Processing system and program

Abstract

We provide a processing system that can properly handle product replacement. [Solution] The processing system 310 includes a CPU 10a as at least one processing unit capable of performing processing related to the replacement of products used in equipment 90a, 90b, and 90c managed by the customer terminal 30. The CPU 10a performs the following: acquire update data for equipment 90a, 90b, and 90c; input the update data into an artificial intelligence model and acquire time-trace data of the load received by the product, which is output as an estimation result by the artificial intelligence model; and generate a product replacement plan based on the time-trace data.

Description

Technical Field

[0001] The disclosure according to this specification relates to a technology for realizing a circular economy.

Background Art

[0002] Patent Document 1 discloses a recycling operation system for a machine as a product. This system calculates the residual value of the current machine and decides to sell it as a used machine if there is a residual value. Also, even if the residual value is low, if it is determined that the parts of the machine are applicable to the regeneration of another machine and the profitability is achievable including the regeneration cost, the system decides to regenerate the other machine.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] As described above, in the technology of Patent Document 1, the current state of the product is judged. However, even if an attempt is made to recover the product such as selling it after obtaining the judgment of the current state, there may be cases where the recovery destination cannot be found immediately or the delivery of the product after replacement cannot be made immediately. Thus, in the technology of Patent Document 1, it is difficult to make a planned response.

[0005] For this reason, it is preferable to estimate in advance the load that the product receives and formulate a future replacement plan. However, there is a concern that when the load received by the product fluctuates discretely, the estimation accuracy decreases, and as a result, the replacement of the product cannot be appropriately realized.

[0006] One of the purposes of the disclosure of this specification is to provide a processing system and a program that can appropriately realize the replacement of a product. [Means for solving the problem]

[0007] One aspect disclosed herein is a processing system comprising at least one processing unit (10a) configured to perform processing relating to the replacement of products (91, 92, 93) used in equipment (90, 90a, 90b, 90c), At least one processing unit, To obtain equipment update data, The process involves inputting update data into an artificial intelligence model and obtaining time-trace data of the load the product experiences, which is then output as an estimation result by the AI ​​model. It is configured to generate a product replacement plan based on time-series data and to perform the following actions.

[0008] Furthermore, one of the disclosed embodiments is a program used for processing the replacement of products (91, 92, 93) used in equipment (90, 90a, 90b, 90c), At least one processor, To obtain equipment update data, The process involves inputting update data into an artificial intelligence model and obtaining time-trace data of the load the product experiences, which is then output as an estimation result by the AI ​​model. The system generates a product replacement plan based on time-series data and then executes the process.

[0009] According to these embodiments, the artificial intelligence model outputs time-trace data of the load on the products used in the equipment, corresponding to the equipment update data. In other words, this time-trace data accurately reflects the impact when the load on the products fluctuates discretely due to equipment updates, by using the equipment update data. Based on this time-trace data, a product replacement plan is generated, making it possible to implement product replacements in a planned manner. Therefore, product replacements can be implemented appropriately.

[0010] The symbols in parentheses included in the claims, etc., are illustrative examples illustrating the correspondence with the embodiments described later, and are not intended to limit the technical scope. [Brief explanation of the drawing]

[0011] [Figure 1] A diagram showing the general configuration of the processing system. [Figure 2] A diagram illustrating the functional architecture of the processing system. [Figure 3] A diagram showing an example of time-series data illustrating the load a product experiences. [Figure 4] A flowchart illustrating an example of a processing method used by a processing system. [Figure 5] A diagram showing the general configuration of the processing system. [Figure 6] A diagram showing the general configuration of the processing system. [Figure 7] A flowchart illustrating an example of a processing method used by a processing system. [Modes for carrying out the invention]

[0012] In this disclosure or claims, the term "processor" means one or more hardware processors configured to execute processing defined by computer program code (i.e., one or more instructions of a computer program) contained in a computer program by reading the code each time. In other words, a "processor" is a hardware device that executes one or more programmed processes. Therefore, computer program code can also be considered software that can define the processing of the processor according to its content. For example, a "processor" may be a general-purpose or specific-purpose processor and may be, but is not limited to, a CPU, microprocessor, GPU, and DFP (Data Flow Processor).

[0013] In this disclosure or claims, the term “memory” means one or more hardware memories that are non-transitional tangible recording media configured to record computer program code and / or data in a manner accessible from a processor. “Memory” can be implemented by memory technology such as SRAM, SDRAM, non-volatile flash memory, or other types of memory. The computer program code that constitutes the program is recorded in memory and executed by a processor, thereby enabling the processor to perform the various functions described above.

[0014] In this disclosure or claims, the term “circuit” refers to one or more logic circuits as hardware, configured to perform specific processing defined based on a pre-designed circuit configuration. In other words (and, in contrast to “processor”), “circuit” in this disclosure or claims refers to a hardware device that performs specific processing based on a circuit configuration, rather than processing defined by software such as the computer program code described above. For example, “circuit” may include custom ICs such as ASICs (Application Specific Integrated Circuits) and FPGAs (Field Programmable Gate Arrays) designed with a Hardware Description Language (HDL). That is, “circuit” in this disclosure or claims includes all hardware circuits except for the processor described above, which performs processing by reading computer program code.

[0015] In this disclosure or in the claims, the expression “at least one circuit and processor” should be interpreted as a disjunctive (logical OR) and not as at least one circuit and at least one processor.

[0016] In the present disclosure or claims, the term "processing unit" means a hardware device that executes processing by a "processor", "circuit", or a combination thereof. When the function of the "processing unit" cannot be realized by a "circuit" and can be realized by a "processor", it may mean the "processor" itself.

[0017] Hereinafter, a plurality of embodiments will be described based on the drawings. In each embodiment, the same reference numerals may be assigned to corresponding components, and redundant descriptions may be omitted. When only a part of the configuration is described in each embodiment, the configuration of other embodiments described previously can be applied to other parts of the said configuration. Further, not only the combinations of configurations explicitly shown in the description of each embodiment, but also the configurations of a plurality of embodiments can be partially combined with each other as long as there is no problem with the combination.

[0018] (First Embodiment) As shown in FIG. 1, the processing system 10 of the first embodiment is a system used to realize a circular economy (circular society). The processing system 10 is configured to be able to execute processing related to product replacement. The product here is a product existing at a predetermined location. The product has a lifespan due to deterioration and is a product for which replacement of itself or a part thereof is assumed.

[0019] As shown in FIG. 1, the product exists at a predetermined location on the earth. The product may be devices 91, 92, 93 such as an inverter, a motor, and an antenna provided in equipment 90 such as power generation equipment, power supply equipment, communication equipment, and signal equipment. When the product is fixedly installed, the location may be referred to as the installation location.

[0020] Here, product replacement means that if the product is equipment 91, 92, 93 installed in a facility, it may mean replacing the entire equipment 91, 92, 93, or it may mean replacing the sub-components that make up equipment 91, 92, 93 (the target product). Replacing the sub-components that make up the target product may correspond to so-called repair or maintenance of the target product.

[0021] As shown in Figure 2, the processing system 10 is configured primarily around, for example, at least one computer. The computer comprising the processing system 10 has at least one CPU (Central Processing Unit) 10a and at least one memory 10b. The memory 10b may be a non-transitory tangible storage medium that non-temporarily stores computer programs, data, artificial intelligence models, etc., that can be read by the CPU 10a. Furthermore, the computer may have a rewritable volatile storage medium such as RAM (Random Access Memory) 10c. The CPU 10a can execute various processes according to the computer programs stored in the memory 10b.

[0022] Furthermore, the computer includes an interface 10d for exchanging data with the outside world. Interface 10d may include a communication interface for connecting the computer to the internet or external devices. Interface 10d may also include an operation interface such as a keyboard or mouse for accepting human input.

[0023] The computer may also include a display 10e. The display 10e may be a liquid crystal display or an OLED display that displays the results of processing by the computer as an image. The display 10e may also be a projection-type display such as a projector.

[0024] The processing system 10 includes an information gathering unit P1, a life estimation unit P2, and a planning unit P3 as functional units whose functions are realized when the CPU 10a executes a computer program stored in memory 10b.

[0025] The information gathering unit P1 is configured to acquire various types of information used for planning product replacements. These types of information may include update data for the equipment 90 on which the product is installed. The update data is data relating to replacements, repairs, additions, reductions, specification changes, etc., that have occurred in the equipment 90 in the past. The update data may include at least one of the following: information identifying the product to be replaced, information regarding the specifications of the product, and information regarding the work period for the replacement. The information regarding the work period for the replacement may include information regarding the start and end dates of the work for the replacement.

[0026] Furthermore, the various information may include operational data of the equipment 90 on which the product is installed. The operational data may include information regarding the past operational status of the equipment 90. For example, if multiple devices 91, 92, and 93 are used in the equipment 90, it is preferable to show the operational status of each device 91, 92, and 93. The operational status may simply indicate whether the equipment or device is operating or stopped at a particular time or period, or it may also indicate the operating mode and output of the equipment or device while it is in operation.

[0027] To obtain update data and operational data for the equipment 90, the processing system 10 may be connected to the equipment 90 via the Internet or other means. The processing system 10 may also obtain update data and operational data from other systems, such as a server of the management company that manages the equipment 90.

[0028] In this way, the various types of information collected by the information collection unit P1, including update data or operational data, are provided to the artificial intelligence I1. More specifically, the information collection unit P1 converts the data acquired from equipment 90, servers on the internet, etc., into a format suitable for input to the artificial intelligence I1, and then outputs it to the artificial intelligence I1.

[0029] Artificial intelligence I1 outputs time-series data showing the load on the product when various information, including update data and operational data of equipment 90, is input. Artificial intelligence I1 may consist of one artificial intelligence model, or it may consist of multiple artificial intelligence models. The artificial intelligence model referred to here is mainly composed of a neural network. A neural network is a mathematical model that mimics the nerve cells of the human brain. The neural network may be a deep neural network used for deep learning, a convolutional neural network, or a recurrent neural network.

[0030] The following describes an example in which artificial intelligence I1 includes multiple load estimation models M1, M2, M3 and a mode selection model M4. Load estimation models M1, M2, and M3 are artificial intelligence models that assume different load modes and perform estimation using learning.

[0031] The load mode referred to here is a mode that indicates the tendency of the load that a product experiences. For example, one load mode is one that assumes that a single device installed in equipment 90 is responsible for all of the output of equipment 90. Another load mode is one that assumes that multiple products installed in equipment 90 share the output of equipment 90. Yet another load mode is one that assumes, for example, that during the construction period of equipment 90, the device operates while suppressing its output in parallel with the construction.

[0032] Then, each load estimation model M1, M2, and M3, upon receiving various information, estimates the load on the device under consideration for replacement in its assumed load mode and outputs time-transition data showing the time progression of the load on the device. The time-transition data may be, for example, a load curve that expresses the load as a function of time. In this case, the load curve may be represented by a polynomial that shows a continuous value with respect to time, and the load estimation models M1, M2, and M3 may be configured to output the coefficients of the polynomial.

[0033] Alternatively, the load estimation models M1, M2, and M3 may be configured to output values ​​at each point on the load curve as pairs of (time value, load value), and the output data may be configured to allow the load curve to be reconstructed using spline interpolation or the like. Furthermore, the load estimation models M1, M2, and M3 may be configured to output a graph of the load curve as image data.

[0034] The mode selection model M4 identifies the timing of load mode changes based on update data and operational data of the equipment 90. The load mode change timing identified here may include predictions of future change timings. For example, the mode selection model M4 may predict load mode changes based on past update data of the equipment 90.

[0035] At the timing of a change in load mode, the load on the product can vary discretely. On the other hand, in continuity intervals where the same load mode persists, the load on the product exhibits continuity. The mode selection model M4 can use a linear regression model to select the appropriate model from among multiple load estimation models M1, M2, and M3 for each continuity interval where the same load mode persists.

[0036] For example, the mode selection model M4 acquires time-lapse data (hereinafter referred to as load measurement data) of loads previously received by products installed in the same equipment 90 or similar equipment 90 in order to select a model. This load measurement data can be acquired by the information collection unit P1 from sources such as equipment 90 and servers on the internet, and provided to the mode selection model M4.

[0037] The mode selection model M4 uses the measured load data as the explanatory variable and the load curve output from one of the load estimation models M1, M2, and M3 as the dependent variable, calculating the sum of squared errors. This process is performed for all load estimation models M1, M2, and M3. Then, the mode selection model M4 decides to adopt the load estimation model that outputs the load curve with the smallest sum of squared errors among the load curves output from each load estimation model M1, M2, and M3.

[0038] Furthermore, when re-estimating the product load for the same continuation period after mode selection, only the selected load estimation model from among the multiple load estimation models M1, M2, and M3 is used; the other load estimation models do not need to be used. In other words, the time-course data (load curve) output by the selected load estimation model is used.

[0039] Thus, the mode selection model M4 outputs time-course data that assumes changes in load modes and the resulting discrete fluctuations in load. For example, as shown in Figure 3, there are continuous intervals PR1 to PR6, and time-course data is output that shows discrete fluctuations in load at the transition points between these intervals. In Figure 3, the load within the same continuous interval is shown as a straight line, but it could also be shown as a curve.

[0040] Here, we will explain how the load mode changes in the time-series data shown in Figure 3. This time-series data shows the load received by the device 91.

[0041] In the first continuation period PR1, for example, before the addition of device 93 shown in Figure 1, the two devices 91 and 92 operate to share the output of equipment 90. In the second continuation period PR2, for example, device 92 of the two devices 91 and 92 is shut down for maintenance, and device 91 operates to handle all of the output of equipment 90. As a result, the load on device 91 increases. In the third continuation period PR3, the state returns to the same state as in continuation period PR1.

[0042] In the fourth continuation section PR4, construction work is underway to add an additional device 93 to equipment 90. As a result of the construction, the output of device 92 has decreased, and the load on device 91 has increased relatively. In the fifth continuation section PR5, the construction is complete, and the three devices 91, 92, and 93 are operating to share the output of equipment 90. However, since the additional device 93 is undergoing trial operation with reduced output, the load on device 91 is lower than in continuation section PR4, but not significantly. In the sixth continuation section PR6, the trial operation of the additional device 93 is completed, and the output of the additional device 94 is increased. As a result, the load on device 91 is lower than in continuation section PR5.

[0043] The mode selection model M4 may perform the above-mentioned calculations by processing a computer program, but it may also perform equivalent functions using an artificial intelligence model.

[0044] The lifespan estimation unit P2 estimates the remaining lifespan of the product based on the time-evolution data output from the artificial intelligence I1. Specifically, if the relationship between the product load and the remaining lifespan has been formulated or is held as data, the lifespan estimation unit P2 estimates the remaining lifespan based on that formula or data. For example, the lifespan estimation unit P2 compares the cumulative load calculated based on the time-evolution data with the upper limit of the cumulative load that is permissible to maintain the state in which the product exhibits the required functions and performance. The lifespan estimation unit P2 then acquires the timing at which the cumulative load based on the time-evolution data reaches the permissible upper limit. This timing becomes the timing at which the product reaches the end of its lifespan (hereinafter referred to as the lifespan timing), and the remaining lifespan is from the present until the timing at which the product reaches the end of its lifespan.

[0045] On the other hand, if such formulas or data are not available, the life estimation unit P2 may estimate the remaining life by comparing information on the product specifications and average life with time-evolution data.

[0046] The planning unit P3 formulates a product replacement plan based on the remaining life estimated by the life estimation unit P2. Formulating the replacement plan may include determining the timing of product replacement. The replacement timing may include a recommended replacement timing. The replacement timing may be a predetermined period before the life timing. The predetermined period may be a buffer time that takes into account, for example, the possibility of replacement being delayed due to a problem.

[0047] The planning of replacements may include proposing ways to extend the lifespan of products or reduce replacement costs. The planning unit P3 may acquire update data for equipment 90 and propose operational means to reduce the load and cost of devices 91, 92, and 93 as products. The planning unit P3 may also propose extending the lifespan of device 91 by relatively reducing the output load of device 91 compared to devices 92 and 93, and shifting the replacement timing to a period when replacement costs are low.

[0048] The development of a replacement plan may include selecting a destination for the product's repurposing (or collection). For example, if the product is used in critical equipment that serves as infrastructure, it may be operated under high safety standards, but it may be possible to repurpose it for equipment that can be operated under lower safety standards than those required for infrastructure (e.g., factory equipment). For this reason, the planning department P3 may acquire information on potential repurposing destinations and select a candidate for repurposing.

[0049] The planning unit P3 then generates display content to show the planned replacement plan. The planning unit P3 displays the generated display content on the display 10e of the processing system 10. By confirming the replacement plan displayed on the display 10e, the user can schedule the replacement and begin arranging for the replacement product and replacement company, negotiating with the recipient, etc.

[0050] Next, an example of how the processing system 10 plans product replacement will be explained using the flowchart in Figure 4. The series of processes in steps S1 to S6 of this flowchart may be realized by the CPU 10a executing a computer program stored in memory 10c.

[0051] In S1, the information gathering unit P1 acquires update data and operational data for the equipment 90. In S2, after processing in S1, the processing system 10 uses artificial intelligence I1 to estimate time-series data through learning. In S3, after processing in S2, the life estimation unit P2 estimates the remaining lifespan of devices 91, 92, and 93 from the time-series data.

[0052] In S4, following the processing in S3, the planning unit P3 formulates a product replacement plan. Specifically, the planning unit P3 selects a replacement destination and calculates the replacement timing. In S5, the processing in S4, the planning unit P3 displays the plan on the display 10e. The series of processes ends with S5.

[0053] According to the first embodiment described above, the artificial intelligence model outputs time-trace data of the load received by devices 91, 92, and 93, which are products used in equipment 90, in response to the update data of equipment 90. That is, this time-trace data reflects the effect when the load received by devices 91, 92, and 93 fluctuates discretely due to equipment updates, by using the update data of equipment 90. Based on this time-trace data, a replacement plan for devices 91, 92, and 93 is generated, making it possible to systematically replace products. Therefore, product replacement can be appropriately implemented.

[0054] Furthermore, according to the first embodiment, the update data includes data relating to the update timing of the equipment 90. The time-series data output by the artificial intelligence model includes data showing that the load fluctuates discretely before and after the update timing. Since the replacement plan for devices 91, 92, and 93 is generated based on the load reflecting these discrete fluctuations before and after the update timing, it becomes possible to achieve product replacement in a more planned manner.

[0055] Furthermore, according to the first embodiment, multiple load estimation models M1, M2, and M3 are provided as artificial intelligence models, and these models have different assumptions about load modes, which are modes that indicate the tendency of load occurrence on the devices 91, 92, and 93 as products. Then, time-transition data for the corresponding load mode is output individually from each load estimation model M1, M2, and M3. Furthermore, multiple time-transition data with different load mode assumptions are compared with past load measurement data, and one time-transition data to be adopted is selected from the multiple time-transition data with different load mode assumptions. By using this selection method, even when the load occurrence tendency changes due to the replacement of equipment 90, etc., it is possible to estimate the load with high accuracy and generate a replacement plan for devices 91, 92, and 93.

[0056] (Second Embodiment) As shown in Figures 5 and 6, the second embodiment is a modified version of the first embodiment. The second embodiment will be described focusing on the differences from the first embodiment.

[0057] In the processing system 210 of the second embodiment, the information collection unit P1 is configured to collect industry data in addition to update data. The industry data is data relating to the industry to which the equipment 90, which houses the product subject to the replacement plan, belongs. For example, if the equipment 90 is semiconductor manufacturing equipment, the industry data will be data relating to the semiconductor industry.

[0058] Industry data may include information indicating industry growth (e.g., growth rate) and information indicating investment in the industry (e.g., investment amount). Industry data may include data showing past performance, but may also include data showing future predictions by experts.

[0059] The information gathering unit P1 may acquire industry data from a cloud server CS via the internet. The cloud server CS refers to a server on a network realized through cloud computing, and may be realized by multiple processing units located in remote locations that are far apart from each other. The information gathering unit P1 may, for example, use a web search engine to search for information on the internet and acquire updated data or operational data from the cloud server CS, which is the source of the retrieved information.

[0060] For example, the information gathering unit P1 collects a large amount of information as industry data, such as online news articles and stock price information related to the industry that are publicly available on the internet, and inputs it into the artificial intelligence I1. For this reason, in the artificial intelligence I1 of the second embodiment, it is preferable that at least one of the load estimation models M1, M2, M3 and mode selection model M4 is a model that can handle at least one type of input from text such as online news articles and images. For example, at least one of the load estimation models M1, M2, M3 and mode selection model M4 may be a multimodal artificial intelligence model that can process two or more types of information (modals) from data, text and images simultaneously.

[0061] These industry data contribute to improving the accuracy of predicting the future expansion and reduction possibilities of equipment 90. For example, if the industry's growth rate and investment are on an upward trend, the likelihood of equipment 90 expansion is predicted to be high and the likelihood of reduction to be low, and the artificial intelligence I1 outputs time-trending load data showing a tendency for the load on a certain device 91 installed in equipment 90 to decrease in the future. Conversely, if the industry's growth rate and investment are on a downward trend, the likelihood of equipment 90 expansion is predicted to be high and the likelihood of reduction to be low, and the artificial intelligence I1 outputs time-trending load data showing a tendency for the load on a certain device 91 installed in equipment 90 to increase.

[0062] According to the second embodiment described above, when the artificial intelligence model receives update data for equipment 90, as well as industry data related to the industry to which equipment 90 belongs, it outputs time-trace data of the load received by devices 91, 92, and 93. In other words, this time-trace data accurately reflects the update history of equipment 490 and future predictions of industry capital investment by using a combination of update data for equipment 90 and industry data. Based on this time-trace data, a replacement plan for devices 91, 92, and 93 is generated, making it possible to systematically replace products. Therefore, product replacement can be appropriately implemented.

[0063] Furthermore, according to the second embodiment, industry data is acquired by searching and collecting a large amount of information describing the industry, including at least one type of text and images from the internet. The artificial intelligence model is a multimodal artificial intelligence model that responds to data input including update data for equipment 90 and at least one type of text and images from the internet as industry data. In this way, by collecting information from the internet and responding flexibly, it becomes possible to generate replacement plans that reflect the latest industry trends with high accuracy.

[0064] (Third embodiment) As shown in Figures 6 and 7, the third embodiment is a modified version of the second embodiment. The third embodiment will be described focusing on the differences from the second embodiment.

[0065] The processing system 310 of the third embodiment is connected to multiple customer terminals 30 via the Internet. The processing system 310 can provide multiple customer terminals with replacement plans for products used in equipment managed by the customers. Since the services provided to each customer are substantially the same, communication with one representative customer terminal 30 will be described below.

[0066] The processing system 310 has a management database (hereinafter referred to as the management DB) 10g. The management DB 10g is configured to include at least one type of non-transitional physical storage medium, such as semiconductor memory, magnetic media, and optical media. The management DB 10g stores data about customers to whom the processing system 310 provides exchange plans. The data about customers may include the IP address of the customer-side terminal 30 for communication with the customer, information about the equipment 90a, 90b, and 90c managed by the customer, and records of bids made by the customer. The data about customers may also include information on whether or not the customer permits automated bidding.

[0067] Furthermore, the management DB10g stores data on contractors who perform product replacements. If the contractor is a seller of the products, the contractor's data may include the latest data on the unit price and delivery date of the products sold by that contractor. If the contractor is a repurposed or recycled contractor, the contractor's data may include the latest data on potential repurposed products or recycling methods. If the contractor is a contractor that performs replacement work, the contractor's data may include the latest data on the contractor's labor costs and availability. In addition, the contractor's data may include records of orders placed with the contractors.

[0068] The processing system 310 should be connected to each vendor's terminal 80a and 80b in a communication-enabled manner. This allows the processing system 310 to obtain the latest data about the vendors from the vendor terminals 80a and 80b. In addition, the processing system 310 can execute order processing to the vendors.

[0069] The customer-side terminal 30 is a system that manages the equipment 90a, 90b, and 90c. The customer-side terminal 30 is mainly composed of, for example, at least one computer. The computer that makes up the customer-side terminal 30 has at least one CPU 30a and at least one memory 30b. The memory 30b may be a non-transitional tangible storage medium that non-temporarily stores computer programs, data, artificial intelligence models, etc. that can be read by the CPU 30a. Furthermore, the computer may have a rewritable volatile storage medium such as RAM 30c. The CPU 30a can execute various processes according to the computer programs stored in the memory 30b.

[0070] Furthermore, the computer includes an interface 30d for exchanging data with the outside world. Interface 30d may include a communication interface for connecting the computer to the internet or external devices. Interface 30d may also include an operation interface such as a keyboard or mouse for accepting human input.

[0071] The computer may also include a display 30e. The display 30e may be a liquid crystal display or an OLED display that displays the results of computer processing as an image. The display 30e may also be a projection-type display such as a projector.

[0072] Furthermore, the customer terminal 30 may be equipped with a management DB 30g. The management DB 30g stores data on the managed equipment 90a, 90b, and 90c that the customer manages. The data on the managed equipment includes information such as the number, status, specifications, location, purchase date, and contract of the managed equipment 90a, 90b, and 90c. The status of the equipment 90a, 90b, and 90c includes the status of the products installed on the equipment 90a, 90b, and 90c. The contract information includes whether or not the customer has permitted automatic bidding, i.e., information on the automatic bidding permission settings. The customer terminal 30 should be able to obtain the latest data for each piece of equipment 90a, 90b, and 90c via communication or other means.

[0073] The customer terminal 30 then periodically, or based on customer operations, requests the processing system 310 to formulate a replacement plan for the requested products among the products used in the managed equipment 90a, 90b, and 90c. The processing system 310 then uses an algorithm similar to that of the first or second embodiment to estimate the remaining lifespan of the requested products and formulate a replacement plan.

[0074] Specifically, the information gathering unit P1 acquires the facilities 90a, 90b, 90c, etc., where the requested product is located, which will be used by the artificial intelligence I1 to output time-trace data of the load. Here, the information gathering unit P1 only needs to acquire the location of the requested product from the customer terminal 30. Then, the time-trace data of the load of the requested product is estimated by learning using the artificial intelligence I1. Next, based on this time-trace data, the life estimation unit P2 estimates the remaining life of the requested product.

[0075] If the customer has permitted automated bidding, the planning unit P3, after formulating a replacement plan as in the first embodiment, transmits the replacement plan and a bidding command for the replacement of the products based on that plan to the customer-side terminal 30. However, the planning unit P3 also selects the vendor that will sell the replaced products and the vendor that will carry out the replacement work.

[0076] The customer terminal 30 stores the received exchange plan in the management DB 30g and transmits the acceptance information of the bid instruction to the processing system 310. The planning unit P3 stores the bid record (the record that the customer terminal 30 accepted the bid) in the management DB 10g.

[0077] Furthermore, the planning unit P3 executes arrangements based on the replacement plan. It places orders for products and / or construction work with the contractors selected in the replacement plan, preferably both. In other words, the order information is transmitted to each contractor's terminal 80a, 80b.

[0078] On the other hand, if the customer does not permit automated bidding, Planning Department P3 will formulate multiple replacement plans with different optimization conditions. Planning Department P3 will formulate replacement plans that prioritize reducing environmental impact, replacement plans that prioritize reducing replacement costs, replacement plans that prioritize reducing the probability of trouble occurring, and balanced replacement plans, etc.

[0079] These exchange plans are displayed as a selectable list on the display 30e of the customer terminal 30, and the customer's manual bid is completed by selecting an exchange plan from among the multiple exchange plans. In other words, the customer terminal 30 transmits bid acceptance information corresponding to the exchange plan selected by the customer to the processing system 310.

[0080] Next, an example of how the processing system 310 plans product replacement will be explained using the flowchart in Figure 7. The series of processes in steps S101 to S110 of this flowchart may be realized by the CPU 10a executing a computer program stored in memory 10c.

[0081] Steps S101 to S103 are the same as steps S1 to S3 in the first embodiment. In step S104, after processing step S103, the planning unit P3 determines whether the customer has permitted automated bidding. If yes, proceed to step S105. If no, proceed to step S108.

[0082] In S105, similar to S5 in the first embodiment, the planning unit P3 formulates a replacement plan based on the remaining lifespan of the product. Next, the planning unit P3 transmits the replacement plan and a bid command. In S106, after processing S105, the planning unit P3 receives bid acceptance information as a reply to the transmission in S105 and records it in the management DB 10g. This confirms the bid. In S107, after processing S106, the planning unit P3 transmits order information to the vendor terminals 80a and 80b based on the replacement plan. The series of processes ends with S107.

[0083] Meanwhile, in S108, the planning unit P3 formulates multiple exchange plans. In S109, after processing S108, the planning unit P3 generates content for the customer to select an exchange plan from the formulated plans and transmits it to be displayed on the display 30e of the customer terminal 30. After processing S109, when the planning unit P3 receives bid acceptance information for the exchange plan selected by the customer, in S110, the planning unit P3 transmits order information to the vendor terminals 80a and 80b based on the exchange plan. The series of processes ends with S110.

[0084] According to the third embodiment described above, the processing system 310 is connected to the customer terminal 30 as a customer-side system and is also connected to the vendor terminals 80a and 80b as vendor-side systems. The CPU 10a of the processing system 310 obtains information regarding the permission settings for automatic bidding from the customer terminal 30. If automatic bidding is permitted, the CPU 10a sends order information to the vendor terminals 80a and 80b, based on the generated replacement plan, which includes at least one of the following: an order for products used in the equipment 90a, 90b, and 90c, and an order for the replacement work of said products. Automatic bidding can reduce the burden on the customer while appropriately promoting the replacement of products.

[0085] Furthermore, according to the third embodiment, the processing system 310 is connected to the customer terminal 30, which is a customer-side system, in a communicative manner. The CPU 10a of the processing system 310 generates multiple replacement plans, each with different conditions, as product replacement plans. The CPU 10a transmits the generated multiple replacement plans to the customer terminal 30. Subsequently, the customer terminal 30 receives the replacement plan selected from among the multiple replacement plans. In this way, by having the customer select the optimal plan from among multiple replacement plans with different conditions, it is possible to promote appropriate product replacement that reflects the customer's situation and judgment.

[0086] Furthermore, according to the third embodiment, in a configuration in which a selection is made from multiple replacement plans, the processing system 310 is further connected to the contractor-side terminals 80a and 80b, which are contractor-side systems, in a communicative manner. The CPU 10a is further configured to transmit order information to the contractor-side terminals 80a and 80b, including at least one of the following: an order for products to be used in the equipment 90a, 90b, and 90c, and an order for the replacement work of the products, based on the selected replacement plan. By having the processing system 310 handle the arrangements on behalf of the customer, the replacement of products can be promoted appropriately while reducing the burden on the customer.

[0087] (Other embodiments) Although several embodiments have been described above, this disclosure is not limited to those embodiments and can be applied to various embodiments and combinations without departing from the spirit of this disclosure.

[0088] In another embodiment, the equipment that houses the product for which load estimation and replacement planning is generated may be subject to movement in location. For example, the equipment may be a mobile vehicle such as an automobile, train, aircraft, drone, robot, ship, or submarine, and the product may be a component mounted on such a mobile vehicle.

[0089] In another embodiment, the time-elapsed data may be represented by discrete data rather than by a function, a curve, or a set of functions or curves provided for each continuation interval. For example, the time-elapsed data may consist of a set of data represented as (numerical value of time, numerical value of load). In this case, if information indicating the boundaries of continuation intervals is added to this set of data, it becomes possible to estimate the remaining life from the load with high accuracy.

[0090] In another embodiment, the planning of the replacement may include selecting a company to replace the products.

[0091] In another embodiment, the mode selection model M4 may use a logistic regression model instead of a linear regression model.

[0092] In another embodiment, the CPU 10a may input industry data into the artificial intelligence model instead of update data, and obtain time-series data as an estimation result.

[0093] In another embodiment, a processor other than a CPU, such as a GPU, may be used for the artificial intelligence model.

[0094] In another embodiment, at least one of the computers constituting the processing systems 10, 210, and 310, the computer constituting the customer-side terminal 30, and the computers constituting the vendor-side terminals 80a and 80b may be configured to implement functions such as load estimation and replacement planning using circuits such as FPGAs together with the processor. [Explanation of Symbols]

[0095] 10,210,310: Processing system, 10a: CPU (processing unit), 90,90a,90b,90c: Equipment, 91,92,93: Device (product)

Claims

1. A processing system comprising at least one processing unit (10a) configured to perform processing related to the replacement of products (91, 92, 93) used in equipment (90, 90a, 90b, 90c), The at least one processing unit is, To acquire update data for the aforementioned equipment, The updated data is input into an artificial intelligence model, and the time-series data of the load received by the product, which is output as an estimation result by the artificial intelligence model, is obtained. A processing system configured to generate a replacement plan for the product based on the aforementioned time-series data.

2. The update data includes data relating to the update timing of the equipment, The processing system according to claim 1, wherein the time-series data output by the artificial intelligence model includes data indicating that the load fluctuates discretely before and after the update timing.

3. The at least one processing unit is, The equipment is further configured to acquire industry data relating to the industry to which it belongs. The processing system according to claim 1, wherein acquiring the aforementioned time-series data includes, in addition to the update data, inputting the industry data into an artificial intelligence model and acquiring time-series data of the load the product will receive, which is output by the artificial intelligence model as a future estimation result.

4. Obtaining the aforementioned industry data includes searching and collecting a large amount of information describing the industry, including at least one type of text and image from the internet. The processing system according to claim 3, wherein the artificial intelligence model is a multimodal artificial intelligence model that corresponds to data input including the update data and the at least one type of industry data.

5. Multiple artificial intelligence models (M1, M2, M3) are provided. Obtaining the aforementioned time-series data means The process involves acquiring the time-series data for each load mode, which is output from multiple artificial intelligence models, each of which has a different assumption for the load mode, which is a mode that indicates the tendency of the load that the product is subjected to. The processing system according to claim 1, comprising: comparing a plurality of time-transition data sets with different assumed load modes with past load measurement data; and selecting the time-transition data set to be adopted from the plurality of time-transition data sets with different assumed load modes.

6. It is connected to the customer's system (30) in a way that allows communication, and is also connected to the vendor's systems (80a, 80b) in a way that allows communication, The aforementioned product is a product managed using the aforementioned customer-side system, The at least one processing unit is, Obtaining information regarding the permission settings for automated bidding from the customer's system, The processing system according to claim 1, further configured to transmit, when the automated bidding is permitted, order information, including at least one of the following, an order for the product and an order for the replacement work of the product, to the contractor's system based on the generated replacement plan.

7. The customer-side system (30) is connected in a way that allows it to communicate with the customer's system. The aforementioned product is a product managed using the aforementioned customer-side system, Generating a replacement plan for the aforementioned product includes generating multiple replacement plans with different conditions, The at least one processing unit is, Sending the generated multiple exchange plans to the customer-side system, The processing system according to claim 1, further configured to receive and execute the exchange plan selected from the plurality of exchange plans from the customer-side system.

8. It is connected to the vendor's system (80a, 80b) in a way that enables communication. The processing system according to claim 5, further configured to transmit order information, including at least one of the following: an order for the product and an order for replacement work for the product, to the contractor's system, based on the selected replacement plan.

9. A program used for processing the replacement of products (91, 92, 93) used in equipment (90, 90a, 90b, 90c), At least one processor, To acquire update data for the aforementioned equipment, The updated data is input into an artificial intelligence model, and the time-series data of the load received by the product, which is output as an estimation result by the artificial intelligence model, is obtained. A program that generates a replacement plan for the product based on the aforementioned time-series data and performs the following actions.

Images