CN116580542B

CN116580542B - Flood early warning method and system

Info

Publication number: CN116580542B
Application number: CN202310863338.9A
Authority: CN
Inventors: 任恩; 刘展雄
Original assignee: Sichuan Sichuan Nuclear Geological Engineering Co ltd
Current assignee: Sichuan Sichuan Nuclear Geological Engineering Co ltd
Priority date: 2023-07-14
Filing date: 2023-07-14
Publication date: 2023-09-15
Anticipated expiration: 2043-07-14
Also published as: CN116580542A

Abstract

According to the flood warning method and system provided by the application, regression analysis processing is carried out on the target information compression coefficient and the rainfall information, so that quantitative evaluation data of the rainfall information is obtained. The regression analysis result, namely the quantized evaluation data, can be obtained relatively rapidly through the data regression analysis capability carried by the coefficient regression analysis thread. The coefficient regression analysis thread is obtained after coefficient optimization, and compared with the thread before coefficient updating, the coefficient regression analysis thread carries relatively accurate data regression analysis capability, and can ensure the reliability of regression analysis results to a certain extent. The rainfall information can be evaluated by adopting the target quantization coefficient determined based on the quantization evaluation data to obtain the evaluation data of the rainfall information, so that the rainfall of the area and surrounding areas can be accurately determined, the possibility of flood occurrence can be accurately determined according to all rainfall information, and the reliability and the confidence of flood early warning are further improved.

Description

Flood early warning method and system

Technical Field

The application relates to the technical field of early warning processing, in particular to a flood early warning method and system.

Background

Flood is a water flow phenomenon caused by natural factors such as storm, flash ice and snow melting, storm tide and the like, wherein the water quantity of rivers, lakes and seas is rapidly increased or the water level is rapidly increased. When heavy rain or snow melt occurs in the river basin to generate runoff, the runoff is collected at the outlet section of the river channel according to the distance. When the runoff in the vicinity arrives, the river flow starts to increase, and the water level rises correspondingly, and the flood is called as rising. And when most of the high-strength surface runoff is converged to the outlet section, the maximum river water flow is called peak flood flow, and the corresponding highest water level is called peak flood level. And when the surface runoff of the river basin and the water quantity stored in the ground, the surface soil and the river network all flow out of the outlet section for a certain time after the storm is stopped, the river water flow and the water level fall back to the original states. The curve of the whole process connection of the flood from rising to peak top to falling is called a flood process line, and the total amount of water flowing out is called the total amount of flood.

Flood is a natural disaster with extremely strong destructive power, and how to improve the accuracy of flood early warning is a technical problem that is difficult to overcome in the prior art at present, so a technical scheme is needed to improve the technical problem.

Disclosure of Invention

In order to improve the technical problems in the related art, the application provides a flood early warning method and a flood early warning system.

In a first aspect, a flood warning method is provided, the method comprising: obtaining rainfall information to be analyzed and a target information compression coefficient of the rainfall information; calling a coefficient regression analysis thread obtained after coefficient optimization, and carrying out regression analysis processing on the target information compression coefficient and the rainfall information to obtain quantized evaluation data of the rainfall information; evaluating the rainfall information by adopting a target quantization coefficient determined based on the quantization evaluation data to obtain evaluation data of the rainfall information; and comparing the evaluation data with the flood occurrence coefficient to obtain a comparison result, and carrying out flood disaster early warning by combining the comparison result.

In an independently implemented embodiment, the quantization evaluation data is used to indicate a regression analysis quantization coefficient, and before the rainfall information is evaluated using the target quantization coefficient determined based on the quantization evaluation data, the method further includes: determining the regression analysis quantized coefficients as target quantized coefficients; or performing intelligent debugging treatment on the regression analysis quantized coefficients to obtain target quantized coefficients.

In an independent embodiment, the performing intelligent debugging on the regression analysis quantization coefficient to obtain a target quantization coefficient includes: evaluating the rainfall information by adopting the regression analysis quantization coefficient to obtain to-be-processed evaluation data of the rainfall information; obtaining an information compression coefficient corresponding to the evaluation data to be processed, and comparing the information compression coefficient corresponding to the evaluation data to be processed with the target information compression coefficient to obtain a comparison result; if the comparison result indicates that the information compression coefficient corresponding to the evaluation data to be processed is associated with the target information compression coefficient, determining the regression analysis quantization coefficient as a target quantization coefficient; and if the comparison result indicates that the information compression coefficient corresponding to the evaluation data to be processed is not associated with the target information compression coefficient, debugging the regression analysis quantization coefficient so as to determine the target quantization coefficient after evaluating the rainfall information according to the debugged regression analysis quantization coefficient.

In an independently implemented embodiment, said tuning said regression analysis quantization coefficients comprises: debugging the regression analysis quantized coefficients according to the undetermined coefficient intervals to which the regression analysis quantized coefficients belong in the multiple undetermined coefficient intervals to obtain the debugged regression analysis quantized coefficients; or according to the debugging range, debugging the regression analysis quantized coefficient to obtain the debugged regression analysis quantized coefficient.

In an independently implemented embodiment, the quantization evaluation data is used to indicate a set of quantization coefficients that encompasses multiple regression analysis quantization coefficients; before the rainfall information is evaluated by adopting the target quantization coefficient determined based on the quantization evaluation data, the method further comprises: determining a regression analysis quantization coefficient from the quantization coefficient group to be determined as a reference quantization coefficient; and determining a target quantization coefficient based on the reference quantization coefficient.

In an independently implemented embodiment, before the estimating the rainfall information using the target quantization coefficient determined based on the quantization estimation data, the method further includes: determining reference quantized evaluation data based on the quantized evaluation data; the target quantized coefficients are determined by a regression analysis quantized coefficients or a set of quantized coefficients indicated by the reference quantized evaluation data.

In an embodiment of the independent implementation, the invoking the coefficient regression analysis thread obtained after coefficient optimization performs regression analysis processing on the target information compression coefficient and the rainfall information to obtain quantized evaluation data of the rainfall information, including: obtaining a coefficient regression analysis thread corresponding to the component of the rainfall information; invoking a coefficient regression analysis thread corresponding to the component of the rainfall information, and carrying out regression analysis processing on the target information compression coefficient and the rainfall information to obtain quantized evaluation data corresponding to the component of the rainfall information; and determining the quantitative evaluation data of the rainfall information through the quantitative evaluation data corresponding to the components of the rainfall information.

In an independently implemented embodiment, the method further comprises: obtaining a rainfall information set; the set of rainfall information covers heterogeneous example rainfall information, the heterogeneous example rainfall information including: one or two of rainfall information with different regional positions and different risk factors; evaluating each example rainfall information according to the undetermined quantized coefficients determined by the undetermined coefficient intervals to obtain configuration information compression coefficients of each example rainfall information; determining an example set through the rainfall information of each example and the configuration information compression coefficient of the rainfall information of each example, determining a to-be-determined quantization coefficient corresponding to the configuration information compression coefficient of the rainfall information of each example as monitoring data of the example set, and carrying out coefficient update on an original coefficient regression analysis thread to obtain a coefficient regression analysis thread.

In an independently implemented embodiment, the updating the coefficients of the original coefficient regression analysis thread includes: for any example set, calling an original coefficient regression analysis thread, and carrying out regression analysis processing on configuration information compression coefficients and example rainfall information included in the any example set to obtain regression analysis quantization coefficients corresponding to the any example set; and debugging the coefficient of the original coefficient regression analysis thread according to the difference between the regression analysis quantized coefficient corresponding to any one of the sample sets and the monitoring data of any one of the sample sets so as to obtain the coefficient regression analysis thread.

In an independently implemented embodiment, the method further comprises: obtaining example rainfall information and configuration information compression coefficients corresponding to the example rainfall information; performing feature extraction processing on the example rainfall information according to the example rainfall information data quantity and the element information value in the example rainfall information to obtain rainfall information features of the example rainfall information; and integrating the coefficients of the original coefficient regression analysis thread through the rainfall information characteristics of the example rainfall information, the configuration information compression coefficient corresponding to the example rainfall information and the undetermined quantization coefficient so as to update the coefficients of the original regression analysis thread and obtain the coefficient regression analysis thread.

In an independent embodiment, the rainfall information to be analyzed covers the rainfall information to be processed or to be cached; the method further comprises the steps of: displaying a target rainfall data window, wherein the target rainfall data window is used for displaying the rainfall information; responding to the coefficient setting operation of the rainfall information, displaying a plurality of undetermined information compression coefficients and rainfall information display evaluation description information corresponding to each undetermined information compression coefficient; and determining the undetermined information compression coefficient screened from the undetermined information compression coefficients according to the screening operation as the target information compression coefficient of the rainfall information.

In an independently implemented embodiment, the method further comprises: checking whether the associated rainfall information evaluation unit is optimized; if yes, a new example set is obtained through the optimized rainfall information evaluation unit, and the coefficient regression analysis thread is updated through the new example set, so that the optimized coefficient regression analysis thread is obtained.

In a second aspect, a flood warning system is provided, comprising a processor and a memory in communication with each other, the processor being adapted to read a computer program from the memory and execute the computer program to implement the method described above.

According to the flood early warning method and system provided by the embodiment of the application, rainfall information to be analyzed and the target information compression coefficient of the rainfall information can be obtained, a coefficient regression analysis thread obtained after coefficient optimization is called, regression analysis processing is carried out on the target information compression coefficient and the rainfall information, and quantitative evaluation data of the rainfall information is obtained. Therefore, the regression analysis result, namely the quantized evaluation data, can be obtained relatively quickly through the data regression analysis capability carried by the coefficient regression analysis thread. And the coefficient regression analysis thread is obtained after coefficient optimization, and compared with the thread before coefficient updating, the coefficient regression analysis thread carries relatively accurate data regression analysis capability, so that the reliability of regression analysis results can be ensured to a certain extent. And then, the rainfall information can be evaluated by adopting the target quantization coefficient determined based on the quantitative evaluation data to obtain the evaluation data of the rainfall information, so that the rainfall of the area and surrounding areas can be accurately determined, the possibility of flood occurrence can be accurately determined according to all rainfall information, and the reliability and the confidence of flood early warning are further improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a flow chart of a flood early warning method according to an embodiment of the present application.

Fig. 2 is a block diagram of a flood warning device according to an embodiment of the present application.

Detailed Description

In order to better understand the above technical solutions, the following detailed description of the technical solutions of the present application is made by using the accompanying drawings and specific embodiments, and it should be understood that the specific features of the embodiments and the embodiments of the present application are detailed descriptions of the technical solutions of the present application, and not limiting the technical solutions of the present application, and the technical features of the embodiments and the embodiments of the present application may be combined with each other without conflict.

Referring to fig. 1, a flood warning method is shown, which may include the following technical solutions described in steps S201-S204.

S201, obtaining rainfall information to be analyzed and a target information compression coefficient of the rainfall information.

The rainfall information to be analyzed is obtained through a weather bureau (wherein the area corresponding to the rainfall information is a plurality of areas, and the rainfall at a plurality of positions is needed to be analyzed, so that the accuracy of the data can be ensured); the target information compression coefficient may be understood as a coefficient obtained by digitizing rainfall information.

S202, calling a coefficient regression analysis thread obtained after coefficient optimization, and carrying out regression analysis processing on the target information compression coefficient and the rainfall information to obtain quantitative evaluation data of the rainfall information.

The coefficient regression analysis thread is a corresponding calculation mode (needs to be arranged according to historical data, and a specific calculation mode is obtained by a person skilled in the art), and can be specifically understood as an artificial intelligent thread and the like, so that the coefficient regression analysis thread can replace manual processing, and the data processing efficiency can be improved.

In one possible implementation, different rainfall information may correspond to different corresponding calculation modes based on the content of the rainfall information. Specifically, one rainfall information and one information compression coefficient may correspond to one quantization coefficient in common; the same rainfall information but different information compression coefficients, or different rainfall information but the same information compression coefficient, can correspond to different quantization coefficients.

And through a coefficient regression analysis thread, carrying out regression analysis processing on the target information compression coefficient and the rainfall information, and particularly determining quantitative evaluation data of the rainfall information according to the indication of a corresponding calculation mode, wherein the quantitative evaluation data can be used for indicating one or more regression analysis quantitative coefficients which correspond to the target information compression coefficient and the rainfall information together. The quantization evaluation data may be an identification of a regression analysis quantization coefficient, or an identification of a quantization coefficient set (covering a plurality of regression analysis quantization coefficients), or may be a regression analysis quantization coefficient itself, which may be used to evaluate rainfall information.

By introducing the coefficient regression analysis thread, for given rainfall information and target information compression coefficient, the coefficient regression analysis thread can directly carry out regression analysis processing, so that quantitative evaluation data of the rainfall information can be rapidly output, control of the information compression coefficient (such as code rate) of the rainfall information based on learning end-to-end is realized, and processing efficiency is improved.

And S203, evaluating the rainfall information by adopting a target quantization coefficient determined based on the quantization evaluation data to obtain evaluation data of the rainfall information.

In order to make the rainfall information evaluation as close as possible to the expectation, after obtaining the quantized evaluation data, a target quantization coefficient determined based on the quantization indication may be obtained first. The target quantized coefficient is a quantized coefficient that enables the result of the rainfall information evaluation to be close to the expected one, and may be the same as or different from the regression-analyzed quantized coefficient indicated by the quantized evaluation data.

S204, comparing the evaluation data with the flood occurrence coefficient to obtain a comparison result, and carrying out flood disaster early warning by combining the comparison result.

When the target quantized coefficient is the same as the regression analysis quantized coefficient indicated by the quantized evaluation data, the transmitted evaluation data is to-be-processed evaluation data obtained by evaluating rainfall information by using the regression analysis quantized coefficient.

By comparing with the historical data (the historical data can be understood as the information of flood occurrence in the past), the accuracy and the confidence of flood early warning can be improved by relying on the historical data.

According to the flood warning method provided by the embodiment of the application, for the rainfall information to be analyzed and the target information compression coefficient of the rainfall information, a coefficient regression analysis thread obtained after coefficient optimization can be called, and regression analysis processing is carried out on the coefficient regression analysis thread to obtain quantitative evaluation data of the rainfall information. In this way, the rainfall information and the target information compression coefficient can be automatically processed through the data regression analysis capability carried by the coefficient regression analysis thread, so that a regression analysis result, namely quantitative evaluation data, can be obtained more quickly. And the coefficient regression analysis thread is obtained after coefficient optimization, and compared with the thread before coefficient updating, the coefficient regression analysis thread carries relatively accurate data regression analysis capability, so that the reliability of regression analysis results can be ensured to a certain extent. And then, the rainfall information can be evaluated by adopting the target quantization coefficient determined based on the quantitative evaluation data to obtain the evaluation data of the rainfall information, so that the rainfall of the area and surrounding areas can be accurately determined, the possibility of flood occurrence can be accurately determined according to all rainfall information, and the reliability and the confidence of flood early warning are further improved.

The flood warning method may be performed by a computer device and may include what is described in the following steps S401 to S405.

S401, obtaining rainfall information to be analyzed and a target information compression coefficient of the rainfall information.

In one embodiment, the rainfall information that needs to be analyzed encompasses the rainfall information that is to be processed or needs to be cached.

First, a target rainfall data window is displayed, which is used to display rainfall information.

And finally, determining the undetermined information compression coefficient screened from the undetermined information compression coefficients according to the screening operation as a target information compression coefficient of the rainfall information.

S402, calling a coefficient regression analysis thread obtained after coefficient optimization, and carrying out regression analysis processing on the target information compression coefficient and the rainfall information to obtain quantitative evaluation data of the rainfall information.

The process of obtaining the coefficient regression analysis thread is described first. In one embodiment, the coefficient regression analysis thread may be obtained by coefficient optimization in the following manner (one) and manner (two).

The first mode is obtained by configuration data configuration. Specifically comprises the following steps 1.1-1.3.

And 1.1, obtaining a rainfall information set.

The rainfall information set covers example rainfall information of different kinds including: one or two of rainfall information with different regional positions and rainfall information with different risk factors.

And 1.2, evaluating each example rainfall information according to the undetermined quantized coefficients determined by the undetermined coefficient intervals to obtain the configuration information compression coefficient of each example rainfall information.

Specifically, a plurality of pending coefficient intervals may be obtained, each pending coefficient interval covering a different pending quantized coefficient, specifically, the value of the pending quantized coefficient is different. Then, the undetermined quantized coefficients used for configuration can be determined from each undetermined coefficient interval, each undetermined quantized coefficient is traversed, the corresponding undetermined quantized coefficient is adopted to evaluate each example rainfall information, the actual information compression coefficient under the undetermined quantized coefficients, such as the actual code rate and the quality of the reconstructed rainfall information, is obtained, and then the actual information compression coefficient is determined to be the configuration information compression coefficient of the corresponding example rainfall information. In this way, each example rainfall information in the set of rainfall information can be generated, corresponding configuration information compression coefficients under the determined to-be-quantized coefficients, one configuration information compression coefficient possibly corresponding to one or more to-be-quantized coefficients.

The configuration data required by the thread configuration covers the example rainfall information and the configuration information compression coefficient of the example rainfall information, and the thread is configured by adopting the configuration data, so that the thread can extract some common characteristics from massive rainfall information and information compression coefficients, the rainfall information and the information compression coefficient are constructed, and the calculation mode corresponding to the quantization coefficient is adopted to carry out regression analysis processing on the rainfall information and the target information compression coefficient.

Step 1.3, determining an example set according to each example rainfall information and the configuration information compression coefficient of each example rainfall information, determining the undetermined quantization coefficient corresponding to the configuration information compression coefficient of each example rainfall information as monitoring data of the example set, and carrying out coefficient update on an original coefficient regression analysis thread to obtain the coefficient regression analysis thread.

In one implementation, an example rainfall information and a configuration information compression coefficient of the example rainfall information may form an example set. Since the information compression coefficients of one example rainfall information evaluated under different pending quantization coefficients may be different, the same example rainfall information is covered in different example sets, but different configuration information compression coefficients.

In one possible implementation, the example rainfall information includes a plurality of components, and the configuration information compression coefficients corresponding to one component of the example rainfall information and the component of the example rainfall information may form one example set. And evaluating the example rainfall information according to the to-be-determined quantization coefficients, specifically evaluating each component of the example rainfall information, and then obtaining configuration information compression coefficients corresponding to each component of the example rainfall information to construct an example set.

The undetermined quantized coefficients corresponding to the configuration information compression coefficients in the example set may be determined as monitoring data for the example set. Because the configuration information compression coefficient is obtained by evaluating the example rainfall information according to the to-be-determined quantization coefficient, the configuration information compression coefficient and the to-be-determined quantization coefficient have corresponding correspondence based on the content of the example rainfall information.

The original coefficient regression analysis thread is one coefficient regression analysis thread covering the original coefficient. The original coefficient regression analysis thread can be a thread obtained through random primordization, or a thread which is preconfigured to carry better processing capacity (such as feature extraction) on rainfall information. And then, updating the coefficient of the original coefficient regression analysis thread through the example set and the monitoring data of the example set, and gradually forming a corresponding calculation mode in the coefficient updating process so as to describe the corresponding relation between the rainfall information and the information compression coefficient to the quantization coefficient.

In one possible implementation, any set of examples is illustrated. The coefficient update is carried out on the original coefficient regression analysis thread, which comprises the following contents: for any example set, an original coefficient regression analysis thread can be called, and regression analysis processing is carried out on the configuration information compression coefficient and the example rainfall information included in any example set to obtain a regression analysis quantization coefficient corresponding to any example set; and then, according to the difference between the regression analysis quantized coefficient corresponding to any one of the example sets and the monitoring data of the example set, debugging the coefficient of the original coefficient regression analysis thread to obtain the coefficient regression analysis thread.

Before or during debugging, the original coefficient regression analysis thread does not form a more accurate corresponding calculation mode, and the original coefficient regression analysis thread is called to carry out regression analysis processing on data in any sample set, so that the obtained regression analysis quantized coefficient of any sample set may not correspond to the configuration information compression coefficient. Therefore, the difference between the monitoring data (i.e. the undetermined quantized coefficients corresponding to the configuration information compression coefficients) of any sample set and the regression analysis quantized coefficients of the any sample set can be calculated.

It can be understood that, for each example set, a corresponding regression analysis quantization coefficient can be obtained, and further, configuration data of a batch formed by a plurality of example sets can be adopted, and based on differences between the regression analysis quantization coefficient corresponding to each example set and corresponding monitoring data, the coefficient of the original coefficient regression analysis thread is debugged once, so as to update the coefficient of the original coefficient regression analysis thread once.

In summary, in the configuration process described in the above steps 1.1 to 1.3, the object of performing thread configuration according to the configuration data is to learn the characteristics and modes of the threads from the data, so as to obtain a thread that can be well correlated with the real data distribution, and in the process of thread configuration, an update method such as gradient descent can be used to minimize the error between the regression analysis value (i.e. the regression analysis quantization coefficient) and the real value (i.e. the quantization coefficient to be determined).

For the first mode, firstly, the configuration information compression coefficient (such as code rate) including the example rainfall information and the example rainfall information can be input into the original coefficient regression analysis thread (such as convolutional neural network), regression analysis processing is carried out on the configuration information compression coefficient and the example rainfall information through the original coefficient regression analysis thread, regression analysis quantization coefficients corresponding to the example set can be output, and then the coefficients of the original coefficient regression analysis thread are debugged according to the differences between the regression analysis quantization parameters corresponding to the example set and the to-be-quantized coefficients corresponding to the example set. Coefficient updating can be realized through thread configuration, and a coefficient regression analysis thread is obtained. In practical application, the coefficient regression analysis thread can be used for inputting rainfall information to be analyzed and a target information compression coefficient (such as target code rate/quality) to realize regression analysis of a quantization coefficient of the rainfall information, and finally, quantization evaluation data for indicating the regression analysis quantization coefficient of the rainfall information can be output so as to further determine the target quantization coefficient to evaluate the rainfall information.

And (2) carrying out coefficient estimation by adopting configuration data. Specifically comprises the following steps 2.1-2.3.

And 2.1, obtaining configuration information compression coefficients corresponding to the example rainfall information.

The configuration information compression coefficients corresponding to the exemplary rainfall information may refer to the content described in step 1.2 in the above manner (one), and detailed description is omitted herein. It should be noted that the obtained exemplary rainfall information may have different configuration information compression coefficients under different to-be-quantized information compression coefficients. The present application is not limited to the number of the exemplary rainfall information obtained in step 2.1 and the number of the configuration information compression coefficients corresponding to the exemplary rainfall information. For ease of understanding, the present application is described with reference to an example rainfall information, and a configuration information compression coefficient of the example rainfall information.

And 2.2, performing feature extraction processing on the example rainfall information according to the example rainfall information data quantity and the element information value in the example rainfall information to obtain rainfall information features of the example rainfall information.

Illustratively, hadamard transform is performed on the example rainfall information to obtain transformed example rainfall information. If the example data of the example rainfall information is more uniformly distributed, the data included in the transformed example rainfall information is more concentrated at corners of a matrix (which is used to represent values of element information in the example rainfall information) to concentrate effective information in the example rainfall information.

Alternatively, instead of performing the conversion processing on the example rainfall information, the values of the respective element information in the example rainfall information and the rainfall information data amount of the example rainfall information may be directly used to determine the rainfall information characteristics of the example rainfall information.

And 2.3, integrating the coefficients of the original coefficient regression analysis thread according to the rainfall information characteristics of the example rainfall information, the configuration information compression coefficient corresponding to the example rainfall information and the undetermined quantization coefficient corresponding to the configuration information compression coefficient so as to update the coefficients of the original regression analysis thread and obtain the coefficient regression analysis thread.

The original coefficient regression analysis thread may be an empirically determined mathematical thread with coefficients, and uses rainfall information features of example rainfall information, configuration information compression coefficients and to-be-determined quantization coefficients to perform coefficient integration (also referred to as coefficient estimation) of the thread coefficients, so as to construct a mapping relationship from (information compression coefficients) to quantization coefficients, that is, a corresponding relationship between (information compression coefficients) and quantization coefficients.

According to the method, p of the original coefficient thread is integrated, and in the integration process, the mapping relation between the rainfall information content and the information compression coefficient and the quantized coefficient can be constructed, so that the coefficient regression analysis thread obtained through coefficient integration is a corresponding calculation mode or comprises a corresponding calculation mode. In this way, for the rainfall information to be evaluated and the given information compression coefficient, the rainfall information can be input into the coefficient regression analysis thread, regression analysis is performed by adopting a corresponding calculation mode, so that a regression analysis quantization coefficient is obtained, and then rainfall information evaluation is performed based on the regression analysis quantization coefficient.

In summary, in the description of the steps 2.1 to 2.3, the coefficient regression analysis thread is obtained based on the coefficient integration method, and the coefficient integration is performed to estimate the coefficient of the thread under the condition of the known thread and the data distribution, so as to better describe the data distribution condition. Thus, the coefficient regression analysis thread can better describe the relationship between rainfall information and information compression coefficient, and corresponds to the quantized coefficient.

In one embodiment, it is also possible to: checking whether the associated rainfall information evaluation unit is optimized; if yes, a new example set is obtained through the optimized rainfall information evaluation unit, and the coefficient regression analysis thread is updated through the new example set, so that the optimized coefficient regression analysis thread is obtained.

The associated rainfall information evaluation unit may be configured to evaluate the exemplary rainfall information according to the to-be-determined quantization coefficient to obtain a configuration information compression coefficient of the exemplary rainfall information. The rainfall information evaluation unit may be an automatic evaluation unit, a variance self-evaluation unit, or the like, which uses various different neural networks.

The updating of the rainfall information estimation unit may be that the network coefficient of the neural network in the rainfall information estimation unit changes, for example, the neural network for estimating the luminance component and the chrominance component uses a new channel number, or may be that other coefficients or structures in the rainfall information estimation unit are updated. When the associated rainfall information evaluation unit is verified to be optimized, the optimized rainfall information evaluation unit can be adopted, and the to-be-determined quantization coefficient is adopted to evaluate the example rainfall information again.

The updating of the rainfall information assessment unit affects the information compression coefficient so that the rainfall information assessment unit and the coefficient regression analysis thread are not associated. Therefore, a new configuration information compression coefficient can be constructed, a new example set is obtained, and the coefficient regression analysis thread is continuously updated according to the steps of the mode (one) or the mode (two) through the new example set, and the original corresponding calculation mode can be further updated. The coefficient regression analysis thread is further updated, so that the optimized coefficient regression analysis thread is associated with the rainfall information evaluation unit, and the coefficient regression analysis thread can be updated along with the update of the associated rainfall information evaluation unit, and further the evaluation effect can be improved.

It can be understood that if the rainfall information evaluation unit associated with the verification is not optimized, the coefficient of the coefficient regression analysis thread is kept unchanged, and the quantization coefficients corresponding to the rainfall information and the information compression coefficient can be analyzed by the coefficient regression analysis thread in a regression mode.

In one possible implementation, the coefficient regression analysis thread is a corresponding calculation mode, so that the coefficient regression analysis thread can be used to describe the correspondence between rainfall information and information compression coefficient to quantization coefficient. In another possible implementation, the coefficient regression analysis thread may be further updated, and the coefficient regression analysis thread after coefficient optimization may generate a corresponding calculation mode, and the coefficient regression analysis thread called here may also be the coefficient regression analysis thread after optimization.

In one embodiment, the step S402 may cover the following sub-steps S4021-S4023 based on the relationship that different components of the rainfall information correspond to different corresponding calculation modes.

S4021, obtaining a coefficient regression analysis thread corresponding to the component of the rainfall information.

The rainfall information may cover a plurality of components, for each of which there is a coefficient regression analysis thread.

S4022, invoking a coefficient regression analysis thread corresponding to the component of the rainfall information, and carrying out regression analysis processing on the target information compression coefficient and the rainfall information to obtain quantized evaluation data corresponding to the component of the rainfall information.

After obtaining the coefficient regression analysis thread corresponding to the component of the rainfall information, the obtained coefficient regression analysis thread can be called, regression analysis processing is carried out on the target information compression coefficient and the corresponding component of the rainfall information, and further quantization evaluation data corresponding to the component is obtained, and the quantization evaluation data corresponding to the component is used for indicating the regression analysis quantization coefficient of the component. In a specific regression analysis process, the quantitative evaluation data can be obtained by following the calculation mode of the component of the rainfall information and the information compression coefficient corresponding to the quantization coefficient.

It can be understood that if the rainfall information covers a plurality of components, a corresponding coefficient regression analysis thread can be obtained for at least one component of the rainfall information, corresponding quantization evaluation data is obtained according to the regression analysis of S4022, and then a target quantization coefficient is determined based on the quantization evaluation data.

For some components covered by the rainfall information, if the corresponding coefficient regression analysis thread is not obtained to carry out regression analysis to obtain the quantized evaluation data, other modes can be adopted to obtain the quantized evaluation data corresponding to the components. Other ways are for example direct search in a manually defined search space to get quantized evaluation data.

S4023, determining quantitative evaluation data of the rainfall information according to the quantitative evaluation data corresponding to the components of the rainfall information.

If each component of the plurality of components covered by the rainfall information adopts a corresponding coefficient regression analysis thread to obtain corresponding quantized evaluation data, the quantized evaluation data corresponding to each component can be combined to obtain quantized evaluation data of the rainfall information. Thus, the quantitative evaluation data of the rainfall information can be used to indicate a plurality of regression analysis quantization coefficients. If one component of the plurality of components covered by the rainfall information adopts a corresponding coefficient regression analysis thread to obtain quantized evaluation data corresponding to the component, the quantized evaluation data corresponding to the component can be determined as quantized evaluation data of the rainfall information.

In summary, the contents introduced in S4021 to S4023 are described, the quantized evaluation data corresponding to the components is obtained by regression analysis with the components of the rainfall information as units, so that the quantized coefficients of the rainfall information can be subjected to finer regression analysis, and the rainfall information is evaluated by adopting the target quantized coefficients through the association between the target quantized coefficients and the components of the rainfall information, so that the evaluation effect of the rainfall information can be further improved. In addition, the quantitative evaluation data of the partial components can be flexibly screened according to the service requirements so as to evaluate the partial components of the rainfall information and meet personalized service requirements.

S403, determining a target quantization coefficient based on the quantization evaluation data.

In one embodiment, the quantitative evaluation data is used to indicate a regression analysis quantization coefficient. The quantized evaluation data itself is a regression analysis quantized coefficient, which can be directly used to evaluate rainfall information, or the quantized evaluation data is a coefficient identifier of a regression analysis quantized coefficient, which may be a number, a letter, a character string, or the like, and a coefficient identifier corresponds to a regression analysis quantized coefficient, and the regression analysis quantized coefficient may be determined based on the quantized evaluation data. For this purpose, a target quantization coefficient is determined based on the quantization evaluation data, specifically including the following modes (1) - (2).

(1) The regression analysis quantized coefficients are determined as target quantized coefficients.

Based on the more accurate corresponding relation described by the corresponding calculation mode, the regression analysis quantization coefficient obtained according to the corresponding calculation mode can be considered to be effective, and the information compression coefficient which is more relevant to the target information compression coefficient can be obtained after the rainfall information is evaluated by adopting the regression analysis quantization coefficient. Therefore, the regression analysis quantized coefficient can be directly determined as the target quantized coefficient, namely, the regression analysis quantized coefficient is directly adopted for subsequent evaluation, and a new quantized coefficient is not required to be determined based on the regression analysis quantized coefficient, so that repeated evaluation and verification of reliability of the regression analysis quantized coefficient are avoided, the overall efficiency of rainfall information evaluation can be improved, and the effect of rainfall information evaluation is ensured to a certain extent.

(2) And performing intelligent debugging treatment on the regression analysis quantized coefficients to obtain target quantized coefficients.

Specifically, intelligent debugging of the regression analysis quantized coefficients is performed based on the results obtained by evaluating the regression analysis quantized coefficients. The rainfall information is evaluated by adopting the regression analysis quantized coefficient, the reliability of the regression analysis quantized coefficient is verified, and then when the reliability of the regression analysis quantized coefficient is low, the regression analysis quantized coefficient can be adaptively debugged.

Because the regression analysis quantized coefficient is a relatively suitable original quantized coefficient determined based on a corresponding calculation mode, in the intelligent debugging process, the regression analysis quantized coefficient is used as a debugging basis to further determine the target quantized coefficient, compared with the search space of manually defined quantized coefficients, the method has the advantages that the number of repeated evaluation can be greatly reduced, the determination process of the target quantized coefficient is simplified, and the evaluation efficiency is improved.

In a possible implementation manner, the intelligent debugging process is performed on the regression analysis quantized coefficients to obtain target quantized coefficients, and the specific implementation content may include the following steps 1 to 4.

Step 1: and evaluating the rainfall information by adopting a regression analysis quantization coefficient to obtain to-be-processed evaluation data of the rainfall information.

Firstly, the regression analysis quantization coefficient can be used for evaluating rainfall information to obtain evaluation data to be processed.

Step 2: and obtaining an information compression coefficient corresponding to the evaluation data to be processed, and comparing the information compression coefficient corresponding to the evaluation data to be processed with the target information compression coefficient to obtain a comparison result.

The information compression coefficient corresponding to the evaluation data to be processed and the target information compression coefficient of the rainfall information cover the coefficients of the same dimension, but specific coefficient values may be different.

In order to verify the reliability of the quantized coefficient of the regression analysis, so that the evaluation result is close to the expected value, the information compression coefficient corresponding to the evaluation data to be processed and the target information compression coefficient can be compared, and the comparison result obtained by the comparison can be used for indicating whether the information compression coefficient corresponding to the evaluation data to be processed and the target information compression coefficient of the rainfall information are related or not.

Step 3: and if the comparison result indicates that the information compression coefficient corresponding to the evaluation data to be processed is associated with the target information compression coefficient, determining the regression analysis quantization coefficient as the target quantization coefficient.

If the comparison result indicates that the information compression coefficient corresponding to the to-be-processed evaluation data is associated with the target information compression coefficient, the result of evaluating the rainfall information by adopting the regression analysis quantization coefficient is close to expected, so that the regression analysis quantization coefficient can be directly determined as the target quantization coefficient, the evaluation data of the rainfall information obtained by evaluation can be directly determined to be the evaluation data of the rainfall information for transmission, the rainfall information is not required to be evaluated, and the evaluation resource is saved. Alternatively, the association here may be that the information compression coefficient corresponding to the evaluation data to be processed and the target information compression coefficient are equal, or that the difference between the coefficient values between the evaluation data to be processed and the target information compression coefficient is within the difference threshold.

Step 4: if the comparison result indicates that the information compression coefficient corresponding to the evaluation data to be processed is not associated with the target information compression coefficient, debugging the regression analysis quantization coefficient so as to evaluate the rainfall information according to the debugged regression analysis quantization coefficient and then determine the target quantization coefficient.

If the comparison result indicates that the information compression coefficient corresponding to the to-be-processed evaluation data is not associated with the target information compression coefficient, the evaluation result obtained by evaluating the rainfall information by adopting the regression analysis quantization coefficient is larger than the expected difference. Therefore, the regression analysis quantized coefficients can be correspondingly debugged, and the debugged regression analysis quantized coefficients are obtained so as to reduce the gap between the debugged regression analysis quantized coefficients and the expected regression analysis quantized coefficients. The debugged regression analysis quantized coefficient obtained by debugging is a new quantized coefficient, and the coefficient value of the debugged regression analysis quantized coefficient is different from the coefficient value of the regression analysis quantized coefficient. And then, the rainfall information can be further evaluated by adopting the debugged regression analysis quantization coefficient according to the contents shown in the steps 1-2 so as to verify the reliability of the debugged regression analysis quantization coefficient. Specifically, whether the regression analysis quantization coefficient can be directly determined as the target quantization coefficient can be further estimated based on the information compression coefficient corresponding to the new evaluation data to be processed obtained by evaluation and the new comparison result obtained by comparing the information compression coefficient with the target information compression coefficient.

Schematically, if the new comparison result indicates that the information compression coefficient corresponding to the to-be-processed evaluation data obtained by evaluating under the debugged quantization coefficient is associated with the target information compression coefficient, the debugged regression analysis quantization coefficient may be directly determined as the target quantization coefficient. Otherwise, continuing to further debug based on the debugged regression analysis quantized coefficient, thereby obtaining a new quantized coefficient. And so on, until the latest comparison result indicates that the latest information compression coefficient is associated with the target information compression coefficient, the latest information compression coefficient can be determined as the target information compression coefficient.

It can be seen that, in the intelligent debugging manner described in the above steps 1 to 4, based on the result obtained by performing the actual evaluation on the rainfall information by using the regression analysis quantization coefficient, the information compression coefficient corresponding to the result of the actual evaluation is compared with the target information compression coefficient to determine whether to correlate with the target information compression coefficient, so as to determine whether to debug the currently obtained regression analysis quantization coefficient into a new quantization coefficient. Therefore, intelligent debugging of the regression analysis quantized coefficients can be realized, and the debugging of the regression analysis quantized coefficients can be more reliable.

Further, in one possible implementation, the debug regression analysis quantization coefficients may be implemented in either of the following modes (1) or (2).

(1) And debugging the regression analysis quantized coefficients according to the undetermined coefficient intervals to which the regression analysis quantized coefficients belong in the undetermined coefficient intervals to obtain the debugged regression analysis quantized coefficients.

Based on the comparison between the coefficient value of the regression analysis quantized coefficient and the coefficient value range constituting the undetermined coefficient section, the undetermined coefficient section to which the regression analysis quantized coefficient belongs can be determined. Then, a quantized coefficient except the regression analysis quantized coefficient can be selected from the undetermined coefficient interval to which the regression analysis quantized coefficient belongs, and the quantized coefficient is determined as the debugged quantized coefficient. The selection from the interval of coefficients to be determined may be a random selection or a selection based on a degree of association between the information compression coefficient of the evaluation data to be processed and the target information compression coefficient. Thus, the debugged regression analysis quantized coefficient and the regression analysis quantized coefficient belong to the same undetermined coefficient interval, but the specific coefficient values are different.

(2) And according to the debugging range, debugging the regression analysis quantized coefficient to obtain the debugged regression analysis quantized coefficient.

Wherein the debug scope is a numerical value used to describe the coefficient debug size. May be a preset setting. Specific implementations of debugging the regression analysis quantization coefficients according to the debugging scope may include: the size relation between the information compression coefficient of the evaluation data to be processed and the target information compression coefficient is obtained first. If the magnitude relation indicates that the information compression coefficient is smaller than the target information compression coefficient, the evaluation of the to-be-processed evaluation data does not meet the evaluation requirement, and the regression analysis quantization coefficient can be debugged based on the relation between the quantization coefficient and the information compression coefficient.

In this way, when the difference between the information compression coefficient corresponding to the evaluation data to be processed and the target information compression coefficient is not large, finer debugging can be performed on the regression analysis quantization coefficient according to the debugging range, so that the regression analysis quantization coefficient is more fine to debug. Therefore, the probability of association between the information compression coefficient corresponding to the to-be-processed evaluation data and the target information compression coefficient, which are obtained based on the regression analysis quantized coefficient evaluation after debugging, can be effectively improved, the number of repeated evaluation is reduced, and the overall evaluation efficiency is improved.

In another embodiment, the quantization evaluation data is used to indicate a set of quantization coefficients that encompasses multiple regression analysis quantization coefficients. Alternatively, one regression analysis quantized coefficient may correspond to one component of the rainfall information, and different regression analysis quantized coefficients may correspond to different components of the rainfall information.

And determining as an achievable way, for any one of the quantized coefficient groups, determining the regression analysis quantized coefficient as a target quantized coefficient, or performing intelligent debugging processing on the regression analysis quantized coefficient to obtain the target quantized coefficient. Reference may be made specifically to the foregoing related description, and details are not described herein. It should be noted that, for each regression analysis quantization coefficient in the quantization coefficient set, a corresponding target quantization coefficient may be determined, so that a plurality of target quantization coefficients may be obtained, and one target quantization coefficient corresponds to one component, so as to evaluate the component of the rainfall information by using the corresponding target quantization coefficient.

S404, evaluating the rainfall information by adopting the target quantization coefficient to obtain evaluation data of the rainfall information.

In one embodiment, a target quantized coefficient is determined based on the quantized evaluation data, and the target quantized coefficient may be a regression-analyzed quantized coefficient indicated by the quantized evaluation data or a new quantized coefficient obtained after debugging based on the regression-analyzed quantized coefficient. In another embodiment, the target quantization coefficient determined based on the quantization evaluation data includes a plurality of target quantization coefficients, and one target quantization coefficient corresponds to one component of the rainfall information. When the rainfall information is evaluated, the components of the rainfall information can be evaluated by adopting the corresponding target quantization coefficients according to the corresponding relation between the target quantization coefficients and the rainfall information components, then the evaluation data corresponding to each component is obtained, and further the evaluation data corresponding to each component is integrated to obtain the evaluation data of the rainfall information.

The method is characterized in that the rainfall information evaluation unit is updated possibly, and before the rainfall information evaluation unit is updated, the information compression coefficient corresponding to the evaluated rainfall information and the target information compression coefficient are not associated, so that whether the information compression coefficient corresponding to the evaluated data and the target information compression coefficient are associated or not can be checked to determine whether to determine a new quantization coefficient, the rainfall information is evaluated again, and further new evaluation data of the rainfall information are obtained.

S405, comparing the evaluation data with the flood occurrence coefficient to obtain a comparison result, and carrying out flood disaster early warning by combining the comparison result.

For the contents shown in the above steps S401 to S405. For the input picture and a given target information compression coefficient (such as target code rate R), the quantized evaluation data can be output through a coefficient regression analysis thread to indicate the regression analysis quantized coefficient, and then the regression analysis quantized coefficient can be directly adopted for evaluation, and evaluation data can be output. Specifically, when there is a large difference between the information compression coefficient corresponding to the evaluation data obtained by evaluating the regression analysis quantization coefficient and the target information compression coefficient, the target quantization coefficient different from the regression analysis quantization coefficient can be determined based on the regression analysis quantization coefficient, and then the target quantization coefficient is adopted for evaluation, so as to output the evaluation data.

According to the flood warning method provided by the embodiment of the application, regression analysis is carried out on the rainfall information and the target information compression coefficient by calling the coefficient regression analysis thread obtained after coefficient optimization, so that quantitative evaluation data can be obtained rapidly, and the regression analysis quantization coefficient is indicated. It can be seen that the scope of the quantized coefficient search can be greatly narrowed by directly obtaining the quantized evaluation data indicating one regression analysis quantized coefficient or one quantized coefficient group through the threaded regression analysis. In the process of determining the target quantized coefficients based on the quantized evaluation data, the regression analysis quantized coefficients can be actually evaluated to verify whether the regression analysis quantized coefficients can be determined as the target quantized coefficients, and the quantized evaluation data can greatly reduce the number of repeated evaluation, further reduce the complexity of the evaluation process and improve the evaluation efficiency. In addition, the coefficient regression analysis thread can be obtained by adopting a thread configuration or coefficient integration mode, so that the end-to-end evaluation processing of rainfall information evaluation can be realized, and the coefficient regression analysis thread can be further updated, so that the coefficient regression analysis thread carries a certain expandability. In the process of updating the obtained coefficient regression analysis thread, different kinds of example rainfall information and different information compression coefficients are adopted, so that the thread can more accurately describe the corresponding relation between the rainfall information and the information compression coefficients to the quantization coefficients.

The flood warning method may be performed by a computer device, and may include what is described in the following steps S701 to S702.

S701, receiving evaluation data of rainfall information, the evaluation data covering a target quantization coefficient.

The target quantization coefficient is determined based on the quantization evaluation data of the rainfall information, wherein the quantization evaluation data is obtained by calling a coefficient regression analysis thread obtained after coefficient optimization and carrying out regression analysis processing on the target information compression coefficient and the rainfall information.

If the target information compression coefficient is a regression analysis quantization coefficient indicated by the quantization evaluation data, evaluating the received rainfall information by adopting the regression analysis quantization coefficient; if the target information compression coefficient is obtained based on the debugging of the regression analysis quantization coefficient indicated by the quantization evaluation data, the evaluation data of the received rainfall information is obtained by evaluating the rainfall information by using a new quantization coefficient.

S702, performing derivative processing on the evaluation data by adopting the target quantization coefficient to obtain derivative rainfall information of the rainfall information.

On the basis of the above, referring to fig. 2, there is provided a flood warning device 200, the device comprising:

A data obtaining module 210, configured to obtain rainfall information to be analyzed, and a target information compression coefficient of the rainfall information;

the data analysis module 220 is configured to invoke a coefficient regression analysis thread obtained after coefficient optimization, and perform regression analysis processing on the target information compression coefficient and the rainfall information to obtain quantized evaluation data of the rainfall information;

a data evaluation module 230, configured to evaluate the rainfall information by using a target quantization coefficient determined based on the quantization evaluation data, to obtain evaluation data of the rainfall information;

and the flood early warning module 240 is configured to compare the evaluation data with the flood occurrence coefficient to obtain a comparison result, and combine the comparison result to perform flood disaster early warning.

On the basis of the above, a flood warning system is shown comprising a processor and a memory in communication with each other, the processor being adapted to read a computer program from the memory and to execute the computer program to implement the method described above.

On the basis of the above, there is also provided a computer readable storage medium on which a computer program stored which, when run, implements the above method.

In summary, based on the above scheme, rainfall information to be analyzed and a target information compression coefficient of the rainfall information can be obtained, a coefficient regression analysis thread obtained after coefficient optimization is called, regression analysis processing is performed on the target information compression coefficient and the rainfall information, and quantitative evaluation data of the rainfall information is obtained. Therefore, the regression analysis result, namely the quantized evaluation data, can be obtained relatively quickly through the data regression analysis capability carried by the coefficient regression analysis thread. And the coefficient regression analysis thread is obtained after coefficient optimization, and compared with the thread before coefficient updating, the coefficient regression analysis thread carries relatively accurate data regression analysis capability, so that the reliability of regression analysis results can be ensured to a certain extent. And then, the rainfall information can be evaluated by adopting the target quantization coefficient determined based on the quantitative evaluation data to obtain the evaluation data of the rainfall information, so that the rainfall of the area and surrounding areas can be accurately determined, the possibility of flood occurrence can be accurately determined according to all rainfall information, and the reliability and the confidence of flood early warning are further improved.

It should be appreciated that the systems and modules thereof shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may then be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or special purpose design hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such as provided on a carrier medium such as a magnetic disk, CD or DVD-ROM, a programmable memory such as read only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system of the present application and its modules may be implemented not only with hardware circuitry such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also with software executed by various types of processors, for example, and with a combination of the above hardware circuitry and software (e.g., firmware).

It should be noted that, the advantages that may be generated by different embodiments may be different, and in different embodiments, the advantages that may be generated may be any one or a combination of several of the above, or any other possible advantages that may be obtained.

While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements and adaptations of the application may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within the present disclosure, and therefore, such modifications, improvements, and adaptations are intended to be within the spirit and scope of the exemplary embodiments of the present disclosure.

Meanwhile, the present application uses specific words to describe embodiments of the present application. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the application. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the application may be combined as suitable.

Furthermore, those skilled in the art will appreciate that the various aspects of the application are illustrated and described in the context of a number of patentable categories or circumstances, including any novel and useful procedures, machines, products, or materials, or any novel and useful modifications thereof. Accordingly, aspects of the application may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the application may take the form of a computer product, comprising computer-readable program code, embodied in one or more computer-readable media.

The computer storage medium may contain a propagated data signal with the computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take on a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. A computer storage medium may be any computer readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated through any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or a combination of any of the foregoing.

The computer program code necessary for operation of portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C ++, c#, vb net, python, etc., a conventional programming language such as C language, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, ruby and Groovy, or other programming languages, etc. The program code may execute entirely on the user's computer or as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any form of network, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or the use of services such as software as a service (SaaS) in a cloud computing environment.

Furthermore, the order in which the elements and sequences are presented, the use of numerical letters, or other designations are used in the application is not intended to limit the sequence of the processes and methods unless specifically recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of example, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the application. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in order to simplify the description of the present disclosure and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are required by the subject application. Indeed, less than all of the features of a single embodiment disclosed above.

In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the numbers allow for adaptive variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations in some embodiments for use in determining the breadth of the range, in particular embodiments, the numerical values set forth herein are as precisely as possible.

Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited herein is hereby incorporated by reference in its entirety. Except for the application history file that is inconsistent or conflicting with this disclosure, the file (currently or later attached to this disclosure) that limits the broadest scope of the claims of this disclosure is also excluded. It is noted that the description, definition, and/or use of the term in the appended claims controls the description, definition, and/or use of the term in this application if there is a discrepancy or conflict between the description, definition, and/or use of the term in the appended claims.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present application. Other variations are also possible within the scope of the application. Thus, by way of example, and not limitation, alternative configurations of embodiments of the application may be considered in keeping with the teachings of the application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly described and depicted herein.

The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and variations of the present application will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. which come within the spirit and principles of the application are to be included in the scope of the claims of the present application.

Claims

1. A flood warning method, the method comprising:

obtaining rainfall information to be analyzed and a target information compression coefficient of the rainfall information;

calling a coefficient regression analysis thread obtained after coefficient optimization, and carrying out regression analysis processing on the target information compression coefficient and the rainfall information to obtain quantized evaluation data of the rainfall information;

evaluating the rainfall information by adopting a target quantization coefficient determined based on the quantization evaluation data to obtain evaluation data of the rainfall information;

comparing the evaluation data with the flood occurrence coefficient to obtain a comparison result, and carrying out flood disaster early warning by combining the comparison result;

wherein the quantization evaluation data is used for indicating a regression analysis quantization coefficient, and before the rainfall information is evaluated by adopting the target quantization coefficient determined based on the quantization evaluation data, the method further comprises:

determining the regression analysis quantized coefficients as target quantized coefficients;

or performing intelligent debugging treatment on the regression analysis quantized coefficients to obtain target quantized coefficients;

the intelligent debugging processing is performed on the regression analysis quantized coefficient to obtain a target quantized coefficient, which comprises the following steps:

Evaluating the rainfall information by adopting the regression analysis quantization coefficient to obtain to-be-processed evaluation data of the rainfall information;

obtaining an information compression coefficient corresponding to the evaluation data to be processed, and comparing the information compression coefficient corresponding to the evaluation data to be processed with the target information compression coefficient to obtain a comparison result;

if the comparison result indicates that the information compression coefficient corresponding to the evaluation data to be processed is associated with the target information compression coefficient, determining the regression analysis quantization coefficient as a target quantization coefficient;

if the comparison result indicates that the information compression coefficient corresponding to the evaluation data to be processed is not associated with the target information compression coefficient, debugging the regression analysis quantization coefficient so as to determine the target quantization coefficient after evaluating the rainfall information according to the debugged regression analysis quantization coefficient;

wherein said debugging said regression analysis quantization coefficients comprises:

debugging the regression analysis quantized coefficients according to the undetermined coefficient intervals to which the regression analysis quantized coefficients belong in the multiple undetermined coefficient intervals to obtain the debugged regression analysis quantized coefficients;

Or according to the debugging range, debugging the regression analysis quantized coefficient to obtain the debugged regression analysis quantized coefficient.

2. The method of claim 1, wherein the quantization evaluation data is used to indicate a set of quantization coefficients that encompasses a plurality of regression analysis quantization coefficients; before the rainfall information is evaluated by adopting the target quantization coefficient determined based on the quantization evaluation data, the method further comprises:

determining a regression analysis quantization coefficient from the quantization coefficient group to be determined as a reference quantization coefficient;

and determining a target quantization coefficient based on the reference quantization coefficient.

3. The method of claim 1, wherein prior to evaluating the rainfall information using the target quantization coefficient determined based on the quantization evaluation data, further comprising:

determining reference quantized evaluation data based on the quantized evaluation data;

the target quantized coefficients are determined by a regression analysis quantized coefficients or a set of quantized coefficients indicated by the reference quantized evaluation data.

4. The method of claim 1, wherein invoking the coefficient regression analysis thread that is obtained after coefficient optimization performs regression analysis processing on the target information compression coefficient and the rainfall information to obtain quantized evaluation data of the rainfall information, and includes:

Obtaining a coefficient regression analysis thread corresponding to the component of the rainfall information;

invoking a coefficient regression analysis thread corresponding to the component of the rainfall information, and carrying out regression analysis processing on the target information compression coefficient and the rainfall information to obtain quantized evaluation data corresponding to the component of the rainfall information;

and determining the quantitative evaluation data of the rainfall information through the quantitative evaluation data corresponding to the components of the rainfall information.

5. The method of claim 1, wherein the method further comprises: obtaining a rainfall information set; the set of rainfall information covers heterogeneous example rainfall information, the heterogeneous example rainfall information including:

one or two of rainfall information with different regional positions and different risk factors;

evaluating each example rainfall information according to the undetermined quantized coefficients determined by the undetermined coefficient intervals to obtain configuration information compression coefficients of each example rainfall information;

determining an example set through the rainfall information of each example and the configuration information compression coefficient of the rainfall information of each example, determining a to-be-determined quantization coefficient corresponding to the configuration information compression coefficient of the rainfall information of each example as monitoring data of the example set, and carrying out coefficient update on an original coefficient regression analysis thread to obtain a coefficient regression analysis thread;

The updating the coefficient of the original coefficient regression analysis thread comprises the following steps:

for any example set, calling an original coefficient regression analysis thread, and carrying out regression analysis processing on configuration information compression coefficients and example rainfall information included in the any example set to obtain regression analysis quantization coefficients corresponding to the any example set;

and debugging the coefficient of the original coefficient regression analysis thread according to the difference between the regression analysis quantized coefficient corresponding to any one of the sample sets and the monitoring data of any one of the sample sets so as to obtain the coefficient regression analysis thread.

6. The method of claim 1, wherein the method further comprises:

obtaining example rainfall information and configuration information compression coefficients corresponding to the example rainfall information;

performing feature extraction processing on the example rainfall information according to the example rainfall information data quantity and the element information value in the example rainfall information to obtain rainfall information features of the example rainfall information;

and integrating the coefficients of the original coefficient regression analysis thread through the rainfall information characteristics of the example rainfall information, the configuration information compression coefficient corresponding to the example rainfall information and the undetermined quantization coefficient so as to update the coefficients of the original regression analysis thread and obtain the coefficient regression analysis thread.

7. The method of claim 1, wherein the rainfall information to be analyzed encompasses rainfall information to be processed or to be cached; the method further comprises the steps of:

displaying a target rainfall data window, wherein the target rainfall data window is used for displaying the rainfall information;

responding to the coefficient setting operation of the rainfall information, displaying a plurality of undetermined information compression coefficients and rainfall information display evaluation description information corresponding to each undetermined information compression coefficient;

determining a pending information compression coefficient screened from the plurality of pending information compression coefficients according to a screening operation as a target information compression coefficient of the rainfall information;

wherein the method further comprises:

checking whether the associated rainfall information evaluation unit is optimized;

if yes, a new example set is obtained through the optimized rainfall information evaluation unit, and the coefficient regression analysis thread is updated through the new example set, so that the optimized coefficient regression analysis thread is obtained.

8. A flood warning system comprising a processor and a memory in communication with each other, the processor being adapted to read a computer program from the memory and execute the computer program to implement the method of any one of claims 1-7.