CN116167892A - Reservoir informatization management system based on digital twinning - Google Patents
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Abstract
The invention discloses a reservoir informatization management system based on digital twinning, which comprises a database, a model library and a computer end service function module, wherein the database is used for storing data of a reservoir; the database comprises basic data, service management data, monitoring data, external sharing data, geographic space data and video data and is used for storing information data related to the reservoir; the model library comprises a flood forecast model, a flood control scheduling model, a hydrodynamic model, a water resource optimization scheduling model and a safety evaluation model, and is used for simulating the operation of the reservoir in various service scenes and adjusting model parameters of the model library according to the simulated operation results; the computer-side business function module comprises a flood prevention scene, a water supply scene, a safety scene, data maintenance and system configuration, and is used for controlling the model library to retrieve information data from the database and executing the content of each business scene. The method solves the problems of reservoir information resource dispersion, poor system applicability and poor disaster defending capability, and comprehensively improves the reservoir informatization management level.
Description
Technical Field
The invention relates to the technical field of water conservancy informatization management application, in particular to a reservoir informatization management system based on digital twinning.
Background
The traditional reservoir informatization management system mainly takes a two-dimensional map loaded with GIS service as a monitoring basis, and superimposes rainfall station, water level station and flow station data, has single functions, and is mutually divided and non-unified with an office system, safety monitoring, water supply forecasting and the like, so that the reservoir business management system has weak comprehensive analysis and decision support capability and low intelligent level.
The existing reservoir informatization management system based on digital twinning has a plurality of problems which are not solved, such as: most of the reservoir peripheral landforms are rendered, and the reservoir peripheral landforms are different from the actual landforms; the digital twin modeling effect is distorted and has larger gap with reality; the method has the advantages that due to the fact that the method involves more departments, integrates hydrology, environmental protection and other departments information resources, the difficulty of integrating and unifying the information resources is high; the hydrogeology around the reservoir is complex, and the model calculation has great challenges; monitoring data loss and abnormality, and affecting monitoring result statistics and model calculation results; the model parameters are not automatically corrected by the calculation results and the actual results, and the calculation accuracy is further and further poorer in the follow-up process or due to environmental changes; most of the systems are not planned and developed based on business scenes, and the applicability is poor. In summary, the informatization degree of the reservoir informatization management system in the prior art is low.
Disclosure of Invention
In view of the above, the invention provides a reservoir informatization management system based on digital twinning, which aims to solve the problem of low informatization degree of the reservoir informatization management system in the prior art.
The specific technical scheme of the invention is as follows:
a reservoir informatization management system based on digital twinning comprises a database, a model library and a computer-side service function module;
the database comprises basic data, service management data, monitoring data, external sharing data, geographic space data and video data and is used for storing information data related to the reservoir;
the model library comprises a flood forecast model, a flood control scheduling model, a hydrodynamic model, a water resource optimization scheduling model and a safety evaluation model, and is used for simulating the operation of the reservoir in various service scenes and adjusting model parameters of the model library according to the simulated operation results;
the computer-side business function module comprises a flood prevention scene, a water supply scene, a safety scene, data maintenance and system configuration, and is used for controlling the model library to retrieve information data from the database and executing the content of each business scene.
Specifically, the basic data comprise reservoir engineering information data, reservoir hydrologic characteristic data, engineering benefit data, building basic information data, water level relation curve data, monitoring station basic information data and safety monitoring basic information data;
the business management data comprises engineering element data, operation and maintenance task data, hidden danger situation data and processing situation data;
the monitoring data comprise rainfall, water level, flow, water quality, safety monitoring and engineering element states;
the geospatial data includes topographical feature data, geospatial point location data, and photographic data.
Specifically, the system further comprises a professional knowledge base, wherein the professional knowledge base comprises a knowledge graph, a plan scheduling scheme, a historical scene mode, business rules, expert experience and a knowledge engine.
Specifically, the knowledge graph comprises laws and regulations, technical standards and technical terms; the historical scene mode comprises historical rainfall and historical flood; the business rules comprise a flood control plan, a flood season scheduling application scheme, a water resource scheduling scheme, a safety management emergency plan and a flood control emergency plan; expert experience includes flood scheduling expert experience and safety management expert experience; the knowledge engine includes knowledge representation, machine reasoning and machine learning.
Specifically, the flood prevention scene comprises flood prevention monitoring, flood forecast scheduling, flood prevention early warning, flood prevention scheduling previewing and flood prevention planning, and is used for controlling a model library to simulate flood prevention working scene business; the water supply scene comprises water supply monitoring, water supply scheduling, water supply early warning, a water supply scheduling plan and a water supply plan, and is used for controlling the model library to simulate water resource scheduling working scene service; the safety scene comprises safety monitoring, engineering operation and maintenance, engineering hidden danger, safety analysis and evaluation, safety prediction, dam break previewing and safety planning, and is used for controlling a model library to simulate dynamic engineering scene service; the data maintenance comprises data acquisition, data filtering and data correction, and is used for intercepting abnormal data and correcting the received abnormal data; the system configuration comprises administrative division configuration, organization configuration, department configuration, post configuration, user configuration, menu configuration, authority configuration, engineering configuration, building configuration, station measurement configuration, measuring point configuration, early warning configuration, engineering element configuration and operation and maintenance configuration, and provides basic configuration information for each function of the system.
Specifically, when a service instruction of a flood prevention scene is sent to a model library, flood prediction and scheduling calculation are performed by using relevant data scheduled from a database by using a flood prediction model and a flood control scheduling model, iterative calculation is performed on calculation results and actual measurement results under different model parameters until a calculation threshold is reached, and the model parameters used in the last calculation are used as optimal parameters to correct.
Specifically, the execution content of the security scene comprises security analysis, security evaluation and security prediction; carrying out regression analysis on the monitored item and the environmental quantity through the actual measurement value, obtaining a forecast value of the monitored item according to a regression equation, calculating a difference value between the forecast value and the actual measurement value and a sample standard deviation, and displaying abnormality if the absolute value of the difference value is greater than three times of the sample standard deviation; the safety evaluation utilizes the historical scene mode and expert experience in the expert knowledge base to divide engineering elements of the reservoir, sets influence weight, gives corresponding scores in combination with hidden danger grades, and establishes a safety comprehensive evaluation system; and the safety prediction utilizes a regression equation to calculate the predicted value of the monitoring project, the safety level is displayed, and dam break previewing is needed if the safety is unsafe.
Specifically, when executing a service of data maintenance, the system identifies abnormal data in real time and intercepts the abnormal data; and the system performs addition and average calculation processing on the data information monitored in the front and the back, so as to obtain recommended modification data corresponding to the abnormal data, and if the user agrees, the abnormal data is replaced by the recommended modification data corresponding to the abnormal data.
Specifically, the system also comprises a mobile terminal service function module, wherein the mobile terminal service function module comprises monitoring information, engineering operation and maintenance and safety supervision.
The invention has the beneficial effects that:
the system is based on a further optimized digital twin technology, a unified database is built, abnormal data automatic interception and maintenance functions are set, an optimized model with parameters automatically corrected is built through scene inversion, a reservoir daily flood prevention, water supply and safety maintenance scenerization system is built through reservoir engineering full-element management, the problems of reservoir information resource dispersion, poor system applicability and poor disaster prevention capability are solved, and the reservoir informatization management level is comprehensively improved.
Drawings
FIG. 1 is a system architecture diagram of an embodiment of a digital twinning-based reservoir informatization management system of the present invention;
FIG. 2 is a schematic functional diagram of a computer end of the reservoir information management system according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of a flood prevention scenario of the present invention;
FIG. 4 is a schematic flow chart of a water supply scenario of the present invention;
FIG. 5 is a flow chart of the security analysis of the present invention;
FIG. 6 is a schematic flow chart of the security assessment of the present invention;
FIG. 7 is a flow chart of the security prediction according to the present invention;
FIG. 8 is a flow chart of data maintenance according to the present invention;
FIG. 9 is a schematic functional structure of an embodiment of a mobile terminal of the reservoir informatization management system of the present invention;
FIG. 10 is a flow chart of the engineering operation and maintenance of the present invention.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The application provides a reservoir informatization management system based on digital twinning, which establishes reservoir digital twinning engineering based on a water conservancy perception network, a unified database, a model library and a knowledge base, and performs service application development on the basis of digital twinning. Specifically, as shown in fig. 1, the system performs carding by collecting relevant standard specifications and service application conditions, and builds a unified database. The bottom data adopted by the database are as follows: basic data, business management data, monitoring data, external sharing data, geospatial data, and video data.
Wherein the base data comprises: reservoir engineering information data, reservoir hydrologic characteristic data, engineering benefit data, building basic information data, water level relation curve data, monitoring station basic information data and safety monitoring basic information data. Basic information data of the building, namely basic information data of station buildings such as water retaining, water conveying, water discharging and the like; the water level relation curve data is the water level reservoir capacity curve and the water level flow curve data; basic information data of the monitoring station, namely basic data of stations such as hydrologic stations, water level stations, rainfall stations, flow stations, evaporation stations, water quality stations, video stations and the like; the basic information data of safety monitoring is the data of section, measuring point and monitoring index.
The service management data includes: engineering element data, operation and maintenance task data, hidden danger situation data and processing situation data. Engineering element data, namely engineering element names, associated contents, responsible persons, element states and the like, wherein the engineering elements comprise buildings, equipment and the like; operation and maintenance task data, namely personnel, task types, task time, execution time, task content and the like; hidden danger situation data, namely personnel, engineering element names, problem description, time and the like; process case data, i.e., personnel, engineering elements, process descriptions, time, etc.
The monitoring data includes: rainfall, water level, flow, water quality, safety monitoring and engineering element status. Wherein the safety monitoring includes: deformation, seepage and stress strain.
The geospatial data includes: DEM data, CAD point data, oblique photography data.
In the application, based on the geospatial data, the digital twin modeling is performed on reservoir engineering, an upstream rain collecting area and a downstream influence area by adopting an engine platform of UnrealEngine 4. The unmanned aerial vehicle is adopted to divide the concerned area in a landform so as to render the concerned area in a corresponding landform and optimize the shadow, so that the concerned area is more approximate to a real scene. Meanwhile, the remote sensing image is utilized to carry out regional contrast analysis in a staged mode, and if the change of the topography and the relief is found to be large, re-measurement is carried out, and the model is modified and optimized.
Referring to modeling, there are necessarily few specialized model libraries. The method utilizes the water conservancy professional model and combines the reservoir condition to construct a model library. The model library specifically comprises: flood forecast model, flood control scheduling model, hydrodynamic model, water resource optimization scheduling model and safety evaluation model. Each model needs to carry out live inversion according to the monitoring data, and parameters of the model are adjusted according to inversion results.
Meanwhile, a professional knowledge base is constructed, and the knowledge base comprises: knowledge graph, plan scheduling scheme, historical scene mode, business rules, expert experience, and knowledge engine. The knowledge graph is legal regulations, technical standards, technical terms and the like; historical scene mode, namely historical rainfall and historical flood; business rules are flood control plans, flood season scheduling and application schemes, water resource scheduling schemes, safety management emergency plans, flood control emergency plans and the like; expert experience is flood scheduling expert experience and safety management expert experience; knowledge engines are knowledge representation, machine reasoning, machine learning.
As shown in fig. 2, the functional module of the computer end of the system is provided with: flood prevention scene, water supply scene, security scene, data maintenance and system configuration. The flood control scene comprises flood control monitoring, flood forecast scheduling, flood control early warning, flood control scheduling previewing and flood control planning, and technical support is provided for flood control work; the water supply scene comprises water supply monitoring, water supply scheduling, water supply early warning, a water supply scheduling plan and a water supply plan, and supports are provided for reservoir water resource safety and optimal scheduling; the safety scene comprises safety monitoring, engineering operation and maintenance, engineering hidden danger, safety analysis and evaluation, safety prediction, dam break previewing and safety planning, and effective support is provided for engineering safety; the data maintenance comprises data acquisition, data filtering and data correction, mainly performs abnormal data interception and correction, and provides a basis for data monitoring and system application calculation; the system configuration comprises administrative division configuration, organization configuration, department configuration, post configuration, user configuration, menu configuration, authority configuration, engineering configuration, building configuration, station measurement configuration, measuring point configuration, early warning configuration, engineering element configuration and operation and maintenance configuration, and provides various function basic information for the system.
The flood prevention scene comprises self-correction of model parameters, as shown in fig. 3, and the implementation steps are as follows: (1) The system displays the data of a water level station, a rain amount station in a rain collecting area, a warehouse-in flow station, a warehouse-out flow station and an evaporation monitoring station, automatically predicts the data of rainfall, flood peak flow, peak time, flood amount and the like according to the set time interval and a flood prediction model and a flood prevention scheduling model of a model base in the flood period, and displays early warning information; (2) The user performs setting of forecast calculation according to actual conditions, including model selection, forecast objects, time, forecast duration, rainfall scheme, evaporation scheme, boundary inflow, model parameter adjustment, water level starting, scheduling mode and the like, and clicks to start flood forecast and scheduling calculation after setting is completed; (3) The system displays and saves the flood forecast and dispatch calculation results, including: a starting water level, a flood peak water level, peak time, a flood volume, a maximum ex-warehouse flow, a flood discharge volume, a scheduling end water level, a scheduling suggestion, a flood forecast and scheduling calculation process chart; (4) Clicking on the flood forecast and dispatch calculation results to start dispatch previewing, and combining a hydrodynamic model according to the calculation results by using a digital twin engineering to start dispatch previewing, dynamically simulating and displaying the opening of a gate, and discharging flow water flow and a downstream submerging range; (5) The system judges the accident level according to the dispatching calculation result and the business rule, and gives corresponding suggestions according to the corresponding plans, including transfer route display, issuing early warning and the like, and a user can perform corresponding operations according to the suggestions of the plans; (6) And (3) selecting an optimal result obtained by comparing the calculation result in the step (3) with the actual measurement result in the same prediction period, performing iterative calculation according to different model parameters and the actual measurement result which are set by comparing the series of optimal results, setting a calculation stopping threshold value, selecting the model parameter used by the last calculation as the optimal parameter when the calculation result reaches the calculation stopping threshold value, and correcting the corresponding model parameter of the model library.
As shown in fig. 4, the water supply scenario is performed as follows: (1) The system displays water quality monitoring, warehouse-in flow monitoring, warehouse-out flow monitoring stations and corresponding monitoring data, regional withering evaluation, water quality evaluation, current reservoir water storage capacity, current water quantity sustainable days and early warning information, wherein the regional withering evaluation is displayed according to the drought forecast condition of the access country, the water quality evaluation is evaluated and displayed according to the water quality standard of the surface water of the country, and the current water quantity sustainable days are calculated and displayed according to the historical water supply demand or daily water demand average; (2) The water supply prediction needs to be provided with a prediction period, a calculation method and parameters, specifically, the water demand prediction and the water supply prediction are firstly carried out, the water demand prediction is calculated by using water report data or a quota method, the water supply prediction comprises rainfall and runoff, wherein the rainfall can be calculated by accessing rainfall forecast data or using historical contemporaneous rainfall data, the runoff calculates the average runoff of a warehouse river for many years through historical data, evaporation and water leakage loss are considered, the water resource optimization configuration model of a model library is subsequently called for carrying out water supply scheduling calculation, and the calculation result comprises a water supply area, a water supply period and water supply quantity; (3) The previewing can utilize digital twin engineering to carry out water supply demonstration of different time periods according to the water supply prediction result; (4) If the regional abundant assessment is drought, corresponding plan suggestions are provided according to drought grade inquiry business rules, and users can report drought, issue early warning and the like.
The execution steps of the security scene are as follows: (1) The system displays the safety monitoring points and corresponding monitoring data, safety comprehensive evaluation scores and grades, monitoring early warning information, engineering operation and maintenance information, engineering hidden danger information and the like; (2) As shown in fig. 5, the safety analysis carries out regression analysis on the monitored items and the environmental quantities through the actual measurement data, finds out the influence weight of each environmental quantity, establishes a regression equation, can obtain the predicted value of the monitored item according to the regression equation, calculates the predicted value, the actual difference value and the sample standard deviation by combining with the specific actual measurement value, displays the abnormal measured value if the absolute value of the predicted value and the actual difference value is more than three times of standard deviation, re-measures three times, carries out early warning if the abnormal measured value is not returned, and filters the abnormal value if the abnormal measured value is returned to be normal; (3) As shown in fig. 6, the safety evaluation divides engineering elements of the reservoir according to historical data and experience analysis, sets influence weight, assigns corresponding scores in combination with hidden danger levels, establishes a safety comprehensive evaluation system comprising safety levels and score areas, associates system monitoring data, operation and maintenance tasks, engineering hidden danger and other data with the engineering elements, performs scoring and sum calculation on the engineering elements according to actual measurement data, and compares the safety comprehensive evaluation system to provide corresponding safety levels, thereby comprehensively evaluating the safety degree of the whole reservoir engineering; (4) As shown in fig. 7, the safety prediction needs to input a prediction object, an influence factor and prediction data, calculates the prediction data of the monitoring project by using a regression equation, inquires about engineering elements related to the prediction data, modifies the scores of the corresponding engineering elements under the current safety score, calculates and displays the total score and the safety grade, finishes the calculation if the result is safe, and performs dam break previewing if the result is unsafe; (5) And comparing the business rules according to the safety prediction calculation result, displaying the proposal suggestion, and carrying out corresponding system operations such as early warning release, dangerous case reporting and the like according to the suggestion by a user.
As shown in fig. 8, the data maintenance is performed as follows: (1) Identifying and intercepting abnormal data, wherein the abnormal data comprises data missing and mutation data, the missing data is data which is not reported according to a specified frequency, and the mutation data is used for respectively setting a judging rule for each monitoring index according to experience conditions, for example, the specified monitoring data is smaller than 0, or the front and rear data exceeds a certain value after being calculated by an empirical formula, and judging the data as data abnormality, marking and intercepting the data; (2) And setting the data after the front and rear monitoring data addition and the average calculation as abnormal data recommendation modification data, directly modifying the abnormal data into recommended data if the user agrees, popping up a data modification frame if the user disagrees, inputting a modification value by the user, and warehousing the modified data.
As shown in fig. 9, the functional module of the mobile terminal of the present system is provided with: monitoring information, engineering operations and maintenance, security supervision, messages, my. The monitoring information comprises water level monitoring, rainfall monitoring, water quantity monitoring, water quality monitoring, evaporation monitoring and safety monitoring, and a palm information base is provided for a user; the engineering operation and maintenance comprises engineering inspection and dispatching and executing engineering maintenance tasks, specifically as shown in fig. 10, inspection, maintenance contents, personnel and frequency are set by PC end operation and maintenance configuration, the system sends work orders to corresponding personnel at a mobile end according to the frequency, maintenance only uploads photos before and after maintenance, hidden trouble reporting is needed if the inspection is abnormal, and circulation processing and treatment information reporting are carried out on newly added hidden trouble.
The beneficial effects of this application lie in:
(1) Integrating information resources of all parties of hydrology, environmental protection, forestry and law enforcement of the reservoir, opening up a scattered construction system, and constructing a unified and fused database;
(2) Through data abnormality judgment and maintenance, a solid data base is built for the intelligent reservoir;
(3) A model with automatically corrected parameters is established, and the calculation accuracy reduction caused by environmental change is greatly improved;
(4) The reservoir and surrounding environment restoration degree is improved by establishing the optimized reservoir digital twin engineering, and the reservoir manager can conveniently carry out daily supervision and business processing by real business scene construction and comprehensive element management, so that the system applicability is improved.
In summary, the system builds a unified database by integrating information resources of all parties, provides a data basis for reservoir monitoring management and forecast calculation by building a reservoir model library, and simultaneously provides a virtual environment for the reservoir by optimized digital twin modeling, so that the model parameter self-correction algorithm improves the environmental adaptability and calculation precision of each forecast calculation of the reservoir, and provides support for management decisions, thereby improving the informatization degree of the reservoir.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (9)
1. The reservoir informatization management system based on digital twinning is characterized by comprising a database, a model library and a computer-side service function module;
the database comprises basic data, service management data, monitoring data, external sharing data, geographic space data and video data, and is used for storing information data related to reservoirs;
the model library comprises a flood forecasting model, a flood control scheduling model, a hydrodynamic model, a water resource optimizing scheduling model and a safety evaluation model, and is used for simulating the operation of the reservoir under each service scene and adjusting model parameters of the model library according to a simulated operation result;
the computer-side business function module comprises a flood prevention scene, a water supply scene, a safety scene, data maintenance and system configuration, and is used for controlling the model library to retrieve information data from the database and executing the content of each business scene.
2. The system of claim 1, wherein the base data comprises reservoir engineering information data, reservoir hydrologic characteristic data, engineering benefit data, building base information data, water level relationship curve data, monitoring station base information data, and safety monitoring base information data;
the business management data comprises engineering element data, operation and maintenance task data, hidden danger situation data and processing situation data;
the monitoring data comprise rainfall, water level, flow, water quality, safety monitoring and engineering element states;
the geospatial data includes topographical feature data, geospatial point location data, and photographic data.
3. The system of claim 1, further comprising a specialized knowledge base comprising knowledge maps, plan scheduling schemes, historical scene patterns, business rules, expert experience, and knowledge engines.
4. The system of claim 3, wherein the knowledge graph comprises laws and regulations, technical standards, and technical terms; the historical scene mode comprises historical rainfall and historical flood; the business rules comprise a flood control plan, a flood season scheduling and application scheme, a water resource scheduling scheme, a safety management emergency plan and a flood control emergency plan; the expert experience comprises flood scheduling expert experience and safety management expert experience; the knowledge engine includes knowledge representation, machine reasoning and machine learning.
5. The system of claim 3, wherein the flood control scenarios comprise flood control monitoring, flood forecast scheduling, flood pre-warning, flood control schedule previewing and flood control plans for controlling the model library to simulate flood control working scenario traffic; the water supply scene comprises water supply monitoring, water supply scheduling, water supply early warning, a water supply scheduling plan and a water supply plan, and is used for controlling the model library to simulate water resource scheduling working scene service; the safety scene comprises safety monitoring, engineering operation and maintenance, engineering hidden danger, safety analysis and evaluation, safety prediction, dam break previewing and safety planning, and is used for controlling the model library to simulate dynamic engineering scene service; the data maintenance comprises data acquisition, data filtering and data correction, and is used for intercepting abnormal data and correcting the received abnormal data; the system configuration comprises administrative division configuration, organization configuration, department configuration, post configuration, user configuration, menu configuration, authority configuration, engineering configuration, building configuration, station measurement configuration, early warning configuration, engineering element configuration and operation and maintenance configuration, and basic configuration information is provided for each function of the system.
6. The system of claim 5, wherein when the business instructions of the flood control scene are sent to the model library, the flood forecast model and the flood control scheduling model are utilized to schedule related data from the database for carrying out flood forecast and scheduling calculation, calculation results and actual measurement results under different model parameters are subjected to iterative calculation until a calculation threshold is reached, and model parameters used in the last calculation are used as optimal parameters for correction.
7. The system of claim 5, wherein the execution of the security scene includes security analysis, security assessment, and security prediction; carrying out regression analysis on a monitoring item and the environmental quantity through an actual measurement value, obtaining a forecast value of the monitoring item according to a regression equation, calculating a difference value between the forecast value and the actual measurement value and a sample standard deviation, and displaying abnormality if the absolute value of the difference value is greater than three times of the sample standard deviation; the safety evaluation utilizes the historical scene mode and the expert experience in the expert knowledge base to divide engineering elements of the reservoir, sets influence weights, gives corresponding scores in combination with hidden danger grades, and establishes a safety comprehensive evaluation system; and calculating the predicted value of the monitoring item by using a regression equation, displaying the safety level, and if the safety level is unsafe, performing dam break previewing.
8. The system according to claim 5, wherein the system recognizes abnormal data in real time and intercepts the abnormal data while performing the service of the data maintenance; and the system performs addition and average calculation processing on the data information monitored in the front and back steps to obtain recommended modification data corresponding to the abnormal data, and if the user agrees, the abnormal data is replaced with the recommended modification data corresponding to the abnormal data.
9. The system of claim 1, further comprising a mobile side business function module, the mobile side business function module comprising monitoring information, engineering operations and security administration.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116739214A (en) * | 2023-06-14 | 2023-09-12 | 上海勘测设计研究院有限公司 | Safety monitoring system and electronic equipment |
| CN116882211A (en) * | 2023-09-06 | 2023-10-13 | 珠江水利委员会珠江水利科学研究院 | Reservoir water condition forecasting simulation method and system based on digital twin |
| CN118092520A (en) * | 2024-04-29 | 2024-05-28 | 长江勘测规划设计研究有限责任公司 | Digital twinning-based flood season water level real-time analysis method and system |
-
2023
- 2023-01-10 CN CN202310030850.5A patent/CN116167892A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116739214A (en) * | 2023-06-14 | 2023-09-12 | 上海勘测设计研究院有限公司 | Safety monitoring system and electronic equipment |
| CN116882211A (en) * | 2023-09-06 | 2023-10-13 | 珠江水利委员会珠江水利科学研究院 | Reservoir water condition forecasting simulation method and system based on digital twin |
| CN116882211B (en) * | 2023-09-06 | 2023-12-19 | 珠江水利委员会珠江水利科学研究院 | Reservoir water condition forecasting simulation method and system based on digital twin |
| CN118092520A (en) * | 2024-04-29 | 2024-05-28 | 长江勘测规划设计研究有限责任公司 | Digital twinning-based flood season water level real-time analysis method and system |
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