A method and system for regulating decentralized toilet blackwater compliance

Abstract

The present application relates to a kind of regulation and control method and system for decentralized toilet blackwater standard discharge.The regulation and control method includes: using outlet end sensing probe to synchronously collect COD, BOD, ammonia nitrogen, total nitrogen, total phosphorus real-time data and transmit to monitoring center;COD and BOD are fused to obtain organic pollution comprehensive concentration value, and effective data is screened;Based on the discharge standard limit value, the regulation and control index of organic pollution comprehensive concentration, ammonia nitrogen, total nitrogen and total phosphorus is calculated;When the regulation and control index is greater than zero, regulation and control instruction is generated and issued to field sewage treatment equipment for adjustment.Compared with prior art, the present application realizes precise online monitoring and self-adaptive regulation of decentralized toilet blackwater quality by multi-index synchronous sensing, data fusion and closed-loop regulation, solves the problem of monitoring and regulation disconnection and poor scene adaptability, ensures stable standard discharge and reduces operation and maintenance cost.

Description

Technical Field

[0001] This invention relates to the fields of environmental monitoring and wastewater treatment technology, and in particular to a control method and system for ensuring that black water from decentralized toilets meets discharge standards. Background Technology

[0002] Currently, in the field of blackwater treatment from decentralized toilets, water quality monitoring largely relies on periodic manual inspections or the use of monitoring equipment developed for centralized municipal treatment scenarios. Manual inspections cannot achieve simultaneous online monitoring of multiple indicators, and the lack of dynamic linkage between monitoring data and wastewater treatment system operating parameters leads to lags in treatment process control, making it difficult to ensure stable blackwater discharge that meets standards. Existing online monitoring equipment is generally bulky and energy-intensive, making it unsuitable for the outdoor installation and low-maintenance requirements of decentralized toilets. Furthermore, existing monitoring solutions for natural water bodies do not address the unique water quality characteristics of blackwater from decentralized toilets, nor do they achieve a deep integration of post-treatment water quality and treatment system control.

[0003] In the prior art, CN108489543A discloses a monitoring device for enterprise wastewater discharge indicators. This device collects indicators such as COD, ammonia nitrogen, total phosphorus, and total nitrogen through multi-parameter sensors, and the data is compared and monitored by a remote monitoring terminal. However, this solution is mainly geared towards quota management scenarios for centralized wastewater discharge enterprises, failing to consider the application requirements of decentralized toilets requiring outdoor installation and low maintenance. Furthermore, it lacks an adaptive control mechanism based on monitoring data, making it difficult to directly adapt to decentralized treatment facilities. CN119985898A discloses an online monitoring method and device for polluted water bodies, extending monitoring nodes to each stage of wastewater treatment and generating intelligent control commands. However, this solution relies on multi-stage deployment including pretreatment and biochemical treatment, resulting in high system complexity. It is not suitable for the relatively simple decentralized toilet blackwater treatment scenario and is not specifically designed for the high concentration and fluctuating water quality characteristics of blackwater.

[0004] In summary, existing technologies generally suffer from poor adaptability of monitoring equipment to different scenarios and a lack of ability to simultaneously monitor and coordinate multiple indicators. There is an urgent need for a solution that can adapt to dispersed scenarios and achieve multi-parameter collaborative sensing and adaptive control. Summary of the Invention

[0005] To address the problems of poor scenario adaptability of existing monitoring equipment and lack of multi-indicator simultaneous monitoring and linkage control capabilities, this application proposes an intelligent monitoring system and its control system for ensuring the compliant discharge of black water from decentralized toilets. This system enables precise online monitoring and adaptive control of the black water quality of decentralized toilets, ensuring stable and compliant discharge of black water.

[0006] The objective of this invention can be achieved through the following technical solutions: The first aspect of this invention provides a method for regulating the discharge of black water from decentralized toilets to meet standards, comprising the following steps: By deploying sensing probes at the outlet of decentralized toilet black water treatment facilities, real-time water quality data of COD, BOD, ammonia nitrogen, total nitrogen, and total phosphorus of the treated black water are collected simultaneously. The real-time water quality data is transmitted to the monitoring center. At the monitoring center, the real-time water quality data of COD and BOD are fused to obtain the comprehensive concentration value of organic pollution, and all water quality data are screened to obtain effective monitoring data. Based on the preset emission standard limits, the control indices corresponding to the comprehensive concentration values ​​of organic pollutants, ammonia nitrogen concentration values, total nitrogen concentration values, and total phosphorus concentration values ​​are calculated respectively. When the control index is greater than zero, control commands corresponding to the on-site wastewater treatment equipment are generated based on the control index of each indicator. The control command is sent to the corresponding on-site wastewater treatment equipment to regulate the black water treatment process of the decentralized toilets.

[0007] Furthermore, the sensing probe includes a chemical sensor for detecting COD, a biosensor for detecting BOD, a fluorescence sensing probe for detecting ammonia nitrogen, a sensing probe for detecting total nitrogen, and a sensing probe for detecting total phosphorus.

[0008] Furthermore, the real-time water quality data of COD and BOD are fused to obtain the comprehensive concentration value of organic pollution, including: using at least one fusion algorithm among weighted average, Kalman filtering or DS evidence theory to process the real-time water quality data of COD and BOD to obtain the comprehensive concentration value of organic pollution.

[0009] Furthermore, based on preset emission standard limits, control indices are calculated, including: in: As an organic pollution control index, , , These are the regulation indices for total nitrogen, total phosphorus, and ammonia nitrogen, respectively. This refers to the comprehensive concentration value of organic pollution represented by the fused COD and BOD data. , , These are the real-time concentration values ​​of total nitrogen, total phosphorus, and ammonia nitrogen measured by the sensing probe, respectively. , , , These are the objective benchmark limits of the corresponding national or industry water quality discharge standards pre-written into the monitoring center.

[0010] Furthermore, based on the control indices of each indicator, control commands corresponding to the on-site wastewater treatment equipment are generated, including: If the organic pollution control index If the error exceeds the corresponding preset tolerance threshold, an instruction is generated to increase the dosage of nitrifying agent or increase the reflux ratio. If the ammonia nitrogen regulation index If the error exceeds the corresponding preset tolerance threshold, an instruction is generated to increase the dosage of nitrifying agent or increase the reflux ratio. If the total nitrogen regulation index or total phosphorus regulation index If the error exceeds the preset fault tolerance threshold corresponding to the fault tolerance threshold, an instruction is generated to increase the dosage of nitrogen and phosphorus removal agents.

[0011] Furthermore, the generation of control instructions corresponding to the on-site wastewater treatment equipment based on the control indices of each indicator also includes: If any of the aforementioned control indices is greater than zero and less than or equal to a preset tolerance deviation threshold, then an instruction is generated to fine-tune the operating parameters of the corresponding on-site wastewater treatment equipment.

[0012] Furthermore, the control command is sent to the corresponding on-site wastewater treatment equipment, including: encapsulating the control command into a standardized digital command through a software-defined universal control protocol interface, and sending it to the on-site wastewater treatment equipment through a physical transmission channel; the on-site wastewater treatment equipment includes at least one of an aeration pump, a dosing pump, an electric valve, and a weak electrical stimulation module.

[0013] Furthermore, after issuing the control command to the corresponding on-site wastewater treatment equipment, the method further includes: receiving the execution status feedback information returned by the on-site wastewater treatment equipment, and returning to execute the step of synchronously collecting real-time water quality data of the treated black water through the sensing probe deployed at the outlet of the decentralized toilet black water treatment facility.

[0014] A second aspect of the present invention provides a control system for achieving compliant discharge of black water from decentralized toilets, comprising: The data acquisition module is used to simultaneously collect real-time water quality data of COD, BOD, ammonia nitrogen, total nitrogen, and total phosphorus of the treated black water through sensing probes deployed at the outlet of the decentralized toilet black water treatment facility. The data transmission program module is used to transmit the real-time water quality data to the monitoring center; The data processing and judgment module is used in the monitoring center to fuse the real-time water quality data of COD and BOD to obtain the comprehensive concentration value of organic pollution, and to screen all water quality data to obtain effective monitoring data. Based on the preset emission standard limits, it calculates the control index corresponding to the comprehensive concentration value of organic pollution, ammonia nitrogen concentration value, total nitrogen concentration value and total phosphorus concentration value respectively. The instruction generation module is used to generate control instructions corresponding to the on-site wastewater treatment equipment based on the control indices of each indicator when the control index is greater than zero. The instruction issuing module is used to issue the control instructions to the corresponding on-site sewage treatment equipment to regulate the black water treatment process of the decentralized toilet.

[0015] Furthermore, the data processing and judgment module is specifically used to: process the real-time water quality data of COD and BOD using at least one fusion algorithm among weighted average, Kalman filtering, or DS evidence theory to obtain the comprehensive concentration value of organic pollution; The instruction generation program module is specifically used for: The organic pollution control index If the error exceeds the corresponding preset tolerance threshold, an instruction is generated to increase the dosage of nitrifying agent or increase the reflux ratio. The ammonia nitrogen regulation index If the error exceeds the corresponding preset tolerance threshold, an instruction is generated to increase the dosage of nitrifying agent or increase the reflux ratio. The total nitrogen regulation index or total phosphorus regulation index If the error exceeds the preset tolerance threshold corresponding to the tolerance threshold, an instruction is generated to increase the dosage of nitrogen and phosphorus removal agents.

[0016] Compared with the prior art, the present invention has the following beneficial effects: The intelligent monitoring system and its control system for achieving compliant discharge of black water from decentralized toilets provided by this invention deploys a lightweight data monitoring terminal integrating multiple types of sensing probes at the water outlet of decentralized toilet treatment facilities, realizing the synchronous in-situ acquisition of multiple water quality data such as COD, BOD, ammonia nitrogen, total nitrogen, and total phosphorus. This solves the problems of traditional equipment being large in size, high in energy consumption, and difficult to adapt to decentralized scenarios.

[0017] The multi-sensor data fusion module in the monitoring center integrates raw COD and BOD data, eliminating the detection errors of single sensors and improving the detection accuracy of comprehensive organic pollution concentration values. The state determination module's built-in dedicated emission standard and control index calculation model quantifies the deviation between water quality data and standard limits into specific control indices, providing objective and accurate triggering basis for subsequent control measures.

[0018] By adjusting the preset instruction mapping rules and software-based universal control protocol interface in the execution equipment, standardized control instructions for field equipment such as aeration pumps, dosing pumps, and electric valves can be automatically generated and issued according to the control index. This achieves adaptive closed-loop control from water quality monitoring to treatment process parameters, solving the bottleneck of existing technologies that only detect but do not control. It significantly improves the stability of black water treatment in decentralized toilets and greatly reduces the cost of manual inspection and maintenance. Attached Figure Description

[0019] Figure 1 This is a structural block diagram of a decentralized toilet black water discharge standard-compliant intelligent monitoring system according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating the workflow of a decentralized toilet black water discharge intelligent monitoring system according to an embodiment of the present invention. Detailed Implementation

[0020] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. Component models, material names, connection structures, circuit structures, control methods, algorithms, and other features not explicitly described in this technical solution are considered common technical features disclosed in the prior art.

[0021] Example 1: This embodiment provides a control method for ensuring the compliant discharge of black water from decentralized toilets. The method uses sensing probes deployed at the outlet of the black water treatment facility in decentralized toilets to simultaneously collect multiple key water quality indicators of the treated black water. Through data transmission, fusion processing, status determination, and command issuance, a complete closed loop from monitoring to control is formed, ensuring the stable and compliant discharge of black water.

[0022] In step S100, real-time water quality data of COD, BOD, ammonia nitrogen, total nitrogen, and total phosphorus of the treated black water are collected simultaneously by sensing probes deployed at the outlet of the decentralized toilet black water treatment facility.

[0023] Specifically, the sensing probes are installed at the end of the blackwater treatment facility's outlet, directly contacting the treated blackwater. Synchronous sampling refers to multiple sensing probes acquiring their respective water quality indicator data within the same sampling time or a very short time window, ensuring that the data for each indicator have a corresponding relationship in the time dimension and avoiding deviations in subsequent fusion and judgment due to sampling time differences. The collected indicators cover COD and BOD, which characterize the degree of organic pollution; ammonia nitrogen, total nitrogen, and total phosphorus, which characterize nitrogen and phosphorus nutrient pollution. These five indicators are the core parameters for evaluating the blackwater treatment effect of decentralized toilets. The specific type and detection principle of the sensing probes will be described in detail in subsequent embodiments; this embodiment does not limit this, as long as it can output the real-time concentration values ​​of the corresponding indicators.

[0024] Step S200: Transmit the real-time water quality data to the monitoring center.

[0025] The monitoring center can be a cloud-based server or a local industrial control computer located on-site. The specific deployment method can be flexibly selected based on the geographical distribution and network conditions of the decentralized toilets. The physical channel for data transmission can be a 4G mobile communication network, a WiFi wireless network, or a wired communication link; this embodiment does not impose any restrictions. During transmission, the data can be accompanied by a timestamp and probe identification information to allow the monitoring center to trace the data source.

[0026] In step S300, at the monitoring center, the real-time water quality data of COD and BOD are fused and processed to obtain the comprehensive concentration value of organic pollution, and all water quality data are screened to obtain effective monitoring data.

[0027] It is important to note that while both COD and BOD are used to characterize the degree of organic pollution in water bodies, their detection principles and response characteristics differ. COD focuses on reflecting the total amount of organic matter under chemical oxidation conditions, while BOD focuses on reflecting the amount of organic matter that can be degraded by microorganisms. Relying solely on either indicator may lead to biases in the judgment of the degree of organic pollution due to sensor drift, interfering substances, or fluctuations in detection conditions. The fusion processing referred to in this embodiment refers to combining data from both COD and BOD sources through a preset fusion strategy to generate a comprehensive concentration value that more fully and reliably reflects the level of organic pollution. The specific algorithm for fusion processing can be weighted averaging, Kalman filtering, DS evidence theory, etc. This embodiment only provides a general overview; specific implementation methods will be elaborated in subsequent embodiments.

[0028] Meanwhile, screening all water quality data means verifying the validity of the collected raw data, removing data points that are obviously beyond the sensor's detection range, have abnormal data jumps, or have errors during transmission, to ensure that the data entering the subsequent judgment stage truly reflects the current water quality status.

[0029] Step S400: Based on the preset emission standard limits, calculate the control index corresponding to the comprehensive concentration of organic pollutants, ammonia nitrogen concentration, total nitrogen concentration and total phosphorus concentration respectively.

[0030] The control index referred to in this embodiment is a dimensionless parameter used to quantify the degree of deviation between the current water quality indicator and the emission standard limit. The core function of the control index is to map water quality indicators of different dimensions and orders of magnitude onto a comparable scale, providing an objective basis for determining whether to trigger control measures and the magnitude of such control. The emission standard limit can be preset based on current national or industry wastewater discharge standards, such as the corresponding Class I or Class II standards in the "Integrated Wastewater Discharge Standard" (GB 8978-1996), or it can be set according to the specific discharge requirements for black water from decentralized toilets. The specific calculation formula and tolerance threshold setting of the control index will be explained in detail in subsequent embodiments. This embodiment only clarifies its higher-level function: when the control index is less than or equal to zero, it indicates that the corresponding indicator meets the emission requirements; when the control index is greater than zero, it indicates that the corresponding indicator has a risk of exceeding the standard or has already exceeded the standard, requiring the initiation of control measures.

[0031] Step S500: When the control index is greater than zero, generate control commands corresponding to the on-site wastewater treatment equipment based on the control index of each indicator.

[0032] The generation of control instructions follows the correspondence between indicators and treatment processes. For example, when the organic pollution control index exceeds the standard, instructions can be generated to adjust aeration intensity or microbial activity; when the ammonia nitrogen control index exceeds the standard, instructions can be generated to adjust the dosage of nitrifying bacteria agents or the reflux ratio; when the total nitrogen or total phosphorus control index exceeds the standard, instructions can be generated to adjust the dosage of nitrogen and phosphorus removal agents. The specific mapping rules and grading strategies for control instructions will be elaborated in subsequent embodiments.

[0033] In step S600, control instructions are sent to the corresponding on-site wastewater treatment equipment to regulate the black water treatment process of decentralized toilets.

[0034] On-site wastewater treatment equipment refers to actuators deployed in blackwater treatment facilities that can directly alter the operating parameters of the treatment process, such as aeration pumps, dosing pumps, electric valves, and low-voltage stimulation modules. Control commands are encapsulated into standardized digital instructions through a software-defined universal control protocol interface and transmitted to the corresponding equipment via a physical transmission channel. After the equipment executes the control commands, its execution status can be fed back to the monitoring center, while the sensing probes continue to collect water quality data after the control is applied, returning to step S100.

[0035] This embodiment, through steps S100 to S600, constructs a complete method and process from synchronous water quality data acquisition, remote transmission, multi-source data fusion and screening, quantitative calculation of control index, automatic generation of control commands, to closed-loop execution by on-site equipment. This method organically connects the monitoring and control stages in the decentralized toilet blackwater treatment process, solving the problem of disconnect between monitoring data and treatment system operating parameters in traditional methods, and achieving real-time, accurate, and adaptive assurance of blackwater treatment effectiveness.

[0036] Example 2: Based on Example 1, this embodiment elaborates on the specific types of sensing probes in step S100 and the specific algorithms for fusion processing in step S300.

[0037] In step S100, the sensing probes deployed at the effluent end include a chemical sensor for COD detection, a biosensor for BOD detection, a fluorescence sensing probe for ammonia nitrogen detection, a sensing probe for total nitrogen detection, and a sensing probe for total phosphorus detection. These five types of sensing probes perform in-situ detection of different pollution indicators after blackwater treatment, each operating based on a different detection principle. The chemical sensor for COD detection typically employs electrochemical sensing principles such as potassium dichromate oxidation or permanganate index methods, quantifying COD concentration by measuring changes in current or potential generated during oxidation. The biosensor for BOD detection utilizes immobilized microbial membranes as recognition elements; when biodegradable organic matter in the water sample is metabolized by microorganisms, it causes changes in dissolved oxygen concentration, which the sensor detects to calculate the BOD value. Conventional sensing probes for detecting total nitrogen and total phosphorus can employ ultraviolet digestion-spectrophotometry or ion-selective electrode methods to convert nitrogen and phosphorus compounds in the water sample into detectable forms for quantitative analysis.

[0038] This embodiment employs a fluorescence sensing probe based on OPA-MOF material for detecting ammonia nitrogen. OPA stands for orthophthalaldehyde, and MOF is a metal-organic framework material. The fluorescence sensing probe consists of a flow cell module, a fluorescence immobilization membrane module, an optical detection module, and a signal processing and automatic reading module. The fluorescence immobilization membrane module immobilizes the OPA reagent within the porous structure of the MOF material, forming a stable fluorescence sensing film. When a water sample containing ammonia nitrogen flows through the flow cell module and comes into contact with the fluorescence immobilization membrane, the ammonia nitrogen undergoes a derivatization reaction with the OPA under alkaline conditions, generating an isoindole complex with strong fluorescence emission characteristics. The excitation light source in the optical detection module illuminates the fluorescence immobilization membrane at a specific wavelength, exciting the generated fluorescence complex to emit light of a characteristic wavelength, which is collected by a photodetector and converted into an electrical signal. The signal processing and automatic reading module amplifies, filters, and performs analog-to-digital conversion on this electrical signal, then calculates the ammonia nitrogen concentration value based on a preset fluorescence intensity-ammonia nitrogen concentration standard curve. This fluorescence detection method based on OPA-MOF material features fast response, high sensitivity, and low reagent consumption, making it particularly suitable for long-term unattended online monitoring needs in outdoor environments with decentralized toilets.

[0039] In step S300, the real-time water quality data of COD and BOD are fused to obtain the comprehensive concentration value of organic pollution. Specifically, at least one fusion algorithm among weighted average, Kalman filtering, or DS evidence theory is used.

[0040] When using a weighted average algorithm, weights are assigned based on the historical detection accuracy and stability of the COD and BOD sensors. In a specific implementation of this example, the COD sensor weight is set to 0.55, and the BOD sensor weight is set to 0.45. The weighted calculation yields the comprehensive organic pollution concentration value. The weight allocation is based on the fact that the COD sensor has a more comprehensive response to chemically oxidized organic matter and relatively smaller data fluctuations, thus it is assigned a slightly higher weight; while the BOD sensor, although better reflecting the actual impact of microbially degradable organic matter, is more susceptible to interference from environmental factors such as temperature and microbial activity, therefore it has a slightly lower weight. This weighting method can effectively smooth out the random errors of a single sensor while retaining the advantages of each type of sensor.

[0041] When employing the Kalman filter algorithm, real-time COD and BOD values ​​are used as two independent observation inputs to establish a state-space model of the comprehensive organic pollution concentration. The Kalman filter, through a predict-update recursive process, utilizes the system state equation and observation equation to recursively calculate the optimal estimate at each sampling time based on the state estimate from the previous time step and the observed value at the current time step. The advantage of this algorithm lies in its ability to automatically adapt to changes in sensor noise characteristics. When a sensor experiences instantaneous drift or interference, the Kalman filter automatically reduces the confidence level of the sensor's observations based on the noise covariance matrix, thereby suppressing the impact of abnormal data on the fusion results.

[0042] When employing the Dempster evidence theory algorithm, the detection results from COD and BOD sensors are treated as two independent sources of evidence. A basic probability assignment function is constructed for each source to characterize the degree of support each sensor provides for determining the level of organic pollution. Then, the basic probability assignments from the two sources are fused using the Dempster synthesis rule to obtain the comprehensive organic pollution concentration value with the highest overall support. The Dempster evidence theory is particularly suitable for handling scenarios where sensor information is conflicting or uncertain. For example, when the COD sensor indicates a high organic matter concentration while the BOD sensor indicates a low concentration, the algorithm can provide a reasonable fusion conclusion based on prior information about the reliability of the two types of sensors under different water quality conditions from historical data, rather than simply taking the average.

[0043] The reason for choosing the aforementioned fusion algorithm to process COD and BOD data is that although both COD and BOD reflect the degree of organic pollution, their detection principles and response characteristics are fundamentally different. COD focuses on chemical oxidation capacity, while BOD focuses on biodegradation characteristics. Relying solely on either indicator may lead to misjudgments under specific water quality conditions. For example, when black water contains a large amount of recalcitrant organic matter, the COD value may be high while the BOD value is relatively low; judging solely based on BOD will underestimate the degree of organic pollution. Conversely, when inorganic reducing substances that interfere with chemical oxidants are present, the COD value may be artificially high; judging solely based on COD will overestimate the degree of pollution. By fusing the information from the two sources, complementing and verifying it, the detection error of a single sensor can be eliminated.

[0044] Example 3: Based on Example 1, this embodiment elaborates on the specific calculation method of the control index in step S400 and the generation rules of the control command in step S500.

[0045] In step S400, based on preset emission standard limits, the control indices corresponding to the comprehensive concentration values ​​of organic pollutants, ammonia nitrogen concentration values, total nitrogen concentration values, and total phosphorus concentration values ​​are calculated respectively, using the following formulas: in: As an organic pollution control index, , , These are the regulation indices for total nitrogen, total phosphorus, and ammonia nitrogen, respectively. This refers to the comprehensive concentration value of organic pollution represented by the fused COD and BOD data. , , These are the real-time concentration values ​​of total nitrogen, total phosphorus, and ammonia nitrogen measured by the sensing probe, respectively. , , , These are the objective benchmark limits of the corresponding national or industry water quality discharge standards pre-written into the monitoring center.

[0046] The design concept of the above formula lies in transforming water quality indicators of different dimensions and orders of magnitude into a dimensionless control index, allowing direct comparison of the degree of deviation between indicators. The control index has a clear physical meaning: when the control index is less than zero, it indicates that the concentration of the indicator is below the emission standard limit, and the treatment effect is good; when the control index is equal to zero, it indicates that the concentration of the indicator is exactly equal to the emission standard limit, and it is in a critical state of compliance; when the control index is greater than zero, it indicates that the concentration of the indicator has exceeded the emission standard limit, and the larger the value, the more severe the exceedance. This method of converting absolute concentration values ​​into relative deviation ratios means that subsequent control decisions no longer depend on the magnitude of the absolute values ​​of each indicator, but are based on a unified deviation scale, simplifying the design of the control logic.

[0047] To facilitate understanding, a specific numerical example is used below. Assume the comprehensive concentration C of organic pollution at a certain moment is... org The measured value was 120 mg / L, while the corresponding emission standard limit C org,lim If the concentration is 100 mg / L, then the organic pollution control index I org =(120-100) / 100=0.2. Similarly, if the measured total nitrogen concentration C TN The total nitrogen emission standard limit is 18 mg / L, which is C. TN,lim If the total nitrogen regulation index is 15 mg / L, then the total nitrogen regulation index I is... TN =(18-15) / 15=0.2. If the measured concentration of ammonia nitrogen C...NH3 The ammonia nitrogen emission standard limit is 6 mg / L, which is C. NH3,lim If the ammonia nitrogen concentration is 5 mg / L, then the ammonia nitrogen regulation index I NH3 =(6-5) / 5=0.2. It can be seen that although the absolute concentration values ​​and standard limits of these three indicators are different, through the calculation of the control index formula, they are all mapped to the same value of 0.2, intuitively reflecting that their respective exceedances are all 20% of the standard limit. This unified quantitative expression provides convenience for subsequent graded control.

[0048] In step S500, the generation of control instructions is not simply based on whether the control index is greater than zero as the sole criterion, but rather introduces the concept of a tolerance deviation threshold T. The tolerance deviation threshold T is an empirical parameter preset based on the maximum treatment capacity of the on-site wastewater treatment equipment. Its physical meaning is: when the control index does not exceed T, the current water quality deviation is still within the range that the on-site equipment can correct by fine-tuning its operating parameters, and there is no need to initiate significant enhanced treatment measures. In this embodiment, the tolerance deviation threshold T is set to 0.2. That is, when the control index is between 0 and 0.2, the system considers the current deviation to be an acceptable small fluctuation, which can be restored to compliance through fine-tuning; when the control index exceeds 0.2, the system considers the current deviation to be beyond the normal fine-tuning capability of the equipment, and enhanced treatment measures are required. Of course, the specific value of T is not fixed at 0.2. In actual deployment, it can be flexibly adjusted according to the buffer capacity of the specific treatment facility process, the strictness of emission standards, and the conservatism of the operation and maintenance strategy. For example, for facilities with small treatment capacity, T can be appropriately reduced to 0.15 or 0.1 to trigger enhanced treatment earlier; for facilities with large treatment capacity, T can also be appropriately increased to 0.25 or 0.3 to reduce unnecessary reagent consumption.

[0049] Based on the above-mentioned fault tolerance threshold T, the generation of control instructions in step S500 follows the following rules.

[0050] If the organic pollution control index I org If the error exceeds the corresponding preset tolerance threshold, an instruction is generated to increase the dosage of nitrifying bacteria agent or increase the reflux ratio; if the ammonia nitrogen control index I... NH3If the deviation exceeds the corresponding preset tolerance threshold, an instruction is generated to increase the dosage of nitrifying bacteria or increase the reflux ratio. The organic pollution control index and ammonia nitrogen control index are grouped into the same treatment strategy because in blackwater biological treatment processes, the degradation of organic matter and the nitrification of ammonia nitrogen are usually completed synergistically by aerobic heterotrophic bacteria and nitrifying bacteria. Increasing the dosage of nitrifying bacteria can simultaneously enhance the removal capacity of both organic matter and ammonia nitrogen, while increasing the reflux ratio can prolong the residence time of activated sludge in the system, which is also beneficial for the deep removal of these two types of pollutants. In practical applications, the system can select one or a combination of these methods based on the on-site equipment configuration. For example, if a nitrifying bacteria dosing device is available on-site, increasing the dosage is preferred because this method has a fast response and direct effect; if no dosing device is available but a reflux pump is installed, increasing the reflux ratio is chosen to indirectly enhance the treatment effect by adjusting process parameters.

[0051] If the total nitrogen regulation index I TN Or total phosphorus regulation index I TP If the total nitrogen and total phosphorus levels exceed the corresponding preset tolerance threshold, an instruction is generated to increase the dosage of nitrogen and phosphorus removal agents. In decentralized toilet black water treatment, the removal of total nitrogen and total phosphorus typically relies on chemical nitrogen and phosphorus removal processes. This involves adding specific chemical agents to convert dissolved nitrogen and phosphorus compounds into insoluble precipitates, which are then separated by sedimentation or filtration. When the control index for total nitrogen or total phosphorus exceeds the tolerance threshold, it indicates that the nitrogen and phosphorus removal capacity of the biological treatment section is insufficient to control the effluent concentration within the standard limits. In this case, it is necessary to activate or increase the dosage of chemical agents. It should be noted that although total nitrogen and total phosphorus share the same instruction generation rule, in actual execution, the system will determine which type of agent to add based on which specific indicator's control index exceeds the standard. For example, when total nitrogen exceeds the standard, a carbon source or denitrification promoter is added; when total phosphorus exceeds the standard, aluminum or iron salt phosphorus removal agents are added. Both can be triggered and executed independently.

[0052] Furthermore, if any control index is greater than zero and less than or equal to a preset tolerance threshold, an instruction is generated to fine-tune the operating parameters of the corresponding on-site wastewater treatment equipment. This rule forms a clear hierarchical logic with the aforementioned enhanced treatment instruction. The essential difference between the fine-tuning instruction and the enhanced treatment instruction lies in the magnitude and method of control: the fine-tuning instruction makes small adjustments to the equipment operating parameters within the current treatment process framework, such as fine-tuning the frequency of the aeration pump from 50Hz to 52Hz, or fine-tuning the stroke of the dosing pump from 30% to 33%, or fine-tuning the reflux ratio from 100% to 105%. These adjustments do not change the basic operating mode of the treatment process; they are merely fine-calibrating the parameters to bring slightly exceeding water quality indicators back to within acceptable ranges, while avoiding energy and chemical waste caused by over-adjustment. The enhanced treatment instruction, on the other hand, means that the current normal operating state of the treatment process is insufficient to cope with the pollution load, and more proactive intervention measures are required, such as starting standby equipment, significantly increasing the dosage of chemicals, or switching to enhanced treatment mode.

[0053] The technical significance of this tiered control logic lies in its avoidance of the on / off oscillation problem common in traditional threshold control methods. Without setting a tolerance threshold and fine-tuning range, the system triggers an enhanced treatment command as soon as the control index crosses zero. Once the enhanced treatment takes effect, the water quality index may quickly drop to levels far below the standard limit, causing the system to immediately stop the enhanced treatment. This results in repeated oscillations between overtreatment and undertreatment, wasting reagents and energy and hindering the stable operation of the biological system. By introducing a tolerance threshold T and a fine-tuning mechanism, the system only performs mild parameter adjustments when the control index is in the (0,T) range. Enhanced treatment is only initiated when the deviation truly exceeds the equipment's fine-tuning capability, thus achieving smooth control of the treatment process and balancing compliance stability with economic efficiency.

[0054] Example 4: This embodiment expands on the method of issuing control commands in step S600 and the feedback after the commands are issued, based on embodiment 1.

[0055] In step S600, control commands are sent to the corresponding on-site wastewater treatment equipment. Specifically, the control commands are encapsulated into standardized digital commands through a software-defined universal control protocol interface and then sent to the on-site wastewater treatment equipment via a physical transmission channel. The software-defined universal control protocol interface refers to a set of command interaction rules defined at the software level, independent of specific hardware devices. Its core function is to transform the control decisions generated by the monitoring center into standardized digital commands that the on-site equipment can recognize and execute, thereby shielding the differences in communication protocols between different manufacturers and types of equipment.

[0056] In one specific implementation of this embodiment, the software-defined general control protocol interface uses Modbus-RTU or Modbus-TCP general communication protocol stacks as the underlying instruction interaction rules. The Modbus protocol, as an open standard widely used in industrial automation, is characterized by its simplicity and strong compatibility, making it particularly suitable for the diverse range of field devices and brands found in decentralized toilet black water treatment facilities. Of course, in other implementations, other industrial communication protocols, such as CANopen, PROFIBUS, or MQTT, can be selected based on the communication capabilities of the field devices, as long as standardized encapsulation and reliable transmission of control commands can be achieved.

[0057] The standardized digital instruction format includes a device identification field, an operation instruction field, a parameter configuration field, and a status feedback field. The device identification field uniquely identifies the target device receiving the instruction, for example, distinguishing which device among the aeration pump, dosing pump, electric valve, or low-voltage stimulation module should execute the instruction using a device address code or device type code. The operation instruction field indicates the specific action the device needs to perform, such as start, stop, frequency adjustment, opening adjustment, or duration setting. The parameter configuration field carries the specific parameter values ​​corresponding to the action, such as the target operating frequency of the aeration pump, the target stroke percentage of the dosing pump, the target opening degree of the electric valve, or the target operating duration of the low-voltage stimulation module. The status feedback field reserves data bits for the device to transmit the execution status back to the monitoring center, ensuring that each issued instruction embeds the definition of a feedback channel.

[0058] The physical transmission channel refers to the physical communication medium that transmits encapsulated standardized digital commands from the monitoring center to the on-site wastewater treatment equipment. In this embodiment, the physical transmission channel can be at least one of RS485 bus, WiFi wireless network, or 4G mobile communication network. RS485 bus is suitable for scenarios where on-site equipment is centrally located and wiring conditions are good, with advantages such as strong anti-interference ability and long transmission distance; WiFi wireless network is suitable for scenarios where there is already wireless LAN coverage on-site, with flexible deployment and no need for additional wiring; 4G mobile communication network is suitable for decentralized toilet scenarios in remote locations without wired network or WiFi coverage, and remote command issuance is achieved through the operator's mobile communication base station. In actual deployment, one or a combination of multiple transmission channels can be flexibly selected according to the specific geographical location of the decentralized toilet, network infrastructure conditions, and cost budget. For example, for treatment facilities that are close to the monitoring center and have concentrated equipment, RS485 bus is preferred to reduce communication costs; for independent toilet units located in remote mountainous areas, 4G mobile communication network is used to achieve remote control.

[0059] The on-site wastewater treatment equipment includes at least one of the following: an aeration pump, a dosing pump, an electric valve, and a low-voltage electrical stimulation module. The aeration pump is used to introduce air into the black water, increasing the dissolved oxygen concentration and promoting the degradation of organic matter and nitrification of ammonia nitrogen by aerobic microorganisms. The dosing pump is used to quantitatively add chemical or biological agents such as nitrifying bacteria, nitrogen removal agents, or phosphorus removal agents to the treatment system. The electric valve is used to regulate the water flow distribution or recirculation ratio between treatment units. The low-voltage electrical stimulation module enhances the metabolic activity of microorganisms and improves the efficiency of biochemical treatment by applying a weak electric field.

[0060] After issuing control commands to the corresponding on-site wastewater treatment equipment, the method in this embodiment further includes: receiving execution status feedback information returned by the on-site wastewater treatment equipment, and returning the step of synchronously collecting real-time water quality data of treated black water through sensing probes deployed at the effluent end of the decentralized toilet black water treatment facility. Specifically, after receiving and executing the control commands, the on-site wastewater treatment equipment will encapsulate the actual execution status into a feedback data packet and send it back to the monitoring center through the same software-defined universal control protocol interface. The feedback data packet typically includes equipment identification, command execution result (success or failure), actual execution parameter values, and execution timestamp. After receiving the feedback information, the monitoring center can confirm whether the control commands have been executed correctly. If execution failure or deviation exceeds the allowable range is found, an alarm can be triggered or the command can be reissued in a timely manner. On the other hand, the monitoring center records the feedback information as a historical control log, providing data support for subsequent control strategy optimization and fault tracing.

[0061] More importantly, after receiving feedback on the execution status, the system does not stop at the completion of a single control operation. Instead, it automatically returns to step S100, where the sensing probe at the effluent end continues to synchronously collect real-time water quality data of the treated black water, including COD, BOD, ammonia nitrogen, total nitrogen, and total phosphorus. This initiates a new round of data transmission, fusion processing, control index calculation, and instruction generation. This control mechanism of acquisition-transmission-judgment-control-feedback-reacquisition allows the system to continuously track the actual effects of control measures and adjust the control strategy based on the dynamic changes in water quality indicators until the control indices of each indicator fall back to within the tolerance threshold or even reach a qualified treatment state.

[0062] Example 5: This embodiment provides a control system for ensuring the compliant discharge of black water from decentralized toilets. This system corresponds to the control method described in Embodiment 1 above, and represents another expression of the same technical solution from the perspective of system architecture and program module division. The control system includes a data acquisition program module, a data transmission program module, a data processing and judgment program module, an instruction generation program module, and an instruction issuance program module. These modules work collaboratively according to a preset data flow direction to jointly realize the complete process from water quality data acquisition to on-site equipment control. For specific implementation, refer to... Figure 1 and Figure 2 The block diagram illustrates an example of one implementation logic.

[0063] The data acquisition module is used to simultaneously collect real-time water quality data of COD, BOD, ammonia nitrogen, total nitrogen, and total phosphorus from the treated black water using sensing probes deployed at the outlet of the decentralized toilet black water treatment facility. Functionally, this module corresponds to step S100 in Example 1, and its core role is to drive multiple sensing probes at the outlet to synchronously acquire the concentration values ​​of each water quality indicator according to a preset sampling cycle. The data acquisition module can run on the microcontroller built into the data monitoring terminal, and is responsible for managing the power supply timing of each sensing probe, sampling trigger signals, and analog-to-digital conversion of the raw signals. The collected real-time water quality data, after being appended with timestamps and probe identification information, is transmitted to the data transmission module in a structured data frame format.

[0064] The data transmission module is used to transmit real-time water quality data to the monitoring center. Functionally, this module corresponds to step S200 in Example 1, responsible for establishing and maintaining a reliable communication link between the data monitoring terminal and the monitoring center. The data transmission module can support multiple communication protocol stacks, such as TCP / IP over a 4G mobile communication network, MQTT over a WiFi wireless network, or Modbus-RTU over an RS485 bus. The specific communication method selected can be flexibly configured based on the network coverage conditions and cost constraints of the geographical location of the distributed toilets. This module also features data caching and breakpoint resume functionality. When the communication link is temporarily interrupted due to network fluctuations, unsuccessfully transmitted data is temporarily stored in the local storage chip and automatically retransmitted after the link is restored, ensuring that the integrity of the monitoring data is not compromised due to communication interruption.

[0065] The data processing and judgment module is used in the monitoring center to fuse real-time water quality data of COD and BOD to obtain the comprehensive concentration value of organic pollution, screen all water quality data to obtain valid monitoring data, and calculate the control index corresponding to the comprehensive concentration value of organic pollution, ammonia nitrogen concentration value, total nitrogen concentration value, and total phosphorus concentration value based on preset emission standard limits. This module is the core computing unit of the control system and functionally covers all the processing logic of steps S300 and S400 in Example 1. The data processing and judgment module is deployed on the server or industrial control computer of the monitoring center. After receiving the raw water quality data uploaded by the data transmission module, it first executes the data screening sub-process to remove abnormal data points through threshold verification and continuity verification to obtain valid monitoring data. Then, it executes the data fusion sub-process to comprehensively process COD and BOD data using at least one fusion algorithm among weighted average, Kalman filtering, or DS evidence theory to generate a comprehensive organic pollution concentration value. Finally, it executes the regulation index calculation sub-process to compare the comprehensive organic pollution concentration value, ammonia nitrogen concentration value, total nitrogen concentration value, and total phosphorus concentration value with the preset emission standard limits, and calculates the regulation index corresponding to each indicator according to the formula given in the aforementioned Example 3.

[0066] The instruction generation module is used to generate control instructions corresponding to the on-site wastewater treatment equipment based on the control indices of each indicator when the control index is greater than zero. Functionally, this module corresponds to step S500 in Example 1. After receiving the control indices of each indicator output by the data processing and judgment module, it makes control decisions based on preset tolerance deviation thresholds and instruction mapping rules. Specifically, the instruction generation module has a built-in mapping logic between control indices and control instructions. When the organic pollution control index or ammonia nitrogen control index is greater than the corresponding tolerance deviation threshold, it generates control instructions for the dosage of nitrifying bacteria agent or the reflux ratio; when the total nitrogen control index or total phosphorus control index is greater than the corresponding tolerance deviation threshold, it generates control instructions for the dosage of nitrogen and phosphorus removal agents; when any control index is greater than zero but does not exceed the tolerance deviation threshold, it generates fine-tuning instructions for the corresponding equipment operating parameters. This hierarchical mapping logic ensures that the generation of control instructions is no longer a simple threshold trigger, but rather automatically matches different levels of control strategies based on the degree of deviation.

[0067] The instruction issuance module is used to issue control instructions to the corresponding on-site wastewater treatment equipment to regulate the black water treatment process of decentralized toilets. Functionally, this module corresponds to step S600 in Example 1, responsible for converting the control decisions generated by the instruction generation module into standardized digital instructions executable by the on-site equipment. The instruction issuance module has a built-in software-defined universal control protocol interface, encapsulating the control instructions according to the format of universal communication protocol stacks such as Modbus-RTU or Modbus-TCP, and issuing them to on-site wastewater treatment equipment such as aeration pumps, dosing pumps, electric valves, or weak current stimulation modules via physical transmission channels such as RS485 bus, WiFi wireless network, or 4G mobile communication network. Simultaneously, this module is also responsible for receiving execution status feedback information returned by the on-site equipment and transmitting the feedback information back to the data processing and judgment module, triggering a new round of water quality data acquisition and processing, thus forming a process from data acquisition, transmission, processing, judgment, instruction generation, instruction issuance to execution feedback.

[0068] The data acquisition module, located at the front end of the entire control system, interacts directly with the sensing probes deployed at the water outlet, responsible for converting physical water quality information into digital signals. The data transmission module acts as a bridge between the front and back ends, reliably transmitting the collected data from the distributed toilet sites to a monitoring center located in the cloud or locally. The data processing and judgment module is the system's decision-making center, cleaning, fusing, and quantitatively analyzing the data gathered at the monitoring center, outputting control indices for each indicator as an objective basis for control decisions. The instruction generation module generates specific control strategies based on the control indices and preset rules, while the instruction issuance module translates these strategies into instructions that can be executed by the field equipment and monitors the execution effect. The modules interact through clearly defined data interfaces, and the specific implementations within each module can be independently upgraded or replaced without affecting the normal operation of other modules. For example, when a more advanced fusion algorithm is required, only the fusion sub-process in the data processing and judgment module needs to be updated; the data acquisition module and instruction issuance module require no modification.

[0069] Example 6: Based on the system architecture described in Embodiment 5, this embodiment further elaborates on the specific functional implementation of the data processing and judgment program module and the instruction generation program module, so that the internal algorithm logic of these two core modules forms a clear correspondence with the method details of steps S400 and S500 in the aforementioned Embodiment 3.

[0070] The data processing and judgment module is specifically used to process real-time water quality data of COD and BOD using at least one fusion algorithm among weighted averaging, Kalman filtering, or DS evidence theory to obtain a comprehensive organic pollution concentration value. This function directly corresponds to the fusion processing step S300 in Example 3 at the system level. Specifically, when the data processing and judgment module is running, it extracts COD and BOD concentration values ​​from the raw water quality data received by the data transmission module as two independent inputs and calls a preset fusion algorithm subroutine for comprehensive calculation. When configured with a weighted average algorithm, the module maintains a set of configurable weight coefficients, such as COD weight 0.55 and BOD weight 0.45. Each time, the two concentration values ​​are multiplied by their corresponding weights and then summed to output the comprehensive organic pollution concentration value. When configured with the Kalman filter algorithm, the module internally establishes a state-space model for the comprehensive organic pollution concentration. Using real-time COD and BOD detection values ​​as inputs, it recursively calculates the optimal estimate for each sampling time through a prediction-update recursive process. During this process, the module automatically adjusts the confidence level of each input observation based on the sensor noise covariance matrix. When configured with the Dempster evidence theory algorithm, the module constructs two basic probability assignment functions from the COD and BOD detection results, respectively. Evidence fusion is then performed using the Dempster synthesis rule, outputting the comprehensive organic pollution concentration value with the highest overall support. The module can integrate multiple algorithms simultaneously, and the currently active algorithm type can be specified via configuration file or remote command to adapt to fusion requirements under different water quality characteristics and sensor operating conditions.

[0071] The instruction generation module is specifically used for: in the organic pollution control index I org If the error exceeds the corresponding preset tolerance threshold, an instruction is generated to increase the dosage of nitrifying bacteria agent or increase the reflux ratio; under the ammonia nitrogen control index I... NH3 If the error exceeds the corresponding preset tolerance threshold, an instruction is generated to increase the dosage of nitrifying bacteria agent or increase the reflux ratio; under the total nitrogen control index I... TN Or total phosphorus regulation index I TP If the error exceeds the corresponding preset tolerance threshold, an instruction is generated to increase the dosage of nitrogen and phosphorus removal agents. This function directly corresponds to the instruction generation rule in step S500 of Example 3 at the system level.

[0072] It should be noted that when executing the above-mentioned judgment logic, the instruction generation module does not simply perform threshold comparisons, but rather incorporates a complete instruction mapping and parameter calculation process. The module first obtains the current value of the control index for each indicator from the data processing and judgment module, and then compares each control index with its corresponding tolerance deviation threshold. The tolerance deviation threshold can be configured as a single global parameter, such as T=0.2, or it can be configured separately for different indicators; for example, the threshold for the organic pollution control index can be set to T. org =0.2, the threshold for the ammonia nitrogen regulation index is set to T. NH3 =0.15, the threshold for the total nitrogen regulation index is set to T. TN =0.2, the threshold for the total phosphorus regulation index is set to T. TP =0.25, this differentiated threshold configuration method allows the system to flexibly adjust the control sensitivity according to the processing difficulty of each indicator and the equipment margin.

[0073] When the organic pollution control index I org When the corresponding tolerance threshold is exceeded, the instruction generation module selects to generate either an instruction to increase the dosage of nitrifying agent or an instruction to increase the reflux ratio, based on the on-site equipment configuration. If both a nitrifying agent dosing device and a reflux pump are installed on-site, the module can prioritize increasing the dosage of nitrifying agent, as this method has a fast response and direct effect; alternatively, it can generate two instructions simultaneously, sending them to the dosing pump and the reflux pump respectively for coordinated control. The specific parameter values ​​carried in the instruction, such as the incremental dosage of nitrifying agent or the target value of the reflux ratio, can be calculated by the module according to a preset proportional relationship based on the extent to which the control index exceeds the threshold. The greater the excess, the larger the increment of the control parameter. The same logic applies to the ammonia nitrogen control index I. NH3 The situation exceeding the threshold. When the total nitrogen regulation index I... TN Or total phosphorus regulation index I TP When the corresponding fault tolerance threshold is exceeded, the instruction generation module generates an instruction to increase the dosage of nitrogen and phosphorus removal agents, and determines the type of agent to be added based on which specific indicator exceeds the standard. When total nitrogen exceeds the standard, a carbon source or denitrification promoter is added accordingly, and when total phosphorus exceeds the standard, an aluminum salt or iron salt phosphorus removal agent is added accordingly. The two are triggered and executed independently.

[0074] Comparative Example 1: This embodiment sets up a comparative example to illustrate the potential defects in the system's determination of the degree of organic pollution when the COD and BOD data fusion processing described in the aforementioned embodiment 2 is not adopted, thereby demonstrating the necessary technical significance of fusion processing for ensuring the accuracy of regulation.

[0075] In this comparative example, the system uses only a single COD sensor data or a single BOD sensor data as the sole source of the comprehensive organic pollution concentration value, without performing the fusion algorithms such as weighted averaging, Kalman filtering, or DS evidence theory described in Example 2. Other steps, including data acquisition, transmission, filtering, control index calculation, and command issuance, remain consistent with Example 1.

[0076] First, we examine the scenario using only a single COD sensor. COD sensors typically operate based on electrochemical sensing principles using potassium dichromate oxidation or permanganate index methods. Their detection electrodes, constantly immersed in the complex aquatic environment of blackwater effluent, are susceptible to interference from certain reducing inorganic substances in the water, such as ferrous ions, sulfides, or chloride ions. When the concentrations of these interfering substances fluctuate, the COD sensor may output a falsely high reading, higher than the actual organic pollutant concentration. For example, in a certain sampling period, the actual comprehensive organic pollutant concentration in the blackwater was 95 mg / L, below the discharge standard limit of 100 mg / L, which should have been considered acceptable. However, due to a temporary increase in sulfide concentration caused by upstream anaerobic reactions, the COD sensor, interfered with, output a reading of 115 mg / L. The system calculates the organic pollution control index I using this single data source. org =(115-100) / 100=0.15, indicating a need for fine-tuning. This triggers a command to fine-tune operating parameters, and may even lead to enhanced treatment commands under certain threshold settings, resulting in unnecessary adjustments to the aeration pump frequency or the addition of nitrifying agents. Such misjudgments not only waste reagents and energy, but frequent ineffective adjustments can also disrupt the stability of the microbial community in the biochemical treatment system, potentially reducing the treatment effect.

[0077] Let's examine the case of using only a single BOD sensor. BOD sensors rely on immobilized microbial biofilms as recognition elements, calculating the BOD value by detecting the rate at which dissolved oxygen is consumed during microbial metabolism of organic matter. The activity of the microbial biofilm is extremely sensitive to temperature, pH, and toxic substances in the water. In the actual operating environment of decentralized toilets, seasonal temperature changes, pH fluctuations in influent due to differences in user habits, and occasional disinfectant residues entering the treatment system can all inhibit the metabolic activity of the microbial biofilm. When microbial biofilm activity is inhibited, the BOD sensor reading will be systematically lower. For example, under low-temperature conditions in winter, the actual comprehensive concentration of organic pollutants in the blackwater is 120 mg / L, exceeding the discharge standard limit of 100 mg / L, which should trigger enhanced treatment. However, due to the significant decrease in the metabolic rate of the microbial biofilm at low temperatures, the BOD sensor reading is only 85 mg / L. The system calculates I based on this. org=(85-100) / 100=-0.15, which was judged as acceptable, thus missing the case of exceeding the standard and failing to generate any control instructions. This missed case led to the direct discharge of black water exceeding the standard, which may cause organic pollution to the receiving water body and violates the original design intention of the system to ensure stable and compliant discharge.

[0078] In contrast, the data fusion processing strategy employed in Example 2 fundamentally changes this situation. When using the weighted average fusion algorithm, the detection results from the COD and BOD sensors participate in the calculation of the comprehensive organic pollution concentration value with weights of 0.55 and 0.45, respectively. When the COD sensor outputs an artificially high reading due to reducing interference substances, the BOD sensor, based on the principle of biological metabolism, is insensitive to such inorganic interference substances, and its output reading remains at a normal level. Although the comprehensive concentration value after weighted averaging may be slightly higher, the deviation is effectively pulled back by the BOD data, usually not enough to trigger a false judgment. Similarly, when the BOD sensor's reading is lower due to low temperature, the COD sensor, based on the principle of chemical oxidation, has detection performance almost unaffected by temperature and can still output a reading close to the true value. The comprehensive concentration value after weighted averaging will not be significantly lower, thus avoiding missed judgments.

[0079] When employing the Kalman filter algorithm, the system recursively estimates the two observed inputs, COD and BOD, using a state-space model. The core mechanism of the Kalman filter lies in its dynamic adjustment of the confidence level for each observed input based on the statistical characteristics of the noise from each sensor, namely the noise covariance matrix. When the noise of a sensor suddenly increases due to interference or performance degradation, the Kalman filter automatically reduces the weight of that sensor at the current moment, relying more on the observations from another sensor for state estimation. This adaptive weight adjustment mechanism makes the fusion result inherently robust to transient anomalies from a single sensor, continuously outputting reliable comprehensive organic pollution concentration values ​​even during sensor performance fluctuations without manual intervention.

[0080] When using the Dempster evidence theory algorithm, COD and BOD, as two evidence sources, each provide a basic probability allocation for the degree of organic pollution. In cases where sensor data conflict—for example, COD indicating high pollution while BOD indicates low pollution—the Dempster evidence theory does not simply average the data or discard information from one source. Instead, it rationally fuses conflicting evidence based on prior information about the reliability of both types of sensors under different water quality conditions, using Dempster's synthesis rules. This approach is particularly suitable for the complex and variable composition of blackwater from decentralized toilets and the occasional discrepancies in judgments between sensors.

[0081] From a technical perspective, the fusion processing strategy in Example 2 organically integrates sensor data from two complementary detection principles, thereby eliminating errors from a single sensor and tolerating abnormal data, significantly improving the data reliability of the comprehensive concentration value of organic pollution.

[0082] Application Example 1: This embodiment uses a specific application scenario as an example to illustrate the deployment and operation process of the control method and control system described in the foregoing embodiments in a real decentralized toilet black water treatment facility, so as to more intuitively demonstrate the actual effect of the technical solution.

[0083] A decentralized black water treatment facility is located in a rural area. This facility employs a combination of biological treatment and chemical phosphorus removal, treating approximately 5 cubic meters of black water daily, serving dozens of households in the surrounding area. Due to its remote location, daily operation and maintenance rely primarily on periodic manual inspections, typically once a week. During these inspection intervals, the facility's operational status and water quality are unknown. Fluctuations in influent load or abnormalities in the treatment process often go undetected until the next inspection, by which time several days of excessive discharge may have already occurred. To address this issue, the control system described in this embodiment is deployed at the facility's outlet.

[0084] The data monitoring terminal is installed at the end of the treatment facility's outlet, with its sensing probes directly contacting the treated blackwater that will be discharged. The sensing probes include a chemical sensor for COD detection, a biosensor for BOD detection, a fluorescent sensor for ammonia nitrogen detection, and conventional sensors for total nitrogen and total phosphorus detection. Each probe operates according to a preset sampling cycle. In this scenario, it is set to collect real-time concentration data of five key water quality indicators of the treated blackwater every 15 minutes. The data monitoring terminal has a built-in 4G communication module, transmitting the collected water quality data in real-time to a monitoring center deployed in the cloud via the operator's mobile communication network. The 4G network was chosen as the transmission channel because, although there is no wired network coverage in this rural area, 4G signal coverage is basically complete, meeting the reliability and real-time requirements of data transmission. Simultaneously, the data monitoring terminal has local caching and breakpoint resume functions. Even in the event of a brief network signal interruption, unsuccessfully transmitted data is temporarily stored in the terminal's built-in industrial-grade storage chip and automatically retransmitted after the network is restored, ensuring no data loss.

[0085] One afternoon, due to a sudden increase in water consumption by nearby residents, the ammonia nitrogen load in the blackwater entering the treatment facility increased significantly within a short period. During the sampling period at 2:00 PM that day, the sensing probe measured a real-time ammonia nitrogen concentration of 6.25 mg / L, while the preset ammonia nitrogen emission standard limit in the monitoring center was 5 mg / L. After receiving the data for this period, the data processing and judgment module of the monitoring center first fused the COD and BOD data to obtain the comprehensive organic pollution concentration value, and then screened and verified all water quality data. After confirming the data's validity, it calculated the control index using formula I. NH3 =(C NH3 -C NH3,lim ) / C NH3,lim Calculations were performed to obtain the ammonia nitrogen regulation index I. NH3 =(6.25-5) / 5=0.25.

[0086] The monitoring center's preset fault tolerance threshold T is 0.2. Because I NH3 Since T=0.25 is greater than T=0.2, the data processing and judgment module determines that the ammonia nitrogen index is in a "requires enhanced treatment" state. The instruction generation module then generates a control instruction to increase the dosage of nitrifying bacteria agent based on this judgment result and a preset instruction mapping rule. This instruction carries specific dosage increment parameters, calculated according to a preset ratio based on the extent to which the control index exceeds the threshold. In this scenario, the dosage of nitrifying bacteria agent is increased by 30% from the existing level. The instruction distribution module encapsulates this control instruction into a standardized Modbus-TCP format digital instruction via a software-defined universal control protocol interface and distributes it via the 4G network to the dosing pump controller located at the treatment facility.

[0087] Upon receiving the instruction, the dosing pump automatically adjusts its stroke frequency and adds nitrifying bacteria agent to the biochemical treatment unit in increments specified in the instruction. After completion, the dosing pump controller sends execution status feedback information, including instruction reception confirmation, actual dosage, and execution timestamp, back to the cloud monitoring center via the same protocol interface. The monitoring center records all information from this control event, forming a complete control log.

[0088] In the next sampling cycle, at 2:15 PM that day, the sensing probe at the effluent end synchronously collected various water quality data of the treated black water again. At this time, due to the supplemental addition of nitrifying bacteria, the activity of the nitrifying bacteria in the biological treatment unit was enhanced, significantly improving the nitrification conversion efficiency of ammonia nitrogen. The real-time ammonia nitrogen concentration measured in this cycle had dropped to 5.25 mg / L, and the ammonia nitrogen control index I was calculated. NH3=(5.25-5) / 5=0.05. Since I_NH3 is greater than zero but less than the fault tolerance threshold T=0.2 at this time, the system determines that the ammonia nitrogen index is in a "fine-tuning required" state, generates a fine-tuning instruction for the dosing pump operating parameters, and adjusts the nitrifying agent dosage back to a maintenance dose slightly higher than the initial level to avoid over-dosing and waste of the agent.

[0089] In the next sampling period, at 14:30 on the same day, the real-time concentration of ammonia nitrogen further decreased to 4.8 mg / L, and the control index I... NH3 =(4.8-5) / 5=-0.04, which is less than zero. The system determines that the ammonia nitrogen index is qualified and maintains the current operating parameters unchanged. Thus, from the detection of ammonia nitrogen exceeding the standard at 14:00 to the restoration to the standard at 14:30, the entire closed-loop process of detection, judgment, control, and verification was automatically completed within 30 minutes, without any manual intervention.

[0090] As can be seen from the above operation process, the control system described in this embodiment demonstrates several key advantages in the real-world scenario of treating black water from decentralized toilets. Firstly, it is unattended. From the detection of water quality anomalies to the generation and issuance of control commands, and then to the tracking and verification of the control effects, everything is completed automatically by the system, eliminating the need for on-site operation by maintenance personnel. This significantly reduces the labor and time costs of operation and maintenance for decentralized toilets in remote rural areas with inconvenient transportation. Secondly, it features adaptive control. The system does not simply trigger on / off control based on fixed thresholds, but automatically selects control strategies of varying intensity, such as fine-tuning or enhanced treatment, based on the specific value of the control index and the tolerance deviation threshold. After control, it continuously tracks water quality changes and dynamically adjusts subsequent commands, avoiding over-treatment or under-treatment. Thirdly, it provides timely response. Under traditional manual inspection methods, it may take several days from the occurrence of a water quality anomaly to its discovery. This system, through online monitoring and real-time control, shortens the response time to minutes, effectively curbing the continued excessive emissions. Fourthly, it is fully traceable. The time of each control event, the triggering indicators, the content of the control instructions, the equipment execution status, and the changes in water quality data before and after the control are all fully recorded in the monitoring center's log system.

[0091] The above description of the embodiments is provided to enable those skilled in the art to understand and use the invention. It will be apparent to those skilled in the art that various modifications can be made to these embodiments, and the general principles described herein can be applied to other embodiments without inventive effort. Therefore, the present invention is not limited to the above embodiments, and any improvements and modifications made by those skilled in the art based on the disclosure of the present invention without departing from the scope of the invention should be within the protection scope of the present invention.

Claims

1. A method for regulating the discharge of black water from decentralized toilets to meet standards, characterized in that, Includes the following steps: By deploying sensing probes at the outlet of decentralized toilet black water treatment facilities, real-time water quality data of COD, BOD, ammonia nitrogen, total nitrogen, and total phosphorus of the treated black water are collected simultaneously. The real-time water quality data is transmitted to the monitoring center. At the monitoring center, the real-time water quality data of COD and BOD are fused to obtain the comprehensive concentration value of organic pollution, and all water quality data are screened to obtain effective monitoring data. Based on the preset emission standard limits, the control indices corresponding to the comprehensive concentration values ​​of organic pollutants, ammonia nitrogen concentration values, total nitrogen concentration values, and total phosphorus concentration values ​​are calculated respectively. When the control index is greater than zero, control commands corresponding to the on-site wastewater treatment equipment are generated based on the control index of each indicator. The control command is sent to the corresponding on-site wastewater treatment equipment to regulate the black water treatment process of the decentralized toilets.

2. The method for regulating the discharge of black water from decentralized toilets to meet standards, as described in claim 1, is characterized in that... The sensing probes include a chemical sensor for detecting COD, a biosensor for detecting BOD, a fluorescence sensing probe for detecting ammonia nitrogen, a sensing probe for detecting total nitrogen, and a sensing probe for detecting total phosphorus.

3. The method for regulating the discharge of black water from decentralized toilets to meet standards, as described in claim 1, is characterized in that... The real-time water quality data of COD and BOD are fused to obtain the comprehensive concentration value of organic pollution. This includes: using at least one fusion algorithm among weighted average, Kalman filtering, or DS evidence theory to process the real-time water quality data of COD and BOD to obtain the comprehensive concentration value of organic pollution.

4. The method for regulating the discharge of black water from decentralized toilets to meet standards, as described in claim 1, is characterized in that... Based on preset emission standard limits, control indices are calculated, including: in: As an organic pollution control index, , , These are the regulation indices for total nitrogen, total phosphorus, and ammonia nitrogen, respectively. This refers to the comprehensive concentration value of organic pollution represented by the fused COD and BOD data. , , These are the real-time concentration values ​​of total nitrogen, total phosphorus, and ammonia nitrogen measured by the sensing probe, respectively. , , , These are the objective benchmark limits of the corresponding national or industry water quality discharge standards pre-written into the monitoring center.

5. The method for regulating the discharge of black water from decentralized toilets to meet standards, as described in claim 4, is characterized in that... Based on the control index of each indicator, control commands corresponding to the on-site wastewater treatment equipment are generated, including: If the organic pollution control index If the error exceeds the corresponding preset tolerance threshold, an instruction is generated to increase the dosage of nitrifying agent or increase the reflux ratio. If the ammonia nitrogen regulation index If the error exceeds the corresponding preset tolerance threshold, an instruction is generated to increase the dosage of nitrifying agent or increase the reflux ratio. If the total nitrogen regulation index or total phosphorus regulation index If the error exceeds the preset fault tolerance threshold corresponding to the fault tolerance threshold, an instruction is generated to increase the dosage of nitrogen and phosphorus removal agents.

6. A method for regulating the discharge of black water from decentralized toilets to meet standards, as described in claim 4 or 5, characterized in that, Based on the control indices of each indicator, control commands are generated corresponding to the on-site wastewater treatment equipment, including: If any of the aforementioned control indices is greater than zero and less than or equal to a preset tolerance deviation threshold, then an instruction is generated to fine-tune the operating parameters of the corresponding on-site wastewater treatment equipment.

7. The method for regulating the discharge of black water from decentralized toilets to meet standards, as described in claim 1, is characterized in that... Sending the control command to the corresponding on-site wastewater treatment equipment includes: encapsulating the control command into a standardized digital command through a software-defined universal control protocol interface, and sending it to the on-site wastewater treatment equipment through a physical transmission channel; The on-site wastewater treatment equipment includes at least one of an aeration pump, a dosing pump, an electric valve, and a weak electrical stimulation module.

8. The method for regulating the discharge of black water from decentralized toilets to meet standards, as described in claim 1, is characterized in that... After issuing the control command to the corresponding on-site wastewater treatment equipment, the method further includes: receiving the execution status feedback information returned by the on-site wastewater treatment equipment, and returning to execute the step of synchronously collecting real-time water quality data of the treated black water through the sensing probe deployed at the outlet of the decentralized toilet black water treatment facility.

9. A control system for ensuring compliant discharge of black water from decentralized toilets, characterized in that, include: The data acquisition module is used to simultaneously collect real-time water quality data of COD, BOD, ammonia nitrogen, total nitrogen, and total phosphorus of the treated black water through sensing probes deployed at the outlet of the decentralized toilet black water treatment facility. The data transmission program module is used to transmit the real-time water quality data to the monitoring center; The data processing and judgment module is used in the monitoring center to fuse the real-time water quality data of COD and BOD to obtain the comprehensive concentration value of organic pollution, and to screen all water quality data to obtain effective monitoring data. Based on the preset emission standard limits, it calculates the control index corresponding to the comprehensive concentration value of organic pollution, ammonia nitrogen concentration value, total nitrogen concentration value and total phosphorus concentration value respectively. The instruction generation module is used to generate control instructions corresponding to the on-site wastewater treatment equipment based on the control indices of each indicator when the control index is greater than zero. The instruction issuing module is used to issue the control instructions to the corresponding on-site sewage treatment equipment to regulate the black water treatment process of the decentralized toilet.

10. A control system for achieving compliant discharge of black water from decentralized toilets according to claim 9, characterized in that, The data processing and judgment module is specifically used to: process the real-time water quality data of COD and BOD using at least one fusion algorithm among weighted average, Kalman filtering, or DS evidence theory to obtain the comprehensive concentration value of organic pollution; The instruction generation program module is specifically used for: The organic pollution control index If the error exceeds the corresponding preset tolerance threshold, an instruction is generated to increase the dosage of nitrifying agent or increase the reflux ratio. The ammonia nitrogen regulation index If the error exceeds the corresponding preset tolerance threshold, an instruction is generated to increase the dosage of nitrifying agent or increase the reflux ratio. The total nitrogen regulation index or total phosphorus regulation index If the error exceeds the preset tolerance threshold corresponding to the tolerance threshold, an instruction is generated to increase the dosage of nitrogen and phosphorus removal agents.

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