Hydrological data analysis method and system based on digital twin

Abstract

The present invention discloses a hydrological data analysis method and system based on digital twins, belonging to the field of data analysis technology. The system includes a data acquisition module, a hydrological analysis module, a water situation warning module and a visualization module; the data acquisition module collects meteorological records, historical logs and three-dimensional model diagrams, and regularly collects hydrological data of rivers in a specified area; the hydrological analysis module divides the sampling area in the three-dimensional model diagram, calculates the abnormal coefficient of each river channel through the hydrological data of the sampling area, calculates the abnormal index according to the meteorological records and historical logs, and identifies the abnormal river channel; the water situation warning module analyzes the water level impact between the abnormal river channel and the downstream river channel and the water level change affected by meteorological factors, thereby calculating the expected water level increase, identifying the dangerous area according to the expected water level increase of the river channel and conducting risk warning; the visualization module displays the abnormal information of the dangerous area and each river channel through the three-dimensional model diagram, and stores the latest hydrological data in the historical log.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and specifically to a hydrological data analysis method and system based on digital twins. Background Art

[0002] Digital twin technology can effectively integrate hydrological data, realize dynamic modeling and simulation of hydrological processes, and provide a powerful decision-making support tool for water resources management. It is one of the key means to promote the development of smart water services. The application prospects of digital twins in the field of hydrological data research are broad, which will help improve the efficiency of water resource utilization and protect the fragile water ecological environment.

[0003] At present, the threshold judgment method is usually used for monitoring and early warning in hydrological management. The water level information at different locations of the river is collected in real time by monitoring equipment, and the pre-set threshold is combined to determine whether to issue an early warning. This method has some problems. On the one hand, the inconsistent riverbed height at different locations of the river leads to different sensitivity to the water level, which easily leads to a large increase difference in the upstream and downstream water levels at the same time. Only independent analysis of the water level information collected by each monitoring device without considering the linkage effect between the upstream and downstream will lead to inefficient and insufficiently intelligent early warning methods. On the other hand, frequent rain and snow in winter cause a large amount of ice and snow to adhere to both sides of the river. When the temperature rises, these ice and snow melt into water and flow into the river, causing the water level to rise rapidly in a short period of time. In this case, it cannot be predicted in advance. Therefore, at this stage, a more efficient and intelligent hydrological data analysis and early warning technology solution is needed to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to provide a hydrological data analysis method and system based on digital twins to solve the problems raised in the above-mentioned background technology.

[0005] In order to solve the above technical problems, the present invention provides a hydrological data analysis method based on digital twins, comprising the following steps:

[0006] S100, collecting meteorological records, historical logs and three-dimensional model diagrams, and regularly collecting hydrological data of rivers in a designated area through monitoring equipment.

[0007] S200, dividing the sampling area according to the river position in the three-dimensional model diagram, calculating the anomaly coefficient of each river channel through the hydrological data of the sampling area, calculating the anomaly index according to the meteorological records and historical logs, and identifying the abnormal river channels.

[0008] S300, analyzing the impact of the water level between the abnormal river channel and the downstream river channel and the water level change caused by meteorological factors, thereby calculating the expected water level increase, identifying the dangerous area according to the expected water level increase of the river channel and issuing risk warnings.

[0009] S400, displays the dangerous areas and abnormal information of each river channel through a three-dimensional model diagram, and stores the latest hydrological data in the historical log.

[0010] In S100, meteorological records include temperature records and snowfall records. Temperature records include ambient temperatures at different times, and snowfall records include the start and end time of each snowfall and the total snowfall during this period. The historical log refers to the hydrological record of the river. Each hydrological record includes the identifier of the monitoring equipment, the collection time and hydrological data. The identifier is used to distinguish different monitoring equipment, which are installed at different locations in the river and collect hydrological data regularly. Hydrological data includes water level values, water temperature, turbidity and pH; the three-dimensional model diagram is a digital twin model of the river in the specified area.

[0011] Meteorological records indicate the snowfall and ambient temperature changes in a specified area. Snowfall records indicate the time period of each snowfall and the total snowfall in that time period. Temperature records indicate the ambient temperature at different times. Snowfall and ambient temperature are actual measured values. Snowfall records are triggered collection, and information is collected only when the snowfall is greater than zero. Temperature records are real-time collection, and the ambient temperature of a specified area is obtained in real time. The specified area is set in advance by the management personnel.

[0012] Hydrological records are collected and generated by monitoring equipment installed at different locations of the river. Each monitoring device regularly collects surrounding hydrological information and generates hydrological records and stores them in historical logs. The three-dimensional model diagram is obtained by using digital twin technology to virtually map the rivers in the specified area in the digital world. The digital twin model is a virtual representation that accurately reflects physical entities through digital technology. It not only contains static three-dimensional geometric graphics, but also includes dynamic models of hydrological data and real-time meteorological information collected by monitoring equipment, which can reflect the real state and dynamics of rivers in the real world.

[0013] S200 includes the following steps:

[0014] S201. Set the snow melting time R. When the interval between the end time of the most recent snowfall in the snowfall record and the current time is less than R, mark all the bifurcations of the river in the three-dimensional model map, and divide the river into N channels according to the bifurcations. Each channel has only one inlet and one outlet. Analyze the number and specific location of monitoring equipment on each channel, set the effective sampling distance U, and divide the circular area with the monitoring equipment location as the center and U as the radius as the sampling area.

[0015] Each river channel includes at least one sampling area. When a river channel does not have a sampling area, it is automatically merged with an adjacent river channel with a sampling area, so that each river channel has a sampling area.

[0016] S202, obtain the hydrological data RD collected by the detection equipment corresponding to each sampling area the most recently now , and the last collected hydrological data RD last ; When the water temperature in both hydrological data is greater than zero, RD now Water level value SW i now 、Water temperature WT i now , turbidity HZ i now and pH i now , and RD last Water level value SW i last 、Water temperature WT i last , turbidity HZ i last and pH i last Substitute them into the anomaly coefficient calculation formula, and calculate the anomaly coefficient of each river channel respectively. The anomaly coefficient calculation formula is:

[0017] ;

[0018] Where AC is the anomaly coefficient, α and β are constants, and j is the number of sampling areas in the river.

[0019] The anomaly coefficient indicates the degree to which the water quality of a river is affected by the melting of ice and snow along the coast. When the water level in the most recently collected hydrological data is higher, the turbidity is higher, the water temperature is lower, and the pH value has changed significantly compared to the previous data, the impact is greater, indicating that the water quality of the river is greatly affected in the real world.

[0020] S203, set the coefficient threshold E, mark the river channel with an abnormal coefficient greater than E and all monitoring equipment in the river channel. Get the end time of the last snowfall in the snowfall record 1 and total snowfall XL 1 , set the melting temperature QT, and retrieve time in the temperature record 1 The time after which the ambient temperature is greater than or equal to QT 2 ; Mark the monitoring device under the collection time distance time 2 The most recent hydrological record is taken as the sample record, and the hydrological records collected after the sample record are taken as the comparison records. The anomaly index of each marked river channel is calculated based on the sample record and the comparison record, and the abnormal river channel is identified.

[0021] In S203, the specific steps of calculating the abnormal index and identifying the abnormal river channel are as follows:

[0022] S20301. Obtain sample records and comparison records of all marked monitoring devices, first divide these records into zones according to whether they belong to the same river channel, then classify the comparison records in the same zone again according to whether the collection time is consistent, and associate each comparison record of the same type with its corresponding sample record.

[0023] S20302. Analyze the water temperature of each pair of associated records, substitute the data in all associated records of the same class whose water temperatures are greater than zero into the anomaly coefficient calculation formula, and calculate the anomaly coefficient of the class; arrange the different classes under each area in order from early to late according to the collection time, and mark the class with anomaly coefficient greater than E.

[0024] S20303. Find the first class combination in which the marked classes appear continuously and the number of classes is greater than p in the order of arrangement, count the number of classes d in the class combination of each zone and calculate the AC of the abnormal coefficient sum , and the collection time range of all associated records in each class combination range and water level extreme value SW range ; Substitute the abnormal index calculation formula to calculate the abnormal index of each marked river channel, set the index threshold H, and take the marked river channel with an abnormal index greater than H as an abnormal river channel; the abnormal index calculation formula is as follows:

[0025] ;

[0026] Where YC is the abnormality index.

[0027] The range of the collection time indicates the duration of the impact of the melting of ice and snow along the river on the river. The shorter the time, the more favorable the environment around the river is for the melting of ice and snow, which makes it melt quickly. Rapid melting is highly destructive to the river. The range of the water level value indicates the impact of the melting of ice and snow along the river on the water level of the river. The anomaly index indicates the overall impact of the river when the ice and snow melt. The greater the sum of the anomaly coefficients and the more total snowfall, the greater the range of the water level value. The smaller the range of the collection time, the greater the overall impact.

[0028] S300 includes the following steps:

[0029] S301. Mark all abnormal river channels in the 3D model map and HD the abnormal river channels according to the river direction. 1 All other channels downstream of the outflow are considered HD 1 Retrieve the hydrological records collected by all monitoring equipment in the abnormal river and its downstream river in the historical log, and calculate the river HD 2 All collection times are time 2 The average water level in the hydrological record SW 2 , SW 2As the River HD 2 In time 2 The average water level.

[0030] S302, HD abnormal river 3 Respectively with HD 3 The downstream river channels are associated with the abnormal river channels, and a training set is established for each pair of associated river channels. The average water level value SW of the associated abnormal river channel and the downstream river channel at the same acquisition time is obtained. yc and SW xy , respectively SW yc As the independent variable SW xy As the dependent variable and packaged as samples, all samples are put into the training set corresponding to the associated river. A linear regression model is established, and the training set of each pair of associated rivers is input into the linear regression model for training to obtain the water level relationship expression of each pair of associated rivers.

[0031] S303, count the number of snowfalls S and the total snowfall of each snowfall in the snowfall record, and calculate the extreme difference of the water level value of each abnormal river after each snowfall according to the content of step S203. Establish a training set for each abnormal river, obtain the total snowfall of each snowfall and the extreme difference of the water level value of the abnormal river, use the total snowfall as the independent variable and the extreme difference of the water level value as the dependent variable and package them into samples, and put these S samples into the training set corresponding to the abnormal river. Establish a linear regression model, input the training set of each abnormal river into the linear regression model for training, and obtain the water level change expression of each abnormal river.

[0032] The specific training content of the expression includes: taking the independent variable of each sample in the training set as the input value of the linear regression model, and taking the difference between the output result of each sample and the dependent variable as the gap coefficient. Setting the error threshold, adjusting the intercept f and the regression coefficient V so that the gap coefficient of all samples in the training set is less than the error threshold, and obtaining the expression after training:

[0033] ;

[0034] In the formula, m is the dependent variable and K is the independent variable.

[0035] S304, obtaining the total snowfall volume XL of the most recent snowfall in the snowfall record 2 , respectively substituted into the water level change expression to calculate the expected water level value of each abnormal river channel, and then substituted the expected water level value into the water level relationship expression to calculate the expected water level value of each downstream river channel, and the expected water level value of the abnormal river channel and the downstream river channel minus their latest water level values ​​to obtain the expected water level increase. Set the increase threshold g, and treat the area where the river channel with an expected water level increase greater than g as a dangerous area and issue a risk warning.

[0036] When a river channel is both an abnormal river channel and the downstream channel of other abnormal river channels, the predicted water level increase in the river channel is summed and then compared with the increase threshold for judgment.

[0037] In S400, the dangerous areas are displayed in the three-dimensional model diagram in descending order according to the water level increase, as well as the abnormal coefficient and hydrological data of each river channel, and the hydrological information collected by the monitoring equipment is stored in the historical log.

[0038] The hydrological data analysis system based on digital twins includes data acquisition module, hydrological analysis module, water situation warning module and visualization module.

[0039] The data acquisition module is used to collect meteorological records, historical logs and three-dimensional model diagrams, as well as to regularly collect hydrological data of rivers in the specified area. The hydrological analysis module is used to divide the sampling area in the three-dimensional model diagram, calculate the abnormal coefficient of each river channel through the hydrological data of the sampling area, calculate the abnormal index and identify the abnormal river channel based on the meteorological records and historical logs. The water situation warning module is used to analyze the water level impact between the abnormal river channel and the downstream river channel and the water level changes affected by meteorological factors, so as to calculate the expected water level increase, identify the dangerous area based on the expected water level increase of the river channel and issue a risk warning. The visualization module is used to display the abnormal information of the dangerous area and each river channel through the three-dimensional model diagram, and store the latest hydrological data in the historical log.

[0040] The data collection module includes a meteorological information collection unit, a hydrological data collection unit and a historical log collection unit.

[0041] The meteorological information collection unit is used to collect temperature records and snowfall records. The temperature record includes the ambient temperature at different times, and the snowfall record includes the start and end time of each snowfall and the total snowfall during this period.

[0042] The hydrological data acquisition unit is used to collect hydrological data and three-dimensional model diagrams. Hydrological data includes water level, water temperature, turbidity and pH. The three-dimensional model diagram is a digital twin model of the river in the specified area.

[0043] The historical log collection unit is used to collect hydrological records of the river, and each hydrological record includes an identifier of the monitoring device, collection time and hydrological data.

[0044] The hydrological analysis module includes a regional division unit and an anomaly analysis unit.

[0045] The area division unit is used to divide the sampling area.

[0046] First, set the snow melting time R. When the interval between the end time of the most recent snowfall in the snowfall record and the current time is less than R, mark all the bifurcations of the river in the 3D model map, and divide the river into N channels with only one inlet and outlet according to the bifurcations. Then analyze the number and specific location of monitoring equipment on each channel, set the effective sampling distance U, and divide the circular area with the monitoring equipment location as the center and U as the radius as the sampling area.

[0047] The abnormal analysis unit is used to analyze the abnormal conditions of the river channel.

[0048] First, obtain the hydrological data RD collected by the detection equipment in each sampling area for the most recent time now , and the last collected hydrological data RD last ; When the water temperature in both hydrological data is greater than zero, obtain RD now Water level value SW i now 、Water temperature WT i now , turbidity HZ i now and pH i now , and RD last Water level value SW i last 、Water temperature WT i last , turbidity HZ i last and pH i last ;

[0049] Secondly, the abnormal coefficient is calculated by the formula Calculate the anomaly coefficient of each river separately; set the coefficient threshold E, mark the river with anomaly coefficient greater than E and all monitoring equipment in the river; obtain the end time of the last snowfall in the snowfall record 1 and total snowfall XL 1 , set the melting temperature QT, and retrieve time in the temperature record 1 The time after which the ambient temperature is greater than or equal to QT 2 ; Among them, α and β are constants, and j is the number of sampling areas in the river.

[0050] Finally, mark the monitoring device under the collection time distance time 2 The most recent hydrological record is used as the sample record, and the hydrological records collected after the sample record are used as the comparison records. The anomaly index of each marked river channel is calculated based on the sample record and the comparison record, and the abnormal river channel is identified.

[0051] The water situation warning module includes a relationship analysis unit and a water potential warning unit.

[0052] The relationship analysis unit is used to analyze the impact and change relationship of river water level.

[0053] First, the abnormal river channel HD in the 3D model 1 All other channels downstream of the outflow are considered HD 1 For the downstream river channel, obtain the hydrological records collected by all monitoring equipment in the abnormal river channel and its downstream river channel, and calculate the average water level at the same collection time in the river channel as the average water level value.

[0054] Secondly, the abnormal river channel is associated with each of its downstream rivers, and a training set is established for each pair of associated rivers. The average water level values ​​at the same acquisition time in the associated rivers are used as independent variables and dependent variables, respectively, and packaged into samples and put into the corresponding training set. A linear regression model is established, and each training set is input for training to obtain the water level relationship expression for each pair of associated rivers.

[0055] Finally, a training set was established for each abnormal river channel, and the water level range of the abnormal river channel after each snowfall was calculated. The total snowfall and the water level range of each snowfall were used as independent variables and dependent variables, respectively, and packaged into samples and put into the corresponding training set. A linear regression model was established, and each training set was input for training to obtain the water level change expression of each abnormal river channel.

[0056] The water potential warning unit is used to identify dangerous areas and issue risk warnings. Substitute them into the water level change expression to calculate the expected water level value of each abnormal river channel, and then substitute the expected water level value into the water level relationship expression to calculate the expected water level value of each downstream river channel. Then subtract the latest water level value from the expected water level value of the river channel to get the expected water level increase. Set the increase threshold g, and treat the area where the river channel with the expected water level increase greater than g as a dangerous area and issue a risk warning.

[0057] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0058] Refined analysis: The present invention divides the river into channels according to the bifurcations, divides the sampling areas in each channel, and calculates the abnormal coefficient of the channel based on the hydrological data of each sampling area. The channels that meet the requirements are screened out and marked by the abnormal coefficient, and the overall degree of impact of the marked channels when the ice and snow melt is analyzed to calculate the abnormal index. The abnormal channels are screened out again according to the abnormal index for further analysis, which is more refined than the traditional technology that analyzes the river as a whole.

[0059] Intelligent prediction: This invention analyzes the water level influence relationship between various river channels, as well as the relationship between snowfall and water level changes in each river channel, and establishes a relational expression. The snowfall is substituted into the relational expression one by one to calculate the expected water level increase of each river channel, and the dangerous area is identified and risk warning is issued based on the expected water level increase. This is more intelligent than the traditional technology that only relies on the mechanical judgment of the pre-set water level threshold.

[0060] In summary, compared with traditional technologies, the present invention has the advantages of refined analysis and intelligent prediction, and can improve the efficiency of hydrological data monitoring and early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0062] Figure 1 It is a schematic diagram of the overall process of the hydrological data analysis method based on digital twins of the present invention;

[0063] Figure 2 It is a schematic diagram of the S200 part of the flow chart of the hydrological data analysis method based on digital twins of the present invention;

[0064] Figure 3 It is a partial flow diagram of S300 of the hydrological data analysis method based on digital twins of the present invention;

[0065] Figure 4 It is a schematic diagram of the overall structure of the hydrological data analysis system based on digital twins of the present invention. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] See also Figure 1 , the present invention provides a hydrological data analysis method based on digital twins, comprising the following steps:

[0068] S100, collecting meteorological records, historical logs and three-dimensional model diagrams, and regularly collecting hydrological data of rivers in a designated area through monitoring equipment.

[0069] S200, dividing the sampling area according to the river position in the three-dimensional model diagram, calculating the anomaly coefficient of each river channel through the hydrological data of the sampling area, calculating the anomaly index according to the meteorological records and historical logs, and identifying the abnormal river channels.

[0070] S300, analyzing the impact of the water level between the abnormal river channel and the downstream river channel and the water level change caused by meteorological factors, thereby calculating the expected water level increase, identifying the dangerous area according to the expected water level increase of the river channel and issuing risk warnings.

[0071] S400, displays the dangerous areas and abnormal information of each river channel through a three-dimensional model diagram, and stores the latest hydrological data in the historical log.

[0072] In S100, meteorological records include temperature records and snowfall records. Temperature records include ambient temperatures at different times, and snowfall records include the start and end time of each snowfall and the total snowfall during this period. The historical log refers to the hydrological record of the river. Each hydrological record includes the identifier of the monitoring equipment, the collection time and hydrological data. The identifier is used to distinguish different monitoring equipment, which are installed at different locations in the river and collect hydrological data regularly. Hydrological data includes water level values, water temperature, turbidity and pH; the three-dimensional model diagram is a digital twin model of the river in the specified area.

[0073] Meteorological records indicate the snowfall and ambient temperature changes in a specified area. Snowfall records indicate the time period of each snowfall and the total snowfall in that time period. Temperature records indicate the ambient temperature at different times. Snowfall and ambient temperature are actual measured values. Snowfall records are triggered collection, and information is collected only when the snowfall is greater than zero. Temperature records are real-time collection, and the ambient temperature of a specified area is obtained in real time. The specified area is set in advance by the management personnel.

[0074] Hydrological records are collected and generated by monitoring equipment installed at different locations of the river. Each monitoring device regularly collects surrounding hydrological information and generates hydrological records and stores them in historical logs. The three-dimensional model diagram is obtained by using digital twin technology to virtually map the rivers in the specified area in the digital world. The digital twin model is a virtual representation that accurately reflects physical entities through digital technology. It not only contains static three-dimensional geometric graphics, but also includes dynamic models of hydrological data and real-time meteorological information collected by monitoring equipment, which can reflect the real state and dynamics of rivers in the real world.

[0075] See also Figure 2 , S200 includes the following steps:

[0076] S201. Set the snow melting time R. When the interval between the end time of the most recent snowfall in the snowfall record and the current time is less than R, mark all the bifurcations of the river in the three-dimensional model map, and divide the river into N channels according to the bifurcations. Each channel has only one inlet and one outlet. Analyze the number and specific location of monitoring equipment on each channel, set the effective sampling distance U, and divide the circular area with the monitoring equipment location as the center and U as the radius as the sampling area.

[0077] Each river channel includes at least one sampling area. When a river channel does not have a sampling area, it is automatically merged with an adjacent river channel with a sampling area, so that each river channel has a sampling area.

[0078] S202, obtain the hydrological data RD collected by the detection equipment corresponding to each sampling area the most recently now , and the last collected hydrological data RD last ; When the water temperature in both hydrological data is greater than zero, RD now Water level value SW i now 、Water temperature WT i now , turbidity HZ i now and pH i now , and RD last Water level value SW i last 、Water temperature WT i last , turbidity HZ i last and pH i last Substitute them into the anomaly coefficient calculation formula, and calculate the anomaly coefficient of each river channel respectively. The anomaly coefficient calculation formula is:

[0079] ;

[0080] Where AC is the anomaly coefficient, α and β are constants, and j is the number of sampling areas in the river.

[0081] The anomaly coefficient indicates the degree to which the water quality of a river is affected by the melting of ice and snow along the coast. When the water level in the most recently collected hydrological data is higher, the turbidity is higher, the water temperature is lower, and the pH value has changed significantly compared to the previous data, the impact is greater, indicating that the water quality of the river is greatly affected in the real world.

[0082] S203, set the coefficient threshold E, mark the river channel with an abnormal coefficient greater than E and all monitoring equipment in the river channel. Get the end time of the last snowfall in the snowfall record1 and total snowfall XL 1 , set the melting temperature QT, and retrieve time in the temperature record 1 The time after which the ambient temperature is greater than or equal to QT 2 ; Mark the monitoring device under the collection time distance time 2 The most recent hydrological record is used as the sample record, and the hydrological records collected after the sample record are used as the comparison records. The anomaly index of each marked river channel is calculated based on the sample record and the comparison record, and the abnormal river channel is identified.

[0083] In S203, the specific steps of calculating the abnormal index and identifying the abnormal river channel are as follows:

[0084] S20301. Obtain sample records and comparison records of all marked monitoring devices, first divide these records into zones according to whether they belong to the same river channel, then classify the comparison records in the same zone again according to whether the collection time is consistent, and associate each comparison record of the same type with its corresponding sample record.

[0085] S20302. Analyze the water temperature of each pair of associated records, substitute the data in all associated records of the same class whose water temperatures are greater than zero into the anomaly coefficient calculation formula, and calculate the anomaly coefficient of the class; arrange the different classes under each area in order from early to late according to the collection time, and mark the class with anomaly coefficient greater than E.

[0086] S20303. Find the first class combination in which the marked classes appear continuously and the number of classes is greater than p in the order of arrangement, count the number of classes d in the class combination of each zone and calculate the AC of the abnormal coefficient sum , and the collection time range of all associated records in each class combination range and water level extreme value SW range ; Substitute the abnormal index calculation formula to calculate the abnormal index of each marked river channel, set the index threshold H, and take the marked river channel with an abnormal index greater than H as an abnormal river channel; the abnormal index calculation formula is as follows:

[0087] ;

[0088] Where YC is the abnormality index.

[0089] The range of the collection time indicates the duration of the impact of the melting of ice and snow along the river on the river. The shorter the time, the more favorable the environment around the river is for the melting of ice and snow, which makes it melt quickly. Rapid melting is highly destructive to the river. The range of the water level value indicates the impact of the melting of ice and snow along the river on the water level of the river. The anomaly index indicates the overall impact of the river when the ice and snow melt. The greater the sum of the anomaly coefficients and the more total snowfall, the greater the range of the water level value. The smaller the range of the collection time, the greater the overall impact.

[0090] See also Figure 3 , S300 includes the following steps:

[0091] S301. Mark all abnormal river channels in the 3D model map and HD the abnormal river channels according to the river direction. 1 All other channels downstream of the outflow are considered HD 1 Retrieve the hydrological records collected by all monitoring equipment in the abnormal river and its downstream river in the historical log, and calculate the river HD 2 All collection times are time 2 The average water level in the hydrological record SW 2 , SW 2 As the River HD 2 In time 2 The average water level.

[0092] S302, HD abnormal river 3 Respectively with HD 3 The downstream river channels are associated with the abnormal river channels, and a training set is established for each pair of associated river channels. The average water level value SW of the associated abnormal river channel and the downstream river channel at the same acquisition time is obtained. yc and SW xy , respectively SW yc As the independent variable SW xy As the dependent variable and packaged as samples, all samples are put into the training set corresponding to the associated river. A linear regression model is established, and the training set of each pair of associated rivers is input into the linear regression model for training to obtain the water level relationship expression of each pair of associated rivers.

[0093] S303, count the number of snowfalls S and the total snowfall of each snowfall in the snowfall record, and calculate the extreme difference of the water level value of each abnormal river after each snowfall according to the content of step S203. Establish a training set for each abnormal river, obtain the total snowfall of each snowfall and the extreme difference of the water level value of the abnormal river, use the total snowfall as the independent variable and the extreme difference of the water level value as the dependent variable and package them into samples, and put these S samples into the training set corresponding to the abnormal river. Establish a linear regression model, input the training set of each abnormal river into the linear regression model for training, and obtain the water level change expression of each abnormal river.

[0094] The specific training content of the expression includes: taking the independent variable of each sample in the training set as the input value of the linear regression model, and taking the difference between the output result of each sample and the dependent variable as the gap coefficient. Setting the error threshold, adjusting the intercept f and the regression coefficient V so that the gap coefficient of all samples in the training set is less than the error threshold, and obtaining the expression after training:

[0095] ;

[0096] In the formula, m is the dependent variable and K is the independent variable.

[0097] S304, obtaining the total snowfall volume XL of the most recent snowfall in the snowfall record 2 , respectively substituted into the water level change expression to calculate the expected water level value of each abnormal river channel, and then substituted the expected water level value into the water level relationship expression to calculate the expected water level value of each downstream river channel, and the expected water level value of the abnormal river channel and the downstream river channel minus their latest water level values ​​to obtain the expected water level increase. Set the increase threshold g, and treat the area where the river channel with an expected water level increase greater than g as a dangerous area and issue a risk warning.

[0098] When a river channel is both an abnormal river channel and the downstream channel of other abnormal river channels, the predicted water level increase in the river channel is summed and then compared with the increase threshold for judgment.

[0099] In S400, the dangerous areas are displayed in the three-dimensional model diagram in descending order according to the water level increase, as well as the abnormal coefficient and hydrological data of each river channel, and the hydrological information collected by the monitoring equipment is stored in the historical log.

[0100] See also Figure 4 The present invention provides a hydrological data analysis system based on digital twins, including a data acquisition module, a hydrological analysis module, a water situation warning module and a visualization module.

[0101] The data acquisition module is used to collect meteorological records, historical logs and three-dimensional model diagrams, as well as to regularly collect hydrological data of rivers in the specified area. The hydrological analysis module is used to divide the sampling area in the three-dimensional model diagram, calculate the abnormal coefficient of each river channel through the hydrological data of the sampling area, calculate the abnormal index and identify the abnormal river channel based on the meteorological records and historical logs. The water situation warning module is used to analyze the water level impact between the abnormal river channel and the downstream river channel and the water level changes affected by meteorological factors, so as to calculate the expected water level increase, identify the dangerous area based on the expected water level increase of the river channel and issue a risk warning. The visualization module is used to display the abnormal information of the dangerous area and each river channel through the three-dimensional model diagram, and store the latest hydrological data in the historical log.

[0102] The data collection module includes a meteorological information collection unit, a hydrological data collection unit and a historical log collection unit.

[0103] The meteorological information collection unit is used to collect temperature records and snowfall records. The temperature record includes the ambient temperature at different times, and the snowfall record includes the start and end time of each snowfall and the total snowfall during this period.

[0104] The hydrological data acquisition unit is used to collect hydrological data and three-dimensional model diagrams. Hydrological data includes water level, water temperature, turbidity and pH. The three-dimensional model diagram is a digital twin model of the river in the specified area.

[0105] The historical log collection unit is used to collect hydrological records of the river, and each hydrological record includes an identifier of the monitoring device, collection time and hydrological data.

[0106] The hydrological analysis module includes a regional division unit and an anomaly analysis unit.

[0107] The area division unit is used to divide the sampling area.

[0108] First, set the snow melting time R. When the interval between the end time of the most recent snowfall in the snowfall record and the current time is less than R, mark all the bifurcations of the river in the 3D model map, and divide the river into N channels with only one inlet and outlet according to the bifurcations. Then analyze the number and specific location of monitoring equipment on each channel, set the effective sampling distance U, and divide the circular area with the monitoring equipment location as the center and U as the radius as the sampling area.

[0109] The abnormal analysis unit is used to analyze the abnormal conditions of the river channel.

[0110] First, obtain the hydrological data RD collected by the detection equipment in each sampling area for the most recent time now , and the last collected hydrological data RD last ; When the water temperature in both hydrological data is greater than zero, obtain RD now Water level value SW i now 、Water temperature WT i now , turbidity HZ i now and pH i now , and RD last Water level value SW i last 、Water temperature WT i last , turbidity HZ i last and pH ilast .

[0111] Secondly, the abnormal coefficient is calculated by the formula Calculate the anomaly coefficient of each river separately. Set the coefficient threshold E, mark the river with anomaly coefficient greater than E and all monitoring equipment in the river. Get the end time of the last snowfall in the snowfall record 1 and total snowfall XL 1 , set the melting temperature QT, and retrieve time in the temperature record 1 The time after which the ambient temperature is greater than or equal to QT 2 Among them, α and β are constants, and j is the number of sampling areas in the river.

[0112] Finally, mark the monitoring device under the collection time distance time 2 The most recent hydrological record is used as the sample record, and the hydrological records collected after the sample record are used as the comparison records. The anomaly index of each marked river channel is calculated based on the sample record and the comparison record, and the abnormal river channel is identified.

[0113] The water situation warning module includes a relationship analysis unit and a water potential warning unit.

[0114] The relationship analysis unit is used to analyze the impact and change relationship of river water level.

[0115] First, the abnormal river channel HD in the 3D model 1 All other channels downstream of the outflow are considered HD 1 For the downstream river channel, obtain the hydrological records collected by all monitoring equipment in the abnormal river channel and its downstream river channel, and calculate the average water level at the same collection time in the river channel as the average water level value.

[0116] Secondly, the abnormal river channel is associated with each of its downstream rivers, and a training set is established for each pair of associated rivers. The average water level values ​​at the same acquisition time in the associated rivers are used as independent variables and dependent variables, respectively, and packaged into samples and put into the corresponding training set. A linear regression model is established, and each training set is input for training to obtain the water level relationship expression for each pair of associated rivers.

[0117] Finally, a training set was established for each abnormal river channel, and the water level range of the abnormal river channel after each snowfall was calculated. The total snowfall and the water level range of each snowfall were used as independent variables and dependent variables, respectively, and packaged into samples and put into the corresponding training set. A linear regression model was established, and each training set was input for training to obtain the water level change expression of each abnormal river channel.

[0118] The water potential warning unit is used to identify dangerous areas and issue risk warnings. Substitute them into the water level change expression to calculate the expected water level value of each abnormal river channel, and then substitute the expected water level value into the water level relationship expression to calculate the expected water level value of each downstream river channel. Then subtract the latest water level value from the expected water level value of the river channel to get the expected water level increase. Set the increase threshold g, and treat the area where the river channel with the expected water level increase greater than g as a dangerous area and issue a risk warning.

[0119] Example 1: Assuming that the number of classes in the class combination B1 in a certain area is 5, the sum of the anomaly coefficients is 1.2, the range of the collection time in all associated records under each class in the class combination is 10h, the range of the water level value is 0.35m, and the total snowfall of the last snowfall in the snowfall record is 25mm, the formula is used to calculate the anomaly index of the marked river corresponding to B1:

[0120] ;

[0121] Then the anomaly index of the marked river channel is 0.21.

[0122] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0123] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Hydrological data analysis method based on digital twins, characterized by: The method includes: S100, collecting meteorological records, historical logs and three-dimensional model diagrams, and regularly collecting hydrological data of rivers in a designated area through monitoring equipment; S200, dividing the sampling area according to the river position in the three-dimensional model map, calculating the abnormal coefficient of each river channel through the hydrological data of the sampling area, calculating the abnormal index according to the meteorological records and historical logs, and identifying the abnormal river channel; S300, analyzing the influence of the water level between the abnormal river channel and the downstream river channel and the water level change caused by meteorological factors, thereby calculating the expected water level increase, identifying the dangerous area according to the expected water level increase of the river channel and issuing risk warning; S400, displays the dangerous areas and abnormal information of each river channel through a three-dimensional model diagram, and stores the latest hydrological data in the historical log; S200 includes: S201. Set the snow melting time R. When the interval between the end time of the most recent snowfall in the snowfall record and the current time is less than R, mark all the bifurcations of the river in the three-dimensional model diagram, and divide the river into N channels according to the bifurcations. Each channel has only one inlet and one outlet. Analyze the number and specific location of monitoring equipment on each channel, set the effective sampling distance U, and divide the circular area with the monitoring equipment location as the center and U as the radius as the sampling area. S202, obtain the hydrological data RD collected by the detection equipment corresponding to each sampling area the most recently now , and the last collected hydrological data RD last ; When the water temperature in both hydrological data is greater than zero, RD now Water level value SW i now 、Water temperature WT i now , turbidity HZ i now and pH i now , and RD last Water level value SW i last 、Water temperature WT i last , turbidity HZ i last and pH i last Substitute them into the anomaly coefficient calculation formula and calculate the anomaly coefficient of each river channel respectively; S203, set the coefficient threshold E, mark the river channel with anomaly coefficient greater than E and all monitoring equipment in the river channel; obtain the end time time1 of the last snowfall and the total snowfall XL1 in the snowfall record, set the melting temperature QT, and search the temperature record for the time time2 after time1 when the ambient temperature is greater than or equal to QT; use the hydrological record with the closest collection time to time2 under the marked monitoring equipment as the sample record, and use the hydrological record with the collection time after the sample record as the comparison record; calculate the anomaly index of each marked river channel according to the sample record and the comparison record and identify the abnormal river channel; S203 includes: S20301. Obtain sample records and comparison records of all marked monitoring devices, first divide these records into different zones according to whether they belong to the same river channel, then classify the comparison records in the same zone again according to whether the collection time is consistent, and associate each comparison record of the same type with its corresponding sample record; S20302, analyzing the water temperature of each pair of associated records, substituting the data in the associated records of the same class whose water temperatures are all greater than zero into the anomaly coefficient calculation formula, and calculating the anomaly coefficient of the class; arranging the different classes in each area in order from early to late according to the collection time, and marking the class with anomaly coefficient greater than E; S20303. Find the first class combination in which the marked classes appear continuously and the number of classes is greater than p in the order of arrangement, count the number of classes d in the class combination of each zone and calculate the AC of the abnormal coefficient sum , and the collection time range of all associated records in each class combination range and water level extreme value SW range ; Substitute the abnormal index calculation formula to calculate the abnormal index of each marked river channel, set the index threshold H, and regard the marked river channels with abnormal index greater than H as abnormal rivers.

2. The hydrological data analysis method based on digital twin according to claim 1 is characterized in that: In S100, meteorological records include temperature records and snowfall records. Temperature records include ambient temperatures at different times, and snowfall records include the start and end time of each snowfall and the total snowfall during this period; historical logs refer to hydrological records of rivers, and each hydrological record includes an identifier of the monitoring equipment, collection time and hydrological data; the identifier is used to distinguish different monitoring equipment, which are installed at different locations of the river and collect hydrological data at regular intervals; hydrological data include water level values, water temperature, turbidity and pH; the three-dimensional model diagram is a digital twin model of the river in the specified area.

3. The hydrological data analysis method based on digital twin according to claim 1 is characterized in that: The calculation formula of abnormal coefficient in S200 is: ; In the formula, AC is the anomaly coefficient, α and β are constants, and j is the number of sampling areas in the river.

4. The hydrological data analysis method based on digital twin according to claim 1 is characterized in that: In S203, the calculation formula of the abnormality index is as follows: ； Where YC is the abnormality index.

5. The hydrological data analysis method based on digital twin according to claim 1 is characterized in that: S300 includes the following steps: S301, marking all abnormal river channels in the three-dimensional model diagram, and taking all other river channels downstream of the outflow of the abnormal river channel HD1 as the downstream river channels of HD1 according to the river direction; searching the historical log for the hydrological records collected by all monitoring equipment in the abnormal river channel and its downstream river channels, calculating the average water level SW2 of all hydrological records collected at time 2 in the river channel HD2, and taking SW2 as the average water level value of the river channel HD2 at time 2; S302, associate the abnormal river channel HD3 with the downstream river channel of HD3 respectively, and establish a training set for each pair of associated rivers; obtain the average water level value SW of the associated abnormal river channel and the downstream river channel at the same collection time yc and SW xy , respectively SW yc As the independent variable SW xy As dependent variables and packaged as samples, all samples are put into the training set corresponding to the associated river channels; a linear regression model is established, and the training set of each pair of associated river channels is input into the linear regression model for training, and the water level relationship expression of each pair of associated river channels is obtained; S303, counting the number of snowfalls S and the total snowfall amount of each snowfall in the snowfall record, and calculating the extreme difference of the water level value of each abnormal river channel after each snowfall according to the content of step S203; establishing a training set for each abnormal river channel, obtaining the total snowfall amount of each snowfall and the extreme difference of the water level value of the abnormal river channel, taking the total snowfall amount as the independent variable and the extreme difference of the water level value as the dependent variable and packaging them into samples, and putting these S samples into the training set corresponding to the abnormal river channel; establishing a linear regression model, and inputting the training set of each abnormal river channel into the linear regression model for training, and obtaining the water level change expression of each abnormal river channel; S304. Obtain the total snowfall XL2 of the most recent snowfall in the snowfall record, substitute it into the water level change expression to calculate the expected water level value of each abnormal river channel, and then substitute the expected water level value into the water level relationship expression to calculate the expected water level value of each downstream river channel. Subtract the latest water level values ​​of the abnormal river channel and the downstream river channel from their expected water level values ​​to obtain the expected water level increase; set the increase threshold g, and treat the area where the river channel with an expected water level increase greater than g as a dangerous area and issue a risk warning.

6. The hydrological data analysis method based on digital twin according to claim 5 is characterized in that: In S400, the dangerous areas are displayed in the three-dimensional model diagram in descending order according to the water level increase, as well as the abnormal coefficient and hydrological data of each river channel, and the hydrological information collected by the monitoring equipment is stored in the historical log.

7. The hydrological data analysis system based on digital twin is applied to the hydrological data analysis method based on digital twin as claimed in claim 1, characterized in that: The system includes data acquisition module, hydrological analysis module, water situation warning module and visualization module; The data acquisition module is used to collect meteorological records, historical logs and three-dimensional model diagrams, as well as regularly collect hydrological data of rivers in a specified area; The hydrological analysis module is used to divide the sampling area in the three-dimensional model map, calculate the anomaly coefficient of each river channel through the hydrological data of the sampling area, calculate the anomaly index based on meteorological records and historical logs, and identify abnormal rivers; The water situation warning module is used to analyze the impact of water levels between abnormal rivers and downstream rivers and water level changes caused by meteorological factors, so as to calculate the expected water level increase, identify dangerous areas based on the expected water level increase of the river channel, and issue risk warnings; The visualization module is used to display the dangerous areas and abnormal information of each river channel through a three-dimensional model diagram, and store the latest hydrological data in the historical log.

8. The hydrological data analysis system based on digital twin according to claim 7 is characterized in that: The data collection module includes a meteorological information collection unit, a hydrological data collection unit and a historical log collection unit; The meteorological information collection unit is used to collect temperature records and snowfall records; the temperature record includes the ambient temperature at different times, and the snowfall record includes the start and end time of each snowfall and the total snowfall during this period; The hydrological data acquisition unit is used to collect hydrological data and three-dimensional model diagrams; the hydrological data includes water level, water temperature, turbidity and pH; the three-dimensional model diagram is a digital twin model of the river in the specified area; The historical log collection unit is used to collect hydrological records of the river, and each hydrological record includes an identifier of the monitoring device, collection time and hydrological data.

9. The hydrological data analysis system based on digital twin according to claim 8, characterized in that: The hydrological analysis module includes a regional division unit and an anomaly analysis unit; The area division unit is used to divide the sampling area for each monitoring device; The anomaly analysis unit is used to analyze and identify abnormal river channels; First, obtain the hydrological data RD collected by the detection equipment in each sampling area for the most recent time now , and the last collected hydrological data RD last ; When the water temperature in both hydrological data is greater than zero, obtain RD now Water level value SW i now 、Water temperature WT i now , turbidity HZ i now and pH i now , and RD last Water level value SW i last 、Water temperature WT i last , turbidity HZ i last and pH i last ; Secondly, the abnormal coefficient is calculated by the formula Calculate the anomaly coefficient of each river channel respectively; set the coefficient threshold E, mark the river channel with anomaly coefficient greater than E and all monitoring equipment in the river channel; obtain the sample records and comparison records of all marked monitoring equipment, divide these records into different zones according to whether they belong to the same river channel, classify the comparison records in the same zone again according to whether the collection time is consistent, and associate each comparison record of the same type with its corresponding sample record; where α and β are both constants, and j is the number of sampling areas in the river channel; Finally, the water temperature of each pair of associated records is analyzed. According to the hydrological data in the associated records in each category where all water temperatures are greater than zero, the anomaly coefficients of each category are calculated and the categories greater than E are marked. All categories in each area are arranged in chronological order according to the acquisition time, and the first combination of p consecutive marked categories is identified. According to the anomaly coefficients of each category in the class combination of each area, the anomaly index of the corresponding marked river channel is calculated, and the index threshold H is set. The marked river channel with an anomaly index greater than H is regarded as an abnormal river channel.

10. The hydrological data analysis system based on digital twin according to claim 9, characterized in that: The water situation warning module includes a relationship analysis unit and a water potential warning unit; The relationship analysis unit is used to analyze the influence and change relationship of river water level; First, all other river channels downstream of the outlet of the abnormal river channel HD1 in the 3D model are regarded as the downstream river channels of HD1, and the hydrological records collected by all monitoring equipment in the abnormal river channel and its downstream river channel are obtained, and the average water level at the same collection time in the river channel is calculated as the average water level value; Secondly, the abnormal river channel is associated with each of its downstream rivers, and a training set is established for each pair of associated rivers. The average water level values ​​of the associated rivers at the same collection time are used as independent variables and dependent variables, respectively, and packaged into samples and put into the corresponding training set. A linear regression model is established, and each training set is input for training to obtain the water level relationship expression for each pair of associated rivers. Finally, a training set is established for each abnormal river channel, and the water level range of the abnormal river channel after each snowfall is calculated. The total snowfall and the water level range of each snowfall are used as independent variables and dependent variables, respectively, and then packaged into samples and put into the corresponding training set; A linear regression model is established, and each training set is input for training to obtain the water level change expression of each abnormal river channel; The water potential warning unit is used to identify dangerous areas and issue risk warnings; First, substitute the most recent total snowfall XL2 into the water level change expression to calculate the expected water level value of each abnormal river channel. Then, substitute the expected water level values ​​into the water level relationship expression to calculate the expected water level value of each downstream river channel. Then, subtract the latest water level value from the expected water level value of the river channel to obtain the expected water level increase. Set the increase threshold g, and treat the area where the river channel with an expected water level increase greater than g as a dangerous area and issue a risk warning.

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