CN117333054A - Water supply network measuring point pressure prediction method, device, equipment and medium - Google Patents
Water supply network measuring point pressure prediction method, device, equipment and medium Download PDFInfo
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Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D3/00—Arrangements for supervising or controlling working operations
- F17D3/01—Arrangements for supervising or controlling working operations for controlling, signalling, or supervising the conveyance of a product
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
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- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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Abstract
The embodiment of the invention discloses a method, a device, equipment and a medium for predicting the pressure of a water supply network measuring point. The method comprises the following steps: acquiring current pipe network state data of a water supply pipe network and parameters affecting the measuring point pressure of the water supply pipe network, wherein the current pipe network state data comprises the current measuring point pressure of the water supply pipe network to be measured; inputting the current pipe network state data and the parameters affecting the pressure of the water supply pipe network measuring points into a pre-trained pipe network state prediction model to obtain the next pipe network state data of the water supply pipe network to be measured; and determining the pressure of the next measuring point of the water supply network to be measured based on the state data of the next pipe network. According to the technical scheme, the pressure of the water supply network is predicted conveniently, and universality and wide applicability of the pressure prediction of the water supply network measuring point are improved.
Description
Technical Field
The embodiment of the invention relates to the technical field of data processing, in particular to a method, a device, equipment and a medium for predicting the pressure of a water supply network measuring point.
Background
The pipe network pressure is an important index for restricting the effective water supply of a tap water system, is also a core factor for influencing the health of the pipe network, maintaining and prolonging the service life, and is also an important cost item of water enterprises. Therefore, a pipe network pressure simulation system is constructed, various control schemes are predicted and evaluated in a prospective mode, and digitization, intellectualization and saving of a water business enterprise are supported effectively. Thus, pipe network pressure prediction becomes critical. At present, the pipe network pressure prediction is usually performed by adopting a manual means, and related experience is needed to be relied on, so that the pipe network pressure prediction has no universality and wide applicability.
Disclosure of Invention
The invention provides a method, a device, equipment and a medium for predicting the pressure of a water supply network measuring point, so that the pressure of the water supply network is predicted conveniently, and the universality and the wide applicability of the pressure prediction of the water supply network measuring point are improved.
According to one aspect of the invention, a water supply network measuring point pressure prediction method is provided, and the method comprises the following steps:
acquiring current pipe network state data of a water supply pipe network and parameters affecting the measuring point pressure of the water supply pipe network, wherein the current pipe network state data comprises the current measuring point pressure of the water supply pipe network to be measured;
inputting the current pipe network state data and the parameters affecting the pressure of the water supply pipe network measuring points into a pre-trained pipe network state prediction model to obtain the next pipe network state data of the water supply pipe network to be measured;
and determining the pressure of the next measuring point of the water supply network to be measured based on the state data of the next pipe network.
Optionally, the method further comprises: acquiring a plurality of sample data and expected data corresponding to each sample data, wherein the sample data comprise pipe network state data of the water supply pipe network at a certain historical moment and parameters affecting the pressure of measuring points of the water supply pipe network, and the expected data are pipe network state data of the water supply pipe network at the next moment of the certain historical moment; inputting each sample data into a pre-constructed initial pipe network state prediction model to obtain an actual output result of the initial pipe network state prediction model for each sample data; and adjusting network parameters of the initial pipe network state prediction model according to expected data and actual output results of each sample data to obtain the trained pipe network state prediction model.
Optionally, the adjusting the network parameters of the initial pipe network state prediction model according to the expected data and the actual output result of each sample data includes: determining first data distribution of actual output results of the initial pipe network state prediction model for each sample data; determining a second data distribution of expected data of the initial pipe network state prediction model for each sample data; and adjusting network parameters of the initial pipe network state prediction model based on the first data distribution and the second data distribution.
Optionally, the adjusting the network parameters of the initial pipe network state prediction model according to the expected data and the actual output result of each sample data includes: and for each sample data, adjusting network parameters of the initial pipe network state prediction model according to expected data and actual output results of the sample data.
Optionally, the method further comprises: acquiring a pre-constructed pipe network water supply decision flow diagram and a pre-constructed pipe network water supply calculation diagram; and constructing the initial pipe network state prediction model based on the pre-constructed pipe network water supply decision flow diagram and the pre-constructed pipe network water supply calculation diagram.
Optionally, the method further comprises: and determining the association relation among all nodes in the pipe network water supply decision flow diagram, and constructing a pipe network water supply calculation diagram based on the association relation.
Optionally, the pre-constructed pipe network water supply decision flow graph comprises at least one environmental state node and at least one decision agent node, wherein the environmental state node comprises a current environmental state sub-node, an environmental state transition sub-node and a next environmental state sub-node.
According to another aspect of the invention, a water supply network measuring point pressure prediction device is provided. The device comprises:
the system comprises a current pipe network state acquisition module, a current control module and a control module, wherein the current pipe network state acquisition module is used for acquiring current pipe network state data of a water supply pipe network and parameters affecting the measuring point pressure of the water supply pipe network, and the current pipe network state data comprise the current measuring point pressure of the water supply pipe network to be measured;
the next pipe network state obtaining module is used for inputting the current pipe network state data and the parameters affecting the pressure of the water supply pipe network measuring points into a pipe network state prediction model which is trained in advance to obtain the next pipe network state data of the water supply pipe network to be measured;
and the next pipe network pressure determining module is used for determining the pressure of the next measuring point of the water supply network to be measured based on the next pipe network state data.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the water supply network site pressure prediction method according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the method for predicting a water supply network site pressure according to any one of the embodiments of the present invention.
According to the technical scheme, the current pipe network state data of the water supply pipe network and the parameters affecting the measuring point pressure of the water supply pipe network are obtained, wherein the current pipe network state data comprise the current measuring point pressure of the water supply pipe network to be measured; inputting the current pipe network state data and the parameters affecting the pressure of the water supply pipe network measuring points into a pre-trained pipe network state prediction model to obtain the next pipe network state data of the water supply pipe network to be measured; and determining the pressure of the next measuring point of the water supply network to be measured based on the state data of the next pipe network. According to the technical scheme, the pressure of the water supply network is predicted conveniently, and universality and wide applicability of the pressure prediction of the water supply network measuring point are improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only 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 schematic flow chart of a method for predicting a water supply network measuring point pressure according to a first embodiment of the present invention;
fig. 2 is a pipe network water supply decision flow chart of a method for predicting a water supply pipe network measuring point pressure according to a first embodiment of the present invention;
fig. 3 is a schematic structural diagram of a water supply network measuring point pressure prediction device according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It will be appreciated that the data (including but not limited to the data itself, the acquisition or use of the data) involved in the present technical solution should comply with the corresponding legal regulations and the requirements of the relevant regulations.
Example 1
Fig. 1 is a schematic flow chart of a water supply network measuring point pressure prediction method according to an embodiment of the present invention, where the method may be performed by a water supply network measuring point pressure prediction device, and the water supply network measuring point pressure prediction device may be implemented in hardware and/or software, and the water supply network measuring point pressure prediction device may be configured in an electronic device such as a computer or a server.
As shown in fig. 1, the method of the present embodiment includes:
s110, acquiring current pipe network state data of a water supply pipe network and parameters affecting the measuring point pressure of the water supply pipe network, wherein the current pipe network state data comprise the current measuring point pressure of the water supply pipe network to be measured.
The current pipe network state data can be understood as state data of the water supply pipe network at the current moment. The water supply network to be measured can be understood as a water supply network requiring pressure prediction. The current pipe network status data may include current site pressure. The current measuring point pressure can be understood as the pressure value of the water supply network measuring point at the current moment. Parameters affecting the water supply network site pressure can be understood as data that has an effect on the water supply network site pressure. Alternatively, parameters affecting water supply network site pressure may include, but are not limited to, user water habit data, network structure, materials, and electromechanical system performance. In the embodiment of the invention, parameters affecting the pressure of the water supply network measuring point can be set according to actual conditions, and are not particularly limited herein.
In the embodiment of the invention, the current measuring point pressure of the water supply network to be measured is obtained, specifically, the measuring point pressure of the water supply network to be measured at the current moment is collected based on a collecting device (such as a pressure sensor), and the current measuring point pressure is obtained.
S120, inputting the current pipe network state data and the parameters affecting the pressure of the water supply pipe network measuring points into a pre-trained pipe network state prediction model to obtain the next pipe network state data of the water supply pipe network to be measured.
The next pipe network state data can be understood as state data of the water supply network to be measured at the next moment of the current moment. The pipe network state prediction module can be understood as being used for obtaining the next pipe network state data of the water supply network to be detected based on the current pipe network state data and parameters affecting the water supply network measuring point pressure.
Specifically, after the current pipe network state data and the parameters affecting the water supply pipe network measuring point pressure are obtained, the current pipe network state data and the parameters affecting the water supply pipe network measuring point pressure can be input into the pipe network state prediction model which is trained in advance, and an output result of the pipe network state prediction model, namely the next pipe network state data of the water supply pipe network to be tested, is obtained.
In an embodiment of the present invention, the method further includes: a plurality of sample data and expected data corresponding to each of the sample data are acquired. The sample data may include pipe network state data of the water supply pipe network at a certain historical moment and parameters affecting the pressure of the water supply pipe network measuring point. The expected data may be pipe network status data of the water supply pipe network at a time next to the certain historical time. After obtaining sample data and expected data corresponding to the sample data, each sample data may be input into a pre-constructed initial pipe network state prediction model, so as to obtain an actual output result of the initial pipe network state prediction model for each sample data. And then, according to expected data and actual output results of each sample data, network parameters of the initial pipe network state prediction model can be adjusted to obtain the trained pipe network state prediction model. The initial pipe network state prediction model can be understood as an initial network model which is built in advance and used for testing pipe network pressure. In the embodiment of the invention, a regression training mode can be adopted for training the initial pipe network state prediction model.
On the basis of the above embodiment, the method further includes: a training set is obtained, wherein the training set includes a plurality of historical state trace data. In the embodiment of the present invention, each piece of history state track data may be a sequence of a series of states in a continuous period including a start state and an end state. In the embodiment of the invention, a pipe network state prediction model constructed based on a decision-making flow diagram, namely a pipe network environment model, can comprise a water consumption prediction module, an intelligent decision-making module and a state transfer module.
In the embodiment of the present invention, the mode of training the model may specifically be that, for each piece of history state track data, training may be performed from the initial state of the track data. That is, the pipe network state at the starting time in the track may be input into the pipe network environment model, to obtain the pipe network prediction state at the next time of the starting time in the track. Further, the pipe network environment model parameters can be optimized based on the pipe network prediction state and the historical real pipe network state at the next moment of the initial moment in the track; based on the above, the pipe network prediction state at the next time of the start time in the track can be further input into the pipe network environment model, so as to obtain the pipe network prediction state at the next time of the start time in the track. Therefore, the pipe network prediction state and the historical real pipe network state at the next moment of the starting moment in the track can be used for optimizing the pipe network environment model parameters. That is, each time the predicted pipe network predicted state is taken as input, the subsequent state is continuously predicted. And (5) ending the prediction until the termination state is obtained by continuous deduction. A track of length N will make N-1 predictions starting from the start state to the end state. Therefore, the training of completing One model by using all tracks in the training set is realized, namely One iteration (One Epoch).
It will be appreciated that in practice, a training process typically requires multiple iterations to learn more information from the data and converge the model to a better state, and thus a trained pipe network environment model can be obtained by continuing training until the model loss is no longer reduced.
After model training is completed, the state data at the current moment can be input into the pipe network environment model, so that the state data at the next moment can be obtained, and in this way, the state data after a period of time can be deduced.
In an embodiment of the present invention, the method further includes: acquiring a pre-constructed pipe network water supply decision flow diagram and a pre-constructed pipe network water supply calculation diagram; and constructing the initial pipe network state prediction model based on the pre-constructed pipe network water supply decision flow diagram and the pre-constructed pipe network water supply calculation diagram.
The pipe network water supply decision flow graph can be a decision graph constructed based on pipe network water supply service characteristics. The pipe network water supply decision flow graph can be used for representing the decision relation among different service parameter characteristics at each time point. In the embodiment of the invention, the input and output of the data flow in the pipe network water supply decision flow diagram cannot form a cycle, that is, the structure of the pipe network water supply decision flow diagram accords with the structure of the directed acyclic graph. The network water supply decision flow graph comprises a plurality of service nodes. In the embodiment of the invention, each service node in the pipe network water supply decision flow diagram can represent a decision process used for calculating the node parameter, and the connection line between the service nodes represents the data flow direction.
In the embodiment of the invention, the nodes in the pipe network water supply decision flow diagram can comprise: weather and holiday nodes, current water consumption nodes, current operation factor nodes, current measuring point pressure nodes, non-operation factor nodes, next water consumption nodes, next operation factor nodes, next measuring point pressure nodes, water consumption prediction nodes, intelligent decision nodes and state transition nodes.
The weather and holiday nodes may include date information, a highest temperature value of the day corresponding to the date information, a lowest temperature value of the day corresponding to the date information, and a precipitation amount of the day corresponding to the date information, wherein the date information is at least one data belonging to the holiday. The current water consumption node can be the total water consumption of the pipe network at the current moment, the regional water consumption and other data related to the water consumption; or, the total water consumption, regional water consumption and other data related to the water consumption of the pipe network in the current period can be obtained. The current time period may be a time period between a current time and a time preset before the current time. The preset time period may be set according to actual requirements, and is not specifically limited herein, for example, 1 day, 7 days, 30 days, or the like.
The current operation factor node may include data that immediately causes a change in the pipe network due to an operation of a pipe network dispatcher at a current time or in a current period, such as pump station outlet pressure, pump station outlet flow, pipe network valve activation conditions in the pipe network, and the like. The current point pressure node may include pressure data for each point in the pipe network at the current time or during the current time period. Non-operational factors may include data including pipe network structure, materials, etc. The next water usage node may include data related to water usage, such as total water usage of the pipe network at the next moment in time, regional water usage, and the like. The next operational factor node may include data that immediately causes a change in the pipe network due to operation of the pipe network dispatcher at a next time, such as pump station outlet pressure, pump station outlet flow, pipe network valve activation, etc. in the pipe network. The next-point pressure node may include pressure data for each point in the lower pipe network at the next time.
The water consumption prediction node can be used for predicting the pipe network water consumption at the next moment according to the data included in the current water consumption node and the data included in the weather and holiday nodes, namely predicting the data included in the next water consumption node. The data included in the current water consumption node can be water consumption data at the current time; or may be water usage data in the current time period. The intelligent decision node can be used for simulating the state change of the operation factor node directly caused by the operation of the dispatching personnel of the pipe network. Specifically, the data in the next operation factor node can be presumed according to the data included in the current water consumption node and the data included in the current measurement point pressure node, the data included in the current operation factor node and the data included in the next water consumption node. The state transition node may be configured to predict data included in a next-measured-point pressure node, that is, predict measured-point pressure information of a lower pipe network at a next time, based on data included in a current water usage node and data included in a current measured-point pressure node, data included in a current operational-factor node, data included in a non-operational-factor node, data included in a next water usage node, and data included in a next operational-factor node.
It should be noted that, in the embodiment of the present invention, the water consumption prediction node and the intelligent decision node may be configured according to actual requirements, and in the case that the water consumption prediction node and the intelligent decision node are not configured, the data included in the corresponding nodes may be obtained by other manners (e.g., manual detection or expert prediction, etc.). That is, if there is accurate future water usage information, i.e., data included in the next water usage node, the water usage prediction module may not be enabled, and the accurate future water usage data may be used; if there is accurate future operational factor information (e.g., scheduling instructions for pump rooms and valves over a known future period of time), i.e., data included by the next operational factor node, the intelligent decision module may not be enabled and accurate future operational information may be used.
The network water supply calculation graph can comprise association relations among all nodes in the network water supply decision flow graph. The method for obtaining the pipe network water supply calculation map may be to determine an association relationship between nodes in the pipe network water supply decision flow chart, and construct the pipe network water supply calculation map based on the association relationship. Specifically, a pipe network water supply calculation graph is constructed based on the service characteristics bound by each service node in the pipe network water supply decision flow graph and the data flow direction information among the service nodes (see the association relationship among the nodes in the pipe network water supply decision flow graph in fig. 2). In the embodiment of the present invention, constructing a water supply calculation graph of a pipe network based on service characteristics bound by each service node in the water supply decision graph of the pipe network and data flow direction information between the service nodes may include: the target decision flow graph can be converted into a calculation graph which can be directly used in environment modeling, namely a pipe network water supply calculation graph, based on the service characteristics bound by each service node in the target decision flow graph and the data flow information among the service nodes.
As an optional embodiment of the present invention, the adjusting the network parameter of the initial pipe network state prediction model according to the expected data and the actual output result of each sample data may include: and for each sample data, adjusting network parameters of the initial pipe network state prediction model according to expected data and actual output results of the sample data.
As an optional embodiment of the present invention, the adjusting the network parameter of the initial pipe network state prediction model according to the expected data and the actual output result of each sample data may include: determining first data distribution of actual output results of the initial pipe network state prediction model for each sample data; determining a second data distribution of expected data of the initial pipe network state prediction model for each sample data; after the first data distribution and the second data distribution are obtained, network parameters of the initial pipe network state prediction model can be adjusted based on the first data distribution and the second data distribution.
The first data distribution may be understood as a data distribution of an actual output result of the initial pipe network state prediction model for each sample data. The second data distribution may be understood as a data distribution of the expected data of the initial pipe network state prediction model for each of the sample data.
In the embodiment of the invention, based on the first data distribution and the second data distribution, the network parameters of the initial pipe network state prediction model are adjusted, and the network parameters of the model can be adjusted from the overall data prediction condition so as to improve the prediction capability of the pipe network state prediction model.
S130, determining the pressure of the next measuring point of the water supply network to be measured based on the state data of the next pipe network.
The pressure of the next measuring point can be understood as the pressure value of the measuring point of the water supply network to be measured at the next moment of the current moment. In the embodiment of the invention, after the next pipe network state data is obtained, the next pipe network state data can be analyzed, so that the pressure of the next measuring point of the water supply network to be measured can be obtained.
According to the technical scheme, the current pipe network state data of the water supply pipe network and parameters affecting the measuring point pressure of the water supply pipe network are obtained, wherein the current pipe network state data comprise the current measuring point pressure of the water supply pipe network to be measured; inputting the current pipe network state data and parameters affecting the pressure of the water supply pipe network measuring points into a pre-trained pipe network state prediction model to obtain the next pipe network state data of the water supply pipe network to be measured; and determining the pressure of the next measuring point of the water supply network to be measured based on the state data of the next pipe network. According to the technical scheme, the pressure of the water supply network is predicted conveniently, and universality and wide applicability of the pressure prediction of the water supply network measuring point are improved.
Example two
Fig. 3 is a schematic structural diagram of a water supply network measuring point pressure prediction device according to a second embodiment of the present invention. As shown in fig. 3, the apparatus includes: the current pipe network state acquisition module 210, the next pipe network state acquisition module 220 and the next pipe network pressure prediction module 230.
The current pipe network state acquisition module 210 is configured to acquire current pipe network state data of a water supply pipe network and parameters affecting a measurement point pressure of the water supply pipe network, where the current pipe network state data includes the current measurement point pressure of the water supply pipe network to be measured; the next pipe network state obtaining module 220 is configured to input the current pipe network state data and the parameters affecting the water supply pipe network measurement point pressure into a pipe network state prediction model that is trained in advance, so as to obtain next pipe network state data of the water supply pipe network to be measured; the next pipe network pressure determining module 230 is configured to determine a next measurement point pressure of the water supply network to be measured based on the next pipe network state data.
According to the technical scheme, the current pipe network state data of the water supply pipe network and the parameters affecting the measuring point pressure of the water supply pipe network are obtained, wherein the current pipe network state data comprise the current measuring point pressure of the water supply pipe network to be measured; inputting the current pipe network state data and the parameters affecting the pressure of the water supply pipe network measuring points into a pre-trained pipe network state prediction model to obtain the next pipe network state data of the water supply pipe network to be measured; and determining the pressure of the next measuring point of the water supply network to be measured based on the state data of the next pipe network. According to the technical scheme, the pressure of the water supply network is predicted conveniently, and universality and wide applicability of the pressure prediction of the water supply network measuring point are improved.
Optionally, the apparatus further comprises a model training module; wherein, training module is used for:
acquiring a plurality of sample data and expected data corresponding to each sample data, wherein the sample data comprise pipe network state data of the water supply pipe network at a certain historical moment and parameters affecting the pressure of measuring points of the water supply pipe network, and the expected data are pipe network state data of the water supply pipe network at the next moment of the certain historical moment;
inputting each sample data into a pre-constructed initial pipe network state prediction model to obtain an actual output result of the initial pipe network state prediction model for each sample data;
and adjusting network parameters of the initial pipe network state prediction model according to expected data and actual output results of each sample data to obtain the trained pipe network state prediction model.
Optionally, the model training module includes a first parameter adjustment unit, where the first parameter adjustment unit is configured to:
determining first data distribution of actual output results of the initial pipe network state prediction model for each sample data;
determining a second data distribution of expected data of the initial pipe network state prediction model for each sample data;
and adjusting network parameters of the initial pipe network state prediction model based on the first data distribution and the second data distribution.
Optionally, the model training module includes a second parameter adjustment unit, where the second parameter adjustment unit is configured to:
and for each sample data, adjusting network parameters of the initial pipe network state prediction model according to expected data and actual output results of the sample data.
Optionally, the apparatus further comprises an initial model building module; an initial model building module for:
acquiring a pre-constructed pipe network water supply decision flow diagram and a pre-constructed pipe network water supply calculation diagram;
and constructing the initial pipe network state prediction model based on the pre-constructed pipe network water supply decision flow diagram and the pre-constructed pipe network water supply calculation diagram.
Optionally, the device further comprises a pipe network water supply calculation map construction module, and the pipe network water supply calculation map construction module is used for:
and determining the association relation among all nodes in the pipe network water supply decision flow diagram, and constructing a pipe network water supply calculation diagram based on the association relation.
Optionally, the pre-constructed pipe network water supply decision flow graph comprises at least one environmental state node and at least one decision agent node, wherein the environmental state node comprises a current environmental state sub-node, an environmental state transition sub-node and a next environmental state sub-node.
The water supply network measuring point pressure prediction device provided by the embodiment of the invention can execute the water supply network measuring point pressure prediction method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
It should be noted that, each unit and module included in the water supply network measuring point pressure prediction device are only divided according to the functional logic, but not limited to the above division, so long as the corresponding functions can be realized; in addition, the specific names of the functional units are also only for distinguishing from each other, and are not used to limit the protection scope of the embodiments of the present invention.
Example III
Fig. 4 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 4, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as the water supply network site pressure prediction method.
In some embodiments, the water supply network site pressure prediction method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the above-described water supply network station pressure prediction method may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the water supply network site pressure prediction method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. The method for predicting the pressure of the water supply network measuring point is characterized by comprising the following steps of:
acquiring current pipe network state data of a water supply pipe network and parameters affecting the measuring point pressure of the water supply pipe network, wherein the current pipe network state data comprises the current measuring point pressure of the water supply pipe network to be measured;
inputting the current pipe network state data and the parameters affecting the pressure of the water supply pipe network measuring points into a pre-trained pipe network state prediction model to obtain the next pipe network state data of the water supply pipe network to be measured;
and determining the pressure of the next measuring point of the water supply network to be measured based on the state data of the next pipe network.
2. The method according to claim 1, wherein the method further comprises:
acquiring a plurality of sample data and expected data corresponding to each sample data, wherein the sample data comprise pipe network state data of the water supply pipe network at a certain historical moment and parameters affecting the pressure of measuring points of the water supply pipe network, and the expected data are pipe network state data of the water supply pipe network at the next moment of the certain historical moment;
inputting each sample data into a pre-constructed initial pipe network state prediction model to obtain an actual output result of the initial pipe network state prediction model for each sample data;
and adjusting network parameters of the initial pipe network state prediction model according to expected data and actual output results of each sample data to obtain the trained pipe network state prediction model.
3. The method of claim 2, wherein said adjusting network parameters of said initial pipe network state prediction model based on expected data and actual output results of each of said sample data comprises:
determining first data distribution of actual output results of the initial pipe network state prediction model for each sample data;
determining a second data distribution of expected data of the initial pipe network state prediction model for each sample data;
and adjusting network parameters of the initial pipe network state prediction model based on the first data distribution and the second data distribution.
4. The method of claim 2, wherein said adjusting network parameters of said initial pipe network state prediction model based on expected data and actual output results of each of said sample data comprises:
and for each sample data, adjusting network parameters of the initial pipe network state prediction model according to expected data and actual output results of the sample data.
5. The method according to claim 2, wherein the method further comprises:
acquiring a pre-constructed pipe network water supply decision flow diagram and a pre-constructed pipe network water supply calculation diagram;
and constructing the initial pipe network state prediction model based on the pre-constructed pipe network water supply decision flow diagram and the pre-constructed pipe network water supply calculation diagram.
6. The method of claim 5, wherein the method further comprises:
and determining the association relation among all nodes in the pipe network water supply decision flow diagram, and constructing a pipe network water supply calculation diagram based on the association relation.
7. The method of claim 5, wherein the nodes in the pre-constructed pipe network water supply decision flow graph comprise weather and holiday nodes, current water usage nodes, current operational factor nodes, current measurement point pressure nodes, non-operational factor nodes, next water usage nodes, next operational factor nodes, next measurement point pressure nodes, water usage prediction nodes, intelligent decision nodes and state transition nodes.
8. The utility model provides a water supply network measurement station pressure prediction device which characterized in that includes:
the system comprises a current pipe network state acquisition module, a current control module and a control module, wherein the current pipe network state acquisition module is used for acquiring current pipe network state data of a water supply pipe network and parameters affecting the measuring point pressure of the water supply pipe network, and the current pipe network state data comprise the current measuring point pressure of the water supply pipe network to be measured;
the next pipe network state obtaining module is used for inputting the current pipe network state data and the parameters affecting the pressure of the water supply pipe network measuring points into a pipe network state prediction model which is trained in advance to obtain the next pipe network state data of the water supply pipe network to be measured;
and the next pipe network pressure determining module is used for determining the pressure of the next measuring point of the water supply network to be measured based on the next pipe network state data.
9. An electronic device, the electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the water supply network site pressure prediction method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to perform the water supply network site pressure prediction method of any one of claims 1-7.
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