CN111290357B - Intelligent fuel management and control system based on Internet of things and big data - Google Patents
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Abstract
The invention relates to an intelligent fuel management and control system based on Internet of things and big data, which comprises an operation overview module, an intelligent loading and unloading and stacking management module, a coal yard intelligent management module, a coal blending and burning intelligent management module, a fuel scheduling management module, a fuel acceptance management module, a coal purchasing guidance module, a coal cost analysis module, a diagnosis cockpit module and a system management module. According to the invention, through the application of automation equipment and information technology, the traditional fuel management mode is improved by means of mechanization, automation and informatization, the intellectualization is promoted around the key link of fuel management, and the working efficiency of the whole process of fuel management is improved.
Description
Technical Field
The invention relates to the technical field of power generation fuel, in particular to a fuel control system of a power plant, and specifically relates to an intelligent fuel control system based on the Internet of things and big data.
Background
Along with the progress of science and technology, mechanized sampling of coal entering a plant and coal entering a furnace is realized, and informatization technology is also applied to daily management work of a power plant in a large amount, such as an automatic identification system of the number of a coal entering the plant, a unified fuel settlement system, a digital coal yard and the like, so that the fuel management level and efficiency are continuously improved.
At present, various links of fuel management of thermal power enterprises have problems, and the problems seriously affect the fuel management efficiency of the enterprises. For example, further improvements are needed in the following links:
(1) And (3) a metering link: some fuel equipment of most thermal power enterprises need manual measurement, the efficiency of an intermediate link is very low, and the problem of data safety exists. Meanwhile, the weighing apparatus cannot automatically identify information such as vehicles, mine types and the like, and problems of repeated weighing or remote control of the weighing apparatus and the like can occur;
(2) A sampling link: and a manual sampling mode is adopted when a plurality of enterprise mechanical sampling devices are in failure. The sampling automation degree is not enough, and a vulnerability exists in the management aspect. The manual sampling has the conditions of over-high labor intensity, non-standard flow, human errors and the like;
(3) A sample preparation link: at present, most enterprises adopt a manual off-line mode to prepare samples, and the sample preparation effect is uncontrollable, so that the samples cannot represent the characteristics of the whole batch of fuel;
(4) And (3) sample sending and storing links: during the transportation of the coal sample, the sealing means of the sample barrel or the sample bag is simple, the safety of the sample cannot be fully guaranteed, and the danger of sample pollution exists;
(5) And (3) assay link: in the process of testing a sample in a laboratory of a thermal power enterprise, various testing data are manually calculated and recorded, and the risk of human errors and changes exists;
(6) And (3) management links: the fuel management personnel still carries out the statistics of fuel measurement, chemical examination data using the manual means, the human error appears easily, and efficiency is lower, and the storage of subregion, branch heap, branch variety, branch quality still can not be accomplished at present to fuel stock management simultaneously, also can't accurately measure fuel parameter information, has brought very big difficulty for fuel blending co-combustion.
Disclosure of Invention
Aiming at the existing problems in each link of fuel management of a thermal power enterprise, the invention provides an intelligent fuel management and control system based on the Internet of things and big data for effectively improving the fuel management efficiency of the enterprise.
The invention is realized by the following technical scheme, and provides an intelligent fuel control system based on the Internet of things and big data, which comprises an operation overview module, an intelligent loading and unloading and stacking management module, a coal yard intelligent management module, a coal blending and burning intelligent management module, a fuel scheduling management module, a fuel acceptance management module, a coal burning purchase guidance module, a coal burning cost analysis module, a diagnosis cockpit module and a system management module, wherein the operation overview module comprises the whole process information from fuel loading to burning result feedback, and an ECharts visual chart library is adopted to display core indexes related to coal burning (including coal loading and unloading operation progress, fuel purchasing guidance suggestions, fuel acceptance statistical conditions, coal storage analysis and coal burning real-time cost of the coal yard), so that the production and operation conditions of enterprises are clearly and intuitively presented to users; the intelligent receiving and discharging stacking management module comprises receiving and discharging stacking management and receiving and discharging stacking equipment for monitoring two parts of contents in real time, a receiving and discharging stacking operation scheme is calculated according to a receiving and discharging stacking model before coal enters a plant and comprises a receiving and discharging path and a stacking position, the receiving and discharging stacking equipment performs work according to a machine instruction converted by the scheme, stores or loads fuel in a partition, stacking, variety and quality mode, and transmits and feeds back receiving and discharging stacking information in real time; the intelligent management module of the coal yard associates coal quality online detection data, mine coal quality data and test coal quality data through a bulk density analysis calculation model, a field loss analysis calculation model, a coal yard dynamic temperature field and an early warning model in combination with three-dimensional dynamic scanning and temperature environment monitoring of the coal yard, and comprehensively, dynamically displays the state of the coal yard in real time in a three-dimensional graph, wherein the state comprises the coal amount, the temperature, the heat value, the total moisture, the sulfur content, the price and the stockpiling time of different areas; the intelligent management module for blending coal and burning generates a blending coal and burning scheme with comprehensive optimal safety, economy and environmental protection indexes according to a blending coal and burning optimization model and by combining factors including boiler design parameters, incoming coal information, current coal storage condition, coal burning cost, blending coal and burning feedback suggestion and equipment operation condition, and also supports generation of a blending coal and burning scheme with optimal single condition; the fuel scheduling management module forms a fire coal scheduling and transporting machine account according to a scheduling and transporting plan, a coming coal forecast, and receiving, unloading, storing and piling management of coal from a railway or a vehicle and a ship, automatically identifies the unique coal code and the weight to be matched with the coming coal forecast information by adding an identification device (such as a radio frequency card) at a factory (port), realizes the bidirectional identification of the factory (port) entrance and exit, and realizes the closed-loop management of each link of scheduling, vehicle receiving, factory entrance, sampling, weighing, unloading and leaving factory; the coal purchasing instruction module is used for planning reasonable purchasing quantity of various heat value coal varieties according to the predicted coal quality entering the power plant when the power plant has the lowest power supply cost and by combining historical mixed combustion data and inventory conditions of the power plant in the same month, and finally generating purchasing opinions of the coal in the same month, wherein the purchasing opinions include suggestions on suppliers, purchasing coal types and purchasing quantity, so that purchasing optimizing management of the power plant with the lowest comprehensive cost is realized; the fuel acceptance management module comprises fuel entering, weighing, coal unloading, peeling, delivery, sampling, sample separating, sample preparation and assay processes, standardizes the fuel acceptance process, improves the control and risk prevention and control capabilities of the fuel acceptance process, realizes the acquisition of field condition data and the monitoring and early warning by being tightly combined with fuel management bottom equipment, and performs dispersed data and centralized display on equipment in each link of remote fuel entering management, metering and sample preparation; the coal-fired cost analysis module analyzes reasons and influence factors of fuel cost rise and fall, analyzes profit and loss trends of unit operation, measures profit and loss balance points, analyzes fuel consumption in the production process of a power plant, and displays change conditions and trends of coal-fired cost; the diagnosis cockpit module statistics summarize all alarm information in the platform, show with Echarts chart and Antd tabular form, can make statistics to various alarm information to later analysis, alarm information includes: abnormal conditions of the loading and unloading device, the sampling and testing device and the weighing device; the operator violates the operational procedures or specifications; alarming when the current coal storage time, temperature and environmental conditions are out of limit; an empirical formula is established by using historical data to judge that the measured data of the equipment deviates from a reasonable range; the system management module is used for managing and configuring various information parameters.
Preferably, the loading and unloading module comprises an operation module, a coal-fired stacking rule base and a coal conveying system process flow base, wherein the operation module is used for calculating and generating a coal loading and unloading operation scheme by using a preference method according to coal forecast information, the running condition of coal conveying equipment and the stacking rule of the current coal storage condition, wherein the coal loading and unloading operation scheme comprises a coal unloading mode, a transfer path and a stacking position.
Preferably, the real-time monitoring of the loading and unloading and stacking device shows the field operation condition through an svg configuration diagram according to the real-time database data, and comprises the following steps: information of entering and leaving factories (ports) and overbalance information (including batch codes, overbalance time, coal varieties and weight) of vehicles (ships); the system comprises a tipper, a sampling machine, an iron remover, a ship unloader, a coal plough, a coal feeder and running state, parameters and alarm information of three-way equipment, and equipment details can be checked for each equipment primitive of a single machine; vehicle (ship) queuing.
Preferably, the bulk density analysis calculation model comprises a bulk density database which records a bulk density change curve corresponding to different coal types, different working conditions and different stacking times.
Preferably, the field loss analysis and calculation model calculates the field loss data and analyzes the field loss factors according to the coal yard coal piling data (including the quantity, the orientation, the coal variety and the storage condition), the coal yard environment monitoring data, the incoming and as-fired coal quality inspection data and the field loss management rules.
Preferably, the coal yard dynamic temperature field and the early warning model acquire the surface temperature value of the coal pile through an infrared scanner, acquire the internal temperature of the coal pile through a temperature measuring rod, establish the dynamic temperature field model aiming at the coal yard by using a function model of an expert in coal-fired temperature research, and give an alarm when the temperature exceeds the early warning value.
In conclusion, the intelligent power station fuel management and control system based on the internet of things and big data improves the traditional fuel management mode through the application of automation equipment and information technology and the mechanized, automatic and informatization means, completes the work of data collection, statistical analysis, operation optimization supervision, decision suggestion, comprehensive display and the like of a fuel system, realizes the sensing, biological, operation automation, diagnosis, intelligence and decision intelligence in the links of fuel receiving, storage, blending, extraction, transportation, acceptance and the like, promotes the intelligence around the key link of fuel management, improves the work efficiency of the whole process of fuel management, and enables the fuel management of thermal power enterprises to be more intelligent.
Drawings
FIG. 1 is a schematic structural diagram of an intelligent fuel management and control system based on the Internet of things and big data in the invention;
FIG. 2 is a schematic diagram of a software architecture of an intelligent fuel management and control system based on the Internet of things and big data according to the present invention;
fig. 3 is a schematic diagram of a hardware connection architecture of an intelligent fuel management and control system based on the internet of things and big data according to the present invention;
Detailed Description
In order to clearly illustrate the technical features of the present invention, the present invention is further illustrated by the following detailed description with reference to the accompanying drawings.
As shown in fig. 1 to 3, an intelligent fuel management and control system based on internet of things and big data comprises an operation overview module, an intelligent loading and unloading and stacking management module, a coal yard intelligent management module, a coal blending intelligent management module, a fuel scheduling management module, a fuel acceptance management module, a coal purchasing guidance module, a coal cost analysis module, a diagnosis cockpit module and a system management module, wherein the operation overview module comprises the whole process information from fuel loading to combustion result feedback, and an ECharts visual chart library is adopted to display core indexes related to coal combustion (including coal loading and unloading operation progress, fuel purchasing guidance suggestions, fuel acceptance statistics, coal yard coal storage analysis and coal combustion real-time cost), so that enterprise production and operation conditions are clearly and intuitively presented to users; the intelligent receiving and discharging stacking management module comprises receiving and discharging stacking management and receiving and discharging stacking equipment for monitoring two parts of contents in real time, a receiving and discharging stacking operation scheme is calculated according to a receiving and discharging stacking model before coal enters a plant and comprises a receiving and discharging path and a stacking position, the receiving and discharging stacking equipment performs work according to a machine instruction converted by the scheme, stores or loads fuel in a partition, stacking, variety and quality mode, and transmits and feeds back receiving and discharging stacking information in real time; the coal yard intelligent management module associates coal quality online detection data, mine coal quality data and test coal quality data through a bulk density analysis calculation model, a field loss analysis calculation model, a coal yard dynamic temperature field and an early warning model in combination with coal yard three-dimensional dynamic scanning and temperature environment monitoring, and dynamically displays the coal yard state in a three-dimensional graph comprehensively in real time, wherein the three-dimensional graph comprises coal amount, temperature, heat value, total moisture, sulfur content, price and stockpiling time in different areas;
the intelligent management module for blending coal and burning generates a blending coal and burning scheme with comprehensive optimal safety, economy and environmental protection indexes according to a blending coal and burning optimization model and by combining factors including boiler design parameters, incoming coal information, current coal storage condition, coal burning cost, blending coal and burning feedback suggestion and equipment operation condition, and also supports generation of a blending coal and burning scheme with optimal single condition; the fuel scheduling management module forms a fire coal scheduling and transporting table net according to a scheduling plan, incoming coal forecast, and receiving, unloading, storing and piling management of coal from a railway or a vehicle and a ship, automatically identifies a unique incoming coal code and matches the weight with incoming coal forecast information by adding an identification device (such as a radio frequency card) at a factory (port) entrance, realizes bidirectional identification of the factory (port) entrance and the factory exit, and realizes closed-loop management of each link of scheduling, receiving, factory entrance, sampling, weighing, unloading and leaving a factory; the coal-fired purchasing instruction module plans reasonable purchasing quantity of various heat value coal varieties according to the predicted coal quality entering the power plant when the power supply cost of the power plant is the lowest, combines the historical mixed burning data and the inventory condition of the power plant in the current month, and finally generates purchasing opinions of the coal-fired in the current month, wherein the purchasing opinions comprise suggestions on suppliers, purchased coal types and purchasing quantity, so that purchasing optimization management with the lowest comprehensive cost of the power plant is realized; the fuel acceptance management module comprises fuel entering, weighing, coal unloading, peeling, delivery, sampling, sample separating, sample preparation and assay processes, standardizes the fuel acceptance process, improves the control and risk prevention and control capabilities of the fuel acceptance process, realizes the acquisition of field condition data and the monitoring and early warning by being tightly combined with fuel management bottom equipment, and performs dispersed data and centralized display on equipment in each link of remote fuel entering management, metering and sample preparation; the coal-fired cost analysis module analyzes reasons and influence factors of fuel cost rise and fall, analyzes profit and loss trends of unit operation, measures profit and loss balance points, analyzes fuel consumption in the production process of a power plant, and displays change conditions and trends of coal-fired cost;
the diagnosis cockpit module statistics summarize all alarm information in the platform, show with Echarts chart and Antd tabular form, can make statistics to various alarm information to later analysis, alarm information includes: abnormal conditions of the loading and unloading device, the sampling and testing device and the weighing device; the operator violates the operational flow or specification; alarming when the current coal storage time, temperature and environmental conditions are out of limit; an empirical formula is established by using historical data to judge that the measured data of the equipment deviates from a reasonable range; the system management module is used for managing and configuring various information parameters.
In this embodiment, the loading/unloading/stacking model includes an operation model, a coal-fired stacking rule base, and a coal conveying system process flow base, the operation model uses an optimization method to calculate and generate a coal loading/unloading/stacking operation scheme according to the coal forecast information, the coal conveying equipment operation condition, and the stacking rule combining the current coal storage condition, wherein the operation scheme includes a coal unloading mode, a transfer path, and a stacking position, the loading/unloading/stacking equipment monitors the operation condition of the site displayed by an svg configuration diagram in real time according to the real-time database data, and the method includes: information of entering and leaving factories (ports) and overbalance information (including batch codes, overbalance time, coal varieties and weight) of vehicles (ships); the system comprises a tipper, a sampling machine, an iron remover, a ship unloader, a coal plough, a coal feeder and three-way equipment, wherein the running state, parameters and alarm information of the three-way equipment can be used for checking the equipment details of each equipment primitive of the single machine; the vehicle (ship) queuing condition, the bulk density analysis calculation model constitute including the bulk density database, recorded different coal types, different operating modes, the corresponding bulk density change curve of different stack time, the field loss analysis calculation model use coal yard pile coal data (quantity, position, coal variety, storage condition), coal yard environmental monitoring data, the coal quality inspection data of entering factory and stove coal and field loss management rule as the basis to calculate the field loss data and to lose the factor to the field and analyze, coal yard dynamic temperature field and early warning model acquire coal pile surface temperature value through infrared scanner, acquire the inside temperature of coal pile through the temperature measurement pole, use the function model of the expert in the aspect of coal-fired temperature research to establish dynamic temperature field model to the coal yard, just report to the police when the temperature exceeds the early warning value and indicate.
Finally, it should be further noted that the above examples and descriptions are not limited to the above embodiments, and technical features of the present invention that are not described may be implemented by or using the prior art, and are not described herein again; the above embodiments and drawings are only for illustrating the technical solutions of the present invention and not for limiting the present invention, and the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that changes, modifications, additions or substitutions within the spirit and scope of the present invention may be made by those skilled in the art without departing from the spirit of the present invention, and shall also fall within the scope of the claims of the present invention.
Claims (1)
1. An intelligent fuel management and control system based on the Internet of things and big data is characterized by comprising an operation overview module, an intelligent loading and unloading and stacking management module, a coal yard intelligent management module, a coal blending and burning intelligent management module, a fuel scheduling management module, a fuel acceptance management module, a coal purchasing guidance module, a coal cost analysis module, a diagnosis cockpit module and a system management module, wherein,
the operation overview module comprises the information of the whole process from fuel entering a factory to combustion result feedback, and an ECharts visual chart library is adopted to display the core indexes related to the fire coal, so that the production and operation conditions of enterprises are clearly and intuitively presented to users;
the intelligent receiving and discharging stacking management module comprises receiving and discharging stacking management and receiving and discharging stacking equipment for monitoring two parts of contents in real time, a receiving and discharging stacking operation scheme is calculated according to a receiving and discharging stacking model before coal enters a plant and comprises a receiving and discharging path and a stacking position, the receiving and discharging stacking equipment performs work according to a machine instruction converted by the scheme, stores or loads fuel in a partition, stacking, variety and quality mode, and transmits and feeds back receiving and discharging stacking information in real time;
the coal yard intelligent management module associates coal quality online detection data, mine coal quality data and test coal quality data through a bulk density analysis calculation model, a field loss analysis calculation model, a coal yard dynamic temperature field and an early warning model in combination with three-dimensional dynamic scanning and temperature environment monitoring of the coal yard, and displays the state of the coal yard comprehensively, in real time and dynamically in a three-dimensional graph, wherein the state comprises the coal quantity, the temperature, the heat value, the total moisture, the sulfur content, the price and the stockpiling time of different areas;
the intelligent management module for blending coal generates a blending coal blending scheme with comprehensive optimal safety, economy and environmental protection indexes by combining factors including boiler design parameters, incoming coal information, current coal storage condition, coal-fired cost, blending coal blending feedback suggestion and equipment operation condition according to a blending coal blending optimization model, and also supports generation of a blending coal blending scheme with optimal single condition;
the fuel scheduling management module forms a fire coal scheduling and transporting platform account according to a scheduling plan, incoming coal forecast, and receiving, unloading, storing and piling management of coal from a railway or a vehicle and a ship, automatically identifies the unique incoming coal code and the matching of the weight and incoming coal forecast information by adding an identification device at a factory entrance, realizes the bidirectional identification of the factory entrance and the factory exit, and realizes the closed-loop management of each link of scheduling, receiving, factory entrance, sampling, weighing, unloading and factory exit;
the coal purchasing instruction module is used for planning reasonable purchasing quantity of various heat value coal varieties according to the predicted coal quality entering the power plant when the power plant has the lowest power supply cost and by combining historical mixed combustion data and inventory conditions of the power plant in the same month, and finally generating purchasing opinions of the coal in the same month, wherein the purchasing opinions include suggestions on suppliers, purchasing coal types and purchasing quantity, so that purchasing optimizing management of the power plant with the lowest comprehensive cost is realized;
the fuel acceptance management module comprises fuel entering, weighing, coal unloading, peeling, delivery, sampling, sample separating, sample preparation and assay processes, standardizes the fuel acceptance process, improves the control and risk prevention and control capabilities of the fuel acceptance process, realizes the acquisition of field condition data and the monitoring and early warning by being tightly combined with fuel management bottom equipment, and performs dispersed data and centralized display on equipment in each link of remote fuel entering management, metering and sample preparation;
the coal-fired cost analysis module analyzes reasons and influence factors of fuel cost rise and fall, analyzes profit and loss trends of unit operation, measures profit and loss balance points, analyzes fuel consumption in the production process of a power plant, and displays change conditions and trends of coal-fired cost;
the diagnosis cockpit module statistics summarize all alarm information in the platform, show with Echarts chart and Antd tabular form, can make statistics to various alarm information to later analysis, alarm information includes: abnormal conditions of the loading and unloading device, the sampling and testing device and the weighing device; the operator violates the operational flow or specification; alarming when the current coal storage time, temperature and environmental conditions are out of limit; an empirical formula is established by using historical data to judge that the measured data of the equipment deviates from a reasonable range;
the system management module is used for managing and configuring various information parameters;
the coal receiving, discharging and stacking operation scheme comprises a coal unloading mode, a transfer path and a stacking position, wherein the coal receiving, discharging and stacking operation scheme is generated by the operation model through calculation by using a preferred method according to coal forecast information, the running condition of coal conveying equipment and the current coal storage condition in combination with stacking rules;
the real-time monitoring of the loading, unloading and stacking equipment shows the field operation condition through an svg configuration diagram according to the real-time database data, and comprises the following steps: vehicle in and out information and over-balance information; the system comprises a tipper, a sampling machine, an iron remover, a ship unloader, a coal plough, a coal feeder and running state, parameters and alarm information of three-way equipment, and equipment details can be checked for each equipment primitive of a single machine; vehicle queuing conditions;
the composition of the bulk density analysis calculation model comprises a bulk density database which records the bulk density change curves corresponding to different coal types, different working conditions and different stacking times;
the field loss analysis and calculation model calculates field loss data and analyzes field loss factors according to coal yard storage data, coal yard environment monitoring data, factory coal quality inspection data and furnace coal quality inspection data and field loss management rules;
the coal yard dynamic temperature field and the early warning model acquire the surface temperature value of the coal pile through an infrared scanner, acquire the internal temperature of the coal pile through a temperature measuring rod, establish a dynamic temperature field model aiming at the coal yard by using a function model of an expert in the coal temperature research aspect, and give an alarm when the temperature exceeds the early warning value.
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| CN111911904B (en) * | 2020-07-23 | 2022-09-13 | 山西晋路同创信息技术有限公司 | New energy boiler Internet of things data acquisition control system and acquisition method thereof |
| CN112325325A (en) * | 2020-12-24 | 2021-02-05 | 武瑞香 | Boiler coal feeding and pulverizing device and method for burning inferior coal |
| CN112734208B (en) * | 2020-12-31 | 2024-05-28 | 深圳市出新知识产权管理有限公司 | Fire coal acceptance monitoring device, method, equipment and readable storage medium |
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