Sewage treatment quality analysis method and system based on image data

Abstract

The invention discloses a sewage treatment quality analysis method and system based on image data. The method comprises the following steps: judging whether a first target sewage surface image of other water quality data is a first mutation sewage surface image or not by adopting a preset image analysis strategy; if the image is a first mutation sewage surface image, determining a first sewage treatment execution scheme of a first sewage pool with other water quality data according to a water quality analysis result corresponding to the second water quality data with other water quality data; acquiring other initial sewage surface images of other sewage pools at the initial moment, and correcting other water quality data at the first moment by adopting a preset time correction strategy to obtain other sudden change moments; and determining other sewage treatment execution schemes of other sewage pools according to the other water quality data according to the water quality analysis results corresponding to the other water quality data. On the premise of ensuring that the water quality reaches the standard, the treatment period is shortened to the maximum extent, and the detection cost is reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of sewage treatment, and particularly relates to a sewage treatment quality analysis method and system based on image data. BACKGROUND

[0002] In the existing sewage treatment process, accurately determining the treatment endpoint (i.e., the water quality has stabilized to reach the predetermined standard) is a key link to achieve energy saving and consumption reduction and improve efficiency. At present, the industry generally relies on periodic sampling and analysis of water quality parameters (such as chemical oxygen demand COD, ammonia nitrogen, turbidity, etc.). The limitations of this approach are obvious: the detection results are lagging, and real-time feedback cannot be provided for process control. More importantly, in order to ensure that the water quality is stable and not accidentally up to standard, the system usually needs to continue running and wait for subsequent consecutive multiple period data to be confirmed stable after detecting the first up-to-standard data before stopping treatment. This conservative strategy, although ensuring reliability, easily leads to "over-treatment", that is, after the water quality has reached the standard, the aeration, stirring, and dosing units are still working invalidly, causing energy and material waste. SUMMARY

[0003] The application provides a sewage treatment quality analysis method and system based on image data, which is used to solve the technical problem that after the water quality has reached the standard, the aeration, stirring, and dosing units are still working invalidly, causing energy and material waste.

[0004] In a first aspect, the application provides a sewage treatment quality analysis method based on image data, comprising: obtaining a first initial sewage surface image of a first sewage pool at an initial time and a first target sewage surface image at a first time, wherein the first sewage pool is the sewage pool with the highest water quality in a set of sewage pools at the initial time, the initial time is any time before sewage treatment, and the first time is any time after the initial time; determining a first target image similarity between the first target sewage surface image and the first initial sewage surface image based on a preset image recognition model, and determining whether the first target image similarity is greater than a first preset threshold; If the first target image similarity is not greater than the first preset threshold, a second target sewage surface image at a second time and each first target historical sewage surface image in a first historical time period are obtained, and whether the first target sewage surface image is a first sudden change sewage surface image is determined according to the second target sewage surface image and the each first target historical sewage surface image by using a preset image analysis strategy, wherein the second time is the next collection time adjacent to the first time, and the first historical time period is a time period before the first time. if the first target sewage surface image is a first mutation sewage surface image, obtaining second water quality data of the first sewage pool at the second time, and determining a first sewage treatment execution scheme of the first sewage pool according to a water quality analysis result corresponding to the second water quality data; obtaining other initial sewage surface images of other sewage pools at an initial time, and correcting the first time by using a preset time correction strategy according to an initial similarity between the other initial sewage surface images and the first initial sewage surface image, to obtain other mutation times; obtaining other water quality data at the other mutation times, and determining other sewage treatment execution schemes of the other sewage pools according to water quality analysis results corresponding to the other water quality data.

[0005] In a second aspect, the present application provides a sewage treatment quality analysis system based on image data, comprising: an obtaining module configured to obtain a first initial sewage surface image of a first sewage pool at an initial time, and a first target sewage surface image at a first time, wherein the first sewage pool is a sewage pool with the highest water quality at the initial time in a set of sewage pools, the initial time is any time before sewage treatment, and the first time is any time after the initial time; a first determining module configured to determine a first target image similarity between the first target sewage surface image and the first initial sewage surface image based on a preset image recognition model, and determine whether the first target image similarity is greater than a first preset threshold; a second determining module configured to, if the first target image similarity is not greater than the first preset threshold, obtain a second target sewage surface image at a second time and each first target historical sewage surface image in a first historical time period, and determine whether the first target sewage surface image is a first mutation sewage surface image by using a preset image analysis strategy according to the second target sewage surface image and the each first target historical sewage surface image, wherein the second time is a next collection time adjacent to the first time, and the first historical time period is a time period before the first time; a first determining module configured to, if the first target sewage surface image is a first mutation sewage surface image, obtain second water quality data of the first sewage pool at the second time, and determine a first sewage treatment execution scheme of the first sewage pool according to a water quality analysis result corresponding to the second water quality data; The correction module is configured to acquire an initial other sewage surface image of the other sewage pool at an initial time point, and correct the first time point by using a preset time correction strategy according to an initial similarity between the initial other sewage surface image and the first initial sewage surface image, to obtain an other mutation time point. The second determination module is configured to acquire other water quality data at the other mutation time point, and determine an other sewage treatment execution scheme of the other sewage pool according to a water quality analysis result corresponding to the other water quality data.

[0006] In a third aspect, an electronic device is provided, which includes at least one processor and a memory connected with the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the image data-based sewage treatment quality analysis method of any of the embodiments.

[0007] In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, and the program instructions are executed by a processor to enable the processor to perform the steps of the image data-based sewage treatment quality analysis method of any of the embodiments.

[0008] The image data-based sewage treatment quality analysis method and system can determine whether the first target image similarity is greater than a first preset threshold by combining the image recognition model, and if the first target image similarity is not greater than the first preset threshold, the second target sewage surface image at the second time point and each first target historical sewage surface image in the first historical time period are acquired, and the first target sewage surface image is determined to be a first mutation sewage surface image by using a preset image analysis strategy according to the second target sewage surface image and the each first target historical sewage surface image. The water quality trend in the sewage treatment process can be accurately captured, the first time point is corrected by using a preset time correction strategy according to the initial similarity between the initial other sewage surface image and the first initial sewage surface image, to obtain the other mutation time point, the mutation time point of the other sewage pool can be determined as quickly as possible on the premise of reducing the image data processing amount, finally, only the single-point water quality data is acquired after the mutation time point of each pool is predicted for verification analysis, and an execution scheme of shutdown or continuous treatment is formulated accordingly, the processing period is maximally shortened and the detection cost is reduced on the premise of ensuring that the water quality meets the standard. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed in the embodiment description. Obviously, the drawings described below are some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0010] Figure 1 A flow chart of a sewage treatment quality analysis method based on image data provided by an embodiment of the present application is shown in Figure 2 A structural block diagram of a sewage treatment quality analysis system based on image data provided by an embodiment of the present application is shown in Figure 3 A structural diagram of an electronic device provided by an embodiment of the present application is shown in DETAILED DESCRIPTION

[0011] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0012] Please refer to Figure 1 , which shows a flow chart of a sewage treatment quality analysis method based on image data.

[0013] As shown in Figure 1 , the sewage treatment quality analysis method based on image data specifically includes the following steps: Step S101, obtaining a first initial sewage surface image of a first sewage pool at an initial time and a first target sewage surface image at a first time, wherein the first sewage pool is a sewage pool with the highest water quality in a sewage pool set at the initial time, the initial time is any time before sewage treatment, and the first time is any time after the initial time.

[0014] In this step, sewage from the same source is stored in multiple sewage pools to obtain a sewage pool set, and the same sewage treatment scheme is used for each sewage pool in the same sewage pool set. At this time, water quality data of each sewage pool at any time before sewage treatment is obtained, and the first sewage pool with the highest water quality is determined according to the analysis result. The high-definition industrial camera deployed at the fixed point directly above the first sewage pool is used to obtain the first initial sewage surface image of the first sewage pool at the initial time and the first target sewage surface image at the first time.

[0015] In step S102, a first target image similarity between the first target sewage surface image and the first initial sewage surface image is determined based on a preset image recognition model, and it is determined whether the first target image similarity is greater than a first preset threshold.

[0016] In this step, the image recognition model is obtained by iteratively training the deep convolutional neural network. The first target sewage surface image and the first initial sewage surface image will sequentially pass through the convolutional layer, the pooling layer, the activation function, etc. of the image recognition model, and finally a fixed-length, high-dimensional feature vector will be obtained before the last fully connected layer or after the global average pooling layer.

[0017] The cosine similarity between the feature vector in the first target sewage surface image and the feature vector in the first initial sewage surface image is calculated, and the first target image similarity between the first target sewage surface image and the first initial sewage surface image is obtained.

[0018] In one specific embodiment, after it is determined whether the first target image similarity is greater than the first preset threshold, if the first target image similarity is greater than the first preset threshold, a second target image similarity between a second target sewage surface image and the first initial sewage surface image is determined based on the image recognition model, and it is continuously determined whether the second target image similarity is greater than the first preset threshold.

[0019] In step S103, if the first target image similarity is not greater than the first preset threshold, a second target sewage surface image at a second time and each first target historical sewage surface image in a first historical time period are obtained, and it is determined whether the first target sewage surface image is a first mutation sewage surface image based on the second target sewage surface image and the each first target historical sewage surface image using a preset image analysis strategy, wherein the second time is a next collection time adjacent to the first time, and the first historical time period is a time period before the first time.

[0020] In the step, the second target sewage surface image and the first target sewage surface image are input into the image recognition model, the image recognition model outputs the image similarity, and it is judged whether the target image similarity is greater than the second preset threshold; if it is not greater than the second preset threshold, the first target sewage surface image is not directly defined as the first mutation sewage surface image; if it is greater than the second preset threshold, the historical image change rate between each first target historical sewage surface image and the first target sewage surface image is determined, and the average of each historical image change rate is taken to obtain the target historical image change rate, wherein the historical image change rate is the ratio of a historical similarity and a historical time interval, the historical similarity is the similarity between a first target historical sewage surface image and the first target sewage surface image, and the historical time interval is the time interval between the historical collection time corresponding to the first target historical sewage surface image and the first time; it is judged whether the target historical image change rate is greater than the preset change rate threshold; if the sequence change rate of the first target historical sewage surface image sequence is not greater than the preset change rate threshold, the first target sewage surface image is defined as the first mutation sewage surface image; if the sequence change rate of the first target historical sewage surface image sequence is greater than the preset change rate threshold, the first target sewage surface image is not defined as the first mutation sewage surface image.

[0021] In the embodiment, when the target image similarity between the second target sewage surface image and the first target sewage surface image is not greater than the second preset threshold, it is indicated that the second target sewage surface image has changed compared with the first target sewage surface image, at this time, the sewage treatment has not tended to be stable, therefore, the first time is not the critical time when the sewage treatment tends to be stable. When the target image similarity between the second target sewage surface image and the first target sewage surface image is greater than the second preset threshold, it is possible that the sewage treatment tends to be stable at this time, or it is possible that a temporary abnormality occurs in the second target sewage surface image, the reason for the temporary abnormality is that the equipment fails or the state is locked (the network transmission is interrupted or unstable, causing the receiving end to repeatedly display the last frame of picture before the flow is cut off) or the environmental condition enters a stable state (external environmental factors reach and maintain a constant state, causing the water surface visual performance to stagnate, but the biochemical reaction is not completed). Therefore, whether the target historical image change rate is greater than the preset change rate threshold is judged to effectively distinguish the normal situation and the temporary abnormal situation.

[0022] In one specific embodiment, after determining whether the first target sewage surface image is the first mutation sewage surface image by using the preset image analysis strategy, if the first target sewage surface image is not the first mutation sewage surface image, it is determined whether the second target image similarity is greater than the first preset threshold; if the second target image similarity is not greater than the first preset threshold, a third target sewage surface image at a third time and each second target historical sewage surface image in a second historical time period are obtained, and whether the second target sewage surface image is the first mutation sewage surface image is determined by using the preset image analysis strategy according to the third target sewage surface image and each second target historical sewage surface image, wherein the third time is a next collection time adjacent to the second time, and the second historical time period is a time period before the second time.

[0023] In step S104, if the first target sewage surface image is the first mutation sewage surface image, the second water quality data of the first sewage pool at the second time is obtained, and the first sewage treatment execution scheme of the first sewage pool is determined according to the water quality analysis result corresponding to the second water quality data.

[0024] In this step, the second time is the next collection time of the first time. Through the steps of steps S101-S103, the critical time when the sewage water quality tends to be stable, i.e. the first time, can be determined more accurately. At this time, the stable water quality data at the second time is directly obtained, and the stable water quality data is analyzed. When the stable water quality data meets the water quality requirement, the sewage treatment of the first sewage pool is directly stopped. If it does not meet the water quality requirement, the sewage treatment of the first sewage pool is continued. Compared with the prior art, which needs to analyze the water quality data at each collection time and terminate the sewage treatment after obtaining continuous multiple stable water quality data, the method can reduce frequent and unnecessary water quality detection, and as much as possible avoid the situation that sewage treatment is still performed when continuous multiple stable water quality data is obtained, thereby saving sewage treatment cost as much as possible.

[0025] In step S105, other initial sewage surface images of other sewage pools at the initial time are obtained, and the initial similarity between the other initial sewage surface images and the first initial sewage surface image is used to correct the first time by using a preset time correction strategy to obtain other mutation times.

[0026] In this step, the second mutation sewage surface image corresponding to the second sewage pool is obtained, and the time interval between the first time and the second mutation time is determined, wherein the second mutation time is the collection time of the second mutation sewage surface image, and the second sewage pool is any sewage pool in a sewage pool set except the first sewage pool. obtaining a second initial similarity between the second initial sewage surface image and the first initial sewage surface image, and obtaining a second initial deviation degree by subtracting a preset similarity from the second initial similarity, wherein the preset similarity is 1, and the second initial sewage surface image is an initial sewage surface image of the second sewage pool at the initial time; defining a target correction coefficient as a ratio between the time interval and the second initial deviation degree; obtaining a third initial similarity between a third initial sewage surface image of the third sewage pool at the initial time and the first initial sewage surface image, and obtaining a third initial deviation degree by subtracting a preset similarity from the third initial similarity, wherein the third sewage pool is any sewage pool in a certain sewage pool set except the first sewage pool and the second sewage pool; multiplying the third initial deviation degree by the target correction coefficient to obtain a corrected time interval, and adding the corrected time interval to the first time to obtain a third mutation time.

[0027] In the embodiment, the identified first sewage pool mutation time (the first time) is used as a reference, the image similarity difference between other sewage pools and the first sewage pool in the initial state (the initial time) is quantified, and the mutation times of the other sewage pools are dynamically predicted and calculated, which can avoid the repeated execution of high-frequency image acquisition and calculation analysis for each sewage pool as much as possible, and effectively reduce the processing amount of image data.

[0028] In step S106, other water quality data at the other mutation times are obtained, and other sewage treatment execution schemes of the other sewage pools are determined according to water quality analysis results corresponding to the other water quality data.

[0029] In this step, the water quality analysis result can be obtained by inputting the water quality data into a pre-constructed water quality recognition model. The water quality recognition model is obtained by training a neural network model in the prior art.

[0030] In summary, the method of the present application first uses the image acquisition device deployed in the initial water quality optimal pool (the first sewage pool) to determine whether the first target image similarity is greater than the first preset threshold value in combination with the image recognition model. If the first target image similarity is not greater than the first preset threshold value, the second target sewage surface image at the second time and each first target historical sewage surface image in the first historical time period are obtained, and whether the first target sewage surface image is a first mutation sewage surface image is determined according to the second target sewage surface image and each first target historical sewage surface image by using a preset image analysis strategy. The water quality tends to be stable in the sewage treatment process can be accurately captured, and the first time is corrected by using a preset time correction strategy according to the initial similarity between the other initial sewage surface images and the first initial sewage surface image to obtain other mutation times. The mutation time of the other sewage pool can be determined as quickly as possible on the premise of reducing the image data processing amount. Finally, only the single-point water quality data after the mutation time predicted by each pool is obtained for verification analysis, and an execution scheme of shutdown or continuous treatment is formulated accordingly. On the premise of ensuring that the water quality meets the standard, the processing period is maximally shortened, and the detection cost is reduced.

[0031] Referring to Figure 2 , a structure block diagram of a sewage treatment quality analysis system based on image data is shown.

[0032] As Figure 2 shown, the sewage treatment quality analysis system 200 comprises an acquisition module 210, a first determination module 220, a second determination module 230, a first determination module 240, a correction module 250, and a second determination module 260.

[0033] The acquisition module 210 is configured to acquire a first initial sewage surface image of a first sewage pool at an initial time and a first target sewage surface image at a first time, the first sewage pool is a sewage pool with the highest water quality in a sewage pool set at the initial time, the initial time is any time before sewage treatment, and the first time is any time after the initial time; the first determination module 220 is configured to determine a first target image similarity between the first target sewage surface image and the first initial sewage surface image based on a preset image recognition model, and determine whether the first target image similarity is greater than a first preset threshold; the second determination module 230 is configured to, if the first target image similarity is not greater than the first preset threshold, acquire a second target sewage surface image at a second time and each first target historical sewage surface image in a first historical time period, and determine whether the first target sewage surface image is a first mutation sewage surface image by using a preset image analysis strategy according to the second target sewage surface image and the each first target historical sewage surface image, the second time is a next collection time adjacent to the first time, and the first historical time period is a time period before the first time; the first determination module 240 is configured to, if the first target sewage surface image is the first mutation sewage surface image, acquire second water quality data of the first sewage pool at the second time, and determine a first sewage treatment execution scheme of the first sewage pool according to a water quality analysis result corresponding to the second water quality data; the correction module 250 is configured to acquire other initial sewage surface images of other sewage pools at the initial time, and correct the first time to obtain other mutation times by using a preset time correction strategy according to an initial similarity between the other initial sewage surface images and the first initial sewage surface image; and the second determination module 260 is configured to acquire other water quality data at the other mutation times, and determine other sewage treatment execution schemes of the other sewage pools according to water quality analysis results corresponding to the other water quality data.

[0034] It should be understood that Figure 2 the modules described in the above Figure 1 correspond to the respective steps in the methods described in the above Figure 2 The operations and features described above for the methods also apply to the modules in the

[0035] In some other embodiments, the present application also provides a computer readable storage medium having stored thereon a computer program, the program instructing a processor to execute the image data based sewage treatment quality analysis method in any of the above method embodiments when the program is executed by the processor. As an implementation form, the computer readable storage medium of the present application stores computer executable instructions, which are configured to: acquire a first initial sewage surface image of a first sewage pool at an initial time and a first target sewage surface image at a first time, wherein the first sewage pool is a sewage pool with the highest water quality in a sewage pool set at the initial time, the initial time is any time before sewage treatment, and the first time is any time after the initial time; determine a first target image similarity between the first target sewage surface image and the first initial sewage surface image based on a preset image recognition model, and determine whether the first target image similarity is greater than a first preset threshold; If the first target image similarity is not greater than the first preset threshold, a second target sewage surface image at a second time and each first target historical sewage surface image in a first historical time period are acquired, and whether the first target sewage surface image is a first mutation sewage surface image is determined based on the second target sewage surface image and the each first target historical sewage surface image using a preset image analysis strategy, wherein the second time is a next collection time adjacent to the first time, and the first historical time period is a time period before the first time; If the first target sewage surface image is the first mutation sewage surface image, second water quality data of the first sewage pool at the second time is acquired, and a first sewage treatment execution scheme of the first sewage pool is determined based on a water quality analysis result corresponding to the second water quality data; acquire other initial sewage surface images of other sewage pools at the initial time, and correct the first time to obtain other mutation times using a preset time correction strategy based on an initial similarity between the other initial sewage surface images and the first initial sewage surface image; acquire other water quality data at the other mutation times, and determine other sewage treatment execution schemes of the other sewage pools based on water quality analysis results corresponding to the other water quality data.

[0036] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the image data-based wastewater treatment quality analysis system, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely configured relative to a processor, which can be connected to the image data-based wastewater treatment quality analysis system via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0037] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the wastewater treatment quality analysis method based on image data described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the image data-based wastewater treatment quality analysis system. The output device 340 may include a display screen or other display device.

[0038] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0039] In one implementation, the above-described electronic device is applied in a wastewater treatment quality analysis system based on image data, for a client, and includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: acquire a first initial sewage surface image of a first sewage pool at an initial time, and a first target sewage surface image at a first time, wherein the first sewage pool is a sewage pool with the highest water quality in a set of sewage pools at the initial time, the initial time is any time before sewage treatment, and the first time is any time after the initial time; determine a first target image similarity between the first target sewage surface image and the first initial sewage surface image based on a preset image recognition model, and determine whether the first target image similarity is greater than a first preset threshold; If the first target image similarity is not greater than the first preset threshold, acquire a second target sewage surface image at a second time and each first target historical sewage surface image in a first historical time period, and determine whether the first target sewage surface image is a first mutation sewage surface image based on the second target sewage surface image and the each first target historical sewage surface image using a preset image analysis strategy, wherein the second time is a next acquisition time adjacent to the first time, and the first historical time period is a time period before the first time. If the first target sewage surface image is the first mutation sewage surface image, acquire second water quality data of the first sewage pool at the second time, and determine a first sewage treatment execution scheme of the first sewage pool based on a water quality analysis result corresponding to the second water quality data. acquire other initial sewage surface images of other sewage pools at the initial time, and correct the first time to obtain other mutation times using a preset time correction strategy based on an initial similarity between the other initial sewage surface images and the first initial sewage surface image. acquire other water quality data at the other mutation times, and determine other sewage treatment execution schemes of the other sewage pools based on water quality analysis results corresponding to the other water quality data.

[0040] From the above description of the embodiments, those skilled in the art can clearly understand that the embodiments can be implemented by means of software plus necessary universal hardware platforms, and of course, can also be implemented by hardware. Based on such understanding, the above technical solutions essentially or in other words, the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a plurality of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of the embodiments or some parts of the embodiments.

[0041] It should be pointed out finally that the above embodiments are only used to illustrate the technical solutions of the present application, but not to limit the same; and although the present application has been described in detail with reference to the foregoing embodiments, it should be appreciated by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features thereof can be replaced equivalently; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method of wastewater treatment quality analysis based on image data, characterized by, The method comprises the following steps: acquiring a first initial sewage surface image of a first sewage pool at an initial time and a first target sewage surface image at a first time, wherein the first sewage pool is a sewage pool with the highest water quality in a set of sewage pools at the initial time, the initial time is any time before sewage treatment, and the first time is any time after the initial time; determining a first target image similarity between the first target sewage surface image and the first initial sewage surface image based on a preset image recognition model, and determining whether the first target image similarity is greater than a first preset threshold; if the first target image similarity is not greater than the first preset threshold, acquiring a second target sewage surface image at a second time and each first target historical sewage surface image in a first historical time period, and determining whether the first target sewage surface image is a first mutation sewage surface image based on the second target sewage surface image and the each first target historical sewage surface image using a preset image analysis strategy, wherein the second time is a next collection time adjacent to the first time, and the first historical time period is a time period before the first time; if the first target sewage surface image is the first mutation sewage surface image, acquiring second water quality data of the first sewage pool at the second time, and determining a first sewage treatment execution scheme of the first sewage pool based on a water quality analysis result corresponding to the second water quality data; acquiring other initial sewage surface images of other sewage pools at the initial time, and correcting the first time to obtain other mutation times using a preset time correction strategy based on an initial similarity between the other initial sewage surface images and the first initial sewage surface image; acquiring other water quality data at the other mutation times, and determining other sewage treatment execution schemes of the other sewage pools based on water quality analysis results corresponding to the other water quality data.

2. The method of claim 1, wherein the method is characterized by, After determining whether the first target image similarity is greater than the first preset threshold, the method further comprises: if the first target image similarity is greater than the first preset threshold, determining a second target image similarity between the second target sewage surface image and the first initial sewage surface image based on the image recognition model, and continuing to determine whether the second target image similarity is greater than the first preset threshold.

3. The method of claim 1, wherein the method is characterized by: The determination of whether the first target sewage surface image is a first mutation sewage surface image based on the second target sewage surface image and the each first target historical sewage surface image using a preset image analysis strategy comprises: inputting the second target sewage surface image and the first target sewage surface image into the image recognition model, and determining whether the target image similarity is greater than a second preset threshold based on an output image similarity of the image recognition model; if greater than the second preset threshold, determining a historical image change rate between each first target historical sewage surface image and the first target sewage surface image, and taking an average of each historical image change rate to obtain a target historical image change rate, wherein a certain historical image change rate is a ratio of a certain historical similarity and a certain historical time interval, the certain historical similarity is a similarity between a certain first target historical sewage surface image and the first target sewage surface image, and the certain historical time interval is a time interval between a historical collection time corresponding to the certain first target historical sewage surface image and the first time; determining whether the target historical image change rate is greater than a preset change rate threshold; if the sequence change rate of the first target historical sewage surface image sequence is not greater than the preset change rate threshold, defining the first target sewage surface image as a first mutation sewage surface image; if the sequence change rate of the first target historical sewage surface image sequence is greater than the preset change rate threshold, not defining the first target sewage surface image as a first mutation sewage surface image.

4. The method of claim 3, wherein the method further comprises: After determining whether the target image similarity is greater than the second preset threshold, the method further comprises: if not greater than the second preset threshold, directly not defining the first target sewage surface image as a first mutation sewage surface image.

5. The method of claim 1, wherein the method is characterized by: After the step of determining whether the first target sewage surface image is a first mutation sewage surface image by using the preset image analysis strategy, the method further comprises: if the first target sewage surface image is not a first mutation sewage surface image, determining whether the second target image similarity is greater than a first preset threshold; if the second target image similarity is not greater than the first preset threshold, obtaining a third target sewage surface image at a third time and each second target historical sewage surface image in a second historical time period, and determining whether the second target sewage surface image is a first mutation sewage surface image by using the preset image analysis strategy according to the third target sewage surface image and the each second target historical sewage surface image, wherein the third time is a next collection time adjacent to the second time, and the second historical time period is a time period before the second time.

6. The method of claim 1, wherein the method further comprises: the step of obtaining other initial sewage surface images of other sewage pools at an initial time, and correcting the first time to obtain other mutation times by using a preset time correction strategy according to an initial similarity between the other initial sewage surface images and the first initial sewage surface image comprises: obtaining a second mutation sewage surface image corresponding to a second sewage pool, and determining a time interval between the first time and a second mutation time, wherein the second mutation time is a collection time of the second mutation sewage surface image, and the second sewage pool is any sewage pool in the certain sewage pool set except the first sewage pool; acquire a second initial similarity between a second initial sewage surface image and the first initial sewage surface image, and subtract a preset similarity from the second initial similarity to obtain a second initial deviation degree, wherein the preset similarity has a value of 1, and the second initial sewage surface image is an initial sewage surface image of a second sewage pool at an initial time; define a ratio between the time interval and the second initial deviation degree as a target correction coefficient; acquire a third initial similarity between a third initial sewage surface image of a third sewage pool at the initial time and the first initial sewage surface image, and subtract the preset similarity from the third initial similarity to obtain a third initial deviation degree, wherein the third sewage pool is any sewage pool in the certain sewage pool set except the first sewage pool and the second sewage pool; multiply the third initial deviation degree by the target correction coefficient to obtain a corrected time interval, and add the corrected time interval to the first time to obtain a third mutation time.

7. An image data-based sewage treatment quality analysis system characterized by comprising: comprise: an acquisition module configured to acquire a first initial sewage surface image of a first sewage pool at an initial time, and a first target sewage surface image at a first time, wherein the first sewage pool is a sewage pool with the highest water quality in a certain sewage pool set at the initial time, the initial time is any time before sewage treatment, and the first time is any time after the initial time; a first judgment module configured to determine a first target image similarity between the first target sewage surface image and the first initial sewage surface image based on a preset image recognition model, and judge whether the first target image similarity is greater than a first preset threshold; a second judgment module configured to, if the first target image similarity is not greater than the first preset threshold, acquire a second target sewage surface image at a second time and each first target historical sewage surface image in a first historical time period, and determine whether the first target sewage surface image is a first mutation sewage surface image according to the second target sewage surface image and the each first target historical sewage surface image using a preset image analysis strategy, wherein the second time is a next collection time adjacent to the first time, and the first historical time period is a time period before the first time; a first determination module configured to, if the first target sewage surface image is the first mutation sewage surface image, acquire second water quality data of the first sewage pool at the second time, and determine a first sewage treatment execution scheme of the first sewage pool according to a water quality analysis result corresponding to the second water quality data; a correction module configured to acquire other initial sewage surface images of other sewage pools at the initial time, and correct the first time to obtain other mutation times using a preset time correction strategy according to an initial similarity between the other initial sewage surface images and the first initial sewage surface image. The second determining module is configured to acquire other water quality data at other mutation time points, and determine other sewage treatment execution schemes of the other sewage pool according to water quality analysis results corresponding to the other water quality data.

8. An electronic device, comprising: The method comprises the following steps: At least one processor and a memory connected in communication with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1 to 6.

9. A computer readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the method of any one of claims 1 to 6.

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