A VOCs quality control autonomous method

Abstract

This invention discloses a self-governing method for VOCs quality control, belonging to the field of VOCs governance technology. The method includes: setting up a VOCs information model for users; improving the VOCs monitoring system based on the VOCs information model; conducting real-time monitoring through the VOCs monitoring system to obtain VOCs monitoring data; matching applicable measures from a preset measure library based on the VOCs information model; performing governance simulation based on the applicable measures and the VOCs information model to obtain simulated governance expected data; displaying the simulated governance expected data in the VOCs information model; acquiring governance needs in real time; analyzing the governance needs based on the VOCs information model; and determining the recommended governance method to meet the governance needs. This invention not only solves the self-governing VOCs quality control problem of a single enterprise, but also achieves data interoperability, resource collaboration, and intelligent decision-making for multiple users by constructing a regional / industry-level comprehensive VOCs governance platform.

Description

Technical Field

[0001] This invention belongs to the field of VOCs treatment technology, specifically a self-management method for VOCs quality control. Background Technology

[0002] Volatile organic compounds (VOCs), as key precursors to the formation of atmospheric ozone and fine particulate matter, have become a core challenge in atmospheric environmental governance. Simultaneously, VOCs control is a core task and significant challenge for corporate environmental management; however, most companies face numerous problems in VOCs management. Under traditional management models, companies rely on manual inspections and offline monitoring to track emissions. Data acquisition is significantly delayed, making it difficult to grasp emission dynamics in real time. This leads to difficulties in timely detection and control of excessive emissions, resulting in hefty fines and the risk of production shutdowns. Regarding multi-technology collaborative governance, companies lack effective integration methods. Different treatment processes operate independently, unable to dynamically adjust parameters according to real-time operating conditions, resulting in low treatment efficiency and high equipment energy consumption.

[0003] Against this backdrop, the present invention provides a self-governing method for VOCs quality control, which is used to solve the problem of self-governing VOCs quality control in enterprises. Summary of the Invention

[0004] To address the problems of the above-mentioned solutions, this invention provides a self-governing method for VOCs quality control.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A self-management method for VOCs quality control, the method comprising:

[0007] Step 1: The platform establishes a measure library in the cloud, which stores various VOCs governance measures and implementation requirements, and the implementation requirements are associated with the VOCs governance measures;

[0008] Furthermore, the platform provider dynamically updates the measures database.

[0009] Step 2: Set up the corresponding VOCs information model for the user, and improve the VOCs monitoring system based on the VOCs information model;

[0010] Furthermore, the VOCs information model is used to display relevant information about the generation, transport, treatment, and emission of VOCs to users.

[0011] Furthermore, the VOCs monitoring system is improved based on the VOCs information model, including:

[0012] The platform provider sets up a monitoring requirement library in the cloud, which stores the monitoring requirement sets corresponding to various VOCs information backgrounds;

[0013] The system identifies the user's VOCs information background, matches the corresponding monitoring requirement set from the monitoring requirement library based on the VOCs information background, extracts features from the VOCs information model using the monitoring requirement set, and obtains corresponding monitoring feature data. The monitoring feature data is analyzed using the monitoring requirement set to identify unmet monitoring requirements, which are then marked as optimization requirements. The VOCs monitoring system is then improved based on the optimization requirements.

[0014] Furthermore, the monitoring feature data is analyzed through a set of monitoring requirements, including:

[0015] Establish a calibration model, the expression for which the calibration model is required is:

[0016] ;

[0017] In the formula: (TA, s) i ) represents the input data, TA represents the monitoring feature data, and s represents the input data. i This represents the corresponding monitoring requirements in the monitoring requirements set, where i = 1, 2, ..., n, and n is the number of monitoring requirements in the monitoring requirements set; TA→s i This indicates that the monitoring characteristic data meets the corresponding monitoring requirements; the output data is the required calibration value YQ(TA, s) i The calibration value must be 1 or 0.

[0018] Identify each monitoring requirement corresponding to the monitoring requirement set, integrate the monitoring requirements and corresponding monitoring feature data into input data, input them into the requirement calibration model for analysis, and obtain the requirement calibration value of the corresponding monitoring requirement.

[0019] Based on the obtained required calibration values, identify the monitoring requirements that are not met.

[0020] Step 3: Conduct real-time monitoring through the VOCs monitoring system to obtain the corresponding VOCs monitoring data, and display the VOCs monitoring data in the corresponding VOCs information model.

[0021] Furthermore, real-time VOCs leakage analysis is performed based on the VOCs information model to obtain leakage analysis results, and corresponding markings are made in the VOCs information model based on the leakage analysis results.

[0022] Furthermore, real-time VOCs leakage analysis is performed based on the VOCs information model, including:

[0023] The system presets factors that affect VOC concentrations and determines the concentration background at the current time based on each factor.

[0024] Based on the concentration impact background, the historical VOCs concentration data without leakage were analyzed to obtain the normal VOCs concentration range under the concentration impact background.

[0025] In the VOCs information model, the normal VOCs concentration range of the corresponding area is dynamically marked. The VOCs concentration range of the corresponding location is compared with the VOCs concentration corresponding to the VOCs monitoring data to determine whether the concentration is abnormal.

[0026] If the concentration is normal, no further action is required.

[0027] If the concentration is abnormal, record the time period of the abnormal concentration, and determine whether there is a VOCs leak based on the time period of the abnormal concentration.

[0028] Step 4: Connect to the cloud-based measure library, match applicable VOCs governance measures from the measure library in real time based on the VOCs information model, and mark the applicable VOCs governance measures as applicable measures; perform governance simulation based on the applicable measures and the VOCs information model to obtain the corresponding simulated governance expected data, and display the simulated governance expected data in the VOCs information model.

[0029] Furthermore, governance simulations are conducted based on applicable measures and VOCs information models, including:

[0030] Based on whether the various applicable measures can be implemented together, a corresponding simulation background is generated; simulation analysis is performed according to the simulation background and VOCs information model to predict the VOCs governance effect of the corresponding simulation background.

[0031] The VOCs treatment effects of each simulation background are integrated into simulated treatment expectation data.

[0032] Step 5: Obtain users' governance needs in real time, analyze the governance needs based on the VOCs information model, determine the recommended governance methods that meet the governance needs, and display the recommended governance methods to users.

[0033] Furthermore, governance needs are analyzed based on the VOCs information model, including:

[0034] Based on the VOCs information model, obtain the corresponding simulated governance expected data, and identify the governance effect of each VOCs corresponding to the simulated governance expected data.

[0035] The treatment effect of each VOCs is calibrated according to the treatment requirements to determine the VOCs treatment effect that meets the treatment requirements.

[0036] Identify the simulated background corresponding to the VOCs treatment effect, and determine the corresponding recommended treatment method based on the simulated background.

[0037] Furthermore, the recommended governance methods are prioritized based on their effectiveness in VOCs governance.

[0038] Furthermore, the optimal governance parameters are determined in real time based on the VOCs information model.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] This invention not only solves the problem of self-governance of VOCs quality control for individual enterprises, but also significantly improves the efficiency of regional atmospheric environmental governance by constructing a regional / industry-level comprehensive VOCs governance platform, enabling data interoperability, resource collaboration, and intelligent decision-making among multiple users. Through platform-based design, it achieves the organic unity of multi-user data interoperability, resource collaboration, intelligent decision-making, and ecosystem expansion, which not only reduces enterprise governance costs and improves regulatory efficiency, but also promotes the standardization of third-party services and the low-carbon transformation of the industry. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0043] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0044] like Figure 1 As shown, a self-management method for VOCs quality control includes:

[0045] Step 1: The platform establishes a measure library in the cloud. This library stores VOCs governance measures applicable to various industries and situations. Subsequently, the platform analyzes the library to determine whether the corresponding VOCs governance measures are suitable for the VOCs governance of the relevant user. This is done based on the application requirements and conditions of the VOCs governance measures, and the results are integrated into implementation requirements for VOCs governance measures. These implementation requirements are then stored in the measure library. The platform will dynamically update the measure library based on the updates and advancements in current VOCs governance technologies.

[0046] Step Two: Set up a corresponding VOCs information model for the user. This VOCs information model is used to intuitively display relevant information such as the generation, transportation, treatment, and emission of VOCs to the user, such as the VOCs generation points of each production workshop and equipment, real-time display of emission concentration, flow rate, and component ratio; the transportation path of VOCs from the generation point to the treatment equipment, marking the pipe material, diameter, length, and key valve positions, real-time monitoring of parameters such as pressure, temperature, and flow rate in the pipes, simulating the direction of exhaust gas flow through color coding or streamline animation, and identifying potential leakage risk points (such as flange connections, elbows, etc.); improve the VOCs monitoring system based on the VOCs information model, that is, to enable the VOCs monitoring system to accurately monitor VOCs throughout the entire process.

[0047] In one embodiment, the VOCs information model is built based on existing technologies such as the Internet of Things, big data, and 3D visualization. It is generally manually set by the platform's staff based on the actual VOCs generation, transportation, treatment, and emission at the user's location.

[0048] In one embodiment, the VOCs information model is built based on digital twin technology, which facilitates subsequent simulation analysis.

[0049] In one embodiment, the VOCs monitoring system can be improved based on the VOCs information model. This can be achieved by identifying the shortcomings of the user's current VOCs monitoring system through existing methods such as manual intervention, and then making targeted improvements.

[0050] In one embodiment, the VOCs monitoring system is improved based on the VOCs information model, including:

[0051] The platform establishes a monitoring requirement database in the cloud. This database stores monitoring requirement sets corresponding to various VOCs information backgrounds. Each monitoring requirement set consists of various monitoring requirements for that VOCs information background. Even for the same VOCs equipment, different monitoring requirement sets may exist due to regional regulations. The VOCs information background is determined based on whether the generation, transportation, treatment, emission, and regional regulations of VOCs can be considered the same. For example, the same set of production and treatment equipment in the same region can be considered the same VOCs information background. Alternatively, various VOCs information backgrounds with autonomous VOCs quality control can be directly statistically analyzed, and a single VOCs information background can be directly applied. That is, one VOCs information background corresponds to one potential user situation. To improve storage and analysis efficiency, clustering algorithms are used to cluster and merge the above-mentioned single VOCs information backgrounds, with the constraint that the same monitoring requirement set can be used. Essentially, the VOCs information background is a dynamic set of characteristics of VOCs from generation to emission in the user scenario, used to quantitatively describe its material composition, spatiotemporal distribution, process correlation, and treatment adaptability.

[0052] The process involves identifying the user's VOCs information background, matching the corresponding monitoring requirement set from the monitoring requirement database based on the VOCs information background, extracting features from the VOCs information model using the monitoring requirement set to obtain corresponding monitoring feature data, i.e., collecting data from the VOCs information model according to each monitoring requirement in the monitoring requirement set to determine whether the monitoring requirement is met; analyzing the monitoring feature data using the monitoring requirement set to identify unmet monitoring requirements, i.e., the corresponding monitoring feature data does not meet the monitoring requirements; and marking the corresponding monitoring requirements as optimization requirements.

[0053] The VOCs monitoring system was improved to meet the optimization requirements.

[0054] In one embodiment, by analyzing the monitoring feature data through the monitoring requirement set, judgments can be made based on existing judgment methods, such as establishing corresponding intelligent models based on machine learning, deep learning algorithms, etc., to make intelligent judgments.

[0055] In one embodiment, the analysis of monitoring feature data using a monitoring requirement set includes:

[0056] Establish a calibration model, the expression for which the calibration model is required is:

[0057] ;

[0058] In the formula: (TA, s) i ) represents the input data, TA represents the monitoring feature data, and s represents the input data. i This represents the corresponding monitoring requirements in the monitoring requirements set, where i = 1, 2, ..., n, and n is the number of monitoring requirements in the monitoring requirements set; TA→s i This indicates that the monitoring characteristic data meets the corresponding monitoring requirements; the output data is the required calibration value YQ(TA, s) i The calibration value is required to be 1 or 0; failure to meet the corresponding monitoring requirements is considered an abnormal situation, and then the corresponding training set is set up using the corresponding historical evaluation data for training.

[0059] Identify each monitoring requirement corresponding to the monitoring requirement set, integrate the monitoring requirements and corresponding monitoring feature data into input data, input them into the requirement calibration model for analysis, and obtain the requirement calibration value of the corresponding monitoring requirement.

[0060] Based on the obtained required calibration values, determine the monitoring requirements that are not met, i.e., the monitoring requirements that require a calibration value of 0.

[0061] Step 3: Perform real-time monitoring through the optimized VOCs monitoring system to obtain the corresponding VOCs monitoring data, and display the VOCs monitoring data in the corresponding VOCs information model.

[0062] In one embodiment, real-time VOCs leakage analysis is performed based on the VOCs information model to obtain the corresponding leakage analysis results, and corresponding markings are made in the VOCs information model based on the leakage analysis results.

[0063] In one embodiment, real-time VOCs leakage analysis based on a VOCs information model includes:

[0064] The system presets factors that affect VOC concentrations and determines the current concentration impact background based on each factor in real time. This background includes factors such as temperature, pressure, flow rate, season, time period, and location range. Based on the concentration impact background, the system analyzes historical VOC concentration data without leakage to obtain the normal VOC concentration range under this impact background.

[0065] In the VOCs information model, the normal VOCs concentration range of the corresponding area is dynamically marked. The VOCs concentration range of the corresponding location is compared with the VOCs concentration corresponding to the VOCs monitoring data to determine whether the concentration is abnormal.

[0066] If the concentration is normal, no further action is required.

[0067] If the concentration is abnormal, record the time period of the abnormal concentration. Determine whether there is a VOCs leak based on the time period of the abnormal concentration. If the time period of the abnormal concentration exceeds the preset time period, it can also be determined by the proportion of the time period of abnormal concentration within a day or other time period.

[0068] In one embodiment, real-time VOCs leakage analysis can be performed based on a VOCs information model. Leakage analysis can also be performed through other methods, such as concentration change curves and threshold comparisons.

[0069] Step 4: Connect to the cloud-based measure library, match applicable VOCs governance measures from the measure library in real time based on the VOCs information model, and make matching judgments according to the corresponding implementation requirements; mark the applicable VOCs governance measures as applicable measures; conduct governance simulation based on the applicable measures and the VOCs information model to obtain the corresponding simulated governance expected data, and display the simulated governance expected data in the VOCs information model.

[0070] In one embodiment, governance simulation is performed based on applicable measures and a VOCs information model, including:

[0071] Based on the relevant historical quality records, determine whether the applicable measures can be implemented together. Based on whether they can be implemented in a coordinated manner, determine the various implementation combinations. For example, if there are three applicable measures, A, B, and C, and A and C cannot be implemented together, then there can be implementation combinations of A, B, C, AB, and BC. Based on the corresponding implementation combinations, generate the corresponding simulation background, which is to simulate the implementation according to the corresponding implementation combinations.

[0072] Based on the simulation background and VOCs information model, simulation analysis is conducted to predict the corresponding VOCs treatment effect. The VOCs treatment effect also includes other relevant information that users are concerned about, such as cost and equipment.

[0073] The VOCs treatment effects corresponding to each simulation background are integrated into simulated treatment expectation data.

[0074] In one embodiment, simulation analysis is performed based on the simulation background and the VOCs information model. For example, if the VOCs information model is established using digital twin technology, then simulation is performed based on the VOCs information model combined with digital twin technology.

[0075] In one embodiment, simulation analysis is performed based on the simulation background and VOCs information model, or other existing simulation technologies can be used to estimate the governance effect when simulating based on the simulation background; for example, the platform provides the corresponding material data, and subsequent analysis and estimation are performed in conjunction with the corresponding material data.

[0076] In one embodiment, the basic effect achieved by displaying the simulated governance expected data in the VOCs information model is as follows:

[0077] Users can view various applicable measures and simulation backgrounds, and understand the corresponding estimated VOCs treatment effects based on the simulation backgrounds; on this basis, other display and interactive functions can be added to facilitate user access.

[0078] Step 5: Obtain users' governance needs in real time. Governance needs refer to various requirements such as the governance effects and cost requirements that users expect to achieve. Analyze the governance needs based on the VOCs information model, determine the recommended governance methods that meet the governance needs, and display the recommended governance methods to users.

[0079] In one embodiment, the analysis of governance needs based on a VOCs information model includes:

[0080] Based on the VOCs information model, obtain the corresponding simulated governance expected data, and identify the governance effect of each VOCs corresponding to the simulated governance expected data.

[0081] The treatment effect of each VOCs is calibrated according to the treatment requirements to determine the VOCs treatment effect that meets the treatment requirements.

[0082] Identify the simulated background corresponding to the VOCs governance effect, and determine the corresponding governance recommendation method based on the simulated background, that is, the combination method formed by various VOCs governance measures corresponding to the simulated background.

[0083] In one embodiment, priority can be ranked according to the VOCs governance effect corresponding to the recommended governance method. For example, it can be ranked directly according to the effect, or according to the cost, or it can be ranked by a comprehensive evaluation of the effect and cost.

[0084] In one embodiment, optimal treatment parameters, such as process operating parameters, purification efficiency parameters, and environmental control parameters, are determined in real time based on a VOCs information model, which facilitates user management.

[0085] Specifically, one approach is to analyze historical governance records based on the current VOCs information context to determine the optimal governance parameters under various conditions, and then perform corresponding matching. However, the most common approach currently is to use intelligent models based on deep learning algorithms to analyze and determine the optimal governance parameters.

[0086] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.

[0087] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A self-managed method for VOCs quality control, characterized in that the method... include: The platform provider establishes a measure library in the cloud, which stores various VOCs governance measures and implementation requirements, and the implementation requirements are associated with the VOCs governance measures. A corresponding VOCs information model is set up for the user. The VOCs information model is used to intuitively display the generation, transportation, treatment and emission information of VOCs to the user; the VOCs monitoring system is improved based on the VOCs information model. Real-time monitoring is performed through a VOCs monitoring system to obtain corresponding VOCs monitoring data, which is then displayed in the corresponding VOCs information model. The system connects to a cloud-based measure library, matches applicable VOCs governance measures from the library in real time based on the VOCs information model, and marks the applicable VOCs governance measures as applicable measures. It then performs governance simulations based on the applicable measures and the VOCs information model to obtain corresponding simulated governance expected data, which is then displayed in the VOCs information model. The system acquires users' governance needs in real time, analyzes these needs based on the VOCs information model, determines the recommended governance methods that meet these needs, and then displays these recommended methods to users. The VOCs monitoring system was improved based on the VOCs information model, including: The platform provider sets up a monitoring requirement library in the cloud, which stores the monitoring requirement sets corresponding to various VOCs information backgrounds; Identify the user's VOCs information background, match the corresponding monitoring requirement set from the monitoring requirement library based on the VOCs information background, and extract features from the VOCs information model through the monitoring requirement set to obtain the corresponding monitoring feature data; By analyzing the monitoring feature data through the monitoring requirement set, unmet monitoring requirements are identified and marked as optimization requirements; the VOCs monitoring system is then improved based on these optimization requirements.

2. The self-management method for VOCs quality control according to claim 1, characterized in that, The platform provider dynamically updates the measures database.

3. The self-management method for VOCs quality control according to claim 1, characterized in that, The monitoring feature data is analyzed through a set of monitoring requirements, including: Establish a calibration model, the expression for which the calibration model is required is: ； In the formula: (TA, s) i ) represents the input data, TA represents the monitoring feature data, and s represents the input data. i This represents the corresponding monitoring requirements in the monitoring requirements set, where i = 1, 2, ..., n, and n is the number of monitoring requirements in the monitoring requirements set; TA→s i This indicates that the monitoring characteristic data meets the corresponding monitoring requirements; the output data is the required calibration value YQ(TA, s) i The calibration value must be 1 or 0. Identify each monitoring requirement corresponding to the monitoring requirement set, integrate the monitoring requirements and corresponding monitoring feature data into input data, input them into the requirement calibration model for analysis, and obtain the requirement calibration value of the corresponding monitoring requirement. Based on the obtained required calibration values, identify the monitoring requirements that are not met.

4. The self-management method for VOCs quality control according to claim 1, characterized in that, Real-time VOCs leakage analysis is performed based on the VOCs information model to obtain leakage analysis results, and corresponding markings are made in the VOCs information model based on the leakage analysis results.

5. A self-management method for VOCs quality control according to claim 4, characterized in that, Real-time VOCs leakage analysis is performed based on the VOCs information model, including: The system presets factors that affect VOC concentrations and determines the concentration background at the current time based on each factor. Based on the concentration impact background, the historical VOCs concentration data without leakage were analyzed to obtain the normal VOCs concentration range under the concentration impact background. In the VOCs information model, the normal VOCs concentration range of the corresponding area is dynamically marked. The VOCs concentration range of the corresponding location is compared with the VOCs concentration corresponding to the VOCs monitoring data to determine whether the concentration is abnormal. If the concentration is normal, no further action is required. If the concentration is abnormal, record the time period of the abnormal concentration, and determine whether there is a VOCs leak based on the time period of the abnormal concentration.

6. A self-management method for VOCs quality control according to claim 1, characterized in that, Governance simulations were conducted based on applicable measures and VOCs information models, including: Based on whether the various applicable measures can be implemented together, a corresponding simulation background is generated; simulation analysis is performed according to the simulation background and VOCs information model to predict the VOCs governance effect of the corresponding simulation background; The VOCs treatment effects of each simulation background are integrated into simulated treatment expectation data.

7. A self-management method for VOCs quality control according to claim 1, characterized in that, The governance needs are analyzed based on the VOCs information model, including: Based on the VOCs information model, obtain the corresponding simulated governance expected data, and identify the governance effect of each VOCs corresponding to the simulated governance expected data. The treatment effect of each VOCs is calibrated according to the treatment requirements to determine the VOCs treatment effect that meets the treatment requirements. Identify the simulated background corresponding to the VOCs treatment effect, and determine the corresponding recommended treatment method based on the simulated background.

8. A self-management method for VOCs quality control according to claim 7, characterized in that, The recommended governance methods are prioritized based on their effectiveness in VOCs governance.

9. A self-management method for VOCs quality control according to claim 1, characterized in that, The optimal governance parameters are determined in real time based on the VOCs information model.

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