CN112541641A - Control method, system, medium and electronic device based on emission data - Google Patents
Control method, system, medium and electronic device based on emission data Download PDFInfo
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Abstract
The invention provides a control method, a system, a medium and electronic equipment based on emission data, wherein the control method based on the emission data comprises the following steps: acquiring data to be detected of industrial equipment; the industrial plant comprises at least: one of production equipment, treatment equipment, monitoring equipment and monitoring equipment; performing data state analysis on the data to be detected and generating a state analysis result; carrying out early warning according to the state analysis result; the early warning at least comprises: one of off-line early warning, abnormal early warning and standard exceeding early warning. The invention carries out a series of measures such as off-line early warning, abnormity early warning and overproof early warning judgment, identification, release, tracking, closing and the like on various devices related to production, can help users to find and solve problems in time, and is more beneficial to helping users to gradually improve the management level.
Description
Technical Field
The invention belongs to the technical field of emission data management and control, relates to a management and control method, and particularly relates to a management and control method, a management and control system, a medium and electronic equipment based on emission data.
Background
The state puts forward the requirement for constructing a whole plant centralized management and control platform for ultralow emission modification of iron and steel enterprises, the whole plant centralized management and control platform is regulated to perform centralized management and control on all monitoring and treatment equipment in an unorganized emission source list in a plant, and the operation conditions of production facilities, dust collection, dust suppression, cleaning and other treatment facility operation data, particle monitoring data and video monitoring historical data related to each unorganized emission source point are recorded.
In actual industrial production, signals of production equipment, treatment equipment and monitoring equipment are collected by a steel enterprise, data are transmitted to an intermediate database, and the intermediate database is accessed to a centralized control platform through an interface protocol, so that the risk of data decoding errors exists; in addition, the production equipment, the treatment equipment and the monitoring equipment may have the conditions of disconnection, production halt, maintenance and the like. Therefore, in order to ensure the effect of signal acquisition and display and help steel enterprises to successfully pass the national ultra-low emission verification, various pre-warning needs to be performed on data of various devices. However, currently, there is no comprehensive data management and control method in the prior art.
Therefore, how to provide a control method, a system, a medium and an electronic device based on emission data to solve the defects that the prior art cannot provide a relatively comprehensive data control method and the like becomes a technical problem to be solved by those skilled in the art.
Disclosure of Invention
In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a method, a system, a medium and an electronic device for managing and controlling emission data, so as to solve the problem that the prior art cannot provide a comprehensive data management and control method.
To achieve the above and other related objects, an aspect of the present invention provides an emission data-based control method, including: acquiring data to be detected of industrial equipment; the industrial plant comprises at least: one of production equipment, treatment equipment, monitoring equipment and monitoring equipment; performing data state analysis on the data to be detected, and generating a state analysis result; carrying out early warning according to the state analysis result; the early warning at least comprises: one of an offline pre-warning, an abnormal pre-warning and an out-of-standard pre-warning.
In an embodiment of the present invention, the step of performing data state analysis on the data to be detected and generating a state analysis result includes: performing offline data analysis on the data to be detected and generating an offline analysis result; performing abnormal data analysis on the data to be detected and generating an abnormal analysis result; and performing standard exceeding data analysis on the data to be detected and generating a standard exceeding analysis result.
In an embodiment of the present invention, the step of performing offline data analysis on the data to be detected and generating an offline analysis result includes: comparing the data to be detected with a preset offline early warning value; and if the data to be detected is larger than the off-line early warning value, determining that the industrial equipment corresponding to the data to be detected is in an off-line state.
In an embodiment of the present invention, the step of performing an abnormal data analysis on the data to be detected and generating an abnormal analysis result includes: comparing the data to be detected with a preset data reference range; and if the data to be detected exceeds the data reference range, judging that the data to be detected is abnormal data.
In an embodiment of the present invention, the step of performing the standard exceeding data analysis on the data to be detected and generating the standard exceeding analysis result includes: comparing the data to be detected with a preset standard exceeding early warning value; and if the data to be detected is larger than the standard exceeding early warning value, judging that the data to be detected is standard exceeding data.
In an embodiment of the present invention, the step of performing the early warning according to the state analysis result includes: generating early warning prompt information according to the state analysis result; and pushing the early warning prompt information.
In an embodiment of the invention, before the step of obtaining the data to be measured of the industrial device, the method for managing and controlling based on the emission data further includes: acquiring and storing historical data of the industrial equipment in advance; determining a data reference value or a data reference range used in the data state analysis process according to the historical data; the data reference range is a reasonable data range in which various data of the industrial equipment are located during normal operation.
To achieve the above and other related objects, another aspect of the present invention provides an emission data based management and control system, including: the data acquisition module is used for acquiring data to be detected of the industrial equipment; the industrial equipment at least comprises: one of production equipment, treatment equipment, monitoring equipment and monitoring equipment; the data analysis module is used for carrying out data state analysis on the data to be detected and generating a state analysis result; the data early warning module is used for early warning according to the state analysis result; the early warning at least comprises: one of off-line early warning, abnormal early warning and standard exceeding early warning.
To achieve the above and other related objects, a further aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, the computer program, when executed by a processor, implementing the emission data based management and control method.
To achieve the above and other related objects, a final aspect of the present invention provides an electronic device, comprising: a processor and a memory; the memory is configured to store a computer program, and the processor is configured to execute the computer program stored in the memory to cause the electronic device to execute the emission data based regulation method.
As described above, the method, the system, the medium and the electronic device for managing and controlling based on the emission data according to the present invention have the following advantages:
the invention carries out a series of measures such as off-line early warning, abnormity early warning and overproof early warning judgment, identification, release, tracking, closing and the like on various devices related to production, can help users to find and solve problems in time, and is more beneficial to helping users to gradually improve the management level. The invention can actively push the early warning prompt information to the user, and can also form a management work order aiming at different early warning events so as to help the user to track the subsequent processing process until the early warning event is closed.
Drawings
Fig. 1 is a schematic flow chart of an emission data based management method according to an embodiment of the present invention.
Fig. 2 is a schematic diagram illustrating a state analysis of an emission data-based control method according to an embodiment of the invention.
Fig. 3 is a flowchart illustrating a reference range setting process of the emission data-based control method according to an embodiment of the invention.
Fig. 4 is a schematic diagram illustrating an embodiment of an emission data-based management and control system according to the present invention.
Fig. 5 is a schematic structural connection diagram of an electronic device according to an embodiment of the invention.
Description of the element reference numerals
4 management and control system based on emission data
41 data acquisition module
42 data analysis module
43 data early warning module
5 electronic device
51 processor
52 memory
S10-S13
S101 to S102
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It is to be noted that the features in the following embodiments and examples may be combined with each other without conflict.
It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention, and the drawings only show the components related to the present invention rather than the number, shape and size of the components in practical implementation, and the type, amount and ratio of the components in practical implementation can be changed freely, and the layout of the components may be complicated.
The emission data-based control method, the system, the medium and the electronic equipment provide a data early warning method for an ultralow emission centralized control platform in the steel enterprise industry, can realize a series of measures such as off-line early warning, abnormity early warning and overproof early warning judgment, identification, release, tracking, closing and the like for various equipment related to production, can help users to find and solve problems in time, and is more beneficial to helping users to gradually improve the management level.
The principle and implementation of the method, system, medium and electronic device for managing and controlling emissions based on emission data according to the present embodiment will be described in detail below with reference to fig. 1 to 5, so that those skilled in the art can understand the method, system, medium and electronic device for managing and controlling emissions based on emission data without creative work.
Referring to fig. 1, a schematic flow chart of an embodiment of a method for emissions-based control according to the present invention is shown. As shown in fig. 1, the method for managing and controlling based on emission data specifically includes the following steps:
s11, acquiring data to be detected of the industrial equipment; the industrial plant comprises at least: one of production equipment, treatment equipment, monitoring equipment and monitoring equipment.
In practical application, the production equipment refers to relevant production equipment such as a sintering process, a pelletizing process, a coking process, a lime production process, a blast furnace iron smelting process, a converter steel-making process, an electric furnace steel-making process and the like; the treatment equipment is also called as pollution treatment equipment and refers to a bag dust collector, an electrostatic dust collector, a point type dust collector, desulfurization and denitrification equipment, dry fog dust suppression equipment such as mist and fog gun, a car washer and the like; the Monitoring equipment refers to air quality micro-stations for air quality environment Monitoring, TSP (Total Suspended Particulate) Monitoring and CEMS (Continuous Emission Monitoring System) Monitoring data; the monitoring equipment refers to high-definition video monitoring equipment arranged in key areas of a whole plant in a steel enterprise factory, an intelligent identification camera used for driving intelligent mist and fog guns in a large stock yard, and the like.
And S12, performing data state analysis on the data to be detected and generating a state analysis result.
In practical applications, the possible states of data include data offline, data exception, and data superscalar.
The data offline refers to the situation that data of production equipment, treatment equipment, air quality micro-stations, TSP, CEMS and video monitoring equipment are not uploaded.
The abnormal data mainly occurs in large-scale dust collectors (treatment equipment), CEMS monitoring data (monitoring equipment), air quality micro-stations and TSP monitoring data (monitoring equipment). The method comprises the following steps:
(1) the data pushed by the owner is inconsistent with the required data, such as current data needing to be detected, and other data (such as speed) pushed by the owner. The normal value of the current data is in the range of dozens of amperes to 100 amperes, and the speed is only 6m/min, so that the numerical difference between 100 and 6 is large in numerical view, and the data is reflected to be abnormal, namely the data is very unreasonable.
(2) When data is decoded wrongly and data is extracted through an interface protocol after the owner pushes the data to the middle database, the data is decoded wrongly, so that the extracted data is strangely different in format, for example, the normal temperature is 35-37 ℃, the temperature of a certain row of ports reaches +/-10 ^ 35-37 ℃, and the data becomes exponential data.
(3) Data collection or pushing program is wrong, for example, TSP dust monitoring data is continuously unchanged, and even 5 bits of data after decimal point are also unchanged.
(4) The treatment equipment is overhauled, so that the concentration monitored by the CEMS at the discharge port is very high. In practice, the environmental protection agency allows a certain proportion of treatment equipment to be overhauled, the CEMS concentration during the overhaul period is not calculated as the sewage discharge of an enterprise, and the CEMS monitoring data during the period is defined as data abnormity.
The data exceeding standard means that the effective data collected by the industrial equipment is too high when the industrial equipment operates normally. Specifically, data exceeding is mainly performed on monitoring data. The method comprises the following steps: monitoring data of the air quality monitoring micro station, TSP monitoring data and CEMS monitoring data. Wherein CEMS has government regulated monitoring factor concentration control value and discharge port annual discharge amount control value; the air quality monitoring value and the TSP monitoring value may not have a government control value, and thus an excessive warning value is preset.
Referring to fig. 2, a state analysis diagram of an embodiment of a method for managing and controlling emissions according to the present invention is shown. As shown in fig. 2, the S12 step includes:
(1) and performing offline data analysis on the data to be detected and generating an offline analysis result. Therefore, early warning can be carried out aiming at the conditions that the data of the production equipment, the treatment equipment, the air quality micro-station and the TSP and CEMS are not uploaded, and on-time and timely early warning based on equipment types is realized.
In one embodiment, the data to be detected is compared with a preset offline early warning value; and if the data to be detected is larger than the offline early warning value, determining that the industrial equipment corresponding to the data to be detected is in an offline state.
Specifically, a tolerable or acceptable time range without data transmission, namely an offline early warning value, is set according to the category of the industrial equipment, so that a certain fault tolerance mechanism is formed. For example, running signals of a belt conveyor, a crusher and a vibrating screen can tolerate the accumulation of 1 hour without data transmission; the data-free transmission time which can be tolerated by the blast furnace and the sintering machine is 5 minutes; the data-free transmission time which can be tolerated by the large dust remover is 10 minutes; the data-free transmission time which can be tolerated by the air quality micro station and the TSP is 1 hour; CEMS can tolerate no data transmission times of 5 minutes, etc. The system compares the received data signal of the equipment with the set corresponding tolerance time, if the data signal reaches or exceeds the set tolerance time, the data is judged to be offline or the equipment corresponding to the data is offline, and offline early warning is started.
Further, in practical applications, the offline data analysis of the data to be tested includes the following processes:
first, the categories of industrial equipment are classified according to the combination of the natural categories of the equipment, the emphasis of government inspection, and the importance of data to the enterprise. The method comprises the following specific steps:
the natural categories of equipment are: production equipment, treatment equipment, monitoring equipment and monitoring equipment.
Based on the comprehensive consideration of government audit and the importance of data to enterprises, the method can be divided into the following categories:
core production equipment (blast furnace, sintering machine, rotary kiln, shaft furnace, converter, electric furnace, etc.);
general production equipment (belt conveyors, crushers, screening machines, distributing trolleys, etc.);
key treatment equipment (large dust collectors, day fog and intelligent fog guns in large storage yards, car washers, etc.);
general treatment equipment (small dust collectors, point dust collectors, non-intelligent fog guns in general material yards, etc.);
CEMS monitoring data of all main discharge ports of the whole plant;
CEMS monitoring data of general discharge ports of the whole plant;
whole plant air quality micro-station and TSP monitoring data;
high-definition video monitoring equipment in key areas of a whole plant, intelligent identification cameras used for driving intelligent fogs and fog guns in a large-scale stock ground, and the like.
And then, respectively setting corresponding equipment offline early warning values according to the classification of the equipment categories. In practical application, a setting interface of an offline early warning value can be provided for a user, and the user with authority, such as an administrator at an environmental protection department, sets and modifies the offline early warning value according to management habits, wherein core production equipment, key treatment equipment and CEMS monitoring data are generally minute-level (5-10 minutes); the key area monitoring equipment is usually in the minute level (5-10 minutes); typical production facilities are typically small-scale (1 hour); air quality micro-station and TSP monitoring is typically on the order of hours (several hours).
And finally, the system judges and identifies the offline early warning event according to the equipment category, the offline early warning value and the data pushing condition. Once the data is offline, the data is used as an offline early warning event and is firstly pushed to an administrator at the environmental protection department, and then a decision instruction sent by the administrator at the environmental protection department according to a determined disposal mode (ignoring or processing) is received, and corresponding processing of the offline early warning is carried out according to the decision instruction. If the administrator selects 'ignore', the offline early warning event is closed, and early warning prompt is not performed any more; if the administrator selects 'processing', an offline early warning event management work order is automatically generated, the subsequent processing process is tracked until the processing is closed, and the offline early warning event management work order is recorded into an offline early warning event management ledger.
In industrial practice, production equipment rarely generates offline early warning; the off-line early warning conditions of the dust remover are a little more, and generally caused by errors in the links of pushing data to a middle database or retrieving data from the middle database; air quality micro stations and TSPs are often off-line and can often be power source unstable (e.g., solar cells are low, switches are turned off, etc.), network transmission unstable (over 4G network), or equipment damaged.
(2) And carrying out abnormal data analysis on the data to be detected and generating an abnormal analysis result. Therefore, the early warning can be performed on data such as current or air volume of production equipment and treatment equipment, invalid or abnormal data acquired by the air quality micro station, TSP (Total suspended particulate) and CEMS (continuous emission monitoring System) monitoring equipment due to abnormal operation of the equipment and invalid or abnormal data caused by decoding errors of the data. The data abnormality form of the production equipment and the treatment equipment mainly comprises that the data such as current, air volume and the like are abnormal large or small, so that the phenomenon is obvious and unreasonable; for monitoring equipment, data caused by equipment damage, maintenance and repair are large in number.
In one embodiment, the data to be tested is compared with a preset data reference range; and if the data to be detected exceeds the data reference range, judging that the data to be detected is abnormal data.
Aiming at production equipment and treatment equipment, the preset data reference range refers to a reasonable data range of various equipment obtained by collecting various signal data of various production equipment and treatment equipment of a large number of iron and steel enterprises in advance and using an artificial intelligence algorithm. The system combines the production running condition according to the collected data, and compares and judges the data with a reasonable range. And early warning is carried out on the abnormal data conditions of the production equipment and the treatment equipment.
Aiming at monitoring equipment, the preset data reference range refers to a reasonable data range of various equipment obtained by collecting various generation processes, air quality positions of various process links, TSP (Total suspended particulate) and CEMS (continuous emission monitoring System) monitoring data of a large number of steel enterprises in advance and by an artificial intelligence algorithm. The system combines the production running condition according to the collected data, and compares and judges the data with a reasonable range. And early warning is carried out on the abnormal condition of the monitoring data. And after early warning, carrying out on-site manual inspection, and finally determining the data to be abnormal after manual confirmation.
Wherein the combined production run condition means: relatively sufficient material metering data are collected on the process line as the representation of the production load. The production-treatment-dust monitoring data are internally related, and within a certain time and space range, the production load is increased, the treatment strength is unchanged, and the dust monitoring data are generally increased.
Production-treatment-dust monitoring data of each process link of each production process are collected in dozens of steel enterprises, and the production-treatment-dust monitoring rules of each process link of each production process are determined by utilizing big data analysis. If the production-treatment-dust monitoring rule of a certain production process link is long, obvious and unreasonable, the system can give data abnormity pre-alarm.
It should be noted that, the data anomaly analysis performed in combination with the production operation condition and the data anomaly analysis performed in a preset reasonable data range may be performed in one of the two independent manners, or may be performed in a combined manner in two manners.
It should be noted that the artificial intelligence algorithm in the present invention may be any algorithm that can determine a required reasonable value range according to a large amount of data, such as a big data analysis algorithm or a distributed gradient enhanced library-machine learning algorithm (xgboost), a random forest classifier (random forest), deep learning (deep learning), a logistic regression-machine learning algorithm (logistic regression), a decision tree-machine learning prediction (decision tree), a support vector machine-classifier (svm) machine learning algorithm, and the like.
(3) And performing standard exceeding data analysis on the data to be detected and generating a standard exceeding analysis result. Therefore, the early warning can be performed when the effective data of various devices are too high.
In one embodiment, the data to be detected is compared with a preset overproof early warning value; and if the data to be detected is larger than the standard exceeding early warning value, judging that the data to be detected is standard exceeding data.
On one hand, aiming at the collected CEMS monitoring data, the concentration of the monitoring factor is compared with the national standard, if the data exceeds the standard, the judgment is carried out, and if the abnormal early warning and the manual site verification are carried out in the time period, the data is marked as abnormal; otherwise, judging the data to be over-standard, and carrying out CEMS monitoring data early warning.
On the other hand, aiming at the collected air micro-station monitoring data, the system compares the provincial control points, judges the air micro-station monitoring data, and carries out the exceeding warning of the air micro-station monitoring data if the air micro-station monitoring data exceeds the control value.
The control value of the air micro station is not a fixed value and is changed, and a setting interface of the control value can be provided for users with authority, such as a senior manager, so that senior manager users can adjust the control value. The air micro-station management and control force of each region is different, dynamic management is also carried out on the same region according to months, and the standards of different months are different, so that the set control value can be dynamically adjusted.
On the other hand, aiming at the collected TSP monitoring data, data statistics and analysis are carried out according to the monitoring data of TSP of a large number of steel plants under various production process lines, various process links and various working conditions, monitoring data control values under various production process lines, various process links and various working conditions are obtained, and if the TSP monitoring data under a certain process line, a certain link and a certain working condition exceed the monitoring data control values, the monitoring data exceed the standard.
In this case, unlike air micro-stations, the TSP is located inside the stockyard, the production shop and the belt corridor, i.e. the TSP is located in the direct pollution area of the production plant or the production activity.
The monitoring value of the TSP is greatly dependent on the process characteristics of the arrangement area, for example, in general, the TSP monitoring value in a sintering or blast furnace return belt conveying link is more than a blending ore conveying link is more than a fine grinding iron concentrate conveying link; TSP monitoring values near the crusher and the vibrating screen are larger than a belt transportation link; the TSP monitoring value of the steel-making workshop is more than that of the sintering burden workshop and more than that of the pelletizing workshop.
Thus, TSP monitoring data has very large process and area characteristics. TSP monitoring data of dozens of iron and steel enterprises are collected in advance, and according to ultralow emission levels (A level, B level and B-level) of the enterprises, TSP monitoring data of each level of enterprises in each process link are respectively counted and analyzed, so that TSP reasonable distribution characteristics of each process link of the A level of enterprises, TSP reasonable distribution characteristics of each process link of the B level of enterprises and TSP reasonable distribution characteristics of each process link of the B-level of enterprises are correspondingly obtained. And performing transverse comparison according to the ultralow emission grade of the user enterprise, and analyzing whether the TSP monitoring data of a certain process area of the user enterprise exceeds the standard or not. For example, TSP monitors are arranged among certain B-level enterprise sintering ingredients, TSP monitoring data among the sintering ingredients of the user enterprises are judged according to reasonable intervals formed by long-term statistical results of TSP among all B-level enterprise sintering ingredients, and if the over-standard accumulation reaches a certain time, the TSP over-standard early warning is given out by the system, and the whole process of post-processing after the over-standard early warning is tracked.
It should be noted that, the analysis of the overproof data may be based on a preset overproof early warning value, and when the overproof early warning value is reached or exceeded, the data is judged to be overproof; the analysis of the overproof data can also be based on whether the time reaching or exceeding the overproof early warning value exceeds a preset overproof time range, and the data is judged to be overproof when the time exceeds the overproof time range.
S13, performing early warning according to the state analysis result; the early warning at least comprises: one of off-line early warning, abnormal early warning and standard exceeding early warning.
In one embodiment, early warning prompt information is generated according to the state analysis result; and pushing the early warning prompt information. Therefore, the problem that the existing centralized management and control system cannot actively analyze, judge and early warn the condition without data transmission is solved. The existing centralized management and control system can classify data pushed by users, and comprises three types, namely: device start, device stop, no data. The former two of them belong to the circumstances that has the data to upload, and current management and control system can carry out the analysis, and the latter is the circumstances of no data transmission, and current management and control system does not carry out the analysis to it.
In one embodiment, S13 includes:
(1) and pushing the early warning prompt information aiming at offline early warning.
Specifically, after the offline early warning is issued and consulted by users at the environment protection department of the enterprise, the users at the environment protection department are allowed to carry out 'neglect' or 'treatment' according to the requirements; and receiving a 'processing' instruction sent by a user at the environment-friendly place, and automatically generating a work order corresponding to the offline early warning so that the user can continuously track the subsequent processing of the offline early warning event until the offline early warning event is closed, thereby being beneficial to helping enterprises to improve the management level. And receiving an 'ignore' instruction sent by a user at the environment-friendly place, and finishing the prompt of the offline early warning.
(2) And pushing the early warning prompt information aiming at the abnormal early warning and the standard exceeding early warning.
Specifically, when the data to be detected exceeds a preset normal operation data range, an early warning prompt is sent to a user, so that the user can manually check the early warning prompt. If the abnormal early warning exists in the time interval and the artificial field verification is carried out, marking the abnormal data as abnormal data; otherwise, judging the standard exceeding, and carrying out the standard exceeding data early warning. Data anomalies are essentially data that is unreasonable, invalid, unusable, or allowed by regulations to be unamplified; data over-standard means that the data is true, valid, and should be exploited.
Taking the CEMS data as an example, the data exception and the data superscript have similar points and different points. The similarity is that the monitoring value is higher than the national standard; but is different in whether the monitoring data is used when calculating the sewage discharge amount and the environmental protection tax.
Further, the abnormal data refers to: data, while reasonable, is invalid. Generally, when treatment equipment is overhauled, the concentration of pollutants at a chimney exhaust port is inevitably over standard. The environmental protection bureau allows a certain proportion of treatment equipment to be overhauled, and when the pollution discharge amount and the environmental protection tax are calculated, CEMS monitoring data in the equipment overhauling period do not participate in calculation, namely are treated as invalid data. The data exceeding is different, the data exceeding refers to normal work of treatment equipment, the related data of the pollution discharge concentration exceeds the national standard probably due to reasons of overlarge production load, low treatment strength, reduced performance of the treatment equipment and the like, and the part of monitoring data is required to be used for calculating the pollution discharge amount and the environmental protection tax, so the data belong to effective data and exceed the standard.
Specifically, when the CEMS monitoring data exceeds a preset normal operation data range, the corresponding operation data of the abatement equipment is obtained, and if the corresponding operation data of the abatement equipment shows that the abatement equipment does not work, the abatement equipment is presumed to be possibly in a damage, maintenance or repair stage, so that the abatement equipment is preliminarily judged to be abnormal data, and needs to be manually verified on site after further issuing an abnormal early warning.
The manual verification is mainly that an administrator at the environmental protection department issues instructions, and each branch plant equipment administrator or environmental protection administrator verifies whether government-approved and legal treatment equipment overhaul behaviors exist or not, provides relevant evidence and marks the evidence as data abnormity. And if not, the system judges that the data exceeds the standard (namely, the data is effective and needs to participate in the calculation of the sewage discharge and the environmental protection tax).
Referring to fig. 3, a flow chart of setting a reference range of an emission data based control method according to an embodiment of the invention is shown. Before the step of S11, the emission data-based control method further includes: and S10. As shown in fig. 3, S10 includes the steps of:
and S101, acquiring and storing historical data of the industrial equipment in advance.
Specifically, a large number of production equipment signals of a sintering process, a pelletizing process, a coking process, a lime production process, a blast furnace iron-making process, a converter steel-making process and an electric furnace steel-making process are collected and stored in advance; the system comprises a bag-type dust collector, an electrostatic dust collector, a desulfurization and denitrification device, a fog gun, a dry fog dust suppression device, a car washer and other pollution control device signals, and air quality environment monitoring, TSP monitoring and CEMS monitoring data.
S102, determining a data reference value or a data reference range used in the data state analysis process according to the historical data; the data reference range is a reasonable data range in which various data of the industrial equipment are located during normal operation.
Specifically, according to the basic process technology of steel generation, a reasonable threshold limit or a reasonable range of collected signals of various devices is preliminarily determined, for example, the current of a dust remover is usually 20-150A, and then, data of the current, the pressure difference, the frequency and the air volume of various dust removers of dozens of steel enterprises are collected in advance, through big data accumulation and analysis, the reasonable current, the pressure difference, the frequency and the air volume of various dust removers can be judged, and the coordination change rule between the current and the frequency and between the current and the air volume can be judged. And if the current value of a certain dust remover is obviously not in a reasonable range or the coordination between the current change curve and the frequency or air volume change curve is unreasonable, judging that the data is abnormal aiming at the treatment equipment of the dust remover.
It should be noted that all the thresholds or threshold ranges related in the present invention can be reasonably adjusted according to actual production management and control needs, for example, the preset threshold can be adjusted to a reasonable threshold range.
The protection scope of the emission data based management and control method is not limited to the execution sequence of the steps listed in the embodiment, and all the schemes of adding, subtracting and replacing the steps in the prior art according to the principle of the present invention are included in the protection scope of the present invention.
The emission data-based management and control system provided by the present embodiment will be described in detail with reference to the drawings. It should be noted that the division of the modules of the following system is only a logical division, and the actual implementation may be wholly or partially integrated into one physical entity or may be physically separated. And the modules can be realized in a mode that all the modules are called by the processing element through software, or in a mode that all the modules are called by hardware, or in a mode that part of the modules are called by the processing element through software, or in a mode that part of the modules are called by hardware. For example: a module may be a separate processing element, or may be integrated into a chip of the system described below. Further, a certain module may be stored in the memory of the following system in the form of program code, and a certain processing element of the following system may call and execute the function of the following certain module. Other modules are implemented similarly. All or part of the modules can be integrated together or can be independently realized. The processing element described herein may be an integrated circuit having signal processing capabilities. In implementation, the steps of the above method or the following modules may be implemented by hardware integrated logic circuits in a processor element or instructions in software.
The following modules may be one or more integrated circuits configured to implement the above methods, for example: one or more Application Specific Integrated Circuits (ASICs), one or more Digital Signal Processors (DSPs), one or more Field Programmable Gate Arrays (FPGAs), and the like. When some of the following modules are implemented in the form of a calling program code by a Processing element, the Processing element may be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. These modules may be integrated together and implemented in the form of a System-on-a-chip (SOC).
Referring to fig. 4, a schematic structural diagram of an emission data based management and control system according to an embodiment of the present invention is shown. As shown in fig. 4, the emission data based management and control system 4 includes: a data acquisition module 41, a data analysis module 42 and a data early warning module 43.
The data acquisition module 41 is configured to acquire data to be detected of the industrial equipment; the industrial plant comprises at least: one of a production facility, an abatement facility, a monitoring facility and a monitoring facility.
The data analysis module 42 is configured to perform data state analysis on the data to be detected, and generate a state analysis result.
In an embodiment, the data analysis module 42 is specifically configured to perform offline data analysis on the data to be detected and generate an offline analysis result; performing abnormal data analysis on the data to be detected and generating an abnormal analysis result; and performing standard exceeding data analysis on the data to be detected and generating a standard exceeding analysis result.
The data early warning module 43 is used for early warning according to the state analysis result; the early warning at least comprises: one of an offline pre-warning, an abnormal pre-warning, and an out-of-standard pre-warning.
In one embodiment, the emission data based management and control system further includes: and a storage module.
The storage module is used for acquiring and storing historical data of the industrial equipment in advance; determining a data reference value or a data reference range used in the data state analysis process according to the historical data; the data reference range is a reasonable data range in which various data of the industrial equipment are located during normal operation.
The principle of the management and control system based on the emission data corresponds to the management and control method based on the emission data, and the management and control system based on the emission data can implement the management and control method based on the emission data, but the implementation device of the management and control method based on the emission data includes, but is not limited to, the structure of the management and control system based on the emission data, which is exemplified in the embodiment, and all the structural modifications and substitutions of the prior art made according to the principle of the invention are included in the protection scope of the invention.
The present embodiment provides a computer-readable storage medium having stored thereon a computer program that, when executed by a processor, implements the emission data-based management and control method.
Those of ordinary skill in the art will understand that: all or part of the steps for implementing the above method embodiments may be performed by hardware associated with a computer program. The aforementioned computer program may be stored in a computer readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned computer-readable storage media comprise: various computer storage media that can store program codes, such as ROM, RAM, magnetic or optical disks.
Please refer to fig. 5, which is a schematic structural connection diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 5, the present embodiment provides an electronic device 5, which specifically includes: a processor 51 and a memory 52; the memory 52 is used for storing computer programs, and the processor 51 is used for executing the computer programs stored in the memory 52 to make the electronic device 5 execute the steps of the emission data based management and control method.
The Processor 51 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete gate or transistor logic device, or discrete hardware components.
The Memory 52 may include a Random Access Memory (RAM), and may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory.
In practical applications, the electronic device may be a computer that includes components such as memory, a memory controller, one or more processing units (CPU), peripheral interfaces, RF circuitry, audio circuitry, speakers, microphone, input/output (I/O) subsystems, a display screen, other output or control devices, and external ports; the computer includes, but is not limited to, Personal computers such as desktop computers, notebook computers, tablet computers, smart phones, Personal Digital Assistants (PDAs), and the like. In other embodiments, the electronic device may also be a server, where the server may be arranged on one or more entity servers according to various factors such as functions and loads, or may be a cloud server formed by a distributed or centralized server cluster, which is not limited in this embodiment. For example, the hardware processing configuration of the electronic device executing the emission data based management and control method is a configuration standard of an industrial PC, including: the system comprises a Windows 7Pro operating system, double Intel Xeon E5-2630 v4 CPUs, an EB-X10 mainboard, a 32G memory, a 128G SSD +1TB HDD hard disk, a GeForce GTX 1660Ti 6GB GPU independent display card and a 6-Port POE Gigabit network server.
In one embodiment, a user receives various warning prompt messages through a terminal, which may be, for example, a fixed terminal, such as a server, a desktop, or the like; or a mobile terminal, such as a notebook computer, a smart phone, or a tablet computer.
In summary, the emission data-based control method, system, medium and electronic device of the present invention perform a series of measures including offline pre-warning, abnormal pre-warning and overproof pre-warning judgment, identification, release, tracking, closing, etc. on various devices involved in production, which not only helps users to find and solve problems in time, but also helps users to gradually improve management level. The invention can actively push the early warning prompt information to the user, and can also form a management work order aiming at different early warning events so as to help the user to track the subsequent processing process until the early warning event is closed. The invention effectively overcomes various defects in the prior art and has high industrial utilization value.
The foregoing embodiments are merely illustrative of the principles and utilities of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or change the above-mentioned embodiments without departing from the spirit and scope of the present invention. Accordingly, it is intended that all equivalent modifications or changes which may be accomplished by those skilled in the art without departing from the spirit and scope of the present invention as set forth in the appended claims.
Claims (10)
1. An emission data based management and control method, characterized in that the emission data based management and control method comprises the following steps:
acquiring data to be detected of industrial equipment; the industrial plant comprises at least: one of production equipment, treatment equipment, monitoring equipment and monitoring equipment;
performing data state analysis on the data to be detected and generating a state analysis result;
carrying out early warning according to the state analysis result; the early warning at least comprises: one of off-line early warning, abnormal early warning and standard exceeding early warning.
2. The emission data-based management and control method according to claim 1, wherein the step of performing data state analysis on the data to be tested and generating a state analysis result comprises:
performing offline data analysis on the data to be detected and generating an offline analysis result;
performing abnormal data analysis on the data to be detected and generating an abnormal analysis result;
and performing standard exceeding data analysis on the data to be detected and generating a standard exceeding analysis result.
3. The emission data-based management and control method according to claim 2, wherein the step of performing offline data analysis on the data to be tested and generating offline analysis results comprises:
comparing the data to be detected with a preset offline early warning value;
and if the data to be detected is larger than the off-line early warning value, determining that the industrial equipment corresponding to the data to be detected is in an off-line state.
4. The emission data-based management and control method according to claim 2, wherein the step of performing abnormal data analysis on the data to be measured and generating an abnormal analysis result comprises:
comparing the data to be detected with a preset data reference range;
and if the data to be detected exceeds the data reference range, judging that the data to be detected is abnormal data.
5. The emission data-based management and control method according to claim 2, wherein the step of performing out-of-standard data analysis on the data to be measured and generating out-of-standard analysis results comprises:
comparing the data to be detected with a preset standard exceeding early warning value;
and if the data to be detected is larger than the standard exceeding early warning value, judging that the data to be detected is standard exceeding data.
6. The emission data-based management and control method according to claim 1, wherein the early warning according to the state analysis result comprises:
generating early warning prompt information according to the state analysis result;
and pushing the early warning prompt information.
7. The emission data-based management and control method according to claim 1, wherein before the step of obtaining data to be measured of the industrial equipment, the emission data-based management and control method further comprises:
acquiring and storing historical data of the industrial equipment in advance;
determining a data reference value or a data reference range used in the data state analysis process according to the historical data; the data reference range is a reasonable data range in which various data of the industrial equipment are located during normal operation.
8. An emission data based management and control system, comprising:
the data acquisition module is used for acquiring data to be detected of the industrial equipment; the industrial plant comprises at least: one of production equipment, treatment equipment, monitoring equipment and monitoring equipment;
the data analysis module is used for carrying out data state analysis on the data to be detected and generating a state analysis result;
the data early warning module is used for early warning according to the state analysis result; the early warning at least comprises: one of off-line early warning, abnormal early warning and standard exceeding early warning.
9. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of emission data based management and control of any one of claims 1 to 7.
10. An electronic device, comprising: a processor and a memory;
the memory is configured to store a computer program, and the processor is configured to execute the computer program stored by the memory to cause the electronic device to perform the emission data based regulation method according to any one of claims 1 to 7.
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