CN117929375B - Water quality detection method and water quality detector based on image processing - Google Patents
Water quality detection method and water quality detector based on image processing Download PDFInfo
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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Abstract
The application relates to a water quality detection method and a water quality detector based on image processing, the method comprises the steps of respectively using image sensors to acquire a plurality of groups of images at a plurality of positions in a test pool, counting the number of characteristic objects and the volume of the characteristic objects at each position according to the images, constructing a space network grid at each position in a position sequence by using each group of characteristic objects, calculating the growth speed of each space network grid, and obtaining a flocculating agent dosage adjustment result according to a calculation result, wherein the flocculating agent dosage adjustment result comprises adjustment and non-adjustment, and obtaining the total amount of interceptors when the flocculating agent dosage adjustment result is non-adjustment. According to the image processing-based water quality detection method and the image processing-based water quality detector, the consumption of the flocculating agent is regulated by means of alum blossom generated in the flocculation process, and then the heavy metal content in a sample is reversely estimated according to the total amount of interceptor, so that the water quality can be rapidly judged in the sample.
Description
Technical Field
The application relates to the technical field of water quality detection, in particular to a water quality detection method and a water quality detector based on image processing.
Background
At present, the detection means of heavy metals in water bodies comprise precipitation method, spectrum method, graduation method, mass spectrometry and the like, all the methods are required to be obtained in a laboratory by means of various reagents and detection equipment, and certain hysteresis exists on results.
The flocculation method is an important method for treating heavy metal wastewater, can remove heavy metal efficiently, and is a simple, quick and low-cost method. Different from the general pollutants which can be removed by oxidative decomposition, heavy metals have the characteristic of being undegradable, and the flocculation method is used for efficiently removing dissolved heavy metal ions and compound heavy metals attached to the surfaces of suspended matters or colloid particles by selecting a proper flocculant aiming at the existence form of the heavy metals in wastewater.
From the above description, it can be seen that flocculation is a means of treating heavy metal wastewater, which, if applied to water quality detection, enables rapid water quality determination at the sample site, but requires further investigation in terms of how to use and standard execution.
Disclosure of Invention
The application provides a water quality detection method and a water quality detector based on image processing, which are used for adjusting consumption of flocculating agent by means of alum blossom generated in the flocculation process, and then reversely estimating heavy metal content in a sample according to total interceptor amount so as to realize rapid judgment of water quality in the sample.
The above object of the present application is achieved by the following technical solutions:
in a first aspect, the present application provides a water quality detection method based on image processing, including:
Acquiring a plurality of groups of images at a plurality of positions in the test pool by using image sensors respectively, wherein each group of images corresponds to one position on the position sequence, each position is provided with the image sensor, and the plurality of positions are arranged according to the flowing direction of water in the test pool;
counting the number of the characteristic objects at each position and the volume of the characteristic objects according to the images;
grouping the feature objects according to the feature object volumes to obtain a plurality of groups of feature objects;
Constructing a spatial network grid using each set of feature objects at each location on the sequence of locations;
Calculating the growth speed of each space network grid, wherein the growth speed comprises the space growth speed and the grid increase density; and
Obtaining a flocculating agent dosage adjustment result according to the calculation result, wherein the flocculating agent dosage adjustment result comprises adjustment and non-adjustment;
Obtaining the total amount of interceptor when the adjustment result of the coagulant dosage is not adjustment;
wherein the number of edges of each of the spatial network grids is the same.
In a possible implementation manner of the first aspect, the number of feature objects at each position and the feature object volume according to the image statistics further need to complement the incomplete feature objects according to the occlusion relation.
In a possible implementation manner of the first aspect, complementing the incomplete feature object includes:
determining a characteristic object with a shielding relation with the incomplete characteristic object;
determining a completion boundary according to the feature objects with shielding relation with the incomplete feature objects;
determining two starting points of the incomplete feature object;
sequentially constructing unit vectors connected end to end at two starting points until the two groups of unit vectors are intersected;
wherein the unit vectors are all located inside the complement boundary.
In a possible implementation manner of the first aspect, the feature objects having an occlusion relationship with the incomplete feature objects are all feature objects having a direct occlusion relationship;
when the feature object having a direct shielding relation with the incomplete feature object has the incomplete feature, the boundary is required to be completed.
In one possible implementation of the first aspect, constructing a spatial network grid using each set of feature objects at each position on the sequence of positions comprises:
Determining position coordinates of the feature objects in each group, wherein the position coordinates comprise direct position coordinates and reference position coordinates;
The spatial network grid is constructed using the location coordinates.
In a possible implementation manner of the first aspect, the position coordinates are any point on the feature object or a center of gravity of the feature object.
In a possible implementation manner of the first aspect, determining the reference position coordinates includes:
Determining a characteristic object with an occlusion relation with the characteristic object, wherein the occlusion relation comprises a direct occlusion relation and an indirect occlusion relation;
Determining coordinates of a feature object with a shielding relation;
Determining coordinates of a plurality of feature objects with shielding relations according to the coordinates of the feature objects to obtain a plurality of coordinates, wherein the number of the coordinates is equal to the number of the feature objects with shielding relations;
and carrying out mean value calculation on the coordinates and obtaining the reference position coordinates.
In a second aspect, the present application provides a water quality detection apparatus based on image processing, comprising:
An image acquisition unit for acquiring a plurality of groups of images at a plurality of positions in the test pool by using the image sensors, respectively, each group of images corresponding to one position on a position sequence, the position sequence referring to the plurality of positions arranged in sequence;
The computing unit is used for counting the number of the characteristic objects and the volume of the characteristic objects at each position according to the image, wherein the characteristic objects are alum flowers;
The grouping unit is used for grouping the characteristic objects according to the characteristic object volumes to obtain a plurality of groups of characteristic objects;
The construction unit is used for acquiring a plurality of groups of images at a plurality of positions in the test pool by using the image sensors respectively, wherein each group of images corresponds to one position on the position sequence, the image sensor is deployed at each position, and the plurality of positions are arranged according to the flowing direction of water in the test pool;
A calculation unit configured to calculate a growth rate of each spatial network grid, the growth rate including a spatial growth rate, which is a volume increase amount of the spatial network grid per unit time, and a grid increase density, which is a feature number increase amount of the spatial network grid per unit time; and
The adjusting unit is used for obtaining an adjusting result of the dosage of the flocculating agent according to the calculation result, wherein the adjusting result of the dosage of the flocculating agent comprises adjustment and non-adjustment;
the result unit is used for obtaining the total amount of the interceptor when the adjustment result of the dosage of the flocculating agent is not adjusted, the interceptor is alum, and the total amount of the interceptor is used as water quality data;
wherein the number of edges of each of the spatial network grids is the same.
In a third aspect, the present application provides a water quality detector based on image processing, the water quality detector comprising:
one or more memories for storing instructions; and
One or more processors configured to invoke and execute the instructions from the memory, to perform the method as described in the first aspect and any possible implementation of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium comprising:
A program which, when executed by a processor, performs a method as described in the first aspect and any possible implementation of the first aspect.
In a fifth aspect, the present application provides a computer program product comprising program instructions which, when executed by a computing device, perform a method as described in the first aspect and any possible implementation of the first aspect.
In a sixth aspect, the present application provides a chip system comprising a processor for implementing the functions involved in the above aspects, e.g. generating, receiving, transmitting, or processing data and/or information involved in the above methods.
The chip system can be composed of chips, and can also comprise chips and other discrete devices.
In one possible design, the system on a chip also includes memory to hold the necessary program instructions and data. The processor and the memory may be decoupled, provided on different devices, respectively, connected by wire or wirelessly, or the processor and the memory may be coupled on the same device.
The beneficial effects of the application are as follows:
according to the image processing-based water quality detection method and the image processing-based water quality detector, the consumption of the flocculating agent is adjusted by combining the alum blossom generated in the flocculation process in an automatic image processing mode, and then the heavy metal content in the sample is reversely estimated according to the total amount of the interceptor.
Drawings
FIG. 1 is a schematic diagram of a water quality detector according to the present application in operation.
FIG. 2 is a schematic block diagram of the flow of steps of a water quality detection method provided by the application.
Fig. 3 is a schematic diagram of a space growth rate provided by the present application.
Fig. 4 is a schematic diagram of a mesh increasing density provided by the present application.
Fig. 5 is a schematic diagram of the completion of incomplete feature objects according to the present application.
Detailed Description
In order to more clearly understand the technical scheme in the present application, the related art will be described first.
The alum blossom is flocculation impurity particles formed by combining a flocculant and impurities in sewage in the flocculation reaction process, and the particle size and compactness of the alum blossom can directly influence the sewage treatment effect, and the concrete expression is as follows:
The growth speed of alum blossom is too fast, the strength of the alum blossom can be weakened, the adsorption bridge can be sheared when strong shearing is encountered in the flowing process, the sheared adsorption bridge is difficult to be continuous, the specific surface area of the alum blossom in water can be drastically reduced when the growth speed of the alum blossom is too fast, some small particles with incomplete reactions lose reaction conditions, the collision probability of the small particles and large particles is drastically reduced, and the alum blossom is difficult to grow again. The alum blossom particles cannot grow too slowly, but are compact, but many particles do not grow to the interception size, and a large number of small particles appear.
The small particles are difficult to intercept, the obtained detection result is lower than the actual result, because in the filtering process of the alum blossom, a part of small particles can pass through the interception net, when the interception net with higher precision is used for intercepting the small particles, the blocking phenomenon of the interception net can be caused, the time for obtaining the result can be prolonged by dredging, and the structure of part of large particles can be damaged, so that the large particles are converted into the small particles.
Firstly, it should be noted that the water quality detection method based on image processing disclosed by the application needs to be implemented by means of a water quality detector based on image processing (hereinafter collectively referred to as a water quality detector), the water quality detector comprises a test pool, a sampler, a dosing device, an image sensor, an image processor and the like, the sampler pumps water samples from a detection position and then sends the water samples into the test pool, the dosing device quantitatively adds flocculating agents into the test pool, the image sensor acquires images in the test pool and then sends the images to the image processor for processing, as shown in fig. 1.
And after the image processor finishes processing, adjusting the adding amount of the flocculating agent according to the processing result, and obtaining the total amount of alum flowers in the test pool when the adding amount of the flocculating agent is adjusted to be proper.
The technical scheme in the application is further described in detail below with reference to the accompanying drawings.
The application discloses a water quality detection method based on image processing, in some examples, referring to FIG. 2, the water quality detection method based on image processing disclosed by the application comprises the following steps:
s101, acquiring a plurality of groups of images at a plurality of positions in a test pool by using image sensors, wherein each group of images corresponds to one position on a position sequence, each position is provided with the image sensor, and the plurality of positions are arranged according to the flowing direction of water in the test pool;
S102, counting the number of the feature objects and the feature object volume at each position according to the image;
s103, grouping the feature objects according to the feature object volumes to obtain a plurality of groups of feature objects;
S104, constructing a spatial network grid at each position on the position sequence by using each group of feature objects;
S105, calculating the growth speed of each space network grid, wherein the growth speed comprises the space growth speed and the grid increase density; and
S106, obtaining a flocculating agent dosage adjustment result according to the calculation result, wherein the flocculating agent dosage adjustment result comprises adjustment and non-adjustment;
s107, obtaining the total amount of interceptor when the adjustment result of the coagulant dosage is not adjustment;
wherein the number of edges of each of the spatial network grids is the same.
In step S101, a plurality of sets of images are acquired at a plurality of positions in the test pool by using image sensors, each set of images corresponds to a position on a sequence of positions, the plurality of positions are located in the test pool, the arrangement order (sequence) of the positions is arranged according to the flow direction of the water body in the test pool, each set of images is deployed at each position, and each set of images is acquired by using the image sensor. The sequence of positions herein refers to a plurality of positions arranged in order.
In step S102, the number of feature objects and the feature object volumes at each position are counted according to the image, wherein the feature objects refer to alum flowers (large particles and small particles), and then in step S103, the feature objects are grouped according to the feature object volumes to obtain a plurality of groups of feature objects.
The grouping of the feature objects is herein determined based on the surface area of the feature objects within the image sensor, one surface area range for each group of feature objects. Groups of feature objects may also be described as grouping feature objects by size.
In step S104, a spatial network mesh is constructed using each set of feature objects at each position on the sequence of positions, the number of edges of each mesh in the spatial network mesh being the same, the limitation being the connection relation determination and mesh shape determination of the feature objects in the assurance. A spatial network grid refers to feature objects in each group as points, which are then connected by line segments.
In S105, the growth rate of each spatial network grid is calculated, and the growth rate is divided into two aspects, namely, a spatial growth rate (shown in fig. 3) which is understood to be the volume increase amount of the spatial network grid in unit time and a grid increase density (shown in fig. 4) which is understood to be the feature quantity increase amount of the spatial network grid in unit time.
It will be appreciated that the corresponding groupings of feature objects change during the volume increase, i.e., the feature objects are transferred to another grouping after the volume increase.
However, as for the relation between the feature objects and the group, there are two cases, namely, the inside of the space network grid and the outside of the space network grid, and the two cases need to be considered, namely, in the application, the space growth speed is divided into the space growth speed and the grid increase density.
For the calculation of the growth speed of the space network grid, a specific calculation mode is to calculate the space growth speed and the grid increase density respectively, and then combine the two calculation results, because the space growth speed and the grid increase density can reflect the growth speed of the space network grid.
Finally, in step S106, a result of adjusting the dosage of the flocculating agent is obtained according to the calculation result, wherein the result of adjusting the dosage of the flocculating agent includes adjustment and non-adjustment, the adjustment means that the dosage of the flocculating agent is unsuitable, and the dosage is influenced by the fact that the dosage is too much or too little, and at the moment, the adjustment is required according to the calculation result.
And repeating the process again after the adjustment is finished until the adjustment result of the dosage of the flocculating agent is not adjusted. Corresponding to the water quality detector, the water sample in the test pool flows unidirectionally, and at least one of the steps S101 to S106 is completed in the process.
Finally, in step S107, when the adjustment result of the dosage of the flocculating agent is that the total amount of the interceptors is not adjusted, the sampler and the dosing device stop working at this time, the interceptor in the test pool is lifted up to intercept alum flowers in the water body, and the interceptors obtained by interception are weighed and calculated to obtain a weight, and according to the weight, the heavy metal content in the water body can be estimated. Or directly using the weight as an evaluation value.
The specific principle is that the volume of the test pool is fixed, the flow speed of the water body is regulated, and the water body volume fixation corresponding to each dosing process, namely the sample mass fixation, can be obtained. The water body has a certain depth, and the image sensor can only obtain images of a part of alum flowers in the water body, so that all the alum flowers in the water body need to be predicted through the space growth speed (the space growth speed and the grid increase density).
In some examples, counting the number of feature objects and the feature object volume at each location from the image also requires complementing incomplete feature objects according to occlusion relationships, the complementing purpose being to determine the size of the feature objects, since in the present application the feature objects need to be grouped according to their size.
The specific steps for complementing the incomplete feature objects are as follows:
S201, determining a feature object with a shielding relation with the incomplete feature object;
S202, determining a completion boundary according to the feature objects with shielding relation with the incomplete feature objects;
S203, determining two starting points of the incomplete feature object;
S204, sequentially constructing end-to-end unit vectors at the two starting points until the two groups of unit vectors are intersected;
wherein the unit vectors are all located inside the complement boundary.
In steps S201 to S204, referring to fig. 5, two starting points of the incomplete feature object are first determined, and then two sets of unit vectors are set with the two starting points as starting points, wherein the unit vectors in each set have equal lengths and are sequentially connected end to end. For the direction determination of the next unit vector, a random manner is used in the present application, but it is necessary to ensure that the unit vectors are all located inside the complement boundary.
In some examples, the feature objects having the occlusion relationship with the incomplete feature object are all feature objects having the direct occlusion relationship, so that the completion boundary mentioned in step S202 may be clearly limited, and the feature objects having the occlusion relationship with the incomplete feature object are limited to feature objects having the direct occlusion relationship in the present application because the completion boundary cannot be expanded without limitation.
The shielding relation is divided into a direct shielding relation and an indirect shielding relation, wherein the direct shielding relation refers to that part of A and part of B are overlapped, and the shielding relation of A and B is the direct shielding relation; the indirect occlusion relationship means that a part of A and a part of B are overlapped, and a part of B and a part of C are overlapped, wherein the occlusion relationship of A and C is the indirect occlusion relationship.
When the feature object with the direct shielding relation with the incomplete feature object has the incomplete feature, the completion boundary is needed, so that the purpose of further defining the completion boundary is achieved.
In some examples, constructing a spatial network grid using each set of feature objects at each location on the sequence of locations includes:
S301, determining position coordinates of the feature objects in each group, wherein the position coordinates comprise direct position coordinates and reference position coordinates;
s302, constructing a space network grid by using the position coordinates.
The direct position coordinates refer to position coordinates with definable depth, the depth determination mode of the feature object is mainly obtained by means of binocular vision calculation, and the depth can be calculated and cannot be calculated for binocular vision calculation, and when the depth cannot be calculated, the reference position coordinates are needed to be used as the position coordinates.
After the position coordinates of the feature objects in each group are obtained, the position coordinates are connected together by line segments to form a space network grid.
In some possible implementations, the location coordinates are any point on the feature object or the center of gravity of the feature object.
The specific steps for determining the reference position coordinates are as follows:
S401, determining a characteristic object with an occlusion relation with the characteristic object, wherein the occlusion relation comprises a direct occlusion relation and an indirect occlusion relation;
S402, determining coordinates of a feature object with an occlusion relationship;
S403, determining coordinates of the feature objects according to the coordinates of the feature objects with the shielding relation to obtain a plurality of coordinates, wherein the number of the coordinates is equal to the number of the feature objects with the shielding relation;
S404, performing mean calculation on the coordinate and obtaining a reference position coordinate.
In step S401, a feature object having an occlusion relationship with the feature object is first determined, where the occlusion relationship includes a direct occlusion relationship and an indirect occlusion relationship, and the direct occlusion relationship and the indirect occlusion relationship are described in the foregoing, which are not described herein.
Then, determining coordinates of the feature objects with shielding relation, determining the coordinates of the feature objects according to the obtained point coordinates, obtaining a plurality of coordinates at the moment, generating the coordinates according to the coordinates of different feature objects with shielding relation, and finally carrying out mean value calculation on the coordinates, namely respectively accumulating X coordinate values, Y coordinate values and Z coordinate values and then averaging to obtain reference position coordinates.
The application also provides a water quality detection device based on image processing, which comprises:
The image acquisition unit is used for acquiring a plurality of groups of images at a plurality of positions in the test pool by using the image sensors respectively, each group of images corresponds to one position on the position sequence, each position is provided with the image sensor, and the plurality of positions are arranged according to the flowing direction of the water body in the test pool;
The computing unit is used for counting the number of the characteristic objects and the volume of the characteristic objects at each position according to the image, wherein the characteristic objects are alum flowers;
The grouping unit is used for grouping the characteristic objects according to the characteristic object volumes to obtain a plurality of groups of characteristic objects;
A construction unit for constructing a spatial network grid using each set of feature objects at each position on the position sequence, the spatial network grid being constructed by using the feature objects in each set as points, and then connecting the points with line segments;
A calculation unit configured to calculate a growth rate of each spatial network grid, the growth rate including a spatial growth rate, which is a volume increase amount of the spatial network grid per unit time, and a grid increase density, which is a feature number increase amount of the spatial network grid per unit time; and
The adjusting unit is used for obtaining an adjusting result of the dosage of the flocculating agent according to the calculation result, wherein the adjusting result of the dosage of the flocculating agent comprises adjustment and non-adjustment;
the result unit is used for obtaining the total amount of the interceptor when the adjustment result of the dosage of the flocculating agent is not adjusted, the interceptor is alum, and the total amount of the interceptor is used as water quality data;
wherein the number of edges of each of the spatial network grids is the same.
Further, counting the number of feature objects and the feature object volume at each position according to the image also requires complementing the incomplete feature objects according to the occlusion relationship.
Further, the method further comprises the following steps:
A first determining unit, configured to determine a feature object having a shielding relationship with the incomplete feature object;
The boundary complementing unit is used for determining a complementing boundary according to the characteristic objects which have shielding relation with the incomplete characteristic objects;
a second determining unit for determining two starting points of the incomplete feature object;
The execution unit is used for sequentially constructing end-to-end unit vectors at the two starting points until the two groups of unit vectors are intersected;
wherein the unit vectors are all located inside the complement boundary.
Further, the feature objects with shielding relation with the incomplete feature objects are all feature objects with direct shielding relation;
when the feature object having a direct shielding relation with the incomplete feature object has the incomplete feature, the boundary is required to be completed.
Further, the method further comprises the following steps:
A third determining unit configured to determine position coordinates of the feature objects in each group, the position coordinates including direct position coordinates and reference position coordinates;
a construction unit for constructing a spatial network grid using the position coordinates.
Further, the position coordinates are any point on the feature object or the center of gravity of the feature object.
Further, the method further comprises the following steps:
a fourth determining unit, configured to determine a feature object having an occlusion relationship with the feature object, where the occlusion relationship includes a direct occlusion relationship and an indirect occlusion relationship;
a fifth determining unit for determining coordinates of the feature object having the occlusion relationship;
The first coordinate calculation unit is used for determining the coordinates of the feature objects according to the coordinates of the feature objects with the shielding relation to obtain a plurality of coordinates, wherein the number of the coordinates is equal to the number of the feature objects with the shielding relation;
And the second coordinate calculation unit is used for carrying out mean value calculation on the coordinates and obtaining the reference position coordinates.
In one example, the unit in any of the above apparatuses may be one or more integrated circuits configured to implement the above methods, for example: one or more application specific integrated circuits (application specific integratedcircuit, ASIC), or one or more digital signal processors (DIGITAL SIGNAL processor, DSP), or one or more field programmable gate arrays (field programmable GATE ARRAY, FPGA), or a combination of at least two of these integrated circuit forms.
For another example, when the units in the apparatus may be implemented in the form of a scheduler of processing elements, the processing elements may be general-purpose processors, such as a central processing unit (central processing unit, CPU) or other processor that may invoke a program. For another example, the units may be integrated together and implemented in the form of a system-on-a-chip (SOC).
Various objects such as various messages/information/devices/network elements/systems/devices/actions/operations/processes/concepts may be named in the present application, and it should be understood that these specific names do not constitute limitations on related objects, and that the named names may be changed according to the scenario, context, or usage habit, etc., and understanding of technical meaning of technical terms in the present application should be mainly determined from functions and technical effects that are embodied/performed in the technical solution.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software 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.
It should also be understood that in various embodiments of the present application, first, second, etc. are merely intended to represent that multiple objects are different. For example, the first time window and the second time window are only intended to represent different time windows. Without any effect on the time window itself, the first, second, etc. mentioned above should not impose any limitation on the embodiments of the present application.
It is also to be understood that in the various embodiments of the application, where no special description or logic conflict exists, the terms and/or descriptions between the various embodiments are consistent and may reference each other, and features of the various embodiments may be combined to form new embodiments in accordance with their inherent logic relationships.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a computer-readable storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned computer-readable storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The application also provides a water quality detector based on image processing, which comprises:
one or more memories for storing instructions; and
One or more processors configured to invoke and execute the instructions from the memory to perform the method as set forth above.
The present application also provides a computer program product comprising instructions which, when executed, cause the water quality detector to perform operations corresponding to the water quality detector of the above method.
The present application also provides a chip system comprising a processor for implementing the functions involved in the above, e.g. generating, receiving, transmitting, or processing data and/or information involved in the above method.
The chip system can be composed of chips, and can also comprise chips and other discrete devices.
The processor referred to in any of the foregoing may be a CPU, microprocessor, ASIC, or integrated circuit that performs one or more of the procedures for controlling the transmission of feedback information described above.
In one possible design, the system on a chip also includes memory to hold the necessary program instructions and data. The processor and the memory may be decoupled, and disposed on different devices, respectively, and connected by wired or wireless means, so as to support the chip system to implement the various functions in the foregoing embodiments. Or the processor and the memory may be coupled to the same device.
Optionally, the computer instructions are stored in a memory.
Alternatively, the memory may be a storage unit in the chip, such as a register, a cache, etc., and the memory may also be a storage unit in the terminal located outside the chip, such as a ROM or other type of static storage device, a RAM, etc., that may store static information and instructions.
It will be appreciated that the memory in the present application can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory.
The non-volatile memory may be a ROM, programmable ROM (PROM), erasable programmable ROM (erasable PROM, EPROM), electrically erasable programmable EPROM (EEPROM), or flash memory.
The volatile memory may be RAM, which acts as external cache. There are many different types of RAM, such as sram (STATIC RAM, SRAM), DRAM (DYNAMIC RAM, DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (double DATA RATE SDRAM, DDR SDRAM), enhanced SDRAM (ENHANCED SDRAM, ESDRAM), synchronous DRAM (SYNCH LINK DRAM, SLDRAM), and direct memory bus RAM.
The embodiments of the present application are all preferred embodiments of the present application, and are not intended to limit the scope of the present application in this way, therefore: all equivalent changes in structure, shape and principle of the application should be covered in the scope of protection of the application.
Claims (8)
1. The water quality detection method based on image processing is characterized by comprising the following steps:
Acquiring a plurality of groups of images at a plurality of positions in the test pool by using image sensors respectively, wherein each group of images corresponds to one position on the position sequence, each position is provided with the image sensor, and the plurality of positions are arranged according to the flowing direction of water in the test pool;
Counting the number of characteristic objects and the volume of the characteristic objects at each position according to the image, wherein the characteristic objects are alum flowers;
grouping the feature objects according to the feature object volumes to obtain a plurality of groups of feature objects;
Constructing a spatial network grid using each set of feature objects at each position on the sequence of positions, the spatial network grid being constructed by using the feature objects in each set as points, and then connecting the points with line segments;
Calculating the growth speed of each space network grid, wherein the growth speed comprises a space growth speed and grid increase density, the space growth speed is the volume increase of the space network grid in unit time, and the grid increase density is the characteristic quantity increase of the space network grid in unit time;
Obtaining a flocculating agent dosage adjustment result according to the calculation result, wherein the flocculating agent dosage adjustment result comprises adjustment and non-adjustment;
obtaining the total amount of interceptors when the adjustment result of the dosage of the flocculating agent is not adjustment, wherein the interceptors are alum flowers, and taking the total amount of interceptors as water quality data;
Wherein the number of edges of each grid in the spatial network grids is the same;
constructing a spatial network grid using each set of feature objects at each location on the sequence of locations comprises:
Determining position coordinates of the feature objects in each group, wherein the position coordinates comprise direct position coordinates and reference position coordinates;
Constructing a spatial network grid by using the position coordinates;
determining the reference position coordinates includes:
Determining a characteristic object with an occlusion relation with the characteristic object, wherein the occlusion relation comprises a direct occlusion relation and an indirect occlusion relation;
Determining coordinates of a feature object with a shielding relation;
Determining coordinates of a plurality of feature objects with shielding relations according to the coordinates of the feature objects to obtain a plurality of coordinates, wherein the number of the coordinates is equal to the number of the feature objects with shielding relations;
and carrying out mean value calculation on the coordinates and obtaining the reference position coordinates.
2. The image processing-based water quality detection method according to claim 1, wherein the number of feature objects and the feature object volume at each position are counted according to the image, and incomplete feature objects are complemented according to the occlusion relation.
3. The image processing-based water quality detection method according to claim 2, wherein the complementing of the incomplete feature object includes:
determining a characteristic object with a shielding relation with the incomplete characteristic object;
determining a completion boundary according to the feature objects with shielding relation with the incomplete feature objects;
determining two starting points of the incomplete feature object;
sequentially constructing unit vectors connected end to end at two starting points until the two groups of unit vectors are intersected;
wherein the unit vectors are all located inside the complement boundary.
4. The image processing-based water quality detection method according to claim 3, wherein the feature objects having a blocking relationship with respect to the incomplete feature object are all feature objects having a direct blocking relationship;
when the feature object having a direct shielding relation with the incomplete feature object has the incomplete feature, the boundary is required to be completed.
5. The image processing-based water quality detection method according to claim 1, wherein the position coordinates are any point on the feature object or a center of gravity of the feature object.
6. A water quality testing device based on image processing, characterized by comprising:
The image acquisition unit is used for acquiring a plurality of groups of images at a plurality of positions in the test pool by using the image sensors respectively, each group of images corresponds to one position on the position sequence, each position is provided with the image sensor, and the plurality of positions are arranged according to the flowing direction of the water body in the test pool;
The computing unit is used for counting the number of the characteristic objects and the volume of the characteristic objects at each position according to the image, wherein the characteristic objects are alum flowers;
The grouping unit is used for grouping the characteristic objects according to the characteristic object volumes to obtain a plurality of groups of characteristic objects;
A construction unit for constructing a spatial network grid using each set of feature objects at each position on the position sequence, the spatial network grid being constructed by using the feature objects in each set as points, and then connecting the points with line segments;
A calculation unit configured to calculate a growth rate of each spatial network grid, the growth rate including a spatial growth rate, which is a volume increase amount of the spatial network grid per unit time, and a grid increase density, which is a feature number increase amount of the spatial network grid per unit time; and
The adjusting unit is used for obtaining an adjusting result of the dosage of the flocculating agent according to the calculation result, wherein the adjusting result of the dosage of the flocculating agent comprises adjustment and non-adjustment;
the result unit is used for obtaining the total amount of the interceptor when the adjustment result of the dosage of the flocculating agent is not adjusted, the interceptor is alum, and the total amount of the interceptor is used as water quality data;
Wherein the number of edges of each grid in the spatial network grids is the same;
A third determining unit configured to determine position coordinates of the feature objects in each group, the position coordinates including direct position coordinates and reference position coordinates;
a construction unit for constructing a spatial network grid using the position coordinates;
a fourth determining unit, configured to determine a feature object having an occlusion relationship with the feature object, where the occlusion relationship includes a direct occlusion relationship and an indirect occlusion relationship;
a fifth determining unit for determining coordinates of the feature object having the occlusion relationship;
The first coordinate calculation unit is used for determining the coordinates of the feature objects according to the coordinates of the feature objects with the shielding relation to obtain a plurality of coordinates, wherein the number of the coordinates is equal to the number of the feature objects with the shielding relation;
And the second coordinate calculation unit is used for carrying out mean value calculation on the coordinates and obtaining the reference position coordinates.
7. A water quality detector based on image processing, the water quality detector comprising:
one or more memories for storing instructions; and
One or more processors to invoke and execute the instructions from the memory to perform the method of any of claims 1 to 5.
8. A computer-readable storage medium, the computer-readable storage medium comprising:
program which, when executed by a processor, performs the method according to any one of claims 1 to 5.
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