CN116433030A - Rapid assessment method, system, medium and equipment for drought loss - Google Patents
Rapid assessment method, system, medium and equipment for drought loss Download PDFInfo
- Publication number
- CN116433030A CN116433030A CN202310463940.3A CN202310463940A CN116433030A CN 116433030 A CN116433030 A CN 116433030A CN 202310463940 A CN202310463940 A CN 202310463940A CN 116433030 A CN116433030 A CN 116433030A
- Authority
- CN
- China
- Prior art keywords
- drought
- calculation formula
- model
- regression model
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000012417 linear regression Methods 0.000 claims abstract description 59
- 238000011156 evaluation Methods 0.000 claims abstract description 58
- 239000011159 matrix material Substances 0.000 claims abstract description 29
- 230000001419 dependent effect Effects 0.000 claims abstract description 18
- 230000006872 improvement Effects 0.000 claims abstract description 18
- 238000012937 correction Methods 0.000 claims abstract description 10
- 238000004364 calculation method Methods 0.000 claims description 65
- 230000006870 function Effects 0.000 claims description 22
- 238000004590 computer program Methods 0.000 claims description 15
- 238000004458 analytical method Methods 0.000 claims description 11
- 238000001507 sample dispersion Methods 0.000 claims description 9
- 239000006185 dispersion Substances 0.000 claims description 4
- 238000004422 calculation algorithm Methods 0.000 claims description 3
- 230000001747 exhibiting effect Effects 0.000 claims description 3
- 238000013210 evaluation model Methods 0.000 abstract description 9
- 238000012544 monitoring process Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 10
- 238000012545 processing Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 230000011218 segmentation Effects 0.000 description 4
- 238000000638 solvent extraction Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000012854 evaluation process Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000003238 somatosensory effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Business, Economics & Management (AREA)
- Mathematical Physics (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Software Systems (AREA)
- Tourism & Hospitality (AREA)
- Algebra (AREA)
- Operations Research (AREA)
- Strategic Management (AREA)
- Databases & Information Systems (AREA)
- Economics (AREA)
- General Engineering & Computer Science (AREA)
- Marketing (AREA)
- Entrepreneurship & Innovation (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- General Business, Economics & Management (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Primary Health Care (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Probability & Statistics with Applications (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of regional drought monitoring, in particular to a rapid assessment method, a rapid assessment system, a rapid assessment medium and rapid assessment equipment for drought loss. The scheme includes extracting drought intensity index and dividing drought degree; performing duration time division according to the drought degree division; adopting a multiple linear regression model to complete linear regression three-dimensional evaluation of drought-enduring population and drought-enduring farmland area, and forming a multiple linear programming model; performing dependent variable correction according to the multiple nonlinear regression to form a multiple nonlinear programming model; constructing a sample matrix and performing classification operation; and comparing the multiple linear regression model evaluation data before and after improvement with the multiple nonlinear regression model evaluation data with actual data, and displaying the difference of drought evaluation accuracy. The scheme provides a quantitative relation between various drought characteristic elements and the drought, and further builds a drought evaluation model to rapidly and accurately evaluate drought loss in drought areas.
Description
Technical Field
The invention relates to the technical field of regional drought monitoring, in particular to a rapid assessment method, a rapid assessment system, a rapid assessment medium and rapid assessment equipment for drought loss.
Background
Drought has become the most widely affected natural disaster worldwide. Current interference patterns and global changes cause drought to have a serious impact on population, cultivated land, productivity, etc., and have attracted considerable attention in countries around the world. At present, a relatively complete drought loss assessment system is not formed at home and abroad. The generation of drought climates brings about serious drought disasters. Currently, there have been some studies on quantitative assessment of drought loss.
Before the technology of the invention, a method is provided for explaining how to evaluate drought frequency, drought loss and drought resistance, and a quantitative relation between drought risk and drought frequency and drought resistance is given. However, the quantitative relation under the combined action of drought frequency and drought resistance is not considered in the research, so that the drought yield loss assessment contains a certain error. Taken together, the prior art focuses on constructing quantitative relationships between drought and single drought signature elements, as the severity of drought is characterized by multiple drought signature elements such as drought intensity and duration. However, prior to the present technology, the existing methods neglected quantitative relationships between various drought characteristic elements and drought, thereby resulting in larger errors in drought assessment results.
Disclosure of Invention
In view of the above problems, the invention provides a rapid assessment method, a rapid assessment system, a rapid assessment medium and rapid assessment equipment for drought loss, provides a quantitative relation between various drought characteristic elements and dryness, and further constructs a drought assessment model to rapidly and accurately assess drought loss in drought areas.
According to a first aspect of embodiments of the present invention, a method for rapid assessment of drought loss is provided.
In one or more embodiments, preferably, the rapid assessment method of drought loss comprises:
extracting drought intensity indexes and dividing drought degrees;
performing duration time division according to the drought degree division;
acquiring data of a drought population, a drought cultivated land area, a drought intensity value and duration, and adopting a multiple linear regression model to complete linear regression three-dimensional evaluation of the drought population, the drought cultivated land area and form a multiple linear programming model;
performing dependent variable correction according to the multiple nonlinear regression to form a multiple nonlinear programming model;
constructing a sample matrix, performing classification operation to obtain an updated sample matrix, and updating a multiple linear regression model and a multiple nonlinear regression model;
and comparing the multiple linear regression model evaluation data before and after improvement with the multiple nonlinear regression model evaluation data with actual data, and displaying the difference of drought evaluation accuracy.
In one or more embodiments, preferably, the extracting the drought intensity index, performing drought degree classification specifically includes:
acquiring drought statistical data, and extracting a drought-affected population and a drought-affected farmland area;
extracting drought intensity values of all stations, and grading according to the drought intensity values;
when the drought intensity value is greater than-0.5, the drought degree is not drought;
when the drought intensity value is greater than-1 and not greater than-0.5, the drought degree is light drought;
when the drought intensity value is greater than-1.5 and not greater than-1, the drought degree is moderate drought;
when the drought intensity value is greater than-2 and not greater than-1.5, the drought degree is heavy drought;
and when the drought intensity value is smaller than-2, the drought degree is extremely drought.
In one or more embodiments, preferably, the dividing of duration according to the drought degree division specifically includes:
obtaining the drought degree division and recording duration time under different grades;
if the duration of drought is 0 days, it is calculated as 0.5 days.
In one or more embodiments, preferably, the acquiring data of the drought-enduring population, the drought-enduring farmland area, the drought intensity value and the duration uses a multiple linear regression model to complete the linear regression three-dimensional evaluation of the drought-enduring population, the drought-enduring farmland area, and forms a multiple linear programming model, which specifically includes:
taking drought intensity as X axis;
taking drought duration as Y axis;
taking the drought loss amount as a Z axis;
substituting X-axis, Y-axis and Z-axis data into a multi-element linear programming model;
setting a multiple linear regression model by using a first calculation formula;
calculating a judgment coefficient by using a second calculation formula;
calculating a multiple linear sample by using a third calculation formula;
the first calculation formula is as follows:
y＝β 0 +β 1 x 1 +β 2 x 2 +...+β n x n +ε
wherein y is a dependent variable, x 1 、x 2 、…、x n N is the number of independent variables, n is the total number of independent variables, beta 0 Is a regression constant; beta 1 、β 2 、…、β n As regression coefficients, ε is the random error;
the second calculation formula is as follows:
wherein R is 2 For the decision coefficients, SSR is the sum of squares of the regression, SSE is the sum of squares of the residuals, SST is the sum of the squares of the total dispersion,
for a multiple linear sample, ++>
For multiple linear samples->
Average value of (2);
the third calculation formula is as follows:
in one or more embodiments, preferably, the correcting the dependent variable according to the multiple nonlinear regression forms a multiple nonlinear programming model, which specifically includes:
setting a fourth calculation formula as a multiple nonlinear regression equation;
performing dependent variable correction according to the multiple nonlinear regression;
the fourth calculation formula is as follows:
wherein b 1 As regression coefficient, a 1 、a 2 、…、a n And b 1 、b 2 、…、b n And c is a random error.
In one or more embodiments, preferably, the constructing a sample matrix and performing a classification operation, and obtaining the updated sample matrix to update the multiple linear regression model and the multiple nonlinear regression model specifically includes:
selecting drought intensity values corresponding to drought loss amounts under different drought grades to construct a sample matrix;
normalizing the sample matrix by using a fifth calculation formula to obtain an index characteristic value;
calculating a sample dispersion square sum by using a fifth calculation formula;
dividing all the samples into k classes, and setting an objective function as a seventh calculation formula form;
when n and k are determined so that the sum of squares of sample dispersion of the class is minimum, ending the algorithm;
calculating an area objective function by using an eighth calculation formula from the initial recursion value;
calculating an update value of the objective function by using a ninth calculation formula;
calculating a classification number acquisition curve by using a tenth calculation formula, and obtaining a corresponding k value as an optimal classification number when the classification number acquisition curve is at a curve inflection point;
the fifth calculation formula is:
x′ ij ＝x ij /x max,j
wherein x is ij For the j index feature value of the i sample, x ij ' is normalized value, x max J is the maximum value in the j index;
the sixth calculation formula is:
wherein D (i, j) is the sum of squares of the sample dispersion, y r As a characteristic value of the data sample,
data sample mean values of class;
the seventh calculation formula is:
wherein F (n, k) is the objective function calculation, n samples are divided into k classes, 1=i 1 ＜i 2 ＜...＜i k ≤i k+1 -1＝n；
The eighth calculation formula is:
wherein F (n, 2) is a regional objective function;
the ninth calculation formula is:
wherein F' (n, k) is the updated value of the objective function;
the tenth calculation formula is:
wherein, beta (k) is the classification number acquisition curve.
In one or more embodiments, preferably, comparing the multiple linear regression before and after the improvement with the multiple nonlinear regression model evaluation data and the actual data, and exhibiting the difference in drought evaluation accuracy, specifically includes:
obtaining estimation data of a multiple linear regression model;
obtaining estimation data of a multi-element nonlinear regression model;
and comparing the multiple linear regression model evaluation data and the multiple nonlinear regression model evaluation data before and after the improvement with the actual data respectively, obtaining the model precision before and after the improvement, and displaying.
According to a second aspect of embodiments of the present invention, a rapid assessment system for drought loss is provided.
In one or more embodiments, preferably, the rapid assessment system of drought loss comprises:
the degree dividing module is used for extracting drought intensity indexes and dividing drought degrees;
the time analysis module is used for dividing duration time according to the drought degree division;
the linear analysis module is used for acquiring data of drought population, drought farmland area, drought intensity value and duration, and adopting a multiple linear regression model to complete linear regression three-dimensional evaluation of the drought population and the drought farmland area so as to form a multiple linear programming model;
the nonlinear analysis module is used for correcting the dependent variable according to the multiple nonlinear regression to form a multiple nonlinear programming model;
the model operation module is used for constructing a sample matrix, performing classification operation to obtain an updated sample matrix, and updating a multiple linear regression model and a multiple nonlinear regression model;
and the model display module is used for comparing the multiple linear regression model evaluation data before and after the improvement with the multiple nonlinear regression model evaluation data with the actual data and displaying the difference of drought evaluation precision.
According to a third aspect of embodiments of the present invention, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement a method according to any of the first aspect of embodiments of the present invention.
According to a fourth aspect of embodiments of the present invention there is provided an electronic device comprising a memory and a processor, the memory being for storing one or more computer program instructions, wherein the one or more computer program instructions are executable by the processor to implement the method of any of the first aspects of embodiments of the present invention.
The technical scheme provided by the embodiment of the invention can comprise the following beneficial effects:
according to the scheme, the evaluation of each drought loss amount is completed by constructing a three-dimensional dynamic evaluation model based on the improved drought loss amount based on the drought-affected population and the drought intensity corresponding to different drought grades is used as an evaluation index.
In the scheme of the invention, the Fi sher optimal segmentation method is adopted to segment the drought loss quantity array, and the drought intensity and duration are evaluated in real time to evaluate each drought loss quantity.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Figure 1 is a flow chart of a method for rapid assessment of drought loss according to one embodiment of the present invention.
FIG. 2 is a flow chart of the method for rapidly evaluating drought loss according to one embodiment of the invention for extracting drought intensity index and dividing drought extent.
FIG. 3 is a flow chart of a method for rapid assessment of drought loss according to one embodiment of the present invention for duration partitioning according to the drought level partitioning.
Fig. 4 is a flowchart of a method for quickly evaluating drought loss according to an embodiment of the present invention, in which data of a drought-affected population, a drought-affected farmland area, a drought intensity value and a duration are obtained, and a multiple linear regression model is used to complete a linear regression three-dimensional evaluation of the drought-affected population, the drought-affected farmland area, so as to form a multiple linear programming model.
FIG. 5 is a flow chart of a method for rapid assessment of drought loss according to one embodiment of the invention for dependent variable correction based on multiple nonlinear regression to form a multiple nonlinear programming model.
FIG. 6 is a flow chart of constructing a sample matrix, performing classification operation, and updating the sample matrix to perform multiple linear regression model and multiple nonlinear regression model in the rapid drought loss assessment method according to an embodiment of the present invention.
FIG. 7 is a flow chart showing the difference in drought assessment accuracy by comparing the multiple linear regression before and after improvement with the multiple nonlinear regression model assessment data and the actual data in the rapid assessment method of drought loss according to one embodiment of the present invention.
Figure 8 is a block diagram of a rapid assessment system for drought loss according to one embodiment of the present invention.
Fig. 9 is a block diagram of an electronic device in one embodiment of the invention.
Detailed Description
In some of the flows described in the specification and claims of the present invention and in the foregoing figures, a plurality of operations occurring in a particular order are included, but it should be understood that the operations may be performed out of order or performed in parallel, with the order of operations such as 101, 102, etc., being merely used to distinguish between the various operations, the order of the operations themselves not representing any order of execution. In addition, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first" and "second" herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, and are not limited to the "first" and the "second" being different types.
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.
Drought has become the most widely affected natural disaster worldwide. Current interference patterns and global changes cause drought to have a serious impact on population, cultivated land, productivity, etc., and have attracted considerable attention in countries around the world. At present, a relatively complete drought loss assessment system is not formed at home and abroad. The generation of drought climates brings about serious drought disasters. Currently, there have been some studies on quantitative assessment of drought loss.
Before the technology of the invention, a method is provided for explaining how to evaluate drought frequency, drought loss and drought resistance, and a quantitative relation between drought risk and drought frequency and drought resistance is given. However, the quantitative relation under the combined action of drought frequency and drought resistance is not considered in the research, so that the drought yield loss assessment contains a certain error. Taken together, the prior art focuses on constructing quantitative relationships between drought and single drought signature elements, as the severity of drought is characterized by multiple drought signature elements such as drought intensity and duration. However, prior to the present technology, the existing methods neglected quantitative relationships between various drought characteristic elements and drought, thereby resulting in larger errors in drought assessment results.
The embodiment of the invention provides a rapid assessment method, a rapid assessment system, a rapid assessment medium and rapid assessment equipment for drought loss. The scheme provides a quantitative relation between various drought characteristic elements and the drought, and further builds a drought evaluation model to rapidly and accurately evaluate drought loss in drought areas.
According to a first aspect of embodiments of the present invention, a method for rapid assessment of drought loss is provided.
Figure 1 is a flow chart of a method for rapid assessment of drought loss according to one embodiment of the present invention.
In one or more embodiments, preferably, the rapid assessment method of drought loss comprises:
s101, extracting an drought intensity index and dividing drought degree;
s102, dividing duration time according to the drought degree division;
s103, acquiring data of a drought-enduring population, a drought-enduring farmland area, a drought intensity value and duration, and adopting a multiple linear regression model to complete linear regression three-dimensional evaluation of the drought-enduring population and the drought-enduring farmland area so as to form a multiple linear programming model;
s104, correcting the dependent variable according to the multi-element nonlinear regression to form a multi-element nonlinear programming model;
s105, constructing a sample matrix, performing classification operation, and updating the multiple linear regression model and the multiple nonlinear regression model by the sample matrix after updating;
s106, comparing the multiple linear regression model evaluation data before and after improvement with the multiple nonlinear regression model evaluation data with actual data, and displaying the difference of drought evaluation accuracy.
In the embodiment of the invention, the problem of low evaluation result precision caused by large discrete degree and weak correlation among arrays of drought loss quantity arrays is solved, an improved three-dimensional dynamic evaluation method for the drought loss quantity by an optimal segmentation method is provided, a three-dimensional dynamic evaluation model for the drought loss quantity based on characteristic elements such as drought intensity, duration and the like is established by adopting a linear regression method and a nonlinear regression method, evaluation of drought population and drought-cultivated land area is completed, and rapid evaluation of the influence of regional extreme drought on the drought loss quantity in a large range is realized. The problems of larger discrete degree and weaker correlation of partial data exist in the drought quantity statistics process, so that the evaluation precision of drought population and drought area is low, and the evaluation precision of the drought population and the drought area can be improved by establishing a three-dimensional dynamic evaluation model of the drought loss quantity.
FIG. 2 is a flow chart of the method for rapidly evaluating drought loss according to one embodiment of the invention for extracting drought intensity index and dividing drought extent.
As shown in fig. 2, in one or more embodiments, preferably, the extracting the drought intensity index, and dividing the drought degree specifically includes:
s201, acquiring drought statistical data, and extracting a drought-affected population and a drought-affected farmland area;
s202, extracting drought intensity values of all stations, and grading according to the drought intensity values;
s203, when the drought intensity value is larger than-0.5, the drought degree is not drought;
s204, when the drought intensity value is greater than-1 and not greater than-0.5, the drought degree is light drought;
s205, when the drought intensity value is greater than-1.5 and not greater than-1, the drought degree is medium drought;
s206, when the drought intensity value is greater than-2 and not greater than-1.5, the drought degree is heavy drought;
s207, when the drought intensity value is smaller than-2, the drought degree is extremely drought.
In the embodiment of the invention, the drought-affected population and the drought-affected farmland area in 2000-2007 and 2015-2018 are counted according to drought information. And (3) extracting the drought intensity value CDI of each site in the region by adopting ARCGIS, and grading the drought according to the CDI. CDI > -0.5 is not drought; CDI is light drought at [ -0.5, -1); CDI is a mid-drought when at [ -1, -1.5); CDI is a heavy drought at [ -1.5, -2); CDI < -2 > is extremely dry.
FIG. 3 is a flow chart of a method for rapid assessment of drought loss according to one embodiment of the present invention for duration partitioning according to the drought level partitioning.
As shown in fig. 3, in one or more embodiments, preferably, the dividing of duration according to the drought degree division specifically includes:
s301, obtaining duration records of the drought degree division under different levels;
s302, when the duration of a plurality of drought is 0 day, calculating according to 0.5 day.
In the embodiment of the invention, the duration statistics is carried out according to the CDI values corresponding to different drought grades, and the drought duration calculation principle is that the number of months occupied by the CDI values in a drought grade interval in a certain year is the duration of drought, and the specific unit is day. If the duration of drought is 0 days, it is calculated as 0.5 days for ease of calculation.
Fig. 4 is a flowchart of a method for quickly evaluating drought loss according to an embodiment of the present invention, in which data of a drought-affected population, a drought-affected farmland area, a drought intensity value and a duration are obtained, and a multiple linear regression model is used to complete a linear regression three-dimensional evaluation of the drought-affected population, the drought-affected farmland area, so as to form a multiple linear programming model.
In one or more embodiments, as shown in fig. 4, preferably, the acquiring data of the drought population, the drought area, the drought intensity value and the duration uses a multiple linear regression model to complete the linear regression three-dimensional evaluation of the drought population and the drought area, so as to form a multiple linear programming model, which specifically includes:
s401, taking drought intensity as an X axis;
s402, taking the drought duration as a Y axis;
s403, taking the drought loss amount as a Z axis;
s404, substituting X-axis, Y-axis and Z-axis data into a multi-element linear programming model;
s405, setting a multiple linear regression model by using a first calculation formula;
s406, calculating a judgment coefficient by using a second calculation formula;
s407, calculating a multi-element linear sample by using a third calculation formula;
the first calculation formula is as follows:
y＝β 0 +β 1 x 1 +β 2 x 2 +...+β n x n +ε
wherein y is a dependent variable, x 1 、x 2 、…、x n N is the number of independent variables, n is the total number of independent variables, beta 0 Is a regression constant; beta 1 、β 2 、…、β n As regression coefficients, ε is the random error;
the second calculation formula is as follows:
wherein R is 2 For the decision coefficients, SSR is the sum of squares of the regression, SSE is the sum of squares of the residuals, SST is the sum of the squares of the total dispersion,
for a multiple linear sample, ++>
For multiple linear samples->
Average value of (2);
the third calculation formula is as follows:
in the embodiment of the invention, the closer the value range of the judgment coefficient is between 0 and 1, the higher the fitting degree of the multiple linear regression equation is, and the lower the fitting degree of the multiple linear regression equation is.
FIG. 5 is a flow chart of a method for rapid assessment of drought loss according to one embodiment of the invention for dependent variable correction based on multiple nonlinear regression to form a multiple nonlinear programming model.
In one or more embodiments, as shown in fig. 5, the performing the dependent variable correction according to the multiple nonlinear regression preferably forms a multiple nonlinear programming model, which specifically includes:
s501, setting a fourth calculation formula as a multiple nonlinear regression equation;
s502, correcting a dependent variable according to multi-element nonlinear regression;
the fourth calculation formula is as follows:
wherein b 1 As regression coefficient, a 1 、a 2 、…、a n And b 1 、b 2 、…、b n And c is a random error.
In the embodiment of the invention, the drought loss amount of the inner Mongolia drought-affected population, the drought-affected farmland area and other drought characteristic elements such as drought intensity and duration do not absolutely show linear response, and the drought formation is often influenced by the common influence of the drought intensity and duration, so that the drought loss amount and the drought characteristic elements may show nonlinear response to a certain extent. Therefore, the study adopts a multiple nonlinear regression model to complete the drought-enduring population and the drought-enduring farmland area.
FIG. 6 is a flow chart of constructing a sample matrix, performing classification operation, and updating the sample matrix to perform multiple linear regression model and multiple nonlinear regression model in the rapid drought loss assessment method according to an embodiment of the present invention.
In one or more embodiments, as shown in fig. 6, preferably, the constructing a sample matrix and performing a classification operation, and obtaining the updated sample matrix to update the multiple linear regression model and the multiple nonlinear regression model specifically includes:
s601, selecting drought intensity values corresponding to drought loss amounts under different drought grades to construct a sample matrix;
s602, carrying out normalization processing on the sample matrix by using a fifth calculation formula to obtain an index characteristic value;
s603, calculating a sample dispersion square sum by using a fifth calculation formula;
s604, dividing all the samples into k classes, and setting an objective function as a seventh calculation formula form;
s605, when the square sum of the sample dispersion of the class is minimum when n and k are determined, ending the algorithm;
s606, calculating an area objective function by using an eighth calculation formula from the initial recursion value;
s607, calculating an update value of the objective function by using a ninth calculation formula;
s608, calculating a classification number acquisition curve by using a tenth calculation formula, and obtaining a corresponding k value as an optimal classification number when the classification number acquisition curve is positioned at a curve inflection point;
the fifth calculation formula is:
x′ ij ＝x ij /x max,j
wherein x is ij For the j index feature value of the i sample, x ij ' is normalized value, x max J is the maximum value in the j index;
the sixth calculation formula is:
wherein D (i, j) is the sum of squares of the sample dispersion, y r As a characteristic value of the data sample,
data sample mean values of class;
the seventh calculation formula is:
wherein F (n, k) is the objective function calculation, n samples are divided into k classes, 1=i 1 ＜i 2 ＜...＜i k ≤i k+1 -1＝n；
The eighth calculation formula is:
wherein F (n, 2) is a regional objective function;
the ninth calculation formula is:
wherein F' (n, k) is the updated value of the objective function;
the tenth calculation formula is:
wherein, beta (k) is the classification number acquisition curve.
In the embodiment of the invention, partial problems still exist in the evaluation process of the drought loss amount, such as the problems of low dynamic evaluation accuracy of the drought loss amount caused by larger degree of dispersion and lower correlation among the drought loss amount data, and the like, so that the requirement of three-dimensional dynamic evaluation of the drought loss amount cannot be met. Aiming at the defects of the existing three-dimensional dynamic evaluation model of the drought loss and the problem of low evaluation precision, the three-dimensional dynamic model of the drought loss, which is improved based on the Fi her optimal segmentation method, is researched and constructed to complete the dynamic evaluation of the internal Mongolian drought loss, the precision of the evaluation model is improved, the mathematical model of reasonably dividing the groups according to the minimum sum of squares of deviations in the sections is adopted, and the evaluation of the drought loss in each group interval is realized by reasonably dividing the drought loss groups, so that the evaluation model is improved.
FIG. 7 is a flow chart showing the difference in drought assessment accuracy by comparing the multiple linear regression before and after improvement with the multiple nonlinear regression model assessment data and the actual data in the rapid assessment method of drought loss according to one embodiment of the present invention.
As shown in fig. 7, in one or more embodiments, preferably, comparing the multiple linear regression before and after the improvement with the multiple nonlinear regression model evaluation data and the actual data, and exhibiting the difference of drought evaluation accuracy specifically includes:
s701, obtaining estimation data of a multiple linear regression model;
s702, obtaining estimation data of a multi-element nonlinear regression model;
s703, comparing the multiple linear regression model evaluation data and the multiple nonlinear regression model evaluation data before and after the improvement with the actual data respectively, obtaining the model precision before and after the improvement, and displaying.
In the embodiment of the invention, in order to show the corresponding progress, after the model is automatically updated, the comparison of the estimation accuracy of the corresponding model is automatically shown in a form of a table.
According to a second aspect of embodiments of the present invention, a rapid assessment system for drought loss is provided.
Figure 8 is a block diagram of a rapid assessment system for drought loss according to one embodiment of the present invention.
In one or more embodiments, preferably, the rapid assessment system of drought loss comprises:
the degree dividing module 801 is used for extracting drought intensity indexes and dividing drought degrees;
a time analysis module 802 for performing duration division according to the drought degree division;
the linear analysis module 803 is used for acquiring data of a drought-enduring population, a drought-enduring farmland area, a drought intensity value and duration, and adopting a multiple linear regression model to complete linear regression three-dimensional evaluation of the drought-enduring population and the drought-enduring farmland area so as to form a multiple linear programming model;
the nonlinear analysis module 804 is configured to perform dependent variable correction according to multiple nonlinear regression to form a multiple nonlinear programming model;
the model operation module 805 is configured to construct a sample matrix, perform classification operation, and update the multiple linear regression model and the multiple nonlinear regression model by using the updated sample matrix;
the model display module 806 is configured to compare the multiple linear regression model evaluation data and the multiple nonlinear regression model evaluation data before and after the improvement with the actual data, and display the difference of the drought evaluation accuracy.
In the embodiment of the invention, in order to automatically perform real-time drought analysis and adjustment, a modularized setting mode is provided on the basis of the method, and efficient and rapid model analysis is realized.
According to a third aspect of embodiments of the present invention, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement a method according to any of the first aspect of embodiments of the present invention.
According to a fourth aspect of an embodiment of the present invention, there is provided an electronic device. Fig. 9 is a block diagram of an electronic device in one embodiment of the invention. The electronic device shown in fig. 9 is a rapid assessment device for general drought loss, which comprises a general computer hardware structure including at least a processor 901 and a memory 902. The processor 901 and the memory 902 are connected by a bus 903. The memory 902 is adapted to store instructions or programs executable by the processor 901. The processor 901 may be a stand-alone microprocessor or may be a set of one or more microprocessors. Thus, the processor 901 performs the process of data and control of other devices by executing the instructions stored in the memory 902, thereby performing the method flow of the embodiment of the present invention as described above. The bus 903 connects the above components together, while connecting the above components to the display controller 904 and display device and input/output (I/O) device 905. Input/output (I/O) device 905 may be a mouse, keyboard, modem, network interface, touch input device, somatosensory input device, printer, and other devices known in the art. Typically, the input/output devices 905 are connected to the system through input/output (I/O) controllers 906.
The technical scheme provided by the embodiment of the invention can comprise the following beneficial effects:
according to the scheme, the evaluation of each drought loss amount is completed by constructing a three-dimensional dynamic evaluation model based on the improved drought loss amount based on the drought-affected population and the drought intensity corresponding to different drought grades is used as an evaluation index.
In the scheme of the invention, a Fisher optimal segmentation method is adopted to segment the drought loss quantity array, and the drought intensity and duration are evaluated in real time to evaluate each drought loss quantity.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. A method for rapid assessment of drought loss, the method comprising:
extracting drought intensity indexes and dividing drought degrees;
performing duration time division according to the drought degree division;
acquiring data of a drought population, a drought cultivated land area, a drought intensity value and duration, and adopting a multiple linear regression model to complete linear regression three-dimensional evaluation of the drought population, the drought cultivated land area and form a multiple linear programming model;
performing dependent variable correction according to the multiple nonlinear regression to form a multiple nonlinear programming model;
constructing a sample matrix, performing classification operation to obtain an updated sample matrix, and updating a multiple linear regression model and a multiple nonlinear regression model;
and comparing the multiple linear regression model evaluation data before and after improvement with the multiple nonlinear regression model evaluation data with actual data, and displaying the difference of drought evaluation accuracy.
2. The method for rapidly assessing drought loss according to claim 1, wherein the extracting drought intensity index and dividing drought degree comprises:
acquiring drought statistical data, and extracting a drought-affected population and a drought-affected farmland area;
extracting drought intensity values of all stations, and grading according to the drought intensity values;
when the drought intensity value is greater than-0.5, the drought degree is not drought;
when the drought intensity value is greater than-1 and not greater than-0.5, the drought degree is light drought;
when the drought intensity value is greater than-1.5 and not greater than-1, the drought degree is moderate drought;
when the drought intensity value is greater than-2 and not greater than-1.5, the drought degree is heavy drought;
and when the drought intensity value is smaller than-2, the drought degree is extremely drought.
3. The method for rapid assessment of drought loss according to claim & wherein said dividing of duration according to said drought degree division comprises:
obtaining the drought degree division and recording duration time under different grades;
if the duration of drought is 0 days, it is calculated as 0.5 days.
4. The method for rapidly evaluating drought loss according to claim 1, wherein the acquiring data of drought areas, drought intensity values and durations of the drought-affected population, the drought-affected cultivated land comprises performing linear regression three-dimensional evaluation of the drought-affected population, the drought-affected cultivated land by using a multiple linear regression model to form a multiple linear programming model, and the method specifically comprises:
taking drought intensity as X axis;
taking drought duration as Y axis;
taking the drought loss amount as a Z axis;
substituting X-axis, Y-axis and Z-axis data into a multi-element linear programming model;
setting a multiple linear regression model by using a first calculation formula;
calculating a judgment coefficient by using a second calculation formula;
calculating a multiple linear sample by using a third calculation formula;
the first calculation formula is as follows:
y＝β 0 +β 1 x 1 +β 2 x 2 +...+β n x n +ε
wherein y is a dependent variable, x 1 、x 2 、…、x n N is the number of independent variables, n is the total number of independent variables, beta 0 Is a regression constant; beta 1 、β 2 、…、β n As regression coefficients, ε is the random error;
the second calculation formula is as follows:
wherein R is 2 For the decision coefficients, SSR is the sum of squares of the regression, SSE is the sum of squares of the residuals, SST is the sum of the squares of the total dispersion,
for a multiple linear sample, ++>
For multiple linear samples->
Average value of (2);
the third calculation formula is as follows:
5. the method for rapidly assessing drought loss according to claim 1, wherein the performing of dependent variable correction according to multiple nonlinear regression forms a multiple nonlinear programming model, specifically comprising:
setting a fourth calculation formula as a multiple nonlinear regression equation;
performing dependent variable correction according to the multiple nonlinear regression;
the fourth calculation formula is as follows:
wherein b 1 As regression coefficient, a 1 、a 2 、…、a n And b 1 、b 2 、…、b n And c is a random error.
6. The method for rapidly evaluating drought loss according to claim 1, wherein the constructing a sample matrix and performing classification operation to obtain an updated sample matrix for updating a multiple linear regression model and a multiple nonlinear regression model comprises:
selecting drought intensity values corresponding to drought loss amounts under different drought grades to construct a sample matrix;
normalizing the sample matrix by using a fifth calculation formula to obtain an index characteristic value;
calculating a sample dispersion square sum by using a fifth calculation formula;
dividing all the samples into k classes, and setting an objective function as a seventh calculation formula form;
when n and k are determined so that the sum of squares of sample dispersion of the class is minimum, ending the algorithm;
calculating an area objective function by using an eighth calculation formula from the initial recursion value;
calculating an update value of the objective function by using a ninth calculation formula;
calculating a classification number acquisition curve by using a tenth calculation formula, and obtaining a corresponding k value as an optimal classification number when the classification number acquisition curve is at a curve inflection point;
the fifth calculation formula is:
x i ' j ＝x ij /x max,j
wherein x is ij For the j index feature value of the i sample, x ij ' is normalized value, x max J is the maximum value in the j index;
the sixth calculation formula is:
wherein D (i, j) is the sum of squares of the sample dispersion, y r As a characteristic value of the data sample,
data sample mean values of class;
the seventh calculation formula is:
wherein F (n, k) is the objective function calculation, n samples are divided into k classes, 1=i 1 ＜i 2 ＜...＜i k ≤i k+1 -1＝n；
The eighth calculation formula is:
wherein F (n, 2) is a regional objective function;
the ninth calculation formula is:
wherein F' (n, k) is the updated value of the objective function;
the tenth calculation formula is:
wherein, beta (k) is the classification number acquisition curve.
7. The method for rapidly assessing drought loss according to claim 1, wherein the comparing the multiple linear regression before and after improvement with the multiple nonlinear regression model assessment data with the actual data and exhibiting differences in drought assessment accuracy, specifically comprises:
obtaining estimation data of a multiple linear regression model;
obtaining estimation data of a multi-element nonlinear regression model;
and comparing the multiple linear regression model evaluation data and the multiple nonlinear regression model evaluation data before and after the improvement with the actual data respectively, obtaining the model precision before and after the improvement, and displaying.
8. A rapid drought loss assessment system for performing the method of any one of claims 1-7 comprising:
the degree dividing module is used for extracting drought intensity indexes and dividing drought degrees;
the time analysis module is used for dividing duration time according to the drought degree division;
the linear analysis module is used for acquiring data of drought population, drought farmland area, drought intensity value and duration, and adopting a multiple linear regression model to complete linear regression three-dimensional evaluation of the drought population and the drought farmland area so as to form a multiple linear programming model;
the nonlinear analysis module is used for correcting the dependent variable according to the multiple nonlinear regression to form a multiple nonlinear programming model;
the model operation module is used for constructing a sample matrix, performing classification operation to obtain an updated sample matrix, and updating a multiple linear regression model and a multiple nonlinear regression model;
and the model display module is used for comparing the multiple linear regression model evaluation data before and after the improvement with the multiple nonlinear regression model evaluation data with the actual data and displaying the difference of drought evaluation precision.
9. A computer readable storage medium, on which computer program instructions are stored, which computer program instructions, when executed by a processor, implement the method of any of claims 1-7.
10. An electronic device comprising a memory and a processor, wherein the memory is configured to store one or more computer program instructions, wherein the one or more computer program instructions are executed by the processor to implement the method of any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310463940.3A CN116433030A (en) | 2023-04-26 | 2023-04-26 | Rapid assessment method, system, medium and equipment for drought loss |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310463940.3A CN116433030A (en) | 2023-04-26 | 2023-04-26 | Rapid assessment method, system, medium and equipment for drought loss |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116433030A true CN116433030A (en) | 2023-07-14 |
Family
ID=87079663
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310463940.3A Pending CN116433030A (en) | 2023-04-26 | 2023-04-26 | Rapid assessment method, system, medium and equipment for drought loss |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116433030A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101784441B1 (en) * | 2016-04-28 | 2017-10-11 | 세종대학교산학협력단 | Method for estimating natural drought index by computer |
| CN108615105A (en) * | 2018-03-30 | 2018-10-02 | 中国科学院遥感与数字地球研究所 | Crops drought loss appraisal system and method based on remotely-sensed data |
| CN110020811A (en) * | 2019-04-16 | 2019-07-16 | 中国水利水电科学研究院 | Arid is to productivity impact evaluation method |
| CN110909933A (en) * | 2019-11-20 | 2020-03-24 | 北京师范大学 | Agricultural drought rapid diagnosis and evaluation method coupling crop model and machine learning language |
| CN115018394A (en) * | 2022-08-08 | 2022-09-06 | 成都数联云算科技有限公司 | Drought evaluation method, device, storage medium, equipment and computer program product |
-
2023
- 2023-04-26 CN CN202310463940.3A patent/CN116433030A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101784441B1 (en) * | 2016-04-28 | 2017-10-11 | 세종대학교산학협력단 | Method for estimating natural drought index by computer |
| CN108615105A (en) * | 2018-03-30 | 2018-10-02 | 中国科学院遥感与数字地球研究所 | Crops drought loss appraisal system and method based on remotely-sensed data |
| CN110020811A (en) * | 2019-04-16 | 2019-07-16 | 中国水利水电科学研究院 | Arid is to productivity impact evaluation method |
| CN111275366A (en) * | 2019-04-16 | 2020-06-12 | 中国水利水电科学研究院 | Method for evaluating influence of drought on productivity |
| CN110909933A (en) * | 2019-11-20 | 2020-03-24 | 北京师范大学 | Agricultural drought rapid diagnosis and evaluation method coupling crop model and machine learning language |
| CN115018394A (en) * | 2022-08-08 | 2022-09-06 | 成都数联云算科技有限公司 | Drought evaluation method, device, storage medium, equipment and computer program product |
Non-Patent Citations (2)
| Title |
|---|
| 朱建平 等: "《统计学——数据的搜集、整理和分析（第三版）》", ，福州：福建科学技术出版社, pages: 155 - 158 * |
| 王义民 等: "水库抗旱调度分期特征确定方法研究", 《自然灾害学报》, vol. 29, no. 1, pages 112 - 120 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109472004B (en) | Comprehensive evaluation method, device and system for influences of climate change and human activities on hydrology and drought | |
| CN109409628B (en) | Acquisition terminal manufacturer evaluation method based on metering big data clustering model | |
| CN109389145B (en) | Electric energy meter manufacturer evaluation method based on metering big data clustering model | |
| CN110442911B (en) | High-dimensional complex system uncertainty analysis method based on statistical machine learning | |
| CN111222678A (en) | Road surface technical condition prediction method | |
| JP5110891B2 (en) | Statistical prediction method and apparatus for influent water quality in water treatment facilities | |
| CN113793057A (en) | Building bidding and tendering data generation method based on regression analysis model | |
| CN116433030A (en) | Rapid assessment method, system, medium and equipment for drought loss | |
| CN116822366A (en) | Construction of runoff pollution load calculation model and runoff pollution load calculation method | |
| Qian et al. | A new nonlinear risk assessment model based on an improved projection pursuit | |
| CN110196797A (en) | Automatic optimization method and system suitable for credit scoring card system | |
| CN102262188B (en) | Sampling inspection method for workpieces | |
| CN114784795A (en) | Wind power prediction method and device, electronic equipment and storage medium | |
| CN109858699B (en) | Water quality quantitative simulation method and device, electronic equipment and storage medium | |
| CN114677052A (en) | Natural gas load fluctuation asymmetry analysis method and system based on TARCH model | |
| CN105488521B (en) | A kind of dilatation screening sample method based on kernel function | |
| Williams et al. | The matrix of good management: defining good management practices and associated nutrient losses across primary industries | |
| CN114021644A (en) | Method for calculating regional representative underground water level based on K-means and de-clustering method | |
| Hoang et al. | A robust parameter approach for estimating CERES-Rice model parameters for the Vietnam Mekong Delta | |
| CN114154686A (en) | Dam deformation prediction method based on ensemble learning | |
| CN107862126B (en) | System reliability assessment method under component-level information diversity condition | |
| CN111861259A (en) | Load modeling method, system and storage medium considering time sequence | |
| CN115481906B (en) | Production line evaluation method, device, equipment and medium | |
| CN115795229B (en) | Quantitative research method suitable for water-dependent ecological system service feedback loop | |
| CN111126768B (en) | Fishery habitat water quality assessment method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |