Aeration control method, device, equipment, medium and program product

Abstract

The present invention provides an aeration control method, device, equipment, medium and program product, which relate to the field of control technology. The aeration control method comprises: obtaining the blower aeration parameters and corresponding water quality related parameters of the sewage treatment system at the current moment; inputting the blower aeration parameters and the water quality related parameters into the pre-acquired effluent ammonia nitrogen classification model to obtain the effluent ammonia nitrogen water quality classification result; the effluent ammonia nitrogen classification model is used to classify and predict the water quality of effluent ammonia nitrogen according to the blower aeration parameters and the water quality related parameters; adjusting the blower aeration parameters according to the effluent ammonia nitrogen water quality classification result. The scheme of the present invention can control the blower aeration according to the water quality of effluent ammonia nitrogen and improve the sewage treatment effect.

Description

Technical Field

[0001] The present invention relates to the field of control technology, and in particular to an aeration control method, device, equipment, medium and program product. Background Art

[0002] At present, most of the blowers in sewage treatment plants mainly adopt artificial aeration control method. However, the artificial aeration control method relies on manual experience to determine the aeration parameters of the blower. The quality of the blower aeration control depends entirely on manual experience and requires manual constant adjustment, which is time-consuming and labor-intensive. There are errors in manual operation, which affects the sewage treatment effect. Moreover, manual operation often uses excessive aeration to prevent the outlet from exceeding the standard, resulting in a large amount of energy waste. Summary of the invention

[0003] The technical solution of the present invention aims to provide an aeration control method, device, equipment, medium and program product to solve the problem that the blower aeration control of the sewage treatment system in the prior art relies on manual operation and has errors, which affects the sewage treatment effect.

[0004] To achieve the above object, the present invention is achieved by:

[0005] In a first aspect, an embodiment of the present invention provides an aeration control method, comprising:

[0006] Obtain the blower aeration parameters and corresponding water quality related parameters of the sewage treatment system at the current moment;

[0007] The blower aeration parameters and the water quality related parameters are input into a pre-acquired effluent ammonia nitrogen classification model to obtain an effluent ammonia nitrogen water quality classification result; the effluent ammonia nitrogen classification model is used to classify and predict the water quality of effluent ammonia nitrogen according to the blower aeration parameters and the water quality related parameters;

[0008] The blower aeration parameters are adjusted according to the effluent ammonia nitrogen water quality classification result.

[0009] Optionally, the aeration control method, wherein obtaining the blower aeration parameters and corresponding water quality related parameters of the sewage treatment system at the current moment, comprises:

[0010] Collecting blower aeration parameters of the sewage treatment system at the current moment and water quality related parameters within a preset time period before the current moment;

[0011] A time series alignment process is performed on the blower aeration parameter at the current moment and the water quality related parameter within the preset time length to obtain the water quality related parameter corresponding to the blower aeration parameter at the current moment.

[0012] Optionally, the aeration control method, wherein the blower aeration parameters are adjusted according to the effluent ammonia nitrogen water quality classification result, comprises:

[0013] According to the control strategy corresponding to the effluent ammonia nitrogen water quality classification result, the blower aeration adjustment direction and the blower aeration adjustment parameter are obtained; the control strategy is used to indicate the blower aeration adjustment direction and the blower aeration adjustment parameter corresponding to different effluent ammonia nitrogen water quality classification results;

[0014] The aeration parameters of the blower are adjusted according to the aeration adjustment direction of the blower and the aeration adjustment parameters of the blower.

[0015] Optionally, the aeration control method further comprises:

[0016] Inputting the adjusted blower aeration parameters into the effluent ammonia nitrogen classification model to obtain an adjusted effluent ammonia nitrogen water quality classification result;

[0017] According to the adjusted effluent ammonia nitrogen water quality classification result, the blower aeration parameters are repeatedly adjusted until the effluent ammonia nitrogen water quality classification result meets the preset classification result.

[0018] Optionally, the aeration control method further comprises:

[0019] According to the following steps, the effluent ammonia nitrogen classification model is obtained:

[0020] Acquire historical data of the sewage treatment system; the historical data includes water quality related parameters, effluent ammonia nitrogen concentration and blower aeration parameters at different times;

[0021] Performing time series alignment processing on the historical data to obtain processed historical data; the processed historical data includes the effluent ammonia nitrogen concentration and the blower aeration parameter at the second historical moment corresponding to the water quality related parameters at the first historical moment, and the blower aeration parameter at the third historical moment corresponding to the water quality related parameters at the first historical moment;

[0022] Using the water quality related parameters and blower aeration parameters in the processed historical data as input features and the effluent ammonia nitrogen concentration in the processed historical data as a target feature, training a machine learning model to obtain a trained machine learning model;

[0023] The effluent ammonia nitrogen classification model is obtained according to the effluent ammonia nitrogen concentration, the ammonia nitrogen water quality standard limit and the trained machine learning model.

[0024] Optionally, in the aeration control method, the blower aeration parameter includes at least one of the following:

[0025] Blower aeration frequency; Blower aeration duration.

[0026] Optionally, in the aeration control method, the water quality related parameters include at least one of the following:

[0027] Influent ammonia nitrogen concentration; influent water volume; ammonia nitrogen concentration in anoxic zone; ammonia nitrogen concentration in aerobic zone; dissolved oxygen concentration in aerobic zone.

[0028] In a second aspect, an embodiment of the present invention further provides an aeration control device, comprising:

[0029] The first acquisition module is used to obtain the blower aeration parameters and corresponding water quality related parameters of the sewage treatment system at the current moment;

[0030] A first acquisition module is used to input the blower aeration parameters and the water quality related parameters into a pre-acquired effluent ammonia nitrogen classification model to obtain an effluent ammonia nitrogen water quality classification result; the effluent ammonia nitrogen classification model is used to classify and predict the water quality of effluent ammonia nitrogen according to the blower aeration parameters and the water quality related parameters;

[0031] The first adjustment module is used to adjust the blower aeration parameters according to the effluent ammonia nitrogen water quality classification result.

[0032] In a third aspect, an embodiment of the present invention further provides an aeration control device, comprising: a processor, a memory, and a program stored in the memory and executable on the processor, wherein when the program is executed by the processor, the aeration control method as described in the first aspect is implemented.

[0033] In a fourth aspect, an embodiment of the present invention further provides a readable storage medium having a program stored thereon, and when the program is executed by a processor, the aeration control method as described in the first aspect is implemented.

[0034] In a fifth aspect, an embodiment of the present invention further provides a computer program product, comprising computer instructions, which, when executed by a processor, implement the aeration control method as described in the first aspect.

[0035] The beneficial effects of the above technical solution of the present invention are as follows:

[0036] In an embodiment of the present invention, the blower aeration parameters and the corresponding water quality related parameters of the sewage treatment system at the current moment are obtained; the blower aeration parameters and the water quality related parameters are input into the pre-acquired effluent ammonia nitrogen classification model to obtain the effluent ammonia nitrogen water quality classification result; the effluent ammonia nitrogen classification model is used to classify and predict the water quality of effluent ammonia nitrogen according to the blower aeration parameters and the water quality related parameters; according to the effluent ammonia nitrogen water quality classification result, the blower aeration parameters are adjusted. In this way, the effluent ammonia nitrogen water quality is predicted by the effluent ammonia nitrogen classification model, and the blower aeration parameters are adjusted according to the effluent ammonia nitrogen water quality classification result, so as to avoid manual operation, realize the intelligent aeration control of the blower, ensure that the effluent ammonia nitrogen water quality meets the standard, improve the sewage treatment effect, and reduce energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic flow chart of the aeration control method according to an embodiment of the present invention;

[0038] Figure 2 A schematic diagram of a flow chart of one implementation of the method described in an embodiment of the present invention;

[0039] Figure 3 A schematic diagram of another implementation of the method according to an embodiment of the present invention;

[0040] Figure 4 It is a structural schematic diagram of the aeration control device according to an embodiment of the present invention;

[0041] Figure 5 This is a hardware block diagram of the aeration control device described in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0043] In various embodiments of the present invention, it should be understood that the size of the serial numbers of the following processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0044] Additionally, the terms "system" and "network" are often used interchangeably herein.

[0045] The terms "first", "second", etc. in the specification and claims of the present invention are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first" and "second" are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0046] When describing the embodiments of the present invention, some concepts used in the following description are first explained.

[0047] Aeration is an important part of sewage treatment. It injects air or oxygen into sewage through a blower to promote the degradation and oxidation of organic matter in sewage and increase the dissolved oxygen content in sewage, thereby effectively removing organic matter and odor in sewage and improving treatment efficiency.

[0048] The embodiment of the present invention, as Figure 1 As shown, an aeration control method is provided, comprising:

[0049] S101, obtaining blower aeration parameters and corresponding water quality related parameters of the sewage treatment system at the current moment.

[0050] Optionally, the blower aeration includes at least one of the following:

[0051] Blower aeration frequency; Blower aeration duration.

[0052] The blower aeration frequency can be understood as the operating frequency of the blower in a single cycle, and the adjustable range is, for example, 38 Hz to 50 Hz; the blower aeration duration can be understood as the operating duration of the blower in a single cycle, and the adjustable range is, for example, 8 min to 20 min.

[0053] Optionally, the water quality related parameters include at least one of the following:

[0054] Influent ammonia nitrogen concentration; influent water volume; ammonia nitrogen concentration in the anoxic zone (the anoxic zone can be called anaerobic and anoxic zone); ammonia nitrogen concentration in the aerobic zone; dissolved oxygen concentration in the aerobic zone.

[0055] It should be noted that the sewage treatment system includes a sewage pool. Optionally, the sewage pool is a sewage pool using a Vertical Flow Labyrinth (VFL) process, and the sewage pool includes anaerobic and anoxic zones; an aerobic zone; and a sedimentation zone. Among them, the anaerobic and anoxic zones are used for anaerobic and anoxic treatment of sewage; in the aerobic zone, a membrane microporous aerator is installed at the bottom of the pool, and a blower aerates from here; the mixed liquid in the aerobic zone flows into the sedimentation zone through the gaps in the partition, and the flow rate decreases from bottom to top, the sludge settles, and the upper clear water flows into the stainless steel drain trough and is discharged.

[0056] Sewage flows into the sewage pool from the water inlet, passes through the anaerobic and anoxic zones, aerobic zones and sedimentation zones in turn, and finally flows out of the treated sewage from the water outlet. The entire sewage treatment process from the water inlet to the water outlet takes a certain amount of time and has a large time lag. Therefore, the treatment effect of the blower aeration parameters at the current moment on the sewage cannot be indicated by the water quality-related parameters at the current moment. In other words, the water quality-related parameters at a certain historical moment correspond to the blower aeration parameters at the current moment.

[0057] Therefore, in order to ensure the consistency and synchronization of data and improve the prediction accuracy of the effluent ammonia nitrogen classification model, it is necessary to obtain the blower aeration parameters of the sewage treatment system at the current moment and the water quality related parameters at the target moment corresponding to the blower aeration parameters at the current moment.

[0058] Furthermore, the aeration control method described in the embodiment of the present invention can be applied to a server of a sewage treatment system, and the server obtains the blower aeration parameters and corresponding water quality related parameters of the sewage treatment system at the current moment through sensors. Through the blower aeration parameters and corresponding water quality related parameters collected in real time, the sewage treatment process can be accurately understood, providing data support for the subsequent aeration control process and avoiding manual operation.

[0059] S102, inputting the blower aeration parameters and the water quality related parameters into a pre-acquired effluent ammonia nitrogen classification model to obtain an effluent ammonia nitrogen water quality classification result; the effluent ammonia nitrogen classification model is used to classify and predict the water quality of effluent ammonia nitrogen according to the blower aeration parameters and the water quality related parameters.

[0060] In an embodiment of the present invention, the blower aeration parameters and the water quality related parameters are input into an outlet ammonia nitrogen classification model, so as to obtain a prediction classification result of the outlet ammonia nitrogen water quality corresponding to the blower aeration parameters at the current moment.

[0061] Optionally, the blower aeration parameters and the water quality related parameters are input into an effluent ammonia nitrogen classification model to obtain an effluent ammonia nitrogen concentration and an effluent ammonia nitrogen water quality classification result corresponding to the effluent ammonia nitrogen concentration.

[0062] It should be noted that the effluent ammonia nitrogen water quality classification result is one of the following four:

[0063] Excellent; Good; Marginal; Exceeded.

[0064] The effluent ammonia nitrogen water quality classification result is related to the effluent ammonia nitrogen concentration. Taking the effluent ammonia nitrogen water quality standard limit of 1.5 g / ml as an example:

[0065] When the effluent ammonia nitrogen concentration is less than 0.2 g / ml, the effluent ammonia nitrogen water quality classification result is marked as "excellent";

[0066] When the effluent ammonia nitrogen concentration is 0.2 g / ml≤<1 g / ml, the effluent ammonia nitrogen water quality classification result is marked as "good";

[0067] When the effluent ammonia nitrogen concentration is 1 g / ml≤<1.5 g / ml, the effluent ammonia nitrogen water quality classification result is marked as "critical";

[0068] When the effluent ammonia nitrogen concentration is ≥1.5 g / ml, the effluent ammonia nitrogen water quality classification result is marked as "exceeding the standard".

[0069] S103, adjusting the blower aeration parameters according to the effluent ammonia nitrogen water quality classification result.

[0070] In an embodiment of the present invention, the aeration parameters of the blower are adjusted according to the effluent ammonia nitrogen water quality classification result to achieve intelligent aeration control guided by the effluent ammonia nitrogen reaching the standard, with high control accuracy, good sewage treatment effect and reduced energy consumption.

[0071] In one embodiment, optionally, S101, obtaining the blower aeration parameters and corresponding water quality related parameters of the sewage treatment system at the current moment includes:

[0072] Collecting blower aeration parameters of the sewage treatment system at the current moment and water quality related parameters within a preset time period before the current moment;

[0073] A time series alignment process is performed on the blower aeration parameter at the current moment and the water quality related parameter within the preset time length to obtain the water quality related parameter corresponding to the blower aeration parameter at the current moment.

[0074] In the embodiment of the present invention, the blower aeration parameters of the sewage treatment system at the current moment and the water quality related parameters within a preset time period before the current moment are collected by sensors.

[0075] It should be noted that the time series alignment process is to obtain the water quality related parameters at the target historical moment corresponding to the blower aeration parameters at the current moment from the water quality related parameters within the preset time period.

[0076] Optionally, the sensor includes at least: a flow meter, an online ammonia nitrogen analyzer and an online dissolved oxygen detector.

[0077] Optionally, the amount of water entering within the preset time period is collected by a flow meter.

[0078] Optionally, the ammonia nitrogen concentration in the inlet water, the ammonia nitrogen concentration in the anoxic zone and the ammonia nitrogen concentration in the aerobic zone within the preset time period are collected by an ammonia nitrogen online analyzer in the corresponding area of ​​the sewage pool.

[0079] Optionally, the ammonia nitrogen concentration in the aerobic zone within the preset time period is collected by an online dissolved oxygen detector.

[0080] Optionally, the preset duration is 24 hours.

[0081] Optionally, the collection frequency of the water quality related parameters within the preset time period, including the inlet ammonia nitrogen concentration, the anoxic zone ammonia nitrogen concentration, the aerobic zone ammonia nitrogen concentration and the aerobic zone dissolved oxygen concentration, is 60 seconds.

[0082] Optionally, the water volume in the water quality related parameters within the preset time period needs to be collected when there is a change.

[0083] It should be noted that, since the blower aeration parameters also need to be collected when they change, the blower aeration parameters may change at the current moment; of course, the blower aeration parameters may not change at the current moment.

[0084] In one embodiment, optionally, in S103, adjusting the aeration parameters of the blower according to the effluent ammonia nitrogen water quality classification result includes:

[0085] According to the control strategy corresponding to the effluent ammonia nitrogen water quality classification result, the blower aeration adjustment direction and the blower aeration adjustment parameter are obtained; the control strategy is used to indicate the blower aeration adjustment direction and the blower aeration adjustment parameter corresponding to different effluent ammonia nitrogen water quality classification results;

[0086] The aeration parameters of the blower are adjusted according to the aeration adjustment direction of the blower and the aeration adjustment parameters of the blower.

[0087] In an embodiment of the present invention, the control strategy includes at least one of the following:

[0088] The effluent ammonia nitrogen water quality classification result is "excellent" and the blower aeration parameter does not reach the minimum limit, and accordingly, the blower aeration adjustment direction is the parameter reduction direction, and the blower aeration adjustment parameter is the first preset value;

[0089] The effluent ammonia nitrogen water quality classification result is "good", and correspondingly, the blower aeration adjustment direction is no adjustment direction, and the blower aeration adjustment parameter is 0;

[0090] The effluent ammonia nitrogen water quality classification result is "critical" and the blower aeration parameter has not reached the maximum limit, and accordingly, the blower aeration adjustment direction is the parameter increase direction, and the blower aeration adjustment parameter is the second preset value;

[0091] The effluent ammonia nitrogen water quality classification result is "exceeding the standard", and correspondingly, the blower aeration adjustment direction is the parameter increasing direction, and the blower aeration adjustment parameter is the third preset value.

[0092] The second preset value may be equal to the third preset value.

[0093] Next, according to the aeration adjustment direction and aeration adjustment parameters indicated by the above control strategy, the aeration parameters of the blower are adjusted. For example, if the effluent ammonia nitrogen water quality classification result is "critical" and the aeration parameters of the blower have not reached the maximum limit, the aeration parameters of the blower at the current moment are increased by a second preset value in the direction of parameter increase to obtain the adjusted aeration control parameters.

[0094] In one embodiment, optionally, the method further comprises:

[0095] Inputting the adjusted blower aeration parameters into the effluent ammonia nitrogen classification model to obtain an adjusted effluent ammonia nitrogen water quality classification result;

[0096] According to the adjusted effluent ammonia nitrogen water quality classification result, the blower aeration parameters are repeatedly adjusted until the effluent ammonia nitrogen water quality classification result meets the preset classification result.

[0097] In an embodiment of the present invention, the adjusted blower aeration parameters and the water quality related parameters corresponding to the blower aeration parameters at the current moment are input into the effluent ammonia nitrogen classification model to obtain the adjusted effluent ammonia nitrogen water quality classification results, wherein the blower aeration parameters at the current moment are the blower aeration parameters before adjustment.

[0098] Here, after obtaining the adjusted effluent ammonia nitrogen water quality classification result, it is necessary to determine whether the adjusted effluent ammonia nitrogen water quality classification result meets the preset classification result. Taking the preset classification result as "good" as an example, if the adjusted effluent ammonia nitrogen water quality classification result is "critical", which means that it does not meet the preset classification result, the blower aeration parameters are repeatedly adjusted to obtain repeatedly adjusted blower aeration parameters, until the repeatedly adjusted blower aeration parameters are input into the effluent ammonia nitrogen classification model, and an effluent ammonia nitrogen water quality classification result that meets the preset classification result is obtained, and the adjustment is ended.

[0099] In one embodiment, optionally, the method further comprises:

[0100] According to the following steps, the effluent ammonia nitrogen classification model is obtained:

[0101] Acquire historical data of the sewage treatment system; the historical data includes water quality related parameters, effluent ammonia nitrogen concentration and blower aeration parameters at different times;

[0102] Performing time series alignment processing on the historical data to obtain processed historical data; the processed historical data includes the effluent ammonia nitrogen concentration and the blower aeration parameter at the second historical moment corresponding to the water quality related parameters at the first historical moment, and the blower aeration parameter at the third historical moment corresponding to the water quality related parameters at the first historical moment;

[0103] Using the water quality related parameters and blower aeration parameters in the processed historical data as input features and the effluent ammonia nitrogen concentration in the processed historical data as a target feature, training a machine learning model to obtain a trained machine learning model;

[0104] The effluent ammonia nitrogen classification model is constructed based on the effluent ammonia nitrogen concentration, the ammonia nitrogen water quality standard limit and the trained machine learning model.

[0105] In the embodiment of the present invention, before performing time series alignment processing on the historical data, the method may further include:

[0106] Calculating the time correlation between the inlet ammonia nitrogen concentration, the anoxic zone ammonia nitrogen concentration and the aerobic zone ammonia nitrogen concentration at each historical moment in the historical data, that is, performing time delay analysis on the water inlet, the anoxic zone and the aerobic zone;

[0107] According to the time correlation between the influent ammonia nitrogen concentration, the anoxic zone ammonia nitrogen concentration and the aerobic zone ammonia nitrogen concentration at each historical moment in the historical data, the historical data is subjected to time series alignment processing to obtain the processed historical data, thereby ensuring the consistency and synchronization of the data.

[0108] Among them, the machine learning model is a decision tree model, which requires no less than 30 days of data for training before use, and the decision tree model has an iteration frequency of 7 days during use.

[0109] It should be noted that after obtaining the historical data of the sewage treatment system, the embodiment of the present invention can also use the water quality related parameters and blower aeration parameters in the historical data that have not been processed by time series alignment as input features, and use the effluent ammonia nitrogen concentration in the historical data that have not been processed by time series alignment as the target feature, and then perform time series alignment processing on the input features and the target features to obtain the processed input features and target features, and train the machine learning model with the processed input features and target features to obtain the trained machine learning model.

[0110] In addition to the above-mentioned sewage pool, server and sensor, the sewage treatment system described in the embodiment of the present invention may also include: a data acquisition and control module, which is connected to the sensor and the server respectively. The server obtains the blower aeration parameters and corresponding water quality-related parameters of the sewage treatment system collected by the sensor at the current moment through the data acquisition and control module, adjusts the blower aeration parameters according to the effluent ammonia nitrogen classification model, and outputs adjustment instructions to the blower through the data acquisition and control module, and the adjustment instructions carry the adjusted blower aeration parameters.

[0111] In addition, the sewage treatment system may further include: a user terminal, which is used to transmit data and instructions between the server and the data acquisition and control module, thereby realizing intelligent and precise control and improving user experience and work efficiency.

[0112] like Figure 2 The following is a flow chart of one embodiment of the aeration control method according to the embodiment of the present invention. According to this embodiment, the aeration control method includes the following steps:

[0113] S201, acquiring historical data, and creating input features according to water quality related parameters and aeration control parameters in the historical data.

[0114] S202, constructing target features according to the effluent ammonia nitrogen concentration in historical data.

[0115] S203, performing time series alignment processing on the input features and the target features to obtain the processed input features and the target features.

[0116] S204, dividing the data set according to the processed input features and target features to obtain a training set and a validation set.

[0117] S205, training a machine learning model based on the training set and the validation set.

[0118] S206, obtaining an effluent ammonia nitrogen classification model.

[0119] like Figure 3This is a flow chart of another embodiment of the aeration control method according to the embodiment of the present invention. In the figure, true means the judgment result is yes, and false means the judgment result is no. According to this embodiment, the aeration control method includes:

[0120] S301, collecting the blower aeration parameters at the current moment and the water quality related parameters within a preset time period before the current moment.

[0121] S302, performing time series alignment processing on the blower aeration parameters at the current moment and the water quality related parameters within a preset time length, to obtain the blower aeration parameters at the current moment and the corresponding water quality related parameters.

[0122] S303, inputting the current blower aeration parameters and the corresponding water quality related parameters into the effluent ammonia nitrogen classification model to obtain the effluent ammonia nitrogen water quality classification result.

[0123] S304, determining whether the ammonia nitrogen water quality classification result is "good".

[0124] If the judgment result of S304 is yes, then enter S305 to return the blower aeration parameters at the current moment.

[0125] If the judgment result of S304 is no, then enter S306 to judge whether the classification result of the ammonia nitrogen water quality is "critical" or "exceeding the standard".

[0126] If the judgment result of S306 is yes, the process proceeds to S307 to judge whether the aeration parameter of the blower is less than the maximum limit.

[0127] If the judgment result of S307 is yes, then enter S308 to adjust the blower aeration parameters, and input the adjusted blower aeration parameters into the effluent ammonia nitrogen classification model to obtain the adjusted effluent ammonia nitrogen water quality classification result.

[0128] S309, determining whether the adjusted effluent ammonia nitrogen water quality classification result is "good".

[0129] If the judgment result of S309 is yes, then enter S305.

[0130] If the judgment result of S309 is no, return to S307.

[0131] If the judgment result of S307 is no, then enter S305.

[0132] If the judgment result of S306 is no, the process proceeds to S310 to judge whether the aeration parameter of the blower is greater than the maximum limit.

[0133] If the judgment result of S310 is yes, then enter S311 to adjust the blower aeration parameters, and input the adjusted blower aeration parameters into the effluent ammonia nitrogen classification model to obtain the adjusted effluent ammonia nitrogen water quality classification result.

[0134] S312, determining whether the adjusted effluent ammonia nitrogen water quality classification result is "good".

[0135] If the judgment result of S312 is yes, then enter S305.

[0136] If the judgment result of S312 is no, return to S310.

[0137] If the judgment result of S310 is no, then enter S305.

[0138] In summary, the aeration control method described in the embodiment of the present invention collects the blower aeration parameters and the corresponding water quality related parameters of the sewage treatment system in real time, can accurately understand the sewage treatment process, and provide data support for the aeration control process. Moreover, the aeration control method described in the embodiment of the present invention can dynamically adjust the blower aeration parameters according to the blower aeration parameters and the corresponding water quality related parameters collected in real time, thereby optimizing energy consumption, ensuring that the effluent ammonia nitrogen water quality meets the standards, improving the sewage treatment effect, avoiding manual operation, realizing intelligent aeration control, and improving the stability and reliability of the system. In addition, time series alignment is used to process data and construct an effluent ammonia nitrogen classification model to ensure data consistency and synchronization and improve the prediction accuracy of the model.

[0139] like Figure 4 As shown, an embodiment of the present invention further provides an aeration control device, comprising:

[0140] The first acquisition module 401 is used to obtain the blower aeration parameters and corresponding water quality related parameters of the sewage treatment system at the current moment;

[0141] The first acquisition module 402 is used to input the blower aeration parameters and the water quality related parameters into the pre-acquired effluent ammonia nitrogen classification model to obtain the effluent ammonia nitrogen water quality classification result; the effluent ammonia nitrogen classification model is used to classify and predict the water quality of effluent ammonia nitrogen according to the blower aeration parameters and the water quality related parameters;

[0142] The first adjustment module 403 is used to adjust the blower aeration parameters according to the effluent ammonia nitrogen water quality classification result.

[0143] Optionally, in the aeration control device, the first acquisition module 401 is specifically used for:

[0144] Collecting blower aeration parameters of the sewage treatment system at the current moment and water quality related parameters within a preset time period before the current moment;

[0145] A time series alignment process is performed on the blower aeration parameter at the current moment and the water quality related parameter within the preset time length to obtain the water quality related parameter corresponding to the blower aeration parameter at the current moment.

[0146] Optionally, in the aeration control device, the first adjustment module 403 is specifically used for:

[0147] According to the control strategy corresponding to the effluent ammonia nitrogen water quality classification result, the blower aeration adjustment direction and the blower aeration adjustment parameter are obtained; the control strategy is used to indicate the blower aeration adjustment direction and the blower aeration adjustment parameter corresponding to different effluent ammonia nitrogen water quality classification results;

[0148] The aeration parameters of the blower are adjusted according to the aeration adjustment direction of the blower and the aeration adjustment parameters of the blower.

[0149] Optionally, the aeration control device further comprises:

[0150] The second acquisition module is used to input the adjusted blower aeration parameters into the effluent ammonia nitrogen classification model to obtain the adjusted effluent ammonia nitrogen water quality classification result;

[0151] The second adjustment module is used to repeatedly adjust the blower aeration parameters according to the adjusted effluent ammonia nitrogen water quality classification result until the effluent ammonia nitrogen water quality classification result meets the preset classification result.

[0152] Optionally, the aeration control device further comprises:

[0153] The second acquisition module is used to obtain the effluent ammonia nitrogen classification model according to the following steps:

[0154] Acquire historical data of the sewage treatment system; the historical data includes water quality related parameters, effluent ammonia nitrogen concentration and blower aeration parameters at different times;

[0155] Performing time series alignment processing on the historical data to obtain processed historical data; the processed historical data includes the effluent ammonia nitrogen concentration and the blower aeration parameter at the second historical moment corresponding to the water quality related parameters at the first historical moment, and the blower aeration parameter at the third historical moment corresponding to the water quality related parameters at the first historical moment;

[0156] Using the water quality related parameters and blower aeration parameters in the processed historical data as input features and the effluent ammonia nitrogen concentration in the processed historical data as a target feature, training a machine learning model to obtain a trained machine learning model;

[0157] The effluent ammonia nitrogen classification model is obtained according to the effluent ammonia nitrogen concentration, the ammonia nitrogen water quality standard limit and the trained machine learning model.

[0158] Optionally, in the aeration control device, the blower aeration parameter includes at least one of the following:

[0159] Blower aeration frequency; Blower aeration duration.

[0160] Optionally, in the aeration control device, the water quality related parameters include at least one of the following:

[0161] Influent ammonia nitrogen concentration; influent water volume; ammonia nitrogen concentration in anoxic zone; ammonia nitrogen concentration in aerobic zone; dissolved oxygen concentration in aerobic zone.

[0162] It should be noted that the aeration control device provided in the embodiment of the present invention is a device capable of executing the above-mentioned aeration control method, and all embodiments of the above-mentioned aeration control method are applicable to the device and can achieve the same or similar technical effects.

[0163] like Figure 5 As shown, an embodiment of the present invention further provides an aeration control device, including: a processor 501; and a memory 502 connected to the processor 501 through a bus interface, the memory 502 is used to store programs and data used by the processor 501 when performing operations, and the processor 501 calls and executes the programs and data stored in the memory 502.

[0164] The processor 501 is used to read the program in the memory 502 and execute the following process:

[0165] Obtain the blower aeration parameters and corresponding water quality related parameters of the sewage treatment system at the current moment;

[0166] The blower aeration parameters and the water quality related parameters are input into a pre-acquired effluent ammonia nitrogen classification model to obtain an effluent ammonia nitrogen water quality classification result; the effluent ammonia nitrogen classification model is used to classify and predict the water quality of effluent ammonia nitrogen according to the blower aeration parameters and the water quality related parameters;

[0167] The blower aeration parameters are adjusted according to the effluent ammonia nitrogen water quality classification result.

[0168] The aeration control device further includes a transceiver 503 for receiving and sending data to the controller of the processor 501 .

[0169] Among them, Figure 5In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically one or more processors represented by processor 501 and various circuits of memory represented by memory 502 are linked together. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface. The transceiver 503 may be a plurality of components, namely, a transmitter and a transceiver, providing a unit for communicating with various other devices on a transmission medium. For different user devices, the user interface 504 may also be an interface capable of externally and internally connecting required devices, and the connected devices include but are not limited to a keypad, a display, a speaker, a microphone, a joystick, and the like.

[0170] The processor 501 is responsible for managing the bus architecture and general processing, and the memory 502 can store data used by the processor 501 when performing operations.

[0171] Optionally, the processor 501 is specifically configured to read a program and execute the following steps:

[0172] Collecting blower aeration parameters of the sewage treatment system at the current moment and water quality related parameters within a preset time period before the current moment;

[0173] A time series alignment process is performed on the blower aeration parameter at the current moment and the water quality related parameter within the preset time length to obtain the water quality related parameter corresponding to the blower aeration parameter at the current moment.

[0174] Optionally, the processor 501 is specifically configured to read a program and execute the following steps:

[0175] According to the control strategy corresponding to the effluent ammonia nitrogen water quality classification result, the blower aeration adjustment direction and the blower aeration adjustment parameter are obtained; the control strategy is used to indicate the blower aeration adjustment direction and the blower aeration adjustment parameter corresponding to different effluent ammonia nitrogen water quality classification results;

[0176] The aeration parameters of the blower are adjusted according to the aeration adjustment direction of the blower and the aeration adjustment parameters of the blower.

[0177] Optionally, the processor 501 is further configured to read a program and execute the following steps:

[0178] Inputting the adjusted blower aeration parameters into the effluent ammonia nitrogen classification model to obtain an adjusted effluent ammonia nitrogen water quality classification result;

[0179] According to the adjusted effluent ammonia nitrogen water quality classification result, the blower aeration parameters are repeatedly adjusted until the effluent ammonia nitrogen water quality classification result meets the preset classification result.

[0180] Optionally, the processor 501 is further configured to read a program and execute the following steps:

[0181] According to the following steps, the effluent ammonia nitrogen classification model is constructed:

[0182] Acquire historical data of the sewage treatment system; the historical data includes water quality related parameters, effluent ammonia nitrogen concentration and blower aeration parameters at different times;

[0183] Performing time series alignment processing on the historical data to obtain processed historical data; the processed historical data includes the effluent ammonia nitrogen concentration and the blower aeration parameter at the second historical moment corresponding to the water quality related parameters at the first historical moment, and the blower aeration parameter at the third historical moment corresponding to the water quality related parameters at the first historical moment;

[0184] Using the water quality related parameters and blower aeration parameters in the processed historical data as input features and the effluent ammonia nitrogen concentration in the processed historical data as a target feature, training a machine learning model to obtain a trained machine learning model;

[0185] The effluent ammonia nitrogen classification model is constructed based on the effluent ammonia nitrogen concentration, the ammonia nitrogen water quality standard limit and the trained machine learning model.

[0186] Optionally, the blower aeration parameters include at least one of the following:

[0187] Blower aeration frequency; Blower aeration duration.

[0188] Optionally, the water quality related parameters include at least one of the following:

[0189] Influent ammonia nitrogen concentration; influent water volume; ammonia nitrogen concentration in anoxic zone; ammonia nitrogen concentration in aerobic zone; dissolved oxygen concentration in aerobic zone.

[0190] A specific embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the above-mentioned aeration control method are implemented and the same technical effect can be achieved. To avoid repetition, it will not be described here.

[0191] In addition, an embodiment of the present invention further provides a computer program product, including computer instructions, which, when executed by a processor, implement the above Figure 1 The various processes of the method embodiment shown can achieve the same technical effect, and will not be described again here to avoid repetition.

[0192] In the several embodiments provided by the present invention, it should be understood that the disclosed methods and devices can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0193] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may be physically included separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0194] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform some steps of the sending and receiving methods described in various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store program codes.

[0195] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An aeration control method, characterized in that: include: Obtain the blower aeration parameters and corresponding water quality related parameters of the sewage treatment system at the current moment; The blower aeration parameters and the water quality related parameters are input into a pre-acquired effluent ammonia nitrogen classification model to obtain an effluent ammonia nitrogen water quality classification result; the effluent ammonia nitrogen classification model is used to classify and predict the water quality of effluent ammonia nitrogen according to the blower aeration parameters and the water quality related parameters; The effluent ammonia nitrogen water quality classification result is related to the effluent ammonia nitrogen concentration; According to the control strategy corresponding to the effluent ammonia nitrogen water quality classification result, the blower aeration adjustment direction and the blower aeration adjustment parameter are obtained; the control strategy is used to indicate the blower aeration adjustment direction and the blower aeration adjustment parameter corresponding to different effluent ammonia nitrogen water quality classification results; adjusting the aeration parameters of the blower according to the aeration adjustment direction of the blower and the aeration adjustment parameters of the blower; Wherein, the effluent ammonia nitrogen classification model is obtained according to the following steps: Acquire historical data of the sewage treatment system; the historical data includes water quality related parameters, effluent ammonia nitrogen concentration and blower aeration parameters at different times; Performing time series alignment processing on the historical data to obtain processed historical data; the processed historical data includes the effluent ammonia nitrogen concentration at a second historical moment corresponding to the water quality related parameters at the first historical moment, and the blower aeration parameters at a third historical moment corresponding to the water quality related parameters at the first historical moment; Using the water quality related parameters and blower aeration parameters in the processed historical data as input features and the effluent ammonia nitrogen concentration in the processed historical data as a target feature, training a machine learning model to obtain a trained machine learning model; The effluent ammonia nitrogen classification model is obtained according to the effluent ammonia nitrogen concentration, the ammonia nitrogen water quality standard limit and the trained machine learning model.

2. The aeration control method according to claim 1, characterized in that: Obtain the blower aeration parameters and corresponding water quality related parameters of the sewage treatment system at the current moment, including: Collecting blower aeration parameters of the sewage treatment system at the current moment and water quality related parameters within a preset time period before the current moment; A time series alignment process is performed on the blower aeration parameter at the current moment and the water quality related parameter within the preset time length to obtain the water quality related parameter corresponding to the blower aeration parameter at the current moment.

3. The aeration control method according to claim 1, characterized in that: The method further comprises: Inputting the adjusted blower aeration parameters into the effluent ammonia nitrogen classification model to obtain an adjusted effluent ammonia nitrogen water quality classification result; According to the adjusted effluent ammonia nitrogen water quality classification result, the blower aeration parameters are repeatedly adjusted until the effluent ammonia nitrogen water quality classification result meets the preset classification result.

4. The aeration control method according to claim 1, characterized in that: The blower aeration parameters include at least one of the following: Blower aeration frequency; Blower aeration duration.

5. The aeration control method according to claim 1, characterized in that: The water quality related parameters include at least one of the following: Influent ammonia nitrogen concentration; influent water volume; ammonia nitrogen concentration in anoxic zone; ammonia nitrogen concentration in aerobic zone; dissolved oxygen concentration in aerobic zone.

6. An aeration control device, characterized in that: include: The first acquisition module is used to obtain the blower aeration parameters and corresponding water quality related parameters of the sewage treatment system at the current moment; A first acquisition module is used to input the blower aeration parameters and the water quality related parameters into a pre-acquired effluent ammonia nitrogen classification model to obtain an effluent ammonia nitrogen water quality classification result; the effluent ammonia nitrogen classification model is used to classify and predict the water quality of effluent ammonia nitrogen according to the blower aeration parameters and the water quality related parameters; The effluent ammonia nitrogen water quality classification result is related to the effluent ammonia nitrogen concentration; A first adjustment module is used to obtain the blower aeration adjustment direction and blower aeration adjustment parameters according to the control strategy corresponding to the effluent ammonia nitrogen water quality classification result; The control strategy is used to indicate the blower aeration adjustment direction and blower aeration adjustment parameters corresponding to different effluent ammonia nitrogen water quality classification results; adjusting the aeration parameters of the blower according to the aeration adjustment direction of the blower and the aeration adjustment parameters of the blower; The second acquisition module is used to obtain the effluent ammonia nitrogen classification model according to the following steps: Acquire historical data of the sewage treatment system; the historical data includes water quality related parameters, effluent ammonia nitrogen concentration and blower aeration parameters at different times; Performing time series alignment processing on the historical data to obtain processed historical data; the processed historical data includes the effluent ammonia nitrogen concentration and the blower aeration parameter at the second historical moment corresponding to the water quality related parameters at the first historical moment, and the blower aeration parameter at the third historical moment corresponding to the water quality related parameters at the first historical moment; Using the water quality related parameters and blower aeration parameters in the processed historical data as input features and the effluent ammonia nitrogen concentration in the processed historical data as a target feature, training a machine learning model to obtain a trained machine learning model; The effluent ammonia nitrogen classification model is obtained according to the effluent ammonia nitrogen concentration, the ammonia nitrogen water quality standard limit and the trained machine learning model.

7. An aeration control device, characterized in that: include: A processor, a memory, and a program stored in the memory and executable on the processor, wherein the program, when executed by the processor, implements the aeration control method according to any one of claims 1 to 5.

8. A readable storage medium, characterized in that: The readable storage medium stores a program, and when the program is executed by a processor, the aeration control method according to any one of claims 1 to 5 is implemented.

9. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor, implement the aeration control method according to any one of claims 1 to 5.

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