CN116504016A - Thermal power plant safety monitoring and early warning method and system based on artificial intelligence - Google Patents
Thermal power plant safety monitoring and early warning method and system based on artificial intelligence Download PDFInfo
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Abstract
The invention relates to the technical field of artificial intelligence, and provides a thermal power plant safety monitoring and early warning method and system based on artificial intelligence, wherein the method comprises the following steps: arranging a plurality of detection sensors in a coal storage bin of a thermal power plant, acquiring a monitoring data set through data monitoring, comparing the monitoring data, and if safety risk data appear, sending the safety risk data to a safety manager of the thermal power plant; logging in a monitoring system of a thermal power plant, and calling a security risk monitoring image of a location area limited by security risk data; acquiring a historical alarm record of a coal storage bin of a thermal power plant, and constructing a safety alarm model through the historical alarm record; the safety risk monitoring image is sent to the safety alarm model, the monitoring early warning result is obtained, the technical problem that the safety monitoring and hidden danger investigation efficacy of the thermal power plant is low is solved, the safety monitoring precision is improved, the safety monitoring and hidden danger investigation efficacy is comprehensively improved, whether the safety risk hidden danger exists or not is judged in real time, and the production normal operation technical effect of the thermal power plant is guaranteed to the maximum extent.
Description
Technical Field
The invention relates to the technical field related to artificial intelligence, in particular to a thermal power plant safety monitoring and early warning method and system based on artificial intelligence.
Background
The production normal operating of thermal power plant need to stock a large amount of coal, and is common, and coal is mostly stored in the open air, and open air storage can cause coal loss, coal quality decline, freeze, store coal water content increase etc. generally, thermal power plant stores up coal in the coal storage storehouse, can greatly reduced above-mentioned harmful effects.
However, there is new potential safety hazard in the coal storage bin of the thermal power plant, including: the coal is stored for a long time, so that the temperature of the coal is increased, the heat conductivity coefficient of the coal is small, the heat can be slowly diffused, when the heat is accumulated in the coal, and the internal temperature of the coal is increased to reach the ignition point of the coal, the coal can be spontaneously combusted, meanwhile, the oxidation speed of the coal can be continuously accelerated along with the temperature increase, and when the temperature and the concentration of combustible gas reach a certain value, the coal is extremely easy to be spontaneously combusted.
The manual investigation of the coal storage bin of the thermal power plant is low in investigation efficiency, high in investigation cost and low in operability, safety risk identification is carried out only through detection data of relevant equipment such as a temperature detection sensor, reliability of identification cannot be guaranteed, risk identification cannot be applied to risk prevention, and the manual investigation is low in operability.
In summary, the technical problems of low efficiency of safety monitoring and hidden trouble investigation of the thermal power plant exist in the prior art.
Disclosure of Invention
The application aims to solve the technical problem that the safety monitoring and hidden trouble investigation efficiency of the thermal power plant in the prior art is low by providing the thermal power plant safety monitoring and early warning method and system based on artificial intelligence.
In view of the above problems, embodiments of the present application provide a thermal power plant safety monitoring and early warning method and system based on artificial intelligence.
According to a first aspect of the disclosure, a thermal power plant safety monitoring and early warning method based on artificial intelligence is provided, wherein the method is applied to a thermal power plant safety monitoring and early warning system, and the thermal power plant safety monitoring and early warning system is in communication connection with various detection sensors, and the method comprises the following steps: the plurality of detection sensors are distributed in a coal storage bin of the thermal power plant, and the plurality of detection sensors comprise a temperature detection sensor, a smoke detection sensor, a combustible gas detection sensor and a carbon monoxide detection sensor; the data monitoring and acquisition are carried out in a coal storage bin of the thermal power plant through the plurality of detection sensors, and a monitoring data set is obtained; comparing the monitoring data through the monitoring data set, and judging whether safety risk data appear or not; if the safety risk data appear, the safety risk data are sent to a safety manager of the thermal power plant; a security manager of the thermal power plant logs in a monitoring system of the thermal power plant, and a security risk monitoring image of a location area limited by the security risk data is called; acquiring a historical alarm record of a coal storage bin of a thermal power plant, and constructing a safety alarm model through the historical alarm record; and sending the security risk monitoring image to the security alarm model to obtain a monitoring and early warning result.
In another aspect of the disclosure, a thermal power plant safety monitoring and early warning system based on artificial intelligence is provided, wherein the thermal power plant safety monitoring and early warning system is in communication connection with a plurality of detection sensors, the system comprises: the sensing layout module is used for layout of the plurality of detection sensors in a coal storage bin of the thermal power plant, wherein the plurality of detection sensors comprise a temperature detection sensor, a smoke detection sensor, a combustible gas detection sensor and a carbon monoxide detection sensor; the monitoring acquisition module is used for carrying out data monitoring acquisition in a coal storage bin of the thermal power plant through the plurality of detection sensors to obtain a monitoring data set; the data comparison module is used for comparing the monitoring data through the monitoring data set and judging whether safety risk data appear or not; the data sending module is used for sending the safety risk data to a safety manager of the thermal power plant if the safety risk data appear; the image calling module is used for a safety manager of the thermal power plant to log in a monitoring system of the thermal power plant and calling a safety risk monitoring image of a position area limited by the safety risk data; the model construction module is used for acquiring a historical alarm record of a coal storage bin of the thermal power plant and constructing a safety alarm model through the historical alarm record; and the early warning result acquisition module is used for sending the security risk monitoring image to the security alarm model to acquire a monitoring early warning result.
In a third aspect of the present application, a thermal power plant safety monitoring and early warning system based on artificial intelligence is provided, including: a processor coupled to a memory for storing a program which, when executed by the processor, causes the system to perform the steps of the method as described in the first aspect.
In a fourth aspect of the present application, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method according to the first aspect.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
because a plurality of detection sensors are distributed in the coal storage bin of the thermal power plant, data monitoring and acquisition are carried out in the coal storage bin of the thermal power plant, a monitoring data set is obtained, monitoring data comparison is carried out, and if safety risk data appear, the safety risk data are sent to a safety manager of the thermal power plant; logging in a monitoring system of a thermal power plant, and calling a security risk monitoring image of a location area limited by security risk data; acquiring a historical alarm record of a coal storage bin of a thermal power plant, and constructing a safety alarm model through the historical alarm record; the safety risk monitoring image is sent to the safety alarm model, the monitoring early warning result is obtained, the safety monitoring precision is improved, the safety monitoring and hidden danger investigation efficacy is comprehensively improved, whether the safety risk hidden danger exists or not is judged in real time, the production normal operation of the thermal power plant is guaranteed to the maximum extent, and the technical effect of the safety monitoring early warning efficiency of the thermal power plant is improved.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
Drawings
Fig. 1 is a schematic flow chart of a thermal power plant safety monitoring and early warning method based on artificial intelligence according to an embodiment of the present application;
fig. 2 is a schematic diagram of a possible flow chart of a detection point positioning mark in a thermal power plant safety monitoring and early warning method based on artificial intelligence;
fig. 3 is a schematic flow chart of determining whether safety risk data is possible in the thermal power plant safety monitoring and early warning method based on artificial intelligence according to the embodiment of the application;
fig. 4 is a schematic diagram of a possible structure of a thermal power plant safety monitoring and early warning system based on artificial intelligence according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an exemplary electronic device of the present application.
Reference numerals illustrate: the system comprises a sensing layout module 100, a monitoring acquisition module 200, a data comparison module 300, a data transmission module 400, an image retrieval module 500, a model construction module 600, an early warning result acquisition module 700, an electronic device 300, a memory 301, a processor 302, a communication interface 303 and a bus architecture 304.
Detailed Description
The embodiment of the application provides a thermal power plant safety monitoring early warning method and system based on artificial intelligence, solves the technical problem that the safety monitoring and hidden danger investigation efficiency of the thermal power plant is low, achieves the purposes of improving safety monitoring precision, comprehensively improving the safety monitoring and hidden danger investigation efficiency, judging whether safety risk hidden danger exists in real time, guaranteeing the production normal operation of the thermal power plant to the maximum extent, and improving the technical effect of the thermal power plant safety monitoring early warning efficiency.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
Example 1
As shown in fig. 1, an embodiment of the present application provides a thermal power plant safety monitoring and early warning method based on artificial intelligence, where the method is applied to a thermal power plant safety monitoring and early warning system, and the thermal power plant safety monitoring and early warning system is communicatively connected with a plurality of detection sensors, and the method includes:
s10: the plurality of detection sensors are distributed in a coal storage bin of the thermal power plant, and the plurality of detection sensors comprise a temperature detection sensor, a smoke detection sensor, a combustible gas detection sensor and a carbon monoxide detection sensor;
Step S10 further includes the steps of:
s11: acquiring structural design information of a coal storage bin of the thermal power plant;
s12: locating and marking a temperature detection point, a smoke detection point, a combustible gas detection point and a carbon monoxide detection point according to the structural design information;
s13: and arranging the plurality of detection sensors through the temperature detection point, the smoke detection point, the combustible gas detection point and the carbon monoxide detection point.
Specifically, the thermal power plant safety monitoring and early warning system is in communication connection with various detection sensors, the communication connection is simply through signal transmission interaction, a communication network is formed between the thermal power plant safety monitoring and early warning system and the various detection sensors, and hardware support is provided for subsequent data analysis;
the coal storage bin of the thermal power plant is a fuel storage position of a target thermal power plant, the plurality of detection sensors comprise a temperature detection sensor, a smoke detection sensor, a combustible gas detection sensor and a carbon monoxide detection sensor, and the plurality of detection sensors are distributed in the coal storage bin of the thermal power plant and specifically comprise: acquiring structural design information of a coal storage bin of the thermal power plant, wherein the structural design information comprises, but is not limited to, coal bin window position information, coal bin gate position information and other related thermal power plant coal storage bin structural information, and positioning and marking temperature detection points, smoke detection points, combustible gas detection points and carbon monoxide detection points on structural drawings corresponding to the structural design information through the structural design information; and respectively arranging the plurality of detection sensors according to the temperature detection point, the smoke detection point, the combustible gas detection point and the carbon monoxide detection point, and providing a basis for data monitoring and acquisition.
As shown in fig. 2, step S12 includes the steps of:
s121: a wind vane is randomly arranged outside a coal storage bin of the thermal power plant, and wind direction data records are obtained;
s122: acquiring a historical wind direction index through the wind direction data record;
s123: and setting a temperature detection point, a smoke detection point, a combustible gas detection point and a carbon monoxide detection point through the structural design information and the historical wind direction index.
Specifically, according to the structural design information, a temperature detection point, a smoke detection point, a combustible gas detection point and a carbon monoxide detection point are positioned and marked, and specifically, in order to ensure the rationality of the setting points of the plurality of detection sensors, wind vanes are randomly arranged outside a coal storage bin of the thermal power plant (randomly, wind vanes arranged at different positions in a coal storage bin area of the thermal power plant are random, and measured wind direction data are basically consistent with wind force data), and wind direction data records are obtained (wind blown from the directions of 337.5-22.5 degrees is called north wind, wind blown from the directions of 315-45 degrees is denoted north wind, and wind direction data expression is carried out according to the specifications);
acquiring a historical wind direction index through the wind direction data record, wherein the historical wind direction index is the historical wind direction data record with the largest occurrence number and the longest duration; through structural design information with history wind direction index (known, the air circulates in the coal bunker of thermal power plant through coal bunker window position/coal bunker gate position, is in the wind gap position, because of the reason of wind helping fire, need pay close attention to temperature, smog, combustible gas and carbon monoxide content in wind gap position), set up temperature check point, smog check point, combustible gas check point and carbon monoxide check point, guarantee that the check point position that sets up is reasonable, provide support for carrying out data detection collection.
Step S123 includes the steps of:
s123-1: acquiring wind power data records through the wind vane;
s123-2: acquiring a historical wind power index through the wind power data record;
s123-3: and setting density constraint on the temperature detection point, the smoke detection point, the combustible gas detection point and the carbon monoxide detection point through the structural design information and the historical wind power index.
Specifically, a temperature detection point, a smoke detection point, a combustible gas detection point and a carbon monoxide detection point are set through the structural design information and the historical wind direction index, specifically, in order to ensure the credibility of key position detection data (avoid the incapability of collecting the key position data due to the fault of a certain detection sensor; likewise, avoid the reduction of the credibility of the detection data due to the detection error of a certain detection sensor), the stability of the detection collection data is improved, and wind data records (the wind power is divided into 18 grades, the minimum is 0 grade, the maximum is 17 grades, and the wind power grade is determined according to the wind speed correspondence) can be obtained by randomly setting wind vanes outside a coal storage bin of the thermal power plant, so that the related normative files such as GB/T28591-2012 wind power grade are satisfied;
Acquiring a historical wind power index through the wind power data record, wherein the historical wind power index is the historical wind power data record with the largest occurrence number and the longest duration; through the structural design information and the historical wind power index (because of wind-assisted fire, the larger wind power place is, the higher the detection precision requirement is, the precision and stability of monitoring acquisition can be improved by setting the density of detection points), and the density constraint is set for the temperature detection points, the smoke detection points, the combustible gas detection points and the carbon monoxide detection points, so that support is provided for data high-precision acquisition.
S20: the data monitoring and acquisition are carried out in a coal storage bin of the thermal power plant through the plurality of detection sensors, and a monitoring data set is obtained;
s30: comparing the monitoring data through the monitoring data set, and judging whether safety risk data appear or not;
as shown in fig. 3, step S30 includes the steps of:
s31: acquiring a safety risk threshold value through the relevant safety specification of the coal storage bin of the thermal power plant;
s32: comparing the monitoring data set with the safety risk threshold value to judge whether safety risk data appear;
s33: if the safety risk data does not appear, sending a monitoring data replacement signal;
S34: and updating the monitoring data set through the monitoring data replacement signal.
Specifically, through the various detection sensors, according to the functions of the detection sensors, data monitoring and acquisition are carried out in a coal storage bin of a thermal power plant to obtain a monitoring data set, wherein the monitoring data set comprises, but is not limited to, temperature monitoring data, smoke monitoring data, combustible gas monitoring data and carbon monoxide monitoring data; and comparing the monitoring data through the monitoring data set, and judging whether safety risk data appear or not, wherein the method specifically comprises the following steps of: acquiring a safety risk threshold through relevant safety specifications of a coal storage bin of the thermal power plant (the relevant safety specifications can be relevant normative literature regulations such as electric power supervision regulations, production safety accident report and investigation treatment regulations and electric power safety accident emergency treatment and investigation treatment regulations, and the like), wherein the safety risk threshold comprises but is not limited to a temperature risk threshold, a smoke risk threshold, a combustible gas risk threshold and a carbon monoxide risk threshold;
comparing the monitoring data set with the safety risk threshold value to judge whether safety risk data appear; and if the safety risk data (the safety risk data: the data meeting the temperature risk threshold, the smoke risk threshold, the combustible gas risk threshold and the carbon monoxide risk threshold) does not exist, sending a monitoring data replacement signal, wherein the monitoring data replacement signal is a functional signal for updating operation, and updating the monitoring data set by utilizing the monitoring data replacement signal so as to provide technical support for continuously updating the monitoring data.
Step S34 includes the steps of:
s341: setting comparison judging circulation through the monitoring data set and the safety risk threshold value;
s342: updating to obtain a first updated monitoring data set through the monitoring data replacement signal;
s343: inputting the first updated monitoring data set into the comparison judging cycle, and carrying out real-time monitoring judging cycle;
s344: after the safety risk data appear, the comparison judging cycle is finished.
Specifically, updating the monitoring data set by the monitoring data replacement signal specifically includes: setting a comparison judging cycle by taking the safety risk threshold value as a comparison judging condition and taking the monitoring data set as input data; updating the monitoring data set through the monitoring data replacing signal to obtain a first updated monitoring data set (the first updated monitoring data set is monitoring data acquired by a next detection data updating time node, and the first updated monitoring data set comprises, but is not limited to, next detection data updating time node temperature monitoring data, next detection data updating time node smoke monitoring data, next detection data updating time node combustible gas monitoring data and next detection data updating time node carbon monoxide monitoring data); inputting the first updated monitoring data set into the comparison judging cycle, and taking the safety risk threshold value as a comparison judging condition to carry out real-time monitoring judging cycle; after the safety risk data appear, the comparison judging cycle is finished, the acquired data obtained by the plurality of detection sensors are synchronized, the comparison judging cycle is set, and support is provided for judging whether the safety risk hidden danger exists in real time.
S40: if the safety risk data appear, the safety risk data are sent to a safety manager of the thermal power plant;
step S40 includes the steps of:
s41: acquiring a safety management duty table of a coal storage bin of a thermal power plant;
s42: the security risk data is sent to a thermal power plant security manager on duty of the current day through the security management duty table;
s43: in a first processing time period, detecting a risk exclusion progress;
s44: if the first processing time period is not finished, acquiring a thermal power plant safety manager in a login thermal power plant monitoring system;
s45: the safety risk data are sent to a thermal power plant safety manager logging in a thermal power plant monitoring system;
s46: in a second processing time period, detecting a risk exclusion progress;
s47: and if the second processing time period is not finished, sending out a safety risk asking for help signal.
Specifically, if the security risk data appears, the security risk data is sent to a security manager of the thermal power plant, and specifically includes: acquiring a safety management duty table (in the safety management duty table, generally, a wheel value) of a coal storage bin of the thermal power plant; the security risk data is sent to a security manager of the thermal power plant on duty (the security manager of the thermal power plant on duty optimally performs risk check first); in a first processing time period (the first processing time period may be ten minutes), performing risk elimination progress detection; if the first processing time period is not finished, acquiring a thermal power plant safety manager (the responsibilities of the thermal power plant safety manager are the same and are all for maintaining the safety of the thermal power plant) in a login thermal power plant monitoring system;
The safety risk data are sent to a thermal power plant safety manager (the thermal power plant safety manager in the thermal power plant monitoring system is high in processing efficiency and is a second-order risk investigation group); in the second processing time period (the second processing time period may be ten minutes), the risk elimination progress detection is performed; if the second processing time period is not completed, a safety risk help signal (the safety risk help signal can be sent to 119 and reported to a fire safety help department aiming at the thermal power potential safety hazard which is difficult to process in time) is sent, and the safety risk help signal comprises a series of related data such as the position of a coal storage bin, temperature monitoring data, smoke monitoring data, combustible gas monitoring data, carbon monoxide monitoring data and the like of the thermal power plant, so that support is provided for comprehensively maintaining the safety of the thermal power plant.
S50: a security manager of the thermal power plant logs in a monitoring system of the thermal power plant, and a security risk monitoring image of a location area limited by the security risk data is called;
s60: acquiring a historical alarm record of a coal storage bin of a thermal power plant, and constructing a safety alarm model through the historical alarm record;
S70: and sending the security risk monitoring image to the security alarm model to obtain a monitoring and early warning result.
Specifically, a thermal power plant security manager only needs to log in a thermal power plant monitoring system, operates the thermal power plant monitoring system on a device background, then performs risk investigation on a region with potential safety hazards after receiving the security risk data, ensures monitoring strength, and reduces the thermal power plant security supervision cost, so that the thermal power plant security manager logs in the thermal power plant monitoring system, and invokes a security risk monitoring image (generally a real-time image, expressed in a video form, of a region with limited position of the security risk data, and needs 5G network support if network congestion exists, thereby shortening network transmission delay to the greatest extent);
based on the data storage unit of the thermal power plant safety monitoring and early warning system, the historical alarm records are retrieved and extracted to obtain the historical alarm records of the thermal power plant coal storage bin, and the historical alarm records are taken as experience data to construct a safety alarm model, which specifically comprises the following steps: the historical alarm records comprise, but are not limited to, historical alarm temperature monitoring data, historical alarm smoke monitoring data, historical alarm combustible gas monitoring data, historical alarm carbon monoxide monitoring data and historical alarm monitoring images, a convolution BP network model is taken as a model basis, the historical alarm monitoring images in the historical alarm records are taken as a training set, the historical alarm temperature monitoring data, the historical alarm smoke monitoring data, the historical alarm combustible gas monitoring data and the historical alarm carbon monoxide monitoring data in the historical alarm records are taken as characteristic data quantity, model convergence training is carried out, model output tends to a stable state, a safety alarm model is determined, and model support is provided for assisting a thermal power plant safety manager to quickly check safety risks;
And taking the security risk monitoring image and the security risk data as input data, sending the input data to an input port of the security alarm model, rapidly checking the security risk through the security alarm model, and acquiring a monitoring and early-warning result, wherein if the monitoring and early-warning result is passed, the content of the monitoring and early-warning result comprises: the temperature risk threshold, the smoke risk threshold, the combustible gas risk threshold and the carbon monoxide risk threshold are adjusted and optimized to remind; if the monitoring and early warning result is failed, the content of the monitoring and early warning result comprises: the safety hazard position, the safety alarm signal, the evacuation reminding of emergency personnel and other various safety emergency instructions are comprehensive improvement of the safety management of the thermal power plant, and the safety monitoring and early warning efficiency of the thermal power plant is improved.
In summary, the thermal power plant safety monitoring and early warning method and system based on artificial intelligence provided by the embodiment of the application have the following technical effects:
1. because a plurality of detection sensors are distributed in the coal storage bin of the thermal power plant, data monitoring and acquisition are carried out in the coal storage bin of the thermal power plant, a monitoring data set is obtained, monitoring data comparison is carried out, and if safety risk data appear, the safety risk data are sent to a safety manager of the thermal power plant; logging in a monitoring system of a thermal power plant, and calling a security risk monitoring image of a location area limited by security risk data; acquiring a historical alarm record of a coal storage bin of a thermal power plant, and constructing a safety alarm model through the historical alarm record; the thermal power plant safety monitoring and early warning method and system based on artificial intelligence are provided, the safety monitoring precision is improved, the safety monitoring and hidden danger investigation efficacy is comprehensively improved, whether the safety risk hidden danger exists or not is judged in real time, the production normal operation of the thermal power plant is guaranteed to the maximum extent, and the technical effect of the thermal power plant safety monitoring and early warning efficiency is improved.
2. Setting comparison judging circulation due to the adoption of a monitoring data set and a safety risk threshold; updating to obtain a first updated monitoring data set through monitoring the data replacement signal, inputting a comparison judging cycle, and carrying out real-time monitoring judging cycle; after the safety risk data appear, the comparison judging cycle is finished, the acquired data obtained by the plurality of detection sensors are synchronized, the comparison judging cycle is set, and support is provided for judging whether the safety risk hidden danger exists in real time.
Example two
Based on the same inventive concept as the thermal power plant safety monitoring and early warning method based on artificial intelligence in the foregoing embodiments, as shown in fig. 4, an embodiment of the present application provides a thermal power plant safety monitoring and early warning system based on artificial intelligence, where the system includes:
the sensor layout module 100 is configured to layout the plurality of detection sensors in a coal storage bin of a thermal power plant, where the plurality of detection sensors include a temperature detection sensor, a smoke detection sensor, a combustible gas detection sensor and a carbon monoxide detection sensor;
the monitoring and collecting module 200 is used for carrying out data monitoring and collecting in a coal storage bin of the thermal power plant through the plurality of detection sensors to obtain a monitoring data set;
The data comparison module 300 is configured to compare the monitoring data set to determine whether safety risk data occurs;
the data sending module 400 is configured to send the security risk data to a security administrator of the thermal power plant if the security risk data appears;
the image calling module 500 is used for a security administrator of the thermal power plant to log in a monitoring system of the thermal power plant and call a security risk monitoring image of the security risk data limited position area;
the model construction module 600 is used for acquiring a historical alarm record of a coal storage bin of the thermal power plant and constructing a safety alarm model according to the historical alarm record;
and the early warning result acquisition module 700 is configured to send the security risk monitoring image to the security alarm model, and acquire a monitoring early warning result.
Further, the system includes:
the structural design information acquisition module is used for acquiring structural design information of the coal storage bin of the thermal power plant;
the detection point positioning mark module is used for positioning and marking a temperature detection point, a smoke detection point, a combustible gas detection point and a carbon monoxide detection point according to the structural design information;
the detection sensor layout module is used for layout of the plurality of detection sensors through the temperature detection point, the smoke detection point, the combustible gas detection point and the carbon monoxide detection point.
Further, the system includes:
the wind direction data record acquisition module is used for randomly setting wind vanes outside a coal storage bin of the thermal power plant to acquire wind direction data records;
the historical wind direction index acquisition module is used for acquiring a historical wind direction index through the wind direction data record;
the first detection point setting module is used for setting a temperature detection point, a smoke detection point, a combustible gas detection point and a carbon monoxide detection point through the structural design information and the historical wind direction index.
Further, the system includes:
the wind power data record acquisition module is used for acquiring wind power data records through the wind vane;
the historical wind power index acquisition module is used for acquiring a historical wind power index through the wind power data record;
the second detection point setting module is used for setting density constraint on the temperature detection point, the smoke detection point, the combustible gas detection point and the carbon monoxide detection point through the structural design information and the historical wind power index.
Further, the system includes:
the safety risk threshold value acquisition module is used for acquiring a safety risk threshold value through the related safety specifications of the coal storage bin of the thermal power plant;
The comparison judging module is used for comparing and judging whether safety risk data appear or not through the monitoring data set and the safety risk threshold value;
the monitoring data replacement signal sending module is used for sending a monitoring data replacement signal if the safety risk data does not appear;
and the monitoring data set updating module is used for updating the monitoring data set through the monitoring data replacing signal.
Further, the system includes:
the comparison judging cycle setting module is used for setting comparison judging cycles through the monitoring data set and the safety risk threshold value;
the first updating monitoring data set acquisition module is used for updating the monitoring data replacement signal to obtain a first updating monitoring data set;
the real-time monitoring and judging circulation module is used for inputting the first updated monitoring data set into the comparison and judging circulation and carrying out real-time monitoring and judging circulation;
and the comparison judging cycle ending module is used for ending the comparison judging cycle after the safety risk data appear.
Further, the system includes:
the safety management duty table acquisition module is used for acquiring a safety management duty table of a coal storage bin of the thermal power plant;
The first data sending module is used for sending the safety risk data to a thermal power plant safety manager on duty through the safety management duty table;
the first risk exclusion progress detection module is used for detecting the risk exclusion progress in a first processing time period;
the system login state acquisition module is used for acquiring a thermal power plant safety manager who logs in a thermal power plant monitoring system if the system login state acquisition module is not completed in the first processing time period;
the second data sending module is used for sending the safety risk data to a thermal power plant safety manager logging in a thermal power plant monitoring system;
the second risk exclusion progress detection module is used for detecting the risk exclusion progress in a second processing time period;
and the safety risk help signal sending module is used for sending a safety risk help signal if the safety risk help signal is not completed in the second processing time period.
Exemplary electronic device
The electronic device of the present application is described below with reference to figure 5,
based on the same inventive concept as the thermal power plant safety monitoring and early warning method based on artificial intelligence in the foregoing embodiments, the present application further provides a thermal power plant safety monitoring and early warning system based on artificial intelligence, including: a processor coupled to a memory for storing a program that, when executed by the processor, causes the system to perform the method of any of the embodiments.
The electronic device 300 includes: a processor 302, a communication interface 303, a memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Wherein the communication interface 303, the processor 302 and the memory 301 may be interconnected by a bus architecture 304; the bus architecture 304 may be a peripheral component interconnect (peripheral component interconnect, PCI) bus, or an extended industry standard architecture (extended industry Standard architecture, EISA) bus, among others. The bus architecture 304 may be divided into address buses, data buses, control buses, and the like. For ease of illustration, only one thick line is shown in fig. 5, but not only one bus or one type of bus.
Processor 302 may be a CPU, microprocessor, ASIC, or one or more integrated circuits for controlling the execution of the programs of the present application.
The communication interface 303 uses any transceiver-like means for communicating with other devices or communication networks, such as ethernet, radio access network (radio access network, RAN), wireless local area network (wireless local area networks, WLAN), wired access network, etc.
The memory 301 may be, but is not limited to, ROM or other type of static storage device that may store static information and instructions, RAM or other type of dynamic storage device that may store information and instructions, or may be an EEPROM (electrically erasable Programmable read-only memory), a compact disc-only memory (CD-ROM) or other optical disk storage, optical disk storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor through bus architecture 304. The memory may also be integrated with the processor.
The memory 301 is used for storing computer-executable instructions for executing the embodiments of the present application, and is controlled by the processor 302 to execute the instructions. The processor 302 is configured to execute computer-implemented instructions stored in the memory 301, so as to implement the thermal power plant safety monitoring and early warning method based on artificial intelligence provided in the foregoing embodiments of the present application.
Alternatively, the computer-executable instructions in the present application may be referred to as application code, which is not specifically limited in this application.
The application provides a thermal power plant safety monitoring and early warning method based on artificial intelligence, wherein, the method is applied to thermal power plant safety monitoring and early warning system, thermal power plant safety monitoring and early warning system and multiple detection sensor communication connection, the method includes: the plurality of detection sensors are distributed in a coal storage bin of the thermal power plant, and the plurality of detection sensors comprise a temperature detection sensor, a smoke detection sensor, a combustible gas detection sensor and a carbon monoxide detection sensor; the data monitoring and acquisition are carried out in a coal storage bin of the thermal power plant through the plurality of detection sensors, and a monitoring data set is obtained; comparing the monitoring data through the monitoring data set, and judging whether safety risk data appear or not; if the safety risk data appear, the safety risk data are sent to a safety manager of the thermal power plant; a security manager of the thermal power plant logs in a monitoring system of the thermal power plant, and a security risk monitoring image of a location area limited by the security risk data is called; acquiring a historical alarm record of a coal storage bin of a thermal power plant, and constructing a safety alarm model through the historical alarm record; and sending the security risk monitoring image to the security alarm model to obtain a monitoring and early warning result.
Those of ordinary skill in the art will appreciate that: the various numbers of first, second, etc. referred to in this application are merely for ease of description and are not intended to limit the scope of this application nor to indicate any order. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one" means one or more. At least two means two or more. "at least one," "any one," or the like, refers to any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one of a, b, or c (species ) may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or plural.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device including one or more servers, data centers, etc. that can be integrated with the available medium. The usable medium may be a magnetic medium (e.g., a floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), etc.
The various illustrative logical units and circuits described herein may be implemented or performed with a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the general purpose processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in the present application may be embodied directly in hardware, in a software element executed by a processor, or in a combination of the two. The software elements may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. In an example, a storage medium may be coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may reside in a terminal. In the alternative, the processor and the storage medium may reside in different components in a terminal. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
Although the present application has been described in connection with specific features and embodiments thereof, it will be apparent that various modifications and combinations can be made without departing from the spirit and scope of the application. Accordingly, the specification and drawings are merely exemplary illustrations of the present application as defined in the appended claims and are considered to cover any and all modifications, variations, combinations, or equivalents that fall within the scope of the present application. It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to include such modifications and variations.
Claims (10)
1. The utility model provides a thermal power plant safety monitoring early warning method based on artificial intelligence, its characterized in that, the method is applied to thermal power plant safety monitoring early warning system, thermal power plant safety monitoring early warning system and multiple detection sensor communication connection, the method includes:
the plurality of detection sensors are distributed in a coal storage bin of the thermal power plant, and the plurality of detection sensors comprise a temperature detection sensor, a smoke detection sensor, a combustible gas detection sensor and a carbon monoxide detection sensor;
the data monitoring and acquisition are carried out in a coal storage bin of the thermal power plant through the plurality of detection sensors, and a monitoring data set is obtained;
comparing the monitoring data through the monitoring data set, and judging whether safety risk data appear or not;
if the safety risk data appear, the safety risk data are sent to a safety manager of the thermal power plant;
a security manager of the thermal power plant logs in a monitoring system of the thermal power plant, and a security risk monitoring image of a location area limited by the security risk data is called;
acquiring a historical alarm record of a coal storage bin of a thermal power plant, and constructing a safety alarm model through the historical alarm record;
and sending the security risk monitoring image to the security alarm model to obtain a monitoring and early warning result.
2. The method of claim 1, wherein the plurality of detection sensors are deployed in a coal storage bin of a thermal power plant, the method comprising:
acquiring structural design information of a coal storage bin of the thermal power plant;
locating and marking a temperature detection point, a smoke detection point, a combustible gas detection point and a carbon monoxide detection point according to the structural design information;
and arranging the plurality of detection sensors through the temperature detection point, the smoke detection point, the combustible gas detection point and the carbon monoxide detection point.
3. The method of claim 2, wherein the locating marks a temperature detection point, a smoke detection point, a combustible gas detection point, and a carbon monoxide detection point by the structural design information, the method comprising:
a wind vane is randomly arranged outside a coal storage bin of the thermal power plant, and wind direction data records are obtained;
acquiring a historical wind direction index through the wind direction data record;
and setting a temperature detection point, a smoke detection point, a combustible gas detection point and a carbon monoxide detection point through the structural design information and the historical wind direction index.
4. A method according to claim 3, wherein said setting a temperature detection point, a smoke detection point, a combustible gas detection point and a carbon monoxide detection point by said structural design information and said historical wind direction index, said method further comprising:
Acquiring wind power data records through the wind vane;
acquiring a historical wind power index through the wind power data record;
and setting density constraint on the temperature detection point, the smoke detection point, the combustible gas detection point and the carbon monoxide detection point through the structural design information and the historical wind power index.
5. The method of claim 1, wherein the comparing of the monitored data by the monitored data set determines whether security risk data is present, the method comprising:
acquiring a safety risk threshold value through the relevant safety specification of the coal storage bin of the thermal power plant;
comparing the monitoring data set with the safety risk threshold value to judge whether safety risk data appear;
if the safety risk data does not appear, sending a monitoring data replacement signal;
and updating the monitoring data set through the monitoring data replacement signal.
6. The method of claim 5, wherein the monitoring dataset is updated by the monitoring data replacement signal, the method comprising:
setting comparison judging circulation through the monitoring data set and the safety risk threshold value;
Updating to obtain a first updated monitoring data set through the monitoring data replacement signal;
inputting the first updated monitoring data set into the comparison judging cycle, and carrying out real-time monitoring judging cycle;
after the safety risk data appear, the comparison judging cycle is finished.
7. The method of claim 1, wherein the sending the security risk data to a security manager of a thermal power plant, the method comprising:
acquiring a safety management duty table of a coal storage bin of a thermal power plant;
the security risk data is sent to a thermal power plant security manager on duty of the current day through the security management duty table;
in a first processing time period, detecting a risk exclusion progress;
if the first processing time period is not finished, acquiring a thermal power plant safety manager in a login thermal power plant monitoring system;
the safety risk data are sent to a thermal power plant safety manager logging in a thermal power plant monitoring system;
in a second processing time period, detecting a risk exclusion progress;
and if the second processing time period is not finished, sending out a safety risk asking for help signal.
8. An artificial intelligence-based thermal power plant safety monitoring and early warning system for implementing the artificial intelligence-based thermal power plant safety monitoring and early warning method according to any one of claims 1 to 7, wherein the thermal power plant safety monitoring and early warning system is in communication connection with a plurality of detection sensors, and the system comprises:
The sensing layout module is used for layout of the plurality of detection sensors in a coal storage bin of the thermal power plant, wherein the plurality of detection sensors comprise a temperature detection sensor, a smoke detection sensor, a combustible gas detection sensor and a carbon monoxide detection sensor;
the monitoring acquisition module is used for carrying out data monitoring acquisition in a coal storage bin of the thermal power plant through the plurality of detection sensors to obtain a monitoring data set;
the data comparison module is used for comparing the monitoring data through the monitoring data set and judging whether safety risk data appear or not;
the data sending module is used for sending the safety risk data to a safety manager of the thermal power plant if the safety risk data appear;
the image calling module is used for a safety manager of the thermal power plant to log in a monitoring system of the thermal power plant and calling a safety risk monitoring image of a position area limited by the safety risk data;
the model construction module is used for acquiring a historical alarm record of a coal storage bin of the thermal power plant and constructing a safety alarm model through the historical alarm record;
and the early warning result acquisition module is used for sending the security risk monitoring image to the security alarm model to acquire a monitoring early warning result.
9. Thermal power plant safety monitoring early warning system based on artificial intelligence, its characterized in that includes: a processor coupled to a memory for storing a program that, when executed by the processor, causes the system to perform the steps of the method of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the method according to any of claims 1 to 7.
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