Digital management method and device for coking wastewater treatment, electronic equipment and medium

Abstract

The embodiment of the invention relates to the field of coking wastewater treatment, in particular to a digital management method and device for coking wastewater treatment, electronic equipment and a medium. A specific embodiment of the method comprises the following steps: acquiring coking wastewater water quality characteristic information; performing comparison treatment to obtain coking wastewater water quality updating information; performing updating processing to obtain target biochemical control information; historical operation mode information obtained in advance is updated, and target operation mode information is obtained; based on the historical coking wastewater characteristic information, performing prediction treatment on the coking wastewater water quality characteristic information to obtain water quality change information; and respectively sending the target biochemical control information, the target operation mode information and the water quality change information to each coking wastewater treatment pond so as to carry out sewage treatment on the coking wastewater. According to the embodiment, the treatment effect of coking wastewater treatment can be more stable.

Description

Technical Field

[0001] The embodiments of this application relate to the field of coking wastewater treatment, specifically to digital management methods, apparatus, electronic devices, and media for coking wastewater treatment. Background Technology

[0002] Biological treatment of coking wastewater is a crucial step in coking wastewater treatment, but many coking wastewater treatment processes often face problems such as low treatment efficiency and large operational fluctuations. The main reasons for this are as follows: I. Complex and highly fluctuating influent water quality: Coking wastewater often experiences significant fluctuations in COD (Chemical Oxygen Demand), ammonia nitrogen, and total nitrogen concentrations due to changes in raw coal sources, coal blending ratios, and coking production loads. These fluctuations far exceed the tolerance range of the biological system, inhibiting microbial activity. Furthermore, toxic and harmful substances such as phenols, cyanides, and polycyclic aromatic hydrocarbons in coking wastewater frequently exceed standards, leading to reduced microbial activity or even death within the biological system.

[0003] II. Low functional bacterial activity and unstable nitrogen removal efficiency: Nitrification is easily inhibited: Nitrifying bacteria (especially nitrifying bacteria) grow slowly and are sensitive to the environment. When the influent ammonia nitrogen is too high (above 1000 mg / L), dissolved oxygen is insufficient (below 2 mg / L), or the temperature is too low (below 15°C), nitrification is prone to stagnation, leading to excessive ammonia nitrogen in the effluent. Insufficient or unbalanced carbon source for denitrification: Coking wastewater has poor biodegradability (BOD5 (Biochemical Oxygen Demand 5) / COD is mostly below 0.3), and the carbon source in the raw water is insufficient to meet the denitrification requirements. It is necessary to add carbon sources such as sodium carbonate, but the dosage is often improperly controlled (too much or too little) because the indicators cannot be fed back in time, resulting in excessive total nitrogen or increased COD, increasing costs.

[0004] 3. Sludge performance deterioration and frequent operational failures: Sludge poisoning and loss: High concentrations of toxic substances can cause a sharp drop in sludge activity, resulting in "dead sludge"; fluctuations in the hydraulic load of the secondary sedimentation tank or malfunctions of the sludge scraper can cause sludge loss, further exacerbating the decline in system treatment efficiency.

[0005] IV. Lagging Process Control and Mismatched Operating Parameters: Parameter control relies on experience: In traditional treatment methods, parameters such as aeration intensity, reflux ratio, and chemical dosage are mostly adjusted manually based on experience, lacking real-time data support. This easily leads to control lag (e.g., failure to increase aeration in time when dissolved oxygen is insufficient), resulting in fluctuations in treatment effects. Poor Coordination Between Units: Unstable pretreatment (e.g., ammonia stripping) results in excessive concentrations of ammonia nitrogen and phenols in the influent, directly impacting the biological system. Poor connection between the biological stage and subsequent advanced treatment units (e.g., excessive SS (Suspended Solids) in the effluent leads to ultrafiltration membrane clogging, affecting overall treatment efficiency). Difficulty in Energy Consumption and Cost Control: To maintain treatment effects, excessive aeration, excessive addition of carbon sources and chemicals are common, leading to high energy consumption (e.g., fan power consumption) and chemical costs. Blindly saving energy (e.g., reducing aeration) can cause a decrease in treatment efficiency, making it difficult to achieve a balance between cost and effect.

[0006] In traditional treatment methods, parameters such as aeration intensity, reflux ratio, and chemical dosage rely heavily on manual experience for adjustment, lacking real-time data support. This leads to control lags (e.g., failure to increase aeration in time when dissolved oxygen is insufficient), resulting in fluctuating treatment effects. Even with extensive IoT control systems in the design phase, some systems claim to incorporate AI (Artificial Intelligence) algorithms, but these often remain at the level of "data visualization" and are not deeply integrated with the actual control logic (e.g., the backend control system is only used for alarms and signal display, unable to guide process parameter optimization). When faced with sudden changes in water quality, the control logic becomes incompatible. For example, if ammonia nitrogen suddenly rises, the conventional dosage may still be used, leading to excessive ammonia nitrogen in the effluent; or there may be overreactions (e.g., blindly limiting flow or increasing aeration), increasing energy and chemical consumption. Coking wastewater treatment needs to balance multiple objectives such as "compliant discharge, minimum energy consumption, minimum chemical consumption, and sludge reduction," but existing systems mostly focus on single-objective control (e.g., only focusing on effluent COD compliance). Summary of the Invention

[0007] The summary section of this application is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description section below. This summary section is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0008] Some embodiments of this application propose a digital management method, apparatus, computer equipment, and computer-readable storage medium for coking wastewater treatment to solve one or more of the technical problems mentioned in the background section above.

[0009] In a first aspect, some embodiments of this application provide a digital management method for coking wastewater treatment. The method includes: in response to receiving an inspection start command, sending preset inspection task information to each inspection terminal, and obtaining coking wastewater water quality characteristic information from each inspection terminal; comparing the previously obtained historical coking wastewater characteristic information with the aforementioned coking wastewater water quality characteristic information to obtain updated coking wastewater water quality information; updating the previously obtained historical biochemical control information based on the updated coking wastewater water quality information to obtain target biochemical control information; updating the previously obtained historical operating mode information based on the updated coking wastewater water quality information and pre-obtained equipment parameter information to obtain target operating mode information; predicting the aforementioned coking wastewater water quality characteristic information based on the aforementioned historical coking wastewater characteristic information to obtain water quality change information; and sending the aforementioned target biochemical control information, the aforementioned target operating mode information, and the aforementioned water quality change information to each coking wastewater treatment pond for wastewater treatment.

[0010] Secondly, some embodiments of this disclosure provide a digital management device for coking wastewater treatment. The device includes: a first sending unit configured to, in response to receiving an inspection start command, send preset inspection task information to each inspection terminal, and acquire coking wastewater water quality characteristic information from each inspection terminal; a comparison unit configured to compare the pre-acquired historical coking wastewater characteristic information with the aforementioned coking wastewater water quality characteristic information to obtain updated coking wastewater water quality information; and a first updating unit configured to update the pre-acquired historical biochemical control information based on the aforementioned updated coking wastewater water quality information. The first processing unit obtains target biochemical control information; the second updating unit is configured to update the pre-acquired historical operation mode information based on the above-mentioned coking wastewater quality update information and pre-acquired equipment parameter information to obtain target operation mode information; the prediction unit is configured to predict the above-mentioned coking wastewater quality characteristic information based on the above-mentioned historical coking wastewater characteristic information to obtain water quality change information; the second sending unit is configured to send the above-mentioned target biochemical control information, the above-mentioned target operation mode information and the above-mentioned water quality change information to each coking wastewater treatment pond for wastewater treatment of coking wastewater.

[0011] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0012] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0013] The above embodiments of this application have the following beneficial effects: 1. More stable treatment effect and significantly improved compliance rate. Digital operation management: This method collects pollutant indicators such as COD and ammonia nitrogen, as well as key parameters such as equipment operating pressure and current in real time through a large number of sensors, and can automatically adjust process parameters such as aeration rate, reflux ratio, and dosage. 2. Significantly reduced costs: Costs are reduced from multiple dimensions such as energy consumption, reagents, and equipment maintenance. In terms of equipment maintenance, fault prediction through data monitoring reduces equipment maintenance costs by 40%. 3. Upgraded management efficiency and stronger risk control capabilities. Digital management: Achieves fully automated control of the entire process, with abnormal alarms directly pushed to the mobile terminal, shortening the response time to about 10 minutes. More efficient resource utilization, in line with the needs of green development. Attached Figure Description

[0014] The above and other features, advantages, and aspects of the embodiments of this application will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0015] Figure 1 This is a flowchart of some embodiments of the digital management method for coking wastewater treatment according to this application; Figure 2 This is a schematic diagram of an Internet of Things (IoT) control system for coking wastewater, based on some embodiments of the digital management method for coking wastewater treatment according to this application. Figure 3 These are schematic diagrams of some embodiments of a digital management device for coking wastewater treatment according to this disclosure; Figure 4 This is a schematic diagram of the structure of a computer device suitable for implementing some embodiments of this application. Detailed Implementation

[0016] Embodiments of this application will now be described in more detail with reference to the accompanying drawings. While some embodiments of this application are shown in the drawings, it should be understood that this application can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this application. It should be understood that the drawings and embodiments of this application are for illustrative purposes only and are not intended to limit the scope of protection of this application.

[0017] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0018] It should be noted that the concepts of "first" and "second" mentioned in this application are only used to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0019] It should be noted that the terms "a" and "a plurality of" used in this application are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0020] The names of the messages or information exchanged between multiple devices in the embodiments of this application are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0021] The present application will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] Figure 1 A flow 100 of some embodiments of a digital management method for coking wastewater treatment according to this application is shown. The digital management method for coking wastewater treatment includes the following steps: Step 101: In response to receiving the inspection start command, the preset inspection task information is sent to each inspection terminal, and the coking wastewater water quality characteristic information is obtained from each inspection terminal.

[0023] In some embodiments, the executing entity of the digital management method for coking wastewater treatment can, in response to receiving an inspection start command, send preset inspection task information to each inspection terminal and obtain coking wastewater water quality characteristic information from each inspection terminal. The inspection start command can be a command received from a user terminal. The user terminal can be a terminal that wants to perform a coking wastewater inspection. The inspection start command can also be a command generated periodically by the executing entity at preset intervals. The inspection start command indicates a desire to inspect the coking wastewater treatment. The preset inspection task information can be a start command pre-generated by the executing entity, which can be used to start the inspection terminals. The inspection task information indicates a desire to collect data from the inspection terminals. The inspection terminals can include, but are not limited to, temperature and humidity sensors, DO (dissolved oxygen) sensors, pH (degree of acidity / oralkalinity) sensors, COD sensors, NH3-N (ammonia nitrogen content) sensors, and high-definition cameras. The inspection terminals can be installed in the coking wastewater IoT control system. The aforementioned water quality characteristics of coking wastewater may include, but are not limited to: seasonal information, current COD value, current ammonia nitrogen value, pH value, ORP (oxidation-reduction potential) in the denitrification zone, current TN (total nitrogen) concentration, current TP removal rate, and ORP (oxidation-reduction potential) in the anaerobic zone. The seasonal information indicates the season in which the above-mentioned water quality characteristics of coking wastewater are collected. Therefore, sludge samples can be collected periodically to observe and compare the sludge settling process. Combined with sludge concentration data, key indicators such as SV30 and SVI (Sludge Volume Index) can be analyzed in real time to avoid delays in treatment due to sludge bulking and other problems.

[0024] In some embodiments, fiber optic interfaces and fiber optic transmission can be used to send preset inspection task information to each inspection terminal and to obtain coking wastewater quality characteristic information from each inspection terminal. This enables seamless integration between the inspection terminal equipment and the back-end management platform, achieving two-way authentication through gateways and routers to ensure the real-time nature and authenticity of water quality and equipment data. Simultaneously, a data storage mechanism is established to provide a foundation for subsequent analysis and traceability.

[0025] As an example, the preset duration could be five minutes, ten minutes, or thirty minutes.

[0026] Specifically, the aforementioned IoT control system for coking wastewater can be referenced. Figure 2 The diagram illustrates a schematic of an Internet of Things (IoT) control system for coking wastewater, representing some embodiments of the digital management method for coking wastewater treatment according to this application. (See attached diagram.) Figure 2As shown, the entire coking wastewater IoT control system can be divided into: an online instrumentation system, an automatic control system, and an automatic alarm and manual assistance system. The online instrumentation system refers to the real-time, continuous, and accurate collection of key process parameters, water quality indicators, and equipment operating status data throughout the entire coking wastewater treatment process. This provides "data basis" for subsequent automatic control and manual decision-making, replacing the lag of traditional manual sampling and testing. This includes flow rate, water quality indicators, process operating parameters such as pH and T (temperature), and current and voltage of electrical equipment. The automatic control system refers to the automatic adjustment of process parameters and control of equipment operating status based on real-time data collected by online instruments, achieving stable and compliant operation under "unmanned intervention," replacing traditional manual experience-based operation. For example, it can automatically adjust the frequency converter frequency of the liquid alkali dosing pump based on the pH of the biological treatment tank to increase or decrease the dosage. Or it can control the frequency of the blower based on the DO concentration in the aerobic tank to increase or decrease the aeration rate. Automatic alert and manual assistance systems are designed for abnormal situations, equipment malfunctions, or complex scenarios requiring manual intervention that exceed the scope of automatic control. They alert staff through multi-channel alarms and task push notifications, compensating for the limitations of automation systems and achieving efficient collaboration with "automation as the primary method and manual intervention as a supplement." For example, SV30 (sludge settling velocity) and sludge concentration still require system alerts and manual sampling for testing. The main source of coking wastewater is residual ammonia water generated during the coking process in coking plants; therefore, its quality and quantity often fluctuate due to process adjustments at the production end, resulting in unstable water quality and quantity. Therefore, automatic online monitoring instruments are installed before the water enters the equalization tank of the treatment system. These instruments can monitor the influent flow rate Q and major pollutant indicators in real time: COD, NH3-N (ammonia nitrogen content), SCN (thiocyanate), and toxic indicators TCN (total cyanide) and S2- (sulfide ions). Alarm values ​​are set for the instruments. For indicators exceeding the standards, operators are notified through both online alarms and central control system alarms. Operators assess the flow rate and general and toxic indicators, notify upstream production departments in real time to strengthen pretreatment, and switch to the emergency tank as needed. For pollutants with slight exceedances, the online biological instruments must be closely monitored, and sampling and testing should be strengthened. The automatic control system should respond to alarms and react to abnormal indicators, automatically controlling the dosing pump, fan frequency, and electric valves to quickly adjust process operating parameters to cope with changes in water quality and quantity. Figure 2 In this context, MLSS can represent Mixed Liquid Suspended Solids, or the concentration of suspended solids in a mixed liquid. O3 can represent ozone.

[0027] In some optional implementations of certain embodiments, the execution entity sending preset inspection task information to each inspection terminal and obtaining coking wastewater quality characteristic information from each inspection terminal may include the following steps: The first step is to send the aforementioned inspection task information to the temperature and humidity sensor to obtain temperature and humidity information. This temperature and humidity information represents both temperature and humidity values.

[0028] The second step is to send the above inspection task information to the DO sensor to obtain the DO value from the DO sensor.

[0029] The third step is to send the above inspection task information to the pH sensor to obtain the pH value from the pH sensor.

[0030] The fourth step is to send the above inspection task information to the COD sensor to obtain the current COD value from the COD sensor.

[0031] The fifth step is to send the above inspection task information to the NH3-N sensor to obtain the NH3-N value from the NH3-N sensor.

[0032] The sixth step is to send the above inspection task information to the high-definition camera to obtain images of the coking wastewater treatment pond from the high-definition camera.

[0033] Step 7: The temperature and humidity information, DO value, pH value, current COD value, NH3-N value, and the image of the coking wastewater treatment pond are fused to obtain the coking wastewater quality characteristic information. Specifically, the fusion of the temperature and humidity information, DO value, pH value, current COD value, NH3-N value, and the image of the coking wastewater treatment pond to obtain the coking wastewater quality characteristic information can be achieved by defining the temperature and humidity information, DO value, pH value, current COD value, NH3-N value, and the image of the coking wastewater treatment pond as the coking wastewater quality characteristic information.

[0034] Optionally, the aforementioned implementing entity may also store the water quality characteristic information of coking wastewater in a water quality database. This water quality database may be a database used to store at least one set of water quality characteristic information for coking wastewater.

[0035] Step 102: Compare and process the previously acquired historical coking wastewater characteristic information with the coking wastewater water quality characteristic information to obtain updated coking wastewater water quality information.

[0036] In some embodiments, the executing entity may compare the pre-acquired historical coking wastewater characteristic information with the aforementioned coking wastewater water quality characteristic information to obtain updated coking wastewater water quality information. The aforementioned historical coking wastewater characteristic information may be historical coking wastewater characteristic information obtained from the aforementioned water quality database. The aforementioned historical time may be a time interval between the current time and the aforementioned preset time period. The aforementioned historical coking wastewater characteristic information may include, but is not limited to: historical COD value, historical ammonia nitrogen value, historical TN concentration value, and historical TP removal rate.

[0037] The aforementioned updated information on coking wastewater quality may include, but is not limited to: COD change rate, ammonia nitrogen change rate, pH value, ORP in the denitrification zone, TN concentration change rate, TP removal change rate, and ORP in the anaerobic zone.

[0038] In some embodiments, the process of comparing the previously acquired historical coking wastewater characteristic information with the coking wastewater water quality characteristic information to obtain updated coking wastewater water quality information may include the following steps: The first step is to determine the ratio of the current COD value included in the above-mentioned coking wastewater water quality characteristic information to the historical COD value included in the above-mentioned historical coking wastewater characteristic information as the COD change rate included in the coking wastewater water quality update information.

[0039] The second step is to determine the ratio of the current ammonia nitrogen value included in the above-mentioned coking wastewater water quality characteristic information to the historical ammonia nitrogen value included in the above-mentioned historical coking wastewater characteristic information as the ammonia nitrogen change rate included in the coking wastewater water quality update information.

[0040] The third step is to determine the ratio of the current TN concentration value included in the above-mentioned coking wastewater water quality characteristic information to the historical TN concentration value included in the above-mentioned historical coking wastewater characteristic information as the TN concentration change rate included in the coking wastewater water quality update information.

[0041] The fourth step is to determine the ratio of the current TP removal rate included in the above-mentioned coking wastewater water quality characteristic information to the historical TP removal rate included in the above-mentioned historical coking wastewater characteristic information as the TP removal change rate included in the coking wastewater water quality update information.

[0042] The fifth step is to determine the pH value, ORP of the denitrification zone, and ORP of the anaerobic zone included in the above-mentioned coking wastewater quality characteristics information as the pH value, ORP of the denitrification zone, and ORP of the anaerobic zone included in the updated coking wastewater quality information.

[0043] Therefore, based on production characteristics, the periodic changes in data can be analyzed to identify the patterns of wastewater discharge at the source of production. For example, coking wastewater is regularly discharged into desulfurization wastewater, which often contains pollutants that can easily cause microbial poisoning. This part of the water can be predicted in advance, and diversion measures can be taken to avoid impacting the biochemical system. For example, during upstream coke oven maintenance, the production wastewater, which is mainly ammonia stripping wastewater, can be changed to flushing water.

[0044] Step 103: Based on the updated water quality information of coking wastewater, update the previously acquired historical biochemical control information to obtain the target biochemical control information.

[0045] In some embodiments, the aforementioned executing entity may update the previously acquired historical biochemical control information based on the aforementioned coking wastewater quality update information to obtain the target biochemical control information.

[0046] In some optional implementations of certain embodiments, the execution entity updates the pre-acquired historical biochemical control information based on the aforementioned coking wastewater quality update information to obtain the target biochemical control information, which may include the following steps: The first step involves determining the target fan control information based on the COD change rate being greater than a preset COD change threshold. This preset fan control information can represent "increasing fan aeration intensity." This, in turn, increases oxygen transfer.

[0047] As an example, the preset COD change threshold mentioned above could be 1.1.

[0048] The second step involves generating target alkali control information based on pre-generated alkali control relationship information, in response to the determination that the ammonia nitrogen change rate exceeds a preset ammonia nitrogen change threshold and the pH value is outside a preset pH range. This pre-generated alkali control relationship information characterizes the correspondence between pH value and sodium hydroxide dosing pump frequency. Generating the target alkali control information based on the pre-generated alkali control relationship information can be achieved by determining the sodium hydroxide dosing pump frequency corresponding to the aforementioned pH value as the target alkali control information. This maintains the pH within a suitable range.

[0049] As an example, the preset ammonia nitrogen change threshold can be 1.1. The preset pH range can be [6.0, 9.0].

[0050] The third step involves determining the target aeration control information based on the fact that the ORP in the denitrification zone is greater than the preset ORP threshold and the TN concentration change rate is greater than the preset TN concentration change threshold. This preset aeration control information can represent "aeration shutdown." This allows for an increase in the internal recirculation ratio, creating a strictly anaerobic environment.

[0051] As an example, the preset ORP threshold for the denitrification zone can be -100 mV. The preset TN concentration change threshold can be 1.1.

[0052] Fourthly, in response to the determination that the TP removal change rate is less than the preset TP removal threshold and the anaerobic zone ORP is greater than the preset anaerobic zone ORP threshold, the preset anaerobic tank control information is determined as the target anaerobic tank control information. This preset anaerobic tank control information can be information indicating "checking for aeration leaks in the anaerobic section and strengthening stirring." This can reduce DO.

[0053] As an example, the preset TP removal threshold can be 0.9. The preset ORP threshold for the anaerobic zone can be -100mV.

[0054] The fifth step involves identifying the target fan control information, the target alkali agent control information, the target aeration control information, and the target anaerobic tank control information as the target fan control information, target alkali agent control information, target aeration control information, and target anaerobic tank control information included in the historical biochemical control information, thereby obtaining the target biochemical control information.

[0055] Optionally, before generating the target alkali control information based on the pre-generated alkali control relationship information, the executing entity may also perform the following steps: The first step is to obtain a set of sample sodium hydroxide dosing pump frequency values ​​and a set of sample pH values ​​within a preset time period. Specifically, the sample sodium hydroxide dosing pump frequency values ​​in the sample sodium hydroxide dosing pump frequency value set and the sample pH values ​​in the sample pH value set correspond one-to-one. These sample sodium hydroxide dosing pump frequency value sets and sample pH value sets can be obtained from a sample database. This sample database can be a database used to store historical sodium hydroxide dosing pump frequency value sets and pH value sets.

[0056] As an example, the preset duration can be, but is not limited to, 10 hours, 12 hours, or 24 hours.

[0057] The second step involves generating a sodium hydroxide dosing pump frequency reference table based on the aforementioned sample sodium hydroxide dosing pump frequency value set and sample pH value set. This generation can be achieved by adding the sample sodium hydroxide dosing pump frequency value set and sample pH value set to an initial sodium hydroxide dosing pump frequency reference table according to their corresponding relationships. This initial sodium hydroxide dosing pump frequency reference table can initially be empty.

[0058] The third step is to determine the sodium hydroxide dosing pump frequency comparison table as the alkali control relationship information.

[0059] Therefore, by analyzing the energy consumption ratio of each process stage through the platform, targeted energy conservation and consumption reduction can be achieved. For example, optimizing the operation mode of electric heating in winter can reduce ineffective power consumption. Recycling treated water that meets standards and using it as recycled water in production can reduce the consumption of fresh water. For high-concentration brine, digital control of the evaporation, crystallization, and salt separation process can ensure that the recovered salt meets industrial standards, achieving resource recycling.

[0060] Step 104: Based on the updated coking wastewater quality information and the pre-acquired equipment parameter information, update the pre-acquired historical operation mode information to obtain the target operation mode information.

[0061] In some embodiments, the executing entity can update the pre-acquired historical operating mode information based on the updated coking wastewater quality information and pre-acquired equipment parameter information to obtain target operating mode information. The equipment parameter information can be obtained from at least one coking wastewater treatment device. This equipment parameter information can characterize the operating information of each coking wastewater treatment device. The equipment parameter information can include a set of equipment operating parameter values. The equipment operating parameter values ​​in the set can be, but are not limited to, pressure, frequency, and current.

[0062] As an example, the aforementioned coking wastewater treatment equipment may be, but is not limited to, heat exchangers, magnetic coagulation sedimentation tanks, biological treatment tanks, secondary sedimentation tanks, or aeration systems.

[0063] In some optional implementations of certain embodiments, the execution entity updates the pre-acquired historical operating mode information based on the updated coking wastewater quality information and pre-acquired equipment parameter information to obtain target operating mode information, which may include the following steps: The first step is to verify the aforementioned equipment parameter information to obtain verification results. This verification process can involve determining a first preset verification result if every equipment operating parameter value in the set of equipment operating parameter values ​​included in the aforementioned equipment parameter information meets the corresponding normal operating conditions. Alternatively, if any equipment operating parameter value in the set of equipment operating parameter values ​​does not meet the corresponding normal operating conditions, a second preset verification result is determined. The first preset verification result can represent "verification passed." The second preset verification result can represent "verification failed."

[0064] For example, when the equipment operating parameter is a voltage value, the corresponding normal operating condition for the equipment can be a voltage value not exceeding 380V (volts). When the equipment operating parameter is a power value, the corresponding normal operating condition for the equipment can be a power value not exceeding 1kW (kilowatts).

[0065] The second step, in response to the determination that the above test results meet the preset safety conditions, is to execute the following mode adjustment sub-step: The first sub-step involves determining, in response to the determination that the aforementioned seasonal information meets preset seasonal conditions, the preset cooling mode information is set as the target heat exchanger operating mode information. Here, the aforementioned preset safety condition can be information indicating "inspection passed" from the aforementioned inspection results. The aforementioned preset seasonal condition can be information indicating "summer" or "spring" from the aforementioned seasonal information. The aforementioned preset cooling mode information can be pre-generated information indicating "cooling on".

[0066] The second sub-step involves determining, in response to the determination that the pH value is greater than a preset maximum pH value, the first preset alkali adjustment information is set as the target alkali adjustment information. This first preset alkali adjustment information may be information characterizing "reducing the dosage of alkali solution".

[0067] As an example, the maximum preset pH value could be 9.0.

[0068] The third sub-step involves determining the target heat exchanger operation mode information and the target alkali solution adjustment information as the target heat exchanger operation mode information and target alkali solution adjustment information included in the historical operation mode information, thereby obtaining the target operation mode information.

[0069] Therefore, the dosing system can automatically adjust the dosage based on real-time water quality data, such as the relationship between pH value and sodium hydroxide dosing pump frequency. By controlling the frequency of the dosing pump to adjust the dosage, precise and efficient dosing of chemicals can be achieved, avoiding overdosing and thus reducing chemical consumption costs. Optionally, the aforementioned implementing entity may also perform the following steps: The first step, in response to the determination that the above test results do not meet the preset safety conditions, is to send preset safety alarm information to the alarm terminal for alarm processing. The preset safety alarm information can be pre-generated warning text or sound. The alarm terminal can be a terminal used to display warning text or issue prompts. Thus, the platform can display the real-time operating status of each process section. Once situations such as water quality exceeding standards, abnormal equipment parameters, or safety issues occur, the system will issue alarms according to preset thresholds and automatically push notifications to the mobile software of the corresponding management personnel. For example, if an abnormal increase in influent TCN is detected, an early warning will be immediately triggered and linked to the influent control plan for the subsequent biological treatment section.

[0070] The second step involves determining that the aforementioned seasonal information does not meet the aforementioned preset seasonal conditions, and then setting the preset heating mode information as the target heat exchanger operating mode information. The aforementioned preset heating mode information can be pre-generated information indicating "heating on". Third, in response to determining that the pH value is less than the preset minimum pH value, the second preset alkali adjustment information is determined as the target alkali adjustment information. This second preset alkali adjustment information can be information representing "increasing the dosage of alkali solution".

[0071] As an example, the minimum preset pH value could be 3.0.

[0072] Alternatively, an experiment can be conducted to find a fuzzy linear relationship between the operating power of the blower and dissolved oxygen. The aeration intensity represents the frequency at which the blower operates. For example, the motor frequency generally operates in the range of 20~50Hz. Record the dissolved oxygen value in the biochemical aerobic tank within the range of 20~50Hz and plot a curve. Conversely, the operating frequency of the blower can be dynamically adjusted based on the dissolved oxygen data.

[0073] Step 105: Based on historical coking wastewater characteristic information, predictive processing of coking wastewater water quality characteristic information is performed to obtain water quality change information.

[0074] In some embodiments, the aforementioned implementing entity can predict and process the water quality characteristics of the coking wastewater based on the aforementioned historical coking wastewater characteristic information to obtain water quality change information. Specifically, this prediction and processing of the water quality characteristics based on the aforementioned historical coking wastewater characteristic information to obtain water quality change information can be achieved by projects identifying patterns in operational data. For example: In northern winters, when the water temperature is 20 degrees Celsius, microbial activity decreases, and the removal rates of organic matter and ammonia nitrogen decrease by 10%. In summer, when the temperature is 30-35 degrees Celsius, dissolved oxygen concentration decreases by 20%, nitrification is restricted, and the ammonia nitrogen removal rate decreases by more than 10-20%. By monitoring the curves between dissolved oxygen and fan frequency, a sustained deviation may indicate reduced fan motor efficiency, requiring shutdown and maintenance. This allows for planned production guidance and ensures stable system operation. A control center is established to achieve centralized back-end management across multiple plants.

[0075] Therefore, based on the operational data accumulated on the platform, optimal process parameters under different operating conditions can be summarized to form a data management library. For example, for biological systems with low carbon-to-nitrogen ratios, the effectiveness of adjustment schemes such as multi-point water inlet and reducing dissolved oxygen in the aerobic zone can be verified through data. Mature experience can be transformed into built-in rules of the platform to continuously improve treatment efficiency and reduce carbon source consumption.

[0076] Step 106: Send the target biochemical control information, target operation mode information, and water quality change information to each coking wastewater treatment pond to treat the coking wastewater.

[0077] In some embodiments, the aforementioned executing entity may send the aforementioned target biochemical control information, the aforementioned target operating mode information, and the aforementioned water quality change information to each coking wastewater treatment pond to treat the coking wastewater.

[0078] The above embodiments of this application have the following beneficial effects: 1. More stable treatment effect and significantly improved compliance rate. Digital operation management: This method collects pollutant indicators such as COD and ammonia nitrogen, as well as key parameters such as equipment operating pressure and current in real time through a large number of sensors, and can automatically adjust process parameters such as aeration rate, reflux ratio, and dosage. 2. Significantly reduced costs: Costs are reduced from multiple dimensions such as energy consumption, reagents, and equipment maintenance. In terms of equipment maintenance, fault prediction through data monitoring reduces equipment maintenance costs by 40%. 3. Upgraded management efficiency and stronger risk control capabilities. Digital management: Achieves fully automated control of the entire process, with abnormal alarms directly pushed to the mobile terminal, shortening the response time to about 10 minutes. More efficient resource utilization, in line with the needs of green development.

[0079] Further reference Figure 3As an implementation of the methods shown in the above figures, this disclosure provides some embodiments of a digital management device for coking wastewater treatment. These device embodiments are similar to... Figure 1 Corresponding to the method embodiments shown, this digital management device for coking wastewater treatment can be specifically applied to various electronic devices.

[0080] like Figure 3 As shown, a digital management device 300 for coking wastewater treatment in some embodiments includes: a first sending unit 301, a comparison unit 302, a first updating unit 303, a second updating unit 304, a prediction unit 305, and a second sending unit 306. The first sending unit 301 is configured to, in response to receiving an inspection start command, send preset inspection task information to each inspection terminal and obtain coking wastewater water quality characteristic information from each inspection terminal; the comparison unit 302 is configured to compare the pre-obtained historical coking wastewater characteristic information with the aforementioned coking wastewater water quality characteristic information to obtain updated coking wastewater water quality information; the first updating unit 303 is configured to update the pre-obtained historical biochemical control information based on the aforementioned updated coking wastewater water quality information to obtain target biochemical control information; the second updating unit 304 is configured to... The new unit 304 is configured to update the previously acquired historical operation mode information based on the aforementioned coking wastewater quality update information and the previously acquired equipment parameter information to obtain target operation mode information; the prediction unit 305 is configured to predict the aforementioned coking wastewater quality characteristic information based on the aforementioned historical coking wastewater characteristic information to obtain water quality change information; the second sending unit 306 is configured to send the aforementioned target biochemical control information, the aforementioned target operation mode information and the aforementioned water quality change information to each coking wastewater treatment pond for wastewater treatment.

[0081] It is understandable that the units described in the digital management device 300 for coking wastewater treatment are consistent with the reference. Figure 1 The steps in the described method correspond accordingly. Therefore, the operations, features, and beneficial effects described above for the method also apply to the digital management device 300 for coking wastewater treatment and the units contained therein, and will not be repeated here.

[0082] This application also provides a computer device 400. For example... Figure 4 As shown, the computer device 400 includes a bus 401, a processor 402, a memory 403, and a communication interface 404. The processor 402, the memory 403, and the communication interface 404 communicate with each other via the bus 401. The computer device 400 can be a server or a terminal device. It should be understood that this application does not limit the number of processors and memories in the computer device 400.

[0083] Bus 401 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus 401 may be represented by a single line, but this does not mean that there is only one bus or one type of bus. The bus 401 may include a path for transmitting information between various components of the computer device 400 (e.g., memory 403, processor 402, communication interface 404).

[0084] Processor 402 may include any one or more processors such as a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0085] Memory 403 may include volatile memory, such as random access memory (RAM). Memory 403 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0086] The memory 403 stores executable program code, and the processor 402 executes the executable program code to implement the functions of the aforementioned first sending unit, comparison unit, first update unit, second update unit, prediction unit, and second sending unit 106, thereby realizing the aforementioned digital management method for coking wastewater treatment. That is, the memory 403 stores instructions for executing the aforementioned digital management method for coking wastewater treatment.

[0087] The communication interface 404 uses transceiver modules such as, but not limited to, network interface cards and transceivers to enable communication between the computer device 400 and other devices or communication networks.

[0088] This application also provides a chip, which includes a processor and a data interface. The processor reads instructions stored in the memory through the data interface to execute the above-described digital management method for coking wastewater treatment.

[0089] This application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium capable of being stored by a computing device, or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive). The computer-readable storage medium includes instructions that instruct the computing device to execute the aforementioned digital management method for coking wastewater treatment.

[0090] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0091] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of this application.

Claims

1. A digital management method for coking wastewater treatment, comprising: In response to receiving the inspection start command, the system sends the preset inspection task information to each inspection terminal and obtains the coking wastewater water quality characteristic information from each inspection terminal. The historical coking wastewater characteristic information obtained in advance is compared with the coking wastewater water quality characteristic information to obtain updated coking wastewater water quality information; Based on the updated coking wastewater quality information, the previously acquired historical biochemical control information is updated to obtain the target biochemical control information. Based on the updated coking wastewater quality information and the pre-acquired equipment parameter information, the pre-acquired historical operation mode information is updated to obtain the target operation mode information. Based on the historical coking wastewater characteristic information, the water quality characteristic information of the coking wastewater is predicted and processed to obtain water quality change information; The target biochemical control information, the target operating mode information, and the water quality change information are respectively sent to each coking wastewater treatment pond to treat the coking wastewater.

2. The digital management method for coking wastewater treatment according to claim 1, characterized in that, The inspection terminal includes: a temperature and humidity sensor, a DO sensor, a pH sensor, a COD sensor, an NH3-N sensor, and a high-definition camera; and the functions of sending preset inspection task information to each inspection terminal and obtaining coking wastewater quality characteristic information from each inspection terminal include: The inspection task information is sent to the temperature and humidity sensor to obtain temperature and humidity information from the temperature and humidity sensor. The inspection task information is sent to the DO sensor to obtain the DO value from the DO sensor; The inspection task information is sent to the pH sensor to obtain the pH value from the pH sensor; The inspection task information is sent to the COD sensor to obtain the current COD value from the COD sensor; The inspection task information is sent to the NH3-N sensor to obtain the NH3-N value from the NH3-N sensor; The inspection task information is sent to a high-definition camera to obtain an image of the coking wastewater treatment pond from the high-definition camera. The temperature and humidity information, the DO value, the pH value, the current COD value, the NH3-N value, and the image of the coking wastewater treatment pond are fused to obtain the water quality characteristic information of the coking wastewater.

3. The digital management method for coking wastewater treatment according to claim 1, characterized in that, The updated coking wastewater quality information includes: COD change rate, ammonia nitrogen change rate, pH value, ORP in the denitrification zone, TN concentration change rate, TP removal change rate, and ORP in the anaerobic zone; and the updating of pre-acquired historical biochemical control information based on the updated coking wastewater quality information to obtain target biochemical control information, including: In response to determining that the COD change rate is greater than a preset COD change threshold, the preset fan control information is determined as the target fan control information; In response to determining that the ammonia nitrogen change rate is greater than a preset ammonia nitrogen change threshold and the pH value is not within a preset pH value range, target alkali control information is generated based on pre-generated alkali control relationship information; In response to determining that the ORP of the denitrification zone is greater than the preset ORP threshold of the denitrification zone and the TN concentration change rate is greater than the preset TN concentration change threshold, the preset aeration control information is determined as the target aeration control information. In response to determining that the TP removal change rate is less than the preset TP removal threshold and the anaerobic zone ORP is greater than the preset anaerobic zone ORP threshold, the preset anaerobic tank control information is determined as the target anaerobic tank control information. The target fan control information, the target alkali agent control information, the target aeration control information, and the target anaerobic tank control information are determined as the target fan control information, target alkali agent control information, target aeration control information, and target anaerobic tank control information included in the historical biochemical control information, thus obtaining the target biochemical control information.

4. The digital management method for coking wastewater treatment according to claim 1, characterized in that, The updated coking wastewater quality information also includes: seasonal information; and the updating process based on the updated coking wastewater quality information and pre-acquired equipment parameter information to obtain target operating mode information, including: The equipment parameter information is processed for verification to obtain the verification results; In response to determining that the test result meets the preset safety conditions, the following mode adjustment steps are performed: In response to determining that the seasonal information meets the preset seasonal conditions, the preset cooling mode information is determined as the target heat exchanger operating mode information; In response to determining that the pH value is greater than the preset maximum pH value, the first preset alkaline solution adjustment information is determined as the target alkaline solution adjustment information; The target heat exchanger operation mode information and the target alkali solution adjustment information are determined as the target heat exchanger operation mode information and target alkali solution adjustment information included in the historical operation mode information to obtain the target operation mode information.

5. The digital management method for coking wastewater treatment according to claim 4, characterized in that, The method further includes: In response to determining that the test result does not meet the preset security conditions, a preset security alarm message is sent to the alarm terminal for alarm processing. In response to determining that the seasonal information does not meet the preset seasonal conditions, the preset heating mode information is determined as the target heat exchanger operating mode information; In response to determining that the pH value is less than the preset minimum pH value, the second preset alkali solution adjustment information is determined as the target alkali solution adjustment information.

6. The digital management method for coking wastewater treatment according to claim 3, characterized in that, Before generating the target alkali control information based on the pre-generated alkali control relationship information, the method further includes: Obtain a set of sample sodium hydroxide dosing pump frequency values ​​and a set of sample pH values ​​within a preset time period, wherein the sample sodium hydroxide dosing pump frequency values ​​in the sample sodium hydroxide dosing pump frequency value set and the sample pH values ​​in the sample pH value set correspond one-to-one. Based on the sample sodium hydroxide dosing pump frequency value set and the sample pH value set, a sodium hydroxide dosing pump frequency comparison table is generated; The sodium hydroxide dosing pump frequency reference table was determined as information for alkali control.

7. A digital management device for coking wastewater treatment, characterized in that, include: The first sending unit is configured to, in response to receiving the inspection start command, send the preset inspection task information to each inspection terminal, and obtain the coking wastewater water quality characteristic information from each inspection terminal. The comparison unit is configured to compare the pre-acquired historical coking wastewater characteristic information with the coking wastewater water quality characteristic information to obtain updated coking wastewater water quality information. The first update unit is configured to update the pre-acquired historical biochemical control information based on the coking wastewater water quality update information to obtain the target biochemical control information. The second update unit is configured to update the pre-acquired historical operation mode information based on the coking wastewater quality update information and the pre-acquired equipment parameter information to obtain the target operation mode information. The prediction unit is configured to perform predictive processing on the water quality characteristic information of the coking wastewater based on the historical coking wastewater characteristic information to obtain water quality change information; The second sending unit is configured to send the target biochemical control information, the target operating mode information, and the water quality change information to each coking wastewater treatment pond for wastewater treatment.

8. An electronic device, characterized in that, include: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 6.

9. A computer-readable medium, characterized in that, It stores a computer program, characterized in that the computer program, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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