Methods, apparatus, terminals and media for generating total nitrogen detection solutions in wastewater

Abstract

This application discloses a method, apparatus, terminal, and medium for generating a total nitrogen detection scheme for wastewater. The method includes: acquiring basic experimental data and dynamic scene data corresponding to the wastewater; mapping the basic experimental data to obtain total nitrogen detection feature data; predicting total nitrogen detection output data based on the total nitrogen detection feature data and the dynamic scene data using a preset total nitrogen detection prediction model; and obtaining a target total nitrogen detection scheme based on the total nitrogen detection output data, the basic experimental data, and the dynamic scene data. This application aims to reduce the error rate of total nitrogen detection in wastewater in a target area and achieve adaptive calibration and precision in the detection process.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, terminal and medium for generating a total nitrogen detection scheme for wastewater. Background Technology

[0002] Total nitrogen is inorganic nitrogen (NO3) in water. - NO2 - NH4 + The sum of nitrogen (etc.) and organic nitrogen (proteins, amino acids, etc.) is the core indicator for measuring eutrophication of water bodies, and its detection results directly support the control of wastewater discharge in compliance with standards.

[0003] However, existing methods for detecting total nitrogen in wastewater have the following shortcomings: The existing technology has a long operational chain (sample pretreatment → high-pressure digestion → cooling and depressurization → colorimetry → standard curve plotting → result calculation), requiring 5 hours for a complete test; even after optimization by replacing the digestion equipment, manual adjustment of parameters such as reagent dosage and digestion time is still required, lacking adaptive adjustment capabilities, and the detection efficiency is only improved to 1.5 hours / test, and the parameters cannot be flexibly adapted to dynamic scenarios such as water sample type (influent / effluent / rainwater) and season; existing technologies use fixed parameters (such as 40 g / L potassium persulfate and 30 min digestion time), making it impossible to adjust the detection strategy for different water quality types and seasons, resulting in spiked recovery rates deviating from the acceptable range of 90%~110% in some scenarios; the detection error of existing technologies is generally ±8%, and even after optimization, it only decreases to ±4%, and the error is greatly affected by manual operation and environmental fluctuations, lacking a real-time calibration mechanism. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, terminal, and medium for generating a total nitrogen detection scheme for wastewater, aiming to reduce the error rate of total nitrogen detection in target areas and achieve adaptive calibration and precision in the detection process.

[0005] To achieve the above objectives, this application provides a method for generating a wastewater total nitrogen detection scheme, the method comprising: Acquire basic experimental data and dynamic scene data corresponding to wastewater. The basic experimental data is used to characterize the experimental results under different experimental conditions for wastewater, and the dynamic scene data is used to characterize different water quality types, different seasons, and different pollution load conditions corresponding to wastewater. The basic experimental data are mapped to obtain total nitrogen detection characteristic data; By using a preset total nitrogen detection prediction model, the total nitrogen detection output data is predicted based on the total nitrogen detection feature data and the dynamic scene data. Based on the total nitrogen detection output data, the basic experimental data, and the dynamic scene data, a target total nitrogen detection scheme is obtained.

[0006] Specifically, the mapping process of the basic experimental data to obtain total nitrogen detection feature data includes: The basic experimental data were subjected to feature correlation analysis to obtain the feature correlation analysis results corresponding to the basic experimental data; Based on the feature correlation analysis results and the basic experimental data, the data to be reduced in dimensionality and improved in efficiency are determined. The data to be reduced in dimensionality and improved in efficiency is subjected to dimensionality reduction processing to obtain the data to be improved in efficiency. The data to be improved are subjected to feature optimization processing to obtain the total nitrogen detection feature data.

[0007] Specifically, the step of performing feature correlation analysis on the basic experimental data to obtain the feature correlation analysis results corresponding to the basic experimental data includes: By using a preset correlation coefficient matrix, the correlation between each feature data in the basic experimental data and the total nitrogen concentration feature corresponding to the wastewater is determined.

[0008] Specifically, determining the data to be reduced in dimensionality and improved in efficiency based on the feature correlation analysis results and the basic experimental data includes: Based on the correlation between each feature data in the basic experimental data and the corresponding total nitrogen concentration feature of the wastewater, the data to be reduced in dimension and improved in the basic experimental data is determined, wherein the data to be reduced in dimension and improved in dimension includes at least one feature data.

[0009] Specifically, the preset total nitrogen detection and prediction model includes an input layer, a hidden layer, and an output layer; The step of predicting total nitrogen detection output data using a preset total nitrogen detection prediction model, based on the total nitrogen detection feature data and the dynamic scene data, includes: Through the input layer, a feature input vector is obtained based on the total nitrogen detection feature data and the dynamic scene data; Through the hidden layer, a strongly correlated core feature vector is obtained based on the feature input vector; The total nitrogen detection output data is obtained through the output layer based on the strongly correlated core feature vector.

[0010] Specifically, the hidden layer includes a first sub-hidden layer and a second sub-hidden layer. The number of nodes in the first sub-hidden layer is three times the number of nodes in the input layer, and the number of nodes in the first sub-hidden layer is twice the number of nodes in the second sub-hidden layer. The process of obtaining a strongly correlated core feature vector through the hidden layer based on the feature input vector includes: The intermediate feature vector is obtained through the first sub-hidden layer based on the feature input vector; The strong correlation core feature vector is obtained through the second sub-hidden layer based on the intermediate feature vector.

[0011] Specifically, the step of obtaining the target total nitrogen detection scheme based on the total nitrogen detection output data, the basic experimental data, and the dynamic scene data includes: Based on different dynamic scenarios in the dynamic scenario data, a fitness function is constructed with the goal of minimizing the deviation rate between the predicted total nitrogen concentration and the measured total nitrogen concentration. The function value corresponding to the fitness function is used to characterize the average deviation rate between the predicted total nitrogen concentration and the measured total nitrogen concentration under the same dynamic scenario. The dynamic scenario is used to characterize the season or collection location of the wastewater. Based on the total nitrogen detection output data, the basic experimental data, and the dynamic scenario data, an optimized target total nitrogen detection scheme corresponding to the minimum function value is obtained. The target total nitrogen detection scheme includes the dynamic scenario corresponding to the wastewater, the sample amount corresponding to the wastewater, the potassium persulfate concentration corresponding to the wastewater, the digestion temperature corresponding to the wastewater, and the digestion time corresponding to the wastewater.

[0012] To achieve the above objectives, this application also provides an apparatus for generating a wastewater total nitrogen detection scheme, the apparatus comprising: The first unit is used to acquire basic experimental data and dynamic scene data corresponding to wastewater. The basic experimental data is used to characterize the experimental results under different experimental conditions for wastewater, and the dynamic scene data is used to characterize different water quality types, different seasons, and different pollution load conditions corresponding to wastewater. The second unit is used to map the basic experimental data to obtain total nitrogen detection feature data. The third unit is used to predict the total nitrogen detection output data based on the total nitrogen detection feature data and the dynamic scene data using a preset total nitrogen detection prediction model. The fourth unit is used to obtain the target total nitrogen detection scheme based on the total nitrogen detection output data, the basic experimental data, and the dynamic scene data.

[0013] To achieve the above objectives, this application also provides a terminal, including a memory storing multiple instructions; the processor loads instructions from the memory to execute the steps in any of the methods provided in this application.

[0014] To achieve the above objectives, this application also provides a medium storing a plurality of instructions adapted for loading by a processor to execute the steps in any of the methods provided in this application.

[0015] This application provides a method, apparatus, terminal, and medium for generating a total nitrogen detection scheme for wastewater. The method first acquires basic experimental data and dynamic scene data corresponding to the wastewater; maps the basic experimental data to obtain total nitrogen detection feature data; predicts total nitrogen detection output data based on the total nitrogen detection feature data and the dynamic scene data using a preset total nitrogen detection prediction model; and obtains a target total nitrogen detection scheme based on the total nitrogen detection output data, the basic experimental data, and the dynamic scene data, thereby reducing the error rate of total nitrogen detection in the target area and achieving adaptive adjustment and precision in the detection process. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the method provided in the embodiments of this application; Figure 2 This is a schematic diagram illustrating the application of the preset total nitrogen detection and prediction model provided in the embodiments of this application; Figure 3 This is a schematic diagram of the device provided in the embodiments of this application; Figure 4 This is a schematic diagram of the terminal structure provided in an embodiment of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] The existing methods for detecting total nitrogen in wastewater have the following shortcomings: The existing technology has a long operational chain (sample pretreatment → high-pressure digestion → cooling and depressurization → colorimetry → standard curve plotting → result calculation), requiring 5 hours for a complete test; even after optimization by replacing the digestion equipment, manual adjustment of parameters such as reagent dosage and digestion time is still required, lacking adaptive adjustment capabilities, and the detection efficiency is only improved to 1.5 hours / test, and the parameters cannot be flexibly adapted to dynamic scenarios such as water sample type (influent / effluent / rainwater) and season; the existing technology uses fixed parameters (such as 40 g / L potassium persulfate and 30 min digestion time), which cannot adjust the detection strategy for different water quality types and seasons, causing the spiked recovery rate to deviate from the acceptable range of 90%~110% in some scenarios; the detection error of the existing technology is generally ±8%, and even after optimization, it is only reduced to ±4%, and the error is greatly affected by manual operation and environmental fluctuations, lacking a real-time calibration mechanism.

[0019] Therefore, this application provides a method, apparatus, terminal, and medium for generating a total nitrogen detection scheme for wastewater to solve practical technical problems.

[0020] In some embodiments, the device may be integrated into an electronic device, such as a terminal or server.

[0021] In some embodiments, the server may also be implemented as a terminal.

[0022] The server can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0023] The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and the server can be connected directly or indirectly through wired or wireless communication, which is not limited herein.

[0024] The following sections provide detailed descriptions of each example. It should be noted that the sequence numbers of the following embodiments are not intended to limit the preferred order of the embodiments.

[0025] This application provides a method for generating a total nitrogen detection scheme for wastewater. The method can reduce the error rate of total nitrogen detection in the target area and achieve adaptive calibration and precision in the detection process.

[0026] In some embodiments, a wastewater treatment plant needs to test the total nitrogen concentration of wastewater influent samples taken during the summer rainy season in June 2024. This water sample belongs to a high pollution load influent scenario (pollution load level: high, normally medium), the water quality type is wastewater treatment plant influent, and the sampling season is summer (rainy season). The core testing requirement is to quickly and accurately obtain a total nitrogen testing solution for this scenario to ensure that the wastewater is discharged in compliance with standards.

[0027] like Figure 1 The specific process of the method can be as follows: S110. Obtain basic experimental data and dynamic scene data corresponding to wastewater, wherein the basic experimental data is used to characterize the experimental results under different experimental conditions for wastewater, and the dynamic scene data is used to characterize different water quality types, different seasons, and different pollution load conditions corresponding to wastewater.

[0028] In some embodiments, historical basic experimental data of the water quality testing group in the area can be retrieved, covering the test results of influent water samples under different experimental conditions during the rainy season. The core features include: sample addition amount (4mL-6mL), potassium persulfate concentration (30g / L-50g / L), sodium hydroxide concentration (20g / L-40g / L), digestion temperature (115℃-125℃), digestion time (25min-35min), absorbance, measured total nitrogen concentration (mg / L), relative error, etc. A total of 80 sets of valid experimental data were collected.

[0029] In some embodiments, dynamic scene data of the influent water samples during the rainy season are collected, including: water quality type (e.g., wastewater treatment plant influent), sampling season (e.g., summer / rainy season), pollution load (e.g., high, COD concentration 80mg / L, ammonia nitrogen 15mg / L, daily COD 50mg / L, ammonia nitrogen 8mg / L), parallel sample test results, spiked recovery rate, etc., and are simultaneously correlated with dynamic scene data of influent water during the summer rainy season of the past 3 years.

[0030] S120. The basic experimental data is mapped to obtain total nitrogen detection characteristic data.

[0031] In some embodiments, the mapping process of the basic experimental data to obtain total nitrogen detection feature data includes the steps A1 to A4 shown below: A1. Perform feature correlation analysis on the basic experimental data to obtain the feature correlation analysis results corresponding to the basic experimental data; in some embodiments, the step of performing feature correlation analysis on the basic experimental data to obtain the feature correlation analysis results corresponding to the basic experimental data includes the following specific implementation process: By using a preset correlation coefficient matrix, the correlation between each feature data in the basic experimental data and the total nitrogen concentration feature corresponding to the wastewater is determined.

[0032] Specifically, the correlation between each feature in the basic experimental data and the total nitrogen concentration feature can be calculated by constructing a Pearson correlation coefficient matrix.

[0033] A2. Based on the results of the feature correlation analysis and the basic experimental data, determine the data to be reduced in dimensionality and improved in efficiency.

[0034] In some embodiments, features with a correlation of ≥0.85 can be selected as data to be used for dimensionality reduction and efficiency improvement, and the following features are finally determined: absorbance, digestion temperature, potassium persulfate concentration, digestion time, and sample addition amount (a total of 5 feature data).

[0035] In some embodiments, determining the data to be reduced in dimensionality and improved efficiency based on the feature correlation analysis results and the basic experimental data includes the following specific implementation process: Based on the correlation between each feature data in the basic experimental data and the corresponding total nitrogen concentration feature of the wastewater, the data to be reduced in dimension and improved in the basic experimental data is determined, wherein the data to be reduced in dimension and improved in dimension includes at least one feature data.

[0036] A3. Perform dimensionality reduction processing on the data to be reduced and improved to obtain the data to be improved.

[0037] In some embodiments, principal component analysis (PCA) is used to reduce the dimensionality of the five data points to be reduced and improved, retaining the principal components with a cumulative variance contribution rate of ≥95%, and finally obtaining four-dimensional data points to be improved, namely, PC1: absorbance mapping value, PC2: digestion condition comprehensive value, PC3: potassium persulfate concentration mapping value, and PC4: sample addition amount mapping value.

[0038] A4. Perform feature optimization processing on the data to be improved to obtain the total nitrogen detection feature data.

[0039] In some embodiments, partial least squares (PLS) method is used to eliminate multicollinearity of "digestion temperature-digestion time" in the data to be improved, construct the latent variable mapping relationship between features and total nitrogen concentration, and finally obtain 4-dimensional total nitrogen detection feature data (numerical range: -3~3, Z-score normalization has been completed).

[0040] S130. Using a preset total nitrogen detection prediction model, the total nitrogen detection output data is predicted based on the total nitrogen detection feature data and the dynamic scene data.

[0041] In some embodiments, such as Figure 2 As shown, the preset total nitrogen detection prediction model includes an input layer, a hidden layer, and an output layer.

[0042] Specifically, the step of predicting the total nitrogen detection output data by using a preset total nitrogen detection prediction model, based on the total nitrogen detection feature data and the dynamic scene data, includes the steps B1 to B3 as shown below: B1. The feature input vector is obtained through the input layer based on the total nitrogen detection feature data and the dynamic scene data.

[0043] In some embodiments, the 4-dimensional total nitrogen detection feature data is fused with dynamic scene data (water quality type unique thermal encoding: influent = 100; seasonal unique thermal encoding: summer = 100; pollution load normalization value: 1.2) to generate a 7-dimensional feature input vector: [1.8, 1.5, 1.2, 0.9, 1, 1, 1.2] (the first 4 bits are the total nitrogen detection feature data, and the last 3 bits are the dynamic scene data encoding / normalization value).

[0044] B2. Through the hidden layer, based on the feature input vector, a strongly correlated core feature vector is obtained.

[0045] In some embodiments, the hidden layer includes a first sub-hidden layer and a second sub-hidden layer, wherein the number of nodes in the first sub-hidden layer is three times the number of nodes in the input layer, and the number of nodes in the first sub-hidden layer is twice the number of nodes in the second sub-hidden layer.

[0046] Specifically, the process of obtaining a strongly correlated core feature vector through the hidden layer based on the feature input vector includes the steps B11 to B12 as shown below: B21. Through the first sub-hidden layer, an intermediate feature vector is obtained based on the feature input vector.

[0047] The first sub-hidden layer performs a linear transformation on the 7-dimensional feature input vector (Z1 = W1 × feature input vector + b1, where W1 is a 12 × 7 weight matrix and b1 is a 12 × 1 bias vector), and then processes it through the ReLU activation function to obtain a 12-dimensional intermediate feature vector, namely [0.85, 0.72, 0.68, 0.55, 0.49, 0.42, 0.38, 0.31, 0.25, 0.18, 0.12, 0.05].

[0048] B22. The strongly correlated core feature vector is obtained through the second sub-hidden layer based on the intermediate feature vector.

[0049] The second sub-hidden layer performs a linear transformation on the 12-dimensional intermediate feature vector (Z2 = W2 × intermediate feature vector + b2, where W2 is a 6 × 12 weight matrix and b2 is a 6 × 1 bias vector), and then processes it through the ReLU activation function to obtain a 6-dimensional strongly correlated core feature vector: [0.92, 0.81, 0.75, 0.63, 0.58, 0.45].

[0050] B3. The total nitrogen detection output data is obtained through the output layer based on the strongly correlated core feature vector.

[0051] The output layer performs a linear transformation on the 6-dimensional strongly correlated core feature vector (Z3 = W3 × strongly correlated core feature vector + b3, where W3 is a 1×6 weight matrix and b3 is a 1×1 bias term), and outputs the result using a linear activation function. After inverse normalization, the total nitrogen detection output data is obtained: 42.5 mg / L (i.e., the predicted total nitrogen concentration).

[0052] S140. Based on the total nitrogen detection output data, the basic experimental data, and the dynamic scene data, a target total nitrogen detection scheme is obtained.

[0053] In some embodiments, obtaining the target total nitrogen detection scheme based on the total nitrogen detection output data, the basic experimental data, and the dynamic scene data includes the following steps C1 to C2: C1. Based on different dynamic scenarios in the dynamic scenario data, a fitness function is constructed with the goal of minimizing the deviation rate between the predicted total nitrogen concentration and the measured total nitrogen concentration. The function value corresponding to the fitness function is used to characterize the average deviation rate between the predicted total nitrogen concentration and the measured total nitrogen concentration under the same dynamic scenario. The dynamic scenario is used to characterize the season or collection location of the wastewater.

[0054] In some embodiments, with the objective of "minimizing the deviation rate between predicted and measured total nitrogen concentrations," a fitness function is constructed for the dynamic scenario of summer rainy season inflow (high pollution load):

[0055] in, This is the fitness function value (characterizing the average deviation rate). For the first The predicted total nitrogen concentration for each sample For the first The measured total nitrogen concentration of each sample The number of samples in this scenario ( =80).

[0056] C2. Based on the total nitrogen detection output data, the basic experimental data, and the dynamic scenario data, optimize the target total nitrogen detection scheme corresponding to the minimum function value. The target total nitrogen detection scheme includes the dynamic scenario corresponding to the wastewater, the sample amount corresponding to the wastewater, the potassium persulfate concentration corresponding to the wastewater, the digestion temperature corresponding to the wastewater, and the digestion time corresponding to the wastewater.

[0057] The total nitrogen detection output data (42.5 mg / L), basic experimental data (80 sets), and dynamic scene data (120 sets) were substituted into a genetic algorithm. With the "minimum fitness function value" as the optimization objective, parameters such as sample amount, potassium persulfate concentration, digestion temperature, and digestion time were encoded and iterated to finally obtain the target total nitrogen detection scheme corresponding to the minimum function value (F=2.8%) as shown below: Dynamic scenario: Summer rainy season water inflow (high pollution load); Sample volume: 5.5 mL; Potassium persulfate concentration: 45 g / L; Digestion temperature: 122℃; Digestion time: 32 min.

[0058] The proposed total nitrogen detection scheme can control the total nitrogen detection deviation rate within ±3% in this dynamic scenario, maintain a detection efficiency of 1.5 hours / time, and adapt to the detection needs of high-load water inflow at airports during the rainy season.

[0059] In summary, this application provides a method for generating a total nitrogen detection scheme for wastewater, which can reduce the error rate of total nitrogen detection in the target area and achieve adaptive calibration and precision in the detection process.

[0060] To better implement the above methods, this application also provides an apparatus for generating a total nitrogen detection scheme for wastewater. This apparatus can be integrated into an electronic device, such as a terminal or server. The terminal can be a mobile phone, tablet computer, smart Bluetooth device, laptop computer, or personal computer; the server can be a single server or a server cluster composed of multiple servers.

[0061] For example, in this embodiment, the method of this application embodiment will be described in detail by taking the device for generating a wastewater total nitrogen detection scheme as specifically integrated into the terminal.

[0062] For example, such as Figure 3 As shown, the apparatus 300 for generating a total nitrogen detection scheme for wastewater may include a first unit 301, a second unit 302, a third unit 303, and a fourth unit 304. The apparatus includes: The first unit 301 is used to acquire basic experimental data and dynamic scene data corresponding to wastewater. The basic experimental data is used to characterize the experimental results under different experimental conditions for wastewater, and the dynamic scene data is used to characterize different water quality types, different seasons, and different pollution load conditions corresponding to wastewater. The second unit 302 is used to perform mapping processing on the basic experimental data to obtain total nitrogen detection feature data; The third unit 303 is used to predict the total nitrogen detection output data based on the total nitrogen detection feature data and the dynamic scene data by using a preset total nitrogen detection prediction model; The fourth unit 304 is used to obtain the target total nitrogen detection scheme based on the total nitrogen detection output data, the basic experimental data, and the dynamic scene data.

[0063] In practice, each of the above units can be implemented as an independent entity or can be arbitrarily combined to be implemented as the same or several entities. For the specific implementation of each of the above units, please refer to the previous method embodiments, which will not be repeated here.

[0064] As can be seen from the above, the embodiments of this application can reduce the error rate of total nitrogen detection in wastewater in the target area and achieve adaptive calibration and precision in the detection process.

[0065] This application also provides an electronic device, which can be a terminal, a server, or other similar device. The terminal can be a mobile phone, tablet computer, smart Bluetooth device, laptop computer, personal computer, etc.; the server can be a single server or a server cluster composed of multiple servers, etc.

[0066] In some embodiments, the product processing device may also be integrated into multiple electronic devices, such as multiple servers, with multiple servers implementing the method for generating wastewater total nitrogen detection scheme of this application.

[0067] In this embodiment, the electronic device will be described in detail as a terminal, for example, such as... Figure 4 As shown, it illustrates a structural schematic diagram of the terminal 400 involved in an embodiment of this application. Specifically: The terminal 400 may include components such as a processor 401 with one or more processing cores, a memory 402 with one or more media, a power supply 403, an input module 404, and a communication module 405. Those skilled in the art will understand that... Figure 4 The terminal 400 structure shown does not constitute a limitation on the terminal 400, and may include more or fewer components than shown, or combine certain components, or have different component arrangements. Wherein: The processor 401 is the control center of the terminal 400. It connects various parts of the terminal 400 via various interfaces and lines, and performs various functions and processes data by running or executing software programs and / or modules stored in the memory 402, and by calling data stored in the memory 402, thereby providing overall monitoring of the terminal 400. In some embodiments, the processor 401 may include one or more processing cores; in some embodiments, the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly handles the operating system, user interface, and applications, and the modem processor mainly handles wireless communication. It is understood that the modem processor may also not be integrated into the processor 401.

[0068] The memory 402 can be used to store software programs and modules. The processor 401 executes various functional applications and data processing by running the software programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as sound playback function, image playback function, etc.), etc.; the data storage area may store data created according to the use of the terminal 400, etc. In addition, the memory 402 may include high-speed random access memory, and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 402 may also include a memory controller to provide the processor 401 with access to the memory 402.

[0069] The terminal 400 also includes a power supply 403 that supplies power to the various components. In some embodiments, the power supply 403 can be logically connected to the processor 401 through a power management system, thereby enabling functions such as charging, discharging, and power consumption management through the power management system. The power supply 403 may also include one or more DC or AC power supplies, recharging systems, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components.

[0070] The terminal 400 may also include an input module 404, which can be used to receive input digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0071] The terminal 400 may also include a communication module 405. In some embodiments, the communication module 405 may include a wireless module. The terminal 400 can perform short-range wireless transmission through the wireless module of the communication module 405, thereby providing users with wireless broadband Internet access. For example, the communication module 405 can be used to help users send and receive emails, browse web pages, and access streaming media.

[0072] Although not shown, terminal 400 may also include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, processor 401 in terminal 400 loads the executable files corresponding to the processes of one or more applications into memory 402 according to the following instructions, and processor 401 runs the applications stored in memory 402 to realize various functions, as follows: Acquire basic experimental data and dynamic scene data corresponding to wastewater. The basic experimental data is used to characterize the experimental results under different experimental conditions for wastewater, and the dynamic scene data is used to characterize different water quality types, different seasons, and different pollution load conditions corresponding to wastewater. The basic experimental data are mapped to obtain total nitrogen detection characteristic data; By using a preset total nitrogen detection prediction model, the total nitrogen detection output data is predicted based on the total nitrogen detection feature data and the dynamic scene data. Based on the total nitrogen detection output data, the basic experimental data, and the dynamic scene data, a target total nitrogen detection scheme is obtained.

[0073] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0074] As can be seen from the above, the embodiments of this application can reduce the error rate of total nitrogen detection in wastewater in the target area and achieve adaptive calibration and precision in the detection process.

[0075] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be accomplished by instructions, or by instructions controlling related hardware. These instructions can be stored in a medium and loaded and executed by a processor.

[0076] Therefore, embodiments of this application provide a medium storing multiple instructions that can be loaded by a processor to execute steps in any of the methods for generating a wastewater total nitrogen detection scheme provided in embodiments of this application. For example, the instructions can execute the following steps: Acquire basic experimental data and dynamic scene data corresponding to wastewater. The basic experimental data is used to characterize the experimental results under different experimental conditions for wastewater, and the dynamic scene data is used to characterize different water quality types, different seasons, and different pollution load conditions corresponding to wastewater. The basic experimental data are mapped to obtain total nitrogen detection characteristic data; By using a preset total nitrogen detection prediction model, the total nitrogen detection output data is predicted based on the total nitrogen detection feature data and the dynamic scene data. Based on the total nitrogen detection output data, the basic experimental data, and the dynamic scene data, a target total nitrogen detection scheme is obtained.

[0077] The medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0078] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a medium. A processor of a computer device reads the computer instructions from the medium and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations of the above embodiments.

[0079] Since the instructions stored in the medium can execute the steps in any of the methods for generating total nitrogen in wastewater provided in the embodiments of this application, the beneficial effects that any of the methods for generating total nitrogen in wastewater provided in the embodiments of this application can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0080] The above provides a detailed description of the method, apparatus, terminal, and medium for generating a wastewater total nitrogen detection scheme according to the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for generating a total nitrogen detection scheme for wastewater, characterized in that, The method includes: Acquire basic experimental data and dynamic scene data corresponding to wastewater. The basic experimental data is used to characterize the experimental results under different experimental conditions for wastewater, and the dynamic scene data is used to characterize different water quality types, different seasons, and different pollution load conditions corresponding to wastewater. The basic experimental data are mapped to obtain total nitrogen detection characteristic data; By using a preset total nitrogen detection prediction model, the total nitrogen detection output data is predicted based on the total nitrogen detection feature data and the dynamic scene data. Based on the total nitrogen detection output data, the basic experimental data, and the dynamic scene data, a target total nitrogen detection scheme is obtained.

2. The method as described in claim 1, characterized in that, The mapping process of the basic experimental data to obtain total nitrogen detection feature data includes: The basic experimental data were subjected to feature correlation analysis to obtain the feature correlation analysis results corresponding to the basic experimental data; Based on the feature correlation analysis results and the basic experimental data, the data to be reduced in dimensionality and improved in efficiency are determined. The data to be reduced in dimensionality and improved in efficiency is subjected to dimensionality reduction processing to obtain the data to be improved in efficiency. The data to be improved are subjected to feature optimization processing to obtain the total nitrogen detection feature data.

3. The method as described in claim 2, characterized in that, The step of performing feature correlation analysis on the basic experimental data to obtain the feature correlation analysis results corresponding to the basic experimental data includes: By using a preset correlation coefficient matrix, the correlation between each feature data in the basic experimental data and the total nitrogen concentration feature corresponding to the wastewater is determined.

4. The method as described in claim 3, characterized in that, The process of determining the data to be reduced in dimensionality and improved in efficiency based on the feature correlation analysis results and the basic experimental data includes: Based on the correlation between each feature data in the basic experimental data and the corresponding total nitrogen concentration feature of the wastewater, the data to be reduced in dimension and improved in the basic experimental data is determined, wherein the data to be reduced in dimension and improved in dimension includes at least one feature data.

5. The method as described in claim 1, characterized in that, The preset total nitrogen detection prediction model includes an input layer, a hidden layer, and an output layer; The step of predicting total nitrogen detection output data using a preset total nitrogen detection prediction model, based on the total nitrogen detection feature data and the dynamic scene data, includes: Through the input layer, a feature input vector is obtained based on the total nitrogen detection feature data and the dynamic scene data; Through the hidden layer, a strongly correlated core feature vector is obtained based on the feature input vector; The total nitrogen detection output data is obtained through the output layer based on the strongly correlated core feature vector.

6. The method as described in claim 5, characterized in that, The hidden layer includes a first sub-hidden layer and a second sub-hidden layer. The number of nodes in the first sub-hidden layer is three times the number of nodes in the input layer, and the number of nodes in the first sub-hidden layer is twice the number of nodes in the second sub-hidden layer. The process of obtaining a strongly correlated core feature vector through the hidden layer based on the feature input vector includes: The intermediate feature vector is obtained through the first sub-hidden layer based on the feature input vector; The strong correlation core feature vector is obtained through the second sub-hidden layer based on the intermediate feature vector.

7. The method as described in claim 1, characterized in that, The method for obtaining the target total nitrogen detection scheme based on the total nitrogen detection output data, the basic experimental data, and the dynamic scene data includes: Based on different dynamic scenarios in the dynamic scenario data, a fitness function is constructed with the goal of minimizing the deviation rate between the predicted total nitrogen concentration and the measured total nitrogen concentration. The function value corresponding to the fitness function is used to characterize the average deviation rate between the predicted total nitrogen concentration and the measured total nitrogen concentration under the same dynamic scenario. The dynamic scenario is used to characterize the season or collection location of the wastewater. Based on the total nitrogen detection output data, the basic experimental data, and the dynamic scenario data, an optimized target total nitrogen detection scheme corresponding to the minimum function value is obtained. The target total nitrogen detection scheme includes the dynamic scenario corresponding to the wastewater, the sample amount corresponding to the wastewater, the potassium persulfate concentration corresponding to the wastewater, the digestion temperature corresponding to the wastewater, and the digestion time corresponding to the wastewater.

8. An apparatus for generating a total nitrogen detection scheme for wastewater, characterized in that, The device includes: The first unit is used to acquire basic experimental data and dynamic scene data corresponding to wastewater. The basic experimental data is used to characterize the experimental results under different experimental conditions for wastewater, and the dynamic scene data is used to characterize different water quality types, different seasons, and different pollution load conditions corresponding to wastewater. The second unit is used to map the basic experimental data to obtain total nitrogen detection feature data. The third unit is used to predict the total nitrogen detection output data based on the total nitrogen detection feature data and the dynamic scene data using a preset total nitrogen detection prediction model. The fourth unit is used to obtain the target total nitrogen detection scheme based on the total nitrogen detection output data, the basic experimental data, and the dynamic scene data.

9. A terminal, characterized in that, The method includes a processor and a memory, the memory storing multiple instructions; the processor loads instructions from the memory to perform the steps of the method as described in any one of claims 1 to 7.

10. A medium, characterized in that, The medium stores a plurality of instructions adapted for loading by a processor to execute the steps of the method according to any one of claims 1 to 7.

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