CN113986897A - Multi-source data fusion method and device based on hydrological robot - Google Patents
Multi-source data fusion method and device based on hydrological robot Download PDFInfo
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Abstract
The invention provides a multi-source data fusion method and device based on a hydrological robot, wherein the data fusion method comprises the following steps: step 1, collecting, transmitting and storing data, collecting and transmitting measurement data of mobile equipment such as a basin hydrological measurement station, a hydrological robot and the like, and storing the data into a hydrological information database after unifying data formats; step 2, constructing multi-source data check rules, including hydrological survey station and robot data mutual check rules and hydrological robot multi-point data check rules; step 3, according to the check rule, diagnosing the abnormal data value and judging the error source; and 4, correcting the hydrological data, converting the time scale of the data and realizing the fusion of the multi-source data. The invention is suitable for the data verification scene of the hydrological survey station and the hydrological robot, and can realize real-time verification and fusion of multi-source data and improve the measurement precision of the hydrological data through multi-machine linkage of the hydrological robot.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a multi-source data fusion method and device based on a hydrological robot.
Background
With the development of the space-air-ground integrated monitoring technology and the upgrading of a water resource management system, the requirement of the hydrological water resource field on the hydrological data quality is further improved. However, at present, the problems that data of the measuring stations on the upstream and the downstream of the drainage basin are difficult to match, the data of a single measuring station deviates from a normal range and the like still exist, so that the data are missing and abnormal.
At present, hydrological measurement stations mainly report hydrological data to various demand departments in a 'measurement and calculation' one-way transmission mode, and the examination and compilation of the hydrological data are usually performed after the completion of the work of hydrological forecasting, water resource management and the like. Therefore, the problem of untimely data verification exists, and better data service is difficult to provide for water resource management work. At present, the technical achievements aiming at the real-time verification of the hydrological data are less, and in the related technologies published and reported, a method for carrying out data verification and fusion on multi-source hydrological monitoring data is absent.
Disclosure of Invention
The invention provides a multi-source data fusion method and device based on a hydrological robot, which are used for solving or at least partially solving the technical problem of low accuracy of hydrological data in the prior art.
In order to solve the technical problem, a first aspect of the present invention provides a multi-source data fusion method based on a hydrographic robot, including:
s1: acquiring hydrologic monitoring data of mobile equipment, transmitting the acquired hydrologic monitoring data to a central computer of an automatic hydrologic measuring and reporting system, converting the hydrologic monitoring data according to a uniform data format and storing the hydrologic monitoring data in a hydrologic information database, wherein the mobile equipment comprises a hydrologic measuring station and a hydrologic robot;
s2: constructing multi-source data check rules, including a hydrological survey station data and robot data mutual check rule and a hydrological robot multi-point data check rule;
s3: judging the abnormal condition of the data according to the multi-source data check rule, and judging the data error source;
s4: and correcting the hydrologic monitoring data according to the data anomaly judgment result and the data error source judgment result, and converting the corrected data into a data set of the same time scale according to a preset time scale.
In one embodiment, in step S1, the unified data format is in the form of a matrix, and the unified data format includes three indexes x, y, ziWherein x is the site name, y is the measurement time, ziAnd (i ═ 1,2, 3.., n) is a corresponding hydrological variable, n is the number of the hydrological variables, and the hydrological variables comprise water level, flow and precipitation.
In one embodiment, the multi-point data check rules of the hydrographic robot in step S2 include different hydrographic variable check rules of the hydrographic robot at different measuring points, the same hydrographic variable check rule at different measuring points, the different hydrographic variable check rules at the same measuring point, and the same hydrographic variable check rule at the same measuring point,
the abnormal condition judgment is carried out according to the hydrological robot at different measuring points and different hydrological variable check rules, and the abnormal condition judgment comprises the following steps: the flow of the current measuring point and the rainfall of the upstream measuring point are mutually verified through the rainfall runoff relation of the hydrological model, and the flow, the water level of the current measuring point, the flow and the water level of the upstream measuring point are mutually verified through the hydraulics model;
the abnormal condition judgment is carried out according to the same hydrological variable check rule of different measuring points, and comprises the following steps: judging the abnormal condition of the hydrological variables according to the same hydrological variable correlation relation of the adjacent sites or the upstream and downstream sites;
the abnormal condition judgment is carried out according to different hydrological variable check rules of the same measuring point, and comprises the following steps: judging the abnormal condition of the data according to the correlation of different hydrological variables of the current measuring point;
the abnormal condition judgment is carried out according to the same hydrological variable check rule at the same measuring point, and the method comprises the following steps: for any single hydrological variable, whether the comparison result is in a normal value range or not is judged through comparison of current measurement data and historical data, and then abnormal conditions of the data are judged.
In one embodiment, the diagnosing the abnormal data values according to the multi-source data verification rule in S3 includes diagnosing abnormal data values according to the multi-source data verification rule of the hydrographic robot, specifically: and acquiring synchronous monitoring data of the hydrological measurement station and the hydrological robot from the same place, and checking the abnormal condition of the data by comparing the difference and the uncertainty of the synchronous monitoring data of the hydrological measurement station and the hydrological robot.
In one embodiment, the step S3 of determining the data error source according to the multi-source data verification rule includes determining the error source according to the hydrological station data and the robot data mutual verification rule, specifically: and respectively analyzing the difference between the hydrological station monitoring data, the hydrological robot monitoring data and the historical monitoring data, judging whether the difference between the hydrological station monitoring data and the historical monitoring data and the difference between the hydrological robot monitoring data and the historical monitoring data are in a normal value range, if so, determining that the measured error is from a random error, otherwise, determining that the measured error is from a system error.
In one embodiment, the step S3 of determining the data error source according to the multi-source data verification rule includes determining the error source according to the multi-source data verification rule of the hydrographic robot, specifically: and (4) checking error sources possibly contained to obtain a final error source.
In one embodiment, the correcting the hydrologic monitoring data according to the data anomaly determination result and the data error source determination result in step S4 includes:
and removing and supplementing abnormal values and correcting error values according to the data abnormality judgment result and the data error source judgment result.
Based on the same inventive concept, the second aspect of the present invention provides a multi-source data fusion device based on a hydrological robot, comprising:
the data storage module is used for acquiring hydrological monitoring data of mobile equipment, transmitting the acquired hydrological monitoring data to a central computer of the hydrological automatic monitoring and reporting system, converting the hydrological monitoring data according to a uniform data format and storing the hydrological monitoring data in a hydrological information database, wherein the mobile equipment comprises a hydrological monitoring station and a hydrological robot;
the verification rule building module is used for building multi-source data verification rules, and the multi-source data verification rules comprise a hydrological survey station data and robot data mutual verification rule and a hydrological robot multi-point data verification rule;
the data diagnosis module is used for judging the abnormal condition of the data according to the multi-source data verification rule and judging the data error source;
and the data fusion module is used for correcting the hydrologic monitoring data according to the data anomaly judgment result and the data error source judgment result and converting the corrected data into a data set of the same time scale according to a preset time scale.
One or more technical solutions in the embodiments of the present application have at least one or more of the following technical effects:
the invention provides a multi-source data fusion method based on a hydrological robot, which comprises the steps of firstly collecting hydrological monitoring data of mobile equipment, converting the hydrological monitoring data according to a uniform data format, storing the hydrological monitoring data in a hydrological information database, and then: constructing a multi-source data check rule, judging the abnormal condition of the data according to the multi-source data check rule, and judging a data error source; and finally, correcting the hydrologic monitoring data according to the data anomaly judgment result and the data error source judgment result, and converting the corrected data into a data set of the same time scale according to a preset time scale. By the method, hydrological data can be automatically acquired from the basin hydrological observation station and the hydrological robot, and are stored according to a uniform format, so that the problem that hydrological basic data are difficult to compile and store is solved, abnormal data conditions can be judged according to multi-source data check rules, data error sources can be judged, abnormal values of data from different sources can be removed and supplemented, error values can be corrected, time scale conversion can be performed on the data, fusion of multi-source hydrological monitoring data is realized, and the accuracy of the hydrological data is 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 introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a multi-source data fusion method based on a hydrological robot in an implementation of the invention;
fig. 2 is a schematic structural diagram of a multi-source data fusion device based on a hydrological robot in the implementation of the present invention.
Detailed Description
The invention mainly aims to provide a real-time verification and fusion method of multi-source hydrological monitoring data, which comprises the following steps: step 1, collecting, transmitting and storing data, collecting measurement data of mobile equipment such as a basin hydrological survey station, a hydrological robot and the like, and storing the data into a hydrological information database after unifying data formats; step 2, constructing multi-source data check rules, including hydrological survey station and robot data mutual check rules and hydrological robot multi-point data check rules; step 3, according to the check rule, diagnosing the abnormal data value and judging the error source; and 4, correcting the hydrological data, converting the time scale of the data and realizing the fusion of the multi-source data. The invention is suitable for the data verification scene of the hydrological survey station and the hydrological robot, and can realize real-time verification and fusion of multi-source data and improve the measurement precision of the hydrological data through multi-machine linkage of the hydrological robot.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
The embodiment of the invention provides a multi-source data fusion method based on a hydrological robot, which comprises the following steps:
s1: acquiring hydrologic monitoring data of mobile equipment, transmitting the acquired hydrologic monitoring data to a central computer of an automatic hydrologic measuring and reporting system, converting the hydrologic monitoring data according to a uniform data format and storing the hydrologic monitoring data in a hydrologic information database, wherein the mobile equipment comprises a hydrologic measuring station and a hydrologic robot;
s2: constructing multi-source data check rules, including a hydrological survey station data and robot data mutual check rule and a hydrological robot multi-point data check rule;
s3: judging the abnormal condition of the data according to the multi-source data check rule, and judging the data error source;
s4: and correcting the hydrologic monitoring data according to the data anomaly judgment result and the data error source judgment result, and converting the corrected data into a data set of the same time scale according to a preset time scale.
Fig. 1 shows a flow of a multi-source data fusion method based on a hydrographic robot according to an embodiment of the present invention.
Specifically, the automatic hydrological measurement and reporting system is a general term for sensors, communication equipment and receiving and processing devices capable of automatically collecting, transmitting and processing various hydrological real-time data. The collected hydrological monitoring data are transmitted to a central computer (central end) from a hydrological monitoring station and a hydrological robot (remote end). In the specific implementation process, data transmission is carried out among the hydrological measurement station at the remote measurement end, the hydrological robot and the central computer through communication equipment.
In a specific example, the hydrological robot is a slide rail type integrated monitoring device, and hydrological variables such as water level, flow and rainfall are automatically monitored along a slide rail mainly in the modes of images, videos and the like.
In one embodiment, in step S1, the unified data format is in the form of a matrix, and the unified data format includes three indexes x, y, ziWherein x is the site name, y is the measurement time, ziAnd (i ═ 1,2, 3.., n) is a corresponding hydrological variable, n is the number of the hydrological variables, and the hydrological variables comprise water level, flow and precipitation.
In the specific implementation process, the station names are hydrological observation stations or hydrological robot numbers. And a hydrological information database is constructed through a unified data format, so that data storage and verification calculation of each measuring point are facilitated.
In one embodiment, the diagnosing the abnormal data values according to the multi-source data verification rule in S3 includes diagnosing abnormal data values according to the multi-source data verification rule of the hydrographic robot, specifically: and acquiring synchronous monitoring data of the hydrological measurement station and the hydrological robot from the same place, and checking the abnormal condition of the data by comparing the difference and the uncertainty of the synchronous monitoring data of the hydrological measurement station and the hydrological robot.
In the specific implementation process, error source discrimination is carried out according to the hydrological survey station data and the robot data mutual verification rule, and the method comprises the following steps: setting the measurement tolerance α1Calculating the relative deviation delta of the synchronous monitoring data of the hydrological measurement station and the hydrological robot1Relative random uncertainty Se1If calculated delta1、Se1Less than the measurement tolerance a1If the measured data is normal, judging the measured data to be abnormal.
In one embodiment, the multi-point data check rules of the hydrographic robot in step S2 include different hydrographic variable check rules of the hydrographic robot at different measuring points, the same hydrographic variable check rule at different measuring points, the different hydrographic variable check rules at the same measuring point, and the same hydrographic variable check rule at the same measuring point,
the abnormal condition judgment is carried out according to the hydrological robot at different measuring points and different hydrological variable check rules, and the abnormal condition judgment comprises the following steps: the flow of the current measuring point and the rainfall of the upstream measuring point are mutually verified through the rainfall runoff relation of the hydrological model, and the flow, the water level of the current measuring point, the flow and the water level of the upstream measuring point are mutually verified through the hydraulics model;
the abnormal condition judgment is carried out according to the same hydrological variable check rule of different measuring points, and comprises the following steps: judging the abnormal condition of the hydrological variables according to the same hydrological variable correlation relation of the adjacent sites or the upstream and downstream sites;
the abnormal condition judgment is carried out according to different hydrological variable check rules of the same measuring point, and comprises the following steps: judging the abnormal condition of the data according to the correlation of different hydrological variables of the current measuring point;
the abnormal condition judgment is carried out according to the same hydrological variable check rule at the same measuring point, and the method comprises the following steps: for any single hydrological variable, whether the comparison result is in a normal value range or not is judged through comparison of current measurement data and historical data, and then abnormal conditions of the data are judged.
In the specific implementation process, the abnormal conditions are judged according to different hydrological variable check rules of the hydrological robot at different measuring points, the mutual check of the flow of the downstream measuring point and the rainfall of the upstream measuring point can be realized through the rainfall runoff relation of the hydrological model, and the mutual check of the flow and the water level of the current measuring point and the flow and the water level of the upstream measuring point can be realized through the hydraulics model. The specific flow of data anomaly determination is as follows: first, a measurement tolerance α is set2Calculating the flow of the downstream measuring point from the rainfall of the upstream measuring point through the hydrological model, and calculating the relative deviation delta between the monitoring data of the downstream measuring point and the data obtained by calculation of the hydrological model2Relative random uncertainty Se2If calculated delta2、Se2Less than the measurement tolerance a2If not, judging the monitoring data to be normal, otherwise, judging the monitoring data to be abnormal. The process of judging data abnormality through the hydraulics model is similar to the process, and details are not repeated here.
The same hydrological variable check rule of different measuring points can be the same hydrological variable correlation relation of adjacent stations or upstream and downstream stations, and the abnormal condition of the hydrological variable is judged. The specific flow of data anomaly determination is as follows: first, a measurement tolerance α is set3Obtaining the correlation between the rainfall of the current station and the rainfall of the peripheral rainfall stations through historical data, obtaining the calculated value of the rainfall of the current station through the actually measured data of the rainfall of the peripheral rainfall stations through the correlation, and calculating the relative deviation delta between the rainfall measured by the current station and the rainfall calculated by the current station3Relative random uncertainty Se3If calculated delta3、Se3Less than the measurement tolerance a3If so, the measured data is judged to be normal,otherwise, it is abnormal.
The check rules of different hydrological variables of the same measuring point can judge the abnormal condition of data through the correlation of different hydrological variables of the current measuring point, such as a water level-flow relation curve, and through the relation between the flow and the water level of the current measuring point. The specific flow of data anomaly determination is as follows: first, a measurement tolerance α is set4Obtaining the correlation between the rainfall of the current station and the rainfall of the peripheral rainfall stations through historical data, obtaining the calculated value of the rainfall of the current station through the actually measured data of the rainfall of the peripheral rainfall stations through the correlation, and calculating the relative deviation delta between the rainfall measured by the current station and the rainfall calculated by the current station4Relative random uncertainty Se4If calculated delta4、Se4Less than the measurement tolerance a4If the measured data is normal, judging the measured data to be abnormal.
The same hydrological variable check rule at the same measuring point comprises the step of judging whether any single hydrological variable is in a normal value range or not by comparing current measured data with historical data, and further judging the abnormal condition of the data.
Note that the relative deviation δ (including δ) in the abnormal situation determination process1、δ2、δ3、δ4) And random uncertainty Se(Se1、Se2、Se3、Se4) Is calculated as follows:
in the formula, zi(i-1, 2,3, …, n) is an observed value of a hydrological variable, zicThe calculated value of the hydrological variable is N, and the number of measurement samples in a certain period is N.
Measurement of the permissible deviation alpha1、α2、α3、α4Can be based on actual conditions(e.g., accuracy requirements) settings.
In the following, the abnormal value diagnosis of the data is described with reference to a specific application scenario.
Scenario one, from the same hydrological variable aspect at different stations.
Based on historical data, a rainfall mapping relation between a current rainfall station and an adjacent rainfall station is established, such as a linear correlation relation, correlation calculation is carried out on rainfall obtained by monitoring different stations, if local rainstorm occurs in the current station, the phenomenon that the rainfall of the adjacent station is obviously increased is caused, and when the rainfall of the current rainfall station is obviously not matched, data abnormality is indicated.
And a second scenario is from different hydrological variables of different stations.
For the rainfall process measured by the upstream station, the runoff process of the downstream section can be calculated according to the hydrological model, the runoff process can be compared with the actual flow process, and when the monitoring data obviously deviates, the data is abnormal.
And a third scenario is from different hydrological variables of a single station.
And for the water level-flow relation curve of a single station, when the monitoring data cannot correspond to the water level-flow relation curve, indicating that the data is abnormal.
Further, in addition to the above embodiments, the error source determination in step S3 in this embodiment is to determine the data error source by way of tracing.
For example, for the hydrological survey station and the robot data mutual verification rule, the error source judgment is carried out as follows: the difference between the hydrological station monitoring data, the hydrological robot monitoring data and the historical monitoring data is analyzed respectively, whether the difference is within a normal value range or not is judged, if the difference is within the normal range, the measured error comes from random errors, and the influence of the errors can be reduced by an interpolation method; if the error is not in the normal range, the error of measurement comes from the system error, and the influence of the error is reduced by performing additional measurement and retesting.
For example, for a multi-point data verification rule of a hydrographic robot, for a verification process of an upstream rainfall and a downstream flow by a hydrographic model, the reasons for data mismatch may include an error of an upstream station measurement data (rainfall), an error of the hydrographic model (rainfall-runoff relationship), and an error of a current station measurement data (flow), and error investigation needs to be performed one by one from the error sources, so as to finally determine the error source.
According to the multi-source data fusion method based on the hydrological robot, hydrological data can be automatically obtained from a plurality of hydrological stations in a drainage basin and the hydrological robot, and are stored according to a uniform format, so that the problem that hydrological basic data are difficult to compile and store is solved; and diagnosing data in modes of a hydrologic variable correlation relation, a hydrologic model, a hydraulics model and the like, and realizing linkage verification of multi-source data.
In one embodiment, the step S3 of determining the data error source according to the multi-source data verification rule includes determining the error source according to the hydrological station data and the robot data mutual verification rule, specifically: and respectively analyzing the difference between the hydrological station monitoring data, the hydrological robot monitoring data and the historical monitoring data, judging whether the difference between the hydrological station monitoring data and the historical monitoring data and the difference between the hydrological robot monitoring data and the historical monitoring data are in a normal value range, if so, determining that the measured error is from a random error, otherwise, determining that the measured error is from a system error.
Error source judgment is carried out according to a data mutual check rule of the hydrological measurement station and the robot, and when the measured error comes from random errors, the influence of the error can be reduced by an interpolation method; when the measured error comes from the system error, the influence of the error is reduced by adding and re-measuring.
In one embodiment, the step S3 of determining the data error source according to the multi-source data verification rule includes determining the error source according to the multi-source data verification rule of the hydrographic robot, specifically: and (4) checking error sources possibly contained to obtain a final error source.
For example, for a multi-point data verification rule of a hydrographic robot, for a verification process of an upstream rainfall and a downstream flow by a hydrographic model, the reasons for data mismatch may include an error of an upstream station measurement data (rainfall), an error of the hydrographic model (rainfall-runoff relationship), and an error of a current station measurement data (flow), and error investigation needs to be performed one by one from the error sources, so as to finally determine the error source.
In one embodiment, the correcting the hydrologic monitoring data according to the data anomaly determination result and the data error source determination result in step S4 includes:
and removing and supplementing abnormal values and correcting error values according to the data abnormality judgment result and the data error source judgment result.
Specifically, the multi-source data fusion is a process of compiling monitoring data from a hydrological survey station and a hydrological robot into a data set of the same time scale after data correction. After the abnormal value diagnosis and the error source judgment in step S3, the abnormal value is removed and supplemented, the error value is corrected, and the data is converted into a data set of the same time scale according to the time scale requirement.
The elimination and supplement of the abnormal value are that after the abnormal value at a certain time point is eliminated, for any hydrologic variable, the average value of data at the time before and after the time point is taken for substitution, if the time points before and after the time point are also abnormal, the most matched historical year is found by a method of dividing the typical year by hydrologic frequency, and the data at the corresponding time of the year is used for substitution.
For example, the water level-flow relation may be used to correct the flow data, or the error may be added to the hydrological model by using a data assimilation method, and the model calculation result may be used to correct the flow data.
Compared with the prior art, the invention has the beneficial effects that:
(1) the multi-source data verification multi-source data fusion method and device based on the hydrological robot can automatically acquire hydrological data from a basin hydrological observation station and the hydrological robot, and store the hydrological data according to a uniform format, so that the problem that hydrological basic data are difficult to compile and store is solved;
(2) the multi-source data check multi-source data fusion method and device based on the hydrological robot can perform mutual check according to the data of a plurality of hydrological measuring points, and diagnose abnormal data values and judge error sources in the modes of hydrological variable correlation, hydrological models, hydraulic models and the like;
(3) the multi-source data verification multi-source data fusion method and device based on the hydrological robot can remove and supplement abnormal values and correct error values of data from different sources, and perform time scale conversion on the data to realize fusion of multi-source hydrological monitoring data.
Example two
Based on the same inventive concept, the embodiment provides a multi-source data fusion device based on a hydrological robot, which comprises:
the data storage module is used for acquiring hydrological monitoring data of mobile equipment, transmitting the acquired hydrological monitoring data to a central computer of the hydrological automatic monitoring and reporting system, converting the hydrological monitoring data according to a uniform data format and storing the hydrological monitoring data in a hydrological information database, wherein the mobile equipment comprises a hydrological monitoring station and a hydrological robot;
the verification rule building module is used for building multi-source data verification rules, and the multi-source data verification rules comprise a hydrological survey station data and robot data mutual verification rule and a hydrological robot multi-point data verification rule;
the data diagnosis module is used for judging the abnormal condition of the data according to the multi-source data verification rule and judging the data error source;
and the data fusion module is used for correcting the hydrologic monitoring data according to the data anomaly judgment result and the data error source judgment result and converting the corrected data into a data set of the same time scale according to a preset time scale.
Fig. 2 shows a structure of a multi-source data fusion device based on a hydrographic robot according to an embodiment of the present invention, and as shown in fig. 2, the multi-source data fusion device of the present embodiment includes a data storage module, a calibration rule construction module, a data diagnosis module, and a data fusion module.
The data fusion module is used for correcting data and converting time scales, and fusion of multi-source data is achieved. The data storage module, the check rule construction module, the data diagnosis module and the data fusion module are all connected in a communication mode and can operate in a linkage mode.
Since the apparatus described in the second embodiment of the present invention is an apparatus used for implementing the multi-source data fusion method based on the hydrographic robot in the first embodiment of the present invention, a person skilled in the art can understand the specific structure and deformation of the apparatus based on the method described in the first embodiment of the present invention, and thus the details are not described herein. All the devices adopted in the method of the first embodiment of the present invention belong to the protection scope of the present invention.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A multi-source data fusion method based on a hydrological robot is characterized by comprising the following steps:
s1: acquiring hydrologic monitoring data of mobile equipment, transmitting the acquired hydrologic monitoring data to a central computer of an automatic hydrologic measuring and reporting system, converting the hydrologic monitoring data according to a uniform data format and storing the hydrologic monitoring data in a hydrologic information database, wherein the mobile equipment comprises a hydrologic measuring station and a hydrologic robot;
s2: constructing multi-source data check rules, including a hydrological survey station data and robot data mutual check rule and a hydrological robot multi-point data check rule;
s3: judging the abnormal condition of the data according to the multi-source data check rule, and judging the data error source;
s4: and correcting the hydrologic monitoring data according to the data anomaly judgment result and the data error source judgment result, and converting the corrected data into a data set of the same time scale according to a preset time scale.
2. The hydrological robot-based multi-source data fusion method of claim 1, wherein in step S1, the unified data format is in a matrix form, and the unified data format comprises three indexes x, y, ziWherein x is the site name, y is the measurement time, ziAnd (i ═ 1,2, 3.., n) is a corresponding hydrological variable, n is the number of the hydrological variables, and the hydrological variables comprise water level, flow and precipitation.
3. The multi-source data fusion method based on hydrological robots as claimed in claim 1, wherein the multi-point data check rules of the hydrological robot in step S2 include different hydrological variable check rules of the hydrological robot at different measuring points, the same hydrological variable check rule at different measuring points, the different hydrological variable check rules at the same measuring point and the same hydrological variable check rule at the same measuring point,
the abnormal condition judgment is carried out according to the hydrological robot at different measuring points and different hydrological variable check rules, and the abnormal condition judgment comprises the following steps: the flow of the current measuring point and the rainfall of the upstream measuring point are mutually verified through the rainfall runoff relation of the hydrological model, and the flow, the water level of the current measuring point, the flow and the water level of the upstream measuring point are mutually verified through the hydraulics model;
the abnormal condition judgment is carried out according to the same hydrological variable check rule of different measuring points, and comprises the following steps: judging the abnormal condition of the hydrological variables according to the same hydrological variable correlation relation of the adjacent sites or the upstream and downstream sites;
the abnormal condition judgment is carried out according to different hydrological variable check rules of the same measuring point, and comprises the following steps: judging the abnormal condition of the data according to the correlation of different hydrological variables of the current measuring point;
the abnormal condition judgment is carried out according to the same hydrological variable check rule at the same measuring point, and the method comprises the following steps: for any single hydrological variable, whether the comparison result is in a normal value range or not is judged through comparison of current measurement data and historical data, and then abnormal conditions of the data are judged.
4. The multi-source data fusion method based on the hydrographic robot as claimed in claim 1, wherein the step S3 of diagnosing abnormal values of data according to the multi-source data verification rule includes the step of diagnosing abnormal values of hydrographic data according to the multi-source data verification rule of the hydrographic robot, specifically: and acquiring synchronous monitoring data of the hydrological measurement station and the hydrological robot from the same place, and checking the abnormal condition of the data by comparing the difference and the uncertainty of the synchronous monitoring data of the hydrological measurement station and the hydrological robot.
5. The multi-source data fusion method based on the hydrographic robot as claimed in claim 1, wherein the step S3 of discriminating the data error source according to the multi-source data verification rule includes discriminating the error source according to the hydrographic survey station data and the robot data mutual verification rule, specifically: and respectively analyzing the difference between the hydrological station monitoring data, the hydrological robot monitoring data and the historical monitoring data, judging whether the difference between the hydrological station monitoring data and the historical monitoring data and the difference between the hydrological robot monitoring data and the historical monitoring data are in a normal value range, if so, determining that the measured error is from a random error, otherwise, determining that the measured error is from a system error.
6. The multi-source data fusion method based on the hydrological robot of claim 1, wherein the step S3 of discriminating the data error source according to the multi-source data verification rule includes discriminating the error source according to the multi-source data verification rule of the hydrological robot, specifically: and (4) checking error sources possibly contained to obtain a final error source.
7. The multi-source data fusion method based on the hydrographic robot as claimed in claim 1, wherein the step S4 of correcting the hydrographic monitoring data according to the data anomaly determination result and the data error source determination result comprises:
and removing and supplementing abnormal values and correcting error values according to the data abnormality judgment result and the data error source judgment result.
8. A multi-source data fusion device based on a hydrological robot is characterized by comprising:
the data storage module is used for acquiring hydrological monitoring data of mobile equipment, transmitting the acquired hydrological monitoring data to a central computer of the hydrological automatic monitoring and reporting system, converting the hydrological monitoring data according to a uniform data format and storing the hydrological monitoring data in a hydrological information database, wherein the mobile equipment comprises a hydrological monitoring station and a hydrological robot;
the verification rule building module is used for building multi-source data verification rules, and the multi-source data verification rules comprise a hydrological survey station data and robot data mutual verification rule and a hydrological robot multi-point data verification rule;
the data diagnosis module is used for judging the abnormal condition of the data according to the multi-source data verification rule and judging the data error source;
and the data fusion module is used for correcting the hydrologic monitoring data according to the data anomaly judgment result and the data error source judgment result and converting the corrected data into a data set of the same time scale according to a preset time scale.
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