7534 results about "Pre treatment" patented technology

The invention relates to the technical field of sewage treatment, and particularly discloses a sewage and wastewater treatment control system and method based on an intelligent optimization algorithm. Water quality data are collected through a data acquisition module, and a water quality characteristic matrix is generated through preprocessing. And the prediction module analyzes the feature matrix by using the trained water quality dynamic prediction model to obtain a water quality prediction result. And processing the prediction result by using a multi-objective optimization algorithm to obtain an initial control parameter. And the parameter optimization module calculates a water load fluctuation ratio, a model confidence coefficient and an equipment state according to the sewage and wastewater treatment data, inputs the water load fluctuation ratio, the model confidence coefficient and the equipment state into the adaptive fuzzy network and generates a multi-target parameter optimization suggestion. And the dynamic optimization module adjusts the multi-objective optimization algorithm parameters according to the parameters, and processes the prediction result again to obtain optimization control parameters. And the control module regulates and controls sewage and wastewater treatment according to the optimized parameters. The system realizes closed-loop management from data acquisition, prediction and optimization to control, can dynamically adapt to water quality change, and operates stably and efficiently.

The invention discloses a water body new pollutant risk assessment method and system. Belongs to the technical field of environmental science. Comprising the steps of collecting multi-source heterogeneous data to generate an initial data set; performing data fusion in the initial data set by using a weighted Bayesian fusion algorithm and a space-time interpolation method to generate a target data set; importing the target data set into a deep learning model for prediction and obtaining a prediction result; and obtaining a preset early warning condition and combining with the prediction result to judge whether risk early warning needs to be carried out. Through constructing a pollutant data acquisition system and data fusion, the monitoring precision of the new pollutants in the water body is improved. The fusion algorithm is used for data preprocessing, and pollutant detection is comprehensive. And the space-time diffusion process of the pollutants can be accurately modeled.

The present invention relates to the technical field of the control of wastewater treatment devices. Disclosed is an intelligent control method for wastewater treatment devices at a dry bulk cargo terminal, which is used for solving the problem of poor control of wastewater treatment devices at a terminal. The method comprises the following steps: installing a plurality of types of sensors at key locations of a dry bulk cargo terminal, and using edge computing nodes to perform real-time data collection and preprocessing; on the basis of historical features and temporal features, using a machine learning model to perform wastewater type classification, thereby realizing efficient dynamic adjustment of operating parameters of wastewater treatment devices; then, by means of weighted voting and confidence evaluation, integrating a plurality of classification results to ensure an optimal treatment effect; and analyzing actual wastewater treatment conditions to continuously optimize device control, thereby preventing faults, extending the service life of devices, and improving the wastewater treatment effect.

The invention provides a sewage treatment system and method based on a large language model and multi-agent collaboration, and the system comprises a sensor network which is used for collecting the water quality parameters of sewage in real time; the monitoring agent is used for performing data verification on the water quality parameters; the at least one edge computing node is used for performing data preprocessing on the water quality parameters; the cloud decision intelligent agent is used for generating a sewage treatment strategy according to the preprocessed water quality parameters based on a large language model; the execution agent is used for executing the sewage treatment strategy and treating the sewage; the evaluation agent is used for analyzing and evaluating the sewage treatment effect and feeding back the sewage treatment effect to the cloud decision agent so as to optimize the sewage treatment strategy; by constructing an intelligent and dynamic treatment system, real-time monitoring, intelligent analysis and self-adaptive treatment of the mariculture sewage are realized, so that the sewage treatment stability and environmental protection benefits are improved, and the sustainable development of the mariculture industry is promoted.

The invention provides a sewage plant effluent prediction method, system and equipment based on an improved Bi-LSTM model, and relates to the technical field of sewage treatment. The method comprises the following steps: acquiring historical operation data of a sewage plant, introducing an attention mechanism, a bidirectional structure and residual connection based on a standard LSTM unit, constructing a Bi-LSTM prediction model, taking a key kinetic equation of a simplified activated sludge model ASM as a physical constraint condition, inputting the operation data subjected to data preprocessing into the Bi-LSTM prediction model, and calculating the operation data of the sewage plant according to the operation data. The prediction result is subjected to multi-objective optimization based on the genetic algorithm to obtain an optimal process parameter combination, and the optimal process parameter combination is converted into an actual process control instruction to realize dynamic parameter adjustment, so that the prediction precision is greatly improved, and the energy consumption is reduced, the stability is improved and the abnormal working condition adaptive capacity is improved through multi-objective optimization.

The invention provides a solid waste filling material ratio optimization method of a magnesium slag-steel slag composite cementing material, and relates to the technical field of solid waste filling. The method comprises the following steps: mixing magnesium slag, steel slag, desulfurized gypsum and mineral slag to prepare a cementing material, and carrying out pretreatment and physicochemical property analysis on raw materials by taking tailings and waste rocks as filling aggregates; then designing a cementing material proportioning scheme and carrying out strength test to obtain an optimal ratio of the cementing material; then considering different slurry concentrations and plastic-to-bone ratios, and carrying out an all-solid waste filling slurry rheological test and an all-solid waste cementitious body strength test; and finally, according to the two test results, establishing a multi-objective optimization model taking the on-way resistance and the filling material cost as optimization objectives and taking the strengths of cementitious bodies at different ages as constraint conditions, and determining optimal parameters of the full-solid waste filling material. According to the invention, resource utilization of the metallurgical solid waste and the mine solid waste is realized, the bottlenecks of high carbon emission and high cost of a cement-based filling material are broken through, and a solution is provided for co-processing of the mine solid waste.

The invention provides a sewage treatment plant terminal effluent carbon source quality detection and evaluation method, which realizes full-flow dynamic evaluation and regulation of carbon source quality through on-line monitoring and intelligent algorithm fusion. According to the method, an online water quality full-spectrum detector is used for collecting original spectrum data flow, and after preprocessing such as variational mode decomposition denoising and mutual information feature selection, organic matter content quantification, variation trend analysis and anomaly detection are completed in combination with algorithms such as a support vector machine and an autoregressive moving average model. An entropy weight method is introduced to dynamically adjust the weight of the evaluation model, model parameters are optimized based on a gradient descent algorithm, a process adjustment instruction is generated through reinforcement learning and fuzzy logic, and an automatic system is linked to execute regulation and control. According to the method, the problems of hysteresis and singleness of traditional offline analysis are solved, multi-dimensional real-time evaluation, abnormal quick response and process dynamic optimization of the quality of the carbon source are realized, the sewage treatment efficiency and the effluent quality stability are improved, and a technical support is provided for continuous standard reaching of the quality of the carbon source.

The invention provides a sewage AI intelligent management and control system based on a neural network algorithm, which comprises multi-source data acquisition units, an algorithm processing unit and an execution feedback unit, and is characterized in that the multi-source data acquisition units are deployed at a water inlet and a water outlet of a sewage treatment assembly line and in a biological reaction tank; comprising a fast parameter detection array composed of a pH sensor, a conductivity sensor, a dissolved oxygen sensor, a turbidity sensor, a temperature sensor and an ORP sensor. The process state monitoring group consists of an aeration equipment rotating speed sensor, a reflux pump flowmeter and a sludge concentration meter; the fast parameter detection array and the process state detection group are connected with the edge computing node through an industrial bus, perform sliding window mean filtering preprocessing on original data, generate regulation and control instructions of aeration rate, reflux ratio and sludge discharge frequency through model calculation, and send the regulation and control instructions to the execution unit; the complete refined AI intelligent control sewage treatment system based on the neural network algorithm is realized.

The invention relates to an intelligent decision-making method, system, equipment and medium for sewage medicament addition, and the method comprises the following steps: detecting and preprocessing sewage water quality parameters, extracting feature vectors, and carrying out clustering analysis to obtain water quality categories. Historical dosing data is retrieved and subjected to statistical analysis, and an initial dosing scheme is generated; a water quality change curve is obtained by simulating the scheme, and then the optimal dosing scheme is obtained through optimization. And finally, generating and issuing an agent adding control instruction, and executing treatment, so that the technical problems that the traditional agent adding mode mostly depends on artificial experience or simple automatic control, and is difficult to cope with the complex working condition of dynamic change of water quality and water quantity, so that the agent adding is inaccurate, and the agent waste is possibly caused are solved.

The invention discloses a charging pile liquid cooling three-level leakage prevention and pump obstacle prevention intelligent control method and system, and relates to the technical field of liquid cooling control. The charging pile liquid cooling three-level leakage prevention and pump obstacle prevention intelligent control method comprises the steps that S1, liquid cooling monitoring data are periodically collected, and the liquid cooling monitoring data are preprocessed; s2, judging whether an abnormal period exists or not, and evaluating whether a cooling liquid leakage trend exists or not in combination with real-time liquid cooling monitoring data; s3, performing coolant leakage risk grade evaluation, judging the leakage risk grade in the current abnormal period, and executing a corresponding leakage prevention strategy; and S4, in the execution process of the anti-leakage strategy, the running state of the circulating pump is evaluated, and whether the pump obstacle prevention and control strategy is triggered or not is judged. The problem that thermal runaway is caused by the fact that cooling abnormity is not intervened in time due to the fact that leakage of trace cooling liquid in an existing liquid cooling system is difficult to recognize in time and pump obstacles are difficult to pre-judge accurately is solved.

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment control process based on dynamic regulation and control of aeration and carbon source addition, which comprises the following steps: acquiring microbial metabolism heat change data in real time; biochemical treatment: monitoring the dielectric constant of a water body and the Reynolds number of sewage flow in real time, and constructing a multi-parameter coupling regulation and control model and dynamically regulating and controlling the aeration rate in combination with microbial metabolism heat change data obtained in the pretreatment step; accurately adding the carbon source according to a calculation result of the carbon source demand prediction model; sewage subjected to biochemical treatment is subjected to ultrafiltration through a nanofiber membrane and then subjected to combined disinfection treatment through ultraviolet rays and ozone, treatment parameters are regulated and controlled in real time according to the membrane flux attenuation trend in the treatment process, and dynamic and accurate control over sewage treatment is achieved. According to the method, the problem of energy consumption waste caused by excessive aeration or low degradation efficiency caused by insufficient aeration in a traditional process is solved, and accurate matching of dissolved oxygen supply and microbial requirements is realized.

The invention discloses ultra-high performance concrete and a preparation method thereof. The ultra-high performance concrete comprises the following components in percentage by mass: 15%-25% of cement, 3%-6% of nano SiO2, 30%-35% of fly ash or steel slag powder, 35%-40% of recycled aggregate, 1%-4% of fiber, 0.5% of a microbial remediation agent and 1%-3% of nano TiO2. The preparation method comprises the following steps: S1, pretreating the recycled aggregate; and S2, packaging the microbial remediation agent: mixing the bacillus pasteurii spores with calcium lactate, and wrapping the mixture in calcium phosphate microspheres. S3, step-by-step stirring; S3.1, dry mixing: uniformly stirring the cement, the fly ash, the nano SiO2, the nano TiO2 and the recycled aggregate; s3.2, wet mixing is carried out, water solution fibers containing the polycarboxylic acid water reducing agent are added, uniform stirring is carried out, and the water-binder ratio of the water solution containing the polycarboxylic acid water reducing agent is 0.18; s3.3, final mixing: adding the packaged microbial remediation agent, and uniformly stirring; and S4, directional curing: performing steam curing at 80 DEG C for 48 hours, performing ultraviolet irradiation, and performing constant-temperature and constant-humidity curing for 7 days. The ultra-high performance concrete has the effects of low cost, low carbon emission, multi-function integration and repeatable crack self-repairing.

The invention discloses a sewage denitrification dosing method and system based on machine learning and a storage medium, and belongs to the technical field of sewage treatment.The method includes the steps that data are collected and preprocessed, and variable data influencing biochemical pool carbon source dosing behaviors are obtained; and lagging influence of carbon source input on the denitrification amount index is analyzed, and the duration time range of the drug effect is determined. And adopting the trained prediction model, and based on the denitrification amount index and the prediction variable of the future t + X period, obtaining the dosage of the (t + 1) th period. Through a correlation analysis method, the correlation rule of nitrogen conversion in the future X period after the carbon source is added is analyzed, the duration time of the drug effect is determined, the lag effect is accurately quantified, and the problem of mismatching of regulation and control opportunities is avoided. The hysteresis effect is captured and subjected to multi-factor coupling analysis based on the prediction model, the carbon source adding amount and time are optimized, system load fluctuation caused by excessive carbon sources or incomplete nitrogen removal caused by insufficient carbon sources are avoided, and the stability of an original sewage ecological system is gradually improved.

The invention provides a multiphase collaborative purification intelligent treatment system for heavy metal wastewater, which relates to the technical field of wastewater treatment and comprises a pretreatment module, a main treatment module, an advanced treatment module, a post-treatment and recycling module, a sludge treatment module and an intelligent control module. The intelligent control module comprises a data acquisition layer, a control layer and a management layer, and intelligent control on each treatment module is realized by monitoring parameters such as pH value, ORP (oxidation-reduction potential), conductivity and heavy metal ion concentration of the wastewater on line. The data acquisition layer acquires processing parameters in real time through various sensors; the control layer executes control logic based on the PLC / DCS and monitors the operation state through SCADA (Supervisory Control And Data Acquisition); the management layer adopts AI algorithms such as a water quality-energy consumption correlation regression model and a time sequence water quality fluctuation learning model to optimize processing parameters. According to the system, the treatment process can be intelligently adjusted according to the characteristics of the wastewater, the maximization of the heavy metal wastewater treatment efficiency, the minimization of chemical consumption and the minimization of energy consumption are realized, and the treatment effect and the system stability are improved.

The invention relates to the technical field of smart city operation, and discloses a pressure regulation and loss reduction method based on steam simulation and condensate water analysis, comprising the following steps: step S1, collecting operation parameters of a steam pipe network system; s2, converting the preprocessed operation parameters into scale space parameters; s3, establishing a condensate water source term equation, a heat source term equation and a condensate water generation model to obtain pressure, temperature and condensate water content; s4, solving the optimal pressure adjustment amount and the condensate water adjustment amount by combining the pressure and condensate water coupling matrix, and optimizing the circulation degree; and S5, correcting model parameters in the condensate water source term equation, the heat source term equation and the condensate water generation model based on a comparison result. By coupling and simulating the condensate water source item, the heat source item and the generation model, dynamic distribution of condensate water can be accurately analyzed, and the problems that in the prior art, calculation efficiency and model precision are difficult to balance, and dynamic coupling is lacked are solved.

The invention relates to the technical field of information, and discloses an agricultural sewage treatment control method and system based on the Internet of Things, and the method comprises the steps: obtaining sewage water quality data in different time periods, and carrying out the preprocessing of the data, and obtaining standard water quality data; extracting time sequence data of the water quality indexes from the water quality indexes, and obtaining smoothed time sequence data by using a time sequence analysis method; according to the smoothed time sequence data, through a trained long-short-term memory neural network model, obtaining prediction data of a future time sequence of the water quality index; according to the prediction data, constructing an objective function of an optimization problem so as to establish a multi-objective optimization model; solving the multi-objective optimization model by adopting a particle swarm optimization algorithm to obtain an optimal combination of process parameters; and converting the optimal combination into a control instruction, issuing the control instruction to execution equipment of the sewage treatment system, and controlling the operation state of the execution equipment. According to the method, the process parameters can be adjusted in time, and the efficiency and the effect of agricultural pollution treatment are improved.

The invention discloses a BIM-based water conservancy project construction quality intelligent management method and system, and relates to the technical field of intelligent management. During operation of the system, various parameters related to the water conservancy project construction quality are acquired based on construction of a BIM model, data from a data acquisition module are preprocessed and integrated, and a BIM model is established; the method comprises the following steps: establishing a mathematical model for water conservancy project quality monitoring, establishing a dynamically updated and optimized model through regression analysis of historical data and a machine learning algorithm, predicting potential risks and quality problems in a construction process, and calculating a dynamic monitoring index Dtjk according to parameters output by a BIM model establishment module, the real-time monitoring and early-warning module is used for monitoring a BIM model, comparing the real-time monitoring and early-warning module with a preset qualified threshold value in the BIM model, giving out early warning according to the preset threshold value, reminding management personnel to take intervention measures, and providing improvement suggestions and optimizing a construction management scheme according to feedback information of the real-time monitoring and early-warning module and changes of a dynamic monitoring index Dtjk.

The invention relates to the technical field of filling materials, and particularly discloses an ardealite-based high-strength anti-seepage filling material and a preparation process thereof, the ardealite-based high-strength anti-seepage filling material comprises 50%-65% of pretreated ardealite, 15%-25% of Portland cement, 5%-15% of superfine slag powder, 5%-10% of electrolytic manganese residues, 5%-8% of silica fume, 3%-6% of metakaolin, 2%-4% of a composite alkali activator and 0.5%-1.2% of a polycarboxylic acid water reducer, 0.3%-0.8% of an organic silicon water repellent and 0.05%-0.1% of cellulose ether; according to the invention, through chemical reaction of Mn < 2 + > in the electrolytic manganese residue and SO4 < 2-> in the phosphogypsum, micron-sized manganite crystals (MnAl2 (SO4) 4. 22H2O) are generated to actively fill internal micro-cracks of the material, so that a filling body is endowed with a self-repairing function and the impermeability is improved, and a composite alkali activator composed of NaOH and water glass is used for synergistically activating potential gelling activity of the superfine slag powder and the silica fume, so that the self-repairing filling material is prepared. And the formation of hydration products is accelerated, and 0.3-0.5 T of pulsed magnetic field is applied in the stirring process, so that the manganite crystals are guided to orderly grow along the expected stress direction of the filling body, and the compressive strength is improved.

The invention relates to the technical field of sewage treatment, in particular to an aerobic aeration treatment system for plateau domestic sewage treatment, which comprises a pretreatment unit, a biological treatment unit, an advanced treatment unit and a recycling unit which are connected in sequence, the system further comprises a data acquisition module, an aerobic aeration intelligent regulation and control module, a biochemical environment constant-temperature maintenance module, a carbon-nitrogen proportion and backflow dynamic regulation and control module, a digital twinning and sludge characteristic simulation module and an energy collaborative scheduling module. Compared with the prior art which adopts a standard aeration strategy and cannot adapt to the special environment of low air pressure and low oxygen partial pressure of the plateau, the system can accurately adapt to the plateau environment by integrating real-time atmospheric pressure and water temperature monitoring, dynamically calculating the oxygen saturation solubility and correcting the aeration control target according to the oxygen saturation solubility, so that the aeration control target is optimized. Stable supply of dissolved oxygen required by biochemical reaction is ensured, and the technical bottleneck of low efficiency of traditional sewage treatment in plateau regions is fundamentally overcome.

The invention relates to a water plant intelligent chemical adding amount prediction method based on data preprocessing, and belongs to the technical field of deep learning and intelligent chemical adding. Calculating a theoretical dosage based on historical flow, pH, water temperature and turbidity; dividing a plurality of clusters and splicing to query historical dosage data; weighting and fusing the theoretical dosing amount and the inquired historical dosing amount as a pre-treatment dosing amount; learning the relationship among the flow, the pH, the water temperature, the turbidity and the pretreatment dosage to perform forward feedback optimization; building an alumen ustum image recognition model, classifying alumen ustum, and associating the alumen ustum with corresponding dosage to form a dosage feedback algorithm model; building a dosage feedback model based on the sedimentation tank outlet water quality monitoring data; and correcting the weight in the preprocessed dosage based on the adjusted data of alumen ustum identification and water quality feedback on the dosage. The method can effectively reduce the influence of the dosage data error on the effectiveness of the model, can reduce the complexity of the algorithm model, and improves the robustness of the model.

The invention provides an intelligent aeration system and a control method, and the system comprises a multi-parameter monitoring module which collects parameters such as water depth, pressure, temperature, dissolved oxygen, inlet water ammonia nitrogen, chemical oxygen demand, and mixed liquid suspended solid concentration in real time; the data analysis and preprocessing module adopts a sliding window algorithm to carry out data cleaning and anomaly detection; the oxygen supply demand prediction module establishes an oxygen demand prediction model based on machine learning, and combines theoretical calculation and deep learning prediction; the multi-target intelligent control module adopts a reinforcement learning algorithm, establishes a Markov decision process model, and optimizes the processing effect and energy consumption at the same time; the execution control module is used for accurately adjusting the rotating speed of a fan, the opening degree of a valve and the running state of an aerator; and the system collaborative optimization module realizes linkage control of the aeration system and the carbon source adding system. The system has the functions of self-adaptive parameter adjustment, fault diagnosis and self-recovery, the DO control precision reaches 95% or above, the daily average electricity is saved by 9.7%, the NH4-N of effluent is reduced by 25%, and the annual comprehensive benefit exceeds 500,000 yuan.

The invention belongs to the crossing field of environmental engineering and hydrology and hydrodynamics, and particularly relates to a traceability method based on coupling hydrodynamics and a pollutant degradation equation, which comprises the following steps: firstly, acquiring and preprocessing multi-source heterogeneous monitoring data, then selecting a one-dimensional Saint-Venant equation or a two-dimensional shallow water equation according to a water body form to solve a hydrodynamic field, and finally, determining the hydrodynamic field. A multi-component convection-diffusion-reaction equation is coupled to simulate pollutant migration and transformation; an LSTM module is introduced to identify suspected pollution events, pollution source parameters are inverted through a two-channel framework of PDE constraint optimization and Bayesian inference, uncertainty is quantified, model parameters are updated online in combination with data assimilation, and finally the uncertainty is quantified and verified. The method considers traceability precision, efficiency and compliance, supports multiple water bodies and multiple data sources, and is suitable for complex water body pollution traceability.

The invention discloses a sewage plant total phosphorus concentration prediction method based on multiple machine learning models and a control system, and belongs to the field of environmental monitoring and treatment. The method comprises the following steps: automatically collecting detection data of a sewage plant, and generating a time sequence data set through intelligent preprocessing; according to the method, a total phosphorus concentration prediction model is constructed by adopting multiple machine learning algorithms, a reference prediction model is automatically selected through evaluation indexes, hyper-parameter tuning is performed by utilizing a swarm intelligence optimization method, and an optimized high-performance prediction model is obtained. The model is deployed to a real-time monitoring system, and through integration with a PLC and monitoring hardware, high-frequency prediction and dynamic regulation and control closed loop are realized; and continuously optimizing model parameters and a regulation and control strategy by returning deviation information to form a'prediction-control-optimization 'closed loop. According to the method, the effluent total phosphorus concentration prediction precision and regulation efficiency are remarkably improved, the agent adding cost is reduced, and the intelligent level of a sewage treatment system is improved.

The invention discloses an intelligent aeration control system and method for a sewage treatment plant, and relates to the technical field of sewage treatment control. Comprising a data acquisition module, an aeration test module, a demand prediction module, a normal state analysis module and an aeration control module, wherein the data acquisition module is used for acquiring real-time working data of a primary sedimentation tank, test data of an aeration tank, historical working data and real-time working data of the aeration tank and preprocessing the data; the technical key points are as follows: an aeration tank is scientifically divided into multiple sections, professional monitoring equipment is deployed in each section, multi-dimensional data is acquired, the data of each section is independently acquired and tested, the operation condition and the processing effect of the aeration equipment in each section can be accurately mastered, and on the basis of the accurate data and in combination with an aeration efficiency evaluation model and a regulation and control strategy, the aeration efficiency of the aeration tank is evaluated. And fine adjustment can be performed according to actual requirements of each section, so that resource waste or poor treatment effect caused by one-step regulation and control is avoided, efficient and accurate operation of the aeration equipment is realized, and the aeration equipment has a good use prospect.

The invention discloses a biological nitrogen and phosphorus removal MBR sewage treatment system and a treatment process thereof. The process comprises the following steps: 1) pretreatment; 2) treatment in an anaerobic tank; (3) treating in an anoxic tank; 4) treatment in an aerobic tank; 5) MBR membrane pool treatment; sewage enters the box body through the water inlet, the moving mechanism drives the aeration mechanism and the scum cleaning mechanism to move, the aeration mechanism performs aeration treatment on the sewage in the box body, and when the aeration mechanism blows gas into the sewage through the branch gas pipe, bubbles can be scattered by spiral blades, so that the contact area between the gas and liquid is further increased, and the aeration effect is enhanced; when the sewage is subjected to aeration treatment, the scum after aeration treatment rises, the scum cleaning mechanism scrapes the scum floating after aeration for multiple times, collects the scraped scum and sends the scum out of the box body, impurities suspended in the middle layer of the sewage are fished, and the sewage after scum cleaning enters the next treatment procedure through the water outlet.

The invention provides a method, a device and equipment for determining a sludge resource utilization path, belongs to the technical field of computer information processing, and solves the problem that economic, environmental and social targets are difficult to dynamically and collaboratively optimize in the current sludge resource treatment process. The method comprises the following steps: acquiring original data, wherein the original data comprises at least one of sludge attribute data, process parameters and external dynamic data; preprocessing the original data to obtain sludge sample data; classifying and integrating the sludge sample data to obtain a multi-dimensional tensor; decomposing and dynamically updating the multi-dimensional tensor to obtain a decomposed factor matrix; inputting the decomposed factor matrix into a path optimization model for processing to obtain a resource optimization path set; and performing optimal verification processing on the resource optimization path set to obtain optimal utilization path data. According to the scheme, dynamic optimal balance of sludge treatment economic benefits, carbon emission reduction and social compliance is realized.

The invention provides a solid waste recycling full life cycle environmental impact evaluation method, which belongs to the technical field of solid waste recycling, and evaluates environmental impact by constructing a system boundary and time dynamic list including solid waste collection, transportation, pretreatment, recycling and product use stages and applying a time weighted characterization model. A time discount rate is introduced to quantify future environmental influence, a multi-scene influence prediction function is adopted to construct a simulation matrix, and an environmental influence time distribution curve is calculated. A four-dimensional index system of resource recovery efficiency, energy substitution efficiency, pollutant emission reduction and environmental impact mitigation is established, a system dynamics and Envi rPred ictor deep learning model is combined to predict a solid waste long-term environmental behavior, an environmental impact comprehensive evaluation matrix is constructed, characterization factors are dynamically adjusted, an iterative optimization mechanism is formed, and the environmental impact mitigation efficiency is improved. Scientific evaluation on the solid waste recycling full life cycle environment influence is realized.

The invention discloses a sewage treatment equipment operation monitoring method based on big data, and relates to the technical field of sewage treatment.The sewage treatment equipment operation monitoring method comprises the steps that original sewage data flow is collected and preprocessed, and multi-source fusion data is generated; performing exception detection processing on the multi-source fusion data to generate exception mark data; performing time sequence prediction on the abnormal mark data by using a time sequence prediction model to generate a sewage prediction sequence, and combining the sewage prediction sequence with the abnormal mark data to form comprehensive risk features; performing early warning analysis on the comprehensive risk characteristics according to a risk level rule and a sewage prediction sequence, and generating pre-risk early warning; and carrying out graded warning and operation decision making on the preposed risk early warning, and generating an operation regulation and control suggestion. According to the method, the prediction result has advantages in two aspects of trend grasp and anomaly adaptation, and the effect of remarkably improving the prediction accuracy and robustness is achieved.

The invention provides a bioreactor aeration self-adaptive control method and system based on machine learning, and the method comprises the steps: collecting the water inlet flow, ammonia nitrogen concentration, dissolved oxygen concentration, water temperature and chemical oxygen demand data of a bioreactor in real time through a sensor, and carrying out the water balance verification and the Kalman filtering pretreatment containing a water temperature compensation item; generating an aeration instruction by adopting multi-model collaboration; outputting a reference aeration rate by a deep reinforcement learning DRL model; the self-adaptive PID controller dynamically adjusts the proportionality coefficient according to the dissolved oxygen deviation and calculates the correction amount; and the LSTM network generates a fusion weight, and combines the seasonal compensation factor and the safety coefficient to synthesize the final aeration rate. The system passes mutation rate constraint and nitrification efficiency verification, an ASM1 model is triggered to recheck when the deviation exceeds the standard, and after passing, an air blower with the precision of + / -2% executes an instruction and performs closed-loop feedback in a period smaller than or equal to 10 seconds. After the method is implemented, the aeration energy consumption is reduced to 0.38 kWh / m, the effluent ammonia nitrogen standard reaching rate is improved to 98.5%, the dissolved oxygen fluctuation range is reduced to + / -0.3 mg / L, and the impact load response time is shortened to be within 15 minutes.

The invention belongs to the technical field of environmental engineering and water treatment, and discloses a sewage treatment process multi-index prediction method based on deep learning, and the method comprises the following steps: S1, collecting original data of sewage treatment; s2, data preprocessing; s3, feature engineering; s4, designing a feature extractor; s5, performing hierarchical contrast learning training; and S6, constructing and training a prediction model. The invention also discloses a sewage treatment process multi-index prediction system based on deep learning. The system comprises a data acquisition module, a data preprocessing module, a feature engineering module, a feature extraction module and a prediction module. A prediction system applying the method meets the requirements of sewage treatment process control in the aspects of prediction precision and stability, and powerful support can be provided for operation optimization and decision making of an actual sewage treatment plant.