Natural gas resource gas supply capacity uncertainty quantification method and system

Abstract

The invention relates to a natural gas resource gas supply capability uncertainty quantification method and system. The method comprises the following steps: determining a natural gas resource division basis; according to the division basis, different gas supply states of the natural gas resources are divided; duration time distribution among different gas supply states is determined; according to the duration time distribution, constructing a natural gas resource state random transfer model; and according to different gas supply state division results of the natural gas resources and historical gas supply data of the natural gas resources, determining gas supply capability compliance distribution of each gas supply level of the natural gas resources, and determining the gas supply capability by using a natural gas resource state random transfer model. The system comprises a first determination module, a division module, a second determination module, a construction module and a third determination module. A theoretical basis is provided for establishment of a more accurate gas supply reliability evaluation model of a natural gas supply chain system by establishing a natural gas resource gas supply capacity uncertainty quantification method and system considering time-varying characteristics and volatility.

Description

Technical Field

[0001] The present invention belongs to the technical field of natural gas supply chain evaluation, and in particular relates to a method and system for quantifying uncertainty in gas supply capacity of natural gas resources. Background Art

[0002] In the process of evaluating the gas supply reliability of the natural gas supply chain system, it is necessary to consider not only the factors related to the pipeline network process and unit failure, but also the uncertainty of the upstream resource supply capacity. Natural gas resources are affected by various factors during the gas supply process, and their gas supply capacity is time-varying and volatile. In the current calculation process of the gas supply reliability of the natural gas supply chain system, there are two types of assumptions used to describe the gas supply capacity of natural gas resources: one is to assume that the gas supply capacity of natural gas resources is a constant, that is, at any time during the system gas supply reliability assessment, natural gas resources can always be stably supplied; the second is to construct the natural gas resources into a probabilistic model, which assumes that the natural gas resources have two states: normal gas supply state and interrupted gas supply state.

[0003] However, in actual projects, natural gas resources are affected by many factors, such as policies, natural disasters, facilities, etc., and their supply capacity is uncertain. Therefore, the assumption that upstream resources can be stably supplied leads to an overestimation of the reliability of the natural gas supply chain, resulting in the reliability calculation results being inconsistent with the actual situation. Therefore, the existing natural gas resource uncertainty quantification methods ignore its time-varying and time-dependent nature. Summary of the invention

[0004] In view of the above problems, the technical solution adopted by the present invention is as follows: a method for quantifying the uncertainty of natural gas resource supply capacity, the method comprising the following steps:

[0005] Determine the basis for natural gas resource division;

[0006] According to the classification basis, different gas supply states of natural gas resources are classified;

[0007] Determine the duration distribution between different air supply states;

[0008] According to the duration distribution, a natural gas resource state random transfer model is constructed;

[0009] According to the results of the classification of different gas supply states of natural gas resources and the historical gas supply data of natural gas resources, the gas supply capacity of each gas supply level of natural gas resources is determined to obey the distribution, and the gas supply capacity is determined using the random transition model of natural gas resource states.

[0010] Furthermore, the basis for determining the division of natural gas resources includes: d Based on the daily contract quantity Q stipulated in the take-or-pay clause d70% of the total gas supply is used as the basis for the natural gas supply level division, and the delivery coefficient ω is introduced to correct the daily contract volume Q d .

[0011] Furthermore, the introduction of the delivery coefficient ω modifies the daily contract quantity Q d include:

[0012] Q d-w =Q d ×ω w ,

[0013] Q d-os =Q d ×ω os ,

[0014]

[0015] Among them, ω w represents the winter delivery factor, ω os Denotes the delivery factor for other seasons, Q d-w represents the daily contract volume in winter, Q d-os represents the daily contract volume in other seasons, Q contract It represents the sum of the gas volume initially agreed upon in the contract and the additional gas volume confirmed by the written agreement.

[0016] Furthermore, the classification of different gas supply states of natural gas resources according to the classification basis includes:

[0017] When the task time of natural gas supply chain system reliability assessment is winter and other seasons, the division means are:

[0018] In winter, the natural gas supply status is divided into:

[0019]

[0020] In other seasons, the natural gas supply status is divided into:

[0021]

[0022] When conducting long-term system reliability assessment, the daily unevenness coefficient is introduced to divide the gas supply status of natural gas resources, specifically:

[0023]

[0024] Among them, i0 represents the normal gas supply state; i1 represents the degraded gas supply state; i2 represents the interrupted gas supply state; K mn represents the daily non-uniformity coefficient; Q h Indicates the gas supply capacity on a certain day in the previous year.

[0025] Furthermore, determining the duration distribution between different air supply states includes: treating the time T required for transition between different air supply states as a random variable, and obtaining the time {T1, T2, ..., T m};

[0026] The Kolmogorov-Smirnov test method is used to determine the appropriate distribution and distribution parameters of the random variable time T, thereby determining the transfer rate matrix and the duration distribution of the transfer from the normal gas supply state i0 to the degraded gas supply state i1.

[0027] Furthermore, the transfer rate matrix is:

[0028]

[0029] The duration distribution includes a probability density distribution f T (t) and the cumulative probability density distribution F T (t), satisfying the following relationship:

[0030]

[0031] Among them, Q trans represents the transfer rate matrix; λ nm (t) represents the rate of transition from state n to state m at time t; P(t<T≤t+dt|T> t) indicates that there is no state transition at time t, and t+dt indicates the probability of state transition at time t; λ nm Indicates the transfer rate from gas supply state n to gas supply state m; the value of n is 0, 1, ... N-1; the value of m is 0, 1, ... N-1, and N refers to the number of states.

[0032] Furthermore, the construction of the natural gas resource state random transfer model according to the duration distribution includes: assuming that the natural gas resource enters the normal gas supply state i0 at time t', then defining the natural gas resource to undergo the next transfer at time t and enter the state i n The probability is:

[0033] K(t,i n |t′,i0)=T(t|t′,i0)dt·C(i n |t,i0);

[0034] Where T(t|t′,i0)dt represents the conditional probability of the natural gas resource undergoing the next state transition at time t, under the condition that the natural gas resource is transferred at time t′ and enters the normal gas supply state i0; C(i n|t,i0) represents the conditional probability that the natural gas resource enters state k under the condition that the initial state of the natural gas resource is k' and the state transition occurs at time t;

[0035] The time t when the next state transition occurs is sampled from the conditional probability T(t|t',k')dt, and the state k of the next state transition is sampled from the conditional probability C(k|k',t); the sampling process is repeated until the task time is reached, thereby obtaining the state at each time point within the task time.

[0036] Furthermore, the method of determining the gas supply capacity of each gas supply level of the natural gas resource according to the different gas supply state classification results of the natural gas resource and the historical gas supply data of the natural gas resource, and determining the gas supply capacity using the natural gas resource state random transition model includes: the method of assuming that the random variable Q represents the gas supply capacity of a certain gas supply level according to the different gas supply state classification results of the natural gas resource and the historical gas supply data of the natural gas resource, forming gas supply capacity samples {q1, q2, ..., q n}; The Kolmogorov-Smirnov test method is used to estimate the probability density function f(q) of the gas supply capacity of the natural gas resource in each gas supply state; and the gas supply capacity is determined by Monte Carlo sampling based on the probability density function.

[0037] In addition, the present invention also proposes a natural gas resource supply capacity uncertainty quantification system, the system includes a first determination module, a division module, a second determination module, a construction module and a third determination module; wherein,

[0038] The first determination module is used to determine the basis for natural gas resource division;

[0039] The division module is used to divide the natural gas resources into different gas supply states according to the division basis;

[0040] The second determination module is used to determine the duration distribution between different gas supply states;

[0041] The construction module is used to construct a natural gas resource state random transfer model according to the duration distribution;

[0042] The third determination module is used to determine the gas supply capacity of each gas supply level of the natural gas resource according to the different gas supply state division results of the natural gas resource and the historical gas supply data of the natural gas resource, and determine the gas supply capacity using the natural gas resource state random transition model.

[0043] Further, the construction module includes a definition unit and a sampling unit, wherein;

[0044] The definition unit is used to assume that the natural gas resource enters the normal gas supply state i0 at time t', and define the natural gas resource to undergo the next transfer at time t and enter the state i n The probability is:

[0045] K(t,i n |t′,i0)=T(t|t′,i0)dt·C(i n |t,i0);

[0046] Where T(t|t′,i0)dt represents the conditional probability of the natural gas resource undergoing the next state transition at time t, under the condition that the natural gas resource is transferred at time t′ and enters the normal gas supply state i0; C(i n |t,i0) represents the conditional probability that the natural gas resource enters state k under the condition that the initial state of the natural gas resource is k' and the state transition occurs at time t;

[0047] The sampling unit is used to sample the time t when the next state transition occurs from the conditional probability T(t|t',k')dt, and to sample the state k of the next state transition from the conditional probability C(k|k',t); the sampling process is repeated until the task time is reached, thereby obtaining the state at each time point within the task time.

[0048] The present invention makes up for the deficiencies of existing research by establishing a method and system for quantifying the uncertainty of natural gas resource supply capacity taking into account time-varying and volatile characteristics, and provides a theoretical basis for establishing a more accurate natural gas supply chain system gas supply reliability assessment model.

[0049] Other features and advantages of the present invention will be described in the following description, and part of them will become obvious from the description, or be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 A flow chart of a method for quantifying uncertainty in gas supply capacity of natural gas resources;

[0052] Figure 2 is (t',i0)→(t,i n )'s transfer state diagram;

[0053] Figure 3 A schematic diagram of a natural gas resource supply capacity uncertainty quantification system. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] like Figure 1 As shown, a method for quantifying uncertainty in gas supply capacity of natural gas resources comprises the following steps: determining the basis for natural gas resource division;

[0056] According to the classification basis, different gas supply states of natural gas resources are classified;

[0057] Determine the duration distribution between different air supply states;

[0058] According to the duration distribution, a natural gas resource state random transfer model is constructed;

[0059] According to the results of the classification of different gas supply states of natural gas resources and the historical gas supply data of natural gas resources, the gas supply capacity of each gas supply level of natural gas resources is determined to obey the distribution, and the gas supply capacity is determined using the random transition model of natural gas resource states.

[0060] Common types of natural gas resources include: domestic conventional oil and gas fields, domestic unconventional oil and gas fields, coal-to-gas projects, pipeline imported gas and LNG (liquefied natural gas) receiving stations, etc. Since natural gas resources are affected by many factors, such as policies, natural disasters, facilities, etc., there is uncertainty in their supply capacity.

[0061] In order to deal with the uncertainty of its supply capacity, natural gas sources sign take-or-pay contracts with consumers to constrain them. The classic "take-or-pay" clause is: "According to the terms of the agreement, the natural gas seller regularly delivers products to the buyer, and the buyer shall pay the seller for the products. Even if the buyer does not extract natural gas, he shall pay the fee according to the agreed extraction volume per period." The specific calculation formula is:

[0062] Q take or pay =(Q contrat ×N)-(P+Q reduce );

[0063] Among them, Q take or pay Indicates take-or-pay quantity, unit is 10 8Nm 3 ;Q contract It represents the sum of the gas volume originally agreed in the contract and the additional gas volume confirmed in the written agreement, in units of 10 8 Nm 3 ; N represents the take-or-pay coefficient (for example, it can be 0.9); P represents the reduction of gas volume due to force majeure factors agreed by both parties, the unit is 10 8 Nm 3 ;Q reduce Indicates the planned gas reduction volume negotiated by both parties, in units of 10 8 Nm 3 .

[0064] Despite the constraints of the take-or-pay clause, changes in gas supply capacity due to natural gas price fluctuations, policy changes, equipment failure and other force majeure factors are inevitable. According to the take-or-pay clause, when the natural gas supply capacity does not reach 70% of the daily contract volume, the shortfall will be accumulated to the take-or-pay volume Q take or pay middle.

[0065] In the embodiment of the present invention, the daily contract quantity Q d Based on the daily contract quantity Q stipulated in the take-or-pay clause d At the same time, since winter is the peak season for natural gas consumption, the delivery coefficient ω is introduced to correct the daily contract volume Q d The correction process is as follows:

[0066] Q d-w =Q d ×ω w ,

[0067] Q d-os =Q d ×ω os ,

[0068]

[0069] Among them, ω w represents the winter delivery factor, ω os Denotes the delivery factor for other seasons, Q d-w represents the daily contract volume in winter, Q d-os Indicates the daily contract volume in other seasons.

[0070] The present invention considers that the random process of natural gas resource supply capacity follows a semi-Markov process, which is a random process involving transitions between distribution states of different durations. The random process of natural gas resource supply capacity from 0 to t is represented by {Q(t), t∈T}, and its supply state space is represented by I={i0,i1,i2,…,i n}. Therefore, the gas supply state of natural gas resources is defined as the supply level, which can be characterized by a finite set of states. The number of gas supply states of natural gas resources can be determined based on historical gas supply data, and the number of divisions is generally not less than three; but too many will result in too few data samples that can be used in each state. Therefore, in the embodiment of the present invention, the number of gas supply states of natural gas resources is set to 3-5.

[0071] Assume that a natural gas resource has three gas supply states: normal gas supply state i0; degraded gas supply state i1; interrupted gas supply state i2. The three gas supply states are described as follows:

[0072] (1) Normal gas supply state i0: Natural gas resources are supplied normally, the demand of downstream users in the natural gas supply chain is met, and there is a small amount of excess gas, which can be stored in pipelines or injected into gas storage for backup;

[0073] (2) Degraded gas supply status i1: The gas supply level of natural gas resources is low, and the demand of downstream users in the natural gas supply chain cannot be met. There is a small amount of gas shortage, which can be supplemented by extracting natural gas from pipelines or using gas storage facilities;

[0074] (3) Gas supply interruption status i2: The natural gas resources can no longer supply gas, the gas supply chain is completely cut off, and other gas sources need to be found as alternatives.

[0075] According to the division basis given in the above steps, the division method of winter and other seasons can be determined as follows:

[0076] In winter, the natural gas supply status is divided into:

[0077]

[0078] In other seasons, the natural gas resource supply status is divided into:

[0079]

[0080] When the task time of the reliability assessment of the natural gas supply chain system is winter and other seasons, the above classification basis can be used to classify the gas supply status of natural gas resources. However, when conducting a system reliability assessment over a longer period, the method of classifying different states according to seasons is relatively cumbersome. Therefore, the present invention proposes a natural gas resource state classification method based on the daily non-uniformity coefficient, which is applicable to any period, as follows:

[0081]

[0082]

[0083] Among them, K mn represents the daily non-uniformity coefficient; Qh Indicates the gas supply capacity on a certain day in the previous year.

[0084] After the natural gas resources are divided into finite states according to the above steps, how to determine the random transitions between states is the key to describing the time-varying nature of natural gas resources. The semi-Markov process jumps between these states according to a transition rate matrix, which describes the possibility of transitioning from one state to another. The duration of each state is determined by various factors, such as equipment, technology, environmental issues, market demand, and infrastructure. These factors may cause the duration to follow an arbitrary distribution, which means that the duration distribution may change from one state to another. The semi-Markov process allows a different duration distribution to be used for each state, making it a more flexible and accurate model for capturing the complex dynamics of natural gas supply capacity. In an embodiment of the present invention, the parameters of the transition rate matrix and the duration distribution are determined based on the KS test.

[0085] After the state division, the time T required for the transition between states is regarded as a random variable, and the time {T1, T2, …, T m}; The Kolmogorov-Smirnov test method is used to determine the appropriate distribution and distribution parameters of the random variable time T, thereby determining the probability density distribution f of the duration T (t) and the cumulative probability density distribution F T (t). The transfer rate matrix is ​​calculated as follows:

[0086]

[0087]

[0088] Among them, Q trans represents the transfer rate matrix; λ nm (t) represents the rate of transition from state n to state m at time t; P(t<T≤t+dt|T> t) indicates that there is no state transition at time t, and t+dt indicates the probability of state transition at time t; λ nm Indicates the transfer rate from gas supply state n to gas supply state m; the value of n is 0, 1, ... N-1; the value of m is 0, 1, ... N-1, and N refers to the number of states.

[0089] According to the constructed natural gas resource semi-Markov process, a natural gas resource state random transition model is constructed based on the time series Monte Carlo method. Figure 2 The state transition diagram is shown. Assuming that the natural gas resource enters state i0 at time t', it is defined that the natural gas resource undergoes the next transition at time t and enters state i n The probability is:

[0090] K(t,i n |t′,i0)=T(t|t′,i0)dt·C(i n |t,i0);

[0091] Where T(t|t',i0)dt represents the conditional probability of the next transfer of natural gas resources at time t, under the condition that the natural gas resources transfer at time t' and enter state i0; C(i n |i0,t) represents the conditional probability that the natural gas resource enters state k given that the initial state of the natural gas resource is k' and the next state transition occurs at time t.

[0092] In the embodiment of the present invention, the sequential Monte Carlo method is used to simulate the system state transition process. The specific steps are as follows: first, the time t of the next state transition is sampled from the conditional probability T(t|t',k')dt, and the state k of the next system transition is sampled from the conditional probability C(k|k',t). The sampling process is repeated until the task time is reached, so that the state at each time point within the task time can be obtained.

[0093] According to the above steps, the supply level of natural gas resources at any time within the mission time can be determined based on the time series Tekalo method. However, in the process of evaluating the reliability of the natural gas supply chain system, the input of the natural gas resource is an exact gas supply capacity value rather than a state value.

[0094] Therefore, based on the gas supply state classification results of the natural gas resources in the above steps and the collected historical gas supply data of natural gas resources, the gas supply capacity of different gas supply levels forms a sample. Assuming that the random variable Q is the gas supply capacity of a certain gas supply level, a sample {q1, q2, ..., q n The nonparametric Kolmogorov-Smirnov test method is used to estimate the probability density function f(q) of the gas supply capacity of natural gas resources in each state. Based on this probability density function, its gas supply capacity can be determined by Monte Carlo sampling.

[0095] At the same time, the present invention also proposes a natural gas resource supply capacity uncertainty quantification system, such as Figure 3 As shown, the system includes a first determination module, a division module, a second determination module, a construction module and a third determination module; wherein,

[0096] The first determination module is used to determine the basis for natural gas resource division;

[0097] The division module is used to divide the natural gas resources into different gas supply states according to the division basis;

[0098] Used to determine the duration distribution between different gas supply states;

[0099] The construction module is used to construct a natural gas resource state random transfer model according to the duration distribution;

[0100] The third determination module is used to determine the gas supply capacity of each gas supply level of the natural gas resource according to the different gas supply state division results of the natural gas resource and the historical gas supply data of the natural gas resource, and determine the gas supply capacity using the natural gas resource state random transition model.

[0101] Specifically, the construction module includes a definition unit and a sampling unit, wherein the definition unit is used to assume that the natural gas resource enters the normal gas supply state i0 at time t', and define that the natural gas resource undergoes the next transfer at time t and enters the state i n The probability is:

[0102] K(t,i n |t′,i0)=T(t|t′,i0)dt·C(i n |t,i0);

[0103] Where T(t|t′,i0)dt represents the conditional probability of the natural gas resource undergoing the next state transition at time t, under the condition that the natural gas resource is transferred at time t′ and enters the normal gas supply state i0; C(i n |t,i0) represents the conditional probability that the natural gas resource enters state k under the condition that the initial state of the natural gas resource is k' and the state transition occurs at time t;

[0104] The sampling unit is used to sample the time t when the next state transition occurs from the conditional probability T(t|t',k')dt, and to sample the state k of the next state transition from the conditional probability C(k|k',t); the sampling process is repeated until the task time is reached, thereby obtaining the state at each time point within the task time.

[0105] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent substitutions for some of the technical features therein; and these modifications or substitutions do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for quantifying uncertainty in natural gas resource supply capacity, characterized in that: The method comprises the following steps: Determine the basis for natural gas resource division; According to the classification basis, different gas supply states of natural gas resources are classified; Determine the duration distribution between different air supply states; According to the duration distribution, a natural gas resource state random transfer model is constructed; According to the results of the classification of different gas supply states of natural gas resources and the historical gas supply data of natural gas resources, the gas supply capacity of each gas supply level of natural gas resources is determined to obey the distribution, and the gas supply capacity is determined using the random transition model of natural gas resource states.

2. The method according to claim 1, characterized in that The basis for determining the division of natural gas resources includes: Daily contract volume Q d Based on the daily contract quantity Q stipulated in the take-or-pay clause d 70% of the total gas supply is used as the basis for the natural gas supply level division, and the delivery coefficient ω is introduced to correct the daily contract volume Q d .

3. The method according to claim 2, characterized in that The introduction of the delivery coefficient ω modifies the daily contract quantity Q d include: Q d-w =Q d ×ω w , Q d-os =Q d ×ω os , Among them, ω w represents the winter delivery factor, ω os Denotes the delivery factor for other seasons, Q d-w represents the daily contract volume in winter, Q d-os represents the daily contract volume in other seasons, Q contract It represents the sum of the gas volume initially agreed upon in the contract and the additional gas volume confirmed by the written agreement.

4. The method according to claim 3, characterized in that According to the classification basis, the different gas supply states of natural gas resources are classified as follows: When the task time of natural gas supply chain system reliability assessment is winter and other seasons, the division means are: In winter, the natural gas supply status is divided into: In other seasons, the natural gas supply status is divided into: When conducting long-term system reliability assessment, the daily unevenness coefficient is introduced to divide the gas supply status of natural gas resources, specifically: Among them, i0 represents the normal gas supply state; i1 represents the degraded gas supply state; i2 represents the interrupted gas supply state; K mn represents the daily non-uniformity coefficient; Q h Indicates the gas supply capacity on a certain day in the previous year.

5. The method according to claim 4, characterized in that Determining the duration distribution between different gas supply states includes: The time T required for the transition between different air supply states is taken as a random variable to obtain the time {T1, T2, …, T m }; The Kolmogorov-Smirnov test method is used to determine the appropriate distribution and distribution parameters of the random variable time T, thereby determining the transfer rate matrix and the duration distribution of the transfer from the normal gas supply state i0 to the degraded gas supply state i1.

6. The method according to claim 5, characterized in that The transfer rate matrix is: The duration distribution includes a probability density distribution f T (t) and the cumulative probability density distribution F T (t), satisfying the following relationship: Among them, Q trans represents the transfer rate matrix; λ nm (t) represents the rate of transition from state n to state m at time t; P(t<T≤t+dt|T> t) indicates that there is no state transition at time t, and t+dt indicates the probability of state transition at time t; λ nm Indicates the transfer rate from gas supply state n to gas supply state m; the value of n is 0, 1, ... N-1; the value of m is 0, 1, ... N-1, and N refers to the number of states.

7. The method according to claim 5 or 6, characterized in that: The constructing of a natural gas resource state random transfer model according to the duration distribution includes: Assuming that the natural gas resource enters the normal gas supply state i0 at time t', it is defined that the natural gas resource undergoes the next transfer at time t and enters the state i n The probability is: K(t,i n |t′,i0)=T(t|t′,i0)dt·C(i n |t,i0); Where T(t|t′,i0)dt represents the conditional probability of the natural gas resource undergoing the next state transition at time t, under the condition that the natural gas resource is transferred at time t′ and enters the normal gas supply state i0; C(i n |t,i0) represents the conditional probability that the natural gas resource enters state k under the condition that the initial state of the natural gas resource is k' and the state transition occurs at time t; The time t when the next state transition occurs is sampled from the conditional probability T(t|t',k')dt, and the state k of the next state transition is sampled from the conditional probability C(k|k',t); the sampling process is repeated until the task time is reached, thereby obtaining the state at each time point within the task time.

8. The method according to claim 4, characterized in that According to the results of the classification of different gas supply states of natural gas resources and the historical gas supply data of natural gas resources, the gas supply capacity of each gas supply level of natural gas resources is determined to obey the distribution, and the gas supply capacity is determined using the random transition model of natural gas resource states, including: According to the classification results of different gas supply states of natural gas resources and the historical gas supply data of natural gas resources, it is assumed that the random variable Q represents the gas supply capacity of a certain gas supply level, and the gas supply capacity samples {q1, q2, ..., q n }; The Kolmogorov-Smirnov test method is used to estimate the probability density function f(q) of the gas supply capacity of natural gas resources in each gas supply state; According to the probability density function, the gas supply capacity is determined by Monte Carlo sampling.

9. A natural gas resource supply capacity uncertainty quantification system, characterized in that: The system includes a first determination module, a division module, a second determination module, a construction module and a third determination module; wherein, The first determination module is used to determine the basis for natural gas resource division; The division module is used to divide the natural gas resources into different gas supply states according to the division basis; The second determination module is used to determine the duration distribution between different gas supply states; The construction module is used to construct a natural gas resource state random transfer model according to the duration distribution; The third determination module is used to determine the gas supply capacity of each gas supply level of the natural gas resource according to the different gas supply state division results of the natural gas resource and the historical gas supply data of the natural gas resource, and determine the gas supply capacity using the natural gas resource state random transition model.

10. The system according to claim 9, characterized in that The building block includes a definition unit and a sampling unit, wherein; The definition unit is used to assume that the natural gas resource enters the normal gas supply state i0 at time t', and define the natural gas resource to undergo the next transfer at time t and enter the state i n The probability is: K(t,i n |t′,i0)=T(t|t′,i0)dt·C(i n |t,i0); Where T(t|t′,i0)dt represents the conditional probability of the natural gas resource undergoing the next state transition at time t, under the condition that the natural gas resource is transferred at time t′ and enters the normal gas supply state i0; C(i n |t,i0) represents the conditional probability that the natural gas resource enters state k under the condition that the initial state of the natural gas resource is k' and the state transition occurs at time t; The sampling unit is used to sample the time t when the next state transition occurs from the conditional probability T(t|t',k')dt, and to sample the state k of the next state transition from the conditional probability C(k|k',t); the sampling process is repeated until the task time is reached, thereby obtaining the state at each time point within the task time.

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