A data determination method, apparatus, electronic device, and storage medium

Abstract

This invention discloses a data determination method, apparatus, electronic device, and storage medium, relating to the field of data processing technology. The method includes: determining historical total consumption fluctuation coefficients and predicted total consumption fluctuation coefficients based on natural gas consumption data; determining historical total inbound volume fluctuation coefficients; calculating predicted total inbound volume fluctuation coefficients based on the linear correlation between historical total consumption fluctuation coefficients and historical total inbound volume fluctuation coefficients; determining historical inbound volume fluctuation coefficients based on historical inbound volume data for each pipeline type; determining a linear regression equation between historical total inbound volume fluctuation coefficients and historical inbound volume fluctuation coefficients for each pipeline type; calculating predicted inbound volume fluctuation coefficients for each pipeline type using the linear regression equation; determining the primary pipeline base load based on the pipeline topology of the region to which the pipeline type belongs; determining peak-shaving margin coefficients and emergency margin coefficients; and determining the pipeline transmission capacity based on the primary pipeline base load, peak-shaving margin coefficient, and emergency margin coefficient for each pipeline type.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a data determination method, apparatus, electronic device, and storage medium. Background Technology

[0002] Pipeline transmission capacity, also known as primary pipeline capacity, refers to the total gas transmission capacity of the main pipeline network connecting gas sources within a certain time period. Pipeline margin reflects the redundancy capacity to cope with predictable peak demand and unpredictable events.

[0003] Existing technologies construct a global pipeline network topology, consider station and pipeline constraints, and employ nonlinear optimization models to solve for the maximum pipeline network's transport capacity, following a local-to-global approach. Alternatively, existing technologies can establish a Mixed-Integer Linear Programming (MILP) model and constraints for the natural gas pipeline network based on pipeline, pipe material, gas storage, economic, and supply-demand data. This model, based on a cost objective function, uses a branch-and-bound algorithm-based MILP solver to solve for the multi-cycle natural gas pipeline network's transport capacity. However, different technical personnel and institutions have inconsistent understandings of the parameters, and their statistical definitions, calculation methods, and analytical results are inconsistent. Therefore, they cannot accurately describe the transport capacity of the national natural gas pipeline network, making it difficult to scientifically assess the reliability of energy supply security. Furthermore, existing technologies do not consider the structural and functional characteristics of different pipelines, potentially leading to discrepancies between the calculated results and actual conditions. Therefore, accurately determining primary pipeline transport capacity has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a data determination method, apparatus, electronic device, and storage medium to solve the problem of inaccurate determination of primary pipeline capacity in the prior art.

[0005] According to one aspect of the present invention, a data determination method is provided, wherein the method includes:

[0006] Based on the natural gas consumption data of all pipeline types, determine the historical total consumption fluctuation coefficient and the predicted total consumption fluctuation coefficient for the target statistical period respectively;

[0007] The historical total inflow fluctuation coefficient is determined based on the historical inflow data of all pipeline types, and the linear correlation between the historical total consumption fluctuation coefficient and the historical total inflow fluctuation coefficient is determined.

[0008] The predicted total consumption fluctuation coefficient is input into the linear correlation relationship, and the predicted total grid connection fluctuation coefficient corresponding to the predicted total consumption fluctuation coefficient is calculated according to the linear correlation relationship.

[0009] Based on the historical inflow data of each pipeline type, determine the historical inflow fluctuation coefficient of each pipeline type, and determine the linear regression equation between the historical total inflow fluctuation coefficient and the historical inflow fluctuation coefficient of each pipeline type.

[0010] Input the predicted total inflow fluctuation coefficient into the linear regression equation to calculate the predicted inflow fluctuation coefficient for each pipeline network type.

[0011] The primary pipeline load for each pipeline type is determined based on the pipeline topology of the region to which each pipeline type belongs; wherein, the pipeline topology is used to characterize the connection relationship between the gas source and the gas source export pipeline;

[0012] The peak-shaving margin coefficient is determined based on the predicted inflow fluctuation coefficient for each pipeline type, and the emergency margin coefficient is determined based on the extreme operating condition impact coefficient for each pipeline type.

[0013] The pipeline transmission capacity is determined based on the primary pipeline base load, the peak shaving margin coefficient, and the emergency margin coefficient for each pipeline type.

[0014] According to another aspect of the present invention, a data determination apparatus is provided, wherein the apparatus comprises:

[0015] The first coefficient determination module is used to determine the historical total consumption fluctuation coefficient and the predicted total consumption fluctuation coefficient corresponding to the target statistical period based on the natural gas consumption data of all pipeline types.

[0016] The linear relationship determination module is used to determine the historical total inbound volume fluctuation coefficient based on the historical inbound volume data of all pipeline types, and to determine the linear correlation between the historical total consumption fluctuation coefficient and the historical total inbound volume fluctuation coefficient.

[0017] The second coefficient determination module is used to input the predicted total consumption fluctuation coefficient into the linear correlation relationship, and calculate the predicted total grid connection fluctuation coefficient corresponding to the predicted total consumption fluctuation coefficient according to the linear correlation relationship.

[0018] The regression equation determination module is used to determine the historical inflow fluctuation coefficient of each pipeline network type based on the historical inflow data of each pipeline network type, and to determine the linear regression equation between the historical total inflow fluctuation coefficient and the historical inflow fluctuation coefficient of each pipeline network type.

[0019] The third coefficient determination module is used to input the predicted total inflow fluctuation coefficient into the linear regression equation and calculate the predicted inflow fluctuation coefficient for each pipeline type.

[0020] The pipeline base load determination module is used to determine the primary pipeline base load for each pipeline type based on the pipeline topology of the region to which each pipeline type belongs; wherein, the pipeline topology is used to characterize the connection relationship between the gas source and the gas source export pipeline;

[0021] The fourth coefficient determination module is used to determine the peak shaving margin coefficient based on the predicted inflow fluctuation coefficient for each pipeline type, and to determine the emergency margin coefficient based on the extreme operating condition impact coefficient for each pipeline type.

[0022] The transmission capacity determination module is used to determine the transmission capacity of the pipeline network based on the primary pipeline base load, the peak shaving margin coefficient, and the emergency margin coefficient for each pipeline network type.

[0023] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0024] At least one processor; and

[0025] A memory communicatively connected to the at least one processor; wherein,

[0026] 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 a data determination method according to any embodiment of the present invention.

[0027] 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 a data determination method according to any embodiment of the present invention.

[0028] The technical solution of this invention involves determining the historical total consumption fluctuation coefficient and the predicted total consumption fluctuation coefficient for the target statistical period based on natural gas consumption data for all pipeline types. It also involves determining the historical total inbound volume fluctuation coefficient based on historical inbound volume data for all pipeline types, establishing a linear correlation between the historical total consumption fluctuation coefficient and the historical total inbound volume fluctuation coefficient, inputting the predicted total consumption fluctuation coefficient into the linear correlation, calculating the predicted total inbound volume fluctuation coefficient corresponding to the predicted total consumption fluctuation coefficient according to the linear correlation, determining the historical inbound volume fluctuation coefficient for each pipeline type based on historical inbound volume data, establishing a linear regression equation between the historical total inbound volume fluctuation coefficient and the historical inbound volume fluctuation coefficient for each pipeline type, and inputting the predicted total inbound volume fluctuation coefficient into the linear regression equation. The system employs equations to calculate the predicted inflow fluctuation coefficient for each pipeline network type, improving the accuracy of determining this coefficient. It also determines the primary pipeline base load for each network type based on its regional network topology, achieving accurate determination of the primary pipeline base load. The network topology characterizes the connection between gas sources and their external pipelines. Peak shaving margin coefficients are determined based on the predicted inflow fluctuation coefficients for each network type, and emergency margin coefficients are determined according to the extreme operating condition influence coefficients for each network type. Finally, the system determines the network transmission capacity based on the primary pipeline base load, peak shaving margin coefficient, and emergency margin coefficient for each network type, enabling separate calculations of the network transmission capacity for each network type. This approach considers the structural and functional characteristics of different network types, improving the accuracy of determining the primary pipeline capacity.

[0029] 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

[0030] 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.

[0031] Figure 1 This is a flowchart of a data determination method provided according to Embodiment 1 of the present invention;

[0032] Figure 2 This is a flowchart of a data determination method provided according to Embodiment 2 of the present invention;

[0033] Figure 3 This is an example diagram of a pipeline topology provided in Embodiment 3 of the present invention;

[0034] Figure 4 This is a schematic diagram of the structure of a data determination device according to Embodiment 3 of the present invention;

[0035] Figure 5 This is a schematic diagram of the structure of an electronic device that implements a data determination method according to an embodiment of the present invention. Detailed Implementation

[0036] 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.

[0037] It should be noted that the terms "first," "second," 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 the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," 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.

[0038] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and their purpose is only to illustrate the feasibility of implementing the technical solution of this application.

[0039] Example 1

[0040] Figure 1 This is a flowchart of a data determination method according to Embodiment 1 of the present invention. This embodiment is applicable to determining the primary transmission capacity of a natural gas pipeline network. The method can be executed by a data determination 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:

[0041] S110. Based on the natural gas consumption data of all pipeline types, determine the historical total consumption fluctuation coefficient and the predicted total consumption fluctuation coefficient corresponding to the target statistical period.

[0042] Pipeline network type refers to different categories of pipeline channels. Generally, pipeline network types can be classified based on spatial distribution characteristics, the topology of the main gas transmission pipeline network, and functional positioning attributes. In one embodiment, pipeline network types include at least onshore gas supply channels, onshore gas consumption channels, and offshore gas supply channels. Each type of pipeline channel includes several gas source areas and non-gas source areas. Non-gas source areas refer to pipeline channels that are not directly connected to gas sources, and channels not directly connected to gas sources are not included in the primary pipeline transmission capacity. For convenient data statistics, the overall pipeline network can be divided into three types according to the main gas supply and consumption functions undertaken by the pipeline channels: onshore gas supply channels, onshore gas consumption channels, and offshore gas supply channels. Generally, onshore gas consumption channels are usually located in the central and eastern regions, and can also be called central and eastern onshore channels. They are located in load centers, have high market peak-shaving demand, and need to have strong emergency and supply guarantee capabilities. In one example, onshore gas supply channels, typically located outside the central and eastern regions, and also referred to as non-central and eastern onshore channels, can include onshore imports and resource loading channels from major gas fields. These channels have large primary gas source channels, high peak-shaving capacity, and strong seasonal fluctuations. For example, non-central and eastern onshore channels may include, but are not limited to, the China-Russia East Route Gas Pipeline and the West-to-East Gas Pipeline. Central and eastern onshore channels can include resource collection and distribution channels within the central and eastern regions. For example, central and eastern onshore channels may include, but are not limited to, the Sichuan-to-East Gas Pipeline. Offshore gas supply channels can include liquefied natural gas (LNG) resource loading channels. Offshore gas supply channels are close to load centers, have strong daily peak-shaving capacity, and their transmission volume is highly sensitive to price.

[0043] Natural gas consumption data can be understood as the national natural gas consumption situation, or simply the consumption situation of natural gas. Generally, natural gas consumption data includes historical natural gas consumption data and predicted natural gas consumption data. Historical data refers to actual data that has already occurred, while predicted data refers to data determined based on demand planning. Natural gas consumption data can be obtained through publicly released designated websites or documents. The target statistical period can be understood as a pre-set time period used to statistically analyze the fluctuations in natural gas consumption data. The target statistical period can be customized according to business needs. In one embodiment, the target statistical period may include, but is not limited to, six months, one year, two years, etc. The historical total consumption fluctuation coefficient can be used to indicate the degree of imbalance in historical natural gas consumption data within the target statistical period. When the target statistical period is one year, the historical total consumption fluctuation coefficient can be an indicator measuring the degree of fluctuation difference between different months of historical natural gas consumption data within one year. The predicted total consumption fluctuation coefficient can be used to indicate the degree of imbalance in predicted natural gas consumption data within the target statistical period. When the target statistical period is one year, the predicted total consumption fluctuation coefficient can be an indicator measuring the degree of fluctuation difference between different months of predicted natural gas consumption data within one year. The target statistical periods for determining the historical total consumption volatility coefficient and the predicted total consumption volatility coefficient are of the same length, but they can be the same or different time periods. For example, the target statistical period for determining the historical total consumption volatility coefficient could be 2022, and the target statistical period for determining the predicted total consumption volatility coefficient could be 2023. The time period length is the same, but the specific time periods can be different.

[0044] In this embodiment, historical and predicted natural gas consumption data for all pipeline types can be obtained from publicly released data from institutions or enterprises. In other words, historical and predicted natural gas consumption data for all pipeline channels are used as the natural gas consumption data. The historical total consumption fluctuation coefficient is determined based on the historical natural gas consumption data, and the predicted total consumption fluctuation coefficient is determined based on the predicted natural gas consumption data. In actual operation, historical natural gas consumption data corresponding to the target statistical period can be extracted, and the maximum and average values ​​of historical natural gas consumption data within the target statistical period can be determined. The ratio of the maximum value to the average value is then used as the historical total consumption fluctuation coefficient. Similarly, predicted natural gas consumption data corresponding to the target statistical period can be extracted, and the maximum and average values ​​of predicted natural gas consumption data within the target statistical period can be determined. The ratio of the maximum value to the average value is then used as the predicted total consumption fluctuation coefficient.

[0045] S120. Determine the historical total inflow fluctuation coefficient based on the historical inflow data of all pipeline types, and determine the linear correlation between the historical total consumption fluctuation coefficient and the historical total inflow fluctuation coefficient.

[0046] Historical inflow data refers to the amount of natural gas entering the pipeline network for each pipeline type at a specific historical moment. Generally, historical inflow data can be presented monthly, representing the monthly inflow at a given historical moment. The historical total inflow fluctuation coefficient indicates the degree of imbalance in historical inflow data for all pipeline types within a target statistical period. When the target statistical period is one year, the historical total inflow fluctuation coefficient can measure the degree of fluctuation difference between different months of the year for all pipeline types. Linear correlation describes the relationship between the historical total consumption fluctuation coefficient and the historical total inflow fluctuation coefficient. In practice, a regression equation can be constructed to indicate this linear correlation. Generally, the linear correlation between the historical total consumption fluctuation coefficient and the historical total inflow fluctuation coefficient can be a univariate linear relationship.

[0047] In this embodiment, the historical total inbound volume fluctuation coefficient can be determined using historical inbound volume data for all pipeline types, and a linear correlation between the historical total consumption fluctuation coefficient and the historical total inbound volume fluctuation coefficient can be established. In actual operation, historical inbound volume data for all pipeline types within the target statistical period can be extracted. The sum of historical inbound volume data for each pipeline type at the same time is taken as the historical total inbound volume data, i.e., the historical inbound volume data for all pipeline types at the same time. The maximum and average values ​​of the historical total inbound volume data within the target statistical period are determined, and the ratio of the maximum value to the average value is taken as the historical total inbound volume fluctuation coefficient. Then, the historical total inbound volume fluctuation coefficient and historical total consumption fluctuation coefficient for the same target statistical period are determined, such as the historical total inbound volume fluctuation coefficient and historical total consumption fluctuation coefficient belonging to the same year. The linear relationship between the historical total inbound volume fluctuation coefficient and the historical total consumption fluctuation coefficient is determined, and this linear relationship is taken as the linear correlation. Alternatively, a regression equation can be determined based on the linear relationship to construct a regression model, and this regression model is taken as the linear correlation.

[0048] S130. Input the predicted total consumption fluctuation coefficient into the linear correlation relationship, and calculate the predicted total grid connection fluctuation coefficient corresponding to the predicted total consumption fluctuation coefficient according to the linear correlation relationship.

[0049] Among them, the predicted total inbound volume fluctuation coefficient can be understood as the total inbound volume fluctuation coefficient of all pipeline types.

[0050] In this embodiment, the predicted total consumption fluctuation coefficient can be input into a linear correlation relationship, and the predicted grid connection fluctuation coefficient corresponding to the predicted total consumption fluctuation coefficient can be calculated according to the linear correlation relationship. That is, the predicted total consumption fluctuation coefficient is input into a regression equation of historical total consumption fluctuation coefficients and historical total grid connection fluctuation coefficients, and the predicted total grid connection fluctuation coefficient is calculated through the regression equation. Within a set statistical period, the linear relationship between the grid connection fluctuation coefficient and the consumption fluctuation coefficient can be considered to conform to a set pattern; therefore, the linear correlation relationship determined by historical data can be applied to determine the predicted total grid connection fluctuation coefficient in the predicted data.

[0051] S140. Determine the historical inflow fluctuation coefficient for each pipeline type based on the historical inflow data, and determine the linear regression equation between the historical total inflow fluctuation coefficient and the historical inflow fluctuation coefficient for each pipeline type.

[0052] The predicted grid connection fluctuation coefficient can be understood as the fluctuation of grid connection volume for each type of pipeline within a given prediction period, with a different predicted grid connection fluctuation coefficient determined for each pipeline type. Generally, the predicted grid connection fluctuation coefficient for each pipeline type can be correlated with the predicted total grid connection fluctuation coefficient for all pipeline types. A linear regression equation can be used to describe the correlation between the historical total grid connection fluctuation coefficient and the historical grid connection fluctuation coefficient for each pipeline type. Generally, the correlation between the historical total grid connection fluctuation coefficient and the historical grid connection fluctuation coefficient for each pipeline type can be a univariate linear relationship. Correspondingly, the linear regression equation can be a linear equation in one variable. This linear regression equation, determined based on historical data, can also be applied to determine the predicted grid connection fluctuation coefficient in the prediction data.

[0053] In this embodiment, historical inflow data for each pipeline type within a target statistical period can be extracted. The average and maximum values ​​of the historical inflow data within the target statistical period are determined, and the ratio of the maximum value to the average value is used as the historical inflow fluctuation coefficient for the corresponding pipeline type. Then, for each pipeline type, the historical total inflow fluctuation coefficient and the historical inflow fluctuation coefficient belonging to the same target period are determined. These historical total inflow fluctuation coefficients and historical inflow fluctuation coefficients belonging to the same target period are used as correlation pairs. Linear regression is performed on these correlation pairs to obtain the linear regression equation between the historical total inflow fluctuation coefficient and the historical inflow fluctuation coefficient for each pipeline type. Different linear regression equations can be obtained for different pipeline types.

[0054] S150. Input the predicted total inflow fluctuation coefficient into the linear regression equation to calculate the predicted inflow fluctuation coefficient for each pipeline type.

[0055] In this embodiment, the predicted total inflow fluctuation coefficient can be input into the linear regression equation corresponding to each pipeline type, and the predicted inflow fluctuation coefficient of the corresponding pipeline type can be calculated according to the linear regression equation.

[0056] S160. Determine the primary pipeline load for each pipeline type based on the pipeline topology of the region to which each pipeline type belongs.

[0057] Pipeline topology is used to characterize the connection relationship between gas sources and their export pipelines. The pipeline topology can be the same or different for regions belonging to different pipeline types. Generally, a pipeline topology can include at least: a gas source region comprising one gas source and one export pipeline; a gas source region comprising one gas source and multiple export pipelines; a gas source region comprising multiple gas sources and multiple export pipelines; or a gas source region comprising multiple gas sources and multiple export pipelines, etc. Primary pipeline baseload refers to the minimum or basic transport capacity that a natural gas transmission pipeline can stably and continuously handle under normal operating conditions.

[0058] In this embodiment, the pipeline topology of the region to which each pipeline type belongs can be determined based on the pipeline network planning map, and the primary pipeline base load of each pipeline type can be determined according to the rules corresponding to each pipeline topology. The region to which each pipeline type belongs usually includes many gas source areas. In actual operation, the method for determining the primary pipeline base load for each type of pipeline topology can be different. For example, when a gas source area includes one gas source and one gas source export pipeline, the primary pipeline transport base load of the gas source area can be the pipeline design capacity of the gas source export pipeline; when a gas source area includes one gas source and multiple gas source export pipelines, the primary pipeline transport base load of the gas source area can be the sum of the pipeline design capacities of the multiple gas source export pipelines; when a gas source area includes multiple gas sources and multiple gas source export pipelines, and the gas sources converge before the trunk line connection point, the primary pipeline transport base load of the gas source area can be the sum of the pipeline design capacities of the multiple gas source export pipelines downstream of the gas source convergence point; when a gas source area includes multiple gas sources and multiple gas source export pipelines, and the gas sources do not converge before the trunk line connection point, the primary pipeline transport base load of the gas source area can be the sum of the pipeline design capacities of the multiple gas source export pipelines.

[0059] S170. Determine the peak shaving margin coefficient based on the predicted inflow fluctuation coefficient for each pipeline type, and determine the emergency margin coefficient based on the extreme operating condition influence coefficient for each pipeline type.

[0060] The peak-shaving margin coefficient can be understood as the first type of influencing factor affecting the transmission capacity of the pipeline network. It is the ratio of the extra gas supply capacity reserved to cope with peak gas demand to the primary pipeline base load. Each pipeline network type has a corresponding peak-shaving margin coefficient. The extreme operating condition influence coefficient can be understood as a parameter affecting the safe operation of the pipeline network. Generally, the extreme operating condition influence coefficient can include temperature limit parameters and efficiency reduction parameters. When extreme environmental temperatures occur, it is necessary to consider the temperature limit parameters to offset the additional gas demand caused by extreme environmental temperatures (such as low winter temperatures). The efficiency reduction parameter refers to the operating efficiency reduction coefficient caused by the aging of pipelines and equipment, characterizing the long-term impact of factors such as material corrosion and mechanical wear on gas transmission capacity. In practical applications, the extreme operating condition influence coefficient can be different for each pipeline network type. The emergency margin coefficient can be understood as the ratio of the extra gas transmission capacity reserved for events that do not occur as stipulated (such as extreme situations, emergency repairs, equipment failures) to the primary pipeline base load. In one embodiment, the peak shaving margin coefficient and the emergency margin coefficient are the same as the pipeline transportation margin.

[0061] In practical applications, the preset rules for determining the peak-shaving margin coefficient for different pipeline types can be different. For example, when the pipeline type is a central-eastern onshore channel or a non-central-eastern onshore channel (also known as an onshore gas supply channel or onshore gas consumption channel), the difference between the predicted inflow fluctuation coefficient and a preset first value can be directly determined, and this difference can be converted into a percentage form as the peak-shaving margin coefficient. In one embodiment, the preset first value can be 1. When the pipeline type is a marine channel (or marine gas supply channel), since the marine gas supply channel undertakes the emergency peak-shaving function of LNG and is affected by daily peak-shaving demand, the peak-shaving margin coefficient of the marine gas supply channel needs to be corrected. In actual operation, a parameter adjustment coefficient can be preset, the difference between the predicted inflow fluctuation coefficient and the preset first value can be determined, the percentage form of this difference can be determined, and then the product of the percentage form of the difference and the parameter adjustment coefficient can be determined as the peak-shaving margin coefficient of the marine channel. In one embodiment, the parameter adjustment coefficient can be determined through historical daily inflow data from the marine pipeline. Meanwhile, the temperature limit parameter and the preset efficiency reduction parameter can be extracted from the channel influence parameters of each pipeline type, and the reciprocal of the product of the temperature limit parameter and the preset efficiency reduction parameter minus 1 can be used as the emergency margin coefficient.

[0062] S180. Determine the pipeline transmission capacity according to the primary pipeline base load, peak shaving margin coefficient, and emergency margin coefficient for each pipeline type.

[0063] Among them, pipeline transmission capacity refers to the total gas transmission capacity of the main pipeline network connecting gas source points within a certain time period (such as one year). It is a key indicator for natural gas pipeline network planning and for measuring pipeline transportation capacity and ensuring energy supply security.

[0064] In this embodiment, the primary pipeline base load for each pipeline network type can be determined separately, and the sum of the peak-shaving margin coefficient, emergency margin coefficient, and a pre-set second value for each pipeline network type can be determined. The product of this sum and the primary pipeline base load for each pipeline network type is then determined, and this product is used as the transmission capacity of the corresponding pipeline network type. In one embodiment, the second value can be 1. In another embodiment, the sum of the transmission capacities of all pipeline network types can be used as the pipeline network transmission capacity.

[0065] This invention, in its embodiment, involves determining the historical total consumption fluctuation coefficient and the predicted total consumption fluctuation coefficient for a target statistical period based on natural gas consumption data for all pipeline types. It also involves determining the historical total inbound volume fluctuation coefficient based on historical inbound volume data for all pipeline types, establishing a linear correlation between the historical total consumption fluctuation coefficient and the historical total inbound volume fluctuation coefficient, inputting the predicted total consumption fluctuation coefficient into the linear correlation, calculating the predicted total inbound volume fluctuation coefficient corresponding to the predicted total consumption fluctuation coefficient according to the linear correlation, determining the historical inbound volume fluctuation coefficient for each pipeline type based on historical inbound volume data, establishing a linear regression equation between the historical total inbound volume fluctuation coefficient and the historical inbound volume fluctuation coefficient for each pipeline type, and inputting the predicted total inbound volume fluctuation coefficient into... Linear regression equations are used to calculate the predicted inflow fluctuation coefficient for each pipeline network type, improving the accuracy of determining the predicted inflow fluctuation coefficient. The primary pipeline base load for each pipeline network type is determined based on the pipeline topology of the region to which each type belongs, achieving accurate determination of the primary pipeline base load based on the pipeline topology. The pipeline topology characterizes the connection relationship between the gas source and the gas source export pipeline. Peak shaving margin coefficients are determined based on the predicted inflow fluctuation coefficients for each pipeline network type, and emergency margin coefficients are determined based on the extreme operating condition influence coefficients for each pipeline network type. The pipeline transmission capacity is determined based on the primary pipeline base load, peak shaving margin coefficient, and emergency margin coefficient for each pipeline network type, enabling separate calculation of the pipeline transmission capacity for each pipeline network type. This approach considers the structural and functional characteristics of different pipeline network types, improving the accuracy of determining the primary pipeline transmission capacity.

[0066] In one embodiment, after determining the pipeline transmission capacity according to the primary pipeline base load, peak-shaving margin factor, and emergency margin factor for each pipeline type, the method further includes:

[0067] The pipeline transmission utilization rate is determined based on the peak-shaving margin coefficient and the emergency margin coefficient.

[0068] Among them, pipeline transmission utilization rate refers to the degree of utilization of pipeline channels during the transmission process, and is a core indicator used to measure the utilization efficiency of resource-based pipelines.

[0069] In this embodiment, the sum of the emergency margin coefficient and peak-shaving margin coefficient for each pipeline network type can be determined. This sum, along with a preset value, is then used as the parameter sum. The reciprocal of this parameter sum is taken as the pipeline network transmission utilization rate, facilitating scheduling strategy optimization based on the pipeline network transmission utilization rate. Generally, an upper limit for the pipeline network transmission utilization rate can be set for each pipeline network type, and scheduling strategy optimization can be performed using both the upper limit and the actual pipeline network transmission utilization rate.

[0070] Example 2

[0071] Figure 2 This is a flowchart of a data determination method according to Embodiment 2 of the present invention. This embodiment is a further optimization and extension based on the above embodiments, and can be combined with various optional technical solutions in the above embodiments. Figure 2 As shown, the method includes:

[0072] S201. Collect historical and predicted natural gas consumption data for all pipeline types.

[0073] Historical natural gas consumption data, also known as historical energy consumption data, can be understood as the natural gas consumption of all pipeline types at a historical point in time. Forecasted natural gas consumption data, also known as forecasted energy consumption data, can be understood as the natural gas consumption of all pipeline types at a predicted point in time. Generally, both historical and forecasted natural gas consumption data can be obtained through publicly available designated websites or documents.

[0074] In this embodiment, historical natural gas consumption data and predicted natural gas consumption data for each pipeline type can be extracted from publicly released data from institutions or enterprises, and monthly inbound volume data from historical years can be extracted as historical inbound volume data.

[0075] S202. Take the average value of historical natural gas consumption data in the target statistical period as the first average value, and extract the maximum value of historical natural gas consumption data in the target statistical period as the historical high value data.

[0076] In this embodiment, historical natural gas consumption data for all pipeline types can be extracted according to the target statistical period. The average value of the historical natural gas consumption data in the target statistical period is determined as the first average value, and the maximum value of the historical natural gas consumption data in the target statistical period is taken as the historical high value data.

[0077] S203. Take the average value of the predicted natural gas consumption data in the target statistical period as the second average value, and extract the maximum value of the predicted natural gas consumption data in the target statistical period as the predicted high value data.

[0078] In this embodiment, predicted natural gas consumption data for all pipeline types can be extracted according to the target statistical period, the average value of the predicted natural gas consumption data in the target statistical period can be determined as the second average value, and the maximum value of the predicted natural gas consumption data in the target statistical period can be taken as the predicted high value data.

[0079] S204. Determine the historical total consumption fluctuation coefficient based on the first average and historical high values, and determine the predicted total consumption fluctuation coefficient based on the second average and predicted high values.

[0080] In this embodiment, the ratio of historical high data to a first mean can be determined as the historical total consumption fluctuation coefficient, and the ratio of predicted high data to a second mean can be determined as the predicted total consumption fluctuation coefficient.

[0081] S205. Determine the sum of historical inflow data for all pipeline types at the same time as the historical inflow total data.

[0082] Among them, the total historical network connection data refers to the sum of historical network connection data for all types of pipeline networks.

[0083] In this embodiment, the historical inflow data belonging to the same time period among all types of pipeline network data can be determined, the sum of the historical inflow data belonging to the same time period can be determined, and the sum can be used as the total historical inflow data.

[0084] S206. Determine the average value of the total historical network access data in the target statistical period as the third average value, and extract the maximum value of the total historical network access data in the target statistical period as the maximum network access volume.

[0085] In this embodiment, the total historical network access data of the target statistical period can be extracted, the average value of the total historical network access data of the target statistical period can be determined as the third average value, the maximum value of the total historical network access data of the target statistical period can be determined, and the maximum value can be taken as the maximum network access volume.

[0086] S207. The ratio of the maximum number of incoming wires to the third average is used as the historical total number of incoming wires fluctuation coefficient.

[0087] In this embodiment, the ratio of the maximum number of new network connections to the third average can be determined, and this ratio can be used as the fluctuation coefficient of the total historical number of new network connections.

[0088] S208. Determine the historical total network access fluctuation coefficient and historical total consumption fluctuation coefficient belonging to the same target statistical period as the associated combination.

[0089] In the embodiment, the historical total network access fluctuation coefficient and the historical total consumption fluctuation coefficient belonging to the same target statistical period can be determined, and the historical network access fluctuation coefficient and the historical total consumption fluctuation coefficient of the same target statistical period can be used as an associated combination.

[0090] S209. Perform linear regression on the associated combination to construct a regression equation as the linear correlation between the historical total consumption fluctuation coefficient and the historical total grid connection fluctuation coefficient.

[0091] S210. Input the predicted total consumption fluctuation coefficient into the linear correlation relationship, and calculate the predicted total grid connection fluctuation coefficient corresponding to the predicted total consumption fluctuation coefficient according to the linear correlation relationship.

[0092] S211. Take the average value of the historical inflow data for each pipeline type in the target statistical period as the fourth average value, and extract the maximum value of the historical inflow data in the target statistical period as the historical maximum inflow.

[0093] In this embodiment, historical inflow data for each pipeline type within the target statistical period can be extracted. The average value of the historical inflow data for each pipeline type within the target statistical period is determined as the fourth average, and the maximum value of the historical inflow data for each pipeline type within the target statistical period is determined as the historical maximum inflow. The above operations are determined separately for each pipeline type.

[0094] S212. The ratio of the historical maximum number of new network connections to the fourth average is used as the historical number of new network connections fluctuation coefficient.

[0095] In this embodiment, the ratio of the historical maximum inflow to the fourth average can be determined, and this ratio can be used as the historical inflow fluctuation coefficient for the corresponding pipeline type. The historical inflow fluctuation coefficient is generally different for each pipeline type.

[0096] S213. Determine the linear regression equations for the historical total inflow fluctuation coefficient and the historical inflow fluctuation coefficient for each pipeline type.

[0097] S214. Input the predicted total inflow fluctuation coefficient into the linear regression equation to calculate the predicted inflow fluctuation coefficient for each pipeline type.

[0098] S215. Identify the pipeline topology of at least one gas source area in the region to which each pipeline type belongs, calculate the primary pipeline base load of the gas source area according to the rules corresponding to the pipeline topology, and sum the primary pipeline base loads of each gas source area as the primary pipeline base load of the region to which each pipeline type belongs.

[0099] The pipeline topology of the gas source area and its corresponding rules include at least one of the following:

[0100] The gas source area includes a gas source and a gas source export pipeline. The primary pipeline transport base load of the gas source area is the pipeline design capacity of the gas source export pipeline.

[0101] If a gas source area includes a gas source and multiple gas source export pipelines, then the primary pipeline transport base load of the gas source area is the sum of the pipeline design transport capacities of the multiple gas source export pipelines.

[0102] The gas source area includes multiple gas sources and multiple gas source export pipelines. The gas sources converge before the trunk line connection point. Therefore, the primary pipeline transport base load of the gas source area is the sum of the pipeline design transport capacity of multiple gas source export pipelines downstream of the gas source convergence point.

[0103] The gas source area includes multiple gas sources and multiple gas source export pipelines. Since the gas sources do not intersect before the trunk line connection point, the primary pipeline transport base load of the gas source area is the sum of the pipeline design transport capacity of the multiple gas source export pipelines.

[0104] In this context, a gas source area refers to the region from which natural gas is supplied to a specific area or user. Generally, a gas source area is a concentrated gas-producing region that is pre-defined within the pipeline network.

[0105] In the embodiment, the pipeline topology of each gas source area in the region to which each pipeline type belongs can be determined, the rules corresponding to the pipeline topology of each gas source area can be determined, the primary pipeline base load of each gas source area can be determined according to the rules, the primary pipeline base load of each gas source area can be accumulated, and the accumulated result can be used as the primary pipeline base load of the region to which each pipeline type belongs. In actual operation, when a gas source area includes one gas source and one gas source export pipeline, the primary pipeline transport base load of the gas source area can be the design pipeline capacity of the gas source export pipeline; when a gas source area includes one gas source and multiple gas source export pipelines, the primary pipeline transport base load of the gas source area can be the sum of the design pipeline capacities of the multiple gas source export pipelines; when a gas source area includes multiple gas sources and multiple gas source export pipelines, and the gas sources converge before the trunk line connection point, the primary pipeline transport base load of the gas source area can be the sum of the design pipeline capacities of the multiple gas source export pipelines; when a gas source area includes multiple gas sources and multiple gas source export pipelines, and the gas sources do not converge before the trunk line connection point, the primary pipeline transport base load of the gas source area can be the sum of the design pipeline capacities of the multiple gas source export pipelines.

[0106] In one embodiment, Figure 3 This is an example diagram of a pipeline topology provided in Embodiment 3 of the present invention, such as... Figure 3As shown, (1) indicates that there is only one gas source and one gas source export pipeline in the region. The calculation method for the primary pipeline base load is: primary pipeline base load = pipeline design capacity; (2) indicates that there is only one gas source and multiple gas source export pipelines in the region. The calculation method for the primary pipeline base load is: primary pipeline base load = the sum of pipeline design capacity; (3) indicates that there are multiple gas sources and multiple gas source export pipelines in the region, and each gas source converges before being connected to the trunk pipeline network. The primary pipeline base load = the sum of pipeline design capacity downstream of the gas source convergence point; (4) indicates that there are multiple gas sources and multiple gas source export pipelines in the region, and each gas source does not converge before being connected to the trunk pipeline network. The primary pipeline base load = the sum of pipeline design capacity downstream of each gas source. Among them, gas sources are the stations in the pipeline network that are responsible for uploading gas field resources, including inbound pipeline gas entry points, LNG receiving terminals, and gas storage facilities; gas source export pipelines are pipelines directly connected to the gas sources that are responsible for exporting natural gas; gas source aggregation points are the stations where all gas sources in the region first converge after being exported through pipelines; trunk line connection points are stations located on the pipeline network.

[0107] S216. Determine the difference between the predicted inflow fluctuation coefficient of each pipeline type and the preset first value as the first result, and determine the pipeline type corresponding to the predicted inflow fluctuation coefficient. Determine the corresponding peak shaving margin coefficient according to the first result and the pipeline type.

[0108] Here, the first value can be understood as a preset value that determines the type of pipeline network. Generally, the first value can be set according to user needs. For example, the first value can be 1.

[0109] In this embodiment, when the preset first value is 1, the difference between the predicted inflow fluctuation coefficient and 1 for each pipeline type can be determined as the first result. The corresponding peak-shaving margin coefficient is then determined according to the pipeline type corresponding to the predicted inflow fluctuation coefficient and the first result. In actual operation, when the pipeline type is an onshore gas supply channel or an onshore gas consumption channel, the peak-shaving margin coefficient is a percentage of the first result. When the pipeline type is an offshore gas supply channel, since the offshore gas supply channel is significantly affected by daily peak-shaving demand, the peak-shaving margin coefficient of the offshore gas supply channel can be corrected by a preset adjustment coefficient. The product of the first result and the preset adjustment coefficient is determined as the first product, and the percentage of the first product is used as the peak-shaving margin coefficient of the offshore gas supply channel. In one embodiment, the preset adjustment coefficient can be the ratio of the high monthly or high daily inflow data of the offshore gas supply channel to the monthly or daily average inflow data of the offshore gas supply channel.

[0110] S217. Extract the pre-set temperature limit parameter and efficiency reduction parameter from the extreme condition influence coefficient of each pipeline network type, determine the product of the temperature limit parameter and efficiency reduction parameter, and subtract 1 from the reciprocal of the product to obtain the emergency margin coefficient of each pipeline network type.

[0111] The temperature limit coefficient is used to offset the additional gas demand caused by extreme ambient temperatures (such as low winter temperatures). It can be preset or determined according to the current pipeline network type. Generally, the temperature limit coefficient for each pipeline network type can be 0.97. The efficiency reduction coefficient refers to the operating coefficient that reduces working efficiency due to pipeline and equipment aging. The efficiency reduction coefficient for each pipeline network type can be preset or dynamically determined based on its key operating indicators (such as operating time, equipment performance degradation, and pipeline condition). In one embodiment, the efficiency reduction coefficient can range from 0.95 to 0.99.

[0112] In the embodiments, the temperature limit coefficient and efficiency reduction coefficient for each pipeline network type can be determined separately, and the product of the temperature limit parameter and the efficiency reduction parameter can be determined. The reciprocal of the product of the temperature limit parameter and the efficiency reduction parameter minus 1 is used as the emergency margin coefficient for each pipeline network type. The above example is expressed in percentage form.

[0113] S218. The sum of the peak-shaving margin coefficient, emergency margin coefficient and the pre-set second value for each pipeline network type is taken as the first sum, and the product of the first sum and the primary pipeline base load is taken as the transmission capacity of the corresponding pipeline network type.

[0114] The second value can be understood as a preset value that determines the transmission capacity. Generally, the second value can be set according to user needs. For example, the second value can be 1.

[0115] In this embodiment, the peak-shaving margin coefficient and emergency margin coefficient for each pipeline network type can be extracted. The sum of the peak-shaving margin coefficient, emergency margin coefficient, and a preset second value for each pipeline network type is determined as a first sum. The product of the first sum and the primary pipeline base load of the pipeline network type is determined as the transmission capacity of the corresponding pipeline network type. The preset second value can be a value set in advance according to business requirements; for example, the preset second value can be 1.

[0116] S219. The sum of the transmission capacities of all pipeline network types shall be taken as the pipeline network transmission capacity.

[0117] In this embodiment of the invention, by determining a first mean, historical high values, a second mean, and predicted high values, the historical total consumption fluctuation coefficient is determined based on the first mean and historical high values, and the predicted total consumption fluctuation coefficient is determined based on the second mean and predicted high values, thus achieving the determination of both the historical and predicted total consumption fluctuation coefficients. Furthermore, by determining the sum of historical inflow data for all pipeline types at the same time as the historical inflow sum data, the historical total inflow fluctuation coefficient and historical total consumption fluctuation coefficient belonging to the same target statistical period are determined as a correlation combination. Linear regression is then performed on this correlation combination to construct a regression equation. The linear correlation between historical total consumption fluctuation coefficient and historical total inbound volume fluctuation coefficient is established. The predicted total consumption fluctuation coefficient is input into this linear correlation to determine the predicted total inbound volume fluctuation coefficient. Historical inbound volume fluctuation coefficients are determined based on historical inbound volume data for each pipeline type within the target statistical period. A linear regression equation is then established between the historical total inbound volume fluctuation coefficient and the historical inbound volume fluctuation coefficient for each pipeline type. The predicted total inbound volume fluctuation coefficient is input into this linear regression equation to calculate the predicted inbound volume fluctuation coefficient for each pipeline type, thus achieving accurate determination of the predicted inbound volume fluctuation coefficient. Furthermore, at least one gas source region within the area to which each pipeline type belongs is identified. Based on the pipeline topology, the primary pipeline baseload of the gas source area is calculated according to the rules corresponding to the pipeline topology. The primary pipeline baseload of each gas source area is accumulated to obtain the primary pipeline baseload of the area to which each pipeline type belongs, thus accurately determining the primary pipeline baseload according to the pipeline topology of the gas source area. The difference between the predicted inflow fluctuation coefficient and the preset first value is used as the first result, and the pipeline type corresponding to the predicted inflow fluctuation coefficient is determined. The corresponding peak-shaving margin coefficient is determined according to the first result and the pipeline type. The preset temperature limit parameter and efficiency reduction parameter are extracted from the extreme operating condition influence coefficient of each pipeline type to determine the temperature. The product of the limiting parameter and the efficiency reduction parameter is used, and the reciprocal of the product minus 1 is taken as the emergency margin coefficient for each pipeline network type. The sum of the peak-shaving margin coefficient, the emergency margin coefficient, and the pre-set second value for each pipeline network type is taken as the first sum. The product of the first sum and the primary pipeline base load is taken as the transmission capacity of the corresponding pipeline network type. The sum of the transmission capacities of all pipeline network types is taken as the pipeline transmission capacity. This allows for the separate calculation of the peak-shaving margin coefficient and the emergency margin coefficient for each pipeline network type, which facilitates the determination of pipeline capacity for each pipeline network type, improves the accuracy of pipeline transmission capacity determination, and reduces the deviation between the calculation results and the actual situation.

[0118] Example 3

[0119] This embodiment, based on the above embodiments, uses primary pipeline capacity as pipeline transmission capacity and emergency safety margin coefficient as emergency margin coefficient as an example to illustrate a specific embodiment of a data determination method.

[0120] Step 1: Classification of Natural Gas Pipeline Network Types. Based on the spatial distribution characteristics of natural gas resources nationwide, the topology of the main gas transmission pipeline network, and its functional positioning attributes, a classification framework for the primary pipeline system can be constructed, including onshore gas supply channels, offshore gas supply channels, and onshore gas consumption channels, to conduct research on their differentiated characteristics. Pipeline network types include onshore gas supply channels, offshore gas supply channels, and onshore gas consumption channels. Onshore gas supply channels include onshore imports and resource loading channels from major gas fields, characterized by being major gas source channels, having a large peak-shaving base, and strong seasonal fluctuations; representative routes are the China-Russia East Route and the West-to-East Gas Pipeline. Offshore gas supply channels include LNG resource loading channels, typically characterized by proximity to load centers, strong daily peak-shaving capacity, and high price sensitivity in terms of transmission volume; representative routes are Tianjin LNG and Jiangsu LNG. Onshore gas consumption channels include resource aggregation and dispersal channels in the central and eastern regions, typically characterized by being located at load centers, having large market peak-shaving demand, and requiring strong emergency and supply guarantee capabilities; representative routes are the Shaanxi-Beijing Line and the Sichuan-to-East Gas Pipeline.

[0121] Step 2: Analysis of the primary pipeline transportation capacity composition of different pipeline network types.

[0122] A single pipeline transport capacity is the total gas transport capacity of the main pipeline network connecting gas source points within a certain time period (usually one year). It is a key indicator for natural gas pipeline network planning and for measuring pipeline transport capacity and ensuring energy supply security.

[0123] The calculation principles include: for onshore gas supply channels, the primary pipeline capacity is the overall transmission capacity of the channel, that is, the sum of the designed transmission capacities of all external transmission channels within the channel.

[0124] Offshore gas supply channels: The primary pipeline capacity is the sum of the designed throughput of each LNG export pipeline.

[0125] Onshore gas transport corridor: The primary pipeline capacity is the sum of the designed transport capacities of all resource-carrying pipelines in the central and eastern regions.

[0126] Generally, if the resources of offshore gas supply channels and onshore gas consumption channels are transferred from multiple primary pipelines to the same secondary pipeline, and the transfer capacity is limited by this pipeline, then the primary pipeline capacity of the pipeline network is calculated based on the design capacity of the secondary pipeline.

[0127] The primary pipeline base load is the minimum or basic transport volume that a long-distance natural gas pipeline can sustainably and stably under normal operating conditions.

[0128] The calculation principle is based on the primary pipeline load of the classified natural gas pipeline network, which is the amount of resources it receives into the network. Natural gas resources entering the network include imported pipeline gas resources, imported LNG resources, and domestically produced gas loading resources, totaling three categories.

[0129] The primary pipeline utilization rate is the ratio of the primary pipeline base load to the primary pipeline capacity, and is a core indicator used to measure the utilization efficiency of resource-based pipelines.

[0130] Primary pipeline utilization rate (pipeline transmission utilization rate) = Primary pipeline base load / Primary pipeline capacity × 100%.

[0131] Step 3: Analysis of the factors that determine the primary pipeline capacity.

[0132] The primary pipeline capacity can be decomposed into three parts: primary pipeline base load, peak-shaving margin coefficient, and emergency margin coefficient. The peak-shaving margin coefficient is mainly used to cope with predictable high monthly peak-shaving demand, while the emergency margin coefficient is used to cope with unpredictable events. Together, they constitute the redundancy capacity of the pipeline network system. In one embodiment, the composition of primary pipeline capacity is analyzed as follows:

[0133] The primary pipeline base load represents normal demand, based on the annual average gas transmission load, reflecting the stable operating flow level of the pipeline network under non-extreme conditions, and does not include any redundant design capacity. The peak-shaving margin coefficient represents peak-shaving demand, based on historical high-month gas consumption fluctuation patterns (such as temperature changes, industrial load cycles, etc.), reserving additional gas transmission capacity to ensure the pipeline network does not exceed its operating limits under high monthly loads. The emergency margin coefficient, also known as the emergency safety margin coefficient, represents emergency demand, a buffer capacity independent of peak-shaving demand, used to cope with instantaneous flow fluctuations or partial supply interruption risks, ensuring the system can maintain safe pressure and flow thresholds even under extreme conditions.

[0134] This application determines primary pipeline capacity using a multiplicative model, which integrates margin coefficients, transforming redundant capacity into relative coefficients. This is based on the primary pipeline base load. This comprehensively reflects the overall margin level, facilitating horizontal comparisons of pipeline networks of different sizes and highlighting the systematic impact of the margin coefficient. Specifically: Primary pipeline capacity = Primary pipeline base load × ( );

[0135] in, This represents the margin factor for a primary pipeline. For pipeline transmission utilization rate.

[0136] in, The peak-shaving margin coefficient reflects the redundancy capacity of the pipeline network system during peak months. The emergency margin coefficient reflects the buffer space or reserve capacity of the pipeline system to maintain safe operation even when the pipeline supply in a local area is interrupted due to an event or extreme operating condition.

[0137] Step 4: Determine the peak shaving margin coefficient.

[0138] This step, based on multi-source data-driven and classification regression modeling, accurately determines the peak-shaving margin coefficient for different types of pipeline networks through data preprocessing, correlation analysis, model building, and parameter correction. The data source is long-term consumption data (natural gas consumption data for all pipeline types), including historical years and the predicted monthly fluctuation coefficient of national natural gas consumption for the forecast year.

[0139] Historical operational data includes monthly data on the national gas supply volume and the monthly gas supply volume for each pipeline type (onshore gas supply channel, onshore gas consumption channel, and offshore gas supply channel) for each historical year (i.e., historical gas supply volume data).

[0140] First, outlier removal was performed, targeting anomalies caused by sudden changes in gas consumption (such as industrial shutdowns or sharp drops in transportation gas consumption) and data inconsistencies. Then, effective data was extracted, selecting monthly data with high continuity and representativeness as the model calculation period to calculate the monthly inflow fluctuation coefficients for national and categorized pipelines. The Pearson correlation coefficient method was used to analyze the linear correlation between the national monthly consumption fluctuation coefficient (including historical and predicted total consumption fluctuation coefficients) and the total inflow fluctuation coefficients for all pipeline types (including historical and predicted total inflow fluctuation coefficients). The results show a significant linear correlation between the national consumption fluctuation coefficient and the total inflow fluctuation coefficients for all pipeline types (coefficient range 0.85-0.93). Furthermore, a linear relationship also exists between the inflow fluctuation coefficients for each pipeline type (including historical and predicted inflow fluctuation coefficients) and the total inflow fluctuation coefficients for all pipeline types (including historical and predicted total inflow fluctuation coefficients). Based on the correlation analysis results, regression models can be constructed to characterize the mapping relationship between the national and various types of pipeline network fluctuation coefficients. An example of the model format is as follows:

[0141] National pipelines: Onshore gas supply channels: Onshore gas supply channels: Offshore gas supply channels: .

[0142] in, X represents the predicted monthly fluctuation coefficient of national consumption (predicted total consumption fluctuation coefficient); X represents the calculated result of the national grid connection fluctuation coefficient model (predicted total grid connection fluctuation coefficient). The model calculation results for the fluctuation coefficient of the onshore gas supply channel's grid connection volume in the predicted year (the predicted fluctuation coefficient of the onshore gas supply channel's grid connection volume). The model calculation results for the fluctuation coefficient of the onshore gas consumption channel in the predicted year (the predicted fluctuation coefficient of the onshore gas consumption channel in the predicted year). The model calculation results for the predicted annual gas supply network fluctuation coefficient of the offshore gas supply channel are shown below. The predicted gas supply network fluctuation coefficients of the onshore gas supply channel, the onshore gas consumption channel, and the offshore gas supply channel are the target regression results for their respective pipeline network types.

[0143] The calculation and correction of peak-shaving margin coefficients include: based on the predicted inflow fluctuation coefficient output by the classification regression model, the high-month peak-shaving margin coefficients for various pipeline network types are calculated according to the following formula:

[0144] .

[0145] The offshore gas supply channel is revised to account for its emergency peak-shaving function for LNG and its significant impact from daily peak-shaving demand. An adjustment coefficient f is introduced to correct its peak-shaving margin coefficient. The revised formula is: .

[0146] Where f = historical inflow data of the highest day in the highest month of the offshore gas supply channel / historical inflow data of the highest average day in the highest month of the offshore gas supply channel (recommended value 1.46). The historical inflow data of the highest day in the highest month is the historical inflow data corresponding to the highest day in the highest month of the historical inflow data in the target statistical period.

[0147] Based on long-term forecast data verification and combined with industry safety standards (such as tolerance for supply interruption risks and requirements for gas storage and peak-shaving capacity), reasonable ranges and reserve levels for peak-shaving margin coefficients of different pipeline network types were determined, including: when the predicted inflow fluctuation coefficient of onshore gas supply channels is 1.24, the peak-shaving margin coefficient is 24%, and the peak-shaving reserve level is medium; when the predicted inflow fluctuation coefficient of onshore gas supply channels is 1.14, the peak-shaving margin coefficient is 14%, and the peak-shaving reserve level is low; when the predicted inflow fluctuation coefficient of offshore gas supply channels is 1.67, the peak-shaving margin coefficient is 98% (after f correction), and the peak-shaving reserve level is high.

[0148] Step 5: Determine the emergency margin coefficient.

[0149] In the existing technology, The method of determination is as follows ;in: When the extreme ambient temperature arrives, the extreme temperature coefficient that offsets the replenishment gas demand must be considered. ; To determine the national economic reserve coefficient that can strengthen the operation of gas pipelines, ; The reliability coefficient of the gas transmission pipeline is determined by factors such as pipeline length, unit type, unit configuration, and reliability maintenance factor. The average time a unit is forced to shut down due to a fault / the average working time of the unit between two faults are jointly determined. The value ranges from 0.862 to 0.998.

[0150] In existing technologies, the emergency safety margin coefficient is defined as: However, this model does not fully consider the actual operating characteristics of pipelines in my country (such as equipment aging and insufficient reserve capacity). Directly using it would lead to repeated calculations of maintenance impacts (because the design of pipelines in my country already implicitly includes uncertainties in maintenance time). Therefore, the model needs to be adaptively modified to take into account the service status of pipelines in my country.

[0151] This application proposes an efficiency reduction factor ( ) Replace the original The combination more accurately reflects the impact of pipeline aging on operating efficiency, and the corrected version The calculation formula is .in, The limiting temperature coefficient (i.e., the preset temperature limit coefficient) is set to 0.97, which is used to offset the additional gas demand caused by extreme ambient temperatures (such as low temperatures in winter). This is the efficiency reduction factor (i.e., the preset efficiency reduction factor), a factor that reduces operating efficiency due to pipeline and equipment aging, characterizing the long-term impact of factors such as material corrosion and mechanical wear on gas transmission capacity. For example, if the average service life of primary pipelines in my country is 10 years, it can be determined that... The value range is 0.95-0.99.

[0152] Considering the differences in service environment, functional positioning, and risk level among different types of pipeline networks, the emergency margin coefficient is determined based on the national benchmark value and combined with the characteristics of the classified pipeline networks. Differential adjustments should be made when the pipeline network type is an onshore gas supply channel. Based on factors such as high population density, frequent extreme weather events (e.g., cold waves), and high dependence on external energy sources, there is a need to strengthen emergency preparedness capabilities. Therefore, the emergency margin coefficient can be adjusted accordingly. Adjust to 6%-8%; when the pipeline type is an onshore gas supply channel, based on sufficient local resources (such as western / northern gas fields), relatively stable consumption demand, and a low probability of unpredictable events, the emergency margin coefficient can be adjusted. Adjust to 4%-6%; when the pipeline type is an offshore gas supply channel, due to geopolitical risks (such as fluctuations in international LNG supply) and high market price sensitivity, flexible peak-shaving capacity needs to be reserved, and the emergency margin coefficient can be adjusted. Adjusted to 6%-8%.

[0153] Step 6: Calculation of primary pipeline capacity and pipeline margin.

[0154] This step's calculation system for primary pipeline capacity and pipeline margin is based on a multi-dimensional margin parameter coupling model. It constructs differentiated margin coefficient combinations and a pipeline utilization upper limit assessment system for the transmission characteristics of different transmission channel types (onshore gas supply channels, onshore gas consumption channels, and offshore gas supply channels). Specific results are as follows:

[0155] ;in, This is the primary pipeline margin factor, which varies depending on the pipeline type. The values ​​can be different.

[0156] Onshore gas supply channels: including peak shaving margin coefficient (Characterizing load fluctuation regulation capability, valued at 14%) and emergency margin coefficient (Characterizing the buffering capacity under sudden operating conditions, with a value range of 4% to 6%), the two are coupled to form the primary pipeline margin coefficient. Onshore gas supply channels: peak shaving margin coefficient (Reflecting the region's ability to dynamically balance supply and demand, with a value of 24%) and the emergency margin coefficient (Considering the emergency response requirements of complex pipeline networks, the value ranges from 6% to 8%), after coupling, the primary pipeline margin coefficient. Offshore gas supply channels: peak-shaving margin coefficient (98% value for matching periodic load changes in long-distance transmission) and emergency margin coefficient (Taking into account the special characteristics of the marine environment, the safety redundancy ranges from 6% to 8%), after coupling, the primary pipeline margin factor. .

[0157] This invention provides a method for calculating the primary transmission capacity and network margin of a classified natural gas pipeline network. By decomposing the components of primary transmission capacity, it classifies and constructs calculation methods for different types of pipeline networks, establishing a system of calculation methods for primary transmission capacity, and standardizing zoning principles, statistical caliber, and calculation procedures. The method proposed in this invention can scientifically assess the matching between natural gas pipeline network capacity and market demand, systematically evaluate the scientific nature of natural gas pipeline network planning, and provide decision support for the safe and efficient operation of the pipeline network.

[0158] Example 4

[0159] Figure 4 This is a schematic diagram of a data determination device according to Embodiment 3 of the present invention. Figure 4 As shown, the device includes: a first coefficient determination module 41, a linear relationship determination module 42, a second coefficient determination module 43, a regression equation determination module 44, a third coefficient determination module 45, a pipeline base load determination module 46, a fourth coefficient determination module 47, and a transmission capacity determination module 48.

[0160] The first coefficient determination module 41 is used to determine the historical total consumption fluctuation coefficient and the predicted total consumption fluctuation coefficient corresponding to the target statistical period based on the natural gas consumption data of all pipeline types.

[0161] The linear relationship determination module 42 is used to determine the historical total inbound volume fluctuation coefficient based on the historical inbound volume data of all pipeline types, and to determine the linear correlation between the historical total consumption fluctuation coefficient and the historical total inbound volume fluctuation coefficient.

[0162] The second coefficient determination module 43 is used to input the predicted total consumption fluctuation coefficient into the linear correlation relationship, and calculate the predicted total grid connection fluctuation coefficient corresponding to the predicted total consumption fluctuation coefficient according to the linear correlation relationship.

[0163] The regression equation determination module 44 is used to determine the historical inflow fluctuation coefficient of each pipeline type based on the historical inflow data of each pipeline type, and to determine the linear regression equation between the historical total inflow fluctuation coefficient and the historical inflow fluctuation coefficient of each pipeline type.

[0164] The third coefficient determination module 45 is used to input the predicted total inflow fluctuation coefficient into the linear regression equation and calculate the predicted inflow fluctuation coefficient for each pipeline type.

[0165] The pipeline base load determination module 46 is used to determine the primary pipeline base load of each pipeline type based on the pipeline topology of the region to which each pipeline type belongs; wherein, the pipeline topology is used to characterize the connection relationship between the gas source and the gas source export pipeline.

[0166] The fourth coefficient determination module 47 is used to determine the peak shaving margin coefficient based on the predicted inflow fluctuation coefficient of each pipeline type, and to determine the emergency margin coefficient according to the extreme operating condition influence coefficient of each pipeline type.

[0167] The transmission capacity determination module 48 is used to determine the transmission capacity of the pipeline network based on the primary pipeline base load, the peak shaving margin coefficient, and the emergency margin coefficient for each pipeline network type.

[0168] The technical solution of this invention involves a first coefficient determination module that determines the historical total consumption fluctuation coefficient and the predicted total consumption fluctuation coefficient for the target statistical period based on natural gas consumption data for all pipeline types. A linear relationship determination module determines the historical total inbound volume fluctuation coefficient based on historical inbound volume data for all pipeline types and establishes a linear correlation between the historical total consumption fluctuation coefficient and the historical total inbound volume fluctuation coefficient. A second coefficient determination module inputs the predicted total consumption fluctuation coefficient into the linear correlation and calculates the predicted total inbound volume fluctuation coefficient corresponding to the predicted total consumption fluctuation coefficient. A regression equation determination module determines the historical inbound volume fluctuation coefficient for each pipeline type based on historical inbound volume data and establishes a linear regression equation between the historical total inbound volume fluctuation coefficient and the historical inbound volume fluctuation coefficient for each pipeline type. A third coefficient determination module then determines the predicted total inbound volume fluctuation coefficient... The system inputs data into a linear regression equation to calculate the predicted inflow fluctuation coefficient for each pipeline network type, improving the accuracy of determining the predicted inflow fluctuation coefficient. The pipeline base load determination module determines the primary pipeline base load for each pipeline network type based on the pipeline topology of the region to which each pipeline network type belongs, achieving accurate determination of the primary pipeline base load based on the pipeline topology. The pipeline topology characterizes the connection relationship between the gas source and the gas source export pipeline. The fourth coefficient determination module determines the peak-shaving margin coefficient based on the predicted inflow fluctuation coefficient for each pipeline network type, and the emergency margin coefficient based on the extreme operating condition influence coefficient for each pipeline network type. The transmission capacity determination module determines the pipeline transmission capacity based on the primary pipeline base load, peak-shaving margin coefficient, and emergency margin coefficient for each pipeline network type, enabling separate calculation of the pipeline transmission capacity for each pipeline network type. This considers the structural and functional characteristics of different pipeline network types, improving the accuracy of determining the primary pipeline capacity.

[0169] In one embodiment, the pipeline baseload determination module 46 includes:

[0170] The pipeline base load determination unit is used to identify the pipeline topology of at least one gas source area in the region to which each pipeline type belongs, calculate the primary pipeline base load of the gas source area according to the rules corresponding to the pipeline topology, and accumulate the primary pipeline base load of each gas source area as the primary pipeline base load of the region to which each pipeline type belongs.

[0171] The pipeline topology of the gas source area and its corresponding rules include at least one of the following:

[0172] The gas source area includes a gas source and a gas source export pipeline. The primary pipeline transport base load of the gas source area is the pipeline design capacity of the gas source export pipeline.

[0173] If a gas source area includes a gas source and multiple gas source export pipelines, then the primary pipeline transport base load of the gas source area is the sum of the pipeline design transport capacities of the multiple gas source export pipelines.

[0174] The gas source area includes multiple gas sources and multiple gas source export pipelines. The gas sources converge before the trunk line connection point. Therefore, the primary pipeline transport base load of the gas source area is the sum of the pipeline design transport capacity of multiple gas source export pipelines downstream of the gas source convergence point.

[0175] The gas source area includes multiple gas sources and multiple gas source export pipelines. Since the gas sources do not intersect before the trunk line connection point, the primary pipeline transport base load of the gas source area is the sum of the pipeline design transport capacity of the multiple gas source export pipelines.

[0176] In one embodiment, the first coefficient determination module 41 includes:

[0177] The data acquisition unit is used to collect historical and predicted natural gas consumption data for all types of pipeline networks.

[0178] The first high value determination unit is used to take the average value of historical natural gas consumption data in the target statistical period as the first average value and extract the maximum value of historical natural gas consumption data in the target statistical period as historical high value data.

[0179] The second high value determination unit is used to take the average value of the predicted natural gas consumption data in the target statistical period as the second average value and extract the maximum value of the predicted natural gas consumption data in the target statistical period as the predicted high value data.

[0180] The total fluctuation coefficient determination unit is used to determine the historical total consumption fluctuation coefficient according to the first mean and historical high value data, and to determine the predicted total consumption fluctuation coefficient according to the second mean and predicted high value data.

[0181] In one embodiment, the linear relationship determination module 42 includes:

[0182] The sum determination unit is used to determine the sum of historical inflow data for all network types at the same time as the historical inflow sum data.

[0183] The maximum value determination unit is used to determine the average value of the total historical network entry volume data in the target statistical period as the third average value, and extract the maximum value of the total historical network entry volume data in the target statistical period as the maximum network entry volume;

[0184] The grid connection fluctuation determination unit is used to take the ratio of the maximum grid connection volume to the third average as the historical total grid connection volume fluctuation coefficient.

[0185] The combination determination unit is used to determine the historical total network access fluctuation coefficient and historical total consumption fluctuation coefficient belonging to the same target statistical period as the associated combination;

[0186] The linear relationship determination unit is used to perform linear regression on the associated combination to construct a regression equation as the linear correlation between the historical total consumption fluctuation coefficient and the historical total grid connection fluctuation coefficient.

[0187] In one embodiment, the regression equation determination module 44 includes:

[0188] The numerical determination unit is used to take the average value of the historical inflow data of each pipeline type in the target statistical period as the fourth average value, and extract the maximum value of the historical inflow data in the target statistical period as the historical maximum inflow.

[0189] The fluctuation coefficient determination unit is used to take the ratio of the historical maximum grid connection volume to the fourth average as the historical grid connection volume fluctuation coefficient.

[0190] In one embodiment, the fourth coefficient determination module 47 includes:

[0191] The margin coefficient determination unit is used to determine the difference between the predicted inflow fluctuation coefficient of each pipeline network type and a preset first value as the first result, and to determine the pipeline network type corresponding to the predicted inflow fluctuation coefficient. The corresponding peak-shaving margin coefficient is determined according to the first result and the pipeline network type; the first value is equal to 1.

[0192] The emergency margin determination unit is used to extract the pre-set temperature limit parameter and efficiency reduction parameter from the extreme condition influence coefficient of each pipeline network type, determine the product of the temperature limit parameter and efficiency reduction parameter, and then subtract 1 from the reciprocal of the product to obtain the emergency margin coefficient of each pipeline network type.

[0193] In one embodiment, the transmission capability determination module 48 includes:

[0194] The first capacity determination unit is used to take the sum of the peak-shaving margin coefficient, emergency margin coefficient and the pre-set second value of each pipeline type as the first sum, and the product of the first sum and the primary pipeline base load as the transmission capacity of the corresponding pipeline type; the second value is equal to 1;

[0195] The transmission capacity determination unit is used to take the sum of the transmission capacities of all pipeline types as the pipeline transmission capacity.

[0196] In one embodiment, the data determination device further includes:

[0197] The utilization rate determination module is used to determine the pipeline transmission utilization rate based on the peak shaving margin coefficient and the emergency margin coefficient.

[0198] The data determination device provided in the embodiments of the present invention can execute the data determination method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method execution.

[0199] Example 5

[0200] Figure 5 This is a schematic diagram of an electronic device implementing a data determination method according to an embodiment of the present invention. 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 (such as 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.

[0201] like Figure 5 As 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 can 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.

[0202] 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.

[0203] 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 a data determination method.

[0204] In some embodiments, a data determination 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 mounted 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 data determination method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform a data determination method by any other suitable means (e.g., by means of firmware).

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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).

[0209] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or 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.

[0210] 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.

[0211] 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.

[0212] 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 data determination method characterized by, The method comprises the following steps: According to the natural gas consumption data of all pipe network types, the historical total consumption fluctuation coefficient corresponding to the target statistical period and the predicted total consumption fluctuation coefficient are determined respectively; According to the historical network entry amount data of all pipe network types, the historical total network entry amount fluctuation coefficient is determined, and the linear correlation between the historical total consumption fluctuation coefficient and the historical total network entry amount fluctuation coefficient is determined; The predicted total consumption fluctuation coefficient is input into the linear correlation, and the predicted total network entry amount fluctuation coefficient corresponding to the predicted total consumption fluctuation coefficient is calculated according to the linear correlation; According to the historical network entry amount data of each pipe network type, the historical network entry amount fluctuation coefficient of each pipe network type is determined, and the linear regression equation of the historical total network entry amount fluctuation coefficient and the historical network entry amount fluctuation coefficient of each pipe network type is determined; The predicted total network entry amount fluctuation coefficient is input into the linear regression equation, and the predicted network entry amount fluctuation coefficient of each pipe network type is calculated; According to the pipe network topology of the region to which each pipe network type belongs, the primary pipe transportation base load of each pipe network type is determined; wherein the pipe network topology is used to represent the connection relationship between the gas source and the gas source external pipeline; According to the predicted network entry amount fluctuation coefficient of each pipe network type, the peak shaving margin coefficient is determined, and the emergency margin coefficient is determined according to the limit working condition influence coefficient of each pipe network type; According to the primary pipe transportation base load, the peak shaving margin coefficient and the emergency margin coefficient of each pipe network type, the pipe network transmission capacity is determined; According to the predicted network entry amount fluctuation coefficient of each pipe network type, the peak shaving margin coefficient is determined, and the emergency margin coefficient is determined according to the limit working condition influence coefficient of each pipe network type, comprising: The difference between the predicted network entry amount fluctuation coefficient of each pipe network type and the first preset value is determined as the first result, and the pipe network type corresponding to the predicted network entry amount fluctuation coefficient is determined, and the corresponding peak shaving margin coefficient is determined according to the first result and the pipe network type; the first value is equal to 1; The temperature limit parameter and the efficiency reduction parameter in the limit working condition influence coefficient of each pipe network type are extracted, the product of the temperature limit parameter and the efficiency reduction parameter is determined, and the reciprocal of the product minus 1 is taken as the emergency margin coefficient of each pipe network type.

2. The method of claim 1, wherein, According to the pipe network topology of the region to which each pipe network type belongs, the primary pipe transportation base load of each pipe network type is determined, comprising: Identify the pipe network topology form of at least one gas source region in the region to which each pipe network type belongs, calculate the primary pipe transportation base load of the gas source region according to the rules corresponding to the pipe network topology form, and accumulate the primary pipe transportation base load of each gas source region as the primary pipe transportation base load of the region to which each pipe network type belongs; The pipe network topology form of the gas source region and the corresponding rules include at least one of the following: If the gas source region includes one gas source and one gas source external pipeline, the primary pipe transportation base load of the gas source region is the pipeline design throughput of the gas source external pipeline; If the gas source region includes one gas source and multiple gas source external pipelines, the primary pipe transportation base load of the gas source region is the sum of the pipeline design throughputs of the multiple gas source external pipelines; The gas source area includes multiple gas sources and multiple gas source external pipelines, and each gas source converges before the trunk line communication point, so that the primary pipe transportation basic load of the gas source area is the sum of the pipeline design flow rates of the multiple gas source external pipelines downstream of the gas source convergence point; The gas source area includes multiple gas sources and multiple gas source external pipelines, and each gas source does not converge before the trunk line communication point, so that the primary pipe transportation basic load of the gas source area is the sum of the pipeline design flow rates of the multiple gas source external pipelines.

3. The method of claim 1, wherein, The method comprises the following steps: Collecting historical natural gas consumption data and predicted natural gas consumption data of all pipe network types; Taking the average value of the historical natural gas consumption data in the target statistical period as a first average value, and extracting the maximum value of the historical natural gas consumption data in the target statistical period as historical high value data; Taking the average value of the predicted natural gas consumption data in the target statistical period as a second average value, and extracting the maximum value of the predicted natural gas consumption data in the target statistical period as predicted high value data; Determining the historical total consumption fluctuation coefficient according to the first average value and the historical high value data, and determining the predicted total consumption fluctuation coefficient according to the second average value and the predicted high value data.

4. The method of claim 1, wherein, The method comprises the following steps: Determining the sum of the historical network entry amount data of all pipe network types at the same time as historical network entry amount sum data; Determining the average value of the historical network entry amount sum data in the target statistical period as a third average value, and extracting the maximum value of the historical network entry amount sum data in the target statistical period as a maximum network entry amount; Taking the ratio of the maximum network entry amount to the third average value as the historical total network entry amount fluctuation coefficient; Determining the historical total network entry amount fluctuation coefficient and the historical total consumption fluctuation coefficient belonging to the same target statistical period as an associated combination; Performing linear regression on the associated combination to construct a regression equation as the linear correlation between the historical total consumption fluctuation coefficient and the historical total network entry amount fluctuation coefficient.

5. The method of claim 1, wherein, The method comprises the following steps: Taking the average value of the historical network entry amount data of each pipe network type in the target statistical period as a fourth average value, and extracting the maximum value of the historical network entry amount data in the target statistical period as a historical maximum network entry amount; Taking the ratio of the historical maximum network entry amount to the fourth average value as the historical network entry amount fluctuation coefficient.

6. The method of claim 1, wherein, The method comprises the following steps: Taking the sum of the peak shaving margin coefficient, the emergency margin coefficient and a second value of each pipe network type as a first sum, and taking the product of the first sum and the primary pipe transportation basic load as the transmission capacity of the corresponding pipe network type; the second value is equal to 1; Taking the sum of the transmission capacities of all pipe network types as the pipe network transmission capacity.

7. A data determination apparatus characterized by comprising: The method comprises the following steps: The first coefficient determination module is configured to determine a historical total consumption fluctuation coefficient and a predicted total consumption fluctuation coefficient corresponding to a target statistical period respectively according to natural gas consumption data of all pipe network types; The linear relationship determination module is configured to determine a historical total inflow fluctuation coefficient according to historical inflow data of all pipe network types, and determine a linear correlation between the historical total consumption fluctuation coefficient and the historical total inflow fluctuation coefficient; The second coefficient determination module is configured to input the predicted total consumption fluctuation coefficient into the linear correlation, and calculate a predicted total inflow fluctuation coefficient corresponding to the predicted total consumption fluctuation coefficient according to the linear correlation; The regression equation determination module is configured to determine a historical inflow fluctuation coefficient of each pipe network type according to historical inflow data of each pipe network type, and determine a linear regression equation between the historical total inflow fluctuation coefficient and the historical inflow fluctuation coefficient of each pipe network type; The third coefficient determination module is configured to input the predicted total inflow fluctuation coefficient into the linear regression equation, and calculate a predicted inflow fluctuation coefficient of each pipe network type; The pipe transmission base load determination module is configured to determine a primary pipe transmission base load of each pipe network type according to a pipe network topology of a region to which each pipe network type belongs; wherein the pipe network topology is used to represent a connection relationship between a gas source and a gas source export pipeline; The fourth coefficient determination module is configured to determine a peak shaving margin coefficient according to the predicted inflow fluctuation coefficient of each pipe network type, and determine an emergency margin coefficient according to a limit working condition influence coefficient of each pipe network type; The transmission capacity determination module is configured to determine a pipe network transmission capacity of each pipe network type according to the primary pipe transmission base load, the peak shaving margin coefficient and the emergency margin coefficient of each pipe network type. The fourth coefficient determination module includes: The margin coefficient determination unit is configured to determine a difference between the predicted inflow fluctuation coefficient of each pipe network type and a first preset value as a first result, determine a pipe network type corresponding to the predicted inflow fluctuation coefficient, and determine a corresponding peak shaving margin coefficient according to the first result and the pipe network type; the first value is equal to 1. The emergency margin determination unit is configured to extract a temperature limit parameter and an efficiency reduction parameter pre-set in the limit working condition influence coefficient of each pipe network type, determine a product of the temperature limit parameter and the efficiency reduction parameter, and determine an inverse of the product minus 1 as an emergency margin coefficient of each pipe network type.

8. An electronic device, comprising: The electronic device includes: at least one processor; and a memory connected with the at least one processor in communication; wherein The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the data determination method of any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for enabling the processor to implement the data determination method of any one of claims 1-6 when executed.

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