CN113112125A - Artificial intelligence-based water resource management method and system - Google Patents

Artificial intelligence-based water resource management method and system Download PDF

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CN113112125A
CN113112125A CN202110300897.XA CN202110300897A CN113112125A CN 113112125 A CN113112125 A CN 113112125A CN 202110300897 A CN202110300897 A CN 202110300897A CN 113112125 A CN113112125 A CN 113112125A
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郭军
王小鹏
徐佳伟
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Zhejiang Heda Technology Co ltd
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Abstract

The embodiment of the invention provides a water resource management method and a device based on artificial intelligence, wherein the method comprises the following steps: acquiring water resource scheduling data in the history record, and acquiring corresponding historical water resource data and geographic attributes; according to the geographic attributes, combining with a geographic attribute weight grade table to obtain corresponding weights of the geographic attributes; inputting historical water resource data, geographic attributes, corresponding weights of the geographic attributes and water resource scheduling data into a convolutional neural network model for training to obtain a trained model; judging whether the current water resource data meets the water resource scheduling requirement; and if the current geographic attribute of the current water resource data is met, obtaining the corresponding weight of the current geographic attribute, inputting the weight to the trained model, and outputting the corresponding water resource scheduling scheme. By adopting the method, the management and scheduling among the water resources of the water supply network can be completed according to the artificial intelligence deep learning, so that the manpower resources are saved, and the efficiency of water resource scheduling is improved.

Description

Artificial intelligence-based water resource management method and system
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a water resource management method and system based on artificial intelligence.
Background
At present, along with the more and more urbanization of China, the resident also more and more passes through the water pipe water, and the water supply network also gradually spreads everywhere, distributes also more and more complicacy to when the water supply network supplies water, still can produce multiple water supply data.
Among the prior art, because the more and more complicated of water supply network, the place that needs to supply water is also more and more, will appear when supplying water in some times, and some local water supply is excessive, and the condition that some local water supply is not enough just needs to adjust the water supply capacity of each place this time, just can not waste the water resource.
According to the above situation, the management means of the related water supply amount scheduling for the water supply network is very complex, a large amount of data (the data may include water consumption data, regional factor data, etc.) needs to be collated and calculated by related workers to obtain a corresponding result, the calculation process is very complex, and the scheduling wastes time, so a management method for the water supply network that can solve the above problems is urgently needed.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a water resource management method and system based on artificial intelligence.
The embodiment of the invention provides a water resource management method based on artificial intelligence, which comprises the following steps:
acquiring water resource scheduling data in a historical record, and acquiring corresponding historical water resource data and geographic attributes according to the water resource scheduling data, wherein the historical water resource data comprises water consumption data, water quality data and reservoir water storage levels of both scheduling parties, and the geographic attributes comprise rainfall and population intensity;
acquiring a preset geographic attribute weight grade table, and obtaining the corresponding weight of the geographic attribute by combining the geographic attribute weight grade table according to the geographic attribute;
inputting the historical water resource data, the geographic attributes, the corresponding weights of the geographic attributes and the water resource scheduling data into a convolutional neural network model for training to obtain a trained convolutional neural network model;
judging whether the current water resource data meets the water resource scheduling requirement;
when the current water resource data meets the water resource scheduling requirement, obtaining the current geographic attribute corresponding to the current water resource data, combining the geographic attribute weight grade table to obtain the weight corresponding to the current geographic attribute, inputting the current water resource data, the current geographic attribute and the weight corresponding to the current geographic attribute into the trained convolutional neural network model, and outputting a corresponding water resource scheduling scheme.
In one embodiment, the method further comprises:
acquiring a current geographic attribute corresponding to current water resource data, acquiring a weight corresponding to the current geographic attribute by combining the geographic attribute weight grade table, and acquiring a water resource data threshold corresponding to the current geographic attribute;
and performing comprehensive calculation on the current water resource data and the weight corresponding to the current geographic attribute, comparing a calculation result with the water resource data threshold value, and judging whether the requirement of a dispatcher in water resource scheduling is met according to a comparison result.
In one embodiment, the method further comprises:
when the current water resource data is judged to be a scheduling party in water resource scheduling according to the comparison result, acquiring corresponding historical water resource data according to the current water resource data, and establishing a corresponding water resource prediction model according to the historical water resource data;
predicting future water demand according to the water resource prediction model, performing scheduling simulation on the current water resource data, and comparing the current water resource data after the scheduling simulation with the future water demand;
and obtaining the current water resource data as the scheduling water resource amount of the scheduling party according to the comparison result.
In one embodiment, the method further comprises:
when the comparison result meets the requirement of a dispatcher in water resource dispatching, outputting a corresponding water resource dispatching scheme, comprising:
and acquiring all scheduling party information in the water resource scheduling, and outputting the all scheduling party information to all scheduling parties and scheduled parties, wherein the all scheduling party information comprises scheduling water quantity data of the scheduling party and current position information of the scheduling party.
In one embodiment, the method further comprises:
acquiring a current geographical attribute corresponding to current water resource data, and acquiring a water resource data threshold corresponding to the current geographical attribute;
and comparing the current water resource data with the water resource data threshold value, and judging whether the requirements of the dispatched party in the water resource dispatching are met according to the comparison result.
In one embodiment, the method further comprises:
when the comparison result meets the requirement of a dispatched party in water resource dispatching, outputting a corresponding water resource dispatching scheme, comprising:
and receiving all scheduling party information in the water resource scheduling, and outputting the all scheduling party information to a water resource management center, wherein the all scheduling party information comprises scheduling water quantity data of a scheduling party and current position information of the scheduling party.
In one embodiment, the method further comprises:
dividing the water resource scheduling data into a training set and a verification set, and inputting the training set and corresponding historical water resource data, geographic attributes and corresponding weights of the geographic attributes into a convolutional neural network model for training to obtain a trained primary convolutional neural network model;
and inputting the verification set and corresponding weights of the historical water resource data, the geographic attributes and the geographic attributes into the trained primary convolutional neural network model for testing, and obtaining the trained convolutional neural network model after the test is finished.
The embodiment of the invention provides a water resource management system based on artificial intelligence, which comprises:
the first acquisition module is used for acquiring water resource scheduling data in a historical record and acquiring corresponding historical water resource data and geographic attributes according to the water resource scheduling data, wherein the historical water resource data comprises water consumption data, water quality data and reservoir water storage levels of both scheduling parties, and the geographic attributes comprise rainfall and population intensity;
the second acquisition module is used for acquiring a preset geographic attribute weight grade table and acquiring the corresponding weight of the geographic attribute by combining the geographic attribute weight grade table according to the geographic attribute;
the training module is used for inputting the historical water resource data, the geographic attributes, the corresponding weights of the geographic attributes and the water resource scheduling data into a convolutional neural network model for training to obtain a trained convolutional neural network model;
the judging module is used for judging whether the current water resource data meets the water resource scheduling requirement;
and the output module is used for obtaining the current geographic attribute corresponding to the current water resource data when the current water resource data meets the water resource scheduling requirement, obtaining the weight corresponding to the current geographic attribute by combining the geographic attribute weight grade table, inputting the current water resource data, the current geographic attribute and the weight corresponding to the current geographic attribute to the trained convolutional neural network model, and outputting a corresponding water resource scheduling scheme.
The embodiment of the invention provides electronic equipment, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the steps of the artificial intelligence-based water resource management method.
Embodiments of the present invention provide a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the artificial intelligence based water resource management method described above.
The artificial intelligence-based water resource management method and the system thereof provided by the embodiment of the invention are used for acquiring water resource scheduling data in a historical record, and acquiring corresponding historical water resource data and geographic attributes according to the water resource scheduling data, wherein the historical water resource data comprises water consumption data, water quality data and reservoir water storage level of both scheduling parties, and the geographic attributes comprise rainfall and population intensity; acquiring a preset geographic attribute weight grade table, and obtaining the corresponding weight of the geographic attribute by combining the geographic attribute weight grade table according to the geographic attribute; inputting historical water resource data, geographic attributes, corresponding weights of the geographic attributes and water resource scheduling data into a convolutional neural network model for training to obtain a trained convolutional neural network model; judging whether the current water resource data meets the water resource scheduling requirement; the current water resource data meet the water resource scheduling requirement, the current geographic attribute corresponding to the current water resource data is obtained, the weight corresponding to the current geographic attribute is obtained by combining the geographic attribute weight grade table, the current water resource data, the current geographic attribute and the weight corresponding to the current geographic attribute are input to the trained convolutional neural network model, and the corresponding water resource scheduling scheme is output. Management scheduling between the water resource of water supply network can be accomplished according to artificial intelligence's degree of depth study like this, when saving manpower resources, also improved water resource scheduling efficiency.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method for artificial intelligence based water resource management in an embodiment of the present invention;
FIG. 2 is a block diagram of an artificial intelligence based water resource management system in an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a metering management method based on artificial intelligence according to an embodiment of the present invention, and as shown in fig. 1, the embodiment of the present invention provides a metering management method based on artificial intelligence, including:
step S101, water resource scheduling data in a historical record are obtained, corresponding historical water resource data and geographic attributes are obtained according to the water resource scheduling data, the historical water resource data comprise water consumption data, water quality data and reservoir water storage levels of both scheduling parties, and the geographic attributes comprise rainfall and population intensity.
Specifically, the water resource scheduling data is basic scheduling data in a water resource database, the water resource scheduling data in the historical scheduling records is obtained from the water resource database, and the historical water resource data and the geographic attributes in the corresponding scheduling records are obtained according to the water resource scheduling data, wherein the historical water resource data refers to water consumption data, water quality data (generally, the lower the water quality data is, the higher the water amount needed in scheduling) and reservoir water storage level of scheduling parties (a scheduling party and a scheduled party) in the corresponding scheduling records, and the geographic attributes refer to attributes in the scheduling records, such as rainfall, population intensity and the like of the geographic positions of the scheduling parties.
Step S102, obtaining a preset geographic attribute weight grade table, and obtaining the corresponding weight of the geographic attribute by combining the geographic attribute weight grade table according to the geographic attribute.
Specifically, the preset geographic attribute weight level table is a corresponding relationship table between geographic attributes and weights, and generally, the higher the water resource demand of the geographic attributes is, that is, the lower the rainfall is and the higher the population density is, the higher the corresponding geographic attribute weight is, whereas the higher the rainfall is and the lower the population density is, the lower the corresponding geographic attribute weight is.
Step S103, inputting the historical water resource data, the geographic attributes, the corresponding weights of the geographic attributes and the water resource scheduling data into a convolutional neural network model for training to obtain the trained convolutional neural network model.
Specifically, historical water resource data, geographic attributes, corresponding weights of the geographic attributes and water resource scheduling data are used as input and input into an input layer of a convolutional neural network model for model training, and the convolutional neural network model is subjected to deep learning through a convolutional layer, a pooling layer and a full connection layer to obtain a trained convolutional neural network model.
And step S104, judging whether the current water resource data meets the water resource scheduling requirement.
Specifically, the method comprises the steps of obtaining current unknown water resource data which is required to be scheduled or not, and judging whether the current water resource data meets the requirement of a water resource scheduling or not, wherein the detection of the requirement of the water resource scheduling is bidirectional, whether the current water resource data meets the requirement of a scheduling party in the water resource scheduling or not needs to be detected, and whether the current water resource data meets the requirement of a scheduled party in the water resource scheduling or not needs to be detected.
Step S105, when the current water resource data meets the water resource scheduling requirement, obtaining the current geographic attribute corresponding to the current water resource data, combining the geographic attribute weight grade table to obtain the weight corresponding to the current geographic attribute, inputting the current water resource data, the current geographic attribute and the weight corresponding to the current geographic attribute to the trained convolutional neural network model, and outputting a corresponding water resource scheduling scheme.
Specifically, when the current water resource data meets the water resource scheduling requirement, which indicates that the current water resource data needs to be subjected to water resource scheduling, the current geographic attribute corresponding to the current water resource data is obtained, the weight corresponding to the current geographic attribute is obtained by combining the geographic attribute weight grade table, the current water resource data, the current geographic attribute and the weight corresponding to the current geographic attribute are used as input and input into the trained convolutional neural network model, and the corresponding water resource scheduling scheme is output through the trained convolutional neural network model.
The embodiment of the invention provides a water resource management method based on artificial intelligence, which comprises the steps of obtaining water resource scheduling data in a historical record, and obtaining corresponding historical water resource data and geographic attributes according to the water resource scheduling data, wherein the historical water resource data comprises water consumption data, water quality data and reservoir water storage positions of both scheduling parties, and the geographic attributes comprise rainfall and population intensity; acquiring a preset geographic attribute weight grade table, and obtaining the corresponding weight of the geographic attribute by combining the geographic attribute weight grade table according to the geographic attribute; inputting historical water resource data, geographic attributes, corresponding weights of the geographic attributes and water resource scheduling data into a convolutional neural network model for training to obtain a trained convolutional neural network model; judging whether the current water resource data meets the water resource scheduling requirement; the current water resource data meet the water resource scheduling requirement, the current geographic attribute corresponding to the current water resource data is obtained, the weight corresponding to the current geographic attribute is obtained by combining the geographic attribute weight grade table, the current water resource data, the current geographic attribute and the weight corresponding to the current geographic attribute are input to the trained convolutional neural network model, and the corresponding water resource scheduling scheme is output. Management scheduling between the water resource of water supply network can be accomplished according to artificial intelligence's degree of depth study like this, when saving manpower resources, also improved water resource scheduling efficiency.
On the basis of the above embodiment, the artificial intelligence-based water resource management method further includes:
acquiring a current geographic attribute corresponding to current water resource data, acquiring a weight corresponding to the current geographic attribute by combining the geographic attribute weight grade table, and acquiring a water resource data threshold corresponding to the current geographic attribute;
and performing comprehensive calculation on the current water resource data and the weight corresponding to the current geographic attribute, comparing a calculation result with the water resource data threshold value, and judging whether the requirement of a dispatcher in water resource scheduling is met according to a comparison result.
In the embodiment of the invention, the current geographical attribute corresponding to the current water resource data is obtained, the weight corresponding to the current geographical attribute is obtained by combining with the geographical attribute weight grade table, and then the water resource data threshold corresponding to the current geographical attribute is obtained, wherein the water resource data threshold refers to that under the current geographical position (such as a cell A), the current water resource data and the weight corresponding to the current geographical attribute are comprehensively calculated according to the water resource data value averagely required by the current geographical position (cell A) in historical data, that is, the current water resource data of the cell A and the corresponding weight (water resource demand weight) are comprehensively calculated to obtain the actual standard of the water resource combination weight of the cell A, and the actual standard is compared with the water resource data threshold to judge whether the cell A meets the demand of a dispatcher in water resource scheduling. Because whether the cell a can be used as a scheduling party is judged, whether the current water resource data of the cell a is larger than a water resource data threshold value is not only needed to be seen, but also sufficient water resource stocks for dealing with various emergencies are ensured after the cell a performs water resource scheduling, so that the current water resource data of the cell a and corresponding weights (water resource demand weights) need to be comprehensively calculated, for example, the population intensity of the cell a is 2 grades, the rainfall grade is 5 grades, and the corresponding weights can be 10, when performing comprehensive calculation, the minimum weight values can be unitized, then the weights 10 can be similarly unitized to obtain corresponding calculated values, and then whether the cell a can be used as a scheduling party can be judged according to the unitized calculated values and the current water resource data.
According to the embodiment of the invention, the corresponding weight of the current water resource data and the current geographic attribute is comprehensively calculated, whether the current water resource data meets the requirement of a dispatcher or not is judged, the current water resource data can be clearly judged to supply water resources, and the corresponding dispatching scheme can be conveniently output subsequently.
On the basis of the above embodiment, the artificial intelligence-based water resource management method further includes:
when the current water resource data is judged to be a scheduling party in water resource scheduling according to the comparison result, acquiring corresponding historical water resource data according to the current water resource data, and establishing a corresponding water resource prediction model according to the historical water resource data;
predicting future water demand according to the water resource prediction model, performing scheduling simulation on the current water resource data, and comparing the current water resource data after the scheduling simulation with the future water demand;
and obtaining the current water resource data as the scheduling water resource amount of the scheduling party according to the comparison result.
In the embodiment of the invention, judging whether the requirement of a dispatcher in water resource dispatching is met according to the comparison result comprises two steps: 1. judging whether the current water resource data can be used as a dispatcher according to a comparison result, 2, scheduling the amount of water needed by the current water resource data, calculating the amount of water to be scheduled after judging that the current water resource data is the dispatcher in water resource scheduling according to the comparison result, specifically establishing a corresponding water resource prediction model according to historical water resource data corresponding to the current water resource data, predicting future water demand (comprehensively calculating by referring to the current water resource data and corresponding weight (water resource demand weight), and obtaining the actual standard of the water resource combination weight), then performing scheduling simulation through the water resource prediction model, comparing the current water resource data after the scheduling simulation with the future water demand, and obtaining the scheduled water resource amount of the current water resource data as the dispatcher according to the comparison result.
After the embodiment of the invention determines that the current water resource data can be used as the scheduling party, the scheduling water resource amount of the water resource data is determined, the high efficiency of water resource scheduling is ensured, and the accuracy of water resource scheduling is improved.
On the basis of the above embodiment, the artificial intelligence-based water resource management method further includes:
when the comparison result meets the requirement of a dispatcher in water resource dispatching, outputting a corresponding water resource dispatching scheme, comprising:
and acquiring all scheduling party information in the water resource scheduling, and outputting the all scheduling party information to all scheduling parties and scheduled parties, wherein the all scheduling party information comprises scheduling water quantity data of the scheduling party and current position information of the scheduling party.
In the embodiment of the invention, when the comparison result meets the requirement of the scheduling party in the water resource scheduling, the corresponding water resource scheduling scheme is output, the water resource scheduling scheme may comprise a plurality of scheduling parties and scheduled parties, and the scheduling parties and the scheduled parties are used as one part of the scheduling parties, the information of all the scheduling parties is obtained, and the information of all the scheduling parties is output to all the scheduling parties and the scheduled parties, so that relevant departments of all the scheduling parties and the scheduled parties can know the actual condition of water quantity scheduling, and the subsequent charging and the record of the file are convenient.
After determining that the current water resource data can be used as the scheduling party, the embodiment of the invention acquires the information of all scheduling parties and outputs the information of all scheduling parties to all scheduling parties and scheduled parties, thereby facilitating the subsequent charging and the record of files.
On the basis of the above embodiment, the artificial intelligence-based water resource management method further includes:
acquiring a current geographical attribute corresponding to current water resource data, and acquiring a water resource data threshold corresponding to the current geographical attribute;
and comparing the current water resource data with the water resource data threshold value, and judging whether the requirements of the dispatched party in the water resource dispatching are met according to the comparison result.
In the embodiment of the invention, the current geographic attribute corresponding to the current water resource data is obtained, and then the water resource data threshold corresponding to the current geographic attribute is obtained, wherein the water resource data threshold refers to comparing the current water resource data of the cell B with the water resource data threshold according to the average water resource data value required by the current geographic position (cell B) in the historical data under the current geographic position (such as the cell B), and judging whether the cell B meets the requirement of a dispatched party in water resource dispatching or not according to the comparison result.
In addition, when the cell B meets the requirements of the dispatched party in water resource dispatching, the corresponding water resource dispatching scheme is received, the water resource dispatching scheme may comprise a plurality of dispatching parties and the dispatched party, all the information of the dispatching parties is sent to the dispatched party by the dispatching parties, the dispatched party receives all the information of the dispatching parties and outputs the information to the water resource management center, and the dispatched party sends all the information of the dispatching parties to the water resource management center, so that subsequent charging and record of files are facilitated.
After determining that the current water resource data can be used as the dispatched party, the embodiment of the invention receives the information of all the dispatchers and outputs the information of all the dispatchers to the water resource management center, thereby facilitating the subsequent charging and the record of the file.
On the basis of the above embodiment, the artificial intelligence-based water resource management method further includes:
dividing the water resource scheduling data into a training set and a verification set, and inputting the training set and corresponding historical water resource data, geographic attributes and corresponding weights of the geographic attributes into a convolutional neural network model for training to obtain a trained primary convolutional neural network model;
and inputting the verification set and corresponding weights of the historical water resource data, the geographic attributes and the geographic attributes into the trained primary convolutional neural network model for testing, and obtaining the trained convolutional neural network model after the test is finished.
In the embodiment of the invention, when the water resource scheduling data and the corresponding historical water resource data, the geographic attribute and the geographic attribute are trained through the convolutional neural network model, the water resource scheduling data are subjected to data grouping, specifically, the data can be divided into 80% of training sets and 20% of verification sets, the training sets and the corresponding historical water resource data, the geographic attribute and the geographic attribute are used for carrying out preliminary training to obtain a preliminary convolutional neural network model, and then the preliminary convolutional neural network model is tested through the verification sets and the corresponding historical water resource data, the geographic attribute and the geographic attribute to obtain the trained convolutional neural network model.
According to the embodiment of the invention, the water resource scheduling data is subjected to data grouping, the preliminary model is established through the training set, and the accuracy of the preliminary model is verified through the verification set, so that the accuracy of the convolutional neural network model is ensured.
Fig. 2 is a water resource management system based on artificial intelligence according to an embodiment of the present invention, which includes: a first obtaining module 201, a second obtaining module 202, a training module 203, a judging module 204 and an output module 205, wherein:
the first acquisition module 201 is configured to acquire water resource scheduling data in a history record, and acquire corresponding historical water resource data and geographic attributes according to the water resource scheduling data, where the historical water resource data includes water consumption data, water quality data, and reservoir water storage levels of both scheduling parties, and the geographic attributes include rainfall and population intensity.
The second obtaining module 202 is configured to obtain a preset geographic attribute weight level table, and obtain, according to the geographic attribute, a corresponding weight of the geographic attribute in combination with the geographic attribute weight level table.
And the training module 203 is configured to input the historical water resource data, the geographic attributes, the corresponding weights of the geographic attributes, and the water resource scheduling data into a convolutional neural network model for training, so as to obtain a trained convolutional neural network model.
And the judging module 204 is configured to judge whether the current water resource data meets the water resource scheduling requirement.
An output module 205, configured to obtain a current geographic attribute corresponding to the current water resource data when the current water resource data meets a water resource scheduling requirement, obtain a weight corresponding to the current geographic attribute by combining the geographic attribute weight level table, input the current water resource data, the current geographic attribute, and the weight corresponding to the current geographic attribute to the trained convolutional neural network model, and output a corresponding water resource scheduling scheme.
In one embodiment, the system may further comprise:
and the third acquisition module is used for acquiring the current geographic attribute corresponding to the current water resource data, obtaining the weight corresponding to the current geographic attribute by combining the geographic attribute weight grade table, and acquiring the water resource data threshold corresponding to the current geographic attribute.
And the calculation module is used for comprehensively calculating the current water resource data and the weight corresponding to the current geographic attribute, comparing the calculation result with the water resource data threshold value, and judging whether the requirement of a dispatcher in water resource dispatching is met according to the comparison result.
In one embodiment, the system may further comprise:
and the model establishing module is used for acquiring corresponding historical water resource data according to the current water resource data when the current water resource data is judged to be a scheduling party in water resource scheduling according to the comparison result, and establishing a corresponding water resource prediction model according to the historical water resource data.
And the comparison module is used for predicting the future water demand according to the water resource prediction model, carrying out scheduling simulation on the current water resource data, and comparing the current water resource data after the scheduling simulation with the future water demand.
And the processing module is used for obtaining the current water resource data according to the comparison result and using the current water resource data as the scheduling water resource amount of the scheduling party.
In one embodiment, the system may further comprise:
the second output module is used for outputting a corresponding water resource scheduling scheme when the comparison result meets the requirement of a scheduling party in water resource scheduling, and the method comprises the following steps: and acquiring all scheduling party information in the water resource scheduling, and outputting the all scheduling party information to all scheduling parties and scheduled parties, wherein the all scheduling party information comprises scheduling water quantity data of the scheduling party and current position information of the scheduling party.
In one embodiment, the system may further comprise:
and the fourth acquisition module is used for acquiring the current geographic attribute corresponding to the current water resource data and acquiring the water resource data threshold corresponding to the current geographic attribute.
And the second comparison module is used for comparing the current water resource data with the water resource data threshold value and judging whether the requirements of the dispatched party in the water resource dispatching are met according to the comparison result.
In one embodiment, the system may further comprise:
the third output module is used for outputting a corresponding water resource scheduling scheme when the comparison result meets the requirement of a scheduled party in water resource scheduling, and the third output module comprises: and receiving all scheduling party information in the water resource scheduling, and outputting the all scheduling party information to a water resource management center, wherein the all scheduling party information comprises scheduling water quantity data of a scheduling party and current position information of the scheduling party.
For specific limitations of the artificial intelligence based water resource management system, reference may be made to the above limitations of the artificial intelligence based water resource management method, which are not described herein again. The modules in the artificial intelligence based water resource management system can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Fig. 3 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 3: a processor (processor)301, a memory (memory)302, a communication Interface (Communications Interface)303 and a communication bus 304, wherein the processor 301, the memory 302 and the communication Interface 303 complete communication with each other through the communication bus 304. The processor 301 may call logic instructions in the memory 302 to perform the following method: acquiring water resource scheduling data in a historical record, and acquiring corresponding historical water resource data and geographic attributes according to the water resource scheduling data, wherein the historical water resource data comprises water consumption data, water quality data and reservoir water storage levels of both scheduling parties, and the geographic attributes comprise rainfall and population intensity; acquiring a preset geographic attribute weight grade table, and obtaining the corresponding weight of the geographic attribute by combining the geographic attribute weight grade table according to the geographic attribute; inputting historical water resource data, geographic attributes, corresponding weights of the geographic attributes and water resource scheduling data into a convolutional neural network model for training to obtain a trained convolutional neural network model; judging whether the current water resource data meets the water resource scheduling requirement; the current water resource data meet the water resource scheduling requirement, the current geographic attribute corresponding to the current water resource data is obtained, the weight corresponding to the current geographic attribute is obtained by combining the geographic attribute weight grade table, the current water resource data, the current geographic attribute and the weight corresponding to the current geographic attribute are input to the trained convolutional neural network model, and the corresponding water resource scheduling scheme is output.
Furthermore, the logic instructions in the memory 302 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the transmission method provided in the foregoing embodiments when executed by a processor, and for example, the method includes: acquiring water resource scheduling data in a historical record, and acquiring corresponding historical water resource data and geographic attributes according to the water resource scheduling data, wherein the historical water resource data comprises water consumption data, water quality data and reservoir water storage levels of both scheduling parties, and the geographic attributes comprise rainfall and population intensity; acquiring a preset geographic attribute weight grade table, and obtaining the corresponding weight of the geographic attribute by combining the geographic attribute weight grade table according to the geographic attribute; inputting historical water resource data, geographic attributes, corresponding weights of the geographic attributes and water resource scheduling data into a convolutional neural network model for training to obtain a trained convolutional neural network model; judging whether the current water resource data meets the water resource scheduling requirement; the current water resource data meet the water resource scheduling requirement, the current geographic attribute corresponding to the current water resource data is obtained, the weight corresponding to the current geographic attribute is obtained by combining the geographic attribute weight grade table, the current water resource data, the current geographic attribute and the weight corresponding to the current geographic attribute are input to the trained convolutional neural network model, and the corresponding water resource scheduling scheme is output.
The above-described system embodiments are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed 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 modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A water resource management method based on artificial intelligence is characterized by comprising the following steps:
acquiring water resource scheduling data in a historical record, and acquiring corresponding historical water resource data and geographic attributes according to the water resource scheduling data, wherein the historical water resource data comprises water consumption data, water quality data and reservoir water storage levels of both scheduling parties, and the geographic attributes comprise rainfall and population intensity;
acquiring a preset geographic attribute weight grade table, and obtaining the corresponding weight of the geographic attribute by combining the geographic attribute weight grade table according to the geographic attribute;
inputting the historical water resource data, the geographic attributes, the corresponding weights of the geographic attributes and the water resource scheduling data into a convolutional neural network model for training to obtain a trained convolutional neural network model;
judging whether the current water resource data meets the water resource scheduling requirement;
when the current water resource data meets the water resource scheduling requirement, obtaining the current geographic attribute corresponding to the current water resource data, combining the geographic attribute weight grade table to obtain the weight corresponding to the current geographic attribute, inputting the current water resource data, the current geographic attribute and the weight corresponding to the current geographic attribute into the trained convolutional neural network model, and outputting a corresponding water resource scheduling scheme.
2. The artificial intelligence based water resource management method of claim 1, wherein said determining whether the current water resource data meets the water resource scheduling requirement comprises:
acquiring a current geographic attribute corresponding to current water resource data, acquiring a weight corresponding to the current geographic attribute by combining the geographic attribute weight grade table, and acquiring a water resource data threshold corresponding to the current geographic attribute;
and performing comprehensive calculation on the current water resource data and the weight corresponding to the current geographic attribute, comparing a calculation result with the water resource data threshold value, and judging whether the requirement of a dispatcher in water resource scheduling is met according to a comparison result.
3. The artificial intelligence based water resource management method of claim 2, further comprising:
when the current water resource data is judged to be a scheduling party in water resource scheduling according to the comparison result, acquiring corresponding historical water resource data according to the current water resource data, and establishing a corresponding water resource prediction model according to the historical water resource data;
predicting future water demand according to the water resource prediction model, performing scheduling simulation on the current water resource data, and comparing the current water resource data after the scheduling simulation with the future water demand;
and obtaining the current water resource data as the scheduling water resource amount of the scheduling party according to the comparison result.
4. The artificial intelligence based water resource management method of claim 2, further comprising:
when the comparison result meets the requirement of a dispatcher in water resource dispatching, outputting a corresponding water resource dispatching scheme, comprising:
and acquiring all scheduling party information in the water resource scheduling, and outputting the all scheduling party information to all scheduling parties and scheduled parties, wherein the all scheduling party information comprises scheduling water quantity data of the scheduling party and current position information of the scheduling party.
5. The artificial intelligence based water resource management method of claim 1, wherein said determining whether the current water resource data meets the water resource scheduling requirement comprises:
acquiring a current geographical attribute corresponding to current water resource data, and acquiring a water resource data threshold corresponding to the current geographical attribute;
and comparing the current water resource data with the water resource data threshold value, and judging whether the requirements of the dispatched party in the water resource dispatching are met according to the comparison result.
6. The artificial intelligence based water resource management method of claim 5, further comprising:
when the comparison result meets the requirement of a dispatched party in water resource dispatching, outputting a corresponding water resource dispatching scheme, comprising:
and receiving all scheduling party information in the water resource scheduling, and outputting the all scheduling party information to a water resource management center, wherein the all scheduling party information comprises scheduling water quantity data of a scheduling party and current position information of the scheduling party.
7. The artificial intelligence based water resource management method of claim 1, wherein the inputting historical water resource data, geographic attributes, corresponding weights of the geographic attributes and the water resource scheduling data into a convolutional neural network model for training to obtain a trained convolutional neural network model comprises:
dividing the water resource scheduling data into a training set and a verification set, and inputting the training set and corresponding historical water resource data, geographic attributes and corresponding weights of the geographic attributes into a convolutional neural network model for training to obtain a trained primary convolutional neural network model;
and inputting the verification set and corresponding weights of the historical water resource data, the geographic attributes and the geographic attributes into the trained primary convolutional neural network model for testing, and obtaining the trained convolutional neural network model after the test is finished.
8. A water resource management system based on artificial intelligence, the system comprising:
the first acquisition module is used for acquiring water resource scheduling data in a historical record and acquiring corresponding historical water resource data and geographic attributes according to the water resource scheduling data, wherein the historical water resource data comprises water consumption data, water quality data and reservoir water storage levels of both scheduling parties, and the geographic attributes comprise rainfall and population intensity;
the second acquisition module is used for acquiring a preset geographic attribute weight grade table and acquiring the corresponding weight of the geographic attribute by combining the geographic attribute weight grade table according to the geographic attribute;
the training module is used for inputting the historical water resource data, the geographic attributes, the corresponding weights of the geographic attributes and the water resource scheduling data into a convolutional neural network model for training to obtain a trained convolutional neural network model;
the judging module is used for judging whether the current water resource data meets the water resource scheduling requirement;
and the output module is used for obtaining the current geographic attribute corresponding to the current water resource data when the current water resource data meets the water resource scheduling requirement, obtaining the weight corresponding to the current geographic attribute by combining the geographic attribute weight grade table, inputting the current water resource data, the current geographic attribute and the weight corresponding to the current geographic attribute to the trained convolutional neural network model, and outputting a corresponding water resource scheduling scheme.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the artificial intelligence based water resource management method according to any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the artificial intelligence based water resource management method according to any one of claims 1 to 7.
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CN108537366A (en) * 2018-03-16 2018-09-14 浙江工业大学 Reservoir operation method based on optimal convolution two dimension
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