A water source detection method and device based on filamentous bacteria state identification

Abstract

Embodiments of the present application provide a water source detection method and device based on filamentous bacteria state identification, a storage medium and an electronic device, relating to the technical field of sewage detection technology. The method comprises: acquiring fluid detection data of a target water source; determining biological state information of filamentous bacteria of the target water source based on the environmental data; and determining state information of the target water source based on the biological state information and the ultrasonic echo data. Through the present application, the problem of low sewage detection efficiency is solved, thereby achieving the effect of improving sewage detection efficiency and precision.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the field of water source detection, and in particular, to a water source detection method and device based on filamentous bacteria state recognition. BACKGROUND

[0002] In a conventional sewage plant, sewage data is generally detected by an online instrument installed on site, and the operation of the biochemical system is judged in combination with the experience of process technicians; this method cannot accurately diagnose the cause of system collapse when the system is subjected to hydraulic impact load and organic impact load, and can only roughly judge the operation of the system through the running parameters of the online data and the apparent characteristics of the system, thereby causing the system to be in an unstable state continuously, and in severe cases, the effluent may not meet the standards. SUMMARY

[0003] Embodiments of the present application provide a water source detection method and device based on filamentous bacteria state recognition, to at least solve the problem of low sewage state detection efficiency in related technologies.

[0004] According to an embodiment of the present application, a water source detection method based on filamentous bacteria state recognition is provided, comprising:

[0005] Obtaining fluid detection data of a target water source, wherein the fluid detection data includes ultrasonic echo data obtained by ultrasonic detection of the target water source by an ultrasonic detection device and environmental data obtained by state detection of activated sludge of the target water source by an environmental detection device;

[0006] Determining biological state information of filamentous bacteria of the target water source based on the environmental data;

[0007] Determining state information of the target water source based on the biological state information and the ultrasonic echo data.

[0008] In one exemplary embodiment, the determining of the biological state information of the filamentous bacteria of the target water source based on the environmental data comprises:

[0009] Determining morphological change data of the activated sludge in a first time period based on the environmental data;

[0010] Determining the biological state information of the filamentous bacteria based on the morphological change data.

[0011] In one exemplary embodiment, the determining of the biological state information of the filamentous bacteria based on the morphological change data comprises:

[0012] Determining light intensity distribution change data of the activated sludge according to the morphological change data;

[0013] perform a first matching between the light intensity distribution change data and preset target light intensity distribution change data;

[0014] determine the biological state information based on a first matching result.

[0015] In one example embodiment, the determining the biological state information of the filamentous bacteria based on the morphology change data comprises:

[0016] determining, according to the morphology change data, reflective light energy distribution change data of the activated sludge;

[0017] performing a second matching between the reflective light energy distribution change data and preset target reflective light energy distribution change data;

[0018] determining the biological state information based on a second matching result.

[0019] According to another embodiment of the present application, a water source detection device based on filamentous bacteria state recognition is provided, comprising:

[0020] a data acquisition module configured to acquire fluid detection data of a target water source, wherein the fluid detection data comprises ultrasonic echo data obtained by performing ultrasonic detection on the target water source by an ultrasonic detection device and environment data obtained by performing state detection on activated sludge of the target water source by an environment detection device;

[0021] a biological state determination module configured to determine biological state information of filamentous bacteria of the target water source based on the environment data;

[0022] a water source state determination module configured to determine state information of the target water source based on the biological state information and the ultrasonic echo data.

[0023] According to still another embodiment of the present application, a computer readable storage medium is also provided, wherein the computer readable storage medium stores a computer program, and the computer program is configured to perform the steps in any of the above method embodiments when executed.

[0024] According to still another embodiment of the present application, an electronic device is also provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to perform the steps in any of the above method embodiments.

[0025] Through the application, since the existence state of the biological object is determined by the ultrasonic echo data and the environmental data together, the water source state detection efficiency and precision are improved without adding additional chemical reagents, thus, the low water source state detection efficiency problem can be solved, and the water source state detection efficiency is improved. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1 is a hardware structure block diagram of a mobile terminal of a water source detection method based on filamentous bacteria state recognition according to an embodiment of the application;

[0027] Figure 2 is a flow chart of a water source detection method based on filamentous bacteria state recognition according to an embodiment of the application;

[0028] Figure 3 is a structure block diagram of a water source detection device based on filamentous bacteria state recognition according to an embodiment of the application. DETAILED DESCRIPTION

[0029] Hereinafter, the embodiments of the application will be described in detail with reference to the accompanying drawings and in conjunction with embodiments.

[0030] It should be noted that the terms "first", "second" and the like in the specification and claims of the application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily have to describe a specific order or sequence.

[0031] The method embodiments provided in the embodiments of the application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking the case of running on a mobile terminal, Figure 1 is a hardware structure block diagram of a mobile terminal of a water source detection method based on filamentous bacteria state recognition according to an embodiment of the application. As shown in Figure 1 , the mobile terminal can include one or more (only one is shown in Figure 1 ) processor 102 (the processor 102 can include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the above-mentioned mobile terminal can further include a transmission device 106 for communication function and an input and output device 108. Those skilled in the art can understand that Figure 1 The structure shown is only schematic, which does not limit the structure of the above-mentioned mobile terminal. For example, the mobile terminal can further include more or less components than Figure 1 shown, or have a different configuration from Figure 1 shown.

[0032] The memory 104 can be used to store computer programs, such as software programs of application software and modules, for example, a computer program corresponding to a water source detection method based on filamentous bacteria state recognition according to an embodiment of the present application. The processor 102 can execute various functions and data processing by running the computer program stored in the memory 104, that is, implement the method described above. The memory 104 can include a high-speed random access memory, and can further include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include a memory remotely arranged with respect to the processor 102, which can be connected to the mobile terminal through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0033] The transmission device 106 is used to receive or send data via a network. Specific examples of the network can include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet in a wireless manner.

[0034] In the present embodiment, a water source detection method based on filamentous bacteria state recognition is provided, Figure 2 According to the present embodiment, a flow chart of a water source detection method based on filamentous bacteria state recognition is shown in FIG. 2, which includes the following steps: Figure 2

[0035] In step S201, fluid detection data of a target water source is obtained, wherein the fluid detection data includes ultrasonic echo data obtained by ultrasonic detection of the target water source by an ultrasonic detection device and environment data obtained by state detection of activated sludge of the target water source by an environment detection device;

[0036] In the present embodiment, when the state of filamentous bacteria changes, the corresponding distribution of filamentous bacteria and the state of activated sludge attached thereto also change. By ultrasonic detection and environment detection of activated sludge, without adding chemical reagents, on the one hand, the information lag of chemical detection is avoided, and on the other hand, the mistakes caused by manual addition of chemical reagents and manual judgment of chemical reactions are avoided, thereby improving the accuracy and efficiency of water source detection.

[0037] ​The target water source can be a sample of part of the water from the water source to be detected, or a small range of water source; the ultrasonic echo data include (but not limited to) echo energy amplitude and energy distribution, echo frequency size and distribution, echo loss, beam characteristics, cavitation characteristics and distribution, power variation and other data.

[0038] The environmental data of the activated sludge include (but not limited to) image data collected by a camera (such as visible light image, infrared image and the like of the water source sample or the water source itself), pH value of the water source collected by an acid-base device, environmental gas data (such as data of gas components such as ammonia, nitric oxide, nitrogen dioxide, sulfur dioxide, carbon dioxide and ozone) of the water source sample or the small range of water source, environmental temperature and humidity data and the like, wherein the environmental gas data can be obtained by detection by corresponding gas sensors, such as K-5S-SO2 sulfur dioxide sensor, K-5S-NO2 nitrogen dioxide sensor and the like, the environmental temperature and humidity data can be obtained by collection by a temperature and humidity sensor, such as DHT11 temperature and humidity sensor, and the environmental image data can be obtained by collection by a laser sensor or a visible light sensor.

[0039] It should be noted that, in order to accurately collect the ultrasonic echo data, the echo collection device needs to be arranged in an array, and generally, 10-20 sensors (which can be microphones or high-precision sound wave sensors) are arranged in a group, the distance between the sensors is generally less than or equal to 0.02 m, and each group is arranged in a spiral arm shape. Of course, each group of sensors can also be arranged in a cross shape, a square shape and the like, and the data collection effect is best in the spiral arm shape.

[0040] In step S202, based on the environmental data, the biological state information of the filamentous bacteria in the target water source is determined.

[0041] In this embodiment, the state of the filamentous bacteria has an impact on the state of the activated sludge, so that the state of the filamentous bacteria can be directly judged by observing the state of the activated sludge. For example, a low pH value is easy to cause a large number of reproduction of the filamentous bacteria, and the large number of reproduction of the filamentous bacteria is easy to cause the activated sludge to present an inflation state; or Microthix parvicella is dominant in the filamentous bacteria group in winter, and Nocardia form, type 0041 or Nostocoida limnicda type filamentous bacteria are easy to reproduce in large quantities in warm seasons, thereby affecting the state of the activated sludge; in addition, organic acids and hydrogen sulfide generated in early anaerobic digestion of sewage before entering the water treatment system can also cause the occurrence of sludge inflation (sulfur bacteria such as Beijerinckia and sulfur filamentous bacteria can obtain energy from hydrogen sulfide oxidation), and such bacteria reproduce in a very long filamentous manner, sometimes up to 1 cm, thereby causing the occurrence of sludge inflation, and further affecting the state of the activated sludge.

[0042] The biological state information includes the development state of the filamentous bacteria, such as filament length, filament distribution, filament quantity, and the like.

[0043] In step S203, the state information of the target water source is determined based on the biological state information and the ultrasonic echo data.

[0044] In this embodiment, the state of the activated sludge is different in different aeration stages of the target water source, and thus the state of the activated sludge is an important criterion for judging the state of the target water source. The activated sludge is usually most obviously affected by the filamentous bacteria, and thus the state of the activated sludge can be determined by judging the state of the filamentous bacteria, and the state of the target water source is further determined. After the biological state of the filamentous bacteria is determined by the activated sludge, the echo of the ultrasonic wave is used for further auxiliary judgment, so as to accurately determine the state of the water source.

[0045] The ultrasonic echo of different organisms is different. For example, the echo of the filamentous bacteria usually has a relatively discrete energy distribution, but the energy distribution is relatively uniform, generally presents a Gaussian distribution, and the energy change in a unit time is relatively stable. The state of the water source usually includes the degree of sedimentation treatment of the sewage, whether there is a new water quality impact or a toxic water quality impact, and the like.

[0046] Through the above steps, the state of the target water source can be accurately determined in real time through digital monitoring, the problem of low water source detection efficiency is solved, and the water source monitoring efficiency is improved.

[0047] It should be noted that, in order to further enhance the discrimination accuracy of the microbial morphology, the image recognition method can also be used for judgment. For example, the pixel point distribution of the filamentous bacteria, such as flagella, pedipalps, and mouthparts, is used to assist in identifying the pixel point distribution, so as to assist in judging the state of the filamentous bacteria. Whether the state of the filamentous bacteria is judged based on the pixel point distribution or the gray value, the relevant object recognition model (such as the yolo series) can be trained through big data, that is, image detection data of the target water source is obtained; pixel feature information of the target water source is determined based on the image detection data, wherein the pixel feature information includes pixel point distribution features and pixel point gray value features; filamentous bacteria state information is determined based on the pixel point distribution features and the pixel point gray value features, wherein the pixel point distribution features include the number of pixel points, the distance between pixel points, and the size of a pixel point set (pixel points with a distance less than a certain threshold are regarded as a pixel point set, and the size of the set is related to the number of pixel points and the distance between pixel points).

[0048] Specifically, after obtaining the pixel point distribution feature and the pixel point gray value feature, a two-dimensional matrix P can be constructed according to a preset rule based on the pixel point distribution feature and the pixel point gray value feature, and the two-dimensional matrix P is matched with a preset two-dimensional matrix p (i.e., a two-dimensional matrix in a normal case), to determine whether the elements in the matrix are within a preset range. If the elements are out of the preset range, it indicates that the related data is abnormal. For example, if the element in the two-dimensional matrix P is 1, the element in the preset two-dimensional matrix p is 5, and the preset range is ±2, it can be determined that the element in the two-dimensional matrix P is far beyond the preset two-dimensional matrix p, so that it is determined that the data constituting the matrix may be abnormal. Similarly, the above method can be used.

[0049] The execution subject of the above steps can be a base station, a terminal, etc., but is not limited thereto.

[0050] In an optional embodiment, the determining of the biological state information of the filamentous bacteria in the target water source based on the environment data comprises:

[0051] In step S2021, the morphological change data of the activated sludge in the first time period is determined based on the environment data.

[0052] In step S2022, the biological state information of the filamentous bacteria is determined based on the morphological change data.

[0053] In this embodiment, the expansion morphology of the activated sludge is different in different development states of the filamentous bacteria, and specifically, the settling speed of the activated sludge and the flocculation time of the sludge are different when settling. At this time, the settling speed and the flocculation time of the activated sludge in a certain time period are observed to determine the corresponding development state of the filamentous bacteria, and then the state of the water source is determined. For example, in the initial expansion state of the filamentous bacteria, the flocculation speed of the activated sludge in the initial settling period is lower than that of the activated sludge with normal performance, and the flocculation time is about 2-4 times longer. At this time, the number and distribution of the filamentous bacteria are more dense than those of other populations.

[0054] The morphological change data includes (but is not limited to) data for detecting the morphology of the settling process of the activated sludge, such as the absorption, reflection, and scattering data of the activated sludge to laser light at different settling times when the activated sludge in the flocculation process is irradiated by laser light, or the color and morphology (lactation, flocculation, etc.) of the activated sludge in the flocculation process, and other data. The first time period can be (but is not limited to) the flocculation time.

[0055] In an optional embodiment, the determining of the biological state information of the filamentous bacteria based on the morphological change data comprises:

[0056] In step S20221, the light intensity distribution change data of the activated sludge is determined based on the morphological change data.

[0057] Step S20222, first matching the light intensity distribution change data with preset target light intensity distribution change data;

[0058] Step S20223, determining the biological state information based on the first matching result.

[0059] In this embodiment, when flocculating to different states, the absorption and reflection of light by the activated sludge are different, thereby the state of the filamentous bacteria can be determined.

[0060] For example, normally, when the filamentous bacteria are weak, the flocculation speed of the activated sludge is fast, and the light intensity distribution is φ1 in 1 min and φ2 in 2 min; when the filamentous bacteria are strong, the flocculation speed of the activated sludge starts to decrease, and it often takes 2 min to reach φ1, and when the filamentous bacteria further grow, it takes 4 min to reach φ1, thereby the state of the filamentous bacteria is determined.

[0061] The light intensity distribution change data includes the change of the light intensity value in a certain time period and the region position matrix of the light intensity distribution.

[0062] In an optional embodiment, the determining the biological state information of the filamentous bacteria based on the morphological change data includes:

[0063] Step S20224, determining the light reflection energy distribution change data of the activated sludge according to the morphological change data;

[0064] Step S20225, second matching the light reflection energy distribution change data with preset target light reflection energy distribution change data;

[0065] Step S20226, determining the biological state information based on the second matching result.

[0066] In this embodiment, in addition to judging the change of the light distribution, the state of the activated sludge can also be determined by the light reflection energy distribution and the energy intensity of the light reflection of the activated sludge, thereby the state of the filamentous bacteria is determined.

[0067] The light reflection energy distribution change data includes the light reflection energy value of each position and the corresponding position matrix of different light reflection energy values.

[0068] Those skilled in the art can clearly understand that the method according to the above-mentioned embodiments can be realized by means of software and necessary general hardware platforms, of course, can also be realized by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a ROM / RAM, a magnetic disk, an optical disk) and includes a plurality of instructions for causing a terminal device (which can be a mobile phone, a computer, a server, or a network device) to execute the method described in the embodiments of the present application.

[0069] In this embodiment, a water source detection device based on filamentous bacteria state recognition is also provided, which is used to realize the above-mentioned embodiments and preferred embodiments, and will not be described again. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably realized in software, hardware, or a combination of software and hardware is also possible and is contemplated.

[0070] Figure 3 is a structural block diagram of a water source detection device based on filamentous bacteria state recognition according to an embodiment of the present application, as shown in Figure 3 The device comprises:

[0071] A data acquisition module 31 is configured to acquire fluid detection data of a target water source, wherein the fluid detection data comprises ultrasonic echo data obtained by ultrasonic detection of the target water source by an ultrasonic detection device and environment data obtained by state detection of activated sludge of the target water source by an environment detection device.

[0072] A biological state determination module 32 is configured to determine biological state information of filamentous bacteria of the target water source based on the environment data.

[0073] A water source state determination module 33 is configured to determine state information of the target water source based on the biological state information and the ultrasonic echo data.

[0074] In an optional embodiment, the biological state determination module 32 comprises:

[0075] A morphology data determination unit is configured to determine morphology change data of the activated sludge in a first time period based on the environment data.

[0076] A biological state determination unit is configured to determine biological state information of the filamentous bacteria based on the morphology change data.

[0077] In an optional embodiment, the biological state determining unit comprises:

[0078] The light intensity distribution subunit is configured to determine light intensity distribution change data of the activated sludge according to the morphological change data.

[0079] The first matching subunit is configured to perform first matching between the light intensity distribution change data and preset target light intensity distribution change data.

[0080] The first biological state subunit is configured to determine the biological state information based on the first matching result.

[0081] In an optional embodiment, the biological state determining unit comprises:

[0082] The light intensity distribution subunit is configured to determine light intensity distribution change data of the activated sludge according to the morphological change data.

[0083] The second matching subunit is configured to perform second matching between the light intensity distribution change data and preset target light intensity distribution change data.

[0084] The second biological state subunit is configured to determine the biological state information based on the second matching result.

[0085] It should be noted that the above-mentioned modules can be implemented by software or hardware, and for the latter, the following implementation manners can be used, but are not limited thereto: the above-mentioned modules are located in the same processor; or the above-mentioned modules are located in different processors in any combination.

[0086] Embodiments of the present application also provide a computer readable storage medium, which stores a computer program, wherein the computer program is configured to execute the steps in any of the above method embodiments when running.

[0087] In an exemplary embodiment, the above-mentioned computer readable storage medium can include, but is not limited to: a U disk, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and various computer program storage media.

[0088] Embodiments of the present application also provide an electronic device, which comprises a memory and a processor, the memory stores a computer program, and the processor is configured to execute the computer program to perform the steps in any of the above method embodiments.

[0089] In one example embodiment, the electronic device described above can further include a transmission device connected to the processor and an input / output device connected to the processor.

[0090] The specific examples in the present embodiment can refer to the examples described in the above embodiments and exemplary implementations, which will not be repeated here.

[0091] Obviously, those skilled in the art should understand that the modules or steps of the present application described above can be realized by general computing devices, which can be centralized on a single computing device or distributed on a network composed of multiple computing devices, which can be realized by program codes executable by the computing devices, so that they can be stored in storage devices and executed by the computing devices, and in some cases, the steps shown or described can be executed in different order, or they can be respectively manufactured into individual integrated circuit modules, or multiple modules or steps among them can be manufactured into a single integrated circuit module to realize. Thus, the present application is not limited to any specific combination of hardware and software.

[0092] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A water source detection method based on filamentous bacteria state identification, characterized in that, include: The method involves acquiring fluid detection data of a target water source, including ultrasonic echo data obtained by ultrasonic testing of the target water source using ultrasonic testing equipment and environmental data obtained by environmental monitoring equipment for the state detection of activated sludge in the target water source. The ultrasonic echo data includes echo energy amplitude and distribution, echo frequency magnitude and distribution, echo loss, beam characteristics, cavitation characteristics and distribution, and power variation. The environmental data includes image data acquired by a camera, pH value of the water source acquired by an acid / alkali testing device, environmental gas data, and environmental temperature and humidity data. Based on the environmental data, the biological state information of the filamentous bacteria in the target water source is determined; Based on the biological state information and the ultrasonic echo data, the state information of the target water source is determined; The method further includes, after acquiring the fluid detection data of the target water source: Acquire image detection data of the target water source; based on the image detection data, determine the pixel feature information of the target water source, wherein the pixel feature information includes pixel distribution features and pixel grayscale value features; based on the pixel distribution features and pixel grayscale value features, determine the filamentous fungal state information, wherein the pixel distribution features include the number of pixels, the distance between pixels, and the size of the pixel set; After obtaining the pixel distribution features and pixel grayscale value features, a two-dimensional matrix is ​​constructed based on the pixel distribution features and pixel grayscale value features according to preset rules. Then, the two-dimensional matrix is ​​matched with a preset two-dimensional matrix under normal conditions to determine whether the elements in the matrix are within a preset range. If they exceed the preset range, it indicates that the relevant data is abnormal.

2. The method according to claim 1, characterized in that, The determination of the biological state information of filamentous bacteria in the target water source based on the environmental data includes: Based on the environmental data, the morphological change data of the activated sludge during the first time period were determined; The biological state information of the filamentous fungus is determined based on the morphological change data.

3. The method according to claim 2, characterized in that, The determination of the biological state information of the filamentous fungus based on the morphological change data includes: Based on the morphological change data, the light intensity distribution change data of the activated sludge was determined; The light intensity distribution change data is first matched with the preset target light intensity distribution change data; The biological state information is determined based on the first matching result.

4. The method according to claim 2, characterized in that, The determination of the biological state information of the filamentous fungus based on the morphological change data includes: Based on the morphological change data, determine the change data of the reflective energy distribution of the activated sludge; The reflected energy distribution change data is then matched with the preset target reflected energy distribution change data in a second manner. The biological state information is determined based on the second matching result.

5. A water source detection device based on filamentous bacteria state identification, characterized in that, include: The data acquisition module is used to acquire fluid detection data of the target water source. The fluid detection data includes ultrasonic echo data obtained by ultrasonic testing of the target water source using ultrasonic testing equipment, and environmental data obtained by environmental monitoring equipment for the activated sludge of the target water source. The ultrasonic echo data includes echo energy amplitude and distribution, echo frequency magnitude and distribution, echo loss, beam characteristics, cavitation characteristics and distribution, and power variation. The environmental data includes image data acquired by a camera, pH value of the water source acquired by an acid / alkali testing device, environmental gas data, and environmental temperature and humidity data. The biological state determination module is used to determine the biological state information of filamentous bacteria in the target water source based on the environmental data. A water source status determination module is used to determine the status information of the target water source based on the biological status information and the ultrasonic echo data. This includes, after acquiring the fluid detection data of the target water source, the following: Acquire image detection data of the target water source; based on the image detection data, determine the pixel feature information of the target water source, wherein the pixel feature information includes pixel distribution features and pixel grayscale value features; based on the pixel distribution features and pixel grayscale value features, determine the filamentous fungal state information, wherein the pixel distribution features include the number of pixels, the distance between pixels, and the size of the pixel set; After obtaining the pixel distribution features and pixel grayscale value features, a two-dimensional matrix is ​​constructed based on the pixel distribution features and pixel grayscale value features according to preset rules. Then, the two-dimensional matrix is ​​matched with a preset two-dimensional matrix under normal conditions to determine whether the elements in the matrix are within a preset range. If they exceed the preset range, it indicates that the relevant data is abnormal.

6. The apparatus according to claim 5, characterized in that, The biological state determination module includes: The morphological data determination unit is used to determine the morphological change data of the activated sludge within a first time period based on the environmental data. A biological state determination unit is used to determine the biological state information of the filamentous fungus based on the morphological change data.

7. The apparatus according to claim 6, characterized in that, The biological state determination unit includes: The light intensity distribution subunit is used to determine the light intensity distribution change data of the activated sludge based on the morphological change data. The first matching subunit is used to perform a first match between the light intensity distribution change data and the preset target light intensity distribution change data. The first biological state subunit is used to determine the biological state information based on the first matching result.

8. The apparatus according to claim 6, characterized in that, The biological state determination unit includes: The reflective energy distribution subunit is used to determine the reflective energy distribution change data of the activated sludge based on the morphological change data. The second matching subunit is used to perform a second matching between the reflected energy distribution change data and the preset target reflected energy distribution change data. The second biological state subunit is used to determine the biological state information based on the second matching result.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to perform the method described in any one of claims 1 to 4 when executed.

10. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method described in any one of claims 1 to 4.

Images