CN115861003A - Intelligent processing method and system for urban water affairs - Google Patents
Intelligent processing method and system for urban water affairs Download PDFInfo
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Abstract
The invention relates to an intelligent processing method and system for urban water affairs, wherein the method comprises the following steps: acquiring wading information of a preset area; establishing a surface and underground integrated mathematical model based on wading information of a preset historical time period of a preset region, pre-acquired artificial activity information and a preset hydrological model, a hydraulic model, a pipe network model, a hydraulic dispatching model, a water quality model, a water ecological model, a water pressure model, a hydrodynamic model and a water environment model; based on the wading information of a preset historical time period of a preset region, performing parameter automatic calibration processing on the surface and underground integrated mathematical model by adopting a big data technology, and acquiring a first surface and underground integrated mathematical model; and processing the wading information in the preset area by adopting the first surface-underground integrated mathematical model to obtain a corresponding processing result.
Description
Technical Field
The invention relates to the technical field of water circulation environment informatization, in particular to an intelligent processing method and system for urban water affairs.
Background
When solving the water-related problems, the related data is not only various in types but also huge in data quantity, such as rainfall, reservoirs, lakes, rivers, pipe networks, dispatching rules, underlying surfaces and the like, and particularly when solving emergency situations such as flood forecasting, urban inland inundation, sudden water pollution tracing analysis and the like, the requirement on the processing and analyzing speed of mass data is very high, and meanwhile, the internal relation and the mutual influence relation among different types of data need to be analyzed.
In the field of wading, the application of mathematical models based on fluid mechanics equations is increasingly common and important. The water quantity and the water quality are two basic attributes of water resources, the two attributes are interdependent and unified, and the key technology for solving the problems is to establish an integrated mathematical model. The objects that solve the problem according to the model can be divided into 4 classes: (1) the reservoir optimization scheduling is taken as a core, and a mathematical model aiming at the river basin flood, water quality and sediment joint optimization scheduling problem is adopted; (2) a mathematical model aiming at the problem of evaluating the bearing capacity of a river network ecological environment system by taking river network water quantity and water quality evolution simulation as a core; (3) a mathematical model aiming at the regulation and control problem of the polluted river gate dam by taking the hydrological environment effect of the river under the control of the gate dam as the key point; (4) the urban inland inundation risk simulation is taken as a core, and a mathematical model for water quantity optimization scheduling is integrated for an urban road network, an underground pipe network and a river network. At present, the water quantity and water quality combined dispatching coupling technology generally adopts loose coupling, namely, a water quality model and a water quantity model are separated and the inherent correlation between the water quality and the water quantity is ignored; urban inland inundation relates to urban plots, pipe networks and riverways, and the relation among the urban plots, the pipe networks and the riverways cannot be ignored when urban inland inundation simulation is carried out, so that coupling hydrodynamic mathematical models of the plots and the pipe networks, the riverways and the pipe networks and the plots and the riverways need to be established.
The modeling of the mathematical model in the water conservancy industry is a complex process, a large amount of basic data needs to be processed and analyzed, and models of different types have respective modeling characteristics. The hydrological model is required to divide hydrological units according to the geographic characteristics of the underlying surface; constructing a river network relation by the river network model, and depicting the shape of a river channel; the two-dimensional surface water model needs mesh generation; the pipe network model needs to be constructed according to the pipe topology relation. These tasks need to be accomplished with the aid of special modeling tools. Parameter tuning is also an important modeling step after data processing and spatial topology modeling are completed. The adjustment is carried out only by a manual mode, which is time-consuming and labor-consuming and has unsatisfactory effect. The results of the model calculation usually need to be presented in a graphic or even video manner, and a good presentation effect is difficult to make without auxiliary tools.
Artificial intelligence has been applied to solving water-related problems in recent years, a computer automatically solves and decides a mathematical model through an artificial intelligence algorithm, and automatically transmits a decision or a conclusion to a previous layer or a next layer to realize integration, for example, in a flood forecasting and dispatching system, a machine learning technology can analyze and compare the current rainfall flood process and the historical rainfall flood process in real time, help related personnel to know the current flood control situation, and make a flood dispatching scheme; the image processing technology based on artificial intelligence can be applied to monitoring and early warning of real-time water and rain conditions, and information elements such as displacement, seepage, water quality, water quantity and the like are fed back in real time and send out early warning signals by utilizing different information processing methods, intelligent algorithms and advanced pattern recognition technologies, so that people can be helped to master the water and rain condition information in time.
However, the current big data and artificial intelligence are still lack of deep application in hydraulic engineering, especially the technology of big data, machine learning and mathematical model close coupling is rarely applied, and the current applications in the fields of intelligent water conservancy, intelligent water conservancy and the like mostly depend on the digital process of knowledge and experience, and lack of deep application after the advanced technology is fused.
(1) The traditional data processing tool and technology have large workload and low efficiency in analyzing and processing water conservancy data, can not meet the industrial requirements of current water management gradually, particularly have high response requirements on quick extraction and analysis of historical data, enterprise pollution discharge, pipe network and river channel water quality data correlation analysis, real-time rainfall and historical rainfall flood similarity analysis and the like when dealing with emergency events such as rainstorm flood, urban waterlogging, sudden water environment pollution and the like, are directly related to the scientificity of leading the efficiency and decision of dealing with the emergency events, and are more directly related to the life and property safety of people.
(2) In the process of combining informatization, the selection and construction of the mathematical model have no unified national and industrial standards. When a business system needs to be updated, models in an old system are often difficult to migrate and integrate. Furthermore, it is difficult to integrate models in existing systems when other systems need to use the same model. These models, like islands, only work within a particular business system, while also solving only a single specific problem, i.e., the model "loose coupling". Other systems want to use the same model, and in addition to manually placing the model in the system, additional development work is required to package or reform the original model by technical means for easy calling. Some mathematical models, such as hydrodynamic models, often require significant computing resources and even a clustered environment to support model operations. When the system utilization rate is not very high, the computing resources invested and constructed for the models are idle, and great waste is caused. In addition, the traditional model construction process is complicated, and a modeling tool with the same standard is not provided, so that the workload of business personnel is large, but the efficiency is low, and even the emergency water safety water environment event cannot be dealt with in time.
(3) The application of technologies such as artificial intelligence in the water conservancy industry is only in a primary stage, and the deep fusion with technologies such as big data and mathematical models is not realized. The water circulation field has wide service range, various monitoring indexes, extremely complex rules and relativity of data in different service fields, difficult completion of the work by artificial or conventional technologies, low efficiency and even no problem essence can be found at all.
Disclosure of Invention
Technical problem to be solved
In view of the above disadvantages and shortcomings of the prior art, the present invention provides an intelligent processing method and system for urban water affairs.
(II) technical scheme
In order to achieve the purpose, the invention adopts the main technical scheme that:
in a first aspect, an embodiment of the present invention provides a method for intelligently processing a municipal water affair, including:
s1, acquiring wading information of a preset area;
the wading information of the preset area includes: the method is used for processing data information required by any one of a hydrological model, a hydraulic model, a pipe network model, a hydraulic scheduling model, a water quality model, a water ecological model, a water pressure model, a hydrodynamic model and a water environmental model, presetting a gate valve scheduling rule in a region, rainfall, emergency team network responsible person contact information, flood waterlogging and loss condition information generated in the same region as the rainfall, water consumption, water discharge and river channel ecological base flow information;
s2, establishing a surface and underground integrated mathematical model based on wading information of a preset historical time period of a preset region, pre-acquired artificial activity information and a preset hydrological model, a hydraulic model, a pipe network model, a hydraulic scheduling model, a water quality model, a water ecological model, a water pressure model, a hydrodynamic model and a water environment model;
the surface and underground integrated mathematical model has the functions of the preset hydrological model, hydraulic model, pipe network model, hydraulic dispatching model, water quality model, water pressure model, water ecological model, hydrodynamic model and water environment model;
s3, based on the wading information of the preset historical time period of the preset region, performing parameter automatic calibration processing on the surface and underground integrated mathematical model by adopting a big data technology, and acquiring a first surface and underground integrated mathematical model;
the first surface and underground integrated mathematical model is a surface and underground integrated mathematical model which is subjected to parameter calibration processing on the surface and underground integrated mathematical model by a big data technology and meets a preset value through the verification accuracy of wading information in a preset historical time period in a preset area;
s4, processing the wading information in the preset area by adopting the first surface and underground integrated mathematical model to obtain a corresponding processing result;
the processing result comprises: and respectively adopting a hydrological model, a hydraulic model, a pipe network model, a hydraulic dispatching model, a water quality model, a water pressure model, a water ecological model, a hydrodynamic model and a water environment model to process results based on the wading information.
Preferably, S1 specifically includes:
s11, collecting corresponding monitoring data by adopting preset sensing equipment, and sending the monitoring data to a preset model cloud platform big data center;
and S12, the preset model cloud platform big data center extracts the monitoring data and the pre-stored information to obtain the wading information.
Preferably, the first and second liquid crystal materials are,
the wading information of the preset area includes: DEM elevation, a water system, a water environment, a hydraulic facility, a bridge, a sunken land, an underlying surface, a pipe network, rainfall, passenger water, drainage, a gate valve regulation rule, a network responsible person of an emergency team, flood waterlogging and loss condition information generated in the same area as the rainfall, water consumption, drainage and river ecological base flow information.
Preferably, the method further comprises:
and S5, aiming at the wading information of the preset area, acquiring flood waterlogging and loss information generated in the preset area.
Preferably, the S5 specifically includes:
s51, aiming at the wading information of the preset area, adopting a preset algorithm to acquire water storage information, drainage information and pure water information of the preset area corresponding to rainfall in the wading information of the preset area respectively;
the preset algorithm comprises the following steps: processing wading information of a preset historical time period of a preset region by adopting a machine learning technology in an artificial intelligence technology, and acquiring an algorithm of a corresponding relation between rainfall and water storage information of the preset region, an algorithm of a corresponding relation between the rainfall and drainage information of the preset region and an algorithm of a corresponding relation between the rainfall and water purification information of the preset region in the wading information of the preset historical time period of the preset region;
the water storage information of the preset area comprises the water storage amount of a pipe network, a gate dam and a river channel in the preset area;
the drainage information of the preset area comprises the drainage quantity of the river channel in the preset area;
the water purification information of the preset area comprises the water purification amount of the wetland and the green garden on the underlying surface in the preset area;
s52, acquiring flood and waterlogging condition information of the preset area based on the water storage information, the drainage information and the water purification information of the preset area;
s53, determining the loss condition of the preset area based on flood waterlogging condition information of the preset area and flood waterlogging and loss condition information generated in an area with the same rainfall in the wading information of the preset area;
the loss condition of the preset area is the same as the loss condition of the area with the same rainfall in the wading information.
Preferably, the human activity information in S2 includes: information of the human activity that caused the water migration cycle.
Preferably, the first and second liquid crystal materials are,
the human activity information includes: the method comprises the steps of presetting building information in an area, presetting reservoir building information on a river channel in the area, and presetting scheduling information of gate pumps in the area.
Preferably, the method further comprises:
s6, acquiring rainfall at the future preset time of a preset area through a preset national high-precision gridding rainfall monitoring and predicting system, and acquiring flood waterlogging condition information at the future preset time of the preset area according to the rainfall at the future preset time of the preset area.
Preferably, the S6 includes:
s61, acquiring water storage information, drainage information and water purification information of the preset region at the future preset time based on the preset algorithm and the rainfall at the future preset time of the preset region;
s62, acquiring future flood and waterlogging condition information of the preset area based on water storage information, drainage information and water purification information of the preset area at the future preset time;
and S63, according to a preset time interval, displaying a preset visual interface according to preset risk levels for the flood waterlogging condition information of the preset area at the future preset time, automatically updating the early warning prompt information according to the waterlogging and flood condition information of the preset area at the future preset time, and determining a corresponding preset emergency plan.
In a second aspect, an embodiment of the present invention provides an intelligent urban water affair processing system, where the system includes:
the plurality of sensing devices are used for acquiring wading information in real time and sending the wading information to the processor;
the processor is used for receiving the wading information and executing the intelligent urban water affair processing method based on the wading information.
(III) advantageous effects
The invention has the beneficial effects that: according to the intelligent processing method and system for the urban water affairs, parameters in the surface and underground integrated mathematical model system are automatically calibrated by adopting a big data technology, and the first surface and underground integrated mathematical model system is obtained; and based on the wading information, the first surface and underground integrated mathematical model system is adopted to process to obtain a corresponding processing result, so that the intelligent processing working efficiency of the urban water affairs is improved.
Drawings
FIG. 1 is a flow chart of an intelligent processing method for urban water affairs according to the present invention;
fig. 2 is a schematic diagram of a water treatment method according to an embodiment of the present invention.
Detailed Description
For the purpose of better explaining the present invention and to facilitate understanding, the present invention will be described in detail by way of specific embodiments with reference to the accompanying drawings.
In order to better understand the above technical solution, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Referring to fig. 1 and fig. 2, the present embodiment provides a method for intelligent treatment of urban water affairs, including:
s1, wading information of a preset area is obtained.
The wading information of the preset area includes: the method is used for processing data information required by any one of a hydrological model, a hydraulic model, a pipe network model, a hydraulic scheduling model, a water quality model, a water ecological model, a water pressure model, a hydrodynamic model and a water environmental model, presetting a gate valve scheduling rule in a region, rainfall, emergency team network responsible person contact information, flood waterlogging and loss condition information generated in the region with the same rainfall, water consumption, water discharge and river channel ecological base flow information.
S2, establishing a surface and underground integrated mathematical model based on preset water-involved information of a preset historical time period of a preset region, the preset artificial activity information and a preset hydrological model, a hydraulic model, a pipe network model, a hydraulic scheduling model, a water quality model, a water ecological model, a water pressure model, a hydrodynamic model and a water environment model.
The surface and underground integrated mathematical model has the functions of the preset hydrological model, hydraulic model, pipe network model, hydraulic dispatching model, water quality model, water pressure model, water ecological model, hydrodynamic model and water environment model.
And S3, based on the wading information of the preset historical time period of the preset region, performing parameter automatic calibration processing on the surface and underground integrated mathematical model by adopting a big data technology, and acquiring a first surface and underground integrated mathematical model.
The first surface and underground integrated mathematical model is a surface and underground integrated mathematical model which is subjected to parameter calibration processing on the surface and underground integrated mathematical model by a big data technology and meets a preset value through the verification accuracy of the wading information in a preset historical time period in a preset area.
And S4, processing the wading information in the preset area by adopting the first surface and underground integrated mathematical model to obtain a corresponding processing result.
The processing result comprises: and respectively adopting a hydrological model, a hydraulic model, a pipe network model, a hydraulic dispatching model, a water quality model, a water pressure model, a water ecological model, a hydrodynamic model and a water environment model to process results based on the wading information.
Preferably in this embodiment, S1 specifically includes:
s11, collecting corresponding monitoring data by adopting preset sensing equipment, and sending the monitoring data to a preset model cloud platform big data center.
And S12, the preset model cloud platform big data center extracts the monitoring data and the pre-stored information to obtain the wading information.
In the embodiment, according to the distribution of the existing sensing devices, problems are taken as guidance, the original multiple departments respectively plan monitoring networks, the sensing devices are readjusted and newly added to form a wading monitoring network, and all monitoring data are gathered in the model cloud platform big data center in real time. The method comprises the steps of obtaining wading information such as DEM elevation, a water system, a water environment, hydraulic facilities, bridges and culverts, a sunken land, an underlying surface, a pipe network, a gate valve regulation rule, rainfall, passenger water, drainage, flood waterlogging and loss conditions generated by network responsible persons of emergency team and areas corresponding to similar rainfall through a model cloud platform big data center, and obtaining information such as water consumption, drainage, river ecological base flow and the like of the corresponding areas. The model cloud platform big data center supports real-time inquiry of wading information and keeps regular updating of the wading information.
Preferably, the wading information of the preset area includes: DEM elevation, a water system, a water environment, a hydraulic facility, a bridge, a sunken land, an underlying surface, a pipe network, rainfall, passenger water, drainage, a gate valve regulation rule, a network responsible person of an emergency team, flood waterlogging and loss condition information generated in the same area as the rainfall, water consumption, drainage and river ecological base flow information.
In this embodiment, the method further includes:
and S5, aiming at the wading information of the preset area, acquiring flood waterlogging and loss information generated in the preset area.
In this embodiment, the S5 specifically includes:
and S51, acquiring water storage information, drainage information and water purification information of the preset area corresponding to rainfall in the wading information of the preset area respectively by adopting a preset algorithm according to the wading information of the preset area.
The preset algorithm comprises the following steps: the method comprises the steps of processing wading information of a preset historical time period of a preset region by adopting a machine learning technology in an artificial intelligence technology, and obtaining an algorithm of a corresponding relation between rainfall and water storage information of the preset region, an algorithm of a corresponding relation between the rainfall and water drainage information of the preset region and an algorithm of a corresponding relation between the rainfall and water purification information of the preset region in the wading information of the preset historical time period of the preset region.
The water storage information of the preset area comprises the water storage amount of a pipe network, a gate dam and a river channel in the preset area.
The drainage information of the preset area comprises the drainage quantity of the river channel in the preset area.
The water purification information of the preset area comprises the water purification amount of the wetland and the green garden on the underlying surface in the preset area.
And S52, acquiring flood and waterlogging condition information of the preset area based on the water storage information, the drainage information and the water purification information of the preset area.
And S53, determining the loss condition of the preset area based on the flood waterlogging condition information of the preset area and the flood waterlogging and loss condition information generated in the area with the same rainfall in the wading information of the preset area.
The loss condition of the preset area is the same as the loss condition of the area with the same rainfall in the wading information.
In the practical application of the embodiment, a dynamic storage capacity is calculated by adopting the machine learning technology in artificial intelligence to extract river and lake water systems, small micro water bodies, pipe network systems, underlying surface water storage and water seepage capacity; drainage capacity of pipe networks, gate dams, river channels and the like; the water purifying capacity of the wetland and green garden with the underlying surface; judging the conditions of waterlogging, flood, loss and the like based on similar rainfall floods; and calculating water storage, drainage and waterlogging flood conditions of the corresponding areas according to future rainfall information and water balance, establishing a mapping relation and the like.
Preferably in this embodiment, the human activity information in S2 includes: information of the human activity that caused the water migration cycle.
Preferably in this embodiment, the human activity information includes: the method comprises the steps of presetting information of buildings built in an area, presetting information of reservoirs built on riverways in the area, and presetting scheduling information of gate pumps in the area.
Preferably in this embodiment, the method further includes:
s6, acquiring rainfall at the future preset time of a preset area through a preset national high-precision gridding rainfall monitoring and predicting system, and acquiring flood waterlogging condition information at the future preset time of the preset area according to the rainfall at the future preset time of the preset area.
Preferably in this embodiment, the S6 includes:
and S61, acquiring water storage information, drainage information and water purification information of the preset region at the future preset time based on the preset algorithm and the rainfall at the future preset time of the preset region.
And S62, acquiring future flood and waterlogging condition information of the preset area based on the water storage information, the drainage information and the water purification information of the preset area at the future preset time.
And S63, according to a preset time interval, displaying a preset visual interface according to the flood waterlogging condition information of the preset area at the future preset time according to a preset risk level, automatically updating the early warning prompt information of the response according to the waterlogging and flood condition information of the preset area at the future preset time, and determining a corresponding preset emergency plan.
In a second aspect, referring to fig. 2, the present embodiment provides an intelligent processing system for urban water affairs, the system includes:
the sensing devices are used for acquiring wading information in real time and sending the wading information to the processor.
The processor is used for receiving the wading information and executing the intelligent urban water affair processing method based on the wading information.
In practical applications of the present embodiment, the present embodiment provides an intelligent urban water affairs processing system having a daily management mode and an emergency management mode.
In a daily management mode, the urban water affair intelligent processing system of the embodiment adopts the first surface-underground integrated mathematical model to process the wading information of the preset area to obtain a corresponding processing result.
The processing result comprises: and respectively adopting a hydrological model, a hydraulic model, a pipe network model, a hydraulic dispatching model, a water quality model, a water pressure model, a water ecological model, a hydrodynamic model and a water environment model to process results based on the wading information.
For example, the water pressure model is used for detecting the water pressure of a water supply network, and corresponding processing results (water pressure information of a water supply pipeline) are obtained in real time; the water quality model is adopted to detect the water quality of the water supply network, and a corresponding treatment result (water quality information of a water supply pipeline) is obtained in real time; performing river network hydrodynamic detection by adopting the hydrodynamic model, and acquiring a corresponding processing result (river network hydrodynamic information) in real time; and (3) detecting the water environment by adopting the water environment model, and acquiring a corresponding processing result (water environment information) in real time.
Under the emergency management mode, the urban water affair intelligent processing system of the embodiment obtains the rainfall capacity of the preset area at the future preset time through the preset national high-precision gridding rainfall monitoring and predicting system, and obtains the flood waterlogging condition information of the preset area at the future preset time according to the rainfall capacity of the preset area at the future preset time.
In this embodiment, the method specifically includes:
and acquiring water storage information, drainage information and water purification information of the preset region at the future preset time based on the preset algorithm and the rainfall at the future preset time of the preset region.
And acquiring future flood and waterlogging condition information of the preset area based on the water storage information, the drainage information and the water purification information of the preset area at the future preset time.
And according to a preset time interval, displaying a preset visual interface according to the preset risk level of the flood waterlogging condition information of the preset area at the future preset time, automatically updating the early warning prompt information of the response according to the flood waterlogging condition information of the preset area at the future preset time, and determining a corresponding preset emergency plan.
In addition, according to flood waterlogging condition information, emergency plans such as riverway pre-drainage water level, gate pump water distribution and risk avoidance and mass transfer can be started in advance, waterlogging early warning information can be pushed to a road traffic management department, traffic control and road dredging are carried out in advance, the influence of disasters on the masses of people is relieved, and therefore water on the sky, the earth surface and the underground is coupled with water conservancy projects, weather forecasts and human activities through a water control model cloud.
Through the operation of the system, more wading data are acquired and put into a big data center. And mining and analyzing data by using a big data technology, putting the latest data into an artificial intelligence sample library, and further verifying and perfecting the water circulation mathematical model to ensure that the forecast is more accurate.
By utilizing a cloud computing technology, business management and model application are served, professional mathematical models such as flood disasters and water environment management are integrated, artificial intelligent algorithms such as big data and machine learning are integrated, a set of water control model cloud platform integrating functions such as hydrologic forecasting, flood scheduling, water environment control, urban waterlogging and pipe network drainage is created, unified authority authentication, unified management tasks and unified resource allocation are carried out, and intelligent analysis decision support is provided for an information platform such as intelligent water affairs.
According to the intelligent urban water affair processing method, by means of a big data technology, parallel analysis and calculation of massive water conservancy information data can be achieved, data processing efficiency and precision are guaranteed, law and correlation analysis is conducted on wading data in different business fields, automatic calibration is conducted on model parameters, the problems of poor data timeliness, low manual calibration efficiency, low model forecast prediction precision and loose coupling of model application in the model operation process are greatly improved, and scientific, efficient, accurate and convenient tools and methods are provided for water safety, water environment and water ecology problem treatment and emergency handling of sudden water safety accidents.
The intelligent treatment method for the urban water affairs provided in the embodiment realizes seamless butt joint integration of models such as hydrology, hydrodynamic force and water quality, simplifies the modeling process, provides a whole standard set of model front and rear treatment tools, greatly saves modeling workload and resources, provides a visual modeling function, and better meets requirements of business daily and emergency work.
The intelligent treatment method for the urban water affairs provided in the embodiment makes full use of advanced technologies, deeply fuses technologies such as artificial intelligence, big data and mathematical models, and widens the application range and depth of the method when solving the problems of water quantity, water quality and water ecology.
Since the system described in the above embodiment of the present invention is a system used for implementing the method of the above embodiment of the present invention, based on the method described in the above embodiment of the present invention, a person skilled in the art can understand the specific structure and the modification of the system/apparatus, and thus the detailed description is omitted here. All systems/devices adopted by the methods of the above embodiments of the present invention are within the intended scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions.
It should be noted that in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention can be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the terms first, second, third and the like are for convenience only and do not denote any order. These words are to be understood as part of the name of the component.
Furthermore, it should be noted that in the description of the present specification, the description of the term "one embodiment", "some embodiments", "examples", "specific examples" or "some examples", etc., means that a specific feature, structure, material or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, the claims should be construed to include preferred embodiments and all changes and modifications that fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention should also include such modifications and variations.
Claims (10)
1. A method for intelligent treatment of urban water affairs is characterized by comprising the following steps:
s1, acquiring wading information of a preset area;
the wading information of the preset area includes: the method is used for processing data information required by any one of a hydrological model, a hydraulic model, a pipe network model, a hydraulic scheduling model, a water quality model, a water ecological model, a water pressure model, a hydrodynamic model and a water environmental model, presetting a gate valve scheduling rule in a region, rainfall, emergency team network responsible person contact information, flood waterlogging and loss condition information generated in the same region as the rainfall, water consumption, water discharge and river channel ecological base flow information;
s2, establishing a surface and underground integrated mathematical model based on wading information of a preset historical time period of a preset region, pre-acquired artificial activity information and a preset hydrological model, a hydraulic model, a pipe network model, a hydraulic scheduling model, a water quality model, a water ecological model, a water pressure model, a hydrodynamic model and a water environment model;
the surface and underground integrated mathematical model has the functions of the preset hydrological model, hydraulic model, pipe network model, hydraulic dispatching model, water quality model, water pressure model, water ecological model, hydrodynamic model and water environment model;
s3, based on the wading information of the preset historical time period of the preset region, performing parameter automatic calibration processing on the surface and underground integrated mathematical model by adopting a big data technology, and acquiring a first surface and underground integrated mathematical model;
the first surface and underground integrated mathematical model is a surface and underground integrated mathematical model which is subjected to parameter calibration processing on the surface and underground integrated mathematical model by a big data technology and meets a preset value through the verification accuracy of wading information in a preset historical time period in a preset area;
s4, processing the wading information in the preset area by adopting the first surface and underground integrated mathematical model to obtain a corresponding processing result;
the processing result comprises: and respectively adopting a hydrological model, a hydraulic model, a pipe network model, a hydraulic scheduling model, a water quality model, a water pressure model, a water ecological model, a hydrodynamic model and a water environment model to process results based on the wading information.
2. The method according to claim 1, wherein S1 specifically comprises:
s11, collecting corresponding monitoring data by adopting preset sensing equipment, and sending the monitoring data to a preset model cloud platform big data center;
and S12, the preset model cloud platform big data center extracts the monitoring data and the pre-stored information to obtain the wading information.
3. The method of claim 2,
the wading information of the preset area includes: DEM elevation, a water system, a water environment, a hydraulic facility, a bridge, a sunken land, an underlying surface, a pipe network, rainfall, passenger water, drainage, a gate valve regulation rule, a network responsible person of an emergency team, flood waterlogging and loss condition information generated in the same area as the rainfall, water consumption, drainage and river ecological base flow information.
4. The method of claim 3, further comprising:
and S5, aiming at the wading information of the preset area, acquiring flood waterlogging and loss information generated in the preset area.
5. The method according to claim 4, wherein the S5 specifically comprises:
s51, aiming at the wading information of the preset area, adopting a preset algorithm to acquire water storage information, drainage information and pure water information of the preset area corresponding to rainfall in the wading information of the preset area respectively;
the preset algorithm comprises the following steps: processing wading information of a preset historical time period of a preset region by adopting a machine learning technology in an artificial intelligence technology, and acquiring an algorithm of a corresponding relation between rainfall and water storage information of the preset region, an algorithm of a corresponding relation between the rainfall and water drainage information of the preset region and an algorithm of a corresponding relation between the rainfall and water purification information of the preset region in the wading information of the preset historical time period of the preset region;
the water storage information of the preset area comprises the water storage amount of a pipe network, a gate dam and a river channel in the preset area;
the drainage information of the preset area comprises the drainage quantity of the river channel in the preset area;
the water purification information of the preset area comprises the water purification amount of the wetland and the green garden on the underlying surface in the preset area;
s52, acquiring flood and waterlogging condition information of the preset area based on the water storage information, the drainage information and the water purification information of the preset area;
s53, determining the loss condition of the preset area based on flood waterlogging condition information of the preset area and flood waterlogging and loss condition information generated in an area with the same rainfall in the wading information of the preset area;
the loss condition of the preset area is the same as the loss condition of the area with the same rainfall in the wading information.
6. The method of claim 5, wherein the human activity information in S2 comprises: information of the human activity that caused the water migration cycle.
7. The method of claim 6,
the human activity information includes: the method comprises the steps of presetting information of buildings built in an area, presetting information of reservoirs built on riverways in the area, and presetting scheduling information of gate pumps in the area.
8. The method of claim 7, further comprising:
s6, acquiring rainfall at the future preset time of a preset area through a preset national high-precision gridding rainfall monitoring and predicting system, and acquiring flood waterlogging condition information at the future preset time of the preset area according to the rainfall at the future preset time of the preset area.
9. The method according to claim 8, wherein the S6 includes:
s61, acquiring water storage information, drainage information and water purification information of the preset region at the future preset time based on the preset algorithm and the rainfall at the future preset time of the preset region;
s62, acquiring future flood and waterlogging condition information of the preset area based on water storage information, drainage information and water purification information of the preset area at the future preset time;
and S63, according to a preset time interval, displaying a preset visual interface according to the flood waterlogging condition information of the preset area at the future preset time according to a preset risk level, automatically updating the early warning prompt information of the response according to the waterlogging and flood condition information of the preset area at the future preset time, and determining a corresponding preset emergency plan.
10. An intelligent urban water affair processing system, which is characterized in that the system comprises:
the sensing devices are used for acquiring wading information in real time and sending the wading information to the processor;
the processor is used for receiving the wading information and executing the urban water affair intelligent processing method according to any one of claims 1 to 9 based on the wading information.
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| CN117094496A (en) * | 2023-08-03 | 2023-11-21 | 北京市市政工程设计研究总院有限公司广东分院 | Scheduling operation system based on expert rules under mechanism and machine learning prediction |
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