching with the previously specified product type X1 is greater than n% (step S122; YES), the specifying unit 32 increments the production quantity of the specified product type X1 by one and stores it. (step S123). The reason why the type identification based on the operation data, which is binary data, is first attempted in this way is that the processing load required for the identification is smaller than the type identification based on the detection data, which is waveform data. is. On the other hand, the reason for trying to identify the product type based on the detection data, which is waveform data, when the probability that the product type identification based on the operation data, which is binary data, is correct is less than the threshold value is that the type identification based on the operation data, which is binary data This is because the accuracy of the identification is higher than that.

In step S122, if the probability of matching with a certain product type X specified immediately before is n% or less (step S122; NO), the identifying unit 32 selects one of the product types X2, X3, X4, . . . is greater than n % (step S124). In this determination as well, the specifying unit 32 attempts to specify the product type based on the operation data, which is binary data, and if the probability of correct specification is less than the threshold, attempts to specify the product type based on the detection data, which is waveform data. Here, if the probability of being any of the product types X2, X3, X4, . The setup period is specified from the specified time to the specified time of the product type X2 (step S125). The setup period is a period during which the production apparatus 10 does not produce products, and is a parts replacement period during which parts are exchanged for producing products in the production apparatus 10 . In this way, when the type of product being produced in the production apparatus 10 is changed from the first type to the second type, the specifying unit 32 determines the period during which the product of the first type is being produced. and the period during which the product of the second type is being produced by exchanging the parts for producing the product of the second type from the product of the first type in the production apparatus 10. Specify that it is a set-up period.

Furthermore, at this time, the learning unit 321 detects the data detected by the sensor 20 during the setup period (that is, the acceleration generated in the production apparatus 10 due to the part replacement work) or the pattern of the operation data generated according to the detected data. It learns in the same way as the method, and memorizes the learning result. In this way, the specifying unit 32 can also specify the setup period by comparing the detection data or operation data with the learning result.

When the type of the product being produced in the production apparatus 10 is specified in this way, the specifying unit 32 increments the production quantity of the specified type (here, type X2) by one and stores it (step S123). .

In step S124, if the probability of any of the product types X2, X3, X4, . If it does not match the operation data corresponding to the detection data detected by the sensor 20 when the product is on, the specifying unit 32 executes the new product type estimation process shown in FIG. End the process.

Next, the details of the new product determination process corresponding to step S126 in FIG. 5 will be described with reference to FIG. In FIG. 6, the identifying unit 32 determines whether or not the waveform data within the same or similar range are continuously repeated in the acquired detection data (step S1261). If it is determined that the waveform data within the same or similar ranges are continuously repeated (step S1261; YES), the specifying unit 32 specifies that a new product type is being produced (step S1262). ). At this time, the learning unit 321 of the identifying unit 32 learns the detection data and patterns of operation data generated from the detection data (that is, appearance patterns of binary data “1” or “0”). Then, the registration unit 34 prompts the user to select whether or not to register the learned pattern in the information processing device 30 . In response to this, when the user selects to register the learned pattern and inputs identification information (name) of a new product, the registration unit 34 causes the identification unit 32 to perform the learning. A registration process is performed to store the patterns of the waveform data and operation data obtained together with the identification information (step S1263). On the other hand, if it is not determined that the waveform data within the same or similar range are continuously repeated (step S1261; NO), the specifying unit 32 specifies that the target period is a period corresponding to noise. (step S1264). In other words, the specifying unit 32 does not specify that a new type of product is being produced.

The result of the identification by the identification unit 32 as described above, that is, the information on the production status of the production apparatus 10 is output by the output unit 33 by a predetermined method. This information includes, for example, the production number or production amount for each product type, the production period required to produce each product type, the operating period and non-operating period (break period and setup period) of the production equipment 10 for each product type. including), etc.

According to the embodiment described above, it is possible to grasp the production status of the production apparatus 10 that does not have the function of recording the production status for each product type. Further, according to the above-described embodiment, even when the production apparatus 10 operates with rest periods or setup periods intervening, it is possible to grasp the production status for each product type.

[Modification]
The invention is not limited to the embodiments described above. The embodiment described above may be modified as follows. Also, two or more of the following modified examples may be combined for implementation. The sensor 20 is not limited to the acceleration sensor exemplified in the embodiment, and may be of any type as long as it detects the physical state of the production apparatus 10 and outputs the detected data. Further, in the above embodiment, the information processing apparatus 30 is illustrated as an example of the information processing apparatus according to the present invention, but the present invention can be applied to any computer that implements the functional blocks illustrated in FIG.

It should be noted that the block diagrams used in the description of the above embodiments show blocks in units of functions. These functional blocks (components) are implemented by any combination of at least one of hardware and software. Also, the method of realizing each functional block is not particularly limited. That is, each functional block may be implemented using one device physically or logically coupled, or directly or indirectly using two or more physically or logically separated devices (e.g. , wired, wireless, etc.) and may be implemented using these multiple devices. A functional block may be implemented by combining software in the one device or the plurality of devices.

Functions include judging, determining, determining, calculating, calculating, processing, deriving, examining, searching, checking, receiving, transmitting, outputting, accessing, resolving, selecting, choosing, establishing, comparing, assuming, expecting, assuming, Broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, assigning, etc. can't For example, a functional block (component) that makes transmission work is called a transmitting unit or a transmitter. In either case, as described above, the implementation method is not particularly limited.

Each aspect/embodiment described in the present disclosure includes LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), FRA (Future Radio Access), NR (new Radio), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark) )), IEEE 802.16 (WiMAX®), IEEE 802.20, UWB (Ultra-WideBand), Bluetooth®, other suitable systems, and extended It may be applied to at least one of the next generation systems. Also, a plurality of systems may be applied in combination (for example, a combination of at least one of LTE and LTE-A and 5G, etc.).

The processing procedures, sequences, flowcharts, etc. of each aspect/embodiment described in this disclosure may be rearranged as long as there is no contradiction. For example, the methods described in this disclosure present elements of the various steps using a sample order, and are not limited to the specific order presented.

Information, etc., may be output from a higher layer (or lower layer) to a lower layer (or higher layer). It may be input and output via multiple network nodes.

Input/output information and the like may be stored in a specific location (for example, memory), or may be managed using a management table. Input/output information and the like can be overwritten, updated, or appended. The output information and the like may be deleted. The entered information and the like may be transmitted to another device.

The determination may be made by a value represented by one bit (0 or 1), by a true/false value (Boolean: true or false), or by numerical comparison (for example, a predetermined value).

Each aspect/embodiment described in the present disclosure may be used alone, may be used in combination, or may be used by switching according to execution. In addition, the notification of predetermined information (for example, notification of “being X”) is not limited to being performed explicitly, but may be performed implicitly (for example, not notifying the predetermined information). good too.
Although the present disclosure has been described in detail above, it should be apparent to those skilled in the art that the present disclosure is not limited to the embodiments described in this disclosure. The present disclosure can be practiced with modifications and variations without departing from the spirit and scope of the present disclosure as defined by the claims. Accordingly, the description of the present disclosure is for illustrative purposes and is not meant to be limiting in any way.

Software, whether referred to as software, firmware, middleware, microcode, hardware description language or otherwise, includes instructions, instruction sets, code, code segments, program code, programs, subprograms, and software modules. , applications, software applications, software packages, routines, subroutines, objects, executables, threads of execution, procedures, functions, and the like.
Software, instructions, information, etc. may also be sent and received over a transmission medium. For example, the software uses wired technology (coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL), etc.) and/or wireless technology (infrared, microwave, etc.) to create websites, Wired and/or wireless technologies are included within the definition of transmission medium when sent from a server or other remote source.

Information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may refer to voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, light fields or photons, or any of these. may be represented by a combination of
The terms explained in this disclosure and the terms necessary for understanding the present disclosure may be replaced with terms having the same or similar meanings. For example, the channel and/or symbols may be signaling. A signal may also be a message. A component carrier (CC) may also be called a carrier frequency, a cell, a frequency carrier, or the like.

As used in this disclosure, the terms "system" and "network" are used interchangeably.

In addition, the information, parameters, etc. described in the present disclosure may be expressed using absolute values, may be expressed using relative values from a predetermined value, or may be expressed using other corresponding information. may be represented. For example, radio resources may be indexed.
The names used for the parameters described above are not limiting names in any way. Further, the formulas, etc., using these parameters may differ from those expressly disclosed in this disclosure. Since the various channels (e.g., PUCCH, PDCCH, etc.) and information elements can be identified by any suitable names, the various names assigned to these various channels and information elements are in no way restrictive names. isn't it.

The terms "determining" and "determining" may encompass a wide variety of actions. "Judgement", "determining" are, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (eg, lookup in a table, database, or other data structure); Also, "judgment" and "determination" are used for receiving (e.g., receiving information), transmitting (e.g., transmitting information), input, output, access (accessing) (for example, accessing data in memory) may include deeming that a "judgment" or "decision" has been made. In addition, "judgment" and "decision" are considered to be "judgment" and "decision" by resolving, selecting, choosing, establishing, comparing, etc. can contain. In other words, "judgment" and "decision" may include considering that some action is "judgment" and "decision". Also, "judgment (decision)" may be read as "assuming", "expecting", "considering", or the like.

The terms "connected", "coupled", or any variation thereof, mean any direct or indirect connection or coupling between two or more elements, It can include the presence of one or more intermediate elements between two elements being "connected" or "coupled." Couplings or connections between elements may be physical, logical, or a combination thereof. For example, "connection" may be read as "access". As used in this disclosure, two elements are defined using at least one of one or more wires, cables, and printed electrical connections and, as some non-limiting and non-exhaustive examples, in the radio frequency domain. , electromagnetic energy having wavelengths in the microwave and optical (both visible and invisible) regions, and the like, can be considered to be “connected” or “coupled” to each other.

As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly specified otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

The “means” in the configuration of each device described above may be replaced with “unit”, “circuit”, “device”, or the like.

Where "include," "including," and variations thereof are used in this disclosure, these terms are inclusive, as is the term "comprising." is intended. Furthermore, the term "or" as used in this disclosure is not intended to be an exclusive OR.

In this disclosure, where articles have been added by translation, such as a, an, and the in English, the disclosure may include the plural nouns following these articles.

In the present disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean that "A and B are different from C". Terms such as "separate," "coupled," etc. may also be interpreted in the same manner as "different."

DESCRIPTION OF SYMBOLS 1... Production management system, 2... Network, 10... Production apparatus, 20... Sensor, 30... Information processing apparatus, 31... Acquisition part, 32... Identification part, 321... Learning part, 33... Output part, 34... Registration part, 3001... Processor, 3002... Memory, 3003... Storage, 3004... Communication device, 3005... Input device, 3006... Output device.

Claims (7)

an acquisition unit that acquires detection data representing a waveform of acceleration detected by the acceleration sensor from the acceleration sensor attached to the production apparatus;
comparing the acquired detection data with a threshold to generate binary data indicating an operating state in which the production device is in operation or a non-operating state in which the production device is not in operation; an identification unit that compares the production period during which the indicated operating state is continuous with the production cycle for each product type, and identifies the product type and the number of the products produced by the production apparatus;
an output unit that outputs information about the result specified by the specifying unit ;
The specifying unit is
When the type of the product specified to have been produced in the production apparatus is changed from the first type to the second type, the production period during which the product of the first type is produced and the The period existing between the production period during which the product of the second type is produced is the part or device for producing the product of the second type from the product of the first type in the production equipment. learning the pattern of the detection data generated by the replacement work and acquired by the acquisition unit during the replacement period,
The acquired detection data and the learned detection data are compared to identify a parts replacement period during which parts are replaced for producing a product in the production apparatus.
An information processing device characterized by: When the probability of identifying the product type based on the pattern of the binary data is equal to or less than a threshold value, the identification unit uses the acquired detection data and the sensor when the known product is being produced. 2. The information processing apparatus according to claim 1 , wherein the type of product produced by said production apparatus is identified by comparing the detection data detected by said production apparatus. When the binary data corresponding to the acquired detection data does not match the binary data corresponding to the detection data detected by the sensor when producing a known product 3. The information processing apparatus according to claim 2 , wherein it specifies that a new type of product is being produced. When the binary data corresponding to the acquired detection data does not match the binary data corresponding to the detection data detected by the sensor when producing a known product 4. The information processing apparatus according to claim 3 , wherein when said binary data corresponding to said acquired detection data is not continuous, it is not specified that a new type of product is being produced. When identifying that a new type of product is being produced, the identifying unit compares detection data detected by the sensor when the new type of product is being produced with a threshold value. 5. The information processing apparatus according to claim 3 , wherein the pattern of the binary data for specifying the new product is learned. a registering unit that prompts a user to select whether or not to register the learned pattern when the pattern of the binary data for specifying the new product type is learned by the specifying unit. 6. The information processing apparatus according to claim 5 . to the computer,
an acquisition unit that acquires detection data representing a waveform of acceleration detected by the acceleration sensor from the acceleration sensor attached to the production apparatus;
comparing the acquired detection data with a threshold to generate binary data indicating an operating state in which the production device is in operation or a non-operating state in which the production device is not in operation; an identification unit that identifies the product type and the number of the products produced by the production equipment by comparing the production period during which the indicated operating state is continuous with the production cycle for each product type , When the product type specified to have been produced in the production equipment is changed from the first type to the second type, the production period during which the product of the first type is produced and the second type The period existing between the production period during which two types of products are being produced is replaced by the replacement of parts or devices for producing the second type of product from the first type of product in the production equipment. the pattern of the detection data generated by the replacement work and acquired by the acquisition unit during the period is learned, and the acquired detection data and the learned detection data are compared. a specifying unit that specifies a parts replacement period during which parts are replaced for producing a product in the production apparatus ;
and an output unit that outputs information about the result specified by the specifying unit .

Images