JP2015021801A - Aggregation-distribution device and sensor device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To realize an aggregation-distribution device for distributing information transferred from a plurality of sensors to all or some of these sensors and a sensor device for observing a desired item, which together make organic linkage of these sensors possible by mutually supplementing a difference in or expanding the observation object, principle, and performance of each individual sensor.SOLUTION: The aggregation-distribution device comprises aggregation means for aggregating information individually transferred from a plurality of sensors via a network and distribution means for distributing the result of the aggregation to all or some of the plurality of sensor via the network.

Description

  The present invention relates to an aggregation / distribution device that distributes information delivered from a plurality of sensors to all or a part of these sensors, and a sensor device that observes a desired item.

Meteorological observation devices (meteorological sensors) such as Doppler meteorological radar, Doppler lidar, and soda have different observation principles, physical quantities, and frequencies, and are individually operated without being directly linked.
For example, even with the same weather radar, the sensitivity to particles in the cloud will differ if the frequency band of the transmitted wave is different, so the reflection intensity distribution observed for the same observation target and the shortest detectable time are also different. Occurs.
On the other hand, in recent years, researches are being conducted to compare the observation results obtained by various meteorological observation devices arranged at high density and to elucidate the mechanism of local short-time heavy rain.
In addition, there existed patent document 1-patent document 5 which are listed below in the prior art relevant to this invention.
(1) “A rain monitoring system comprising a plurality of radar bases having a radar device for measuring radar information including a rainfall region within an observation range partially overlapping each other, wherein each of the radar bases has its own radar device. A radar information transmitter that transmits the radar information measured in step 1 to another adjacent radar base that overlaps the observation range, and a radar information receiver that receives radar information transmitted from another radar base. Radar information synthesis for synthesizing radar information obtained by removing the overlap area from other radar information received from its own radar information receiver and radar information measured by its own radar device And a display unit configured to display and output the radar information composed of the radar information combining unit ”, a small radar having a narrow observation range is provided. A rain monitoring system characterized by the fact that even a radar base having a device can ensure a wide observation range with a precision as high as that of a large radar device.
(2) “The center device and the terminal device are connected by center-end type communication via the communication line, and the terminal device requests the center device to transfer the data required by the terminal device and the calculation means and processing means. The center device transfers the data included in the received transfer request, the calculation means, and the processing means to the terminal device, and the terminal device executes processing by the received data, the calculation means, and the processing means. Output the result of "the local prediction that specifies the location and time according to the individual's individual needs, short-term prediction or limited target, or the personalized prediction is delivered from the center device By running global or global data and a learning and prediction program on a terminal device such as a personal computer, a proxy with a high level of expertise can be used as a proxy. Provides a predictive service that eliminates the need for expert intervention ”.
(3) “A plurality of observation means each installed at a predetermined observation point and provided at the analysis center and connected to each other via the network and the plurality of observation means. Information processing means for analyzing meteorological observation data and predicting the weather, and controlling the start and stop of the distributed weather information processing system that conforms to the prescribed standard specifications for the integration of the distributed system A distributed system management function for performing (or distributed simulation management function), a distributed object management function for managing objects such as the weather observation data from the plurality of observation means and data generated by the weather information processing means, A time management function that synchronizes time in data transfer between the plurality of observation means and the weather information processing means; and A distributed inter-object communication management function (or declaration management function) for managing a list of relationships between objects to be transferred and transfer destinations so as to enable transfer of desired objects between the measurement means and the weather information processing means. And a distributed system integrated control means for integrated control of the entire distributed weather information processing system. Each of the plurality of observation means and the weather information processing means cooperates with the distributed system integrated control means. And complying with the predetermined standard specification that enables the transfer of a desired object between the plurality of observation means and the weather information processing means via the distributed system integrated control means in synchronization with time. By having distributed system control means, "the connection specifications between the observation device, center device, and terminal device are defined in IEEE1516. Distributed simulation connection integration architecture HLA (High Level Architecture) standard specification for distributed system integration with distributed system management function, distributed object management function, time management function and distributed object communication management function Distributing meteorological information processing system characterized by the fact that it can be reconstructed efficiently and inexpensively by adding new observation devices by unifying ... Patent Document 3
(4) “A plurality of information input processing means for acquiring weather observation information in the vicinity of the observed area obtained by a plurality of types of weather observation sensors, and a plurality of weather observation sensors acquired by the plurality of information input processing means. A plurality of information unified processing means for converting observation information into a unified format, and a plurality of meteorological observation information converted into a unified format by the plurality of information unified processing means into information of predetermined three-dimensional lattice points, respectively. By providing information integration means for distributing and integrating information for each grid point, and 3D information presentation means for presenting information of 3D grid points integrated by this information integration means, This system can provide the integrated weather observation information obtained from the sensors and contribute to improving the accuracy of local weather prediction. ... Patent Document 4
(5) “Thundercloud cell extraction processing unit that extracts thundercloud cells from observation data obtained by weather radar equipment, and identification of past thundercloud cells from the current thundercloud cell and thundercloud cell database extracted by the thundercloud cell extraction processing unit” A thundercloud cell identification processing unit that outputs the movement amount between both cells as a displacement, and calculates the movement specification and the rise and fall change amount of the thundercloud cell based on the displacement output. Based on thundercloud cell data from the thundercloud cell extraction processing unit, the thundercloud cell database and the thundercloud cell prediction processing unit. Based on the rise / fall situation determination processing means for judging the rise / fall situation of the previous thundercloud cell, and the data from the respective processing units, the processing means and the thundercloud cell database, the present, past and desired time The thundercloud cell includes a display processing unit for displaying the thundercloud cell and its rise / fall status, locus, etc., and the thundercloud cell extraction processing unit performs movement accumulation processing in a three-dimensional radar data area in accordance with the thundercloud cell size. By providing an integration processing means and a cell threshold processing means for determining whether or not a thundercloud cell by performing a threshold process with a predetermined threshold on the movement integration result, and extracting a thundercloud cell, Thunder characterized in that it provides more accurate thunder information by utilizing the obtained three-dimensional information and determining and predicting thunder clouds in consideration of changes in actual thunder clouds that exist in units of cells. Observation system ... Patent Literature 5

JP-A-8-240663 Japanese Patent Laid-Open No. 10-21190 JP 2001-208862 A JP 2002-156467 A Japanese Patent No. 4067999

By the way, in the above-described prior art, observation results obtained by individual observation devices are collected and analyzed, but these observation devices operate independently without being linked to each other.
Moreover, since the results of such analysis can only be obtained within the constraints of the characteristics and performance unique to each observation device, there is flexibility in each stage of observation target generation, development, and decline. Adaptation was difficult.

  The present invention provides an aggregation / distribution device and a sensor device that enable organic linkage of these sensors by compensating for or expanding the differences in observation targets, principles, and performance of individual sensors. With the goal.

  In the first aspect of the present invention, the aggregating means consolidates information individually delivered from a plurality of sensors via a network. The distribution unit distributes the result of the aggregation to all or a part of the plurality of sensors via the network.

  That is, any of the plurality of sensors can compensate for differences and shortages in the observation target, range, accuracy, principle, performance, and the like by obtaining a result of aggregating information delivered from other sensors.

  According to a second aspect of the present invention, the aggregating means aggregates the observation result and the observation result delivered from another sensor device or a specific device via the network. The distribution unit distributes the aggregation result to the other sensor device or the specific device via the network.

  That is, the sensor device according to the present invention obtains a result of observation obtained by the sensor device and a result of aggregation of information delivered from other sensors and devices, thereby providing an object, range, accuracy, and principle of observation. It is possible to compensate for differences and shortages in performance, etc., and to deliver the result of the aggregation to these other sensors or devices.

  According to a third aspect of the present invention, in the aggregation / distribution apparatus according to the first aspect, the aggregation unit may include a movement history for each item indicated by information individually delivered from the plurality of sensors via the network. The geographical location, the applied detection method, the difference at the time of identification, the result of the prediction interpolating the difference, the difference in the observation area of the sensor subjected to the identification, The aggregation is performed based on the degree of correlation of all or part of the attributes of the plurality of sensors.

  In other words, each of the plurality of sensors is configured in a form suitable for the behavior, state and change of each object indicated by information delivered from other sensors, and the method and principle of each sensor. By obtaining the result of aggregation, it is possible to compensate for differences and shortages in the observation target, range, accuracy, principle, performance, and the like.

  According to a fourth aspect of the present invention, in the sensor device according to the second aspect, the aggregation means includes a movement history and an item for each item indicated by information individually delivered from the other sensor via the network. The geographical location, the method of detection applied, the difference at the time of identification, the result of the prediction interpolating the difference, the difference in the observation area of the sensor subjected to the identification, and The aggregation is performed based on the degree of correlation of all or part of the attributes of a plurality of sensors.

  That is, the aggregation / distribution device according to the present invention has a form suitable for the behavior, state and change of each item indicated by the information obtained by the aggregation / distribution device, and the method and principle of each sensor. By obtaining a result of information aggregation, it is possible to compensate for differences or shortages in observation targets, range, accuracy, principle, performance, etc., and to deliver the result of the aggregation to these other sensors or devices.

  In the sensor device and the observation system according to the present invention, it is possible to link in a form not achieved in the conventional example, and the observation target and the dynamic range can be expanded.

  In addition, in the sensor device and the observation system according to the present invention, not only the object to be observed but also the method and the principle can be coordinated, and the object to be observed and the dynamic range can be expanded accurately and stably. In addition, the convenience and reliability of observation can be improved easily.

  Therefore, in the observation system to which the present invention is applied, existing technologies and devices (sensors) are utilized at low cost, and these link together to realize observation and measurement in a form not achieved in the conventional example. , Added value is increased, and flexible adaptation to various needs becomes possible.

It is a figure which shows one Embodiment of this invention. It is an operation | movement flowchart of the weather observation sensor in this embodiment. It is an operation | movement flowchart of the integrated analysis apparatus in this embodiment.

Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.
FIG. 1 is a diagram showing an embodiment of the present invention.
In the present embodiment, the meteorological observation sensors 10-1 to 10-N and the integrated analysis device 30 facing the meteorological observation sensors 10-1 to 10-N via the network 20 are configured.

  In the meteorological observation sensor 10-1, the output of the observation device 11-1 is connected to the input of the feature quantity extraction unit 12-1, and one output of the feature quantity extraction unit 12-1 is sent via the transmission unit 13-1. Connected to the network 20. The network 20 is connected to the input of the reception unit 14-1, and the output of the reception unit 14-1 is input to the target attribute determination unit 17-1 via the coordinate conversion unit 15-1 and the measurement range extraction unit 16-1. Connected to. The output of the target attribute determination unit 17-1 is connected to the control input of the observation device 11-1. The other output of the feature quantity extraction unit 12-1 is connected to the input of the data stack 19-1 via the identification processing unit 18-1, and the output of the data stack 19-1 is the other of the target attribute determination unit 17-1. Connected to the input.

  The configuration of the meteorological observation sensors 10-2 to 10-N is the same as that of the meteorological observation sensor 10-1. Therefore, in the following, reference numerals “2” to “N” are added to the reference numerals of the corresponding components. Here, illustration and description are omitted.

  In addition, in the following, for items that apply to any of the meteorological observation sensors 10-1 to 10-N, it is attached to the reference numerals of the corresponding constituent elements, meaning that any of “1” to “N” can be applied. It is described by adding the letter “c”.

  On the other hand, in the integrated analysis device 30, the network 20 is connected to the input of the reception unit 31, and the output of the reception unit 31 is output from the data stack 35 via the unified coordinate conversion unit 32, the identification processing unit 33, and the data combination unit 34. Connected to input. The output of the data stack 35 is connected to the network 20 via the tracking information generation unit 36 and the transmission unit 37.

FIG. 2 is an operation flowchart of the weather observation sensor in the present embodiment.
FIG. 3 is an operation flowchart of the integrated analysis apparatus according to this embodiment.
The operation of this embodiment will be described below with reference to FIGS.

In the meteorological observation sensor 10-c, the observation device 11-c performs the following processing.
(1) Observation is performed with respect to predetermined items (step S1 in FIG. 2).
(2) The result of the observation (hereinafter referred to as “observation data”) is subjected to processing for ensuring accuracy and quality such as noise removal and interference removal (step S2 in FIG. 2).
(3) The results of these processes are converted into information on the three-dimensional coordinates of the space in which the corresponding item is to be observed (hereinafter referred to as “three-dimensional space information”) (step S3 in FIG. 2).

  The feature quantity extraction unit 12-c calculates a physical quantity that serves as an index for the tracking process of the observation target such as a thundercloud based on the three-dimensional spatial information (step S4 in FIG. 2).

  Here, the physical quantity is determined according to the observation target to be observed by the observation device 11-c and the configuration and performance of the observation device 11-c. For example, in the case of a weather radar, items listed below Or a combination of all or some of them.

(1) Radar reflection intensity
(2) Vertical integrated moisture content (VIL)
(3) Echo peak altitude
(4) Echo area
(5) Convergence field at low elevation angle
(6) Vertical velocity

Note that the local coordinate system having the origin at the position where the observation device 11-c is installed is applied to the feature quantity including these physical quantities, and includes the following items.
(1) Identification number ID of observation device 11-c (any of 1 to N)
(2) Location information for each observation target
(3) Time of observation

  The transmission unit 13-c transmits such a feature amount to the integrated analysis device 30 via the network 20 at a predetermined cycle or interval (step S5 in FIG. 2).

Note that the transmission unit 13-c or the feature amount extraction unit 12-c considers a missing measurement in any of the following cases and cancels the transmission of the feature amount.
(1) When the period of observation performed by the observation device 11-c is significantly longer than a predetermined upper limit value (data collection interval at which the integrated analysis device 30 should collect the above-mentioned feature amount)
(2) When a problem such as competition between the meteorological observation sensors 10-1 to 10-N is expected due to a sudden change in the state of the observation target, etc.

  In addition, the identification processing unit 18-c refers to the feature amount calculated by the feature amount extraction unit 12-c as appropriate, so that a pair of the identifier ID and the position information given to the corresponding observation item (hereinafter, “ Is generated and stored in the data stack 19-c (step S6 in FIG. 2).

  On the other hand, in the integrated analysis device 30, the receiving unit 31 delivers the feature amount received from the weather observation sensors 10-1 to 10-N via the network 20 to the unified coordinate conversion unit 32 (step S1 in FIG. 3).

  The unified coordinate conversion unit 32 maps (map-converts) these feature quantities into a common coordinate system (hereinafter referred to as “unified coordinate system”) (step S2 in FIG. 3).

  The unified coordinate system is a three-dimensional coordinate system that indicates a space that can be observed by the meteorological observation sensors 10-1 to 10-N (observation devices 11-1 to 11-N). , Any of a barometric coordinate system, a temperature coordinate system, a σ coordinate system, an eta coordinate system, and the like.

  The identification processing unit 33 identifies the distribution and location of the observation target based on the individual feature values given in the unified coordinate system (step S3 in FIG. 3), and is configured as a combination of the following items corresponding to each observation target. A “target list for each weather observation sensor” is generated (step S4 in FIG. 3).

(1) Unique identifier ID
(2) Location information
(3) Observation time (band)

Note that the processing performed by the identification processing unit 33 is closely related to the processing performed by the identification processing unit 18-c included in the weather observation sensor 10-c, as will be described later.
The data combining unit 34 generates the “list for each observation target” by aggregating the “target list for each weather observation sensor” for each common observation target, and accumulates it in the data stack 35 (step in FIG. 3). S5).

  Note that in this aggregation process, duplication of the “target list” due to multiple weather observation sensors observing the same target is due to the proximity of the relative position of each weather observation sensor to the observation target, The same observation target is identified and identified based on the feature amount given by the meteorological observation sensor.

The tracking information generation unit 36 performs the following processing for each observation target.
(1) A tracking data set is created based on the association or correlation between the past “list of observation targets” accumulated in the data stack 35 and the movement history (step S6 in FIG. 3). For example, a tracking filter such as a least square filter, an α-β filter, or a Kalman filter may be applied to the association or correlation.

(2) It is determined whether the corresponding observation target is in the “occurrence stage”, “development stage”, or “decay stage” (step S7 in FIG. 3). Hereinafter, the result of such determination is referred to as “stage information”.

(3) The “integrated target list” is generated as a combination of the latest position information of the corresponding observation target, the “stage information”, and the “representative observation time” indicating the time when the aggregation is performed (step S8 in FIG. 3). .

  The transmission unit 37 distributes the “integrated target list” to the weather observation sensor 10-c (may be all or any combination of the weather observation sensors 10-1 to 10-N) via the network 20. (Step S9 in FIG. 3).

  In the meteorological observation sensor 10-c, the receiving unit 14-c receives the “integrated target list” distributed in this way, and delivers it to the coordinate conversion unit 15-c (step S7 in FIG. 2).

  The coordinate conversion unit 15-c generates a “local target list” by mapping the “integrated target list” from the above-described unified coordinate system to the local coordinate system (step S8 in FIG. 2).

  The measurement range extracting unit 16-c includes only a target list (hereinafter, “extraction”) that is included in the observation range of the meteorological observation sensor 10-c among the observation targets indicated in the “local target list”. Only the “target list” is extracted (step S9 in FIG. 2).

  The target attribute determination unit 17-c includes the “extraction target list” extracted in this way, and a plurality of scans generated by the identification processing unit 18-c and accumulated in the data stack 19-c as described above. The “attribute list” corresponding to each observation target is generated by performing the following processing based on the correlation result (step S10 in FIG. 2) and the following processing. 2 step S11).

(1) Observation targets included in the “Extracted Target List” and the latest “Own Sensor Target List”, or observations not included in the “Extracted Target List” but included in the latest “Own Sensor Target List” An attribute ID (hereinafter referred to as “current observation target”) is assigned to the target.
(2) The observation targets that are included in the “extraction target list” but are not included in the latest “own sensor target list” are classified as follows, and individually assigned an identifier ID.

(2-1) Based on the current stage (any of “occurrence stage”, “development stage”, “decay stage”), observation characteristics of observation sensor 10-c, and past multiple scans Based on the obtained observation result, a target that can be identified (detected) in the future (hereinafter referred to as “future observation target”) and its attributes are identified, and an attribute ID (hereinafter “future observation target ID”) is identified. And so on).

(2-2) Does not fall under either “Current Observation Target” or “Future Observation Target” and is based on the “stage information” described above and the observation characteristics of the observation sensor 10-c. A target (hereinafter referred to as “past observation target”) and its attribute, and an attribute ID (hereinafter referred to as “past observation target ID”) is assigned.

(2-3) Identify a target that does not fall under either (2-1) or (2-2) above (hereinafter referred to as “missing target”) and its attributes, and an attribute ID (hereinafter referred to as “missing target”). Measurement target ID ").

The “past observation target” corresponds to, for example, the following observation targets.
(a) Thunderclouds in the “development stage” and “decay stage” are radar reflected waves in the W band that arrive at the cloud radar from higher altitudes than the rainfall altitude. Observation targets that are obstructed due to being too small

 (b) Observation targets that are difficult to observe with meteorological satellites because they are located under the “Kana-Toko cloud” formed at the top of the existing cumulonimbus clouds.

(3) The above-mentioned “future observation target” attribute and “future observation target ID” pair and the “current observation target” attribute and “current observation target ID” pair are delivered to the observation device 11-c.
On the other hand, the observation device 11-c applies the attribute and ID pairs thus delivered in the form listed below, thereby more accurately identifying a desired target or item (step S12 in FIG. 2). .

(1) Applicable to complement observation data and 3D spatial information as described above and improve accuracy and quality.
(2) Refer to the calculation target of the (radar) signal processing that should be performed originally.
(3) Expand the accuracy and possibilities of signal processing to be complemented or implemented (radar) signal processing.
(4) To improve convenience and performance by adding to an instruction image (not shown).

  Therefore, according to the present embodiment, the plurality of N observation apparatuses 11-1 to 11-N share the data of the observations performed individually, thereby expanding the area or the object to be observed, It is possible to complement (interpolate) or improve the accuracy and reliability of observation, and to flexibly realize added value improvement that has not been achieved with conventional observation devices.

In the present embodiment, the following matters may be anything.
(1) Models and types of observation devices 11-1 to 11-N (for example, meteorological satellites, infrared cameras, and riders)
(2) Observation objects and forms to be performed by the observation devices 11-1 to 11-N
(3) Functions of function distribution and load distribution performed by the cooperation of the meteorological observation sensors 10-1 to 10-N and the integrated analysis device 30

(4) All or part of the topology, communication procedure, transmission method, multiple access method, and exchange method applied to the network interposed between the meteorological observation sensors 10-1 to 10-N and the integrated analysis device 30

  In the present embodiment, with respect to the above-described processing performed by the integrated analysis device 30, both or either of the function and the load are meteorological observation sensors 10-1 to 10-N (observation devices 11-1 to 11). -N).

Furthermore, in the present embodiment, the target of correlation performed for the above-described aggregation may be any combination of the following items that are equal for each observation target.
(1) Movement history
(2) Position

(3) Applied detection method
(4) Differences at the time of identification
(5) Prediction process result that interpolates the difference
(6) Differences in the observation area of the sensor used for identification
(7) Multiple sensor attributes

  Further, the present invention is not limited to the above-described embodiments, and various configurations of the embodiments are possible within the scope of the present invention, and any improvements may be made to all or some of the components.

DESCRIPTION OF SYMBOLS 10 Meteorological observation sensor 11 Observation apparatus 12 Feature-value extraction part 13,37 Transmission part 14,31 Reception part 15 Coordinate conversion part 16 Measurement range extraction part 17 Target attribute determination part 18,33 Identification process part 19,35 Data stack 20 Network 30 Integrated analyzer 32 Unified coordinate converter 34 Data combiner 36 Tracking information generator

Claims (4)

An aggregation means for aggregating information individually delivered from a plurality of sensors via a network;
An aggregation / distribution device comprising: a distribution unit that distributes the aggregation result to all or a part of the plurality of sensors via the network. A sensor device for observing a desired item,
Aggregating means for aggregating the observation results and the observation results delivered from another sensor device or a specific device via the network;
A sensor device comprising: distribution means for distributing the result of aggregation to the other sensor device or a specific device via the network. The aggregation / distribution device according to claim 1,
The aggregation means includes
For each item indicated by information individually delivered from the plurality of sensors via the network, the movement history, the position, the applied detection method, the difference at the time of identification, the difference The aggregation is performed based on a prediction result for interpolating the difference, an observation area difference of the sensor used for identification, and a degree of correlation of all or part of the attributes of the plurality of sensors. Dispensing device. The sensor device according to claim 2,
The aggregation means includes
For each item indicated by information individually delivered from the other sensor via the network, the movement history, the position, the applied detection method, the difference at the time of identification, the difference The aggregation is based on the result of prediction for interpolating the difference, the difference in the observation area of the sensor used for identification, and the degree of correlation of all or part of the attributes of the plurality of sensors. apparatus.