CN114510514B - A method and system for monitoring all factors of thermal power fuel based on big data platform - Google Patents
A method and system for monitoring all factors of thermal power fuel based on big data platform Download PDFInfo
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Abstract
The invention is suitable for the technical field of computers, and particularly relates to a thermal power fuel full-factor supervision method and system based on a big data platform, wherein the method comprises the steps of acquiring historical power generation data and real-time environment data; the method comprises the steps of constructing a power generation database according to historical power generation data, storing the historical power generation data into the power generation database, calling the historical power generation data according to time sequence, constructing a power generation data curve according to the historical power generation data, estimating the fuel use condition according to real-time environment data, the power generation data curve and a preset fuel transportation time value, and generating a fuel estimation report. According to the invention, through analyzing the historical consumption and usage of the fuel, the prejudgment of the fuel use condition is realized, so that the fuel can meet the daily power generation requirement, the occurrence of fuel accumulation or fuel deficiency can be avoided, the pressure of fuel storage is reduced, and the consumption of the fuel can be made dependent.
Description
Technical Field
The invention belongs to the technical field of computers, and particularly relates to a thermal power fuel full-element supervision method and system based on a big data platform.
Background
With the advent of the cloud age, big data has attracted more and more attention. Big data is often used to account for the large amounts of unstructured and semi-structured data created by a company, which can take excessive time and money when downloaded to a relational database for analysis. Big data analysis is often tied to cloud computing because real-time big data set analysis requires tens, hundreds, or even thousands of computer distribution efforts.
The power generation system using energy contained in combustible materials and the like is collectively called thermal power generation. According to the power generation mode, thermal power generation is divided into coal turbine power generation, fuel turbine power generation, gas-steam combined cycle power generation and internal combustion engine power generation. Thermal power still occupies most of the markets of electric power, and only the thermal power technology must be continuously improved and developed to meet the requirements of harmonious society.
In the current thermal power generation process, a large amount of fuel is required to be used, however, the generated energy is not stable and unchanged, so that the fuel is required to be subjected to full-factor supervision, but the existing fuel supervision method is single, intelligent analysis on the use condition of the fuel cannot be performed, and the problems of insufficient fuel or fuel accumulation are easy to occur.
Disclosure of Invention
The invention aims to provide a thermal power fuel full-element supervision method based on a big data platform, which aims to solve the problems that the existing fuel supervision method is single, intelligent analysis cannot be performed on the use condition of fuel, and insufficient fuel or fuel accumulation easily occurs.
The embodiment of the invention is realized in such a way that the thermal power fuel full-element supervision method based on the big data platform comprises the following steps:
acquiring historical power generation data and real-time environment data, wherein the historical power generation data comprises historical environment information data, historical power generation amount statistical data and historical fuel consumption data;
Constructing a power generation database according to the historical power generation data, and storing the historical power generation data into the power generation database;
The historical power generation data are called according to the time sequence, and a power generation data curve is constructed according to the historical power generation data;
And estimating the fuel use condition according to the real-time environment data, the power generation data curve and the preset fuel transportation time value, and generating a fuel estimation report, wherein the fuel estimation report at least comprises a fuel consumption meter and a fuel feeding meter.
Preferably, the step of constructing a power generation database according to the historical power generation data and storing the historical power generation data into the power generation database specifically includes:
Dividing historical power generation data according to a preset time step to obtain periodic power generation data;
dividing storage areas of the built power generation database according to the number of the periodic power generation data, and reserving areas to be stored, wherein the areas to be stored are used for storing power generation data generated in the future;
the periodic power generation data is stored in a power generation database in time order, and a time index is constructed.
Preferably, the step of retrieving historical power generation data according to a time sequence and constructing a power generation data curve according to the historical power generation data specifically includes:
The method comprises the steps of calling periodic power generation data in historical power generation data according to time sequence, and analyzing the periodic power generation data to obtain periodic environment data, periodic power generation data and periodic fuel consumption data;
constructing a power generation data curve according to time sequence by taking periodic environment data, periodic power generation data and periodic fuel consumption data as references, wherein the power generation data curve comprises an environment power generation curve, an environment fuel curve and a power generation fuel curve;
the relation between the environmental parameters contained in the periodic environmental data and the power generation amount and the fuel usage amount, respectively, is determined.
Preferably, the step of estimating the fuel usage according to the real-time environmental data, the power generation data curve and the preset fuel transportation time value and generating a fuel estimation report specifically includes:
analyzing the real-time environment data, and determining the fuel cycle consumption according to the power generation data curve to obtain a fuel consumption meter;
and determining a fuel feeding period according to a fuel transportation time value preset by the fuel period consumption, obtaining a fuel feeding table, and generating a fuel estimation report.
Preferably, the power generation data curve is also used for analysis of fuel anomaly analysis.
Preferably, the real-time environmental data and the historical environmental information data each include air temperature information, humidity information and date information.
Another object of an embodiment of the present invention is to provide a thermal power fuel full-factor monitoring system based on a big data platform, the system including:
The data acquisition module is used for acquiring historical power generation data and real-time environment data, wherein the historical power generation data comprises historical environment information data, historical power generation amount statistical data and historical fuel consumption data;
The database construction module is used for constructing a power generation database according to the historical power generation data and storing the historical power generation data into the power generation database;
the model construction module is used for calling historical power generation data according to the time sequence and constructing a power generation data curve according to the historical power generation data;
The data analysis module is used for estimating the fuel use condition according to the real-time environment data, the power generation data curve and the preset fuel transportation time value and generating a fuel estimation report, wherein the fuel estimation report at least comprises a fuel consumption meter and a fuel feeding meter.
Preferably, the composition, preferably,
Preferably, the model building module includes:
The data analysis unit is used for calling the periodic power generation data in the historical power generation data according to the time sequence, and analyzing the periodic power generation data to obtain periodic environment data, periodic power generation data and periodic fuel consumption data;
the curve modeling unit is used for constructing a power generation data curve according to the time sequence by taking the periodic environment data, the periodic power generation data and the periodic fuel consumption data as the basis, wherein the power generation data curve comprises an environment power generation curve, an environment fuel curve and a power generation fuel curve;
And a variable analysis unit for determining a relationship between the environmental parameter contained in the periodic environmental data and the power generation amount and the fuel usage amount, respectively.
Preferably, the data analysis module includes:
The fuel consumption analysis unit is used for analyzing the real-time environment data, determining the fuel period consumption according to the power generation data curve and obtaining a fuel consumption meter;
the fuel feeding analysis unit is used for determining a fuel feeding period according to a fuel transportation time value preset by the fuel period consumption, obtaining a fuel feeding table and generating a fuel estimated report.
According to the thermal power fuel full-element supervision method based on the big data platform, through big data analysis on the historical consumption and usage of the fuel, the use condition of the fuel is prejudged, so that the fuel can meet the daily power generation requirement, fuel accumulation or insufficient fuel can be avoided, the pressure of fuel storage is reduced, and the consumption of the fuel is dependent.
Drawings
FIG. 1 is a flow chart of a thermal power fuel full-element supervision method based on a big data platform provided by an embodiment of the invention;
FIG. 2 is a flowchart showing steps for constructing a power generation database according to historical power generation data and storing the historical power generation data into the power generation database according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating steps for retrieving historical power generation data in a time sequence and constructing a power generation data curve according to the historical power generation data according to an embodiment of the present invention;
FIG. 4 is a flowchart showing the steps of estimating fuel usage according to real-time environmental data, a power generation data curve and a preset fuel transportation time value, and generating a fuel estimation report according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a thermal power fuel full-element supervision system based on a big data platform according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a database building block according to an embodiment of the present invention;
FIG. 7 is a schematic diagram of a model building module according to an embodiment of the present invention;
fig. 8 is a schematic diagram of a data analysis module according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
It will be understood that the terms "first," "second," and the like, as used herein, may be used to describe various elements, but these elements are not limited by these terms unless otherwise specified. These terms are only used to distinguish one element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script, without departing from the scope of this disclosure.
The power generation system using energy contained in combustible materials and the like is collectively called thermal power generation. According to the power generation mode, thermal power generation is divided into coal turbine power generation, fuel turbine power generation, gas-steam combined cycle power generation and internal combustion engine power generation. Thermal power still occupies most of the markets of electric power, and only the thermal power technology must be continuously improved and developed to meet the requirements of harmonious society. In the current thermal power generation process, a large amount of fuel is required to be used, however, the generated energy is not stable and unchanged, so that the fuel is required to be subjected to full-factor supervision, but the existing fuel supervision method is single, intelligent analysis on the use condition of the fuel cannot be performed, and the problems of insufficient fuel or fuel accumulation are easy to occur.
According to the invention, through analyzing the historical consumption and usage of the fuel, the prejudgment of the fuel use condition is realized, so that the fuel can meet the daily power generation requirement, the occurrence of fuel accumulation or fuel deficiency can be avoided, the pressure of fuel storage is reduced, and the consumption of the fuel can be made dependent.
As shown in fig. 1, a flowchart of a thermal power fuel full-element supervision method based on a big data platform according to an embodiment of the present invention is provided, where the method includes:
and S100, acquiring historical power generation data and real-time environment data, wherein the historical power generation data comprises historical environment information data, historical power generation amount statistical data and historical fuel consumption data.
In the step, historical power generation data and real-time environmental data are acquired, wherein the historical power generation data comprises historical environmental information data, historical power generation statistical data and historical fuel consumption data, the historical environmental information data refers to daily environmental data in the past power generation process, the real-time environmental data and the historical environmental information data comprise air temperature information, humidity information and date information, the historical power generation records the power generation amount of each day in the past power generation process, and the data recorded by the historical fuel consumption data are the fuel amount used for daily power generation.
S200, a power generation database is built according to the historical power generation data, and the historical power generation data is stored in the power generation database.
In the step, a power generation database is built according to historical power generation data, in order to estimate the power generation amount in the future power generation process and the fuel used in power generation, the historical data is taken as a basis to carry out big data analysis, and the power generation database is built first, so that the historical power generation data is recorded in the power generation database, in the recording process, in order to ensure that the data can be quickly searched after being stored, an index is created in the storing process, and the index can be created according to a time sequence or according to parameters in environmental data.
S300, historical power generation data are called according to the time sequence, and a power generation data curve is constructed according to the historical power generation data.
In this step, historical power generation data are acquired according to a time sequence, in order to analyze the amount of future fuel and the amount of power generation, large data analysis is required first, a power generation data curve is constructed according to historical environmental information data, historical power generation statistical data and historical fuel consumption data contained in the historical power generation data, and the relationship between the historical environmental information data and the historical power generation statistical data and the historical fuel consumption data is analyzed through the curve, so that the amount of future fuel and the amount of power generation are estimated according to the relationship.
S400, estimating the fuel use condition according to the real-time environment data, the power generation data curve and the preset fuel transportation time value, and generating a fuel estimation report, wherein the fuel estimation report at least comprises a fuel consumption meter and a fuel feeding meter.
In the step, after a power generation data curve is established, real-time environment data is used as independent variables, so that the consumption condition of fuel under the current environment condition is judged according to the power generation data curve, the power generation quantity of the fuel in the past period under different environment parameters is analyzed, the consumption condition and the power generation quantity of the fuel are determined, a fuel estimation report is generated, the fuel estimation report at least comprises a fuel consumption meter and a fuel feeding meter, the power generation data curve is also used for analyzing abnormal analysis of fuel, namely judging the theoretical power generation quantity range of the same fuel under the current environment condition according to historical data, and judging the abnormal condition if the current actual power generation quantity is not in the theoretical power generation range, and carrying out diagnostic analysis on a system.
As shown in fig. 2, as a preferred embodiment of the present invention, the step of constructing a power generation database according to the historical power generation data and storing the historical power generation data into the power generation database specifically includes:
S201, dividing historical power generation data according to a preset time step to obtain periodic power generation data.
In this step, the historical power generation data is divided according to a preset time step, so as to facilitate data storage, the time step may be one year or one month, and specifically determined according to the historical power generation data, if the historical power generation data includes the historical data, the longer the time step, the conversely, the shorter the year of the historical data, the shorter the time step.
S202, dividing storage areas of the power generation database according to the number of the periodic power generation data, and reserving areas to be stored, wherein the areas to be stored are used for storing the power generation data generated in the future.
In this step, the storage area division is performed on the power generation database according to the number of the periodic power generation data, specifically, if the historical power generation data is divided into N groups of periodic power generation data, N storage areas with the same size are created in the power generation database, and the storage areas to be stored are reserved, where the storage areas to be stored are used for storing the power generation data generated in the future.
And S203, storing the periodic power generation data in a power generation database according to the time sequence, and constructing a time index.
In this step, the periodic power generation data is stored in the power generation database in time sequence, and in this process, the area storing the periodic power generation data is further divided, specifically, if the periodic power generation data is divided by year, the periodic power generation data is divided by month or day when further divided, and a time index is constructed.
As shown in fig. 3, as a preferred embodiment of the present invention, the steps of retrieving historical power generation data in time sequence and constructing a power generation data curve according to the historical power generation data specifically include:
S301, the periodic power generation data in the historical power generation data are called according to the time sequence, and the periodic power generation data are analyzed to obtain periodic environment data, periodic power generation data and periodic fuel consumption data.
In this step, the periodic power generation data in the history power generation data is retrieved in time series, and the periodic power generation data includes three contents, which are the periodic environment data, the periodic power generation data, and the periodic fuel consumption data, respectively, and thus is extracted.
S302, a power generation data curve is constructed according to time sequence based on the periodic environment data, the periodic power generation data and the periodic fuel consumption data, wherein the power generation data curve comprises an environment power generation curve, an environment fuel curve and a power generation fuel curve.
In the step, a power generation data curve is constructed according to a time sequence by taking periodic environment data, periodic power generation data and periodic fuel consumption data as references, wherein the power generation data curve comprises an environment power generation curve, an environment fuel curve and a power generation fuel, the environment power generation curve is used for analyzing the relation between environment parameters and power generation, the environment fuel curve is used for analyzing the relation between environment parameters and fuel consumption, the power generation fuel curve is used for analyzing the relation between fuel and power generation, and the environment power generation curve, the environment fuel curve and the power generation fuel curve are all drawn according to the time sequence.
S303, determining the relation between the environmental parameters contained in the periodic environmental data and the generated energy and the fuel usage amount respectively.
In this step, the relation between the environmental parameters contained in the periodic environmental data and the power generation amount and the fuel usage amount is determined, and in this process, all the environmental parameters contained in the periodic environmental data are sorted, so that the correspondence between the environmental parameters and the power generation amount and the fuel usage amount is determined in descending or ascending order, respectively, and accordingly, a corresponding coordinate system is established, for example, M kinds of environmental parameters are used, and a relation curve between M kinds of power generation amounts and the fuel usage amount is drawn in a rectangular coordinate system.
As shown in fig. 4, as a preferred embodiment of the present invention, the steps of estimating the fuel usage according to the real-time environmental data, the power generation data curve and the preset fuel transportation time value, and generating a fuel estimation report specifically include:
S401, analyzing the real-time environment data, and determining the fuel cycle consumption according to the power generation data curve to obtain the fuel consumption meter.
In the step, the real-time environmental data is analyzed, namely, the current environmental parameters and the planned power generation amount are extracted, so that the environmental factors are determined, and then the power generation data curve or the relation curve between the power generation amount and the fuel consumption amount is inquired, so that the fuel consumption amount is determined, and the fuel consumption meter is obtained.
S402, determining a fuel feeding period according to a fuel transportation time value preset by the fuel period consumption, obtaining a fuel feeding table, and generating a fuel estimation report.
In this step, the fuel delivery cycle is determined according to the fuel transportation time value preset for the fuel cycle, and since the fuel needs a certain time period from the production place to the power generation place, a certain margin needs to be reserved in order to ensure that the fuel can be stably provided, i.e. if the cycle of raw material transportation is P, controlling the raw material transportation amount each time can ensure the fuel requirements in two material transportation cycles, thereby determining the delivery cycle and generating a fuel estimation report.
As shown in fig. 5, a thermal power fuel full-element monitoring system based on a big data platform according to an embodiment of the present invention includes:
The data acquisition module 100 is configured to acquire historical power generation data and real-time environmental data, where the historical power generation data includes historical environmental information data, historical power generation statistics, and historical fuel consumption data.
In the system, the data acquisition module 100 acquires historical power generation data and real-time environmental data, the historical power generation data comprises historical environmental information data, historical power generation statistical data and historical fuel consumption data, the historical environmental information data refers to daily environmental data in the past power generation process, the real-time environmental data and the historical environmental information data comprise air temperature information, humidity information and date information, the historical power generation records the power generation amount of the past power generation process, and the data recorded by the historical fuel consumption data are the fuel quantity used in the daily power generation process.
The database construction module 200 is configured to construct a power generation database according to the historical power generation data, and store the historical power generation data into the power generation database.
In the system, the database construction module 200 constructs a power generation database according to historical power generation data, in order to be able to estimate the power generation amount in the future power generation process and the fuel used in the power generation process, the historical data is needed to be used as a basis for large data analysis, and the power generation database is constructed first, so that the historical power generation data is recorded in the power generation database, in the recording process, in order to ensure that the data can be quickly searched after being stored, an index is created in the storing process, and the index can be created according to a time sequence or according to parameters in the environmental data.
The model construction module 300 is configured to retrieve historical power generation data according to a time sequence, and construct a power generation data curve according to the historical power generation data.
In the system, the model building module 300 retrieves historical power generation data according to time sequence, and in order to analyze the future fuel consumption and power generation amount, large data analysis is needed first, a power generation data curve is built according to historical environment information data, historical power generation amount statistical data and historical fuel consumption data contained in the historical power generation data, and the relationship between the historical environment information data and the historical power generation amount statistical data and the historical fuel consumption data is analyzed through the curve, so that the future fuel consumption and power generation amount are estimated according to the relationship.
The data analysis module 400 is configured to predict a fuel usage situation according to the real-time environmental data, the power generation data curve and a preset fuel transportation time value, and generate a fuel prediction report, where the fuel prediction report at least includes a fuel consumption meter and a fuel feeding meter.
In the system, after the power generation data curve is established, the data analysis module 400 takes real-time environment data as independent variables, so that the consumption condition of fuel under the current environment condition is judged according to the power generation data curve, the power generation quantity of the fuel in the past under different environment parameters is analyzed, the consumption condition and the power generation quantity of the fuel are determined, a fuel estimation report is generated, the fuel estimation report at least comprises a fuel consumption meter and a fuel feeding meter, and the power generation data curve is also used for analyzing abnormal analysis of the fuel.
As shown in fig. 6, as a preferred embodiment of the present invention, the database construction module 200 includes:
The data dividing unit 201 is configured to divide the historical power generation data according to a preset time step to obtain periodic power generation data.
In this module, the data dividing unit 201 divides the historical power generation data according to a preset time step, so as to facilitate data storage, and the time step may be one year or one month, specifically determined according to the historical power generation data, if the historical power generation data includes the historical data with a longer year, the time step is longer, and conversely, the historical data with a shorter year, the time step is shorter.
The memory dividing unit 202 is configured to divide storage areas of the power generation database according to the number of the periodic power generation data, and reserve a to-be-stored area, where the to-be-stored area is used for storing power generation data generated in the future.
In this module, the memory dividing unit 202 divides the storage area of the power generation database according to the number of the periodic power generation data, specifically, if the historical power generation data is divided into N groups of periodic power generation data, N storage areas with the same size are created in the power generation database, and a to-be-stored area is reserved, where the to-be-stored area is used for storing future generated power generation data.
A data storage unit 203 for storing the periodic power generation data in the power generation database in time order, and constructing a time index.
In this module, the data storage unit 203 stores the periodic power generation data in the power generation database in time sequence, in which process the stored periodic power generation data area is further divided, specifically, if the periodic power generation data is divided by year, the periodic power generation data is divided by month or day when further divided, and a time index is constructed.
As shown in fig. 7, as a preferred embodiment of the present invention, the model building module 300 includes:
The data analysis unit 301 is configured to retrieve the periodic power generation data in the historical power generation data according to a time sequence, and analyze the periodic power generation data to obtain periodic environment data, periodic power generation data, and periodic fuel consumption data.
In this module, the data analysis unit 301 extracts periodic power generation data in the historical power generation data, which includes three parts of content, that is, periodic environment data, periodic power generation data, and periodic fuel consumption data, according to a time sequence.
A curve modeling unit 302, configured to construct a power generation data curve according to a time sequence based on the periodic environment data, the periodic power generation data, and the periodic fuel consumption data, where the power generation data curve includes an environment power generation curve, an environment fuel curve, and a power generation fuel curve.
In this module, the curve modeling unit 302 builds a power generation data curve according to a time sequence based on the periodic environmental data, the periodic power generation data and the periodic fuel consumption data, where the power generation data curve includes an environmental power generation curve, an environmental fuel curve and a power generation fuel, the environmental power generation curve is used for analyzing a relationship between an environmental parameter and a power generation, the environmental fuel curve is used for analyzing a relationship between an environmental parameter and a fuel consumption, and the power generation fuel curve is used for analyzing a relationship between a fuel and a power generation, and the environmental power generation curve, the environmental fuel curve and the power generation fuel curve are all drawn according to the time sequence.
And a variable analysis unit 303 for determining the relationship between the environmental parameters contained in the periodic environmental data and the power generation amount and the fuel usage amount, respectively.
In this module, the variable analysis unit 303 determines the relationship between the environmental parameters included in the periodic environmental data and the power generation amount and the fuel usage amount, respectively, and in this process, ranks all the environmental parameters included in the periodic environmental data so as to determine the correspondence between the environmental parameters and the power generation amount and the fuel usage amount, respectively, in descending order or ascending order of the environmental parameters, thereby establishing a corresponding coordinate system, for example, M environmental parameters, and drawing a relationship curve between M power generation amounts and the fuel usage amounts in a rectangular coordinate system.
As shown in fig. 8, as a preferred embodiment of the present invention, the data analysis module 400 includes:
The fuel consumption analysis unit 401 is configured to analyze the real-time environmental data, determine the fuel cycle consumption according to the power generation data curve, and obtain the fuel consumption meter.
In this module, the fuel consumption analysis unit 401 analyzes the real-time environmental data, that is, extracts the current environmental parameter and the planned power generation amount, so as to determine the environmental factor, and further queries the power generation data curve or the relation curve between the power generation amount and the fuel consumption amount, so as to determine the fuel consumption amount, and obtain the fuel consumption meter.
The fuel feeding analysis unit 402 is configured to determine a fuel feeding period according to a fuel transportation time value preset by a fuel period consumption, obtain a fuel feeding table, and generate a fuel estimation report.
In this module, the fuel feed analysis unit 402 determines a fuel feeding period according to a fuel transportation time value preset by the fuel period usage, and since a certain time period is required for transporting fuel from a production place to a power generation place, a certain margin needs to be reserved in order to ensure that fuel can be stably provided, i.e. if the period of raw material transportation is P, controlling the raw material transportation amount each time can ensure the fuel requirements in two material transportation periods, thereby determining the feeding period and generating a fuel estimation report.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (9)
1. The thermal power fuel full-element supervision method based on the big data platform is characterized by comprising the following steps of:
acquiring historical power generation data and real-time environment data, wherein the historical power generation data comprises historical environment information data, historical power generation amount statistical data and historical fuel consumption data;
Constructing a power generation database according to the historical power generation data, and storing the historical power generation data into the power generation database;
The historical power generation data are called according to the time sequence, and a power generation data curve is constructed according to the historical power generation data;
The method comprises the steps of estimating fuel use conditions according to real-time environment data, a power generation data curve and a preset fuel transportation time value, and generating a fuel estimation report, wherein the fuel estimation report at least comprises a fuel consumption meter and a fuel feeding meter:
S301, periodic power generation data in the historical power generation data are called according to a time sequence, and are analyzed to obtain periodic environment data, periodic power generation data and periodic fuel consumption data;
The method comprises the steps of calling periodic power generation data in historical power generation data according to time sequence, wherein the periodic power generation data comprises three parts of content which are periodic environment data, periodic power generation data and periodic fuel consumption data respectively, so that the periodic environment data, the periodic power generation data and the periodic fuel consumption data are extracted;
s302, constructing a power generation data curve according to time sequence by taking periodic environment data, periodic power generation data and periodic fuel consumption data as references, wherein the power generation data curve comprises an environment power generation curve, an environment fuel curve and a power generation fuel curve, and constructing a power generation data curve according to time sequence by taking the periodic environment data, the periodic power generation data and the periodic fuel consumption data as references, wherein the power generation data curve comprises an environment power generation curve, an environment fuel curve and a power generation fuel, the environment power generation curve is used for analyzing the relation between environment parameters and power generation, the environment fuel curve is used for analyzing the relation between environment parameters and fuel consumption, the power generation fuel curve is used for analyzing the relation between fuel and power generation, and the environment power generation curve, the environment fuel curve and the power generation fuel curve are all drawn according to time sequence;
S303, determining the relation between the environmental parameters contained in the periodic environmental data and the generated energy and the fuel consumption respectively;
And determining the relation between the environmental parameters contained in the periodic environmental data and the generated energy and the fuel consumption respectively, and in the process, sequencing all the environmental parameters contained in the periodic environmental data so as to respectively determine the corresponding relation between the environmental parameters and the generated energy and the fuel consumption according to the descending order or the ascending order of the environmental parameters, thereby establishing a corresponding coordinate system.
2. The thermal power fuel full-factor supervision method based on a big data platform according to claim 1, wherein the step of constructing a power generation database according to historical power generation data and storing the historical power generation data into the power generation database specifically comprises:
Dividing historical power generation data according to a preset time step to obtain periodic power generation data;
dividing storage areas of the built power generation database according to the number of the periodic power generation data, and reserving areas to be stored, wherein the areas to be stored are used for storing power generation data generated in the future;
the periodic power generation data is stored in a power generation database in time order, and a time index is constructed.
3. The thermal power fuel full-factor supervision method based on a big data platform according to claim 1, wherein the step of predicting fuel usage according to real-time environmental data, a power generation data curve and a preset fuel transportation time value and generating a fuel prediction report specifically comprises the following steps:
analyzing the real-time environment data, and determining the fuel cycle consumption according to the power generation data curve to obtain a fuel consumption meter;
and determining a fuel feeding period according to a fuel transportation time value preset by the fuel period consumption, obtaining a fuel feeding table, and generating a fuel estimation report.
4. The full factor thermal power fuel supervision method based on the big data platform according to claim 1, wherein the power generation data curve is further used for analyzing fuel anomaly analysis.
5. The big data platform based all-factor thermal power fuel supervision method according to claim 1, wherein the real-time environment data and the historical environment information data each include air temperature information, humidity information and date information.
6. A thermal power fuel full-factor supervision system based on a big data platform, adopting the thermal power fuel full-factor supervision method of claim 1, characterized in that the system comprises:
The data acquisition module is used for acquiring historical power generation data and real-time environment data, wherein the historical power generation data comprises historical environment information data, historical power generation amount statistical data and historical fuel consumption data;
The database construction module is used for constructing a power generation database according to the historical power generation data and storing the historical power generation data into the power generation database;
the model construction module is used for calling historical power generation data according to the time sequence and constructing a power generation data curve according to the historical power generation data;
The data analysis module is used for estimating the fuel use condition according to the real-time environment data, the power generation data curve and the preset fuel transportation time value and generating a fuel estimation report, wherein the fuel estimation report at least comprises a fuel consumption meter and a fuel feeding meter.
7. The big data platform based full factor thermal power fuel monitoring system of claim 6, wherein the database construction module comprises:
the data segmentation unit is used for dividing the historical power generation data according to a preset time step to obtain periodic power generation data;
The memory dividing unit is used for dividing storage areas of the built power generation database according to the number of the periodic power generation data, reserving a to-be-stored area, and storing the future generated power generation data;
and the data storage unit is used for storing the periodic power generation data in the power generation database according to the time sequence and constructing a time index.
8. The big data platform based thermal power fuel full factor supervisory system according to claim 7, wherein the model building module comprises:
The data analysis unit is used for calling the periodic power generation data in the historical power generation data according to the time sequence, and analyzing the periodic power generation data to obtain periodic environment data, periodic power generation data and periodic fuel consumption data;
the curve modeling unit is used for constructing a power generation data curve according to the time sequence by taking the periodic environment data, the periodic power generation data and the periodic fuel consumption data as the basis, wherein the power generation data curve comprises an environment power generation curve, an environment fuel curve and a power generation fuel curve;
And a variable analysis unit for determining a relationship between the environmental parameter contained in the periodic environmental data and the power generation amount and the fuel usage amount, respectively.
9. The big data platform based full factor thermal power fuel monitoring system of claim 8, wherein the data analysis module comprises:
The fuel consumption analysis unit is used for analyzing the real-time environment data, determining the fuel period consumption according to the power generation data curve and obtaining a fuel consumption meter;
the fuel feeding analysis unit is used for determining a fuel feeding period according to a fuel transportation time value preset by the fuel period consumption, obtaining a fuel feeding table and generating a fuel estimated report.
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