CN113139743A - Sewage discharge index analysis method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention relates to the field of data analysis, and discloses a sewage discharge index analysis method, which comprises the following steps: collecting sewage data in a preset time period, and performing data cleaning and noise elimination on the sewage data to obtain target sewage data; performing time series component on the target sewage data to obtain a plurality of component sewage data; detecting the sewage discharge index of each component sewage data by using a plurality of pre-trained data analysis models, wherein the data analysis models and the component sewage data have one-to-one correspondence; and merging the sewage discharge indexes of each component sewage data to obtain the final sewage discharge index of the sewage data. In addition, the invention also relates to a block chain technology, and the component sewage data can be stored in the block chain. In addition, the invention also provides a sewage discharge index analysis device, electronic equipment and a storage medium. The invention can realize flexible monitoring of the sewage discharge index and improve the analysis accuracy of the sewage discharge index.
Description
Technical Field
The invention relates to the field of data analysis, in particular to a sewage discharge index analysis method and device, electronic equipment and a computer readable storage medium.
Background
With the continuous development of science and technology, great convenience is brought to people, meanwhile, great challenges are brought to ecological environment, and especially in the field of water environment, the problem of sewage discharge is always a hot spot of continuous attention of people. At present, sewage discharge is generally discharged based on time periods and discharge quantity divided by environmental departments, but due to complexity of actual business scenes and multiterminal change of sewage discharge data, sewage discharge in each time period is difficult to monitor and is not flexible enough, so that the sewage discharge in each time period cannot be accurately known.
Disclosure of Invention
The invention provides a sewage discharge index analysis method, a sewage discharge index analysis device, electronic equipment and a computer readable storage medium, and mainly aims to realize flexible monitoring of sewage discharge indexes and improve the analysis accuracy of the sewage discharge indexes.
In order to achieve the above object, the present invention provides a method for analyzing a wastewater discharge index, comprising:
collecting sewage data in a preset time period, carrying out data cleaning on the sewage data to obtain standard sewage data, and carrying out noise elimination on the standard sewage data to obtain target sewage data;
performing time series component on the target sewage data to obtain a plurality of component sewage data;
detecting sewage discharge indexes of the plurality of component sewage data by using a plurality of pre-trained data analysis models to obtain the sewage discharge index of each component sewage data, wherein the data analysis models and the component sewage data have a one-to-one correspondence relationship;
and merging the sewage discharge indexes of each component sewage data to obtain the final sewage discharge index of the sewage data.
Optionally, the performing data cleaning on the sewage data to obtain standard sewage data includes:
deleting abnormal data in the sewage data to obtain initial sewage data, and detecting whether the initial sewage data has a data missing value;
if the initial sewage data does not have a data missing value, taking the initial sewage data as standard sewage data;
and if the initial sewage data has the data missing value, performing data filling on the data missing value to obtain standard sewage data.
Optionally, the populating the data missing value includes:
acquiring a missing position of data to be filled, presetting filling parameters at the missing position, and calculating the missing value probability of the filling parameters;
and generating a data missing value of the data to be filled according to the missing position, the filling parameter and the missing value probability.
Optionally, the performing noise elimination on the standard sewage data to obtain target sewage data includes:
acquiring a data field of the standard sewage data, and identifying the data attribute of the standard sewage data according to the data field;
clustering standard sewage data with the same data attribute to obtain a plurality of clustering central points;
and calculating the mean sewage data of each clustering central point, and summarizing the mean sewage data to obtain target sewage data.
Optionally, the performing time-series components on the target sewage data to obtain a plurality of component sewage data includes:
acquiring the acquisition time point of the target sewage data in the preset time period, and constructing a data time series curve of the target sewage data according to the acquisition time point;
identifying a maximum value point and a minimum value point of target sewage data in the data time series curve, and respectively fitting the data time series curve by adopting a spline difference function according to the maximum value point and the minimum value point to obtain a maximum value time series curve and a minimum value time series curve;
and selecting target sewage data of the same amplitude point from the data time series curve according to the maximum value time series curve and the minimum value time series curve to obtain a plurality of component sewage data.
Optionally, the selecting, according to the maximum time series curve and the minimum time series curve, target sewage data of the same amplitude point from the data time series curve to obtain a plurality of component sewage data includes:
calculating a mean time series curve of the maximum time series curve and the minimum time series curve;
calculating a difference value sequence curve of the data time sequence curve and the mean value time sequence curve;
and taking the target sewage data with the same amplitude point in the difference sequence curve as component sewage data to obtain a plurality of component sewage data.
Optionally, before the detecting the sewage discharge indicator of the plurality of component sewage data by using the plurality of pre-trained data analysis models, the method further includes:
acquiring historical data of the component sewage data and corresponding real sewage discharge indexes;
calculating a state value of the historical data by using an input gate in a pre-constructed data analysis model;
calculating an activation value of the historical data by using a forgetting gate in the pre-constructed data analysis model;
calculating a state update value of the historical data according to the state value and the activation value;
calculating the sewage discharge sequence of the state updating value by using an output gate in the pre-constructed data analysis model to obtain a predicted sewage discharge index of the historical data;
calculating the loss values of the predicted sewage discharge index and the real sewage discharge index;
if the loss value is larger than a preset threshold value, adjusting parameters of the pre-constructed data analysis model, and returning to the step of calculating the state value of the historical data by using an input gate in the pre-constructed data analysis model;
and if the loss value is not greater than the preset threshold value, obtaining a trained data analysis model.
In order to solve the above problems, the present invention also provides a sewage discharge index analyzing apparatus, comprising:
the data preprocessing module is used for acquiring sewage data in a preset time period, performing data cleaning on the sewage data to obtain standard sewage data, and performing noise elimination on the standard sewage data to obtain target sewage data;
the data component module is used for carrying out time sequence component on the target sewage data to obtain a plurality of component sewage data;
the index detection module is used for detecting sewage discharge indexes of the plurality of component sewage data by utilizing a plurality of pre-trained data analysis models to obtain the sewage discharge index of each component sewage data, wherein the data analysis models and the component sewage data have a one-to-one correspondence relationship;
and the index merging module is used for merging the sewage discharge indexes of the component sewage data to obtain the final sewage discharge index of the sewage data.
In order to solve the above problem, the present invention also provides an electronic device, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to implement the sewage discharge indicator analysis method described above.
In order to solve the above problem, the present invention also provides a computer-readable storage medium having at least one computer program stored therein, the at least one computer program being executed by a processor in an electronic device to implement the sewage discharge index analysis method described above.
According to the embodiment of the invention, data cleaning and noise elimination are firstly carried out on the original sewage data, some useless data in the collected sewage data can be screened out, and the speed of subsequent data analysis is improved; secondly, the time series component is carried out on the target sewage data to obtain a plurality of component sewage data, the data with the same time scale characteristic can be classified together, the time series complexity of the sewage data is effectively reduced, the flexible monitoring of sewage data discharge is realized, and the accuracy of sewage data discharge is improved; furthermore, in the embodiment of the invention, one component sewage data corresponds to one data analysis model so as to realize sewage discharge index detection of each component sewage data, and the detected sewage discharge indexes are combined to obtain the final sewage discharge index of the sewage data, so that the monitoring accuracy of sewage data discharge can be further improved. Therefore, the sewage discharge index analysis method, the sewage discharge index analysis device, the electronic equipment and the storage medium can realize flexible monitoring of the sewage discharge index and improve the analysis accuracy of the sewage discharge index.
Drawings
FIG. 1 is a schematic flow chart of a method for analyzing a wastewater discharge index according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart illustrating a detailed process of one step of the method for analyzing a wastewater discharge index provided in FIG. 1 according to a first embodiment of the present invention;
fig. 3 is a schematic block diagram of a sewage discharge index analysis apparatus according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an internal structure of an electronic device for implementing a method for analyzing a wastewater discharge index according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a sewage discharge index analysis method. The execution subject of the sewage discharge index analysis method includes, but is not limited to, at least one of electronic devices, such as a server, a terminal, and the like, which can be configured to execute the method provided by the embodiments of the present application. In other words, the sewage discharge index analysis method may be performed by software or hardware installed in a terminal device or a server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Fig. 1 is a schematic flow chart of a sewage discharge index analysis method according to an embodiment of the present invention. In an embodiment of the present invention, the method for analyzing a wastewater discharge index includes:
s1, collecting sewage data in a preset time period, carrying out data cleaning on the sewage data to obtain standard sewage data, and carrying out noise elimination on the standard sewage data to obtain target sewage data.
In the embodiment of the invention, the sewage data comprises production sewage data and domestic sewage data. The production sewage data includes industrial sewage data, agricultural sewage data, medical sewage data and the like, and the domestic sewage data refers to sewage data generated in daily life, and is obtained by various forms of complex mixtures of inorganic matters and organic matters, such as floating and suspended solid particles, colloidal and gelatinous diffusions, pure solutions and the like.
Further, in the embodiment of the present invention, the sewage discharge index of the sewage data generated in the preset time period is calculated by collecting the sewage data in the preset time period, where the preset time period is set based on different user requirements, for example, the requirement of the user a is to collect the sewage data generated every hour for three days, the requirement of the user B is to collect the sewage data generated every day for one month, and the collection of the sewage data can be obtained by the sewage data collecting instrument.
It should be further understood that some missing data and abnormal data may exist in the collected sewage data, and in order to increase the calculation speed of the subsequent sewage data, the embodiment of the present invention performs data cleaning on the sewage data to reduce the data amount of the subsequent data analysis.
In an optional embodiment of the present invention, the performing data cleaning on the sewage data to obtain standard sewage data includes: deleting abnormal data in the sewage data to obtain initial sewage data, and detecting whether the initial sewage data has a data missing value; if the initial sewage data does not have a data missing value, taking the initial sewage data as standard sewage data; and if the initial sewage data has the data missing value, performing data filling on the data missing value to obtain standard sewage data.
In an alternative embodiment, the abnormal data is deleted by a normal distribution algorithm.
In an alternative embodiment, the detection of the missing data value may be implemented by a detection function in a currently known missing data value detection tool, such as a mismap function detection function in an Amelia package tool.
In an optional embodiment, the populating the data missing value includes: acquiring a missing position of data to be filled, presetting a filling parameter at the missing position, calculating the missing value probability of the filling parameter, and generating a data missing value of the data to be filled according to the missing position, the filling parameter and the missing value probability. Optionally, the missing data value is filled by using the following formula:
wherein, L (theta)) Indicating a filled data missing value, xiIndicating the missing position of the ith data missing value, theta indicating the filling parameter corresponding to the filled data missing value, n indicating the quantity of the original sewage data after the weight is removed, and p (x)i| θ) represents the missing value probability of the padding parameter.
Further, because the data in the standard sewage data is complex and diverse in acquisition time period and data source, which easily causes noise, such as severe signal intensity jitter, to exist in the data in the standard sewage data, the embodiment of the present invention performs noise elimination processing on the standard sewage data to ensure smoothness of the standard sewage data, so as to accelerate the speed of subsequent data analysis.
In an optional embodiment of the present invention, the performing noise elimination on the standard sewage data to obtain target sewage data includes: acquiring a data field of the standard sewage data, identifying the data attribute of the standard sewage data according to the data field, clustering the standard sewage data with the same data attribute to obtain a plurality of clustering central points, calculating the mean sewage data of each clustering central point, and summarizing the mean sewage data to obtain target sewage data.
The data field is used for representing entity object parameters of the standard sewage data, the data attribute is used for representing data types of the standard sewage data, such as data names, data characteristics and the like, and the mean sewage data is calculated by a moving average filtering method.
And S2, performing time series component on the target sewage data to obtain a plurality of component sewage data.
It should be understood that the target sewage data is collected based on a preset time period, and in order to better understand the signal change condition of the sewage data at each time point in the preset time period, the invention classifies the data with the same scale time characteristics by performing time series components on the target sewage data, so that the target sewage data has more regularity at each time point in the preset time period, the influence of signal change presented by each sewage data at each time point in the preset time period is reduced, and the analysis accuracy of subsequent sewage data is improved.
In an alternative embodiment of the present invention, referring to fig. 2, the performing time-series components on the target sewage data to obtain a plurality of component sewage data includes:
s20, acquiring the acquisition time point of the target sewage data in the preset time period, and constructing a data time series curve of the target sewage data according to the acquisition time point;
s21, identifying a maximum value point and a minimum value point of target sewage data in the data time series curve, and respectively fitting the data time series curve by adopting a spline difference function according to the maximum value point and the minimum value point to obtain a maximum value time series curve and a minimum value time series curve;
and S22, selecting target sewage data of the same amplitude point from the data time series curve according to the maximum value time series curve and the minimum value time series curve to obtain a plurality of component sewage data.
The data time series curve takes a collection time point as an X-axis direction, the amplitude of target sewage data as a Y-axis direction, and the root of the data time series curve is used for reflecting the signal change condition of the target sewage data along with the collection time point, the maximum value point refers to a point with the maximum amplitude in the data time series curve, the minimum value point refers to a point with the minimum amplitude in the data time series curve, and the spline difference function comprises a cubic spline function.
In an optional embodiment, the selecting, according to the maximum time series curve and the minimum time series curve, target sewage data of the same amplitude point from the data time series curve to obtain a plurality of component sewage data includes: calculating a mean time series curve of the maximum time series curve and the minimum time series curve, calculating a difference value series curve of the data time series curve and the mean time series curve, and taking target sewage data with the same amplitude point in the difference value series curve as component sewage data to obtain a plurality of component sewage data.
Further, in order to ensure high availability and reusability of the component sewage data, the component sewage data may also be stored in a block chain node.
S3, detecting sewage discharge indexes of the component sewage data by using a plurality of pre-trained data analysis models to obtain the sewage discharge index of each component sewage data, wherein the data analysis models and the component sewage data have a one-to-one correspondence relationship.
As can be seen from the above, if the component sewage data have the same scale time characteristic at different time points, the multiple component sewage data have multiple different scale time characteristics, for example, there are N component sewage data: { Y11，Y21，Y31，...，YT1}，{Y12，Y22，Y32，...，YT2}，…，{Y1N，Y2N，Y3N，...，YTNAnd each component sewage data has a same scale time characteristic, so that the embodiment of the invention performs sewage discharge index detection on each component sewage data through a plurality of pre-trained data analysis models to identify the sewage discharge amount of each component sewage data and help a user to plan a better sewage discharge scheme. It should be noted that, because the scale time characteristics of each component sewage data are different, in the embodiment of the present invention, each component sewage data corresponds to one data analysis model to accurately detect the sewage discharge index of each component sewage data, that is, the data analysis model and the component sewage data have a one-to-one correspondence relationship, for example, a component sewage data i corresponds to a data analysis model i, and a component sewage data j corresponds to a data analysis model j.
Further, in an optional embodiment of the present invention, before detecting the sewage discharge indicator of each of the component sewage data by using a plurality of pre-trained data analysis models, the method further includes: training a data analysis model for each of the component sewage data, in detail, the training the data analysis model for each of the component sewage data includes: acquiring historical data of the component sewage data and corresponding real sewage discharge indexes, and calculating a state value of the historical data by using an input gate in a pre-constructed data analysis model; calculating an activation value of the historical data by using a forgetting gate in the pre-constructed data analysis model; calculating a state update value of the historical data according to the state value and the activation value; calculating the sewage discharge sequence of the state updating value by using an output gate in the pre-constructed data analysis model to obtain a predicted sewage discharge index of the historical data; calculating the loss values of the predicted sewage discharge index and the real sewage discharge index; and if the loss value is not greater than the preset threshold value, obtaining a trained data analysis model.
The real sewage discharge index refers to the sewage discharge amount of the historical data in the past time period, the pre-constructed data analysis model is constructed through a Long Short-Term Memory network (LSTM) which is used for solving the problem of Long-Term dependence of a recurrent neural network, and in the invention, the pre-constructed data analysis model is used for predicting the discharge index of the sewage data.
In an alternative embodiment, the state values of the historical data are calculated using the following formula:
wherein itThe value of the state is represented by,
indicates the offset of the cell unit in the input gate, wiDenotes the activation factor of the input gate, ht-1Representing the peak, x, of the historical data at time t-1 of the input gatetRepresenting eye-sensitive text at time t, biIndication inputThe weight of the cell units in the entry.
In an alternative embodiment, the activation value of the historical data is calculated using the following formula:
wherein f istThe value of the activation is represented by,
indicating the bias of the cell unit in the forgetting gate, wfAn activation factor that indicates that the door was forgotten,
represents the peak value, x, of the history data at the moment t-1 of the forgetting gatetRepresenting historical data input at time t, bfRepresenting the weight of the cell unit in the forgetting gate.
In an alternative embodiment, the state update value for the historical data is calculated using the following formula:
wherein, ctRepresents the state update value, ht-1Representing the peak of the target image at time t-1 of the input gate,
indicating the peak of the sensitive text at the moment of forgetting the gate t-1.
In an alternative embodiment, the sequence of effluent discharges for the state update values is calculated using the following formula:
ot＝tan h(ct)
wherein o istRepresenting the sewage discharge sequence, tan h representing the activation function of the output gate, ctRepresenting the state update value.
In an alternative embodiment, the loss values of the predicted wastewater discharge indicator and the actual wastewater discharge indicator are calculated using the following formula:
LC＝mg logmp+(1-mg)log(1-mp)
wherein LC represents a loss value, mgM represents a predicted wastewater discharge indexpAnd the real sewage discharge index is represented. Optionally, the preset threshold is 0.1, and may also be set according to an actual service scenario.
In an alternative embodiment, the parameters of the pre-constructed data analysis model include weights and offsets, and optionally, the parameters of the pre-constructed data analysis model are updated using a gradient descent algorithm, such as a random gradient descent algorithm.
Further, in the embodiment of the present invention, each of the component sewage data is input into a corresponding trained data analysis model to obtain a sewage discharge index of each of the component sewage data, where the sewage discharge index refers to a sewage discharge amount of each of the component sewage data in a future time period.
And S4, merging the sewage discharge indexes of each component sewage data to obtain the final sewage discharge index of the sewage data.
It should be understood that the sewage discharge index is the discharge amount of each component sewage data, not the collected sewage data discharge amount, and therefore, in the embodiment of the present invention, the sewage discharge indexes of each component sewage data are combined, that is, all the sewage discharge indexes are added to obtain the final sewage discharge index of the sewage data, and the sewage discharge amount of the sewage data in the future time period within the preset time period can be predicted based on the final sewage discharge index, so that a user can be helped to make a sewage discharge scheme more efficiently.
According to the embodiment of the invention, data cleaning and noise elimination are firstly carried out on the original sewage data, some useless data in the collected sewage data can be screened out, and the speed of subsequent data analysis is improved; secondly, the time series component is carried out on the target sewage data to obtain a plurality of component sewage data, the data with the same time scale characteristic can be classified together, the time series complexity of the sewage data is effectively reduced, the flexible monitoring of sewage data discharge is realized, and the accuracy of sewage data discharge is improved; furthermore, in the embodiment of the invention, one component sewage data corresponds to one data analysis model so as to realize sewage discharge index detection of each component sewage data, and the detected sewage discharge indexes are combined to obtain the final sewage discharge index of the sewage data, so that the monitoring accuracy of sewage data discharge can be further improved. Therefore, the sewage discharge index analysis method provided by the invention can realize flexible monitoring of the sewage discharge index and improve the analysis accuracy of the sewage discharge index.
FIG. 3 is a functional block diagram of the sewage discharge index analyzer according to the present invention.
The sewage discharge index analyzing apparatus 100 according to the present invention may be installed in an electronic device. According to the realized functions, the sewage discharge index analysis device can comprise a data preprocessing module 101, a data component module 102, an index detection module 103 and an index merging module 104. A module according to the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the present embodiment, the functions regarding the respective modules/units are as follows:
the data preprocessing module 101 is configured to collect sewage data of a preset time period, perform data cleaning on the sewage data to obtain standard sewage data, and perform noise elimination on the standard sewage data to obtain target sewage data.
In the embodiment of the invention, the sewage data comprises production sewage data and domestic sewage data. The production sewage data includes industrial sewage data, agricultural sewage data, medical sewage data and the like, and the domestic sewage data refers to sewage data generated in daily life, and is obtained by various forms of complex mixtures of inorganic matters and organic matters, such as floating and suspended solid particles, colloidal and gelatinous diffusions, pure solutions and the like.
Further, in the embodiment of the present invention, the sewage discharge index of the sewage data generated in the preset time period is calculated by collecting the sewage data in the preset time period, where the preset time period is set based on different user requirements, for example, the requirement of the user a is to collect the sewage data generated every hour for three days, the requirement of the user B is to collect the sewage data generated every day for one month, and the collection of the sewage data can be obtained by the sewage data collecting instrument.
It should be further understood that some missing data and abnormal data may exist in the collected sewage data, and in order to increase the calculation speed of the subsequent sewage data, the embodiment of the present invention performs data cleaning on the sewage data to reduce the data amount of the subsequent data analysis.
In an optional embodiment of the present invention, the data cleaning is performed on the sewage data to obtain standard sewage data, and the data preprocessing module 101 is implemented in the following manner: deleting abnormal data in the sewage data to obtain initial sewage data, and detecting whether the initial sewage data has a data missing value; if the initial sewage data does not have a data missing value, taking the initial sewage data as standard sewage data; and if the initial sewage data has the data missing value, performing data filling on the data missing value to obtain standard sewage data.
In an alternative embodiment, the abnormal data is deleted by a normal distribution algorithm.
In an alternative embodiment, the detection of the missing data value may be implemented by a detection function in a currently known missing data value detection tool, such as a mismap function detection function in an Amelia package tool.
In an optional embodiment, the data missing value is filled, and the data preprocessing module 101 performs the following steps: acquiring a missing position of data to be filled, presetting a filling parameter at the missing position, calculating the missing value probability of the filling parameter, and generating a data missing value of the data to be filled according to the missing position, the filling parameter and the missing value probability. Optionally, the missing data value is filled by using the following formula:
wherein L (θ) represents a filled data missing value, xiIndicating the missing position of the ith data missing value, theta indicating the filling parameter corresponding to the filled data missing value, n indicating the quantity of the original sewage data after the weight is removed, and p (x)i| θ) represents the missing value probability of the padding parameter.
Further, because the data in the standard sewage data is complex and diverse in acquisition time period and data source, which easily causes noise, such as severe signal intensity jitter, to exist in the data in the standard sewage data, the embodiment of the present invention performs noise elimination processing on the standard sewage data to ensure smoothness of the standard sewage data, so as to accelerate the speed of subsequent data analysis.
In an optional embodiment of the present invention, the data preprocessing module 101 performs noise elimination on the standard sewage data to obtain target sewage data, and performs the following steps: acquiring a data field of the standard sewage data, identifying the data attribute of the standard sewage data according to the data field, clustering the standard sewage data with the same data attribute to obtain a plurality of clustering central points, calculating the mean sewage data of each clustering central point, and summarizing the mean sewage data to obtain target sewage data.
The data field is used for representing entity object parameters of the standard sewage data, the data attribute is used for representing data types of the standard sewage data, such as data names, data characteristics and the like, and the mean sewage data is calculated by a moving average filtering method.
The data component module 102 is configured to perform time series component on the target sewage data to obtain multiple component sewage data.
It should be understood that the target sewage data is collected based on a preset time period, and in order to better understand the signal change condition of the sewage data at each time point in the preset time period, the invention classifies the data with the same scale time characteristics by performing time series components on the target sewage data, so that the target sewage data has more regularity at each time point in the preset time period, the influence of signal change presented by each sewage data at each time point in the preset time period is reduced, and the analysis accuracy of subsequent sewage data is improved.
In an optional embodiment of the present invention, the time-series component is performed on the target sewage data to obtain a plurality of component sewage data, and the data component module 102 performs the following steps:
step I, acquiring a collection time point of the target sewage data in the preset time period, and constructing a data time series curve of the target sewage data according to the collection time point;
II, identifying a maximum value point and a minimum value point of target sewage data in the data time series curve, and respectively fitting the data time series curve by adopting a spline difference function according to the maximum value point and the minimum value point to obtain a maximum value time series curve and a minimum value time series curve;
and III, selecting target sewage data of the same amplitude point from the data time series curve according to the maximum value time series curve and the minimum value time series curve to obtain a plurality of component sewage data.
The data time series curve takes a collection time point as an X-axis direction, the amplitude of target sewage data as a Y-axis direction, and the root of the data time series curve is used for reflecting the signal change condition of the target sewage data along with the collection time point, the maximum value point refers to a point with the maximum amplitude in the data time series curve, the minimum value point refers to a point with the minimum amplitude in the data time series curve, and the spline difference function comprises a cubic spline function.
In an optional embodiment, the selecting, according to the maximum time series curve and the minimum time series curve, target sewage data of the same amplitude point from the data time series curve to obtain a plurality of component sewage data, and the data component module 102 performs the following steps: calculating a mean time series curve of the maximum time series curve and the minimum time series curve, calculating a difference value series curve of the data time series curve and the mean time series curve, and taking target sewage data with the same amplitude point in the difference value series curve as component sewage data to obtain a plurality of component sewage data.
Further, in order to ensure high availability and reusability of the component sewage data, the component sewage data may also be stored in a block chain node.
The index detection module 103 is configured to detect sewage discharge indexes of the multiple pieces of component sewage data by using multiple pre-trained data analysis models to obtain a sewage discharge index of each piece of component sewage data, where the data analysis models and the component sewage data have a one-to-one correspondence relationship.
As can be seen from the above, if the component sewage data have the same scale time characteristic at different time points, the multiple component sewage data have multiple different scale time characteristics, for example, there are N component sewage data: { Y11，Y21，Y31，...，YT1}，{Y12，Y22，Y32，...，YT2}，…，{Y1N，Y2N，Y3N，...，YTNAnd each component sewage data has a same scale time characteristic, so that the embodiment of the invention performs sewage discharge index detection on each component sewage data through a plurality of pre-trained data analysis models to identify the sewage discharge amount of each component sewage data and help a user to plan a better sewage discharge scheme. It should be noted that, in the embodiment of the present invention, the number of each component sewage is different because the scale time characteristics of each component sewage data are differentAnd (3) accurately detecting the sewage discharge index of each component sewage data according to a corresponding data analysis model, namely the data analysis model and the component sewage data have one-to-one correspondence, for example, a component sewage data i corresponds to a data analysis model i, and a component sewage data j corresponds to a data analysis model j.
Further, in an optional embodiment of the present invention, before detecting the sewage discharge indicator of each of the component sewage data by using a plurality of pre-trained data analysis models, the indicator detecting module 103 further includes: and training a data analysis model of each component sewage data.
In detail, the training of the data analysis model of each of the component sewage data, the index detection module 103 is executed in the following manner: acquiring historical data of the component sewage data and corresponding real sewage discharge indexes, and calculating a state value of the historical data by using an input gate in a pre-constructed data analysis model; calculating an activation value of the historical data by using a forgetting gate in the pre-constructed data analysis model; calculating a state update value of the historical data according to the state value and the activation value; calculating the sewage discharge sequence of the state updating value by using an output gate in the pre-constructed data analysis model to obtain a predicted sewage discharge index of the historical data; calculating the loss values of the predicted sewage discharge index and the real sewage discharge index; and if the loss value is not greater than the preset threshold value, obtaining a trained data analysis model.
The real sewage discharge index refers to the sewage discharge amount of the historical data in the past time period, the pre-constructed data analysis model is constructed through a Long Short-Term Memory network (LSTM) which is used for solving the problem of Long-Term dependence of a recurrent neural network, and in the invention, the pre-constructed data analysis model is used for predicting the discharge index of the sewage data.
In an alternative embodiment, the indicator detection module 103 calculates the state value of the historical data using the following formula:
wherein itThe value of the state is represented by,
indicates the offset of the cell unit in the input gate, wiDenotes the activation factor of the input gate, ht-1Representing the peak, x, of the historical data at time t-1 of the input gatetRepresenting eye-sensitive text at time t, biRepresenting the weight of the cell units in the input gate.
In an alternative embodiment, the indicator detection module 103 calculates the activation value of the historical data using the following formula:
wherein f istThe value of the activation is represented by,
indicating the bias of the cell unit in the forgetting gate, wfAn activation factor that indicates that the door was forgotten,
represents the peak value, x, of the history data at the moment t-1 of the forgetting gatetRepresenting historical data input at time t, bfRepresenting the weight of the cell unit in the forgetting gate.
In an alternative embodiment, the indicator detection module 103 calculates the state update value of the historical data using the following formula:
wherein, ctRepresents the state update value, ht-1Representing the peak of the target image at time t-1 of the input gate,
indicating the peak of the sensitive text at the moment of forgetting the gate t-1.
In an alternative embodiment, the index detection module 103 calculates the sewage discharge sequence of the state update value using the following formula:
ot＝tan h(ct)
wherein o istRepresenting the sewage discharge sequence, tan h representing the activation function of the output gate, ctRepresenting the state update value.
In an alternative embodiment, the index detection module 103 calculates the loss values of the predicted wastewater discharge index and the actual wastewater discharge index by using the following formula:
LC＝mglogmp+(1-mg)log(1-mp)
wherein LC represents a loss value, mgM represents a predicted wastewater discharge indexpAnd the real sewage discharge index is represented. Optionally, the preset threshold is 0.1, and may also be set according to an actual service scenario.
In an alternative embodiment, the parameters of the pre-constructed data analysis model include weights and offsets, and optionally, the parameters of the pre-constructed data analysis model are updated using a gradient descent algorithm, such as a random gradient descent algorithm.
The index merging module 104 is configured to merge the sewage discharge indexes of each component sewage data to obtain a final sewage discharge index of the sewage data.
It should be understood that the sewage discharge index is the discharge amount of each component sewage data, not the collected sewage data discharge amount, and therefore, in the embodiment of the present invention, the sewage discharge indexes of each component sewage data are combined, that is, all the sewage discharge indexes are added to obtain the final sewage discharge index of the sewage data, and the sewage discharge amount of the sewage data in the future time period within the preset time period can be predicted based on the final sewage discharge index, so that a user can be helped to make a sewage discharge scheme more efficiently.
According to the embodiment of the invention, data cleaning and noise elimination are firstly carried out on the original sewage data, some useless data in the collected sewage data can be screened out, and the speed of subsequent data analysis is improved; secondly, the time series component is carried out on the target sewage data to obtain a plurality of component sewage data, the data with the same time scale characteristic can be classified together, the time series complexity of the sewage data is effectively reduced, the flexible monitoring of sewage data discharge is realized, and the accuracy of sewage data discharge is improved; furthermore, in the embodiment of the invention, one component sewage data corresponds to one data analysis model so as to realize sewage discharge index detection of each component sewage data, and the detected sewage discharge indexes are combined to obtain the final sewage discharge index of the sewage data, so that the monitoring accuracy of sewage data discharge can be further improved. Therefore, the sewage discharge index analysis device provided by the invention can realize flexible monitoring of the sewage discharge index and improve the analysis accuracy of the sewage discharge index.
Fig. 4 is a schematic structural diagram of an electronic device for implementing the method for analyzing a wastewater discharge index according to the present invention.
The electronic device may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may further include a computer program, such as a sewage discharge indicator analysis program, stored in the memory 11 and executable on the processor 10.
In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same function or different functions, and includes one or more Central Processing Units (CPUs), a microprocessor, a digital Processing chip, a graphics processor, a combination of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device by running or executing programs or modules (for example, executing a sewage discharge index analysis program and the like) stored in the memory 11 and calling data stored in the memory 11.
The memory 11 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 11 may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device. The memory 11 may be used not only to store application software installed in the electronic device and various types of data, such as codes of a sewage discharge index analysis program, etc., but also to temporarily store data that has been output or will be output.
The communication bus 12 may be a Peripheral Component Interconnect (PCI) bus or an extended industry standard architecture (ELSA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.
The communication interface 13 is used for communication between the electronic device and other devices, and includes a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 4 shows only an electronic device having components, and those skilled in the art will appreciate that the structure shown in fig. 4 does not constitute a limitation of the electronic device, and may include fewer or more components than those shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management and the like are realized through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.
It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.
The sewage discharge index analysis program stored in the memory 11 of the electronic device is a combination of a plurality of computer programs, and when running in the processor 10, can realize:
collecting sewage data in a preset time period, carrying out data cleaning on the sewage data to obtain standard sewage data, and carrying out noise elimination on the standard sewage data to obtain target sewage data;
performing time series component on the target sewage data to obtain a plurality of component sewage data;
detecting sewage discharge indexes of the plurality of component sewage data by using a plurality of pre-trained data analysis models to obtain the sewage discharge index of each component sewage data, wherein the data analysis models and the component sewage data have a one-to-one correspondence relationship;
and merging the sewage discharge indexes of each component sewage data to obtain the final sewage discharge index of the sewage data.
Specifically, the processor 10 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer program, which is not described herein again.
Further, the electronic device integrated module/unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a non-volatile computer-readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).
The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:
collecting sewage data in a preset time period, carrying out data cleaning on the sewage data to obtain standard sewage data, and carrying out noise elimination on the standard sewage data to obtain target sewage data;
performing time series component on the target sewage data to obtain a plurality of component sewage data;
detecting sewage discharge indexes of the plurality of component sewage data by using a plurality of pre-trained data analysis models to obtain the sewage discharge index of each component sewage data, wherein the data analysis models and the component sewage data have a one-to-one correspondence relationship;
and merging the sewage discharge indexes of each component sewage data to obtain the final sewage discharge index of the sewage data.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.
The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A sewage discharge index analysis method is characterized by comprising the following steps:
collecting sewage data in a preset time period, carrying out data cleaning on the sewage data to obtain standard sewage data, and carrying out noise elimination on the standard sewage data to obtain target sewage data;
performing time series component on the target sewage data to obtain a plurality of component sewage data;
detecting sewage discharge indexes of the plurality of component sewage data by using a plurality of pre-trained data analysis models to obtain the sewage discharge index of each component sewage data, wherein the data analysis models and the component sewage data have a one-to-one correspondence relationship;
and merging the sewage discharge indexes of each component sewage data to obtain the final sewage discharge index of the sewage data.
2. The method for analyzing sewage discharge index according to claim 1, wherein the performing data cleaning on the sewage data to obtain standard sewage data comprises:
deleting abnormal data in the sewage data to obtain initial sewage data, and detecting whether the initial sewage data has a data missing value;
if the initial sewage data does not have a data missing value, taking the initial sewage data as standard sewage data;
and if the initial sewage data has the data missing value, performing data filling on the data missing value to obtain standard sewage data.
3. The sewage discharge index analysis method of claim 2, wherein the populating the missing data values includes:
acquiring a missing position of data to be filled, presetting filling parameters at the missing position, and calculating the missing value probability of the filling parameters;
and generating a data missing value of the data to be filled according to the missing position, the filling parameter and the missing value probability.
4. The method of analyzing a wastewater discharge index according to claim 1, wherein the performing noise elimination on the standard wastewater data to obtain target wastewater data comprises:
acquiring a data field of the standard sewage data, and identifying the data attribute of the standard sewage data according to the data field;
clustering standard sewage data with the same data attribute to obtain a plurality of clustering central points;
and calculating the mean sewage data of each clustering central point, and summarizing the mean sewage data to obtain target sewage data.
5. The method according to any one of claims 1 to 4, wherein the performing time-series components on the target sewage data to obtain a plurality of component sewage data comprises:
acquiring the acquisition time point of the target sewage data in the preset time period, and constructing a data time series curve of the target sewage data according to the acquisition time point;
identifying a maximum value point and a minimum value point of target sewage data in the data time series curve, and respectively fitting the data time series curve by adopting a spline difference function according to the maximum value point and the minimum value point to obtain a maximum value time series curve and a minimum value time series curve;
and selecting target sewage data of the same amplitude point from the data time series curve according to the maximum value time series curve and the minimum value time series curve to obtain a plurality of component sewage data.
6. The method of analyzing a wastewater discharge index according to claim 5, wherein the step of selecting target wastewater data of the same magnitude point from the data time-series curve according to the maximum time-series curve and the minimum time-series curve to obtain a plurality of component wastewater data comprises:
calculating a mean time series curve of the maximum time series curve and the minimum time series curve;
calculating a difference value sequence curve of the data time sequence curve and the mean value time sequence curve;
and taking the target sewage data with the same amplitude point in the difference sequence curve as component sewage data to obtain a plurality of component sewage data.
7. The method of analyzing sewage discharge indicators of claim 1, wherein before detecting the sewage discharge indicators of the plurality of component sewage data using the plurality of pre-trained data analysis models, the method further comprises:
acquiring historical data of the component sewage data and corresponding real sewage discharge indexes;
calculating a state value of the historical data by using an input gate in a pre-constructed data analysis model;
calculating an activation value of the historical data by using a forgetting gate in the pre-constructed data analysis model;
calculating a state update value of the historical data according to the state value and the activation value;
calculating the sewage discharge sequence of the state updating value by using an output gate in the pre-constructed data analysis model to obtain a predicted sewage discharge index of the historical data;
calculating the loss values of the predicted sewage discharge index and the real sewage discharge index;
if the loss value is larger than a preset threshold value, adjusting parameters of the pre-constructed data analysis model, and returning to the step of calculating the state value of the historical data by using an input gate in the pre-constructed data analysis model;
and if the loss value is not greater than the preset threshold value, obtaining a trained data analysis model.
8. An apparatus for analyzing a wastewater discharge index, the apparatus comprising:
the data preprocessing module is used for acquiring sewage data in a preset time period, performing data cleaning on the sewage data to obtain standard sewage data, and performing noise elimination on the standard sewage data to obtain target sewage data;
the data component module is used for carrying out time sequence component on the target sewage data to obtain a plurality of component sewage data;
the index detection module is used for detecting sewage discharge indexes of the plurality of component sewage data by utilizing a plurality of pre-trained data analysis models to obtain the sewage discharge index of each component sewage data, wherein the data analysis models and the component sewage data have a one-to-one correspondence relationship;
and the index merging module is used for merging the sewage discharge indexes of the component sewage data to obtain the final sewage discharge index of the sewage data.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
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 sewage discharge indicator analysis method of any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the sewage discharge index analyzing method according to any one of claims 1 to 7.
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