A coal gas consumption management method, device, equipment and storage medium
By standardizing the initial calorific value data of coal gas and training machine learning models, the problem of inaccurate statistical calculations in coal gas consumption management was solved, and efficient and accurate coal gas consumption management was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SGIS SONGSHAN CO LTD
- Filing Date
- 2023-02-02
- Publication Date
- 2026-07-03
AI Technical Summary
Existing methods for managing gas consumption contain errors in the statistical calculation of volume and calorific value, resulting in inaccurate statistical results and cumbersome calculation steps, which affects management efficiency.
The initial calorific value data of the sample gas is standardized to determine the standard calorific value data, which is then stored in the calorific value dataset. Based on the standard calorific value data and the heat per unit volume data, the calorific value state volume data is determined. A gas consumption evaluation model is trained using a machine learning model, and the gas consumption is evaluated by combining the calorific value dataset and the volume dataset.
This improved the accuracy and efficiency of evaluating gas consumption, enabling automated management and precise evaluation of gas consumption.
Smart Images
Figure CN116070951B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of gas fuel management technology, and in particular to a method, apparatus, equipment and storage medium for managing coal gas consumption. Background Technology
[0002] Currently, both volumetric and calorific value statistics are used for the statistical analysis of coal gas metering. Typically, coal gas metering instruments are equipped with flow meters, pressure gauges, thermometers, and calorific value meters to calculate the volume and calorific value of coal gas, allowing for the management of coal gas consumption based on the results. However, existing coal gas consumption management methods, which use coal gas volume as the statistical criterion, do not consider the influence of calorific value on the volume of coal gas, leading to errors in the statistical calculations. Conversely, using calorific value as the statistical criterion for calculating coal gas consumption involves cumbersome calculation steps, hindering daily data management. Therefore, improving the accuracy of coal gas statistical calculations and the efficiency of coal gas consumption management is a problem that needs to be addressed. Summary of the Invention
[0003] This invention provides a method, apparatus, equipment, and storage medium for managing coal gas consumption, which can improve the accuracy of coal gas statistical calculations and increase the efficiency of coal gas consumption management.
[0004] According to one aspect of the present invention, a method for managing gas consumption is provided, comprising:
[0005] The initial calorific value data of the sample gas is standardized to determine the standard calorific value data, and the standard calorific value data is stored in the calorific value dataset.
[0006] Based on the standard calorific value data, the constituent gases of the sample gas are determined from the candidate gases; the candidate gases include: blast furnace gas, coke oven gas, and converter gas;
[0007] Based on the standard calorific value data and the unit volume heat data of the sample gas, the calorific value state volume data of the sample gas corresponding to the standard calorific value data is determined, and the calorific value state volume data is stored in the volume data set.
[0008] The machine learning model is trained based on the calorific value dataset, the volume dataset, and the composition of the sample gas. The gas consumption evaluation model is determined based on the training results. The gas consumption evaluation model is used to evaluate the consumption of the gas to be evaluated.
[0009] According to another aspect of the present invention, a gas consumption management device is provided, the device comprising:
[0010] The standard calorific value data acquisition module is used to standardize the initial calorific value data of the sample gas, determine the standard calorific value data, and store the standard calorific value data in the calorific value dataset.
[0011] The gas composition determination module is used to determine the composition of the sample gas from the candidate gases based on the standard calorific value data; the candidate gases include: blast furnace gas, coke oven gas, and converter gas;
[0012] The calorific value state volume determination module is used to determine the calorific value state volume data of the sample gas corresponding to the standard calorific value data based on the standard calorific value data and the unit volume heat data of the sample gas, and store the calorific value state volume data in the volume dataset.
[0013] The model training module is used to train the machine learning model based on the calorific value dataset, the volume dataset, and the composition of the sample gas, and to determine the gas consumption evaluation model based on the training results; the gas consumption evaluation model is used to evaluate the consumption of the gas to be evaluated.
[0014] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:
[0015] At least one processor; and
[0016] A memory communicatively connected to the at least one processor; wherein,
[0017] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the gas consumption management method according to any embodiment of the present invention.
[0018] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the gas consumption management method according to any embodiment of the present invention.
[0019] The technical solution of this invention standardizes the initial calorific value data of the sample gas to determine standard calorific value data, and stores the standard calorific value data in a calorific value dataset. Based on the standard calorific value data, the constituent gas of the sample gas is determined from candidate gases. Based on the standard calorific value data and the unit volume heat data of the sample gas, the calorific value state volume data of the sample gas corresponding to the standard calorific value data is determined, and the calorific value state volume data is stored in a volume dataset. Based on the calorific value dataset, the volume dataset, and the constituent gas of the sample gas, a machine learning model is trained, and a gas consumption evaluation model is determined based on the training results. The gas consumption evaluation model is used to evaluate the consumption of the gas to be evaluated. This solves the problem of inaccurate results from statistical calculations of gas consumption. The above solution fully considers the impact of gas calorific value data and gas volume heat data on gas consumption when managing gas consumption. When managing coal gas consumption, the standard calorific value data of the sample coal gas is stored in a calorific value dataset, and the calorific value volumetric data of the sample coal gas is stored in a volumetric dataset. This facilitates subsequent analysis of coal gas consumption based on the calorific value dataset and the volumetric dataset. Furthermore, a machine learning model is trained using the calorific value dataset, the volumetric dataset, and the composition of the sample coal gas to obtain a coal gas consumption evaluation model. This achieves automated evaluation of coal gas consumption, improving the efficiency and accuracy of the evaluation.
[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description
[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0022] Figure 1 A flowchart of a gas consumption management method provided in Embodiment 1 of the present invention;
[0023] Figure 2 This is a flowchart of a gas consumption management method provided in Embodiment 2 of the present invention;
[0024] Figure 3 This is a schematic diagram of a gas consumption management device provided in Embodiment 3 of the present invention;
[0025] Figure 4This is a schematic diagram of the structure of an electronic device provided in Embodiment 4 of the present invention. Detailed Implementation
[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0027] It should be noted that the terms "candidate" and "target," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "etc.", and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.
[0028] Example 1
[0029] Figure 1 This is a flowchart illustrating a gas consumption management method according to Embodiment 1 of the present invention. This embodiment is applicable to situations involving the management of gas consumption. The method can be executed by a gas consumption management device, which can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes:
[0030] S110. Standardize the initial calorific value data of the sample gas to determine the standard calorific value data, and store the standard calorific value data in the calorific value dataset.
[0031] The sample gas refers to the gas used in the heating furnace that was collected in advance.
[0032] It should be noted that the calorific value of coal gas is a fluctuating value, and generally has a range of fluctuation. For example, the calorific value of blast furnace gas fluctuates within a range of 0.003 GJ / m³. 3 ~0.004GJ / m 3 .
[0033] Specifically, the initial calorific value data of the sample gas is acquired using a calorific value acquisition instrument within the sampling period. The method for standardizing the initial calorific value data of the sample gas can be as follows: sum the initial calorific value data within the sampling period and calculate the average. A calorific value dataset is then set up to store the standard calorific value data. The average value of the calculated standard calorific value data is used as the standard calorific value data, and this standard calorific value data is stored in the calorific value dataset.
[0034] Optionally, the median of the initial calorific value data can be used as the standard calorific value data.
[0035] For example, a method for standardizing the initial calorific value data of sample gas can be as follows: determine the initial calorific value data of sample gas based on the gas parameter information of sample gas, filter the initial calorific value data to determine the effective calorific value data, determine the average effective calorific value of the effective calorific value data, and use the average effective calorific value as the standard calorific value data of sample gas.
[0036] The gas parameters of the sample gas may include: blast furnace gas combustion parameters, combustion air parameters, and chemical reaction parameters. The chemical reaction refers to the chemical reaction that occurs during the combustion of the sample gas and combustion air. Fuel analysis is performed on the sample gas based on these gas parameters to determine its initial calorific value. This initial calorific value data is then filtered to remove invalid data, and the remaining data is considered valid. For example, invalid data may be initial calorific value data that does not meet preset calorific value conditions. The valid calorific value data are summed and averaged; this average is then used as the standard calorific value of the sample gas.
[0037] Understandably, the above scheme can improve the reliability of the standard calorific value data of the sample gas.
[0038] S120. Based on the standard calorific value data, determine the composition of the sample gas from the candidate gas.
[0039] Candidate gases include: blast furnace gas, coke oven gas, and converter gas.
[0040] Specifically, generally speaking, the calorific value of blast furnace gas fluctuates within a range of 0.003 GJ / m³. 3 ~0.004GJ / m 3 The calorific value of coke oven gas fluctuates between 0.0170 and 0.0175 GJ / m³. 3 The calorific value of the converter gas fluctuates between 0.005 and 0.007 GJ / m³. 3Due to varying heating process requirements, the gas used in the heating furnace can be any one of the three types of gas: blast furnace gas, coke oven gas, and converter gas; a mixture of these three types; or a mixture of any two of these three types. The composition of the sample gas can be determined from the candidate gases based on standard calorific value data, the calorific value fluctuation range of blast furnace gas, coke oven gas, and converter gas.
[0041] S130. Based on the standard calorific value data and the unit volume heat data of the sample gas, determine the calorific value state volume data of the sample gas corresponding to the standard calorific value data, and store the calorific value state volume data in the volume data set.
[0042] The calorific value volume refers to the volume of the sample gas under standard calorific value data.
[0043] Specifically, the ratio of the standard calorific value data to the unit volume heat data of the sample gas is used as the calorific value volume data of the sample gas corresponding to the standard calorific value data. A volume dataset is set up to store the calorific value volume data, and the calorific value volume data is stored in the calorific value dataset.
[0044] S140. Based on the calorific value dataset, volume dataset, and composition of the sample gas, train the machine learning model and determine the gas consumption evaluation model based on the training results; the gas consumption evaluation model is used to evaluate the consumption of the gas to be evaluated.
[0045] Specifically, the standard calorific value data from the calorific value dataset, the calorific value-state volume data from the volume dataset, and the composition of the sample gas are divided into model training samples and model testing samples. The machine learning model is trained using the standard calorific value data, calorific value-state volume data, and composition of the sample gas from the model training samples. The trained machine learning model is then tested using the same standard calorific value data, calorific value-state volume data, and composition of the sample gas from the model testing samples. The accuracy of the trained machine learning model is determined based on the test results. If the accuracy of the trained machine learning model exceeds a preset accuracy threshold, it is used as the gas consumption evaluation model. This model is then used to evaluate the consumption of the gas to be evaluated.
[0046] For example, when evaluating the consumption of the gas to be evaluated based on the gas consumption evaluation model, the volume data to be evaluated and the composition of the gas to be evaluated can be determined based on the calorific value data to be evaluated of the gas to be evaluated; the calorific value data to be evaluated, the volume data to be evaluated, and the composition of the gas to be evaluated are used as input data for the gas consumption evaluation model, and the gas consumption evaluation data of the gas to be evaluated is determined based on the output data of the gas consumption evaluation model.
[0047] The above scheme provides a method for evaluating the gas consumption of the gas to be evaluated based on a gas consumption evaluation model, which improves the efficiency of evaluating gas consumption and can obtain accurate gas consumption evaluation data, making it easier for maintenance personnel to analyze and manage gas consumption based on the gas consumption evaluation data.
[0048] The technical solution provided in this embodiment standardizes the initial calorific value data of the sample gas to determine standard calorific value data, which is then stored in a calorific value dataset. Based on the standard calorific value data, the constituent gas of the sample gas is determined from candidate gases. Based on the standard calorific value data and the unit volume heat data of the sample gas, the calorific value state volume data of the sample gas corresponding to the standard calorific value data is determined, and this calorific value state volume data is stored in a volume dataset. A machine learning model is trained using the calorific value dataset, the volume dataset, and the constituent gas of the sample gas. A gas consumption evaluation model is determined based on the training results. This gas consumption evaluation model is used to evaluate the consumption of the gas to be evaluated. This solves the problem of inaccurate results from statistical calculations of gas consumption. The above solution fully considers the impact of gas calorific value data and gas volume heat data on gas consumption when managing gas consumption. When managing coal gas consumption, the standard calorific value data of the sample coal gas is stored in a calorific value dataset, and the calorific value volumetric data of the sample coal gas is stored in a volumetric dataset. This facilitates subsequent analysis of coal gas consumption based on the calorific value dataset and the volumetric dataset. Furthermore, a machine learning model is trained using the calorific value dataset, the volumetric dataset, and the composition of the sample coal gas to obtain a coal gas consumption evaluation model. This achieves automated evaluation of coal gas consumption, improving the efficiency and accuracy of the evaluation.
[0049] Example 2
[0050] Figure 2 This is a flowchart of a gas consumption management method provided in Embodiment 2 of the present invention. This embodiment optimizes the above embodiment and provides a preferred implementation method for training a machine learning model based on a calorific value dataset, a volume dataset, and the composition of sample gas, and determining a gas consumption evaluation model based on the training results. Specifically, as shown... Figure 2 As shown, the method includes:
[0051] S210 standardizes the initial calorific value data of the sample gas, determines the standard calorific value data, and stores the standard calorific value data in the calorific value dataset.
[0052] S220. Based on standard calorific value data, determine the composition of the sample gas from the candidate gas.
[0053] Candidate gases include: blast furnace gas, coke oven gas, and converter gas.
[0054] S230. Based on the standard calorific value data and the unit volume heat data of the sample gas, determine the calorific value state volume data of the sample gas corresponding to the standard calorific value data, and store the calorific value state volume data in the volume data set.
[0055] S240. Determine the standard calorific value difference of the standard calorific value data at a specified time in the standard calorific value data, and determine the volume difference of the calorific value state volume at a specified time in the volume data.
[0056] The specified time can be the time point when the calorific value data is collected for every two adjacent standard calorific value data in the value dataset.
[0057] Specifically, in the calorific value dataset, the interval between the calorific value data collection time points corresponding to every two adjacent standard calorific value data points is the same; similarly, in the volume dataset, the interval between the volume data collection time points corresponding to every two adjacent calorific value state volumes is also the same, and the calorific value data collection time points and volume data collection time points correspond. The standard calorific value difference is determined by pairwise subtraction of adjacent standard calorific value data points in the calorific value dataset at a specified time; similarly, the volume difference is determined by pairwise subtraction of calorific value state volumes in the volume dataset at a specified time.
[0058] S250. Determine the gas consumption evaluation data of the sample gas based on the standard calorific value difference and volume difference.
[0059] Among them, coal gas consumption evaluation data refers to evaluation information that assesses the consumption of coal gas.
[0060] Specifically, the ratio of the standard calorific value difference to the volume difference is calculated, and the gas consumption evaluation data of the sample gas is determined based on the ratio of the standard calorific value difference to the volume difference.
[0061] For example, a method for determining the gas consumption evaluation data of the sample gas may be: calculating the ratio of the standard calorific value difference to the volume difference, and determining the gas consumption evaluation level corresponding to the ratio; and using the gas consumption evaluation level as the gas consumption evaluation data of the sample gas.
[0062] The gas consumption evaluation data can be a specified gas consumption evaluation level, which can include: Level 1 consumption status, Level 2 consumption status, and Level 3 consumption status. Level 1 consumption status refers to the gas consumption evaluation data when the gas consumption is at its optimal level.
[0063] Specifically, the ratio of the standard calorific value difference to the volume difference is calculated, and the gas consumption evaluation data of the sample gas is determined based on this ratio. For example, a pre-defined correspondence between the gas consumption evaluation level and the ratio range can be established. The gas consumption evaluation data of the sample gas is then determined based on the ratio of the standard calorific value difference to the volume difference, and the pre-defined correspondence between the gas consumption evaluation level and the ratio range.
[0064] Understandably, determining the gas consumption evaluation level based on the ratio of the standard calorific value difference to the volume difference, and using the gas consumption evaluation level as the gas consumption evaluation data for the sample gas, achieves a quantitative assessment of gas consumption and improves the calculation efficiency of gas consumption evaluation data.
[0065] S260. Based on the standard calorific value difference, volume difference, gas consumption evaluation data, and the composition of sample gas, the machine learning model is trained to determine the gas consumption evaluation model; the gas consumption evaluation model is used to evaluate the consumption of the gas to be evaluated.
[0066] Specifically, the standard calorific value difference, volume difference, gas consumption evaluation data, and the composition of the sample gas are divided into model training samples and model test samples. The machine learning model is trained using the standard calorific value difference, volume difference, gas consumption evaluation data, and the composition of the sample gas from the model training samples. The trained machine learning model is then tested using the same standard calorific value difference, volume difference, gas consumption evaluation data, and the composition of the sample gas from the model test samples. The accuracy of the trained machine learning model is determined based on the test results. If the accuracy of the trained machine learning model is greater than a preset accuracy threshold, then the trained machine learning model is used as the gas consumption evaluation model; this model is then used to evaluate the consumption of the gas to be evaluated.
[0067] For example, the standard calorific value difference, volume difference, and composition of sample gas can be used as training data for the machine learning model, and the gas consumption evaluation data can be used as supervision data for the machine learning model; the machine learning model is trained under supervision based on the training data and supervision data, and the trained machine learning model is used as the gas consumption evaluation model.
[0068] The technical solution of this embodiment, when training a machine learning model to determine a gas consumption evaluation model, determines the gas consumption evaluation data of the sample gas based on the standard calorific value difference and the volume difference of the calorific value state volume of the standard calorific value data. Based on the standard calorific value difference, volume difference, gas consumption evaluation data, and the composition of the sample gas, the machine learning model is trained to determine the gas consumption evaluation model used to evaluate the consumption of the gas to be evaluated. This solution, when training the machine learning model to obtain the gas consumption evaluation model, fully considers the influence of the calorific value data, the calorific value state volume of the sample gas, and the composition of the sample gas on the gas consumption evaluation data. Training the machine learning model based on the standard calorific value difference, volume difference, gas consumption evaluation data, and the composition of the sample gas can improve the accuracy of the obtained gas consumption evaluation model.
[0069] Example 3
[0070] Figure 3 This is a schematic diagram of a gas consumption management device according to Embodiment 3 of the present invention. This embodiment is applicable to situations where gas consumption is managed. Figure 3 As shown, the gas consumption management device includes: a standard calorific value data acquisition module 310, a gas composition determination module 320, a calorific value state volume determination module 330, and a model training module 340.
[0071] The standard calorific value data acquisition module 310 is used to standardize the initial calorific value data of the sample gas, determine the standard calorific value data, and store the standard calorific value data in the calorific value dataset.
[0072] The gas composition determination module 320 is used to determine the composition of the sample gas from the candidate gases based on standard calorific value data; the candidate gases include: blast furnace gas, coke oven gas and converter gas;
[0073] The calorific value state volume determination module 330 is used to determine the calorific value state volume data of the sample gas corresponding to the standard calorific value data based on the standard calorific value data and the unit volume heat data of the sample gas, and store the calorific value state volume data in the volume dataset.
[0074] The model training module 340 is used to train the machine learning model based on the calorific value dataset, volume dataset, and composition of the sample gas, and to determine the gas consumption evaluation model based on the training results; the gas consumption evaluation model is used to evaluate the consumption of the gas to be evaluated.
[0075] The technical solution provided in this embodiment standardizes the initial calorific value data of the sample gas to determine standard calorific value data, which is then stored in a calorific value dataset. Based on the standard calorific value data, the constituent gas of the sample gas is determined from candidate gases. Based on the standard calorific value data and the unit volume heat data of the sample gas, the calorific value state volume data of the sample gas corresponding to the standard calorific value data is determined, and this calorific value state volume data is stored in a volume dataset. A machine learning model is trained using the calorific value dataset, the volume dataset, and the constituent gas of the sample gas. A gas consumption evaluation model is determined based on the training results. This gas consumption evaluation model is used to evaluate the consumption of the gas to be evaluated. This solves the problem of inaccurate results from statistical calculations of gas consumption. The above solution fully considers the impact of gas calorific value data and gas volume heat data on gas consumption when managing gas consumption. When managing coal gas consumption, the standard calorific value data of the sample coal gas is stored in a calorific value dataset, and the calorific value volumetric data of the sample coal gas is stored in a volumetric dataset. This facilitates subsequent analysis of coal gas consumption based on the calorific value dataset and the volumetric dataset. Furthermore, a machine learning model is trained using the calorific value dataset, the volumetric dataset, and the composition of the sample coal gas to obtain a coal gas consumption evaluation model. This achieves automated evaluation of coal gas consumption, improving the efficiency and accuracy of the evaluation.
[0076] For example, model training module 340 includes:
[0077] The standard calorific value difference determination unit is used to determine the standard calorific value difference of standard calorific value data at a specified time in the standard calorific value data, and to determine the volume difference of calorific value state volume at a specified time in the volume data;
[0078] The evaluation data determination unit is used to determine the gas consumption evaluation data of the sample gas based on the standard calorific value difference and volume difference.
[0079] The evaluation model determination unit is used to train the machine learning model based on the standard calorific value difference, volume difference, gas consumption evaluation data and the composition of sample gas, and to determine the gas consumption evaluation model.
[0080] For example, the evaluation model determination unit is specifically used for:
[0081] The standard calorific value difference, volume difference, and composition of sample gas were used as training data for the machine learning model, and the gas consumption evaluation data were used as supervision data for the machine learning model.
[0082] The machine learning model is trained under supervision based on training and supervision data, and the trained machine learning model is used as a gas consumption evaluation model.
[0083] For example, the evaluation data determination unit is specifically used for:
[0084] Calculate the ratio of the standard calorific value difference to the volume difference, and determine the gas consumption evaluation level corresponding to the ratio;
[0085] The gas consumption evaluation level is used as the gas consumption evaluation data for the sample gas.
[0086] For example, the standard calorific value data acquisition module 310 is specifically used for:
[0087] Based on the gas parameter information of the sample gas, the initial calorific value data of the sample gas is determined, and the initial calorific value data is filtered to determine the effective calorific value data.
[0088] Determine the average effective calorific value of the effective calorific value data, and use the average effective calorific value as the standard calorific value data of the sample gas.
[0089] For example, the above-mentioned gas consumption management device further includes:
[0090] The coal gas analysis module is used to determine the volume data and composition of the coal gas to be evaluated based on the calorific value data of the coal gas to be evaluated.
[0091] The evaluation data determination module is used to take the calorific value data to be evaluated, the volume data to be evaluated, and the composition of the gas to be evaluated as input data for the gas consumption evaluation model, and determine the gas consumption evaluation data of the gas to be evaluated based on the output data of the gas consumption evaluation model.
[0092] The gas consumption management device provided in this embodiment can be applied to the gas consumption management method provided in any of the above embodiments, and has the corresponding functions and beneficial effects.
[0093] Example 4
[0094] Figure 4 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.
[0095] like Figure 4As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.
[0096] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.
[0097] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as gas consumption management methods.
[0098] In some embodiments, the gas consumption management method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the gas consumption management method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the gas consumption management method by any other suitable means (e.g., by means of firmware).
[0099] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.
[0100] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.
[0101] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.
[0102] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).
[0103] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.
[0104] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.
[0105] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.
[0106] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.
Claims
1. A method for managing coal gas consumption, characterized in that, include: The initial calorific value data of the sample gas is standardized to determine the standard calorific value data, and the standard calorific value data is stored in the calorific value dataset. Based on the standard calorific value data, the constituent gases of the sample gas are determined from the candidate gases; the candidate gases include: blast furnace gas, coke oven gas, and converter gas; Based on the standard calorific value data and the unit volume heat data of the sample gas, the calorific value state volume data of the sample gas corresponding to the standard calorific value data is determined, and the calorific value state volume data is stored in the volume data set. The machine learning model is trained based on the calorific value dataset, the volume dataset, and the composition of the sample gas. The gas consumption evaluation model is determined based on the training results. The gas consumption evaluation model is used to evaluate the consumption of the gas to be evaluated. The process includes training a machine learning model based on the calorific value dataset, the volume dataset, and the composition of the sample gas, and determining a gas consumption evaluation model based on the training results, including: The standard calorific value difference of standard calorific value data at a specified time in the standard calorific value data is determined, and the volume difference of calorific value state volume at a specified time in the volume dataset is determined; wherein, the specified time in the standard calorific value data is the calorific value data collection time point corresponding to every two adjacent standard calorific value data in the calorific value dataset; the time interval between the calorific value data collection time points corresponding to every two adjacent standard calorific value data in the calorific value dataset is the same, and the time interval between the volume data collection time points corresponding to every two adjacent calorific value state volumes in the volume dataset is the same, and the calorific value data collection time points and the volume data collection time points correspond; Calculate the ratio of the standard calorific value difference to the volume difference, and determine the gas consumption evaluation level corresponding to the ratio; The gas consumption evaluation level is used as the gas consumption evaluation data of the sample gas. Based on the standard calorific value difference, the volume difference, the gas consumption evaluation data, and the composition of the sample gas, the machine learning model is trained to determine the gas consumption evaluation model. The standardization process for the initial calorific value data of the sample gas to determine the standard calorific value data includes: Based on the gas parameter information of the sample gas, the initial calorific value data of the sample gas is determined, and the initial calorific value is filtered to determine the effective calorific value data. The effective calorific value average of the effective calorific value data is determined, and the effective calorific value average is used as the standard calorific value data of the sample gas.
2. The method according to claim 1, characterized in that, Based on the standard calorific value difference, the volume difference, the gas consumption evaluation data, and the composition of the sample gas, a machine learning model is trained to determine the gas consumption evaluation model, including: The standard calorific value difference, the volume difference, and the composition of the sample gas are used as training data for the machine learning model, and the gas consumption evaluation data are used as supervision data for the machine learning model. The machine learning model is trained under supervision based on the training data and the supervision data, and the trained machine learning model is used as a gas consumption evaluation model.
3. The method according to claim 1, characterized in that, Also includes: Based on the calorific value data of the gas to be evaluated, determine the volume data of the gas to be evaluated and the composition of the gas to be evaluated. The calorific value data to be evaluated, the volume data to be evaluated, and the composition of the gas to be evaluated are used as input data for the gas consumption evaluation model, and the gas consumption evaluation data of the gas to be evaluated is determined based on the output data of the gas consumption evaluation model.
4. A gas consumption management device, characterized in that, include: The standard calorific value data acquisition module is used to standardize the initial calorific value data of the sample gas, determine the standard calorific value data, and store the standard calorific value data in the calorific value dataset. The gas composition determination module is used to determine the composition of the sample gas from the candidate gases based on the standard calorific value data; the candidate gases include: blast furnace gas, coke oven gas, and converter gas; The calorific value state volume determination module is used to determine the calorific value state volume data of the sample gas corresponding to the standard calorific value data based on the standard calorific value data and the unit volume heat data of the sample gas, and store the calorific value state volume data in the volume dataset. The model training module is used to train a machine learning model based on the calorific value dataset, the volume dataset, and the composition of the sample gas, and to determine a gas consumption evaluation model based on the training results; the gas consumption evaluation model is used to evaluate the consumption of the gas to be evaluated. The model training module includes: A standard calorific value difference determination unit is used to determine the standard calorific value difference of standard calorific value data at a specified time in the standard calorific value data, and to determine the volume difference of calorific value state volumes at a specified time in the volume dataset; wherein, the specified time in the standard calorific value data refers to the calorific value data collection time point corresponding to every two adjacent standard calorific value data in the calorific value dataset; the time interval between the calorific value data collection time points corresponding to every two adjacent standard calorific value data in the calorific value dataset is the same, and the time interval between the volume data collection time points corresponding to every two adjacent calorific value state volumes in the volume dataset is the same, and the calorific value data collection time point and the volume data collection time point correspond; The evaluation data determination unit is used to calculate the ratio of the standard calorific value difference to the volume difference, and determine the gas consumption evaluation level corresponding to the ratio; the gas consumption evaluation level is used as the gas consumption evaluation data of the sample gas. The evaluation model determination unit is used to train the machine learning model based on the standard calorific value difference, the volume difference, the gas consumption evaluation data, and the composition of the sample gas, and to determine the gas consumption evaluation model. The standard calorific value data acquisition module is specifically used for: determining the initial calorific value data of the sample gas based on the gas parameter information of the sample gas; filtering the initial calorific value data to determine the effective calorific value data; determining the average effective calorific value of the effective calorific value data; and using the average effective calorific value as the standard calorific value data of the sample gas.
5. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the gas consumption management method according to any one of claims 1-3.
6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the gas consumption management method according to any one of claims 1-3.