CN115809748B - Method for predicting river channel defects for smart city - Google Patents
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Abstract
The invention discloses a method for predicting river channel defects for smart cities, and belongs to the technical field of urban inland river treatment. The prediction method comprises the following steps: dividing a water area interval of a river channel; acquiring environmental information in a river channel water area interval; acquiring predicted growth trend data of water plants on river dike foundation stones by utilizing environmental information in a river channel water area interval; and obtaining a river channel defect prediction result based on a river channel cleaning period by using the predicted growth trend data of water planting on river dike foundation stones. The river channel is divided into a plurality of water area intervals according to the structure and the humanistic activity of the urban inland river, the water planting growth condition in the urban inland river is predicted in a segmented mode, the water planting growth on river bank bedstones is linked to the condition that the river bank bedstones are corroded, the operation defect of the river channel caused by damage of the river banks is predicted, the river channel is prevented from rapidly overflowing due to the water planting, the ecological effect of the water areas is achieved, assistance is provided for inland river basin management, and fine management of capillary phenomena when the river bank bedstones are corroded is facilitated.
Description
Technical Field
The invention belongs to the technical field of urban inland river treatment, and particularly relates to a method for predicting river channel defects for smart cities.
Background
The urban inland river is the most severe place for changing the natural environment of the human society, and the relation between the harmonious urban river and the human water is a necessary meaning in the novel urbanization problem. The urban inland river refers to a river reach flowing through the inside of an urban area, and the inland river basin governance object mainly refers to a water collection area taking the urban area as a main body.
The deterioration of the small environment of the river channel is only the centralized expression of the deterioration of the large environment of the drainage basin. The river channel defects mainly comprise the problem of basin pollution caused by water quality change; river running problems caused by river channel silting and river bank damage; the problem of insufficient river water caused by general water shortage in cities and incapability of realizing scale recycling of reclaimed water and the like.
Among the above-mentioned river course defects, water quality deterioration can accelerate the rapid flooding of vegetation and microorganisms in the river course, affecting the ecology of the water area, and the vegetation with developed root system is rooted in the gap of the river bank foundation stone to accelerate the capillary phenomenon of the river bank wall, so that the river bank is eroded and the water seepage situation is aggravated.
Disclosure of Invention
The purpose of the invention is as follows: the method for predicting the river channel defects for the smart city is provided to solve the problems in the prior art.
The technical scheme is as follows: a method for predicting river channel defects for smart cities comprises the following steps:
dividing a water area interval of a river channel;
acquiring environmental information in a river channel water area interval;
acquiring predicted growth trend data of water plants on river dike foundation stones by utilizing environmental information in a river channel water area interval;
and obtaining a river channel defect prediction result based on a river channel cleaning period by using the predicted growth trend data of water planting on river dike foundation stones.
Further, dividing the water area interval of the river channel includes:
dividing a confluence section and a fishing section of a river channel into element-rich areas;
and dividing a water restraining section and a turning section of the river channel into silt attachment areas.
Further, acquiring environmental information in the river channel water area interval includes:
acquiring a detection result of nutrient elements in the water area and the sediment attachment amount on the river bed stones of the river levees in the water area;
wherein, the detection result of the nutrient elements comprises the types and the contents of the nutrient elements.
Further, it includes to acquire the silt attachment amount on the interval river levee bed stone in waters:
the silt attachment quantity is related to the water area interval of the sub-channel and the water area parameters; wherein the water area parameters comprise flow speed, flow and mud content; the thickness H of the silt attachment amount is determined by the formula:
Wherein t is the time for attaching the sediment; rho is the mud content; q is the flow; v is the flow velocity; lambda [ alpha ] A Is the flow rate coefficient; alpha is an adhesion coefficient including alpha 1 、α 2 And alpha 3 ，α 1 Is the attachment coefficient of the water-binding segment, alpha 2 For the coefficient of adhesion of the turn section, alpha 3 The attachment coefficient of the element-rich area is related to the structure of the river bank;
the actual thickness of the sediment attachment is collected regularly, the attachment coefficient and the attachment constant are corrected by using the actual thickness of the sediment attachment, and the sediment attachment is reset after the river bank bedrock is cleaned every time.
Further, the step of obtaining the data of the predicted growth trend of the water plants on the river bank bedrock by using the environmental information in the river channel water area interval comprises the following steps:
judging the type of water plants growing on the river levee bed stones according to the silt attachment amount on the river levee bed stones in the water area interval;
acquiring soil nutrient supply indexes by utilizing the silt attachment amount and the water planting type;
acquiring water area nutrition supply indexes by utilizing the detection result of the nutrient elements and the water planting type;
constructing a database, wherein the database comprises growth trends of the length of the water plant root system under different soil and water area nutrient supply indexes;
establishing a first water planting growth model according to growth trends of the lengths of the water planting roots under different soil and water area feeding indexes;
and acquiring actual soil and water area nutrient supply indexes, and outputting predicted growth trend data of water planting on river dike bedrock according to the first water planting growth model.
Further, the method for judging the type of the water plants growing on the river levee bed stones according to the silt attachment amount on the river levee bed stones in the water area interval comprises the following steps:
if the thickness H of the silt attachment amount is not more than 15mm, the water planting type grown on the river levee foundation stones is a single type, and the single type is peat moss;
otherwise, the water plants growing on the river levee foundation are mixed plants, and the mixed plants comprise peat moss and root system water plants.
Further, the environmental information further includes biological information in the water area interval, and the obtaining of the predicted growth trend data of the water plants on the river bank bedrock by using the environmental information in the river channel water area interval includes:
acquiring biological information in a water area interval;
acquiring an influence amplitude value of biological activities of different biological information on the predicted growth trend data of the water planting;
correcting the first water planting growth model according to the influence amplitude value of the biological activities of different biological information on the predicted growth trend data of the water planting;
and acquiring actual environment information, and outputting predicted growth trend data of water planting on the river levee bedrock based on the corrected first water planting growth model.
Further, obtaining the values of the influence amplitude of the biological activities of different biological information on the predicted growth trend data of the water plant comprises:
the biological information is positively correlated with the detection result of the nutrient elements in the element-rich area, the biological information is the type and the quantity of the herbivorous organisms, the biological activity is the edible water planting of the herbivorous organisms, and the influence range of the biological information on ideal growth trend data is obtained based on the edible water planting quantity of the herbivorous organisms.
Further, the method for obtaining the river channel defect prediction result based on the river channel cleaning cycle by using the predicted growth trend data of water plants on the river dike foundation stones comprises the following steps:
obtaining the length of the water planting root system at the predicted time point by using the predicted growth trend data;
the length of the water-planted root system is used for calculating the depth of the water-planted root system for eroding the river levee foundation stone;
representing the damaged depth of the river bank by using the depth of the water-planted root system eroding the river bank bedrock;
and fitting the damaged depth of the river bank based on the river channel cleaning period to obtain a river channel defect prediction result.
Further, the prediction method further comprises:
regularly acquiring the actual length of the water planting root system, and correcting the water planting growth model by using the actual length of the water planting root system;
and regularly surveying the actual depth of erosion of the river levee bedstones, and correcting the calculation process of simulating the erosion of the river levee bedstones by the water plant root systems by using the actual depth of erosion of the river levee bedstones.
Has the beneficial effects that: the river channel is divided into a plurality of water area intervals according to the structure and the humanistic activity of the urban inland river, the water planting growth condition in the urban inland river is predicted in a segmented mode, the water planting growth on river bank bedstones is linked to the condition that the river bank bedstones are corroded, the operation defect of the river channel caused by damage of the river banks is predicted, the river channel is prevented from rapidly overflowing due to the water planting, the ecological effect of the water areas is achieved, assistance is provided for inland river basin management, and fine management of capillary phenomena when the river bank bedstones are corroded is facilitated.
Drawings
FIG. 1 is a flow chart of a method for monitoring water quality of an urban inland river basin.
Detailed Description
In the following description, numerous specific details are set forth in order to provide a more thorough understanding of the present invention. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without one or more of these specific details. In other instances, well-known features have not been described in order to avoid obscuring the invention.
The first embodiment is as follows: urban inland river treatment is an important content of river basin treatment, and a river basin defect prediction method for erosion of river banks caused by water vegetation in urban inland river riverways is provided at present so as to assist the inland river basin treatment and provide help for fine management of water area ecology development.
As shown in fig. 1, a method for predicting river channel defects in smart cities includes:
step 1: dividing a water area interval of a river channel;
step 2: acquiring environmental information in a river channel water area interval;
and step 3: acquiring predicted growth trend data of water plants on river dike foundation stones by utilizing environmental information in a river channel water area interval;
and 4, step 4: and obtaining a river channel defect prediction result based on a river channel cleaning period by using the predicted growth trend data of water plants on river dike foundation stones.
The method divides the river channel into a plurality of water area sections through the structure and the humanistic activity of the urban inland river, predicts the water plant growth condition in the urban inland river by utilizing environmental information in the river channel in sections, and links the water plant growth on the river bank bedrock to the erosion condition of the river bank bedrock, thereby realizing the prediction of the operation defect of the river channel caused by the damage of the river bank, playing an important role in preventing the river channel from rapidly overflowing due to the water plant to influence the ecology of the water area, providing assistance for the management of the inland river basin and being beneficial to the fine management of the capillary phenomenon when the river bank bedrock is eroded.
The step 1 specifically comprises the following steps:
dividing a confluence section and a fishing section of a river channel into element-rich areas; the confluence section of river course can take place multiunit tributary and merge, the riverbed disturbance releases the nutrient, make aquatic life abundant, and the section of fishing is that the artificial riverbed of selecting is flat, the low river surface of velocity of flow fluctuates little waters, the section of fishing possesses the characteristics of regularly putting in the fish and eating, so aquatic life is abundant also very abundant, classify the division with this kind of waters interval that possesses abundant nutrient, when acquireing environmental information, need more intensive nutrient element to gather the appearance point, with the accuracy of nutrient element in improving environmental information, reduce the influence of nutrient element change to the prediction result.
Dividing a water-restraining section and a turning section of a river channel into silt attachment areas; the river bank structure of the river channel water binding section is narrowed, the turning section changes the flowing direction of river water, the river bank structure changes, silt can be rapidly gathered on the side wall of the river bank in the water area interval with the change of river water impact area, the collection density of silt attachment is increased when environmental information is obtained, and the period of river channel cleaning is also shorter.
The river channel is divided into a plurality of water area intervals according to the urban inland river structure and the humanistic activities, and reasonable information acquisition is carried out on the river channel according to the characteristics of each interval, so that the accuracy of information acquisition is favorably exerted, and the river channel information is better reflected.
The step 2 specifically comprises the following steps:
acquiring a detection result of nutrient elements in the water area and the sediment attachment amount on the river bed stones of the river levees in the water area;
wherein, the detection result of the nutrient elements comprises the types and the content of the nutrient elements.
The detection result of the nutrient elements and the attachment amount of the silt are taken as important factors influencing the growth vigor of the water plants, and the important factors can be manually acquired or automatically acquired by a detector.
The step 2 of obtaining silt attachment amount on the river levee foundation of the water area interval comprises the following steps:
the silt attachment quantity is related to the water area interval of the sub-channel and the water area parameters; wherein the water area parameters comprise flow speed, flow and mud content; the thickness H of the silt attachment amount is determined by the formula:
wherein t is the time for the silt to adhere; rho is the mud content; q is the flow; v is the flow velocity; lambda [ alpha ] A Is the flow rate coefficient; alpha is an adhesion coefficient including alpha 1 、α 2 And alpha 3 ，α 1 Is the attachment coefficient of the water-binding segment, alpha 2 For the adhesion coefficient of the turning section (alpha is selected for the upstream surface of the turning section) 2 The back water surface of the turning section is not impacted by river water, the difficulty of gathering silt on the river bank bedrock is higher, and the attachment coefficient alpha of the element-rich area can be selected 3 ），α 3 The attachment coefficient of the element-rich area is related to the structure of the river bank;
the actual thickness of the sediment attachment is collected regularly, the attachment coefficient and the attachment constant are corrected by using the actual thickness of the sediment attachment, and the sediment attachment is reset after the river bank bedrock is cleaned every time. Along with the increase of the actual thickness and the correction times of the collected sediment attachment amount, the thickness of the sediment attachment amount is more attached to the actual situation of the river channel, and the accuracy of a prediction result is facilitated.
The step 3 specifically comprises the following steps:
step 31: judging the type of water plants growing on the river levee bedrock according to the silt attachment amount on the river levee bedrock in the water area interval;
step 32: acquiring soil nutrient supply indexes by using the silt attachment amount and the water planting type;
step 33: acquiring water area nutrition supply indexes by utilizing the detection result of the nutrient elements and the water planting type;
step 34: constructing a database, wherein the database comprises growth trends of the length of the water plant root system under different soil and water area nutrient supply indexes;
step 35: establishing a first water planting growth model according to growth trends of water planting root length under different soil and water area nutrient supply indexes;
and step 36: and acquiring actual soil and water area nutrient supply indexes, and outputting predicted growth trend data of water planting on river dike bedrock according to the first water planting growth model.
Step 31 specifically includes:
if the thickness H of the silt attachment amount is not more than 15mm, the water planting type grown on the river levee bed stones is a single type, and the single type is peat moss; otherwise, the water planting type growing on the river levee bed stone is a mixed type, and the mixed type comprises peat moss and root system water planting.
The peat moss is a general name of various moss organisms, has low requirement on the thickness of silt attachment when attached to river levee bedstones for growth, has small erosion effect on the river levee bedstones, can attract herbivorous organisms to gather, and also has the effect of inhibiting the growth of root system water plants; the root system water planting is a general name of the aquatic plants growing from root systems, the root systems are fine and are easy to root into gaps and cracks of the river levee bedstones, the capability of eroding the river levee bedstones is strong, and the growth conditions have requirements on the thickness of the silt attachment amount.
If the environmental information does not contain biological information, the method for acquiring the predicted growth trend data needs to consider the influence of the biological information on the predicted growth trend data if a biological community is allowed to exist in the inland river of the city.
When the environmental information further includes biological information within the water area section,
and 5: the method for acquiring the predicted growth trend data of the water plants on the river bank bedrock by utilizing the environmental information in the river channel water area interval comprises the following steps:
step 51: acquiring biological information in a water area interval;
step 52: obtaining an influence amplitude value of biological activities of different biological information on the predicted growth trend data of the water planting;
step 53: correcting the first water planting growth model according to the influence amplitude value of the biological activities of different biological information on the predicted growth trend data of the water planting;
step 54: and acquiring actual environment information, and outputting predicted growth trend data of water planting on the river levee bedrock based on the corrected first water planting growth model.
Step 52 specifically includes:
biological information is positively correlated with the nutrient element detection result of the rich element area, namely, the content of nitrogen and phosphorus elements in the nutrient element detection result is high, the breeding environment of plankton is good, and the number of plankton is more, thereby inducing biological clustering, the biological information is richer, the biological information is the type and the number of the herbivorous organisms, the biological activity is the edible water planting of the herbivorous organisms, and the influence amplitude of the biological information on ideal growth trend data is obtained based on the edible water planting amount of the herbivorous organisms.
The step 4 specifically comprises the following steps:
step 41: obtaining the length of the water planting root system at the predicted time point by using the predicted growth trend data;
step 42: calculating the depth of the water plant root system for eroding the river levee bed stones by using the length of the water plant root system;
step 43: representing the damaged depth of the river bank by using the depth of the water-planted root system eroding the river bank bedrock;
and step 44: and fitting the damaged depth of the river bank based on the river channel cleaning period to obtain a river channel defect prediction result.
Regarding the setting of the river channel cleaning period, the early stage of the prediction method in use can be set according to the working capacity of a river channel management part, the river channel cleaning period can be set in week or month, after a certain period of training, the formula and the model in the prediction method are gradually mature, the setting of the river channel cleaning period can be flexibly adjusted according to the prediction value of the water plant root system length in the water plant growth model and the prediction result of the river channel defects, after one river channel cleaning period is finished, when the water plant growth model predicts that the water plant overgrows at a certain time point, the river channel cleaning period is set to be before the time point, the damage of the river channel is increased along with the increase of the river channel cleaning times, the superposition effect of the river channel defects is more remarkable, the river channel cleaning period is shortened along with the increase of the river channel cleaning times, the reasonable setting of the river channel cleaning period is favorable for preventing the rapid growth of the root system water plant, and the serious erosion essence caused by improper management is avoided.
In addition, after the river bank bedrock is cleaned by silt and water plants, erosion suffered by the river bank bedrock cannot disappear, the capillary phenomenon on the river bank is favorable for growth of the water plants, and when the damaged depth of the river bank under a plurality of river channel cleaning periods is fitted, a period coefficient needs to be configured for the total damaged depth of the river bank, and the period coefficient is increased along with the increase of the period quantity.
The prediction method further comprises the following steps:
acquiring the actual length of the water-planted root system periodically, and correcting the water-planted growth model by using the actual length of the water-planted root system;
and regularly surveying the actual depth of the river bank bedrock eroded, and correcting the calculation process of simulating the erosion of the river bank bedrock by the water planting root system by using the actual depth of the river bank bedrock eroded.
The river channel defect prediction method provided by the invention plays an important role in urban inland river management, provides timely clear indication for ecological environment management of urban inland rivers, and has an obvious effect in defense of water planting erosion river levees.
The preferred embodiments of the present invention have been described in detail with reference to the accompanying drawings, however, the present invention is not limited to the specific details of the embodiments, and various equivalent changes can be made to the technical solution of the present invention within the technical idea of the present invention, and these equivalent changes are within the scope of the present invention.
Claims (6)
1. A method for predicting river channel defects for smart cities is characterized by comprising the following steps:
dividing a water area interval of a river channel; dividing a confluence section and a fishing section of the river channel into element-rich areas, and dividing a water-restraining section and a turning section of the river channel into sediment attachment areas;
acquiring environmental information in a river channel water area interval; the method comprises the steps of obtaining environmental information in a river channel water area interval, wherein the obtaining of the environmental information comprises obtaining a detection result of nutrient elements in the water area interval and the attachment amount of silt on river dike bedrock in the water area interval, and the detection result of the nutrient elements comprises nutrient element types and nutrient element content;
wherein, it includes to acquire the silt attachment amount on the interval river levee bed stone in waters:
the silt attachment quantity is related to the water area interval of the sub-channel and the water area parameters; wherein the water area parameters comprise flow speed, flow and mud content; the thickness H of the silt attachment amount is determined by the formula:
so as to obtain the compound with the characteristics of,
wherein t is the time for attaching the sediment; rho is the mud content; q is the flow; v is the flow velocity; lambda [ alpha ] A Is the flow rate coefficient; alpha is an adhesion coefficient including alpha 1 、α 2 And alpha 3 ，α 1 Is the attachment coefficient of the water-binding segment, alpha 2 For the coefficient of adhesion of the turn section, alpha 3 The attachment coefficient of the element-rich area is related to the structure of the river levee; collecting the actual thickness of the sediment attachment amount at regular intervals, correcting the attachment coefficient and the attachment constant by using the actual thickness of the sediment attachment amount, and resetting the sediment attachment amount after each time of river bank foundation stone cleaning;
acquiring predicted growth trend data of water plants on river dike foundation stones by utilizing environmental information in a river channel water area interval; the method for acquiring the predicted growth trend data of the water plants on the river bank bedrock by utilizing the environmental information in the river channel water area interval comprises the following steps: judging the type of water plants growing on the river levee bed stones according to the silt attachment amount on the river levee bed stones in the water area interval; acquiring soil nutrient supply indexes by using the silt attachment amount and the water planting type; acquiring water area nutrition supply indexes by utilizing the detection result of the nutrient elements and the water planting type; constructing a database, wherein the database comprises growth trends of the length of the water plant root system under different soil and water area nutrient supply indexes; establishing a first water planting growth model according to growth trends of the lengths of the water planting roots under different soil and water area feeding indexes; acquiring actual soil and water area nutrient supply indexes, and outputting predicted growth trend data of water planting on river dike bedrock according to the first water planting growth model;
and obtaining a river channel defect prediction result based on a river channel cleaning period by using the predicted growth trend data of water plants on river dike foundation stones.
2. The method of claim 1, wherein the step of determining the type of water plants growing on the river bed stones according to the amount of silt deposited on the river bed stones in the water area comprises:
if the thickness H of the silt attachment amount is not more than 15mm, the water planting type grown on the river levee bed stones is a single type, and the single type is peat moss;
otherwise, the water planting type growing on the river levee bed stone is a mixed type, and the mixed type comprises peat moss and root system water planting.
3. The method as claimed in claim 2, wherein the environmental information further includes biological information in the water area zone, and the obtaining of the data of the predicted growth trend of the water plants on the river bank rocks using the environmental information in the water area zone of the river includes:
acquiring biological information in a water area interval;
acquiring an influence amplitude value of biological activities of different biological information on the predicted growth trend data of the water planting;
correcting the first water planting growth model according to the influence amplitude value of the biological activities of different biological information on the predicted growth trend data of the water planting;
and acquiring actual environment information, and outputting predicted growth trend data of water planting on the river levee bedrock based on the corrected first water planting growth model.
4. The method as claimed in claim 3, wherein the obtaining of the magnitude of the influence of the biological activities of different biological information on the data of the predicted growth tendency of water plants comprises:
the biological information is positively correlated with the detection result of the nutrient elements in the element-rich area, the biological information is the type and the quantity of the herbivorous organisms, the biological activity is the edible water planting of the herbivorous organisms, and the influence range of the biological information on ideal growth trend data is obtained based on the edible water planting quantity of the herbivorous organisms.
5. The method of claim 4, wherein the step of obtaining the predicted river channel defect based on the river channel cleaning cycle by using the predicted growth trend data of the water plants on the river dike foundation stones comprises:
obtaining the length of the water plant root system at the predicted time point by using the predicted growth trend data;
calculating the depth of the water plant root system for eroding the river levee bed stones by using the length of the water plant root system;
representing the damaged depth of the river bank by using the depth of the water-planted root system eroding the river bank bedrock;
and fitting the damaged depth of the river bank based on the river channel cleaning period to obtain a river channel defect prediction result.
6. The method as claimed in claim 5, wherein the method further comprises:
regularly acquiring the actual length of the water planting root system, and correcting the water planting growth model by using the actual length of the water planting root system;
and regularly surveying the actual depth of erosion of the river levee bedstones, and correcting the calculation process of simulating the erosion of the river levee bedstones by the water plant root systems by using the actual depth of erosion of the river levee bedstones.
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| CN108256177A (en) * | 2017-12-28 | 2018-07-06 | 中国水利水电科学研究院 | A kind of parameter optimization method and system of river Water-sand model |
| CN114819390A (en) * | 2022-05-19 | 2022-07-29 | 厦门市城市规划设计研究院有限公司 | Model method for estimating urban river sediment deposition |
| CN115219682A (en) * | 2022-07-14 | 2022-10-21 | 武汉鸿榛园林绿化工程有限公司 | River water environment treatment monitoring and analyzing system based on artificial intelligence |
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| CN108256177A (en) * | 2017-12-28 | 2018-07-06 | 中国水利水电科学研究院 | A kind of parameter optimization method and system of river Water-sand model |
| CN114819390A (en) * | 2022-05-19 | 2022-07-29 | 厦门市城市规划设计研究院有限公司 | Model method for estimating urban river sediment deposition |
| CN115219682A (en) * | 2022-07-14 | 2022-10-21 | 武汉鸿榛园林绿化工程有限公司 | River water environment treatment monitoring and analyzing system based on artificial intelligence |
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