KR102583270B1 - Water-environment measurement data management system - Google Patents

Abstract

The present invention relates to a water environment measurement data management system and, more specifically, to a water environment measurement data management system which collects and purifies various measurement data from water and sewage facilities, separates measurement data and location information of the measurement data and stores them, and can analyze a pattern variation cause based on a pattern of the measurement data.

Description

Water environment measurement data management system {Water-environment measurement data management system}

The present invention relates to a water environment measurement data management system, and more specifically, to collect and purify various measurement data from water and sewage facilities, to separate and store measurement data and location information of the measurement data, and to use the pattern of the measurement data as the basis. This is about a water environment measurement data management system that can analyze the causes of pattern changes.

Water and sewage facilities purify water taken from the river at a water purification plant and then provide it to each home or factory through a drainage basin and water pipe. Used water is purified at a sewage treatment plant through a sewer pipe and returned to the river.

In addition, during rainy weather, rainwater is quickly discharged into the river through stormwater pipes to prevent urban flooding.

In other words, the water quality and flow rate of the river affect the treatment process of the water purification plant, the amount of water used affects the capacity of the sewer pipe and the capacity of the sewage treatment plant, and the treatment and water reuse of the sewage treatment plant affect the water quality and flow rate of the river. It has a circular structure.

Therefore, there is a need to provide various services related to water by utilizing various measurement data generated from water supply and sewerage facilities.

In order to solve the above-mentioned problems, the technical task to be achieved by the present invention is to collect and purify various measurement data from water and sewage facilities, separate and store the measurement data and the location information of the measurement data, and create a pattern based on the pattern of the measurement data. The goal is to present a water environment measurement data management system that can analyze the causes of variation.

The problems of the present invention are not limited to those mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the description below.

As a means to solve the above-mentioned technical problem, the water environment measurement data management system according to the present invention transmits measurement data generated in water and sewage facilities and attribute information of the measurement data to a cloud DB, and location information of the measurement device. and a plurality of water supply and sewerage management devices that transmit attribute information of the measurement data to a water and sewage integrated server; a cloud server that purifies measurement data received from the plurality of water supply and sewerage management devices and stores the purified measurement data and attribute information of the measurement data in the cloud DB; And a water supply and sewerage integrated server that stores location information and attribute information received from the plurality of water supply and sewerage management devices; wherein the cloud server includes a cloud communication unit that communicates with a plurality of water supply and sewerage management devices and a water supply and sewerage integrated server; a data purification unit that purifies missing data and unobserved data that are not normal measured data among the measured data using the measured data received from the plurality of water supply and sewerage management devices and attribute information of the measured data; A cloud DB that receives and stores normal measurement data, refined measurement data, and attribute information of the measurement data from the data purification unit; And when a request to read measurement data is received from the water and sewage integrated server, it is characterized by including a cloud control unit that processes the measurement data stored in the cloud DB in CSV file format using Open API.

Additionally, the data purification unit may include a data classification unit that classifies missing data and unobserved data among the measurement data; and a data correction unit that corrects missing data and unobserved data classified through the data classification unit.

In addition, the data classification unit may include a short-term missing data classification unit that classifies missing data within a predetermined reference time among the measurement data as short-term missing data; a long-term missing data classification unit that classifies data missing for a predetermined reference time or more among the measurement data as long-term missing data; and an unobserved data classification unit that classifies unobserved data among the measured data.

In addition, the data correction unit includes a first measurement data generation unit that generates refined measurement data from short-term missing data using a data interpolation algorithm; a second measurement data generator that generates refined measurement data from long-term missing data using a data conversion algorithm; and a third measurement data generator that generates refined measurement data from unobserved data using a non-linear statistical algorithm.

In addition, the data interpolation algorithm is one of linear interpolation, second-order polynomial interpolation, and spline interpolation, the data conversion algorithm is one of moving average or exponential smoothing, and the nonlinear statistical algorithm is AR (Autoregressive). , It is characterized as one of MA (Moving Average), ARMA (Autoregressive Moving Average), and ARIMA (Autoregressive Integrated Moving Average).

In addition, the water and sewage integrated server is characterized in that the received location information and attribute information are individually encrypted and stored.

In addition, the attribute information of the measurement data includes identification information and measurement time information of the measuring device that measured the measurement data, and the water and sewage integrated server has the attributes of the measurement data commonly stored together with the measurement data and location information. The measurement data is read from a cloud server and analyzed based on the information.

In addition, the water and sewage integrated server reads the measurement data stored in the cloud DB and analyzes the pattern, and if it is determined that the analyzed pattern has changed suddenly compared to the reference pattern, the cause of the sudden change is determined through learning, but It is characterized by comparing and analyzing classified measurement data change cause items and big data related to social issues to identify highly correlated items as the cause of the sudden change.

In addition, the previously classified measurement data change cause items are created by category, and the categories include facility transformation, industrial type change, climate, disaster, and tourist destinations.

The water environment measurement data management system according to the present invention collects and purifies various measurement data from water and sewage facilities, improves the security of the measurement data by separately storing the measurement data and the location information of the measurement data, and based on the pattern of the measurement data. By analyzing the cause of pattern variation, you can accurately identify the exact cause of pattern variation and predict and prepare for future use of measurement data.

The effects of the present invention are not limited to those mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the description below.

1 is a diagram showing a water environment measurement data management system according to an embodiment of the present invention;
Figure 2 is a conceptual diagram for explaining the operation of water supply and sewerage facilities;
Figure 3 is a block diagram of the data purification unit;
Figure 4 is a block diagram of the data classification unit;
5 is a block diagram of the data correction unit;
Figure 6 is a diagram showing the operation of reading data using Open API, and
Figure 7 is an example diagram showing a case where the pattern of measurement data changes suddenly.

The above objects, other objects, features and advantages of the present invention will be easily understood through the following preferred embodiments related to the attached drawings. However, the present invention is not limited to the embodiments described herein and may be embodied in other forms. Rather, the embodiments introduced herein are provided so that the disclosed content will be thorough and complete and so that the spirit of the present invention can be sufficiently conveyed to those skilled in the art.

In this specification, when an element is referred to as being on another element, it means that it may be formed directly on the other element or that a third element may be interposed between them. Additionally, in the drawings, the thickness of components is exaggerated for effective explanation of technical content.

If an element, component, device, or system is referred to herein as including a component consisting of a program or software, even if explicitly stated, that element, component, device, or system is intended to include a component consisting of a program or software. It should be understood as including hardware necessary for execution or operation (e.g., memory, CPU, etc.) or other programs or software (e.g., operating system or drivers required to run hardware, etc.).

In addition, unless specifically stated in the implementation of an element (or component), it should be understood that the element (or component) may be implemented in any form of software, hardware, or software and hardware.

Hereinafter, the present invention will be described in detail with reference to the drawings. In describing specific embodiments below, various specific details have been written to explain the invention in more detail and to aid understanding. However, a reader with sufficient knowledge in the field to understand the present invention can recognize that it can be used without these various specific details.

In some cases, it is mentioned in advance that when describing the invention, parts that are commonly known but are not significantly related to the invention are not described in order to prevent confusion without any reason in explaining the invention.

Figure 1 is a diagram illustrating a water environment measurement data management system according to an embodiment of the present invention.

Referring to Figure 1, the water environment measurement data management system according to an embodiment of the present invention includes a plurality of water supply and sewerage management devices (100, 100-1, ..., 100-n, n is a constant), a cloud server 200, and It may include a water and sewage integrated server 300.

A plurality of water supply and sewerage management devices (100, 100-1, ..., 100-n) collect measurement data generated from water supply and sewerage facilities (10, 10-1, ..., 10-n) and attribute information of the measurement data and It can be transmitted to the cloud server 200 to be stored in the cloud DB 230, and the location information of the measuring device that obtained the measurement data can be transmitted to the water and sewage integrated server 300 along with the attribute information of the corresponding measurement data.

Figure 2 is a conceptual diagram for explaining the operation of water supply and sewerage facilities.

Referring to Figure 2, water supply and sewage facilities include a number of facilities related to water treatment, such as water intake plants, water purification plants, drainage ponds, water pipes, sewer pipes, and sewage treatment plants. Water taken from the river through a water intake is purified at a water purification plant and then supplied to homes or factories through a drainage basin and water pipe. Water used in homes or factories is purified at a sewage treatment plant through a sewer pipe and returns to the river.

Multiple water supply and sewerage facilities are equipped with multiple measuring devices to measure the status of each water and sewage facility, and the multiple measuring devices record measured data (hereinafter referred to as 'measurement data') along with attribute information of the measured data. It can be transmitted to water supply and sewage management devices (100, 100-1, ..., 100-n) respectively connected to water supply and sewage facilities. The attribute information of the measurement data may include identification information (unique ID) of the measurement device and information on the time when the measurement data was acquired.

[Table 1] shows the types of measurement data obtained from multiple water supply and sewerage facilities.

water and sewage facilities Measurement data type River Water level, flow rate, water quality water intake station flow rate, water quality water purification plant Inflow flow rate, inflow water quality, supply flow rate, supply water quality, operation information Bae Su-ji flow rate, water quality water pipe Pressure, flow, water quality sewer pipe Water level, flow rate, water quality sewage treatment plant Inflow flow rate, inflow water quality, supply flow rate, supply water quality, operation information

In [Table 1], the type of measurement data may refer to the type of measuring device installed in the water supply and sewerage facility.

A number of water supply and sewerage management devices (100, 100-1, ..., 100-n) shown in FIG. 1 are connected to or provided with water supply and sewerage facilities (10, 10-1, ..., 10-n) as shown in FIG. It may include a river management device, a water intake management device, a water purification plant management device, a drainage basin management device, a water pipe management device, a sewer pipe management device, and a sewage treatment management device.

A plurality of water supply and sewerage management devices (100, 100-1, ..., 100-n) may each include a management communication unit 110, a management memory 120, and a management control unit 130, and hereinafter, provided at the water purification plant. The description will be made using the water supply and sewerage management device 100 as an example.

The management communication unit 119 includes a communication interface circuit for communicating with a plurality of measuring devices (not shown) installed in the water supply and sewage facility (10, water purification plant), the cloud server 200, and the water and sewage integrated server 300. The management communication unit 119 receives measurement data and attribute information of the measurement data from a plurality of measuring devices (not shown).

Management memory 120 may include volatile memory and/or non-volatile memory. The management memory 120 includes commands or data related to the components 110 to 130, one or more programs and/or software, an operating system, etc., in order to implement and/or provide the functions provided by the water and sewage management device 100. It can be saved.

Additionally, the management memory 120 further stores a mapping table that maps and stores identification information of the measuring device and location information of the measuring device. The location information of the measuring device is information on the location where the measuring device is installed in the water supply and sewerage facility connected to the water and sewage management device 100.

The management control unit 130 executes one or more programs stored in the management memory 120 to control the overall operation of the water supply and sewerage facility 10 or the water supply and sewage management device 100.

For example, the management control unit 130 converts the received measurement data and measurement data attribute information into a processable form and stores it in the management memory 120. The management control unit 130 checks the measuring device identification information included in the attribute information of the measurement data among the converted data, and checks the location information of the measuring device mapped to the confirmed identification information in the mapping table.

The management control unit 130 transmits the measurement data and attribute information of the measurement data to the cloud server 200, and sends the location information of the measuring device that measured the measurement data confirmed from the mapping table and the attribute information of the measurement data to the water and sewage integrated server ( Controls the management communication unit 119 to transmit to 300).

In addition, the management control unit 130 analyzes measurement data received from measuring devices provided in the connected water supply and sewage facility 10 to determine the status of the water supply and sewage facility 10, and generates a signal for driving control according to the determination result. This can be transmitted to the water supply and sewerage facility (10).

Meanwhile, the cloud server 200 may purify measurement data and attribute information received from a plurality of water and sewage management devices 100, 100-1, ..., 100-n and store them in the cloud DB 230. To this end, the cloud server 200 may include a cloud communication unit 210, a data purification unit 220, a cloud DB 230, a cloud memory 240, and a cloud control unit 250.

The cloud communication unit 210 communicates with a plurality of water and sewerage management devices (100, 100-1, ..., 100-n) and the water and sewage integrated server 300.

The data purification unit 220 performs corrections for missing data and unobserved data that are not normal measurement data among the measurement data collected from the plurality of water and sewage management devices 100, 100-1, ..., 100-n.

In the case of the above measurement data, missingness or unobservation may occur during the maintenance and repair period due to a failure of the sensor, communication equipment, or software. Additionally, missingness or unobservation may occur due to external causes or sensor-specific errors. there is.

Therefore, to purify such missing data and unobserved data, the data purification unit 220 may further include a data classification unit 221 and a data correction unit 222, as shown in FIGS. 3 to 5.

Here, missing data refers to data due to malfunction of multiple water supply and sewerage management devices, etc., and unobserved data refers to data due to failure of multiple water supply and sewage management devices.

First, data classification among the measurement data must be performed through the data classification unit 221. For this purpose, the data classification unit 221 includes a short-term missing data classification unit 221a, a long-term missing data classification unit 221b, and unobserved data. It further includes a classification unit 221c.

The short-term missing data classification unit 221a classifies missing data within a predetermined standard time among the measurement data as short-term missing data. Here, the predetermined standard time may be a time set by an administrator.

The long-term missing data classification unit 221b classifies data that is missing for a predetermined standard time or more among the measurement data as long-term missing data. Here, the predetermined standard time may preferably be a time set by an administrator.

The unobserved data classification unit 221c classifies unobserved data among the measured data.

Next, in order to correct the long-term/short-term missing data and unobserved data classified through the data classification unit 221, the data correction unit 222 generates the first measurement data generation unit 222a and the second measurement data. It further includes a unit 222b and a third measurement data generation unit 222c.

The first measurement data generator 222a generates refined measurement data using short-term missing data using a data interpolation algorithm. In this case, the interpolation algorithm may be any one of linear interpolation, second-order polynomial interpolation, and spline interpolation. . In other words, if missing information occurs for a short period of time, it can be corrected using various interpolation methods.

The second measurement data generator 222b generates refined measurement data from long-term missing data using a data conversion algorithm. In this case, the data conversion algorithm may be either a moving average method or an exponential smoothing method. In other words, if long-term missingness occurs, it can be corrected using measurement data values from past measurement data on the same day of the week and at the same time.

The third measurement data generator 222c generates refined measurement data using unobserved data using a non-linear statistical algorithm. At this time, the non-linear statistical algorithm includes Autoregressive (AR), Moving Average (MA), and Autoregressive Moving (ARMA). Average) or ARIMA (Autoregressive Integrated Moving Average), preferably the ARIMA algorithm. The ARIMA algorithm is a time series data-based analysis technique that can generate predictive data (refined measurement data) for unobserved data using past measurement data. In other words, the similarity of other measurement items that are related to the measurement item at the point where unobserved data was generated (e.g., how the flow rate at that point affects water quality), points nearby the point where unobserved data was generated. Applies to events that have an absolute impact on flow rate and water quality, such as the similarity of measurement items with other measurement items (e.g., how water quality at upstream points affects downstream points), upstream discharge volume, water intake volume, and pollutant discharge volume. Predicted data can be generated by analyzing the correlation between measurement items.

[Table 2] shows the weights and application items used by the ARIMA algorithm to generate prediction data.

classification Applicable items basic score weight raw measurements Measurement time Measuring point Measures Same point, same point measurement value Metrics 10 Metric correlation Measures 10 Metric correlation time series elements month 10 slot 10 Day of the week 5 day off 3 consecutive holidays 2 number of consecutive holidays 2 Holiday classification One climatic factors Air temperature/water temperature 10 wind speed 10 solar radiation 10 Average temperature for the previous hour 9 Average temperature for the previous 3 hours 8 Average temperature for the previous 6 hours 7 Average temperature for the previous 12 hours 6 Average temperature for the previous 24 hours 5 Average temperature for the previous 48 hours 4 Average temperature for the previous 7 days 3 Average temperature for the previous 14 days 2 Average temperature for the previous 30 days One Cumulative rainfall in the previous hour 9 Cumulative rainfall in the previous 3 hours 8 Cumulative rainfall in the previous 6 hours 7 Cumulative rainfall in the previous 12 hours 6 Accumulated rainfall in the previous 24 hours 5 Cumulative rainfall in the previous 48 hours 4 Cumulative rainfall over the previous 7 days 3 Cumulative rainfall over the previous 14 days 2 Cumulative rainfall over the previous 30 days One Solar radiation for the previous hour 9 Sunshine amount for the previous 3 hours 8 Solar radiation in the previous 6 hours 7 Solar radiation in the previous 12 hours 6 Solar radiation in the previous 24 hours 5 Solar radiation in the previous 48 hours 4 Sunshine amount for the previous 7 days 3 Solar radiation for the previous 14 days 2 Sunshine amount for the previous 30 days One Number of consecutive dry season days1 10 Number of consecutive dry season days 2 (less than 5mm) 10 Number of consecutive rainy days1 10 Number of days with continuous rainfall 2 (more than 5mm) 10 Nearby point measurements Metrics 5 By distance Measures 5 By distance Upstream flow water quality (1~n) Upstream inlet flow One weight by distance Upstream inlet water quality One Distance/item weighting Upstream non-point pollutants (1~n) Upstream contamination flow One weight by distance Upstream polluted water quality One Distance/item weighting Dam/Weir (1~n) Water level One Discharge One water intake source water intake One Water intake quality One sewage treatment plant Discharge One Discharged water quality One

The cloud DB 230 can store purified measurement data received from a plurality of water and sewage management devices 100, 100-1, ..., 100-n and attribute information of the measurement data together with the received time. Attribute information of measurement data includes identification information (unique ID) of the measuring device that measured the measurement data and information on the measured time.

Cloud memory 240 may include volatile memory and/or non-volatile memory. One or more programs and/or software, operating systems, data, etc. may be stored in the cloud memory 240 in order to implement and/or provide functions provided by the cloud server 200.

The cloud control unit 250 controls the overall operation of the cloud server 200 by executing one or more programs stored in the cloud memory 240. For example, the cloud control unit 250 refines the measurement data received by the cloud communication unit 210 and processes the refined measurement data and attribute information of the measurement data to be stored in the cloud DB 230.

In addition, when a request to read measurement data is received from the water and sewage integrated server 300, the cloud control unit 250 stores the data in the cloud DB 230 in a CSV (Comma-Separated Values) file format using an Open API (Application Program Interface). The stored measurement data can be processed for reading.

Meanwhile, the water and sewage integrated server 300 can store location information and attribute information of measurement data received from a plurality of water and sewage management devices (100, 100-1, ..., 100-n). To this end, the water and sewage integrated server 300 may include an integrated communication unit 310, an integrated DB 320, an integrated memory 330, and an integrated control unit 340.

The integrated communication unit 310 communicates with a plurality of water and sewage management devices 100, 100-1, ..., 100-n and the cloud server 200.

The integrated DB 320 can store the location information where the measurement data received from the plurality of water and sewage management devices 100, 100-1, ..., 100-n is measured by mapping it to the attribute information of the measuring device. The attribute information of the measuring device includes the attribute information of the measuring data (identification information of the measuring device, measurement time information), measurement items and measurement cycle of the measuring device, and water supply and sewage management devices (100, 100-1, …, 100-n). Includes transmission cycle and pipe network information. Attribute information of measuring devices can be created and managed in the integrated control unit 340.

Integrated memory 330 may include volatile memory and/or non-volatile memory. The integrated memory 330 includes commands or data related to the components 310 to 340, one or more programs and/or software, an operating system, etc., in order to implement and/or provide the functions provided by the water and sewage integrated server 300. It can be saved.

In addition, the integrated memory 330 includes identification information (unique number or unique ID) of measuring devices installed in multiple water supply and sewerage facilities, installation location of the measuring device, measurement items and measurement cycle measured by the measuring device, and transmission of measurement data. Additional information on measuring devices, including information on the water supply and sewerage network of the area where the measuring devices are installed, is further stored. The water and sewage pipe network map includes information about water and sewage pipes, such as their location, pipe diameter, pipe material, slope, and construction date. Additional information on measuring devices stored in the integrated memory 330 can be stored in advance by the manager or provided from a number of water and sewage management devices 100, 100-1, ..., 100-n.

Additionally, in the integrated memory 330, if a change occurs in the pattern of the measurement data, previously classified change cause items can be stored by category so that the cause of the change in the measurement data can be identified.

[Table 3] shows an example of variation cause items.

category Variation cause item Facility transformation New facilities, expansions, breakdowns, changes in construction methods, etc. Change in industrial form New construction of large-scale gathering facilities, industrial facilities, shopping malls, etc., population inflow and outflow, etc. climate factors Rainfall, temperature, fine dust, wind speed, etc. disaster factors Major accidents, earthquakes, fires, epidemics, etc. tourist destination factors Tourist events, local government events, large-scale performances, etc. other weights Mobile phone usage by region, lodging usage rate, event ticket sales volume, etc.

Referring to [Table 3], categories include facility transformation, industrial type change, climate, disaster, tourist destination, and other weights. Other weights include rapid or rapid declines in mobile phone usage, rapid or rapid declines in lodging usage, and changes in ticket sales.

The integrated control unit 340 executes one or more programs stored in the integrated memory 330 to control the overall operation of the water and sewage integrated server 300.

For example, the integrated control unit 340 processes the cloud communication unit 210 to store the received measurement data and attribute information of the measurement data in the cloud DB 220. In particular, when the measurement data and the property information of the measurement data are received, the integrated control unit 340 creates measurement device property information using the property information of the measurement data and the information stored in the integrated memory 330, and creates the properties of the created measurement device. Information and received measurement data can be mapped and stored in the integrated DB (320). In detail, the integrated control unit 340 analyzes the attribute information of the measurement data to confirm the unique information of the measuring device, and stores the measuring device additional information having the same unique information as the identified unique information among the measuring device additional information in the integrated memory ( 330). Then, measuring device attribute information is created by mapping the attribute information of the measurement data and the confirmed measuring device additional information.

Additionally, the integrated control unit 340 may encrypt the location information of the measurement data and the attribute information of the measuring device and store them in the integrated DB 320. The encryption method may be one of various methods, such as a blockchain method.

Additionally, the integrated control unit 340 can access the cloud server 200 through Open API and read measurement data stored in the cloud DB 230, as shown in FIG. 6. The integrated control unit 340 searches for and reads measurement data having matching attribute information from the cloud DB 230 using the identification information of the measuring device to be searched and the measured time (i.e., attribute information of the measurement data). You can.

In addition, the integrated control unit 340 reads the measurement data stored in the cloud DB 230, analyzes the pattern of the measurement data, and, if it is determined that the analyzed pattern has changed suddenly compared to the previously stored reference pattern, learns the cause of the sudden change. It can be figured out by The degree of sudden change can be judged as a sudden change if the difference between the reference pattern at the same predetermined time and the currently analyzed pattern is greater or less than the reference value.

Figure 7 is an example diagram showing a case where the pattern of measurement data changes suddenly.

Referring to Figure 7, the measured data collected in area A shows a higher than normal water usage during the tourist season, and then the water usage decreased and then sharply increased again. First, in the section where the number decreased sharply, it can be seen that the number of tourists decreased due to the flu epidemic, and in the section where the number increased rapidly, it can be seen that water use increased due to a large fire. However, conventionally, only the measurement data shown in FIG. 7 is stored, and the cause of the change according to the change in the measurement data is not separately confirmed and stored.

Accordingly, in an embodiment of the present invention, the integrated control unit 340 continuously or periodically compares the pattern of the measurement data and the reference pattern to determine a suddenly changed section. When a sudden change section is determined, the integrated control unit 340 checks the time when the sudden change section was determined and stores accumulated big data such as SNS (Social Network Service), Internet news, and search terms on portal sites corresponding to the confirmed time in advance. By comparing and analyzing the items causing changes in measurement data classified and stored in the integrated memory 330, items with a high degree of correlation can be identified as the cause of the sudden change.

For example, in Figure 7, when the water usage pattern used in December has drastically decreased compared to the reference pattern, the integrated control unit 340 collects big data that occurred in December in area A, and ranks search terms or search terms from the collected big data. Identify the ranking of the most frequently occurring words (e.g., most included in news), and individually compare the identified search term rankings, word rankings, and the items causing changes in [Table 2]. If there is a match, a correlation You can calculate the degree and identify the item with the highest correlation (for example, the item with the highest search term ranking among matching items) as the cause of the variation.

According to the above-described embodiment of the present invention, the measurement data and the location information where the measurement data were measured are separated and stored separately in the cloud DB 230 and the integrated DB 320, thereby strengthening the security of the measurement data, which is public data. , Even if measurement data is leaked, location information cannot be leaked, so illegal use of measurement data can be prevented.

In addition, efforts are being made to detect abnormal situations in facilities and store measurement data in large quantities when traditional analysis techniques for measurement data deviate from existing patterns. However, when there is a change in the surrounding environment, such as moving into a large residential complex, the usability of previously stored measurement data decreases rapidly, and the amount of change according to specific situations, such as when various accidents occur or diseases such as the flu are prevalent, is unknown. can not do it.

As shown in FIG. 7, if only the measurement data is stored for a long period of time, after several years, it is not known for what reason the measurement data in FIG. 7 was measured or predicted. Therefore, in an embodiment of the present invention, variables that affect water usage or water-related measurement data, that is, items causing variation, are systematically classified, measurement data is secured through measurement, and then data affecting the measurement is obtained. You can extract information from the cloud (SNS, big data, etc.) and store and manage it by classifying it according to access rights. This allows you to predict the cause and prepare for water use if a similar measurement data pattern occurs again.

In the above, even though all the components constituting the embodiment of the present invention have been described as being combined or operated in combination, the present invention is not necessarily limited to this embodiment. That is, as long as it is within the scope of the purpose of the present invention, all of the components may be operated by selectively combining one or more of them. In addition, although all of the components may be implemented as a single independent hardware, a program module in which some or all of the components are selectively combined to perform some or all of the combined functions in one or more pieces of hardware. It may also be implemented as a computer program having. The codes and code segments that make up the computer program can be easily deduced by a person skilled in the art of the present invention. Such a computer program can be stored in a computer-readable storage medium and read and executed by a computer, thereby implementing embodiments of the present invention.

Meanwhile, although the preferred embodiments have been described and illustrated to illustrate the technical idea of the present invention, the present invention is not limited to the configuration and operation as shown and described, and does not deviate from the scope of the technical idea. Without limitation, those skilled in the art will understand that many changes and modifications can be made to the present invention. Accordingly, all such appropriate changes, modifications and equivalents should be considered to fall within the scope of the present invention. Therefore, the true scope of technical protection of the present invention should be determined by the technical spirit of the attached registration claims.

10, 10-1, … , 10-n: Water and sewage facilities
100, 100-1, … , 100-n: Multiple water supply and sewage management devices
200: Cloud server
300: Water supply and sewage integrated server

Claims (9)

A number of water supply and sewerage systems transmit measurement data generated from water supply and sewerage facilities and attribute information of the measurement data to a cloud DB, and transmit location information of the measuring device that measured the measurement data and attribute information of the measurement data to a water and sewage integrated server. management devices;
a cloud server that purifies measurement data received from the plurality of water supply and sewerage management devices and stores the purified measurement data and attribute information of the measurement data in the cloud DB; and
A water and sewage integrated server that stores location information and attribute information received from the plurality of water and sewage management devices;
Including,
The cloud server is,
A cloud communication unit that communicates with multiple water and sewage management devices and a water and sewage integrated server;
a data purification unit that purifies missing data and unobserved data that are not normal measured data among the measured data using the measured data received from the plurality of water supply and sewerage management devices and attribute information of the measured data;
A cloud DB that receives and stores normal measurement data, refined measurement data, and attribute information of the measurement data from the data purification unit; and
When a request to read measurement data is received from the water and sewage integrated server, a cloud control unit that processes the reading of measurement data stored in the cloud DB in CSV file format using Open API;
Includes,
The data purification unit,
a data classification unit that classifies missing data and unobserved data among the measurement data; and
a data correction unit that corrects missing data and unobserved data classified through the data classification unit;
Including,
The data correction unit,
a first measurement data generator that generates refined measurement data from short-term missing data using a data interpolation algorithm;
a second measurement data generator that generates refined measurement data from long-term missing data using a data conversion algorithm; and
a third measurement data generator that generates refined measurement data from unobserved data using a non-linear statistical algorithm;
Includes,
The water and sewage integrated server,
Includes unique information on the measuring devices provided in the above-mentioned multiple water supply and sewerage facilities, installation locations of the measuring devices, measurement items and measurement cycles measured by the measuring devices, transmission cycle of measurement data, and water supply and sewerage network map information in the area where the measuring devices are installed. An integrated memory in which additional information on measuring devices is stored; and
Confirm the unique information of the corresponding measuring device by analyzing the attribute information of the received measurement data, and check the additional information of the measuring device that has the same unique information as the confirmed unique information among the additional information of the measuring device stored in the integrated memory. , Measuring device attribute information is created by mapping the confirmed measuring device additional information and the attribute information of the received measurement data, and the location information of the received measuring device is mapped to the created measuring device additional information, encrypted, and integrated DB. Integrated control unit for storing;
Including,
The attribute information of the measurement data includes identification information of the measuring device that measured the measurement data and measurement time information,
The water and sewage integrated server reads and analyzes the measurement data from a cloud server based on attribute information of the measurement data that is commonly stored together with the measurement data and location information,
Measurement data stored in the cloud DB is read and the pattern is analyzed, the analyzed pattern is compared with the reference pattern, the section where the pattern difference exceeds the standard value is determined to be a rapidly changing section, and the time when the determined sudden change section occurs is confirmed. And, the big data accumulated in the search terms of SNS (Social Network Service), Internet news, and portal sites corresponding to the confirmed period are compared and analyzed with the cause of change in measurement data stored in advance to identify the cause of the sudden change section. A cloud-based water environment measurement data management system characterized by:
delete According to paragraph 1,
The data classification unit,
a short-term missing data classification unit that classifies missing data within a predetermined reference time among the measurement data as short-term missing data;
a long-term missing data classification unit that classifies data missing for a predetermined reference time or more among the measurement data as long-term missing data; and
an unobserved data classification unit that classifies unobserved data among the measured data;
A water environment measurement data management system comprising:
delete According to paragraph 1,
The data interpolation algorithm is,
One of linear interpolation, second-order polynomial interpolation, and spline interpolation,
The data conversion algorithm is,
Either the moving average method or the exponential smoothing method,
The nonlinear statistical algorithm is,
A water environment measurement data management system characterized by one of AR (Autoregressive), MA (Moving Average), ARMA (Autoregressive Moving Average), and ARIMA (Autoregressive Integrated Moving Average).
delete delete delete According to paragraph 1,
The previously classified measurement data change cause items are created by category,
The above category is a water environment measurement data management system that includes facility transformation, industrial type change, climate, disaster, and tourist destinations.

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