CN117339759B - Digital twin system and method for dust remover - Google Patents
Digital twin system and method for dust remover Download PDFInfo
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- CN117339759B CN117339759B CN202311641672.6A CN202311641672A CN117339759B CN 117339759 B CN117339759 B CN 117339759B CN 202311641672 A CN202311641672 A CN 202311641672A CN 117339759 B CN117339759 B CN 117339759B
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- G06F3/0481—Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
- G06F3/04815—Interaction with a metaphor-based environment or interaction object displayed as three-dimensional, e.g. changing the user viewpoint with respect to the environment or object
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- B03C—MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- B03C—MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
- B03C3/00—Separating dispersed particles from gases or vapour, e.g. air, by electrostatic effect
- B03C3/34—Constructional details or accessories or operation thereof
- B03C3/66—Applications of electricity supply techniques
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
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Abstract
The invention provides a digital twin system and a digital twin method for a dust remover, comprising the following steps: the system comprises a data perception module, a data analysis module and a man-machine interaction module; the data perception module is used for acquiring coal quality analysis data, the dust remover and real-time operation data of a boiler host machine and auxiliary equipment connected with the dust remover; the data analysis module is used for inputting the real-time operation data and the coal quality analysis data into a preset data processing model and outputting a real-time analysis result; the data processing model is constructed and obtained in advance based on historical operation data of the dust remover and the boiler host and auxiliary equipment, structural parameter data of the dust remover and the boiler host and auxiliary equipment and control experience data of the dust remover and the boiler host and auxiliary equipment; and the man-machine interaction module is used for displaying and/or processing the real-time analysis result according to the operation instruction if the operation instruction of the user is received. The system relieves the technical problem of lower automation degree in the prior art, and improves the experience of users.
Description
Technical Field
The invention relates to the technical field of industrial dust removal, in particular to a digital twin system and method of a dust remover.
Background
Currently, an intelligent service platform for a dust remover generally judges operation parameters of the dust remover based on preset parameters so as to control the dust remover. However, the above prior art has a low degree of automation, resulting in a poor user experience.
Disclosure of Invention
The invention aims to provide a digital twin system and a digital twin method for a dust remover, which solve the technical problem of lower automation degree in the prior art and improve the experience of users.
In a first aspect, an embodiment of the present invention provides a digital twinning system for a dust collector, including: the system comprises a data perception module, a data analysis module and a man-machine interaction module which are connected in sequence; the data perception module is used for acquiring coal quality analysis data, a dust remover and real-time operation data of a boiler host and auxiliary equipment connected with the dust remover; the auxiliary equipment comprises a denitration ammonia escape instrument, a low-temperature economizer outlet flue gas thermometer, an ash conveying bin pump, a desulfurization circulating pump and a desulfurization system outlet flue dust instrument; the data analysis module is used for inputting the real-time operation data and the coal quality analysis data into a preset data processing model and outputting a real-time analysis result; the data processing model is constructed in advance based on historical operation data of the dust collector, the boiler main machine and the auxiliary machine equipment, structural parameter data of the dust collector, the boiler main machine and the auxiliary machine equipment and control experience data of the dust collector, the boiler main machine and the auxiliary machine equipment; and the man-machine interaction module is used for displaying and/or processing the real-time analysis result according to the operation instruction if the operation instruction of the user is received.
In a preferred embodiment of the present invention, the digital twin system for a dust collector further includes: the data storage module is connected with the data perception module; the data storage module includes: caching a database and a time sequence database; the cache database and the time sequence database are used for storing the real-time operation data.
In a preferred embodiment of the present invention, the real-time running data is provided with a data update identification bit; the data perception module is also used for judging whether the numerical value corresponding to the real-time operation data is updated or not according to the data update identification bit; if yes, storing the updated real-time operation data into the time sequence database, and updating the data update identification bit of the real-time operation data of the cache database.
In a preferred embodiment of the present invention, the real-time operation data includes: the high-voltage power supply operation parameters of the dust remover, the vibration parameters of cathode vibration and anode vibration, the heating parameters of porcelain bushing electric heating, the vibration failure states of cathode vibration and anode vibration, the ash accumulation condition of an ash bucket, dust removal efficiency, power consumption, air leakage rate, structural safety monitoring data of a steel bracket and the ash bucket, the specific dust collection area of an anode plate, the oxygen content of an inlet and an outlet, humidity, smoke pressure and inlet and outlet smoke dust concentration; boiler load of the boiler main machine, boiler soot blowing and coal feeding amount data, ammonia escape data of a denitration system, start-stop data of an ash conveying system, outlet smoke temperature of the low-temperature economizer, fault data, start-stop data of the desulfurization circulating pump, fault data and outlet smoke dust meter data of the desulfurization system.
In a preferred embodiment of the present invention, the data processing model includes: a concentration prediction sub-model, a digital twin sub-model, a structural safety evaluation sub-model, a deep energy-saving optimization sub-model and a smoke characteristic analysis sub-model; the data perception module is also used for determining the boiler ash and slag ratio, load, total fuel amount and ash data of the boiler main machine according to the real-time operation data; the concentration prediction sub-model is used for calculating the boiler ash-slag ratio, the load, the total fuel quantity, the coal type entering the boiler and ash data according to a preset smoke concentration calculation formula to obtain the smoke concentration prediction of the inlet of the dust remover; the soot concentration calculation formula is constructed in advance based on control experience data of the dust collector, the boiler main machine and the auxiliary machine equipment; the digital twin sub-model is used for constructing a real-time mapping model of the dust removal characteristic of the dust remover through a mechanism model of the dust remover and a data driving model of the dust remover; the data driving model is used for indicating a relation model between the dust removal efficiency and the operation parameters, which are obtained by training the initial neural network through the historical operation data; the data perception module is also used for acquiring real-time mechanical data of a preset position of the dust remover; the structural safety assessment sub-model analyzes the safety states of the shell, the ash bucket and the steel bracket of the dust remover according to the real-time mechanical data; the structural safety evaluation model is pre-established based on a finite element model of the dust remover; the deep energy-saving optimization sub-model is used for predicting the dust concentration of the outlet of the dust remover according to the real-time operation data based on a preset pollutant removal model and an operation energy consumption material consumption cost model, and comparing the dust concentration of the outlet of the dust remover with a preset dust concentration control target value to obtain a comparison result; outputting an operation parameter control signal of the dust remover according to the comparison result; the pollutant removal model and the operation energy consumption cost model are used for indicating an energy resource allocation model constructed based on the dust remover operation logic and preset constraint parameters; the flue gas characteristic analysis submodel is used for calculating and obtaining fire stability index, burnout characteristic index, coal powder firing index, slagging characteristic index, coal powder abrasion characteristic index, dust specific resistance of the coal powder according to the real-time operation data and the coal quality analysis data, and obtaining the adaptability data of the dust remover to coal types, wherein the inlet flue gas quantity of the dust remover, ammonium bisulfate components in flue gas and the dust remover are used for solving the problems of low coal quality and low coal quality.
In a preferred embodiment of the present invention, the data processing model further includes: a health real-time analysis sub-model, a fault early warning and diagnosis sub-model and a device life assessment sub-model; the health real-time analysis sub-model is used for determining the technical performance, fault condition, service life condition, safety condition, operation environment condition and corrosion condition of the dust remover according to the real-time operation data and the real-time mechanical data; calculating the health score of the dust remover according to the technical performance, the fault condition, the service life condition, the safety condition, the running environment condition, the corrosion condition and the corresponding weight and scoring rules; outputting a target health grade corresponding to the health score according to the health grade preset by the health score control; the fault early warning and diagnosing sub-model is used for determining the fault type of the dust remover according to the real-time operation data, analyzing the fault type based on preset cause analysis data and outputting an analysis result; comparing the analysis result with a preset fault cause screening rule, and determining a target fault cause corresponding to the analysis result; the real-time operation data further includes: the service life of the components of the boiler main machine and the auxiliary machine equipment affects factor data; the above component lifetime influence factor data includes: ambient temperature, power flashover conditions, IGBT temperature, transformer temperature, environmental acid-base corrosiveness, environmental dust and environmental humidity; the equipment life evaluation submodel is used for calculating real-time life evaluation data of the dust remover and the components according to the environmental temperature of the dust remover, the power flashover working condition of the power supply, the IGBT temperature, the transformer temperature, the environmental acid-base corrosiveness, the environmental dust, the environmental humidity and the influence coefficient of preset life influence factors.
In a preferred embodiment of the present invention, the fault early warning and diagnosing sub-model is further configured to compare the analysis result with a preset warning rule; and if the analysis result accords with the alarm rule, generating an alarm signal corresponding to the analysis result, and transmitting the alarm signal to a designated alarm terminal for alarm operation.
In a preferred embodiment of the present invention, the man-machine interaction module includes: displaying a page; a dust remover three-dimensional model corresponding to the dust remover is built on the display page in advance; the substructure of the three-dimensional model of the duster is labeled with a preset color based on the real-time operational data.
In a preferred embodiment of the present invention, the display page includes: a plurality of interactive controls; each interaction control corresponds to a preset interaction function; and each interaction control is used for displaying and/or processing the real-time analysis result corresponding to the interaction function on the three-dimensional model according to the operation instruction if the operation instruction of the user is received.
In a second aspect, the embodiment of the invention also provides a digital twin method of the dust remover, which is applied to the digital twin system of the dust remover; the method comprises the following steps: acquiring coal analysis data, a dust remover and real-time operation data of a boiler host machine and auxiliary equipment connected with the dust remover; the auxiliary equipment comprises a denitration ammonia escape instrument, a low-temperature economizer outlet flue gas thermometer, an ash conveying bin pump, a desulfurization circulating pump and a desulfurization system outlet flue dust instrument; inputting the real-time operation data and the coal quality analysis data into a preset data processing model, and outputting a real-time analysis result; the data processing model is constructed in advance based on historical operation data of the dust collector, the boiler main machine and the auxiliary machine equipment, structural parameter data of the dust collector, the boiler main machine and the auxiliary machine equipment and control experience data of the dust collector, the boiler main machine and the auxiliary machine equipment; and if an operation instruction of a user is received, displaying and/or processing the real-time analysis result according to the operation instruction.
The embodiment of the invention has the following beneficial technical effects:
the embodiment of the invention provides a digital twin system and a digital twin method for a dust remover, comprising the following steps: the system comprises a data perception module, a data analysis module and a man-machine interaction module which are connected in sequence; the data perception module is used for acquiring coal quality analysis data, a dust remover and real-time operation data of a boiler host and auxiliary equipment connected with the dust remover; the auxiliary equipment comprises a denitration ammonia escape instrument, a low-temperature economizer outlet flue gas thermometer, an ash conveying bin pump, a desulfurization circulating pump and a desulfurization system outlet flue dust instrument; the data analysis module is used for inputting the real-time operation data and the coal quality analysis data into a preset data processing model and outputting a real-time analysis result; the data processing model is constructed in advance based on historical operation data of the dust collector, the boiler main machine and the auxiliary machine equipment, structural parameter data of the dust collector, the boiler main machine and the auxiliary machine equipment and control experience data of the dust collector, the boiler main machine and the auxiliary machine equipment; and the man-machine interaction module is used for displaying and/or processing the real-time analysis result according to the operation instruction if the operation instruction of the user is received. The system relieves the technical problem of lower automation degree in the prior art, and improves the experience of users.
Additional features and advantages of the present embodiments will be set forth in the description which follows, or in part will be obvious from the description, or may be learned by practice of the techniques of the present disclosure.
The foregoing objects, features and advantages of the disclosure will be more readily apparent from the following detailed description of the preferred embodiments taken in conjunction with the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a digital twinning system of a dust collector according to an embodiment of the present invention;
FIG. 2 is a schematic view of a usage scenario of a digital twinning system of a dust collector according to an embodiment of the present invention;
fig. 3 is a schematic flow chart of a digital twin method of a dust remover according to an embodiment of the present invention.
Reference numerals illustrate: 11-a data perception module; 12-a data analysis module; 13-a man-machine interaction module; 21-a boiler main unit; a 22-deduster digital twin system; a 23-server; 24-a firewall; 25-a display; 26-unidirectional isolation device.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Currently, an intelligent service platform for a dust remover generally judges operation parameters of the dust remover based on preset parameters so as to control the dust remover. However, the above prior art has a low degree of automation, resulting in a poor user experience.
Based on the above, the embodiment of the invention provides a digital twin system and a digital twin method for a dust remover, which relieve the technical problem of lower automation degree in the prior art and improve the user experience. For the sake of understanding the embodiments of the present invention, a digital twin system for dust collectors disclosed in the embodiments of the present invention will be described in detail.
Example 1
Fig. 1 is a schematic structural diagram of a digital twin system of a dust collector according to an embodiment of the present invention.
As seen in fig. 1, the system comprises: the system comprises a data perception module 11, a data analysis module 12 and a human-computer interaction module 13 which are connected in sequence.
In this embodiment, the data sensing module 11 is configured to obtain real-time operation data of the coal quality analysis data, the dust remover, and the boiler host and auxiliary equipment connected to the dust remover; the auxiliary equipment comprises a denitration ammonia escape instrument, a low-temperature economizer outlet flue gas thermometer, an ash conveying bin pump, a desulfurization circulating pump and a desulfurization system outlet flue dust instrument; the data analysis module 12 is configured to input the real-time operation data and the coal analysis data into a preset data processing model, and output a real-time analysis result; the data processing model is constructed in advance based on historical operation data of the dust collector, the boiler main machine and the auxiliary machine equipment, structural parameter data of the dust collector, the boiler main machine and the auxiliary machine equipment and control experience data of the dust collector, the boiler main machine and the auxiliary machine equipment; the man-machine interaction module 13 is configured to display and/or process the real-time analysis result according to the operation instruction if the operation instruction of the user is received.
Wherein, above-mentioned dust remover is the electrostatic precipitator.
Here, the real-time operation data includes: the high-voltage power supply operation parameters of the dust remover, the vibration parameters of cathode vibration and anode vibration, the heating parameters of porcelain bushing electric heating, the vibration failure states of cathode vibration and anode vibration, the ash accumulation condition of an ash bucket, dust removal efficiency, power consumption, air leakage rate, structural safety monitoring data of a steel bracket and the ash bucket, the specific dust collection area of an anode plate, the oxygen content of an inlet and an outlet, humidity, smoke pressure and inlet and outlet smoke dust concentration; boiler load of the boiler main machine, boiler soot blowing and coal feeding amount data, ammonia escape data of a denitration system, start-stop data of an ash conveying system, outlet smoke temperature of the low-temperature economizer, fault data, start-stop data of the desulfurization circulating pump, fault data and outlet smoke dust meter data of the desulfurization system.
Further, the digital twin system of the dust remover further comprises: the data storage module is connected with the data perception module; the data storage module includes: caching a database and a time sequence database; the cache database and the time sequence database are used for storing the real-time operation data.
In actual operation, the time sequence database is also used for storing the historical operation data; the data storage module further includes: a relational database; the relational database is used for storing business data used by the system.
Further, a data update identification bit is arranged on the real-time operation data; the data sensing module 11 is further configured to determine whether a value corresponding to the real-time running data is updated according to the data update identifier bit; if yes, storing the updated real-time operation data into the time sequence database, and updating the data update identification bit of the real-time operation data of the cache database.
Further, the data analysis module 12 includes: a concentration prediction sub-model, a digital twin sub-model, a structural safety evaluation sub-model, a deep energy-saving optimization sub-model and a smoke characteristic analysis sub-model; the data perception module 11 is also used for determining the boiler ash and slag ratio, load, total fuel amount, and coal type and ash data of the boiler main engine according to the real-time operation data; the concentration prediction sub-model is used for calculating the boiler ash-slag ratio, the load, the total fuel quantity, the coal type entering the boiler and ash data according to a preset smoke concentration calculation formula to obtain the smoke concentration prediction of the inlet of the dust remover; the soot concentration calculation formula is constructed in advance based on control experience data of the dust collector, the boiler main machine and the auxiliary machine equipment; the digital twin sub-model is used for constructing a real-time mapping model of the dust removal characteristic of the dust remover through a mechanism model of the dust remover and a data driving model of the dust remover; the data driving model is used for indicating a relation model between the dust removal efficiency and the operation parameters, which are obtained by training the initial neural network through the historical operation data; the data sensing module 11 is further configured to obtain real-time mechanical data of a preset position of the dust remover; the structural safety assessment sub-model analyzes the safety states of the shell, the ash bucket and the steel bracket of the dust remover according to the real-time mechanical data; the structural safety evaluation model is pre-established based on a finite element model of the dust remover; the deep energy-saving optimization sub-model is used for predicting the dust concentration of the outlet of the dust remover according to the real-time operation data based on a preset pollutant removal model and an operation energy consumption material consumption cost model, and comparing the dust concentration of the outlet of the dust remover with a preset dust concentration control target value to obtain a comparison result; outputting an operation parameter control signal of the dust remover according to the comparison result; the pollutant removal model and the operation energy consumption cost model are used for indicating an energy resource allocation model constructed based on the dust remover operation logic and preset constraint parameters; the flue gas characteristic analysis submodel is used for calculating and obtaining fire stability index, burnout characteristic index, coal powder firing index, slagging characteristic index, coal powder abrasion characteristic index, dust specific resistance of the coal powder according to the real-time operation data and the coal quality analysis data, and obtaining the adaptability data of the dust remover to coal types, wherein the inlet flue gas quantity of the dust remover, ammonium bisulfate components in flue gas and the dust remover are used for solving the problems of low coal quality and low coal quality.
In actual operation, the structural safety assessment model is pre-established based on the finite element model, the structural damage mechanism, the structural destructive test and the physical simulation test of the dust remover.
Furthermore, the structural safety evaluation model is used for inputting real-time mechanical data into a preset calculation formula to calculate and outputting the real-time weight of the fly ash in the ash bucket. The calculation formula is pre-constructed based on the corresponding relation between the weight of the fly ash in the ash hopper and the real-time mechanical data. Further, the real-time level height of the fly ash in the ash bucket is calculated according to a preset calculation formula of the level height of the fly ash in the ash bucket. Wherein, the calculation formula of the fly ash level height in the ash bucket is constructed in advance based on the fly ash weight, the ash bucket structural parameters and the fly ash density empirical data.
Further, the data processing model further includes: a health real-time analysis sub-model, a fault early warning and diagnosis sub-model and a device life assessment sub-model; the health real-time analysis sub-model is used for determining the technical performance, fault condition, service life condition, safety condition, operation environment condition and corrosion condition of the dust remover according to the real-time operation data and the real-time mechanical data; calculating the health score of the dust remover according to the technical performance, the fault condition, the service life condition, the safety condition, the running environment condition, the corrosion condition and the corresponding weight and scoring rules; outputting a target health grade corresponding to the health score according to the health grade preset by the health score control; the fault early warning and diagnosing sub-model is used for determining the fault type of the dust remover according to the real-time operation data, analyzing the fault type based on preset cause analysis data and outputting an analysis result; comparing the analysis result with a preset fault cause screening rule, and determining a target fault cause corresponding to the analysis result; the real-time operation data further includes: the service life of the components of the boiler main machine and the auxiliary machine equipment affects factor data; the above component lifetime influence factor data includes: ambient temperature, power flashover conditions, IGBT temperature, transformer temperature, environmental acid-base corrosiveness, environmental dust and environmental humidity; the equipment life evaluation submodel is used for calculating real-time life evaluation data of the dust remover and the components according to the environmental temperature of the dust remover, the power flashover working condition of the power supply, the IGBT temperature, the transformer temperature, the environmental acid-base corrosiveness, the environmental dust, the environmental humidity and the influence coefficient of preset life influence factors.
Further, the fault early warning and diagnosis sub-model is further used for comparing the analysis result with a preset warning rule; and if the analysis result accords with the alarm rule, generating an alarm signal corresponding to the analysis result, and transmitting the alarm signal to a designated alarm terminal for alarm operation.
Further, the man-machine interaction module includes: displaying a page; a dust remover three-dimensional model corresponding to the dust remover is built on the display page in advance; the substructure of the three-dimensional model of the duster is labeled with a preset color based on the real-time operational data.
Wherein the real-time lifetime assessment data comprises: hidden danger operation, over-period operation, saturation operation and normal operation. Here, the color of the hidden trouble operation corresponds to red, the color of the out-of-date operation corresponds to orange, the color of the saturated operation corresponds to yellow, and the color of the normal operation corresponds to green.
Further, the display page includes: a plurality of interactive controls; each interaction control corresponds to a preset interaction function; and each interaction control is used for displaying and/or processing the real-time analysis result corresponding to the interaction function on the three-dimensional model according to the operation instruction if the operation instruction of the user is received.
In this embodiment, the above-mentioned relational database adopts MySQL relational database management system, the cache database adopts Redis-log type, key-Value database which can be based on memory and also can be persistent, and the time sequence database adopts InfluxDB open source distributed time sequence, event and index database. The Redis cache database is used for caching real-time data acquired by the data sensing module, the InfluxDB time sequence database is used for storing the real-time data acquired by the data sensing module as historical data, and the MySQL database is used for storing basic configuration information of a smoke concentration prediction sub-model, a digital twin sub-model, a structural safety evaluation sub-model, a deep energy saving optimization sub-model, a smoke characteristic analysis sub-model, a health real-time analysis sub-model, a fault early warning and diagnosis sub-model, a device life evaluation sub-model and the like of a plurality of analysis models related to the multi-dimensionality of dust collector safety, energy saving, environmental protection, health and intelligent operation and maintenance, and service data and alarm record data of fault early warning.
The three-dimensional model of the dust remover comprises three-dimensional high-low voltage equipment, three-dimensional component names of equipment component levels, component IDs, attributes, association relations between three-dimensional components and acquired data, association relations between three-dimensional components and configured related video animations.
Further, a system setting module in the man-machine interaction module is used for receiving configuration information sent by engineering implementation personnel; and configuring basic configuration information of a plurality of analysis models related to the safety, energy conservation, environmental protection, health and intelligent operation and maintenance of the dust collector, such as a smoke concentration prediction sub-model, a digital twin sub-model, a structural safety evaluation sub-model, a deep energy conservation optimization sub-model, a smoke characteristic analysis sub-model, a health real-time analysis sub-model, a fault early warning and diagnosis sub-model, an equipment life evaluation sub-model and the like according to the configuration information, and storing the configuration information into the data storage module.
In one embodiment, the data analysis module 12 determines whether the value in the cache database is updated, if so, analyzes the real-time data in the cache database, synchronously stores the real-time data in the time sequence database, updates the flag bit state of the data in the cache database to be stored, and waits for the sensing module to acquire the new data and then updates the cached real-time data and the updated flag bit state; otherwise, the real-time data in the cache database is not updated and is not stored in the time sequence database. Further, when the real-time data is cached, the data communication bits of the equipment such as the high-frequency power supply, the pulse power supply and the like are judged, if the communication fault exists, the related data of the corresponding power supply are marked in the cache database, and the marked data are not stored in the time sequence database in real time.
Further, the data analysis module 12 is further configured to read data related to the request from the data storage module according to the request of each sub-module and send the data; when the system service processes the request sent by the front-end browsing module, the real-time data per second refreshing service is provided for the front end through the Websocket, and after the real-time data is analyzed and repackaged, the real-time data is sent to the man-machine interaction module 13 in real time for display in a JSON file format.
In one embodiment, the data analysis module 12 processes the request of the man-machine interaction module 13 in the following manner: the man-machine interaction module 13 sends a request to the data analysis module 12, the data analysis module 12 queries data corresponding to the request in a database according to the URL and request parameters sent by the man-machine interaction module 13 after receiving the request, the data analysis module 12 packages the obtained data into a JSON format and sends the JSON format to the man-machine interaction module, and a front-end browsing mode displays the JSON format to a user.
The man-machine interaction module is used for man-machine interaction, and the data sent by the data analysis data module are displayed in a corresponding form according to the selection of a user; the form comprises a dust remover three-dimensional model constructed based on BIM technology, and corresponding real-time data and states are displayed in a three-dimensional component of the three-dimensional model; when a request is sent to the data analysis module 12 for the first time, the human-computer interaction module establishes a long connection with the data analysis module 12, and the data analysis module 12 sends real-time data to the human-computer interaction module through the long connection. The man-machine interaction module is used as a man-machine interface of the digital twin system of the dust remover for a user to acquire the information of the three-dimensional component, the equipment and component monitoring data, the association relation between the three-dimensional component and the equipment and component monitoring data, the alarm real-time data, the alarm historical data and the association relation between the three-dimensional component and the alarm real-time data stored in the data storage module from the data analysis module 12, establishes the three-dimensional module for display based on the existing BIM technology, displays corresponding data and the running state of the equipment in the three-dimensional model in real time, and realizes linkage display of the dust remover monitoring data and the three-dimensional digital model, display of alarm information and inquiry of historical alarm information.
Here, the three-dimensional member includes a dust collector body model and a high-voltage power supply model.
Further, the dust remover body model comprises a dust remover body, cathode vibration, anode vibration, a disconnecting switch box, an insulator chamber, an inlet bell mouth inner piece, an outlet bell mouth inner piece, an ash bucket, porcelain shaft electric heating, porcelain sleeve electric heating, ash bucket bottom ash conveying bin pump, a pavement, a support, an anode plate and a cathode system.
Wherein the cathode rapping comprises: the device comprises a motor, a shaft, large and small needle wheels, a hammer head, a dust bearing and a porcelain shaft; the above-mentioned anodic rapping comprises: a motor, a shaft, a hammer head and a dust bearing; the insulator chamber includes: porcelain bushing and heater; the inlet flare inner piece includes: perforated plate, tube support and deflector; the outlet flare trim includes: groove-shaped plates and tube supports; the ash bucket comprises: pipe support, wind shield, material level gauge and gasification device; the cathode system includes: a cathode frame and a cathode line.
Further, the high-frequency power supply model comprises a high-frequency power supply whole, a high-frequency inverter box, a high-frequency control box, a high-frequency distribution box, a power supply bottom and a transformer.
Wherein, above-mentioned high frequency inverter box includes: the control transformer, the composite busbar, the current transformer, the thin film capacitor, the resonant capacitor, the IGBT module, the IGBT driving plate, the silicon controlled rectifier module, the switching power supply, the axial flow fan, the door plate, the radiator, the IGBT interface plate, the high-frequency open circuit module and the high-frequency silicon controlled rectifier driving plate.
Further, the high-frequency control box includes: the device comprises a signal isolation module, a voltage transmitter, a control transformer, an alternating current contactor, an intermediate relay, a miniature circuit breaker, a motor starter, a high-frequency controller, a current transmitter, a wiring terminal, a switching power supply, the intermediate relay, a ceramic melting core, a high-frequency signal conditioning control board and a high-frequency DCS control module.
Further, the high-frequency distribution box includes: miniature circuit breaker, moulded case circuit breaker and insulating column.
Further, the power supply bottom includes: a high-frequency fan and a high-frequency underframe of a flat wheel.
Further, the transformer includes: pressure relief valve, platinum thermal resistor, float switch, and high frequency sampling plate.
Further: the pulse power supply model comprises a pulse chassis fan, a pulse control box, a left pulse box, a right pulse box and a pulse transformer.
Wherein, the pulse control box includes: the device comprises a control transformer, a current transformer, an alternating current contactor, a switching power supply, an intermediate relay base, a DCS signal isolation module, a pulse analog board, a pulse high-frequency control board A board and a pulse controller.
Further, the left pulse box includes: the device comprises a current transformer, an axial flow fan, a resonant capacitor, an absorption capacitor, a high-power thick film resistor, an alternating current contactor, a fast recovery diode module, an M6 screw type temperature probe, a front cover plate and a pulse left radiator.
Further, the right pulse box includes: voltage transmitter, current transmitter, control transformer, silicon controlled rectifier module, IGBT module, switching power supply, intermediate relay base, M6 screw type temperature probe, pulse right radiator, pulse three-phase trigger plate, pulse generating line sampling board, pulse resistance-capacitance absorption board, IGBT drive plate, front shroud, axial fan, reverse welded pulse generating line sampling board and pulse three same step board module.
Further, the pulse transformer includes: a pulse secondary voltage sampling plate, a pressure release valve, a float switch and a platinum thermal resistor.
In actual operation, the man-machine interaction module 13 includes an intelligent home page module, a digital twin module, a security management and control module, a deep energy-saving efficiency improvement module, an intelligent analysis module, a health management module, an alarm and fault management module, an equipment digital archive module, an operation management module, a maintenance management module, a system setting module and a system management module.
When the intelligent home page module is used for sending a request to the data analysis module 12, the data analysis module 12 obtains the dust remover display video data from the server and sends the dust remover display video data to the front end for display. The intelligent home page module requests the data analysis module 12 for the data which is up to standard in real time, the data analysis module 12 queries the last period of monitoring data which is up to standard in real time from the time sequence database, the data are analyzed and packaged and then fed back to the front end to show the operation trend of the last period of outlet emission in the form of a graph; the intelligent home page module requests the data of pollution reduction and carbon reduction, safety control, fault diagnosis, equipment control and health assessment from the data analysis module 12 in real time, the data analysis module 12 queries the corresponding data of pollution reduction and carbon reduction, safety control, fault diagnosis, equipment control and health assessment from the database according to the request, the data are packaged after being processed and fed back to the intelligent home page module, the intelligent home page module renders and displays the data, wherein the equipment control data are displayed in a bar graph form, and the health assessment data are displayed in a radar graph form.
Further, the digital twin module sends a request to the data analysis module 12, the data analysis module 12 extracts corresponding three-dimensional component information, alarm information, equipment and component monitoring data and three-dimensional model files in the database according to the request, and feeds back the three-dimensional component information, alarm information, equipment and component monitoring data to the digital twin module, and the digital twin module renders and displays the three-dimensional model files in the corresponding layout to support the functions of moving, zooming, rotating and selecting the model. The digital twin module screens the running state information and fault information in the monitoring data of the equipment and the components returned by the data analysis module 12, renders the running state and the fault state of the equipment such as a high-frequency power supply, a pulse power supply, porcelain bushing electric heating, cathode vibration, anode vibration and the like in real time, if the equipment has faults, renders the corresponding equipment on the three-dimensional model into bright red and fast flashes, if the equipment does not have faults, continues to judge the running state of the equipment, the equipment is in red but does not flash, otherwise, the equipment is in green, and the equipment is in a stop state at the moment. When the three-dimensional component in the three-dimensional model is selected, the digital twin module initiates a request to the data analysis module 12, the data analysis module 12 reads corresponding real-time monitoring data from the real-time data cache according to the requested parameters, analyzes and packages the data, and feeds the data back to the digital twin module. And selecting part of the equipment in the three-dimensional model, checking the animation demonstration video of the equipment and jumping to check the component-level three-dimensional model of the equipment, wherein the component-level three-dimensional model of the equipment displays the levels and all three-dimensional components of the equipment and the components in a model tree mode, and meanwhile, the levels and all three-dimensional component models of the equipment and the components also support hiding, moving, zooming, rotating and selecting functions. In the three-dimensional display page after the jump, the digital twin module requests real-time monitoring data, alarm information, running information, equipment names, equipment rules, parameters and other basic information of the equipment, maintenance information of the equipment and other data from the data analysis module 12, and the data analysis module 12 acquires corresponding data from the real-time cache and the database according to the URL and the request parameters requested by the digital twin module, analyzes and processes the corresponding data, packages the data and feeds the data back to the digital twin module.
Further, the security management and control module sends a request to the data analysis module 12, and the data analysis module 12 feeds corresponding data back to the security management and control module according to the request. The data analysis module 12 receives a request of the safety control module for the ash bucket three-dimensional model, three-dimensional model data in a server, three-dimensional model component information in a database, equipment monitoring data, association relation of the three-dimensional model component information and the equipment monitoring data, alarm real-time data, association relation of the three-dimensional model component information and the alarm real-time data and alarm history data are sent to the safety control module, the safety control module displays the three-dimensional model according to the received three-dimensional model, if corresponding equipment in the three-dimensional model has the corresponding alarm real-time data, the three-dimensional model displays red to indicate that the equipment has real-time alarm, if the three-dimensional model does not have the real-time alarm, the three-dimensional model component information and the equipment monitoring data are displayed normally, the corresponding equipment is selected for clicking, and the history alarm data of the equipment can be checked. The data analysis module 12 receives the request of the security management and control module for the real-time monitoring data and the historical data, acquires the real-time data from the real-time cache database and the database, and feeds the corresponding historical data back to the security management and control module, and the security management and control module displays the real-time monitoring data in a table and displays the received historical monitoring data in a curve chart form. The data analysis module 12 receives the request of the real-time alarm information from the database, acquires the real-time alarm information from the database, performs grading statistics and then feeds back to the safety control module, and the safety control module receives the alarm real-time data fed back by the data analysis module 12, performs grading display and color rendering according to the alarm grade, wherein the primary alarm display is red, the secondary alarm display is orange, the tertiary alarm display is yellow, and the quaternary alarm display is green.
Further, the deep energy-saving efficiency improving module sends a request to the data analyzing module 12, and the data analyzing module 12 feeds corresponding data back to the deep energy-saving efficiency improving module according to the request, wherein the deep energy-saving efficiency improving module comprises three menu pages of intelligent optimal control, energy management and energy saving evaluation.
The intelligent optimization control comprises four label pages of an optimization control home page, a classical algorithm, an artificial intelligent algorithm and an operation trend:
here, the optimization control home page: the optimization control home page sends a request to the data analysis module 12, and the data analysis module 12 feeds corresponding data back to the optimization control home page according to the request. The data analysis module 12 receives a request of the optimizing control front page to the flue gas flow model of the dust remover, acquires the flue gas flow model from the server and feeds back the flue gas flow model to the optimizing control front page, queries corresponding data from the database, calculates the flue gas concentration data of each electric field, packages the flue gas concentration data, feeds back the flue gas concentration data to the optimizing control front page, displays the received flue gas flow model, renders the flue gas flow model according to the flue gas concentration of each electric field, and the higher the flue gas concentration is, the darker the electric field display color corresponding to the three-dimensional model is. The data analysis module 12 receives a real-time request of the optimizing control home page to the historical data of the outlet emission and the plant power consumption, acquires the historical data of a period of time from the time sequence database and feeds the historical data back to the optimizing control home page, and the optimizing control home page displays the data change trend of the period of time in a graph. The data analysis module 12 receives the request of the smoke concentration predicted value of the entrance and exit of the optimizing control home page, the dust removing efficiency of each electric field and the ash accumulation thickness predicted value, acquires corresponding data from the database and feeds the corresponding data back to the optimizing control home page, and the optimizing control home page displays the acquired data in a form of a table.
Further, the classical algorithm page sends a request to the data analysis module 12, and the data analysis module 12 feeds corresponding data back to the classical algorithm page according to the request. The data analysis module 12 receives the request of the classical algorithm page for real-time data, obtains the real-time data such as the outlet concentration, the optimized power value, the high compaction time power, the real-time power duty ratio, the compensation power duty ratio, the residence time, the plate current density, the linear current density, the electric field intensity, the voltage real-time, the voltage coefficient, the current real-time, the current coefficient, the control coefficient and the like from the database, and feeds back the real-time data to the classical algorithm page, and the classical algorithm page displays the received data. The data analysis module 12 receives a request of the classical algorithm page for the historical data, acquires corresponding historical data from the time sequence database and feeds the corresponding historical data back to the classical algorithm page, and the classical algorithm page displays the change trend of the received data for a period of time by using a curve chart. The data analysis module 12 receives the request of the classical algorithm page for the guiding parameters of each electric field, acquires the guiding set values of the secondary voltage and the secondary current of each electric field from the database, and feeds back the guiding set values to the classical algorithm page, and the classical algorithm page displays the received data.
Further, the artificial intelligence algorithm page sends a request to the data analysis module 12, and the data analysis module 12 feeds corresponding data back to the artificial intelligence algorithm page according to the request. The data analysis module 12 receives a request of the artificial intelligence algorithm page for the inlet and outlet concentration data, acquires historical data of inlet and outlet concentration prediction from the time sequence database, and feeds back the historical data to the artificial intelligence algorithm page, and the artificial intelligence algorithm page receives the data to display the change trend of the data in a period of time by using a curve chart. The data analysis module 12 receives the request of the artificial intelligence algorithm page for real-time data, acquires the data of the secondary current, the secondary voltage, the vibration state, the body parameters and the like of each electric field from the database, packages the data, and feeds the data back to the artificial intelligence algorithm page, and the artificial intelligence algorithm page displays the received data. The data analysis module 12 receives the request of the artificial intelligence algorithm page for the historical data, acquires corresponding historical data from the time sequence database and feeds the corresponding historical data back to the artificial intelligence algorithm page, and the artificial intelligence algorithm page displays the change trend of the received data in a curve chart for a period of time. The data analysis module 12 receives the request of the artificial intelligence algorithm page for the guiding parameters of each electric field, acquires the secondary voltage, secondary current and vibrating guiding set values of each electric field from the database, and feeds the secondary voltage, secondary current and vibrating guiding set values back to the artificial intelligence algorithm page, and the artificial intelligence algorithm page displays the received data.
Further, the running trend page sends a request to the data analysis module 12, and the data analysis module 12 feeds corresponding data back to the running trend page according to the request. The data analysis module 12 receives the request of the running trend page for real-time data, acquires the real-time data of the secondary voltage and the secondary current of each electric field from the database, calculates the real-time power of each electric field and the real-time total power of the dust remover, feeds back the real-time power to the running trend page, and displays the real-time total power in a pie chart and the real-time power of each electric field in a bar chart. The data analysis module 12 receives the request of the operation trend page for the historical data, acquires the historical data of the outlet emission and the boiler load from the time sequence database, and feeds back the historical data to the operation trend page, and the operation trend page displays the received data in a curve chart to show the change trend of the data in a period of time.
Further, the energy management page is configured to send a request to the data analysis module 12, and the data analysis module 12 feeds corresponding data back to the energy management page according to the request. The data analysis module 12 receives the request of the energy management page for real-time data, acquires three-phase real-time data package feedback of power grid voltage, power grid current, load current, equipment current, IGBT temperature and the like from the database, and the energy management page displays the received feedback data. The data analysis module 12 receives the request of the energy management page for the current harmonic content data, acquires corresponding data from the database and feeds the corresponding data back to the energy management page, and the energy management page displays the received feedback data in a form of a bar graph.
Further, the energy-saving evaluation comprises three label pages of pollution-reducing and carbon-reducing billboard, pollution-reducing and carbon-reducing comparison and station service power analysis. The pollution-reducing and carbon-reducing billboard is used for sending a request to the data analysis module 12, the data analysis module 12 feeds corresponding data back to the pollution-reducing and carbon-reducing billboard according to the request, and the pollution-reducing and carbon-reducing billboard displays the received data. Further, the pollution reduction and carbon reduction contrast sends a request to the data analysis module 12, and the data analysis module 12 feeds corresponding data back to the pollution reduction and carbon reduction contrast according to the request, and the pollution reduction and carbon reduction contrast is displayed according to the received data. Further, the station power analysis sends a request to the data analysis module 12, the data analysis module 12 feeds corresponding data back to the station power analysis according to the request, and the station power analysis displays according to the received data.
Further, the intelligent analysis module sends a request to the data analysis module 12, and the data analysis module 12 feeds corresponding data back to the intelligent analysis module according to the request, wherein the intelligent analysis module comprises six menu pages of operation analysis, fault analysis, flue gas analysis, flow field analysis, performance analysis and circuit analysis. The operation analysis comprises two label pages for index pair mark and environment protection index analysis. Further, the index pair target page sends a request to the data analysis module 12, the data analysis module 12 feeds corresponding data back to the index pair target page according to the request, and the index pair target page displays according to the received data. The index pair target page selects the corresponding measuring point on the editing page of the related index, when the index pair target page sends a request to the data analysis module 12, the data analysis module 12 obtains real-time data of the corresponding measuring point to analyze, and feeds back the real-time data to the index pair target page after packaging, and the index pair target page displays the received data, so that real-time data change of the related index can be monitored. Further, the environmental protection index analysis page sends a request to the data analysis module 12, the data analysis module 12 queries historical data in a time period of the request from the time sequence database according to the URL of the request and the request parameter, feeds back the obtained historical data to the environmental protection index analysis page, and the environmental protection index analysis page displays the received data in the form of a pie chart and a graph. The fault analysis page sends a request to the data analysis module 12, the data analysis module 12 acquires corresponding alarm history data from the database according to the request, judges according to the parameter of the request, sorts according to the electric field, sorts according to the alarm type, sorts according to the equipment type, compares the data to obtain the first three of the fault rate ranks, gives a corresponding conclusion, packages the data, feeds the packaged data back to the fault analysis page, and displays the data in a histogram after the fault analysis page receives the data. The flue gas analysis comprises two tag pages for coal adaptability analysis and fuel property evaluation. The coal type adaptability analysis sends a request to the data analysis module 12, and the data analysis module 12 feeds corresponding data back to the coal type adaptability analysis according to the request. The user performs the operations of adding, deleting and checking the coal data on the coal adaptability analysis page, and the data analysis module 12 performs corresponding operations in the database according to the received request URL and the request parameters. The user selects corresponding coal types on the coal type adaptability analysis page, an evaluation request is sent to the data analysis module 12, the data analysis module 12 receives the request and then calls corresponding coal type data and configured coal type evaluation related configuration data from the database, a series of operations are carried out, a matched evaluation result is fed back to the coal type adaptability analysis page, and the coal type adaptability analysis page displays the received returned data. Further, the fuel property evaluation sends a request to the data analysis module 12, and the data analysis module 12 feeds back corresponding data to the fuel property evaluation according to the request. The user inputs the coal composition data on the fuel property evaluation page, clicks and saves the coal composition data to the data analysis module 12, the data analysis module 12 checks the coal composition data after receiving the request, and the data is updated to the database after confirming the error. The user sends a recalculation request to the data analysis module 12 at the result output page of the fuel property evaluation, after the data analysis module 12 receives the request, a series of operations are performed according to the relevant configuration data configured in the database and the coal composition data input by the user, the evaluation result matched after the calculation is fed back to the result output page of the fuel property evaluation, and the fuel property evaluation page displays the received returned data. Further, the system supports engineering implementation personnel to carry out custom configuration on a calculation formula and a conclusion of a result, the coal components and calculated intermediate data can be defined and named as variables, the variables and operator numbers are combined into the calculation formula, judgment conditions for the conclusion matching are customized, a plurality of conditions can be used for realizing complex condition association matching conclusion through a self-defined logic expression, and the configured data are stored in a database. Further, the flow field analysis sends a request to the data analysis module 12, the data analysis module 12 feeds corresponding data and pictures back to the flow field analysis page according to the requested parameters, and the flow field analysis page displays the received data and pictures at corresponding positions. Further, the performance analysis sends a request to the data analysis module 12, and the data analysis module 12 feeds corresponding data back to the performance analysis page according to the request, and the historical data are displayed according to a curve chart. Further, the circuit analysis sends a request to the data analysis module 12, and the data analysis module 12 feeds corresponding data and pictures back to the circuit analysis page according to the request. Further, the data analysis module 12 receives a request of the circuit analysis interface for checking the waveform, and according to the requested parameters, historical data of 30 seconds, 1 minute, 10 minutes and 30 minutes of corresponding parameters are respectively obtained from the time sequence database and fed back to the circuit analysis page, and the circuit analysis page is displayed in a curve chart. Further, the data analysis module 12 receives a request of the circuit analysis page for viewing the drive, feeds back different waveform pictures and real-time data according to the result, and displays the corresponding data and pictures after the circuit analysis page receives the corresponding data and pictures.
The health management module sends a request to the data analysis module 12, and the data analysis module 12 feeds corresponding data back to the health management module according to the request. Further, the engineering implementation personnel configure relevant parameters in the health management module, the data analysis module 12 checks the received configuration data after receiving the configuration request, and the configuration data passes the check and is stored in the database. Further, the data analysis module 12 calculates the scores of six factors and each sub-factor according to the relevant data configured by the health management module and the real-time data acquired by the data sensing module, such as technical performance, fault condition, service life condition, safety condition, running environment condition and corrosion condition, obtains the score of six factors according to the calculated score and the duty ratio of each sub-factor, calculates the overall health score of the dust remover according to the score of six factors and the duty ratio of each factor, and matches the score to the corresponding conclusion. Further, the data analysis module 12 evaluates the factor scores and calculates the overall scores of the dust collector in real time, and stores the results in a database in real time for the modules needing to display health management results to call the data.
The alarm and fault management module sends a request to the data analysis module 12, and the data analysis module 12 feeds corresponding data back to the alarm and fault management module according to the request. Further, engineering implementation personnel configure alarm information in alarm configuration, wherein the alarm information comprises basic alarm information, alarm rule matching information, alarm fault reason information and alarm fault reason matching rule information. Further, the data analysis module 12 calls the real-time data collected by the data perception module in real time to perform rule judgment according to the alarm configuration, and stores the corresponding alarm record when the alarm rule is triggered, and stores all real-time operation data of the current moment of the alarm so as to analyze the reason of the alarm later.
Further, the alarm processing and positioning page sends a request to the data analysis module 12, and the data analysis module 12 screens out the corresponding historical alarm record from the alarm record table of the database according to the URL and the request parameters of the request and feeds the historical alarm record back to the alarm processing and positioning page. Further, after receiving the operation request for the alarm record, the data analysis module 12 performs corresponding data processing according to the request, and updates the processed alarm record into the database. Further, after receiving the request for checking the fault record, the data analysis module 12 queries the time sequence database for historical data of relevant parameters of the first 30 seconds, the first 1 minute, the first 10 minutes and the first 30 minutes when the alarm record occurs, and feeds back the historical data to an alarm processing and positioning page, wherein the alarm processing and positioning page displays the received data in a curve chart form. After receiving the request for checking the fault diagnosis, the data analysis module 12 acquires the fault reason and the picture of the alarm configuration corresponding to the alarm record from the database, and feeds the fault reason and the picture back to the alarm processing and positioning page, wherein the alarm processing and positioning page displays the received data. After receiving the request for screening the fault reasons, the data analysis module 12 acquires the fault reasons and pictures of the alarm configuration corresponding to the alarm records from the database, screens according to the alarm of the alarm configuration and the matching rule information of the alarm fault reasons, feeds back the screened fault reasons to the alarm processing and positioning page, and displays the received data by the alarm processing and positioning page.
Further, the historical alarm statistics page sends a request to the data analysis module 12, and the data analysis module 12 feeds data back to the historical alarm statistics page according to the request, wherein the historical alarm statistics page displays the data in the form of a ring chart, a discount chart and the like. The historical alarm records are respectively counted according to alarm grades, time, equipment type, fault type, unit, dust collector, room and electric field.
Further, the history alarm inquiry page sends a request to the data analysis module 12, and the data analysis module 12 feeds back data to the history alarm inquiry page according to the URL of the request and the request parameters. Further, after receiving the request for checking the fault record, the data analysis module 12 queries the historical data of the relevant parameters of the first 30 seconds, the first 1 minute, the first 10 minutes and the first 30 minutes when the alarm record occurs from the time sequence database, feeds back the historical data to the historical alarm query page, and the historical alarm query page displays the received data in a curve chart form. After receiving the request for checking the fault diagnosis, the data analysis module 12 acquires the fault reason and the picture of the alarm configuration corresponding to the alarm record from the database, feeds back the fault reason and the picture to the historical alarm inquiry page, and displays the received data by the historical alarm inquiry page. After receiving the request for screening the fault reasons, the data analysis module 12 acquires the fault reasons and pictures of the alarm configuration corresponding to the alarm records from the database, screens according to the alarm of the alarm configuration and the matching rule information of the alarm fault reasons, feeds back the screened fault reasons to the historical alarm inquiry page, and displays the received data by the historical alarm inquiry page.
Further, the device digital profile module sends a request to the data analysis module 12, and the data analysis module 12 feeds back data to the device digital profile module according to the request. The engineering implementation personnel configures object model data in an object model configuration module, a user selects an object model configured by the engineering implementation personnel in a device digital file to generate a device, a data analysis module 12 receives a request of a new device, acquires corresponding object model information from a database according to the requested device information, judges whether the object model has sub-level devices, generates device information according to the device configuration information and the object model information if the sub-level devices do not exist, stores the device information in the database, generates device information according to the device configuration information and the object model information if the sub-level devices do not exist, acquires information from the database for all the sub-level devices of the object model selected by the device, generates sub-level device information according to the object model information, stores the sub-level device information in the database, continuously inquires whether the sub-level device still exists or not, and repeats the operation until all the sub-level device level devices are generated.
Further, the device information generated by the device digital archive module is stored in a database, and the data analysis module 12 performs correlation operation according to the configured device information and the correlation coefficient, the device lifetime adjustment type, the conversion coefficient and the like of the whole life cycle of the device configured by the engineering implementation personnel, and stores the calculated initial conversion operation accumulated time of the device in the database. The data analysis module 12 then performs an operation once an hour, and updates the cumulative operating time of the device plus the converted operating time of the latest one hour to the database as the latest cumulative operating time. The data analysis module 12 calculates the accumulated running time of the equipment and the service life of equipment configuration to obtain the coefficient of the accumulated running time of the equipment to the service life of the equipment, and evaluates the health state of the service life of the equipment, wherein the accumulated running time of the equipment is divided into four levels of hidden danger operation, over-period operation, saturated operation and normal operation. Further, when the equipment digital archive module inquires the equipment, the characters in the health state are marked with different colors, the normal operation display is green, the saturated operation display is yellow, the out-of-date operation display is orange, and the hidden danger operation display is red.
The operation management module sends a request to the data analysis module 12, and the data analysis module 12 feeds back data to the operation management module according to the request. The user configures the timed work task at the operation management module, the data analysis module 12 stores the configuration data in the database after receiving the configuration information, and the user can modify, edit and delete the configured timed work task. Further, the data analysis module 12 generates a timing task at regular time according to the configured timing task configuration data, stores the timing task in the database, and the user can check the timing task generated at the time when the timing task checking page, perform related operations on the timing task, perform corresponding processing on the data after receiving the corresponding request by the data analysis module 12, and store the processing result in the database.
The maintenance management module sends a request to the data analysis module 12, and the data analysis module 12 feeds back data to the maintenance management module according to the request. Further, the user can select any number of devices to start the maintenance task in the device digital archive module, and the data analysis module 12 can generate the maintenance task for the devices that start the maintenance task. The generated maintenance tasks are in the states of to-be-maintained, normal, completed, overdue and the like, and each device for starting the maintenance tasks has one maintenance task which is in the states of to-be-maintained, normal and overdue, and possibly has 0 or more completed maintenance tasks. The data analysis module 12 automatically updates the to-be-maintained, normal and overdue states of the maintenance task according to the equipment related maintenance information configured by the equipment digital archive module, and the maintenance task is restarted to time according to the current time until the user completes the current maintenance of the equipment.
The engineering implementation personnel perform setting in a system setting module, the system setting module sends a request to the data analysis module 12, and the data analysis module 12 feeds back data to the system setting module according to the request.
The engineering implementation personnel perform data initial configuration at the system management module, the system management module sends a request to the data analysis module 12, and the data analysis module 12 feeds data back to the system management module according to the request. The user performs related operations by using the system management module daily, and the data analysis module 12 processes the data according to the received request URL and the request parameter, stores the processed data in the database, and feeds back the corresponding data to the system management module.
For ease of understanding, fig. 2 is a schematic structural diagram of another digital twin system of a dust collector according to an embodiment of the present invention. As seen in fig. 2, the boiler host 21 is connected to the aforementioned dust catcher digital twin system 22 via a server 23 and a firewall 24. Further, for convenience of manager, the server 23 is connected to a peripheral display 25 through a unidirectional isolation device 26.
Here, the unidirectional isolating device is a unidirectional isolating gatekeeper.
The embodiment of the invention provides a digital twin system of a dust remover, which comprises the following components: the system comprises a data perception module, a data analysis module and a man-machine interaction module which are connected in sequence; the data perception module is used for acquiring coal quality analysis data, a dust remover and real-time operation data of a boiler host and auxiliary equipment connected with the dust remover; the auxiliary equipment comprises a denitration ammonia escape instrument, a low-temperature economizer outlet flue gas thermometer, an ash conveying bin pump, a desulfurization circulating pump and a desulfurization system outlet flue dust instrument; the data analysis module is used for inputting the real-time operation data and the coal quality analysis data into a preset data processing model and outputting a real-time analysis result; the data processing model is constructed in advance based on historical operation data of the dust collector, the boiler main machine and the auxiliary machine equipment, structural parameter data of the dust collector, the boiler main machine and the auxiliary machine equipment and control experience data of the dust collector, the boiler main machine and the auxiliary machine equipment; and the man-machine interaction module is used for displaying and/or processing the real-time analysis result according to the operation instruction if the operation instruction of the user is received. The system analyzes, stores and displays the coal quality analysis data, the dust remover and the real-time operation data of the boiler host and auxiliary equipment connected with the dust remover through a digital means, thereby improving the automation degree of the operation monitoring of the dust remover.
Example 2
On the basis of embodiment 1, fig. 3 is a schematic flow chart of a digital twin method of a dust remover provided by the embodiment of the invention. Here, the digital twin method of the dust remover is applied to the digital twin system of the dust remover.
As seen in fig. 3, the method includes:
step S301: acquiring coal analysis data, a dust remover and real-time operation data of a boiler host machine and auxiliary equipment connected with the dust remover; the auxiliary equipment comprises a denitration ammonia escape instrument, a low-temperature economizer outlet flue gas thermometer, an ash conveying bin pump, a desulfurization circulating pump and a desulfurization system outlet flue dust instrument.
Step S302: inputting the real-time operation data and the coal quality analysis data into a preset data processing model, and outputting a real-time analysis result; the data processing model is constructed in advance based on historical operation data of the dust collector and the boiler main unit and the auxiliary equipment, structural parameter data of the dust collector and the boiler main unit and the auxiliary equipment, and control experience data of the dust collector and the boiler main unit and the auxiliary equipment.
Step S303: and if an operation instruction of a user is received, displaying and/or processing the real-time analysis result according to the operation instruction.
The digital twin method for the dust remover provided by the embodiment of the disclosure has the same implementation principle and technical effects as those of the embodiment of the system, and for the purposes of brief description, the corresponding contents in the embodiment of the system are referred to for the parts of the embodiment of the method which are not mentioned.
Finally, it should be noted that: the foregoing examples are merely specific embodiments of the present disclosure, and are not intended to limit the scope of the disclosure, but the present disclosure is not limited thereto, and those skilled in the art will appreciate that while the foregoing examples are described in detail, it is not limited to the disclosure: any person skilled in the art, within the technical scope of the disclosure of the present disclosure, may modify or easily conceive changes to the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features thereof; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the disclosure, and are intended to be included within the scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
Claims (7)
1. A digital twinning system for a dust collector, comprising: the system comprises a data perception module, a data analysis module and a man-machine interaction module which are connected in sequence;
the data perception module is used for acquiring coal quality analysis data, a dust remover and real-time operation data of a boiler host and auxiliary equipment connected with the dust remover; the auxiliary equipment comprises a denitration ammonia escape instrument, a low-temperature economizer outlet flue gas thermometer, an ash conveying bin pump, a desulfurization circulating pump and a desulfurization system outlet flue dust instrument; the real-time operation data includes: the high-voltage power supply operation parameters of the dust remover, the vibration parameters of cathode vibration and anode vibration, the heating parameters of porcelain bushing electric heating, the vibration failure states of cathode vibration and anode vibration, the ash accumulation condition of an ash bucket, dust removal efficiency, power consumption, air leakage rate, structural safety monitoring data of a steel bracket and the ash bucket, the specific dust collection area of an anode plate, the oxygen content of an inlet and an outlet, humidity, smoke pressure and inlet and outlet smoke dust concentration; the boiler main engine comprises boiler load, boiler soot blowing and coal feeding amount data, ammonia escape data of a denitration system, start-stop data of an ash conveying system, outlet smoke temperature of the low-temperature economizer, fault data, start-stop data of the desulfurization circulating pump, fault data and outlet smoke dust meter data of the desulfurization system;
The data analysis module is used for inputting the real-time operation data and the coal quality analysis data into a preset data processing model and outputting a real-time analysis result; the data processing model is constructed and obtained in advance based on historical operation data of the dust remover, the boiler host and the auxiliary equipment, structural parameter data of the dust remover, the boiler host and the auxiliary equipment and control experience data of the dust remover, the boiler host and the auxiliary equipment;
wherein the data processing model comprises: a concentration prediction sub-model, a digital twin sub-model, a structural safety evaluation sub-model, a deep energy-saving optimization sub-model, a smoke characteristic analysis sub-model, a health real-time analysis sub-model, a fault early warning and diagnosis sub-model and a device life evaluation sub-model;
the data perception module is also used for determining the boiler ash and slag ratio, load, total fuel amount, and the coal type and ash data of the boiler according to the real-time operation data;
the concentration prediction sub-model is used for calculating the boiler ash-to-slag ratio, the load, the total fuel quantity, the coal type entering the boiler and ash data according to a preset smoke concentration calculation formula to obtain the smoke concentration prediction of the inlet of the dust remover; the smoke concentration calculation formula is constructed in advance based on control experience data of the dust remover and the boiler main machine and the auxiliary machine equipment;
The digital twin sub-model is used for constructing a real-time mapping model of the dust removal characteristic of the dust remover through a mechanism model of the dust remover and a data driving model of the dust remover; the data driving model is used for indicating a relation model between the dust removal efficiency and the operation parameters, which are obtained by training the initial neural network through the historical operation data;
the data perception module is also used for acquiring real-time mechanical data of a preset position of the dust remover; the structural safety assessment sub-model analyzes the safety states of the shell, the ash bucket and the steel bracket of the dust remover according to the real-time mechanical data; the structural safety evaluation model is pre-established based on a finite element model of the dust remover;
the deep energy-saving optimization sub-model is used for predicting the dust concentration at the outlet of the dust remover according to the real-time operation data based on a preset pollutant removal model and an operation energy consumption material consumption cost model, and comparing the dust concentration at the outlet of the dust remover with a preset dust concentration control target value to obtain a comparison result; outputting an operation parameter control signal of the dust remover according to the comparison result; the pollutant removal model and the operation energy consumption cost model are used for indicating an energy resource allocation model constructed based on the dust remover operation logic and preset constraint parameters;
The flue gas characteristic analysis submodel is used for calculating and obtaining fire stability index, burnout characteristic index, coal powder firing index, slagging characteristic index, coal powder abrasion characteristic index, dust specific resistance of the coal powder according to the real-time operation data and the coal quality analysis data, and obtaining the inlet flue gas quantity of the dust remover, ammonium bisulfate components in flue gas and adaptability data of the dust remover to coal types;
the health real-time analysis sub-model is used for determining the technical performance, fault condition, service life condition, safety condition, operation environment condition and corrosion condition of the dust remover according to the real-time operation data and the real-time mechanical data; calculating the health score of the dust remover according to the technical performance, the fault condition, the service life condition, the safety condition, the running environment condition, the corrosion condition, the corresponding weight and the scoring rule; outputting a target health grade corresponding to the health score according to the health score control preset health grade;
the fault early warning and diagnosing sub-model is used for determining the fault type of the dust remover according to the real-time operation data, analyzing the fault type based on preset cause analysis data and outputting an analysis result; comparing the analysis result with a preset fault cause screening rule, and determining a target fault cause corresponding to the analysis result;
The real-time operational data further includes: the service life of components of the boiler main machine and the auxiliary equipment affects factor data; the component life influence factor data comprises: ambient temperature, power flashover conditions, IGBT temperature, transformer temperature, environmental acid-base corrosiveness, environmental dust and environmental humidity; the equipment life evaluation submodel is used for calculating real-time life evaluation data of the dust remover and the components according to the environmental temperature of the dust remover, the power flashover working condition of the power supply, the IGBT temperature, the transformer temperature, the environmental acid-base corrosivity, the environmental dust, the environmental humidity and the influence coefficient of preset life influence factors; and the man-machine interaction module is used for displaying and/or processing the real-time analysis result according to the operation instruction if the operation instruction of the user is received.
2. The digital twinning system of a duster of claim 1, further comprising: the data storage module is connected with the data perception module; the data storage module includes: caching a database and a time sequence database;
The cache database and the time sequence database are used for storing the real-time operation data.
3. The digital twinning system of a dust remover according to claim 2, wherein the real-time operation data is provided with a data update identification bit;
the data perception module is also used for judging whether the numerical value corresponding to the real-time operation data is updated or not according to the data update identification bit; if yes, storing the updated real-time operation data into the time sequence database, and updating the data update identification bit of the real-time operation data of the cache database.
4. The digital twinning system of a dust collector of claim 1, wherein the fault pre-warning and diagnostic sub-model is further configured to compare the analysis result with a preset warning rule; and if the analysis result accords with the alarm rule, generating an alarm signal corresponding to the analysis result, and transmitting the alarm signal to a designated alarm terminal for alarm operation.
5. The digital twinning system of a precipitator of claim 1, wherein the human-machine interaction module comprises: displaying a page; a three-dimensional model of the dust remover corresponding to the dust remover is built on the display page in advance; the substructure of the three-dimensional model of the duster is labeled with a preset color based on the real-time operational data.
6. The digital twinning system of a scrubber of claim 5, wherein the display page comprises: a plurality of interactive controls; each interaction control corresponds to a preset interaction function;
and each interaction control is used for displaying and/or processing the real-time analysis result corresponding to the interaction function on the three-dimensional model according to the operation instruction if the operation instruction of the user is received.
7. A dust remover digital twin method applied to the dust remover digital twin system as claimed in any one of claims 1 to 6; the method comprises the following steps:
acquiring coal analysis data, a dust remover and real-time operation data of a boiler host and auxiliary equipment connected with the dust remover; the auxiliary equipment comprises a denitration ammonia escape instrument, a low-temperature economizer outlet flue gas thermometer, an ash conveying bin pump, a desulfurization circulating pump and a desulfurization system outlet flue dust instrument; the real-time operation data includes: the high-voltage power supply operation parameters of the dust remover, the vibration parameters of cathode vibration and anode vibration, the heating parameters of porcelain bushing electric heating, the vibration failure states of cathode vibration and anode vibration, the ash accumulation condition of an ash bucket, dust removal efficiency, power consumption, air leakage rate, structural safety monitoring data of a steel bracket and the ash bucket, the specific dust collection area of an anode plate, the oxygen content of an inlet and an outlet, humidity, smoke pressure and inlet and outlet smoke dust concentration; the boiler main engine comprises boiler load, boiler soot blowing and coal feeding amount data, ammonia escape data of a denitration system, start-stop data of an ash conveying system, outlet smoke temperature of the low-temperature economizer, fault data, start-stop data of the desulfurization circulating pump, fault data and outlet smoke dust meter data of the desulfurization system;
Inputting the real-time operation data and the coal quality analysis data into a preset data processing model, and outputting a real-time analysis result; the data processing model is constructed and obtained in advance based on historical operation data of the dust remover, the boiler host and the auxiliary equipment, structural parameter data of the dust remover, the boiler host and the auxiliary equipment and control experience data of the dust remover, the boiler host and the auxiliary equipment; wherein the data processing model comprises: a concentration prediction sub-model, a digital twin sub-model, a structural safety evaluation sub-model, a deep energy-saving optimization sub-model, a smoke characteristic analysis sub-model, a health real-time analysis sub-model, a fault early warning and diagnosis sub-model and a device life evaluation sub-model; the data perception module is also used for determining the boiler ash and slag ratio, load, total fuel amount, and the coal type and ash data of the boiler according to the real-time operation data; the concentration prediction sub-model is used for calculating the boiler ash-to-slag ratio, the load, the total fuel quantity, the coal type entering the boiler and ash data according to a preset smoke concentration calculation formula to obtain the smoke concentration prediction of the inlet of the dust remover; the smoke concentration calculation formula is constructed in advance based on control experience data of the dust remover and the boiler main machine and the auxiliary machine equipment; the digital twin sub-model is used for constructing a real-time mapping model of the dust removal characteristic of the dust remover through a mechanism model of the dust remover and a data driving model of the dust remover; the data driving model is used for indicating a relation model between the dust removal efficiency and the operation parameters, which are obtained by training the initial neural network through the historical operation data; the data perception module is also used for acquiring real-time mechanical data of a preset position of the dust remover; the structural safety assessment sub-model analyzes the safety states of the shell, the ash bucket and the steel bracket of the dust remover according to the real-time mechanical data; the structural safety evaluation model is pre-established based on a finite element model of the dust remover; the deep energy-saving optimization sub-model is used for predicting the dust concentration at the outlet of the dust remover according to the real-time operation data based on a preset pollutant removal model and an operation energy consumption material consumption cost model, and comparing the dust concentration at the outlet of the dust remover with a preset dust concentration control target value to obtain a comparison result; outputting an operation parameter control signal of the dust remover according to the comparison result; the pollutant removal model and the operation energy consumption cost model are used for indicating an energy resource allocation model constructed based on the dust remover operation logic and preset constraint parameters; the flue gas characteristic analysis submodel is used for calculating and obtaining fire stability index, burnout characteristic index, coal powder firing index, slagging characteristic index, coal powder abrasion characteristic index, dust specific resistance of the coal powder according to the real-time operation data and the coal quality analysis data, and obtaining the inlet flue gas quantity of the dust remover, ammonium bisulfate components in flue gas and adaptability data of the dust remover to coal types; the health real-time analysis sub-model is used for determining the technical performance, fault condition, service life condition, safety condition, operation environment condition and corrosion condition of the dust remover according to the real-time operation data and the real-time mechanical data; calculating the health score of the dust remover according to the technical performance, the fault condition, the service life condition, the safety condition, the running environment condition, the corrosion condition, the corresponding weight and the scoring rule; outputting a target health grade corresponding to the health score according to the health score control preset health grade; the fault early warning and diagnosing sub-model is used for determining the fault type of the dust remover according to the real-time operation data, analyzing the fault type based on preset cause analysis data and outputting an analysis result; comparing the analysis result with a preset fault cause screening rule, and determining a target fault cause corresponding to the analysis result; the real-time operational data further includes: the service life of components of the boiler main machine and the auxiliary equipment affects factor data; the component life influence factor data comprises: ambient temperature, power flashover conditions, IGBT temperature, transformer temperature, environmental acid-base corrosiveness, environmental dust and environmental humidity; the equipment life evaluation submodel is used for calculating real-time life evaluation data of the dust remover and the components according to the environmental temperature of the dust remover, the power flashover working condition of the power supply, the IGBT temperature, the transformer temperature, the environmental acid-base corrosivity, the environmental dust, the environmental humidity and the influence coefficient of preset life influence factors; and if an operation instruction of a user is received, displaying and/or processing the real-time analysis result according to the operation instruction.
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| CN111077869A (en) * | 2019-12-31 | 2020-04-28 | 国电九江发电有限公司 | Optimization control method and system for big data intelligent control bag-type dust collector |
| CN211450981U (en) * | 2019-12-31 | 2020-09-08 | 国电九江发电有限公司 | Cloth bag dust removal equipment of coal-fired boiler and intelligent optimization control device thereof |
| CN213435054U (en) * | 2020-05-29 | 2021-06-15 | 浙江大维高新技术股份有限公司 | Embedded electric precipitation operation optimization system |
| CN113339787A (en) * | 2021-06-11 | 2021-09-03 | 华北电力大学（保定） | Fluidized bed boiler operation optimization method and system based on digital twinning |
| CN113976323A (en) * | 2021-09-10 | 2022-01-28 | 华能曲阜热电有限公司 | Multi-signal optimization energy-saving electric precipitation control method |
| CN115200033A (en) * | 2022-07-15 | 2022-10-18 | 西京学院 | Rotary air preheater thermal monitoring system based on digital twinning technology |
| CN116341214A (en) * | 2023-03-03 | 2023-06-27 | 上海元蜃数字科技有限公司 | Digital twin system of industrial dedusting equipment of washing tower and construction method thereof |
| CN117150677A (en) * | 2023-09-06 | 2023-12-01 | 福建龙净环保股份有限公司 | Method and device for determining type selection parameters of electric dust collector |
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