CN114860833B - Data center station and data processing method applied to digital twin hydraulic engineering - Google Patents
Data center station and data processing method applied to digital twin hydraulic engineering Download PDFInfo
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Abstract
The application provides a data center station and a data processing method applied to digital twin hydraulic engineering, wherein the data center station comprises: the data source management module is used for acquiring and managing a data source which can be accessed to the data center station, and analyzing, defining and connecting test configuration is carried out on the data source; the data integration module is used for collecting target data; the data management module is used for managing the service domain information of the target hydraulic engineering and setting processing items of the target data and/or the data source; and the data application module is used for providing report data calculation and analysis operations related to the target hydraulic engineering and the target data and generating a report design program. The data center station applied to the digital twin hydraulic engineering can accurately collect, analyze, manage and the like hydraulic engineering data based on the digital twin hydraulic integrated architecture.
Description
Technical Field
The application relates to the field of Internet of things and cloud platforms, in particular to a data center station and a data processing method applied to digital twin hydraulic engineering.
Background
The water resource is used as one of natural resources, environmental resources and economic resources which are most important for maintaining human survival, life and production, and the efficient development and utilization of the water resource are important bases for guaranteeing the sustainable development of human society, so that the development and management of hydraulic engineering are particularly important.
However, with the continuous development of hydraulic engineering technology, the volume and complexity of hydraulic engineering data are gradually improved, and the development and management modes and methods of the traditional hydraulic engineering are not in accordance with the time requirements. Therefore, in the face of water conservancy data with high volume and complexity, it is necessary to provide a data center station and a data processing method applied to digital twin hydraulic engineering.
Disclosure of Invention
The embodiment of the application aims to provide a data processing method and a system applied to digital twin hydraulic engineering, and the embodiment of the application can remarkably improve the accuracy of monocular image depth estimation. The specific technical scheme is as follows:
in a first aspect of an embodiment of the present application, there is provided a data center station applied to digital twin hydraulic engineering, including:
the data source management module is used for acquiring and managing a data source which can be accessed to the data center station, and analyzing, defining and connecting test configuration is carried out on the data source; the data source comprises one or more of a text, a video, a relational database, a network interface and an open source platform;
The data integration module is used for collecting target data; the target data are from the target hydraulic engineering, the acquisition mode comprises real-time acquisition and timing acquisition, and the target data comprise one or more of flood prevention data, water resource data, target hydraulic engineering operation and maintenance data and supervision data;
the data management module is used for managing the service domain information of the target hydraulic engineering and setting processing items of the target data and/or the data source; the service domain information comprises service names, classification, coding, description and responsible person information, and supports the functions of visualization and keyword query; the processing items comprise one or more of operator task establishment, data off-line processing, data timing processing, data real-time processing and relational database processing;
the data application module is used for providing report data calculation and analysis operations related to the target hydraulic engineering and the target data and generating a report design program; the report corresponding to the report data comprises one or more of a real-time monitoring table, an operation management table, a maintenance table and a flood pre-report.
Optionally, the data source management module further includes a metadata management module, where the metadata management module is specifically configured to:
Performing profiling management on metadata of the data source;
establishing an acquisition task of the target data, and performing a first management operation on the acquisition task; the first management operation comprises one or more of changing a warehousing strategy of the metadata, changing an execution mode, an execution period and an execution time of the acquisition task;
establishing and managing a data standard, wherein the data standard is established according to the metadata and the service domain information and is used for representing a specification of acquisition and management of the target data; the data standards include data source standards, data set standards, data dictionary standards, external metadata standards, and data normalization processes.
Optionally, the data center further includes a value-added module, where the value-added module is configured to generate a value-added service of the data center, and the value-added module includes:
the main data capability stack is used for providing at least one value added service of main data standardization, main data approval, main data resource catalogue and main data blood margin;
the intelligent multi-bin capacity stack is used for providing at least one value-added service of data model construction, index blood margin, multi-bin planning and a data map;
And the hydraulic operator capability stack is used for providing at least one value-added service of hydraulic model construction, offline processing operators, model data standardization and real-time processing operators.
Optionally, the data integration module includes a task management sub-module and a schedule management sub-module, wherein:
the task management submodule is used for configuring tasks according to the information of the data sources and the service requirements, and the tasks are used for collecting the target data and sending the target data to the data center;
the scheduling management submodule is used for generating a task scheduling instruction, and the task scheduling instruction is used for triggering and checking one or more of the name, the execution period, the last execution state, the last execution time consumption, the next running time, the release time, the scheduling state, the disabling, the executing and the log operation of the acquisition task.
Optionally, the data governance module further includes a function definition module and an image data processing module, wherein:
the function definition module is used for generating a custom function used for processing the target data and information of the custom function, wherein the information of the custom function comprises a function type, a name, a code, a resource, a version, an execution class, description information, an input parameter and an output parameter;
The image data processing module is used for analyzing water level, water area, floaters and abnormal events related to the target hydraulic engineering based on the image data in the target data to obtain analysis data, and carrying out early warning and processing on the analysis data.
Optionally, the data application module further includes a fusion application module, where the fusion application module is configured to integrate applications of the target hydraulic engineering, where the applications include a flood control service application, a water resource management and allocation application, an intelligent service application, and an innovation service application;
the report also comprises a dimension table and a fact table, wherein the dimension table is used for recording equipment data, engineering data and space data of the target hydraulic engineering, and the fact table is used for recording a dynamic domain and a static domain of the target hydraulic engineering; the dynamic domain comprises rainwater data and working condition data, and the static domain comprises a construction safety threshold and a design value of the target hydraulic engineering.
Optionally, the data center station further includes a hydraulic engineering data processing model building module, where the hydraulic engineering data processing model building module is configured to build a hydraulic engineering data processing model, and the hydraulic engineering data processing model is configured to generate a processing policy for the target hydraulic engineering and/or the target data based on the collected target data.
In still another aspect of the embodiment of the present invention, a data processing method applied to digital twin hydraulic engineering is provided, including:
acquiring and managing a data source which can be accessed to the data center station, and analyzing, defining and connecting test configuration to the data source; the data source comprises one or more of a text, a video, a relational database, a network interface and an open source platform;
collecting target data; the target data are from the target hydraulic engineering, the acquisition mode comprises real-time acquisition and timing acquisition, and the target data comprise one or more of flood prevention data, water resource data, target hydraulic engineering operation and maintenance data and supervision data;
managing service domain information of the target hydraulic engineering and setting processing items of the target data and/or the data source; the service domain information comprises service names, classification, coding, description and responsible person information, and supports the functions of visualization and keyword query; the processing items comprise one or more of operator task establishment, data off-line processing, data timing processing, data real-time processing and relational database processing;
Providing report data calculation and analysis operations related to the target hydraulic engineering and the target data, and generating a report design program; the report corresponding to the report data comprises one or more of a real-time monitoring table, an operation management table, a maintenance table and a flood pre-report.
In a further aspect of the embodiments of the present application, there is provided a computer readable storage medium having stored thereon a computer program which when executed implements the steps of the method as described above.
In yet another aspect of the embodiments of the present application, a computer device is provided, comprising a processor, a memory and a computer program stored on the memory, the processor implementing the steps of the method as described above when executing the computer program.
From the above, the embodiment of the application develops and improves various aspects of the hydraulic engineering such as data source, acquisition mode, service definition, data management, data application and the like through the digital twin hydraulic integrated architecture, so that hydraulic engineering data can be accurately acquired, analyzed, managed and applied.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic view of an application scenario of a data center station applied to digital twin hydraulic engineering according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a data center station applied to digital twin hydraulic engineering according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a data processing method applied to digital twin hydraulic engineering according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a computer device according to an embodiment of the present application.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
The terms "a," "an," "the," and/or "the" are not specific to the singular, but may include the plural, unless the context clearly indicates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
In the embodiment of the application, digital twin refers to the full life cycle process of the corresponding entity equipment by fully utilizing data such as a physical model, sensor update, operation history and the like, integrating simulation processes of multiple disciplines, multiple physical quantities, multiple scales and multiple probabilities and completing mapping in a virtual space. Digital twinning is a beyond-the-reality concept that can be seen as a digital mapping system of one or more important, mutually dependent equipment systems.
Digital twinning is a universally adapted theoretical technology system, can be applied in a plurality of fields, and has more application in the fields of product design, product manufacturing, medical analysis, engineering construction and the like. The most deep application in China is in the engineering construction field, the highest attention is paid, and the hottest research is in the intelligent manufacturing field.
Fig. 1 is a schematic diagram of an application scenario 100 of a data center station applied to digital twin hydraulic engineering according to some embodiments of the present disclosure.
In some embodiments, a data center applied to digital twin hydraulic engineering may manage hydraulic engineering data by implementing the methods and/or processes disclosed in the present specification.
As shown in fig. 1, an application scenario 100 according to an embodiment of the present specification may include a processing device 1, a network 120, a storage device 130, an acquisition terminal 140, and a water supplier 150. In some embodiments, components in application scenario 100 may be connected and/or in communication with each other via network 120. For example, the processing device 110 may connect the storage device 130, the acquisition terminal 140, and the water provider 150 through a network to access information and/or data. For another example, the processing device 110 may acquire acquisition data and/or information from the acquisition terminal 140 and process the acquired data and/or information.
The processing device 110 may be used to process information and/or data related to the application scenario 100, such as rainfall data, rain collection data, etc. The processing device 110 may process data, information, and/or processing results obtained from other devices or system components and execute program instructions based on such data, information, and/or processing results to perform one or more functions described herein.
The network 120 may connect components of the application scenario 100 and/or connect the application scenario 100 with external resource portions. The network enables communication between the components and other parts of the application scenario 100 that facilitate the exchange of data and/or information. The network may be a local area network, a wide area network, the internet, etc., and may be a combination of various network structures.
Storage device 130 may be used to store data, instructions, and/or any other information. In some embodiments, the storage device 130 may store data and/or instructions that the processing device 110 uses to perform or use to accomplish the exemplary methods described in this specification. In some embodiments, the storage device 130 may be connected to the network 120 to communicate with at least one component of the application scenario 100 (e.g., the processing device 110, the acquisition terminal 140). For example, the storage device 130 may store rainfall data, rainwater collection data.
The acquisition terminal 140 may be used to acquire data and/or information. Such as rainfall data, rain collection data, etc. In some embodiments, the acquisition terminal 140 may include a water gauge 140-1, a rain gauge 140-2, a water quality detector 140-3, and the like. In some embodiments, the acquisition terminal 140 may send the acquired data and/or information to other components of the application scenario 100 (e.g., the processing device 11 0) over the network 120. For more on the acquisition terminal see fig. 2 and its description.
The water provider 150 may be used to collect and/or store stormwater resources, and may also be used to supply and/or schedule water resources. For example, the water supplier may supply water resources to the user. For another example, the water provider may schedule water resources in areas where the water resources are abundant to areas where the water resources are scarce. Exemplary water suppliers may include water service bureaus or various utility water supply companies subordinate to the water service bureaus.
It should be noted that the application scenario is provided for illustrative purposes only and is not intended to limit the scope of the present description. Many modifications and variations will be apparent to those of ordinary skill in the art in light of the present description. For example, the application scenario may also include a database. As another example, application scenarios may be implemented on other devices to implement similar or different functionality. However, variations and modifications do not depart from the scope of the present description.
The embodiment of the application provides a data center station applied to digital twin hydraulic engineering, and fig. 2 shows a schematic structural diagram of the data center station applied to digital twin hydraulic engineering, where the data center station includes:
a data source management module 201, configured to acquire and manage a data source that can be accessed to the data center, and analyze, define and connect a test configuration to the data source; the data source comprises one or more of a text, a video, a relational database, a network interface and an open source platform;
a data integration module 202 for collecting target data; the target data are from the target hydraulic engineering, the acquisition mode comprises real-time acquisition and timing acquisition, and the target data comprise one or more of flood prevention data, water resource data, target hydraulic engineering operation and maintenance data and supervision data;
the data management module 203 is configured to manage service domain information of the target hydraulic engineering, and set processing items for the target data and/or the data source; the service domain information comprises service names, classification, coding, description and responsible person information, and supports the functions of visualization and keyword query; the processing items comprise one or more of operator task establishment, data off-line processing, data timing processing, data real-time processing and relational database processing;
The data application module 204 is configured to provide report data calculation and analysis operations related to the target hydraulic engineering and the target data, and generate a report design program; the report corresponding to the report data comprises one or more of a real-time monitoring table, an operation management table, a maintenance table and a flood pre-report.
The data source management module 201 further includes a metadata management module, where the metadata management module is specifically configured to:
performing profiling management on metadata of the data source;
establishing an acquisition task of the target data, and performing a first management operation on the acquisition task; the first management operation comprises one or more of changing a warehousing strategy of the metadata, changing an execution mode, an execution period and an execution time of the acquisition task;
establishing and managing a data standard, wherein the data standard is established according to the metadata and the service domain information and is used for representing a specification of acquisition and management of the target data; the data standards include data source standards, data set standards, data dictionary standards, external metadata standards, and data normalization processes.
The metadata management module can collect and manage metadata of data sources of each service domain of the target hydraulic engineering in a unified mode. The services in the service domain may include a plurality of services, which may include, for example, but not limited to, electricity, water, traffic, travel, aerospace, e-commerce, and the like.
The metadata management module supports collection and extraction of metadata such as MySQL, postgreSQL, oracle, supports real-time tracking of metadata in the computing/storage engine, builds a unified metadata model through abstraction of different types of storage metadata, supports rapid expansion of multiple types of metadata, realizes rich and various metadata, unified standards and stability of the metadata through construction of a metadata center, and provides powerful metadata guarantee for data map and data management.
The management of the acquisition task can comprise task detail management and acquisition task log management. The task detail management may define or edit task names, data source names, warehouse entry policies, states, and the like. The collected task log management can be used for checking the execution log of the task, including collecting the task name, the result description of task execution, the task execution result state, the execution time and the like.
Optionally, the data center further includes a value-added module, where the value-added module is configured to generate a value-added service of the data center, and the value-added module includes:
the main data capability stack is used for providing at least one value added service of main data standardization, main data approval, main data resource catalogue and main data blood margin;
The intelligent multi-bin capacity stack is used for providing at least one value-added service of data model construction, index blood margin, multi-bin planning and a data map;
and the hydraulic operator capability stack is used for providing at least one value-added service of hydraulic model construction, offline processing operators, model data standardization and real-time processing operators.
Optionally, the data integration module includes a task management sub-module and a schedule management sub-module, wherein:
the task management submodule is used for configuring tasks according to the information of the data sources and the service requirements, and the tasks are used for collecting the target data and sending the target data to the data center;
the scheduling management submodule is used for generating a task scheduling instruction, and the task scheduling instruction is used for triggering and checking one or more of the name, the execution period, the last execution state, the last execution time consumption, the next running time, the release time, the scheduling state, the disabling, the executing and the log operation of the acquisition task. Wherein the execution cycle may be real-time or non-real-time, the scheduling state may be enabled or disabled, the logging operation may be enabled, disabled, or other more options.
Optionally, the data governance module further includes a function definition module and an image data processing module, wherein:
the function definition module is used for generating a custom function used for processing the target data and information of the custom function, wherein the information of the custom function comprises a function type, a name, a code, a resource, a version, an execution class, description information, an input parameter and an output parameter;
the image data processing module is used for analyzing water level, water area, floaters and abnormal events related to the target hydraulic engineering based on the image data in the target data to obtain analysis data, and carrying out early warning and processing on the analysis data.
Specifically, when the method is applied, the data center station can firstly acquire the image of the hydraulic engineering scene (such as a reservoir) acquired by the image acquisition equipment (such as a camera), then set a related image recognition algorithm through a preset function, such as an image recognition algorithm for recognizing unknown personnel or objects in the reservoir, and after the abnormality is recognized through the image recognition algorithm, the data center station can send the abnormal image and the abnormality prompt message to related external equipment (such as an external PC (personal computer) end and an alarm), so that the integrated operation of analyzing, recognizing and early warning the target data of the hydraulic engineering can be realized.
Optionally, the data application module further includes a fusion application module, where the fusion application module is configured to integrate applications of the target hydraulic engineering, where the applications include a flood control service application, a water resource management and allocation application, an intelligent service application, and an innovation service application;
the report also comprises a dimension table and a fact table, wherein the dimension table is used for recording equipment data, engineering data and space data of the target hydraulic engineering, and the fact table is used for recording a dynamic domain and a static domain of the target hydraulic engineering; the dynamic domain comprises rainwater data and working condition data, and the static domain comprises a construction safety threshold and a design value of the target hydraulic engineering.
Optionally, the data center station further includes a hydraulic engineering data processing model building module, where the hydraulic engineering data processing model building module is configured to build a hydraulic engineering data processing model, and the hydraulic engineering data processing model is configured to generate a processing policy for the target hydraulic engineering and/or the target data based on the collected target data.
The hydraulic engineering data processing model may be a model for determining a processing strategy of the target hydraulic engineering. In some embodiments, each processing strategy may correspond to the use of at least one hydraulic engineering data processing model. For example, a stormwater treatment strategy may correspond to the use of a hydraulic engineering data treatment model for stormwater data.
The hydraulic engineering data processing model may refer to a trained machine learning model. In some embodiments, the hydraulic engineering data processing model may include any one or combination of a deep neural network model, a recurrent neural network model, a convolutional neural network, or other custom model structure, etc.
In some embodiments, the input to the hydraulic engineering data processing model may be any type of target data, including, for example, rainfall, water level, irrigation volume, and the like. For example, rainfall data and water level height in a certain time range of a certain area are input into a hydraulic engineering data processing model, and a processing strategy of rainwater collected by the area can be output, for example, whether waste is directly discharged or irrigation is performed after the processing.
In some embodiments, the hydraulic engineering data processing model may be obtained based on training. Training of hydraulic engineering data processing models can be performed by the data platform of the embodiment of the application.
In some embodiments, when training the hydraulic engineering data processing model, a plurality of training samples with labels may be used to perform training through a plurality of methods (for example, gradient descent method), so that parameters of the model may be learned, and when the trained model meets a preset condition, training is ended, and a trained hydraulic engineering data processing model is obtained.
The training samples can comprise target data acquired in months, quarters and years, the labels of the training samples can be historical target data based on historical time nodes, and the labels of the training samples can be acquired through manual labeling. In some embodiments, the hydraulic engineering data processing model may be trained in additional devices or modules.
Some embodiments of the present disclosure determine a processing policy for a target hydraulic engineering and/or target data based on a hydraulic engineering data processing model, and by defining the processing policy related to the hydraulic engineering of each area in advance, it is beneficial to execute an application related to the target hydraulic engineering, a subsequent acquisition mode and a data management mode in advance.
In the following, taking the treatment strategy as an example for constructing the water storage balance of the target hydraulic engineering, the balance water amount can be expressed as follows:
H t ＝H t-1 +I t-1 +UQ t-1 -DW t-1 -IW t-1 -AW t-1 -EW t-1 -OW t-1 -HT t-1 -ET t-1 -DQ t-1
wherein H is t 、H t-1 The initial water storage capacity and the final water storage capacity of the period t of the reservoir lake are respectively; i t-1 Reservoir inflow for time period t; UQ (UQ) t-1 Discarding the amount of drain for the period upstream; DW (DW) t-1 、IW t-1 、AW t-1 、EW t-1 、 OW t-1 Respectively the domestic water consumption, the urban water consumption, the rural water consumption and the environmentWater usage and other water usage; ET (electric T) t-1 、HT t-1 The evaporation amount and the leakage amount are respectively; DQ (digital versatile disc) t-1 The water drainage quantity is discarded from the reservoir or the water drainage quantity is discharged from a normal water supply area; t is a period.
The water split point and control node water balance can be expressed as:
the water diversion node is as follows:
and (3) a control node:
wherein, the liquid crystal display device comprises a liquid crystal display device,the diversion amount is the diversion amount of the diversion point i and the diversion point k time period t; p (k, i, t) is a distribution coefficient of water quantity distributed from a water distribution point i to a water distribution point k in a period t; />For all ingress traffic of a node +.>All outgoing traffic for the node.
For disaster early warning or other disaster coping strategies of the target hydraulic engineering, initial parameters, iteration times, model convergence criteria and likelihood functions of model optimization can be set based on the SCE-UA algorithm. According to hydrological actual measurement data, planning, design and construction scheme data, optimizing n and k parameters in a Nash model for flood and waterlogging based on a moment method; and determining the value optimal range of the parameter and generating the initial value of the parameter set.
The likelihood function S (D) and the root mean square error RMSE can be expressed as:
S(D)＝RMSE D
wherein D represents a shape parameter; n represents the total number of simulated floods and waterlogging; n represents the moment of simulating flood and waterlogging; q (Q) sim，n 、Q mea，n The hydrographic simulation value and the actual measurement and design value at time n are respectively shown.
From the above, the embodiment of the application develops and improves various aspects of the hydraulic engineering such as data source, acquisition mode, service definition, data management, data application and the like through the digital twin hydraulic integrated architecture, so that hydraulic engineering data can be accurately acquired, analyzed, managed and applied.
Fig. 3 is a schematic flow chart of a data processing method applied to digital twin hydraulic engineering, as shown in fig. 3, according to an embodiment of the present application, the data processing method applied to digital twin hydraulic engineering includes the following steps:
step 301, acquiring and managing a data source which can be accessed to the data center station, and analyzing, defining and connecting the data source to test configuration.
Wherein the data source comprises one or more of text, video, relational database, network interface, and open source platform.
Step 302, collecting target data.
The system comprises a target hydraulic engineering, a target data acquisition system, a control system and a control system, wherein the target data is from the target hydraulic engineering, the acquisition mode comprises real-time acquisition and timing acquisition, and the target data comprises one or more of flood control data, water resource data, target hydraulic engineering operation and maintenance data and supervision data;
step 303, managing service domain information of the target hydraulic engineering, and setting processing matters for the target data and/or the data source.
The service domain information comprises service names, classification, coding, description and responsible person information, and supports the functions of visualization and keyword query; the processing items comprise one or more of operator task establishment, data off-line processing, data timing processing, data real-time processing and relational database processing;
And 304, providing report data calculation and analysis operations related to the target hydraulic engineering and the target data, and generating a report design program.
The report corresponding to the report data comprises one or more of a real-time monitoring table, an operation management table, a maintenance table and a flood pre-report.
From the above, the embodiment of the application develops and improves various aspects of the hydraulic engineering such as data source, acquisition mode, service definition, data management, data application and the like through the digital twin hydraulic integrated architecture, so that hydraulic engineering data can be accurately acquired, analyzed, managed and applied.
In some embodiments, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used for storing relevant data of the image acquisition device. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by the processor is used for realizing a data processing method and system applied to digital twin hydraulic engineering.
In some embodiments, a computer device is provided, which may be a terminal, and the internal structure of which may be as shown in fig. 4. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless mode can be realized through WIFI, an operator network, NFC (near field communication) or other technologies. The computer program, when executed by the processor, implements a data processing method and system for digital twin hydraulic engineering. The display screen of the computer equipment can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer equipment can be a touch layer covered on the display screen, can also be keys, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In some embodiments, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.
In some embodiments, a computer readable storage medium is provided, storing a computer program which, when executed by a processor, implements the steps of the method embodiments described above.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, or the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can be in various forms such as static random access memory (Static Random Access Memory, SR AM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), etc.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
In summary, the data processing method applied to digital twin hydraulic engineering provided by the application comprises the following steps:
acquiring and managing a data source which can be accessed to the data center station, and analyzing, defining and connecting test configuration to the data source; the data source comprises one or more of a text, a video, a relational database, a network interface and an open source platform;
Collecting target data; the target data are from the target hydraulic engineering, the acquisition mode comprises real-time acquisition and timing acquisition, and the target data comprise one or more of flood prevention data, water resource data, target hydraulic engineering operation and maintenance data and supervision data;
managing service domain information of the target hydraulic engineering and setting processing items of the target data and/or the data source; the service domain information comprises service names, classification, coding, description and responsible person information, and supports the functions of visualization and keyword query; the processing items comprise one or more of operator task establishment, data off-line processing, data timing processing, data real-time processing and relational database processing;
providing report data calculation and analysis operations related to the target hydraulic engineering and the target data, and generating a report design program; the report corresponding to the report data comprises one or more of a real-time monitoring table, an operation management table, a maintenance table and a flood pre-report.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments provided in the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that: like reference numerals and letters in the following figures denote like items, and thus once an item is defined in one figure, no further definition or explanation of it is required in the following figures, and furthermore, the terms "first," "second," "third," etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the above examples are only specific embodiments of the present application, and are not intended to limit the scope of the present application, but it should be understood by those skilled in the art that the present application is not limited thereto, and that the present application is described in detail with reference to the foregoing examples: any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or perform equivalent substitution of some of the technical features, while remaining within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the corresponding technical solutions. Are intended to be encompassed within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (2)
1. A data center applied to digital twin hydraulic engineering, comprising:
the data source management module is used for acquiring and managing a data source which can be accessed to the data center station, and analyzing, defining and connecting test configuration is carried out on the data source; the data source comprises one or more of a text, a video, a relational database, a network interface and an open source platform;
the data integration module is used for collecting target data; the target data is from a target hydraulic engineering, the acquisition mode comprises real-time acquisition and timing acquisition, and the target data comprises one or more of flood control data, water resource data, target hydraulic engineering operation and maintenance data and supervision data;
the data management module is used for managing the service domain information of the target hydraulic engineering and setting processing items of the target data and/or the data source; the service domain information comprises service names, classification, coding, description and responsible person information, and supports the functions of visualization and keyword query; the processing items comprise one or more of operator task establishment, data off-line processing, data timing processing, data real-time processing and relational database processing;
The data application module is used for providing report data calculation and analysis operations related to the target hydraulic engineering and the target data and generating a report design program; the report corresponding to the report data comprises one or more of a real-time monitoring table, an operation management table, a maintenance table and a flood pre-report;
the data source management module further comprises a metadata management module, and the metadata management module is specifically configured to:
performing profiling management on metadata of the data source;
establishing an acquisition task of the target data, and performing a first management operation on the acquisition task; the first management operation comprises one or more of changing a warehousing strategy of the metadata, changing an execution mode, an execution period and an execution time of the acquisition task;
establishing and managing a data standard, wherein the data standard is established according to the metadata and the service domain information and is used for representing a specification of acquisition and management of the target data; the data standards comprise data source standards, data set standards, data dictionary standards, external metadata standards and data standardization processing; the data center also includes a value-added module for generating a value-added service for the data center, the value-added module including:
The main data capability stack is used for providing at least one value added service of main data standardization, main data approval, main data resource catalogue and main data blood margin;
the intelligent multi-bin capacity stack is used for providing at least one value-added service of data model construction, index blood margin, multi-bin planning and a data map;
the hydraulic operator capability stack is used for providing at least one value-added service of hydraulic model construction, offline processing operators, model data standardization and real-time processing operators;
the data integration module comprises a task management sub-module and a scheduling management sub-module, wherein:
the task management submodule is used for configuring tasks according to the information of the data sources and the service requirements, and the tasks are used for collecting the target data and sending the target data to the data center;
the scheduling management submodule is used for generating a task scheduling instruction, and the task scheduling instruction is used for triggering and checking one or more of the name, the execution period, the last execution state, the last execution time consumption, the next running time, the release time, the scheduling state, the disabling, the executing and the log operation of the acquisition task; the data management module further comprises a function definition module and an image data processing module, wherein:
The function definition module is used for generating a custom function used for processing the target data and information of the custom function, wherein the information of the custom function comprises a function type, a name, a code, a resource, a version, an execution class, description information, an input parameter and an output parameter;
the image data processing module is used for analyzing water levels, water areas, floaters and abnormal events related to the target hydraulic engineering based on the image data in the target data to obtain analysis data, and carrying out early warning and processing on the analysis data; the data application module further comprises a fusion application module, wherein the fusion application module is used for integrating applications of the target hydraulic engineering, and the applications comprise flood control service application, water resource management and allocation application, intelligent service application and innovation service application;
the report also comprises a dimension table and a fact table, wherein the dimension table is used for recording equipment data, engineering data and space data of the target hydraulic engineering, and the fact table is used for recording a dynamic domain and a static domain of the target hydraulic engineering; the dynamic domain comprises rainwater data and working condition data, and the static domain comprises a construction safety threshold and a design value of the target hydraulic engineering.
2. The data center applied to digital twin hydraulic engineering according to claim 1, further comprising a hydraulic engineering data processing model building module, wherein the hydraulic engineering data processing model building module is used for building a hydraulic engineering data processing model, and the hydraulic engineering data processing model is used for generating a processing strategy for the target hydraulic engineering and/or the target data based on the collected target data.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111984709A (en) * | 2019-05-23 | 2020-11-24 | 云南青年学园科技有限公司 | Visual big data middle station-resource calling and algorithm |
| KR20210019312A (en) * | 2019-08-12 | 2021-02-22 | 한국수자원공사 | Construct methode and apparatus for managing of water desaster database |
| CN112416898A (en) * | 2020-11-18 | 2021-02-26 | 尚禹科技有限公司 | Intelligent water conservancy data center construction method |
| CN112767058A (en) * | 2021-02-05 | 2021-05-07 | 深圳市爱云信息科技有限公司 | AIOT DaaS digital twin cloud platform |
| CN113626648A (en) * | 2021-07-30 | 2021-11-09 | 联通(广东)产业互联网有限公司 | Water conservancy data processing system, method and storage medium |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9251226B2 (en) * | 2013-03-15 | 2016-02-02 | International Business Machines Corporation | Data integration using automated data processing based on target metadata |
| US20170270454A1 (en) * | 2016-03-15 | 2017-09-21 | Waterfind USA, Inc. | Systems and Methods for Optimization of Groundwater Resource Usage in a Groundwater Basin |
| US11403541B2 (en) * | 2019-02-14 | 2022-08-02 | Rockwell Automation Technologies, Inc. | AI extensions and intelligent model validation for an industrial digital twin |
| US11928103B2 (en) * | 2020-08-18 | 2024-03-12 | Electronics And Telecommunications Research Institute | Method and apparatus for configurating digital twin |
-
2022
- 2022-05-30 CN CN202210596687.4A patent/CN114860833B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111984709A (en) * | 2019-05-23 | 2020-11-24 | 云南青年学园科技有限公司 | Visual big data middle station-resource calling and algorithm |
| KR20210019312A (en) * | 2019-08-12 | 2021-02-22 | 한국수자원공사 | Construct methode and apparatus for managing of water desaster database |
| CN112416898A (en) * | 2020-11-18 | 2021-02-26 | 尚禹科技有限公司 | Intelligent water conservancy data center construction method |
| CN112767058A (en) * | 2021-02-05 | 2021-05-07 | 深圳市爱云信息科技有限公司 | AIOT DaaS digital twin cloud platform |
| CN113626648A (en) * | 2021-07-30 | 2021-11-09 | 联通(广东)产业互联网有限公司 | Water conservancy data processing system, method and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 浅析数字孪生技术在水利水电工程地质的应用方案;王国岗;赵文超;陈亚鹏;陈建辉;;水利技术监督(第05期);全文 * |
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