Enterprise emission monitoring and regulation method and device based on dynamic factor library

Abstract

The invention discloses an enterprise emission monitoring and regulation method and device based on a dynamic factor library. The method comprises the following steps: firstly, obtaining basic information of each target enterprise in a target area and key processes in a process type; determining emission factors of each target enterprise based on the basic information and the key process; constructing a dynamic factor library according to the emission factors of the target enterprises; and finally performing emission monitoring and regulation and control on each target enterprise in a to-be-controlled period based on the dynamic factor library. A dynamic factor library of a reaction process level is constructed, and the accuracy of emission factors can be improved, so that the refinement of enterprise emission monitoring and regulation is improved.

Description

Technical Field

[0001] This invention belongs to the field of air pollution control technology, specifically relating to a method and device for monitoring and controlling enterprise emissions based on a dynamic factor library. Background Technology

[0002] Emission factors are key indicators for assessing pollutant emission levels, and their representativeness directly affects the accuracy of emission inventories and the effectiveness of pollution control measures. Currently, the construction and application of existing emission factor databases still face the following problems: insufficient accuracy—existing factors mostly adopt a broad "industry-overall" model, failing to reflect the differences in emissions from different production processes and procedures within the same industry, and lacking refined factors at the process level, resulting in insufficient accuracy in the calculation results; and lagging timeliness—current factor update cycles are long, making it difficult to reflect the impact of enterprise technological transformation or environmental facility upgrades in a timely manner, leading to poor timeliness. These problems restrict the scientific validity and applicability of emission inventories, especially in supporting scenarios such as emergency control of heavy pollution weather and precise enterprise control, affecting the accuracy, precision, and timeliness of control measures.

[0003] Therefore, how to improve the precision of enterprise emission control has become a key technical problem that urgently needs to be solved by those in the field. Summary of the Invention

[0004] The purpose of this invention is to solve the technical problem that the accuracy of emission factors in the prior art is not high, which leads to a low precision in monitoring and controlling enterprise emissions.

[0005] To achieve the above technical objectives, on the one hand, the present invention provides a method for enterprise emission monitoring and control based on a dynamic factor library, the method comprising:

[0006] Obtain basic information, key processes, and process types for each target enterprise in the target region;

[0007] The emission factors for each target enterprise are determined based on the aforementioned basic information, key processes, and process types.

[0008] A dynamic factor library is constructed based on the emission factors of each target enterprise.

[0009] Based on the aforementioned dynamic factor library, emissions monitoring and control are conducted for each target enterprise during the period of pending regulation.

[0010] Furthermore, determining the emission factors of each target enterprise based on the aforementioned basic information, key processes, and process types specifically includes, for any one of the target enterprises... The emission factors of pollutants are determined as shown in the following formula:

[0011] ;

[0012] In the formula, Let k be the emission factor of pollutant k in process i. For time, specifically in hours. Let k be the concentration of contaminant k in process i at time t. Let be the exhaust gas flow rate at time t. For monitoring time intervals, This refers to the activity level data for process i.

[0013] Furthermore, after obtaining the basic information, the method further includes:

[0014] Determine whether the data integrity of the basic information is less than a preset ratio;

[0015] If so, the emission factor is determined based on the conventional emission factor and the correction factor, wherein the correction factor is specifically determined based on meteorological conditions, the efficiency of pollutant treatment facilities and the operating status of equipment.

[0016] Furthermore, the emission factor is determined based on the conventional emission factor and the correction coefficient, as shown in the following formula:

[0017] ;

[0018] In the formula, for Process Emission factors of pollutants, for Process Conventional emission factors of pollutants, This is a correction factor.

[0019] Furthermore, the correction coefficient Specifically, it is determined using the following formula:

[0020] ;

[0021] In the formula, This is a correction factor for pollutant treatment efficiency determined based on the efficiency of pollutant treatment facilities. These are the meteorological condition correction factors determined based on meteorological conditions. This is a correction factor for the equipment operating status determined based on the equipment's operating condition.

[0022] Furthermore, emissions monitoring is specifically conducted using the following formula:

[0023] ;

[0024] ;

[0025] In the formula, for Process Pollutant emissions for Process Emission factors of pollutants, For the activity level data of process i, For enterprises Pollutant emissions This represents the total number of processes in the enterprise's production process.

[0026] Furthermore, the regulation specifically refers to, when the When pollutant emissions exceed a preset threshold, the location is determined. pollutants Process, then reduce Activity level data for the process.

[0027] Furthermore, the method also includes:

[0028] Based on the target company's basic information and emission factors, the pre-set model to be trained is obtained by training the pre-set model;

[0029] When the deviation between the online monitoring data of the target enterprise and the historical average value is greater than a preset threshold, the preset model is trained and updated, and the updated emission factor is obtained based on the updated preset model.

[0030] On the other hand, the present invention also provides an enterprise emission monitoring and control device based on a dynamic factor library, the device comprising:

[0031] The acquisition module is used to acquire basic information about each target enterprise in the target area and key processes in the process type;

[0032] The determination module is used to determine the emission factors of each target enterprise based on the basic information and key processes.

[0033] The module is used to build a dynamic factor library based on the emission factors of each target enterprise.

[0034] The monitoring and control module is used to monitor and control the emissions of each target enterprise during the period to be controlled, based on the dynamic factor library.

[0035] This invention provides a method and apparatus for enterprise emission monitoring and control based on a dynamic factor library. Compared with existing technologies, this method first acquires basic information and key processes of each target enterprise in a target area; then, based on the basic information and key processes, it determines the emission factors of each target enterprise; next, it constructs a dynamic factor library based on the emission factors of each target enterprise; finally, it monitors and controls the emissions of each target enterprise during the period to be controlled based on the dynamic factor library. The construction of a process-level dynamic factor library improves the accuracy of emission factors, thereby enhancing the precision of enterprise emission monitoring and control. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments or prior art of this specification, the drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this specification. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0037] Figure 1 The diagram shown is a flowchart illustrating the enterprise emission monitoring and control method based on a dynamic factor library provided in the embodiments of this specification.

[0038] Figure 2 The diagram shown is a structural schematic of the enterprise emission monitoring and control device based on a dynamic factor library provided in the embodiments of this specification. Detailed Implementation

[0039] To enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0040] like Figure 1The diagram illustrates a flowchart of an enterprise emission monitoring and control method based on a dynamic factor library, as provided in the embodiments of this specification. While this specification provides the method operation steps or device structure shown in the embodiments or accompanying drawings, based on conventional methods or without creative effort, the method or device may include more or fewer operation steps or module units after partial merging. In steps or structures where there is no logically necessary causal relationship, the execution order of these steps or the module structure of the device is not limited to the execution order or module structure shown in the embodiments or accompanying drawings of this specification. When the method or module structure is applied in actual devices, servers, or terminal products, it can be executed sequentially or in parallel according to the method or module structure shown in the embodiments or accompanying drawings (e.g., in a parallel processor or multi-threaded processing environment, or even in a distributed processing or server cluster implementation environment).

[0041] The enterprise emission monitoring and control method based on a dynamic factor library provided in the embodiments of this specification is as follows: Figure 1 As shown, the method specifically includes the following steps:

[0042] Step S101: Obtain basic information and key processes in the process types of each target enterprise in the target area.

[0043] Specifically, a database of enterprise information for target companies in the target region is constructed, such as a localized database of key industry enterprises. Basic data on local development level and pollution sources are obtained, key industries, technological processes, and operational procedures are identified, and pollution source emission characteristics are analyzed based on on-site surveys. A localized database of key industry enterprises is then constructed, and local industrial sources are decomposed into four-level operational units according to the *National Industrial Classification of Economic Activities* (GB / T4754) and the *Code Rules for Stationary Pollution Sources* (HJ 608). Scientific and technological literature is collected, and emission factors for various key pollution sources (i.e., the conventional emission factors described below) are obtained through methods such as researching the *Emission Source Statistical Survey Production and Discharge Accounting Methods and Coefficients Handbook*, the *AP-42 Emission Factor Handbook Recommended by the U.S. Environmental Protection Agency*, sharing authoritative emission factor data, and literature reviews.

[0044] Acquire target enterprise production and operation data (such as enterprise DCS system data, raw material usage, product output, etc.), online monitoring data (CEMS, such as particulate matter, SO2, NOx), manual monitoring data (third-party testing reports), environmental parameter data (wind speed, humidity, temperature, etc.), and equipment attribute data (environmental protection facility ledgers, such as dust collector filtration velocity and equipment operating hours). Data preprocessing methods employ the 3σ criterion to remove outliers from monitoring data, align multi-source data using a unified time granularity, and fill in missing values ​​using nearest neighbor interpolation. Online monitoring data is used to determine pollutant concentrations, flue gas flow rates, and other data.

[0045] In this embodiment of the application, after obtaining the basic information, the method further includes:

[0046] Determine whether the data integrity of the basic information is less than a preset ratio;

[0047] If so, the emission factor is determined based on the conventional emission factor and the correction factor, wherein the correction factor is specifically determined based on meteorological conditions, the efficiency of pollutant treatment facilities and the operating status of equipment.

[0048] Specifically, when the data completeness of basic information is less than a preset ratio, it is first determined whether there are emission factors obtained from observation data for the corresponding industry in the local public data. If so, the localized emission factor, i.e., the conventional emission factor, is directly obtained based on the emission factor. If there are no emission factors calculated based on actual measurements in the local public data, the emission factors based on actual measurements are obtained from the public data of the corresponding industry in other regions as the first emission factor, and the emission factors recommended for the same process in the corresponding industry are obtained from the public data as the second emission factor. When there are only the first emission factor or the second emission factor, the first emission factor or the second emission factor is directly used as the conventional emission factor. When there are both the first emission factor and the second emission factor, the first emission factor and the second emission factor are weighted and then combined to obtain the conventional emission factor.

[0049] Introducing correction coefficients The emission factor is dynamically adjusted based on local meteorological conditions (such as wind speed and humidity), equipment aging, and differences in pollution control technologies. The emission factor is determined based on the conventional emission factor and the correction coefficient, as shown in the following formula:

[0050] ;

[0051] In the formula, for Process The emission factors of pollutants, that is, the corrected emission factors. for Process Conventional emission factors of pollutants, This is a correction factor.

[0052] The correction coefficient Specifically, it is determined using the following formula:

[0053] ;

[0054] In the formula, This is a correction factor for pollutant treatment efficiency determined based on the efficiency of pollutant treatment facilities. These are the meteorological condition correction factors determined based on meteorological conditions. This is a correction factor for the equipment operating status determined based on the equipment's operating condition.

[0055] Specifically, the pollutant treatment efficiency correction factor is the actual efficiency of the pollutant treatment facility divided by its design efficiency. The meteorological condition correction factor is obtained through regression analysis of historical data; for example, in high humidity environments, the efficiency of wet desulfurization... A value of 0.92~1.05 is acceptable. When using an SCR denitrification system, if the catalyst temperature is below 300℃, a low-temperature correction factor is triggered. ).

[0056] The equipment operating status correction factor is determined as shown in the following formula:

[0057] ;

[0058] In the formula, The equipment attenuation coefficient, To account for the operating load of the treatment facility, the equipment attenuation coefficient is based on the equipment manufacturer's reference value. If the manufacturer does not provide this value, it is obtained by dividing the equipment's usage time by its scrapping time.

[0059] Step S102: Determine the emission factors of each target enterprise based on the basic information and key processes.

[0060] In this embodiment of the application, determining the emission factors of each target enterprise based on the basic information, key processes, and process types specifically includes, for any one of the target enterprises The emission factors of pollutants are determined as shown in the following formula:

[0061] ;

[0062] In the formula, for Process Emission factors of pollutants, For time, specifically in hours. At time t Process Pollutant concentration, Let be the exhaust gas flow rate at time t. For monitoring time intervals, This refers to the activity level data for process i.

[0063] Among them, the amount of raw materials used and the output of products are used to determine In the calculation formula, A represents the concentration of pollutants monitored online, which is used to determine... In the calculation formula Online monitoring of pollutant exhaust gas flow rate is used to determine In the calculation formula .

[0064] Step S103: Construct a dynamic factor library based on the emission factors of each target enterprise.

[0065] Specifically, the above scheme can be used to obtain the emission factors of each target enterprise, thereby constructing a dynamic factor library for the target area.

[0066] The method further includes:

[0067] Based on the target company's basic information and emission factors, the pre-set model to be trained is obtained by training the pre-set model;

[0068] When the deviation between the online monitoring data of the target enterprise and the historical average value is greater than a preset threshold, the preset model is trained and updated, and the updated emission factor is obtained based on the updated preset model.

[0069] Specifically, a machine learning model (such as random forest or LSTM) is established. This model is a conventional machine learning model that automatically optimizes emission factors based on online monitoring data and enterprise technological upgrade information, enabling iterative updates to the factor library. The model iteration strategy is as follows: when the online monitoring data deviates from the historical average by 20%, model training is triggered, and the model is retrained and updated using data from a preset time period. This method of updating emission factors based on a large amount of data avoids the occurrence of outliers at any given moment, directly using outliers as the basis for updating emissions data.

[0070] Step S104: Based on the dynamic factor library, conduct emission monitoring and control for each target enterprise during the period to be controlled.

[0071] Emissions monitoring is specifically conducted using the following formula:

[0072] ;

[0073] ;

[0074] In the formula, for Process Pollutant emissions for Process Emission factors of pollutants, For the activity level data of process i, For enterprises Pollutant emissions This represents the total number of processes in the enterprise's production process.

[0075] The regulation specifically refers to the following: when the When pollutant emissions exceed a preset threshold, the location is determined. pollutants Process, then reduce Process activity level data. Enables process-level control within the enterprise. Preset thresholds can be set based on historical data, industry best practices, or environmental standards.

[0076] This invention breaks through the limitations of traditional emission inventory algorithms, which rely on extensive calculations at the industry level, and enables process-level emission monitoring and control. By combining online monitoring, production records, and on-site measurements, it improves the accuracy of localized emission factors. This solution can determine an accurate emission factor database for each enterprise, enabling monitoring and control of emissions during periods of regulatory oversight. Furthermore, by coupling machine learning with local environmental parameters, this invention addresses the issue of emission factor lag, improving the timeliness of emission factors.

[0077] In specific application scenarios, taking the steel industry as an example, the enterprise emission monitoring and control method based on a dynamic factor library provided in this application can be as follows:

[0078] S201. Construct a localized database of steel enterprise information.

[0079] Specifically, this includes basic data collection: collecting local environmental statistics, pollution source census data, historical emission inventories, steel enterprise directories, and other information to identify key processes (coking, sintering, ironmaking, converter, rolling) and process types (such as blast furnace volume, rolling mill type, etc.) in the local steel industry.

[0080] On-site survey: Based on basic pollution source data, typical local steel companies were selected for on-site surveys. Survey forms were created to collect information on company production processes, production activity levels, pollutant CEMS data, environmental protection facilities (such as wet desulfurization, SCR denitrification, etc.), equipment operating years, and production load rates, forming a "company-process-pollutant" mapping table.

[0081] Feature analysis: Analyze the key emission periods and emission intensities of typical pollutants (such as particulate matter, SO2, NOx).

[0082] S202. Obtain emission coefficients for each process in the steel industry from publicly available data, taking particulate matter emissions from sintering machine heads as an example.

[0083] Based on the "National Industrial Classification of Economic Activities" (GB / T4754) and the "Code Rules for Stationary Pollution Sources" (HJ 608), local industrial sources are decomposed into four-level process units. Pollution generation coefficients for various key pollution sources are obtained through methods such as collecting scientific and technological literature, researching the "Emission Source Statistical Survey Methods and Coefficients Handbook," the "AP-42 Emission Coefficients Handbook Recommended by the U.S. Environmental Protection Agency," sharing authoritative emission factor data, and literature review. The emission factor is the pollution generation coefficient multiplied by (1 - end-of-pipe treatment technology removal efficiency). Details are shown in Table 1 below.

[0084] Table 1

[0085] S203, Multi-source data acquisition and fusion (taking particulate matter emissions from sintering machine head as an example).

[0086] Data sources include:

[0087] Production data: Sinter output, coke consumption, coal consumption, and iron ore consumption in the sintering section.

[0088] Monitoring data: Hourly concentrations (mg / m³) of SO2 and NOx particulate matter in sintering machine flue gas collected by CEMS. 3 ), and flue gas flow rate (m 3 / h);

[0089] Environmental parameters: hourly data such as wind speed and humidity from the factory's weather station.

[0090] Data preprocessing methods:

[0091] Outliers in CEMS were removed using the 3σ criterion. Data exceeding the mean ± 3 standard deviations were removed from the particulate matter concentration data in CEMS.

[0092] Align time granularity: Unify production data (hourly level) and monitoring data (15-minute level) to hourly granularity, and use linear interpolation to complete the timestamps;

[0093] Missing values ​​are filled by nearest neighbor interpolation. For example, particulate matter data during a bag filter malfunction is filled by the average of data from the same operating period over the past three days.

[0094] S204, Process-level Emission Factor Calculation.

[0095] The formula for calculating the particulate matter emission factor of the sintering die head is:

[0096] ;

[0097] In the formula: The particulate matter emission factor is g / kg; The concentration of particulate matter at time t (mg / m³) 3 ); The exhaust gas flow rate (m³) at time t 3 / h); Data on process activity levels (e.g., annual output of sinter, tons / year).

[0098] The annual production data of the sintering process (output of 3.5 million tons / year) was obtained, and the hourly particulate matter monitoring data of the CEMS at the sintering machine head was obtained. The particulate matter emission factor of the sintering machine head was calculated to be 0.05 g / kg sintered ore.

[0099] S205, Dynamic Correction of Emission Factors.

[0100] Corrected model (taking sintering die head particles as an example):

[0101] ;

[0102] β1 (dust removal efficiency): The electrostatic precipitator at the sintering machine head of the enterprise is designed for an efficiency of 99%, and the actual operating efficiency is 92%. β1 = 0.92 / 0.99 = 0.93.

[0103] β2 (meteorological): Relative humidity 65%, trigger electrostatic dust removal correction factor β2=0.95;

[0104] β3 (equipment load): equipment load rate 75%, equipment service life 10 years (scrapping age 20 years), γ=10 / 20=0.5, β3=1-0.5×(1-0.75)=0.875.

[0105] Revised The unit is g / kg.

[0106] S206, Factor library is dynamically updated.

[0107] The LSTM neural network (time series prediction) was used, with training data from January 2023 to December 2024 (80% training set, 20% validation set). The target prediction error was <5%, meaning the training data was set for a one-year period. The model was first trained using the training data for the set period. When the online particulate matter concentration exceeded the historical average for the same period by ±20% for 3 consecutive hours, the latest 10 days of data were collected, and the LSTM model was retrained. An updated emission factor (e.g., corrected to 0.06 kg / t sintered ore) was generated and pushed to the enterprise's environmental management system, with a version number marked.

[0108] S207, Enterprise Emission Monitoring and Control.

[0109] ;

[0110] Assuming the company's daily sinter production is 1200 tons / day, the particulate matter emission of this process on that day would be 72 kg.

[0111] A multi-level early warning system is set up for process control. During routine control, separate early warning values ​​(such as 80% of the ultra-low emission limit) and alarm values ​​(such as the ultra-low emission limit) are set for each process. During heavy pollution weather control, if the emission reduction ratio is 20%, the early warning value for the process is set to 60% of the daily emission, and the alarm value is set to 80% of the daily emission. During different control periods, when the emission volume reaches the threshold at each level, different levels of early warning are triggered.

[0112] S208, dynamic factor library, application of real-time monitoring and control technology.

[0113] Application scenarios include: precise environmental protection supervision, where local environmental protection bureaus can access real-time dynamic emission data at the enterprise's process level (such as dynamic factors of particulate matter in sintering machine heads and real-time emission volumes) via API interfaces, constructing a process-level supervision model that supports real-time monitoring and intelligent early warning, improving the efficiency of off-site enforcement; aggregating real-time emission data, analyzing emission trends, and accurately assessing the effectiveness of emergency emission reduction during heavily polluted weather, providing quantitative basis for environmental policy formulation and adjustment; and enterprise self-inspection and optimization, where the application of a dynamic factor library can automatically benchmark the enterprise's emission factors against national guidelines or industry benchmarks, identifying potential problems in the operation of treatment facilities; and in the event of abnormal emissions, it can quickly locate the abnormal process and trace the source of the abnormality (such as production fluctuations, treatment facilities, and the impact of meteorological conditions), achieving intelligent control from passive response to proactive optimization.

[0114] Based on the above-described enterprise emission monitoring and control method based on a dynamic factor library, one or more embodiments of this specification also provide a platform or terminal for enterprise emission monitoring and control based on a dynamic factor library. This platform or terminal may include devices, software, modules, plug-ins, servers, clients, etc., using the methods described in the embodiments of this specification, combined with necessary hardware implementation. Based on the same innovative concept, the systems in one or more embodiments provided in this specification are as described in the following embodiments. Since the implementation schemes and methods for solving the system problem are similar, the specific system implementation in the embodiments of this specification can refer to the implementation of the aforementioned methods. Repeated details will not be repeated. The terms "unit" or "module" used below can refer to a combination of software and / or hardware that achieves a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, hardware implementation, and a combination of software and hardware, are also possible and contemplated.

[0115] Specifically, Figure 2 This is a schematic diagram of the module structure of an embodiment of the enterprise emission monitoring and control device based on a dynamic factor library provided in this specification, as shown below. Figure 2 As shown, the enterprise emission monitoring and control based on a dynamic factor library provided in this specification includes:

[0116] Module 201 is used to acquire basic information and key processes in the process type of each target enterprise in the target area;

[0117] The determination module 202 is used to determine the emission factors of each target enterprise based on the basic information and key processes;

[0118] Module 203 is used to construct a dynamic factor library based on the emission factors of each target enterprise;

[0119] The monitoring and control module 204 is used to monitor and control the emissions of each target enterprise during the period to be controlled, based on the dynamic factor library.

[0120] It should be noted that the system described above may include other implementation methods based on the description of the corresponding method embodiments. The specific implementation methods can be referred to the description of the corresponding method embodiments above, and will not be elaborated here.

[0121] This application also provides an electronic device, including:

[0122] processor;

[0123] Memory used to store the processor's executable instructions;

[0124] The processor is configured to perform the methods provided in the embodiments described above.

[0125] The electronic device provided in this application stores executable instructions for a processor in its memory. When the processor executes these instructions, it first acquires basic information and key processes in the process types of each target enterprise in the target area; then, based on the basic information and key processes, it determines the emission factors of each target enterprise; next, it constructs a dynamic factor library based on the emission factors of each target enterprise; finally, based on the dynamic factor library, it monitors and regulates the emissions of each target enterprise during the period to be controlled. This improves the accuracy of emission factors, thereby enhancing the precision of emission control for enterprises.

[0126] The foregoing has described specific embodiments of this specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0127] The methods or apparatus described in the embodiments provided in this specification can implement business logic through a computer program and record it on a storage medium. The storage medium can be read and executed by a computer to achieve the effects of the solutions described in the embodiments of this specification, such as:

[0128] Obtain basic information and key processes in the process types of each target enterprise in the target region;

[0129] The emission factors of each target enterprise are determined based on the aforementioned basic information and key processes.

[0130] A dynamic factor library is constructed based on the emission factors of each target enterprise.

[0131] Based on the aforementioned dynamic factor library, emissions monitoring and control are conducted for each target enterprise during the period of pending regulation.

[0132] The storage medium can include physical devices for storing information, typically digitizing the information and then storing it using electrical, magnetic, or optical methods. The storage medium can include: devices that store information using electrical energy, such as various types of memory, like RAM and ROM; devices that store information using magnetic energy, such as hard disks, floppy disks, magnetic tapes, magnetic core memory, bubble memory, and USB flash drives; and devices that store information using optical methods, such as CDs or DVDs. Of course, there are other readable storage media, such as quantum memories and graphene memories.

[0133] The embodiments in this specification are not limited to conforming to industry communication standards, standard computer resource data update and data storage rules, or the situations described in one or more embodiments of this specification. Slightly modified implementations based on certain industry standards or custom methods or embodiments can also achieve the same, equivalent, or similar, or predictable, implementation effects as described above. Embodiments that utilize these modified or modified methods for data acquisition, storage, judgment, and processing still fall within the scope of optional implementations of the embodiments in this specification.

[0134] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, ASICs, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0135] The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or plug-ins may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, indirect coupling or communication connection between devices or units, and may be electrical, mechanical, or other forms.

[0136] These computer program instructions can also be loaded onto a computer or other programmable resource data updating device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0137] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, system embodiments are basically similar to method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. In the description of this specification, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this specification. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0138] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A method for enterprise emission monitoring and regulation based on dynamic factor library, characterized in that, The method comprises: acquiring basic information and key processes in process types of each target enterprise in a target area; determining an emission factor of each target enterprise based on the basic information and the key processes; constructing a dynamic factor library according to the emission factor of each target enterprise; performing emission monitoring and control on each target enterprise in a period to be controlled based on the dynamic factor library.

2. The dynamic factor library based enterprise emission monitoring and regulation method of claim 1, wherein, The emission factor of each target enterprise is determined based on the basic information and the key process, and specifically includes The emission factor of the pollutant is determined according to the following formula: ； wherein is process emission factor of the pollutant, is time, in particular hours, is the time t process pollutant concentration, is the exhaust gas flow at time t, is the monitoring time interval, is the activity level data of the i process.

3. The dynamic factor library based enterprise emission monitoring and regulation method of claim 1, wherein, After the basic information is acquired, the method further comprises: judging whether the data completeness of the basic information is less than a preset proportion; if yes, determining an emission factor based on a conventional emission factor and a correction coefficient, the correction coefficient being determined according to meteorological conditions, efficiency of a pollution control facility and an equipment operation state.

4. The dynamic factor library based enterprise emission monitoring and regulation method of claim 3, wherein, The determination of the emission factor based on the conventional emission factor and the correction coefficient is specifically shown in the following formula: ； wherein is process emission factor of the pollutant, is process conventional emission factor of the pollutant, is a correction factor.

5. The dynamic factor library based enterprise emission monitoring and regulation method of claim 3, wherein, the correction factor is determined in particular by the following equation: ； In the formula, a pollutant treatment efficiency correction factor determined according to the efficiency of the pollutant treatment facility, a meteorological condition correction factor determined according to the meteorological condition, a device operation state correction factor determined according to the device operation state.

6. The dynamic factor library based enterprise emission monitoring and regulation method of claim 1, wherein, The emission monitoring is specifically performed through the following formula: ； ； wherein is process pollutant emissions, is process emission factor of the pollutant, is activity level data for process i, is the enterprise pollutant emissions, is the total number of processes in the enterprise production process.

7. The dynamic factor library based enterprise emission monitoring and regulation method of claim 6, wherein, The regulating specifically comprises positioning the pollutant emission greater than a preset threshold, positioning the pollutant emission greater than a preset threshold, positioning the pollutant emission greater than a preset threshold, positioning the pollutant emission greater than a preset threshold, positioning the 8. The dynamic factor library based enterprise emission monitoring and regulation method of claim 1, wherein, The method further comprises: training a preset model according to the basic information and the emission factor of the target enterprise to obtain a trained preset model; when online monitoring data of the target enterprise deviates from a historical average value by more than a preset threshold, training and updating the preset model, and obtaining an updated dynamic factor library according to the updated preset model.

9. An enterprise emission monitoring and regulation device based on dynamic factor library, characterized in that, The device comprises: an acquisition module, configured to acquire basic information and key processes in process types of each target enterprise in a target area; a determination module, configured to determine an emission factor of each target enterprise based on the basic information and the key processes; a construction module, configured to construct a dynamic factor library according to the emission factor of each target enterprise; a monitoring and control module, configured to perform emission monitoring and control on each target enterprise in a period to be controlled based on the dynamic factor library.

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