A mechanical assistant intelligent auxiliary system for chemical dosing in wastewater treatment

Abstract

This invention discloses an intelligent auxiliary system for robotic arm dosing in wastewater treatment, relating to the field of intelligent control technology. It includes a construction module, a simulation module, and an optimization module. The system converts key coordinates, sets operating parameters, simulates wastewater treatment, performs initial adjustments to the operating parameters, establishes an initial mapping relationship, simulates power consumption, and selects the optimal trajectory. This invention uses the construction module to represent the key points of the robotic arm in coordinates, enabling high-precision dosing operations. It can automatically complete dosing operations based on simulation results and the optimized trajectory, improving dosing efficiency. Adjusting operating parameters based on simulation results avoids reagent waste. Selecting the optimal trajectory through power consumption simulation reduces operating costs. Automatically adjusting operating parameters according to different wastewater parameters and operating conditions adapts to various complex wastewater treatment scenarios and effectively protects the health of operators.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to an intelligent auxiliary system for chemical dosing in wastewater treatment. Background Technology

[0002] In recent years, some advanced intelligent dosing systems have gone beyond simply adding a single agent. They can also comprehensively consider multiple water quality parameters and, through big data analysis and mathematical models, achieve intelligent adjustment of parameters such as aeration volume and sludge return flow in wastewater treatment plants, thereby reducing production and operating costs. Intelligent dosing control technology will not only be limited to single stages such as phosphorus removal and flocculation, but will also be extended to other treatment stages in wastewater treatment plants such as nitrogen removal and ammonia removal, and even intelligent control can be achieved in wastewater collection systems.

[0003] Currently, Chinese invention patent CN111571583A discloses a robotic arm control method. This method receives actual operation commands input by the user for a physical robotic arm, identifies the command type, robotic arm brand and model in the actual operation command, and forms a unique command identifier for the actual operation command. The unique command identifier is matched with preset rules to form a virtual command. It then determines whether the virtual command is correctly executed by the physical robotic arm. If the determination result is yes, the current state and position information of the physical robotic arm are directly output; otherwise, the virtual command is formatted into a unified command according to a preset format and sent. The virtual robotic arm executes commands and generates feedback flags based on unified instructions. These flags then form the virtual robotic arm's execution status and position information, which is sent to the human-machine interface. However, related technologies do not accurately represent spatial points based on the conversion relationship between images formed by industrial cameras and actual coordinates. This is detrimental to the precision of robotic arm control. Furthermore, the lack of planning for the robotic arm's trajectory based on obstacles and path points can easily lead to collisions or bumps, compromising operational safety. Finally, the lack of detailed planning for the robotic arm's trajectory based on energy efficiency hinders economic efficiency and is not conducive to energy conservation and environmental protection, thus presenting certain limitations. Summary of the Invention

[0004] The technical problem solved by this invention is that related technologies do not accurately represent spatial points based on the conversion relationship between images formed by industrial cameras and actual coordinates, which is not conducive to the precision of robotic arm control; they do not plan the trajectory of the robotic arm based on obstacles and waypoints, which can easily lead to collisions or bumps, which is not conducive to the safety of operation; and they do not plan the trajectory of the robotic arm in detail based on cost-saving performance, which is not conducive to the economy of operation, energy conservation and environmental protection, and has certain limitations.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a mechanical assistant intelligent auxiliary system for chemical dosing in wastewater treatment, comprising a construction module, a simulation module and an optimization module;

[0006] The construction module transforms the coordinate system and represents the key points of the robotic arm based on the transformed coordinate system to obtain the key coordinates;

[0007] The simulation module sets the operation parameters based on the knowledge graph and the original wastewater parameters. The knowledge graph is constructed based on historical operation data. The wastewater treatment is simulated based on the operation parameters to obtain simulation results. The operation parameters are adjusted based on the wastewater acceptance results, and a first mapping relationship is constructed between the adjusted operation parameters and the original wastewater parameters.

[0008] The optimization module sets the trajectory of the robotic arm based on the simulation results to obtain the trajectory curve. It then performs a power consumption simulation based on the trajectory curve and the first adjusted operating parameters. Based on the power consumption simulation results, it selects the optimal trajectory and processes the wastewater to be treated based on the optimal trajectory and the first adjusted operating parameters.

[0009] As a preferred embodiment of the intelligent auxiliary system for wastewater treatment chemical dosing described in this invention, the logic for transforming the coordinate system includes:

[0010] Using any corner point at the bottom of the factory as the origin, and the sides adjacent to the origin as the x-axis, y-axis, and z-axis, a three-dimensional coordinate system is constructed. Based on the transformation relationship between the world coordinate system and the camera coordinate system, each point in the three-dimensional coordinate system is converted into a point in the camera coordinate system. Based on the relationship between the camera coordinate system and the image physical coordinate system, the points in the camera coordinate system are projected from perspective into points in the image physical coordinate system. Based on the transformation relationship between the image physical coordinate system and the pixel coordinate system, the points in the image physical coordinate system are converted into points in the pixel coordinate system. The first correspondence between the coordinate points in the three-dimensional coordinate system and the corresponding pixel coordinate points is established.

[0011] The pixel coordinates refer to the coordinates of the pixel coordinate system in the image captured by the industrial camera. They are two-dimensional coordinates, and the coordinates of the pixel coordinate system are determined by the camera's intrinsic parameters, which include focal length, principal point, and distortion parameters.

[0012] Input pixel coordinates into the first correspondence to obtain the corresponding coordinates in the three-dimensional coordinate system;

[0013] Based on the first correspondence, the key coordinates of the robotic arm are set, including the coordinates of the movement key point and the coordinates of the blocking key point.

[0014] As a preferred embodiment of the intelligent auxiliary system for chemical dosing in wastewater treatment described in this invention, the key points of movement include the geometric center point of the mechanical joint, the geometric center point of the mechanical link, and the geometric center point of the manipulator.

[0015] The key blocking points include the edge points of the drug storage tank, the edge points of the delivery pipeline, the edge points of the reaction tank fence, the edge points of the sedimentation tank fence, the edge points of the filter tank fence, the edge points of the mixer, the edge points of the blower, and the edge points of the operator. Each edge point is obtained by machine vision identifying the type of object and then performing edge detection on the identified object.

[0016] Each reaction tank, sedimentation tank, and filtration tank is equipped with a corresponding robotic arm, reagent storage tank, conveying pipeline, mixer, blower, and operator.

[0017] As a preferred embodiment of the intelligent auxiliary system for wastewater treatment chemical dosing described in this invention, the logic for object type identification includes:

[0018] Obtain standard images of each object, which are retrieved from the task image database;

[0019] The operator collects images of N faces of each standard object, sets them as the corresponding object names, and enters them into the work image database.

[0020] First shape features are extracted from the standard image, and second shape features are extracted from the image of the object to be identified. The similarity between the first and second features is calculated using the cosine similarity formula. A first value is set as the similarity threshold. The similarity is compared with the first value. When the similarity is greater than or equal to the first value, the name of the object in the standard image is set as the name of the object to be identified. When the similarity is less than the first value, the process jumps to the next standard image of the standard object and repeats the logic of comparing similarity. When the similarity of any standard image of the standard object is greater than or equal to the first value, the name of the object in the standard image is set as the name of the object to be identified.

[0021] When all standard images of the standard object have been traversed, and the similarity of all standard images of the standard object is less than the first value, jump to any standard image of the next standard object and repeat the above logic until the name of the object to be identified is set, then stop the identification operation.

[0022] As a preferred embodiment of the intelligent auxiliary system for chemical dosing in wastewater treatment described in this invention, the knowledge graph represents the operational parameters corresponding to wastewater parameters in historical wastewater treatment processes.

[0023] The wastewater parameters include carbon-to-nitrogen ratio, phosphorus concentration, pH value, and particle size and concentration of impurities.

[0024] The operational parameters include drug type, drug dosage, and drug administration time.

[0025] The simulation results include the sequence of key coordinate transit points and the wastewater acceptance results. The wastewater acceptance results represent the parameters of the treated wastewater. The standard values ​​of the wastewater parameters that meet the standards are obtained through water quality treatment standards.

[0026] The sequence of key coordinate transit points is represented by the order in which the moving key point passes through any point around the blocking key point from its original position, starting from the operator's input of the dosing command. The surrounding points are represented by the straight-line distance between the moving key point and the geometric center of the object represented by the blocking key point, which is the first distance.

[0027] As a preferred embodiment of the intelligent auxiliary system for wastewater treatment chemical dosing described in this invention, the knowledge graph is specifically represented as follows:

[0028] The types of drugs corresponding to the carbon-nitrogen ratio include acetic acid, sodium acetate, and methanol. The timing of drug administration for the carbon-nitrogen ratio is before the denitrification stage. The formula for calculating the drug dosage for the carbon-nitrogen ratio is:

[0029] Dosage = (TN concentration - BOD5 concentration) / BOD5 equivalent of carbon source;

[0030] The types of drugs corresponding to the phosphorus concentration include aluminum sulfate, polyaluminum chloride, ferric chloride, and polyferric sulfate. The time point for drug addition corresponding to the phosphorus concentration is before the reaction tank starts working. The dosage of the drug corresponding to the phosphorus concentration is a molar ratio of total phosphorus in the drug to total phosphorus in the wastewater of 1.5 to 3.0.

[0031] The types of drugs corresponding to the pH value include sulfuric acid, hydrochloric acid, lime, and sodium hydroxide. The timing of drug addition is before the sedimentation tank begins operation. The dosage of the drug is to adjust the acidity or alkalinity of the wastewater to the target pH value, which is preset by the operator.

[0032] The types of drugs corresponding to the size and concentration of impurity particles include polyaluminum chloride and polyacrylamide. The timing of drug addition is before the sedimentation tank starts working. The dosage of drugs is adjusted to the target size and concentration of impurity particles in the wastewater. The target size and concentration of impurity particles are preset by the operator.

[0033] As a preferred embodiment of the intelligent auxiliary system for chemical dosing in wastewater treatment described in this invention, the wastewater treatment is simulated using simulation software. Wastewater parameters are input into the simulation software, and process parameters are set, including the size of each treatment tank, retention time, aeration rate, and reflux ratio. Operating conditions are set, including internal reflux flow rate, external reflux flow rate, sludge discharge rate, and dissolved oxygen control rate. Modules are built by dragging and dropping modules and connecting them with lines. Simulation conditions are set, including simulation step size and iteration accuracy. After a first time period, simulation results are obtained.

[0034] As a preferred embodiment of the intelligent auxiliary system for wastewater treatment chemical dosing described in this invention, the logic for first adjusting the operating parameters based on the wastewater acceptance results includes:

[0035] Obtain the simulation results, compare any value of the simulation results with the corresponding standard value of the qualified wastewater parameter, and when the value is greater than the corresponding standard value of the qualified wastewater parameter, set the third value as the corresponding drug dosage change gradient, and continuously increase the drug dosage until the value is less than or equal to the corresponding standard value of the qualified wastewater parameter, then stop changing the drug dosage.

[0036] Iterate through the values ​​in each simulation result and loop through the above logic to obtain the first adjusted operating parameters, and construct the first mapping relationship between the first adjusted operating parameters and the original wastewater parameters;

[0037] By inputting wastewater parameters into the first mapping relationship, the first adjusted operating parameters corresponding to the wastewater parameters are obtained.

[0038] As a preferred embodiment of the intelligent auxiliary system for wastewater treatment chemical dosing described in this invention, the logic of the optimization module for setting the trajectory of the robotic arm based on simulation results includes:

[0039] Obtain the first distance corresponding to each blocking key point, and calculate the second distance from the moving key point to each blocking key point at this time;

[0040] Set the first planning condition to be that the first distance is greater than any second distance;

[0041] The second planning condition is set to the trajectory curve passing through key coordinate points;

[0042] Set the third programming condition to the point that the trajectory curve has no poles;

[0043] The trajectory curves are obtained using mathematical software;

[0044] Based on each trajectory curve and the drug delivery time point, power consumption simulation is performed to obtain power consumption simulation results, which are expressed as the total power consumed by the robotic arm when the processing is completed.

[0045] As a preferred embodiment of the intelligent auxiliary system for wastewater treatment chemical dosing described in this invention, the logic for selecting the optimal trajectory based on power consumption simulation results includes:

[0046] Iterate through each total power, sort the total power in ascending order, and select the trajectory curve corresponding to the total power with the smallest value;

[0047] Set the trajectory curve as the optimal trajectory;

[0048] The wastewater to be treated is processed based on the optimal trajectory and the operating parameters after the first adjustment.

[0049] The beneficial effects of this invention are as follows: By constructing modules to represent the key points of the robotic arm in coordinates, high-precision dosing operations can be achieved. The robotic arm can accurately deliver chemicals according to the set trajectory and position, avoiding errors from manual operation. It can automatically complete the dosing operation based on simulation results and optimized trajectories, reducing manual intervention and improving dosing efficiency. Simulation based on sewage parameters and historical operation data optimizes the dosing dosage and operation parameters. Adjusting operation parameters through simulation results ensures that the amount of chemicals used achieves the best effect and avoids waste. Selecting the optimal trajectory through power consumption simulation not only optimizes the energy consumption of the dosing operation but also reduces operating costs. The dynamic control mechanism ensures that the sewage treatment effect always meets the standards and improves the stability of effluent quality. The operation parameters are automatically adjusted according to different sewage parameters and operating conditions to adapt to various complex sewage treatment scenarios. The application of robotic arms and intelligent auxiliary systems reduces the necessity of manual operation and reduces labor intensity, especially in harsh working environments, effectively protecting the health of operators. Attached Figure Description

[0050] Figure 1 This is a basic flowchart of a mechanical assistant intelligent auxiliary system for chemical dosing in wastewater treatment, provided as an embodiment of the present invention. Detailed Implementation

[0051] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0052] Example, refer to Figure 1As an embodiment of the present invention, a mechanical assistant intelligent auxiliary system for wastewater treatment chemical dosing is provided, including a construction module, a simulation module and an optimization module;

[0053] The construction module transforms the coordinate system and represents the key points of the robotic arm based on the transformed coordinate system to obtain the key coordinates;

[0054] The simulation module sets the operation parameters based on the knowledge graph and the original wastewater parameters. The knowledge graph is constructed based on historical operation data. The wastewater treatment is simulated based on the operation parameters to obtain simulation results. The operation parameters are adjusted based on the wastewater acceptance results, and a first mapping relationship is constructed between the adjusted operation parameters and the original wastewater parameters.

[0055] The optimization module sets the trajectory of the robotic arm based on the simulation results to obtain the trajectory curve. It then performs a power consumption simulation based on the trajectory curve and the first adjusted operating parameters. Based on the power consumption simulation results, it selects the optimal trajectory and processes the wastewater to be treated based on the optimal trajectory and the first adjusted operating parameters.

[0056] This invention uses a modular approach to represent the key points of a robotic arm in coordinates, enabling high-precision dosing operations. The robotic arm can accurately deliver chemicals according to a set trajectory and position, avoiding errors from manual operation. It can automatically complete the dosing operation based on simulation results and optimized trajectories, reducing manual intervention and improving dosing efficiency. Simulations based on wastewater parameters and historical operation data optimize dosing dosage and operating parameters. Adjusting operating parameters based on simulation results ensures optimal chemical usage and avoids waste. Selecting the optimal trajectory through power consumption simulation not only optimizes energy consumption for dosing operations but also reduces operating costs. The dynamic control mechanism ensures that wastewater treatment results consistently meet standards, improving the stability of effluent quality. Automatic adjustment of operating parameters based on different wastewater parameters and operating conditions adapts to various complex wastewater treatment scenarios. The application of the robotic arm and intelligent auxiliary system reduces the necessity of manual operation and lowers labor intensity, effectively protecting the health of operators, especially in harsh working environments.

[0057] The logic for transforming the coordinate system includes:

[0058] Using any corner point at the bottom of the factory as the origin, and the sides adjacent to the origin as the x-axis, y-axis, and z-axis, a three-dimensional coordinate system is constructed. Based on the transformation relationship between the world coordinate system and the camera coordinate system, each point in the three-dimensional coordinate system is converted into a point in the camera coordinate system. Based on the relationship between the camera coordinate system and the image physical coordinate system, the points in the camera coordinate system are projected from perspective into points in the image physical coordinate system. Based on the transformation relationship between the image physical coordinate system and the pixel coordinate system, the points in the image physical coordinate system are converted into points in the pixel coordinate system. The first correspondence between the coordinate points in the three-dimensional coordinate system and the corresponding pixel coordinate points is established.

[0059] The pixel coordinates refer to the coordinates of the pixel coordinate system in the image captured by the industrial camera. They are two-dimensional coordinates, and the coordinates of the pixel coordinate system are determined by the camera's intrinsic parameters, which include focal length, principal point, and distortion parameters.

[0060] Input pixel coordinates into the first correspondence to obtain the corresponding coordinates in the three-dimensional coordinate system;

[0061] Based on the first correspondence, the key coordinates of the robotic arm are set, including the coordinates of the movement key point and the coordinates of the blocking key point.

[0062] The key points of motion include the geometric center points of mechanical joints, mechanical links, and manipulators;

[0063] The key blocking points include the edge points of the drug storage tank, the edge points of the delivery pipeline, the edge points of the reaction tank fence, the edge points of the sedimentation tank fence, the edge points of the filter tank fence, the edge points of the mixer, the edge points of the blower, and the edge points of the operator. Each edge point is obtained by machine vision identifying the type of object and then performing edge detection on the identified object.

[0064] Each reaction tank, sedimentation tank, and filtration tank is equipped with a corresponding robotic arm, reagent storage tank, conveying pipeline, mixer, blower, and operator.

[0065] In practice, a precise mapping from space to image is achieved by converting points in the 3D coordinate system to points in the camera coordinate system, and then further converting them to points in the image physical coordinate system and pixel coordinate system. This multi-level transformation ensures high-precision positioning of the target location by the robotic arm during operation. Ultimately, the 3D coordinates are converted to pixel coordinates, and utilizing the high-resolution imaging capabilities of industrial cameras, millimeter-level or even higher positioning accuracy can be achieved. This is crucial for precise dosing in drug dispensing operations. By inputting pixel coordinate points, the corresponding 3D coordinate points can be obtained in real time, enabling dynamic adjustments to the robotic arm's operation. This real-time feedback mechanism can adapt to complex operating environments and changing working conditions. For example, it can make immediate adjustments based on the drug's placement and effect during dosing, and adjust according to different factory structures and equipment layouts, demonstrating strong versatility and adaptability. Regardless of changes in the shape and size of the factory, a 3D coordinate system can be constructed by selecting a suitable origin and coordinate axes, thereby achieving precise robotic arm operation. By setting the coordinates of key obstruction points, the robotic arm can automatically identify and avoid obstacles during operation, reducing the risk of collisions. This safety mechanism effectively protects equipment and personnel, reducing the probability of accidents. Through precise coordinate transformation and robotic arm operation, chemicals can be accurately delivered to designated locations, ensuring thorough mixing between the chemicals and wastewater and improving dosing effectiveness. This precise delivery reduces chemical waste while improving the efficiency and quality of wastewater treatment.

[0066] The logic for object type identification includes:

[0067] Obtain standard images of each object, which are retrieved from the task image database;

[0068] The operator collects images of N faces of each standard object, sets them as the corresponding object names, and enters them into the work image database.

[0069] First shape features are extracted from the standard image, and second shape features are extracted from the image of the object to be identified. The similarity between the first and second features is calculated using the cosine similarity formula. A first value is set as the similarity threshold. The similarity is compared with the first value. When the similarity is greater than or equal to the first value, the name of the object in the standard image is set as the name of the object to be identified. When the similarity is less than the first value, the process jumps to the next standard image of the standard object and repeats the logic of comparing similarity. When the similarity of any standard image of the standard object is greater than or equal to the first value, the name of the object in the standard image is set as the name of the object to be identified.

[0070] When all standard images of the standard object have been traversed, and the similarity of all standard images of the standard object is less than the first value, jump to any standard image of the next standard object and repeat the above logic until the name of the object to be identified is set, then stop the identification operation.

[0071] In practice, operators collect images of multiple faces of various standard objects, assign them corresponding object names, and input them into the work image database. This multi-angle image acquisition method comprehensively covers all features of the object, improving recognition accuracy. Shape features are extracted from both the standard image and the image of the object to be identified, and similarity is calculated using the cosine similarity formula. This method accurately compares the shape features of objects, ensuring the reliability of the recognition results. By setting a similarity threshold (first value), the strictness of recognition can be adjusted according to actual needs. When the similarity is greater than or equal to the threshold, the object name is confirmed; when the similarity is less than the threshold, the system continues to compare the next standard object. This dynamic adjustment mechanism effectively avoids misidentification. If no match is found after traversing all standard objects, the system jumps to any standard image of the next standard object and continues recognition. This fault-tolerant mechanism can handle complex scenarios, avoiding recognition failure due to mismatch of a single standard image. The recognition logic can be adjusted according to actual needs, such as increasing or decreasing the number of standard images or adjusting the similarity threshold, exhibiting strong adaptability and scalability. The entire recognition process is automated, reducing the need for manual intervention. Operators only need to collect standard images and enter them into the database, and the system can automatically complete object recognition, improving work efficiency.

[0072] The knowledge graph represents the operational parameters corresponding to the wastewater parameters in the historical wastewater treatment process;

[0073] The wastewater parameters include carbon-to-nitrogen ratio, phosphorus concentration, pH value, and particle size and concentration of impurities.

[0074] The operational parameters include drug type, drug dosage, and drug administration time.

[0075] The simulation results include the sequence of key coordinate transit points and the wastewater acceptance results. The wastewater acceptance results represent the parameters of the treated wastewater. The standard values ​​of the wastewater parameters that meet the standards are obtained through water quality treatment standards.

[0076] The sequence of key coordinate transit points is represented by the order in which the moving key point passes through any point around the blocking key point from its original position, starting from the operator's input of the dosing command. The surrounding points are represented by the straight-line distance between the moving key point and the geometric center of the object represented by the blocking key point, which is the first distance.

[0077] The knowledge graph is specifically represented as follows:

[0078] The types of drugs corresponding to the carbon-nitrogen ratio include acetic acid, sodium acetate, and methanol. The timing of drug administration for the carbon-nitrogen ratio is before the denitrification stage. The formula for calculating the drug dosage for the carbon-nitrogen ratio is:

[0079] Dosage = (TN concentration - BOD5 concentration) / BOD5 equivalent of carbon source;

[0080] The types of drugs corresponding to the phosphorus concentration include aluminum sulfate, polyaluminum chloride, ferric chloride, and polyferric sulfate. The time point for drug addition corresponding to the phosphorus concentration is before the reaction tank starts working. The dosage of the drug corresponding to the phosphorus concentration is a molar ratio of total phosphorus in the drug to total phosphorus in the wastewater of 1.5 to 3.0.

[0081] The types of drugs corresponding to the pH value include sulfuric acid, hydrochloric acid, lime, and sodium hydroxide. The timing of drug addition is before the sedimentation tank begins operation. The dosage of the drug is to adjust the acidity or alkalinity of the wastewater to the target pH value, which is preset by the operator.

[0082] The types of drugs corresponding to the size and concentration of impurity particles include polyaluminum chloride and polyacrylamide. The timing of drug addition is before the sedimentation tank starts working. The dosage of drugs is adjusted to the target size and concentration of impurity particles in the wastewater. The target size and concentration of impurity particles are preset by the operator.

[0083] In practice, the knowledge graph clearly identifies the types of chemicals corresponding to different water quality parameters (such as carbon-to-nitrogen ratio, phosphorus concentration, pH value, and particle size and concentration of impurities), ensuring the scientific and targeted selection of chemicals. The timing of each chemical's administration is carefully designed to ensure it works at its optimal moment. For example, chemicals corresponding to the carbon-to-nitrogen ratio are added before the denitrification stage, and chemicals corresponding to the phosphorus concentration are added before the reaction tank begins operation. This helps improve the utilization efficiency of the chemicals. The knowledge graph provides specific calculation methods or ratio ranges for chemical dosages, avoiding the uncertainty of human experience. For example, the dosage for the carbon-to-nitrogen ratio is calculated using a formula, and the dosage for the phosphorus concentration is controlled by the molar ratio. This quantitative management ensures the accuracy of chemical dosage. Through precise chemical administration and dosage control, the effectiveness and stability of wastewater treatment can be effectively improved. For example, by adjusting the pH value and the concentration of impurity particles, the operation of the sedimentation tank can be optimized, the effluent quality can be improved, and reasonable timing of dosing and dosage control can reduce unnecessary chemical consumption and equipment operation time, thereby reducing the system's energy consumption. The knowledge graph, built based on historical operation data, can provide the system with rich experience data, helping the system to better cope with various situations and improve the level of intelligence in operation.

[0084] Wastewater treatment is simulated using simulation software. Wastewater parameters are input into the simulation software, and process parameters are set, including the size of each treatment tank, retention time, aeration rate, and reflux ratio. Operating conditions are set, including internal reflux flow rate, external reflux flow rate, sludge discharge rate, and dissolved oxygen control rate. Modules are built by dragging and dropping modules and connecting them with lines. Simulation conditions are set, including simulation step size and iteration accuracy. After the first time period, simulation results are obtained.

[0085] The logic for initial adjustment of operational parameters based on wastewater acceptance results includes:

[0086] Obtain the simulation results, compare any value of the simulation results with the corresponding standard value of the qualified wastewater parameter, and when the value is greater than the corresponding standard value of the qualified wastewater parameter, set the third value as the corresponding drug dosage change gradient, and continuously increase the drug dosage until the value is less than or equal to the corresponding standard value of the qualified wastewater parameter, then stop changing the drug dosage.

[0087] Iterate through the values ​​in each simulation result and loop through the above logic to obtain the first adjusted operating parameters, and construct the first mapping relationship between the first adjusted operating parameters and the original wastewater parameters;

[0088] By inputting wastewater parameters into the first mapping relationship, the first adjusted operating parameters corresponding to the wastewater parameters are obtained.

[0089] In practice, by comparing the simulated values ​​with the standard values ​​for qualified wastewater parameters, the required drug dosage can be accurately identified, ensuring that all indicators after wastewater treatment meet the standard requirements. When the simulated values ​​exceed the standard values, the system will continuously increase the drug dosage until the values ​​meet the standard. This continuous control mechanism can effectively improve the compliance rate of wastewater treatment. The drug dosage change gradient is set according to the third value. This gradient adjustment can avoid excessive increase of drug dosage and reduce waste of agents. The system iterates through the values ​​in each simulated result and loops the above logic. This looping mechanism can ensure that the system can effectively control parameters under different operating conditions. Operators can set the third value, i.e., the drug dosage change gradient, according to the actual situation. The system can flexibly adjust the operating parameters according to these settings.

[0090] The logic for setting the trajectory of the robotic arm based on the simulation results in the optimization module includes:

[0091] Obtain the first distance corresponding to each blocking key point, and calculate the second distance from the moving key point to each blocking key point at this time;

[0092] Set the first planning condition to be that the first distance is greater than any second distance;

[0093] The second planning condition is set to the trajectory curve passing through key coordinate points;

[0094] Set the third programming condition to the point that the trajectory curve has no poles;

[0095] The trajectory curves are obtained using mathematical software;

[0096] Based on each trajectory curve and the drug delivery time point, power consumption simulation is performed to obtain power consumption simulation results, which are expressed as the total power consumed by the robotic arm when the processing is completed.

[0097] In practice, by calculating the distances from the moving key points to each blocking key point and setting the first planning condition as a first distance greater than any second distance, it can be ensured that the robotic arm avoids obstacles during operation, reducing the risk of collision. Setting the third planning condition as the trajectory curve has no extreme points can prevent the robotic arm from suddenly turning or stopping during movement, improving the smoothness and safety of operation. Setting the second planning condition as the trajectory curve passes through key coordinate points can ensure that the robotic arm operates according to the predetermined path, improving the accuracy and efficiency of drug dispensing. By obtaining each trajectory curve through mathematical software, precise trajectory planning can be achieved, ensuring that the movement trajectory of the robotic arm reaches the optimal level. Based on each trajectory curve and the drug dispensing time point, power consumption simulation can be performed to predict the energy consumption under different trajectories, select the optimal trajectory to reduce energy consumption, and continuously optimize the trajectory curve and operating parameters based on the results of each operation to achieve continuous improvement and performance enhancement.

[0098] The logic for selecting the optimal trajectory based on electricity consumption simulation results includes:

[0099] Iterate through each total power, sort the total power in ascending order, and select the trajectory curve corresponding to the total power with the smallest value;

[0100] Set the trajectory curve as the optimal trajectory;

[0101] The wastewater to be treated is processed based on the optimal trajectory and the operating parameters after the first adjustment.

[0102] This invention uses a modular approach to represent the key points of a robotic arm in coordinates, enabling high-precision dosing operations. The robotic arm can accurately deliver chemicals according to a set trajectory and position, avoiding errors from manual operation. It can automatically complete the dosing operation based on simulation results and optimized trajectories, reducing manual intervention and improving dosing efficiency. Simulations based on wastewater parameters and historical operation data optimize dosing dosage and operating parameters. Adjusting operating parameters based on simulation results ensures optimal chemical usage and avoids waste. Selecting the optimal trajectory through power consumption simulation not only optimizes energy consumption for dosing operations but also reduces operating costs. The dynamic control mechanism ensures that wastewater treatment results consistently meet standards, improving the stability of effluent quality. Automatic adjustment of operating parameters based on different wastewater parameters and operating conditions adapts to various complex wastewater treatment scenarios. The application of the robotic arm and intelligent auxiliary system reduces the necessity of manual operation and lowers labor intensity, effectively protecting the health of operators, especially in harsh working environments.

[0103] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A mechanical assistant intelligent auxiliary system for chemical dosing in wastewater treatment, characterized in that, It includes a building module, a simulation module, and an optimization module; The construction module transforms the coordinate system and represents the key points of the robotic arm based on the transformed coordinate system to obtain the key coordinates; The logic for transforming the coordinate system includes: Using any corner point at the bottom of the factory as the origin, and the sides adjacent to the origin as the x-axis, y-axis, and z-axis, a three-dimensional coordinate system is constructed. Based on the transformation relationship between the world coordinate system and the camera coordinate system, each point in the three-dimensional coordinate system is converted into a point in the camera coordinate system. Based on the relationship between the camera coordinate system and the image physical coordinate system, the points in the camera coordinate system are projected from perspective into points in the image physical coordinate system. Based on the transformation relationship between the image physical coordinate system and the pixel coordinate system, the points in the image physical coordinate system are converted into points in the pixel coordinate system. The first correspondence between the coordinate points in the three-dimensional coordinate system and the corresponding pixel coordinate points is established. The pixel coordinates refer to the coordinates of the pixel coordinate system in the image captured by the industrial camera. They are two-dimensional coordinates, and the coordinates of the pixel coordinate system are determined by the camera's intrinsic parameters, which include focal length, principal point, and distortion parameters. Input pixel coordinates into the first correspondence to obtain the corresponding coordinates in the three-dimensional coordinate system; Based on the first correspondence, the key coordinates of the robotic arm are set, including the coordinates of the movement key point and the coordinates of the blocking key point. The simulation module sets the operation parameters based on the knowledge graph and the original wastewater parameters. The knowledge graph represents the operation parameters corresponding to the wastewater parameters in the historical wastewater treatment process. The knowledge graph is constructed based on historical operation data. The wastewater treatment is simulated based on the operation parameters to obtain simulation results. The operation parameters are first adjusted based on the wastewater acceptance results, and a first mapping relationship between the first adjusted operation parameters and the original wastewater parameters is constructed. The simulation results include the sequence of key coordinate transit points and the wastewater acceptance results, which represent the parameters of the treated wastewater. The optimization module sets the trajectory of the robotic arm based on the simulation results to obtain the trajectory curve. It then performs a power consumption simulation based on the trajectory curve and the first adjusted operating parameters. Based on the power consumption simulation results, it selects the optimal trajectory and treats the wastewater to be treated based on the optimal trajectory and the first adjusted operating parameters. The logic for setting the trajectory of the robotic arm based on the simulation results in the optimization module includes: The key blocking points include the edge points of the drug storage tank, the edge points of the delivery pipeline, the edge points of the reaction tank fence, the edge points of the sedimentation tank fence, the edge points of the filter tank fence, the edge points of the mixer, the edge points of the blower, and the edge points of the operator. Each edge point is obtained by machine vision identifying the type of object and then performing edge detection on the identified object. Obtain the first distance corresponding to each blocking key point, and calculate the second distance from the moving key point to each blocking key point at this time; Set the first planning condition to be that the first distance is greater than any second distance; The second planning condition is set to the trajectory curve passing through key coordinate points; The sequence of key coordinate transit points is represented as the order in which the moving key point passes through any point around the blocking key point from its original position, starting from the operator's input of the dosing command. The surrounding points are represented by the first distance, which is the straight-line distance between the moving key point and the geometric center of the object represented by the blocking key point. Set the third programming condition to the point that the trajectory curve has no poles; The trajectory curves are obtained using mathematical software; Based on each trajectory curve and the drug delivery time point, power consumption simulation is performed to obtain power consumption simulation results, which are expressed as the total power consumed by the robotic arm when the processing is completed.

2. The intelligent auxiliary system for chemical dosing in wastewater treatment as described in claim 1, characterized in that: The key points of motion include the geometric center points of mechanical joints, mechanical links, and manipulators; Each reaction tank, sedimentation tank, and filtration tank is equipped with a corresponding robotic arm, reagent storage tank, conveying pipeline, mixer, blower, and operator.

3. The intelligent auxiliary system for chemical dosing in wastewater treatment as described in claim 1, characterized in that: The logic for object type identification includes: Obtain standard images of each object, which are retrieved from the task image database; The operator collects images of N faces of each standard object, sets them as the corresponding object names, and enters them into the work image database. First shape features are extracted from the standard image, and second shape features are extracted from the image of the object to be identified. The similarity between the first and second features is calculated using the cosine similarity formula. A first value is set as the similarity threshold. The similarity is compared with the first value. When the similarity is greater than or equal to the first value, the name of the object in the standard image is set as the name of the object to be identified. When the similarity is less than the first value, the process jumps to the next standard image of the standard object and repeats the logic of comparing similarity. When the similarity of any standard image of the standard object is greater than or equal to the first value, the name of the object in the standard image is set as the name of the object to be identified. When all standard images of the standard object have been traversed, and the similarity of all standard images of the standard object is less than the first value, jump to any standard image of the next standard object and repeat the above logic until the name of the object to be identified is set, then stop the identification operation.

4. The intelligent auxiliary system for chemical dosing in wastewater treatment as described in claim 1, characterized in that: The wastewater parameters include carbon-to-nitrogen ratio, phosphorus concentration, pH value, and particle size and concentration of impurities. The operational parameters include drug type, drug dosage, and drug administration time. Obtain the standard values ​​for qualified wastewater parameters, which are obtained through water quality treatment standards.

5. The intelligent auxiliary system for chemical dosing in wastewater treatment as described in claim 4, characterized in that: The knowledge graph is specifically represented as follows: The types of drugs corresponding to the carbon-nitrogen ratio include acetic acid, sodium acetate, and methanol. The timing of drug administration for the carbon-nitrogen ratio is before the denitrification stage. The formula for calculating the drug dosage for the carbon-nitrogen ratio is: Dosage = (TN concentration - BOD5 concentration) / BOD5 equivalent of carbon source; The types of drugs corresponding to the phosphorus concentration include aluminum sulfate, polyaluminum chloride, ferric chloride, and polyferric sulfate. The time point for drug addition corresponding to the phosphorus concentration is before the reaction tank starts working. The dosage of the drug corresponding to the phosphorus concentration is a molar ratio of total phosphorus in the drug to total phosphorus in the wastewater of 1.5 to 3.

0. The types of drugs corresponding to the pH value include sulfuric acid, hydrochloric acid, lime, and sodium hydroxide. The timing of drug addition is before the sedimentation tank begins operation. The dosage of the drug is to adjust the acidity or alkalinity of the wastewater to the target pH value, which is preset by the operator. The types of drugs corresponding to the size and concentration of impurity particles include polyaluminum chloride and polyacrylamide. The timing of drug addition is before the sedimentation tank starts working. The dosage of drugs is adjusted to the target size and concentration of impurity particles in the wastewater. The target size and concentration of impurity particles are preset by the operator.

6. The intelligent auxiliary system for chemical dosing in wastewater treatment as described in claim 1, characterized in that: Wastewater treatment is simulated using simulation software. Wastewater parameters are input into the simulation software, and process parameters are set, including the size of each treatment tank, retention time, aeration rate, and reflux ratio. Operating conditions are set, including internal reflux flow rate, external reflux flow rate, sludge discharge rate, and dissolved oxygen control rate. Modules are built by dragging and dropping modules and connecting them with lines. Simulation conditions are set, including simulation step size and iteration accuracy. After the first time period, simulation results are obtained.

7. The intelligent auxiliary system for chemical dosing in wastewater treatment as described in claim 6, characterized in that: The logic for initial adjustment of operational parameters based on wastewater acceptance results includes: Obtain the simulation results, compare any value of the simulation results with the corresponding standard value of the qualified wastewater parameter, and when the value is greater than the corresponding standard value of the qualified wastewater parameter, set the third value as the corresponding drug dosage change gradient, and continuously increase the drug dosage until the value is less than or equal to the corresponding standard value of the qualified wastewater parameter, then stop changing the drug dosage. Iterate through the values ​​in each simulation result and loop through the above logic to obtain the first adjusted operating parameters, and construct the first mapping relationship between the first adjusted operating parameters and the original wastewater parameters; By inputting wastewater parameters into the first mapping relationship, the first adjusted operating parameters corresponding to the wastewater parameters are obtained.

8. The intelligent auxiliary system for chemical dosing in wastewater treatment as described in claim 1, characterized in that: The logic for selecting the optimal trajectory based on electricity consumption simulation results includes: Iterate through each total power, sort the total power in ascending order, and select the trajectory curve corresponding to the total power with the smallest value; Set the trajectory curve as the optimal trajectory; The wastewater to be treated is processed based on the optimal trajectory and the operating parameters after the first adjustment.

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