CN117451959A - Water pollution treatment method, system and readable storage medium - Google Patents
Water pollution treatment method, system and readable storage medium Download PDFInfo
- Publication number
- CN117451959A CN117451959A CN202311444724.0A CN202311444724A CN117451959A CN 117451959 A CN117451959 A CN 117451959A CN 202311444724 A CN202311444724 A CN 202311444724A CN 117451959 A CN117451959 A CN 117451959A
- Authority
- CN
- China
- Prior art keywords
- turbidity
- information
- detection
- pollution
- pollution treatment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000003911 water pollution Methods 0.000 title claims abstract description 43
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000001514 detection method Methods 0.000 claims abstract description 161
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 93
- 239000008213 purified water Substances 0.000 claims abstract description 32
- 238000004590 computer program Methods 0.000 claims description 9
- 238000004659 sterilization and disinfection Methods 0.000 abstract description 15
- 230000000694 effects Effects 0.000 abstract description 6
- 238000004891 communication Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 238000011109 contamination Methods 0.000 description 3
- 241000894006 Bacteria Species 0.000 description 2
- VEXZGXHMUGYJMC-UHFFFAOYSA-M Chloride anion Chemical compound [Cl-] VEXZGXHMUGYJMC-UHFFFAOYSA-M 0.000 description 2
- 238000005286 illumination Methods 0.000 description 2
- 244000005700 microbiome Species 0.000 description 2
- 238000000746 purification Methods 0.000 description 2
- 230000005855 radiation Effects 0.000 description 2
- 241000700605 Viruses Species 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000005416 organic matter Substances 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 238000001556 precipitation Methods 0.000 description 1
- 238000004062 sedimentation Methods 0.000 description 1
- 230000011664 signaling Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Classifications
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/30—Treatment of water, waste water, or sewage by irradiation
- C02F1/32—Treatment of water, waste water, or sewage by irradiation with ultraviolet light
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/18—Water
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/761—Proximity, similarity or dissimilarity measures
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/11—Turbidity
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/42—Liquid level
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Life Sciences & Earth Sciences (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Chemical & Material Sciences (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Toxicology (AREA)
- Organic Chemistry (AREA)
- Water Supply & Treatment (AREA)
- Environmental & Geological Engineering (AREA)
- Hydrology & Water Resources (AREA)
- Computing Systems (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- Quality & Reliability (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Multimedia (AREA)
- Food Science & Technology (AREA)
- Medicinal Chemistry (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Physical Water Treatments (AREA)
Abstract
The invention provides a water pollution treatment method, a system and a readable storage medium, wherein the method comprises the following steps: acquiring turbidity information of water to be treated in the purified water tank based on a detection device; acquiring pollution information of the water to be treated based on the turbidity information and a three-dimensional navigation chart of the purified water tank; generating a first pollution treatment point group { (Mi, ni) } based on the pollution information and light emission information of a light emission device, mi being i robot positions for performing treatment, ni being an illuminable volume of the light emitter when the underwater robot is at the robot position; the underwater robot is controlled to move to each Mi of the first pollution treatment point group { (Mi, ni) } in turn, and emits treatment light to the water to be treated by the light emitting device. By the mode, the disinfection effect of the water to be treated can be effectively improved.
Description
Technical Field
The invention relates to the technical field of water pollution, in particular to a water pollution treatment method, a water pollution treatment system and a readable storage medium.
Background
In the prior art, after solid garbage precipitation, suspended matter sedimentation and organic matter/harmful matter treatment are carried out on the polluted water, the polluted water is further required to be disinfected, so that the safety of water quality is ensured.
At present, chloride and ultraviolet radiation are generally utilized for disinfection, but the chloride disinfection generally brings pungent smell, the ultraviolet radiation irradiates a clean water tank by utilizing a row of ultraviolet lamps, and the ultraviolet lamps cannot complete sufficient irradiation due to different depths/turbidity of water, so that the disinfection effect is poor.
Disclosure of Invention
An object of the present invention is to provide a water pollution treatment method, system and readable storage medium, which are used for solving the above technical problems.
Embodiments of the invention may be implemented as follows: in a first aspect, the present invention provides a water pollution treatment method, the water pollution treatment method being applied to a water pollution treatment system, the water pollution treatment system comprising a detection device and an underwater robot, the underwater robot comprising a light emitting device, the detection device being for being mounted on a side wall of a clean water tank, the underwater robot being for running in the clean water tank;
the method comprises the following steps:
acquiring turbidity information of water to be treated in the purified water tank based on the detection device;
acquiring pollution information of the water to be treated based on the turbidity information and a three-dimensional navigation chart of the purified water tank;
generating a first pollution treatment point group { (Mi, ni) } based on the pollution information and the light emission information of the light emission device, the Mi being i robot positions for performing treatment, the Ni being an illuminable volume of the light emitter when the underwater robot is at the robot position;
the underwater robot is controlled to sequentially move to each Mi of the first pollution treatment point group { (Mi, ni) }, and to emit treatment light to the water to be treated by the light emitting device.
In a second aspect, the invention provides a water pollution treatment system, comprising a controller, a detection device connected with the controller and an underwater robot, wherein the underwater robot comprises a light emitting device, and the detection device is used for being arranged on the side wall of a purified water tank;
the detection device is used for acquiring turbidity information of water to be treated in the purified water tank;
the controller is used for acquiring pollution information of the water to be treated based on the turbidity information and a three-dimensional navigation chart of the purified water tank;
the controller is further configured to generate a first pollution treatment point group { (Mi, ni) } based on the pollution information and the light emission information of the light emission device, where Mi is i robot positions for performing a treatment, and Ni is an illuminable volume of the light emitter when the underwater robot is at the robot position;
the controller is further configured to control the underwater robot to move to each Mi of the first pollution treatment point group { (Mi, ni) } in sequence, and to emit treatment light to the water to be treated by the light emitting device.
In a third aspect, the present invention provides a water pollution treatment system comprising a processor and a memory, the memory storing a computer program executable by the processor, the processor being executable by the computer program to implement the water pollution treatment method of the first aspect.
In a fourth aspect, the present invention provides a readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the water pollution treatment method according to the first aspect.
The water pollution treatment method, the system and the readable storage medium provided by the invention are used for acquiring turbidity information of water to be treated in the purified water tank based on the detection device; then, pollution information of the water to be treated is obtained based on the turbidity information and a three-dimensional navigation chart of the purified water tank; generating a first pollution treatment point group { (Mi, ni) } based on the pollution information and the light emission information of the light emission device, the Mi being i robot positions for performing treatment, the Ni being an illuminable volume of the light emitter when the underwater robot is at the robot position; the underwater robot is controlled to sequentially move to each Mi of the first pollution treatment point group { (Mi, ni) }, and to emit treatment light to the water to be treated by the light emitting device. Compared with the prior art, the movable underwater robot is utilized to emit treatment light for disinfection treatment, so that the flexibility and the disinfection capability of the underwater robot are effectively improved, the disinfection coverage area is improved, the disinfection effect is improved, a first pollution treatment point group (Mi, ni) is generated based on pollution information and light emission information of the light emitting device, the travel of the first pollution treatment point group (Mi, ni) is utilized, the treatment of a larger irradiation volume with less M is effectively realized, the running path and time of the underwater robot are greatly saved, and the cost is saved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following description will briefly explain the drawings needed in the embodiments, it being understood that the following drawings illustrate only some embodiments of the invention and are therefore not to be considered limiting of its scope, since other related drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of an embodiment of a water pollution treatment method according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating the substeps of step S11 of FIG. 1;
FIG. 3 is a flow chart illustrating the substep of step S113 of FIG. 2;
fig. 4 is a schematic block diagram of an embodiment of a water pollution treatment system according to an embodiment of the present invention.
Fig. 5 is a block diagram of another embodiment of a water pollution treatment system according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
In the description of the present invention, it should be noted that, if the terms "upper", "lower", "inner", "outer", and the like indicate an azimuth or a positional relationship based on the azimuth or the positional relationship shown in the drawings, or the azimuth or the positional relationship in which the inventive product is conventionally put in use, it is merely for convenience of describing the present invention and simplifying the description, and it is not indicated or implied that the apparatus or element referred to must have a specific azimuth, be configured and operated in a specific azimuth, and thus it should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, if any, are used merely for distinguishing between descriptions and not for indicating or implying a relative importance.
It should be noted that the features of the embodiments of the present invention may be combined with each other without conflict.
As shown in fig. 1, an embodiment of the present invention provides a water pollution treatment method, which is particularly applicable to a water pollution treatment system.
In an alternative embodiment, water pollution treatment system 10 includes detection device 100, underwater robot 200, and controller 400 coupled to detection device 100 and underwater robot 200.
The detection device 100 can be used for being installed on the side wall of a purified water tank, and can be used for detecting turbidity of water to be treated in the purified water tank.
In an alternative embodiment, the detection device 100 may be a light sensor, which is not limited herein.
The underwater robot 200 may be used to operate in a clean water tank, has a waterproof function, and may be electrically or remotely connected to the controller 400, and the underwater robot 200 further includes a light emitting device which may be used to emit treatment light.
Optionally, the treatment light may be ultraviolet light, and ultraviolet light with a certain wave band may inactivate bacteria, viruses and other microorganisms in the water to be treated.
Alternatively, the underwater robot 200 may be a sphere, and the light emitting devices are arranged in an array to cover a portion of the sphere to achieve a wide range of treatment light emission.
Alternatively, the underwater robot 200 can be other shapes, and the light emitting devices are arranged in an array to cover one or more surfaces of the underwater robot 200.
Alternatively, the light emitting device may be just a point light source, and may emit the processing light diffused in a cone shape.
S11, acquiring turbidity information of water to be treated in the purified water tank based on a detection device.
First, turbidity information of water to be treated in the purified water tank is acquired based on the detection device 100.
Optionally, the detecting device 100 includes a plurality of detecting devices and is distributed on each side wall of the purifying water tank in an array, and is used for emitting detecting light to the water to be treated in the purifying water tank and receiving the detecting light to determine the corresponding turbidity information.
Optionally, the detecting device 100 includes a transmitting end and a receiving end, where the transmitting end and the receiving end may be disposed on each side wall of the purifying water tank, or the transmitting end may be located in the center of the purifying water tank, the receiving end may be distributed on each side wall of the purifying water tank, or the transmitting end may be located on each side wall of the purifying water tank, and the receiving ends may be located in the purifying water tank, which is not limited herein.
In an alternative embodiment, the purifying water tank may include a plurality of detection areas, each detection area having a three-dimensional structure with a certain length, width and height.
Referring to fig. 2, fig. 2 is a schematic flow chart of the substeps of step S11 in fig. 1, which specifically includes:
s111, acquiring turbidity values of each detection area in the plurality of detection areas based on the detection device.
Turbidity values are obtained for each of a plurality of detection zones based on the detection device 100.
Alternatively, for each monitoring area, there may be a corresponding detection device 100, and the detection device 100 may independently detect the turbidity value of the detection area.
And S112, binding the turbidity value with each detection area correspondingly.
After the turbidity value is obtained, the corresponding turbidity value is bound with the detection area based on the binding relation between the detection device 100 and the detection area.
S113, taking a plurality of adjacent detection areas with turbidity value similarity larger than a preset value as the same turbidity area, and calculating the average value of the same turbidity area as the turbidity value of the turbidity area.
Then, a plurality of adjacent detection areas with turbidity value similarity larger than a preset value can be used as the same turbidity area, and the average value of the same turbidity area is calculated as the turbidity value of the turbidity area.
That is, similar to clustering, a plurality of detection areas having relatively similar turbidity values and adjacent to each other are regarded as the same turbidity area, and the average value of all detection areas in the same turbidity area is calculated again as the turbidity value of the turbidity area.
Referring to fig. 3, fig. 3 is a schematic flow chart of the substeps of step S113 in fig. 2, which specifically includes:
s1131, traversing each detection area in the plurality of detection areas according to a preset sequence, and taking the traversed first detection area as an independent detection area.
Alternatively, each of the plurality of detection areas is first traversed in a preset order, and the traversed first detection area is taken as an independent detection area.
Alternatively, a three-dimensional coordinate system may be established based on one vertex of the entire purified water tank, and coordinates (x, y, z) of any one point in any one detection area may be made such that x is greater than or equal to 0, y is greater than or equal to 0, and z is greater than or equal to 0.
In an alternative embodiment, the traversing is performed with the detection area where the origin is located as a starting point, i.e. the detection area where the origin is located is used as an independent detection area, and then the traversing of other detection areas surrounding the independent detection area is performed based on the independent detection area according to a preset sequence.
For the detection area that has been traversed before, the traversal is not repeated.
S1132, obtaining the similarity of the turbidity values of all adjacent detection areas around the independent detection area and the turbidity value of the independent detection area as the turbidity value approximation degree.
Alternatively, reference herein to adjacent detection zones means that the two detection zones have one face that is adjacent or coplanar.
The preset sequence of traversing the detection areas around the independent detection areas may specifically be, but is not limited to, traversing the detection areas of the level above the independent detection areas, traversing the detection areas of the level where the independent detection areas are located, and traversing the detection areas of the level below the independent detection areas.
The similarity of turbidity values for each detection zone to the independent detection zone is then calculated and used as a turbidity value approximation.
S1133, if the turbidity approximation degree is larger than the preset value, the detection area is used as the same turbidity area of the independent detection area.
And if the turbidity approximations of the adjacent detection areas and the independent detection areas are larger than the preset value, the detection areas are used as the same turbidity area of the independent detection areas.
I.e. the detection zone is incorporated into a separate detection zone and acts as the same turbidity zone.
S1134, calculating the similarity between the turbidity values of other adjacent detection areas of the detection areas and the turbidity value of the independent detection area as the turbidity value approximation degree, and returning to the step of taking the detection area as the same turbidity area of the independent detection area if the turbidity approximation degree of the adjacent detection area and the independent detection area is larger than the preset value;
and optionally, if the turbidity approximations of the detection area and the independent detection area are greater than the preset value, i.e. after the detection area is integrated into the same turbidity area of the independent detection area, taking the detection area as a reference, and calculating the similarity of the turbidity values of other adjacent detection areas of the detection area and the turbidity value of the independent detection area as the turbidity value approximations.
Subsequently, the process returns to step S1133.
So that the detection area connected to the independent detection area (adjacent/adjacent, etc.) satisfies that the turbidity value approximation degree is larger than the preset value based on the independent detection area as a reference, can be incorporated into the independent detection area and serve as the same turbidity area.
S1135, if the turbidity approximation degree is not greater than the preset value, the detection area is used as an independent detection area, and the step of acquiring the similarity between the turbidity values of all the adjacent detection areas around the independent detection area and the turbidity value of the independent detection area is returned to be used as the turbidity value approximation degree.
Alternatively, in the traversal process, if the turbidity approximation degree between the traversal to a detection area and the previous independent detection area is not greater than the preset value, the detection area may be used as the independent detection area.
And returns to step S1132.
It should be noted that, for any one independent detection area, all the traversals based on the independent detection areas (i.e. to the boundaries of multiple detection areas or to other independent detection areas) need to be completed, so that the traversals of the next independent detection area can be performed.
Taking the first independent detection area as an example, if the turbidity approximation degree of one detection area to the independent detection area is not larger than the preset value in the process of traversing based on the independent detection area, marking the detection area as a second independent detection area, and then skipping the detection area to continue the calculation of the next adjacent detection area. And sequentially labeling the encountered independent detection areas according to the sequence, wherein the second independent area can be traversed only after all the traversal of the independent detection areas is completed.
And S114, taking all turbidity areas and corresponding turbidity values as the turbidity information.
After one or more turbidity areas are acquired, all turbidity areas and corresponding turbidity values are used as the turbidity information.
S12, acquiring pollution information of the water to be treated based on the turbidity information and the three-dimensional navigation map of the purified water tank.
Then, the pollution information of the water to be treated can be obtained based on the turbidity information and the three-dimensional navigation map of the purified water tank.
Alternatively, the three-dimensional navigation map information is map information for navigating the underwater robot 200, which is associated by matching with coordinates of the turbidity area, thereby binding turbidity values with the three-dimensional navigation map information to form pollution information of the water to be treated.
S13, generating a first pollution treatment point group { (Mi, ni) } based on the pollution information and the light emission information of the light emitting device, where Mi is i robot positions for performing treatment, and Ni is an illuminable volume of the light emitter when the underwater robot is at the robot position.
Then, a first set of contamination treatment points { (Mi, ni) } can be generated based on the contamination information and the light emission information of the light emitting device, where Mi is i robot positions for performing the treatment, and Ni is an illuminable volume of the light emitter when the underwater robot is at the robot position.
It should be noted that, the robot position, that is, the position 200 of the underwater robot, in this application specifically includes a position and an attitude of the underwater robot 200, where the position is coordinates (x, y, z) of the underwater robot on a three-dimensional map, and the attitude refers to an orientation angle θ of a main direction of the underwater robot relative to a plane in which the (x, y) is located, that is, the robot position may be generally expressed as (x, y, z, θ).
In an alternative embodiment, a second set of pollution treatment points { (Mu, nu) }, where Mu is u robot positions generated based on the pollution information, and Nu is an illuminable volume of the light emitter when the underwater robot is at the robot position, may be constructed first.
In an alternative embodiment, u is greater than i.
Specifically, for each turbidity area in the three-dimensional navigation chart, it may be first determined whether the turbidity value corresponding to the turbidity area is greater than a preset turbidity.
If yes, that is, the turbidity value corresponding to the turbidity area is greater than the preset turbidity, a plurality of robot positions are generated based on the center point of each detection area constituting the turbidity area, that is, (x, y, z) constituting M is formed based on the center point, although a plurality of θ are randomly generated so as to constitute (x, y, z, θ) with the (x, y, z), respectively.
If the coordinates of the center point of a certain detection area are (x 1, y1, z 1), and randomly generated θ is θ1, θ2, θ3 … θj, j M are formed, and are (x 1, y1, z1, θ1), (x 1, y1, z1, θ2), (x 1, y1, z1, θ3) … (x 1, y1, z1, θj).
That is, in an alternative embodiment, if the turbidity value is too large, it indicates that the penetrability of the treatment light is poor, and long-distance penetration and disinfection cannot be performed, and intensive generation of a plurality of robot positions is required, specifically, for the center of each detection area of the turbidity area, which is used as a basis for generating the robot positions.
Alternatively, since the light emitting device of the underwater robot 200 may not be fully covered, a different θ may also affect the illuminable volume of the light emitting device of its underwater robot 200.
If not, namely the turbidity value corresponding to the turbidity area is smaller than or equal to the preset turbidity, n preset points are randomly generated in the turbidity area based on the number M of detection areas contained in the turbidity area, then a plurality of M are generated based on the preset points, and similarly, a random number θ is added based on (x, y, z) of the preset points to form a random number M.
In an alternative embodiment, if the turbidity value of a certain turbidity area is low, the corresponding penetrability is better, and no robot positions are needed to be set too much, n preset points can be randomly generated in the turbidity area based on the number m of detection areas contained in the turbidity area.
Alternatively, the n=m/2.
In other embodiments, n may have other relationships with m, which is not limited herein.
In other embodiments, m center points may be selected from the center points of the n detection areas as preset points, which are not limited herein.
Then, for each robot position of each turbidity region, an illuminable volume of the processing light is determined based on the light emission information of the processing light and the turbidity value of the turbidity region, and the illuminable volume is taken as N corresponding to the M.
Alternatively, where the turbidity value is high, the illuminable volume is low due to losses caused by reflection/scattering, whereas the illuminable volume is high, the light emission information of the processing light including the illumination range, wavelength, intensity, and the like of the processing light, which can calculate the illuminable volume of the processing light, that is, the effective illumination range of the processing light, based on the corresponding turbidity value.
It should be noted that, the effective irradiation range ensures that all the water to be treated in the range can bear the treatment light irradiation with a certain threshold intensity so as to ensure the disinfection effect.
Subsequently, the first pollution treatment spot set { (Mi, ni) } is selected from the second pollution treatment spot set { (Mu, nu) }.
After the second pollution treatment spot set { (Mu, nu) }, the first pollution treatment spot set { (Mi, ni) } is selected from the second pollution treatment spot set { (Mu, nu) }.
Alternatively, the second set of pollution treatment points { (Mu, nu) } is traversed first, and a plurality of random sets of random numbers M are randomly acquired, and optionally, the random sets are a subset of the second set of pollution treatment points { (Mu, nu) }.
Wherein the random set of M may specifically include a random number of M, which is less than or equal to u.
Then, the union of N corresponding to each robot position of the underwater robot in any group of random groups is calculated, namely, the union of all N of any group of random groups is calculated.
Then, the union sets of N corresponding to the plurality of sets of random groups are compared, and if the number of M of the corresponding random group is the smallest when the union set of N is the largest, the random group containing the smallest number of M is determined as the first contamination processing dot group { (Mi, ni) }.
I.e. when the union of N is maximum (e.g. equal to the volume of the entire purification tank), if the number of M needed is minimum, this means that its robot position is minimum, i.e. the robot position where the underwater robot 200 needs to operate is also minimum, which can be an efficient saving of travel of the underwater robot 200.
S14, controlling the underwater robot to sequentially move to each Mi in the first pollution treatment point group (Mi, ni) and emitting treatment light to water to be treated through the light emitting device.
Alternatively, after the first pollution treatment point group { (Mi, ni) } is determined, the underwater robot 200 is controlled to sequentially move to each Mi of the first pollution treatment point group { (Mi, ni) }, and then the light emitting device emits treatment light to the water to be treated, and the water to be treated is sterilized by the light emitting device, so that microorganisms, bacteria and the like thereof can be effectively killed, and the purification degree and safety degree of the water to be treated are greatly improved.
In the above embodiment, the turbidity information of the water to be treated in the purified water tank is obtained based on the detection device; then, pollution information of the water to be treated is obtained based on the turbidity information and a three-dimensional navigation chart of the purified water tank; generating a first pollution treatment point group { (Mi, ni) } based on the pollution information and the light emission information of the light emission device, the Mi being i robot positions for performing treatment, the Ni being an illuminable volume of the light emitter when the underwater robot is at the robot position; the underwater robot is controlled to sequentially move to each Mi of the first pollution treatment point group { (Mi, ni) }, and to emit treatment light to the water to be treated by the light emitting device. Compared with the prior art, the movable underwater robot is utilized to emit treatment light for disinfection treatment, so that the flexibility and the disinfection capability of the underwater robot are effectively improved, the disinfection coverage area is improved, the disinfection effect is improved, a first pollution treatment point group (Mi, ni) is generated based on pollution information and light emission information of the light emitting device, the travel of the first pollution treatment point group (Mi, ni) is utilized, the treatment of a larger irradiation volume with less M is effectively realized, the running path and the running time of the underwater robot are greatly saved, and the cost is saved.
The present invention also provides a water pollution treatment system, as shown in fig. 4, fig. 4 is a schematic block diagram of a water pollution treatment system provided in the present application, the water pollution treatment system includes a controller 400, and a detection device 100 and an underwater robot 200 connected to the controller 400, wherein the underwater robot 200 includes a light emitting device, and the detection device is used for being installed on a side wall of a purifying water tank.
The detection device 100 is used for acquiring turbidity information of water to be treated in the purified water tank;
the controller 400 is used for acquiring pollution information of the water to be treated based on the turbidity information and a three-dimensional navigation chart of the purified water tank;
the controller 400 is further configured to generate a first pollution treatment point group { (Mi, ni) } based on the pollution information and the light emission information of the light emitting device, where Mi is i robot positions for performing a treatment, and Ni is an illuminable volume of the light emitter when the underwater robot 200 is at the robot position;
the controller 40 is also configured to control the underwater robot 200 to sequentially move to each Mi of the first pollution treatment point group { (Mi, ni) }, and to emit treatment light to the water to be treated by the light emitting device.
The invention also provides a control device of the water pollution treatment System, and each functional module in the control device of the water pollution treatment System provided by the embodiment of the invention can be stored in a memory in the form of software or Firmware (Firmware) or solidified in Operating equipment (OS) of the water pollution treatment System and can be executed by a processor in the water pollution treatment System. Meanwhile, data, codes of programs, and the like required to execute the above-described modules may be stored in the memory.
Therefore, the embodiment of the invention also provides a water pollution treatment system, as shown in fig. 5, and fig. 5 is a block schematic diagram of the water pollution treatment system provided by the embodiment of the invention. The water pollution treatment system 300 includes a communication interface 301, a processor 302, and a memory 303. The processor 302, the memory 303 and the communication interface 301 are electrically connected directly or indirectly to each other to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 303 may be used to store software programs and modules, such as program instructions/modules corresponding to the water pollution treatment method provided in the embodiment of the present invention, and the processor 302 executes the software programs and modules stored in the memory 303, thereby performing various functional applications and data processing. The communication interface 301 may be used for communication of signaling or data with other node devices. The water pollution treatment system 300 may have a plurality of communication interfaces 301 in the present invention.
The Memory 303 may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (Programmable Read-Only Memory, PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc.
The processor 302 may be an integrated circuit chip with signal processing capabilities. The processor may be a general-purpose processor including a central processing unit (Central Processing Unit, CPU), a network processor (NetworM Processor, NP), etc.; but also digital signal processors (Digital Signal Processing, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.
The embodiment of the present invention also provides a readable storage medium having stored thereon a computer program which, when executed by a processor, implements the water pollution treatment method according to any of the foregoing embodiments. The computer readable storage medium may be, but is not limited to, a usb disk, a removable hard disk, ROM, RAM, PROM, EPROM, EEPROM, a magnetic disk, or an optical disk, etc. various media capable of storing program codes.
In summary, by providing a water pollution treatment method, turbidity information of water to be treated in a purified water tank is obtained based on a detection device; acquiring pollution information of the water to be treated based on the turbidity information and a three-dimensional navigation chart of the purified water tank; generating a first pollution treatment point group { (Mi, ni) } based on the pollution information and light emission information of a light emission device, mi being i robot positions for performing treatment, ni being an illuminable volume of the light emission device when an underwater robot is at the robot position; the underwater robot is controlled to move to each Mi of the first pollution treatment point group { (Mi, ni) } in turn, and emits treatment light to the water to be treated by the light emitting device. Through the mode, the disinfection effect of the water to be treated can be effectively improved, and the cost is saved.
The present invention is not limited to the above embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. The water pollution treatment method is characterized by being applied to a water pollution treatment system, wherein the water pollution treatment system comprises a detection device and an underwater robot, the underwater robot comprises a light emitting device, the detection device is used for being installed on the side wall of a purified water tank, and the underwater robot is used for running in the purified water tank;
the method comprises the following steps:
acquiring turbidity information of water to be treated in the purified water tank based on the detection device;
acquiring pollution information of the water to be treated based on the turbidity information and a three-dimensional navigation chart of the purified water tank;
generating a first pollution treatment point group { (Mi, ni) } based on the pollution information and the light emission information of the light emission device, the Mi being i robot positions for performing treatment, the Ni being an illuminable volume of the light emitter when the underwater robot is at the robot position;
the underwater robot is controlled to sequentially move to each Mi of the first pollution treatment point group { (Mi, ni) }, and to emit treatment light to the water to be treated by the light emitting device.
2. The method of claim 1, wherein the purified water tank includes a plurality of detection zones, the obtaining turbidity information of water to be treated in the purified water tank based on the detection device, comprising:
acquiring a turbidity value of each detection zone of the plurality of detection zones based on the detection device;
binding the turbidity value with each detection area correspondingly;
taking a plurality of adjacent detection areas with turbidity value approximation degree larger than a preset value as the same turbidity area, and calculating the average value of the same turbidity area as the turbidity value of the turbidity area;
and taking all turbidity areas and corresponding turbidity values as the turbidity information.
3. The method of claim 2, wherein said identifying adjacent ones of the plurality of detection zones having turbidity value approximations greater than a predetermined value as the same turbidity region comprises:
traversing each detection zone in the plurality of detection zones according to a preset sequence, and taking the traversed first detection zone as an independent detection zone;
acquiring the similarity between the turbidity values of all adjacent detection areas around the independent detection area and the turbidity value of the independent detection area as the turbidity value approximation degree;
if the turbidity approximation degree is larger than the preset value, the detection area is used as the same turbidity area of the independent detection area;
calculating the similarity between the turbidity values of other adjacent detection areas of the detection areas and the turbidity value of the independent detection area as the turbidity value approximation degree, and returning to the step of taking the detection area as the same turbidity area of the independent detection area if the turbidity approximation degree of the adjacent detection area and the independent detection area is larger than the preset value;
and if the turbidity approximation degree is not greater than the preset value, taking the detection area as an independent detection area, and returning to the step of acquiring the similarity between the turbidity values of all adjacent detection areas around the independent detection area and the turbidity value of the independent detection area as the turbidity value approximation degree.
4. The method according to claim 2, wherein the pollution information of the water to be treated is obtained based on the turbidity information and a three-dimensional navigation map of the purified water tank; comprising the following steps:
and binding turbidity values corresponding to the turbidity areas with the three-dimensional navigation chart based on the turbidity areas in the turbidity information and the three-dimensional navigation chart to form the pollution information.
5. The method according to claim 2, wherein the generating a first pollution treatment point group { (Mi, ni) } based on the pollution information and the light emission information of the light emitting device includes:
constructing a second pollution treatment point group { (Mu, nu) }, wherein Mu is u robot positions generated based on the pollution information, and Nu is the illuminable volume of the light emitter when the underwater robot is at the robot position;
the first pollution treatment spot set { (Mi, ni) } is selected from the second pollution treatment spot set { (Mu, nu) }.
6. The method according to claim 5, wherein said selecting said first pollution treatment spot set { (Mi, ni) } from said second pollution treatment spot set { (Mu, nu) }, comprises:
traversing the second set of pollution treatment points { (Mu, nu) }, and randomly obtaining a plurality of sets of random sets comprising random numbers M;
calculating a union of N corresponding to each robot position of the underwater robot in any group of random groups;
and comparing the union sets of N corresponding to the plurality of groups of random sets, and if the number of M of the corresponding random sets is the smallest when the union set of N is the largest, determining the random set with the smallest number of M as the first pollution treatment point set { (Mi, ni) }.
7. The method of claim 5, wherein said constructing a second set of pollution treatment points { (Mu, nu) }, comprises:
judging whether the turbidity value of each turbidity area in the three-dimensional navigation chart is larger than a preset turbidity or not;
if yes, generating a plurality of M based on the central point of each detection area forming the turbidity area;
if not, randomly generating n preset points in the turbidity area based on the number M of detection areas contained in the turbidity area, and then generating a plurality of M based on the n preset points, wherein n=m/2;
an illuminable volume of the processing light is determined based on light emission information of the processing light and a turbidity value of the turbidity region, and the illuminable volume is taken as N corresponding to the M.
8. The water pollution treatment system is characterized by comprising a controller, a detection device connected with the controller and an underwater robot, wherein the underwater robot comprises a light emission device, and the detection device is used for being arranged on the side wall of a purified water tank;
the detection device is used for acquiring turbidity information of water to be treated in the purified water tank;
the controller is used for acquiring pollution information of the water to be treated based on the turbidity information and a three-dimensional navigation chart of the purified water tank;
the controller is further configured to generate a first pollution treatment point group { (Mi, ni) } based on the pollution information and the light emission information of the light emission device, where Mi is i robot positions for performing a treatment, and Ni is an illuminable volume of the light emitter when the underwater robot is at the robot position;
the controller is further configured to control the underwater robot to move to each Mi of the first pollution treatment point group { (Mi, ni) } in sequence, and to emit treatment light to the water to be treated by the light emitting device.
9. A water pollution treatment system comprising a processor and a memory, the memory storing a computer program executable by the processor, the processor being executable by the computer program to implement the water pollution treatment method of any one of claims 1-7.
10. A readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the water pollution treatment method according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311444724.0A CN117451959B (en) | 2023-10-31 | 2023-10-31 | Water pollution treatment method, system and readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311444724.0A CN117451959B (en) | 2023-10-31 | 2023-10-31 | Water pollution treatment method, system and readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117451959A true CN117451959A (en) | 2024-01-26 |
| CN117451959B CN117451959B (en) | 2024-06-25 |
Family
ID=89594422
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311444724.0A Active CN117451959B (en) | 2023-10-31 | 2023-10-31 | Water pollution treatment method, system and readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117451959B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20130114765A (en) * | 2012-04-10 | 2013-10-21 | 이학섭 | Water purification system and automatic controled eco-water measurement using network |
| CN107219275A (en) * | 2017-03-25 | 2017-09-29 | 南宁市广千信息技术有限公司 | Water quality monitoring ship humanoid robot |
| CN112067517A (en) * | 2020-09-11 | 2020-12-11 | 杭州市地下管道开发有限公司 | Intelligent monitoring method, equipment and system for river and lake water body and readable storage medium |
| CN116300980A (en) * | 2022-12-02 | 2023-06-23 | 广州市城市排水有限公司 | Real-time visual navigation obstacle avoidance method for underwater dredging robot |
| CN116718555A (en) * | 2023-08-11 | 2023-09-08 | 广东省建科建筑设计院有限公司 | Method, system and medium for detecting indoor pollutants of building |
-
2023
- 2023-10-31 CN CN202311444724.0A patent/CN117451959B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20130114765A (en) * | 2012-04-10 | 2013-10-21 | 이학섭 | Water purification system and automatic controled eco-water measurement using network |
| CN107219275A (en) * | 2017-03-25 | 2017-09-29 | 南宁市广千信息技术有限公司 | Water quality monitoring ship humanoid robot |
| CN112067517A (en) * | 2020-09-11 | 2020-12-11 | 杭州市地下管道开发有限公司 | Intelligent monitoring method, equipment and system for river and lake water body and readable storage medium |
| CN116300980A (en) * | 2022-12-02 | 2023-06-23 | 广州市城市排水有限公司 | Real-time visual navigation obstacle avoidance method for underwater dredging robot |
| CN116718555A (en) * | 2023-08-11 | 2023-09-08 | 广东省建科建筑设计院有限公司 | Method, system and medium for detecting indoor pollutants of building |
Non-Patent Citations (1)
| Title |
|---|
| 刘瑞鹏;刘桥;祁志美;: "基于散射光功率比值测量的抗扰浊度探测器", 光学精密工程, vol. 19, no. 06, 15 June 2011 (2011-06-15), pages 1221 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117451959B (en) | 2024-06-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11328429B2 (en) | Method and apparatus for detecting ground point cloud points | |
| US11768495B2 (en) | Path planning in mobile robots | |
| Pérez et al. | An RFID-based intelligent vehicle speed controller using active traffic signals | |
| CN111383480B (en) | Method, apparatus, device and medium for hazard warning of vehicles | |
| CN104933218A (en) | Method and Device for Establishing a Trajectory for a Vehicle | |
| CN111562784B (en) | Disinfection method and equipment for mobile disinfection robot | |
| JP2013101485A (en) | Peripheral object detection device | |
| CN115308101B (en) | Water quality monitoring management method and system | |
| CN113835102B (en) | Lane line generation method and device | |
| CN117451959B (en) | Water pollution treatment method, system and readable storage medium | |
| Korthals et al. | Multi-modal detection and mapping of static and dynamic obstacles in agriculture for process evaluation | |
| Simon et al. | Bioinspired sonar reflectors as guiding beacons for autonomous navigation | |
| CN111220959B (en) | Indoor multipath false target identification method and device, electronic equipment and storage medium | |
| CN110618413A (en) | Passable area detection method and device based on multi-line laser radar | |
| CN117171695B (en) | Method and system for evaluating ecological restoration effect of antibiotic contaminated soil | |
| CN101689086A (en) | Method of processing data generated by a user input device for indicating positions | |
| CN116718555B (en) | Method, system and medium for detecting indoor pollutants of building | |
| US8483478B1 (en) | Grammar-based, cueing method of object recognition, and a system for performing same | |
| CN115605776A (en) | Method for determining the position of an object using ultrasound and driver assistance system for determining the position of an object in the surroundings of a vehicle | |
| Ngwenya et al. | Virtual obstacles for sensors incapacitation in robot navigation: A systematic review of 2D path planning | |
| CN112558035A (en) | Method and apparatus for estimating ground | |
| CN115166700A (en) | Ground point cloud filtering method, system, equipment and storage medium for laser radar | |
| CN114779207A (en) | Noise data identification method, device and storage medium | |
| CN112364888A (en) | Point cloud data processing method and device, computing equipment and computer storage medium | |
| CN109752730B (en) | Laser positioning method and system based on V-groove detection |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant |