CN113112125B - Water resource management method and system based on artificial intelligence - Google Patents
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Abstract
The embodiment of the invention provides a water resource management method and device based on artificial intelligence, wherein the method comprises the following steps: acquiring water resource scheduling data in a history record, and acquiring corresponding historical water resource data and geographic attributes; according to the geographic attribute, combining the geographic attribute weight level table to obtain the corresponding weight of the geographic attribute; the historical water resource data, the geographic attributes, the corresponding weights of the geographic attributes and the water resource scheduling data are input into a convolutional neural network model for training, and a trained model is obtained; judging whether the current water resource data meets the water resource scheduling requirement or not; and when the current geographical attribute of the current water resource data is met, obtaining the weight corresponding to the current geographical attribute, inputting the weight to the trained model, and outputting a corresponding water resource scheduling scheme. By adopting the method, the management and the scheduling between the water resources of the water supply pipe network can be finished according to the deep learning of the artificial intelligence, the manpower resources are saved, and the water resource scheduling efficiency is improved.
Description
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 increasing urban of China, residents use more water through water pipes, water supply networks are gradually distributed throughout, distribution is more and more complex, and various water supply data can be generated when the water supply networks supply water.
In the prior art, because the water supply network is more and more complex, more places need to be supplied with water, water is excessively supplied in some places when water is supplied, and water is insufficiently supplied in some places, so that the water supply amount of each place needs to be adjusted, and water resources cannot be wasted.
According to the above situation, the management means for the related water supply scheduling of the water supply pipe network is very complex, and the related staff is required to obtain the corresponding result through a large amount of data (the data may include water data, regional factor data, etc.) sorting calculation, the calculation process is very complex, and the scheduling is very time-consuming, so that a management method for the water supply pipe network is needed to solve the above problems.
Disclosure of Invention
Aiming at the problems existing 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 two scheduling parties, and the geographic attributes comprise rainfall and population concentration;
acquiring a preset geographic attribute weight level table, and combining the geographic attribute weight level table according to the geographic attribute to obtain the corresponding weight of the geographic attribute;
inputting the historical water resource data, the geographic attribute, the corresponding weight of the geographic attribute 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 or not;
when the current water resource data meets the water resource scheduling requirement, acquiring the current geographic attribute corresponding to the current water resource data, combining the geographic attribute weight level 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, combining the geographic attribute weight level table to acquire a weight corresponding to the current geographic attribute, and acquiring a water resource data threshold corresponding to the current geographic attribute;
and comprehensively calculating the weight corresponding to the current water resource data and the current geographic attribute, comparing a calculation result with the water resource data threshold value, and judging whether the requirement of a dispatching party in water resource dispatching is met according to the 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 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, wherein the water resource dispatching scheme comprises the following steps:
and acquiring all the scheduling party information in the water resource scheduling, and outputting the all the scheduling party information to all the scheduling parties and the scheduled party, wherein the all the 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 geographic attribute corresponding to current water resource data, and acquiring a water resource data threshold corresponding to the current geographic attribute;
and comparing the current water resource data with the water resource data threshold value, and judging whether the requirement of a scheduled party in water resource scheduling is met according to a comparison result.
In one embodiment, the method further comprises:
when the comparison result meets the requirement of the scheduled party in the water resource scheduling, the corresponding water resource scheduling scheme is output, and the method comprises the following steps:
and receiving all the dispatching party information in the water resource dispatching, and outputting the all the dispatching party information to a water resource management center, wherein the all the dispatching party information comprises dispatching water quantity data of a dispatching party and current position information of the dispatching party.
In one embodiment, the method further comprises:
dividing the water resource scheduling data into a training set and a verification set, 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, and obtaining a trained preliminary convolutional neural network model;
and inputting the verification set, the historical water resource data, the geographic attributes and the corresponding weights of the geographic attributes into the trained preliminary convolutional neural network model for testing, and obtaining the trained convolutional neural network model after the testing is completed.
The embodiment of the invention provides a water resource management system based on artificial intelligence, which comprises the following components:
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 level of both scheduling parties, and the geographic attributes comprise rainfall and population concentration;
the second acquisition module is used for acquiring a preset geographic attribute weight level table, and according to the geographic attribute, combining the geographic attribute weight level table to obtain the corresponding weight of the geographic attribute;
the training module is used for inputting the historical water resource data, the geographic attribute, the corresponding weight of the geographic attribute and the water resource scheduling data into the 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 or not;
and the output module is used for acquiring the current geographic attribute corresponding to the current water resource data when the current water resource data meets the water resource scheduling requirement, combining the geographic attribute weight level table to acquire 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.
The embodiment of the invention provides electronic equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the steps of the water resource management method based on artificial intelligence when executing the program.
Embodiments of the present invention provide a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the artificial intelligence based water resource management method described above.
According to the water resource management method and system based on artificial intelligence, water resource scheduling data in a history record are obtained, corresponding historical water resource data and geographical 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 level of two scheduling parties, and the geographical attributes comprise rainfall and population concentration; acquiring a preset geographic attribute weight level table, and acquiring the corresponding weight of the geographic attribute according to the geographic attribute by combining the geographic attribute weight level table; the historical water resource data, the geographic attributes and the corresponding weights of the geographic attributes are input into a convolutional neural network model for training, and a trained convolutional neural network model is obtained; judging whether the current water resource data meets the water resource scheduling requirement or not; the current water resource data meets 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 with the geographic attribute weight level table, the current water resource data, the current geographic attribute and the weight corresponding to the current geographic attribute are input into the trained convolutional neural network model, and the corresponding water resource scheduling scheme is output. Therefore, the management and the scheduling between the water resources of the water supply pipe network can be finished according to the deep learning of the artificial intelligence, the manpower resources are saved, and meanwhile, the water resource scheduling efficiency is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an artificial intelligence based water resource management method in an embodiment of the invention;
FIG. 2 is a block diagram of an artificial intelligence based water resource management system in accordance with an embodiment of the invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a schematic flow chart of an artificial intelligence based metering management method according to an embodiment of the present invention, and as shown in fig. 1, the embodiment of the present invention provides an artificial intelligence based metering management method, including:
step S101, acquiring water resource scheduling data in a history 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 concentration.
Specifically, the water resource scheduling data is basic scheduling data in a water resource database, the water resource scheduling data in a history scheduling record is obtained from the water resource database, and the history water resource data and the geographical attribute in a corresponding scheduling record are obtained according to the water resource scheduling data, wherein the history water resource data refers to water consumption data, water quality data (generally, the lower the water quality data is, the higher the water quantity required in scheduling) of two scheduling parties (a scheduling party and a scheduled party), a reservoir water level and the geographical attribute refers to the attribute of influencing water such as rainfall, population concentration and the like of the geographical positions of the two scheduling parties in the scheduling record.
Step S102, a preset geographic attribute weight level table is obtained, and according to the geographic attribute, the corresponding weight of the geographic attribute is obtained by combining the geographic attribute weight level table.
Specifically, the preset geographic attribute weight level table is a corresponding relation table of geographic attributes and weights, in general, the higher the demand of the geographic attributes for water resources is, namely, the lower the rainfall is, the higher the population density is, the higher the corresponding geographic attribute weight is, otherwise, the higher the rainfall is, the lower the population density is, and the lower the corresponding geographic attribute weight is.
And step S103, inputting the historical water resource data, the geographic attribute, the corresponding weight of the geographic attribute and the water resource scheduling data into a convolutional neural network model for training, and obtaining 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 inputs and are input into an input layer of a convolutional neural network model for model training, and the convolutional neural network model carries out deep learning through a convolutional layer, a pooling layer and a full connection layer to obtain the trained convolutional neural network model.
Step S104, judging whether the current water resource data meets the water resource scheduling requirement.
Specifically, whether currently unknown water resource data needing to be scheduled is obtained, whether the current water resource data meets the water resource scheduling requirement is judged, wherein the detection of the water resource scheduling requirement is bidirectional, whether the current water resource data meets the requirement of a scheduler in water resource scheduling or not is required to be detected, and whether the current water resource data meets the requirement of the scheduled side in water resource scheduling is also required to be detected.
Step S105, when the current water resource data meets the water resource scheduling requirement, acquiring a current geographic attribute corresponding to the current water resource data, combining the geographic attribute weight level table to obtain a 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.
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 geographic attribute weight level table is combined to obtain the weight corresponding to the current geographic attribute, the current water resource data, the current geographic attribute and the weight corresponding to the current geographic attribute are input into the trained convolutional neural network model, and the corresponding water resource scheduling scheme is output through the trained convolutional neural network model.
According to the water resource management method based on artificial intelligence, water resource scheduling data in a historical record are obtained, corresponding historical water resource data and geographical 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 two scheduling parties, and the geographical attributes comprise rainfall and population concentration; acquiring a preset geographic attribute weight level table, and acquiring the corresponding weight of the geographic attribute according to the geographic attribute by combining the geographic attribute weight level table; the historical water resource data, the geographic attributes and the corresponding weights of the geographic attributes are input into a convolutional neural network model for training, and a trained convolutional neural network model is obtained; judging whether the current water resource data meets the water resource scheduling requirement or not; the current water resource data meets 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 with the geographic attribute weight level table, the current water resource data, the current geographic attribute and the weight corresponding to the current geographic attribute are input into the trained convolutional neural network model, and the corresponding water resource scheduling scheme is output. Therefore, the management and the scheduling between the water resources of the water supply pipe network can be finished according to the deep learning of the artificial intelligence, the manpower resources are saved, and meanwhile, the water resource scheduling efficiency is improved.
On the basis of the above embodiment, the water resource management method based on artificial intelligence further includes:
acquiring a current geographic attribute corresponding to current water resource data, combining the geographic attribute weight level table to acquire a weight corresponding to the current geographic attribute, and acquiring a water resource data threshold corresponding to the current geographic attribute;
and comprehensively calculating the weight corresponding to the current water resource data and the current geographic attribute, comparing a calculation result with the water resource data threshold value, and judging whether the requirement of a dispatching party in water resource dispatching is 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, the weight corresponding to the current geographic attribute is obtained by combining with a geographic attribute weight level table, then the water resource data threshold corresponding to the current geographic attribute is obtained, wherein the water resource data threshold refers to the fact that under the current geographic position (such as a cell A), according to the water resource data value required by the current geographic position (the cell A) in the historical data on average, the current water resource data and the weight corresponding to the current geographic attribute are comprehensively calculated, namely, the current water resource data of the cell A and the weight corresponding to the water resource demand weight are comprehensively calculated, so that the actual standard of the water resource combination weight of the cell A is obtained, the actual standard is compared with the water resource data threshold, and whether the cell A meets the demand of a dispatching party in the water resource dispatching is judged. Because it is determined whether the cell a can be used as a scheduler, it is not only necessary to see whether the current water resource data of the cell a is greater than the water resource data threshold, but also to ensure that after the cell a performs water resource scheduling, there is enough water resource stock for coping with various emergencies, so that comprehensive calculation needs to be performed on the current water resource data of the cell a and the corresponding weight (water resource demand weight), for example, the population density of the cell a is 2, the rainfall level is 5, the corresponding weight may be 10, when the comprehensive calculation is performed, the minimum weight value may be unitized, then the weight 10 may be unitized equally, so as to obtain the corresponding calculation value, and then, according to the unitized calculation value and the current water resource data, it is determined whether the cell a can be used as a scheduler.
According to the embodiment of the invention, the weight corresponding to the current water resource data and the current geographic attribute is comprehensively calculated, so that whether the current water resource data meets the demands of a dispatcher is judged, the current water resource data can be clearly judged to be capable of supplying water resources, and the corresponding dispatching scheme is convenient to output subsequently.
On the basis of the above embodiment, the water resource management method based on artificial intelligence 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 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 demand 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 dispatching party according to the comparison result, 2, judging how much water quantity dispatching is needed by the current water resource data, when the current water resource data is judged to be the dispatching party in the water resource dispatching according to the comparison result, calculating how much water quantity dispatching is needed, specifically, building a corresponding water resource prediction model through historical water resource data corresponding to the current water resource data, predicting future water demand (referring to the actual standard of the current water resource data and the corresponding weight (water resource demand weight), combining the obtained water resource with the weight), carrying out dispatching simulation through the water resource prediction model, comparing the current water resource data after dispatching simulation with future water demand, and obtaining the current water resource data as the dispatching water resource quantity of the dispatching party according to the comparison result.
According to the embodiment of the invention, after the current water resource data can be used as the scheduling party, the scheduling water resource amount of the water resource data is defined, so that the high-efficiency scheduling of the water resource is ensured, and the accuracy of the scheduling of the water resource is improved.
On the basis of the above embodiment, the water resource management method based on artificial intelligence further includes:
when the comparison result meets the requirement of a dispatcher in water resource dispatching, outputting a corresponding water resource dispatching scheme, wherein the water resource dispatching scheme comprises the following steps:
and acquiring all the scheduling party information in the water resource scheduling, and outputting the all the scheduling party information to all the scheduling parties and the scheduled party, wherein the all the 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 demands of the schedulers in the water resource scheduling, a corresponding water resource scheduling scheme is output, wherein the water resource scheduling scheme possibly comprises a plurality of schedulers and schedulers, and is used as a part of the schedulers, information of all schedulers is acquired, and the information of all schedulers is output to all schedulers and schedulers so that relevant departments of all schedulers and schedulers can know the actual conditions of water quantity scheduling, thereby facilitating subsequent charging and archival recording.
After the current water resource data can be used as the dispatcher, the embodiment of the invention acquires all dispatcher information, and outputs all dispatcher information to all dispatchers and dispatched parties, thereby facilitating subsequent charging and file recording.
On the basis of the above embodiment, the water resource management method based on artificial intelligence further includes:
acquiring a current geographic attribute corresponding to current water resource data, and acquiring a water resource data threshold corresponding to the current geographic attribute;
and comparing the current water resource data with the water resource data threshold value, and judging whether the requirement of a scheduled party in water resource scheduling is met according to a 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 whether the current water resource data and the water resource data threshold of the cell B are compared according to the water resource data value required by the current geographic position (the cell B) in the historical data in the current geographic position (the cell B), and whether the cell B meets the requirement of a scheduled party in the water resource scheduling is judged according to the comparison result.
In addition, when the cell B meets the demands of the scheduled party in the water resource scheduling, a corresponding water resource scheduling scheme is received, wherein the water resource scheduling scheme possibly comprises a plurality of scheduled parties and scheduled parties, the scheduled parties send all the information of the scheduled parties to the scheduled parties, the scheduled parties receive all the information of the scheduled parties and output the information to a water resource management center, and the scheduled parties send all the information of the scheduled parties to the water resource management center, so that the follow-up charging and file recording are convenient.
After the current water resource data can be used as the scheduled party, the embodiment of the invention receives all the information of the scheduled party, and outputs all the information of the scheduled party to the water resource management center, thereby facilitating the subsequent charging and file recording.
On the basis of the above embodiment, the water resource management method based on artificial intelligence further includes:
dividing the water resource scheduling data into a training set and a verification set, 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, and obtaining a trained preliminary convolutional neural network model;
and inputting the verification set, the historical water resource data, the geographic attributes and the corresponding weights of the geographic attributes into the trained preliminary convolutional neural network model for testing, and obtaining the trained convolutional neural network model after the testing is completed.
In the embodiment of the invention, when the water resource scheduling data and the corresponding historical water resource data, geographic attribute and geographic attribute are trained through the convolutional neural network model, the data of the water resource scheduling data are grouped, the data can be specifically divided into 80% of training set and 20% of verification set, the training set and the corresponding historical water resource data, geographic attribute and geographic attribute are used for carrying out preliminary training to obtain a preliminary convolutional neural network model, and then the verification set and the corresponding historical water resource data, geographic attribute and geographic attribute are used for testing the preliminary convolutional neural network model to obtain the trained convolutional neural network model.
According to the embodiment of the invention, the water resource scheduling data are subjected to data grouping, the primary model is established through the training set, and the accuracy of the primary model is verified through the verification set, so that the accuracy of the convolutional neural network model is ensured.
FIG. 2 is a schematic diagram of an artificial intelligence-based water resource management system according to an embodiment of the present invention, including: a first acquisition module 201, a second acquisition module 202, a training module 203, a judgment module 204 and an output module 205, wherein:
the first obtaining module 201 is configured to obtain water resource scheduling data in a history record, and obtain corresponding historical water resource data and geographical attributes according to the water resource scheduling data, where the historical water resource data includes water consumption data, water quality data, and reservoir water level of both scheduling parties, and the geographical attributes include rainfall and population concentration.
The second obtaining module 202 is configured to obtain a preset geographic attribute weight level table, and combine the geographic attribute weight level table according to the geographic attribute to obtain a corresponding weight of the geographic attribute.
The training module 203 is configured to input the historical water resource data, the geographic attribute, the corresponding weight of the geographic attribute, and the water resource scheduling data to a convolutional neural network model for training, and obtain a trained convolutional neural network model.
The judging module 204 is configured to judge whether the current water resource data meets the water resource scheduling requirement.
And the output module 205 is configured to obtain a current geographical attribute corresponding to the current water resource data when the current water resource data meets the water resource scheduling requirement, and combine the geographical attribute weight level table to obtain a weight corresponding to the current geographical attribute, input the current water resource data, the current geographical attribute, and the weight corresponding to the current geographical 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, combining the geographic attribute weight level table to obtain the weight corresponding to the current geographic attribute, and acquiring the water resource data threshold corresponding to the current geographic attribute.
And the calculation module is used for comprehensively calculating the weight corresponding to the current geographical attribute and the current water resource data, 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 building module is used for obtaining corresponding historical water resource data according to the current water resource data and building a corresponding water resource prediction model according to the historical 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 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 scheduling simulation with the future water demand.
And the processing module is used for obtaining the current water resource data as the scheduling water resource quantity of the scheduling party according to the comparison result.
In one embodiment, the system may further comprise:
and the second output module is used for outputting a corresponding water resource scheduling scheme when the comparison result meets the requirement of a scheduler in water resource scheduling, and comprises the following steps: and acquiring all the scheduling party information in the water resource scheduling, and outputting the all the scheduling party information to all the scheduling parties and the scheduled party, wherein the all the 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 requirement of a scheduled party in water resource scheduling is met according to the comparison result.
In one embodiment, the system may further comprise:
and a third output module, configured to output a corresponding water resource scheduling scheme when the comparison result meets a requirement of a scheduled party in water resource scheduling, where the water resource scheduling scheme includes: and receiving all the dispatching party information in the water resource dispatching, and outputting the all the dispatching party information to a water resource management center, wherein the all the dispatching party information comprises dispatching water quantity data of a dispatching party and current position information of the dispatching party.
Specific limitations regarding the artificial intelligence based water resource management system may be found in the above limitations regarding the artificial intelligence based water resource management method, and will not be described in detail herein. The various modules in the artificial intelligence-based water resource management system described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
Fig. 3 illustrates a physical schematic diagram of an electronic device, as shown in fig. 3, where the electronic device may include: 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 perform 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 history 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 concentration; acquiring a preset geographic attribute weight level table, and acquiring the corresponding weight of the geographic attribute according to the geographic attribute by combining the geographic attribute weight level table; the historical water resource data, the geographic attributes and the corresponding weights of the geographic attributes are input into a convolutional neural network model for training, and a trained convolutional neural network model is obtained; judging whether the current water resource data meets the water resource scheduling requirement or not; the current water resource data meets 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 with the geographic attribute weight level table, the current water resource data, the current geographic attribute and the weight corresponding to the current geographic attribute are input into the trained convolutional neural network model, and the corresponding water resource scheduling scheme is output.
Further, the logic instructions in memory 302 described above may be implemented in the form of software functional units and stored in a computer readable storage medium when sold or used as a stand alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, embodiments of the present invention further provide a non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor is implemented to perform the transmission method provided in the above embodiments, for example, including: acquiring water resource scheduling data in a history 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 concentration; acquiring a preset geographic attribute weight level table, and acquiring the corresponding weight of the geographic attribute according to the geographic attribute by combining the geographic attribute weight level table; the historical water resource data, the geographic attributes and the corresponding weights of the geographic attributes are input into a convolutional neural network model for training, and a trained convolutional neural network model is obtained; judging whether the current water resource data meets the water resource scheduling requirement or not; the current water resource data meets 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 with the geographic attribute weight level table, the current water resource data, the current geographic attribute and the weight corresponding to the current geographic attribute are input into the trained convolutional neural network model, and the corresponding water resource scheduling scheme is output.
The system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the 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 scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. An artificial intelligence-based water resource management method, comprising the steps of:
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 two scheduling parties, and the geographic attributes comprise rainfall and population concentration;
acquiring a preset geographic attribute weight level table, and combining the geographic attribute weight level table according to the geographic attribute to obtain the corresponding weight of the geographic attribute, wherein the lower the rainfall is, the higher the corresponding weight of the geographic attribute is when the population density is higher, and the lower the corresponding weight of the geographic attribute is when the rainfall is higher and the population density is lower;
inputting the historical water resource data, the geographic attribute, the corresponding weight of the geographic attribute and the water resource scheduling data into a convolutional neural network model for training to obtain a trained convolutional neural network model;
judging whether current water resource data meets water resource scheduling requirements or not, wherein the water resource scheduling requirements comprise requirements of a scheduling party in water resource scheduling and requirements of a scheduled party in water resource scheduling;
when the current water resource data meets the water resource scheduling requirement, acquiring the current geographic attribute corresponding to the current water resource data, combining the geographic attribute weight level 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 the 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, combining the geographic attribute weight level table to acquire a weight corresponding to the current geographic attribute, and acquiring a water resource data threshold corresponding to the current geographic attribute;
and comprehensively calculating the weight corresponding to the current water resource data and the current geographic attribute, comparing a calculation result with the water resource data threshold value, and judging whether the requirement of a dispatching party in water resource dispatching is met according to the 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 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, wherein the water resource dispatching scheme comprises the following steps:
and acquiring all the scheduling party information in the water resource scheduling, and outputting the all the scheduling party information to all the scheduling parties and the scheduled party, wherein the all the 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 the 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, and acquiring a water resource data threshold corresponding to the current geographic attribute;
and comparing the current water resource data with the water resource data threshold value, and judging whether the requirement of a scheduled party in water resource scheduling is met according to a comparison result.
6. The artificial intelligence based water resource management method of claim 5, further comprising:
when the comparison result meets the requirement of the scheduled party in the water resource scheduling, the corresponding water resource scheduling scheme is output, and the method comprises the following steps:
and receiving all the dispatching party information in the water resource dispatching, and outputting the all the dispatching party information to a water resource management center, wherein the all the dispatching party information comprises dispatching water quantity data of a dispatching party and current position information of the dispatching party.
7. The artificial intelligence-based water resource management method according to claim 1, wherein the step of inputting the historical water resource data, the geographical attribute, the corresponding weight of the geographical attribute, and the water resource scheduling data into the 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, 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, and obtaining a trained preliminary convolutional neural network model;
and inputting the verification set, the historical water resource data, the geographic attributes and the corresponding weights of the geographic attributes into the trained preliminary convolutional neural network model for testing, and obtaining the trained convolutional neural network model after the testing is completed.
8. An artificial intelligence based water resource management system, 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 level of both scheduling parties, and the geographic attributes comprise rainfall and population concentration;
the second acquisition module is used for acquiring a preset geographic attribute weight level table, combining the geographic attribute weight level table according to the geographic attribute to obtain the corresponding weight of the geographic attribute, wherein the lower the rainfall is, the higher the population density is, the higher the corresponding weight of the geographic attribute is, and the higher the rainfall is, the lower the population density is, the lower the corresponding weight of the geographic attribute is;
the training module is used for inputting the historical water resource data, the geographic attribute, the corresponding weight of the geographic attribute and the water resource scheduling data into the 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 or not, wherein the water resource scheduling requirement comprises the requirement of a scheduling party in water resource scheduling and the requirement of a scheduled party in water resource scheduling;
and the output module is used for acquiring the current geographic attribute corresponding to the current water resource data when the current water resource data meets the water resource scheduling requirement, combining the geographic attribute weight level table to acquire 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.
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 performs the steps of the artificial intelligence based water resource management method as claimed in any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the artificial intelligence based water resource management method of any one of claims 1 to 7.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108537366A (en) * | 2018-03-16 | 2018-09-14 | 浙江工业大学 | Reservoir operation method based on optimal convolution two dimension |
| CN109816142A (en) * | 2018-12-18 | 2019-05-28 | 深圳市东深电子股份有限公司 | A kind of water resource precision dispensing system and distribution method |
| CN110991687A (en) * | 2019-09-26 | 2020-04-10 | 深圳市东深电子股份有限公司 | Water resource scheduling optimization method based on empirical model |
-
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- 2021-03-22 CN CN202110300897.XA patent/CN113112125B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108537366A (en) * | 2018-03-16 | 2018-09-14 | 浙江工业大学 | Reservoir operation method based on optimal convolution two dimension |
| CN109816142A (en) * | 2018-12-18 | 2019-05-28 | 深圳市东深电子股份有限公司 | A kind of water resource precision dispensing system and distribution method |
| CN110991687A (en) * | 2019-09-26 | 2020-04-10 | 深圳市东深电子股份有限公司 | Water resource scheduling optimization method based on empirical model |
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