CN116335116B - Soft soil solidification intelligent flow control system - Google Patents
Soft soil solidification intelligent flow control system Download PDFInfo
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- CN116335116B CN116335116B CN202310326206.2A CN202310326206A CN116335116B CN 116335116 B CN116335116 B CN 116335116B CN 202310326206 A CN202310326206 A CN 202310326206A CN 116335116 B CN116335116 B CN 116335116B
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- 239000002689 soil Substances 0.000 title claims abstract description 90
- 238000007711 solidification Methods 0.000 title claims abstract description 12
- 230000008023 solidification Effects 0.000 title claims abstract description 12
- 239000003795 chemical substances by application Substances 0.000 claims abstract description 67
- 238000003756 stirring Methods 0.000 claims abstract description 51
- 238000002347 injection Methods 0.000 claims abstract description 36
- 239000007924 injection Substances 0.000 claims abstract description 36
- 238000010276 construction Methods 0.000 claims abstract description 32
- 238000001514 detection method Methods 0.000 claims abstract description 32
- 239000000843 powder Substances 0.000 claims abstract description 26
- 238000005457 optimization Methods 0.000 claims abstract description 23
- 229920002554 vinyl polymer Polymers 0.000 claims abstract description 14
- 239000000835 fiber Substances 0.000 claims abstract description 11
- 238000001917 fluorescence detection Methods 0.000 claims abstract description 11
- 239000000523 sample Substances 0.000 claims abstract description 11
- 230000000694 effects Effects 0.000 claims abstract description 6
- 238000004458 analytical method Methods 0.000 claims abstract description 5
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- 238000000034 method Methods 0.000 claims description 33
- 239000003550 marker Substances 0.000 claims description 24
- 239000004576 sand Substances 0.000 claims description 21
- 238000004364 calculation method Methods 0.000 claims description 20
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 18
- 239000000203 mixture Substances 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 10
- 235000008733 Citrus aurantifolia Nutrition 0.000 claims description 9
- 239000004115 Sodium Silicate Substances 0.000 claims description 9
- 235000011941 Tilia x europaea Nutrition 0.000 claims description 9
- 239000004568 cement Substances 0.000 claims description 9
- 239000010883 coal ash Substances 0.000 claims description 9
- 239000004571 lime Substances 0.000 claims description 9
- 239000002893 slag Substances 0.000 claims description 9
- NTHWMYGWWRZVTN-UHFFFAOYSA-N sodium silicate Chemical compound [Na+].[Na+].[O-][Si]([O-])=O NTHWMYGWWRZVTN-UHFFFAOYSA-N 0.000 claims description 9
- 229910052911 sodium silicate Inorganic materials 0.000 claims description 9
- 239000004575 stone Substances 0.000 claims description 9
- 238000007405 data analysis Methods 0.000 claims description 6
- 230000005284 excitation Effects 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 6
- 239000004372 Polyvinyl alcohol Substances 0.000 claims description 3
- 229920001807 Urea-formaldehyde Polymers 0.000 claims description 3
- GZCGUPFRVQAUEE-SLPGGIOYSA-N aldehydo-D-glucose Chemical compound OC[C@@H](O)[C@@H](O)[C@H](O)[C@@H](O)C=O GZCGUPFRVQAUEE-SLPGGIOYSA-N 0.000 claims description 3
- 229920002689 polyvinyl acetate Polymers 0.000 claims description 3
- 239000011118 polyvinyl acetate Substances 0.000 claims description 3
- 229920002451 polyvinyl alcohol Polymers 0.000 claims description 3
- 239000002344 surface layer Substances 0.000 claims description 3
- 238000004886 process control Methods 0.000 claims 10
- 238000011835 investigation Methods 0.000 abstract description 5
- 238000013461 design Methods 0.000 abstract description 2
- 238000013528 artificial neural network Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
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- 238000011160 research Methods 0.000 description 1
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- 238000012795 verification Methods 0.000 description 1
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Classifications
-
- E—FIXED CONSTRUCTIONS
- E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
- E02D—FOUNDATIONS; EXCAVATIONS; EMBANKMENTS; UNDERGROUND OR UNDERWATER STRUCTURES
- E02D3/00—Improving or preserving soil or rock, e.g. preserving permafrost soil
- E02D3/12—Consolidating by placing solidifying or pore-filling substances in the soil
-
- C—CHEMISTRY; METALLURGY
- C04—CEMENTS; CONCRETE; ARTIFICIAL STONE; CERAMICS; REFRACTORIES
- C04B—LIME, MAGNESIA; SLAG; CEMENTS; COMPOSITIONS THEREOF, e.g. MORTARS, CONCRETE OR LIKE BUILDING MATERIALS; ARTIFICIAL STONE; CERAMICS; REFRACTORIES; TREATMENT OF NATURAL STONE
- C04B28/00—Compositions of mortars, concrete or artificial stone, containing inorganic binders or the reaction product of an inorganic and an organic binder, e.g. polycarboxylate cements
- C04B28/24—Compositions of mortars, concrete or artificial stone, containing inorganic binders or the reaction product of an inorganic and an organic binder, e.g. polycarboxylate cements containing alkyl, ammonium or metal silicates; containing silica sols
- C04B28/26—Silicates of the alkali metals
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- C—CHEMISTRY; METALLURGY
- C09—DYES; PAINTS; POLISHES; NATURAL RESINS; ADHESIVES; COMPOSITIONS NOT OTHERWISE PROVIDED FOR; APPLICATIONS OF MATERIALS NOT OTHERWISE PROVIDED FOR
- C09K—MATERIALS FOR MISCELLANEOUS APPLICATIONS, NOT PROVIDED FOR ELSEWHERE
- C09K17/00—Soil-conditioning materials or soil-stabilising materials
- C09K17/40—Soil-conditioning materials or soil-stabilising materials containing mixtures of inorganic and organic compounds
-
- E—FIXED CONSTRUCTIONS
- E02—HYDRAULIC ENGINEERING; FOUNDATIONS; SOIL SHIFTING
- E02D—FOUNDATIONS; EXCAVATIONS; EMBANKMENTS; UNDERGROUND OR UNDERWATER STRUCTURES
- E02D3/00—Improving or preserving soil or rock, e.g. preserving permafrost soil
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N1/00—Sampling; Preparing specimens for investigation
- G01N1/28—Preparing specimens for investigation including physical details of (bio-)chemical methods covered elsewhere, e.g. G01N33/50, C12Q
- G01N1/38—Diluting, dispersing or mixing samples
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/90—Dynamic range modification of images or parts thereof
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
-
- C—CHEMISTRY; METALLURGY
- C04—CEMENTS; CONCRETE; ARTIFICIAL STONE; CERAMICS; REFRACTORIES
- C04B—LIME, MAGNESIA; SLAG; CEMENTS; COMPOSITIONS THEREOF, e.g. MORTARS, CONCRETE OR LIKE BUILDING MATERIALS; ARTIFICIAL STONE; CERAMICS; REFRACTORIES; TREATMENT OF NATURAL STONE
- C04B2111/00—Mortars, concrete or artificial stone or mixtures to prepare them, characterised by specific function, property or use
- C04B2111/00474—Uses not provided for elsewhere in C04B2111/00
- C04B2111/00732—Uses not provided for elsewhere in C04B2111/00 for soil stabilisation
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- C—CHEMISTRY; METALLURGY
- C09—DYES; PAINTS; POLISHES; NATURAL RESINS; ADHESIVES; COMPOSITIONS NOT OTHERWISE PROVIDED FOR; APPLICATIONS OF MATERIALS NOT OTHERWISE PROVIDED FOR
- C09K—MATERIALS FOR MISCELLANEOUS APPLICATIONS, NOT PROVIDED FOR ELSEWHERE
- C09K2103/00—Civil engineering use
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
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- Engineering & Computer Science (AREA)
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- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Structural Engineering (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Soil Sciences (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Mining & Mineral Resources (AREA)
- Inorganic Chemistry (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Civil Engineering (AREA)
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- Environmental & Geological Engineering (AREA)
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- Chemical Kinetics & Catalysis (AREA)
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- Health & Medical Sciences (AREA)
- Consolidation Of Soil By Introduction Of Solidifying Substances Into Soil (AREA)
Abstract
The application relates to a soft soil solidification intelligent flow control system, which utilizes a remote sensing image to obtain geological parameters and carries out formula design according to the geological parameters; therefore, the investigation speed and accuracy can be greatly improved, and the accurate construction is realized; the formula optimization is carried out by utilizing the geological parameters detected on site, so that the problem of low accuracy of remote sensing geological parameters is solved, the accuracy of curing construction can be further improved, and the stability of construction is ensured; the novel curing agent formula is provided, fibers, water-soluble vinyl polymers and the like are added in the novel formula, and the curing effect of the curing agent is greatly improved; and the remote sensing images are registered based on the ground surface markers, so that the analysis accuracy is improved. The components of the curing agent are doped with fluorescent powder in a fixed proportion before being added into the stirring injection module, and the curing agent detection module judges the proportion of the components in the curing agent through the intensity of fluorescence emitted by the fluorescent powder with different wavelengths received by the fluorescence detection probe.
Description
Technical Field
The application relates to the field of soft soil curing, in particular to an intelligent soft soil curing flow control system and an intelligent soft soil curing flow control method.
Background
The expressway in the current construction passes through a large number of soft soil areas, and the soft soil has the engineering characteristics of large pores, high natural water content, poor permeability, low shear strength, obvious rheological property, complex distribution and the like, if the soft soil is improperly treated, the problems of settlement and deformation of the roadbed and the like are caused in the engineering, the stability and strength of the highway are reduced, and even the problems of pavement cracking, bridge abutment damage, bridge head vehicle jumping, body or channel sinking and the like are possibly caused under the action of external load on the roadbed, so that the service life of the highway is seriously shortened, and the running safety and smoothness of the expressway are threatened. Therefore, deep research on the expressway soft soil foundation treatment technology is very necessary, a novel curing agent sludge curing mechanism is revealed by researching and developing an early sludge curing agent, and the soft soil curing intelligent flow control system is researched and developed, so that the safety performance of the roadbed is comprehensively improved.
In the current soft soil curing construction, geological parameters of an area to be cured are generally collected in advance, and a specific curing agent ratio is designed based on the geological parameters. However, the construction process is complicated, the verification process is long, the construction period is easy to be prolonged, and the construction progress is slow. Meanwhile, the large regional range can cause great investigation workload during field investigation, and the investigation effect is difficult to ensure under the condition of insufficient technicians.
The survey of earth surface geological parameters based on the remote sensing technology has gained a great deal of attention in recent years, and how to integrate survey, formulation, construction and feedback based on the remote sensing technology and the Beidou positioning technology is a problem to be solved at present.
Disclosure of Invention
In order to solve the problems, the application provides an intelligent soft soil curing flow control system which comprises an upper computer and a curing construction machine.
The upper computer is connected with a remote sensing data acquisition module, a positioning matching module and a formula calculation module;
the remote sensing data acquisition module is connected with the remote sensing image database, acquires a plurality of satellite remote sensing images of the area to be solidified from the remote sensing image database, and processes the satellite remote sensing images;
the positioning matching module registers the acquired coordinates of a plurality of remote sensing images and sends the registered images to the upper computer;
remote sensing analysis is carried out on the registered remote sensing images in the upper computer, and remote sensing geological parameters corresponding to each coordinate of the position to be hardened are obtained;
the formula calculation module acquires coordinates and corresponding remote sensing geological parameters from the upper computer, and calculates the formula of the curing agent according to the remote sensing geological parameters;
the upper computer is in wireless connection with the curing construction machine, and the curing construction machine comprises a formula optimizing module, a Beidou positioning module, a curing agent detection module, a soil detection module and a stirring injection module;
the Beidou positioning module is used for acquiring Beidou positioning coordinates and uploading the Beidou positioning coordinates to the upper computer in real time; the stirring injection module is used for stirring each component of the curing agent and injecting the components into the soil surface layer;
the curing agent detection module is arranged in the stirring injection module and used for detecting the state of the curing agent in the stirring injection module in real time;
the soil detection module is used for detecting on-site geological parameters of soil in real time and uploading the on-site geological parameters to the formula optimization module in real time; the upper computer sends the remote sensing geological parameters to a formula optimization module of the curing construction machine; the formula optimization module compares the on-site geological parameters with the remote sensing geological parameters, optimizes the mixture ratio of the curing agent, and further sends the optimized mixture ratio to the stirring injection module.
Further, the remote sensing image database comprises CBERS-2, CBERS-2b, CBERS, landsat, resourceSat, S-NPP, terra and UK-DMC 2 data;
of course, instead of the above mentioned common remote sensing image database, remote sensing data based on the observation of the base station or remote sensing data based on hyperspectral observation of the unmanned aerial vehicle may also be used.
The remote sensing data acquisition module acquires at least 3 remote sensing image data in the range of the area to be detected from a remote sensing image database, and sends the remote sensing image data and the space coordinates corresponding to each pixel point in the remote sensing image to the positioning matching module;
and the positioning matching module acquires at least 3 remote sensing image data in the range of the area to be solidified, and performs positioning registration on the remote sensing image data.
Further, the positioning matching module carries out binarization processing on the acquired remote sensing image data, so that the remote sensing image is segmented, and a marker region in the remote sensing image is separated;
the marker area is a water system area, a vegetation area, a mountain area or an artificial engineering area;
and the positioning matching module performs shape matching on the marker region and performs coordinate registration by taking the marker region as a reference.
Because ponds, forest areas, roads, houses or other artifacts and the like in the remote sensing have clear outlines and stable reference characteristics, different remote sensing images can be aligned by using the characteristics as reference objects based on the characteristics; specific registration methods, such as edge method, multi-point registration method, etc., have been widely studied in the prior art, and are not described herein.
Further, remote sensing data analysis is carried out on the registered remote sensing images in the upper computer, so that remote sensing geological parameters of the area to be solidified are obtained; the remote sensing geological parameters at least comprise: the water content, the type of the sludge, the depth of the sludge and the sand content of the sludge;
the method of converting the remote sensing image into the geological parameters can directly use a neural network, an SVM or other modeling methods. Those skilled in the art will readily implement this by the instructions given based on this scheme and will not be described in detail herein.
The formula calculation module divides the area to be solidified into a plurality of subareas, and obtains remote sensing geological parameters in each subarea from the upper computer; the formula calculation module averages the remote sensing geological parameters in each subarea and calculates the formula of the curing agent according to the average value.
The calculation method of the formula can directly use a neural network, an SVM or other modeling methods, and can also be based on empirical formulas and the like. Those skilled in the art will readily implement this by the instructions given based on this scheme and will not be described in detail herein.
Further, the number of the subareas is at least 16, and the actual area of each subarea is not less than 0.5 square kilometer;
the formula of the curing agent comprises the following components: slag, coal ash powder, water-soluble vinyl polymer, stone, cement, fiber, lime and sodium silicate;
the slag is 5-20%, the coal ash is less than 10-20%, the water-soluble vinyl polymer is 1-5%, the stone is 10-30%, the cement is 5-15%, the fiber is 1-5%, the lime is 5-20%, and the sodium silicate is 1-5%.
Further, the curing agent detection module is arranged in the stirring injection module and arranged in the stirring section for analyzing the components of the curing agent in stirring;
the components of the curing agent are doped with fluorescent powder in a fixed proportion before being added into the stirring injection module, and the emission wavelength of the fluorescent powder doped by each component is different; the proportion of the fluorescent powder to each component of the curing agent is less than 0.5 percent, so that the curing effect is not affected;
the curing agent detection module is provided with a fluorescence detection probe and an ultraviolet light emission head; the ultraviolet light emitting head is used for emitting ultraviolet excitation light to the curing agent in stirring; the fluorescence detection probe is used for detecting fluorescence emitted by the trace fluorescent powder after being excited by ultraviolet excitation light;
the curing agent detection module judges the proportion of each component in the curing agent through the intensity of fluorescence emitted by fluorescent powder with different wavelengths received by the fluorescence detection probe.
Further, the soil detection module acquires in real time on-site geological parameters of the soil at the solidification position, wherein the on-site geological parameters at least comprise: soil moisture, soil hardness, and soil sand content;
the formula optimization module optimizes the process as follows:
let the water content in the remote sensing geological parameter be R 1 The sludge type is L, and the sand content of the sludge is S 1 The method comprises the steps of carrying out a first treatment on the surface of the The soil humidity in the on-site geological parameters is R 2 The soil hardness is H, and the soil sand content is S 2 ；
Then the recipe is optimized:
the adjusting proportion of the water-soluble vinyl polymer and the fiber is as follows: m=k 1 ·L·H 0 /H；
The adjustment proportion of stone, slag and coal ash powder is as follows: n=k 2 ·S 1 / S 2
The adjustment proportion of cement and lime is as follows: o=k 3 ·R 2 /R 1
The adjusting proportion of the sodium silicate is as follows: p= (n.o) 1/3
Wherein the numerical value of L is 1, 2, 3 and 4, which correspond to the branch-shaped sludge, the net-shaped sludge, the sandy sludge and the mud-shaped sludge respectively; r is R 1 And R is 2 All are percentage values of water; s is S 1 And S is 2 Is the percentage value of sand; h 0 The soil hardness is preset; k (k) 1 、k 2 、k 3 To adjust the constant.
Further, the water-soluble vinyl polymer is polyvinyl acetate, urea-formaldehyde resin or polyvinyl alcohol.
The soft soil curing intelligent flow control method uses the soft soil curing intelligent flow control system, and comprises the following steps:
step one, a remote sensing data acquisition module acquires at least 3 remote sensing image data in a region range to be detected from a remote sensing image database, and sends the remote sensing image data and space coordinates corresponding to each pixel point in the remote sensing image to a positioning matching module;
the positioning matching module acquires at least 3 remote sensing image data in the range of the area to be solidified, and performs positioning registration on the remote sensing image data;
step two, the positioning matching module carries out binarization processing on the acquired remote sensing image data, so as to segment the remote sensing image and separate out a marker region in the remote sensing image;
the marker area is a water system area, a vegetation area, a mountain area or an artificial engineering area;
the positioning matching module performs shape matching on the marker region and performs coordinate registration by taking the marker region as a reference;
thirdly, carrying out remote sensing data analysis on the registered remote sensing images in the upper computer so as to obtain remote sensing geological parameters of the area to be solidified; the remote sensing geological parameters at least comprise: the water content, the type of the sludge, the depth of the sludge and the sand content of the sludge;
the formula calculation module divides the area to be solidified into a plurality of subareas, and obtains remote sensing geological parameters in each subarea from the upper computer; the formula calculation module averages the remote sensing geological parameters in each subarea and calculates the formula of the curing agent according to the average value;
fourth, during site construction, the soil detection module acquires site geological parameters of the soil at the solidification position in real time, wherein the site geological parameters at least comprise: soil moisture, soil hardness, and soil sand content;
step five, the soil detection module detects the field geological parameters of the soil in real time and uploads the field geological parameters to the formula optimization module in real time; the upper computer sends the remote sensing geological parameters to a formula optimization module of the curing construction machine; the formula optimization module compares the on-site geological parameters with the remote sensing geological parameters and optimizes the proportion of the curing agent;
step six, the formula optimizing module sends the optimized mixture ratio to the stirring injection module, and the stirring injection module performs stirring injection according to the optimized formula, wherein the amount of each stirring injection is less than 5% of the required dosage of the subarea;
and step seven, after stirring and injecting for one time, repeating the steps four to six until the whole subarea is cured.
Further, the number of the subareas is at least 16, and the actual area of each subarea is not less than 0.5 square kilometer.
The beneficial effects of the application are as follows:
firstly, a new soft soil solidification intelligent flow control system is provided, geological parameters are obtained by utilizing remote sensing images, and formula design is carried out according to the geological parameters; therefore, the investigation speed and accuracy can be greatly improved, and the accurate construction is realized;
meanwhile, the formula optimization is carried out by utilizing the geological parameters detected on site, so that the problem of low accuracy of remote sensing geological parameters is solved, the accuracy of curing construction can be further improved, and the stability and uniformity of construction are ensured;
in addition, the application provides a new curing agent formula, which is added with fibers, water-soluble vinyl polymers and the like, so that the curing effect of the curing agent is greatly improved;
the application registers the remote sensing images based on the surface markers, thereby improving the accuracy of analysis.
The components of the curing agent are doped with fluorescent powder in fixed proportion before being added into the stirring injection module, the emission wavelength of the fluorescent powder doped with each component is different, and the curing agent detection module judges the proportion of each component in the curing agent through the intensity of fluorescence emitted by the fluorescent powder with different wavelengths and received by the fluorescence detection probe.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the application, and that other drawings can be obtained from these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a schematic diagram of the overall architecture of the present application.
Detailed Description
Example 1:
referring to fig. 1, a soft soil curing intelligent flow control system comprises an upper computer and a curing construction machine.
The upper computer is connected with a remote sensing data acquisition module, a positioning matching module and a formula calculation module;
the remote sensing data acquisition module is connected with the remote sensing image database, acquires a plurality of satellite remote sensing images of the area to be solidified from the remote sensing image database, and processes the satellite remote sensing images;
the positioning matching module registers the acquired coordinates of a plurality of remote sensing images and sends the registered images to the upper computer;
remote sensing analysis is carried out on the registered remote sensing images in the upper computer, and remote sensing geological parameters corresponding to each coordinate of the position to be hardened are obtained;
the formula calculation module acquires coordinates and corresponding remote sensing geological parameters from the upper computer, and calculates the formula of the curing agent according to the remote sensing geological parameters;
the upper computer is in wireless connection with the curing construction machine, and the curing construction machine comprises a formula optimizing module, a Beidou positioning module, a curing agent detection module, a soil detection module and a stirring injection module;
the Beidou positioning module is used for acquiring Beidou positioning coordinates and uploading the Beidou positioning coordinates to the upper computer in real time; the stirring injection module is used for stirring each component of the curing agent and injecting the components into the soil surface layer;
the curing agent detection module is arranged in the stirring injection module and used for detecting the state of the curing agent in the stirring injection module in real time;
the soil detection module is used for detecting on-site geological parameters of soil in real time and uploading the on-site geological parameters to the formula optimization module in real time; the upper computer sends the remote sensing geological parameters to a formula optimization module of the curing construction machine; the formula optimization module compares the on-site geological parameters with the remote sensing geological parameters, optimizes the mixture ratio of the curing agent, and further sends the optimized mixture ratio to the stirring injection module.
Further, the remote sensing image database comprises CBERS-2, CBERS-2b, CBERS, landsat, resourceSat, S-NPP, terra and UK-DMC 2 data;
of course, instead of the above mentioned common remote sensing image database, remote sensing data based on the observation of the base station or remote sensing data based on hyperspectral observation of the unmanned aerial vehicle may also be used.
The remote sensing data acquisition module acquires at least 3 remote sensing image data in the range of the area to be detected from a remote sensing image database, and sends the remote sensing image data and the space coordinates corresponding to each pixel point in the remote sensing image to the positioning matching module;
and the positioning matching module acquires at least 3 remote sensing image data in the range of the area to be solidified, and performs positioning registration on the remote sensing image data.
Further, the positioning matching module carries out binarization processing on the acquired remote sensing image data, so that the remote sensing image is segmented, and a marker region in the remote sensing image is separated;
the marker area is a water system area, a vegetation area, a mountain area or an artificial engineering area;
and the positioning matching module performs shape matching on the marker region and performs coordinate registration by taking the marker region as a reference.
Because ponds, forest areas, roads, houses or other artifacts and the like in the remote sensing have clear outlines and stable reference characteristics, different remote sensing images can be aligned by using the characteristics as reference objects based on the characteristics; specific registration methods, such as edge method, multi-point registration method, etc., have been widely studied in the prior art, and are not described herein.
Further, remote sensing data analysis is carried out on the registered remote sensing images in the upper computer, so that remote sensing geological parameters of the area to be solidified are obtained; the remote sensing geological parameters at least comprise: the water content, the type of the sludge, the depth of the sludge and the sand content of the sludge;
the method of converting the remote sensing image into the geological parameters can directly use a neural network, an SVM or other modeling methods. Those skilled in the art will readily implement this by the instructions given based on this scheme and will not be described in detail herein.
The formula calculation module divides the area to be solidified into a plurality of subareas, and obtains remote sensing geological parameters in each subarea from the upper computer; the formula calculation module averages the remote sensing geological parameters in each subarea and calculates the formula of the curing agent according to the average value.
The calculation method of the formula can directly use a neural network, an SVM or other modeling methods, and can also be based on empirical formulas and the like. Those skilled in the art will readily implement this by the instructions given based on this scheme and will not be described in detail herein.
Further, the number of the subareas is at least 16, and the actual area of each subarea is not less than 0.5 square kilometer;
the formula of the curing agent comprises the following components: slag, coal ash powder, water-soluble vinyl polymer, stone, cement, fiber, lime and sodium silicate;
the slag is 5-20%, the coal ash is less than 10-20%, the water-soluble vinyl polymer is 1-5%, the stone is 10-30%, the cement is 5-15%, the fiber is 1-5%, the lime is 5-20%, and the sodium silicate is 1-5%.
Further, the curing agent detection module is arranged in the stirring injection module and arranged in the stirring section for analyzing the components of the curing agent in stirring;
the components of the curing agent are doped with fluorescent powder in a fixed proportion before being added into the stirring injection module, and the emission wavelength of the fluorescent powder doped by each component is different; the proportion of the fluorescent powder to each component of the curing agent is less than 0.5 percent, so that the curing effect is not affected;
the curing agent detection module is provided with a fluorescence detection probe and an ultraviolet light emission head; the ultraviolet light emitting head is used for emitting ultraviolet excitation light to the curing agent in stirring; the fluorescence detection probe is used for detecting fluorescence emitted by the trace fluorescent powder after being excited by ultraviolet excitation light;
the curing agent detection module judges the proportion of each component in the curing agent through the intensity of fluorescence emitted by fluorescent powder with different wavelengths received by the fluorescence detection probe.
Further, the soil detection module acquires in real time on-site geological parameters of the soil at the solidification position, wherein the on-site geological parameters at least comprise: soil moisture, soil hardness, and soil sand content;
the formula optimization module optimizes the process as follows:
let the water content in the remote sensing geological parameter be R 1 The sludge type is L, and the sand content of the sludge is S 1 The method comprises the steps of carrying out a first treatment on the surface of the The soil humidity in the on-site geological parameters is R 2 The soil hardness is H, and the soil sand content is S 2 ；
Then the recipe is optimized:
the adjusting proportion of the water-soluble vinyl polymer and the fiber is as follows: m=k 1 ·L·H 0 /H；
The adjustment proportion of stone, slag and coal ash powder is as follows: n=k 2 ·S 1 / S 2
The adjustment proportion of cement and lime is as follows: o=k 3 ·R 2 /R 1
The adjusting proportion of the sodium silicate is as follows: p= (n.o) 1/3
Wherein the numerical value of L is 1, 2, 3 and 4, which correspond to the branch-shaped sludge, the net-shaped sludge, the sandy sludge and the mud-shaped sludge respectively; r is R 1 And R is 2 All are percentage values of water; s is S 1 And S is 2 Is the percentage value of sand; h 0 The soil hardness is preset; k (k) 1 、k 2 、k 3 To adjust the constant.
Further, the water-soluble vinyl polymer is polyvinyl acetate, urea-formaldehyde resin or polyvinyl alcohol.
Example 2:
the soft soil curing intelligent flow control method uses the soft soil curing intelligent flow control system, and comprises the following steps:
step one, a remote sensing data acquisition module acquires at least 3 remote sensing image data in a region range to be detected from a remote sensing image database, and sends the remote sensing image data and space coordinates corresponding to each pixel point in the remote sensing image to a positioning matching module;
the positioning matching module acquires at least 3 remote sensing image data in the range of the area to be solidified, and performs positioning registration on the remote sensing image data;
step two, the positioning matching module carries out binarization processing on the acquired remote sensing image data, so as to segment the remote sensing image and separate out a marker region in the remote sensing image;
the marker area is a water system area, a vegetation area, a mountain area or an artificial engineering area;
the positioning matching module performs shape matching on the marker region and performs coordinate registration by taking the marker region as a reference;
thirdly, carrying out remote sensing data analysis on the registered remote sensing images in the upper computer so as to obtain remote sensing geological parameters of the area to be solidified; the remote sensing geological parameters at least comprise: the water content, the type of the sludge, the depth of the sludge and the sand content of the sludge;
the formula calculation module divides the area to be solidified into a plurality of subareas, and obtains remote sensing geological parameters in each subarea from the upper computer; the formula calculation module averages the remote sensing geological parameters in each subarea and calculates the formula of the curing agent according to the average value;
fourth, during site construction, the soil detection module acquires site geological parameters of the soil at the solidification position in real time, wherein the site geological parameters at least comprise: soil moisture, soil hardness, and soil sand content;
step five, the soil detection module detects the field geological parameters of the soil in real time and uploads the field geological parameters to the formula optimization module in real time; the upper computer sends the remote sensing geological parameters to a formula optimization module of the curing construction machine; the formula optimization module compares the on-site geological parameters with the remote sensing geological parameters and optimizes the proportion of the curing agent;
step six, the formula optimizing module sends the optimized mixture ratio to the stirring injection module, and the stirring injection module performs stirring injection according to the optimized formula, wherein the amount of each stirring injection is less than 5% of the required dosage of the subarea;
and step seven, after stirring and injecting for one time, repeating the steps four to six until the whole subarea is cured.
Further, the number of the subareas is at least 16, and the actual area of each subarea is not less than 0.5 square kilometer.
Because the amount of each stirring injection is less than 5% of the amount required by the subareas, that is, the formula is re-optimized for at least 20 times in each subarea, the flexibility of site construction can be greatly improved. Meanwhile, the formula is designed in advance through remote sensing, so that the raw materials are transferred very accurately in actual construction, and the waste of material conveying working hours is avoided.
The description of the foregoing embodiments has been provided for the purposes of illustration and description. It is not intended to be exhaustive or to limit the disclosure. Individual elements or features of a particular embodiment are generally not limited to the particular embodiment, but, where applicable, may be interchanged and used with the selected embodiment even if not specifically shown or described. The same elements or features may also be varied in many ways. Such variations are not to be regarded as a departure from the disclosure, and all such modifications are intended to be included within the scope of the disclosure.
Example embodiments are provided so that this disclosure will be thorough, and will fully convey the scope to those skilled in the art. Numerous details are set forth, such as examples of specific parts, devices, and methods, in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent to one skilled in the art that the exemplary embodiments may be embodied in many different forms without the use of specific details, and neither should be construed to limit the scope of the disclosure. In certain example embodiments, well-known processes, well-known device structures, and well-known techniques are not described in detail.
The terminology used herein is for the purpose of describing particular example embodiments only and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises" and "comprising" are inclusive and, therefore, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order discussed and illustrated, unless specifically indicated. It should also be appreciated that additional or alternative steps may be employed.
Claims (10)
1. The utility model provides a weak soil solidification intelligence flow control system, includes host computer and solidification construction machine, its characterized in that:
the upper computer is connected with a remote sensing data acquisition module, a positioning matching module and a formula calculation module;
the remote sensing data acquisition module is connected with the remote sensing image database, acquires a plurality of satellite remote sensing images of the area to be solidified from the remote sensing image database, and processes the satellite remote sensing images;
the positioning matching module registers the acquired coordinates of a plurality of remote sensing images and sends the registered images to the upper computer;
remote sensing analysis is carried out on the registered remote sensing images in the upper computer, and remote sensing geological parameters corresponding to each coordinate of the position to be hardened are obtained;
the formula calculation module acquires coordinates and corresponding remote sensing geological parameters from the upper computer, and calculates the formula of the curing agent according to the remote sensing geological parameters;
the upper computer is in wireless connection with the curing construction machine, and the curing construction machine comprises a formula optimizing module, a Beidou positioning module, a curing agent detection module, a soil detection module and a stirring injection module;
the Beidou positioning module is used for acquiring Beidou positioning coordinates and uploading the Beidou positioning coordinates to the upper computer in real time; the stirring injection module is used for stirring each component of the curing agent and injecting the components into the soil surface layer;
the curing agent detection module is arranged in the stirring injection module and used for detecting the state of the curing agent in the stirring injection module in real time;
the soil detection module is used for detecting on-site geological parameters of soil in real time and uploading the on-site geological parameters to the formula optimization module in real time; the upper computer sends the remote sensing geological parameters to a formula optimization module of the curing construction machine; the formula optimization module compares the on-site geological parameters with the remote sensing geological parameters, optimizes the mixture ratio of the curing agent, and further sends the optimized mixture ratio to the stirring injection module.
2. The intelligent soft soil curing process control system according to claim 1, wherein:
the remote sensing image database comprises CBERS-2, CBERS-2b, CBERS, landsat, resourceSat, S-NPP, terra and UK-DMC 2 data;
the remote sensing data acquisition module acquires at least 3 remote sensing image data in the range of the area to be detected from a remote sensing image database, and sends the remote sensing image data and the space coordinates corresponding to each pixel point in the remote sensing image to the positioning matching module;
and the positioning matching module acquires at least 3 remote sensing image data in the range of the area to be solidified, and performs positioning registration on the remote sensing image data.
3. The intelligent soft soil curing process control system according to claim 2, wherein:
the positioning matching module carries out binarization processing on the acquired remote sensing image data so as to segment the remote sensing image and separate out a marker region in the remote sensing image;
the marker area is a water system area, a vegetation area, a mountain area or an artificial engineering area;
and the positioning matching module performs shape matching on the marker region and performs coordinate registration by taking the marker region as a reference.
4. The intelligent soft soil curing process control system according to claim 2, wherein:
the registered remote sensing images are subjected to remote sensing data analysis in the upper computer, so that remote sensing geological parameters of the area to be solidified are obtained; the remote sensing geological parameters at least comprise: the water content, the type of the sludge, the depth of the sludge and the sand content of the sludge;
the formula calculation module divides the area to be solidified into a plurality of subareas, and obtains remote sensing geological parameters in each subarea from the upper computer; the formula calculation module averages the remote sensing geological parameters in each subarea and calculates the formula of the curing agent according to the average value.
5. The intelligent soft soil curing process control system as claimed in claim 4, wherein:
the number of the subareas is at least 16, and the actual area of each subarea is not less than 0.5 square kilometer;
the formula of the curing agent comprises the following components: slag, coal ash powder, water-soluble vinyl polymer, stone, cement, fiber, lime and sodium silicate;
the slag is 5-20%, the coal ash is less than 10-20%, the water-soluble vinyl polymer is 1-5%, the stone is 10-30%, the cement is 5-15%, the fiber is 1-5%, the lime is 5-20%, and the sodium silicate is 1-5%.
6. The intelligent soft soil curing process control system as claimed in claim 5, wherein:
the curing agent detection module is arranged in the stirring injection module and arranged in the stirring section for analyzing the components of the curing agent in stirring;
the components of the curing agent are doped with fluorescent powder in a fixed proportion before being added into the stirring injection module, and the emission wavelength of the fluorescent powder doped by each component is different; the proportion of the fluorescent powder to each component of the curing agent is less than 0.5 percent, so that the curing effect is not affected;
the curing agent detection module is provided with a fluorescence detection probe and an ultraviolet light emission head; the ultraviolet light emitting head is used for emitting ultraviolet excitation light to the curing agent in stirring; the fluorescence detection probe is used for detecting fluorescence emitted by the trace fluorescent powder after being excited by ultraviolet excitation light;
the curing agent detection module judges the proportion of each component in the curing agent through the intensity of fluorescence emitted by fluorescent powder with different wavelengths received by the fluorescence detection probe.
7. The intelligent soft soil curing process control system as claimed in claim 6, wherein:
the soil detection module acquires in real time on-site geological parameters of the soil at the solidification position, wherein the on-site geological parameters at least comprise: soil moisture, soil hardness, and soil sand content;
the formula optimization module optimizes the process as follows:
let the water content in the remote sensing geological parameter be R 1 The sludge type is L, and the sand content of the sludge is S 1 The method comprises the steps of carrying out a first treatment on the surface of the The soil humidity in the on-site geological parameters is R 2 The soil hardness is H, and the soil sand content is S 2 ；
Then the recipe is optimized:
the adjusting proportion of the water-soluble vinyl polymer and the fiber is as follows: m=k 1 ·L·H 0 /H；
The adjustment proportion of stone, slag and coal ash powder is as follows: n=k 2 ·S 1 / S 2
The adjustment proportion of cement and lime is as follows: o=k 3 ·R 2 /R 1
The adjusting proportion of the sodium silicate is as follows: p= (n.o) 1/3
Wherein the numerical value of L is 1, 2, 3 and 4, which correspond to the branch-shaped sludge, the net-shaped sludge, the sandy sludge and the mud-shaped sludge respectively; r is R 1 And R is 2 All are percentage values of water; s is S 1 And S is 2 Is the percentage value of sand; h 0 The soil hardness is preset; k (k) 1 、k 2 、k 3 To adjust the constant.
8. The intelligent soft soil curing process control system as claimed in claim 6, wherein:
the water-soluble vinyl polymer is polyvinyl acetate, urea-formaldehyde resin or polyvinyl alcohol.
9. A soft soil curing intelligent process control method, which uses the soft soil curing intelligent process control system of claim 6, and is characterized by comprising the following steps:
step one, a remote sensing data acquisition module acquires at least 3 remote sensing image data in a region range to be detected from a remote sensing image database, and sends the remote sensing image data and space coordinates corresponding to each pixel point in the remote sensing image to a positioning matching module;
the positioning matching module acquires at least 3 remote sensing image data in the range of the area to be solidified, and performs positioning registration on the remote sensing image data;
step two, the positioning matching module carries out binarization processing on the acquired remote sensing image data, so as to segment the remote sensing image and separate out a marker region in the remote sensing image;
the marker area is a water system area, a vegetation area, a mountain area or an artificial engineering area;
the positioning matching module performs shape matching on the marker region and performs coordinate registration by taking the marker region as a reference;
thirdly, carrying out remote sensing data analysis on the registered remote sensing images in the upper computer so as to obtain remote sensing geological parameters of the area to be solidified; the remote sensing geological parameters at least comprise: the water content, the type of the sludge, the depth of the sludge and the sand content of the sludge;
the formula calculation module divides the area to be solidified into a plurality of subareas, and obtains remote sensing geological parameters in each subarea from the upper computer; the formula calculation module averages the remote sensing geological parameters in each subarea and calculates the formula of the curing agent according to the average value;
fourth, during site construction, the soil detection module acquires site geological parameters of the soil at the solidification position in real time, wherein the site geological parameters at least comprise: soil moisture, soil hardness, and soil sand content;
step five, the soil detection module detects the field geological parameters of the soil in real time and uploads the field geological parameters to the formula optimization module in real time; the upper computer sends the remote sensing geological parameters to a formula optimization module of the curing construction machine; the formula optimization module compares the on-site geological parameters with the remote sensing geological parameters and optimizes the proportion of the curing agent;
step six, the formula optimizing module sends the optimized mixture ratio to the stirring injection module, and the stirring injection module performs stirring injection according to the optimized formula, wherein the amount of each stirring injection is less than 5% of the required dosage of the subarea;
and step seven, after stirring and injecting for one time, repeating the steps four to six until the whole subarea is cured.
10. The intelligent soft soil curing process control method according to claim 9, wherein:
the number of the subareas is at least 16, and the actual area of each subarea is not less than 0.5 square kilometer.
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