CN112668591A - Intelligent river water burst treatment method and related device - Google Patents
Intelligent river water burst treatment method and related device Download PDFInfo
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Abstract
The embodiment of the application provides an intelligent river surge water treatment method and a related device, which are applied to a sewage treatment system, wherein the method comprises the following steps: acquiring a target image, wherein the target image comprises a target river channel; if the target river channel is determined to be a suspected polluted river channel according to the target image, obtaining a river water sample of the target river channel; detecting the river water sample to obtain a target detection result; and if the target river channel is determined to be the polluted river channel by the target detection result, carrying out evolution treatment on the sewage of the target river channel, and improving the intelligence during sewage treatment.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an intelligent river surge water processing method and a related device.
Background
With the continuous development of cities, certain environmental pollution is brought along with the continuous development of cities. A lot of rivers that flow through the city all receive pollution of different degree, when handling the sewage in the river course at present, adopt artificial intervention's mode usually, detect through the manual work whether river is polluted, carry out sewage treatment again after polluting, the intelligence that has leaded to when sewage treatment is lower.
Disclosure of Invention
The embodiment of the application provides an intelligent river water burst treatment method and a related device, which can improve the intelligence during sewage treatment.
The first aspect of the embodiment of the application provides an intelligent river surge water treatment method, which is applied to a sewage treatment system, and the method comprises the following steps:
acquiring a target image, wherein the target image comprises a target river channel;
if the target river channel is determined to be a suspected polluted river channel according to the target image, obtaining a river water sample of the target river channel;
detecting the river water sample to obtain a target detection result;
and if the target river channel is determined to be the polluted river channel by the target detection result, carrying out evolution treatment on the sewage of the target river channel.
With reference to the first aspect, in a possible implementation manner, the determining, according to the target image, that the target river is a suspected contaminated river includes:
extracting the features of the target image to obtain feature data;
determining river channel environment information and river bank environment information of the target river channel according to the characteristic data;
determining a target probability value of the target river channel being polluted according to the river channel environment information and the river bank environment information;
and if the first target probability value is higher than a first preset probability value, determining that the target river channel is a suspected polluted river channel.
With reference to the first aspect, in one possible implementation manner, the determining, according to the river channel environment information and the river bank environment information, a target probability value that the target river channel is polluted includes:
determining a first probability value that the target river channel is polluted according to the first color, and determining a second probability value that the target river channel is polluted according to the first quantity;
determining a third probability value according to the river bank environment information;
determining the target probability value according to the first probability value, the second probability value, and the third probability value.
With reference to the first aspect, in one possible implementation manner, the determining a third probability value according to the information about the river bank environment includes:
determining a reference probability value according to the river bank environment information;
if the reference probability value is higher than a second preset probability value, splitting the target image to obtain N sub-target images;
acquiring sub-river channel images in the N sub-target images to obtain K sub-river channel images, wherein K is a positive integer less than or equal to N;
determining the number of foams of the corresponding sub-riverway according to the K sub-riverway images to obtain K first numbers;
determining the plankton swarm movement trend of the corresponding sub-riverway according to the K sub-riverway images so as to obtain at least one movement trend;
determining at least one motion track according to the at least one motion trend;
determining the third probability value according to the first number and the at least one motion trail.
With reference to the first aspect, in one possible implementation manner, the method further includes:
determining the pollution level of the target river channel according to the target detection result;
determining alarm information according to the pollution level;
and displaying the alarm information.
A second aspect of the embodiment of the application provides an intelligence river gushes water treatment facilities, is applied to sewage treatment system, the device includes:
the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring a target image, and the target image comprises a target river channel;
the second acquisition unit is used for acquiring a river water sample of the target river channel if the target river channel is determined to be a suspected polluted river channel according to the target image;
the detection unit is used for detecting the river water sample to obtain a target detection result;
and the processing unit is used for carrying out evolution treatment on the sewage of the target river channel if the target river channel is determined to be the polluted river channel by the target detection result.
With reference to the second aspect, in a possible implementation manner, in the aspect that the target river is determined to be a suspected contaminated river according to the target image, the second obtaining unit is configured to:
extracting the features of the target image to obtain feature data;
determining river channel environment information and river bank environment information of the target river channel according to the characteristic data;
determining a target probability value of the target river channel being polluted according to the river channel environment information and the river bank environment information;
and if the first target probability value is higher than a first preset probability value, determining that the target river channel is a suspected polluted river channel.
With reference to the second aspect, in a possible implementation manner, the river environment information includes a first color of river water and a first number of floating objects, and in the determining, according to the river environment information and the river bank environment information, a target probability value that the target river is polluted, the second obtaining unit is configured to:
determining a first probability value that the target river channel is polluted according to the first color, and determining a second probability value that the target river channel is polluted according to the first quantity;
determining a third probability value according to the river bank environment information;
determining the target probability value according to the first probability value, the second probability value, and the third probability value.
With reference to the second aspect, in one possible implementation manner, in the determining a third probability value according to the information about the river bank environment, the second obtaining unit is configured to:
determining a reference probability value according to the river bank environment information;
if the reference probability value is higher than a second preset probability value, splitting the target image to obtain N sub-target images;
acquiring sub-river channel images in the N sub-target images to obtain K sub-river channel images, wherein K is a positive integer less than or equal to N;
determining the number of foams of the corresponding sub-riverway according to the K sub-riverway images to obtain K first numbers;
determining the plankton swarm movement trend of the corresponding sub-riverway according to the K sub-riverway images so as to obtain at least one movement trend;
determining at least one motion track according to the at least one motion trend;
determining the third probability value according to the first number and the at least one motion trail.
With reference to the second aspect, in one possible implementation manner, the apparatus is further configured to:
determining the pollution level of the target river channel according to the target detection result;
determining alarm information according to the pollution level;
and displaying the alarm information.
A third aspect of the embodiments of the present application provides a terminal, including a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, where the memory is used to store a computer program, and the computer program includes program instructions, and the processor is configured to call the program instructions to execute the step instructions in the first aspect of the embodiments of the present application.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program makes a computer perform part or all of the steps as described in the first aspect of embodiments of the present application.
A fifth aspect of embodiments of the present application provides a computer program product, wherein the computer program product comprises a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps as described in the first aspect of embodiments of the present application. The computer program product may be a software installation package.
The embodiment of the application has at least the following beneficial effects:
by obtaining a target image, wherein the target image comprises a target river channel, if the target river channel is determined to be a suspected polluted river channel according to the target image, a river water sample of the target river channel is obtained, detecting the river water sample to obtain a target detection result, if the target detection result determines that the target river channel is a polluted river channel, then the sewage of the target river channel is evolved and treated, compared with the existing scheme, whether the river water is polluted or not is detected by manpower, the sewage treatment is carried out after the pollution, the river water can be detected after the target river channel is determined to be a suspected polluted river channel by analyzing and treating the image comprising the target river channel, when confirming the river course to be contaminated river course according to the testing result, handle the sewage in target river course, promoted the intellectuality when handling sewage.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic view of a wastewater treatment system according to an embodiment of the present disclosure;
fig. 2A is a schematic flow chart of an intelligent river surge water treatment method provided in the embodiment of the present application;
FIG. 2B is a schematic structural diagram of a sewage treatment module according to an embodiment of the present disclosure;
fig. 3 is a schematic flow chart of another intelligent river surge water treatment method provided in the embodiment of the present application;
fig. 4 is a schematic structural diagram of a terminal according to an embodiment of the present application;
fig. 5 provides a schematic structural diagram of an intelligent river surge water treatment device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to better understand the intelligent river surge water treatment method provided by the embodiment of the application, a brief introduction is given below to a sewage treatment system applying the intelligent river surge water treatment method. Fig. 1 is a schematic view of a sewage treatment system provided in an embodiment of the present application, as shown in fig. 1, the sewage treatment system includes a camera, a detection device, a processor (not shown) and a sewage treatment device, the camera acquires a target image, the target image includes a target river channel, the processor processes the target image, it is determined whether the target river channel is a suspected polluted river channel according to the target image, if it is determined that the target river channel is the suspected polluted river channel, a river water sample of the target river channel is acquired by the detection device, the detection device detects the river water sample to obtain a detection result, the processor determines whether the target river channel is the polluted river channel according to the detection result, and if it is determined that the target river channel is the polluted river channel, the sewage is evolved and treated by the sewage treatment device. Therefore, in current scheme, whether detect the river through the manual work and be polluted, carry out sewage treatment again after polluting, can be through carrying out analysis processes to the image including the target river course to and after confirming that the target river course is the suspected contaminated river course, detect the river again, when confirming the river course for being contaminated river course according to the testing result, handle the sewage of target river course, promoted the intelligence when handling sewage.
Referring to fig. 2A, fig. 2A is a schematic flow chart of an intelligent river surge treatment method according to an embodiment of the present application. As shown in fig. 2A, the treatment method is applied to a sewage treatment system, and the treatment method includes:
201. and acquiring a target image, wherein the target image comprises a target river channel.
The sewage treatment system acquires a target image through the camera, and the mode of acquiring the target highlight can be a mode of acquiring at regular time or a mode of acquiring under a specific condition. In a specific case, the acquisition may be, for example, when the target river channel is dumped by the user, the object may be a pollutant, and then the target image is acquired at this time, so that the accuracy of determining whether the river channel is polluted can be improved, and the energy consumption can be reduced.
202. And if the target river channel is determined to be a suspected polluted river channel according to the target image, obtaining a river water sample of the target river channel.
A processor in the sewage treatment system judges whether the target river channel is a suspected polluted river channel or not according to the target image, during judgment, feature extraction can be carried out on the target image, and whether the target river channel is the suspected polluted river channel or not is judged according to feature data. The river water sample of the target river channel can be obtained through the detection device, and the detection device is arranged in the river channel and can be used for extracting the river water to obtain the river water sample. When the river water is extracted, filtration treatment or the like may be performed.
203. And detecting the river water sample to obtain a target detection result.
The river water sample is detected by a river water detection method in the existing scheme, and a target detection result is obtained.
204. And if the target river channel is determined to be the polluted river channel by the target detection result, carrying out evolution treatment on the sewage of the target river channel.
And judging whether the target river channel is a polluted river channel or not according to the detection result, comparing the target detection result with the existing river channel pollution index, and if the target river channel accords with the river channel pollution index, determining that the target river channel is the polluted river channel, wherein the existing river channel pollution index can be acquired through the Internet and other modes.
The method for carrying out evolution treatment on the sewage of the target river channel can be that the sewage is treated by a sewage treatment device. As shown in FIG. 2B, FIG. 2B shows a schematic view of a sewage treatment module. The system consists of three parts, namely a sludge collecting device, a mud-water separating device and an ecological soil water purifying system. Wherein, stagger about in the sludge collection device and arrange partly sponge, slow down the water velocity of flow, let silt attach to or submerge on the sponge, can play sand suction and cross two kinds of effects of water. The silt can fall off the sponge under the action of a self-descending or high-pressure water gun after being adsorbed for a period of time. The silt that drops is collected through the sludge pump in the bottom, specifically is put into the aquatic with the iron ladle, and the inside bottom installation sludge pump of iron ladle, iron ladle periphery link up to the lower extreme and stay the groove and advance the mud pipe, stays the groove and advance mud pipe both ends and seal, and the pipe upper portion is sealed, the lower part separately a plurality of osculums, and pressure differential effect makes mud follow mud hole entering sludge pipe, and sludge pump during operation, mud is siphoned away by the sludge pump.
The height of the designed iron bucket needs to be higher than the height of the water surface, and the sludge in the water body enters the sludge pipe under the action of the sludge pump and then enters the sludge-water separation device by utilizing the water pressure generated by the difference between the water level inside and outside the iron bucket. Mud-water separation equipment contains four major structures of glass linker, the outlet pipe, sludge pipe and mud pump, and mud passes through the sludge pipe and gets into the sedimentation tank and carry out mud-water separation, and three (including but not limited to three) little U type sedimentation tank is separated into with the wall body to big sedimentation tank, through certain dwell time, and mud deposit to the bottom of the pool, and supplementary glass linker observes the sediment sludge volume, takes away with the mud pump after reaching certain sludge volume, and the upper water gets into ecological soil water purification system through the overflow. The ecological soil water purification system is an ecological water purification unit, and in order to improve pollutant removal efficiency and ensure monomer maintenance or recuperation adjustment, a large system is divided into a plurality of small systems by using a wall body, and sewage flowing into each small system can completely filter the whole system by intermittent (electronic valve control) water inlet. Each system is provided with a special vent pipe, two layers of vent pipes are transversely arranged and are longitudinally distributed with the vent pipes, and the vent pipes are communicated with each other. A main water outlet pipe and a plurality of water outlet pipes are arranged at the bottom of the pool, each water outlet pipe is connected with the main water outlet pipe, one end of the main water outlet pipe is sealed, the other end of the main water outlet pipe is connected with a drainage channel, two ends of each water outlet pipe are sealed, grooves are reserved on the side edges, filtered water enters each branch pipe firstly and then collects the main water outlet pipe, and the filtered water is drained into the drainage channel from the outlet of the main water outlet pipe after being collected uniformly. The system comprises a plant layer and a filler layer from top to bottom, and the filler layer comprises a fine sand layer, a coarse sand layer, an activated carbon layer and a crushed stone layer from top to bottom. The ecological soil water purification system has the main purification effect that the fillers in all layers are adsorbed and trapped, and the plant purification effect is assisted.
In a possible implementation manner, one possible method for determining the target river as the suspected contaminated river according to the target image may be:
a1, performing feature extraction on the target image to obtain feature data;
the method of extracting the features of the target image may be a local binary method or the like. The feature data may be a gradation value, a luminance value, an RGB value, or the like.
A2, determining river channel environment information and river bank environment information of the target river channel according to the characteristic data;
the river channel environment information includes the color of river water, the number of floating objects and the like, and the river bank environment information may be the growth state information of trees on the river bank and the like.
The number of the floaters can be determined according to the contour extraction of the river surface and the number of the obtained closed loop contours.
A3, determining a target probability value of the target river channel being polluted according to the river channel environment information and the river bank environment information;
the corresponding pollution probability values can be respectively determined according to the river channel environment information and the river bank environment information, the target probability value is determined according to the corresponding pollution probability values, and the method for determining the target probability value according to the corresponding pollution probability values can be methods such as weight calculation.
And A4, if the first target probability value is higher than a first preset probability value, determining that the target river channel is a suspected polluted river channel.
The first preset probability value is set by an empirical value or historical data.
In this example, the target probability value is determined through the river channel environment information and the river bank environment information, and when the target probability value is higher than the first preset probability value, the target river channel is determined to be a suspected polluted river channel, so that the accuracy of determining the suspected polluted river channel can be improved.
In one possible implementation, the river channel environment information includes a first color of river water and a first number of floating objects, and a possible method for determining a target probability value that the target river channel is polluted according to the river channel environment information and the river bank environment information includes:
b1, determining a first probability value that the target river is polluted according to the first color, and determining a second probability value that the target river is polluted according to the first quantity;
the different colors correspond to different values of probability of contamination, e.g., the darker the first color, the greater the first probability value, and the bluer the first color, the smaller the first probability value. Different floater quantity corresponds to different pollution probability values, and the larger the first quantity of the floater is, the larger the second probability value is, and the smaller the first quantity is, the smaller the second probability value is.
B2, determining a third probability value according to the river bank environment information;
the bank information may include the growth status of plants including the bank. The reaction of different plants to pollution can be different, then the growth states of the plants can be different, the growth states of all the plants can be analyzed, the plants with larger pollution reactions can also be analyzed, and the growth states of the plants with larger pollution can be greatly changed after the plants are polluted.
Determining a first growth state of a target plant according to the information of the river bank environment, wherein the target plant can be a preset plant, for example, a plant which has a large response to pollution; a third probability value is determined based on the first growth state. The target plant may have a plurality of growth states, different growth states having different probability values of contamination, and the third probability value may be determined directly from the growth states.
Or comparing the first growth state with the normal growth state to obtain the offset between the first growth state and the normal growth state; a third probability value is determined based on the offset. The larger the offset, the larger the third probability value, the smaller the offset, the smaller the third probability value, and when the offset is 0, the third probability value is zero, i.e., not contaminated. The third probability value may also be determined in other manners, for example, obtaining the growth statuses of a plurality of plants, and determining the third probability value according to the growth statuses of the plurality of plants, which is only an example and is not limited specifically herein.
B3, determining the target probability value according to the first probability value, the second probability value and the third probability value.
The first probability value, the second probability value, and the third probability value may be weighted to obtain a target probability value. The first probability value may correspond to the first weight, the second probability value may correspond to the second weight, and the third probability value may correspond to the third weight, and then the first weight, the second weight, and the third weight may be used to perform weight operation on the first probability value, the second probability value, and the third probability value, so as to obtain the target probability value. Of course, the maximum probability value of the three probability values may be determined as the target probability value, and the like.
In this example, a first probability value is determined according to the color of the river channel, a second probability value is determined according to the number of the floating objects, a third probability value is determined according to the river bank environment information, a target probability value is determined according to the first probability value, the second probability value and the third probability value, the target probability value can be determined according to specific information of the river channel, and accuracy in determining the target probability value is improved.
In one possible implementation, another possible method for determining the third probability value according to the information about the river bank environment includes:
c1, determining a reference probability value according to the riparian environment information;
the method for determining the reference probability value according to the information about the river bank environment may refer to the method for determining the third probability value according to the information about the river bank environment in the foregoing methods, and details thereof are not repeated herein.
C2, splitting the target image to obtain N sub-target images if the reference probability value is higher than a second preset probability value;
the preset second probability value is set by an empirical value or historical data.
The method for splitting the target image may be to split the image uniformly to obtain N sub-images, where uniform splitting may be understood as splitting in a manner of equal area, splitting in a manner of equal area and size, or splitting in a manner of non-uniform splitting, and is not particularly limited.
C3, acquiring sub-river channel images in the N sub-target images to obtain K sub-river channel images, wherein K is a positive integer less than or equal to N;
the method for acquiring the sub-river channel images can acquire N sub-river channel images in a characteristic extraction mode. Certainly, the sub-river image may also be extracted through an image extraction model, the image extraction model may be a model obtained by training a sample image in advance, and the sample may include a sub-target image with a river mark, and the like.
C4, determining the number of foams of the corresponding sub-riverway according to the K sub-riverway images to obtain K first numbers;
the method for acquiring the number of bubbles in the river channel may be acquired by means of feature extraction, for example, acquiring the number of bubbles from feature data. The number of foams in the river channel can reflect the pollution condition of the river channel, and the number of foams in the polluted river channel is higher than that of foams in the uncontaminated river channel under normal conditions.
C5, determining the plankton swarm movement trend of the corresponding sub-riverway according to the K sub-riverway images to obtain at least one movement trend;
the community shape of the plankton community can be determined according to the sub-river image, and the movement trend is determined according to the community shape. The moving tendency may be determined by a colony-shaped convex portion, for example, if the colony shape is an irregular shape with a convex portion, the direction corresponding to the convex portion may be determined as the moving direction of the plankton, and the moving direction may be determined as the moving tendency. If a plurality of convex parts exist, the direction corresponding to the convex part with the largest convex area is determined as the moving direction. The moving direction may be determined as a center line of the direction corresponding to the plurality of projections.
C6, determining at least one motion track according to the at least one motion trend;
the method for determining the motion trail according to the motion trend can be as follows: the type of the plankton group can be determined, the corresponding motion trail set is determined, and at least one motion trail is determined from the motion trail set according to the motion trend. The different motion trends may correspond to at least one motion trajectory, and the similarity between the at least one motion trajectory is greater than a preset threshold.
C7, determining the third probability value according to the first number and the at least one motion trail.
The larger the first number is, the larger the third probability value is, and the smaller the coincidence rate of the motion trail in the flowing direction of river water in the river is, the larger the third probability value is. The coincidence rate of the motion trajectory and the flowing direction of the river water can be understood as the coincidence rate between the direction of the tangent of the motion estimation at different vertexes and the flowing direction of the river water, the tangent direction can be understood as the direction along the forward direction of the motion trajectory, and the forward direction of the motion trajectory can be understood as the direction of the motion.
In this example, when the reference probability value is greater than the second preset probability value, N sub-images of the target image are obtained, and then the third probability value is determined according to the number of foams in the sub-images in the sub-channel and the movement trend of the plankton population, so that the accuracy of determining the third probability value can be improved.
In one possible implementation, the intelligent river surge water treatment method further includes:
d1, determining the pollution level of the target river channel according to the target detection result;
d2, determining alarm information according to the pollution level;
d3, displaying the alarm information.
The target detection result may be a performance detection result, and the pollution level may be determined according to a relationship between the performance detection result and a preset performance index. For example, if the preset performance index corresponds to the pollution level, the corresponding pollution level may be determined according to the detection result.
Different pollution levels have different alarm information, the alarm information corresponding to the pollution level can be determined, and the alarm information can be displayed in a text mode, a voice mode, a video mode and the like.
In this example, after the target detection result is obtained, the pollution level is determined, and the alarm information corresponding to the pollution level is displayed, so that a user or a manager can quickly obtain the pollution condition of the river channel, and convenience is improved.
In a possible implementation manner, before displaying the alarm information, the following steps may be further included:
e1, acquiring a first fingerprint image;
e2, dividing the first fingerprint image into a plurality of areas;
e3, determining the distribution density of the feature points of each of the multiple regions to obtain a feature point distribution density set, wherein each region corresponds to one feature point distribution density;
e4, determining a target mean value and a target mean square error corresponding to the feature point distribution density set;
e5, determining a target image enhancement algorithm corresponding to the target average value according to the mapping relation between the preset average value and the image enhancement algorithm;
e6, determining a target fine tuning coefficient corresponding to the target mean square error according to a mapping relation between a preset mean square error and the fine tuning coefficient;
e7, adjusting the algorithm control parameters of the target image enhancement algorithm according to the target fine adjustment coefficients to obtain target algorithm control parameters;
e8, carrying out image enhancement processing on the first fingerprint image according to the target algorithm control parameter and the target image enhancement algorithm to obtain a second fingerprint image;
e9, matching the second fingerprint image with a preset fingerprint template;
e10, when the second fingerprint image is successfully matched with the preset fingerprint template, executing the step of displaying the alarm information.
The first fingerprint image can be a fingerprint image of a system administrator or other users who can operate the sewage management system.
In the embodiment of the application, the preset fingerprint template can be pre-stored in the electronic device. In specific implementation, the electronic device may acquire the first fingerprint image, and further, the first fingerprint image may be divided into a plurality of regions, the size of each region in the plurality of regions is within a preset area range, the size of each region in the plurality of regions may be the same or different, and the preset area range may be set by a user or default to a system.
Further, the electronic device may determine a feature point distribution density of each of the plurality of regions to obtain a feature point distribution density set, where the feature point distribution density set includes a plurality of feature point distribution densities, and each region corresponds to one feature point distribution density, that is, the number of feature points of each of the plurality of regions and a corresponding region area may be determined, and a ratio between the number of feature points and the corresponding region area is used as the feature point distribution density. The electronic device may determine a target average value and a target mean square error corresponding to the feature point distribution density set, that is, the target average value is the total number of feature points/the number of regions corresponding to the feature point distribution density set, and may determine the target mean square error corresponding to the feature point distribution density set based on the target average value and the feature point distribution density set.
In addition, in this embodiment of the application, the image enhancement algorithm may be at least one of the following: histogram equalization, wavelet transformation, gray stretching, Retinex algorithm, etc., without limitation. Each image enhancement algorithm corresponds to an algorithm control parameter, and the algorithm control algorithm is used for controlling the image enhancement degree. The electronic device may pre-store a mapping relationship between a preset average value and an image enhancement algorithm, and a mapping relationship between a preset mean square error and a fine tuning coefficient. The average value reflects the overall characteristics of the image, and the mean square error reflects the relevance between the regions, so that the corresponding image enhancement algorithm and the corresponding algorithm control parameters can be selected by combining the overall characteristics and the regional relevance of the image, and the image enhancement efficiency is favorably improved, namely the quality of the fingerprint image is improved.
Furthermore, the electronic device may determine a target image enhancement algorithm corresponding to the target average value according to a mapping relationship between a preset average value and an image enhancement algorithm, and may determine a target fine-tuning coefficient corresponding to the target mean-square error according to a mapping relationship between a preset mean-square error and a fine-tuning coefficient, and then, the electronic device may adjust an algorithm control parameter of the target image enhancement algorithm according to the target fine-tuning coefficient to obtain a target algorithm control parameter, and perform an image enhancement process on the first fingerprint image according to the target algorithm control parameter and the target image enhancement algorithm to obtain a second fingerprint image, and further, since the second fingerprint image has been subjected to the image enhancement process, the electronic device may match the second fingerprint image with the preset fingerprint template, and perform the step of obtaining the data transmission request when the second fingerprint image is successfully matched with the preset fingerprint template, otherwise, the user can be prompted to continue inputting the fingerprint image, and therefore the fingerprint identification efficiency can be improved.
Referring to fig. 3, fig. 3 is a schematic flow chart of another intelligent river surge water treatment method according to the embodiment of the present application. As shown in fig. 3, the treatment method is applied to a sewage treatment system, and the treatment method comprises the following steps:
301. acquiring a target image, wherein the target image comprises a target river channel;
302. if the target image is subjected to feature extraction, feature data are obtained;
303. determining river channel environment information and river bank environment information of the target river channel according to the characteristic data;
304. determining a target probability value of the target river channel being polluted according to the river channel environment information and the river bank environment information;
305. if the first target probability value is higher than a first preset probability value, determining that the target river channel is a suspected polluted river channel, and acquiring a river water sample of the target river channel;
306. detecting the river water sample to obtain a target detection result;
307. and if the target river channel is determined to be the polluted river channel by the target detection result, carrying out evolution treatment on the sewage of the target river channel.
In this example, the target probability value is determined through the river channel environment information and the river bank environment information, and when the target probability value is higher than the first preset probability value, the target river channel is determined to be a suspected polluted river channel, so that the accuracy of determining the suspected polluted river channel can be improved.
In accordance with the foregoing embodiments, please refer to fig. 4, where fig. 4 is a schematic structural diagram of a terminal provided in an embodiment of the present application, and as shown in the figure, the terminal includes a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, where the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the program includes instructions for performing the following steps;
acquiring a target image, wherein the target image comprises a target river channel;
if the target river channel is determined to be a suspected polluted river channel according to the target image, obtaining a river water sample of the target river channel;
detecting the river water sample to obtain a target detection result;
and if the target river channel is determined to be the polluted river channel by the target detection result, carrying out evolution treatment on the sewage of the target river channel.
The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It is understood that the terminal includes corresponding hardware structures and/or software modules for performing the respective functions in order to implement the above-described functions. Those of skill in the art will readily appreciate that the present application is capable of hardware or a combination of hardware and computer software implementing the various illustrative elements and algorithm steps described in connection with the embodiments provided herein. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiment of the present application, the terminal may be divided into the functional units according to the above method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
In accordance with the above, please refer to fig. 5, fig. 5 is a schematic structural diagram of an intelligent river surge water treatment device according to an embodiment of the present application. As shown in fig. 5, the apparatus is applied to a sewage treatment system, the apparatus comprising:
a first obtaining unit 501, configured to obtain a target image, where the target image includes a target river;
a second obtaining unit 502, configured to obtain a river water sample of the target river channel if the target river channel is determined to be a suspected polluted river channel according to the target image;
the detection unit 503 is configured to detect the river water sample to obtain a target detection result;
and the processing unit 504 is configured to, if the target detection result determines that the target river is a polluted river, perform evolution processing on the sewage of the target river.
In a possible implementation manner, in the aspect that the target river is determined to be a suspected contaminated river according to the target image, the second obtaining unit 502 is configured to:
extracting the features of the target image to obtain feature data;
determining river channel environment information and river bank environment information of the target river channel according to the characteristic data;
determining a target probability value of the target river channel being polluted according to the river channel environment information and the river bank environment information;
and if the first target probability value is higher than a first preset probability value, determining that the target river channel is a suspected polluted river channel.
In a possible implementation manner, the river environment information includes a first color of river water and a first number of floating objects, and in terms of determining a target probability value that the target river is polluted according to the river environment information and the river bank environment information, the second obtaining unit 502 is configured to:
determining a first probability value that the target river channel is polluted according to the first color, and determining a second probability value that the target river channel is polluted according to the first quantity;
determining a third probability value according to the river bank environment information;
determining the target probability value according to the first probability value, the second probability value, and the third probability value.
In a possible implementation manner, in the determining a third probability value according to the information about the river bank environment, the second obtaining unit 502 is configured to:
determining a reference probability value according to the river bank environment information;
if the reference probability value is higher than a second preset probability value, splitting the target image to obtain N sub-target images;
acquiring sub-river channel images in the N sub-target images to obtain K sub-river channel images, wherein K is a positive integer less than or equal to N;
determining the number of foams of the corresponding sub-riverway according to the K sub-riverway images to obtain K first numbers;
determining the plankton swarm movement trend of the corresponding sub-riverway according to the K sub-riverway images so as to obtain at least one movement trend;
determining at least one motion track according to the at least one motion trend;
determining the third probability value according to the first number and the at least one motion trail.
In one possible implementation, the apparatus is further configured to:
determining the pollution level of the target river channel according to the target detection result;
determining alarm information according to the pollution level;
and displaying the alarm information.
Embodiments of the present application also provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any one of the intelligent river surge water treatment methods as described in the above method embodiments.
Embodiments of the present application also provide a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program causes a computer to execute some or all of the steps of any one of the intelligent river surge water treatment methods as described in the above method embodiments.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed 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 can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may be implemented in the form of a software program module.
The integrated units, if implemented in the form of software program modules and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory comprises: various media capable of storing program codes, such as a usb disk, a read-only memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and the like.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash memory disks, read-only memory, random access memory, magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. An intelligent river water burst treatment method is applied to a sewage treatment system, and comprises the following steps:
acquiring a target image, wherein the target image comprises a target river channel;
if the target river channel is determined to be a suspected polluted river channel according to the target image, obtaining a river water sample of the target river channel;
detecting the river water sample to obtain a target detection result;
and if the target river channel is determined to be the polluted river channel by the target detection result, carrying out evolution treatment on the sewage of the target river channel.
2. The method of claim 1, wherein the determining the target river as the suspected contaminated river from the target image comprises:
extracting the features of the target image to obtain feature data;
determining river channel environment information and river bank environment information of the target river channel according to the characteristic data;
determining a target probability value of the target river channel being polluted according to the river channel environment information and the river bank environment information;
and if the first target probability value is higher than a first preset probability value, determining that the target river channel is a suspected polluted river channel.
3. The method of claim 2, wherein the river channel environment information comprises a first color of river water and a first number of floating objects, and the determining a target probability value that the target river channel is polluted according to the river channel environment information and the river bank environment information comprises:
determining a first probability value that the target river channel is polluted according to the first color, and determining a second probability value that the target river channel is polluted according to the first quantity;
determining a third probability value according to the river bank environment information;
determining the target probability value according to the first probability value, the second probability value, and the third probability value.
4. The method of claim 3, wherein determining a third probability value according to the information about the riparian environment comprises:
determining a reference probability value according to the river bank environment information;
if the reference probability value is higher than a second preset probability value, splitting the target image to obtain N sub-target images;
acquiring sub-river channel images in the N sub-target images to obtain K sub-river channel images, wherein K is a positive integer less than or equal to N;
determining the number of foams of the corresponding sub-riverway according to the K sub-riverway images to obtain K first numbers;
determining the plankton swarm movement trend of the corresponding sub-riverway according to the K sub-riverway images so as to obtain at least one movement trend;
determining at least one motion track according to the at least one motion trend;
determining the third probability value according to the first number and the at least one motion trail.
5. The method according to any one of claims 1-4, further comprising:
determining the pollution level of the target river channel according to the target detection result;
determining alarm information according to the pollution level;
and displaying the alarm information.
6. The intelligent river water burst treatment device is applied to a sewage treatment system, and the method comprises the following steps:
the system comprises a first acquisition unit, a second acquisition unit and a third acquisition unit, wherein the first acquisition unit is used for acquiring a target image, and the target image comprises a target river channel;
the second acquisition unit is used for acquiring a river water sample of the target river channel if the target river channel is determined to be a suspected polluted river channel according to the target image;
the detection unit is used for detecting the river water sample to obtain a target detection result;
and the processing unit is used for carrying out evolution treatment on the sewage of the target river channel if the target river channel is determined to be the polluted river channel by the target detection result.
7. The apparatus of claim 6, wherein in the determining of the target river as the suspected contaminated river from the target image, the second obtaining unit is configured to:
extracting the features of the target image to obtain feature data;
determining river channel environment information and river bank environment information of the target river channel according to the characteristic data;
determining a target probability value of the target river channel being polluted according to the river channel environment information and the river bank environment information;
and if the first target probability value is higher than a first preset probability value, determining that the target river channel is a suspected polluted river channel.
8. The apparatus according to claim 2, wherein the river channel environment information includes a first color of river water and a first number of floating objects, and in the determining of the target probability value that the target river channel is polluted according to the river channel environment information and the river bank environment information, the second obtaining unit is configured to:
determining a first probability value that the target river channel is polluted according to the first color, and determining a second probability value that the target river channel is polluted according to the first quantity;
determining a third probability value according to the river bank environment information;
determining the target probability value according to the first probability value, the second probability value, and the third probability value.
9. A terminal, comprising a processor, an input device, an output device, and a memory, the processor, the input device, the output device, and the memory being interconnected, wherein the memory is configured to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method of any of claims 1-5.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program comprising program instructions that, when executed by a processor, cause the processor to carry out the method according to any one of claims 1-5.
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