A sewage discharge early warning analysis method based on metal processing monitoring

Abstract

The present invention provides a sewage discharge early warning analysis method based on metal processing monitoring. The method includes generating early warning rules from monitoring data and inputting the monitoring variables generated by these rules and a communication topology matrix into a metal processing process model. The improved early warning rules generate sewage discharge rules, and the changes in the monitoring variables generated by these rules and the communication topology are used as perturbations to be input into the model. By judging whether the system variables and perturbations cause conflicts, and using an anti-rule interference observer to correct the monitoring strategy when a conflict occurs, and feeding the corrected strategy back to the model and the early warning rules, the present invention can achieve effective early warning and control of sewage discharge during the metal processing process.

Description

Technical Field

[0001] The present invention relates to the fields of environmental monitoring and industrial automation. More specifically, the present invention relates to a sewage discharge early warning analysis method based on metal processing monitoring. Background Art

[0002] In the metal processing industry, sewage discharge is an important environmental issue. Traditional metal processing processes often generate a large amount of wastewater and pollutants. If these wastewaters are directly discharged into the environment without effective treatment and monitoring, they will cause serious damage to water bodies and ecosystems. Existing metal processing monitoring technologies mainly focus on processing efficiency and product quality, and pay insufficient attention to the sewage discharge problems generated during the processing. Although there are some monitoring systems that can collect and analyze data during the processing, they usually lack the ability to warn and analyze sewage discharge, and cannot provide effective warning information before sewage discharge occurs.

[0003] When dealing with sewage discharge problems, existing technologies often rely on post-treatment, that is, treatment is carried out after sewage has been generated and discharged. This not only increases the treatment cost, but also is difficult to avoid damage to the environment. In addition, existing monitoring systems have limitations in data collection and analysis, and cannot reflect the sewage discharge situation during metal processing in real time and accurately, resulting in a slow response speed and low warning accuracy of the warning system.

[0004] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the existing technologies: existing metal processing monitoring systems lack the ability to monitor and warn sewage discharge in real time, and cannot provide effective warning information before sewage discharge occurs, resulting in environmental pollution problems being difficult to be controlled and solved in a timely manner. In addition, existing monitoring strategies and warning rules are often too simple to adapt to the complex and changeable metal processing environment, and a more intelligent and flexible warning analysis method is needed to improve the accuracy and timeliness of warnings. Summary of the Invention

[0005] The present invention provides a sewage discharge early warning analysis method and device based on metal processing monitoring.

[0006] In the first aspect of the present invention, a sewage discharge early warning analysis method based on metal processing monitoring is provided, including:

[0007] Generating a sewage discharge early warning rule based on the monitoring data of the metal processing process, and inputting the first relative state variable to be monitored in the metal processing process and the first communication topology matrix generated by the sewage discharge early warning rule as system variables into the metal processing process model, wherein the sewage discharge early warning rule includes a metal processing process monitoring strategy;

[0008] By improving the sewage discharge warning rule, a sewage discharge rule is generated, and the second relative state change and the second communication topology to be monitored in the metal processing process generated by the sewage discharge rule are used as disturbances and input into the metal processing process model;

[0009] Determine whether the system variables and the disturbances in the metal processing process model will cause conflicts; and when the conflicts occur, use an anti-rule interference observer to correct the metal processing process monitoring strategy and feedback the corrected metal processing process monitoring strategy to the metal processing process model and the sewage discharge warning rule.

[0010] Further, using an anti-rule interference observer to correct the metal processing process monitoring strategy further includes: inputting the sewage discharge output result and the machine decision output result into the anti-rule interference observer;

[0011] The anti-rule interference observer simulates the change items after the metal processing process model receives the disturbances; and uses the change items to assist in correcting the metal processing process monitoring strategy to offset the influence of the disturbances on the metal processing process model.

[0012] Further, the anti-rule interference observer z is represented by the following formula:

[0013]

[0014] Where, is the metal processing process monitoring strategy of the sewage discharge warning rule, is the metal processing process monitoring strategy of the sewage discharge rule, is the time delay between every two adjacent sampling times The monitoring strategy threshold within, is and The error of, and is the sign function.

[0015] Further, it further includes: when the conflicts are not caused, using the sewage discharge output result to improve the metal processing process monitoring strategy; and feedbacking the improved metal processing process monitoring strategy to the metal processing process model and the sewage discharge warning rule.

[0016] Further, the monitoring strategy threshold is determined by the following formula as :

[0017]

[0018] Where, and The time of the k-th sampling point respectively and the time of the (k + 1)-th sampling point at the monitoring strategy threshold value.

[0019] Furthermore, the monitoring data of the metal processing process is ， where x is the relative state variable , where r is the relative distance in the metal processing process, is the line-of-sight angle, is the relative velocity along the line-of-sight direction in the metal processing process, is the relative velocity perpendicular to the line-of-sight direction in the metal processing process;

[0020] is the communication topology matrix , where is the element in the i-th row and j-th column of the communication topology matrix A; and is the monitoring strategy , where is the component of the acceleration along the connection line direction of the metal processing process, and is the component of the acceleration perpendicular to the connection line direction of the metal processing process.

[0021] Furthermore, the metal processing process model further includes: a metal processing process dynamics model, a metal processing process kinematics model, and a metal processing process communication topology matrix.

[0022] Furthermore, the metal processing process model is , and the state variable threshold σF within the time delay Δt between every two adjacent sampling times is defined by the following formula:

[0023]

[0024] where is the relative state variable , where is the relative distance in the metal processing process, is the line-of-sight angle, is the relative velocity along the line-of-sight direction in the metal processing process, is the relative velocity perpendicular to the line-of-sight direction in the metal processing process;

[0025] is the communication topology matrix , where is the communication topology matrix of the i-th row and j-th column element; and is the monitoring strategy , where is the component of the acceleration along the connection direction of the metal processing process during the metal processing process, and is the component of the acceleration perpendicular to the connection direction of the metal processing process during the metal processing process.

[0026] In a second aspect of the present invention, a sewage discharge early warning analysis device based on metal processing monitoring is provided, including:

[0027] A consistency protocol module for generating monitoring data of the metal processing process;

[0028] A machine decision rule and system variable generation module for generating machine decision rules based on the consistency protocol, and taking the first relative state variable and the first communication topology matrix to be monitored in the metal processing process generated by the machine decision rules as system variables, where the machine decision rules include metal processing process monitoring strategies;

[0029] A sewage discharge rule and disturbance generation module for receiving the machine decision rules and generating sewage discharge rules by improving the machine decision rules, and taking the second relative state change and the second communication topology to be monitored in the metal processing process generated by the sewage discharge rules as disturbances;

[0030] A metal processing process model module for receiving the system variables and the disturbances and inputting them into the metal processing process model;

[0031] A conflict judgment module for judging whether the system variables and the disturbances in the metal processing process model will cause conflicts; and an anti-rule interference observer for correcting the metal processing process monitoring strategy when conflicts are caused and feeding back the corrected metal processing process monitoring strategy to the metal processing process model module and the consistency protocol module.

[0032] Further, it includes: a sewage discharge output module and a machine decision output module, where the sewage discharge output module is connected to the conflict judgment module and provides a sewage discharge output result to the anti-rule interference observer;

[0033] The machine decision output module is connected to the conflict judgment module and provides a machine decision output result to the anti-rule interference observer; and the anti-rule interference observer simulates the changed items after the metal processing process model receives the disturbances, and uses the changed items to assist in correcting the metal processing process monitoring strategy to offset the influence of the disturbances on the metal processing process model.

[0034] The above embodiments of the present invention have at least the following beneficial effects: The sewage discharge early warning analysis method based on metal processing monitoring provided by the present invention can effectively monitor the sewage discharge situation during the metal processing process and generate early warning rules through real-time data analysis. This method can predict potential conflicts and adjust the monitoring strategy in a timely manner through improved sewage discharge rules and disturbance input, thereby reducing or avoiding the impact of sewage discharge on the environment. In addition, through the application of an anti-rule interference observer, the accuracy and response speed of the monitoring strategy can be further improved, ensuring the environmental protection and sustainability of the metal processing process.

[0035] In addition, the present invention also provides a sewage discharge early warning analysis device based on metal processing monitoring. This device can realize all the functions of the above method, including a consistency protocol module, a machine decision rule and system variable generation module, a sewage discharge rule and disturbance generation module, a metal processing process model module, a conflict judgment module, and an anti-rule interference observer, etc. The application of this device can improve the environmental monitoring ability of the metal processing industry, reduce the risk of environmental pollution, and at the same time improve production efficiency and product quality. Through real-time monitoring and early warning, this device helps enterprises achieve a more environmentally friendly and sustainable production method, meeting the current social requirements for green manufacturing and clean production. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understood. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, wherein:

[0037] Figure 1 It is a schematic flow chart of the sewage discharge early warning analysis method based on metal processing monitoring provided by an embodiment of the present invention;

[0038] Figure 2 It is a schematic structural diagram of the sewage discharge early warning analysis device based on metal processing monitoring provided by an embodiment of the present invention;

[0039] Figure 3 It schematically shows a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are given only to enable those skilled in the art to better understand and then implement the present invention, and do not limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to be able to fully convey the scope of the present invention to those skilled in the art.

[0041] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, equipment, method, or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0042] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0043] The following refers to Figure 1 , Figure 1 which is a schematic flowchart of a sewage discharge early warning analysis method based on metal processing monitoring provided for an embodiment of the present invention. As Figure 1 shown, a sewage discharge early warning analysis method 100 based on metal processing monitoring includes:

[0044] Step 101, generating a sewage discharge early warning rule based on the monitoring data of the metal processing process, and inputting the first relative state variable and the first communication topology matrix to be monitored in the metal processing process generated by the sewage discharge early warning rule into the metal processing process model as system variables, where the sewage discharge early warning rule includes a metal processing process monitoring strategy;

[0045] Step 102, improving the sewage discharge early warning rule to generate a sewage discharge rule, and inputting the second relative state change and the second communication topology to be monitored in the metal processing process generated by the sewage discharge rule into the metal processing process model as disturbances;

[0046] Step 103, determining whether the system variables and the disturbances in the metal processing process model will cause conflicts; and when conflicts are caused, using an anti-rule interference observer to correct the metal processing process monitoring strategy and feeding the corrected metal processing process monitoring strategy back to the metal processing process model and the sewage discharge early warning rule.

[0047] It should be noted that first, a sewage discharge early warning rule is generated based on the monitoring data of the metal processing process. This step is to ensure that during the metal processing process, risk factors that may lead to sewage discharge can be detected in a timely manner. To this end, the monitoring data will be used to create a set of early warning rules, which will guide how to monitor the metal processing process and determine which parameters need special attention.

[0048] Specifically, these monitoring data include parameters such as relative distance, line-of-sight angle, relative speed, etc. during the metal processing, as well as a communication topology matrix, which describes the communication relationships between various parts during the processing. The warning rules will set thresholds based on these parameters, and when the monitored parameter values exceed these thresholds, the system will trigger a warning. For example, if the relative speed exceeds the preset safe range, the system will issue a warning signal.

[0049] Preferably, these warning rules can also include the monitoring of the acceleration components during the metal processing, namely the component along the connection line direction of the metal processing and the component perpendicular to this direction. In this way, the risk of sewage discharge can be predicted more accurately.

[0050] Furthermore, the generation of the warning rules can also combine historical data and machine learning algorithms to improve the accuracy and adaptability of the warning. In practical applications, the parameter settings in the warning rules can be adjusted according to the specific metal processing environment and requirements to achieve the best monitoring effect.

[0051] In some embodiments, using an anti-rule interference observer to correct the metal processing process monitoring strategy further includes: inputting the sewage discharge output result and the machine decision output result into the anti-rule interference observer;

[0052] The anti-rule interference observer simulates the change item after the disturbance is received by the metal processing process model; and uses the change item to assist in correcting the metal processing process monitoring strategy to offset the influence of the disturbance on the metal processing process model.

[0053] It should be noted that the implementation manner of this method involves using an anti-rule interference observer to correct the metal processing process monitoring strategy. This step is to be able to adjust the strategy in a timely manner to maintain the stability and efficiency of the metal processing process when the monitoring strategy is affected by disturbances. The anti-rule interference observer is an advanced control strategy that can simulate and predict the behavior of the system when it is disturbed and adjust the control strategy accordingly.

[0054] Specifically, the anti-rule interference observer takes the sewage discharge output result and the machine decision output result as inputs. These output results reflect the current state and potential problems of the metal processing process. The observer compares this information with a preset model to determine whether there is a disturbance and the nature of the disturbance. For example, if the sewage discharge suddenly increases, the observer will identify this change and analyze its impact on the metal processing process.

[0055] Preferably, the implementation of the anti-rule interference observer may include setting specific monitoring strategy thresholds. These thresholds can be set according to the specific requirements and historical data of the metal processing process. For example, a threshold for sewage discharge volume based on historical data can be set, and when the actual discharge volume exceeds this threshold, the observer will trigger a correction mechanism.

[0056] Furthermore, the correction mechanism of the observer can adopt various algorithms, such as PID control, fuzzy logic control, or adaptive control, etc., to adapt to different processing environments and requirements. During the implementation process, the introduction of self-learning ability can also be considered to enable the observer to continuously optimize the correction strategy according to new data.

[0057] In some embodiments, the anti-rule interference observer z is represented by the following formula:

[0058]

[0059] where is the monitoring strategy for the metal processing process of the sewage discharge warning rule, is the monitoring strategy for the metal processing process of the sewage discharge rule, is the time delay between every two adjacent sampling times the monitoring strategy threshold within, is and the error of, and is the sign function.

[0060] Specifically, the calculation formula of the anti-rule interference observer involves several key parameters: the monitoring strategy for the metal processing process of the sewage discharge warning rule, the monitoring strategy for the metal processing process of the sewage discharge rule, the monitoring strategy threshold, and the error.

[0061] More specifically, these parameters jointly determine how the observer simulates the changes in the metal processing process model after receiving disturbances and accordingly corrects the monitoring strategy. For example, the monitoring strategy threshold can be set as a dynamically adjustable parameter and adjusted according to the change trends of real-time monitoring data and historical data.

[0062] Preferably, the calculation formula of the anti-rule interference observer can be further refined. For example, various methods can be used for calculating the error, such as absolute error, square error, or weighted error, etc., to adapt to different monitoring accuracy requirements.

[0063] Furthermore, the choice of the sign function can also be adjusted according to the actual application scenario to ensure that the observer can accurately simulate and predict the behavior of the system when it is disturbed. In practical applications, an adaptive mechanism can also be considered to enable the observer to continuously optimize the parameter settings in its calculation formula based on new data, so as to improve the adaptability and accuracy of the correction strategy.

[0064] In some embodiments, it further includes: when no such conflict is caused, using the sewage discharge output result to improve the metal processing process monitoring strategy; and feeding back the improved metal processing process monitoring strategy to the metal processing process model and the sewage discharge warning rule.

[0065] It should be noted that the implementation of this method includes using the sewage discharge output result to improve the metal processing process monitoring strategy when no conflict is caused. This means that when the system variables and disturbance inputs do not cause a conflict, the system will adopt a feedback mechanism to optimize the monitoring strategy by analyzing the actual output result of the sewage discharge.

[0066] Specifically, this method will involve the collection and analysis of the sewage discharge output results. These output results may include key parameters such as the chemical composition, discharge volume, and discharge frequency of the sewage.

[0067] More specifically, through these data, the effectiveness of the current monitoring strategy can be evaluated, and the areas that need improvement can be identified. For example, if it is found that the discharge volume of a certain chemical substance exceeds the preset safety threshold, then the monitoring strategy may need to be adjusted to more closely monitor the generation and discharge of this chemical substance.

[0068] Preferably, the improved monitoring strategy may include resetting the monitoring parameters, such as adjusting the monitoring frequency, adding new monitoring points, or using more sensitive monitoring equipment. In addition, machine learning algorithms can also be considered to analyze the sewage discharge data, so as to automatically adjust the monitoring strategy to make it more intelligent and adaptive.

[0069] Furthermore, historical sewage discharge data can be used to train a prediction model that can predict future discharge trends and adjust the monitoring strategy in advance accordingly. This method can not only improve the efficiency of monitoring, but also take preventive measures before potential environmental risks occur.

[0070] In some embodiments, the monitoring strategy threshold σg is determined by the following formula:

[0071]

[0072] where and are the time of the k-th sampling point respectively The time of the (k + 1)-th sampling point at the monitoring strategy threshold value.

[0073] It should be noted that the implementation of this method optimizes the monitoring strategy by determining the monitoring strategy threshold value . The monitoring strategy threshold value is a key parameter that defines the range of change of the monitoring strategy between consecutive sampling points.

[0074] Specifically, the monitoring strategy threshold value is determined by comparing the monitoring strategy values of two consecutive sampling points. For example, if the absolute difference between the monitoring strategy value of the k-th sampling point and the monitoring strategy value of the (k + 1)-th sampling point does not exceed a preset threshold value , then it can be considered that the monitoring strategy is stable between these two sampling points. This comparison can be applied to various monitoring parameters, such as the relative distance, line-of-sight angle, relative speed, etc. in the metal processing process. The parameter settings can be adjusted according to the actual processing environment and requirements to ensure that the monitoring strategy is both sensitive and stable.

[0075] Preferably, the determination of the monitoring strategy threshold value can adopt various methods, including statistical analysis, empirical setting, or model-based optimization. For example, the reasonable range of can be determined by analyzing historical data, or an initial value can be set according to expert experience and then adjusted based on the performance during actual operation.

[0076] Furthermore, an adaptive mechanism can be considered to enable to dynamically adjust according to real-time data to adapt to the changes in the processing process. This dynamic adjustment can be based on machine learning algorithms, and the monitoring strategy threshold value can be optimized by continuously learning the dynamic characteristics of the processing process, thereby improving the adaptability and accuracy of the monitoring strategy.

[0077] The monitoring data of the metal processing process is ， where x is the relative state variable , where is the relative distance in the metal processing process, is the line-of-sight angle, is the relative speed along the line-of-sight direction in the metal processing process, is the relative speed perpendicular to the line-of-sight direction in the metal processing process;

[0078] is the communication topology matrix , where is the element in the i-th row and j-th column of the communication topology matrix A; and is the monitoring strategy , where is the component of the acceleration along the direction of the metal processing process connection line during the metal processing process, and is the component of the acceleration perpendicular to the direction of the metal processing process connection line during the metal processing process.

[0079] It should be noted that the implementation of this method involves using specific monitoring data to generate sewage discharge warning rules. These monitoring data include relative state variables and communication topology matrices during the metal processing process, which are the basis for evaluating sewage discharge risks and formulating warning rules.

[0080] Specifically, the monitoring data includes relative distance , line-of-sight angle , relative velocity along the line-of-sight direction and relative velocity perpendicular to the line-of-sight direction . These parameters can provide dynamic information during the metal processing process to help identify abnormal situations that may lead to sewage discharge.

[0081] More specifically, the communication topology matrix describes the communication relationships between various parts during the processing process, which is crucial for understanding the dynamic behavior of the entire system and formulating effective warning rules. The monitoring strategy includes the component of the acceleration along the direction of the metal processing process connection line and the component perpendicular to this direction , and these parameters help evaluate the dynamic changes during the processing process.

[0082] Preferably, the collection and processing of the monitoring data can adopt advanced sensor technologies and data analysis methods. For example, high-precision laser sensors can be used to measure relative distances and speeds, and high-speed cameras can be used to capture changes in the line-of-sight angle. The construction of the communication topology matrix can be based on the actual layout of the processing equipment and communication protocols.

[0083] Furthermore, the formulation of the monitoring strategy can combine expert knowledge and historical data to ensure the accuracy and practicality of the warning rules. During the implementation process, machine learning algorithms can also be considered to analyze the monitoring data, thereby automatically adjusting the warning rules to make them more intelligent and adaptive. This method can not only improve the efficiency of monitoring but also take preventive measures before potential environmental risks occur.

[0084] In some embodiments, the metal processing process model further includes: a metal processing process dynamics model, a metal processing process kinematics model, and a metal processing process communication topology matrix.

[0085] It should be noted that the implementation of this method covers multiple aspects of the metal processing process model, including the kinetic model, kinematic model, and communication topology matrix. These models together constitute a comprehensive framework for simulating and analyzing the possible sewage discharge situations in the metal processing process.

[0086] Specifically, the kinetic model of the metal processing process focuses on the conversion of force and energy during the processing, the kinematic model describes the movement trajectory and speed of the processing components, while the communication topology matrix defines the information exchange method between various components in the processing process.

[0087] More specifically, the parameter settings of these models need to be determined according to the actual processing equipment and process requirements. For example, the kinetic model may need to consider parameters such as cutting force and torque, the kinematic model may need to consider parameters such as speed and acceleration, while the communication topology matrix needs to be constructed according to the actual network layout and communication protocol.

[0088] Preferably, the implementation of these models can adopt advanced simulation technologies and real-time data processing methods. For example, computer-aided engineering (CAE) software can be used to establish and simulate the kinetic and kinematic models, and at the same time, a real-time data acquisition system can be used to monitor the actual parameters during the processing.

[0089] Furthermore, the construction of the communication topology matrix can be based on modern industrial Internet of Things (IIoT) technology to achieve efficient data exchange and processing. During the implementation process, an adaptive control strategy can also be considered to enable the model to dynamically adjust according to real-time data to adapt to the changes in the processing process. This method can not only improve the accuracy of monitoring but also take preventive measures before potential environmental risks occur.

[0090] In some embodiments, the metal processing process model is , and the state variable threshold within the time delay Δt between every two adjacent sampling times is defined by the following formula :

[0091]

[0092] Wherein, is the relative state variable , wherein, is the relative distance in the metal processing process, is the line-of-sight angle, is the relative velocity along the line-of-sight direction in the metal processing process, is the relative velocity perpendicular to the line-of-sight direction in the metal processing process;

[0093] is the communication topology matrix , where is the element in the i-th row and j-th column of the communication topology matrix ; and is the monitoring strategy , where is the component of the acceleration along the connection direction of the metal processing process during the metal processing process, and is the component of the acceleration perpendicular to the connection direction of the metal processing process during the metal processing process.

[0094] It should be noted that the implementation of this method ensures the stability of the metal processing process and the accuracy of early warning analysis by defining the state variable threshold within the time delay Δt between every two adjacent sampling times. This method uses the state variable threshold to monitor and evaluate the changes in the metal processing process, so as to timely identify the risks that may lead to sewage discharge.

[0095] Specifically, the state variable threshold is based on the relative distance , line-of-sight angle , relative velocity along the line-of-sight direction and relative velocity perpendicular to the line-of-sight direction during the metal processing process. These parameters jointly describe the dynamic state of the metal processing process, and the state variable threshold defines the acceptable range of changes in these parameters between consecutive sampling points. For example, if the relative velocity changes by more than the preset threshold between two consecutive samplings, the system will consider that there is a potential risk and may trigger the early warning mechanism.

[0096] Preferably, the state variable threshold can be set by various methods, including historical data analysis based on statistical analysis, expert experience or machine learning algorithms. For example, the normal fluctuation range of each parameter can be determined by analyzing historical data, and the threshold can be set accordingly.

[0097] Furthermore, a real-time feedback mechanism can be considered to enable the threshold to be dynamically adjusted according to the current processing conditions and environmental changes. For example, if a significant change in the temperature or humidity of the processing environment is detected, the system can automatically adjust the threshold to adapt to these changes, thereby improving the adaptability and accuracy of the early warning system. This method can not only improve the real-time performance of monitoring, but also take preventive measures before potential environmental risks occur.

[0098] The above embodiments of the present invention have the following beneficial effects: The sewage discharge early warning analysis method based on metal processing monitoring of the present invention can provide a comprehensive monitoring and early warning mechanism to monitor and analyze the sewage discharge situation in the metal processing process in real time. This method can effectively predict and analyze the possibility of sewage discharge by generating sewage discharge early warning rules and inputting the monitoring data as system variables into the metal processing process model. In addition, by improving the sewage discharge early warning rules, generating sewage discharge rules, and taking the relative state changes and communication topologies generated by these rules as perturbations and inputting them into the model, the accuracy and adaptability of the early warning system can be further enhanced. This method also includes judging whether the system variables and perturbations will cause conflicts, and using an anti-rule interference observer to correct the monitoring strategy when conflicts occur, so as to ensure the stability and environmental protection of the metal processing process.

[0099] Through this method, the sewage discharge in the metal processing process can be monitored and warned in real time, thus reducing environmental pollution. It can simulate the changed items after the metal processing process model receives perturbations, and use these changed items to assist in correcting the monitoring strategy to offset the impact of perturbations on the model. This can not only improve the effectiveness of the monitoring strategy, but also reduce the environmental risks caused by sewage discharge. In addition, this method also includes using the sewage discharge output results to improve the monitoring strategy when no conflict occurs, and feeding back the improved strategy to the model and early warning rules, so as to continuously optimize the monitoring strategy and improve the accuracy and practicability of early warning analysis.

[0100] As Figure 2 shown, a sewage discharge early warning analysis device 200 based on metal processing monitoring in some embodiments, the device 200 includes:

[0101] A consistency protocol module 201, configured to generate monitoring data of the metal processing process;

[0102] A machine decision rule and system variable generation module 202, configured to generate machine decision rules based on the consistency protocol, and use the first relative state variable to be monitored in the metal processing process and the first communication topology matrix generated by the machine decision rules as system variables, wherein the machine decision rules include metal processing process monitoring strategies;

[0103] A sewage discharge rule and perturbation generation module 203, configured to receive the machine decision rules and generate sewage discharge rules by improving the machine decision rules, and use the second relative state change to be monitored in the metal processing process and the second communication topology generated by the sewage discharge rules as perturbations;

[0104] A metal processing process model module 204, configured to receive the system variables and the perturbations and input them into the metal processing process model;

[0105] A conflict judgment module 205 for judging whether the system variables and the disturbances in the metal processing process model will cause conflicts; and an anti-rule interference observer for correcting the metal processing process monitoring strategy and feeding the corrected metal processing process monitoring strategy back to the metal processing process model module and the consistency protocol module when the conflict is caused.

[0106] It can be understood that the various modules described in the sewage discharge early warning analysis device 200 based on metal processing monitoring correspond to the respective steps in the sewage discharge early warning analysis method based on metal processing monitoring described in the reference Figure 1 Therefore, the operations, features, and beneficial effects described above for the sewage discharge early warning analysis method based on metal processing monitoring also apply to the sewage discharge early warning analysis device 200 based on metal processing monitoring and the modules included therein, and will not be elaborated here.

[0107] In some embodiments, it further includes: a sewage discharge output module and a machine decision output module, wherein the sewage discharge output module is connected to the conflict judgment module and provides the sewage discharge output result to the anti-rule interference observer;

[0108] The machine decision output module is connected to the conflict judgment module and provides the machine decision output result to the anti-rule interference observer; and the anti-rule interference observer simulates the changed items after the metal processing process model receives the disturbance, and uses the changed items to assist in correcting the metal processing process monitoring strategy to offset the influence of the disturbance on the metal processing process model.

[0109] It should be noted that the implementation mode of this device includes multiple modules, which work together to realize the early warning analysis of sewage discharge in the metal processing process. Specifically, the consistency protocol module is responsible for generating monitoring data, the machine decision rule and system variable generation module generates machine decision rules based on these data, and the sewage discharge rule and disturbance generation module improves these rules to generate sewage discharge rules.

[0110] Specifically, the consistency protocol module can be a network-based communication protocol to ensure the accuracy and real-time nature of the data. The machine decision rule and system variable generation module will use these data to generate an initial monitoring strategy, including which parameters to monitor and how to monitor.

[0111] More specifically, the sewage discharge rule and disturbance generation module further analyzes these strategies, generates more refined sewage discharge rules, and determines possible disturbance factors. The parameter settings of these modules need to be adjusted according to the actual metal processing process to ensure that they can effectively monitor and give early warnings of sewage discharge.

[0112] Preferably, the implementation of such a device may further include a sewage discharge output module and a machine decision output module, which are connected to the conflict judgment module and provide necessary output results to the anti-rule interference observer. These modules can adopt advanced data processing algorithms to ensure the accuracy of the output results. For example, data fusion technology can be used to integrate data from multiple sensors, or pattern recognition algorithms can be used to identify potential sewage discharge patterns.

[0113] Furthermore, the anti-rule interference observer can adopt advanced control algorithms, such as adaptive control or robust control, to improve the system's resistance to disturbances. In practical applications, artificial intelligence technologies, such as deep learning, can also be considered to further improve the device's early warning ability and adaptability. This method can not only improve the accuracy of monitoring but also take preventive measures before potential environmental risks occur.

[0114] Next, refer to Figure 3 , which shows a schematic structural diagram of an electronic device 300 suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Tablet Computers), PMPs (Portable Multimedia Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal devices shown are merely examples and should not impose any limitations on the functions and usage scopes of the embodiments of the present invention.

[0115] As Figure 3 shown, the electronic device 300 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to the programs stored in the read-only memory (ROM) 302 or the programs loaded from the storage device 308 into the random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other through a bus 304. The input / output (I / O) interface 305 is also connected to the bus 304.

[0116] Typically, the following devices can be connected to the I / O interface 305: an input device 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 308 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 309. The communication device 309 can allow the electronic device 300 to communicate with other devices wirelessly or wiredly to exchange data. Although Figure 3 an electronic device 300 with various devices is shown, it should be understood that it is not required to implement or have all the shown devices. More or fewer devices can be alternatively implemented or had. Figure 3 Each block shown in can represent one device or multiple devices as needed.

[0117] Furthermore, the storage medium of the embodiments of the present application stores program instructions capable of implementing all the above methods. Among them, the program instructions can be stored in the above storage medium in the form of a software product, including several instructions to cause a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. And the foregoing storage medium includes: various media that can store program codes such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc, or a terminal device such as a computer, a server, a mobile phone, a tablet, etc.

[0118] The above description is only some preferred embodiments of the present invention and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, technical solutions formed by mutually replacing the above features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A wastewater discharge early warning analysis method based on metal processing monitoring, characterized in that: include: Generate a sewage discharge warning rule based on monitoring data of the metal processing process, and input a first relative state variable to be monitored in the metal processing process and a first communication topology matrix generated by the sewage discharge warning rule as system variables into a metal processing process model, wherein the sewage discharge warning rule includes a metal processing process monitoring strategy; The monitoring data of the metal processing process is minV(x,A,g)(t), where x is the relative state variable x=[r,λ,Vr,Vλ], where r is the relative distance in the metal processing process, λ is the line of sight angle, Vr is the relative speed along the line of sight direction in the metal processing process, and Vλ is the relative speed perpendicular to the line of sight direction in the metal processing process; A is a communication topology matrix A=[aij], where aij is the i-th row and j-th column element of the communication topology matrix A; and g is a monitoring strategy g=[AFr,AFλ], where AFr is the component of the acceleration in the metal processing process along the direction of the metal processing process line, and AFλ is the component of the acceleration in the metal processing process perpendicular to the direction of the metal processing process line; The metal processing process model further includes: a metal processing process dynamics model, a metal processing process kinematics model and a metal processing process communication topology matrix; The metal processing process model is F(x, A, g)(t), and the state variable threshold σF within the time delay Δt between each two adjacent sampling times is defined by the following formula: ΔF=|F(x,A,g)(t k+1 )-F(x,A,g)(t k )|≤σ F Among them, x is the relative state variable x = [r, λ, Vr, Vλ], where r is the relative distance in the metal processing process, λ is the line of sight angle, Vr is the relative speed along the line of sight in the metal processing process, and Vλ is the relative speed perpendicular to the line of sight in the metal processing process; A is a communication topology matrix A=[aij], where aij is the i-th row and j-th column element of the communication topology matrix A; and g is a monitoring strategy g=[AFr,AFλ], where AFr is the component of the acceleration in the metal processing process along the direction of the metal processing process line, and AFλ is the component of the acceleration in the metal processing process perpendicular to the direction of the metal processing process line; By improving the sewage discharge warning rule, a sewage discharge rule is generated, and the second relative state change to be monitored in the metal processing process and the second communication topology generated by the sewage discharge rule are input as disturbances into the metal processing process model; Determine whether the system variables and the disturbance in the metal processing process model will cause a conflict; and when the conflict occurs, correct the metal processing process monitoring strategy using an anti-rule interference observer and feed the corrected metal processing process monitoring strategy back to the metal processing process model and the sewage discharge warning rule; The anti-regular interference observer z is expressed by the following formula: Among them, g is the metal processing process monitoring strategy of the wastewater discharge warning rule, σ g is the monitoring strategy threshold within the delay Δt between each two adjacent sampling times, Δg is the sum of g and The error, is the metal processing process monitoring strategy for the wastewater discharge rule, and sign(.) is the sign function.

2. The wastewater discharge early warning analysis method based on metal processing monitoring according to claim 1 is characterized in that: Correcting the metal processing process monitoring strategy using the rule-resistant interference observer further includes: inputting the wastewater discharge output result and the machine decision output result into the rule-resistant interference observer; The rule-resistant disturbance observer simulates the change item of the metal machining process model after receiving the disturbance; and uses the change item to assist in correcting the metal machining process monitoring strategy to offset the influence of the disturbance on the metal machining process model.

3. The wastewater discharge early warning analysis method based on metal processing monitoring according to claim 1 is characterized in that: Further including: When said conflicts are not caused, improving metalworking process monitoring strategies using wastewater discharge output results; And feeding back the improved metal processing process monitoring strategy to the metal processing process model and the wastewater discharge early warning rules.

4. The wastewater discharge early warning analysis method based on metal processing monitoring according to claim 3 is characterized in that: The monitoring strategy threshold σg is determined by the following formula: Δg=|g(t k+1 )-g(t k )|≤σ g Among them, g(t k+1 ) and g(t k ) are the time t of the kth sampling point k and the time t of the k+1th sampling point k+1 The monitoring strategy threshold at .

5. A sewage discharge early warning analysis device based on metal processing monitoring, applied to a sewage discharge early warning analysis method based on metal processing monitoring as claimed in any one of claims 1 to 4, characterized in that: include: A consistency protocol module for generating monitoring data for metalworking processes; A machine decision rule and system variable generation module, used to generate a machine decision rule based on a consistency protocol, and use a first relative state variable to be monitored in a metal processing process generated by the machine decision rule and a first communication topology matrix as system variables, wherein the machine decision rule includes a metal processing process monitoring strategy; A sewage discharge rule and disturbance generation module, used for receiving the machine decision rule and generating a sewage discharge rule by improving the machine decision rule, and using the second relative state change and the second communication topology generated by the sewage discharge rule to be monitored in the metal processing process as a disturbance; a metal processing process model module, for receiving the system variables and the disturbance and inputting them into the metal processing process model; A conflict judgment module is used to judge whether the system variables and the disturbance in the metal processing process model will cause a conflict; and an anti-rule interference observer is used to correct the metal processing process monitoring strategy when the conflict occurs and feed back the corrected metal processing process monitoring strategy to the metal processing process model module and the consistency protocol module.

6. The sewage discharge early warning and analysis device based on metal processing monitoring according to claim 5 is characterized in that: Further comprising: a sewage discharge output module and a machine decision output module, wherein the sewage discharge output module is connected to the conflict judgment module and provides a sewage discharge output result to the anti-rule interference observer; The machine decision output module is connected to the conflict judgment module and provides the machine decision output result to the anti-rule interference observer; and the anti-rule interference observer simulates the change item of the metal processing process model after receiving the disturbance, and uses the change item to assist in correcting the metal processing process monitoring strategy to offset the influence of the disturbance on the metal processing process model.

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