Decision-making method based on natural gas analysis data

Abstract

The invention discloses a decision-making method based on natural gas analysis data. The method comprises the following steps: acquiring natural gas component data and equipment transmission data, performing combustion based on the natural gas component data, acquiring combustion performance, and extracting influence data associated with the combustion performance according to the equipment transmission data and time sequence characteristics; a cross-model collaborative decision framework is constructed according to the natural gas component data, the equipment transmission data and the equipment maintenance strategy, and the natural gas supply efficiency is cooperatively adjusted according to the equipment state; and collecting decision implementation effect data, comparing the decision implementation effect data with historical data, carrying out quantitative evaluation on the decision effect data, and optimizing the decision method according to an evaluation result. Through automatic data acquisition, processing and analysis, the decision-making efficiency is improved, and the data model and the decision-making method can be adjusted in time according to the decision-making implementation effect, so that the decision-making method can adapt to constantly changing market environments and production conditions, and effectiveness and adaptability of decision-making are kept.

Description

Technical Field

[0001] The present invention relates to the field of natural gas data analysis, and in particular to a decision-making method based on natural gas analysis data. Background Art

[0002] Natural gas is primarily composed of methane (85%), with smaller amounts of ethane (9%), propane (3%), nitrogen (2%), and butane (1%). It is primarily used as a fuel and also as a feedstock for the production of chemicals such as acetaldehyde, acetylene, ammonia, carbon black, ethanol, formaldehyde, hydrocarbon fuels, hydrogenated oil, methanol, nitric acid, synthesis gas, and vinyl chloride.

[0003] At present, in the production, transportation, and sales of natural gas, data such as its composition, pressure, flow, and temperature contain a wealth of information. Effectively analyzing this data and making reasonable decisions based on the analysis results are crucial to ensuring the efficient, safe, and stable operation of the natural gas industry.

[0004] However, traditional decision-making methods based on natural gas analysis data present numerous challenges. For one thing, most rely on a single dimension of data for analysis. For example, adjusting pipeline pressure based solely on natural gas flow data ignores the impact of changes in natural gas composition on the relationship between flow and pressure, resulting in low accuracy and reliability in decision-making. Furthermore, existing decision-making methods lack the ability to dynamically process and update data in real time, making them unable to adapt to the complex and ever-changing conditions of natural gas production, transportation, and sales, and thus struggling to meet the demands of actual production operations. Summary of the Invention

[0005] Based on this, the purpose of the present invention is to provide a decision-making method based on natural gas analysis data, aiming to solve the problem that the current decision-making method of natural gas analysis data has a single analysis and lacks dynamic processing and real-time updating capabilities.

[0006] To achieve the above objectives, the present invention proposes a decision-making method based on natural gas analysis data, which includes: Obtaining natural gas composition data and equipment delivery data, performing combustion based on the natural gas composition data, and obtaining combustion performance, and extracting data influencing the combustion performance based on the equipment delivery data and time series features; Based on the natural gas composition data, equipment delivery data, and equipment maintenance strategies, a cross-model collaborative decision-making framework is constructed to collaboratively adjust the natural gas supply efficiency according to the equipment status; Collect decision-making implementation effect data, compare historical data, conduct quantitative evaluation of decision-making effect data, and optimize decision-making methods based on the evaluation results.

[0007] According to one aspect of the above technical solution, in the steps of obtaining natural gas composition data and equipment delivery data, performing combustion based on the natural gas composition data, and obtaining combustion performance, and extracting data influencing the combustion performance from time series features based on the equipment delivery data: Obtain the volume fraction of each component in natural gas, perform combustion based on the current natural gas composition data, and obtain combustion performance; At least the pressure, flow and temperature data of each node of the natural gas transmission pipeline are collected, and at least the vibration signal and pressure fluctuation of the transmission pipeline are collected through sensors installed in the transmission pipeline, and the equipment fault level is classified according to the transmission pipeline record; Extract equipment delivery data in different time periods, compare the impact of equipment delivery data in different time periods on combustion performance, and record the impact data.

[0008] According to one aspect of the above technical solution, the steps of constructing a cross-model collaborative decision-making framework based on the natural gas composition data, equipment delivery data, and equipment maintenance strategy, and collaboratively adjusting the natural gas supply efficiency according to the equipment status include: Based on the natural gas component data, a natural gas combustion model is constructed. The volume fraction of each component in the natural gas is recorded as , , ,in, Expressed as The volume proportion of the components; Calculating the Higher Heating Value of Natural Gas :

[0009] in, For natural gas The standard high calorific value of the components, is the model error term, reflecting the non-ideal combustion or component interaction; Based on the high calorific value of natural gas , build a natural gas combustion model :

[0010]

[0011] in, is the comprehensive index of combustion performance, and is the weight coefficient, is the combustion rate of the current component, For the optimal combustion speed, The target high calorific value upper limit, The volume fraction under standard conditions is The burning rate of the components, is the standard proportion of the current component in the standard natural gas composition, is 298K, is the mixed gas temperature, and is the fitting parameter.

[0012] According to one aspect of the above technical solution, after constructing the natural gas combustion model, a transportation model is established based on the pressure, flow and temperature data of each node of the natural gas transmission pipeline. :

[0013] in, is the standardized performance index of pressure, is the standardized performance index of flow, is the standardized performance index of temperature, is the transmission efficiency index, For security penalties, , , , and is the weight coefficient.

[0014] According to one aspect of the above technical solution, the steps after constructing the transport model include: collecting vibration signals of a natural gas transmission pipeline using a vibration sensor, performing bandpass filtering on the vibration signals to extract fault characteristic frequency bands, performing frequency domain feature extraction and time domain feature extraction based on the fault characteristic frequency bands, and converting the results of the frequency domain feature extraction and the results of the time domain feature extraction into an observation sequence; Establish a fault prediction model based on the observation sequence :

[0015] in, is the accuracy standardization indicator, is the recall standardization indicator, is the AUC value standardization indicator, is the computational efficiency index, is the false positive penalty item, , , , is the weight coefficient.

[0016] According to one aspect of the above technical solution, a cross-model collaborative decision-making framework is constructed based on the natural gas combustion model, transportation model, and fault prediction model:

[0017] in, is the decision variable vector, The proportion of natural gas components, is the transport parameter, For equipment maintenance strategy, represents the transposed vector;

[0018] in, Output vector for the frame;

[0019] in, is the objective function of the cross-model collaborative decision-making framework, expressed as maximizing comprehensive benefits. For the purpose of economic efficiency, To ensure safety, Reliable target for equipment , and is the weight coefficient.

[0020] According to one aspect of the above technical solution, in the step of collecting decision implementation effect data, comparing historical data, quantitatively evaluating the decision effect data, and optimizing the decision method based on the evaluation results: Acquire multiple sets of historical natural gas composition data, historical equipment delivery data, and historical equipment maintenance strategies, and based on the multiple sets of historical data, collect historical economic efficiency target data, historical delivery safety target data, and historical equipment reliability target data; Input multiple sets of the historical data into the cross-model collaborative decision-making framework, and obtain multiple sets of decision data, compare the multiple sets of decision data with historical economic efficiency target data, historical transportation safety target data, and historical equipment reliability target data, and compare the results to adjust the adoption number for optimizing the cross-model collaborative decision-making framework.

[0021] The present invention further proposes a decision-making system based on natural gas analysis data, which is used to implement the above-mentioned decision-making method based on natural gas analysis data. The system includes: An extraction module is used to obtain natural gas composition data and equipment delivery data, perform combustion based on the natural gas composition data, and obtain combustion performance, and extract influencing data related to the combustion performance based on the equipment delivery data and time series features; A collaborative module is used to build a cross-model collaborative decision-making framework based on the natural gas composition data, equipment delivery data, and equipment maintenance strategy, and collaboratively adjust the natural gas supply efficiency according to the equipment status; The optimization module is used to collect decision-making implementation effect data, compare historical data, conduct quantitative evaluation of decision-making effect data, and optimize the decision-making method based on the evaluation results.

[0022] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned decision-making method based on natural gas analysis data.

[0023] The present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the above-mentioned decision-making method based on natural gas analysis data is implemented.

[0024] In summary, a decision-making method based on natural gas analysis data proposed in the present invention greatly reduces the time of manual operation and analysis through automated data collection, processing and analysis, can quickly generate decision support information, meet the rapidly changing market needs of the natural gas industry, and improve decision-making efficiency; combines the professional knowledge and historical data of the natural gas industry to construct a data model, takes into account various factors of natural gas in various links, makes decisions more scientific and reasonable, can effectively reduce decision-making risks, and improve the economic benefits and market competitiveness of enterprises; through the decision evaluation and optimization links, the data model and decision-making method can be adjusted in time according to the decision implementation effect, so that the decision-making method can adapt to the ever-changing market environment and production conditions, and maintain the effectiveness and adaptability of the decision.

[0025] Additional aspects and advantages of the present invention will be given in part in the description which follows and in part will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of a decision-making method based on natural gas analysis data in Example 1 of the present invention; Figure 2 This is a schematic diagram of the structure of a decision-making system based on natural gas analysis data in Example 2 of the present invention; Figure 3 This is a structural block diagram of an electronic device in embodiment 4 of the present invention. DETAILED DESCRIPTION

[0027] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The drawings illustrate several embodiments of the present invention. However, the present invention may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and comprehensive understanding of the present invention.

[0028] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be an intermediate element. When an element is referred to as being "connected to" another element, it may be directly connected to the other element or there may be an intermediate element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one skilled in the art to which this invention pertains. The terms used in this specification of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0030] Example 1 like Figure 1 FIG. 1 is a flow chart of a decision-making method based on natural gas analysis data in a first embodiment of the present invention. The decision-making method based on natural gas analysis data includes the following steps S01 to S03, wherein: S01. Obtain natural gas component data and equipment delivery data, perform combustion based on the natural gas component data, and obtain combustion performance. According to the equipment delivery data, extract influencing data associated with the combustion performance using time series features.

[0031] Since natural gas is composed of various gases, such as CH4, C2H6, H 2S , CO2, etc. By adjusting the volume fraction of different component gases, the combustion performance indicators of natural gas can be adjusted, thereby improving the combustion effect of natural gas. Therefore, it is necessary to obtain the volume fraction of each component in natural gas, burn according to the current natural gas component data, and obtain the combustion performance.

[0032] At the same time, in order to study the impact of natural gas pipeline equipment data on natural gas transmission or combustion, at least the pressure, flow and temperature data of each node of the natural gas transmission pipeline are collected, and at least the vibration signal and pressure fluctuation of the transmission pipeline are collected through sensors installed in the transmission pipeline. According to the transmission pipeline ledger, the equipment failure level is divided, the equipment transmission data of different time periods are extracted, the impact of the equipment transmission data of different time periods on the combustion performance is compared, and the impact data is recorded.

[0033] S02. Based on the natural gas composition data, equipment delivery data, and equipment maintenance strategy, a cross-model collaborative decision-making framework is constructed to collaboratively adjust the natural gas supply efficiency according to the equipment status.

[0034] To analyze natural gas data and develop efficient and accurate strategies for natural gas, maximizing overall benefits while ensuring reliable equipment and safe transportation, this embodiment constructs natural gas combustion models, transportation models, and fault prediction models. These models consider natural gas composition data, equipment pressure, flow rate, and temperature, as well as safety performance. A cross-model collaborative decision-making framework is then used to make joint decisions, maximizing overall benefits.

[0035] Assume that natural gas consists of n components, such as methane (CH4), ethane (C2H6), propane (C3H8), nitrogen (N2), carbon dioxide (CO2), etc., and record the volume fraction of each component as , , ,in, Expressed as Based on the volume proportion of various components, a natural gas combustion model is constructed to calculate the high calorific value of natural gas. :

[0036] in, For natural gas The standard high calorific value of the components, is the model error term, reflecting the non-ideal combustion or component interaction, It can be obtained through laboratories or databases; Based on the high calorific value of natural gas , build a natural gas combustion model :

[0037]

[0038] in, is the comprehensive index of combustion performance, and is the weight coefficient, is the combustion rate of the current component, For the optimal combustion speed, The target high calorific value upper limit, The volume fraction under standard conditions is The burning rate of the components, is the standard proportion of the current component in the standard natural gas composition, is 298K, is the mixed gas temperature, and is the fitting parameter.

[0039] when <0.8× When the gas composition data is adjusted, an early warning is triggered, such as increasing the proportion of methane. is the de-ignition critical value.

[0040] After building the natural gas combustion model, a transportation model is established based on the pressure, flow and temperature data of each node of the natural gas transmission pipeline. :

[0041] in, is the standardized performance index of pressure, is the standardized performance index of flow, is the standardized performance index of temperature, is the transmission efficiency index, For security penalties, , , , and is the weight coefficient.

[0042] The natural gas transmission pipeline is divided into N sections. A node is set on each section to measure the pressure of the section:

[0043] in, Expressed as the pressure of the ith node, The average pressure of all pipeline nodes, is the pressure standard deviation, is the number of nodes, Measures the uniformity of pressure distribution, Indicates the relative deviation of the node pressure from the mean. The smaller the deviation, the The closer it is to 1, the smaller the pressure fluctuation is and the higher the delivery stability is.

[0044]

[0045] in, is the actual delivery flow, is the rated design flow of the natural gas transmission pipeline, is the flow fluctuation amplitude, is the average flow rate, Reflects traffic utilization, Reflects flow stability. When the actual flow is close to the rated value and the fluctuation is small, Approaching 1.

[0046]

[0047] in, is the temperature of the i-th node, For the optimal delivery temperature, is the temperature sensitivity coefficient, and the exponential function reflects the degree of penalty for temperature deviation from the optimal value. The closer , The closer it is to 1, the more the design takes into account the impact of temperature on transportation energy consumption. For example, if the temperature is too low, it will easily lead to hydrate formation, and if it is too high, it will increase heating energy consumption.

[0048] Further, The closer it is to 1, the higher the energy utilization efficiency.

[0049] The steps after building the conveying model include: collecting vibration signals of a natural gas transmission pipeline using a vibration sensor, performing bandpass filtering on the vibration signals to extract fault characteristic frequency bands, performing frequency domain feature extraction and time domain feature extraction based on the fault characteristic frequency bands, and converting the results of the frequency domain feature extraction and the results of the time domain feature extraction into an observation sequence; Establish a fault prediction model based on the observation sequence :

[0050] in, is the accuracy standardization indicator, is the recall standardization indicator, is the AUC value standardization indicator, is the computational efficiency index, is the false positive penalty item, , , , is the weight coefficient.

[0051] Based on the natural gas combustion model, transportation model, and fault prediction model, a cross-model collaborative decision-making framework is constructed:

[0052] in, is the decision variable vector, The proportion of natural gas components, is the transport parameter, For equipment maintenance strategy, represents the transposed vector;

[0053] in, Output vector for the frame;

[0054] in, is the objective function of the cross-model collaborative decision-making framework, expressed as maximizing comprehensive benefits. For the purpose of economic efficiency, To ensure safety, Reliable target for equipment , and is the weight coefficient.

[0055] S03. Collect decision-making implementation effect data, compare historical data, conduct quantitative evaluation of decision-making effect data, and optimize decision-making methods based on the evaluation results.

[0056] Acquire multiple sets of historical natural gas composition data, historical equipment delivery data, and historical equipment maintenance strategies, and based on the multiple sets of historical data, collect historical economic efficiency target data, historical delivery safety target data, and historical equipment reliability target data; Input multiple sets of the historical data into the cross-model collaborative decision-making framework, and obtain multiple sets of decision data, compare the multiple sets of decision data with historical economic efficiency target data, historical transportation safety target data, and historical equipment reliability target data, and compare the results to adjust the adoption number for optimizing the cross-model collaborative decision-making framework.

[0057] In summary, a decision-making method based on natural gas analysis data proposed in the present invention greatly reduces the time of manual operation and analysis through automated data collection, processing and analysis, can quickly generate decision support information, meet the rapidly changing market needs of the natural gas industry, and improve decision-making efficiency; combines the professional knowledge and historical data of the natural gas industry to construct a data model, takes into account various factors of natural gas in various links, makes decisions more scientific and reasonable, can effectively reduce decision-making risks, and improve the economic benefits and market competitiveness of enterprises; through the decision evaluation and optimization links, the data model and decision-making method can be adjusted in time according to the decision implementation effect, so that the decision-making method can adapt to the ever-changing market environment and production conditions, and maintain the effectiveness and adaptability of the decision.

[0058] Example 2 Another aspect of the present invention is to provide a decision-making system based on natural gas analysis data. Figure 2 , which is a schematic diagram of the structure of a decision-making system based on natural gas analysis data in a second embodiment of the present invention, includes: Extraction module 11, for obtaining natural gas composition data and equipment delivery data, performing combustion based on the natural gas composition data, and obtaining combustion performance, and extracting data related to the combustion performance based on the equipment delivery data and time series features; A collaboration module 12 is configured to construct a cross-model collaborative decision-making framework based on the natural gas composition data, equipment delivery data, and equipment maintenance strategy, and collaboratively adjust the natural gas supply efficiency according to the equipment status; The optimization module 13 is used to collect decision implementation effect data, compare historical data, conduct quantitative evaluation of the decision effect data, and optimize the decision method based on the evaluation results.

[0059] Since natural gas is composed of various gases, such as CH4, C2H6, H 2S , CO2, etc. By adjusting the volume fraction of different component gases, the combustion performance indicators of natural gas can be adjusted, thereby improving the combustion effect of natural gas. Therefore, it is necessary to obtain the volume fraction of each component in natural gas, burn according to the current natural gas component data, and obtain the combustion performance.

[0060] At the same time, in order to study the impact of natural gas pipeline equipment data on natural gas transmission or combustion, at least the pressure, flow and temperature data of each node of the natural gas transmission pipeline are collected, and at least the vibration signal and pressure fluctuation of the transmission pipeline are collected through sensors installed in the transmission pipeline. According to the transmission pipeline ledger, the equipment failure level is divided, the equipment transmission data of different time periods are extracted, the impact of the equipment transmission data of different time periods on the combustion performance is compared, and the impact data is recorded.

[0061] To analyze natural gas data and develop efficient and accurate strategies for natural gas, maximizing overall benefits while ensuring reliable equipment and safe transportation, this embodiment constructs natural gas combustion models, transportation models, and fault prediction models. These models consider natural gas composition data, equipment pressure, flow rate, and temperature, as well as safety performance. A cross-model collaborative decision-making framework is then used to make joint decisions, maximizing overall benefits.

[0062] Assume that natural gas consists of n components, such as methane (CH4), ethane (C2H6), propane (C3H8), nitrogen (N2), carbon dioxide (CO2), etc., and record the volume fraction of each component as , , ,in, Expressed as Based on the volume proportion of various components, a natural gas combustion model is constructed to calculate the high calorific value of natural gas. :

[0063] in, For natural gas The standard high calorific value of the components, is the model error term, reflecting the non-ideal combustion or component interaction, It can be obtained through laboratories or databases; Based on the high calorific value of natural gas , build a natural gas combustion model :

[0064]

[0065] in, is the comprehensive index of combustion performance, and is the weight coefficient, is the combustion rate of the current component, For the optimal combustion speed, The target high calorific value upper limit, The volume fraction under standard conditions is The burning rate of the components, is the standard proportion of the current component in the standard natural gas composition, is 298K, is the mixed gas temperature, and is the fitting parameter.

[0066] when <0.8× When the gas composition data is adjusted, an early warning is triggered, such as increasing the proportion of methane. is the de-ignition critical value.

[0067] After building the natural gas combustion model, a transportation model is established based on the pressure, flow and temperature data of each node of the natural gas transmission pipeline. :

[0068] in, is the standardized performance index of pressure, is the standardized performance index of flow, is the standardized performance index of temperature, is the transmission efficiency index, For security penalties, , , , and is the weight coefficient.

[0069] The natural gas transmission pipeline is divided into N sections. A node is set on each section to measure the pressure of the section:

[0070] in, Expressed as the pressure of the ith node, The average pressure of all pipeline nodes, is the pressure standard deviation, is the number of nodes, Measures the uniformity of pressure distribution, Indicates the relative deviation of the node pressure from the mean. The smaller the deviation, the The closer it is to 1, the smaller the pressure fluctuation is and the higher the delivery stability is.

[0071]

[0072] in, is the actual delivery flow, is the rated design flow of the natural gas transmission pipeline, is the flow fluctuation amplitude, is the average flow rate, Reflects traffic utilization, Reflects flow stability. When the actual flow is close to the rated value and the fluctuation is small, Approaching 1.

[0073]

[0074] in, is the temperature of the i-th node, For the optimal delivery temperature, is the temperature sensitivity coefficient, and the exponential function reflects the degree of penalty for temperature deviation from the optimal value. The closer , The closer it is to 1, the more the design takes into account the impact of temperature on transportation energy consumption. For example, if the temperature is too low, it will easily lead to hydrate formation, and if it is too high, it will increase heating energy consumption.

[0075] Further, The closer it is to 1, the higher the energy utilization efficiency.

[0076] The steps after building the conveying model include: collecting vibration signals of a natural gas transmission pipeline using a vibration sensor, performing bandpass filtering on the vibration signals to extract fault characteristic frequency bands, performing frequency domain feature extraction and time domain feature extraction based on the fault characteristic frequency bands, and converting the results of the frequency domain feature extraction and the results of the time domain feature extraction into an observation sequence; Establish a fault prediction model based on the observation sequence :

[0077] in, is the accuracy standardization indicator, is the recall standardization indicator, is the AUC value standardization indicator, is the computational efficiency index, is the false positive penalty item, , , , is the weight coefficient.

[0078] Based on the natural gas combustion model, transportation model, and fault prediction model, a cross-model collaborative decision-making framework is constructed:

[0079] in, is the decision variable vector, The proportion of natural gas components, is the transport parameter, For equipment maintenance strategy, represents the transposed vector;

[0080] in, Output vector for the frame;

[0081] in, is the objective function of the cross-model collaborative decision-making framework, expressed as maximizing comprehensive benefits. For the purpose of economic efficiency, To ensure safety, Reliable target for equipment , and is the weight coefficient.

[0082] Acquire multiple sets of historical natural gas composition data, historical equipment delivery data, and historical equipment maintenance strategies, and based on the multiple sets of historical data, collect historical economic efficiency target data, historical delivery safety target data, and historical equipment reliability target data; Input multiple sets of the historical data into the cross-model collaborative decision-making framework, and obtain multiple sets of decision data, compare the multiple sets of decision data with historical economic efficiency target data, historical transportation safety target data, and historical equipment reliability target data, and compare the results to adjust the adoption number for optimizing the cross-model collaborative decision-making framework.

[0083] In summary, a decision-making system based on natural gas analysis data proposed in the present invention greatly reduces the time of manual operation and analysis through automated data collection, processing and analysis, can quickly generate decision support information, meet the rapidly changing market needs of the natural gas industry, and improve decision-making efficiency; combines the professional knowledge and historical data of the natural gas industry to build a data model, takes into account the various factors of natural gas in various links, makes decisions more scientific and reasonable, can effectively reduce decision-making risks, and improve the economic benefits and market competitiveness of enterprises; through the decision evaluation and optimization links, can timely adjust the data model and decision-making method according to the decision implementation effect, so that the decision-making method can adapt to the ever-changing market environment and production conditions, and maintain the effectiveness and adaptability of the decision.

[0084] Example 3 Another aspect of the present invention further provides a computer-readable storage medium having one or more computer programs stored thereon, which implement the above-mentioned decision-making method based on natural gas analysis data when executed by a processor.

[0085] Those skilled in the art will appreciate that the logic or steps represented in a flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable storage medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, a "computer-readable storage medium" can be any device that can contain, store, communicate, propagate, or transmit a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0086] More specific examples (a non-exhaustive list) of computer-readable storage media include the following: an electrical connection with one or more wires (electronic device), a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable storage medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0087] Example 4 Figure 3This is a structural block diagram of an electronic device provided in Example 4. The electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the decision-making method based on natural gas analysis data in the above embodiment is implemented. Figure 3 The electronic device 30 shown is only an example and should not limit the functionality and scope of use of the embodiments of the present invention.

[0088] like Figure 3 As shown, the electronic device 30 may be a general-purpose computing device, such as a server device. Components of the electronic device 30 may include, but are not limited to, the at least one processor 31, the at least one memory 32, and a bus 33 connecting different system components (including the memory 32 and the processor 31).

[0089] The bus 33 includes a data bus, an address bus, and a control bus.

[0090] The memory 32 may include a volatile memory, such as a RAM 321 (Random Access Memory), and / or a cache memory 322 , and may further include a ROM 323 (Read Only Memory).

[0091] The memory 32 may also include a program tool 325 having a set (at least one) of program modules 324, such program modules 324 including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.

[0092] The processor 31 executes various functional applications and data processing by running computer programs stored in the memory 32 , such as the decision-making method based on natural gas analysis data as described above in the present invention.

[0093] The electronic device 30 can also communicate with one or more external devices 34 (e.g., keyboard, pointing device, etc.). Such communication can be performed via an I / O interface 35 (input / output interface). In addition, the model generating electronic device 30 can also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via a network adapter 36. Figure 3 As shown, the network adapter 36 communicates with other modules of the model-generated electronic device 30 via the bus 33. It should be understood that, although not shown in the figures, other hardware and / or software modules may be used in conjunction with the model-generated electronic device 30, including but not limited to microcode, device drivers, redundant processors, disk drive arrays, RAID (RAID) systems, tape drives, and data backup storage systems.

[0094] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of the present invention, the features and functions of two or more units / modules described above may be embodied in a single unit / module. Conversely, the features and functions of a single unit / module described above may be further divided and embodied by multiple units / modules.

[0095] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0096] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

Claims

1. A decision-making method based on natural gas analysis data, characterized in that: The decision-making method based on natural gas analysis data includes: Obtaining natural gas composition data and equipment delivery data, performing combustion based on the natural gas composition data, and obtaining combustion performance, and extracting data influencing the combustion performance based on the equipment delivery data and time series features; Based on the natural gas composition data, equipment delivery data, and equipment maintenance strategies, a cross-model collaborative decision-making framework is constructed to collaboratively adjust the natural gas supply efficiency according to the equipment status; Collect decision-making implementation effect data, compare historical data, conduct quantitative evaluation of decision-making effect data, and optimize decision-making methods based on the evaluation results.

2. The decision-making method based on natural gas analysis data according to claim 1, characterized in that: In the step of obtaining natural gas component data and equipment delivery data, performing combustion based on the natural gas component data, and obtaining combustion performance, and extracting data influencing the combustion performance based on the equipment delivery data through time series features: Obtain the volume fraction of each component in natural gas, perform combustion based on the current natural gas composition data, and obtain combustion performance; At least the pressure, flow and temperature data of each node of the natural gas transmission pipeline are collected, and at least the vibration signal and pressure fluctuation of the transmission pipeline are collected through sensors installed in the transmission pipeline, and the equipment fault level is classified according to the transmission pipeline record; Extract equipment delivery data in different time periods, compare the impact of equipment delivery data in different time periods on combustion performance, and record the impact data.

3. The decision-making method based on natural gas analysis data according to claim 1, characterized in that: The steps of constructing a cross-model collaborative decision-making framework based on the natural gas composition data, equipment delivery data, and equipment maintenance strategy, and collaboratively adjusting the natural gas supply efficiency according to the equipment status include: Based on the natural gas component data, a natural gas combustion model is constructed. The volume fraction of each component in the natural gas is recorded as , , ,in, Expressed as The volume proportion of the components; Calculating the Higher Heating Value of Natural Gas : in, For natural gas The standard high calorific value of the components, is the model error term, reflecting the non-ideal combustion or component interaction; Based on the high calorific value of natural gas , build a natural gas combustion model : in, is the comprehensive index of combustion performance, and is the weight coefficient, is the combustion rate of the current component, For the optimal combustion speed, The target high calorific value upper limit, The volume fraction under standard conditions is The burning rate of the components, is the standard proportion of the current component in the standard natural gas composition, is 298K, is the mixed gas temperature, and is the fitting parameter.

4. The decision-making method based on natural gas analysis data according to claim 3, characterized in that: After building the natural gas combustion model, a transportation model is established based on the pressure, flow and temperature data of each node of the natural gas transmission pipeline. : in, is the standardized performance index of pressure, is the standardized performance index of flow, is the standardized performance index of temperature, is the transmission efficiency index, For security penalties, , , , and is the weight coefficient.

5. The decision-making method based on natural gas analysis data according to claim 4, characterized in that: The steps after building the conveying model include: collecting vibration signals of a natural gas transmission pipeline using a vibration sensor, performing bandpass filtering on the vibration signals to extract fault characteristic frequency bands, performing frequency domain feature extraction and time domain feature extraction based on the fault characteristic frequency bands, and converting the results of the frequency domain feature extraction and the results of the time domain feature extraction into an observation sequence; Establish a fault prediction model based on the observation sequence : in, is the accuracy standardization indicator, is the recall standardization indicator, is the AUC value standardization indicator, is the computational efficiency index, is the false positive penalty item, , , , is the weight coefficient.

6. The decision-making method based on natural gas analysis data according to claim 5, characterized in that: Based on the natural gas combustion model, transportation model, and fault prediction model, a cross-model collaborative decision-making framework is constructed: in, is the decision variable vector, The proportion of natural gas components, is the transport parameter, For equipment maintenance strategy, represents the transposed vector; in, Output vector for the frame; in, is the objective function of the cross-model collaborative decision-making framework, expressed as maximizing comprehensive benefits. For the purpose of economic efficiency, To ensure safety, Reliable target for equipment , and is the weight coefficient.

7. The decision-making method based on natural gas analysis data according to claim 1, characterized in that: In the step of collecting decision implementation effect data, comparing historical data, quantitatively evaluating the decision effect data, and optimizing the decision method based on the evaluation results: Acquire multiple sets of historical natural gas composition data, historical equipment delivery data, and historical equipment maintenance strategies, and based on the multiple sets of historical data, collect historical economic efficiency target data, historical delivery safety target data, and historical equipment reliability target data; Input multiple sets of the historical data into the cross-model collaborative decision-making framework, and obtain multiple sets of decision data, compare the multiple sets of decision data with historical economic efficiency target data, historical transportation safety target data, and historical equipment reliability target data, and compare the results to adjust the adoption number for optimizing the cross-model collaborative decision-making framework.

8. A decision-making system based on natural gas analysis data, characterized in that: The decision-making system based on natural gas analysis data is used to implement the decision-making method based on natural gas analysis data according to any one of claims 1 to 7, and the system includes: An extraction module is used to obtain natural gas composition data and equipment delivery data, perform combustion based on the natural gas composition data, and obtain combustion performance, and extract influencing data related to the combustion performance based on the equipment delivery data and time series features; A collaborative module is used to build a cross-model collaborative decision-making framework based on the natural gas composition data, equipment delivery data, and equipment maintenance strategy, and collaboratively adjust the natural gas supply efficiency according to the equipment status; The optimization module is used to collect decision-making implementation effect data, compare historical data, conduct quantitative evaluation of decision-making effect data, and optimize the decision-making method based on the evaluation results.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the decision-making method based on natural gas analysis data as described in any one of claims 1 to 7 is implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the decision-making method based on natural gas analysis data according to any one of claims 1 to 7 is implemented.

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