123results about How to "Generate efficiently" patented technology

The invention discloses a free-form surface three-axis ball-end cutter equal approximation error finish machining tool path generation method which comprises the following steps: firstly, importing a free-form surface model to be machined, and setting data such as the radius, the line spacing, the approximation error maximum allowable value and the precision of a ball-end cutter; secondly, a group of section planes are planned according to the row spacing to intersect with the curved surface, the intersecting line serves as a cutter contact curve, and an equal-bow-height-error cutter contact iterative search method for driving cutter contact adjustment through the geometric distance is provided for the cutter contact curve on the section planes; the maximum distance between a cutter cutting envelope surface and a cutter contact track line is used as an approximation error, and an adaptive discrete method is adopted to carry out approximation error calculation; and finally, the cutter location points of the equal-bow-height-error cutter contact points serve as initial values, an equal-error cutter location point calculation method of step length self-adaptive adjustment iteration is provided, approximation errors between the cutter location points are all within an allowable range, and therefore the free-form surface three-axis ball-end cutter equal approximation error finish machining cutter path is obtained.

The invention discloses a multi-modal semantic topology and constraint reasoning CAD sequence generation method based on a time scale, and belongs to the technical field of crossing of computer aided design and artificial intelligence. According to the method, an end-to-end automatic generation framework from multi-modal input to an editable parameterized CAD model is constructed, and semantic consistency, structural rationality and logic correctness of a generated sequence are remarkably improved; according to the method, multi-source data such as images, sketches and point clouds can be deeply fused, a design intention is accurately captured through feature extraction of time scale perception and dynamic topological graph evolution, and meanwhile, the problems of error accumulation and logic drift in a traditional autoregression model are effectively solved through a sequence synthesis mechanism guided by grammar and geometric constraints; and in combination with a geometric feedback driving optimization engine, efficient and steady generation of the parameterized model is realized, so that systematic breakthrough in the aspects of generation quality, automation degree and cross-scene applicability is realized, and a key technical support is provided for intelligent manufacturing and intelligent design.

The invention discloses a defect detection sample data generation method and related equipment, and relates to the technical field of defect detection sample data generation, and the defect detection sample data generation method comprises the steps: obtaining a good product background image and an expected defect direction; based on the expected defect direction, respectively generating a structure guide mask and a semantic guide text cue word according to the defect knowledge base; inputting the non-defective product background image, the structure guiding mask and the semantic guiding text cue word into a local redrawing model, synthesizing a local defect image, and determining defect labeling information of the local defect image according to the structure guiding mask; and integrating the local defect image and the defect labeling information, and outputting defect detection sample data. According to the method, the structure guide mask and the semantic guide text cue word are generated through the defect knowledge base and are input into the local redrawing model to synthesize the local defect image, so that the defect image is effectively generated, and finally, the beneficial effect of improving the accuracy when the defect sample image is used for defect detection is achieved.

The invention discloses a virtual simulation data intelligent generation and enhancement method based on deep learning. The method comprises the following steps: step 1, collecting and converging multi-source heterogeneous simulation data; 2, simulation data preprocessing and feature extraction; step 3, constructing and configuring a depth generation model; step 4, iterative training and optimization of the depth generation model; step 5, intelligently generating and synthesizing new simulation data; step 6, generating data quality verification and enhancement feedback; step 7, carrying out controllability adjustment and directional enhancement on the generated data; and 8, model online learning and data generation closed-loop optimization are carried out. The method can automatically learn the internal law and distribution of the simulation data, efficiently generate highly-vivid and diversified new simulation data meeting specific requirements, and deeply enhance the existing limited data, thereby significantly reducing the simulation data acquisition cost, improving the data quality and richness, and improving the simulation data acquisition efficiency. And powerful data support is provided for development and test of a virtual simulation system and simulation-based AI model training.

The invention discloses an image generation method and device, electronic equipment and a storage medium, and belongs to the technical field of electronic equipment.The method comprises the steps that a motion vector field is calculated based on a video sequence; wherein the motion vector field comprises pixel-level motion information of each image frame in the video sequence; generating a dynamic region mask based on the amplitude information of the motion vector field; wherein the dynamic region mask is used for identifying dynamic pixels and static pixels in the video sequence; determining a static background and at least one dynamic element from the video sequence based on the dynamic region mask; and performing synthesis processing on the dynamic element and the static background to generate a dynamic low-light image.

The present application relates to the technical field of soil remediation, and discloses a composite microbial inoculum for activating reverse conversion of oxidized iron in dry land soil and application thereof.The composite microbial inoculum comprises aerobic iron-reducing bacteria and phenazine electron shuttles.The present application mediates the aerobic iron-reducing process of the aerobic iron-reducing bacteria strain through the phenazine electron shuttles, significantly improves the electron transfer efficiency, enhances the iron-reducing capacity, and can efficiently generate high-activity weak-crystalline iron oxide under aerobic conditions, thereby breaking through the limitation of traditional iron reduction relying on anaerobic environment;by promoting the reverse conversion of high-crystalline oxidized iron to weak-crystalline oxidized iron, the mineral interface activity is improved, the fixation capacity for Cd, Pb, As and other multi-metals in dry land soil is significantly enhanced, the biological availability thereof is reduced, the biological toxicity and migration risk thereof are greatly weakened, the remediation process is stable, the risk of secondary pollution is low, and the present application has good application prospect and popularization value in the field of heavy metal contaminated soil remediation.

This invention provides a novel view synthesis method and system based on a real-time rendering generalizable neural radiation field. The method includes: constructing a generalizable neural radiation field algorithm network model based on the spatial feature similarity of image patches; wherein the generalizable neural radiation field algorithm network model introduces global information from the input image through the similarity of spatial features of image patches; training the generalizable neural radiation field algorithm network model with data collected by a data acquisition device; wherein the data includes images, depth, and motion data of real-world scenes; and performing novel view synthesis of free-viewpoint video using the trained generalizable neural radiation field algorithm network model. Applying this invention can adapt to different scenarios and tasks, accelerate the 3D scene reconstruction process, improve rendering quality, provide more refined visual effects, and reduce unnecessary computational resource consumption.

This invention discloses a wide-interlayer-spacing molybdenum disulfide catalyst, its preparation method, and its application, belonging to the field of water electrolysis for hydrogen production energy conversion technology. The preparation method includes the following steps: preparing a molybdenum disulfide precursor solution using a molybdenum source and a sulfur source; then subjecting the molybdenum disulfide precursor solution to a hydrothermal reaction with a carbon-containing interlayer-spacing regulator; and obtaining the wide-interlayer-spacing molybdenum disulfide catalyst after centrifugation and washing. The carbon-containing interlayer-spacing regulator is used to insert into the interlayer of molybdenum disulfide during the hydrothermal reaction, increasing the interlayer spacing of the (002) crystal planes of the obtained molybdenum disulfide catalyst. The wide-interlayer-spacing molybdenum disulfide catalyst of this invention facilitates electrolyte penetration and hydrogen bubble release, effectively avoids active site blockage, enhances the intrinsic conductivity of the catalyst, accelerates internal charge transport, increases active sites for hydrogen evolution reaction, and improves the intrinsic hydrogen evolution catalytic activity of the catalyst.

The present application belongs to the field of wastewater advanced oxidation treatment technology, and proposes a wastewater treatment device with ozone oxidation and hydraulic cavitation synergistic effect. A primary Venturi tube, a ring air guide structure, a ring gap and a gas-liquid initial mixing section are arranged. The hydraulic cavitation is realized through the primary Venturi tube, the cavitation bubbles formed are extended to the downstream along the expansion section, and the synergistic effect with the ozone entering through the ring gap is realized. In the extreme environment generated by the collapse of the cavitation bubbles, the dissolution of the ozone gas into the liquid and the generation of the hydroxyl radicals are promoted. To some extent, the dependence on the pressure gas source is reduced, and the energy consumption is reduced. The gas phase is preliminarily dispersed before entering the secondary Venturi tube, the uniformity of the gas-liquid in the flow space is realized to the maximum extent, and the choked flow is easy to form. On the one hand, the bubbles can be further micronized, and the mass transfer is strengthened. At the same time, the gas-liquid interface changes sharply in the process of bubble micronization, which is also beneficial to the efficient generation of the hydroxyl radicals, so as to further improve the wastewater treatment efficiency.

This application pertains to the technical field of enterprise data analysis and provides a method and system for constructing a risk prevention and control system based on enterprise digital transformation. The method involves first acquiring multiple access request information and access permission information, then generating authentication result information based on this. If the total number of authentication failures exceeds a conventional threshold, risk event information is generated. This application can promptly detect abnormal behavior during monitoring periods, such as a large number of users attempting unauthorized access to the power supply bureau's core system data. It accurately identifies potential risk events such as privilege abuse, brute-force attacks, internal personnel violations, or external penetration attacks. It provides real-time alerts and decision support for security management personnel, facilitating rapid deployment of countermeasures and effectively preventing the leakage of sensitive electricity consumption information, power grid operating parameters, and user privacy data. This significantly enhances the reliability and proactive defense capabilities of the power supply bureau's data security risk prevention and control system.

The invention discloses a method, a system and equipment for generating ECharts chart resources based on a server side. The method is executed in a Node.js environment and comprises the following steps: receiving and analyzing a chart generation request; loading an ECharts configuration template and obtaining original data, and generating a complete configuration item after standardization processing; creating a virtual DOM (Document Object Model) environment and container elements through the JSDOM; creating an off-screen canvas based on Canvas, initializing an ECharts instance and executing rendering; encoding the rendering result into an image data stream; and performing format adaptation and distribution according to the terminal type. The invention further provides a cross-end consistency guarantee mechanism, a block rendering optimization mechanism, an intelligent distribution mechanism and the like. Complete decoupling of the ECharts and the server environment is achieved, static chart resources can be efficiently generated, large data volume processing is supported, cross-terminal display consistency is ensured, the performance and safety of chart generation are remarkably improved, and the application range of chart generation is remarkably widened.

This application provides a method for generating ID photos, including the following steps: acquiring a face image and a background image; aligning the face in the face image with the face in the background image; overlaying the aligned face in the face image with the face in the background image to adapt the face in the face image to the background image; and performing a face-swapping operation on the adapted face in the background image to generate the ID photo in a specified scene. The ID photo generation method provided by this application matches the face in a user-provided face image with the background image and performs a face-swapping operation on the background image, thereby efficiently generating high-quality ID photos. This application also provides an ID photo generation device and an electronic device.

The application discloses a method for quickly optimizing a trajectory of a robot of an arbitrary shape based on RC-ESDF, which comprises the following steps: generating an occupancy grid map according to the shape of the robot, thereby establishing RC-ESDF on the mechanical system of the robot, wherein the ESDF value of a grid vertex inside the robot is negative and the absolute value is the nearest distance to the surface of the robot, and the ESDF value of a grid vertex outside the robot is 0; obtaining an initial trajectory of the robot; based on the initial trajectory, constructing a target function for trajectory optimization, wherein the target function comprises a collision penalty item for the joint optimization of the position and the yaw angle of the robot; and solving the target function to obtain an optimized trajectory, so that the robot moves according to the optimized trajectory. The application reduces the calculation consumption based on RC-ESDF and is suitable for robots of an arbitrary shape; and based on the joint optimization of the position and the yaw angle, the robot can pass through a narrow area in a complex environment in a suitable pose.

An axle box bearing fault diagnosis method based on residual errors and data scaling comprises the steps that original one-dimensional vibration signals of an axle box bearing are collected, and samples of standardized signals are obtained after preprocessing; features sensitive to faults are extracted to form fault state features, then standardization processing is carried out, and standardized fault features are obtained; a Transform encoder time sequence prediction model based on an attention mechanism is constructed to serve as a reference model; inputting the standardized fault features into the reference model, and calculating a training fault residual error between a model prediction value and an actual observation value; carrying out local random scaling on the training fault residual error to construct a mixed training set, and training a classification model by using the mixed training set; and obtaining a residual error corresponding to a to-be-detected vibration signal, inputting the residual error corresponding to the to-be-detected vibration signal into the trained classification model, and finally outputting a fault category. The method can improve the generalization insensitivity of the model to the fault degree, and achieves the robust recognition of a wide-range unknown-size fault.

The invention provides a surface mine live-action modeling system and method. The method comprises the following steps: automatically acquiring oblique photography images and laser point cloud data of a target mine area through an intelligent data acquisition unit; the data is stably transmitted back to a local data processing center through the data communication and transmission unit; performing automatic preprocessing and quality evaluation on the returned data through a data preprocessing and quality evaluation unit to form a quality control closed loop; a live-action three-dimensional model which is high in precision and supports real-time rendering is generated through a live-action three-dimensional modeling engine unit on the basis of a 3D Gaussian technology and by fusing multi-source data and mine semantic constraints; according to the method, the high-precision live-action three-dimensional model supporting real-time interaction is efficiently and automatically generated, and a powerful data basis is provided for mine digital twinning, safety production and intelligent decision making.

The embodiment of the invention discloses an incidence relation graph adjusting method and device, equipment, a medium and a program product. A specific embodiment of the method comprises the steps of obtaining a target association relation graph; utilizing a preset object identification rule model to generate first object identification information corresponding to each piece of equipment information in the target association relation graph; according to the first object identification information, determining whether to use a pre-trained object identification information generation model to identify the same natural object; in response to determination of use, generating second object identification information corresponding to each piece of equipment information by using an object identification information generation model; and according to the second object identification information, performing graph adjustment on the target association graph to obtain an adjusted association graph. The implementation mode is related to artificial intelligence, and the same natural object identification can be accurately and effectively carried out on each piece of equipment information in the target association relation graph so as to determine each piece of natural object information corresponding to the target association relation graph.

The invention discloses a new-generation cryptographic system and method based on a physics quantum dynamic public key and a cloud entropy synchronous private key, a terminal, a server and a medium, and relates to the technical field of quantum information technology, cryptography and Internet of Things security crossing. The system comprises a terminal, a server and a cloud entropy synchronization private key library, and the terminal is configured to generate a quantum random number by using a quantum random number generation module, generate a synchronization parameter based on the quantum random number, and send the synchronization parameter or a physics quantum dynamic public key exported by the synchronization parameter to the cloud entropy synchronization server; and the terminal and the cloud entropy synchronization server are further configured to determine the same target entropy material from the cloud entropy synchronization private key library by applying the same cloud entropy mapping algorithm based on the synchronization parameter, and generate the same session key based on the target entropy material. According to the key system, non-interactive key establishment, forward secrecy and endogenous quantum computing attack resistance are realized.

The invention belongs to the technical field of oil field logging, and discloses an oil field logging data optimization method based on machine learning and a related device. Original oil field logging data are collected through multi-source logging equipment and preprocessed to generate a preprocessed data set; feature correlation analysis and model importance evaluation are carried out on the data set to extract a key feature set through a dimension reduction algorithm, then the key feature set is adopted to train a pre-constructed machine learning model to obtain a logging data optimization model, and finally the model is applied to optimize to-be-processed logging data and output an optimization result. By adopting the method, the effectiveness and representativeness of logging data characteristics can be remarkably improved, the real condition of an underground reservoir can be accurately reflected, the full-well-section high-resolution logging data can be simply, conveniently and quickly obtained at low cost, a reliable basis is provided for a shale oil and gas development scheme, and the rationality of the development scheme and the overall development efficiency are further enhanced.

The application relates to the technical field of artificial intelligence, and discloses a large model expert library construction based on a knowledge graph, which realizes real-time acquisition of multi-source data, marks an update point through a hash fingerprint algorithm and semantic similarity calculation, and stores the update point in a message queue in combination with metadata determination; a low-dimensional semantic vector is generated by extracting features and constructing a fusion network, and cross-modal mapping is established; an attribute graph is built with the semantic vector as a node, and the attribute graph contains basic facts, reasoning rules and time sequence edges; a user request is parsed as a graph traversal task, and a result is returned through counterfactual reasoning; sensitive data and relationships are encrypted and indexed; source field graph features are migrated to optimize target field graph, and the construction of the expert library is accelerated. The application realizes efficient data processing, semantic correlation analysis, safe query and rapid construction of the expert library, and improves data utilization and knowledge reasoning efficiency.

This invention relates to the field of flexible job shop scheduling and control technology, specifically a large language model-assisted flexible job shop scheduling algorithm, comprising the following steps: initialization of a pre-basic meme algorithm framework generation, thought chain generation, multi-LLM co-evolution and code generation, module replacement, algorithm verification and feedback, iteration termination and module accumulation, and final meme algorithm integration and output. This invention eliminates the need for professionals to manually customize algorithm structures and parameters for different FJS scenarios, reducing labor costs and time consumption. Simultaneously, the collaboration of multiple LLMs utilizes their respective prior knowledge bases to generate diverse functional modules, improving the algorithm's adaptability. It can quickly and effectively generate scheduling schemes that meet diverse needs, and the generated algorithms are superior in solution quality, computational efficiency, and generalization performance, aligning with the demands for efficient and precise scheduling in actual production.

This invention discloses a method for preparing tin dioxide hydrated colloidal precursors via dual-anode synchronous electrolysis, belonging to the field of electrochemical preparation technology of inorganic functional materials. The method includes the following steps: a) preparing an electrolyte containing 0.01 M hydrochloric acid and adjusting the initial pH value; b) using a high-purity tin plate as anode 1 and a titanium-based size-stabilized electrode as anode 2; using the titanium-based size-stabilized electrode as a common cathode; c) applying a DC voltage for constant-voltage synchronous electrolysis; anode 1 dissolves to generate stannous ions, anode 2 electrolyzes chloride ions to generate hypochlorous acid, and the hypochlorous acid instantly oxidizes the stannous ions to tetravalent tin ions in the bulk solution far from the cathode, forming a translucent tin dioxide hydrated colloidal precursor in situ in an acidic medium; d) drying the resulting colloid under reduced pressure and then calcining it at 200–250℃ to obtain high-purity nano-tin dioxide. This invention achieves integrated "dissolution-oxidation-gelation" through dual-anode synergy, avoiding the loss of hypochlorous acid during cathode reduction.

This utility model discloses a foaming device, belonging to the field of microbubble cleaning technology, specifically relating to a micro / nanobubble generating device, comprising: a cavity with a fluid inlet and a mixed fluid outlet, the fluid inlet being used to receive a gas-liquid mixed fluid with a certain pressure; and a multi-stage vortex module disposed within the cavity; the multi-stage vortex module includes a flow channel composed of several blade units, used to cause the passing gas-liquid mixed fluid to undergo at least two rotational directional changes in opposite directions, thereby breaking the gas into microbubbles under the repeated impact and shearing action of the liquid. The blades in this utility model are assembled, and different numbers of blades can be selected depending on the length of the pipe; the blade shape is fluid-mechanically designed to facilitate the rotation of the liquid flow, achieving efficient and stable microbubble generation while significantly improving cost control, maintainability, and adaptability.

This invention discloses a multi-ship cooperative target search method and system for complex search scenarios. The multi-ship cooperative target search system is installed on the ship's hull and includes an information acquisition module, a top-level task coordination and allocation module, a main control module, a communication module, a ship motion decision-making module, and a shore-based interactive control center. This invention proposes a multi-ship cooperative collision avoidance motion planning method based on the MADDPG algorithm combined with the APF algorithm. An APF-MADDPG model is constructed and trained using an LSTM network structure. The trained deep network adaptively designs parameters, enabling each intelligent ship to efficiently generate navigation and collision avoidance routes. Furthermore, a regularly symmetrical elliptical safety zone is defined for each ship, divided into 18 regions. The distance information and collision risk of each ship to other intelligent ships within each refined safety detection region are added to the state space of each ship, enhancing the model's robustness and generalization ability.

The application relates to a Transformer-based power multi-modal full-element sample fusion labeling method and system, which comprises the following steps: S1: obtaining multi-modal original data of a production operation site, and obtaining a preprocessed multi-modal data set through preprocessing; S2: performing multi-modal feature coding and Transformer representation modeling according to the preprocessed multi-modal data, and obtaining a fusion feature representation through cross-modal feature fusion and information completion; S3: acquiring a multi-modal semantic alignment label set through semantic consistency detection and a semantic mapping mechanism according to the fusion feature representation and a corresponding candidate label set; S4: constructing a label system structure tree according to the multi-modal semantic alignment label set; and S5: constructing an automatic labeling module to generate preliminary labeling based on the fusion feature representation and the label system structure tree, and directly inferring labels based on similar samples and category probabilities. The application realizes efficient automatic label generation and sample dynamic classification.

The invention is suitable for the technical field of deep learning, and provides a nucleic acid aptamer generation and screening method and device, electronic equipment and a program product. The method comprises the following steps: inputting a target protein into a protein structure encoder, and extracting a first protein feature of the target protein; inputting the guiding condition and the guiding intensity coefficient into a condition diffusion model to generate at least one candidate nucleic acid aptamer; inputting each candidate nucleic acid aptamer into a nucleic acid encoder, and extracting a first nucleic acid sequence feature of each candidate nucleic acid aptamer; inputting each first nucleic acid sequence feature and the first protein feature into a semantic space alignment module, and calculating a prediction score of each candidate nucleic acid aptamer; adding each candidate nucleic acid aptamer and the predicted score thereof into a nucleic acid candidate pool; and determining the candidate nucleic acid aptamer with the highest predicted score in the nucleic acid candidate pool as the nucleic acid aptamer with the optimal binding capacity with the target protein. According to the invention, efficient generation and screening of the high-affinity nucleic acid aptamer can be realized.

An object of the present embodiment is to efficiently generate a design document and a code from a system configuration graph. A system according to an embodiment includes a reception unit, an analysis unit, and a generation unit. The receiving unit inputs a system configuration diagram. The analysis unit analyzes the demand on the basis of the system configuration graph input by the reception unit. The generation unit generates a design document and a code on the basis of the demand analyzed by the analysis unit.

The application provides an artificial intelligence-based test script generation method and device. A specific embodiment of the method includes: obtaining full-link data of a plurality of service requests corresponding to a target service in a first operating environment through a preset link tracking tool; generating a plurality of initial test information corresponding to the target service based on the full-link data of the plurality of service requests; inputting each initial test information into a pre-trained first prediction model to generate an adaptation degree of each initial test information to a second operating environment and a test success rate of each initial test data in the target service in the second operating environment by the first prediction model; determining a plurality of target test information from the plurality of initial test information according to the adaptation degree and the test success rate of each initial test information, and generating a plurality of test cases for execution in the second operating environment according to the plurality of target test information; and generating a test script corresponding to each test case based on a pre-set script template and each test case.

The embodiment of the application relates to the technical field of motor testing, and provides a model testing method and system based on a double-fed motor model, which first acquires a plurality of actually measured output power information of a target double-fed motor and a plurality of simulated output power information of a motor simulation model based on preset test parameter set information, then effectively generates data difference value information according to the actually measured output power information and the simulated output power information, and finally accurately generates simulation result information based on the data difference value information. The embodiment of the application can realize accurate judgment on the motor simulation model, ensure that the simulation model accurately simulates dynamic performance and steady-state characteristics of a double-fed speed-regulating motor, significantly reduce the risk of evaluating an actual double-fed speed-regulating motor due to dependence on an inaccurate simulation model, reduce decision-making errors caused by simulation deviation, and greatly improve testing precision.

The invention provides a teaching annotation data generation method and device for a body agent and electronic equipment. The method comprises the steps of obtaining simulation original data and obtaining a running task of a target body agent; based on the simulation original data, obtaining an action semantic annotation and a scene semantic annotation matched with the simulation original data; decomposing a running task of the target body agent into a plurality of running sub-tasks, and labeling each running sub-task to obtain a sub-task set label matched with the running task; and on the basis of the action semantic annotation, and / or the scene semantic annotation, and / or the subtask set annotation, obtaining teaching annotation data of executing the running task by the target body agent. The teaching annotation data can be automatically and efficiently generated.

The utility model discloses a kind of high-concentration hemicellulose wastewater treatment systems, it is related to high-concentration hemicellulose wastewater treatment technical field, including the adjusting pool, fenton reaction pool, fenton sedimentation tank, multistage hydrolysis acidification pool and lifting pool connected in order, the adjusting pool water inlet is connected with high-concentration hemicellulose wastewater pipeline, non-ferrous wastewater pipeline and acidic wastewater pipeline, adjusting pool and fenton reaction pool between being provided with dispensing room, and ferrous sulfate storage tank, hydrogen peroxide storage tank and PAM storage tank are provided in dispensing room, ferrous sulfate storage tank and hydrogen peroxide storage tank are connected with fenton reaction pool water inlet respectively, and PAM storage tank is connected with fenton reaction pool, fenton reaction pool is also connected with alkali tank, the fenton sedimentation tank bottom is provided with sludge pump, and the lifting pool water outlet is connected with polymeric ferric sulfate storage tank. Through pre-fenton system combination multistage anaerobic treatment unit realizes the efficient decomposition of hemicellulose, lignin and other refractory substances, and cooperates subsequent advanced treatment to ensure that effluent COD is stable and up to standard.