Production wastewater discharge management method and device based on industrial internet of things, terminal and medium

Abstract

The application discloses a production wastewater discharge management method and device based on an industrial Internet of Things, a terminal and a medium. The method comprises the following steps: acquiring pH data corresponding to production wastewater, wastewater flow data corresponding to the production wastewater, chemical oxygen demand data corresponding to the production wastewater, and suspended solids concentration data corresponding to the production wastewater; predicting production wastewater prediction parameters in a preset future time period by a preset wastewater discharge parameter prediction model according to the pH data, the wastewater flow data, the chemical oxygen demand data and the suspended solids concentration data; and obtaining a target production wastewater discharge management scheme based on the production wastewater prediction parameters, preset target parameters corresponding to the production wastewater prediction parameters and a preset control coefficient. The application aims to improve the efficiency and accuracy of production wastewater discharge of a factory.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, device, terminal and medium for managing industrial wastewater discharge based on the Industrial Internet of Things. Background Technology

[0002] Factory wastewater discharge management is a core aspect of industrial environmental protection. Its core objective is to ensure that discharge indicators meet the requirements of the "Integrated Wastewater Discharge Standard" (GB8978-1996) and industry-specific standards by monitoring and controlling key water quality and flow parameters during the wastewater discharge process, while improving the operational efficiency of the discharge process and reducing energy consumption and environmental risks.

[0003] However, existing methods for managing industrial wastewater discharge have the following shortcomings: Existing technologies largely rely on real-time monitoring data from industrial sensors for "post-event alarms," ​​triggering warnings only when parameters exceed compliance thresholds, lacking the ability to predict wastewater parameters over future periods; existing technologies often adjust discharge valves using a "single threshold triggering fixed adjustment amount" approach (e.g., increasing valve opening by 10% when pH is below 6), without incorporating multi-dimensional calculations, thus failing to achieve sufficient accuracy; existing technologies lack a systematic connection between perceived data, predicted results, and control schemes, still requiring manual intervention for decision-making, failing to achieve full-process automation and intelligence in discharge management, resulting in insufficient discharge management efficiency. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, terminal and medium for managing industrial wastewater discharge based on the Industrial Internet of Things, which aims to improve the efficiency and accuracy of industrial wastewater discharge in factories.

[0005] To achieve the above objectives, this application provides a method for managing industrial wastewater discharge based on the Industrial Internet of Things, the method comprising: Obtain data on the pH, flow rate, chemical oxygen demand, and suspended solids concentration of the production wastewater. By using a preset wastewater discharge parameter prediction model, based on the pH data, wastewater flow rate data, chemical oxygen demand data, and suspended solids concentration data, the predicted parameters for production wastewater within a preset future time period are obtained. Based on the predicted production wastewater parameters, the preset target parameters corresponding to the predicted production wastewater parameters, and the preset control coefficients, a target production wastewater discharge management scheme is obtained, wherein the target production wastewater discharge management scheme includes adjustment parameters corresponding to the production wastewater discharge valve.

[0006] Specifically, the preset wastewater discharge parameter prediction model includes an input layer, a hidden layer, and an output layer; The method of predicting production wastewater parameters for a preset future time period using a preset wastewater discharge parameter prediction model, based on the pH data, wastewater flow rate data, chemical oxygen demand data, and suspended solids concentration data, includes: Through the input layer, an input three-dimensional tensor is obtained based on the pH data, wastewater flow rate data, chemical oxygen demand data, and suspended solids concentration data; Through the hidden layer, a hidden state vector is obtained based on the input three-dimensional tensor; Through the output layer, a predicted two-dimensional tensor is obtained based on the hidden state vector. The predicted two-dimensional tensor includes production wastewater sample labels and production wastewater prediction parameters corresponding to the production wastewater sample labels.

[0007] Specifically, the hidden layer includes a first LSTM layer and a second LSTM layer, and the hidden state vector includes the hidden state vector at the last time step. The process of obtaining the hidden state vector through the hidden layer based on the input 3D tensor includes: Through the first LSTM layer, the hidden state vectors for all time steps are obtained based on the input three-dimensional tensor; The hidden state vector of the last time step is obtained through the second LSTM layer based on the hidden state vectors of all time steps.

[0008] Specifically, the preset control coefficients include a first preset control coefficient, a second preset control coefficient, and a third preset control coefficient; The process of obtaining a target production wastewater discharge management scheme based on the predicted production wastewater parameters, the corresponding preset target parameters, and preset control coefficients includes: Based on the predicted parameters of the production wastewater, the preset target parameters, and the first preset control coefficient, the result of the first calculation item is calculated. Based on the predicted parameters of the production wastewater, the preset target parameters, and the second preset control coefficient, the result of the second calculation item is calculated. Based on the predicted parameters of the production wastewater, the preset target parameters, and the third preset control coefficient, the result of the third calculation item is calculated. Based on the results of the first calculation item, the second calculation item, and the third calculation item, the adjustment parameters corresponding to the production wastewater discharge valve are obtained.

[0009] Specifically, the calculation of the first calculation item result based on the predicted production wastewater parameters, the preset target parameters, and the first preset control coefficient includes: Calculate the difference between the predicted parameters of the production wastewater and the preset target parameters to obtain the error value; The product of the error value and the first preset control coefficient is calculated to obtain the result of the first calculation item.

[0010] Specifically, the calculation of the second calculation item result based on the predicted parameters of the production wastewater, the preset target parameters, and the second preset control coefficient includes: Calculate the difference between the predicted parameters of the production wastewater and the preset target parameters to obtain the error value; Calculate the cumulative sum of error values ​​over the preset future time period to obtain the cumulative sum of error values; The product of the cumulative sum of the error values ​​and the second preset control coefficient is calculated to obtain the result of the second calculation item.

[0011] Specifically, the calculation of the third calculation item result based on the predicted parameters of the production wastewater, the preset target parameters, and the third preset control coefficient includes: Calculate the difference between the predicted parameters of the production wastewater and the preset target parameters to obtain the error value; Calculate the rate of change of the error value within the preset future time period to obtain the rate of change of the error value; The product of the error value change rate and the third preset control coefficient is calculated to obtain the result of the third calculation item.

[0012] To achieve the above objectives, this application also provides a production wastewater discharge management device based on the Industrial Internet of Things, the device comprising: The first unit is used to acquire data on pH, flow rate, chemical oxygen demand and suspended solids concentration of the production wastewater. The second unit is used to predict the production wastewater prediction parameters for a preset future time period by using a preset wastewater discharge parameter prediction model, based on the pH data, the wastewater flow rate data, the chemical oxygen demand data, and the suspended solids concentration data. The third unit is used to obtain a target production wastewater discharge management scheme based on the production wastewater prediction parameters, the preset target parameters corresponding to the production wastewater prediction parameters, and the preset control coefficients, wherein the target production wastewater discharge management scheme includes adjustment parameters corresponding to the production wastewater discharge valve.

[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, device, terminal, and medium for managing industrial wastewater discharge based on the Industrial Internet of Things (IIoT). It first acquires data on the pH, flow rate, chemical oxygen demand (COD), and suspended solids concentration of the industrial wastewater. Then, using a preset wastewater discharge parameter prediction model, it predicts wastewater discharge parameters for a preset future time period based on the pH, flow rate, COD, and suspended solids concentration data. Finally, based on these predicted parameters, corresponding preset target parameters, and preset control coefficients, a target wastewater discharge management scheme is obtained to improve the efficiency and accuracy of industrial wastewater discharge. Attached Figure Description

[0016] Figure 1 A flowchart illustrating the method provided in the embodiments of this application; Figure 2 A schematic diagram illustrating the application of the preset wastewater discharge parameter 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] Existing methods for managing industrial wastewater discharge have the following shortcomings: Firstly, existing technologies rely heavily on real-time monitoring data from industrial sensors for "post-event alarms," ​​triggering warnings only when parameters exceed compliance thresholds, lacking the ability to predict wastewater parameters over future periods. Secondly, existing technologies often adjust discharge valves using a "single threshold triggering fixed adjustment amount" approach (e.g., increasing valve opening by 10% when pH is below 6), without incorporating multi-dimensional calculations, thus failing to achieve sufficient accuracy. Thirdly, existing technologies lack a systematic connection between perceived data, predicted results, and control schemes, still requiring manual intervention for decision-making, and cannot achieve full automation and intelligence in discharge management, resulting in insufficient discharge management efficiency.

[0019] Therefore, this application provides a method, device, terminal, and medium for managing industrial wastewater discharge based on the Industrial Internet of Things to solve practical technical problems.

[0020] In some embodiments, the penetration of technologies such as the Industrial Internet of Things (IIoT) and machine learning into the industrial field is transforming the management of production wastewater discharge from the traditional model of "manual inspection + threshold alarm" to an intelligent model of "real-time perception - intelligent prediction - precise control".

[0021] In some embodiments, the device may be integrated into an electronic device, such as a terminal or server.

[0022] In some embodiments, the server may also be implemented as a terminal.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] This application provides a method for managing industrial wastewater discharge based on the Industrial Internet of Things, which can improve the efficiency and accuracy of industrial wastewater discharge in factories.

[0027] In some embodiments, the synthesis workshop of a fine chemical enterprise mainly produces organic intermediates, and the production wastewater contains pollutants such as benzene series compounds, acids, alkalis and salts. The compliant discharge indicators are pH∈6~9, COD≤80mg / L, SS≤50mg / L, and instantaneous discharge flow rate of wastewater≤20m³ / h; the preset future time period is the next 15 minutes.

[0028] like Figure 1 The specific process of the method can be as follows: S110. Obtain the pH data, flow rate data, chemical oxygen demand data, and suspended solids concentration data of the production wastewater.

[0029] In some embodiments, industrial IoT sensor nodes deployed at the wastewater discharge outlet of the workshop are used to collect real-time production wastewater data for the first 30 time steps (one time step every 30 seconds) within the past 15 minutes: pH data: [4.2, 4.5, 4.3, 5.1, 4.9, ..., 5.3] (30 values ​​in total, unit: none) Wastewater flow rate data: [12,13,11,14,15,...,16] (30 values ​​in total, unit: m³ / h) Chemical oxygen demand (COD) data: [75,78,76,82,80,...,83] (30 values ​​in total, unit: mg / L) Suspended solids (SS) concentration data: [42,45,43,48,46,...,49] (30 values ​​in total, unit: mg / L).

[0030] S120. Using a preset wastewater discharge parameter prediction model, based on the pH data, wastewater flow rate data, chemical oxygen demand data, and suspended solids concentration data, predict the production wastewater parameters for a preset future time period.

[0031] In some embodiments, such as Figure 2 As shown, the preset wastewater discharge parameter prediction model includes an input layer, a hidden layer, and an output layer.

[0032] Specifically, the step of predicting production wastewater parameters for a preset future time period using a preset wastewater discharge parameter prediction model, based on the pH data, wastewater flow rate data, chemical oxygen demand data, and suspended solids concentration data, includes the steps A1 to A3 shown below: A1. Through the input layer, based on the pH data, wastewater flow rate data, chemical oxygen demand data, and suspended solids concentration data, an input three-dimensional tensor is obtained.

[0033] In some embodiments, the collected pH data, wastewater flow data, chemical oxygen demand data, and suspended solids concentration data are reconstructed according to the dimension of "time step × parameter type × number of samples" to obtain an input three-dimensional tensor: the dimension is 30 (time step) × 4 (parameter type: pH, flow, COD, SS) × 1 (number of samples, i.e. a set of continuous data collected in this instance).

[0034] A2. Through the hidden layer, the hidden state vector is obtained based on the input three-dimensional tensor.

[0035] In some embodiments, the hidden layer includes a first LSTM layer and a second LSTM layer, and the hidden state vector includes the hidden state vector at the last time step.

[0036] Specifically, obtaining the hidden state vector through the hidden layer based on the input three-dimensional tensor includes the steps A21 to A22 as shown below: A21. Through the first LSTM layer, the hidden state vectors of all time steps are obtained based on the input three-dimensional tensor.

[0037] In some embodiments, the feature information of each time step is extracted by the gating units (input gate, forget gate, output gate) in the first LSTM layer, and the hidden state vector of all time steps is output: the dimension is 30 (time step) × 128 (number of hidden units), which contains the feature vectors of each of the 30 time steps (e.g., the hidden state vector of the first time step is [0.12, 0.35, ..., 0.21], and the hidden state vector of the 30th time step is [0.28, 0.41, ..., 0.33]).

[0038] A22. The hidden state vector of the last time step is obtained by using the second LSTM layer based on the hidden state vectors of all time steps.

[0039] In some embodiments, the second LSTM layer performs secondary extraction and fusion of temporal features on all time step hidden state vectors, focusing on the key time step features corresponding to the next 15 minutes, and finally outputs the last time step hidden state vector: the dimension is 1×128, specifically [0.32,0.45,...,0.38], which is the core feature for wastewater parameter prediction.

[0040] A3. Through the output layer, a predicted two-dimensional tensor is obtained based on the hidden state vector, wherein the predicted two-dimensional tensor includes production wastewater sample labels and production wastewater prediction parameters corresponding to the production wastewater sample labels.

[0041] In some embodiments, the hidden state vector at the last time step is mapped through a fully connected layer to generate a prediction two-dimensional tensor: the dimension is 4 (sample labels) × 2 (labels + prediction parameters), and the prediction two-dimensional tensor may include: Production wastewater sample label: pH value; predicted parameters for production wastewater: 5.8. Production wastewater sample label: wastewater flow data, production wastewater prediction parameter: 18 m³ / h; Production wastewater sample label: Chemical oxygen demand data (COD concentration), predicted parameters for production wastewater: 85 mg / L; Production wastewater sample label: Suspended solids concentration data (SS concentration), production wastewater predicted parameter: 52 mg / L.

[0042] S130. Based on the predicted production wastewater parameters, the preset target parameters corresponding to the predicted production wastewater parameters, and the preset control coefficient, a target production wastewater discharge management scheme is obtained, wherein the target production wastewater discharge management scheme includes adjustment parameters corresponding to the production wastewater discharge valve.

[0043] In some embodiments, the preset control coefficients include a first preset control coefficient, a second preset control coefficient, and a third preset control coefficient.

[0044] Specifically, the preset control coefficients are the first preset control coefficient, i.e., Kp=0.8; The second preset control coefficient is Ki = 0.3; The third preset control coefficient is Kd=0.5.

[0045] Specifically, the step of obtaining the target production wastewater discharge management scheme based on the predicted production wastewater parameters, the preset target parameters corresponding to the predicted production wastewater parameters, and the preset control coefficients includes the steps B1 to B4 shown below: B1. Based on the predicted parameters of the production wastewater, the preset target parameters, and the first preset control coefficient, the result of the first calculation item is calculated.

[0046] In some embodiments, the calculation of the first calculation result based on the production wastewater prediction parameters, the preset target parameters, and the first preset control coefficient includes the following specific implementation process: Calculate the difference between the predicted parameters of the production wastewater and the preset target parameters to obtain the error value; The product of the error value and the first preset control coefficient is calculated to obtain the result of the first calculation item.

[0047] Specifically, the error value is calculated as follows: predicted production wastewater parameter (85 mg / L) - preset target parameter (80 mg / L) = 5 mg / L.

[0048] Calculate the result of the first calculation item: Error value × First preset control coefficient = 5 × 0.8 = 4.

[0049] B2. Based on the predicted parameters of the production wastewater, the preset target parameters, and the second preset control coefficient, the result of the second calculation item is calculated.

[0050] In some embodiments, the calculation of the second calculation item result based on the production wastewater prediction parameters, the preset target parameters, and the second preset control coefficient includes the following specific implementation process: Calculate the difference between the predicted parameters of the production wastewater and the preset target parameters to obtain the error value; Calculate the cumulative sum of error values ​​over the preset future time period to obtain the cumulative sum of error values; The product of the cumulative sum of the error values ​​and the second preset control coefficient is calculated to obtain the result of the second calculation item.

[0051] Specifically, the error value is calculated as follows: the steps are the same as above, and will not be repeated. It is 5 mg / L.

[0052] Calculate the cumulative error: The cumulative error over the next 15 minutes (30 time steps) = 5 × 30 = 150 mg / L step.

[0053] Calculate the result of the second calculation item: cumulative error value × second preset control coefficient = 150 × 0.3 = 45.

[0054] B3. Based on the predicted parameters of the production wastewater, the preset target parameters, and the third preset control coefficient, the result of the third calculation item is calculated.

[0055] In some embodiments, the calculation of the third calculation item result based on the production wastewater prediction parameters, the preset target parameters, and the third preset control coefficient includes the following specific implementation process: Calculate the difference between the predicted parameters of the production wastewater and the preset target parameters to obtain the error value; Calculate the rate of change of the error value within the preset future time period to obtain the rate of change of the error value; The product of the error value change rate and the third preset control coefficient is calculated to obtain the result of the third calculation item.

[0056] Specifically, the error value is calculated as follows: the steps are the same as above, and will not be repeated. It is 5 mg / L.

[0057] Calculate the rate of change of error value: The rate of change of error value within the next 15 minutes = (final time step error value - initial time step error value) / total number of time steps = (5-3) / 30 ≈ 0.0667 mg·L - ¹·step - ¹, where the initial time step error value can be derived from historical data as 3 mg / L.

[0058] The result of the third calculation item is: Error value change rate × third preset control coefficient = 0.0667 × 0.5 ≈ 0.03335.

[0059] B4. Based on the results of the first calculation item, the second calculation item, and the third calculation item, the adjustment parameters corresponding to the production wastewater discharge valve are obtained.

[0060] In some embodiments, the results of the first calculation item, the second calculation item, and the third calculation item are summed to obtain the valve opening adjustment parameter corresponding to the chemical oxygen demand data: 4+45+0.03335≈49.033%.

[0061] Similarly, the valve adjustment parameters corresponding to pH, wastewater flow rate, and SS concentration are calculated to be -6.2% (the negative sign indicates a decrease in valve opening), 9.5%, and 3.1%, respectively.

[0062] In some embodiments, by considering the adjustment requirements of the four types of parameters, the final target production wastewater discharge management plan is determined: the opening of the wastewater discharge valve in the synthesis workshop is increased by 12% (which can be the value after comprehensive weighting), and the reagent dosing device of the wastewater treatment system can be linked to ensure that the wastewater parameters meet the compliance standards within the next 15 minutes.

[0063] In summary, this application provides a method for managing production wastewater discharge based on the Industrial Internet of Things (IIoT). This method enables accurate prediction of wastewater discharge parameters, and then optimizes multi-objective discharge decisions through algorithms. Combined with the real-time sensing and control capabilities of the IIoT, it improves the efficiency and accuracy of factory production wastewater discharge.

[0064] To better implement the above methods, this application also provides a production wastewater discharge management device based on the Industrial Internet of Things. This device can be integrated into an electronic device, such as a terminal or server. The terminal can be a mobile phone, tablet, smart Bluetooth device, laptop, or personal computer; the server can be a single server or a server cluster composed of multiple servers.

[0065] For example, in this embodiment, the method of this application embodiment will be described in detail by taking the industrial Internet of Things-based production wastewater discharge management device specifically integrated into the terminal as an example.

[0066] For example, such as Figure 3 As shown, the industrial IoT-based wastewater discharge management device 300 may include a first unit 301, a second unit 302, and a third unit 303. The device includes: The first unit is used to acquire data on pH, flow rate, chemical oxygen demand and suspended solids concentration of the production wastewater. The second unit is used to predict the production wastewater prediction parameters for a preset future time period by using a preset wastewater discharge parameter prediction model, based on the pH data, the wastewater flow rate data, the chemical oxygen demand data, and the suspended solids concentration data. The third unit is used to obtain a target production wastewater discharge management scheme based on the production wastewater prediction parameters, the preset target parameters corresponding to the production wastewater prediction parameters, and the preset control coefficients, wherein the target production wastewater discharge management scheme includes adjustment parameters corresponding to the production wastewater discharge valve.

[0067] 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.

[0068] As can be seen from the above, the embodiments of this application can improve the efficiency and accuracy of factory wastewater discharge.

[0069] 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.

[0070] In some embodiments, the product processing device may also be integrated into multiple electronic devices, such as multiple servers, which can then implement the industrial Internet of Things-based wastewater discharge management method of this application.

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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: Obtain data on the pH, flow rate, chemical oxygen demand, and suspended solids concentration of the production wastewater. By using a preset wastewater discharge parameter prediction model, based on the pH data, wastewater flow rate data, chemical oxygen demand data, and suspended solids concentration data, the predicted parameters for production wastewater within a preset future time period are obtained. Based on the predicted production wastewater parameters, the preset target parameters corresponding to the predicted production wastewater parameters, and the preset control coefficients, a target production wastewater discharge management scheme is obtained, wherein the target production wastewater discharge management scheme includes adjustment parameters corresponding to the production wastewater discharge valve.

[0077] For details on the implementation of each of the above operations, please refer to the previous examples, which will not be repeated here.

[0078] As can be seen from the above, the embodiments of this application can improve the efficiency and accuracy of factory wastewater discharge.

[0079] 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.

[0080] To this end, embodiments of this application provide a medium storing multiple instructions that can be loaded by a processor to execute steps in any of the industrial IoT-based wastewater discharge management methods provided in this application. For example, the instructions can execute the following steps: Obtain data on the pH, flow rate, chemical oxygen demand, and suspended solids concentration of the production wastewater. By using a preset wastewater discharge parameter prediction model, based on the pH data, wastewater flow rate data, chemical oxygen demand data, and suspended solids concentration data, the predicted parameters for production wastewater within a preset future time period are obtained. Based on the predicted production wastewater parameters, the preset target parameters corresponding to the predicted production wastewater parameters, and the preset control coefficients, a target production wastewater discharge management scheme is obtained, wherein the target production wastewater discharge management scheme includes adjustment parameters corresponding to the production wastewater discharge valve.

[0081] The medium may include: read-only memory (ROM), random access memory (RAM), disk or optical disk, etc.

[0082] 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.

[0083] Since the instructions stored in the medium can execute the steps of any of the industrial IoT-based wastewater discharge management methods provided in the embodiments of this application, the beneficial effects that any of the industrial IoT-based wastewater discharge management methods provided in the embodiments of this application can achieve can be realized, as detailed in the preceding embodiments, and will not be repeated here.

[0084] The above provides a detailed description of a production wastewater discharge management method, device, terminal, and medium based on the Industrial Internet of Things (IIoT) provided in the embodiments of this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are 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 managing industrial wastewater discharge based on the Industrial Internet of Things, characterized in that, The method includes: Obtain data on the pH, flow rate, chemical oxygen demand, and suspended solids concentration of the production wastewater. By using a preset wastewater discharge parameter prediction model, based on the pH data, wastewater flow rate data, chemical oxygen demand data, and suspended solids concentration data, the predicted parameters for production wastewater within a preset future time period are obtained. Based on the predicted production wastewater parameters, the preset target parameters corresponding to the predicted production wastewater parameters, and the preset control coefficients, a target production wastewater discharge management scheme is obtained, wherein the target production wastewater discharge management scheme includes adjustment parameters corresponding to the production wastewater discharge valve.

2. The method as described in claim 1, characterized in that, The preset wastewater discharge parameter prediction model includes an input layer, a hidden layer, and an output layer; The method of predicting production wastewater parameters for a preset future time period using a preset wastewater discharge parameter prediction model, based on the pH data, wastewater flow rate data, chemical oxygen demand data, and suspended solids concentration data, includes: Through the input layer, an input three-dimensional tensor is obtained based on the pH data, wastewater flow rate data, chemical oxygen demand data, and suspended solids concentration data; Through the hidden layer, a hidden state vector is obtained based on the input three-dimensional tensor; Through the output layer, a predicted two-dimensional tensor is obtained based on the hidden state vector. The predicted two-dimensional tensor includes production wastewater sample labels and production wastewater prediction parameters corresponding to the production wastewater sample labels.

3. The method as described in claim 2, characterized in that, The hidden layer includes a first LSTM layer and a second LSTM layer, and the hidden state vector includes the hidden state vector at the last time step. The process of obtaining the hidden state vector through the hidden layer based on the input 3D tensor includes: Through the first LSTM layer, the hidden state vectors for all time steps are obtained based on the input three-dimensional tensor; The hidden state vector of the last time step is obtained through the second LSTM layer based on the hidden state vectors of all time steps.

4. The method as described in claim 1, characterized in that, The preset control coefficients include a first preset control coefficient, a second preset control coefficient, and a third preset control coefficient; The process of obtaining a target production wastewater discharge management scheme based on the predicted production wastewater parameters, the corresponding preset target parameters, and preset control coefficients includes: Based on the predicted parameters of the production wastewater, the preset target parameters, and the first preset control coefficient, the result of the first calculation item is calculated. Based on the predicted parameters of the production wastewater, the preset target parameters, and the second preset control coefficient, the result of the second calculation item is calculated. Based on the predicted parameters of the production wastewater, the preset target parameters, and the third preset control coefficient, the result of the third calculation item is calculated. Based on the results of the first calculation item, the second calculation item, and the third calculation item, the adjustment parameters corresponding to the production wastewater discharge valve are obtained.

5. The method as described in claim 4, characterized in that, The calculation of the first calculation item result based on the predicted parameters of the production wastewater, the preset target parameters, and the first preset control coefficient includes: Calculate the difference between the predicted parameters of the production wastewater and the preset target parameters to obtain the error value; The product of the error value and the first preset control coefficient is calculated to obtain the result of the first calculation item.

6. The method as described in claim 4, characterized in that, The calculation of the second calculation item result based on the predicted parameters of the production wastewater, the preset target parameters, and the second preset control coefficient includes: Calculate the difference between the predicted parameters of the production wastewater and the preset target parameters to obtain the error value; Calculate the cumulative sum of error values ​​over the preset future time period to obtain the cumulative sum of error values; The product of the cumulative sum of the error values ​​and the second preset control coefficient is calculated to obtain the result of the second calculation item.

7. The method as described in claim 4, characterized in that, The calculation of the third calculation item based on the predicted parameters of the production wastewater, the preset target parameters, and the third preset control coefficient includes: Calculate the difference between the predicted parameters of the production wastewater and the preset target parameters to obtain the error value; Calculate the rate of change of the error value within the preset future time period to obtain the rate of change of the error value; The product of the error value change rate and the third preset control coefficient is calculated to obtain the result of the third calculation item.

8. A production wastewater discharge management device based on the Industrial Internet of Things, characterized in that, The device includes: The first unit is used to acquire data on pH, flow rate, chemical oxygen demand and suspended solids concentration of the production wastewater. The second unit is used to predict the production wastewater prediction parameters for a preset future time period by using a preset wastewater discharge parameter prediction model, based on the pH data, the wastewater flow rate data, the chemical oxygen demand data, and the suspended solids concentration data. The third unit is used to obtain a target production wastewater discharge management scheme based on the production wastewater prediction parameters, the preset target parameters corresponding to the production wastewater prediction parameters, and the preset control coefficients, wherein the target production wastewater discharge management scheme includes adjustment parameters corresponding to the production wastewater discharge valve.

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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