Oil mist purification method and system

Abstract

The present application provides an oil mist purification method and system, which relates to the field of oil mist purification technology. The method includes: analyzing the oil mist distribution based on the workshop process and ventilation standards, and determining the emission trend and periodic nodes. Digitally simulate the internal flow field of the purifier and analyze the airflow characteristics. Combine the underlying control logic and intelligent control model to determine the purification plan. Convert the control program to the single-chip microcomputer, establish an interface circuit connection, and control the oil mist purification circuit. The present application can solve the problems of inaccurate prediction of oil mist emission trends, poor adaptability and low degree of automation in the prior art, resulting in poor oil mist purification effect, further affecting the cleanliness of the production environment and production efficiency, realize intelligent oil mist purification circuit control, and improve the efficiency of oil mist purification.

Description

Technical Field

[0001] The present application relates to the technical field of oil mist purification, and in particular to an oil mist purification method and system. Background Art

[0002] In the mechanical processing, metal processing, heat treatment, cooking and other industries, a large amount of oil mist is often generated. If these oil mists are not effectively treated, they will not only pollute the air and affect the health of employees, but also cause equipment failure and affect production efficiency.

[0003] At present, traditional oil mist purification systems often lack accurate prediction of oil mist emission trends and cannot automatically adjust purification strategies according to changes in the production process. Due to the lack of accurate analysis of the internal flow field, existing oil mist purifiers may have deficiencies in airflow characteristics and oil mist flow rate, resulting in low purification efficiency. Traditional oil mist purification systems may not take into account the optimal use of energy, resulting in high operating costs.

[0004] In summary, the existing technology has poor oil mist purification effect due to inaccurate prediction of oil mist emission trend, poor adaptability and low degree of automation, which further affects the cleanliness of the production environment and production efficiency. Summary of the invention

[0005] The purpose of the present application is to provide an oil mist purification method and system to solve the problem that the prior art has poor oil mist purification effect due to inaccurate prediction of oil mist emission trend, poor adaptability and low degree of automation.

[0006] In view of the above problems, the present application provides an oil mist purification method and system.

[0007] In a first aspect, the present application provides an oil mist purification method, which is implemented by an oil mist purification system, wherein the method includes: based on the production process and ventilation standards of the target workshop, performing a correlation analysis with the oil mist distribution to determine the oil mist emission trend, wherein the oil mist emission trend has a periodic node identifier; for the internal flow field of the oil mist purifier, performing an airflow characteristic analysis based on digital simulation to determine the internal flow field situation, wherein the airflow characteristics include the oil mist flow rate and the airflow uniformity; interacting with the underlying control logic of the oil mist purifier, combining the oil mist emission trend with the internal flow field situation, assisting the intelligent control model, making a directional inhalation purification decision to determine a pre-control plan, wherein the oil mist purification plan is a periodic plan, including a main line control strategy and a regulation strategy; based on the oil mist purification plan, performing control program conversion and single-chip microcomputer burning; establishing a single-chip microcomputer interface connection with the oil mist purifier and the interface circuit of the sensor device to perform loop control of the oil mist purification of the target workshop, wherein the sensor device at least includes a distributed smoke sensor.

[0008] In a second aspect, the present application further provides an oil mist purification system for executing an oil mist purification method as described in the first aspect, wherein the system comprises: a correlation analysis module, wherein the correlation analysis module is used to perform a correlation analysis with the oil mist distribution based on the production process and ventilation standards of the target workshop, and determine the oil mist emission trend, wherein the oil mist emission trend carries a periodic node identifier; an airflow characteristic analysis module, wherein the airflow characteristic analysis module is used to perform an airflow characteristic analysis on the internal flow field of the oil mist purifier based on digital simulation, and determine the internal flow field situation, wherein the airflow characteristics include the oil mist flow rate and the airflow uniformity; a control scheme determination module, wherein the control scheme determination module The block is used to interact with the underlying control logic of the oil mist purifier, combine the oil mist emission trend with the internal flow field situation, assist the intelligent control model, make directional inhalation purification decisions and determine the pre-control plan, wherein the oil mist purification plan is a periodic plan, including a main line control strategy and a regulation strategy; the plan burning module, the plan burning module is used to perform control program conversion and single-chip microcomputer burning based on the oil mist purification plan; the loop control module, the loop control module is used to establish a single-chip microcomputer interface and the interface circuit connection between the oil mist purifier and the sensor equipment, and perform loop control of the oil mist purification of the target workshop, and the sensor equipment at least includes a distributed smoke sensor.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages: Based on the production process and ventilation standards of the target workshop, a correlation analysis is conducted with the oil mist distribution to determine the oil mist emission trend, which is marked with periodic nodes; the internal flow field of the oil mist purifier is analyzed for airflow characteristics based on digital simulation to determine the internal flow field situation, where the airflow characteristics include the oil mist flow rate and airflow uniformity; the underlying control logic of the oil mist purifier is interactively combined with the oil mist emission trend and the internal flow field situation to assist the intelligent control model, and make a directional inhalation purification decision to determine the pre-control plan, where the oil mist purification plan is a periodic plan. It includes a main line control strategy and a regulation strategy; based on the oil mist purification scheme, control program conversion and single-chip microcomputer burning are performed; a single-chip microcomputer interface is connected with the interface circuit of the oil mist purifier and the sensor equipment to perform loop control of the oil mist purification of the target workshop. The sensor equipment at least includes a distributed smoke sensor, which effectively solves the problem that the prior art has poor oil mist purification effect due to inaccurate prediction of the oil mist emission trend, poor adaptability and low degree of automation, further affecting the cleanliness of the production environment and production efficiency, realizes intelligent oil mist purification loop control, and improves the efficiency of oil mist purification.

[0010] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented according to the contents of the specification, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are specifically cited below. It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present application, nor is it intended to limit the scope of the present application. Other features of the present application will become easy to understand through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the present application or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0012] Figure 1 A schematic diagram of a process of oil mist purification method for this application; Figure 2 This is a structural schematic diagram of an oil mist purification system for this application.

[0013] Description of reference numerals: Correlation analysis module 11, airflow characteristic analysis module 12, control scheme determination module 13, scheme burning module 14, loop control module 15. DETAILED DESCRIPTION

[0014] The present application provides an oil mist purification method and system, which solves the problem that the prior art has poor oil mist purification effect due to inaccurate prediction of oil mist emission trend, poor adaptability and low degree of automation, further affecting the cleanliness of the production environment and production efficiency, realizes intelligent oil mist purification circuit control, and improves the efficiency of oil mist purification.

[0015] Below, the technical solutions in the present application will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments of the present application. It should be understood that the present application is not limited to the example embodiments described herein. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application. It should also be noted that, for the convenience of description, only the parts related to the present application are shown in the accompanying drawings, rather than all of them. Embodiment 1

[0016] Please see attached Figure 1The present application provides an oil mist purification method, wherein the method is applied to an oil mist purification system, and the method specifically comprises the following steps: S1: Based on the production process and ventilation standards of the target workshop, a correlation analysis is performed with the oil mist distribution to determine the oil mist emission trend, which is marked with periodic nodes.

[0017] Specifically, the production process data of the workshop is collected, including information such as machine tool type, processing materials, cutting fluid used, production shifts, etc. At the same time, the ventilation data of the workshop is collected, such as the design parameters of the ventilation system, air flow conditions, etc. Distributed smoke sensors are installed in the workshop to monitor the oil mist concentration at different locations and at different time points in real time. Statistical methods or machine learning algorithms are used to analyze the collected data to find the correlation between the oil mist concentration and the production process and ventilation conditions. For example, the changing pattern of oil mist concentration during different production shifts and different machine tool operations can be analyzed. Based on the analysis results, the peak and low peak periods of oil mist emission are identified, and these periods are periodic nodes.

[0018] S2: Analyze the airflow characteristics of the internal flow field of the oil mist purifier based on digital simulation to determine the internal flow field state, where the airflow characteristics include the oil mist flow rate and airflow uniformity.

[0019] Specifically, create an accurate geometric model of the inside of the oil mist purifier, including the inlet, outlet, filter, fan, duct and other related components. Divide the mesh on the geometric model. The mesh is to divide the continuous geometric space into discrete small units. Use computational fluid dynamics CFD software for simulation. In CFD simulation, define the initial conditions such as the inlet velocity, temperature, oil mist concentration of the flow field and the boundary conditions such as the outlet pressure and wall conditions. Run CFD software for simulation calculation. Calculate the characteristics of the flow field based on fluid mechanics equations. Analyze the simulation results, including oil mist flow rate, air flow uniformity, pressure distribution, oil mist concentration distribution, etc.

[0020] S3: Interact with the underlying control logic of the oil mist purifier, combine the oil mist emission trend with the internal flow field situation, assist the intelligent control model, make a directional inhalation purification decision and determine the pre-control plan, wherein the oil mist purification plan is a periodic plan, including a main line control strategy and a regulation strategy.

[0021] Specifically, the collected oil mist emission trend data, internal flow field simulation results and purifier operation data are integrated into a unified platform. Use machine learning or optimization algorithms to establish an intelligent control model. This model adjusts the operating parameters of the purifier based on real-time data and preset goals, such as minimum energy consumption, maximum purification efficiency, etc. Based on the intelligent control model, a periodic oil mist purification plan is formulated. Including mainline control strategy and regulation strategy. The mainline control strategy defines the basic operating mode of the purifier, while the regulation strategy is used to respond to emergencies or changes in the production process. The intelligent control model will calculate the optimal suction purification strategy based on the oil mist emission trend and the internal flow field situation. For example, the model may find that the oil mist concentration in a certain area is particularly high during a certain period of time, and adjust the speed and direction of the fan to enhance the oil mist collection effect in that area.

[0022] S4: Based on the oil mist purification solution, control program conversion and microcontroller burning are performed.

[0023] Specifically, the control strategy in the oil mist purification scheme is converted into a specific algorithm. Including decision logic, PID control algorithm, fuzzy logic control, etc., which are used to process real-time data and make control decisions. According to the algorithm, write the control program using C, C++, Python, etc. This program will be responsible for reading sensor data, executing control algorithms, and sending control commands to actuators such as fans, valves, etc. Test the control program in the development environment to ensure that the program can correctly perform all predetermined functions and can handle various abnormal situations. Convert the control program that has been tested correctly into machine code that can be recognized by the microcontroller. Use a program burner to burn the converted machine code into the flash memory of the microcontroller. After the burning is completed, perform hardware testing on the microcontroller to ensure that the program can run correctly in the actual hardware environment.

[0024] S5: Establishing a connection between the single-chip microcomputer interface and the interface circuit of the oil mist purifier and the sensor device to perform loop control of the oil mist purification of the target workshop, wherein the sensor device at least includes a distributed smoke sensor.

[0025] Specifically, according to the needs of the oil mist purifier and sensing equipment, the hardware interface of the single-chip microcomputer is designed. Including I / O ports, analog input ports, communication interfaces such as RS-485, Ethernet, wireless modules, etc. Ensure that the electrical characteristics and physical dimensions of the interface match the purifier and sensing equipment. Install sensing equipment such as distributed smoke sensors at key locations in the workshop, and the output of the sensor is connected to the single-chip microcomputer through analog input ports or digital input ports. Actuators in the oil mist purifier, such as fans, pumps, valves, etc., are also connected to the single-chip microcomputer. These interfaces are used to control the start and stop of the actuators and adjust the working parameters. Use appropriate connectors, cables, and circuit protection components to connect the interface of the single-chip microcomputer to the interface circuits of the sensing equipment and actuators. Ensure that all connections meet electrical safety standards and electromagnetic compatibility requirements. Write an interface program in the control program of the single-chip microcomputer to read the data of the sensor and process the control signal of the actuator. Design a closed-loop control logic according to the needs of oil mist purification. For example, when the smoke sensor detects that the oil mist concentration exceeds the threshold, the single-chip microcomputer will start the fan and adjust its speed to increase the suction and purification efficiency of the oil mist. After the connections are completed, the entire system is tested to ensure that all sensors and actuators can correctly perform their functions through the microcontroller, including simulating different oil mist concentrations and operating conditions to check the system's response and stability.

[0026] Furthermore, step S1 of the present application also includes: Based on the production process, a process periodicity analysis of oil mist generation and escape is performed to determine a first spatial flow field, which is a dynamic flow field; based on the ventilation standard, a convergence and dispersion deviation analysis of the oil mist is performed to determine a second spatial flow field; the first spatial flow field and the second spatial flow field are mapped and fitted to determine the oil mist emission trend.

[0027] Specifically, collect production process data, including machine tool operation cycle, material processing type, cutting fluid and lubricant used, etc. Analyze the mechanism of oil mist generation in the production process and determine the key stages and periodic characteristics of oil mist generation. Use statistical methods or machine learning algorithms, such as time series analysis, to identify the periodic patterns of oil mist generation and escape. According to the results of process periodicity analysis, simulate the air flow and oil mist distribution in the workshop to obtain the first spatial flow field. The first spatial flow field is a dynamic flow field because it changes with different stages of the production process. Collect the design parameters of the workshop's ventilation system, including ventilation volume, air outlet location, air flow direction, etc. Analyze the influence of the ventilation system on the gathering and dispersion of oil mist and determine the movement trend of oil mist under ventilation. Based on the ventilation standards, use computational fluid dynamics CFD software to simulate the airflow in the workshop to obtain the second spatial flow field. The second spatial flow field reflects the influence of the ventilation system on the distribution of oil mist, including the gathering and dispersion bias of oil mist. Map and fit the first spatial flow field and the second spatial flow field to determine the actual dispersion trend of oil mist. Use multi-physics field coupling simulation to consider the comprehensive influence of production process and ventilation system.

[0028] Furthermore, step S2 of the present application also includes: Through the digital simulation model, the internal flow field is analyzed for bias flow to determine the internal bias flow situation; taking into account both purification quality and purification efficiency, the oil mist flow rate is set, and based on the oil mist flow rate and the airflow uniformity, the internal bias flow situation is traversed to perform reverse bias marking of the flow field distribution to determine the internal flow field situation, wherein the oil mist flow rate is an interval vector.

[0029] Specifically, a digital simulation model of the oil mist purifier is established using computational fluid dynamics (CFD) software. The physical properties of the oil mist, such as density, viscosity, and particle size, are considered in the simulation. The simulation is run to analyze the deviation phenomenon in the flow field, that is, the unevenness of the oil mist flow. Based on the simulation results, the high and low flow velocity areas in the flow field, as well as the locations where the oil mist gathers and disperses, are identified. The impact of these areas on the oil mist collection efficiency is analyzed to determine the internal deviation flow situation. According to the requirements of purification quality and purification efficiency, a reasonable oil mist flow velocity range is set. The setting of the oil mist flow velocity should ensure that the oil mist particles are effectively sucked into the purifier while avoiding excessive energy consumption. Within the set oil mist flow velocity range, different flow velocity values ​​are traversed. For each flow velocity value, the flow field distribution is analyzed, especially the movement trajectory and residence time of the oil mist in the flow field. For each flow velocity value, the high deviation flow areas in the flow field are marked, which are the difficulties in oil mist collection. Anti-deflection marking refers to identifying areas that need to be improved for subsequent optimization design. The optimal internal flow field situation is determined by comprehensively considering the oil mist flow velocity and air flow uniformity. The optimal situation is to achieve higher purification efficiency while ensuring purification quality.

[0030] Furthermore, step S5 of the present application also includes: The particle size of the oil mist in the target workshop is determined. If the particle size of the oil mist is smaller than a preset particle size, a particle integration analysis is performed on the wind wheel structure in the oil mist purifier to determine a wind wheel control signal, wherein the wind wheel control signal is determined based on the rotation speed, the steering direction, and the time limit. Based on the wind wheel control signal, a particle collision integration control is performed in response to the oil mist purifier before the oil mist purification step.

[0031] Specifically, the particle size of the oil mist in the workshop is measured using a particle size analyzer or other suitable detection equipment. The measurement results are compared with the preset particle size threshold. The preset particle size threshold can be determined based on the design and processing capacity of the purifier. If the oil mist particle size is smaller than the preset particle size, a particle integration analysis of the wind wheel structure is performed. The influence of factors such as the geometry of the wind wheel, blade angle, and rotation speed on the movement trajectory and collision effect of the oil mist particles is analyzed. Based on the results of the particle integration analysis, the control signal of the wind wheel is determined. The control signal includes rotation speed, direction, and time limit, which will affect the movement and collection efficiency of the oil mist particles in the purifier. Before the oil mist purification step, the operating state of the wind wheel is adjusted according to the wind wheel control signal. By optimizing the integrated control of particle collisions, the collision probability of oil mist particles with wind wheel blades can be increased, thereby improving the collection efficiency. The wind wheel control signal is transmitted to the control system of the oil mist purifier. The control system adjusts the rotation speed and direction of the wind wheel according to the signal to achieve optimal collection of oil mist particles.

[0032] Furthermore, step S5 of the present application also includes: Based on the sensing device, the oil mist signal is collected and the signal is corrected and fed back, wherein the signal correction is based on the field interference factors of the target workshop; based on the single chip microcomputer, a control logic analysis based on the fed-back oil mist signal is performed to determine a command signal based on oil mist purification; the oil mist purifier receives the command signal and performs a control response for smoke purification.

[0033] Specifically, a distributed smoke sensor is installed in the target workshop to monitor the oil mist concentration in real time. The sensor outputs a corresponding signal based on the characteristics of the oil mist, such as particle size and concentration. Since there may be various field interference factors in the workshop environment, such as temperature, humidity, other gases, etc., the sensor signal needs to be calibrated. The calibration can be achieved by comparing the output of the sensor with a known standard or using a calibration device to ensure the accuracy and reliability of the signal. The calibrated signal needs to be transmitted back to the microcontroller. The feedback can be achieved through wired or wireless communication, such as RS-485, Ethernet, Wi-Fi, Bluetooth, etc. After receiving the returned oil mist signal, the microcontroller performs a preset control logic analysis. The control logic includes algorithm processing, data filtering, threshold judgment, etc., which is used to determine whether the oil mist concentration exceeds the safety standard. According to the results of the control logic analysis, the microcontroller determines the corresponding command signal. The command signal will contain control commands for the oil mist purifier, such as start, stop, adjust the fan speed, etc. After receiving the command signal, the oil mist purifier executes the corresponding control response according to the signal content. For example, if the oil mist concentration exceeds a preset threshold, the purifier will start the fan to increase the air intake, or adjust the filtration system to improve purification efficiency.

[0034] Furthermore, the present application also includes: Perform situation monitoring on the internal flow field of the oil mist purifier, determine the bias flow information and make an over-limit judgment, and generate a bias flow adjustment signal, wherein the over-limit judgment standard is a preset bias flow threshold, and the preset bias flow threshold is a non-fixed value; the single-chip microcomputer receives the bias flow adjustment signal and determines a control instruction based on shunt diffusion; based on the control instruction, perform loop feedback control on the oil mist purifier.

[0035] Specifically, sensors installed inside the oil mist purifier, such as flow rate sensors and pressure sensors, are used to monitor the internal flow field. Flow field data, including flow rate, flow direction, pressure distribution and other information, are collected in real time. The collected flow field data is analyzed to identify the bias flow phenomenon in the flow field, that is, the unevenness of the oil mist flow. The bias flow information includes the position, range and intensity of the bias flow area. The over-limit judgment is made according to the preset bias flow threshold. The preset bias flow threshold is not a fixed value, and it can be dynamically adjusted according to factors such as purification efficiency, energy consumption, and safety standards. If the monitored bias flow exceeds the preset threshold, it is judged as over-limit. When the judgment result is over-limit, a bias flow adjustment signal is generated. The adjustment signal contains information about the bias flow phenomenon. The single-chip microcomputer receives the bias flow adjustment signal through the communication interface. The single-chip microcomputer processes the signal and prepares to execute the corresponding control instruction. The single-chip microcomputer determines the control instruction based on the diversion diffusion according to the bias flow adjustment signal. The control instruction includes reducing the bias flow phenomenon and optimizing the flow field distribution by adjusting the fan speed and changing the airflow direction. The single chip microcomputer sends control instructions to the actuators of the oil mist purifier, such as fans, valves, etc. The actuators adjust according to the instructions to achieve real-time feedback control of the flow field.

[0036] Furthermore, the present application also includes: Determine the emission influencing factors, wherein the emission influencing factors include at least temperature and humidity; determine the emission influence function with the emission influencing factors as independent variables and the oil mist emission status as dependent variables; and perform reverse control of the oil mist distribution in the target workshop based on the emission influence function.

[0037] Specifically, the factors affecting the dissipation of oil mist from the source to other areas of the workshop. In addition to temperature and humidity, they may also include air flow rate, workshop layout, configuration of production equipment, personnel activities, etc. Collect data such as temperature, humidity, and air flow rate of the target workshop. At the same time, record the dissipation of oil mist under different conditions, such as oil mist concentration, distribution range, etc. Use statistical methods or machine learning algorithms, such as regression analysis, to determine the dissipation influence function. Take temperature, humidity, etc. as independent variables and oil mist dissipation status as dependent variables to analyze the relationship between them. By analyzing the dissipation influence function, identify the degree of influence of different factors on oil mist dissipation. For example, it is found that the dissipation of oil mist increases when the temperature rises, and the dissipation of oil mist decreases when the humidity increases. Based on the dissipation influence function, formulate a counter-regulation strategy. Counter-regulation refers to reducing the dissipation and distribution of oil mist by adjusting the environmental conditions of the workshop. According to the dissipation influence function and the counter-regulation strategy, the environmental conditions of the workshop are regulated. For example, the temperature and humidity can be controlled by adjusting the air conditioning system, or the air flow rate can be controlled by improving the ventilation system.

[0038] In summary, the oil mist purification method provided by this application has the following technical effects: Based on the production process and ventilation standards of the target workshop, a correlation analysis is conducted with the oil mist distribution to determine the oil mist emission trend, which is marked with periodic nodes; the internal flow field of the oil mist purifier is analyzed for airflow characteristics based on digital simulation to determine the internal flow field situation, where the airflow characteristics include the oil mist flow rate and airflow uniformity; the underlying control logic of the oil mist purifier is interactively combined with the oil mist emission trend and the internal flow field situation to assist the intelligent control model, and make a directional inhalation purification decision to determine the pre-control plan, where the oil mist purification plan is a periodic plan. It includes a main line control strategy and a regulation strategy; based on the oil mist purification scheme, control program conversion and single-chip microcomputer burning are performed; a single-chip microcomputer interface is connected with the interface circuit of the oil mist purifier and the sensor equipment to perform loop control of the oil mist purification of the target workshop. The sensor equipment at least includes a distributed smoke sensor, which effectively solves the problem that the prior art has poor oil mist purification effect due to inaccurate prediction of the oil mist emission trend, poor adaptability and low degree of automation, further affecting the cleanliness of the production environment and production efficiency, realizes intelligent oil mist purification loop control, and improves the efficiency of oil mist purification. Embodiment 2

[0039] Based on the oil mist purification method in the above embodiment, the present application also provides an oil mist purification system, see the attached Figure 2 , the system comprising: The correlation analysis module 11 is used to perform correlation analysis on the oil mist distribution based on the production process and ventilation standard of the target workshop, and determine the oil mist emission trend, wherein the oil mist emission trend has a periodic node mark.

[0040] The airflow characteristic analysis module 12 is used to analyze the internal flow field of the oil mist purifier based on digital simulation to determine the internal flow field state, wherein the airflow characteristics include the oil mist flow rate and the airflow uniformity.

[0041] The control scheme determination module 13 is used to interact with the underlying control logic of the oil mist purifier, combine the oil mist emission trend with the internal flow field situation, assist the intelligent control model, make a directional inhalation purification decision and determine the pre-control scheme, wherein the oil mist purification scheme is a periodic scheme, including a main line control strategy and a regulation strategy.

[0042] The scheme burning module 14 is used for performing control program conversion and single chip microcomputer burning based on the oil mist purification scheme.

[0043] The loop control module 15 is used to establish a connection between the single-chip microcomputer interface and the interface circuit of the oil mist purifier and the sensor device to perform loop control of the oil mist purification of the target workshop. The sensor device at least includes a distributed smoke sensor.

[0044] Furthermore, the correlation analysis module 11 in the system is also used for: Based on the production process, a process periodicity analysis of oil mist generation and escape is performed to determine a first spatial flow field, which is a dynamic flow field; based on the ventilation standard, a convergence and dispersion deviation analysis of the oil mist is performed to determine a second spatial flow field; the first spatial flow field and the second spatial flow field are mapped and fitted to determine the oil mist emission trend.

[0045] Furthermore, the airflow characteristic analysis module 12 in the system is also used for: Through the digital simulation model, the internal flow field is analyzed for bias flow to determine the internal bias flow situation; taking into account both purification quality and purification efficiency, the oil mist flow rate is set, and based on the oil mist flow rate and the airflow uniformity, the internal bias flow situation is traversed to perform reverse bias marking of the flow field distribution to determine the internal flow field situation, wherein the oil mist flow rate is an interval vector.

[0046] Furthermore, the loop control module 15 in the system is also used for: The particle size of the oil mist in the target workshop is determined. If the particle size of the oil mist is smaller than a preset particle size, a particle integration analysis is performed on the wind wheel structure in the oil mist purifier to determine a wind wheel control signal, wherein the wind wheel control signal is determined based on the rotation speed, the steering direction, and the time limit. Based on the wind wheel control signal, a particle collision integration control is performed in response to the oil mist purifier before the oil mist purification step.

[0047] Furthermore, the loop control module 15 in the system is also used for: Based on the sensing device, the oil mist signal is collected and the signal is corrected and fed back, wherein the signal correction is based on the field interference factors of the target workshop; based on the single chip microcomputer, a control logic analysis based on the fed-back oil mist signal is performed to determine a command signal based on oil mist purification; the oil mist purifier receives the command signal and performs a control response for smoke purification.

[0048] Furthermore, the system further comprises a feedback control module, wherein the feedback control module is used to: Perform situation monitoring on the internal flow field of the oil mist purifier, determine the bias flow information and make an over-limit judgment, and generate a bias flow adjustment signal, wherein the over-limit judgment standard is a preset bias flow threshold, and the preset bias flow threshold is a non-fixed value; the single-chip microcomputer receives the bias flow adjustment signal and determines a control instruction based on shunt diffusion; based on the control instruction, perform loop feedback control on the oil mist purifier.

[0049] Furthermore, the system further comprises a counter-regulation module, which is used to: Determine the emission influencing factors, wherein the emission influencing factors include at least temperature and humidity; determine the emission influence function with the emission influencing factors as independent variables and the oil mist emission status as dependent variables; and perform reverse control of the oil mist distribution in the target workshop based on the emission influence function.

[0050] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1 The oil mist purification method and specific examples in the first embodiment are also applicable to an oil mist purification system in the present embodiment. Through the above detailed description of an oil mist purification method, those skilled in the art can clearly understand an oil mist purification system in the present embodiment, so for the sake of brevity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0051] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

[0052] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of the present application and its equivalent technology, the present application is also intended to include these modifications and variations.

Claims

1. An oil mist purification method, characterized in that: The method comprises: Based on the production process and ventilation standards of the target workshop, a correlation analysis is performed with the oil mist distribution to determine the oil mist emission trend, which is marked with periodic nodes; The internal flow field of the oil mist purifier is analyzed based on digital simulation to determine the internal flow field situation, where the air flow characteristics include the oil mist flow rate and air flow uniformity; Interact with the underlying control logic of the oil mist purifier, combine the oil mist emission trend with the internal flow field situation, assist the intelligent control model, make directional inhalation purification decisions and determine the pre-control plan, wherein the oil mist purification plan is a periodic plan, including a main line control strategy and a regulation strategy; Based on the oil mist purification scheme, control program conversion and single chip microcomputer burning are performed; Establish a connection between the single-chip microcomputer interface and the interface circuit of the oil mist purifier and the sensor device to perform loop control of the oil mist purification of the target workshop, and the sensor device at least includes a distributed smoke sensor.

2. The oil mist purification method according to claim 1, characterized in that: Conduct correlation analysis with oil mist distribution, including: Based on the production process, a process periodicity analysis of oil mist generation and escape is performed to determine a first spatial flow field, where the first spatial flow field is a dynamic flow field; Based on the ventilation standard, the gathering and dispersion direction of the oil mist is analyzed to determine the second spatial flow field; The first spatial flow field and the second spatial flow field are mapped and fitted to determine the oil mist emission trend.

3. The oil mist purification method according to claim 1, characterized in that: The airflow characteristics analysis based on digital simulation to determine the internal flow field situation includes: By using a digital simulation model, a bias flow analysis is performed on the internal flow field to determine the internal bias flow situation; Taking both purification quality and purification efficiency into consideration, the oil mist flow rate is set, and based on the oil mist flow rate and the airflow uniformity, the internal bias flow situation is traversed to perform reverse bias marking of the flow field distribution to determine the internal flow field situation, wherein the oil mist flow rate is an interval vector.

4. The oil mist purification method according to claim 1, characterized in that: The loop control for oil mist purification of the target workshop includes: Determine the particle size of the oil mist in the target workshop. If the particle size of the oil mist is smaller than a preset particle size, perform particle integration analysis on the wind wheel structure in the oil mist purifier to determine a wind wheel control signal, wherein the wind wheel control signal is determined based on a rotation speed, a steering direction, and a time limit; Based on the wind wheel control signal, the particle collision integration control before the oil mist purification step is performed in response to the oil mist purifier.

5. The oil mist purification method according to claim 1, characterized in that: Conducting loop control of oil mist purification in the target workshop includes: Based on the sensing device, oil mist signals are collected and signal correction and feedback are performed, wherein the signal correction is based on the field interference factors of the target workshop; Based on the single chip microcomputer, a control logic analysis based on the returned oil mist signal is performed to determine a command signal based on oil mist purification; The oil mist purifier receives the command signal and performs a control response for smoke purification.

6. The oil mist purification method according to claim 5, characterized in that: Conducting loop control of oil mist purification in the target workshop includes: Performing situation monitoring on the internal flow field of the oil mist purifier, determining the bias flow information and making an over-limit judgment, and generating a bias flow adjustment signal, wherein the over-limit judgment standard is a preset bias flow threshold, and the preset bias flow threshold is a non-fixed value; The single chip microcomputer receives the bias current adjustment signal and determines a control instruction based on shunt diffusion; Based on the control instruction, loop feedback control is performed on the oil mist purifier.

7. The oil mist purification method according to claim 1, characterized in that: The method further comprises: Determining dissipation influencing factors, wherein the dissipation influencing factors at least include temperature and humidity; Taking the dispersion influencing factors as independent variables and the oil mist dispersion conditions as dependent variables, a dispersion influencing function is determined; Based on the dispersion influence function, the oil mist distribution of the target workshop is reversely regulated.

8. An oil mist purification system, characterized in that: For implementing the steps of an oil mist purification method according to any one of claims 1 to 7, the system comprises: A correlation analysis module, wherein the correlation analysis module is used to perform correlation analysis on the oil mist distribution based on the production process and ventilation standard of the target workshop, and determine the oil mist emission trend, wherein the oil mist emission trend is marked with a periodic node mark; An airflow characteristic analysis module, which is used to analyze the internal flow field of the oil mist purifier based on digital simulation to determine the internal flow field state, wherein the airflow characteristics include the oil mist flow rate and the airflow uniformity; A control scheme determination module, which is used to interact with the underlying control logic of the oil mist purifier, combine the oil mist emission trend with the internal flow field situation, assist the intelligent control model, make a directional inhalation purification decision and determine the pre-control scheme, wherein the oil mist purification scheme is a periodic scheme, including a main line control strategy and a regulation strategy; A program burning module, the program burning module is used to perform control program conversion and single-chip microcomputer burning based on the oil mist purification program; A loop control module is used to establish a connection between the single-chip microcomputer interface and the interface circuit of the oil mist purifier and the sensor device to perform loop control of the oil mist purification of the target workshop. The sensor device at least includes a distributed smoke sensor.

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