Distributed system and data processing method

Abstract

To provide a decentralized system and a data processing method that, when an abnormality occurs on an edge device, identify another edge device adopting the component that caused the abnormality.SOLUTION: A decentralized system 101 comprises an edge device 20 that converts status data indicating status of a component as a constituent of a replaceable unit constituting an edge device to diagnostic data according to a prescribed diagnostic algorithm and a diagnosis CS 30 that acquires the diagnostic data from a first edge device to identify a second edge device adopting a component relevant to the component adopted in the first edge device.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a distributed system and a data processing method. [Background technology]

[0002] Patent document 1 describes an external fault diagnosis device that diagnoses faults that occur in a vehicle. The fault diagnosis device acquires fault information detected in the vehicle, and when a fault is detected in the vehicle, changes the fault occurrence determination criteria for faults in other vehicles of the same model as the vehicle to make it easier to determine a fault than the default setting, thereby determining whether the fault is specific to the vehicle or is one that can generally occur in vehicles of the same model as the vehicle. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2006-349429 A Summary of the Invention [Problem to be solved by the invention]

[0004] According to the technology described in Patent Document 1, when a fault occurs in a vehicle, it is possible to determine whether or not the same fault is likely to occur in other vehicles of the same model as the faulty vehicle. However, the vehicles for which it is possible to determine the possibility of the same fault occurring are limited to the same model as the faulty vehicle.

[0005] Furthermore, edge devices such as vehicles are composed of numerous components, and each component is used in a wide variety of edge devices, not limited to the same model of vehicle. Therefore, if a failure or abnormality occurs in an edge device and the cause is a specific component, edge devices in which a failure or abnormality may occur in the future will not be limited to the same model of vehicle as the edge device in which the failure or abnormality occurred. Therefore, it is desirable to be able to determine whether a failure or abnormality may occur and to output an alert for a wide variety of edge devices that use the component. However, such technology has not yet been established.

[0006] The present invention has been made in consideration of the above-mentioned points, and aims to make it possible, when an abnormality occurs in an edge device, to identify other edge devices that use the component that caused the abnormality. [Means for solving the problem]

[0007] The present application includes a number of means for solving at least part of the above problems, examples of which are as follows.

[0008] In order to solve the above problem, a distributed system according to one embodiment of the present invention is a distributed system comprising an edge device which is at least one of an automatically operable mobile body and equipment, and a diagnostic data calculator, wherein the edge device converts status data representing the status of components as components of replaceable units which constitute the edge device into diagnostic data according to a predetermined diagnostic algorithm, and the diagnostic data calculator acquires the diagnostic data from a first edge device, refers to a database relating to the configuration of the edge device, and identifies a second edge device which employs a component related to the component employed in the first edge device. Effect of the Invention

[0009] According to the present invention, when an abnormality occurs in an edge device, it is possible to identify other edge devices that employ the component that caused the abnormality.

[0010] Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a diagram illustrating an example of the configuration of a distributed system according to a first embodiment of the present invention. [Diagram 2] FIG. 2 is a diagram illustrating an application example of a distributed system. [Diagram 3] FIG. 3 is a diagram illustrating an example of a hardware configuration of a computer. [Figure 4] FIG. 4 is a diagram illustrating an example of a configuration of a distributed system according to the second embodiment of the present invention. [Diagram 5] FIG. 5 is a diagram showing an example of a transmission and reception protocol for status data. [Figure 6] FIG. 6 is a diagram showing an example of a cause analysis result screen for edge device manufacturers and repairers. [Figure 7] FIG. 7 is a diagram showing an example of a cause analysis result screen for an edge device operator. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] In the following embodiments, for convenience, when necessary, the description will be divided into multiple sections or embodiments, but unless otherwise expressly stated, they are not unrelated to each other and one is a partial or complete variation, detail, supplementary explanation, etc. of the other.

[0013] In addition, in the following embodiments, when referring to the number of elements (including the number, numerical values, amounts, ranges, etc.), unless specifically stated or clearly limited to a specific number in principle, the number is not limited to that specific number and may be more than or less than the specific number.

[0014] Furthermore, it goes without saying that in the following embodiments, the components (including element steps, etc.) are not necessarily essential unless specifically stated otherwise or unless they are clearly considered essential in principle.

[0015] Similarly, in the following embodiments, when referring to the shapes, positional relationships, etc. of components, etc., unless otherwise specified or considered to be obviously different in principle, it includes those that are substantially similar or similar to the shapes, etc. The same applies to the above numerical values ​​and ranges.

[0016] In addition, in all the drawings for explaining the embodiments, the same members are generally designated by the same reference numerals, and the repeated explanation thereof will be omitted. Hereinafter, each embodiment of the present invention will be explained with reference to the drawings.

[0017] <Configuration Example of Distributed System 101 According to the First Embodiment of the Present Invention> FIG. 1 shows an example of the configuration of a distributed system 101 according to a first embodiment of the present invention.

[0018] The distributed system 101 includes an edge device 20 and a diagnostic cloud server (CS) 30. The edge device 20 is connected to the diagnostic cloud server 30 via a network 1. The network 1 is a two-way communication network represented by, for example, a mobile phone communication network, the Internet (including Ethernet), and the like. Note that, although only one edge device 20 is shown in the figure, in reality, a wide variety of edge devices 20 operated by various service providers exist and are connected to the diagnostic cloud server 30 via the network 1.

[0019] The edge device 20 is assumed to be, for example, a moving body capable of automatic operation (e.g., a vehicle, a drone, a robot) or equipment (e.g., a robot arm, a machine tool, a numerically controlled lathe, etc.). Here, automatic operation includes automatic driving. Here, automatic operation refers to operation by electronic control, and includes not only fully automatic operation that does not require human control, but also partial automatic operation in which some of the moving parts are automatically electronically controlled, and automatic operation under limited conditions. As an example, the edge device 20 can be a vehicle capable of automatic driving.

[0020] The edge device 20 includes an intra-edge controller 21, a plurality of components 25, and a communication unit 26.

[0021] The edge controller 21 is composed of a computer such as an ECU (Electronic Control Unit), a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), etc. The edge controller 21 has a control unit 22, a data collection and diagnosis unit 23, and a sequence storage memory 24.

[0022] The control unit 22 controls the operations of the data collection and diagnosis unit 23 and the components 25. For example, the control unit 22 controls actuators (not shown) included in the components 25 based on status data collected by the data collection and diagnosis unit 23 from the components 25.

[0023] The data collection and diagnosis unit 23 collects status data of the components of the component 25, such as sensors, power supplies, and actuators (none of which are shown), from the memory 252 of each component 25 in accordance with the collection conditions. In addition, the data collection and diagnosis unit 23 converts the status data collected from the component 25 into diagnostic data representing the internal status of the intra-edge controller 21 in accordance with a predetermined diagnostic algorithm, and outputs the data to the communication unit 26. Specifically, for example, the status data is compared with a predetermined threshold value, and the data is converted into diagnostic data based on the comparison result.

[0024] The sequence storage memory 24 stores beforehand collection conditions, such as the type, period, and procedure of status data to be collected, and a diagnostic algorithm for converting status data into diagnostic data.

[0025] The components 25 are functional, logical, or physical components for realizing various replaceable units such as a moving mechanism or an operating mechanism included in the edge device 20. For example, if the edge device 20 is a vehicle, the units include, for example, an engine, a motor, a brake, a car navigation system, an ECU, and the like.

[0026] The component 25 includes a data acquisition unit 251 and a memory 252 .

[0027] The data acquisition unit 251 acquires state data representing the state of each component 25 from registers, log data, and the like within each component 25 .

[0028] The memory 252 temporarily stores the state data acquired by the data acquisition unit 251 .

[0029] The communication unit 26 assigns identification information of the edge device 20 to the diagnostic data input from the data collection and diagnosis unit 23, and transmits the data to the diagnostic cloud server 30 via the network 1. The communication unit 26 also receives collection conditions and diagnostic algorithms transmitted from the diagnostic cloud server 30 via the network 1, and outputs the data to the control unit 22.

[0030] The diagnostic cloud server 30 is composed of one or more computers existing on the network 1. In this embodiment, instead of a cloud server such as the diagnostic cloud server 30, one or more locally existing computers may be used. Conversely, instead of a local computer, a cloud server may be used. The diagnostic cloud server 30 corresponds to the diagnostic computer of the present invention.

[0031] The diagnostic cloud server 30 includes a communication unit 31, a processing memory 32, an edge device DB (database) 33, and a processing unit .

[0032] The communication unit 31 receives diagnostic data transmitted from the edge device 20 via the network 1. The communication unit 31 also transmits control information to the edge device 20 via the network 1.

[0033] The processing memory 32 temporarily stores diagnostic data of the edge device 20 received by the communication unit 31, various processing results by the processing unit 34, and the like.

[0034] The edge device DB33 stores information related to the edge device 20, such as the model of the edge device 20, configuration information of the edge device 20 (the model of the components 25 used in the units that make up the edge device 20, the locations where the components 25 are used, etc.), the manufacturer of the edge device 20, the service provider that operates the edge device 20, the repair company, etc., in association with each other.

[0035] The processing unit 34 executes a diagnosis process P1, a factor analysis process P2, an edge group search process P3, an intra-group diagnosis process P4, and an abnormality determination process P5. Each process will be described below.

[0036] <Overview of Distributed System 101> In the distributed system 101 configured as above, the data collection and diagnosis unit 23 of the edge device 20 collects status data from the memory 252 of the component 25 according to collection conditions transmitted in advance from the diagnostic cloud server 30 and stored in the sequence storage memory 24, and converts the status data into diagnostic data according to a diagnostic algorithm. Then, the communication unit 26 transmits the diagnostic data to the diagnostic cloud server 30 via the network 1 according to a predetermined communication protocol.

[0037] In the diagnostic cloud server 30, the communication unit 31 receives the diagnostic data transmitted from the edge device 20 and stores it in the processing memory 32. Then, as a diagnostic process P1, the processing unit 34 diagnoses the presence or absence of an abnormality or failure in the edge device 20 based on the diagnostic data of the edge device 20 stored in the processing memory 32. The diagnostic result is stored in the processing memory 32 in association with the diagnostic data. Here, the edge device 20 diagnosed as to the presence or absence of an abnormality or failure corresponds to the first edge device of the present invention.

[0038] Next, the processing unit 34 analyzes the diagnostic data of the edge device 20 as a cause analysis process P2, and identifies the component 25 that caused the abnormality or failure. The causes of the abnormality or failure include a hardware failure of the component 25 and an abnormality in the operation by the software. The method of analyzing the cause of the failure, for example, is to create a database of correlations between the diagnostic data and the component 25 that caused the abnormality, and to find the cause of the failure based on the database. The method of implementing this database may be a list of conditions and results, or machine learning using a neural network may be used. In other words, any method may be used as long as it is a format and a method of searching for correlations between the diagnostic data and the cause of the abnormality or failure in the edge device. The method of identifying the location where the abnormality or failure has occurred may be to find the location using the correlation between the state data and the cause of the abnormality or failure.

[0039] Next, the processing unit 34 performs edge group search processing P3 by referring to the edge device DB 33 to search for a group of edge devices 20 that employ a component related to the component 25 that caused the abnormality or failure. Here, the component related to the component 25 that caused the abnormality or failure includes not only a component that is completely identical to the component 25 that caused the abnormality or failure, but also, for example, a component that has a common basic design but has different specifications or versions, an OEM product, etc. Hereinafter, the component 25 that caused the abnormality or failure is referred to as a cause component, and a component related to the cause component is referred to as a related component. Note that the relationship of the component to the cause component may be set by the user in addition to the above-mentioned example.

[0040] In this group search, edge devices 20 that employ related components are grouped from various viewpoints. For example, edge devices 20 that employ related components and are of the same model may be grouped, edge devices 20 that employ related components and are manufactured by the same manufacturer may be grouped, edge devices 20 that employ related components and are operated by the same service provider may be grouped, edge devices 20 that employ related components and have approximately the same usage environment (usage area, etc.) or usage status (for example, if the edge device is a vehicle, mileage, etc.) may be grouped, edge devices 20 that employ related components regardless of manufacturer or service provider and that employ the related components in the same mechanism may be grouped, or edge devices 20 that employ related components and have the same or similar connection form between the related components and other components in the edge devices 20 may be grouped. The types of grouping are not limited to the above examples. The conditions for grouping can be determined by the user.

[0041] Next, as an intra-group diagnosis process P4, the processing unit 34 diagnoses the degree of association between the edge device 20 in which the cause component is adopted and an edge device that belongs to the same group as the edge device 20, i.e., that adopts a related component. For example, if the cause component and the related component are adopted in the same mechanism in each edge device 20, the degree of association is diagnosed as high, and if they are adopted in different mechanisms, the degree of association is diagnosed as low. An edge device that belongs to the same group as the edge device 20 corresponds to the second edge device of the present invention.

[0042] Next, as an anomaly determination process P5, the processing unit 34 determines the possibility of occurrence of the same anomaly or failure as that of the edge device 20 diagnosed as having an anomaly or failure, based on the diagnostic data of other edge devices belonging to the same group as the edge device 20 diagnosed as having an anomaly or failure, according to the relevance of each of the grouped edge devices. Specifically, for example, a database may be created of the correlation between the time series change in the diagnostic data and the possibility (probability) of occurrence of the anomaly or failure, and the possibility of occurrence of the anomaly or failure may be determined based on the database. The database may be implemented by listing the time series change and probability of the status data, or by using machine learning using a neural network.

[0043] Furthermore, in the abnormality determination process P5, an alert is output for other edge devices that belong to the same group as the edge device 20 diagnosed as having an abnormality or failure and that are determined to have the possibility of an abnormality or failure occurring, to the manufacturers, repairers, operating companies, etc. (hereinafter collectively referred to as the service provider) of the other edge devices. Note that the alerts to be output can be classified as emergency, warning, caution, report, etc., depending on the probability of an abnormality or failure occurring. In addition, the granularity of the alert can be arbitrarily set by the user, such as by edge device, mechanism, component, etc.

[0044] As described above, according to the distributed system 101, the edge device 20 and the diagnostic cloud server 30 external to the edge device 20 cooperate with each other to diagnose an abnormality or failure that has occurred in the edge device 20 and identify the causal component. In addition, a group of edge devices in which a component related to the causal component is adopted can be searched for, and the possibility that the same abnormality or failure as that of the edge device 20 diagnosed as having an abnormality or failure will occur in other edge devices belonging to the group can be diagnosed, and an alert according to the diagnosis result can be output to a service provider or the like related to the other edge devices. This allows the service provider or the like to perform maintenance on the edge device before an abnormality or failure actually occurs, and can prevent the edge device from having an abnormality or failure, thereby improving the availability rate of the edge device.

[0045] <Application example of Distributed Systems 101> Next, FIG. 2 shows a data flow in a distributed system 1000 which is an application example of the distributed system 101 shown in FIG.

[0046] The distributed system 1000 is multi-tiered and includes one or more edge devices 20, one or more cloud servers (or computers) included in a data management layer 40, and one or more cloud servers (or computers) included in a data utilization layer 50.

[0047] 1 is divided into a diagnostic management cloud server 301 and an analytical outsourcing cloud server 302. The diagnostic management cloud server 301 is disposed in the data management layer 40, and the analytical outsourcing cloud server 302 is disposed in the data utilization layer 50.

[0048] The edge device 20 includes an in-edge controller 21 , a movement / actuation mechanism 221 , and a sensor 222 .

[0049] The edge controller 21 is, for example, an ECU, and controls a moving / actuating mechanism 221. The moving / actuating mechanism 221 is at least one of a moving mechanism and an actuating mechanism. The moving mechanism is, for example, an engine or a motor in a moving body. The actuating mechanism is, for example, an actuator such as a motor or hydraulics in equipment. The moving / actuating mechanism 221 corresponds to a unit composed of components 25 (FIG. 1).

[0050] In relation to the moving / operating mechanism 221, "operation" means at least "performing a certain action, causing the equipment to change its state in accordance with the operating command (in accordance with JIS B 0132)."

[0051] In the following description, for the sake of simplicity, an example will be described in which the edge device 20 is a moving body and the movement / operation mechanism 221 is a moving mechanism. The intra-edge controller 21 is abbreviated as an ECTL (Edge Controller) 21.

[0052] The ECTL 21 is responsible for at least a part of the processing for realizing the automatic operation of the edge device 20. Therefore, the ECTL 21 is becoming more complex in terms of hardware or software. For example, an example of the complexity of the hardware may include a GPU (Graphics Processing Unit), an FPGA (Field-Programmable Gate Array), a processor dedicated to neural networks, or other hardware that accelerates machine learning in order to introduce machine learning processing or processing for recognizing and determining data input from various sensors and cameras in real time.

[0053] The data management layer 40 is a virtual layer or group introduced for the sake of simplicity. The cloud server (or computer) included in the data management layer 40 is mainly used to store data generated by the edge device 20.

[0054] The data management layer 40 may include cloud servers (or computers) for purposes other than those described above. Also, the data management layer 40 may omit cloud servers (or computers) for the purposes described above.

[0055] In the case of the same figure, the data management layer 40 includes an edge device manufacturer's computer 41, an edge data distribution company's cloud server (CS) 42, and a diagnosis management cloud server (CS) 301.

[0056] The edge device manufacturer computer 41 is a computer owned by a company that designs or manufactures the edge device 20. The manufacturing company manages edge data related to the edge device 20 for the development and maintenance of its own products, and therefore is included in the data management layer 40 for convenience. The edge device manufacturer computer 41 transmits update data D23 of the edge data to the edge data distribution company cloud server 42.

[0057] The edge data distribution company cloud server 42 stores edge data related to the edge device 20, and transmits unprocessed or processed edge data D25 to the requesting cloud server in response to a request from another cloud server. The edge data D25 is assumed to be, for example, information about the components of the edge device 20.

[0058] Here, data processing refers to, for example, changing the data expression format, calculating differential values, calculating statistical values, encryption, decryption, compression, decompression, removal of unnecessary data, adding or removing redundant codes, data extraction, etc., but if the processing entity changes some or all of the data it received and then transmits it, this may be considered processing.

[0059] The diagnostic management cloud server 301 stores diagnostic data (hereinafter also referred to as edge controller diagnostic data or EXTL diagnostic data) D11, and transmits unprocessed or processed diagnostic data D21, D27 in response to requests from other cloud servers. Note that EXTL diagnostic data is an example of edge data.

[0060] From one perspective, a company that has a cloud server (or a computer) included in the data management layer 40 may be considered to be a company that designs or manufactures the edge device 20 or its parts, or a company that distributes edge data. From this perspective, the cloud servers of a communication company, a company that provides a car navigation program executed on a smartphone, and a manufacturer of a mobile device can be considered to be included in the data management layer 40.

[0061] The data utilization layer 50 is a virtual layer or group introduced for the sake of simplicity. The cloud servers (or computers) included in the data utilization layer 50 are used for purposes that are closer to providing services than the cloud servers included in the data management layer 40.

[0062] Services to be provided by the cloud servers included in the data utilization layer 50 include edge device-related services that utilize the edge device 20, target entities related to the edge device 20, or target the edge device 20 itself.

[0063] Here, entities may be, for example, a group or group of people, animals, or equipment (a traffic light, a ship transporting the edge device 20, or an equipment outside the edge device 20 that supports the automatic operation of the edge device 20).

[0064] In the same figure, the data utilization layer 50 includes cloud servers of various service providers such as a dealer / repair company cloud server (CS) 51, an insurance company cloud server (CS) 52, a transportation company cloud server (CS) 53, a MaaS company cloud server (CS) 54, a rental company cloud server (CS) 55, and an analysis outsourcing cloud server (CS) 302.

[0065] The dealer / repair company cloud server 51 receives the edge data D25 or the post-analysis data D32 provided from the analysis outsourcing cloud server 302, in order to support the arrangement of repairs and maintenance of the edge device 20 and the work of the dealer or repair company that repairs the edge device 20, and generates information necessary for maintenance services (failure prevention, repairs, etc.) or repair services (accident response) and presents it to the user.

[0066] The insurance company cloud server 52 executes a part or all of the processes for determining insurance rates by referring to the edge data D25 or the post-analysis data D32 provided from the analysis outsourcing cloud server 302 at an insurance company that underwrites a predetermined risk related to the business of a company that performs a predetermined function using the edge device 20. Note that the risk underwritten by the insurance company may be a risk related to an entity that uses the edge device 20. For example, automobile insurance contracted by an individual is envisioned.

[0067] The transportation company cloud server 53 executes processing for providing transportation services based on the edge data D25 or the post-analysis data D32 provided from the analysis outsourcing cloud server 302 at a transportation company that directly uses the edge device 20 or uses the services of other transportation companies to operate a transportation business. Examples of the processing include vehicle dispatch planning for the edge device 20, fare calculation, and analysis processing for revising service specifications including fares.

[0068] The MaaS (Mobility as a Service) company cloud server 54 receives the edge data D25 or the post-analysis data D31 provided from the analysis outsourcing cloud server 302 and generates information required for a maintenance plan for the owned vehicles, in order to support the business of the MaaS company that directly uses the edge device 20 or provides a transportation means service using the services of other companies. The information is provided to employees of the MaaS company, etc.

[0069] The rental company cloud server 55, like the MaaS company cloud server 54, directly uses the edge device 20 or supports the business of a rental company that provides a means of transportation service using the services of other companies, and receives the edge data D25 or the post-analysis data D31 provided from the analysis outsourcing cloud server 302 and generates information required for a maintenance plan for the owned vehicles, etc. The information is provided to employees of the rental company, etc.

[0070] The analysis outsourcing cloud server 302 is a cloud server to which companies included in the data utilization layer 50 outsource the analysis of service-related analysis processes necessary for providing or improving services. The analysis outsourcing cloud server 302 is in charge of outsourcing the analysis of status data and diagnostic data (especially EXTL diagnostic data) D11, but can also be in charge of outsourcing analysis of edge device manufacturers and the like included in the data management layer 40. The service-related analysis process by the analysis outsourcing cloud server 302 corresponds to the factor analysis process regarding the internal state of the edge device 20 or EXTL 21 in each solution described later.

[0071] The division into the data management layer 40 and the data utilization layer 50 is not limited to the example shown in the figure, and may not be exclusive. For example, the cloud server of a communications company corresponds to the edge data distribution company cloud server 42 included in the data management layer 40 from the perspective of the distribution of edge data described above, but it can also be considered to be included in the data utilization layer 50 from the perspective of providing "communication services" to the edge device 20.

[0072] Furthermore, from another perspective, a company that has a cloud server included in the data utilization layer 50 can be considered as a company that does not design or manufacture the edge device 20 or its parts.

[0073] <Example of the configuration of the computer 100 that constitutes the cloud server> Next, FIG. 3 shows an example of a hardware configuration of a computer 100 that constitutes a cloud server such as the diagnostic cloud server 30. As shown in FIG.

[0074] The computer 100 includes a processor 101, a memory 102, an external storage device 103, a voice output device 104, a biometric information input device 105, an operation input device 106, an output device 107, and a communication device 108, all of which are connected via a data bus 109.

[0075] The processor 101 includes a CPU, a GPU, an FPGA, etc., and controls the entire computer 100. The memory 102 is a main storage device such as a RAM (Random Access Memory), etc. The external storage device 103 is a non-volatile storage device capable of storing digital information, such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), or a flash memory.

[0076] The audio output device 104 includes a speaker, etc. The biometric information input device 105 includes a camera, a gaze input device, a microphone, etc. The operation input device 106 includes a keyboard, a mouse, a touch panel, etc. The output device 107 includes a display, a printer, etc.

[0077] The communication device 108 is composed of a NIC (Network Interface Card) or the like. The communication device 108 communicates with other devices connected to the same network by at least one of wired communication and wireless communication. For this communication, packet communication according to TCP / IP (Transmission Control Protocol / Internet Protocol) is adopted, but the present invention is not limited to this, and communication according to other protocols such as UDP (User Datagram Protocol) may also be adopted.

[0078] The hardware configuration of the computer 100 is not limited to the above example, and some of the above components may be omitted or other components may be included. The computer 100 may be any of various information processing devices, such as a server computer, a personal computer, a notebook computer, a tablet computer, a smartphone, or a television device.

[0079] The computer 100 can store and externally load programs such as an OS (Operating System), middleware, and application programs, and can execute various types of processing by the processor 101 executing these programs.

[0080] <Configuration Example of Distributed System 102 According to the Second Embodiment of the Present Invention> FIG. 4 shows an example of the configuration of a distributed system 102 according to the second embodiment of the present invention.

[0081] The distributed system 102 is obtained by adding a collection condition change process P6 to the processes executed by the processing unit 34 of the diagnostic cloud server 30 in the distributed system 101.

[0082] The collection condition change process P6 makes it possible to change the collection conditions (type of status data to be collected, period, procedure, etc.) when status data is collected from an edge device that has been determined in the abnormality determination process P5 to be at risk of an abnormality or failure, converted into diagnostic data, and transmitted to the diagnostic cloud server 30.

[0083] This makes it possible to quickly detect abnormalities or failures in other edge devices that belong to the same group as the edge device 20 diagnosed as having an abnormality or failure.

[0084] For example, since electronic devices as components 25 are prone to errors at high temperatures, location information is acquired so that the usage environment (area) of other edge devices 20 can be determined, and when the device is traveling in a warm area in summer, a change is instructed to shorten the collection period of diagnostic data.

[0085] Also, for example, the values ​​of status registers, etc. that have not been read during normal operation when no abnormality or failure has been diagnosed are collected as status data, and a change is instructed to send them as is as diagnostic data.

[0086] Also, for example, if a problem occurs inside component 25 that is likely to cause cable deterioration, a change is instructed to send the number of communication errors inside component 25, which was not monitored under normal circumstances, as diagnostic data.

[0087] The processing unit 34 of the diagnostic cloud server 30 can instruct to restore the collection conditions instructed to be changed in the collection condition change process P6 to the collection conditions before the change in response to a predetermined trigger (for example, an operation by a user or a determination by the processing unit 34). This can prevent the edge device 20 from becoming overloaded due to a change in the collection conditions, which would cause problems in normal operation.

[0088] According to the distributed system 102 described above, in addition to the same actions and effects as those of the distributed system 101, by changing the collection conditions, it is possible to diagnose with higher accuracy the possibility of an abnormality or failure occurring in other edge devices belonging to the same group as the edge device 20 diagnosed as having an abnormality or failure. Therefore, by performing maintenance before an abnormality or failure occurs, it is possible to improve the operating rate of the edge devices and reduce maintenance costs.

[0089] Next, FIG. 5 illustrates an example of a transmission / reception protocol between the edge device 20 and the diagnostic cloud server 30 in the distributed system 102.

[0090] In the edge device 20 , the data collection and diagnosis unit 23 of the intra-edge controller 21 periodically collects status data from the components 25 at a predetermined cycle and converts it into diagnostic data. The communication unit 26 transmits the diagnostic data to the diagnostic cloud server 30 .

[0091] Next, the diagnostic cloud server 30 stores the received diagnostic data in the processing memory 32, and executes a diagnostic process P1, a factor analysis process P2, an edge group search process P3, an intra-group diagnostic process P4, and an abnormality determination process P5 based on the diagnostic data. Then, as a collection condition change process P6, an instruction to change the collection conditions of an edge device diagnosed as having a possibility of an abnormality or failure (for example, an instruction to shorten the cycle of collecting status data) is generated and transmitted to the edge device.

[0092] Next, in the edge device that has received the instruction to change the collection conditions, status data is collected from the components 25 in accordance with the changed collection conditions, and the communication unit 26 transmits the diagnostic data to the diagnostic cloud server 30.

[0093] Then, the diagnostic cloud server 30 executes diagnostic processing P1, factor analysis processing P2, edge group search processing P3, intra-group diagnostic processing P4, and abnormality determination processing P5 based on the diagnostic data collected and transmitted according to the changed collection conditions.

[0094] 5, when an edge device 20 is diagnosed as having an abnormality or failure, the collection conditions can be changed for edge devices that belong to the same group as the edge device 20. This makes it possible to quickly and accurately detect abnormalities or failures in other edge devices that belong to the same group as the edge device 20 diagnosed as having an abnormality or failure.

[0095] <Example of the cause analysis result screen> 6 shows an example of a cause analysis result screen 500 for manufacturers and repairers that is displayed on the edge device manufacturer's computer 41, dealer and repair company cloud server 51 (both in FIG. 2), etc. The cause analysis result screen 500 is generated by the processing unit 34, and is displayed on the screen of the edge device manufacturer's computer 41, etc. that has connected to the diagnostic cloud server 30.

[0096] The cause analysis result screen 500 displays the name, model, and user history of the edge device 20 diagnosed by the diagnostic cloud server 30 as having an abnormality or a malfunction, as well as the cause analysis result.

[0097] The cause analysis results include items such as the location where the abnormality occurred, devices managed by the same operator, collection conditions / environmental information, and abnormality causes.

[0098] The item of abnormality occurrence location displays the name of the causative component in the edge device 20 that has been diagnosed as having an abnormality or failure, the date of occurrence, and the time of occurrence.

[0099] The item for devices managed by the same operator displays the name, model, and degree of association of other edge devices that are managed by the operator that operates the edge device 20 diagnosed as having an abnormality or failure and that use components related to the causal component.

[0100] The collection conditions / environmental information item displays the rule type, temperature, and humidity, which indicate the collection conditions of the status data.

[0101] The "Cause of Abnormality" item displays the specific cause of the abnormality in the cause component. The cause of abnormality also includes information to determine whether it is a software or hardware abnormality. This is because the methods of resolving software and hardware abnormalities often differ significantly.

[0102] By viewing the cause analysis result screen 500, the manufacturer or repairer of the edge device 20 can identify the edge device 20 in which the abnormality or failure occurred and the cause component that caused the abnormality or failure. In addition, the manufacturer or repairer can identify the names, models, and degrees of association of other edge devices that use related components of the cause component. Thus, the manufacturer of the edge device 20 can, for example, review the basic design of the cause component or the related components, or consider replacing them with other components. The repairer of the edge device 20 can, for example, prepare replacement parts for the cause component or the related components in preparation for repairs that may be requested in the future.

[0103] 7 shows a display example of a factor analysis result screen 600 for an operator who operates the edge device 20, which is displayed on the transportation company cloud server 53, the MaaS company cloud server 54, the rental company cloud server 55 (all of which are shown in FIG. 2), etc. The screen information of the factor analysis result screen 600 is generated by the processing unit 34, and is displayed on the screen of the transportation company cloud server 53, etc., that has connected to the diagnosis cloud server 30.

[0104] The cause analysis result screen 600, like the cause analysis result screen 500 (FIG. 5), displays the name, model, and user history of the edge device 20 diagnosed by the diagnostic cloud server 30 as having an abnormality or a malfunction, as well as the cause analysis result.

[0105] The cause analysis results include items such as the location where the abnormality occurred, the location where the abnormality occurred, the urgency of the abnormality response, devices within the group that require response, the response content, and the abnormality cause and abnormality occurrence history.

[0106] The item of abnormality occurrence location displays the name of the causative component in the edge device 20 that was diagnosed as having an abnormality or failure, the date of occurrence, and the time of occurrence. The item of abnormality occurrence location displays the address and latitude / longitude indicating the location where the abnormality or failure occurred.

[0107] The urgency of the abnormality response item highlights whether the abnormality requires immediate replacement or repair, response after return, or response during regular maintenance.

[0108] The item of in-group device requiring support displays the name, model, and relevance of edge devices 20 that require support such as repair among the edge devices 20 operated by the operating company.

[0109] The "Response Details" field displays the specific details of the response, such as repairs, etc. The "Anomaly Cause / Anomaly Occurrence History" field displays the specific anomaly cause in the factor component and the anomaly occurrence history in the factor component.

[0110] By looking at the cause analysis result screen 600, the operator of the edge device 20 can identify the edge device 20 in which an abnormality or failure has occurred and the component 25 that caused the abnormality. Furthermore, the operator can understand the urgency of the response to the abnormality and the repair cost.

[0111] This allows the operator of the edge device 20 to select one of the following measures.

[0112] Countermeasure method 1: Prioritize repair work on edge devices 20 where an abnormality has occurred farther than the base location of the operator. An example of a case in which this priority is reflected is the arrangement of a replacement edge device 20.

[0113] Countermeasure method 2: Resolve an abnormal condition with a low urgency before it becomes an abnormal condition with a high urgency. For example, if an abnormal condition is not fatal to the edge device 20 by itself, the priority can be set to low, but if an abnormal condition with a high correlation is of high urgency, the former abnormal condition is set to a higher priority than low, such as medium or high.

[0114] Countermeasure 3: Prioritize repair work based on urgency. In particular, when abnormal conditions with different urgency levels occur within the same edge device 20, resolving the abnormal condition with a higher urgency may also resolve the abnormal condition with a lower urgency.

[0115] Next, we will explain the application of this solution to an insurance company.

[0116] Even if the insurance premium is low, if the insurance payment conditions are strict, the contracting entity will not enter into a contract. On the other hand, if the insurance payment conditions are too relaxed, the insurance company's balance sheet will deteriorate too much and the service will not be able to continue. Therefore, by referring to the processed data provided by the analysis outsourcing cloud server 302, the insurance payment conditions can be set taking into account the frequency of occurrence of abnormal conditions inside the edge device 20.

[0117] For example, if it is found that the frequency of occurrence of an abnormal state inside the edge device 20 is high under certain conditions (e.g., type of edge device 20, usage environment, type of usage entity), it becomes possible to optimize the insurance premium by increasing the insurance premium for those conditions and decreasing the premium for conditions where the frequency of occurrence is low.

[0118] Furthermore, by monitoring the state of the edge device 20, it becomes possible to take action before a functional failure occurs, increasing the probability of preventing opportunity loss due to accidents or breakdowns. In that case, insurance payments can be reduced, and the insurance premium rate can be lowered. This advantage becomes even greater when the number of abnormalities related to the edge device 20 increases due to the spread of automated operation.

[0119] The technology according to the present invention is not limited to a distributed system and a data transmission method, but can be provided in various forms such as a computer and a computer-readable program.

[0120] The present invention is not limited to the above-described embodiment, and various modifications are possible. For example, the above-described embodiment has been described in detail to clearly explain the present invention, and is not necessarily limited to those having all of the configurations described. In addition, it is possible to replace a part of the configuration of one embodiment with a configuration of another embodiment, or to add a part of the configuration of another embodiment.

[0121] In addition, each of the above configurations, functions, processing units, processing means, etc. may be realized in hardware, for example, by designing a part or all of them in an integrated circuit. In addition, each of the above configurations, functions, etc. may be realized in software by a processor interpreting and executing a program that realizes each function. Information such as a program, table, file, etc. that realizes each function can be placed in a memory, a recording device such as a hard disk or SSD, or a recording medium such as an IC card, an SD card, or a DVD. In addition, the control lines and information lines are shown as those that are considered necessary for explanation, and not all control lines and information lines are necessarily shown in the product. In reality, it may be considered that almost all configurations are connected to each other. [Explanation of symbols]

[0122] 1 network, 101, 102 distributed system, 20 edge device, 21 edge controller, 22 control unit, 23 data collection and diagnosis unit, 24 sequence storage memory, 25 component, 251 data acquisition unit, 252 memory, 26 communication unit, 30 diagnostic cloud server, 31 communication unit, 32 processing memory, 34 processing unit, 40 data management layer, 41 edge device manufacturer computer, 42 edge data distribution company cloud server, 50 data utilization layer, 51 dealer and repair company cloud server, 52 maintenance insurance company cloud server, 53···transportation company cloud server, 54···MaaS company cloud server, 55···rental company cloud server, 100···computer, 101···processor, 102···memory, 103···external storage device, 104···audio output device, 105···biometric information input device, 106···operation input device, 107···output device, 108···communication device, 109···data bus, 221···movement and actuation mechanism, 222···sensor, 301···diagnosis management cloud server, 302···analysis outsourcing cloud server, 500, 600···cause analysis result screen, 1000··distributed system

Claims

1. A distributed system including a plurality of edge devices that are at least one of automatically operable mobile objects and equipment, and a diagnostic data calculator, The edge device includes: converting status data representing a status of a component as a constituent element of a replaceable unit constituting the edge device into diagnostic data according to a predetermined diagnostic algorithm; The diagnostic data calculator includes: Obtaining the diagnostic data from the edge device; referring to a database relating to the configuration of the edge device, and identifying a second edge device employing a component related to the component employed in the first edge device diagnosed as at least one of an abnormality and a failure based on the diagnosis data acquired from the first edge device; determining a degree of association between the first edge device and the second edge device; determining a possibility of at least one of an abnormality and a failure occurring in the second edge device based on the degree of association and diagnostic data acquired from the second edge device; Instructing the second edge device in which at least one of an abnormality and a failure may occur to change collection conditions regarding collection of the status data and transmission of the diagnostic data. Distributed systems.

2. 2. The distributed system of claim 1, The diagnostic data calculator includes: Diagnosing at least one of an abnormality and a failure of the first edge device based on the diagnosis data acquired from the first edge device; Identifying a causal component that caused at least one of an abnormality and a failure of the first edge device based on the diagnosis data acquired from the first edge device; Identifying the second group of edge devices employing the related component for the factor component. Distributed systems.

3. 2. The distributed system of claim 1, The diagnostic data calculator includes: Instructing the second edge device, which has instructed the change of the collection condition, to restore the collection condition. Distributed systems.

4. 2. The distributed system of claim 1, The diagnostic data calculator includes: An alert is output to a service operator regarding the second edge device in which at least one of an abnormality and a failure may occur. Distributed systems.

5. 2. The distributed system of claim 1, The diagnostic data calculator includes: Transmitting the diagnostic algorithm to the edge device Distributed systems.

6. A data processing method for a distributed system including a plurality of edge devices that are at least one of automatically operable mobile objects and equipment, and a diagnostic data calculator, comprising: The edge device includes: converting status data representing a status of a component as a constituent element of a replaceable unit constituting the edge device into diagnostic data according to a predetermined diagnostic algorithm; The diagnostic data calculator includes: Obtaining the diagnostic data from the edge device; referring to a database relating to the configuration of the edge device, and identifying a second edge device employing a component related to the component employed in the first edge device diagnosed as at least one of an abnormality and a failure based on the diagnosis data acquired from the first edge device; determining a degree of association between the first edge device and the second edge device; determining a possibility of at least one of an abnormality and a failure occurring in the second edge device based on the degree of association and diagnostic data acquired from the second edge device; Instructing the second edge device in which at least one of an abnormality and a failure may occur to change collection conditions regarding collection of the status data and transmission of the diagnostic data. Data processing methods.

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