Water quality prediction method, system, device and readable storage medium

Abstract

The application provides a water quality prediction method, system, device and readable storage medium. The water quality prediction method comprises the following steps: obtaining initial water quality data; inputting the initial water quality data into a sewage treatment model; and performing rolling calculation on the initial water quality data based on the sewage treatment model to obtain water quality prediction data. According to the water quality prediction data, the effluent water quality of the sewage plant can be simulated in advance, the effluent water quality of the sewage plant is warned in advance, relevant measures are taken in time, the effluent water quality of the sewage plant is ensured to be stable and up to standard, and the possibility of the effluent water of the sewage plant polluting the environment is reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of water quality prediction, and relates to a water quality prediction method, in particular to a water quality prediction method, system, device and readable storage medium. BACKGROUND

[0002] In the process of sewage treatment by the current sewage plant, due to environmental factors, improper addition of drugs and chemicals, improper operation and management and other factors, the effluent water quality after treatment by the sewage plant is out of standard, which brings great threat to rivers, soil, people's health and life. Therefore, how to predict the effluent water quality after treatment by the sewage plant has become one of the technical problems to be solved. SUMMARY

[0003] The application provides a water quality prediction method, system, device and readable storage medium, which are used for predicting the effluent water quality of a sewage plant in advance.

[0004] In a first aspect, an embodiment of the application provides a water quality prediction method, which comprises: acquiring initial water quality data; inputting the initial water quality data into a sewage treatment model; and performing rolling calculation on the initial water quality data based on the sewage treatment model to obtain water quality prediction data.

[0005] In an implementation manner of the first aspect, the inputting of the initial water quality data into the sewage treatment model comprises: acquiring first water quality source data; performing a preprocessing operation on the first water quality source data to obtain second water quality source data; sending the second water quality source data to a water quality database; establishing a connection between the water quality database and the sewage treatment model; extracting a group of the second water quality source data from the water quality database as the initial water quality data, and inputting the initial water quality data into the sewage treatment model.

[0006] In an implementation manner of the first aspect, the performing of the rolling calculation on the initial water quality data based on the sewage treatment model to obtain the water quality prediction data comprises: determining time length information, frequency information and output step length information of the rolling calculation; and performing the rolling calculation on the initial water quality data based on the sewage treatment model according to the time length information, the frequency information and the output step length information to obtain the water quality prediction data.

[0007] In an implementation form of the first aspect, the rolling calculation of the initial water quality data based on the sewage treatment model comprises: inputting the initial water quality data into the sewage treatment model to obtain first water quality prediction data; inputting first water quality data in last water quality prediction data into the sewage treatment model to obtain the current water quality prediction data, and repeating the step until a termination condition is met, the first water quality data being static index data in the water quality prediction data.

[0008] In an implementation form of the first aspect, second water quality data in the water quality prediction data is used to predict effluent water quality of the sewage plant corresponding to the sewage treatment model within a preset time.

[0009] In an implementation form of the first aspect, the water quality prediction method further comprises: if the second water quality data is less than or equal to a water quality threshold, determining that the effluent water quality of the sewage plant within the preset time is normal water quality; and if the second water quality data is greater than the water quality threshold, determining that the effluent water quality of the sewage plant within the preset time is abnormal water quality.

[0010] In an implementation form of the first aspect, the construction method of the sewage treatment model comprises: constructing an initial water quality model; correcting the initial water quality model based on historical water quality data to obtain a corrected water quality model; and verifying the corrected water quality model based on real-time water quality data to obtain the sewage treatment model.

[0011] The water quality prediction method provided by the embodiments of the present application can perform rolling calculation on initial water quality data based on a sewage treatment model to obtain water quality prediction data. In this way, the effluent water quality of the sewage plant can be simulated in advance. The staff can adjust the sewage treatment link of the sewage plant in a timely manner according to the water quality prediction data, so as to ensure that the effluent water quality of the sewage plant is normal water quality and reduce the possibility of water pollution of the environment by the sewage plant.

[0012] In a second aspect, the embodiments of the present application provide a water quality prediction system, comprising: a water quality data acquisition module configured to acquire initial water quality data; a water quality data input module configured to input the initial water quality data into a sewage treatment model; and a water quality data prediction module configured to perform rolling calculation on the initial water quality data based on the sewage treatment model to obtain water quality prediction data.

[0013] In a third aspect, the embodiments of the present application provide an electronic device, comprising a memory and a processor; the memory is configured to store a computer program; and the processor is configured to execute the computer program and implement the water quality prediction method according to any one of the first aspect of the embodiments of the present application when executing the computer program.

[0014] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, having stored thereon a computer program, wherein the computer program is executed by a processor to implement the water quality prediction method according to any one of the first aspect of the embodiments of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0015] FIG. 1A FIG. 1 shows a schematic diagram of an application scenario of the water quality prediction method according to an embodiment of the present application.

[0016] FIG. 1B FIG. 2 shows a flowchart of the water quality prediction method according to an embodiment of the present application.

[0017] FIG. 2 FIG. 3 shows a flowchart of inputting initial water quality data into a sewage treatment model according to an embodiment of the present application.

[0018] FIG. 3 FIG. 4 shows a flowchart of determining water quality prediction data according to an embodiment of the present application.

[0019] FIG. 4 FIG. 5 shows a flowchart of performing rolling calculation on the initial water quality data based on the sewage treatment model according to an embodiment of the present application.

[0020] FIG. 5 FIG. 6 shows a flowchart of constructing the sewage treatment model according to an embodiment of the present application.

[0021] FIG. 6 FIG. 7 shows a schematic diagram of the sewage treatment model according to an embodiment of the present application.

[0022] FIG. 7 FIG. 8 shows another flowchart of performing rolling calculation on the initial water quality data based on the sewage treatment model according to an embodiment of the present application.

[0023] FIG. 8 FIG. 9 shows a schematic diagram of a water quality prediction system according to an embodiment of the present application.

[0024] FIG. 9 FIG. 10 shows a structural schematic diagram of an electronic device according to an embodiment of the present application.

[0025] ELEMENT NUMBER EXPLANATION

[0026] S11-S13 steps

[0027] S21-S25 steps

[0028] S31-S32 steps

[0029] S41-S42 steps

[0030] S51-S53 steps

[0031] S701-S712 steps

[0032] 600 sewage treatment model

[0033] 601 water input model

[0034] 602 carbon source input model

[0035] 603 biochemical reactor

[0036] 604 carbon source input model

[0037] 605 biochemical reactor

[0038] 606 first sedimentation tank model

[0039] 607 second sedimentation tank model

[0040] 608 second sedimentation tank model

[0041] 609 membrane filtration model

[0042] 610 membrane filtration model

[0043] 611 chemical disinfection model

[0044] 612 output model

[0045] 80 water quality prediction system

[0046] 81 water quality data acquisition module

[0047] 82 water quality data input module

[0048] 83 water quality data prediction module

[0049] 90 electronic device

[0050] 91 processor

[0051] 92 non-volatile storage medium

[0052] 93 system bus

[0053] 94 internal memory

[0054] 95 network interface DETAILED DESCRIPTION

[0055] Following, the advantages and effects of the present application can be easily understood by those skilled in the art from the description. The present application can also be implemented or applied by different specific embodiments, and the details in the description can be modified or changed based on different views and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0056] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concepts of the present application in a schematic manner, and only the components related to the present application are shown in the diagrams, not the number, shape and size of the components when actually implemented. The shapes, numbers and proportions of the components when actually implemented can be arbitrarily changed, and the layout of the components can be more complex.

[0057] The prior art lacks a water quality prediction method for predicting the effluent quality of a sewage plant in advance.

[0058] To at least solve the above problems, the present application provides a water quality prediction method. In the water quality prediction method, initial water quality data is obtained, the initial water quality data is input into a sewage treatment model, and the initial water quality data is calculated based on the sewage treatment model to obtain water quality prediction data, which can solve the technical problem that the prior art cannot predict the effluent quality of a sewage plant in advance.

[0059] FIG. 1A An application scenario of the water quality prediction method provided by an embodiment of the present application is shown. As shown in FIG. 1A The application scenario includes a data acquisition device and an electronic device, and the water quality database is located in the electronic device. The data acquisition device is used to acquire data related to water quality prediction, including a water quality analyzer, a water quality sensor, etc.

[0060] The electronic device of the present application is used to transmit the initial water quality data in the water quality database to a data processing device through a network.

[0061] The data processing device of the present application includes a water quality analyzer, a water quality sensor, etc., which is used to receive the initial water quality data sent by the electronic device and simulate the process of sewage treatment in a sewage plant in advance based on the initial water quality data.

[0062] The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application.

[0063] FIG. 1B A flowchart of the water quality prediction method in an embodiment of the present application is shown. As shown in FIG. 1BAs shown, the water quality prediction method provided by the embodiments of the present application includes the following steps S11 to S13.

[0064] S11, obtaining initial water quality data.

[0065] The data indicators corresponding to the initial water quality data include total suspended solids, total phosphorus, chemical oxygen demand, ammonia nitrogen and biochemical oxygen demand, pH value, etc.

[0066] The initial water quality data is pre-processed data.

[0067] For example, the initial water quality data is extracted from the water quality database of the sewage plant.

[0068] It should be noted that the above-mentioned initial water quality data is the water quality data corresponding to the sewage to be treated in the sewage plant.

[0069] S12, inputting the initial water quality data into the sewage treatment model.

[0070] Before inputting the initial water quality data into the sewage treatment model, the data input format of the sewage treatment model is confirmed, and the data format of the initial water quality data is modified to a format that can be input into the sewage treatment model according to the data input format of the sewage treatment model.

[0071] For example, the data input format of the sewage treatment model can be CSV, TXT, etc.

[0072] It should be noted that the above-mentioned data input format of the sewage treatment model is only used for illustrative purposes, and in actual application, the data input format of the sewage treatment model can be reasonably set according to actual needs, and the present application does not limit this.

[0073] Specifically, the process of inputting the initial water quality data into the sewage treatment model can include: finding the corresponding data input cell on the data input page of the sewage treatment model, and inputting the collected initial water quality data into the corresponding cell one by one.

[0074] Further specifically, if the sewage treatment model is executed based on a program design language, the process of inputting the initial water quality data into the sewage treatment model can also include inputting the initial water quality data in the program design input interface where the sewage treatment model is located.

[0075] S13, based on the sewage treatment model, the initial water quality data is calculated to obtain water quality prediction data.

[0076] The water quality prediction data is the prediction result of the water quality data corresponding to the effluent water quality after the sewage plant treats the sewage in a future period of time.

[0077] For example, the process of rolling calculation is as follows: the initial water quality data is input into the wastewater treatment model to perform model calculation, and water quality prediction data t1 is obtained. The first water quality data t10 in the water quality prediction data t1 is input into the wastewater treatment model to perform model calculation, and water quality prediction data t2 is obtained; the second water quality data t11 in the water quality prediction data t1 is directly output from the output port of the wastewater treatment model, and is used to predict the effluent water quality of the wastewater treatment plant. The first water quality data t20 in the water quality prediction data t2 is input into the wastewater treatment model to perform model calculation, and water quality prediction data t2 is obtained; the second water quality data t21 in the water quality prediction data t2 is directly output from the output port of the wastewater treatment model, and is used to predict the effluent water quality of the wastewater treatment plant. The first water quality data tn0 in the water quality prediction data tn is input into the wastewater treatment model to perform model calculation, and water quality prediction data tn+1 is obtained; the second water quality data tn1 in the water quality prediction data tn is directly output from the output port of the wastewater treatment model, and is used to predict the effluent water quality of the wastewater treatment plant. The above model calculation steps are repeated until the termination condition is met.

[0078] In the water quality prediction method provided in the embodiment of the present application, the obtained initial water quality data is input into the wastewater treatment model, the initial water quality is rolling calculated in the wastewater treatment model, and the water quality prediction result is obtained, so that the full process simulation of the wastewater treatment plant is realized, and the technical problem that the effluent water quality after the wastewater treatment plant is processed cannot be predicted is solved.

[0079] FIG. 2 A flowchart for inputting the initial water quality data into the wastewater treatment model in the embodiment of the present application is shown. As shown in FIG. 2 The process of inputting the initial water quality data into the wastewater treatment model in the embodiment of the present application includes the following steps S21 to S25.

[0080] S21, obtaining first water quality source data.

[0081] The first water quality source data is directly extracted from untreated wastewater.

[0082] It should be noted that the data index corresponding to the first water quality source data is the same as the data index corresponding to the initial water quality data, that is, the first water quality source data includes a plurality of water quality data.

[0083] For example, the data index can include total suspended solids, total phosphorus, chemical oxygen demand, ammonia nitrogen, etc. The water quality data in the first water quality source data at least includes water quality data corresponding to total suspended solids, water quality data corresponding to total phosphorus, water quality data corresponding to chemical oxygen demand, and water quality data corresponding to ammonia nitrogen.

[0084] Exemplarily, the first water quality source data in sewage can be extracted based on a water quality analyzer. The water quality analyzer can be any one of a multi-parameter water quality analyzer, a handheld spectral analyzer, etc.

[0085] Exemplarily, the first water quality source data in sewage can also be extracted based on a water quality sensor. The water quality sensor can be any one of a pH sensor, a turbidity sensor, a conductivity sensor, etc.

[0086] It should be noted that the water quality analyzer and the water quality sensor used for extracting the first water quality source data in sewage are only used for illustrative purposes. In actual applications, appropriate instruments / devices can be selected to extract the first water quality source data in sewage according to specific conditions, and the present application does not limit this.

[0087] S22, performing a preprocessing operation on the first water quality source data to obtain second water quality source data.

[0088] The preprocessing operation includes any one or a combination of multiple operations of data cleaning, data conversion, data verification, data reduction, etc.

[0089] Exemplarily, if there is any one or more of data missing, data duplication, data anomaly, etc. in the first water quality source data extracted directly from sewage, the data cleaning operation in the preprocessing operation can be performed on the first water quality source data. After the first water quality source data is subjected to the data cleaning operation, the second water quality source data is obtained.

[0090] Exemplarily, if the data formats of multiple data in the first water quality source data are not uniform, the first water quality source data can be subjected to data conversion according to the situation, the data format of the first water quality source data can be adjusted, or the first water quality source data can be normalized, etc. to obtain the second water quality source data.

[0091] The data conversion includes any one or a combination of multiple operations of normalization and standardization, data discretization, data encoding, etc.

[0092] It should be noted that when the preprocessing operation is performed on the first water quality source data, any one or multiple data operation modes in the preprocessing operation can be selected according to the actual data processing condition of the first water quality source data, and the present application does not limit this.

[0093] S23, sending the second water quality source data to a water quality database.

[0094] The water quality database stores multiple sets of second water quality source data.

[0095] Exemplarily, before sending the second water quality source data to the water quality database, it is required to establish a connection between the second water quality source data and the water quality database. After the connection is established, the second water quality source data is sent to the water quality database using an insert command of the database. After all the second water quality source data is inserted into the water quality database, the connection of the water quality database is closed.

[0096] The insert command of the water quality database can be an SQL insert statement, an SQL insert default value, etc.

[0097] It should be noted that the above-mentioned insert command of the water quality database is only used for exemplary illustration, and in actual application, a suitable insert command can be selected according to the specific situation, and the present application does not limit this.

[0098] S24, establishing a connection between the water quality database and the sewage treatment model.

[0099] Before establishing a connection between the water quality database and the sewage treatment model, the connection information of the water quality database is confirmed.

[0100] Exemplarily, the water quality database and the sewage treatment model can be connected by any one of the two connection modes of direct connection and middleware connection.

[0101] The direct connection is suitable for the case that the sewage treatment model directly interacts with the water quality database.

[0102] Specifically, the middleware connection refers to using middleware services to bridge the water quality database and the sewage treatment model. The middleware services can be API services, message queue services, etc.

[0103] It should be noted that in addition to the two connection modes given in the present application for connecting the water quality database and the sewage treatment model, in actual application, any other suitable connection mode can be selected according to the specific situation, and the present application does not limit this.

[0104] S25, extracting a set of second water quality source data from the water quality database as the initial water quality data, and inputting the initial water quality data into the sewage treatment model.

[0105] The set of second water quality source data includes all data indicators.

[0106] Specifically, before the effluent water quality of the sewage plant needs to be predicted, a set of second water quality source data is extracted from the water quality database in advance. The extracted set of second water quality source data is used as the initial water quality data.

[0107] The embodiment of the present application provides a method for inputting initial water quality data into a sewage treatment model. Through data preprocessing operation on the first water quality source data, data cleaning operation can be performed on the abnormal data in the first water quality source data, or the data format of all data in the first water quality source data is uniformly adjusted to obtain adjusted second water quality source data, thereby providing an accurate data basis for subsequent input of initial water quality data into the sewage treatment model. The preprocessed multiple sets of second water quality source data are sent to a water quality database for storage, so as to provide subsequent extraction of a set of second water quality source data as initial water quality data input into the sewage treatment model. The method avoids the reduction of the processing efficiency of the sewage treatment model due to the data error of the initial water quality data. Through the data preprocessing operation, the accuracy of the initial water quality data is improved, and the processing efficiency of the subsequent rolling calculation of the initial water quality data in the sewage treatment model is improved.

[0108] FIG. 3 A flowchart for determining water quality prediction data in an embodiment of the present application is shown. As shown in FIG. 3 the process for determining water quality prediction data in the embodiment of the present application includes the following steps S31 to S32.

[0109] S31, determine the time length information, frequency information and output step length information of the rolling calculation.

[0110] The time length information represents the calculation interval of the rolling calculation, the frequency information represents the update frequency of the initial water quality data, and the output step length information represents the time interval of the calculation result output.

[0111] The rolling calculation can be performed in the sewage treatment model.

[0112] Specifically, the time length information, frequency information and output step length information of the rolling calculation are determined before the rolling calculation.

[0113] For example, the time length information of the rolling calculation is set to 30 days, the frequency information is set to 1 day, that is, the initial water quality data is updated every day within the 30-day time period, and the output step length information is set to 3 days, that is, the water quality prediction result is output every 3 days.

[0114] It should be noted that the above specific time settings of the time length information, frequency information and output step length information are only used for exemplary description, and in actual application, appropriate time corresponding to the time length information, frequency information and output step length information can be selected according to the specific application scene, and the present application itself does not limit this.

[0115] S32, performing rolling calculation on the initial water quality data based on the sewage treatment model according to the time length information, the frequency information and the output step length information, to obtain the water quality prediction data.

[0116] Specifically, after determining the time length information, the frequency information and the output step length information, the initial water quality data is input into the sewage treatment model, and the initial water quality data is calculated based on the sewage treatment model to obtain the water quality prediction data.

[0117] The embodiments of the present application determine the time length information, the frequency information and the output step length information of rolling calculation, perform rolling calculation on the initial water quality data based on the sewage treatment model according to the time length information, the frequency information and the output step length information of rolling calculation, obtain the water quality prediction data, simulate the effluent water quality of the sewage plant in advance according to the water quality prediction data, give early warning of the effluent water quality of the sewage plant in advance, and take timely measures to ensure that the effluent water quality of the sewage plant is stable and up to standard.

[0118] FIG. 4 A flowchart for performing rolling calculation on the initial water quality data based on the sewage treatment model in an embodiment of the present application is shown. As shown in FIG. 4, the process of performing rolling calculation on the initial water quality data based on the sewage treatment model in an embodiment of the present application includes the following steps S41-S42. FIG. 4

[0119] S41, inputting the initial water quality data into the sewage treatment model to obtain first water quality prediction data.

[0120] The water quality prediction data includes first water quality data.

[0121] Specifically, according to the previously set step length information, after inputting the initial water quality data into the sewage treatment model for model calculation, the first water quality prediction data is output, that is, the first water quality prediction data is the water quality prediction result corresponding to the first step length information.

[0122] S42, inputting the first water quality data in the last water quality prediction data into the sewage treatment model to obtain the current water quality prediction data, and repeating this step until a termination condition is met.

[0123] The first water quality data is static index data in the numerical prediction data, and the static index data specifically includes PH value, biochemical oxygen demand, chemical oxygen demand, hardness, alkalinity and / or dissolved oxygen, etc.

[0124] ​The termination condition can be time length information, and the rolling calculation is terminated when the running time of the rolling calculation is greater than a time interval corresponding to the time length information. In addition, the termination condition can also be the number of obtained water quality prediction data or any other suitable termination behavior of the rolling calculation, and the present application does not limit the termination condition.

[0125] The specific process of the rolling calculation is as follows: the initial water quality data is input into the sewage treatment model for calculation in the first cycle, and the output of the sewage treatment model is the prediction data obtained by the first water quality prediction, which is referred to as the first water quality prediction data. The first water quality data in the first water quality prediction data is input into the sewage treatment model for calculation, and the output of the sewage treatment model is the second water quality prediction data. The first water quality data in the second water quality prediction data is input into the sewage treatment model for calculation, and the output of the sewage treatment model is the third water quality prediction data. Similarly, the training is stopped when the termination condition is met.

[0126] In some embodiments, the second water quality data in the water quality prediction data is used to predict the effluent water quality of the sewage treatment plant corresponding to the sewage treatment model within a preset time. The second water quality data is the data after weakening processing.

[0127] The second water quality data represents the water quality state of the sewage treatment plant at a specific time point or time period.

[0128] The preset time can be one week, two weeks, or one month in the future, etc.

[0129] It should be noted that the above-mentioned preset time can be reasonably determined according to the specific operation condition of the sewage treatment plant, and the present application does not limit the preset time.

[0130] In some embodiments, the water quality prediction method further comprises: if the second water quality data is less than or equal to a water quality threshold, determining that the effluent water quality of the sewage treatment plant within the preset time is normal water quality; if the second water quality data is greater than the water quality threshold, determining that the effluent water quality of the sewage treatment plant within the preset time is abnormal water quality.

[0131] Specifically, the specific setting of the water quality threshold should comply with the setting of the environmental protection agency or relevant regulations. The specific setting of the water quality threshold can be reasonably set according to the requirements or purposes in different regions and different scenes, and the present application does not limit the specific setting of the water quality threshold.

[0132] For example, if the second water quality data is less than or equal to the water quality threshold, it is determined that the effluent water quality of the sewage plant in the preset time is normal water quality. At this time, it indicates that each water treatment submodule of the sewage plant operates normally in the preset time, and any modification of each water treatment submodule in the sewage plant is not required. On the contrary, if the second water quality data is greater than the water quality threshold, it is determined that the effluent water quality of the sewage plant in the preset time is abnormal water quality. At this time, the corresponding water treatment submodule in the sewage plant needs to be adjusted appropriately. For example, if the carbon-nitrogen ratio (C / N) in the second water quality data is greater than the carbon-nitrogen ratio (C / N) in the water quality threshold, and the carbon-phosphorus ratio (C / P) is greater than the carbon-phosphorus ratio (C / P) in the water quality threshold, the carbon source addition amount in the carbon source addition module in the sewage plant needs to be increased in time to promote better nitrification and denitrification of microorganisms and removal of nitrogen and phosphorus.

[0133] In the embodiment of the present application, a method for rolling calculation of initial water quality data based on a sewage treatment model is provided. The initial water quality data is input into the sewage treatment model to obtain first water quality prediction data. Then, the first water quality data in the last water quality prediction data is input into the sewage treatment model again for calculation to obtain current water quality prediction data. The above steps are repeated until a termination condition is met. Through rolling calculation, the effluent water quality of the sewage plant is tracked throughout the process, the effluent water quality of the sewage plant is simulated in advance, water quality prediction data is obtained, and appropriate measures are taken in time for each water treatment submodule of the sewage plant according to the water quality prediction data, so as to ensure that the effluent of the sewage plant is stable and meets the standards.

[0134] Please refer to FIG. 5 , FIG. 5 The flowchart shows the process of constructing a sewage treatment model in an embodiment of the present application. As shown in FIG. 5 , the process of constructing a sewage treatment model in an embodiment of the present application includes the following steps S51 to S53.

[0135] S51, constructing an initial water quality model.

[0136] For example, the initial water quality model is constructed based on the process line of the sewage plant.

[0137] S52, correcting the initial water quality model based on historical water quality data to obtain a corrected water quality model.

[0138] The historical water quality data includes historical laboratory data, historical online monitoring and operation data.

[0139] Specifically, after the initial water quality model is constructed, historical water quality data is input to the initial water quality model for correction. In the correction process, each parameter in the initial water quality model can be adjusted according to the data output of the initial water quality model, so that the initial water quality model approximates the actual treatment effect of the sewage plant.

[0140] S53, verifying the corrected water quality model based on real-time water quality data to obtain the sewage treatment model.

[0141] The real-time water quality data is data of the sewage plant when treating sewage.

[0142] For example, the data of the sewage plant when treating sewage is extracted and input to the corrected water quality model. After data processing of the corrected water quality model, output water quality data is obtained. The sewage plant outputs treated water, and the water quality data of the treated water of the sewage plant is compared with the water quality data output by the corrected water quality model. If the water quality error between the water quality data of the treated water of the sewage plant and the water quality data output by the corrected water quality model is within the error allowable range, it means that the corrected water quality model passes the verification, and the corrected water quality model is the sewage treatment model. Otherwise, if the water quality error between the water quality data of the treated water of the sewage plant and the water quality data output by the corrected water quality model is not within the error allowable range, the corrected water quality model is verified again based on real-time water quality data until the water quality error between the water quality data of the treated water of the sewage plant and the water quality data output by the corrected water quality model is within the error allowable range.

[0143] The embodiment of the present application provides a sewage treatment model construction method. An initial water quality model is constructed through a process line of a sewage plant, and the constructed initial water quality model is corrected based on historical water quality data and verified based on real-time water quality data to obtain a sewage treatment model, so as to ensure the accuracy of the sewage treatment model and provide an accurate model basis for subsequent rolling calculation based on the sewage treatment model.

[0144] As shown in FIG. 6, FIG. 6 In an embodiment, the sewage treatment model 600 of the present application includes an influent input model 601, carbon source input models 602 and 604, biochemical reactors 603 and 605, a first sedimentation tank model 606, second sedimentation tank models 607 and 608, membrane filtration models 609 and 610, a chemical disinfection model 611, and an output model 612.

[0145] In the influent input model 601, influent input includes COD of municipal sewage, nitrogen of municipal sewage, phosphorus of municipal sewage, and the like.

[0146] The general composition of municipal wastewater COD is: ASM2d contains 9 dissolved substances (S_S is not included) and 10 particulate substances, and the dissolved and particulate states are represented by S_* and X_*, respectively. In order to explain the phenomenon of the dominant reproduction and elimination of certain species of microorganisms in the activated sludge system, the biomass part in the influent needs to be considered. If the content of bacteria in the influent is high, even in the high load state that may be eliminated, the reaction process may occur.

[0147] In order to explain the value-added and eliminated phenomenon of phosphorus accumulating bacteria and autotrophic bacteria in the system, the phosphorus accumulating bacteria and autotrophic bacteria in the influent are included in the influent parameters in the embodiments of the present application. In most cases, the content of autotrophic bacteria X_AUT in the influent is very low, and the content of phosphorus accumulating bacteria X_PAO is also very low. It should be noted that X_PAO does not include stored polyhydroxyalkanoate X_PHA, which is generally considered to be an independent compound. Similarly, the polyphosphate stored in the phosphorus accumulating bacteria is also considered to be an independent component, which means that the contribution of the phosphorus accumulating bacteria to the total suspended solids comes from three parts: X_PAO, X_PHA and X_PP.

[0148] In addition, the influent component model can be divided into two types of models based on composition and component. Total COD, TKN and ammonia nitrogen are used as main influent characteristic inputs, and model components include soluble inert COD (si), easily biodegradable substrate (ss, sf or slf, etc.), particulate inert substance (xi), non-biodegradable cell decay product (xu) and biomass concentration (xbh, xba and xbp) are specified by the proportion fraction of total COD. The main difference lies in the determination of organic nitrogen and phosphorus components. The former uses explicit static variables such as dissolved total organic nitrogen and particulate total organic nitrogen, and the latter uses component proportion of static variables of COD to simulate organic nitrogen and phosphorus, and uses ammonia nitrogen and phosphorus in the influent as main correction parameters to maintain the input of nitrogen and phosphorus in the model. The model based on the composition analysis method is: ASM1, Barker & Dold and UCT PHO+, and the model based on the component analysis method is: ASM2d, ASM3, ASM3+ Bio-P and TU Delft.

[0149] In the activated sludge system in the endogenous respiration state, wastewater is added, and the respiration rate is detected until it returns to the endogenous level. Integrating the respiration rate curve with respect to time is equivalent to the soluble easily degradable organic matter Ss and Xs.

[0150]

[0151] wherein V T represents the reactor volume, V W represents the wastewater volume, Ss and Xs represent the soluble easily degradable organic matter, Y Hwherein, Y represents the yield coefficient, t1 represents the time of rapid degradation of organic matter by microorganisms, t2 represents the time of slow degradation of organic matter by microorganisms, R1 represents the OUR (Oxygen Uptake Rate) change curve when the microorganisms rapidly degrade the organic matter, R2 represents the OUR change curve when the microorganisms slowly degrade the organic matter, and R3 represents the OUR change curve when the microorganisms undergo endogenous respiration.

[0152] In some implementations, the rapid oxygen demand Ss can be decomposed and calculated through the material balance equation of continuous experiments, or through the inflection point analysis of the multi-component substrate respiration rate curve.

[0153] Estimating Xs, affecting the denitrification performance of the system: Method 1: the ratio of BOD to (1-fBOD) is BCOD, wherein fBOD is 0.15, used to compensate for the biologically undegradable inert residual substances generated by biomass decay, and the difference between BCOD and Ss is Xs; Method 2: the respiratory response of XS is very small, the experimental measurement is long, and the nitrification effect is superimposed, and an optimal parameter estimation algorithm is used to eliminate the nitrification effect, and the formula is:

[0154]

[0155] wherein, represents the proportion of slowly degradable organic matter to COD, COD represents the chemical oxygen demand, BOD U represents the final biochemical oxygen demand, S S represents the rapidly biodegradable organic matter.

[0156] Estimating Xi, affecting the sludge production and affecting the MLSS and the settling performance: the difference between COD and BOD is Si and Xi, Xi is calculated by knowing Si, and the formula is:

[0157]

[0158] wherein, represents the content of inert particulate organic matter, represents the content of inert soluble organic matter, COD represents the chemical oxygen demand, S I represents the biologically undegradable dissolved organic matter, BOD U represents the final biochemical oxygen demand, BOD5 represents the 5-day biochemical oxygen demand, f BOD represents the ratio of BOD5 / BOD U .

[0159] The main component of total nitrogen in municipal wastewater is reduced nitrogen, or exists in the form of ammonia nitrogen, or exists in the form of amino compounds in organic matter. Among them, ammonia nitrogen is the main reduced component, usually accounting for 60% to 70% of total Kjeldahl nitrogen, mainly from urea, which is quickly hydrolyzed, so it is rarely found in wastewater treatment plants. In raw sewage, low concentrations of nitrate or nitrite can be found, usually ranging from 0 to 1 mg / L, mainly from infiltration water, and if the concentration is high, it may come from groundwater and industrial wastewater. In the component-based model, organic nitrogen is generally divided into dissolved and particulate states, and the particulate state must undergo a hydrolysis step to be converted into dissolved state before it can be converted into ammonia nitrogen through ammonification reaction (ASM1). By measuring ammonia nitrogen and TN, deducting nitrate and nitrite, calculating TKN, assuming the proportion of ammonia nitrogen in dissolved TKN, calculating sTKN, and calculating xTKN from TKN and sTKN. In ASM2d, organic nitrogen is further accounted for using the component ratio of the static variable of COD, and ammonia nitrogen and total nitrogen (or TKN) in the influent are used as the main correction parameters to maintain the input of nitrogen in the model.

[0160] Determine the N component by measuring the ammonia nitrogen and TN in the influent:

[0161] S TKN = S NH / f rsNH

[0162] Where S TKN represents the concentration of dissolved Kjeldahl nitrogen, S NH represents the concentration of ammonia nitrogen, and f rsNH represents the proportion of ammonia nitrogen in dissolved Kjeldahl nitrogen.

[0163] Carbon source input models 602 and 604 are used to input chemical doses or water in the wastewater treatment process. It is convenient to convert typical chemical components into state variables used in wastewater treatment models.

[0164] In biochemical reactors 603 and 605, biochemical tanks are mainly used for removal of organic matter and denitrification and phosphorus removal, so the modified version of activated sludge model No. 2 (ASM2d) can be used to simulate the biochemical tank. ASM2d model adds two processes to explain that PAOs can use intracellular organic storage for denitrification, unlike ASM2 which assumes that PAOs only grow under aerobic conditions, ASM2d includes denitrifying PAOs. ASM2d includes four main functions: carbon oxidation, nitrification, denitrification, and biological phosphorus removal.

[0165] In the first sedimentation tank model 606, the common first sedimentation tank model includes one or more of the following: point sedimentation tank model, volumetric ideal sedimentation tank model, layered sedimentation tank model, CFD sedimentation tank model, and reaction sedimentation tank model.

[0166] For individual discrete non-flocculating particles (homogeneous, e.g. sand), the settling velocity is the same regardless of its concentration. The settling velocity of a single particle is only related to shape, size (diameter) and density. The settling behavior of the MLSS mixture from an activated sludge reactor is very different from a sand particle suspension, mainly due to their flocculating nature. At the beginning of the SVI or zone settling rate test, the sludge containing both organic floes and inorganic particles will settle as flocculated aggregates (free settling), and the settling velocity will be strongly influenced by the MLSS concentration.

[0167] The zone settling process goes through free settling, flocculated settling, and laminar settling until no layer of the original MLSS concentration exists - it has settled through a transition zone into the compression zone (compression settling). Compression settling is distinctly different from zone settling - the particles support each other, and compression is achieved by squeezing water out of the sludge floc. The settling velocity is no longer a function of the sludge concentration, but depends on the void pressure and the compressibility and permeability of the sludge.

[0168] In the second settling tank models 607 and 608, this physico-chemical process is approximated by a combination of the respective phosphorus removal unit and a filtration settling model. The stoichiometric ratio is used as a basis for the settling model, but the complexity of the wastewater chemistry, including the influence of pH, alkalinity and other elements, means that the stoichiometric ratio is only the maximum phosphorus removal that can be achieved with a large excess of chemicals. In practice, the removal is much lower, as not all of the chemicals added are used for phosphorus addition. However, the fraction that is available, and thus the required dosage, can be estimated or calibrated for different locations and specified in the model.

[0169] In the membrane filtration models 609 and 610, the cloth filter tank is chosen as a zero- volume membrane filter tank model, in which the soluble state variables are not affected, and the particulate state variables are calculated based on a solids separation fraction factor (empirical removal efficiency) and mass conservation.

[0170] In the chemical disinfection model 611, the chemical disinfection model calculates the fraction of surviving E. coli based on the residence time in the disinfection device. The state variables are not affected by this model.

[0171] In the effluent model 612, the effluent quality index model in the effluent model 612 uses user-specified concentration limits and specific compound weighting factors to estimate.

[0172] The wastewater quality index represents the wastewater pollution load to the receiving water body, and is estimated using the expression provided in the COST simulation benchmark:

[0173]

[0174] wherein Q(t) represents the instantaneous effluent flow rate, n represents the number of compounds in the EQI estimation, w i represents the weight coefficient of the compounds in the EQI, S i (t) represents the instantaneous concentration of the compounds in the EQI estimation, T represents the time period of the moving average calculation, and t represents the starting time of the moving average calculation.

[0175] FIG. 7 A flowchart showing another process of rolling calculation of initial water quality data based on a sewage treatment model in an embodiment of the present application is shown. As shown in the flowchart, the method of rolling calculation of initial water quality data based on a sewage treatment model provided by the embodiment of the present application includes the following steps S700 to S712. FIG. 7

[0176] S700, obtaining first water quality source data.

[0177] The first water quality source data is directly extracted from untreated sewage.

[0178] S701, obtaining second water quality source data.

[0179] Specifically, the second water quality source data is obtained after the first water quality source data is preprocessed.

[0180] S702, sending to a water quality database.

[0181] The second water quality source data is sent to the water quality database.

[0182] S703, extracting initial water quality data.

[0183] Specifically, a group of second water quality source data is extracted from the water quality database as the initial water quality data.

[0184] S704, determining time length information.

[0185] S705, determining frequency information.

[0186] S706, determining output step information.

[0187] S707, inputting a sewage treatment model.

[0188] Specifically, the determined time length information, frequency information and output step information are inputted into the sewage treatment model to set corresponding parameters of the sewage treatment model.

[0189] S708, performing model calculation.

[0190] Specifically, the initial water quality data is calculated in the sewage treatment model.

[0191] ​S709, obtaining water quality prediction data.

[0192] Specifically, after the initial water quality data is calculated in the sewage treatment model, the water quality prediction data is obtained.

[0193] S710, obtaining first water quality data.

[0194] Specifically, the water quality prediction data includes the first water quality data and the second water quality data, and the first water quality data extracted from the water quality prediction data will enter the sewage treatment model again for model calculation. The step of inputting the first water quality data into the sewage treatment model again for model calculation is repeated until the termination condition is met.

[0195] S711, obtaining second water quality data.

[0196] S712, predicting effluent water quality of the sewage plant.

[0197] Specifically, the effluent water quality of the sewage plant is predicted based on the second water quality data, and the effluent water is pre-warned in advance, and the aeration amount is adjusted in time or the carbon source is added to ensure that the effluent water of the sewage plant is stable and up to standard.

[0198] It should be noted that the specific process in each step of the above rolling calculation of the initial water quality data based on the sewage treatment model in the embodiments of the present application is similar to the above FIG. 1B to FIG. 6 or steps, and the present application will not be repeated here.

[0199] The protection scope of the water quality prediction method of the embodiments of the present application is not limited to the step execution order listed in the embodiments. Any scheme realized by adding, reducing or replacing steps of the prior art according to the principle of the present application is included in the protection scope of the present application.

[0200] The embodiments of the present application also provide a water quality prediction system, which can realize the water quality prediction method of the present application. However, the implementation system of the water quality prediction method of the present application includes but is not limited to the structure of the feature vector extraction device listed in the embodiments. Any structure deformation and replacement of the prior art according to the principle of the present application is included in the protection scope of the present application.

[0201] As shown in FIG. 8 , in an embodiment, the water quality prediction system 80 of the present application includes a water quality data acquisition module 81, a water quality data input module 82, and a water quality data prediction module 83.

[0202] The water quality data acquisition module 81 is used to acquire initial water quality data.

[0203] The water quality data input module 82 is used to input the initial water quality data into the sewage treatment model.

[0204] The water quality data prediction module 83 is configured to perform a rolling calculation on the initial water quality data based on the sewage treatment model to obtain water quality prediction data.

[0205] The water quality data acquisition module 81, the water quality data input module 82, and the water quality data prediction module 83 correspond to the steps of the water quality prediction method in one-to-one correspondence, and thus will not be described again here.

[0206] In several embodiments provided in the present application, it should be understood that the disclosed apparatus or method can be implemented by other manners. For example, the apparatus embodiments described above are only illustrative, for example, the division of the modules / units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of modules or units can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the displayed or discussed modules can be indirect coupling or communication connection between the modules or units through some interfaces, and can be electrical, mechanical or other forms.

[0207] The modules / units described as separate components can be or can not be physically separated, and the components displayed as modules / units can be or can not be physical modules, that is, can be located in one place, or can be distributed on a plurality of network units. Part or all of the modules / units can be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in each embodiment of the present application can be integrated in one processing module, or each module / unit can be physically separated, or two or more modules / units can be integrated in one module / unit.

[0208] Those of ordinary skill in the art should further realize that the units and steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in general terms in the above description. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A skilled person can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0209] This application also provides a computer-readable storage medium. Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing a processor, and the program can be stored in a computer-readable storage medium. The storage medium is a non-transitory medium, such as random access memory, read-only memory, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof. The above storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0210] This application also provides an electronic device. FIG. 9 The diagram shown is a structural schematic of an electronic device 90 in one embodiment of this application. The water quality prediction method provided in this embodiment can be applied to… FIG. 9 The electronic device shown is 90, but it is not limited to this. For example... FIG. 9 As shown, the electronic device 90 includes a processor 91, a memory, a system bus 93, and a network interface 95. The memory may include a non-volatile storage medium 92 and internal memory 94.

[0211] The non-volatile storage medium 92 can store an operating system and a computer program. The computer program includes program instructions that, when executed, cause the processor to perform any of the water quality prediction methods provided in the embodiments of this application.

[0212] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0213] The internal memory 94 provides an environment for the execution of a computer program in a non-volatile storage medium. When the computer program is executed by the processor, it enables the processor to execute any of the water quality prediction methods provided in the embodiments of this application.

[0214] This network interface 95 is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... FIG. 1A The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0215] It should be understood that the processor 91 can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0216] The electronic device 90 of the embodiments of the present application can be applied to terminal devices such as a tablet computer, a notebook computer, a mobile phone, a supercomputer, a smart wearable device, etc., and can also be applied to a database, a server, and a service response system based on terminal artificial intelligence. The embodiments of the present application do not limit the specific type of the electronic device.

[0217] For example, the electronic device can be a station (STATION, ST) in a WLAN, and can be a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, a computer, a laptop, a handheld communication device, a handheld computing device, and / or other equipment for communicating over a wireless system, and a next-generation communication system, for example, a mobile terminal in a 5G network, a mobile terminal in a future evolved public land mobile network (PLMN), or a mobile terminal in a future evolved non-terrestrial network (NTN), etc.

[0218] As an example but not limitation, when the electronic device is a wearable device, the wearable device can also be a general term of application of wearable technology to the intelligent design of daily wear, and development of wearable devices, such as gloves, watches and other devices configured with near field communication modules. The wearable device is a portable device directly worn on the body or integrated into the user's clothes or accessories, which performs payment, authentication and other operations through attachment to the user's body and pre-bound electronic cards. The wearable device is not only a hardware device, but also a powerful function through software support and data interaction, cloud interaction. The general wearable smart device includes a full function, large size, and can realize complete or partial functions without relying on a smart phone, such as a smart watch or smart glasses, and only focuses on a certain application function, and needs to cooperate with other devices such as a smart phone, such as various smart watches with display screens, smart bracelets and the like.

[0219] The description of the flow or structure corresponding to each of the above figures has its own emphasis, and the part not described in detail in a certain flow or structure can refer to the related description of other flows or structures.

[0220] The above embodiments are only illustrative of the principles and effects of the present application, and are not used to limit the present application. Any person skilled in the art can modify or change the above embodiments without departing from the spirit and scope of the present application. Therefore, all equivalent modifications or changes completed by those skilled in the art without departing from the spirit and technical idea of the present application should be covered by the claims of the present application.

Claims

1. A water quality prediction method, characterized in that, The method includes: Obtain initial water quality data; The initial water quality data is input into the wastewater treatment model; the wastewater treatment model includes an influent input model, a carbon source addition input model, a biochemical reactor, a first sedimentation tank model, a second sedimentation tank model, a membrane filtration model, a chemical disinfection model, and an output model; The step of inputting the initial water quality data into the wastewater treatment model includes: acquiring first water quality source data; performing preprocessing operations on the first water quality source data to obtain second water quality source data; sending the second water quality source data to a water quality database; establishing a connection between the water quality database and the wastewater treatment model; extracting a set of the second water quality source data from the water quality database as the initial water quality data, and inputting the initial water quality data into the wastewater treatment model; Based on the wastewater treatment model, the initial water quality data is calculated in a rolling manner to obtain water quality prediction data; The rolling calculation of the initial water quality data based on the wastewater treatment model includes: inputting the initial water quality data into the wastewater treatment model to obtain the first water quality prediction data; inputting the first water quality data from the previous water quality prediction data into the wastewater treatment model to obtain the current water quality prediction data; repeating this step until the termination condition is met, wherein the first water quality data is the static index data in the water quality prediction data; The step of performing rolling calculations on the initial water quality data based on the wastewater treatment model to obtain water quality prediction data includes: determining the duration, frequency, and output step size information of the rolling calculations; and performing rolling calculations on the initial water quality data based on the wastewater treatment model according to the duration, frequency, and output step size information to obtain the water quality prediction data.

2. The water quality prediction method according to claim 1, characterized in that, The second water quality data in the water quality prediction data is used to predict the effluent water quality of the wastewater treatment plant corresponding to the wastewater treatment model within a preset time period.

3. The water quality prediction method according to claim 2, characterized in that, The water quality prediction method also includes: If the second water quality data is less than or equal to the water quality threshold, then the effluent water quality of the sewage treatment plant within the preset time period is determined to be normal water quality. If the second water quality data is greater than the water quality threshold, then the effluent water quality of the sewage treatment plant within the preset time period is determined to be abnormal.

4. The water quality prediction method according to claim 1, characterized in that, The method for constructing the wastewater treatment model includes: Construct an initial water quality model; The initial water quality model is corrected based on historical water quality data to obtain the corrected water quality model. The corrected water quality model is validated based on real-time water quality data to obtain the wastewater treatment model.

5. A water quality prediction system, characterized in that, The water quality prediction system includes: The water quality data acquisition module is used to acquire initial water quality data; A water quality data input module is used to input the initial water quality data into a wastewater treatment model. The wastewater treatment model includes an influent input model, a carbon source addition input model, a biochemical reactor, a first sedimentation tank model, a second sedimentation tank model, a membrane filtration model, a chemical disinfection model, and an output model. Inputting the initial water quality data into the wastewater treatment model includes: acquiring first water quality source data; performing preprocessing operations on the first water quality source data to obtain second water quality source data; sending the second water quality source data to a water quality database; establishing a connection between the water quality database and the wastewater treatment model; extracting a set of the second water quality source data from the water quality database as the initial water quality data, and inputting the initial water quality data into the wastewater treatment model. The water quality data prediction module is used to perform rolling calculations on the initial water quality data based on the wastewater treatment model to obtain water quality prediction data. The rolling calculations on the initial water quality data based on the wastewater treatment model include: inputting the initial water quality data into the wastewater treatment model to obtain the first water quality prediction data; inputting the first water quality data from the previous water quality prediction data into the wastewater treatment model to obtain the current water quality prediction data; repeating this step until a termination condition is met, wherein the first water quality data is the static index data in the water quality prediction data. The step of performing rolling calculations on the initial water quality data based on the wastewater treatment model to obtain water quality prediction data includes: determining the duration, frequency, and output step size information of the rolling calculations; and performing rolling calculations on the initial water quality data based on the wastewater treatment model according to the duration, frequency, and output step size information to obtain the water quality prediction data.

6. An electronic device, characterized in that, The electronic device includes a memory and a processor; The memory is used to store computer programs; The processor is used to execute the computer program and, in executing the computer program, implement the water quality prediction method as described in any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the water quality prediction method according to any one of claims 1 to 4.

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