Mechanical assistant intelligent auxiliary system for dosing in sewage treatment

Abstract

The invention discloses a mechanical assistant intelligent auxiliary system for sewage treatment dosing, and relates to the technical field of intelligent control, the mechanical assistant intelligent auxiliary system comprises a construction module, a simulation module and an optimization module, conversion is carried out to obtain key coordinates, operation parameters are set, sewage treatment is simulated, the operation parameters are subjected to first regulation and control, and a first mapping relation is constructed; and performing power utilization simulation, and selecting an optimal track. The key points of the mechanical arm are subjected to coordinate representation through the construction module, high-precision chemical adding operation can be achieved, chemical adding operation can be automatically completed according to the simulation result and the optimized track, chemical adding efficiency is improved, operation parameters are regulated and controlled through the simulation result, chemical waste is avoided, the optimal track is selected through electricity utilization simulation, and chemical adding efficiency is improved. The operation cost is reduced, the operation parameters are automatically adjusted according to different sewage parameters and working conditions, the system adapts to various complex sewage treatment scenes, and the health of operators can be effectively protected.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control, and in particular to a mechanical assistant intelligent auxiliary system for sewage treatment dosing. Background Art

[0002] In recent years, some advanced intelligent dosing systems are not limited to the addition of a single agent, but can also comprehensively consider multiple water quality parameters. Through big data analysis and mathematical models, they can realize intelligent adjustment of parameters such as the aeration volume and sludge return volume of sewage treatment plants, thereby reducing production and operation costs. Intelligent dosing control technology will not only be limited to single links such as phosphorus removal and flocculation, but will also be extended to other treatment links such as nitrogen removal and ammonia removal in sewage treatment plants, and even intelligent control can be achieved in sewage collection systems.

[0003] At present, in the Chinese invention patent with publication number CN111571583A, a manipulator control method is disclosed. The method receives the actual operation instruction for the physical manipulator input by the user, identifies the instruction category, the brand and model of the manipulator in the actual operation instruction and forms a unique instruction identifier of the actual operation instruction, matches the unique instruction identifier with the preset rules and forms a virtual instruction, judges whether the virtual instruction is correctly executed by the physical manipulator, and if the judgment result is yes, directly outputs the current state and position information of the physical manipulator, otherwise the virtual instruction is formed into a unified instruction command according to the preset format and sent To the virtual manipulator and execute, based on the unified instruction command, a feedback mark is formed, and the execution status and position information of the virtual manipulator are formed according to the feedback mark, and sent to the human-machine interface. However, the relevant technology does not accurately represent the points in space based on the conversion relationship between the image formed by the industrial camera and the actual coordinates, which is not conducive to the accuracy of the manipulator control. The manipulator trajectory is not planned according to obstacles and passing points, which may easily cause collisions or bumps, which is not conducive to the safety of the operation. The manipulator trajectory is not planned in detail according to the energy-saving performance, which is not conducive to the economy of the operation, energy saving and environmental protection, and has certain limitations. Summary of the Invention

[0004] The technical problem solved by the present invention is: the related technology does not accurately represent the points in space based on the conversion relationship between the image formed by the industrial camera and the actual coordinates, which is not conducive to the accuracy of the robot arm control; the robot arm trajectory is not planned according to obstacles and waypoints, which easily leads to collisions or bumps, which is not conducive to the safety of the operation; the robot arm trajectory is not planned in detail according to the energy-saving performance, which is not conducive to the economy of the operation, energy saving and environmental protection, and has certain limitations.

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

[0006] The construction module transforms the coordinate system, represents the key points of the robotic arm according to the transformed coordinate system, and obtains key coordinates;

[0007] The simulation module sets the operation parameters according to the knowledge spectrum and the sewage parameters, the knowledge spectrum is constructed according to the historical operation data, simulates the sewage treatment according to the operation parameters, obtains the simulation results, performs a first adjustment on the operation parameters according to the sewage acceptance results, and constructs a first mapping relationship between the first adjusted operation parameters and the sewage parameters;

[0008] The optimization module sets the trajectory of the robotic arm according to the simulation results to obtain a trajectory curve, performs power consumption simulation according to the trajectory curve and the operating parameters, selects the optimal trajectory according to the power consumption simulation results, and treats the wastewater to be treated according to the optimal trajectory and the operating parameters after the first adjustment.

[0009] As a preferred solution of the mechanical assistant intelligent auxiliary system for sewage treatment dosing described in the present invention, the coordinate system conversion logic includes:

[0010] Taking any corner point at the bottom of the factory building 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, obtaining a first line connecting the geometric center of the lens and the geometric center of the body of the industrial camera, obtaining a first plane where the first line is located, and obtaining a second plane where any two axes of the three-dimensional coordinate system are located, identifying the angle between the first plane and each second plane, selecting a second plane corresponding to an angle less than 90 degrees, setting the angle between the second plane and the first plane as a conversion angle, converting each point in the three-dimensional coordinate system into a point in the camera coordinate system according to the transformation relationship between the world coordinate system and the camera coordinate system, perspectively projecting the points in the camera coordinate system into points in the image physical coordinate system according to the relationship between the camera coordinate system and the image physical coordinate system, and converting the points in the image physical coordinate system into points in the pixel coordinate system according to the transformation relationship between the image physical coordinate system and the pixel coordinate system, and establishing a first correspondence between the coordinate points of the three-dimensional coordinate system and the corresponding pixel coordinate points;

[0011] The pixel coordinate point represents the coordinate point of the pixel in the picture taken by the industrial camera, which is a two-dimensional coordinate point. The coordinate point of the pixel is determined by the intrinsic parameters of the camera, and the intrinsic parameters of the camera include focal length, principal point and distortion parameters;

[0012] Inputting a pixel coordinate point into the first corresponding relationship to obtain a corresponding coordinate point in a three-dimensional coordinate system;

[0013] According to the first corresponding relationship, key coordinates of the robot arm are set, where the key coordinates include moving key point coordinates and blocking key point coordinates.

[0014] As a preferred solution of the mechanical assistant intelligent auxiliary system for sewage treatment dosing described in the present invention, wherein: the key points of movement include the geometric center point of the mechanical joint, the geometric center point of the mechanical connecting rod, and the geometric center point of the manipulator;

[0015] The key blocking points include the edge points of the reagent storage tank, the edge points of the conveying pipeline, the edge points of the reaction pool fence, the edge points of the sedimentation tank fence, the edge points of the filter pool 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 performing edge detection on the identified object after the machine vision recognizes the type of object;

[0016] Corresponding robotic arms, reagent storage tanks, conveying pipelines, mixers, blowers and operators are installed next to the reaction tank, sedimentation tank and filtration tank.

[0017] As a preferred solution of the mechanical assistant intelligent auxiliary system for sewage treatment dosing described in the present invention, the logic of object type recognition includes:

[0018] Obtaining a standard image of each object, wherein the standard image is obtained from a job image database;

[0019] The operator collects N pictures of each standard object, sets them as the names of the corresponding objects, and enters them into the operation picture database;

[0020] Extracting a first shape feature of the standard image, extracting a second shape feature of the image of the object to be identified, calculating the similarity of the first feature and the second feature using a cosine similarity formula, setting a first value as a similarity threshold, comparing the similarity with the first value, and when the similarity is greater than or equal to the first value, setting the name of the object in the standard image as the name of the object to be identified; when the similarity is less than the first value, jumping to the next standard image of the standard object and repeating the logic of comparing similarities; and when there is any standard image of the standard object whose similarity is greater than or equal to the first value, setting the name of the object in the standard image as the name of the object to be identified;

[0021] When all the standard pictures of the standard objects are traversed and the similarities of all the standard pictures of the standard objects are less than the first value, jump to any standard picture of the next standard object and repeat the above logic until the name of the object to be identified is set, and then stop the recognition operation.

[0022] As a preferred solution of the mechanical assistant intelligent auxiliary system for sewage treatment dosing described in the present invention, wherein: the knowledge spectrum represents the operating parameters corresponding to the sewage parameters in the historical sewage treatment process;

[0023] The wastewater parameters include carbon-nitrogen ratio, phosphorus concentration, pH value and impurity particle size and concentration;

[0024] The operation parameters include drug type, drug dosage and drug delivery time point;

[0025] The simulation results include a sequence of key coordinate waypoints and a sewage acceptance result. The sewage acceptance result represents the parameters of the treated sewage, and the qualified standard values ​​of the sewage parameters are obtained. The qualified standard values ​​of the sewage parameters are obtained according to the water quality treatment standards.

[0026] The sequence of the key coordinate passing points is represented by the order in which the moving key point passes through any point around the blocking key point from the original position since the operator inputs the dosing instruction, and the surrounding is represented by the straight-line distance between the moving key point and the geometric center point of the object represented by the blocking key point as the first distance.

[0027] As a preferred solution of the mechanical assistant intelligent auxiliary system for sewage treatment dosing described in the present invention, the knowledge spectrum is specifically represented as follows:

[0028] The types of drugs corresponding to the carbon-nitrogen ratio include acetic acid, sodium acetate and methanol. The drug addition time point corresponding to the carbon-nitrogen ratio is before the denitrification stage. The calculation expression of the drug dosage corresponding to the carbon-nitrogen ratio is:

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

[0030] The types of drugs corresponding to phosphorus concentration include aluminum sulfate, polyaluminum chloride, ferric chloride and polyferric sulfate. The drug addition time point corresponding to phosphorus concentration is before the reaction tank. The drug dosage corresponding to phosphorus concentration is the 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 time point for adding the drugs corresponding to the pH value is before the sedimentation tank. The dosage of the drugs corresponding to the pH value is to adjust the pH of the sewage to the target pH value, which is preset by the operator;

[0032] The types of drugs corresponding to the impurity particle size and concentration include polyaluminum chloride and polyacrylamide. The time point for adding the drugs corresponding to the impurity particle size and concentration is to add them in front of the sedimentation tank. The drug dosage corresponding to the impurity particle size and concentration is to adjust the impurity particle size and concentration of the sewage to the target impurity particle size and target impurity particle concentration. The target impurity particle size and target impurity particle concentration are preset by the operator.

[0033] As a preferred solution of the mechanical assistant intelligent auxiliary system for sewage treatment dosing described in the present invention, sewage treatment is simulated by simulation software, sewage parameters are input into the simulation software, process parameters are set, the process parameters include the size of each treatment tank, residence time, aeration volume and reflux ratio, operating conditions are set, the operating conditions include internal reflux volume, external reflux volume, sludge discharge volume and dissolved oxygen control rate, module construction is completed by dragging modules and connecting lines, simulation conditions are set, the simulation conditions include simulation step size and iteration accuracy, and simulation results are obtained after the first time period.

[0034] As a preferred embodiment of the mechanical assistant intelligent auxiliary system for sewage treatment dosing according to the present invention, the logic for first regulating the operation parameters according to the sewage acceptance result includes:

[0035] Acquiring a simulation result, comparing any numerical value of the simulation result with a corresponding standard numerical value of a qualified sewage parameter, and when the numerical value is greater than the corresponding standard numerical value of the qualified sewage parameter, setting a third value as a change gradient of the corresponding drug dosage, continuously increasing the drug dosage until the numerical value is less than or equal to the corresponding standard numerical value of the qualified sewage parameter, and stopping changing the drug dosage;

[0036] Traversing the values ​​in each simulation result and looping the above logic to obtain the first regulated operating parameters, and constructing a first mapping relationship between the first regulated operating parameters and the sewage parameters;

[0037] By inputting sewage parameters into the first mapping relationship, first regulated operation parameters corresponding to the sewage parameters are obtained.

[0038] As a preferred solution of the mechanical assistant intelligent auxiliary system for sewage treatment dosing described in the present invention, the logic of the optimization module for setting the trajectory of the robotic arm according to the simulation results includes:

[0039] Get the first distance corresponding to each blocking key point, and calculate the second distance from the moving key point to each blocking key point;

[0040] Setting the first planning condition as the first distance being greater than any second distance;

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

[0042] The third planning condition is set to that the trajectory curve does not have a pole;

[0043] Each trajectory curve is obtained through mathematical software;

[0044] The power consumption simulation is performed according to each trajectory curve and the drug delivery time point to obtain the power consumption simulation result, which is represented by the total power consumed by the robot arm when the processing is completed.

[0045] As a preferred solution of the mechanical assistant intelligent auxiliary system for sewage treatment dosing described in the present invention, the logic of selecting the optimal trajectory according to the power consumption simulation results includes:

[0046] Traverse 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] Setting the trajectory curve to an optimal trajectory;

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

[0049] The beneficial effects of the present invention are as follows: by constructing modules to represent the coordinates of the key points of the robotic arm, high-precision dosing operations can be achieved, the robotic arm can accurately deliver the agent according to the set trajectory and position, avoiding errors in manual operation, and can automatically complete the dosing operation according to the simulation results and the optimized trajectory, reducing manual intervention and improving dosing efficiency. Simulation is performed according to sewage parameters and historical operation data to optimize the dosing dosage and operating parameters. The operating parameters are regulated by the simulation results to ensure that the dosage of the agent achieves the best effect and avoid waste of the agent. The optimal trajectory is selected by power simulation, which not only optimizes the energy consumption of the dosing operation, but also reduces the operating cost. The dynamic control mechanism can ensure that the sewage treatment effect always meets the standards and improves the stability of the effluent quality. The operating parameters are automatically adjusted according to different sewage parameters and working conditions to adapt to various complex sewage treatment scenarios. The application of the robotic arm and the intelligent auxiliary system reduces the necessity of manual operation and reduces labor intensity, especially in harsh working environments, which can effectively protect the health of operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of the basic flow of a mechanical assistant intelligent assistance system for sewage treatment dosing provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0052] Example, see Figure 1, as an embodiment of the present invention, provides a mechanical assistant intelligent assistance system for sewage treatment dosing, including a construction module, a simulation module and an optimization module;

[0053] The construction module transforms the coordinate system, represents the key points of the robotic arm according to the transformed coordinate system, and obtains key coordinates;

[0054] The simulation module sets the operation parameters according to the knowledge spectrum and the sewage parameters, the knowledge spectrum is constructed according to the historical operation data, simulates the sewage treatment according to the operation parameters, obtains the simulation results, performs a first adjustment on the operation parameters according to the sewage acceptance results, and constructs a first mapping relationship between the first adjusted operation parameters and the sewage parameters;

[0055] The optimization module sets the trajectory of the robotic arm according to the simulation results to obtain a trajectory curve, performs power consumption simulation according to the trajectory curve and the operating parameters, selects the optimal trajectory according to the power consumption simulation results, and treats the wastewater to be treated according to the optimal trajectory and the operating parameters after the first adjustment.

[0056] The present invention uses a construction module to represent the coordinates of the key points of the robotic arm, which can achieve high-precision dosing operations. The robotic arm can accurately deliver the agent according to the set trajectory and position, avoiding errors in manual operation. The dosing operation can be automatically completed according to the simulation results and the optimized trajectory, reducing manual intervention and improving dosing efficiency. Simulation is performed according to sewage parameters and historical operation data to optimize the dosing dosage and operating parameters. The operating parameters are regulated by the simulation results to ensure that the dosage of the agent achieves the best effect and avoid waste of the agent. The optimal trajectory is selected by power simulation, which not only optimizes the energy consumption of the dosing operation, but also reduces the operating cost. The dynamic control mechanism can ensure that the sewage treatment effect always meets the standard and improves the stability of the effluent quality. The operating parameters are automatically adjusted according to different sewage parameters and working conditions to adapt to various complex sewage treatment scenarios. The application of the robotic arm and the intelligent auxiliary system reduces the necessity of manual operation and reduces labor intensity, especially in harsh working environments, which can effectively protect the health of operators.

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

[0058] Taking any corner point at the bottom of the factory building 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, obtaining a first line connecting the geometric center of the lens and the geometric center of the body of the industrial camera, obtaining a first plane where the first line is located, and obtaining a second plane where any two axes of the three-dimensional coordinate system are located, identifying the angle between the first plane and each second plane, selecting a second plane corresponding to an angle less than 90 degrees, setting the angle between the second plane and the first plane as a conversion angle, converting each point in the three-dimensional coordinate system into a point in the camera coordinate system according to the transformation relationship between the world coordinate system and the camera coordinate system, perspectively projecting the points in the camera coordinate system into points in the image physical coordinate system according to the relationship between the camera coordinate system and the image physical coordinate system, and converting the points in the image physical coordinate system into points in the pixel coordinate system according to the transformation relationship between the image physical coordinate system and the pixel coordinate system, and establishing a first correspondence between the coordinate points of the three-dimensional coordinate system and the corresponding pixel coordinate points;

[0059] The pixel coordinate point represents the coordinate point of the pixel in the picture taken by the industrial camera, which is a two-dimensional coordinate point. The coordinate point of the pixel is determined by the intrinsic parameters of the camera, and the intrinsic parameters of the camera include focal length, principal point and distortion parameters;

[0060] Inputting a pixel coordinate point into the first corresponding relationship to obtain a corresponding coordinate point in a three-dimensional coordinate system;

[0061] According to the first corresponding relationship, key coordinates of the robot arm are set, where the key coordinates include moving key point coordinates and blocking key point coordinates.

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

[0063] The key blocking points include the edge points of the reagent storage tank, the edge points of the conveying pipeline, the edge points of the reaction pool fence, the edge points of the sedimentation tank fence, the edge points of the filter pool 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 performing edge detection on the identified object after the machine vision recognizes the type of object;

[0064] Corresponding robotic arms, reagent storage tanks, conveying pipelines, mixers, blowers and operators are installed next to the reaction tank, sedimentation tank and filtration tank.

[0065] In practice, precise mapping from space to image is achieved by converting points in a 3D coordinate system into points in the camera coordinate system, and then further into points in the image physical coordinate system and pixel coordinate system. This multi-stage conversion ensures high-precision positioning of the target position during operation by the robotic arm. Ultimately, the 3D coordinates are converted into pixel coordinates. Leveraging the high-resolution imaging capabilities of industrial cameras, positioning accuracy of millimeters or even higher can be achieved, which is crucial for precise dosing during dosing operations. By inputting pixel coordinates, the corresponding 3D coordinates can be retrieved in real time, enabling dynamic adjustment of the robotic arm's operation. This real-time feedback mechanism can adapt to complex operating environments and changing working conditions. For example, during dosing, adjustments can be made based on the placement and effect of the drug, as well as to different plant structures and equipment layouts. It is highly versatile and adaptable. Regardless of the shape and size of the plant, the 3D coordinate system can be constructed by selecting the appropriate origin and coordinate axes, thereby achieving precise operation of the robotic arm. By setting the coordinates of key blocking 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 conversion and robotic arm operation, the reagent can be precisely delivered to the designated location, ensuring thorough mixing of the reagent and sewage, thereby improving dosing effectiveness. This precise delivery reduces reagent waste while improving the efficiency and quality of sewage treatment.

[0066] The logic of object type recognition includes:

[0067] Obtaining a standard image of each object, wherein the standard image is obtained from a job image database;

[0068] The operator collects N pictures of each standard object, sets them as the names of the corresponding objects, and enters them into the operation picture database;

[0069] Extracting a first shape feature of the standard image, extracting a second shape feature of the image of the object to be identified, calculating the similarity of the first feature and the second feature using a cosine similarity formula, setting a first value as a similarity threshold, comparing the similarity with the first value, and when the similarity is greater than or equal to the first value, setting the name of the object in the standard image as the name of the object to be identified; when the similarity is less than the first value, jumping to the next standard image of the standard object and repeating the logic of comparing similarities; and when there is any standard image of the standard object whose similarity is greater than or equal to the first value, setting the name of the object in the standard image as the name of the object to be identified;

[0070] When all the standard pictures of the standard objects are traversed and the similarities of all the standard pictures of the standard objects are less than the first value, jump to any standard picture of the next standard object and repeat the above logic until the name of the object to be identified is set, and then stop the recognition operation.

[0071] In specific implementations, operators collect images of multiple sides of each standard object, assign them corresponding object names, and enter them into the job image database. This multi-angle image acquisition method comprehensively covers all object features, improving recognition accuracy. Shape features are extracted from 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 object's shape features, ensuring the reliability of the recognition results. By setting a similarity threshold (the first value), the strictness of the 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 comparison proceeds to the next standard object. This dynamic adjustment mechanism effectively prevents 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 and avoid recognition failures caused by mismatches in 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. It is highly adaptable and scalable, and the entire recognition process is automated, reducing the need for manual intervention. Operators only need to collect standard pictures and enter them into the database, and the system can automatically complete object recognition, thereby improving work efficiency.

[0072] The knowledge spectrum represents the operation parameters corresponding to the sewage parameters in the historical sewage treatment process;

[0073] The wastewater parameters include carbon-nitrogen ratio, phosphorus concentration, pH value and impurity particle size and concentration;

[0074] The operation parameters include drug type, drug dosage and drug delivery time point;

[0075] The simulation results include a sequence of key coordinate waypoints and a sewage acceptance result. The sewage acceptance result represents the parameters of the treated sewage, and the qualified standard values ​​of the sewage parameters are obtained. The qualified standard values ​​of the sewage parameters are obtained according to the water quality treatment standards.

[0076] The sequence of the key coordinate passing points is represented by the order in which the moving key point passes through any point around the blocking key point from the original position since the operator inputs the dosing instruction, and the surrounding is represented by the straight-line distance between the moving key point and the geometric center point of the object represented by the blocking key point as the first distance.

[0077] The knowledge spectrum graph is specifically expressed as:

[0078] The types of drugs corresponding to the carbon-nitrogen ratio include acetic acid, sodium acetate and methanol. The drug addition time point corresponding to the carbon-nitrogen ratio is before the denitrification stage. The calculation expression of the drug dosage corresponding to the carbon-nitrogen ratio is:

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

[0080] The types of drugs corresponding to phosphorus concentration include aluminum sulfate, polyaluminum chloride, ferric chloride and polyferric sulfate. The drug addition time point corresponding to phosphorus concentration is before the reaction tank. The drug dosage corresponding to phosphorus concentration is the 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 time point for adding the drugs corresponding to the pH value is before the sedimentation tank. The dosage of the drugs corresponding to the pH value is to adjust the pH of the sewage to the target pH value, which is preset by the operator;

[0082] The types of drugs corresponding to the impurity particle size and concentration include polyaluminum chloride and polyacrylamide. The time point for adding the drugs corresponding to the impurity particle size and concentration is to add them in front of the sedimentation tank. The drug dosage corresponding to the impurity particle size and concentration is to adjust the impurity particle size and concentration of the sewage to the target impurity particle size and target impurity particle concentration. The target impurity particle size and target impurity particle concentration are preset by the operator.

[0083] In specific implementation, the knowledge graph clearly identifies the drug types corresponding to different water quality parameters (such as carbon-nitrogen ratio, phosphorus concentration, pH, and impurity particle size and concentration), ensuring scientific and targeted drug selection. The timing of each drug's administration is carefully designed to ensure optimal effectiveness. For example, drugs corresponding to the carbon-nitrogen ratio are added before the denitrification stage, while drugs corresponding to phosphorus concentration are added before the reaction tank. This helps improve drug utilization efficiency. The knowledge graph provides specific calculation methods or ratio ranges for drug dosage, eliminating the uncertainty of human experience. For example, drug dosage for the carbon-nitrogen ratio is calculated using a formula, while drug dosage for phosphorus concentration is controlled using a molar ratio. This quantitative management ensures the accuracy of drug dosage. Through precise drug administration and dosage control, the effectiveness and stability of wastewater treatment can be effectively improved. For example, by adjusting pH and impurity particle concentration, the operation of sedimentation tanks can be optimized and effluent quality can be improved. Appropriate timing and dosage control reduce unnecessary drug consumption and equipment operating time, thereby reducing system energy consumption. The knowledge graph, built based on historical operation data, provides the system with rich empirical data, helping it better respond to various situations and enhance operational intelligence.

[0084] Sewage treatment is simulated using simulation software. Sewage parameters are input into the simulation software, and process parameters are set, including the size, residence time, aeration volume, and reflow ratio of each treatment tank. Operating conditions are set, including internal reflow volume, external reflow volume, sludge discharge volume, and dissolved oxygen control rate. Module construction is completed by dragging modules and connecting lines, and simulation conditions are set, including simulation step size and iteration accuracy. After the first time period, simulation results are obtained.

[0085] The logic for the first regulation of operating parameters based on the sewage acceptance results includes:

[0086] Acquiring a simulation result, comparing any numerical value of the simulation result with a corresponding standard numerical value of a qualified sewage parameter, and when the numerical value is greater than the corresponding standard numerical value of the qualified sewage parameter, setting a third value as a change gradient of the corresponding drug dosage, continuously increasing the drug dosage until the numerical value is less than or equal to the corresponding standard numerical value of the qualified sewage parameter, and stopping changing the drug dosage;

[0087] Traversing the values ​​in each simulation result and looping the above logic to obtain the first regulated operating parameters, and constructing a first mapping relationship between the first regulated operating parameters and the sewage parameters;

[0088] By inputting sewage parameters into the first mapping relationship, first regulated operation parameters corresponding to the sewage parameters are obtained.

[0089] In specific implementation, by comparing the numerical value of the simulation result with the standard numerical value of the qualified sewage parameter, the drug dosage that needs to be adjusted can be accurately identified to ensure that the various indicators after sewage treatment meet the standard requirements. When the numerical value of the simulation result exceeds the standard value, the system will continuously increase the drug dosage until the numerical value meets the standard. This continuous control mechanism can effectively improve the compliance rate of sewage treatment. The drug dosage change gradient is set according to the third value. This gradient adjustment can avoid excessive increase in drug dosage and reduce waste of drugs. The numerical values ​​in each simulation result are traversed, and the above logic is looped. This loop mechanism can ensure that the system can perform effective parameter control under different working conditions. The operator can set the third value according to the actual situation, that is, the drug dosage change gradient, and the system can flexibly adjust the operating parameters according to these settings.

[0090] The logic of the optimization module for setting the trajectory of the robotic arm according to the simulation results includes:

[0091] Get the first distance corresponding to each blocking key point, and calculate the second distance from the moving key point to each blocking key point;

[0092] Setting the first planning condition as the first distance being greater than any second distance;

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

[0094] The third planning condition is set to that the trajectory curve does not have a pole;

[0095] Each trajectory curve is obtained through mathematical software;

[0096] The power consumption simulation is performed according to each trajectory curve and the drug delivery time point to obtain the power consumption simulation result, which is represented by the total power consumed by the robot arm when the processing is completed.

[0097] In the specific implementation, by calculating the distance from the moving key point to each blocking key point, and setting the first planning condition that the first distance is greater than any second distance, it can ensure that the robot arm avoids obstacles during operation and reduce the risk of collision. Setting the third planning condition that the trajectory curve has no extreme points can avoid sudden turns or stops of the robot arm during movement, thereby improving the smoothness and safety of operation. Setting the second planning condition that the trajectory curve passes through the key coordinate waypoint can ensure that the robot arm operates according to the predetermined path, improving the accuracy and efficiency of drug addition. By obtaining each trajectory curve through mathematical software, accurate trajectory planning can be achieved to ensure that the movement trajectory of the robot arm is optimal. According to the power consumption simulation of each trajectory curve and the time point of drug delivery, the energy consumption under different trajectories can be predicted, and the optimal trajectory can be selected to reduce energy consumption. The trajectory curve and operation parameters are continuously optimized according to the results of each operation to achieve continuous improvement and performance enhancement.

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

[0099] Traverse 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] Setting the trajectory curve to an optimal trajectory;

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

[0102] The present invention uses a construction module to represent the coordinates of the key points of the robotic arm, which can achieve high-precision dosing operations. The robotic arm can accurately deliver the agent according to the set trajectory and position, avoiding errors in manual operation. The dosing operation can be automatically completed according to the simulation results and the optimized trajectory, reducing manual intervention and improving dosing efficiency. Simulation is performed according to sewage parameters and historical operation data to optimize the dosing dosage and operating parameters. The operating parameters are regulated by the simulation results to ensure that the dosage of the agent achieves the best effect and avoid waste of the agent. The optimal trajectory is selected by power simulation, which not only optimizes the energy consumption of the dosing operation, but also reduces the operating cost. The dynamic control mechanism can ensure that the sewage treatment effect always meets the standard and improves the stability of the effluent quality. The operating parameters are automatically adjusted according to different sewage parameters and working conditions to adapt to various complex sewage treatment scenarios. The application of the robotic arm and the intelligent auxiliary system reduces the necessity of manual operation and reduces labor intensity, especially in harsh working environments, which can effectively protect the health of operators.

[0103] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may 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 may 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 read-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 memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple 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 the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A mechanical assistant intelligent auxiliary system for sewage treatment dosing, characterized in that: Includes building module, simulation module and optimization module; The construction module transforms the coordinate system, represents the key points of the robotic arm according to the transformed coordinate system, and obtains key coordinates; The simulation module sets the operation parameters according to the knowledge spectrum and the sewage parameters, the knowledge spectrum is constructed according to the historical operation data, simulates the sewage treatment according to the operation parameters, obtains the simulation results, performs a first adjustment on the operation parameters according to the sewage acceptance results, and constructs a first mapping relationship between the first adjusted operation parameters and the sewage parameters; The optimization module sets the trajectory of the robotic arm according to the simulation results to obtain a trajectory curve, performs power consumption simulation according to the trajectory curve and the operating parameters, selects the optimal trajectory according to the power consumption simulation results, and treats the wastewater to be treated according to the optimal trajectory and the operating parameters after the first adjustment.

2. The mechanical assistant intelligent auxiliary system for sewage treatment dosing according to claim 1, characterized in that: The logic for transforming the coordinate system includes: Taking any corner point at the bottom of the factory building 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, obtaining a first line connecting the geometric center of the lens and the geometric center of the body of the industrial camera, obtaining a first plane where the first line is located, and obtaining a second plane where any two axes of the three-dimensional coordinate system are located, identifying the angle between the first plane and each second plane, selecting a second plane corresponding to an angle less than 90 degrees, setting the angle between the second plane and the first plane as a conversion angle, converting each point in the three-dimensional coordinate system into a point in the camera coordinate system according to the transformation relationship between the world coordinate system and the camera coordinate system, perspectively projecting the points in the camera coordinate system into points in the image physical coordinate system according to the relationship between the camera coordinate system and the image physical coordinate system, and converting the points in the image physical coordinate system into points in the pixel coordinate system according to the transformation relationship between the image physical coordinate system and the pixel coordinate system, and establishing a first correspondence between the coordinate points of the three-dimensional coordinate system and the corresponding pixel coordinate points; The pixel coordinate point represents the coordinate point of the pixel in the picture taken by the industrial camera, which is a two-dimensional coordinate point. The coordinate point of the pixel is determined by the intrinsic parameters of the camera, and the intrinsic parameters of the camera include focal length, principal point and distortion parameters; Inputting a pixel coordinate point into the first corresponding relationship to obtain a corresponding coordinate point in a three-dimensional coordinate system; According to the first corresponding relationship, key coordinates of the robot arm are set, where the key coordinates include moving key point coordinates and blocking key point coordinates.

3. The mechanical assistant intelligent auxiliary system for sewage treatment dosing according to claim 2, characterized in that: The key points of movement include the geometric center points of the mechanical joints, the geometric center points of the mechanical links, and the geometric center points of the manipulator; The key blocking points include the edge points of the reagent storage tank, the edge points of the conveying pipeline, the edge points of the reaction pool fence, the edge points of the sedimentation tank fence, the edge points of the filter pool 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 performing edge detection on the identified object after the machine vision recognizes the type of object; Corresponding robotic arms, reagent storage tanks, conveying pipelines, mixers, blowers and operators are installed next to the reaction tank, sedimentation tank and filtration tank.

4. The mechanical assistant intelligent auxiliary system for sewage treatment dosing according to claim 3, characterized in that: The logic of object type recognition includes: Obtaining a standard image of each object, wherein the standard image is obtained from a job image database; The operator collects N pictures of each standard object, sets them as the names of the corresponding objects, and enters them into the operation picture database; Extracting a first shape feature of the standard image, extracting a second shape feature of the image of the object to be identified, calculating the similarity of the first feature and the second feature using a cosine similarity formula, setting a first value as a similarity threshold, comparing the similarity with the first value, and when the similarity is greater than or equal to the first value, setting the name of the object in the standard image as the name of the object to be identified; when the similarity is less than the first value, jumping to the next standard image of the standard object and repeating the logic of comparing similarities; and when there is any standard image of the standard object whose similarity is greater than or equal to the first value, setting the name of the object in the standard image as the name of the object to be identified; When all the standard pictures of the standard objects are traversed and the similarities of all the standard pictures of the standard objects are less than the first value, jump to any standard picture of the next standard object and repeat the above logic until the name of the object to be identified is set, and then stop the recognition operation.

5. The mechanical assistant intelligent auxiliary system for sewage treatment dosing according to claim 1, characterized in that: The knowledge spectrum represents the operation parameters corresponding to the sewage parameters in the historical sewage treatment process; The wastewater parameters include carbon-nitrogen ratio, phosphorus concentration, pH value and impurity particle size and concentration; The operation parameters include drug type, drug dosage and drug delivery time point; The simulation results include a sequence of key coordinate waypoints and a sewage acceptance result. The sewage acceptance result represents the parameters of the treated sewage, and the qualified standard values ​​of the sewage parameters are obtained. The qualified standard values ​​of the sewage parameters are obtained according to the water quality treatment standards. The sequence of the key coordinate passing points is represented by the order in which the moving key point passes through any point around the blocking key point from the original position since the operator inputs the dosing instruction, and the surrounding is represented by the straight-line distance between the moving key point and the geometric center point of the object represented by the blocking key point as the first distance.

6. The mechanical assistant intelligent auxiliary system for sewage treatment dosing according to claim 5, characterized in that: The knowledge spectrum graph is specifically expressed as: The types of drugs corresponding to the carbon-nitrogen ratio include acetic acid, sodium acetate and methanol. The drug addition time point corresponding to the carbon-nitrogen ratio is before the denitrification stage. The calculation expression of the drug dosage corresponding to the carbon-nitrogen ratio is: Dosage = (TN concentration - BOD5 concentration) / carbon source BOD5 equivalent; The types of drugs corresponding to phosphorus concentration include aluminum sulfate, polyaluminum chloride, ferric chloride and polyferric sulfate. The drug addition time point corresponding to phosphorus concentration is before the reaction tank. The drug dosage corresponding to phosphorus concentration is the 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 time point for adding the drugs corresponding to the pH value is before the sedimentation tank. The dosage of the drugs corresponding to the pH value is to adjust the pH of the sewage to the target pH value, which is preset by the operator; The types of drugs corresponding to the impurity particle size and concentration include polyaluminum chloride and polyacrylamide. The time point for adding the drugs corresponding to the impurity particle size and concentration is to add them in front of the sedimentation tank. The drug dosage corresponding to the impurity particle size and concentration is to adjust the impurity particle size and concentration of the sewage to the target impurity particle size and target impurity particle concentration. The target impurity particle size and target impurity particle concentration are preset by the operator.

7. The mechanical assistant intelligent auxiliary system for sewage treatment dosing according to claim 1, characterized in that: Sewage treatment is simulated using simulation software. Sewage parameters are input into the simulation software, and process parameters are set, including the size, residence time, aeration volume, and reflow ratio of each treatment tank. Operating conditions are set, including internal reflow volume, external reflow volume, sludge discharge volume, and dissolved oxygen control rate. Module construction is completed by dragging modules and connecting lines, and simulation conditions are set, including simulation step size and iteration accuracy. After the first time period, simulation results are obtained.

8. The mechanical assistant intelligent auxiliary system for sewage treatment dosing according to claim 7, characterized in that: The logic for the first regulation of operating parameters based on the sewage acceptance results includes: Acquiring a simulation result, comparing any numerical value of the simulation result with a corresponding standard numerical value of a qualified sewage parameter, and when the numerical value is greater than the corresponding standard numerical value of the qualified sewage parameter, setting a third value as a change gradient of the corresponding drug dosage, continuously increasing the drug dosage until the numerical value is less than or equal to the corresponding standard numerical value of the qualified sewage parameter, and stopping changing the drug dosage; Traversing the values ​​in each simulation result and looping the above logic to obtain the first regulated operating parameters, and constructing a first mapping relationship between the first regulated operating parameters and the sewage parameters; By inputting sewage parameters into the first mapping relationship, first regulated operation parameters corresponding to the sewage parameters are obtained.

9. The mechanical assistant intelligent auxiliary system for sewage treatment dosing according to claim 1, characterized in that: The logic of the optimization module for setting the trajectory of the robotic arm according to the simulation results includes: Get the first distance corresponding to each blocking key point, and calculate the second distance from the moving key point to each blocking key point; Setting the first planning condition as the first distance being greater than any second distance; The second planning condition is set as the trajectory curve passing through the key coordinate waypoint; The third planning condition is set to that the trajectory curve does not have a pole; Each trajectory curve is obtained through mathematical software; The power consumption simulation is performed according to each trajectory curve and the drug delivery time point to obtain the power consumption simulation result, which is represented by the total power consumed by the robot arm when the processing is completed.

10. The mechanical assistant intelligent auxiliary system for sewage treatment dosing according to claim 9, characterized in that: The logic for selecting the optimal trajectory based on the power consumption simulation results includes: Traverse each total power, sort the total power in ascending order, and select the trajectory curve corresponding to the total power with the smallest value; Setting the trajectory curve to an optimal trajectory; The wastewater to be treated is treated according to the optimal trajectory and the operating parameters after the first adjustment.

Images