CN113361772A - Method and device for predicting water quantity of mixed flow pipe network - Google Patents
Method and device for predicting water quantity of mixed flow pipe network Download PDFInfo
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Abstract
The invention relates to a method and a device for predicting water quantity of a mixed flow pipe network. The method comprises the following steps: acquiring water quantity monitoring data of a drainage pipe network, which is sent by preset water quantity monitoring equipment, and preprocessing the water quantity monitoring data; the pretreatment comprises the following steps: cleaning invalid data and optimizing the time resolution of water quantity monitoring data; carrying out data mining analysis processing on the pretreated water quantity monitoring data to construct a water quantity monitoring data item; training the water quantity monitoring data item, and constructing a multilayer perceptron model; and (5) predicting the water quantity of the pipe network by using a multilayer perceptron model. The method improves the accuracy and the practicability of the water quantity prediction of the drainage pipe network.
Description
Technical Field
The invention relates to the technical field of water quantity prediction of pipe networks, in particular to a method and a device for predicting water quantity of a mixed flow pipe network.
Background
In the urban drainage system, a drainage pipe network collects and conveys urban rainwater and sewage to a treatment unit, and plays an important role in starting and stopping. The water yield of a drainage pipe network is fluctuated usually in reality, and the impact load can be caused to the sewage treatment unit, so that the energy consumption is increased, or the overflow is caused, and the operation difficulty of the sewage treatment unit is increased. Therefore, the water quantity of the drainage pipe network is simulated and predicted, the running condition of the sewage treatment unit can be adjusted in advance, and the running parameters are optimized, so that the running of the sewage treatment unit is more efficient. In China, a great amount of combined drainage pipe networks exist, and the phenomenon of mixed misconnection also exists in the split drainage pipe networks. Therefore, the water volume rule of the drainage pipe network is influenced by factors such as the production and living water rule, seasons, date types (working days and rest days) and the like, and inflow infiltration caused by rainfall has a great influence on the water volume of the drainage pipe network. The factors are complex and have certain randomness, so that the water quantity of the pipe network is difficult to predict by using a mechanism model, and the method is suitable for adopting a machine learning algorithm.
At present, the prediction mode of the water quantity of a drainage pipe network comprises the following steps: the artificial fish swarm neural network is used for predicting the water inflow of the sewage plant with higher time precision by adopting an exponential smoothing model, but the consideration on rainfall factors is simpler and the artificial fish swarm neural network is not suitable for mixed flow pipe network manufacturing; and the sewage quantity is predicted by adopting the ELMAN neural network, the mode takes the factors of domestic water, rainfall, continuous weather and the like into consideration, but the prediction time is a daily scale, and the precision is low. Therefore, high time precision prediction for the mixed flow drainage system still remains to be broken through.
Disclosure of Invention
In view of this, the present invention provides a method and an apparatus for predicting water flow in a mixed flow pipe network to overcome the disadvantages of the prior art. The problem of low prediction accuracy of the water quantity of the existing pipe network is solved.
In order to achieve the purpose, the invention adopts the following technical scheme:
a prediction method of mixed flow pipe network water quantity comprises the following steps:
acquiring water quantity monitoring data of a drainage pipe network, which is sent by preset water quantity monitoring equipment, and preprocessing the water quantity monitoring data; the pretreatment comprises the following steps: cleaning invalid data and optimizing the time resolution of the water quantity monitoring data;
carrying out data mining analysis processing on the preprocessed water quantity monitoring data to construct a water quantity monitoring data item;
training the water quantity monitoring data item to construct a multilayer perceptron model;
and predicting the water quantity of the pipe network by using the multilayer perceptron model.
Optionally, the water amount monitoring data includes: monitoring data of water quantity per minute, monitoring data of instantaneous rainfall per minute, monitoring data of temperature per minute, monitoring data of humidity per minute and monitoring data acquisition time;
acquire the water yield monitoring data of the drain pipe network that preset water yield monitoring facilities sent, include:
receiving the minute-by-minute monitoring data of the water quantity monitored by a water meter arranged on a drainage pipe network;
receiving the instantaneous rainfall minute-by-minute monitoring data monitored by a rain gauge arranged in a water collecting piece area of a drainage pipe network, the temperature minute-by-minute monitoring data monitored by a thermometer and the humidity minute-by-minute monitoring data monitored by a hygrometer;
and extracting specific acquisition time of the water quantity minute-by-minute monitoring data, the instantaneous rainfall minute-by-minute monitoring data, the temperature minute-by-minute monitoring data and the humidity minute-by-minute monitoring data as respective monitoring data acquisition time, and determining a date type corresponding to the acquisition time.
Optionally, the preprocessing the water amount monitoring data includes:
carrying out data cleaning on missing values, abnormal values and error values in the water amount monitoring data;
carrying out resolution reconstruction on the water quantity monitoring data after data cleaning, and adjusting the data time precision of the water quantity monitoring data from minute-level precision to hour-level precision; and adjusting the water quantity minute-by-minute monitoring data into water quantity time-by-time monitoring data, adjusting the instantaneous rainfall minute-by-minute monitoring data into instantaneous rainfall time-by-time monitoring data, adjusting the temperature minute-by-minute monitoring data into temperature time-by-time monitoring data, and adjusting the humidity minute-by-minute monitoring data into humidity time-by-time monitoring data.
Optionally, the water amount monitoring data item includes: a numeric data item and a categorical data item;
the data mining analysis processing is carried out on the preprocessed water quantity monitoring data, and a water quantity monitoring data item is constructed, and the method comprises the following steps:
calculating the single-field accumulated rainfall according to the instantaneous rainfall hourly monitoring data and determining a rainfall influence stage;
constructing the numerical data item by the water quantity time-by-time monitoring data, the instantaneous rainfall time-by-time monitoring data, the temperature time-by-time monitoring data, the humidity time-by-time monitoring data, the single-field accumulated rainfall and the monitoring data acquisition time;
constructing the category-type data item from the rainfall impact period and the date type.
Optionally, the calculating the single-field accumulated rainfall according to the instantaneous rainfall hourly monitoring data includes:
trial calculating the variation coefficient of actual rainfall intervals under different preset minimum rainfall time intervals according to the instantaneous rainfall hourly monitoring data and the acquired hourly rainfall historical data of the same pipe network catchment area;
selecting a minimum rainfall time interval with the variation coefficient of 1 according to the trial calculation result;
dividing rainfall fields according to the minimum rainfall time interval;
and calculating the single-field accumulated rainfall according to the rainfall field times and the instantaneous rainfall hourly monitoring data.
Optionally, the determining the rainfall impact stage includes:
screening the instantaneous rainfall hourly monitoring data according to a set standard, selecting dry-land water volume data, and dividing the dry-land water volume data into date types; the date types include: working days and non-working days;
carrying out statistical analysis on the water quantity data of the dry sky to obtain 24-hour hourly average water quantity of the dry sky pipe network;
obtaining the working day dry-sky pipe network basic water volume and the non-working day dry-sky pipe network basic water volume according to the time-by-time average water volume of the dry-sky pipe network and the date type;
and determining the rainfall influence stage by combining the rainfall field, the working day dry sky pipe network basic water volume and the non-working day dry sky pipe network basic water volume.
Optionally, the training the water amount monitoring data item to construct a multilayer perceptron model includes:
constructing data characteristics according to the water quantity monitoring data items;
screening the data characteristics by using a grid search method to obtain optimal parameters of model trial calculation;
training and verifying set division is carried out on the water quantity monitoring data items;
training an initial multilayer perceptron model by using the training set according to the model trial calculation optimal parameters, judging a prediction result of the initial multilayer perceptron model by using the verification set, and determining the initial multilayer perceptron model with the prediction result error meeting preset conditions as the multilayer perceptron model.
Optionally, the screening the instantaneous rainfall hourly monitoring data according to a set standard, and selecting the water quantity data in dry weather includes:
determining whether the date of the monitoring data acquisition time corresponding to the instantaneous rainfall hourly monitoring data is rainfall or not according to the instantaneous rainfall hourly monitoring data;
if the rainfall exists, judging that the date of the rainfall is rainy;
and eliminating the instantaneous rainfall hourly monitoring data corresponding to the date and the instantaneous rainfall hourly monitoring data corresponding to the set days before and after the date, and taking the residual instantaneous rainfall hourly monitoring data as the dry-weather water quantity data.
Optionally, the rainfall affecting stage comprises: pre-rain, mid-rain and post-rain phases; the mid-rain stage is a stage from the beginning of rainfall to the stopping of the rainfall in a single rain field; the post-rain stage is a stage from the end of the mid-rain stage to the recovery of the water quantity of the pipe network to the daily basic water quantity of the pipe network; the pre-rain stage is from the post-rain stage to the next rain stage.
A prediction device of mixed flow pipe network water yield comprises:
the system comprises a monitoring data acquisition module, a water quantity monitoring module and a water quality monitoring module, wherein the monitoring data acquisition module is used for acquiring water quantity monitoring data of a drainage pipe network, which are sent by preset water quantity monitoring equipment, and preprocessing the water quantity monitoring data;
the data item construction module is used for carrying out data mining analysis processing on the preprocessed water quantity monitoring data to construct a water quantity monitoring data item;
the model building module is used for training the water quantity monitoring data item and building a multilayer perceptron model;
and the water quantity prediction module is used for predicting the water quantity of the pipe network by utilizing the multilayer sensor model.
The technical scheme provided by the application can comprise the following beneficial effects:
the application discloses a method for predicting water quantity of a mixed flow pipe network, which comprises the following steps: firstly, acquiring water quantity monitoring data of a drainage pipe network, and preprocessing the water quantity monitoring data; the water quantity monitoring data is detected by water quantity monitoring equipment arranged in a drainage pipe network. Then carrying out data mining analysis processing on the pretreated water quantity monitoring data to construct a water quantity monitoring data item; training the water quantity monitoring data item to construct a multilayer perceptron model; and predicting the water quantity of the pipe network by using the multilayer perceptron model. According to the method, firstly, invalid data in water quantity monitoring data of a drainage pipe network monitored in real time are cleaned, the time resolution of the data is improved, then the water quantity monitoring data after pretreatment are mined to construct a water quantity monitoring data item, then a multilayer sensor model is constructed according to the water quantity monitoring data item, the water quantity of the pipe network is predicted by using the model, in the method, the water quantity monitoring data item is cleaned and the resolution is reconstructed, so that high-quality data in the water quantity monitoring data are selected, then the water quantity is predicted by using the model constructed by the high-quality water quantity monitoring data item, and the prediction accuracy of the multilayer sensor model is improved. Meanwhile, due to the high time resolution of the water amount monitoring data, the time resolution of a prediction result is high, and the practicability of water amount prediction by utilizing a multilayer sensor model is greatly improved.
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 described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flow chart of a method for predicting water flow of a mixed flow pipe network according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for constructing water monitoring data items according to an embodiment of the present invention;
FIG. 3 is a flow chart of a method for constructing a multi-layered perceptron model according to an embodiment of the present invention;
fig. 4 is a structural diagram of a device for predicting the water flow of a mixed flow pipe network according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of the basic water volume of a pipe network in dry days on weekdays and off-weekdays according to an embodiment of the invention;
FIG. 6 is a schematic illustration of a rainfall affecting stage provided by an embodiment of the present invention;
FIG. 7 is a schematic diagram illustrating comparison between a model fitting value and an actual measurement value according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. All other embodiments, which can be derived by a person skilled in the art from the examples given herein without any inventive step, are within the scope of the present invention.
Fig. 1 is a flowchart of a method for predicting the water flow of a mixed flow pipe network according to an embodiment of the present invention. Referring to fig. 1, a method for predicting water flow of a mixed flow pipe network includes:
step 101: acquiring water quantity monitoring data of a drainage pipe network, which is sent by preset water quantity monitoring equipment, and preprocessing the water quantity monitoring data; the pretreatment comprises the following steps: cleaning invalid data and optimizing the time resolution of water quantity monitoring data.
When predicting the water quantity of the drainage pipe network in the embodiment, the relevant water quantity monitoring data of the drainage pipe network needs to be acquired at first, for example: the method comprises the following steps of monitoring data of water quantity by minutes, monitoring data of instantaneous rainfall by minutes, monitoring data of temperature by minutes, monitoring data of humidity by minutes and monitoring data acquisition time. The water quantity minute-by-minute monitoring data is obtained by monitoring a water meter arranged on a drainage pipe network, the instantaneous rainfall minute-by-minute monitoring data is obtained by monitoring a rain gauge arranged in a drainage pipe network catchment area, the temperature minute-by-minute monitoring data is obtained by monitoring a thermometer arranged in the drainage pipe network catchment area, and the humidity minute-by-minute monitoring data is obtained by monitoring the thermometer. Meanwhile, extracting the specific time of the acquisition of the monitoring data to obtain the time of year, month, day and hour as the respective monitoring data acquisition time; and then dividing the monitoring data into working days and non-working days according to the date type of the monitoring data sampling. It should be noted that in this embodiment, the drainage pipe network may be a branch pipe, a main pipe or a trunk pipe, and the selection of the specific drainage pipe network is not fixed, and may be determined according to actual conditions as long as the above data required for water quantity prediction can be monitored.
After the water quantity monitoring data of the drainage pipe network are obtained, the water quantity monitoring data need to be preprocessed, specifically, missing data, abnormal data and error data in the water quantity monitoring data are cleaned firstly. The missing data processing specifically includes: if the rainfall data, the temperature data or the humidity data are missing, directly discarding the data nodes with the missing data; and if the water quantity monitoring data of the drainage pipe network is missing, filling the missing data through a naive Bayesian algorithm. The error data processing specifically includes: and eliminating the data which has no practical significance and exceeds the range of the monitoring equipment, and then discarding or filling the data according to missing data processing. The exception data processing specifically includes: and describing data distribution by adopting a mathematical statistical method to obtain the overall distribution characteristics of the data, mining specific data to find abnormal data, and finally performing professional judgment by combining field knowledge to determine whether an abnormal value is removed or retained.
And then, adjusting the time precision of the water quantity monitoring data subjected to data cleaning from minute-level precision to hour-level precision, and storing the time precision in an hour-by-hour time sequence.
Step 102: and carrying out data mining analysis processing on the pretreated water quantity monitoring data to construct a water quantity monitoring data item.
Step 103: and training the water quantity monitoring data item to construct a multilayer perceptron model.
Step 104: and (5) predicting the water quantity of the pipe network by using a multilayer perceptron model.
fig. 2 is a flowchart of a method for constructing a water monitoring data item according to an embodiment of the present invention. Referring to fig. 2, constructing the water amount monitoring data item includes:
step 201: and calculating the single-field accumulated rainfall according to the instantaneous rainfall hourly monitoring data and determining the rainfall influence stage.
Wherein, calculating the single-field accumulated rainfall comprises:
firstly, trial calculation is carried out on the variation coefficient of the actual rainfall interval under different preset minimum rainfall time intervals according to instantaneous rainfall hourly monitoring data and hourly rainfall historical data; the variation coefficient is the ratio of the standard deviation to the average value, and then the rainfall minimum time interval with the trial calculation time variation coefficient of 1 is selected. The preset minimum rainfall time interval during trial calculation can be determined by personnel according to actual conditions, multiple trial calculations are required in the step, and multiple different preset minimum rainfall time intervals are set to ensure that the coefficient of variation is 1. Meanwhile, in order to improve the accuracy of the variation coefficient, the data used in trial calculation not only include instantaneous rainfall hourly monitoring data, but also use the hourly rainfall historical data in the same catchment area as the instantaneous rainfall hourly monitoring data.
And then dividing the rainfall field according to the minimum rainfall time interval. In the same rainfall, the sum of all instantaneous rainfall monitoring data time by time before the current time is used as the single-field accumulated rainfall.
Meanwhile, the determining of the rainfall influence stage specifically comprises the following steps:
firstly, instantaneous rainfall hourly monitoring data are screened, and water quantity data in dry weather are selected. During screening, if rainfall occurs in the day, the screening is divided into rainy days, and the rest are dry days; removing the dry-land water volume data on the rainy day and the set days before and after the rainy day, and dividing the date type of the residual dry-land water volume data into a working day and a non-working day; carrying out statistical analysis on the water volume of the pipe network of the remaining dry-day water volume data to obtain 24-hour hourly average water volume of the dry-day pipe network, and further obtain the basic water volume of the dry-day pipe network in working days and the basic water volume of the dry-day pipe network in non-working days; and finally, determining the rainfall influence stage by combining the rainfall field, the basic water volume of the working day dry sky pipe network and the basic water volume of the non-working day dry sky pipe network. The selection of the specific days of the set days in this embodiment is not fixed, and may be specifically determined according to the area where the pipe network is located and the city.
Specifically, the rainfall influence phase comprises: pre-rain, mid-rain and post-rain. The stage in the rain is the stage from the beginning of rainfall to the rain stop of single-field rainfall, and the water volume and the water level of a pipe network are influenced by the rainfall in the stage; the method comprises the following steps of (1) recovering the water quantity of a pipe network to the daily basic water quantity of the pipe network from the end of a raining stage, and defining the stage as a post-raining stage; the post-rain phase to the next mid-rain phase is defined as the pre-rain phase.
Step 202: and constructing a numerical data item by using water quantity time-by-time monitoring data, instantaneous rainfall time-by-time monitoring data, temperature time-by-time monitoring data, humidity time-by-time monitoring data single-field accumulated rainfall and monitoring data acquisition time.
Step 203: category-type data items are constructed from rainfall impact phase and date types.
In more detail, step 103, namely training the water quantity monitoring data item, the specific implementation process of constructing the multilayer perceptron model is as follows:
FIG. 3 is a flowchart of a method for constructing a multi-layered perceptron model according to an embodiment of the present invention. Referring to fig. 3, the water quantity monitoring data item is trained to construct a multilayer perceptron model, which includes:
step 301: and constructing data characteristics according to the water quantity monitoring data items. And taking the data characteristics as input data of the multilayer perceptron model. The numerical data items are directly used as data features, and the category data items are vector-coded and then used as the data features. Then, the data is standardized, so that the data format meets the requirements of the model.
Step 302: and screening the data characteristics by using a grid search method to obtain the optimal parameters of model trial calculation. Wherein the parameters include: the method comprises the steps of inputting the number of data nodes, predicting step length, the number of layers of a multilayer perceptron, the number of nodes of each layer, an activation function, iteration times and the like.
In the process of trial calculation of the optimal parameters, a user firstly sets various parameter combinations, such as: the number of data nodes is set to be 8, 12, 16, 20 and 24, the prediction step length is 2, 3 and 4, the number of layers of the multilayer perceptron is 1, 2 and 3, the number of nodes of each layer is 8, 16, 24 and 32, the activation function is sigmod, tanh and ReLU, the number of iterations is 100, 200, 300 and 400, and 2160 choices are provided in total, and each choice is respectively substituted into the model for trial calculation. And finally obtaining model trial calculation optimal parameters.
Step 303: and carrying out training set and verification set division on the water quantity monitoring data items.
And dividing the optimal parameters obtained in the steps into non-rainfall stage data nodes and rainfall stage data nodes, then respectively randomly extracting the data nodes, and dividing a training set and a verification set to balance the data proportion of the non-rainfall stage (a pre-rainfall stage) and the rainfall stage (a mid-rain stage and a post-rain stage).
Step 304: training the initial multilayer sensor model by using the training set according to the model trial calculation optimal parameters, judging the prediction result of the initial multilayer sensor model by using the verification set, and determining the initial multilayer sensor model with the prediction result error meeting the preset conditions as the multilayer sensor model.
After an initial model is obtained through a training set, the initial multi-layer sensor model is used for prediction through a verification set, Relative Mean Absolute Error (RMAE) is selected according to the prediction result of data of the verification set to judge the quality of the prediction result, iterative optimization is continuously carried out on the model, the accuracy of the model is improved, and finally the initial model with the optimal prediction result is used as the multi-layer sensor model.
In the embodiment, the accuracy of prediction is improved on the basis of high-quality data; the semi-automatic preprocessing of the monitoring data is realized, the quality of the data is improved, and therefore the accuracy of prediction is improved. Meanwhile, the influence of rainfall on the water volume of the pipe network is deeply considered in the water volume monitoring data in the embodiment, and the prediction is more reasonable; based on the actual monitoring data of the drainage pipe network and the rainfall, the influence of the rainfall on the water quantity of the pipe network is deeply analyzed, the actual situation is fully reflected, and the reliability of the calculation result is high.
Meanwhile, the time resolution of the monitoring data is optimized from a minute level to an hour level, so that the randomness and the volatility of the data are reduced while the prediction timeliness is basically not sacrificed, the method is closer to the actual situation, and the actual application value of the data is improved; moreover, the prediction result in the application reaches the hour precision, and the operation of a drainage system can be guided: the operation state of the processing unit is adjusted in advance, and the operation parameters of the processing unit are optimized, so that the operation of the processing unit is more efficient.
The multi-layer perceptron model trained in the application has strong robustness; the parameter selection is carried out by combining the monitoring data analysis and the grid searching method, the whole process is simple and convenient, and the popularization is easy. The prestoring method is suitable for pipe networks of different levels and is high in universality. The difference of water supply laws of the branch pipe, the main pipe and the main pipe is considered, the model is respectively constructed, parameters are selected for prediction, and the method is strong in universality and wide in application range.
To more clearly describe the water amount prediction method in the present application, the following description will be made by way of example:
the system is introduced by taking a tail end water inlet source of a sewage plant as a flow distribution and drainage system of a certain basin in the north, and taking a small number of mixed and staggered connections and partial flow distribution areas as scenes. The specific implementation process is as follows:
1. monitoring data collection, preprocessing and storage
Aiming at the drainage system, arranging a water meter on a main pipe to obtain minute-by-minute monitoring data of water quantity of the main pipe; a rain gauge with temperature and humidity functions is arranged in a water collecting sheet area of the drainage system, and rainfall, temperature and humidity minute-by-minute monitoring data are obtained.
Preprocessing the collected monitoring data, and eliminating error values and abnormal values;
and reconstructing the time resolution of the preprocessed monitoring data into an hour level, wherein each data node comprises a numerical data item (instantaneous rainfall, pipe network water quantity, temperature, humidity and specific time) and a category data item (date type and rainfall stage), the date type is divided into a working day and a non-working day, and the specific time is divided according to a 24-hour system from 0 hour to 23 hours.
2. Monitoring data analysis and information mining
Analyzing the daily water fluctuation rule of the pipe network to obtain the dry-day basic water volume of the pipe network, which is shown in fig. 5 under the working day and non-working day conditions respectively;
combining rainfall monitoring data and rainfall duration data to obtain rainfall interval time when the variation coefficient of the rainfall interval time is 1 and the rainfall interval is 13h, and dividing the rainfall field according to the rainfall interval time;
according to the division of the rainfall field times, single-field accumulated rainfall is obtained and is added to data items of data nodes as numerical data items;
and (3) judging the rainfall influence stage by combining the division of the rainfall field and the basic water amount of the pipe network in dry days (as shown in figure 6): the stage in the rain, namely the stage from the beginning of rainfall to the rain stop of single-field rainfall; the method comprises the following steps of (1) recovering the water quantity of a pipe network to the daily basic water quantity of the pipe network from the end of a raining stage, and defining the stage as a post-raining stage; the post-rain phase to the next mid-rain phase is defined as the pre-rain phase.
3. Constructing a multilayer perceptron model and predicting the water quantity of a pipe network
For the type data in the data nodes, a single-hot coding mode is adopted to construct data characteristics; directly using numerical data in the data nodes as data characteristics;
the data is subjected to a standardization process and,X ′ ＝(X-Xstd)/Xmeanwherein:X ′in order to normalize the processed input values,Xin the form of the original input value,XstdandXmeanstandard deviation and mean;
determining parameters of a multilayer perceptron model by using a grid search method, wherein the parameters comprise: the step length of input time sequence data is 8, the prediction step length of a trunk pipe is 2, the number of layers of a multilayer perceptron is 3, the number of nodes of each layer is 16, an activation function is ReLU, and the number of iterations is 300;
randomly extracting data, wherein 80% of samples are used as a training set, 20% of samples are used as a verification set, and the proportion of data in a non-rainfall stage (a pre-rainfall stage) and a rainfall stage (a mid-rain stage and a post-rain stage) is 1:1 during sampling;
comparison of model fit values with measured values as shown in fig. 7, the relative mean error of the current validation set was 10%. And with the accumulation of data, the model is continuously subjected to iterative optimization, and the accuracy of the model is continuously improved.
The embodiment of the invention also provides a device for predicting the water quantity of the mixed flow pipe network. Please see the examples below.
Fig. 4 is a structural diagram of a device for predicting the water flow of a mixed flow pipe network according to an embodiment of the present invention. Referring to fig. 4, a device for predicting the water flow of a mixed flow pipe network comprises:
the monitoring data acquisition module 401 is configured to acquire water volume monitoring data of the drainage pipe network sent by the preset water volume monitoring device, and preprocess the water volume monitoring data.
A data item constructing module 402, configured to perform data mining, analysis and processing on the preprocessed water amount monitoring data, and construct a water amount monitoring data item.
And a model building module 403, configured to train the water amount monitoring data item, and build a multilayer perceptron model.
And a water quantity prediction module 404, configured to predict the water quantity of the pipe network by using the multilayer sensor model.
The data item construction module 402 is specifically configured to: calculating the single-field accumulated rainfall according to the instantaneous rainfall hourly monitoring data and determining a rainfall influence stage; constructing the numerical data item by the water quantity time-by-time monitoring data, the instantaneous rainfall time-by-time monitoring data, the temperature time-by-time monitoring data, the humidity time-by-time monitoring data, the single-field accumulated rainfall and the monitoring data acquisition time; constructing the category-type data item from the rainfall impact period and the date type.
The model building module 403 is specifically configured to: constructing data characteristics according to the water quantity monitoring data items; screening the data characteristics by using a grid search method to obtain optimal parameters of model trial calculation; training and verifying set division is carried out on the water quantity monitoring data items; training an initial multilayer perceptron model by using the training set according to the model trial calculation optimal parameters, judging a prediction result of the initial multilayer perceptron model by using the verification set, and determining the initial multilayer perceptron model with the prediction result error meeting preset conditions as the multilayer perceptron model.
According to the device, the multilayer perceptron model is constructed by monitoring the water quantity monitoring data of the drainage pipe network, the device is based on high-quality data, the prediction accuracy is improved, the influence of rainfall on the water quantity of the pipe network is deeply considered, the prediction is more reasonable, the prediction time resolution is higher, and the practical application value is high.
In order to more clearly introduce a hardware system for implementing the embodiment of the invention, the embodiment of the invention also provides a device for predicting the water quantity of the mixed flow pipe network, which corresponds to the method for predicting the water quantity of the mixed flow pipe network provided by the embodiment of the invention. Please see the examples below.
A prediction device of mixed flow pipe network water quantity comprises:
a processor, and a memory coupled to the processor;
the memory is used for storing a computer program, and the computer program is at least used for executing the prediction method of the mixed flow pipe network water quantity; the processor is used for calling and executing the computer program in the memory.
It is understood that the same or similar parts in the above embodiments may be mutually referred to, and the same or similar parts in other embodiments may be referred to for the content which is not described in detail in some embodiments.
It should be noted that the terms "first," "second," and the like in the description of the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present invention, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (10)
1. A prediction method of mixed flow pipe network water quantity is characterized by comprising the following steps:
acquiring water quantity monitoring data of a drainage pipe network, which is sent by preset water quantity monitoring equipment, and preprocessing the water quantity monitoring data; the pretreatment comprises the following steps: cleaning invalid data and optimizing the time resolution of the water quantity monitoring data;
carrying out data mining analysis processing on the preprocessed water quantity monitoring data to construct a water quantity monitoring data item;
training the water quantity monitoring data item to construct a multilayer perceptron model;
and predicting the water quantity of the pipe network by using the multilayer perceptron model.
2. The method of claim 1, wherein the water volume monitoring data comprises: monitoring data of water quantity per minute, monitoring data of instantaneous rainfall per minute, monitoring data of temperature per minute, monitoring data of humidity per minute and monitoring data acquisition time;
acquire the water yield monitoring data of the drain pipe network that preset water yield monitoring facilities sent, include:
receiving the minute-by-minute monitoring data of the water quantity monitored by a water meter arranged on a drainage pipe network;
receiving the instantaneous rainfall minute-by-minute monitoring data monitored by a rain gauge arranged in a water collecting piece area of a drainage pipe network, the temperature minute-by-minute monitoring data monitored by a thermometer and the humidity minute-by-minute monitoring data monitored by a hygrometer;
and extracting specific acquisition time of the water quantity minute-by-minute monitoring data, the instantaneous rainfall minute-by-minute monitoring data, the temperature minute-by-minute monitoring data and the humidity minute-by-minute monitoring data as respective monitoring data acquisition time, and determining a date type corresponding to the acquisition time.
3. The method of claim 2, wherein the pre-processing the water volume monitoring data comprises:
carrying out data cleaning on missing values, abnormal values and error values in the water amount monitoring data;
carrying out resolution reconstruction on the water quantity monitoring data after data cleaning, and adjusting the data time precision of the water quantity monitoring data from minute-level precision to hour-level precision; and adjusting the water quantity minute-by-minute monitoring data into water quantity time-by-time monitoring data, adjusting the instantaneous rainfall minute-by-minute monitoring data into instantaneous rainfall time-by-time monitoring data, adjusting the temperature minute-by-minute monitoring data into temperature time-by-time monitoring data, and adjusting the humidity minute-by-minute monitoring data into humidity time-by-time monitoring data.
4. The method of claim 3, wherein the water volume monitoring data items comprise: a numeric data item and a categorical data item;
the data mining analysis processing is carried out on the preprocessed water quantity monitoring data, and a water quantity monitoring data item is constructed, and the method comprises the following steps:
calculating the single-field accumulated rainfall according to the instantaneous rainfall hourly monitoring data and determining a rainfall influence stage;
constructing the numerical data item by the water quantity time-by-time monitoring data, the instantaneous rainfall time-by-time monitoring data, the temperature time-by-time monitoring data, the humidity time-by-time monitoring data, the single-field accumulated rainfall and the monitoring data acquisition time;
constructing the category-type data item from the rainfall impact period and the date type.
5. The method of claim 4, wherein said calculating a single-field cumulative rainfall from said instantaneous rainfall hourly monitoring data comprises:
trial calculating the variation coefficient of actual rainfall intervals under different preset minimum rainfall time intervals according to the instantaneous rainfall hourly monitoring data and the acquired hourly rainfall historical data of the same pipe network catchment area;
selecting a minimum rainfall time interval with the variation coefficient of 1 according to the trial calculation result;
dividing rainfall fields according to the minimum rainfall time interval;
and calculating the single-field accumulated rainfall according to the rainfall field times and the instantaneous rainfall hourly monitoring data.
6. The method of claim 5, wherein said determining a rainfall impact period comprises:
screening the instantaneous rainfall hourly monitoring data according to a set standard, selecting dry-land water volume data, and dividing the dry-land water volume data into date types; the date types include: working days and non-working days;
carrying out statistical analysis on the water quantity data of the dry sky to obtain 24-hour hourly average water quantity of the dry sky pipe network;
obtaining the working day dry-sky pipe network basic water volume and the non-working day dry-sky pipe network basic water volume according to the time-by-time average water volume of the dry-sky pipe network and the date type;
and determining the rainfall influence stage by combining the rainfall field, the working day dry sky pipe network basic water volume and the non-working day dry sky pipe network basic water volume.
7. The method of claim 1, wherein training the water monitoring data item to construct a multi-layered perceptron model comprises:
constructing data characteristics according to the water quantity monitoring data items;
screening the data characteristics by using a grid search method to obtain optimal parameters of model trial calculation;
training and verifying set division is carried out on the water quantity monitoring data items;
training an initial multilayer perceptron model by using the training set according to the model trial calculation optimal parameters, judging a prediction result of the initial multilayer perceptron model by using the verification set, and determining the initial multilayer perceptron model with the prediction result error meeting preset conditions as the multilayer perceptron model.
8. The method of claim 6, wherein the step-by-step screening of the instantaneous rainfall time-by-time monitoring data according to a set standard to select the data of the water amount in dry weather comprises:
determining whether the date of the monitoring data acquisition time corresponding to the instantaneous rainfall hourly monitoring data is rainfall or not according to the instantaneous rainfall hourly monitoring data;
if the rainfall exists, judging that the date of the rainfall is rainy;
and eliminating the instantaneous rainfall hourly monitoring data corresponding to the date and the instantaneous rainfall hourly monitoring data corresponding to the set days before and after the date, and taking the residual instantaneous rainfall hourly monitoring data as the dry-weather water quantity data.
9. The method of claim 6, wherein the rainfall affecting period comprises: pre-rain, mid-rain and post-rain phases;
the mid-rain stage is a stage from the beginning of rainfall to the stopping of the rainfall in a single rain field; the post-rain stage is a stage from the end of the mid-rain stage to the recovery of the water quantity of the pipe network to the daily basic water quantity of the pipe network; the pre-rain stage is from the post-rain stage to the next rain stage.
10. The utility model provides a prediction unit of mixed flow pipe network water yield which characterized in that includes:
the system comprises a monitoring data acquisition module, a water quantity monitoring module and a water quality monitoring module, wherein the monitoring data acquisition module is used for acquiring water quantity monitoring data of a drainage pipe network, which are sent by preset water quantity monitoring equipment, and preprocessing the water quantity monitoring data;
the data item construction module is used for carrying out data mining analysis processing on the preprocessed water quantity monitoring data to construct a water quantity monitoring data item;
the model building module is used for training the water quantity monitoring data item and building a multilayer perceptron model;
and the water quantity prediction module is used for predicting the water quantity of the pipe network by utilizing the multilayer sensor model.
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