A method and system for monitoring all factors of thermal power fuel based on big data platform

Abstract

The present invention is applicable to the field of computer technology, and in particular to a method and system for monitoring all factors of thermal power fuel based on a big data platform, the method comprising: obtaining historical power generation data and real-time environmental data; constructing a power generation database based on the historical power generation data, and storing the historical power generation data in the power generation database; retrieving the historical power generation data in chronological order, and constructing a power generation data curve based on the historical power generation data; 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. The present invention performs big data analysis on the historical usage and usage of fuel, thereby realizing the prediction of fuel usage, which can not only ensure that the fuel can meet the daily power generation demand, but also avoid the occurrence of fuel accumulation or fuel shortage, reduce the pressure of fuel storage, and make the fuel usage have a basis to rely on.

Description

Technical Field

[0001] The present invention belongs to the field of computer technology, and in particular relates to a method and system for supervising all factors of thermal power fuel based on a big data platform. Background Art

[0002] With the advent of the cloud era, big data has attracted more and more attention. Big data is usually used to describe a large amount of unstructured and semi-structured data created by a company, which will take too much time and money to download to a relational database for analysis. Big data analysis is often associated with cloud computing because real-time analysis of large data sets requires dozens, hundreds or even thousands of computers to distribute the work.

[0003] The method of generating electricity by using the energy contained in combustibles is collectively referred to as thermal power generation. According to the power generation method, thermal power generation is divided into coal-fired steam turbine power generation, oil-fired steam turbine power generation, gas-steam combined cycle power generation and internal combustion engine power generation. Thermal power still occupies most of the electricity market. Only when thermal power technology is continuously improved and developed can it meet the requirements of a harmonious society.

[0004] In the current thermal power generation process, a large amount of fuel is needed. However, since the power generation is not stable, the fuel needs to be fully regulated. However, the existing fuel supervision method is single and cannot perform intelligent analysis of fuel usage, which can easily lead to problems such as fuel shortage or fuel accumulation. Summary of the invention

[0005] The purpose of the embodiment of the present invention is to provide a method for full-factor supervision of thermal power fuel based on a big data platform, aiming to solve the problem that the existing fuel supervision method is single and cannot perform intelligent analysis on the use of fuel, and is prone to fuel shortage or fuel accumulation. The present invention performs full-factor supervision on fuel, thereby performing intelligent prediction of fuel use to determine the fuel usage and avoid the problem of fuel shortage or fuel accumulation.

[0006] The embodiment of the present invention is implemented as follows: a method for supervising all factors of thermal power fuel based on a big data platform, the method comprising:

[0007] Acquire historical power generation data and real-time environmental data, wherein the historical power generation data includes historical environmental information data, historical power generation statistics data, and historical fuel consumption data;

[0008] Building a power generation database based on historical power generation data, and storing the historical power generation data in the power generation database;

[0009] Retrieve historical power generation data in chronological order and construct a power generation data curve based on the historical power generation data;

[0010] The fuel usage is estimated according to the real-time environmental data, the power generation data curve and the preset fuel transportation time value, and a fuel estimation report is generated, wherein the fuel estimation report includes at least a fuel usage table and a fuel feed table.

[0011] Preferably, the step of constructing a power generation database according to the historical power generation data and storing the historical power generation data in the power generation database specifically includes:

[0012] Divide the historical power generation data according to the preset time step to obtain periodic power generation data;

[0013] The power generation database is divided into storage areas according to the amount of periodic power generation data, and a storage area is reserved, wherein the storage area is used to store power generation data generated in the future;

[0014] The periodic power generation data is stored in the power generation database in chronological order, and a time index is constructed.

[0015] Preferably, the step of retrieving historical power generation data in chronological order and constructing a power generation data curve according to the historical power generation data specifically includes:

[0016] Retrieving the periodic power generation data in the historical power generation data in chronological order, and parsing the periodic power generation data to obtain periodic environmental data, periodic power generation data, and periodic fuel consumption data;

[0017] Constructing a power generation data curve in chronological order based on the periodic environmental data, the periodic power generation data and the periodic fuel consumption data, wherein the power generation data curve includes an environmental power generation curve, an environmental fuel curve and a power generation fuel curve;

[0018] Determine the relationship between the environmental parameters included in the periodic environmental data and the power generation and fuel usage, respectively.

[0019] Preferably, the step of estimating fuel usage according to real-time environmental data, power generation data curve and preset fuel transportation time value and generating a fuel estimation report specifically includes:

[0020] Analyze real-time environmental data and determine the fuel cycle usage based on the power generation data curve to obtain a fuel usage table;

[0021] The fuel delivery cycle is determined based on the preset fuel transportation time value of the fuel cycle usage, a fuel delivery table is obtained, and a fuel estimation report is generated.

[0022] Preferably, the power generation data curve is also used to analyze fuel anomaly analysis.

[0023] Preferably, the real-time environmental data and historical environmental information data both include temperature information, humidity information and date information.

[0024] 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 comprising:

[0025] A data acquisition module, used to acquire historical power generation data and real-time environmental data, wherein the historical power generation data includes historical environmental information data, historical power generation statistics data, and historical fuel consumption data;

[0026] A database construction module, used to construct a power generation database according to historical power generation data, and store the historical power generation data in the power generation database;

[0027] A model building module is used to retrieve historical power generation data in chronological order and build a power generation data curve based on the historical power generation data;

[0028] The data analysis module is used to estimate the fuel usage according to the real-time environmental data, the power generation data curve and the preset fuel transportation time value, and generate a fuel estimation report, wherein the fuel estimation report includes at least a fuel usage table and a fuel feed table.

[0029] Preferred, preferred,

[0030] Preferably, the model building module includes:

[0031] A data analysis unit is used to retrieve the periodic power generation data in the historical power generation data in chronological order, and analyze the periodic power generation data to obtain periodic environmental data, periodic power generation data and periodic fuel consumption data;

[0032] A curve modeling unit, used to construct a power generation data curve in chronological order based on periodic environmental data, periodic power generation data and periodic fuel consumption data, wherein the power generation data curve includes an environmental power generation curve, an environmental fuel curve and a power generation fuel curve;

[0033] The variable analysis unit is used to determine the relationship between the environmental parameters included in the periodic environmental data and the power generation and fuel usage.

[0034] Preferably, the data analysis module includes:

[0035] A fuel usage analysis unit is used to analyze real-time environmental data and determine the fuel cycle usage according to the power generation data curve to obtain a fuel usage table;

[0036] The fuel feed analysis unit is used to determine the fuel delivery cycle according to the preset fuel transportation time value of the fuel cycle usage, obtain the fuel feed table, and generate a fuel estimation report.

[0037] The embodiment of the present invention provides a method for monitoring all factors of thermal power fuel based on a big data platform. By performing big data analysis on the historical usage and usage of the fuel, the fuel usage can be predicted. This can not only ensure that the fuel can meet the daily power generation demand, but also avoid the occurrence of fuel accumulation or fuel shortage, reduce the pressure on fuel storage, and make the fuel usage reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 A flowchart of a method for supervising all factors of thermal power fuel based on a big data platform provided in an embodiment of the present invention;

[0039] Figure 2 A flowchart of the steps of building a power generation database according to historical power generation data and storing the historical power generation data in the power generation database provided by an embodiment of the present invention;

[0040] Figure 3 A flowchart of the steps of retrieving historical power generation data in chronological order and constructing a power generation data curve based on the historical power generation data provided by an embodiment of the present invention;

[0041] Figure 4 A flowchart of the steps of estimating fuel usage according to real-time environmental data, power generation data curves and preset fuel transportation time values ​​and generating a fuel estimation report provided by an embodiment of the present invention;

[0042] Figure 5 An architecture diagram of a thermal power fuel full-factor monitoring system based on a big data platform provided in an embodiment of the present invention;

[0043] Figure 6 An architectural diagram of a database construction module provided in an embodiment of the present invention;

[0044] Figure 7 An architectural diagram of a model building module provided in an embodiment of the present invention;

[0045] Figure 8 An architectural diagram of a data analysis module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0047] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, 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.

[0048] The method of generating electricity by using the energy contained in combustibles is collectively referred to as thermal power generation. According to the power generation method, thermal power generation is divided into coal-fired steam turbine power generation, oil-fired steam turbine power generation, gas-steam combined cycle power generation and internal combustion engine power generation. Thermal power still occupies most of the electricity market. Only when thermal power technology is continuously improved and developed can it meet the requirements of a harmonious society. In the current thermal power generation process, a large amount of fuel is needed. However, since the power generation is not stable, it is necessary to supervise the fuel in all aspects. However, the existing fuel supervision method is single and cannot perform intelligent analysis of the fuel usage, which is prone to fuel shortage or fuel accumulation.

[0049] The present invention performs big data analysis on the historical usage and usage of fuel, thereby predicting the fuel usage, which can not only ensure that the fuel can meet the daily power generation needs, but also avoid the occurrence of fuel accumulation or fuel shortage, reduce the pressure on fuel storage, and make the fuel usage reliable.

[0050] like Figure 1 FIG. 1 is a flowchart of a method for supervising all factors of thermal power fuel based on a big data platform provided by an embodiment of the present invention, the method comprising:

[0051] S100, acquiring historical power generation data and real-time environmental data, wherein the historical power generation data includes historical environmental information data, historical power generation statistics data, and historical fuel consumption data.

[0052] In this step, historical power generation data and real-time environmental data are obtained. The historical power generation data includes historical environmental information data, historical power generation statistics data and historical fuel consumption data. The historical environmental information data refers to the daily environmental data in the previous power generation process. The real-time environmental data and the historical environmental information data both include temperature information, humidity information and date information. The historical power generation records the daily power generation in the previous power generation process, and the historical fuel consumption data records the amount of fuel used for power generation every day.

[0053] S200, constructing a power generation database according to historical power generation data, and storing the historical power generation data in the power generation database.

[0054] In this step, a power generation database is constructed based on historical power generation data. In order to be able to estimate the power generation and fuel used in the future power generation process, it is necessary to use historical data as a basis for big data analysis. First, a power generation database is constructed to enter the historical power generation data into it. During the entry process, in order to ensure that the data can be quickly retrieved after storage, an index is created during storage. The index can be created in chronological order or according to the parameters in the environmental data.

[0055] S300, retrieving historical power generation data in chronological order, and constructing a power generation data curve based on the historical power generation data.

[0056] In this step, historical power generation data is retrieved in chronological order. In order to analyze future fuel usage and power generation, big data analysis is required first. A power generation data curve is constructed based on the historical environmental information data, historical power generation statistics data, and historical fuel consumption data contained in the historical power generation data. The relationship between the historical environmental information data and the historical power generation statistics data and the historical fuel consumption data is analyzed through the curve, and future fuel usage and power generation are estimated based on this.

[0057] S400, estimating fuel usage according to real-time environmental data, power generation data curve and preset fuel transportation time value, and generating a fuel estimation report, wherein the fuel estimation report at least includes a fuel usage table and a fuel feed table.

[0058] In this step, after the power generation data curve is established, the real-time environmental data is used as the independent variable, so as to judge the fuel consumption under the current environmental conditions according to the power generation data curve, and analyze the power generation of the past fuel under different environmental parameters, so as to determine the fuel consumption and power generation, and generate a fuel estimation report. The fuel estimation report includes at least a fuel usage table and a fuel feed table; the power generation data curve is also used to analyze fuel anomaly analysis, that is, to judge the theoretical power generation range of the same fuel under the current environmental conditions according to historical data. If the current actual power generation is not within the theoretical power generation range, it is regarded as an abnormality and is used for diagnostic analysis of the system.

[0059] like Figure 2 As shown, as a preferred embodiment of the present invention, the step of constructing a power generation database according to historical power generation data and storing the historical power generation data in the power generation database specifically includes:

[0060] S201, dividing historical power generation data according to a preset time step to obtain periodic power generation data.

[0061] In this step, the historical power generation data is divided according to a preset time step. In order to facilitate data storage, the historical power generation data is divided. The time step can be one year or one month, which is specifically determined according to the historical power generation data. If the historical power generation data contains more years of historical data, the time step will be longer. Conversely, the shorter the years of historical data, the shorter the time step will be.

[0062] S202, dividing the power generation database into storage areas according to the amount of periodic power generation data, and reserving a storage area, wherein the storage area is used to store power generation data generated in the future.

[0063] In this step, the storage area of ​​the power generation database is divided according to the number of periodic power generation data. Specifically, if the historical power generation data is divided into N groups of periodic power generation data, N storage areas of the same size are created in the power generation database, and an area to be stored is reserved, and the area to be stored is used to store the power generation data generated in the future.

[0064] S203, storing the periodic power generation data in a power generation database in chronological order, and constructing a time index.

[0065] In this step, the periodic power generation data is stored in the power generation database in chronological order. 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, it is further divided by month or day, and a time index is constructed.

[0066] like Figure 3 As shown, as a preferred embodiment of the present invention, the step of retrieving historical power generation data in chronological order and constructing a power generation data curve according to the historical power generation data specifically includes:

[0067] S301, retrieve the periodic power generation data in the historical power generation data in chronological order, and parse the periodic power generation data to obtain periodic environmental data, periodic power generation data, and periodic fuel consumption data.

[0068] In this step, the periodic power generation data in the historical power generation data is retrieved in chronological order. The periodic power generation data includes three parts, namely periodic environmental data, periodic power generation data and periodic fuel consumption data, so they are extracted.

[0069] S302, constructing a power generation data curve in chronological order based on periodic environmental data, periodic power generation data and periodic fuel consumption data, wherein the power generation data curve includes an environmental power generation curve, an environmental fuel curve and a power generation fuel curve.

[0070] In this step, a power generation data curve is constructed in chronological order based on periodic environmental data, periodic power generation data and periodic fuel consumption data, wherein the power generation data curve includes an environmental power generation curve, an environmental fuel curve and power generation fuel. The environmental power generation curve is used to analyze the relationship between environmental parameters and power generation, the environmental fuel curve is used to analyze the relationship between environmental parameters and fuel consumption, and the power generation fuel curve is used to analyze the relationship between fuel and power generation. The above-mentioned environmental power generation curve, environmental fuel curve and power generation fuel curve are all drawn in chronological order.

[0071] S303, determining the relationship between the environmental parameters included in the periodic environmental data and the power generation and fuel usage.

[0072] In this step, the relationships between the environmental parameters contained in the periodic environmental data and the power generation and fuel usage are determined. In this process, all environmental parameters contained in the periodic environmental data are sorted, so as to determine the corresponding relationships between the environmental parameters and the power generation and fuel usage in descending or ascending order, and a corresponding coordinate system is established accordingly. For example, if there are M kinds of environmental parameters, then the relationship curves between the M kinds of power generation and fuel usage are drawn in the rectangular coordinate system.

[0073] like Figure 4 As shown, as a preferred embodiment of the present invention, 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:

[0074] S401, analyzing real-time environmental data, and determining fuel cycle usage according to the power generation data curve to obtain a fuel usage table.

[0075] In this step, the real-time environmental data is analyzed, that is, the current environmental parameters and planned power generation are extracted to determine the environmental factors, and then the power generation data curve or the relationship curve between power generation and fuel usage is queried to determine the fuel usage and obtain the fuel usage table.

[0076] S402, determining the fuel purchase cycle according to the preset fuel transportation time value of the fuel cycle usage, obtaining the fuel supply table, and generating a fuel estimation report.

[0077] In this step, the fuel purchase cycle is determined based on the preset fuel transportation time value of the fuel cycle usage. Since it takes a certain period of time to transport the fuel from the production site to the power generation site, in order to ensure a stable supply of fuel, a certain margin needs to be reserved. That is, if the raw material transportation cycle is P, then controlling the raw material transportation volume each time can ensure the fuel demand within two material transportation cycles, thereby determining the purchase cycle and generating a fuel estimation report.

[0078] like Figure 5 As shown, a thermal power fuel full-factor monitoring system based on a big data platform is provided in an embodiment of the present invention, and the system includes:

[0079] The data acquisition module 100 is used to acquire historical power generation data and real-time environmental data, wherein the historical power generation data includes historical environmental information data, historical power generation statistics data and historical fuel consumption data.

[0080] In this system, the data acquisition module 100 acquires historical power generation data and real-time environmental data. The historical power generation data includes historical environmental information data, historical power generation statistics data and historical fuel consumption data. The historical environmental information data refers to the daily environmental data in the previous power generation process. The real-time environmental data and the historical environmental information data both include temperature information, humidity information and date information. The historical power generation records the daily power generation in the previous power generation process, and the historical fuel consumption data records the amount of fuel used for power generation every day.

[0081] The database construction module 200 is used to construct a power generation database according to the historical power generation data, and store the historical power generation data in the power generation database.

[0082] In this system, the database construction module 200 constructs a power generation database based on historical power generation data. In order to be able to estimate the power generation and fuel used in the future power generation process, it is necessary to use historical data as a basis for big data analysis. The power generation database is first constructed to enter the historical power generation data into it. During the entry process, in order to ensure that the data can be quickly retrieved after storage, an index is created during storage. The index can be created in chronological order or according to the parameters in the environmental data.

[0083] The model building module 300 is used to retrieve the historical power generation data in chronological order and build a power generation data curve based on the historical power generation data.

[0084] In this system, the model building module 300 retrieves historical power generation data in chronological order. In order to analyze future fuel usage and power generation, it is necessary to first perform big data analysis. Based on the historical environmental information data, historical power generation statistics data and historical fuel consumption data contained in the historical power generation data, a power generation data curve is constructed. The relationship between the historical environmental information data and the historical power generation statistics data and the historical fuel consumption data is analyzed through the curve, and the future fuel usage and power generation are estimated based on this.

[0085] The data analysis module 400 is used to estimate the fuel usage according to the real-time environmental data, the power generation data curve and the preset fuel transportation time value, and generate a fuel estimation report, wherein the fuel estimation report includes at least a fuel usage table and a fuel feed table.

[0086] In this system, after establishing the power generation data curve, the data analysis module 400 uses the real-time environmental data as an independent variable, thereby judging the fuel consumption under the current environmental conditions based on the power generation data curve, and analyzing the power generation of the past fuel under different environmental parameters, so as to determine the fuel consumption and power generation, and generate a fuel estimation report, which includes at least a fuel usage table and a fuel feed table; the power generation data curve is also used to analyze fuel anomaly analysis.

[0087] like Figure 6 As shown, as a preferred embodiment of the present invention, the database construction module 200 includes:

[0088] The data segmentation unit 201 is used to divide the historical power generation data according to a preset time step to obtain periodic power generation data.

[0089] In this module, the data segmentation unit 201 divides the historical power generation data according to a preset time step. In order to facilitate data storage, the historical power generation data is divided. The time step can be one year or one month, which is specifically determined according to the historical power generation data. If the historical power generation data contains more years of historical data, the time step is longer. Conversely, the shorter the number of years of historical data, the shorter the time step is.

[0090] The memory division unit 202 is used to divide the storage area of ​​the power generation database according to the amount of periodic power generation data, and reserve a storage area, wherein the storage area is used to store power generation data generated in the future.

[0091] In this module, the memory division unit 202 divides the storage area of ​​the power generation database according to the number of periodic power generation data. Specifically, if the historical power generation data is divided into N groups of periodic power generation data, N storage areas of the same size are created in the power generation database, and an area to be stored is reserved. The area to be stored is used to store the power generation data generated in the future.

[0092] The data storage unit 203 is used to store the periodic power generation data in the power generation database in chronological order and construct a time index.

[0093] In this module, the data storage unit 203 stores the periodic power generation data in the power generation database in chronological order. In this process, the area for storing the periodic power generation data is further divided. Specifically, if the periodic power generation data is divided by year, it is further divided by month or day, and a time index is constructed.

[0094] like Figure 7 As shown, as a preferred embodiment of the present invention, the model building module 300 includes:

[0095] The data analysis unit 301 is used to retrieve the periodic power generation data in the historical power generation data in chronological order, and analyze the periodic power generation data to obtain periodic environmental data, periodic power generation data and periodic fuel consumption data.

[0096] In this module, the data analysis unit 301 retrieves the periodic power generation data in the historical power generation data in chronological order. The periodic power generation data includes three parts, namely periodic environmental data, periodic power generation data and periodic fuel consumption data, so it is extracted.

[0097] The curve modeling unit 302 is used to construct a power generation data curve in chronological order based on periodic environmental data, periodic power generation data and periodic fuel consumption data, wherein the power generation data curve includes an environmental power generation curve, an environmental fuel curve and a power generation fuel curve.

[0098] In this module, the curve modeling unit 302 constructs a power generation data curve in chronological order based on periodic environmental data, periodic power generation data and periodic fuel consumption data, wherein the power generation data curve includes an environmental power generation curve, an environmental fuel curve and power generation fuel. The environmental power generation curve is used to analyze the relationship between environmental parameters and power generation, the environmental fuel curve is used to analyze the relationship between environmental parameters and fuel consumption, and the power generation fuel curve is used to analyze the relationship between fuel and power generation. The above-mentioned environmental power generation curve, environmental fuel curve and power generation fuel curve are all drawn in chronological order.

[0099] The variable analysis unit 303 is used to determine the relationship between the environmental parameters included in the periodic environmental data and the power generation and fuel usage.

[0100] In this module, the variable analysis unit 303 determines the relationship between the environmental parameters contained in the periodic environmental data and the power generation and fuel usage. In this process, all environmental parameters contained in the periodic environmental data are sorted, so as to determine the corresponding relationship between the environmental parameters and the power generation and fuel usage in descending or ascending order, and establish a corresponding coordinate system accordingly. For example, if there are M kinds of environmental parameters, then the relationship curves between the M kinds of power generation and fuel usage are drawn in the rectangular coordinate system.

[0101] like Figure 8 As shown, as a preferred embodiment of the present invention, the data analysis module 400 includes:

[0102] The fuel usage analysis unit 401 is used to analyze the real-time environmental data and determine the fuel cycle usage according to the power generation data curve to obtain a fuel usage table.

[0103] In this module, the fuel usage analysis unit 401 parses the real-time environmental data, that is, extracts the current environmental parameters and the planned power generation to determine the environmental factors, and then queries the power generation data curve or the relationship curve between power generation and fuel usage to determine the fuel usage and obtain the fuel usage table.

[0104] The fuel feed analysis unit 402 is used to determine the fuel delivery cycle according to the fuel transportation time value preset by the fuel cycle usage, obtain the fuel feed table, and generate a fuel estimation report.

[0105] In this module, the fuel feed analysis unit 402 determines the fuel purchase cycle based on the preset fuel transportation time value of the fuel cycle usage. Since it takes a certain period of time to transport the fuel from the production site to the power generation site, in order to ensure a stable supply of fuel, a certain margin needs to be reserved. That is, if the raw material transportation cycle is P, then controlling the raw material transportation volume each time can ensure the fuel demand within two material transportation cycles, thereby determining the purchase cycle and generating a fuel estimation report.

[0106] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0107] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many 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).

[0108] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0109] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring all factors of thermal power fuel based on a big data platform, characterized in that: The method comprises: Acquire historical power generation data and real-time environmental data, wherein the historical power generation data includes historical environmental information data, historical power generation statistics data, and historical fuel consumption data; Building a power generation database based on historical power generation data, and storing the historical power generation data in the power generation database; Retrieve historical power generation data in chronological order and construct a power generation data curve based on the historical power generation data; The fuel usage is estimated according to the real-time environmental data, the power generation data curve and the preset fuel transportation time value, and a fuel estimation report is generated, wherein the fuel estimation report at least includes a fuel usage table and a fuel feed table; the step of retrieving the historical power generation data in chronological order and constructing the power generation data curve according to the historical power generation data specifically includes: S301, retrieving periodic power generation data from historical power generation data in chronological order, and parsing the periodic power generation data to obtain periodic environmental data, periodic power generation data, and periodic fuel consumption data; The periodic power generation data in the historical power generation data is retrieved in chronological order. The periodic power generation data contains three parts, namely periodic environmental data, periodic power generation data and periodic fuel consumption data, so it is extracted; S302, constructing a power generation data curve in chronological order based on the periodic environmental data, the periodic power generation data and the periodic fuel consumption data, wherein the power generation data curve includes an environmental power generation curve, an environmental fuel curve and a power generation fuel curve; constructing a power generation data curve in chronological order based on the periodic environmental data, the periodic power generation data and the periodic fuel consumption data, wherein the power generation data curve includes an environmental power generation curve, an environmental fuel curve and a power generation fuel, wherein the environmental power generation curve is used to analyze the relationship between environmental parameters and power generation, the environmental fuel curve is used to analyze the relationship between environmental parameters and fuel consumption, and the power generation fuel curve is used to analyze the relationship between fuel and power generation, and the above environmental power generation curve, environmental fuel curve and power generation fuel curve are all drawn in chronological order; S303, determining the relationship between the environmental parameters included in the periodic environmental data and the power generation and fuel usage; Determine the relationship between the environmental parameters contained in the periodic environmental data and the power generation and fuel usage. In this process, all environmental parameters contained in the periodic environmental data are sorted, so as to determine the corresponding relationship between the environmental parameters and the power generation and fuel usage in descending or ascending order, and establish the corresponding coordinate system accordingly.

2. The method for monitoring all factors of thermal power fuel based on a big data platform according to claim 1 is characterized in that: The step of constructing a power generation database based on historical power generation data and storing the historical power generation data in the power generation database specifically includes: Divide the historical power generation data according to the preset time step to obtain periodic power generation data; The power generation database is divided into storage areas according to the amount of periodic power generation data, and a storage area is reserved, wherein the storage area is used to store power generation data generated in the future; The periodic power generation data is stored in the power generation database in chronological order, and a time index is constructed.

3. The method for monitoring all factors of thermal power fuel based on a big data platform according to claim 1 is characterized in that: The step of estimating fuel usage according to real-time environmental data, power generation data curve and preset fuel transportation time value and generating a fuel estimation report specifically includes: Analyze real-time environmental data and determine the fuel cycle usage based on the power generation data curve to obtain a fuel usage table; The fuel delivery cycle is determined based on the preset fuel transportation time value of the fuel cycle usage, a fuel delivery table is obtained, and a fuel estimation report is generated.

4. The method for monitoring all factors of thermal power fuel based on a big data platform according to claim 1 is characterized in that: The power generation data curve is also used to analyze fuel anomaly analysis.

5. The method for monitoring all factors of thermal power fuel based on a big data platform according to claim 1 is characterized in that: The real-time environmental data and historical environmental information data both include temperature information, humidity information and date information.

6. A thermal power fuel full-factor supervision system based on a big data platform, using the thermal power fuel full-factor supervision method according to claim 1, characterized in that: The system comprises: A data acquisition module, used to acquire historical power generation data and real-time environmental data, wherein the historical power generation data includes historical environmental information data, historical power generation statistics data, and historical fuel consumption data; A database construction module, used to construct a power generation database according to historical power generation data, and store the historical power generation data in the power generation database; A model building module is used to retrieve historical power generation data in chronological order and build a power generation data curve based on the historical power generation data; The data analysis module is used to estimate the fuel usage according to the real-time environmental data, the power generation data curve and the preset fuel transportation time value, and generate a fuel estimation report, wherein the fuel estimation report includes at least a fuel usage table and a fuel feed table.

7. The thermal power fuel full-factor monitoring system based on the big data platform according to claim 6 is characterized in that: The database construction module includes: A data segmentation unit, used to divide the historical power generation data according to a preset time step to obtain periodic power generation data; A memory division unit, used to divide the storage area of ​​the power generation database according to the amount of periodic power generation data, and reserve a storage area, wherein the storage area is used to store power generation data generated in the future; The data storage unit is used to store the periodic power generation data in the power generation database in chronological order and construct a time index.

8. The thermal power fuel full-factor monitoring system based on the big data platform according to claim 7 is characterized in that: The model building module includes: A data analysis unit is used to retrieve the periodic power generation data in the historical power generation data in chronological order, and analyze the periodic power generation data to obtain periodic environmental data, periodic power generation data and periodic fuel consumption data; A curve modeling unit, used to construct a power generation data curve in chronological order based on periodic environmental data, periodic power generation data and periodic fuel consumption data, wherein the power generation data curve includes an environmental power generation curve, an environmental fuel curve and a power generation fuel curve; The variable analysis unit is used to determine the relationship between the environmental parameters included in the periodic environmental data and the power generation and fuel usage.

9. The thermal power fuel full-factor monitoring system based on the big data platform according to claim 8 is characterized in that: The data analysis module includes: A fuel usage analysis unit is used to analyze real-time environmental data and determine the fuel cycle usage according to the power generation data curve to obtain a fuel usage table; The fuel feed analysis unit is used to determine the fuel delivery cycle according to the preset fuel transportation time value of the fuel cycle usage, obtain the fuel feed table, and generate a fuel estimation report.

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