CN116484319B - Ship lock upstream and downstream reservoir area multi-source data fusion method and system based on machine learning - Google Patents
Ship lock upstream and downstream reservoir area multi-source data fusion method and system based on machine learning Download PDFInfo
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Abstract
The invention discloses a machine learning-based multi-source data fusion method and system for a ship lock upstream and downstream reservoir areas, and relates to the technical field of river water environment monitoring, wherein the method comprises the following steps: acquiring multi-source data of an ultra-long drainage basin; extracting satellite spectrum images in satellite monitoring data, searching and determining satellite pollution areas in the satellite spectrum images, and obtaining a coordinate set of the satellite pollution areas; extracting unmanned aerial vehicle spectral images in unmanned aerial vehicle monitoring data, searching and determining unmanned aerial vehicle pollution areas in the unmanned aerial vehicle spectral images, acquiring a coordinate set of the unmanned aerial vehicle pollution areas, setting a pollution area comparison model, comparing satellite pollution areas with the unmanned aerial vehicle pollution areas, and determining that the unmanned aerial vehicle pollution areas and the satellite pollution areas belong to the same area; extracting ground point source data in ground monitoring data, extracting a ground point source coordinate set in the ground point source data, setting a pollution area registration model, and registering by combining with an unmanned aerial vehicle pollution area.
Description
Technical Field
The invention belongs to the technical field of river water environment monitoring, and particularly relates to a method and a system for multi-source data fusion of a ship lock upstream and downstream reservoir areas based on machine learning.
Background
The three gorges reservoir area is a natural water system channel connecting the Sichuan basin and the two lakes plain, and has special topography, complex natural environment and fragile ecological environment.
At present, the reservoir area water environment monitoring capability mainly comprises traditional point source monitoring and manual detection, the intelligent monitoring means and the area monitoring means are fewer, and the real-time property and the application property of data cannot be met. The water quality, the water environment and the water pollution are mainly detected by manual traditional detection means for regular water quality sampling and laboratory detection, the current water environment supervision requirement is difficult to meet to a certain extent, but timeliness of information acquisition and problem discovery is mainly limited by factors such as point source network density, point source network position, manual sampling frequency, manual sampling timeliness and the like, the current situation of water environment monitoring application in three gorges reservoir areas cannot be responded, the point source monitoring result is difficult to reflect the overall water environment condition of the watershed, the means for near-real-time monitoring and analysis of the whole area of the reservoir areas of the watershed are lacked, black and odorous water body screening, river and lake water quality change real-time perception, water environment project operation supervision, early warning and treatment effect evaluation related to the first-line service of the water environment are all urgent to improve the comprehensive capacity of water environment monitoring analysis of the reservoir areas.
1. The current situation and the main problems
1) The pollution problem in the three gorges reservoir area is serious
The problem of point source pollution in the three gorges reservoir is still common. The sewage collection pipe network facilities of the villages and towns are not perfect, the pollution collection treatment efficiency is generally low, the existing sewage treatment process selection and scale design of the sewage plants in the areas and counties are not scientific, the effluent quality is unstable, the denitrification and dephosphorization effects are poor, and long-term stable operation and standard emission are difficult to realize. The drainage ports in part of the drainage basin are numerous, and the drainage total amount limiting measure of the drainage basin needs to be lifted. Besides sewage outlets of sewage treatment plants in part of the areas, sewage and wastewater discharge outlets which are processed simply or unprocessed by a certain number of industries and enterprises exist, and the discharge outlets can directly lead to pollution of watershed water bodies and have great influence on water quality.
The surface source pollution of the warehouse area is still a main source of warehouse-in pollution of the warehouse area. The structure of the reservoir area planting industry is unreasonable, the whole level is not high, the production mode is still rough, the development of the green ecological industry is still immature, and the prevention and control effects of pesticide and fertilizer loss pollution, crop straw pollution, cultivation pollution, rural life pollution and other non-point source pollution are poor; the shallow hills in the reservoir area are more, the distribution of the hillside farmland is wide, the water and soil loss of the hillside farmland is serious in a part of areas, the heavy rainfall is encountered, the water flow is mixed with the soil to directly enter the river channel, and the influence on the water environment of the river domain is large.
Part of the watershed has serious endogenous pollution and is easy to cause eutrophication of water. Partial river channel is deposited, and a large amount of sediment containing pollutants such as organic matters, nitrogen, phosphorus, heavy metals and the like is deposited to cause endogenous pollution; after the reservoir area is subjected to water storage operation, a large-area falling area is formed, and pollutants in the falling area rise along with surface runoff or reservoir water level to enter a water area, so that the pollutants also become an important cause of river basin pollution.
2) The water environment monitoring means is urgent to be perfected
The water environment monitoring mainly adopts ground monitoring and remote sensing monitoring. The ground monitoring is time-consuming and labor-consuming, and most of the ground monitoring is point source information, so that the ground monitoring is difficult to reflect the spatial distribution condition and the change trend of the water quality in a large range. Satellite remote sensing makes up the defect of space continuity of ground monitoring, but is influenced by the space resolution, and is mainly applied to large-area water bodies such as lakes, oceans and the like at present. Water quality monitoring based on unmanned aerial vehicle remote sensing images tends to be changed from qualitative description to quantitative analysis, meanwhile, the water quality parameters which can be monitored are gradually increased, inversion accuracy is also continuously improved, but the accuracy of a remote sensing inversion model is greatly influenced by factors such as areas, seasons and the like, ground monitoring data auxiliary verification and model correction are required, and the fusion of multi-source data and the automatic optimization of the inversion model are lacking at present.
Disclosure of Invention
In order to solve the technical problems, the invention provides a ship lock upstream and downstream reservoir area multi-source data fusion method based on machine learning, which comprises the following steps:
acquiring multi-source data of an ultralong basin, wherein the multi-source data comprises: ground monitoring data, satellite monitoring data and unmanned aerial vehicle monitoring data;
extracting satellite spectrum images in the satellite monitoring data, searching and determining satellite pollution areas in the satellite spectrum images, and obtaining a coordinate set of the satellite pollution areas;
extracting unmanned aerial vehicle spectral images in the unmanned aerial vehicle monitoring data, searching and determining unmanned aerial vehicle pollution areas in the unmanned aerial vehicle spectral images, acquiring a coordinate set of the unmanned aerial vehicle pollution areas, setting a pollution area comparison model, comparing the satellite pollution areas with the unmanned aerial vehicle pollution areas, and determining that the unmanned aerial vehicle pollution areas and the satellite pollution areas belong to the same area;
extracting ground point source data in the ground monitoring data, extracting a ground point source coordinate set in the ground point source data, setting a pollution area registration model, and registering by combining the unmanned aerial vehicle pollution area to finally generate a pollution area multi-source spectrum image.
Further, the acquiring the coordinate set of the satellite pollution area includes: acquiring a satellite boundary coordinate set of the satellite pollution area, wherein the satellite boundary coordinate set is as follows:
，
wherein A is a coordinate point, X, Y and Z are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript;
the acquiring the coordinate set of the unmanned aerial vehicle pollution area comprises the following steps: acquiring an unmanned aerial vehicle boundary coordinate set of the unmanned aerial vehicle pollution area, wherein the unmanned aerial vehicle boundary coordinate set is as follows:
，
wherein B is a coordinate point, m, n and q are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript.
Further, the contaminated area comparison model is:
，
wherein ,for the coincidence degree->For adjusting the factor, for adjusting the coincidence ratio of the A coordinate point and the B coordinate point, +.>Is the overlap threshold.
Further, comparing the satellite pollution area with the unmanned aerial vehicle pollution area further includes: and when the unmanned aerial vehicle pollution area and the satellite pollution area do not belong to the same area, re-acquiring the satellite monitoring data and the unmanned aerial vehicle monitoring data until the unmanned aerial vehicle pollution area in the re-acquired unmanned aerial vehicle monitoring data and the satellite pollution area in the re-acquired satellite monitoring data belong to the same area.
Further, the setting of the pollution area registration model, and the registration in combination with the unmanned aerial vehicle pollution area, further includes: searching unmanned aerial vehicle point source data in the unmanned aerial vehicle pollution area, and extracting unmanned aerial vehicle point source coordinates in the unmanned aerial vehicle point source data, wherein the unmanned aerial vehicle point source coordinates set is as follows:
，
wherein C is a coordinate point, h, j and k are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript.
Further, the contaminated region registration model is:
，
，
wherein D is a ground point source coordinate point, t, Y and u are coordinates of an X axis, a Y axis and a Z axis respectively, i is a subscript,is the coordinate threshold value, when->And->The difference of (2) is less than +.>And when the method is used, the following steps are carried out:
，
where E is the registered point source coordinate point,the coefficients are adjusted for precision.
When (when)And->The difference of (2) is greater than +.>And respectively marking two points on the multi-source spectrum image of the polluted area.
The invention also provides a machine learning-based multi-source data fusion system for the upstream and downstream storage areas of the ship lock, which comprises the following steps:
the data acquisition module is used for acquiring multi-source data of the ultra-long drainage basin, wherein the multi-source data comprises: ground monitoring data, satellite monitoring data and unmanned aerial vehicle monitoring data;
the satellite pollution area extracting module is used for extracting satellite spectrum images in the satellite monitoring data, searching and determining satellite pollution areas in the satellite spectrum images, and acquiring coordinate sets of the satellite pollution areas;
the unmanned aerial vehicle monitoring system comprises an unmanned aerial vehicle monitoring module, a satellite pollution area comparison module, a satellite pollution area detection module and a satellite pollution area detection module, wherein the unmanned aerial vehicle monitoring module is used for extracting unmanned aerial vehicle spectral images in unmanned aerial vehicle monitoring data, searching and determining unmanned aerial vehicle pollution areas in the unmanned aerial vehicle spectral images, acquiring a coordinate set of the unmanned aerial vehicle pollution areas, setting a pollution area comparison model, comparing the satellite pollution areas with the unmanned aerial vehicle pollution areas, and determining that the unmanned aerial vehicle pollution areas and the satellite pollution areas belong to the same area;
the pollution area spectrum image generation module is used for extracting ground point source data in the ground monitoring data, extracting a ground point source coordinate set in the ground point source data, setting a pollution area registration model, and registering by combining the unmanned aerial vehicle pollution area, so that a pollution area multi-source spectrum image is finally generated.
Further, the acquiring the coordinate set of the satellite pollution area includes: acquiring a satellite boundary coordinate set of the satellite pollution area, wherein the satellite boundary coordinate set is as follows:
，
wherein A is a coordinate point, X, Y and Z are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript;
the acquiring the coordinate set of the unmanned aerial vehicle pollution area comprises the following steps: acquiring an unmanned aerial vehicle boundary coordinate set of the unmanned aerial vehicle pollution area, wherein the unmanned aerial vehicle boundary coordinate set is as follows:
，
wherein B is a coordinate point, m, n and q are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript.
Further, the contaminated area comparison model is:
，
wherein ,for the coincidence degree->For adjusting the factor for adjusting the coincidence of the A-coordinate point and the B-coordinate pointDegree (f)>Is the overlap threshold.
Further, comparing the satellite pollution area with the unmanned aerial vehicle pollution area further includes: and when the unmanned aerial vehicle pollution area and the satellite pollution area do not belong to the same area, re-acquiring the satellite monitoring data and the unmanned aerial vehicle monitoring data until the unmanned aerial vehicle pollution area in the re-acquired unmanned aerial vehicle monitoring data and the satellite pollution area in the re-acquired satellite monitoring data belong to the same area.
In general, the above technical solutions conceived by the present invention have the following beneficial effects compared with the prior art:
according to the technical scheme, multi-channel, all-dimensional and dead-angle-free supervision information can be provided, intelligent and unmanned analysis means such as visual analysis are utilized to carry out interpretation analysis on drainage basin video monitoring, multi-channel pictures, texts and the like, and a large data center based on multi-source terminal data is constructed by combining ground observation, manual sampling and the like.
Drawings
FIG. 1 is a flow chart of the method of embodiment 1 of the present invention;
fig. 2 is a block diagram of a system of embodiment 2 of the present invention.
Detailed Description
In order to better understand the above technical solutions, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.
The method provided by the invention can be implemented in a terminal environment, wherein the terminal can comprise one or more of the following components: processor, storage medium, and display screen. Wherein the storage medium has stored therein at least one instruction that is loaded and executed by the processor to implement the method described in the embodiments below.
The processor may include one or more processing cores. The processor connects various parts within the overall terminal using various interfaces and lines, performs various functions of the terminal and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the storage medium, and invoking data stored in the storage medium.
The storage medium may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (ROM). The storage medium may be used to store instructions, programs, code sets, or instructions.
The display screen is used for displaying a user interface of each application program.
In addition, it will be appreciated by those skilled in the art that the structure of the terminal described above is not limiting and that the terminal may include more or fewer components, or may combine certain components, or a different arrangement of components. For example, the terminal further includes components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, a power supply, and the like, which are not described herein.
Example 1
As shown in fig. 1, an embodiment of the present invention provides a method for fusing multi-source data in an upstream and downstream reservoir area of a ship lock based on machine learning, including:
step 101, obtaining multi-source data of an ultra-long river basin, wherein the multi-source data comprises: ground monitoring data, satellite monitoring data and unmanned aerial vehicle monitoring data;
102, extracting satellite spectrum images in the satellite monitoring data, searching and determining satellite pollution areas in the satellite spectrum images, and obtaining a coordinate set of the satellite pollution areas;
step 103, extracting an unmanned aerial vehicle spectrum image in the unmanned aerial vehicle monitoring data, searching and determining an unmanned aerial vehicle pollution area in the unmanned aerial vehicle spectrum image, acquiring a coordinate set of the unmanned aerial vehicle pollution area, setting a pollution area comparison model, comparing the satellite pollution area with the unmanned aerial vehicle pollution area, and determining that the unmanned aerial vehicle pollution area and the satellite pollution area belong to the same area;
specifically, the acquiring the coordinate set of the satellite pollution area includes: acquiring a satellite boundary coordinate set of the satellite pollution area, wherein the satellite boundary coordinate set is as follows:
，
wherein A is a coordinate point, X, Y and Z are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript;
specifically, the acquiring the coordinate set of the unmanned aerial vehicle pollution area includes: acquiring an unmanned aerial vehicle boundary coordinate set of the unmanned aerial vehicle pollution area, wherein the unmanned aerial vehicle boundary coordinate set is as follows:
，
wherein B is a coordinate point, m, n and q are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript.
Specifically, the pollution area comparison model is as follows:
，
wherein ,for the coincidence degree->For adjusting the factor, for adjusting the coincidence ratio of the A coordinate point and the B coordinate point, +.>Is the overlap threshold.
Specifically, comparing the satellite pollution area with the unmanned aerial vehicle pollution area further includes: and when the unmanned aerial vehicle pollution area and the satellite pollution area do not belong to the same area, re-acquiring the satellite monitoring data and the unmanned aerial vehicle monitoring data until the unmanned aerial vehicle pollution area in the re-acquired unmanned aerial vehicle monitoring data and the satellite pollution area in the re-acquired satellite monitoring data belong to the same area.
And 104, extracting ground point source data in the ground monitoring data, extracting a ground point source coordinate set in the ground point source data, setting a pollution area registration model, and registering by combining the unmanned aerial vehicle pollution area to finally generate a pollution area multi-source spectrum image.
Specifically, the setting of the pollution area registration model, and the registration in combination with the unmanned aerial vehicle pollution area, further includes: searching unmanned aerial vehicle point source data in the unmanned aerial vehicle pollution area, and extracting unmanned aerial vehicle point source coordinates in the unmanned aerial vehicle point source data, wherein the unmanned aerial vehicle point source coordinates set is as follows:
，
wherein C is a coordinate point, h, j and k are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript.
Specifically, the contaminated region registration model is:
，
，
wherein D is a ground point source coordinate point, t, Y and u are coordinates of an X axis, a Y axis and a Z axis respectively, i is a subscript,is the coordinate threshold value, when->And->The difference of (2) is less than +.>And when the method is used, the following steps are carried out:
，
where E is the registered point source coordinate point,for the precision adjustment factor, +.>The larger the E, the higher the accuracy.
When (when)And->The difference of (2) is greater than +.>And respectively marking two points on the multi-source spectrum image of the polluted area.
Example 2
As shown in fig. 2, the embodiment of the invention further provides a system for multi-source data fusion of a ship lock upstream and downstream reservoir areas based on machine learning, which comprises:
the data acquisition module is used for acquiring multi-source data of the ultra-long drainage basin, wherein the multi-source data comprises: ground monitoring data, satellite monitoring data and unmanned aerial vehicle monitoring data;
the satellite pollution area extracting module is used for extracting satellite spectrum images in the satellite monitoring data, searching and determining satellite pollution areas in the satellite spectrum images, and acquiring coordinate sets of the satellite pollution areas;
the unmanned aerial vehicle monitoring system comprises an unmanned aerial vehicle monitoring module, a satellite pollution area comparison module, a satellite pollution area detection module and a satellite pollution area detection module, wherein the unmanned aerial vehicle monitoring module is used for extracting unmanned aerial vehicle spectral images in unmanned aerial vehicle monitoring data, searching and determining unmanned aerial vehicle pollution areas in the unmanned aerial vehicle spectral images, acquiring a coordinate set of the unmanned aerial vehicle pollution areas, setting a pollution area comparison model, comparing the satellite pollution areas with the unmanned aerial vehicle pollution areas, and determining that the unmanned aerial vehicle pollution areas and the satellite pollution areas belong to the same area;
specifically, the acquiring the coordinate set of the satellite pollution area includes: acquiring a satellite boundary coordinate set of the satellite pollution area, wherein the satellite boundary coordinate set is as follows:
，
wherein A is a coordinate point, X, Y and Z are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript;
specifically, the acquiring the coordinate set of the unmanned aerial vehicle pollution area includes: acquiring an unmanned aerial vehicle boundary coordinate set of the unmanned aerial vehicle pollution area, wherein the unmanned aerial vehicle boundary coordinate set is as follows:
，
wherein B is a coordinate point, m, n and q are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript.
Specifically, the pollution area comparison model is as follows:
，
wherein ,for the coincidence degree->For adjusting the factor, for adjusting the coincidence ratio of the A coordinate point and the B coordinate point, +.>Is the overlap threshold.
Specifically, comparing the satellite pollution area with the unmanned aerial vehicle pollution area further includes: and when the unmanned aerial vehicle pollution area and the satellite pollution area do not belong to the same area, re-acquiring the satellite monitoring data and the unmanned aerial vehicle monitoring data until the unmanned aerial vehicle pollution area in the re-acquired unmanned aerial vehicle monitoring data and the satellite pollution area in the re-acquired satellite monitoring data belong to the same area.
The pollution area spectrum image generation module is used for extracting ground point source data in the ground monitoring data, extracting a ground point source coordinate set in the ground point source data, setting a pollution area registration model, and registering by combining the unmanned aerial vehicle pollution area, so that a pollution area multi-source spectrum image is finally generated.
Specifically, the setting of the pollution area registration model, and the registration in combination with the unmanned aerial vehicle pollution area, further includes: searching unmanned aerial vehicle point source data in the unmanned aerial vehicle pollution area, and extracting unmanned aerial vehicle point source coordinates in the unmanned aerial vehicle point source data, wherein the unmanned aerial vehicle point source coordinates set is as follows:
，
wherein C is a coordinate point, h, j and k are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript.
Specifically, the contaminated region registration model is:
，
，
wherein D is a ground point source coordinate point, t, Y and u are coordinates of an X axis, a Y axis and a Z axis respectively, i is a subscript,is the coordinate threshold value, when->And->The difference of (2) is less than +.>When in use, then
，
Where E is the registered point source coordinate point,for the precision adjustment factor, +.>The larger the E, the higher the accuracy.
When (when)And->The difference of (2) is greater than +.>And respectively marking two points on the multi-source spectrum image of the polluted area.
Example 3
The embodiment of the invention also provides a storage medium which stores a plurality of instructions for realizing the multi-source data fusion method of the ship lock upstream and downstream reservoir areas based on machine learning.
Alternatively, in this embodiment, the storage medium may be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.
Alternatively, in the present embodiment, the storage medium is configured to store program code for performing the steps of: step 101, obtaining multi-source data of an ultra-long river basin, wherein the multi-source data comprises: ground monitoring data, satellite monitoring data and unmanned aerial vehicle monitoring data;
102, extracting satellite spectrum images in the satellite monitoring data, searching and determining satellite pollution areas in the satellite spectrum images, and obtaining a coordinate set of the satellite pollution areas;
step 103, extracting an unmanned aerial vehicle spectrum image in the unmanned aerial vehicle monitoring data, searching and determining an unmanned aerial vehicle pollution area in the unmanned aerial vehicle spectrum image, acquiring a coordinate set of the unmanned aerial vehicle pollution area, setting a pollution area comparison model, comparing the satellite pollution area with the unmanned aerial vehicle pollution area, and determining that the unmanned aerial vehicle pollution area and the satellite pollution area belong to the same area;
specifically, the acquiring the coordinate set of the satellite pollution area includes: acquiring a satellite boundary coordinate set of the satellite pollution area, wherein the satellite boundary coordinate set is as follows:
，
wherein A is a coordinate point, X, Y and Z are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript;
specifically, the acquiring the coordinate set of the unmanned aerial vehicle pollution area includes: acquiring an unmanned aerial vehicle boundary coordinate set of the unmanned aerial vehicle pollution area, wherein the unmanned aerial vehicle boundary coordinate set is as follows:
，
wherein B is a coordinate point, m, n and q are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript.
Specifically, the pollution area comparison model is as follows:
，
wherein ,for the coincidence degree->For adjusting the factor, for adjusting the coincidence ratio of the A coordinate point and the B coordinate point, +.>Is the overlap threshold.
Specifically, comparing the satellite pollution area with the unmanned aerial vehicle pollution area further includes: and when the unmanned aerial vehicle pollution area and the satellite pollution area do not belong to the same area, re-acquiring the satellite monitoring data and the unmanned aerial vehicle monitoring data until the unmanned aerial vehicle pollution area in the re-acquired unmanned aerial vehicle monitoring data and the satellite pollution area in the re-acquired satellite monitoring data belong to the same area.
And 104, extracting ground point source data in the ground monitoring data, extracting a ground point source coordinate set in the ground point source data, setting a pollution area registration model, and registering by combining the unmanned aerial vehicle pollution area to finally generate a pollution area multi-source spectrum image.
Specifically, the setting of the pollution area registration model, and the registration in combination with the unmanned aerial vehicle pollution area, further includes: searching unmanned aerial vehicle point source data in the unmanned aerial vehicle pollution area, and extracting unmanned aerial vehicle point source coordinates in the unmanned aerial vehicle point source data, wherein the unmanned aerial vehicle point source coordinates set is as follows:
，
wherein C is a coordinate point, h, j and k are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript.
Specifically, the contaminated region registration model is:
，
，
wherein D is a ground point source coordinate point, t, Y and u are coordinates of an X axis, a Y axis and a Z axis respectively, i is a subscript,is the coordinate threshold value, when->And->The difference of (2) is less than +.>And when the method is used, the following steps are carried out:
，
where E is the registered point source coordinate point,for the precision adjustment factor, +.>The larger the E, the higher the accuracy.
When (when)And->The difference of (2) is greater than +.>And respectively marking two points on the multi-source spectrum image of the polluted area.
Example 4
The embodiment of the invention also provides electronic equipment, which comprises a processor and a storage medium connected with the processor, wherein the storage medium stores a plurality of instructions, and the instructions can be loaded and executed by the processor so that the processor can execute the machine learning-based lock upstream and downstream reservoir area multi-source data fusion method.
Specifically, the electronic device of the present embodiment may be a computer terminal, and the computer terminal may include: one or more processors, and a storage medium.
The storage medium can be used for storing software programs and modules, such as a method for fusing multi-source data in upstream and downstream storage areas of a ship lock based on machine learning in the embodiment of the invention, and the processor executes various functional applications and data processing by running the software programs and the modules stored in the storage medium, namely, the method for fusing multi-source data in upstream and downstream storage areas of the ship lock based on machine learning is realized. The storage medium may include a high-speed random access storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memory, or other non-volatile solid-state storage medium. In some examples, the storage medium may further include a storage medium remotely located with respect to the processor, and the remote storage medium may be connected to the terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The processor may invoke the information stored in the storage medium and the application program via the transmission system to perform the following steps: step 101, obtaining multi-source data of an ultra-long river basin, wherein the multi-source data comprises: ground monitoring data, satellite monitoring data and unmanned aerial vehicle monitoring data;
102, extracting satellite spectrum images in the satellite monitoring data, searching and determining satellite pollution areas in the satellite spectrum images, and obtaining a coordinate set of the satellite pollution areas;
step 103, extracting an unmanned aerial vehicle spectrum image in the unmanned aerial vehicle monitoring data, searching and determining an unmanned aerial vehicle pollution area in the unmanned aerial vehicle spectrum image, acquiring a coordinate set of the unmanned aerial vehicle pollution area, setting a pollution area comparison model, comparing the satellite pollution area with the unmanned aerial vehicle pollution area, and determining that the unmanned aerial vehicle pollution area and the satellite pollution area belong to the same area;
specifically, the acquiring the coordinate set of the satellite pollution area includes: acquiring a satellite boundary coordinate set of the satellite pollution area, wherein the satellite boundary coordinate set is as follows:
，
wherein A is a coordinate point, X, Y and Z are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript;
specifically, the acquiring the coordinate set of the unmanned aerial vehicle pollution area includes: acquiring an unmanned aerial vehicle boundary coordinate set of the unmanned aerial vehicle pollution area, wherein the unmanned aerial vehicle boundary coordinate set is as follows:
，
wherein B is a coordinate point, m, n and q are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript.
Specifically, the pollution area comparison model is as follows:
，
wherein ,for the coincidence degree->For adjusting the factor, for adjusting the coincidence ratio of the A coordinate point and the B coordinate point, +.>Is the overlap threshold.
Specifically, comparing the satellite pollution area with the unmanned aerial vehicle pollution area further includes: and when the unmanned aerial vehicle pollution area and the satellite pollution area do not belong to the same area, re-acquiring the satellite monitoring data and the unmanned aerial vehicle monitoring data until the unmanned aerial vehicle pollution area in the re-acquired unmanned aerial vehicle monitoring data and the satellite pollution area in the re-acquired satellite monitoring data belong to the same area.
And 104, extracting ground point source data in the ground monitoring data, extracting a ground point source coordinate set in the ground point source data, setting a pollution area registration model, and registering by combining the unmanned aerial vehicle pollution area to finally generate a pollution area multi-source spectrum image.
Specifically, the setting of the pollution area registration model, and the registration in combination with the unmanned aerial vehicle pollution area, further includes: searching unmanned aerial vehicle point source data in the unmanned aerial vehicle pollution area, and extracting unmanned aerial vehicle point source coordinates in the unmanned aerial vehicle point source data, wherein the unmanned aerial vehicle point source coordinates set is as follows:
，
wherein C is a coordinate point, h, j and k are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript.
Specifically, the contaminated region registration model is:
，
，
wherein D is a ground point source coordinate point, t, Y and u are coordinates of an X axis, a Y axis and a Z axis respectively, i is a subscript,is the coordinate threshold value, when->And->The difference of (2) is less than +.>When in use, then
，
Where E is the registered point source coordinate point,for the precision adjustment factor, +.>The larger the E, the higher the accuracy.
When (when)And->The difference of (2) is greater than +.>And respectively marking two points on the multi-source spectrum image of the polluted area.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the embodiments provided in the present invention, it should be understood that the disclosed technology may be implemented in other manners. The system embodiments described above are merely exemplary, and for example, the division of the units is merely a logic function division, and there may be another division manner in actual implementation, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or partly in the form of a software product or all or part of the technical solution, which is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, randomAccess Memory), a removable hard disk, a magnetic disk, or an optical disk, or the like, which can store program codes.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary here nor is it exhaustive of all embodiments. While still being apparent from variations or modifications that may be made by those skilled in the art are within the scope of the invention.
Claims (8)
1. A multi-source data fusion method for a ship lock upstream and downstream reservoir areas based on machine learning is characterized by comprising the following steps:
acquiring multi-source data of an ultralong basin, wherein the multi-source data comprises: ground monitoring data, satellite monitoring data and unmanned aerial vehicle monitoring data;
extracting satellite spectrum images in the satellite monitoring data, searching and determining satellite pollution areas in the satellite spectrum images, and obtaining a coordinate set of the satellite pollution areas;
extracting unmanned aerial vehicle spectral images in the unmanned aerial vehicle monitoring data, searching and determining unmanned aerial vehicle pollution areas in the unmanned aerial vehicle spectral images, acquiring a coordinate set of the unmanned aerial vehicle pollution areas, setting a pollution area comparison model, comparing the satellite pollution areas with the unmanned aerial vehicle pollution areas, and determining that the unmanned aerial vehicle pollution areas and the satellite pollution areas belong to the same area;
extracting ground point source data in the ground monitoring data, extracting a ground point source coordinate set in the ground point source data, setting a pollution area registration model, and carrying out registration in combination with the unmanned aerial vehicle pollution area to finally generate a pollution area multi-source spectrum image, wherein the setting of the pollution area registration model, and carrying out registration in combination with the unmanned aerial vehicle pollution area, further comprises: searching unmanned aerial vehicle point source data in the unmanned aerial vehicle pollution area, and extracting unmanned aerial vehicle point source coordinates in the unmanned aerial vehicle point source data, wherein the unmanned aerial vehicle point source coordinates set is as follows:
，
wherein C is a coordinate point, h, j and k are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript;
the contaminated area registration model is:
,
,
wherein D is a ground point source coordinate point, t, Y and u are coordinates of an X axis, a Y axis and a Z axis respectively, i is a subscript,is the coordinate threshold value, when->And->The difference of (2) is less than +.>When in use, then
,
Where E is the registered point source coordinate point,the precision adjustment coefficient is adopted;
when (when)And->The difference of (2) is greater than +.>And respectively marking two points on the multi-source spectrum image of the polluted area.
2. The machine learning based lock up and down stock area multisource data fusion method of claim 1, wherein the acquiring of the coordinate set of the satellite contaminated area comprises: acquiring a satellite boundary coordinate set of the satellite pollution area, wherein the satellite boundary coordinate set is as follows:
,
wherein A is a coordinate point, X, Y and Z are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript;
the acquiring the coordinate set of the unmanned aerial vehicle pollution area comprises the following steps: acquiring an unmanned aerial vehicle boundary coordinate set of the unmanned aerial vehicle pollution area, wherein the unmanned aerial vehicle boundary coordinate set is as follows:
,
wherein B is a coordinate point, m, n and q are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript.
3. The machine learning based ship lock upstream and downstream reservoir area multisource data fusion method according to claim 2, wherein the pollution area comparison model is as follows:
,
wherein ,for the coincidence degree->For adjusting the factor, for adjusting the coincidence ratio of the A coordinate point and the B coordinate point, +.>Is the overlap threshold.
4. The machine learning based lock up and down stock area multisource data fusion method of claim 1, wherein comparing the satellite contaminated area with the unmanned contaminated area further comprises: and when the unmanned aerial vehicle pollution area and the satellite pollution area do not belong to the same area, re-acquiring the satellite monitoring data and the unmanned aerial vehicle monitoring data until the unmanned aerial vehicle pollution area in the re-acquired unmanned aerial vehicle monitoring data and the satellite pollution area in the re-acquired satellite monitoring data belong to the same area.
5. A machine learning based lock upstream and downstream pool area multi-source data fusion system, comprising:
the data acquisition module is used for acquiring multi-source data of the ultra-long drainage basin, wherein the multi-source data comprises: ground monitoring data, satellite monitoring data and unmanned aerial vehicle monitoring data;
the satellite pollution area extracting module is used for extracting satellite spectrum images in the satellite monitoring data, searching and determining satellite pollution areas in the satellite spectrum images, and acquiring coordinate sets of the satellite pollution areas;
the unmanned aerial vehicle monitoring system comprises an unmanned aerial vehicle monitoring module, a satellite pollution area comparison module, a satellite pollution area detection module and a satellite pollution area detection module, wherein the unmanned aerial vehicle monitoring module is used for extracting unmanned aerial vehicle spectral images in unmanned aerial vehicle monitoring data, searching and determining unmanned aerial vehicle pollution areas in the unmanned aerial vehicle spectral images, acquiring a coordinate set of the unmanned aerial vehicle pollution areas, setting a pollution area comparison model, comparing the satellite pollution areas with the unmanned aerial vehicle pollution areas, and determining that the unmanned aerial vehicle pollution areas and the satellite pollution areas belong to the same area;
the pollution area spectrum image generation module is used for extracting ground point source data in the ground monitoring data, extracting a ground point source coordinate set in the ground point source data, setting a pollution area registration model, registering in combination with the unmanned aerial vehicle pollution area, and finally generating a pollution area multi-source spectrum image, wherein the pollution area registration model is set, registering in combination with the unmanned aerial vehicle pollution area, and further comprises: searching unmanned aerial vehicle point source data in the unmanned aerial vehicle pollution area, and extracting unmanned aerial vehicle point source coordinates in the unmanned aerial vehicle point source data, wherein the unmanned aerial vehicle point source coordinates set is as follows:
,
wherein C is a coordinate point, h, j and k are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript;
the contaminated area registration model is:
,
,
wherein D is a ground point source coordinate point, t, Y and u are coordinates of an X axis, a Y axis and a Z axis respectively, i is a subscript,is the coordinate threshold value whenAnd->The difference of (2) is less than +.>When in use, then
,
Where E is the registered point source coordinate point,the precision adjustment coefficient is adopted;
when (when)And->Is greater than/>And respectively marking two points on the multi-source spectrum image of the polluted area.
6. The machine learning based lock up and down pool area multisource data fusion system of claim 5, wherein the acquiring the set of coordinates of the satellite contaminated area comprises: acquiring a satellite boundary coordinate set of the satellite pollution area, wherein the satellite boundary coordinate set is as follows:
,
wherein A is a coordinate point, X, Y and Z are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript;
the acquiring the coordinate set of the unmanned aerial vehicle pollution area comprises the following steps: acquiring an unmanned aerial vehicle boundary coordinate set of the unmanned aerial vehicle pollution area, wherein the unmanned aerial vehicle boundary coordinate set is as follows:
,
wherein B is a coordinate point, m, n and q are coordinates of an X axis, a Y axis and a Z axis respectively, and i is a subscript.
7. The machine learning based lock up and down pool area multisource data fusion system of claim 6, wherein the contaminated area comparison model is:
,
wherein ,for the coincidence degree->For adjusting the factor, for adjusting the coincidence ratio of the A coordinate point and the B coordinate point, +.>Is the overlap threshold.
8. The machine learning based lock up and down stream pool area multisource data fusion system of claim 6, wherein said comparing said satellite contaminated area to said unmanned contaminated area further comprises: and when the unmanned aerial vehicle pollution area and the satellite pollution area do not belong to the same area, re-acquiring the satellite monitoring data and the unmanned aerial vehicle monitoring data until the unmanned aerial vehicle pollution area in the re-acquired unmanned aerial vehicle monitoring data and the satellite pollution area in the re-acquired satellite monitoring data belong to the same area.
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