CN113112127A - 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 a metering management device based on artificial intelligence, wherein the method comprises the following steps: connecting an external data source, and acquiring monitoring data through the external data source, wherein the monitoring data comprises household meter data, large meter 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, acquiring 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 water supply network data management can be completed according to the artificial intelligence deep learning, the manpower resource is saved, the management efficiency is improved, and a corresponding correlation 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 increasingly urbanization of China, residents have more and more water through water pipes, water supply networks are gradually distributed everywhere, the distribution is more and more complex, and when the water supply networks supply water, various water supply data can be generated, such as household meter data taking a household as a unit, large meter data taking a building or a building as a unit, water supply quantity data of the whole community and the like.
In the prior art, because the water supply network is more and more complicated, the operation when carrying out relevant management to various data in the water supply network is also more and more complicated, for example when carrying out water quantity scheduling, need carry out the water yield, the investigation of water supply network district position, then arrange relevant scheme through relevant staff, then carry out the water quantity scheduling between the region, for example when carrying out the leakage analysis, also arrange the analysis abnormal data through relevant staff to abnormal data, obtain after receiving the abnormal information that the user uploaded, then confirm the position, dispatch relevant maintainer and carry out relevant maintenance operation etc..
In light of the above situation, the management means for the related data of the water supply network is very complicated, and a management method for the related data of the water supply network that can solve the above problems is urgently needed.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a metering management method and system based on artificial intelligence.
The embodiment of the invention provides a metering management method based on artificial intelligence, which comprises the following steps:
connecting an external data source, and acquiring monitoring data through the external data source, wherein the monitoring data comprises user meter data, big meter 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;
acquiring a geographical position corresponding to the monitoring data, inputting the monitoring data and the corresponding geographical position into a trained convolutional neural network model, and obtaining an association 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 group into historical user table data, historical large table data and historical cell data according to different data types, and adding classification identifiers to different types of data in the historical data group;
and constructing a three-dimensional data set by the historical data set, the corresponding geographic position and the association scheme according to the classification identification, 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 the scheme type of the associated scheme corresponding to the monitoring data, and acquiring a scheduling party and a scheduled party of the associated scheme corresponding to the monitoring data when the scheme type is a water scheduling type;
acquiring corresponding scheduling water quantity values of the scheduling party and the scheduled party, and detecting whether the scheduling water quantity values meet the requirements of the associated schemes corresponding to the monitoring data;
and when the scheduling water quantity value does not meet the requirement of the association scheme corresponding to the monitoring data, acquiring the geographic position and the scheduling water quantity value corresponding to the monitoring data, and determining a standby scheduling scheme according to the geographic position and the scheduling water quantity value corresponding to the monitoring data.
In one embodiment, the method further comprises:
detecting the scheme type of the associated scheme corresponding to the monitoring data, and when the scheme type is a leakage analysis type, acquiring a leakage reason and a leakage repairing scheme in the associated scheme corresponding to the monitoring data;
and acquiring corresponding staff terminal information according to the leakage reason, and sending the leakage reason and the leakage repairing scheme to a corresponding terminal according to the staff terminal information.
In one embodiment, the method further comprises:
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 a convolutional neural network model for training to obtain a trained primary convolutional neural network model;
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 finished.
In one embodiment, the method further comprises:
and taking the monitoring data, the corresponding geographic position and the association scheme as verification data, and performing 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 a relevant department according to a geographical 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 a metering management system based on artificial intelligence, which comprises:
the device comprises a connection module, a data processing module and a data processing module, wherein the connection module is used for connecting an external data source and acquiring monitoring data through the external data source, and the monitoring data comprises user meter data, big meter 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 association scheme into a convolutional neural network model for training to obtain 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 an association 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 which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the steps of the metering management method based on artificial intelligence.
Embodiments of the present invention provide a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the above-mentioned artificial intelligence-based metering management method.
The metering management method and system based on artificial intelligence provided by the embodiment of the invention are connected with an external data source, and monitor data are obtained through the external data source, wherein the monitor data comprise household meter data, large meter 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, 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 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, data of the water supply network can be managed according to the deep learning of artificial intelligence, the management efficiency is improved while manpower resources are saved, and corresponding associated 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 used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a method for artificial intelligence based metering management in an embodiment of the present 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 an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device in an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flow chart of a metering management method based on artificial intelligence according to an embodiment of the present invention, and as shown in fig. 1, the embodiment of the present invention provides a metering management method based on artificial intelligence, including:
step S101, connecting an external data source, and acquiring monitoring data through the external data source, wherein the monitoring data comprises user meter data, big meter data and cell data.
Specifically, the external data source can include various remote water meters (personal meter, floor meter, cell meter, etc.) or centralized management devices of water meter data, connect the external data source, acquire corresponding monitoring data through the external data source, the monitoring data includes household meter data, big meter data, cell data, wherein, the monitoring data who acquires can be as shown in fig. 2, carry out the data show, show data can include current data, yesterday data, newly-increased data and the trend of change between the data, make things convenient for relevant staff to carry out data and look over.
And S102, 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.
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 daily (7 th day) water consumption data (household meter data) of the cell a, the historical daily water consumption data of the cell a is obtained, and 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 corresponding association scheme when the historical data of the cell is abnormal, for example, the water consumption data of the cell a in the 1 st, 2 nd and 3 th days is in a normal range, the corresponding association scheme is to normally charge the water consumption of the cell a, the water consumption in the 4 th and 5 th days exceeds 1340%, the corresponding association scheme may be to determine that the total pipe network of the cell leaks after the related staff performs investigation, the total pipe network of the cell leaks, the water consumption in the 6 th day exceeds 56%, the corresponding association scheme may be after the related staff performs investigation, and in addition, the specific content of the association scheme can also be different due to different geographic positions, for example, when the local temperature of the A cell rises and the water consumption of the A cell exceeds 26% in the 6 th day, the corresponding association scheme still carries out normal charging on the water consumption of the A cell.
And S103, inputting the historical data set, the corresponding geographic position and the association scheme into a convolutional neural network model for training to obtain the trained convolutional neural network model.
Specifically, the historical data set and the corresponding geographic position (including the factors such as topography, altitude, longitude and latitude, climate and the like which may influence the water consumption of a user) and the association scheme are used as input and input into an input layer of the convolutional neural network model for model training, and the convolutional neural network model is deeply learned through a convolutional layer, a pooling layer and a full connection layer to obtain the trained convolutional neural network model.
And step S104, 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 the association scheme corresponding to the monitoring data through the trained convolutional neural network model.
Specifically, the geographical position corresponding to the monitoring data (including factors such as topography, altitude, longitude and latitude, climate and the like which may affect the water consumption of the user) is obtained, the monitoring data and the corresponding geographical position are input into the trained convolutional neural network model, and the convolutional neural network model obtains the output corresponding association scheme by taking the monitoring data and the geographical position corresponding to the monitoring data as input.
In addition, the association scheme can be of various types according to different requirements of workers, for example, the workers need to obtain corresponding business charging through the monitoring data and the geographic position corresponding to the monitoring data, and then corresponding water consumption charging is carried out according to the monitoring data and the geographic position corresponding to the monitoring data; when the staff needs to carry out water production scheduling, the water shortage section and the water surplus section can be determined through the monitoring data and the geographic position corresponding to the monitoring data, and water scheduling is carried out; when the staff need carry out the leakage analysis of pipe network through the monitoring data, can confirm the leakage condition and the position of pipe network through the abnormal data in the geographical position that monitoring data and monitoring data correspond.
And 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 the specific output process may be to acquire a binding terminal corresponding to the monitoring data, acquire a communication terminal of an association department according to a geographic position corresponding to the monitoring data, and send the association scheme corresponding to the monitoring data to the binding terminal and the communication terminal of the association department, for example, when the water consumption is measured, the fee information may be sent to the binding water user terminal and the communication terminal of the association department which collects the water fee; when 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 relevant departments carrying out the water quantity scheduling, so that relevant personnel can know the relevant scheme about pipe network water supply processing at the first time, and the user experience and the processing efficiency are improved.
The metering management method based on artificial intelligence provided by the embodiment of the invention is connected with an external data source, and acquires monitoring data through the external data source, wherein the monitoring data comprises household meter data, large meter 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, 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 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, data of the water supply network can be managed according to the deep learning of artificial intelligence, the management efficiency is improved while manpower resources are saved, and corresponding associated solutions can be provided for the data of the water supply network.
On the basis of the above embodiment, the metering management method based on artificial intelligence further includes:
dividing the historical data group into historical user table data, historical large table data and historical cell data according to different data types, and adding classification identifiers to different types of data in the historical data group;
and constructing a three-dimensional data set by the historical data set, the corresponding geographic position and the association scheme according to the classification identification, 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, a historical data group is classified, the historical data group is classified into historical user table data, historical large table data and historical cell data, corresponding classification identifiers are added to 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, historical user table data, corresponding geographic positions and association schemes, b, historical large table data, corresponding geographic positions and association schemes, c, historical cell data, corresponding geographic positions and association schemes, the three-dimensional data are input into an input layer of a convolutional neural network model, and the three-dimensional data group is subjected to cross training through the convolutional neural network model to obtain the cross relationship between the historical user table data, the historical large table data and the historical cell data and the geographic positions and the association schemes.
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 represent the difference of corresponding associated data (geographic position and associated scheme) when the data types in the historical data set are different, and the result of the subsequent associated scheme is more accurate.
On the basis of the above embodiment, the metering management method based on artificial intelligence further includes:
detecting the scheme type of the associated scheme corresponding to the monitoring data, and acquiring a scheduling party and a scheduled party of the associated scheme corresponding to the monitoring data when the scheme type is a water scheduling type;
acquiring corresponding scheduling water quantity values of the scheduling party and the scheduled party, and detecting whether the scheduling water quantity values meet the requirements of the associated schemes corresponding to the monitoring data;
and when the scheduling water quantity value does not meet the requirement of the association scheme corresponding to the monitoring data, acquiring the geographic position and the scheduling water quantity value corresponding to the monitoring data, and determining a standby scheduling scheme according to the geographic position and the scheduling water quantity value corresponding to the monitoring data.
In the embodiment of the invention, when the scheme type of the association scheme is a water scheduling type, scheduling is performed on water resources through monitoring data to obtain a scheduling party and a scheduled party of the association scheme, then obtaining a scheduling water quantity value corresponding to the scheduling party and the scheduled party, for example, when water quantity scheduling is performed between a cell A and a cell C, whether the scheduling water quantity value between AC cells meets the requirement of the association scheme corresponding to the monitoring data is detected, when the scheduling water quantity value does not meet the requirement, the actual condition of the AC cells cannot complete water quantity scheduling, an error exists in the output association scheme, a geographic position corresponding to the monitoring data (AC cells) and a water quantity value capable of being scheduled are obtained, then a standby scheduling scheme is determined according to the geographic position corresponding to the monitoring data and the scheduling water quantity value, namely, a cell D, a cell F and the like are determined, and water quantity scheduling is completed alternatively.
According to the embodiment of the invention, when the associated scheme has errors and the water quantity scheduling cannot be completed, the completion of the water quantity scheduling is ensured through the alternative scheme, and the user quantity of related users is ensured.
On the basis of the above embodiment, the metering management method based on artificial intelligence further includes:
detecting the scheme type of the associated scheme corresponding to the monitoring data, and when the scheme type is a leakage analysis type, acquiring a leakage reason and a leakage repairing scheme in the associated scheme corresponding to the monitoring data;
and acquiring corresponding staff terminal information according to the leakage reason, and sending the leakage reason and the leakage repairing scheme to a corresponding terminal according to the staff terminal information.
In the embodiment of the invention, when the scheme type of the association scheme is a leakage analysis type, the leakage condition of the water resource is analyzed through the monitoring data, the leakage reason and the leakage repairing scheme about the water supply pipe in the association scheme are obtained, the terminal information of professional workers (maintenance) related to the leakage reason is obtained, and the leakage reason and the leakage repairing scheme are sent to the corresponding terminals according to the terminal information of the workers.
According to the embodiment of the invention, the leakage reason and the leakage repairing scheme can be timely sent to the terminals of the related workers, so that the related workers can timely maintain the leakage point, and the larger loss is avoided.
On the basis of the above embodiment, the metering management method based on artificial intelligence further includes:
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 a convolutional neural network model for training to obtain a trained primary convolutional neural network model;
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 finished.
In the embodiment of the invention, when the historical data group and the corresponding geographical position and association scheme are trained through the convolutional neural network model, the historical data group is subjected to data grouping, specifically, the data can be divided into 80% of training sets and 20% of verification sets, preliminary training is performed through the geographical position and association scheme corresponding to the training sets and the data of the training sets to obtain a preliminary convolutional neural network model, and then the preliminary convolutional neural network model is tested through the geographical position and association scheme corresponding to the data of the verification sets and the data of the verification sets to obtain the trained convolutional neural network model.
In addition, after the monitoring data, the corresponding geographic position and the corresponding association scheme are obtained, the monitoring data, the corresponding geographic position and the corresponding association scheme are used as verification data of the same verification set, the trained convolutional neural network model is subjected to iterative updating, and the novelty of model data in the convolutional neural network model is guaranteed.
According to the embodiment of the invention, the accuracy of the convolutional neural network model is ensured by carrying out data grouping on the historical data set, establishing the primary model through the training set and carrying out accuracy verification on the primary model through the verification set.
Fig. 3 is a measurement management system based on artificial intelligence according to an embodiment of the present invention, including: 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 acquire monitoring data through the external data source, where the monitoring data includes user meter data, large meter 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.
And the training module 203 is configured to input the historical data set, the corresponding geographic position, and the association scheme into a convolutional neural network model for training, so as to obtain a trained convolutional neural network model.
A second obtaining module 204, configured to obtain a geographic position corresponding to the monitoring data, input the monitoring data and the corresponding geographic position into the trained convolutional neural network model, and obtain, through the trained convolutional neural network model, an association scheme corresponding to the monitoring data.
And the output module 205 is configured to output the association scheme corresponding to the monitoring data.
In one embodiment, the system may further comprise:
and the dividing module is used for dividing the historical data group into historical user table data, historical large table data and historical cell data according to different data types and adding classification identifiers to the different types of data in the historical data group.
And the input module is used for constructing a three-dimensional data set by the historical data set and the corresponding geographic position and the association scheme according to the classification identification, 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:
and the detection module is used for detecting the scheme type of the associated scheme corresponding to the monitoring data, and acquiring a scheduling party and a scheduled party of the associated scheme corresponding to the monitoring data when the scheme type is a water scheduling type.
And the third acquisition module is used for acquiring the corresponding scheduling water quantity values of the scheduling party and the scheduled party and detecting whether the scheduling water quantity values meet the requirements of the associated schemes corresponding to the monitoring data.
And the fourth obtaining module is used for obtaining the geographic position and the scheduling water quantity value corresponding to the monitoring data when the scheduling water quantity value does not meet the requirement of the association scheme corresponding to the monitoring data, and determining a standby scheduling scheme according to the geographic position and the scheduling water quantity value corresponding to the monitoring data.
In one embodiment, the system may further comprise:
and the second detection module is used for detecting the scheme type of the associated scheme corresponding to the monitoring data, and when the scheme type is a leakage analysis type, acquiring a leakage reason and a leakage repairing scheme in the associated scheme corresponding to the monitoring data.
And the sending module is used for obtaining corresponding staff terminal information according to the leakage reason and sending the leakage reason and the leakage repairing scheme to the corresponding terminal according to the staff terminal information.
In one embodiment, the system may further comprise:
and 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 a convolutional neural network model for training, and obtaining a trained preliminary convolutional neural network model.
And the test module is used for inputting the verification set, the corresponding geographic position and the correlation scheme into the trained preliminary convolutional neural network model for testing, and obtaining the trained convolutional neural network model after the test is finished.
In one embodiment, the system may further comprise:
and the updating module is used for 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 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 associated 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 of the metering management system based on artificial intelligence, reference may be made to the above limitations of the metering management method based on artificial intelligence, which will not be described herein again. The modules in the artificial intelligence based metering management system can be wholly or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor)301, a memory (memory)302, a communication Interface (Communications Interface)303 and a communication bus 304, wherein the processor 301, the memory 302 and the communication Interface 303 complete communication with each other through the communication bus 304. The processor 301 may call logic instructions in the memory 302 to perform the following method: connecting an external data source, and acquiring monitoring data through the external data source, wherein the monitoring data comprises household meter data, large meter 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, 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 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.
Furthermore, the logic instructions in the memory 302 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented to perform the transmission method provided in the foregoing embodiments when executed by a processor, and for example, the method includes: connecting an external data source, and acquiring monitoring data through the external data source, wherein the monitoring data comprises household meter data, large meter 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, 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 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 above-described system embodiments are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A metering management method based on artificial intelligence is characterized by comprising the following steps:
connecting an external data source, and acquiring monitoring data through the external data source, wherein the monitoring data comprises user meter data, big meter 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;
acquiring a geographical position corresponding to the monitoring data, inputting the monitoring data and the corresponding geographical position into a trained convolutional neural network model, and obtaining an association scheme corresponding to the monitoring data through the trained convolutional neural network model;
and outputting the association scheme corresponding to the monitoring data.
2. The artificial intelligence based metering management method of claim 1, wherein the inputting historical data sets and corresponding geographical locations and association schemes into a convolutional neural network model for training comprises:
dividing the historical data group into historical user table data, historical large table data and historical cell data according to different data types, and adding classification identifiers to different types of data in the historical data group;
and constructing a three-dimensional data set by the historical data set, the corresponding geographic position and the association scheme according to the classification identification, 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.
3. The artificial intelligence based metering management method according to claim 1, wherein after obtaining the association scheme corresponding to the monitoring data through the trained convolutional neural network model, the method further comprises:
detecting the scheme type of the associated scheme corresponding to the monitoring data, and acquiring a scheduling party and a scheduled party of the associated scheme corresponding to the monitoring data when the scheme type is a water scheduling type;
acquiring corresponding scheduling water quantity values of the scheduling party and the scheduled party, and detecting whether the scheduling water quantity values meet the requirements of the associated schemes corresponding to the monitoring data;
and when the scheduling water quantity value does not meet the requirement of the association scheme corresponding to the monitoring data, acquiring the geographic position and the scheduling water quantity value corresponding to the monitoring data, and determining a standby scheduling scheme according to the geographic position and the scheduling water quantity value corresponding to the monitoring data.
4. The artificial intelligence based metering management method according to claim 1, wherein after obtaining the association scheme corresponding to the monitoring data through the trained convolutional neural network model, the method further comprises:
detecting the scheme type of the associated scheme corresponding to the monitoring data, and when the scheme type is a leakage analysis type, acquiring a leakage reason and a leakage repairing scheme in the associated scheme corresponding to the monitoring data;
and acquiring corresponding staff terminal information according to the leakage reason, and sending the leakage reason and the leakage repairing scheme to a corresponding terminal according to the staff terminal information.
5. The artificial intelligence based metering management method according to claim 1, wherein the inputting the historical data set, the corresponding geographic location and the association scheme into a convolutional neural network model for training to obtain a trained convolutional neural network model comprises:
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 a convolutional neural network model for training to obtain a trained primary convolutional neural network model;
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 finished.
6. The artificial intelligence based metering management method according to claim 1, wherein after obtaining the association scheme corresponding to the monitoring data through the trained convolutional neural network model, the method further comprises:
and taking the monitoring data, the corresponding geographic position and the association scheme as verification data, and performing iterative updating on the trained convolutional neural network model.
7. The method for metering management 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 a relevant department according to a geographical 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.
8. An artificial intelligence based metering management system, the system comprising:
the device comprises a connection module, a data processing module and a data processing module, wherein the connection module is used for connecting an external data source and acquiring monitoring data through the external data source, and the monitoring data comprises user meter data, big meter 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 association scheme into a convolutional neural network model for training to obtain 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 an association 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.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the artificial intelligence based metering management method according to any one of claims 1 to 7 are implemented when the program is executed by the processor.
10. A non-transitory computer readable storage medium, having stored thereon a computer program, wherein the computer program, when executed by a processor, performs the steps of the artificial intelligence based metering management method according to any one of claims 1 to 7.
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