METHOD FOR CONFIGURING SLOPE REGULATION AND STORAGE MEASURES BASED ON FLOOD REGULATION AND STORAGE CAPACITY AND WATER CONSUMPTION CONTROL

Abstract

Provided is a method for configuring slope regulation and storage measures based on flood regulation and storage capacity and water consumption control, including the following steps: S1, collecting and organizing precipitation data of a basin; S2, constructing an optimized configuration model based on a balance between flood regulation and storage capacity improvement and slope water consumption control; S3, initializing settings; S4, computing an optimal comprehensive benefit of the slope regulation and storage measures with a genetic algorithm; and S5, determining whether a number of iterations is maximized; if the number of iterations is maximized, terminating an iteration and outputting data, such that a computation is completed; and if the number of iterations is not maximized, returning to the S4, and restarting the computation.The optimized configuration model of this application can resolve a balanced relationship between flood regulation and storage capacity improvement and slope water consumption control.

Description

l METHOD FOR CONFIGURING SLOPE REGULATION AND STORAGE MEASURES BASED ON FLOOD REGULATION AND STORAGE CAPACITY AND WATER CONSUMPTION CONTROL TECHNICAL FIELD [OOOl]The present disclosure relates to the technical field of hydraulic engineering, and especially relates to the technical fields of flood regulation and storage and water conservation. Specifically, the present disclosure provides a method for optimized configuration of slope regulation and storage measures based on a balance between flood regulation and storage capacity improvement and slope water consumption control. BACKGROUND [OOOZ]The ecological engineering prevention and control has gradually become a significant action for addressing flood disasters in river basins. The scientific configuration of regulation and storage measures during a rainfallrunoff generation process on a slope is a key for the ecological engineering prevention and control. Under extreme precipitation, slope regulation and storage measures such as forest and grass vegetation projects and natural restoration projectsaffecttherunoffgeneration,sedimentyield,andsoil moisture on slopes. On the one hand, the slope regulation and storage measures can attenuate the flood peak, prolong the flood duration, and mitigate the flood damage. On the other hand, the arrangement of vegetation construction measures on slopes will cause specified water consumption and reduce the runoffs entering river channels. The scientific configuration of slope regulation and storage measures is crucial for balancing flood control / mitigation and slope water generation / consumption. Currently, most of the relevant studiesfocusontheinfluenceofindexessuchasslopegradient andvegetationcoveragedegraaonthesinglegoalssuchasslope flood regulation and storage capacity or slope water consumption. However, there are few studies on the changes in coupling between flood regulation and storage capacity and water consumption for slopes under extreme precipitation. Therefore, it is necessary to propose a method for optimized configuration based.on a balance between flood regulation and storage capacity improvement and slope water consumption control, so as to achieve the reasonable allocation of flood control / mitigation and water resources for river basins. SUMMARY

[0003] An objective of the present disclosure is to provide a method for optimized configuration of slope regulation and storage measures based on a balance between flood regulation and storage capacity improvement and slope water consumption control. This method can resolve a balanced relationship between flood regulation and storage capacity improvement and slope water consumption control. That is, this method can maximize the comprehensive benefit. with 51 maximum. flood regulation and storage capacity and a minimum slope water consumption as constraint objectives. Unlike the existing studies focusing on the influence of indexes such as slope gradient and vegetation coverage degree on the single goals such as slope flood regulation and storage capacity or slope water consumption, the present disclosure can achieve the reasonable allocation of flood control / mitigation and water resources for river basins.

[0004] To achieve the above objective, the present disclosure adopts the following technical solutions:

[0005] A.method for configuring slope regulation and storage measures based on flood regulation and storage capacity and water consumption control is provided, including the following steps:

[0006] Sl, collecting and organizing precipitation data of a basin;

[0007] S2, constructing an<optimized.configurationlnodel based on a balance between flood regulation and storage capacity improvement.and.slope water consumption control, including the following steps:

[0008] 821, flood regulation and storage objective: maximizing a water storage ratio and a flood peak attenuation rate in a control unit to improve a flood regulation and storage capacity FRi, which is specifically as follows:

[0009] FRLMax = Max(WSiFPRi (1)

[0010] where,

[0011] WSi =P'PiRi (2) R,- = f(PiSi Vi) (3) FPRi :w (4) FPM

[0012] In the above formulas, WSi represents a water storage ratio in a unit i; FPRi represents a flood peak attenuation rate in the unit i; Pi represents a precipitation in the unit i, mm; R,- represents a total runoff yield in the unit i, mm; f represents a functional relationship of Ri with a precipitation, a slope gradient, and a vegetation coverage degree; 5,- represents a slope gradient in the unit i; V,- represents a vegetation coverage degree in the unit i; FPbl. represents a flood peak flow rate in the unit 1' without a slope vegetation construction measure, m3 / s; and FPv,i represents a flood peak flow rate in the unit i with a slope vegetation construction measure, m3 / s;

[0013] 822, water consumption control objective: minimizing a water demand and a transpiration of a vegetation on a slope to allow the water consumption control objective WC,- on the slope, which is specifically as follows:

[0014] WGL-M = Min(WP'Îi,EPTii) (5)

[0015] where,

[0016] WDi = WDgi + WD- = g(Vi) (6)

[0017] ETi=<p(Vi) (7)

[0018] In the above formulas, WDl- represents a water demand of a vegetation in the unit i, mm; WDW- represents a water demand of a grassland in the unit i, mm; WD- represents a water demand of a forestland in the unit i, mm; g represents a functional relationship of WDi with a vegetation coverage degree; V,- represents a vegetation coverage degree in the unit i; ET,- represents a transpiration of a vegetation in the unit i, mm; and (p represents a functional relationship of ET,- with a vegetation coverage degree;

[0019] 82-3, based on a maximum flood regulation and storage capacity and a minimum slope water consumption for multiobjective optimized configuration of the slope regulation and storage measures, transforming a multiobjective function into a singleobjective function with a comprehensive benefit index CEi as a target; and calculating a maximum value of CEi through a tradeoff relationship between flood regulation and storage and slope water consumption, where a corresponding slope gradient and coverage degree are optimal solutions, and a calculation formula for the comprehensive benefit index is as follows:

[0020] CEi=w1xï2x (8) FR WC

[0021] where,

[0022] w1+w2=1w1>0w2>0 (9)

[0023] In the above formulas, CEi represents a comprehensive benefit index for slope regulation and storage measures in the unit i; FRi represents a flood regulation and storage capacity index for the slope regulation and storage measures in the unit i; FR represents an average flood regulation and storage capacity for slope regulation and storage measures in different units; WC; represents a water consumption control index for the slope regulation and storage measures in the unit i; WC represents an average water consumption control value for slope regulation and storage measures in different units; and 001 and wz represent weight values, which are determined according to simulation experiments;

[0024] S3, initializing settings;

[0025] S4, computing an optimal comprehensive benefit of the slope regulation and storage measures with a genetic algorithm: calculating a fitness of each individual with the genetic algorithm, and evaluating performance of each individual according to a target, where an individual with a high fitness represents a highquality solution, each individual is provided with a slope gradient and a coverage degree, and the target is the comprehensive benefit index for the slope regulation and storage measures; and

[0026] SS, determining whether a number of iterations is maximized; if the number of iterations is maximized, terminatingauliterationanxioutputtingwdatatx>produceaislope gradient and a coverage degree corresponding to the optimal comprehensive benefit of the slope regulation and storage measures, such that a computation is completed; and if the number of iterations is not maximized, returning to the S4, and restarting the computation.

[0027] The collecting and organizing precipitation data of a basin in the Sl includes: collecting observation data from rainfall stations and hydrologic stations within the basin, and selecting data of floodtriggering rainfall events as representative precipitation data.

[0028] Further, the 82-3 includes the following steps:

[0029] (a) determining a flood.regulation.and storage capacity of the slope regulation and storage measures with a maximum floodregulationandstoragecapacityincludimganximumwater storage ratio and.alnaximum.flood.peak attenuation rate in the control unit as a first criterion; and

[0030] (b) integrating flood regulation and storage capacity and.water consumption control data of the slope regulation and storageIneasures toeastablish.the comprehensive benefit index; according to the comprehensive benefit index, determining an optimal slope gradient and vegetation coverage degree; and based on a threshold of a total slope regulation and storage capacity in the control unit, conducting secondary configuration to determine a zoning and classification optimization solution.

[0031] Further, the initializing settings in the S3 includes: with a slope gradient set to Si=(Y and a boundary condition set to 0°S£Si5590°, calculating a step size ASi=ÏF; and with a vegetation coverage degree set to %==() and. a boundary condition set to O S Vi S 100%, calculating a step size AV,- = 1%.

[0032] Further, the computation with the genetic algorithm in the S4 includes: 1) initialization: creating an initial population, where each individual in the initial population represents a potential solution, each individual is provided with a slope gradient and a vegetation coverage degree, and each individual is represented by a chromosome; 2) fitness evaluation: calculating the fitness of each individual to determine a quality of each individual, and evaluating the performance of each individual with a fitness function according to a target of a problem, where an individual with a high fitness to the target of the problem represents a highquality solution, and the target of the problem is the comprehensive benefit index for the slope regulation and storage measures; 3) selection: based on fitness values, selectingaiplurality of individuals fronla current population as parents for breeding of offspring; 4) crossover: combining the selected parents through crossover operations to produce new offspring individuals; 5) mutation: conducting mutation operations for the offspring individuals: randomly modifying gene values at some chromosomal positions for simulating mutation processes in biological evolution to ensure a diversity of a population; 6) replacement: replacing individualsjjïtheoriginalpopulationwithîjuanewindividuals to produce a newgeneration population; and 7) repetition: repeating the evaluation, the selection, the crossover, and the mutation until a termination condition is met, where the termination condition is that the number of iterations is maximized.

[0033] The above technical solutions have the following beneficial effects: An optimized configuration. model is constructed and used.in combination.with.a genetic algorithm, actual observations, etc. to resolve a balanced relationship between flood regulation and storage capacity improvement and slopewaterconsumptioncontrolwithzamaximumfloodregulation and storage capacity and.a minimum.slope water consumption as constraint objectives, so as to maximize the comprehensive benefit of reasonable configuration of flood control / mitigation and water resources for a river basin. BRIEF DESCRIPTION OF THE DRAWING

[0034] The present disclosure will be further described in detail below with reference to accompanying drawings and specific implementations.

[0035] FIG. 1 is a schematic diagram of a model construction flow in the method of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0036] Example 1

[0037] A.method for configuring slope regulation and storage measures based on flood regulation and storage capacity and water consumption control was provided, including the following steps:

[0038] Sl. Precipitation data of a basin was collected and organized: Observation data from rainfall stations and hydrologic stations within the basin was collected, and data of floodtriggering rainfall events was selected as representative precipitation data.

[0039] 52. An optimized.configuration model based on a balance between flood regulation and storage capacity improvement and slopexater consumption control was constructed, including the following steps:

[0040] 82-1. Flood regulation and storage objective: A water storage ratio and a flood peak attenuation rate in a control unit were maximized_to improve a flood regulation and storage capacity FP], which was specifically as follows:

[0041] FRiMax=Max(WSiFPRi (1)

[0042] where,

[0043] WSizpiPZR (2)

[0044] Ri=](PLSLVÜ (3)

[0045] FPRizw (4) FPM

[0046] In the above formulas, VV represents a water storage ratiojjlaunit Ù FPRirepresentseafloodpeakattenuationrate in the unit Ù 1% represents a precipitation in the unit L mm; l represents a total runoff yield in the unit i, mm; f represents a functional relationship of Ri with a precipitation, a slope gradient, and a vegetation coverage degree; Si represents a. slope gradient ill the unit i; % represents a vegetation coverage degree in the unit i; FPüi representseaflood peak flow rateijlthe unitiiwithouteislope vegetation construction measure, m3 / s; and FB represents a flood peak flow rate in the unit i with a slope vegetation construction measure, m3 / s.

[0047] SZ-2. Water consumption control objective: A water demand and a transpiration of a vegetation on a slope were minimized to allow the water consumption control objective VVCion the slope, which was specifically as follows:

[0048] WCiMi=Min(WPL;iEP7;i) (5)

[0049] where,

[0050] WDi=WDgli+WDfli=g(Vi) (6)

[0051] ET}==@(W) (7)

[0052] In the above formulas, WU} represents a water demand of a vegetation.in the unit L mm; HUI represents a water demand of a grassland in the unit L mm; WDfi represents a water demand of a forestland in the uniti, mm; g represents a functional relationship of Uh with a vegetation coverage degree; W represents a vegetation coverage degree in the unit i; En represents a transpiration of a vegetation in the unit L mm; and @ represents a functional relationship of E with a vegetation coverage degree.

[0053] 523. Based on a maximum flood regulation and storage capacity and a minimum slope water consumption for multiobjective optimized configuration of the slope regulation and storage measures, a multiobjective function was transformed into a singleobjective function with a comprehensive benefit index as a target. A maximum value of CEi was calculated through a tradeoff relationship between flood regulation and storage and slope water consumption. A corresponding slope gradient and coverage degree were optimal solutions.

[0054] (a) A flood regulation and storage capacitgiof the slope regulation and storage measures was determined with a maximum floodregulationandstoragecapacityincludimganaximmnwater storage ratio and aInaximum flood peak attenuation rate in the control unit as a first criterion.

[0055] (b) Flood regulation and storage capacity and water consumption control data of the slope regulation and storage measures was integrated to establish the comprehensive benefit index. According to the comprehensive benefit index, an optimal slope gradient and vegetation coverage degree were determined. Based on a threshold of a total slope regulation and storage capacity in the control unit (which was determined by formulas (1) to (4) ) , secondary configuration was conducted to determine a zoning and classification optimization solution.

[0056] A calculation formula for the comprehensive benefit index was as follows: [OO57]CEi=w1X%2x% (8)

[0058] where,

[0059] w1+w2=1,w1>0,w2>0 (9)

[0060] In the above formulas, CEi represents a comprehensive benefit index for slope regulation and storage measures in the unit i; FRi represents a flood regulation and storage capacity index for the slope regulation and storage measures in the unit i; FR represents an average flood regulation and storage capacity for slope regulation and storage measures in different units; WC; represents a water consumption control index for the slope regulation and storage measures in the unit i; WC represents an average water consumption control value for slope regulation and storage measures in different units; and 001 and 002 represent weight values.

[0061] S3. Settings were initialized: With a slope gradient set to Si = 0° and a boundary condition set to 0° S 5,- S 90°, a step size A5,- = 1° was calculated. With a vegetation coverage degree set to Vi =O and a boundary condition set to 0 SV,- S 100%, a step size AV,- = 1% was calculated.

[0062] S4. An optimal comprehensive benefit of the slope regulation and storage measures was computed with a genetic algorithm: A fitness of each individual was calculated with the genetic algorithm, and performance of each individual was evaluated according to a target. An individual with a high fitness represented a highquality solution. Each individual was provided with a slope gradient and a coverage degree. The target was the comprehensive benefit index for the slope regulation and storage measures.

[0063] The computation.with.the genetic algorithnlincluded: 1) Initialization: An initial population was created. Each individual in the initial population represented.a potential solution.Theindividualinthepresentdisclosurewasprovided with a slope gradient and a vegetation coverage degree. Each individualwasrepresentedbyeachromosome(itcouldbeeabinary, a.real number, or another data structure, andeuïinitialization mode could. be random. generation or specific rulebased generation). 2) fitness evaluation: The fitness of each individual was calculated to determine a quality of each individual. The performance of each individual was evaluated with a fitness function according to a target of a problem. An individual with a high fitness to the target of the problem representecla highquality solution. The target of the problem in the present disclosure was the comprehensive benefit index for the slope regulation and storage measures. 3) Selection: Based on fitness values, a plurality of individuals were selected.fron1a current population as parents for breeding of offspring (common selection methods included roulette wheel selection, tournament.selection, rankbased.selection, etc.). 4) Crossover: The selected parents were combined through crossover operations (also known as recombination) to produce new offspring individuals (this usually involved the exchange éipart.oftuagenetic information <æfchromosomes oftuaparents to mimic the gene recombination in biogenetics). 5) Mutation: Mutation operations were conducted for the offspring individuals. Gene values were randomly' modified. at some chromosomal positions for simulating mutation processes in biological evolution to ensure a diversity of a population. 6) Replacement: Individuals in the original population (parents) were replaced with the new individuals (offspring) to produce a newgeneration. population (there were many replacement strategies, including complete replacement and partial replacement). 7) Repetition: The evaluation, the selection, the crossover, and.the1nutation.were repeatecìuntil a termination condition was met. The termination condition in the present disclosure was that the number of iterations was maximized.

[0064] 85. Whether a number of iterations was maximized was determined. If the number of iterations was maximized, an iteration was terminated, and data was output to produce a slope gradient and a coverage degree corresponding to the optimal comprehensive benefit of the slope regulation and storage measures, such.that a computation.was completed If the number of iterations was not maximized, it returned to the S4, and the computation was restarted. With a comprehensive benefit of the slope regulation and storage measures as a final index, a.Pi River basin_was optimized with the method in this example. Optimization results were as follows: a slope gradient: 28°C, and a vegetation coverage degree: 60%.

[0065] The above description.is proposed merely as a technical solution that can be implemented by the present disclosure, and does not serve as a single restriction on the technical solution itself.

Claims

l. Procedure for configuring slope control lation and storage measures based on flood protection control and storage capacity and water consumption management, comprising the following steps: Sl, the collection and organization of precipitation data from a basin; 82, building an optimized configuration model part based on a balance between improvement of over flow control and storage capacity and control of water consumption on slopes, comprising the following steps: S2l, objective' for flood regulation and on stroke: maximizing a water storage ratio and a degree of weakening. for flood peaks II a. board ring unit for a flood regulation and storage capacity to improve city FPi, which is specifically as follows: FR,-Max = Max(WSiFPRi) (1) whereby ws, = P'PiR' <2) Ri =f(PiSiVi) (3) FPRi = FP"F';ZP* <4) where WSi a water storage ratio in a unit i displays; FËRi a degree of attenuation for overflow reflects peaks in the unit i; EQ a reflection in the unit i represents, mm; Ri a total discharge output in the unit i represents, mm; f a functional relationship from Ri with a precipitation, a slope gradient and a vege represents coverage rate; Si a slope gradient in the unit i represents; DQ a vegetation cover rate in represents the unit i; FPÈi a flood peak degree in the unit i represents without construction measure of the slope vegetation, H / s; and ETQJ a flood peak degree in the unit i represents with a construction measure gel for slope vegetation, m³ / s; 822, objective for water consumption control: the minimizing water requirements and a transpiration of vegetation on a slope to the objective for wa consumption control on the slope, MU}, possible to ma know, which is specifically as follows: WC,-M = Min (WP132%") (5) whereby WD,- = WDgi + WD = g(Vi) (6) ETi = (PU / i) (7) where WDi a water requirement of vegetation in the unit i represents, mm; WDi the water requirement of a grassland represents in the unit i, mm; WDLi a water management represents the depth of a forest area in the unit i, mm; g a functional relationship of WDi with a vegetation cover degree represents; LG a vegetation cover rate in the one heid i represents; ETi a transpiration of vegetation in the unit i represents, mm; and @ a functional relation tie of ET} displays with a vegetation cover; 823, based on maximum flood regulation and storage capacity and minimal water consumption on hel lings for an optimized configuration of the hel sedation regulation and multi-objective storage measures lingen, converting a position with Heerdere objective statements in a position with a single objective with an extensive CEi benefits index as an objective; and the calculating a maximum value of CEi by a deviation relationship between flood regulation and storage and slope water consumption, where a corresponding hel lingsgradient and coverage rate are optimal solutions, and a calculation formula for the expanded benefits index is as follows: CEi=w1x%wzx% (8) whereby, w1+w2=1 w1>0,w2>0 (9) where CEi an extensive benefit index for slope gulation <a] opslagmaatregelen lll eenheid _i weergeeft; eri a flood regulation and storage capacity index for the slope regulation and storage measures in unit i displays; FR an average flood regulation and storage capacity for slope regulation and storage measures represents len in different units; WCi a water consumption control index for slope control and on represents striking measures in unit i; DVC an average the value for water consumption management for slope regulation and storage measures in various units displays; and display wl and wzgewicht values; 53, initializing settings; S4, calculating optimal expanded benefits of the slope regulation and storage measures. with a genetic algorithm: calculating the fitness of each individual with the genetic algorithm, and evaluating the performance of each individual according to an objective, where an individual with high fitness a high-quality represents solution, every individual is provided of a slope gradient and a coverage rate, and the objective the extended benefit index for the slope regulation and storage measures is; and 85, determining whether a number of iterations is maximized seerd; when the number of iterations is maximized, the terminating an iteration and executing data to produce a slope gradient and a coverage rate which corresponds to the optimal extended benefit of the slope regulation and storage measures, so that a drawing is completed; and if the number of iterations is not is maximized, the return to the 54 and the Restart the calculation.

2. Procedure for configuring slope control tie and storage measures based on flood regulations tie and storage capacity, and water consumption management' vol according to conclusion 1, where the collecting and organizing of precipitation data from a basin in the 81 includes: the collecting observation data from precipitation stations and hydrological stations in the basin, and selecting of data on flood-causing rainfall as representative precipitation data.

3. Procedure for configuring slope control tie and storage measures based on flood regulations tie and storage capacity. and water consumption management' full according to conclusion 1, where the 523 the following steps to vat: a) determining a flood regulation and on stroke capacity of the slope regulation and storage measures len, with maximum flood regulation and storage ca capacity, comprising a maximum water storage ratio and a maximum attenuation degree for flood peaks in the control unit as the first criterion; and b) integrating flood regulation and storage capacity and water consumption management data of the heath ling regulation and storage measures to the extensive to determine the benefits index; according to the extended determine benefit index. of an optimal slope gradient and vegetation cover; and based on a threshold of a total slope regulation and storage capacity in the be control unit, performing secondary configuration for a solution for zoning and classification to determine optimization.

4. Procedure for configuring slope control tie and storage measures based on flood regulations tie and storage capacity, and water consumption management' vol according to conclusion 1, where the initialization of the setting lings in the 53 includes: with a slope gradient set at Si = 0° and a boundary condition set to 0° S Si S 90°, calculating a step size ASi: 1°; and with a vegetation cover. set. to Vi = 0 and. a border condition set to (7 S Vi S 100%, calculating a step size AVi=1%.

5. Procedure for configuring slope control tie and storage measures based on flood regulations tie and storage capacity. and water consumption management' full gene conclusion 1, where the calculation with the genetic algorithm in the 54 includes: 1) initialization: creating of an initial population, where each individual in the initial population represents a possible solution, each individual is provided with a slope gradient and a vegetation cover, and each individual is preceded established by a chromosome; 2) Fitness evaluation: the attainment knowing the fitness of each individual to the quality of to determine each individual, and evaluating the performance ties of each individual with a fitness function according to a objective' of a problem where an individual Inet a high level of fitness for the objective of the problem represents' high-quality solution, and the objective definition of the problem the expanded benefits index for the slope regulation and storage measures is; 3) selection: on based on fitness values, selecting a number individuals from a current population as parents for the breeding of offspring; 4) crossing: the combining of the selected parents by means of crossbreeding gene to produce new offspring; 5) mutation: the performing Umtation operations for the offspring: the randomly changing gene values ​​on some chromosomes the positions for simulating mutation processes in the biological evolution to ensure the diversity of a population to guarantee; 6) replacement: the replacement of individuals in the original population by the new individuals to produce a new generation of population; and 7) repetition: repeating the evaluation, the selection, the crossing and the mutation until to a termination for value has been met, whereby the termination condition is that the number of iterations is maximized. ooo FIG. 1 PATENT APPLICATION NO.: NO 200217 RESEARCH REPORT CONCERNING THE RESULT OF THE STATE OF THE ART RESEARCH RELEVANT LITERATURE 1 Literature with, where necessary, indication of of particular importance for Classification (IPC) Category text sections or figures. conclusion(s) no: X US 2019 / 354873 A1 (PESCARMONA ENRIQUE 1-5 INV. MENOTTI [AR]) G06Q10 / 04 November 21, 2019 (2019-11-21) G06Q10 / 063 * figures 1-8 * G06Q50 / 02 * paragraphs [0001] - [0040], [0049] - G06Q50 / 06 [0112] * G06Q50 / 26 ----- A CN 115 935 862 A (BEIJING INTELLIWAY 1-5 ENVIRONMENT SCIENCE & TECH CO LTD) April 7, 2023 (2023-04-07) * the entire document * ----- Investigated areas of technology G06Q If amended conclusions have been submitted, this report relates to the conclusions submitted on: Place of investigation: Date on which the investigation was conducted Competent official: completed: The Hague, March 9, 2026 Meijs, Koen 1 CATEGORY OF THE SIGNED LITERATURE X: the conclusion is deemed not new or not inventive T: after the filing date or the priority date considered in relation to this literature published literature that is not detrimental to the patent application, but is mentioned for clarification of Y: the conclusion is considered non-inventive at 1 the theory or principle that underlies the in relation to the combination of this literature with other cited literature of the same category, invention where the combination is obvious to the skilled person E: earlier patent (application), published on or after the is deemed to be the filing date on which the same invention is described A: literature not belonging to category X or Y that the D: stated in the patent application describes the state of the art O: non-written state of the art L: literature mentioned for other reasons P: between the priority date and the filing date &: member of the same patent family or corresponding published literature patent publication EOB FORM 02.83 (P0414B) NO200217 NL2040264 09-03-2026 US2019354873 A1 21-11-2019 AR 109623A1 09-01-2019 BR102019003180A2 10-09-2019 CO 2018010363A1 30-09-2019 EP 3534187A2 04-09-2019 ES 2894877T3 16-02-2022 JP 7001626B2 19-01-2022 JP 2019194424A 07-11-2019 MY 193943A 02-11-2022 PT 3534187T 25-01-2022 US 2019354873A1 21-11-2019 US 2021326715A1 21-10-2021 ----------------------------------------------------------------------- CN115935862 A 07-04-2023 NONE ----------------------------------------------------------------------- APPENDIX TO THE REPORT CONCERNING THE STATE OF THE ART EXAMINATION CONDUCTED IN THE PATENT APPLICATION NO. The appendix contains a list of patent applications or patents published elsewhere (so-called members of the same patent family) that correspond to patent documents mentioned in the report.The statement has been compiled on the basis of data from the computer file of the European Patent Office as of [date]. The accuracy and completeness of this statement is not guaranteed by the European Patent Office nor by the Industrial Property Office; the data are provided for information purposes. Patent document mentioned in the report Date of publication Corresponding document(s) Date of publication General information regarding this appendix has been published in the 'Official Journal' of the European Patent Office No. 12 / 82 p. 44 8 et seq.

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