CN113112127B - Metering management method and system based on artificial intelligence - Google Patents
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Abstract
The embodiment of the invention provides a metering management method and device based on artificial intelligence, wherein the method comprises the following steps: the method comprises the steps of connecting an external data source, and obtaining monitoring data through the external data source, wherein the monitoring data comprises user table data, large table data and cell data; acquiring a corresponding historical data set according to the type of the monitoring data, and acquiring a geographic position and an association scheme corresponding to the historical data set; inputting the historical data set, the corresponding geographic position and the association scheme into a convolutional neural network model for training to obtain a trained model, obtaining the geographic position corresponding to the monitoring data, inputting the monitoring data and the geographic position into the trained model, and obtaining the association scheme corresponding to the monitoring data through the trained model; and outputting the association scheme of the monitoring data. By adopting the method, the data management of the water supply network can be completed according to the deep learning of the artificial intelligence, the manpower resources are saved, the management efficiency is improved, and a corresponding association solution is provided for the data of the water supply network.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a metering 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, a water supply network is gradually distributed throughout the places, the distribution is more and more complex, and various water supply data, such as household table data in household units, large table data in span or building units, water supply data of the whole community and the like, are also generated when the water supply network supplies water.
In the prior art, as the water supply network is more and more complex, operations during relevant management on various data in the water supply network are more and more complex, for example, when water quantity scheduling is performed, water quantity and water supply network section positions are required to be inspected, then relevant schemes are arranged through relevant staff, then water quantity scheduling among areas is performed, for example, when leakage analysis is performed, abnormal data are arranged and analyzed through relevant staff, after abnormal information uploaded by a user is acquired, positions are determined, relevant maintenance staff are dispatched to perform relevant maintenance operations, and the like.
In view of the above, the management means for the related data of the water supply network are complex, and there is a need for a management method for the related data of the water supply network, which can solve the above problems.
Disclosure of Invention
Aiming at the problems existing in the prior art, the embodiment of the invention provides a metering management method and system based on artificial intelligence.
The embodiment of the invention provides an artificial intelligence based metering management method, which comprises the following steps:
the method comprises the steps of connecting an external data source, and obtaining monitoring data through the external data source, wherein the monitoring data comprises household table data, large table data and cell data;
acquiring a corresponding historical data set according to the type of the monitoring data, and acquiring a geographic position and an association scheme corresponding to the historical data set;
inputting the historical data set, the corresponding geographic position and the corresponding association scheme into a convolutional neural network model for training, and obtaining a trained convolutional neural network model;
acquiring a geographic position corresponding to the monitoring data, inputting the monitoring data and the corresponding geographic position into a trained convolutional neural network model, and obtaining a correlation scheme corresponding to the monitoring data through the trained convolutional neural network model;
and outputting the association scheme corresponding to the monitoring data.
In one embodiment, the method further comprises:
dividing the historical data set into historical user table data, historical large table data and historical cell data according to different data types, and adding classification identifiers for the data of different types in the historical data set;
and constructing a three-dimensional data set according to the historical data set, the corresponding geographic position and the corresponding association scheme according to the classification mark, inputting the three-dimensional data set into the convolutional neural network model, and performing cross training on the three-dimensional data set through the convolutional neural network model.
In one embodiment, the method further comprises:
detecting a scheme type of an association scheme corresponding to the monitoring data, and acquiring a dispatcher and a dispatched party of the association scheme corresponding to the monitoring data when the scheme type is water dispatching type;
acquiring the scheduling water quantity values corresponding to the scheduling party and the scheduled party, and detecting whether the scheduling water quantity values meet the requirements of the association schemes corresponding to the monitoring data;
and when the dispatching water quantity value does not meet the requirements of the association scheme corresponding to the monitoring data, acquiring the geographic position and the dispatching water quantity value corresponding to the monitoring data, and determining a standby dispatching scheme according to the geographic position and the dispatching water quantity value corresponding to the monitoring data.
In one embodiment, the method further comprises:
detecting a scheme type of an association scheme corresponding to the monitoring data, and acquiring a leakage reason and a leakage repairing scheme in the association scheme corresponding to the monitoring data when the scheme type is leakage analysis type;
and acquiring corresponding staff terminal information according to the leakage reasons, and transmitting the leakage reasons and the leakage repairing scheme to the corresponding terminals according to the staff terminal information.
In one embodiment, the method further comprises:
the historical data components are divided into a training set and a verification set, the training set, the corresponding geographic position and the corresponding association scheme are input into a convolutional neural network model for training, and a trained preliminary convolutional neural network model is obtained;
and inputting the verification set, the corresponding geographic position and the association scheme into the trained preliminary convolutional neural network model for testing, and obtaining the trained convolutional neural network model after the testing is completed.
In one embodiment, the method further comprises:
and taking the monitoring data, the corresponding geographic position and the association scheme as verification data, and carrying out iterative updating on the trained convolutional neural network model.
In one embodiment, the method further comprises:
acquiring a binding terminal corresponding to the monitoring data, and acquiring a communication terminal of an associated department according to the geographic position corresponding to the monitoring data;
and sending the association scheme corresponding to the monitoring data to the binding terminal and the communication terminal of the association department.
The embodiment of the invention provides an artificial intelligence-based metering management system, which comprises the following components:
the connection module is used for connecting an external data source and acquiring monitoring data through the external data source, wherein the monitoring data comprises household table data, large table data and cell data;
the first acquisition module is used for acquiring a corresponding historical data set according to the type of the monitoring data and acquiring a geographic position and an association scheme corresponding to the historical data set;
the training module is used for inputting the historical data set, the corresponding geographic position and the corresponding association scheme into the convolutional neural network model for training, and obtaining a trained convolutional neural network model;
the second acquisition module is used for acquiring the geographic position corresponding to the monitoring data, inputting the monitoring data and the corresponding geographic position into the trained convolutional neural network model, and obtaining a correlation scheme corresponding to the monitoring data through the trained convolutional neural network model;
and the output module is used for outputting the association scheme corresponding to the monitoring data.
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 metering 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 metering management method described above.
According to the metering management method and system based on artificial intelligence, which are provided by the embodiment of the invention, the metering management method and system based on artificial intelligence are connected with an external data source, and monitoring data are obtained through the external data source, wherein the monitoring data comprise household table data, large table data and cell data; acquiring a corresponding historical data set according to the type of the monitoring data, and acquiring a geographic position and an association scheme corresponding to the historical data set; inputting the historical data set, the corresponding geographic position and the association scheme into a convolutional neural network model for training to obtain a trained convolutional neural network model, obtaining the geographic position corresponding to the monitoring data, inputting the monitoring data and the corresponding geographic position into the trained convolutional neural network model, and obtaining the association scheme corresponding to the monitoring data through the trained convolutional neural network model; and outputting the association scheme corresponding to the monitoring data. Therefore, the management of the data of the water supply network can be completed according to the deep learning of the artificial intelligence, the management efficiency is improved while the human resources are saved, and corresponding association solutions can be provided for the data of the water supply network.
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 metering management method in an embodiment of the invention;
FIG. 2 is a schematic diagram of an artificial intelligence based metering management system interface in an embodiment of the present invention;
FIG. 3 is a block diagram of an artificial intelligence based metering management system in accordance with an embodiment of the present invention;
fig. 4 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, connecting an external data source, and acquiring monitoring data through the external data source, wherein the monitoring data comprises user table data, large table data and cell data.
Specifically, the external data source may include various remote water meters (personal use meter, floor use meter, district use meter, etc.) or centralized management device of water meter data, connect the external data source, obtain corresponding monitoring data through the external data source, the monitoring data includes household table data, big table data, district data, wherein, the obtained monitoring data may be as shown in fig. 2, data display is performed, display data may include current data, yesterday data, newly added data, change trend among the data, etc., and the related staff is convenient to view the data.
Step S102, a corresponding historical data set is obtained according to the type of the monitoring data, and a geographic position and an association scheme corresponding to the historical data set are obtained.
Specifically, a corresponding historical data set is obtained according to the type of the monitoring data, for example, the type of the monitoring data is the water consumption data (household table data) of the cell A on the day (day 7), the historical daily water consumption data of the cell A is obtained, the geographic position corresponding to the cell A and the association scheme corresponding to the cell A are obtained, wherein the association scheme corresponding to the historical data set refers to the association scheme corresponding to the cell when the historical data is abnormal, for example, the water consumption data of the cell A on days 1, 2 and 3 is in a normal range, the corresponding association scheme is to perform normal charging on the water consumption of the cell A, the water consumption on days 4 and 5 exceeds 1340%, the corresponding association scheme can be to perform leak repairing on the cell main network after the related staff checks, the water consumption on day 6 exceeds 56%, the corresponding association scheme can be to perform leak repairing on the B of the cell after the related staff checks, in particular content of the association scheme can also be different geographic positions corresponding to the cell A26%, and the water consumption of the cell A is still normally charged for the corresponding to the cell A on the different days.
And step S103, inputting the historical data set, the corresponding geographic position and the corresponding association scheme into a convolutional neural network model for training, and obtaining the trained convolutional neural network model.
Specifically, the historical data set, the corresponding geographical position (including factors such as terrain, altitude, longitude and latitude, climate and the like which may influence the water consumption of a user) and the association scheme are taken 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 the convolutional layer, the pooling layer and the full connection layer, so that the trained convolutional neural network model is obtained.
Step S104, obtaining the geographic position corresponding to the monitoring data, inputting the monitoring data and the corresponding geographic position into a trained convolutional neural network model, and obtaining a correlation scheme corresponding to the monitoring data through the trained convolutional neural network model.
Specifically, the geographic position (including factors such as topography, altitude, longitude and latitude, climate and the like which may influence the water consumption of a user) corresponding to the monitoring data is obtained, the monitoring data and the corresponding geographic position are input into a trained convolutional neural network model, and the convolutional neural network model obtains the corresponding association scheme of the output by taking the monitoring data and the geographic position corresponding to the monitoring data as input.
In addition, the association scheme can be of various types according to different requirements of the staff, for example, the staff needs to acquire corresponding business charging through the monitoring data and the geographic position corresponding to the monitoring data, and then carries out corresponding water consumption charging according to the monitoring data and the geographic position corresponding to the monitoring data; when the staff needs to carry out water production scheduling, determining a water shortage area and a water abundance area through monitoring data and geographic positions corresponding to the monitoring data, and carrying out water scheduling; when staff needs to analyze the leakage of the pipe network through the monitoring data, the leakage condition and the position of the pipe network can be determined through the monitoring data and the abnormal data in the geographic position corresponding to the monitoring data.
Step S105, outputting the association scheme corresponding to the monitoring data.
Specifically, after determining the association scheme corresponding to the monitoring data, outputting the monitoring data for reference by related personnel, wherein a specific output process may be to obtain a binding terminal corresponding to the monitoring data, obtain a communication terminal of an association department according to a geographic position corresponding to the monitoring data, send the association scheme corresponding to the monitoring data to the binding terminal and the communication terminal of the association department, for example, when using water, can send fee information to the binding water user terminal and the communication terminal of the related department for collecting water fee; when the water quantity scheduling is carried out, the information of the water quantity scheduling can be sent to the terminals of both scheduling parties and the communication terminals of the related departments for carrying out the water quantity scheduling, so that related personnel can know related schemes about water supply treatment of a pipe network in the first time, and the user experience and the treatment efficiency are improved.
The metering management method based on the artificial intelligence is connected with an external data source, and monitoring data is obtained through the external data source, wherein the monitoring data comprises household table data, large table data and cell data; acquiring a corresponding historical data set according to the type of the monitoring data, and acquiring a geographic position and an association scheme corresponding to the historical data set; inputting the historical data set, the corresponding geographic position and the association scheme into a convolutional neural network model for training to obtain a trained convolutional neural network model, obtaining the geographic position corresponding to the monitoring data, inputting the monitoring data and the corresponding geographic position into the trained convolutional neural network model, and obtaining the association scheme corresponding to the monitoring data through the trained convolutional neural network model; and outputting the association scheme corresponding to the monitoring data. Therefore, the management of the data of the water supply network can be completed according to the deep learning of the artificial intelligence, the management efficiency is improved while the human resources are saved, and corresponding association solutions can be provided for the data of the water supply network.
Based on the above embodiment, the metering management method based on artificial intelligence further includes:
dividing the historical data set into historical user table data, historical large table data and historical cell data according to different data types, and adding classification identifiers for the data of different types in the historical data set;
and constructing a three-dimensional data set according to the historical data set, the corresponding geographic position and the corresponding association scheme according to the classification mark, inputting the three-dimensional data set into the convolutional neural network model, and performing cross training on the three-dimensional data set through the convolutional neural network model.
In the embodiment of the invention, the type of the historical data group is divided, the historical data group is divided into historical user table data, historical large table data and historical cell data, corresponding classification identifiers are added for different types of data, then a three-dimensional data group is constructed according to the classification identifiers, the three-dimensional data group respectively comprises a, the historical user table data, corresponding geographic positions and association schemes, b, the historical large table data, corresponding geographic positions and association schemes, c, the historical cell data, the corresponding geographic positions and association schemes, the three-dimensional data is input into an input layer of a convolutional neural network model, and cross training is carried out on the three-dimensional data group through the convolutional neural network model, so that the cross relation among the historical user table data, the historical large table data, the historical cell data, the geographic positions and the association schemes is obtained.
According to the embodiment of the invention, the three-dimensional data set is input into the convolutional neural network model for cross training, so that the trained convolutional neural network model can more obviously characterize the difference of corresponding association data (geographic position and association scheme) when the data types in the historical data set are different, and the subsequent association scheme result is more accurate.
Based on the above embodiment, the metering management method based on artificial intelligence further includes:
detecting a scheme type of an association scheme corresponding to the monitoring data, and acquiring a dispatcher and a dispatched party of the association scheme corresponding to the monitoring data when the scheme type is water dispatching type;
acquiring the scheduling water quantity values corresponding to the scheduling party and the scheduled party, and detecting whether the scheduling water quantity values meet the requirements of the association schemes corresponding to the monitoring data;
and when the dispatching water quantity value does not meet the requirements of the association scheme corresponding to the monitoring data, acquiring the geographic position and the dispatching water quantity value corresponding to the monitoring data, and determining a standby dispatching scheme according to the geographic position and the dispatching water quantity value corresponding to the monitoring data.
In the embodiment of the invention, when the scheme type of the associated scheme is water scheduling, scheduling water resources through monitoring data, acquiring a scheduling party and a scheduled party of the associated scheme, acquiring scheduling water quantity values corresponding to the scheduling party and the scheduled party, for example, when water quantity scheduling is carried out between an A cell and a C cell, detecting whether the scheduling water quantity values among AC cells meet the requirements of the associated scheme corresponding to the monitoring data, when the scheduling water quantity values are not met, indicating that the actual situation of the AC cells can not complete water quantity scheduling, acquiring geographic positions corresponding to the monitoring data (AC cells) and the water quantity values capable of being scheduled, then determining a standby scheduling scheme according to the geographic positions corresponding to the monitoring data and the scheduling water quantity values, namely, determining a D cell, an F cell and the like, and carrying out alternative completion water quantity scheduling.
According to the embodiment of the invention, when the water quantity scheduling cannot be completed due to errors in the association scheme, the completion of the water quantity scheduling is ensured through the alternative scheme, and the user quantity of related users is ensured.
Based on the above embodiment, the metering management method based on artificial intelligence further includes:
detecting a scheme type of an association scheme corresponding to the monitoring data, and acquiring a leakage reason and a leakage repairing scheme in the association scheme corresponding to the monitoring data when the scheme type is leakage analysis type;
and acquiring corresponding staff terminal information according to the leakage reasons, and transmitting the leakage reasons and the leakage repairing scheme to the corresponding terminals according to the staff terminal information.
In the embodiment of the invention, when the scheme type of the associated scheme is leakage analysis, the leakage condition of the water resource is analyzed through the monitoring data, the leakage reason and the leakage repairing scheme of the associated scheme about the water supply pipe are obtained, the terminal information of professional working (maintenance) personnel related to the leakage reason is obtained, and the leakage reason and the leakage repairing scheme are sent to the corresponding terminal according to the terminal information of the working personnel.
The embodiment of the invention can timely send the leakage reasons and the leakage repairing schemes to the terminals of related staff, so that the related staff can timely repair the leakage points, and larger losses are avoided.
Based on the above embodiment, the metering management method based on artificial intelligence further includes:
the historical data components are divided into a training set and a verification set, the training set, the corresponding geographic position and the corresponding association scheme are input into a convolutional neural network model for training, and a trained preliminary convolutional neural network model is obtained;
and inputting the verification set, the corresponding geographic position and the association scheme 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 historical data set and the corresponding geographic position and association scheme are trained through the convolutional neural network model, the data of the historical data set is grouped, specifically, the data can be divided into 80% of training set and 20% of verification set, the initial convolutional neural network model is obtained through the initial training of the training set and the geographic position and association scheme corresponding to the training set data, and then the initial convolutional neural network model is tested through the geographic position and association scheme corresponding to the verification set and the verification set data, so that the trained convolutional neural network model is obtained.
In addition, after the monitoring data and the corresponding geographic position and the corresponding association scheme are obtained, the monitoring data and the corresponding geographic position and the corresponding association scheme are used as verification data of the same verification set, and the trained convolutional neural network model is subjected to iterative updating, so that the novelty of model data in the convolutional neural network model is ensured.
According to the embodiment of the invention, the historical data sets are subjected to data grouping, the preliminary model is built 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. 3 is a schematic diagram of an artificial intelligence based metering management system according to an embodiment of the present invention, including: the device comprises a connection module 201, a first acquisition module 202, a training module 203, a second acquisition module 204 and an output module 205, wherein:
the connection module 201 is configured to connect to an external data source, and obtain monitoring data through the external data source, where the monitoring data includes user table data, large table data, and cell data.
The first obtaining module 202 is configured to obtain a corresponding historical data set according to the type of the monitoring data, and obtain a geographic location and an association scheme corresponding to the historical data set.
The training module 203 is configured to input the historical data set, the corresponding geographic location, and the association scheme to the convolutional neural network model for training, and obtain a trained convolutional neural network model.
The second obtaining module 204 is configured to obtain a geographic location corresponding to the monitoring data, input the monitoring data and the corresponding geographic location to a trained convolutional neural network model, and obtain an association scheme corresponding to the monitoring data through the trained convolutional neural network model.
And the output module 205 is configured to output an association scheme corresponding to the monitoring data.
In one embodiment, the system may further comprise:
the dividing module is used for dividing the historical data set into historical user table data, historical large table data and historical cell data according to different data types, and adding classification identifiers for the data of different types in the historical data set.
And the input module is used for constructing a three-dimensional data set according to the historical data set, the corresponding geographic position and the corresponding association scheme according to the classification identifier, inputting the three-dimensional data set into the convolutional neural network model, and performing cross training on the three-dimensional data set through the convolutional neural network model.
In one embodiment, the system may further comprise:
the detection module is used for detecting the scheme type of the association scheme corresponding to the monitoring data, and when the scheme type is water scheduling, the scheduling party and the scheduled party of the association scheme corresponding to the monitoring data are obtained.
And the third acquisition module is used for acquiring the scheduling water quantity values corresponding to the scheduled party and the scheduled party, and detecting whether the scheduling water quantity values meet the requirements of the association scheme corresponding to the monitoring data.
And the fourth acquisition module is used for acquiring the geographic position and the dispatching water quantity value corresponding to the monitoring data when the dispatching water quantity value does not meet the requirements of the association scheme corresponding to the monitoring data, and determining a standby dispatching scheme according to the geographic position and the dispatching water quantity value corresponding to the monitoring data.
In one embodiment, the system may further comprise:
the second detection module is used for detecting the scheme type of the association scheme corresponding to the monitoring data, and acquiring the leakage reason and the leakage repairing scheme in the association scheme corresponding to the monitoring data when the scheme type is leakage analysis type.
And the sending module is used for acquiring corresponding staff terminal information according to the leakage reasons and sending the leakage reasons and the leakage repairing scheme to the corresponding terminal according to the staff terminal information.
In one embodiment, the system may further comprise:
the second training module is used for dividing the historical data into a training set and a verification set, inputting the training set, the corresponding geographic position and the association scheme into the convolutional neural network model for training, and obtaining a trained preliminary convolutional neural network model.
The testing module is used for inputting the verification set, the corresponding geographic position and the association scheme into the trained preliminary convolutional neural network model for testing, and obtaining the trained convolutional neural network model after the testing is completed.
In one embodiment, the system may further comprise:
and the updating module is used for iteratively updating the trained convolutional neural network model by taking the monitoring data, the corresponding geographic position and the corresponding association scheme as verification data.
In one embodiment, the system may further comprise:
and the fifth acquisition module is used for acquiring the binding terminal corresponding to the monitoring data and acquiring the communication terminal of the related department according to the geographic position corresponding to the monitoring data.
And the second sending module is used for sending the association scheme corresponding to the monitoring data to the binding terminal and the communication terminal of the association department.
For specific limitations regarding the artificial intelligence based metering management system, reference may be made to the limitations of the artificial intelligence based metering management method hereinabove, and will not be described in detail herein. The various modules in the artificial intelligence based metering management system described above may be implemented in whole or in part in 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. 4 illustrates a physical schematic diagram of an electronic device, as shown in fig. 4, which 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: the method comprises the steps of connecting an external data source, and obtaining monitoring data through the external data source, wherein the monitoring data comprises user table data, large table data and cell data; acquiring a corresponding historical data set according to the type of the monitoring data, and acquiring a geographic position and an association scheme corresponding to the historical data set; inputting the historical data set, the corresponding geographic position and the association scheme into a convolutional neural network model for training to obtain a trained convolutional neural network model, obtaining the geographic position corresponding to the monitoring data, inputting the monitoring data and the corresponding geographic position into the trained convolutional neural network model, and obtaining the association scheme corresponding to the monitoring data through the trained convolutional neural network model; and outputting the association scheme corresponding to the monitoring data.
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: the method comprises the steps of connecting an external data source, and obtaining monitoring data through the external data source, wherein the monitoring data comprises user table data, large table data and cell data; acquiring a corresponding historical data set according to the type of the monitoring data, and acquiring a geographic position and an association scheme corresponding to the historical data set; inputting the historical data set, the corresponding geographic position and the association scheme into a convolutional neural network model for training to obtain a trained convolutional neural network model, obtaining the geographic position corresponding to the monitoring data, inputting the monitoring data and the corresponding geographic position into the trained convolutional neural network model, and obtaining the association scheme corresponding to the monitoring data through the trained convolutional neural network model; and outputting the association scheme corresponding to the monitoring data.
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 (5)
1. An artificial intelligence based metering management method, comprising the following steps:
the method comprises the steps of connecting an external data source, and obtaining monitoring data through the external data source, wherein the monitoring data comprises household table data, large table data and cell data;
acquiring a corresponding historical data set according to the type of the monitoring data, and acquiring a geographic position and an association scheme corresponding to the historical data set;
inputting the historical data set, the corresponding geographic position and the corresponding association scheme into a convolutional neural network model for training, and obtaining a trained convolutional neural network model;
acquiring a geographic position corresponding to the monitoring data, inputting the monitoring data and the corresponding geographic position into a trained convolutional neural network model, and obtaining a correlation scheme corresponding to the monitoring data through the trained convolutional neural network model;
outputting an association scheme corresponding to the monitoring data;
the geographic positions corresponding to the historical data sets and the geographic positions corresponding to the monitoring data comprise terrain, altitude, longitude and latitude and climate which influence the water consumption of the user;
the step of inputting the historical data set, the corresponding geographic position and the association scheme into a convolutional neural network model for training comprises the following steps:
dividing the historical data set into historical user table data, historical large table data and historical cell data according to different data types, and adding classification identifiers for the data of different types in the historical data set;
constructing a three-dimensional data set according to the classification mark, wherein the three-dimensional data set respectively comprises a, historical user table data, corresponding geographic positions and corresponding association schemes, b, historical large table data, corresponding geographic positions and corresponding association schemes, c, historical cell data, corresponding geographic positions and corresponding association schemes, inputting the three-dimensional data set into the convolutional neural network model, and performing cross training on the three-dimensional data set through the convolutional neural network model;
the association scheme comprises multiple types according to different requirements of staff, and the staff needs to acquire corresponding business charging through the monitoring data and the geographic positions corresponding to the monitoring data, so that corresponding water consumption charging is carried out according to the monitoring data and the geographic positions corresponding to the monitoring data; when the staff needs to carry out water production scheduling, determining a water shortage area and a water abundance area through the monitoring data and the geographic positions corresponding to the monitoring data, and carrying out water scheduling; when staff needs to analyze the leakage of the pipe network through the monitoring data, determining the leakage condition and the position of the pipe network through the monitoring data and the abnormal data in the geographic position corresponding to the monitoring data;
after obtaining the association scheme corresponding to the monitoring data through the trained convolutional neural network model, the method further comprises the following steps:
detecting a scheme type of an association scheme corresponding to the monitoring data, and acquiring a dispatcher and a dispatched party of the association scheme corresponding to the monitoring data when the scheme type is water dispatching type;
acquiring the scheduling water quantity values corresponding to the scheduling party and the scheduled party, and detecting whether the scheduling water quantity values meet the requirements of the association schemes corresponding to the monitoring data;
when the dispatching water quantity value does not meet the requirements of the association scheme corresponding to the monitoring data, acquiring the geographic position and the dispatching water quantity value corresponding to the monitoring data, and determining a standby dispatching scheme according to the geographic position and the dispatching water quantity value corresponding to the monitoring data;
when the scheme type is leakage analysis type, acquiring leakage reasons and leakage repairing schemes in the associated schemes corresponding to the monitoring data;
acquiring corresponding staff terminal information according to the leakage reasons, and transmitting the leakage reasons and the leakage repairing scheme to corresponding terminals according to the staff terminal information;
inputting the historical data set, the corresponding geographic position and the corresponding association scheme into a convolutional neural network model for training, and obtaining a trained convolutional neural network model, wherein the method comprises the following steps of:
the historical data components are divided into a training set and a verification set, the training set, the corresponding geographic position and the corresponding association scheme are input into a convolutional neural network model for training, and a trained preliminary convolutional neural network model is obtained;
inputting the verification set, the corresponding geographic position and the association scheme into the trained preliminary convolutional neural network model for testing, and obtaining the trained convolutional neural network model after the testing is completed;
after obtaining the association scheme corresponding to the monitoring data through the trained convolutional neural network model, the method further comprises the following steps:
and taking the monitoring data, the corresponding geographic position and the association scheme as verification data, and carrying out iterative updating on the trained convolutional neural network model.
2. The metering management method based on artificial intelligence according to claim 1, wherein the outputting the association scheme corresponding to the monitoring data comprises:
acquiring a binding terminal corresponding to the monitoring data, and acquiring a communication terminal of an associated department according to the geographic position corresponding to the monitoring data;
and sending the association scheme corresponding to the monitoring data to the binding terminal and the communication terminal of the association department.
3. An artificial intelligence based metering management system employing an artificial intelligence based metering management method as claimed in any one of claims 1 to 2, the system comprising:
the connection module is used for connecting an external data source and acquiring monitoring data through the external data source, wherein the monitoring data comprises household table data, large table data and cell data;
the first acquisition module is used for acquiring a corresponding historical data set according to the type of the monitoring data and acquiring a geographic position and an association scheme corresponding to the historical data set;
the training module is used for inputting the historical data set, the corresponding geographic position and the corresponding association scheme into the convolutional neural network model for training, and obtaining a trained convolutional neural network model;
the second acquisition module is used for acquiring the geographic position corresponding to the monitoring data, inputting the monitoring data and the corresponding geographic position into the trained convolutional neural network model, and obtaining a correlation scheme corresponding to the monitoring data through the trained convolutional neural network model;
the output module is used for outputting the association scheme corresponding to the monitoring data;
the step of inputting the historical data set, the corresponding geographic position and the association scheme into a convolutional neural network model for training comprises the following steps:
dividing the historical data set into historical user table data, historical large table data and historical cell data according to different data types, and adding classification identifiers for the data of different types in the historical data set;
and constructing a three-dimensional data set according to the historical data set, the corresponding geographic position and the corresponding association scheme according to the classification mark, inputting the three-dimensional data set into the convolutional neural network model, and performing cross training on the three-dimensional data set through the convolutional neural network model.
4. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the artificial intelligence based metering management method of any one of claims 1 to 2 when the program is executed by the processor.
5. 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 metering management method according to any of claims 1 to 2.
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