Data management system, data management device, and data management method

Abstract

To provide a data management system, a data management device, and a data management method that can efficiently compress and manage a large amount of time-series data.SOLUTION: A data management system includes an information processing apparatus that processes time-series data. The information processing apparatus estimates the characteristics of the waveform of the time-series data to be processed when processing the time-series data, specifies information indicating the characteristics of a compression method suitable for the processing based on the estimated characteristics of the waveform of the time-series data and application data, and determines the compression method based on the specified information indicating the characteristics of the compression method. The information processing apparatus compresses the time-series data according to the determined compression method to generate compressed time-series data, and stores the compressed time-series data in a storage device.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a data management system, a data management apparatus, and a data management method.

Background Art

[0002] Standardization for the utilization of data related to electric power (hereinafter referred to as "electric power data") collected from smart meters introduced into each house and the like is being promoted both at home and abroad. As a technology that can be used for the utilization of electric power data, Patent Document 1 utilizes parametric coding for compression of electric power data, assigns a bit rate with the amplitude of the fundamental wave as the maximum value, and performs compression by the difference between frames, based on the feature that the components of the electric power waveform are fundamental wave + harmonic wave, and discloses a sensor signal processing apparatus.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In order to handle a large amount of time-series data such as data collected from smart meters, it is required to efficiently compress and manage a large amount of time-series data. The present invention has been made to solve the above problems. That is, one of the objects of the present invention is to provide a data management system, a data management apparatus, and a data management method capable of efficiently compressing and managing a large amount of time-series data.

Means for Solving the Problems

[0005] To solve the above problems, the data management system of the present invention is a data management system comprising an information processing device for processing a plurality of time series data and a storage device, wherein the storage device stores application data including information indicating the waveform characteristics and compression method characteristics of the time series data associated with each other, and the information processing device is configured to estimate the waveform characteristics of the time series data to be processed when processing the time series data, identify information indicating the characteristics of a compression method suitable for processing based on the estimated waveform characteristics of the time series data and the application data, determine a compression method based on the identified information indicating the characteristics of the compression method, generate compressed time series data by compressing the time series data with the determined compression method, and store the compressed time series data in the storage device.

[0006] The data management device of the present invention is a data management device that includes an information processing device for processing a plurality of time series data, wherein the information processing device includes a storage device that stores application data including waveform characteristics and compression method characteristics of the time series data associated with each other, and the information processing device is configured to estimate the waveform characteristics of the time series data to be processed when processing the time series data, identify information indicating the characteristics of a compression method suitable for processing based on the estimated waveform characteristics of the time series data and the application data, determine a compression method based on the identified information indicating the characteristics of the compression method, generate compressed time series data by compressing the time series data with the determined compression method, and store the compressed time series data in the storage device.

[0007] The data management method of the present invention is a data management method using an information processing device that processes a plurality of time series data, and a storage device that stores application data including information indicating the waveform characteristics and compression method characteristics of the time series data associated with each other, wherein the information processing device estimates the waveform characteristics of the time series data to be processed when processing the time series data, identifies information indicating the characteristics of a compression method suitable for processing based on the estimated waveform characteristics of the time series data and the application data, determines a compression method based on the identified information indicating the characteristics of the compression method, generates compressed time series data by compressing the time series data with the determined compression method, and stores the compressed time series data in the storage device. [Effects of the Invention]

[0008] According to the present invention, large amounts of time-series data can be efficiently compressed and managed. [Brief explanation of the drawing]

[0009] [Figure 1] Figure 1 is a system configuration diagram of a system including a power waveform analysis system according to an embodiment of the present invention. [Figure 2] Figure 2 is a diagram illustrating the data type and accuracy data. [Figure 3] Figure 3 is a diagram illustrating data management information. [Figure 4] Figure 4 is a diagram illustrating the task. [Figure 5] Figure 5 is a block diagram illustrating an example of the hardware configuration of an information processing device. [Figure 6] Figure 6 is a diagram illustrating the outline of the present invention. [Figure 7] Figure 7 is a diagram illustrating the outline of the present invention. [Figure 8] Figure 8 is a sequence diagram illustrating an example of the process a system uses to store data. [Figure 9]Figure 9 is a flowchart showing the processing flow performed by the data feature estimation function and the processing method selection function. [Figure 10] Figure 10 is a sequence diagram illustrating an example of system processing when a data user (terminal) accesses data. [Modes for carrying out the invention]

[0010] Embodiments of the present invention will be described below with reference to the drawings. In all drawings of the embodiments, the same or corresponding parts may be denoted by the same reference numerals.

[0011] In the following explanation, various types of information may be described using terms such as "table," but these types of information may also be represented using other data structures. Furthermore, when describing identification information, terms such as "ID," "name," and "identification information" will be used, but these are interchangeable. In addition, in the following explanation, processing may be described with a functional block as the subject, but the subject of the processing may be the CPU or device instead of the functional block.

[0012] <<Embodiment>> <Structure> Figure 1 is a system configuration diagram of a system including a power waveform analysis system 300 according to an embodiment of the present invention. The power waveform analysis system 300 may also be referred to as a "data management system" for convenience. As shown in Figure 1, the system includes a smart meter 100, a power waveform data transmission device 200, a power waveform analysis system 300, an external system 400, and a terminal 500. The power waveform data transmission device 200 and the power waveform analysis system 300 are configured to transmit information from the power waveform data transmission device 200 to the power waveform analysis system 300 via a network (not shown). The power waveform analysis system 300, the external system 400, and the terminal 500 are configured to send and receive information from each other via a network (not shown).

[0013] The smart meter 100 is a device connected to the power grid of a residence (house, home), building, factory, or city, and has the function of collecting electricity usage data for each power grid. When the smart meter 100 is installed in a general residence, examples of connected devices include refrigerators, air conditioners, microwave ovens, televisions, fluorescent lights, and washing machines. When the smart meter 100 is installed in a factory, examples of connected devices include machine tools, conveyor belts, compressors, and lathes. When the smart meter 100 is installed in a building, examples of connected devices include lighting, elevators, automatic doors, escalators, air conditioning, surveillance cameras, and broadcasting equipment. The devices connected to the smart meter 100 can be any devices that operate using electricity, and are not limited to the examples above. Furthermore, the smart meter 100 may be connected to these devices via a centralized device such as a distribution board. The smart meter 100 sequentially measures power data and transmits the measured power data to the power waveform analysis system 300 at predetermined intervals using the power waveform data transmission device 200. The power data includes time-series power data (power waveform data). The power waveform data is data in which time-series power data (power waveform data) from multiple devices are superimposed. In this example, the power waveform data is the target of processing by the power waveform analysis system. Note that the power waveform data may also be current waveform data in which current waveform data from multiple devices are superimposed.

[0014] The power waveform data transmission device 200 is connected to the smart meter 100 to receive information. The power waveform data transmission device 200 is a device that transmits power waveform data collected by the smart meter 100. The power waveform data transmission device 200 includes an encoding function 210 and a compression function 220. The encoding function 210 receives and encodes the power waveform data collected by the smart meter 100.

[0015] The compression function 220 creates compressed power data by compressing the encoded power waveform data. The power waveform data transmission device 200 transmits the compressed power data created by the encoding function 210 and the compression function 220 to the power waveform analysis system 300.

[0016] The power waveform analysis system 300 includes a data input processing device 310, a data processing method selection device 320, an execution control device 330, a data management device 340, a data acquisition processing device 350, and a system cooperation device 360.

[0017] The data input processing device 310 is a device that receives the compressed power data input from the outside and performs "processing necessary for data storage" and "processing necessary for data processing method selection" in the data management device 340.

[0018] "Processing necessary for data storage" may include decompression of compressed data such as the decoding function 311, association of transmission source information with the data, association of data reception date and time, etc., conversion of data format, correction of missing data, and deletion of abnormal data. In FIG. 1, only the decoding function 311 is shown as an example of that function. The decoding function 311 generates (restores) power waveform data by decompressing and decoding the compressed power data. Note that the compressed power data may be compressed by reversible compression or irreversible compression.

[0019] "Processing necessary for data processing method selection" may include separation, normalization, feature extraction, and selection of specific data of the power waveform based on the rules of the input data. By clarifying the characteristics and attributes of the data through these processes, it becomes possible to select a process suitable for the characteristics and uses of the power waveform data in the data processing method selection device 320.

[0020] Figure 1 shows only the power waveform separation function 312 as an example of the above functions. The power waveform separation function 312 separates the power waveform data into power waveform data for each of the multiple devices connected to the smart meter 100. Various algorithms are known for separating waveform data such as voice and current, but in this example, the main focus is on processing the separated data, so the algorithm for separation can be any algorithm. Different processing is performed on the separated power waveform data according to the characteristics and applications of each power waveform data.

[0021] The data processing method selection device 320 includes a data feature estimation function 321, a processing method selection function 322, data type / precision data 323, and data management information 324.

[0022] The data processing method selection device 320 has the function of selecting a data processing function according to the characteristics of the data and transferring the data to another device. The data processing method selection device 320 may also act as an intermediary for data when moving data from one device to another. After selecting a data processing method, the data processing method selection device 320 may transfer the data to another device by specifying a destination and sending the data to the destination, or by instructing the data processing function to read the target data. In this example, the data processing method selection device 320 acts as an intermediary for data.

[0023] The data feature estimation function 321 is a function that estimates the features of the power waveform data. The features of the power waveform are the peak value of the power (current), the periodicity of the peak value, the average power (average current) excluding the peak value, and the waveform pattern (waveform shape) classified based on elements such as the characteristics of the waveform, amplitude, and frequency, or combinations of these elements, which are approximated by the sine wave and square wave included in the waveform.

[0024] Electrical devices have distinctive waveform patterns that correspond to the combination of electrical components that make up the device, such as circuits, motors, and capacitors, as well as the device's power consumption pattern and power usage trends.

[0025] The data feature estimation function 321 evaluates (analyzes) the elements of the waveform pattern as described above, and estimates (determines, identifies) the waveform pattern that is closest to the characteristics (waveform pattern) of the power waveform corresponding to the equipment, thereby estimating (determines, identifies) the classification of specific equipment.

[0026] Specifically, the data feature estimation function 321 uses the data type and precision data 323 to determine which pattern (waveform pattern) of the pattern correspondence data 323a of the data type and precision data 323 matches or is similar to the data features (waveform pattern). Based on the result, it stores identification information for identifying / managing the determined data, information for specifying the data interval (time interval), and information based on the determination result in the data management information 324. Since devices with the same features are thought to have similar waveforms, processing efficiency (compression efficiency) can be improved by collecting waveforms with similar features and processing (compressing) them together.

[0027] The processing method selection function 322 selects a processing method and transmits the data based on the waveform pattern determined (identified) by the data feature estimation function 321. Whether or not to select a processing method is managed by the data type / precision data 323.

[0028] Figure 2 is a diagram illustrating the data type / accuracy data 323. The data type / accuracy data 323 is a data store that stores data used in the data feature estimation function 321 and the processing method selection function 322. As shown in Figure 2, the data type / accuracy data 323 includes pattern matching data 323a, application data 323b, pattern detail data 323c, processing method selection data 323d, and processing service detail data 323e.

[0029] Pattern-corresponding data 323a is data that manages the relationship between the pattern (waveform pattern) of the power waveform data and the criteria for determining the waveform pattern of the power waveform data. Pattern-corresponding data 323a includes columns for storing information (values): pattern 322a1, criteria 1322a2, criteria 2322a3, criteria 3323a4, criteria 4324a5, criteria 5325a6, and criteria 6326a6. In pattern-corresponding data 323a, information corresponding to each column regarding the relationship between the waveform pattern of the power waveform data and the criteria is associated with each other and stored as row-level information (records). Specifically, pattern 323a1 stores the name of the waveform pattern. Each of criteria 1322a2 through 6323a7 stores information indicating the criteria that must be met to determine each waveform pattern. There may be multiple criteria, and the criteria may be in the form of binary data such as success or failure of a criterion, or rational / irrational numbers. In this example, the information indicating the criteria that must be met to determine (identify) each waveform pattern is either "1" or "0". "1" indicates that the criteria must be met to determine (identify) that it is the corresponding waveform pattern, and "0" indicates that the criteria must be met to determine (identify) that it is the corresponding waveform pattern. The criteria are set based on characteristics of shape, characteristics of peak value, characteristics of period, etc.

[0030] Application data 323b is data that manages the relationship between the application of power waveform data and the waveform pattern. Application data 323b includes the following columns for storing information (values): Application ID 323b1, Application 323b2, Pattern 323b3, Required data precision 323b4, Required resolution 323b5, and Required frequency 323b6. In application data 323b, information corresponding to each column regarding the relationship between application and waveform pattern is associated with each other and stored as row-level information (records). Specifically, Application ID 323b1 stores an ID for identifying the application. Application 323b2 stores the name of the application. Pattern 323b3 stores the name of the waveform pattern. Required data precision 323b4 stores the required data precision, which indicates the degree of data mismatch that is allowed due to compression and decoding. Required resolution 323b5 stores the required resolution, which indicates the data resolution. The required frequency 323b6 stores the frequency of the data necessary for the application.

[0031] Pattern detail data 323c is data that manages the detailed content of each pattern. Pattern detail data 323c includes the following columns for storing information (values): major equipment classification 323c1, medium equipment classification 323c2, minor equipment classification 323c3, regional characteristics 323c4, and approximate pattern 323c5. In pattern detail data 323c, the information corresponding to each column regarding the details of each waveform pattern is associated with each other and stored as row-level information (records). Specifically, major equipment classification 323c1 stores information indicating the classification of the equipment's characteristics. Medium equipment classification 323c2 stores information indicating the more detailed functions that the equipment possesses. Minor equipment classification 323c3 stores information indicating the area in which the equipment functions appropriately. Regional characteristics 323c4 stores information indicating the environment in which the equipment was operating. Approximate pattern 323c5 stores the name indicating the waveform pattern.

[0032] The processing method selection data 323d is data that manages the content of the processing method. The processing method selection data 323d includes the processing service ID 323d1, the activation classification 323d2, the matching pattern 323d3, the utilization rate 323d4, and the processing classification 323d5 as columns for storing information (values).

[0033] The processing method selection data 323d stores information corresponding to each column related to the content of the processing method, with the information linked to each other and stored as row-level information (records). Specifically, the processing service ID 323d1 stores an ID indicating the right to use a computing resource such as a server (information processing device) that performs data processing, or a computing resource on the cloud. The types of computing resources can be assumed to be SaaS type, VM type (started), VM type (not started), container type (started), container type (not started), FaaS type, etc. The computing resource that processes the data is ready to execute processing at any time. The computing resource may normally be not started or not instantiated, and may only operate when data processing is required.

[0034] The startup classification 323d2 stores information indicating whether the corresponding processing service (computer resource) is immediately available. By linking the information stored in the startup classification 323d2 to how to prepare computer resources, more efficient management of computer resources can be achieved.

[0035] The matching pattern 323d3 stores the name of the waveform pattern. The utilization rate 323d4 stores the current utilization rate of computing resources (the percentage of currently used resources relative to the amount of available resources used).

[0036] The processing classification 323d5 stores information indicating the processing classification that serves as a criterion for selecting (identifying) a processing service when there are multiple types of processing for a waveform pattern (for example, when there are multiple types of processing such as compression and decompression). In this example, the processing classification 323d5 stores either "0" or "1" as information indicating the processing classification. "0" indicates that the processing type of the processing service is data compression, and "1" indicates that the processing type of the processing service is data decompression (decryption).

[0037] The processing service detail data 323e is data that manages the algorithmic characteristics of each processing service, such as the algorithm and data accuracy. The processing service detail data 323e includes the processing service ID 323e1, algorithm 323e2, data accuracy 323e3, resolution 323e4, and frequency 323e5 as columns for storing information (values).

[0038] The processing service details data 323e stores information corresponding to each column regarding the details of the processing service, with the information linked to each other and stored as row-level information (records). The processing service ID 323e1 stores the same information as the processing service ID 323d1 mentioned above. The algorithm 323e2 stores the name of the data processing algorithm executed by the computing resources. Examples of algorithms include algorithms corresponding to compression methods such as lossy compression (high compression), lossless compression (low compression), and no compression. Note that the compression examples shown here are just examples, and the processing itself is not limited to compression. The data precision 323e3 stores information indicating the data precision of the data processing. The resolution 323e4 stores the data resolution of the data processing. The frequency 323e5 stores the frequency of the data processing.

[0039] For example, a single power waveform data set may have multiple applications. In this case, the processing to be performed may differ depending on the application. For instance, if the processing to be performed is data compression, it is not possible to apply compression that would compromise the required accuracy. However, some applications may require high accuracy, while others may only require low accuracy. Thus, when multiple applications are set, the processing should be done using the compression method that has the lowest compression ratio and does not compromise accuracy. In this case, based on the processing service details data 323e, a processing service that matches the waveform patterns and corresponds to the data accuracy and algorithm suitable for all applications is selected based on the processing service details data 323e. Examples of compression based on such accuracy include lossy compression (high compression), lossless compression (low compression), and no compression, but the compression examples shown here are just examples, and the processing itself is not limited to compression. In the case of compression, it is not possible to perform superimposed processing, but if the data processing for each application does not significantly affect the results of the other, both processing methods may be combined to handle multiple waveform patterns.

[0040] Figure 3 is a diagram illustrating the data management information 324. The data management information 324 is management data for managing the relationships between the data, waveform pattern, required data accuracy, required resolution, and required frequency determined by the data processing method selection device 320. The data management information 324 includes, as columns for storing information (values), a data ID 324a1, a start time 324a2, a planned end time 324a3, a change point 324a4, an approximate pattern 324a5, an application ID 324a6, a required data accuracy 324a7, a required resolution 324a8, and a required frequency 324a9.

[0041] The data management information 324 stores information corresponding to each column of the above management data, with the information associated with each other, as row-level information (records).

[0042] Specifically, the data ID 324a1 stores the name of the data, which is identification information for identifying and managing data whose waveform pattern has been determined. The start time 324a2 stores the start time of the section (time interval) in which the applied waveform pattern is valid. The scheduled end time 324a3 stores the end time of the section (time interval) in which the applied waveform pattern is valid. The change point 324a stores information indicating whether or not there is a change point, which indicates whether or not another waveform pattern has been registered with the same data after the end of this section. In this example, "1" indicates that such a change point exists, and "0" indicates that such a change point does not exist. The approximate pattern 324a5 stores the name of the approximate pattern that represents a waveform pattern that is identical or closest to the waveform characteristics of the data. The application ID 324a6 stores an ID indicating the application. The required data precision 324a7 stores the required data precision. The required resolution 324a8 stores the required data resolution. The required frequency 324a9 stores the required data frequency.

[0043] Furthermore, the data management information 324 may also include a column for storing the processing method (compression method). In addition, the data management information 324 may also include columns included in other usage data 323b. Continuous data in a time series of a certain type does not always result in the same approximate pattern (waveform pattern). For example, in the case of air conditioners, where the power consumption trends differ between summer and winter, it may be more appropriate to apply different waveform patterns to each. In this case, as with data B, even if the data is the same, it may be registered in separate intervals, and different waveform patterns may be registered in the data management information 324 for each interval.

[0044] The execution control device 330 may operate not only in response to external data inputs and outputs, but also in response to periodic events or changes in data. The execution control device 330 includes an execution control function 331 that performs processing in response to periodic events or changes in data, and a task 332 that manages the processing to be executed.

[0045] The execution control function 331 executes the data collection process specified in task 332 based on the execution interval stored in task 332, and executes the process specified in task 332 based on the judgment criteria specified in task 332. Furthermore, the execution control function 331 has an API (Application Programming Interface) and executes the process specified in task 332 based on the judgment criteria specified for each event ID in task 332, based on events notified from the data input processing device 310, etc. The execution control function 331 has an API (Application Programming Interface) and executes the process specified in task 332 based on the judgment criteria specified for each event ID in task 332, based on events notified from the data input processing device 310, etc. By using the execution control function 331, batch processing and stream processing of data become possible.

[0046] Figure 4 is a diagram illustrating task 332. Task 332 manages the data necessary for the execution control function 331. As shown in Figure 4, task 332 includes the following columns for storing information (values): task ID 332a1, related event ID 332a2, execution interval 332a3, data collection process 332a4, judgment process 332a5, judgment parameter 332a6, process 1332a7, process 2332a8, and process 3332a9. Task 332 stores information corresponding to each column regarding the data necessary for the execution control function 331, with the information being associated with each other as row-level information (records). Task 332 may also store other information, such as the judgment criteria mentioned above.

[0047] Specifically, task 332a1 stores a task ID, which is identification information for identifying the task. Related event ID 332a2 stores an event ID, which is identification information for identifying the event that triggers the processing. Execution interval 332a3 stores the execution interval (time interval) for specifying the processing interval. Data collection process 332a4 stores information for processing data collection at each execution interval. Judgment process 332a5 stores information for processing to judge the collected data. Judgment parameters 332a6 stores information indicating the parameters to be given to the judgment process. Processes 1332a7 to 3332a9 store information for executing individual processes according to the result of the judgment process. Note that processes 1 to 3 do not necessarily have to be three; parameters to be given to each process may be specified separately.

[0048] The data management device 340 includes data processing function A341, data processing function B342, data processing function C343, data processing function D344, power waveform data 345, separated data A346, separated data B347, authorization data 348, and processed data 349.

[0049] Data processing function A341 and data processing function B342 each perform compression processing according to the algorithm specified in the processing service detail data 323e. Although Figure 1 illustrates data processing function A341 and data processing function B342 as an example, the data management device 340 may include other data processing functions.

[0050] Each of the data processing functions C343 and D344 performs decoding according to the algorithm specified in the processing service detail data 323e. Although Figure 1 illustrates data processing functions C343 and D344 as an example, the data management device 340 may include other data processing functions.

[0051] The power waveform data 345 stores data collected from homes, buildings, factories, towns, and other locations.

[0052] Separated data A346 and separated data B347 each store compressed data obtained by applying the corresponding data processing (compression) to each separated power waveform using the data processing function. For example, separated data A346 and separated data B347 are databases created for each waveform pattern. In Figure 1, separated data A346 and separated data B347 are shown as examples, but the data management device 340 may also include other separated data.

[0053] The authorization data 344 stores data that manages the relationship between the data owner, the users, devices, and systems authorized to use the data, and the scope of authorization. The processed data 349 stores data obtained by processing the collected data. The processed data 349 may also store the results of data processing by the execution control unit 330. Note that the data processing by the execution control unit 330 may be performed on an external system 400.

[0054] The data acquisition and processing device 350 is a device for sending and receiving separated data, processed data, or data necessary for the operation of the power waveform analysis system 300. The data acquisition and processing device 350 includes a data type / accuracy update function 351, a task update function 352, an access control function 353, and an authentication function 354.

[0055] The data type / accuracy update function 351 registers information to the data type / accuracy data 323 and updates the information registered in the data type / accuracy data 323. For example, the data type / accuracy update function 351 registers information to the data type / accuracy data 323 and updates the information in the data type / accuracy data 323 based on information input from the terminal 500, which acts as an input device.

[0056] The task update function 352 updates tasks. The access control function 353 allows reading and updating of specified data based on the permissions assigned as a result of authentication. The authentication function 354 performs authentication using IDs, passwords, tokens, certificates, biometric information, device authentication codes, one-time passwords, etc., and grants data access rights appropriate to the authenticated data user or system. This function may be used not only for external data users but also for data input and output from external systems. Here, external systems may include data analysis systems, internal corporate systems such as ERP (Enterprise Resource Planning), and power management systems such as HEMS (Home Energy Management System) and BEMS (Building and Energy Management System).

[0057] The system integration device 360 ​​is a device for coordinating with the external system 400, and an API-GW is an example of this. The execution control device 330 described above may read the separated data at the timing specified by task 332, send the data to the external system 400 via the system integration device 360, receive the processed data, and store it in the processed data.

[0058] The external system 400 is a system that processes and analyzes data. The external system 400 may be, for example, an internal enterprise system such as a data analysis system or ERP (Enterprise Resource Planning), or a power management system such as HEMS (Home Energy Management System) or BEMS (Building and Energy Management System).

[0059] Each component of the power waveform analysis system 300 described above may be implemented in various ways, such as a hardware module, one or more servers (information processing devices), or libraries on a server. The information processing device may also be a virtual information processing device on the cloud. Each component may be connected to each other via a network so that information can be sent and received from one another.

[0060] Figure 5 is a block diagram illustrating an example of the hardware configuration of an information processing device. The information processing device 510 includes a CPU 511, ROM 512, RAM 513, storage device (HDD) 514, network interface 515, and input / output interface 516, etc. These are connected to each other via a bus 517 so that they can communicate with one another. For convenience, the information processing device 510 is also referred to as "information processing device".

[0061] The CPU 1511 loads various programs (not shown) stored in the ROM 512 and / or storage device 514 into the RAM 513, and executes the programs loaded into the RAM 513 to realize various functions. As described above, the RAM 513 is loaded with various programs to be executed by the CPU 511, and temporarily stores data used by the CPU 511 when executing these programs. The ROM 512 is a non-volatile storage medium in which various programs are stored. The storage device 514 is a non-volatile storage medium in which data can be read and written. The network interface 515 is an interface for the information processing device 510 to connect to a network. The input / output interface 516 is an interface for connecting to external devices (for example, operating devices such as keyboards and mice, and displays (display devices)).

[0062] The power waveform analysis system 300 may also be a power waveform analyzer equipped with an information processing device. In this case, the data input processing device 310 consists of a network interface and a program stored in the ROM 512 and / or storage device 514. The decoding function 311 and the power waveform separation function correspond to the program.

[0063] The data processing method selection device 320 consists of a program stored in the ROM 512 and / or storage device 514, and the storage device 514 itself. The data feature estimation function 321 corresponds to the program, and the data type / precision data 323 and data management information are stored in the storage device 514. The execution control device 330 consists of an execution control function 331 and a task 332. The execution control function 331 corresponds to the program, and the task 332 is stored in the storage device 514.

[0064] The data management device 340 consists of a program stored in the ROM 512 and / or the storage device 514, and the storage device 514 itself. Data processing functions A, B342, C343, and C344 correspond to the program, while power waveform data 345, separated data A346, separated data B347, authorization data 348, and processed data 349 are stored in the storage device 514. The data acquisition processing device 350 consists of an input / output interface, a network interface, and a program stored in the ROM 512 and / or the storage device 514. The data type / accuracy update function 351, task update function 352, access control function 353, and authentication function 354 are configured by the program.

[0065] The system integration device 360 ​​consists of a network interface and a program stored in the ROM 512 and / or storage device 514. The power waveform analyzer may also be referred to as a "data management device" for convenience.

[0066] <Summary of the Invention> The outline of the present invention will be described with reference to Figures 6 and 7. Figures 6 and 7 are diagrams illustrating the outline of the present invention. In Figure 6, an air conditioner Ms1, a range Ms2, and a washing machine Ms3 are shown as examples of equipment installed in house HM1. Hereinafter, the air conditioner Ms1, range Ms2, and washing machine Ms3 may be referred to as "equipment Ms" when there is no need to distinguish between them. As shown in explanatory text St1, each of the power waveforms of equipment Ms has a waveform pattern corresponding to each equipment Ms.

[0067] The power waveform analysis system 300 receives power waveform data D10, which is a superimposed power waveform data of each device Ms collected by the smart meter 100 installed in the distribution board 600, and separates it into power waveform data 10a to 10c for each device Ms. For example, as shown in explanatory text St2, the device Ms is estimated by comparing the waveform patterns and combinations thereof with the waveforms of the separated power waveform data. Based on the estimation result, the power waveform data D10 is separated into power waveform data 10a to 10c for each device Ms by extracting the power waveform of each device Ms from the power waveform data D10.

[0068] The power waveform data 10a to 10c for each of these devices Ms can be used for a variety of purposes and tasks.

[0069] For example, as shown in explanatory text St3, the time spent at home can be estimated from the state of the air conditioner Ms1 based on power waveform data 10a, and can be used for visiting customers. For example, as shown in explanatory text St4, the break time can be determined from the state of the range Ms2 based on power waveform data 10b, and can be used for visiting customers. For example, as shown in explanatory text St5, the lifestyle rhythm, such as night owl or day owl, can be determined from the washing machine Ms3 information based on power waveform data 10c, and can be used for marketing.

[0070] In the future, the amount of power waveform data collected by smart meters 100 is expected to increase significantly, potentially expanding by several to several hundred times. Therefore, there is a need to efficiently manage the power waveform data D10 of each device Ms collected by smart meters 100.

[0071] Therefore, as shown in Figure 7, the power waveform analysis system 300 of the present invention estimates the characteristics (waveform patterns) of the power waveforms of the separated power waveform data D20, and selects a compression method for each of the power waveform data 10a to 10c based on the estimated characteristics (waveform patterns). The power waveform analysis system 300 generates compressed power data, which is the compressed data for each of the power waveform data 10a to 10c, by compressing each of the power waveform data 10a to 10c using the selected compression method. The power waveform analysis system 300 stores and manages each of the compressed power data in a database corresponding to its waveform pattern (a database created for each power waveform characteristic (waveform pattern)). In this case, the power waveform analysis system 300 may compress multiple power waveform data that share common characteristics (waveform patterns) together. Furthermore, waveform patterns can be analyzed not only by comparing data at the same time, such as over 24 hours or a week, but also by separating the parts of the waveform that change significantly from those that change little, and then comparing the parts with significant changes regardless of the duration of the parts with little change to determine agreement or disagreement. This is because the characteristics of equipment usage are often evident during periods of intensive use, and by making such comparisons, the number of patterns can be reduced, thereby improving compression efficiency.

[0072] In other words, as explained in St11, the application (purpose, data characteristics, required accuracy) of power waveform data differs depending on the characteristics of the power waveform. Therefore, when compressing and managing power waveform data, it is preferable to compress it using an appropriate compression method according to the application. Nevertheless, if power waveform data is compressed using a uniform compression method, it may not be possible to compress and manage the data efficiently.

[0073] Therefore, the power waveform analysis system 300 compresses each separated power waveform data using a data processing method (compression method) suitable for its application according to the characteristics of the power waveform. As shown in explanatory text St12, this makes it possible to optimize the compression method, data arrangement, and processing method (reprocessing method (recovery method) when using the compressed data) for data units that share common power waveform characteristics. Furthermore, as shown in explanatory text St13, it becomes easier to analyze the relationships between data that share common power waveform characteristics. Thus, the power waveform analysis system 300 can efficiently compress and manage each separated power waveform data 10a to 10c.

[0074] <Overview of Operation> The following is an overview of how the system works.

[0075] (Overview of data separation and storage) The data input processing device 310 decodes the power waveform data received by the decoding function 311, then separates the power waveform data into power waveform data for each device, apparatus, and system using the power waveform separation function 312, and stores the separated power waveform data in the power waveform data 345 of the data management device 340.

[0076] The data processing method selection device 320 reads power waveform data for each device, apparatus, and system from the power waveform data 345 and evaluates the data trend.

[0077] The processing method selection function 322 of the data processing method selection device 320 determines the data processing method, etc., based on the evaluation results and using the data type and accuracy data 323. The processing method selection function 322 stores the selected method, etc., in the data management information 324 and issues data processing instructions to the data processing function of the data management device 340.

[0078] The data processing function of the data management device 340 processes power waveform data for each device, apparatus, and system in a specified manner, generates separated data, and stores the separated data.

[0079] (Overview of data usage) The data acquisition processing device 350 determines, based on the data acquisition request received from the terminal 500, whether the data user is legitimate and has the necessary permissions, based on authentication information and other information associated with the data reception request.

[0080] If the data user has the correct authentication information and authorization, the data acquisition processing device 350 performs a data recovery process on the separated and compressed data based on the data management information 324 and returns the data to the data user's terminal 500.

[0081] When the data acquisition processing device 350 receives a data access request from a data user's terminal 500 or an external system 400, it first requests authentication from the data user's terminal 500 or the external system 400. At this time, the data user's terminal 500 or the external system 400 transmits authentication information such as an ID and password to the data acquisition processing device 350. The communication channel at this time may be an encrypted channel using SSL or the like, or it may be a dedicated line or encrypted and scrambled using pre-shared information. When the authentication function 354 of the data acquisition processing device 350 receives the authentication information, it may authenticate the data user (terminal 500) or the external system 400 using the authorization data 348 located in the data management device 340 in the diagram. Here, the authorization data includes at least information such as an ID to identify the object of authentication, information such as a password to authenticate the object of authentication, and information on the authorization granted as a result of authentication. Information such as passwords is not directly stored, but the result calculated using a hash algorithm such as SHA256 is stored, and this may be combined with encryption and anonymization processing, such as comparing the result of processing the input information with the same algorithm.

[0082] If authentication is successful, the authentication function 354 passes the permissions assigned to the ID to the access control function 353. The permissions may include information about the ID or path and scope of the data that can be accessed, and the access level, such as read-only or writable.

[0083] After authentication, the access control function 353 determines whether the ID or path to the data accessed from the data user's terminal 500 or external system 400 is within the scope of the granted permissions, and also determines whether the processing is read, updated, etc., and whether the access falls within the scope of the granted permissions. If access is permitted, the access control function 353 processes the separated data or processed data using data processing function C343 or data processing function D344, etc., based on the information for specifying the processing method for reading or updating, which is described (stored) in the data management information 324, and returns the processing result to the data user's terminal 500 or external system 400.

[0084] <Specific operation> The specific operation of the system will be described below. Figure 8 is a sequence diagram illustrating an example of the process the system uses to store data. As shown in Figure 8, the system separates and stores power waveform data by sequentially performing the processes S801 to S808 described below.

[0085] S801: The power waveform data transmission device 200 transmits compressed power data, obtained by encoding and compressing the power waveform data acquired from the smart meter 100, to the data input processing device 310.

[0086] S802: The data input processing device 310 receives compressed power data.

[0087] S803: The data input processing device 310 decodes the compressed power data and generates power waveform data.

[0088] S804: The data input processing device 310 separates the power waveform data into power waveform data for each device and transmits the separated power waveform data to the data processing method selection device 320.

[0089] S805: The data processing method selection device 320 estimates data characteristics for each power waveform data. Data characteristics are the waveform patterns of the power waveform data described above. The process for estimating these data characteristics will be described in detail later using Figure 9.

[0090] S806: The data processing method selection device 320 selects a data processing method (compression method) based on the estimated data characteristics. This processing method selection process will be described in detail later with reference to Figure 9.

[0091] S807: The data management device 340 performs data compression processing using the selected data processing method (compression method), and other necessary processing as appropriate.

[0092] S808: The data management device 340 stores the compressed power waveform data in separated data (for example, separated data A) corresponding to the waveform pattern.

[0093] Figure 9 is a flowchart showing the processing flow executed by the data feature estimation function 321 and the processing method selection function 322. The data feature estimation function 321 starts processing from step 900 and proceeds to step 901. In step 901, when power waveform data to be used for data feature (waveform feature) estimation is input, it sequentially executes the processes in steps 902 and 903 described below and proceeds to step 904.

[0094] Step 902: The data feature estimation function 321 assigns the value "1" to variable i and the number of judgments registered in variable j (the number of judgments registered in pattern-corresponding data 323a (number of judgment columns)).

[0095] Step 903: The data feature estimation function 321 performs the judgment process for judgment i of the pattern-corresponding data 323a. Note that "i" in judgment i is the value of the variable i currently assigned.

[0096] When the data feature estimation function 321 proceeds to step 904, it determines whether or not the criteria for judgment i have been met.

[0097] If the criteria for judgment i are not met, the data feature estimation function 321 determines "No" in step 904 and proceeds to step 906, sets the value of judgment i to "0", and proceeds to step 908. Note that "i" in judgment i is the value of the variable i currently assigned.

[0098] If the criteria for judgment i are met, the data feature estimation function 321 determines "Yes" in step 904 and proceeds to step 907, sets the value of judgment i to "1", and proceeds to step 908. Note that "i" in judgment i is the value of the variable i currently assigned.

[0099] When the data feature estimation function 321 proceeds to step 908, it determines whether the value of variable i and the value of variable j are the same.

[0100] If the values ​​of variable i and variable j are not the same, the data feature estimation function 321 determines "No" in step 908 and proceeds to step 909, where it increases the value of variable i by "1" and assigns the result to variable i. After that, the data feature estimation function 321 returns to step 903, performs an appropriate process from steps 903 to 907 described above, and proceeds to step 908.

[0101] If the values ​​of variable i and variable j are the same, the data feature estimation function 321 determines "Yes" in step 908 and proceeds to step 910 and step 911, which are described below, in order, before moving on to step 912.

[0102] Step 910: The data feature estimation function 321 searches for a waveform pattern that matches or is closest to the judgment result within the pattern-matching data 323a. For example, if the judgment results are judgment 1=1, judgment 2=0, judgment 3=0, judgment 4=0, judgment 5=0, and judgment 6=0, this matches the judgment result corresponding to pattern 1, so pattern 1 is searched for (identified).

[0103] Step 911: The data feature estimation function 321 identifies the application (associated application) associated with the retrieved waveform pattern by referring to the application data 323b.

[0104] When the data feature estimation function 321 proceeds to step 912, it determines whether there are two or more applications in the application data 323b that are associated with the searched waveform pattern.

[0105] If there are two or more applications, the data feature estimation function 321 determines "Yes" in step 912 and proceeds to step 913. In the application data 323b, the highest required data accuracy, required resolution, and required frequency corresponding to the identified applications are set as the common required data accuracy, required resolution, and required frequency, and the process proceeds to step 914.

[0106] If there is only one application, the data feature estimation function 321 determines "No" in step 912, and in the application data 323b, sets the required data accuracy, required resolution, and required frequency corresponding to that application, and proceeds directly to step 914.

[0107] When the data feature estimation function 321 proceeds to step 914, it registers information about the data to be estimated, as well as information about the selected processing method (required data accuracy, required resolution, and required frequency), etc., in the data management information 324.

[0108] When information regarding the processing method selected by the data feature estimation function 321 is registered in the data management information 324, the processing method selection function 322 uses the processing method selection data 323d and the processing service detail data 323e in step 915 to select a processing service that has an algorithm with data accuracy suitable for processing the estimated waveform pattern.

[0109] Subsequently, the processing method selection function 322 executes the processes described in steps 916 to 918 in order, then proceeds to step 919, and terminates this processing flow.

[0110] Step 916: The processing method selection function 322 sends data to the processing service at an appropriate time according to the computing resource activation classification and utilization rate.

[0111] Step 917: The processing method selection function 322 causes the selected processing service to perform data processing (compression processing) on ​​the data (power waveform data).

[0112] Step 918: The processing method selection function 322 stores the results (compressed power data) performed by the selected processing service in the classified data (separated data).

[0113] Figure 10 is a sequence diagram illustrating an example of system processing when a data user (terminal 500) accesses data. As shown in Figure 10, the system executes the processing when terminal 500 accesses data by sequentially performing the processes described below, from S1001 to S1023.

[0114] S1001: Terminal 500 transmits the authentication ID and password entered into Terminal 500 by the data user to the data acquisition processing device 350.

[0115] S1002: The data acquisition processing device 350 receives the ID and password and performs authentication based on the received ID and password.

[0116] S1003: Terminal 500 requests data from data acquisition processing device 350.

[0117] S1004: When the data acquisition processing device 350 receives a data request, it checks whether the data user has the right to use the data being requested.

[0118] S1005: The data acquisition processing device 350 confirms the processing method (information related to the processing method) of the data (compressed power data) corresponding to the data requested by the data user (for example, by referring to the data management information 324, etc., to confirm the processing method (information related to the processing method) used when the compressed power data was generated), and transmits the processing method (information related to the processing method) to the data processing method selection device 320.

[0119] S1006: The data processing method selection device 320 receives a processing method (information related to the processing method) and, based on the received processing method (information related to the processing method), specifies an appropriate data processing method to the data management device 340.

[0120] S1007: The data management device 340 processes (restores) the compressed power data corresponding to the data requested by the data user using the specified data processing method.

[0121] S1008: The data management device 340 transmits the restored data to the data acquisition processing device 350.

[0122] S1009: The data acquisition processing device 350 receives the restored data.

[0123] S1010: The data acquisition processing device 350 encrypts the data.

[0124] S1011: The data acquisition processing device 350 transmits the encrypted data to the terminal 500.

[0125] S1012: Terminal 500 receives encrypted data.

[0126] <Effects> As described above, the power waveform analysis system 300 according to the embodiment of the present invention can efficiently compress a large amount of power waveform data by compressing power waveform data with common waveform patterns together. Furthermore, the power waveform analysis system 300 according to the embodiment of the present invention can appropriately manage the power waveform data of each device by compressing the power waveform data of each device using an appropriate compression method according to the application, for power waveform data with common waveform patterns.

[0127] <<Variation>> The present invention is not limited to the embodiments described above, and various modifications can be adopted within the scope of the present invention.

[0128] In the above embodiment, the data feature estimation function 321 may estimate the waveform pattern using a computational model that takes separated radio wave waveform data as input and outputs data features (waveform patterns).

[0129] The above embodiment aims to efficiently manage power waveform data from various devices collected from smart meters by collecting power waveform data for each device and compressing it according to the characteristics of the power waveform data, thereby reducing the cost of storing and processing the power waveform data. The features of this above embodiment are not only effective for managing power waveform data, but can also be applied to the management of multiple types of time-series data with unique characteristics.

[0130] The features of the above embodiment can be applied to the management of sensor data (time-series data of sensors) acquired by various sensors on factory equipment. In managing sensor data from factory equipment, if vibration sensors, temperature sensors, torque sensors, acoustic sensors, etc., are attached to each piece of equipment, the data will be managed for each piece of equipment. However, the characteristics of the waveform obtained by concatenating the sensor values ​​in time series differ for each sensor, and the characteristics of the waveform obtained by concatenating the sensor values ​​also differ depending on the type of equipment and the type of material being processed. Therefore, by preparing waveform processing patterns according to the type of sensor, type of equipment, type of material, etc., and selecting an appropriate processing pattern according to the waveform pattern during data processing, it is possible to reduce the cost of data storage and processing.

[0131] The features of the above embodiment may also be applied to data related to human biological information acquired by various sensors. For example, by managing the sensor values ​​of human biological information such as body temperature, blood pressure, blood oxygen saturation, heart rate, weight, body fat percentage, and electrocardiogram in a time-series manner, similar reductions in data storage and processing costs can be expected.

[0132] The features of the above embodiment may also be applied to data related to vehicle body information acquired by various sensors (for example, sensor values ​​such as speed, engine speed, accelerator, brake, and steering wheel rotation angular velocity, which are vehicle body information).

[0133] Furthermore, the present invention can also take the following configuration.

[0134] [1] An information processing device that processes multiple time-series data, Memory device and including, A data management system, The storage device stores application data including information indicating the waveform characteristics and compression method characteristics of the time-series data that are associated with each other. The aforementioned information processing device is When processing the aforementioned time-series data, the waveform characteristics of the time-series data to be processed are estimated, and based on the estimated waveform characteristics of the time-series data and the application data, information indicating the characteristics of the compression method suitable for processing is identified. Based on the information indicating the characteristics of the identified compression method, the compression method is determined. Compressed time-series data is generated by compressing the time-series data using the determined compression method, and the compressed time-series data is stored in the storage device. It is configured in such a way. Data management system.

[0135] [2] In the data management system described in [1], The aforementioned information processing device is Multiple time-series data that share the same compression method are compressed together and stored in the memory device. It is configured in such a way. Data management system.

[0136] [3] In the data management system described in [1], The storage device stores processing detail data including corresponding compression methods and information indicating the characteristics of the compression methods. The aforementioned information processing device is Based on the processing details data, the compression method is determined by identifying a compression method corresponding to the information indicating the characteristics of the identified compression method. It is configured in such a way. Data management system.

[0137] [4] In the data management system described in [1], The aforementioned application data includes applications associated with information indicating the characteristics of the waveform and the characteristics of the compression method. Data management system.

[0138] [5] In the data management system described in [4], If there are multiple pieces of information in the application data indicating the characteristics of the application and compression method corresponding to the waveform characteristics of the estimated time series data, Based on predetermined criteria, information representing the characteristics of a compression method suitable for processing is identified from among information representing the characteristics of multiple compression methods. It is configured in such a way. Data management system.

[0139] [6] In the data management system described in [5], The information indicating the characteristics of the aforementioned compression method is: Including data accuracy, data resolution and data frequency, Data management system.

[0140] [7] In the data management system described in [3], The information processing device includes an input device operated by a user for inputting information, The aforementioned information processing device is Based on the information input from the input device, the system can register the compression method and information indicating the characteristics of the compression method to the processing detail data, and update the compression method and information indicating the characteristics of the compression method in the processing detail data. It is configured in such a way. Data management system.

[0141] [8] In the data management system described in [4], The information processing device includes an input device operated by a user for inputting information, The aforementioned information processing device is Based on the information input from the input device, the system can register the waveform characteristics, information indicating the compression method characteristics, and the application to the application data, and update the waveform characteristics, information indicating the compression method characteristics, and the application to the application data. It is configured in such a way. Data management system.

[0142] [9] In the data management system described in [4], The aforementioned information processing device is If a single time-series data contains multiple time intervals with different estimated waveform characteristics, the compression method corresponding to the waveform characteristics is determined for each time interval. It is configured in such a way. Data management system.

[0143]

[10] In the data management system described in [9], The aforementioned information processing device is The storage device stores data management information including identification information for identifying the corresponding compressed time series data, information indicating the time interval in which the waveform characteristics of the compressed time series data appear, the waveform characteristics of the compressed time series data, the intended use of the compressed time series data, information indicating the characteristics of the compression method, and information indicating whether or not another waveform characteristic of the same compressed time series data has been registered after the end of the time interval. It is configured in such a way. Data management system.

[0144]

[11] In the data management system described in

[10] , The aforementioned information processing device is configured to be able to send and receive information with an external device. The aforementioned information processing device is In response to a request from the external device, the compressed time-series data is restored based on the data management information, and at least one of the restored time-series data and the processed data based on the restored time-series data is transmitted to the external device. It is configured in such a way. Data management system.

[0145]

[12] In the data management system described in [1], The aforementioned information processing device is The process involves separating power waveform data from smart meters and processing multiple time-series data sets. It is configured in such a way. Data management system.

[0146]

[13] In the data management system described in

[12] , The information processing device is configured to receive power waveform data from a power waveform data transmission device configured to transmit the power waveform data measured by the smart meter to the information processing device. The information processing device generates a plurality of power waveform data by separating the power waveform data received from the power waveform data transmission device. It is configured in such a way. Data management system.

[0147]

[14] A data management device including an information processing device that processes multiple time-series data, The aforementioned information processing device is Includes a storage device that stores application data including information indicating the waveform characteristics and compression method characteristics of the time-series data that are associated with each other, The aforementioned information processing device is When processing the aforementioned time-series data, the waveform characteristics of the time-series data to be processed are estimated, and based on the estimated waveform characteristics of the time-series data and the application data, information indicating the characteristics of the compression method suitable for processing is identified. Based on the information indicating the characteristics of the identified compression method, the compression method is determined. Compressed time-series data is generated by compressing the time-series data using the determined compression method, and the compressed time-series data is stored in the storage device. It is configured in such a way. Data management device.

[0148]

[15] An information processing device that processes multiple time-series data, A data management method using a storage device that stores application data including information indicating the waveform characteristics and compression method characteristics of the time-series data associated with each other, The information processing device, When processing the aforementioned time-series data, the waveform characteristics of the time-series data to be processed are estimated, and based on the estimated waveform characteristics of the time-series data and the application data, information indicating the characteristics of the compression method suitable for processing is identified. Based on the information indicating the characteristics of the identified compression method, the compression method is determined. Compressed time-series data is generated by compressing the time-series data using the determined compression method, and the compressed time-series data is stored in the storage device. Data management methods. [Explanation of Symbols]

[0149] 100...Smart meter, 200...Power waveform data transmission device, 300...Power waveform analysis system, 310...Data input processing device, 320...Data processing method selection device, 330...Execution control device, 340...Data management device, 350...Data acquisition processing device, 360...System linkage device, 400...External system, 500...Terminal

Claims

1. An information processing device that processes multiple time-series data, Memory device and including, A data management system, The storage device stores application data including information indicating the waveform characteristics and compression method characteristics of the time-series data that are associated with each other. The aforementioned information processing device is When processing the aforementioned time-series data, the waveform characteristics of the time-series data to be processed are estimated, and based on the estimated waveform characteristics of the time-series data and the application data, information indicating the characteristics of the compression method suitable for processing is identified. Based on the information indicating the characteristics of the identified compression method, the compression method is determined. Compressed time-series data is generated by compressing the time-series data using the determined compression method, and the compressed time-series data is stored in the storage device. It is configured in such a way. Data management system.

2. In the data management system according to claim 1, The aforementioned information processing device is Multiple time-series data that share the same compression method are compressed together and stored in the memory device. It is configured in such a way. Data management system.

3. In the data management system according to claim 1, The storage device stores processing detail data including corresponding compression methods and information indicating the characteristics of the compression methods. The aforementioned information processing device is Based on the processing details data, the compression method is determined by identifying a compression method corresponding to the information indicating the characteristics of the identified compression method. It is configured in such a way. Data management system.

4. In the data management system according to claim 1, The aforementioned application data includes applications associated with information indicating the characteristics of the waveform and the characteristics of the compression method. Data management system.

5. In the data management system according to claim 4, If there are multiple pieces of information in the application data indicating the characteristics of the application and compression method corresponding to the waveform characteristics of the estimated time series data, Based on predetermined criteria, information representing the characteristics of a compression method suitable for processing is identified from among information representing the characteristics of multiple compression methods. It is configured in such a way. Data management system.

6. In the data management system according to claim 5, The information indicating the characteristics of the aforementioned compression method is: Including data accuracy, data resolution and data frequency, Data management system.

7. In the data management system described in claim 3, The information processing device includes an input device operated by a user for inputting information, The aforementioned information processing device is Based on the information input from the input device, the system can register the compression method and information indicating the characteristics of the compression method to the processing detail data, and update the compression method and information indicating the characteristics of the compression method in the processing detail data. It is configured in such a way. Data management system.

8. In the data management system according to claim 4, The information processing device includes an input device operated by a user for inputting information, The aforementioned information processing device is Based on the information input from the input device, the system can register the waveform characteristics, information indicating the compression method characteristics, and the application to the application data, and update the waveform characteristics, information indicating the compression method characteristics, and the application to the application data. It is configured in such a way. Data management system.

9. In the data management system according to claim 4, The aforementioned information processing device is If a single time-series data contains multiple time intervals with different estimated waveform characteristics, the compression method corresponding to the waveform characteristics is determined for each time interval. It is configured in such a way. Data management system.

10. In the data management system according to claim 9, The aforementioned information processing device is The storage device stores data management information including identification information for identifying the corresponding compressed time series data, information indicating the time interval in which the waveform characteristics of the compressed time series data appear, the waveform characteristics of the compressed time series data, the intended use of the compressed time series data, information indicating the characteristics of the compression method, and information indicating whether or not another waveform characteristic of the same compressed time series data has been registered after the end of the time interval. It is configured in such a way. Data management system.

11. In the data management system according to claim 10, The aforementioned information processing device is configured to be able to send and receive information with an external device. The aforementioned information processing device is In response to a request from the external device, the compressed time-series data is restored based on the data management information, and at least one of the restored time-series data and the processed data based on the restored time-series data is transmitted to the external device. It is configured in such a way. Data management system.

12. In the data management system according to claim 1, The aforementioned information processing device is The process involves separating power waveform data from smart meters and processing multiple time-series data sets. It is configured in such a way. Data management system.

13. In the data management system according to claim 12, The information processing device is configured to receive power waveform data from a power waveform data transmission device configured to transmit the power waveform data measured by the smart meter to the information processing device. The information processing device generates a plurality of power waveform data by separating the power waveform data received from the power waveform data transmission device. It is configured in such a way. Data management system.

14. A data management device including an information processing device that processes multiple time-series data, The aforementioned information processing device is Includes a storage device that stores application data including information indicating the waveform characteristics and compression method characteristics of the time-series data that are associated with each other, The aforementioned information processing device is When processing the aforementioned time-series data, the waveform characteristics of the time-series data to be processed are estimated, and based on the estimated waveform characteristics of the time-series data and the application data, information indicating the characteristics of the compression method suitable for processing is identified. Based on the information indicating the characteristics of the identified compression method, the compression method is determined. Compressed time-series data is generated by compressing the time-series data using the determined compression method, and the compressed time-series data is stored in the storage device. It is configured in such a way. Data management device.

15. A data management method executed by an information processing device that processes multiple time-series data using a storage device, The storage device stores application data including information indicating the waveform characteristics and compression method characteristics of the time-series data that are associated with each other. The aforementioned information processing device When processing the aforementioned time-series data, the waveform characteristics of the time-series data to be processed are estimated, and based on the estimated waveform characteristics of the time-series data and the application data, information indicating the characteristics of the compression method suitable for processing is identified. Based on the information indicating the characteristics of the identified compression method, the compression method is determined. Compressed time-series data is generated by compressing the time-series data using the determined compression method, and the compressed time-series data is stored in the storage device. Data management methods.

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