Gas station risk level assessment method

Abstract

The invention provides a gas station risk level assessment method. The method comprises the following steps: carrying out structure division on the gas station, and determining equipment and parts forming the gas station; determining a signal monitoring point of the key equipment, and acquiring a monitoring signal in real time from a sensor installed at the signal monitoring point; and estimating the risk level of the gas station based on the monitoring signal and the equipment parameters, and performing safety situation prediction by establishing a hidden Markov model. According to the method, the gas station is subjected to structure division and key signal monitoring point setting, the risk of the gas station is subjected to grade quantification, and the safety situation prediction is carried out based on the hidden Markov model, so that the risk assessment of the gas station is more accurate and reliable.

Description

Technical Field

[0001] The present invention belongs to the technical field of risk assessment, and in particular relates to a method for assessing the risk level of a gas station. Background Art

[0002] With the continuous development of the economy and the continuous improvement of people's living standards, the supply and demand of industrial and domestic energy has grown rapidly. In recent years, the energy structure has begun to transform and develop. The original structure with coal and oil as the core energy will be transformed and transitioned to new energy sources such as cleaner and more environmentally friendly hydropower, nuclear energy, and natural gas. Natural gas, as a high-quality energy, has received unprecedented attention and development, and its status in China's energy strategy is also constantly improving. my country's main natural gas production areas are concentrated in the west, and most of the natural gas resources are imported from abroad, far away from the core consumption areas of the southeast coast of China, so natural gas long-distance pipelines and gas supply stations have become infrastructure construction connecting natural gas production areas and consumption areas. As an important part of the long-distance pipelines between urban and rural areas, gas stations undertake the core task of ensuring the safe and stable operation of gas transmission and pipelines, and play a vital role.

[0003] Gas stations are the most basic network units in the natural gas transmission system network. Any urban and rural natural gas pipeline transportation must first pass this checkpoint. There are a large number of flammable and explosive substances with high risk levels such as natural gas and hydrocarbons in gas stations, as well as a large number of gas industrial equipment and implementation. Once natural gas leaks due to equipment failure and other reasons, it will directly cause fires or even explosions in the gas supply station, which will cause significant losses to the gas company and the energy supply of the entire city and the property and personal safety of the people. Therefore, the safety work of gas stations is the top priority in the operation of gas companies. Monitoring the operating status of key equipment in gas stations, conducting risk assessments of gas stations, and proposing effective improvement measures based on the results of risk assessments are important components of gas station safety work. Summary of the invention

[0004] In order to solve the above problems existing in the prior art, the present invention provides a method for assessing the risk level of a gas station.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions.

[0006] A method for assessing the risk level of a gas station comprises the following steps:

[0007] Divide the structure of the gas station and determine the equipment and components that make up the gas station;

[0008] Determine the signal monitoring points of key equipment and obtain monitoring signals in real time from sensors installed at the signal monitoring points;

[0009] Estimate the risk level of the gas station based on the monitoring signals and equipment parameters, and predict the safety situation by establishing a hidden Markov model.

[0010] Furthermore, the gas station includes pipelines, valves, pressure regulators, filters, relief pipes, and gas-using equipment.

[0011] Furthermore, the signal monitoring points are set at the inlets and outlets of key equipment, important nodes of pipelines, and equally divided points in the pipeline length direction.

[0012] Furthermore, the method for estimating the risk level of the gas station includes:

[0013] Calculate the process processing degree E, which is used to characterize the comprehensive ability of the gas station to process, transform, and handle gas;

[0014] Calculate the layout density F, which is used to characterize the strength of the association and cooperation between one signal monitoring point and other signal monitoring points;

[0015] Calculate the risk level of the gas station based on the process processing degree E and the layout density F.

[0016] Even further, the method for calculating the risk level of the gas station based on E and F includes:

[0017] Set the process processing degree threshold E0 and the layout density threshold F0;

[0018] If E ≤ E0 and F ≤ F0, the risk level is level 1;

[0019] If E > E0 and F ≤ F0, the risk level is level 2;

[0020] If E ≤ E0 and F > F0, the risk level is level 3;

[0021] If E > E0 and F > F0, the risk level is level 4.

[0022] Even further, the calculation formula for the layout density F is:

[0023] F = k1F1 + k2F2 (1)

[0024] In the formula, k1 and k2 are weighting coefficients, 0 < k1 < 1, 0 < k2 < 1, k1 + k2 = 1, F1 is the normalized value of the average distance between on-site equipment, which is equal to the ratio of the average value of the distances between adjacent equipment to the maximum distance, F2 is the equipment layout efficiency index, and the calculation formula for F2 is:

[0025] F2 = S v / S z ×(1 - ε) (2)

[0026] In the formula, S v is the actual floor space occupied by all equipment, S z is the area occupied by the equipment, ε is the equipment complexity coefficient, which is equal to the mean of all equipment complexity coefficients. The complexity coefficient of a single device is determined by the complexity of the device shape and takes a value between 0 and 1. The larger the value, the more complex the device.

[0027] Furthermore, the calculation formula of the process degree E is:

[0028] E=E1×E2×E3×E4 (3)

[0029] Where E1 is the normalized value of the number of key equipment, E2 is the normalized value of the average value of equipment performance parameters, E3 is the equipment operation status index, and E4 is the synergy coefficient.

[0030] Furthermore, the methods for predicting security situations based on hidden Markov models include:

[0031] S1, based on the risk level observation sequence O = {O t}Establish hidden Markov model: λ=[π,A,B];

[0032] Among them, O t is the observed value of the risk level at the t-th moment, t=1,2,......,T, T is the current moment; the i-th risk level S i The value of is i, i = 1, 2, 3, 4; π is the initial state probability vector, π = {π i}, π i The risk level at the first moment is S i The probability of state transition; A is the state transition probability matrix, denoted by A[a ij ], a ij The risk level S at the t-1th moment i Transfer to the risk level S at the tth moment j The probability of; B is the output observation probability matrix, denoted as B[b ij ], b ij The risk level is S i When the risk level observation value is S j probability;

[0033] S2, initialization:

[0034] α1(i)=π i b i (O1) (4)

[0035] In the formula, α1(i) is the risk level S at the first moment i The probability of i (O1) = bij , O1=S j , i=1,2,3,4;

[0036] S3, iterative calculation:

[0037]

[0038] In the formula, α t+1 (i) The risk level at the t+1th moment is S i The probability of i (O t+1 )=b ij , O t+1 =S j , i=1,2,3,4, t=1,2,...,T-1;

[0039] S4. Probability of risks in exporting gas stations:

[0040]

[0041] According to formula (5), the probability of each risk level can be predicted, and according to formula (6), the probability of the gas station having risk can be predicted.

[0042] Furthermore, the method also includes: establishing a time series of monitoring signals, decomposing the time series into a trend series, a periodic series and a residual series, and predicting the monitoring signals in the future.

[0043] Furthermore, the monitoring signals in the future are predicted based on the SARIMA model.

[0044] Compared with the prior art, the present invention has the following beneficial effects.

[0045] The present invention divides the structure of the gas station, determines the equipment and components that constitute the gas station, determines the signal monitoring points of key equipment, obtains monitoring signals in real time from sensors installed at the signal monitoring points, estimates the risk level of the gas station based on the monitoring signals and equipment parameters, and predicts the safety situation by establishing a hidden Markov model, thereby realizing the assessment of the risk level of the gas station. The present invention divides the structure of the gas station and sets key signal monitoring points, quantifies the level of the gas station risk, and predicts the safety situation based on the hidden Markov model, making the risk assessment of the gas station more accurate and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 The present invention is a flowchart of a method for assessing the risk level of a gas station.

[0047] Figure 2 The figure is a schematic diagram of the structure of a gas station.

[0048] Figure 3 This is a schematic diagram of the mapping relationship between the hidden state and the observed state of the risk level of the gas station.

[0049] Figure 4 Schematic diagram of ACF and PACF. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solution and advantages of the present invention clearer and more understandable, the present invention is further described below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only 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 making creative work are within the scope of protection of the present invention.

[0051] Figure 1 This is a flow chart of a method for assessing the risk level of a gas station according to an embodiment of the present invention, comprising the following steps:

[0052] Step 101, structurally divide the gas station and determine the equipment and components that constitute the gas station;

[0053] Step 102, determining the signal monitoring point of the key equipment, and acquiring the monitoring signal in real time from the sensor installed at the signal monitoring point;

[0054] Step 103, estimating the risk level of the gas station based on the monitoring signal and equipment parameters, and predicting the safety situation by establishing a hidden Markov model.

[0055] In this embodiment, step 101 is mainly used to divide the structure of the gas station. Since the state change law of the gas station equipment operation is not easy to describe directly, and there are multiple and complex data volumes, data forms and analysis methods in the gas station, it is necessary to first determine the system structure of the gas station to facilitate its in-depth analysis. The gas station is divided into the following 5 links:

[0056] Gas transportation: Gas transportation is the basic function of a gas station, which ensures that gas is safely and efficiently transmitted from the gas source to the user end. Pipelines are the main carrier of gas transportation, connecting the gas source and various usage points. Various valves are used to control the flow direction and flow of gas to ensure the safety and flexibility of the transportation process. Through the coordinated work of pipelines and valves, gas stations can achieve continuous and stable gas transportation to meet users' gas needs.

[0057] Gas start and stop link: The gas start and stop link controls the supply and cut-off of gas, which is crucial to ensure the safety and stability of the system. Valves play a key role in the start and stop link, and control the flow of gas by opening and closing the valve. In an emergency, the gas supply can be quickly cut off to prevent the accident from expanding; under normal circumstances, the flow of gas is adjusted according to demand.

[0058] Gas pressure regulation and flow control: During the transportation and use of gas, pressure and flow may need to be adjusted to meet the gas needs of different users or adapt to different transportation conditions. The pressure regulator is a key device to achieve changes in gas pressure and flow. The pressure regulator reduces the high-pressure gas in the transmission pipeline to a pressure suitable for downstream users. At the same time, it adjusts the gas flow according to user needs and prevents gas backflow when the downstream pressure is higher than the upstream. Through the adjustment of the pressure regulator, the gas station can ensure the stability and safety of the gas under various conditions of use.

[0059] Gas safety handling: As a flammable and explosive substance, gas safety handling is an indispensable part of gas stations. Filters play an important role in gas safety handling. They can remove impurities and pollutants in gas to ensure the cleanliness of gas. The vent pipe is used to discharge gas under certain circumstances to prevent excessive pressure or other safety hazards. Through filtering and discharge measures, gas stations can ensure the safe use of gas and reduce the risk of accidents.

[0060] Gas use link: The ultimate purpose of gas is to meet the gas needs of users, so the use link is the final link of the gas station system. Through the processing and guarantee of the previous links, gas can eventually be safely and efficiently supplied to the user end to meet the user's daily life and industrial production needs.

[0061] In this embodiment, step 102 is mainly used to determine the signal monitoring point and obtain the monitoring signal. After the gas station is structurally divided, signal monitoring points are set for the transportation, start and stop, processing and use of gas, and corresponding sensors are installed at the monitoring points to obtain monitoring signals from the sensors. Monitoring signals include gas pressure, temperature and flow, etc.

[0062] In this embodiment, step 103 is mainly used to perform risk assessment on the gas station. In this embodiment, the risk level of the gas station is first estimated based on the obtained monitoring signals and equipment parameters, that is, the risk level is quantified, for example, quantified into 1 to 4 risk levels, and a time series of the risk level is established. Then, a hidden Markov model is established to predict the safety situation, for example, predict the risk level of the gas station and the probability of risk.

[0063] As an optional embodiment, the gas station includes pipelines, valves, pressure regulators, filters, vent pipes and gas use equipment.

[0064] This embodiment provides the equipment that constitutes the gas station. According to the five links of the gas station divided above, the equipment belonging to each link is counted to obtain the equipment included in the gas station, such as pipelines, valves, pressure regulators, filters, vent pipes and gas use equipment. Figure 2 A structural schematic diagram of a gas station is given.

[0065] As an optional embodiment, the signal monitoring points are set at the inlet and outlet of key equipment, important nodes of the pipeline and equally divided points in the direction of the pipe length.

[0066] This embodiment provides a method for setting signal monitoring points. After the gas station is structurally divided, signal monitoring points are set for the transportation, start and stop, processing, and use of gas. The setting principles are as follows:

[0067] Signal monitoring points are set at the inlet and outlet of key equipment: A signal monitoring point is set at the inlet and outlet of each key equipment in the gas station, such as pressure regulators, filters, valves, etc. The inlet and outlet flow, pressure and other parameters of key equipment are monitored and extracted in real time.

[0068] Set up signal monitoring points at important nodes of the pipeline: Set up signal monitoring points at important nodes of the gas pipeline, such as branch points, confluence points, and midpoints or three-points of the pipeline length. These nodes are important links in the pipeline system, and by setting up signal monitoring points, the gas flow in the pipeline can be monitored.

[0069] Set up signal monitoring points on the load side: Set up signal monitoring points on the load side of the gas station, that is, the gas use point. For large gas users or key facilities, signal monitoring points should be set up at their incoming lines to monitor gas usage and consumption.

[0070] As an optional embodiment, the method for estimating the risk level of a gas station includes:

[0071] Calculate the process degree E, which is used to characterize the comprehensive ability of the gas station to process, transform and treat the gas;

[0072] Calculate the layout density F, where the layout density is used to characterize the strength of the association and coordination between a signal monitoring point and other signal monitoring points;

[0073] The risk level of the gas station is calculated based on the process degree E and layout density F.

[0074] This embodiment provides a technical solution for estimating the risk level of a gas station. In order to achieve a quantitative assessment of the risk of a gas station, this embodiment proposes two indicators, process degree and layout density, and calculates the risk level of a gas station based on the size of these two indicators. Process degree is a comprehensive reflection of the number of key equipment in a gas station and its processing capacity, which reflects the comprehensive ability of the gas station to process, transform and treat gas. Specifically, process degree can be quantified by counting the number of key equipment in the gas station, which includes but is not limited to pressure regulators, filters, etc. These equipment not only affect the process degree in quantity, but their performance, specifications and operating status are also important factors in evaluating the process degree. When describing the safety status of gas station operation and maintenance, layout density refers to the strength of the association and coordination of an observation node with other nodes. The greater the layout density, the more likely the node is to affect the normal operation of the entire gas station, thereby easily causing safety risk problems. Therefore, the layout density can reflect the risk status of the gas station.

[0075] As an optional embodiment, the method for calculating the risk level of a gas station based on E and F includes:

[0076] Set the process degree threshold E0 and layout density threshold F0;

[0077] If E≤E0, F≤F0, the risk level is 1;

[0078] If E>E0, F≤F0, the risk level is 2;

[0079] If E≤E0, F>F0, the risk level is 3;

[0080] If E>E0, F>F0, the risk level is 4.

[0081] This embodiment is a technical solution for calculating the risk level of a gas station based on E and F. This embodiment divides the process degree and layout density into larger and smaller values ​​(greater than the threshold is a larger value, less than or equal to the threshold is a smaller value) by setting a process degree threshold E0 and a layout density threshold F0 respectively, and then combines the larger and smaller values ​​of the process degree with the larger and smaller values ​​of the layout density to obtain four risk levels 1 to 4. For example, when both the process degree and the layout density take smaller values, the lowest risk level is 1; when both take larger values, the highest risk level is 4. The thresholds E0 and F0 are generally taken as the average values ​​of the process degree and the layout density, respectively.

[0082] Specifically for level 4 risks, a level 1 risk indicates that both the process processing degree and the layout density of the gas station are at a relatively low level, and the operation of the station is relatively safe; a level 2 risk indicates that the gas station has a low layout density, but there are many key devices with a high degree of process processing, and its potential risk is higher than that of a level 1 risk; a level 3 risk means that the gas station has a large layout density. Even if the process processing degree is not high, it has a high failure rate, and the operation of the gas station faces greater risks; a level 4 risk means that both indicators are in a high state, which is the highest risk level. Once an abnormality occurs during operation, it will cause serious consequences and immediate emergency measures need to be taken for intervention. The measures to be taken for each risk level are shown in Table 1.

[0083] Table 1 Measures for Responding to Multi-level Risks

[0084]

[0085]

[0086] As an optional embodiment, the calculation formula for the layout density F is:

[0087] F = k1F1 + k2F2 (1)

[0088] In the formula, k1 and k2 are weighting coefficients, 0 < k1 < 1, 0 < k2 < 1, k1 + k2 = 1, F1 is the normalized value of the average spacing of on-site equipment, which is equal to the ratio of the average value of the spacing between adjacent equipment to the maximum spacing, F2 is the equipment layout efficiency index, and the calculation formula for F2 is:

[0089] F2 = S v / S z ×(1 - ε) (2)

[0090] In the formula, S v is the actual floor area of all equipment, S z is the area occupied by the equipment, ε is the equipment complexity coefficient, which is equal to the average value of the complexity coefficients of all equipment. The complexity coefficient of a single equipment is determined by the shape complexity of the equipment and takes a value between 0 and 1. The larger the value, the more complex the equipment.

[0091] This embodiment gives a technical solution for calculating the layout density F. The layout density is equal to the weighted sum of the average spacing of on-site equipment (normalized value) and the equipment layout efficiency index, as shown in formula (1). The equipment layout efficiency is mainly determined by the ratio of the actual floor area of all equipment to the area occupied by the equipment, and is also negatively correlated with the equipment complexity. The specific calculation formula is as shown in formula (2).

[0092] As an optional embodiment, the calculation formula for the process processing degree E is:

[0093] E = E1 × E2 × E3 × E4 (3)

[0094] Where E1 is the normalized value of the number of key equipment, E2 is the normalized value of the average value of equipment performance parameters, E3 is the equipment operation status index, and E4 is the synergy coefficient.

[0095] This embodiment provides a technical solution for calculating the process degree E. The process degree E is equal to the product of four factors: the number of key equipment (normalized value) E1, the average value of equipment performance parameters E2, the equipment operation status index E3 and the synergy coefficient E4. The number of key equipment reflects the basic scale and processing capacity of the gas station, and can be quantified according to the actual number of key equipment in the gas station. The average value of equipment performance parameters represents the efficiency of the equipment in processing gas, which can be calculated by weighted averaging the performance parameters of key equipment. The equipment operation status index takes into account factors such as equipment stability, failure rate and maintenance status, and can be calculated based on the actual operation data of the equipment. For example, it can be expressed by the ratio of normal operation time of the equipment to total operation time. The synergy coefficient reflects the degree of coordination between equipment and the overall operating efficiency. This coefficient can be evaluated based on the actual synergy between the equipment, and it needs to rely on the experience and judgment of experts to obtain a coefficient between 0 and 1. The higher the coefficient, the better the synergy effect.

[0096] As an optional embodiment, a method for predicting security situation based on a hidden Markov model includes:

[0097] S1, based on the risk level observation sequence O = {O t}Establish hidden Markov model: λ=[π,A,B];

[0098] Among them, O t is the observed value of the risk level at the t-th moment, t=1,2,......,T, T is the current moment; the i-th risk level S i The value of is i, i = 1, 2, 3, 4; π is the initial state probability vector, π = {π i}, π i The risk level at the first moment is S i The probability of state transition; A is the state transition probability matrix, denoted by A[a ij ], a ij The risk level S at the t-1th moment i Transfer to the risk level S at the tth moment j The probability of; B is the output observation probability matrix, denoted as B[b ij ], b ij The risk level is S i The observed value of risk level is S j probability;

[0099] S2, initialization:

[0100] α1(i)=π i b i (O1) (4)

[0101] In the formula, α1(i) is the risk level S at the first moment i The probability of b i (O1) = b ij , O1=S j , i=1,2,3,4;

[0102] S3, iterative calculation:

[0103]

[0104] In the formula, α t+1 (i) The risk level at the t+1th moment is S i The probability of b i (O t+1 )=b ij , O t+1 =S j , i=1,2,3,4, t=1,2,...,T-1;

[0105] S4. Probability of risks in exporting gas stations:

[0106]

[0107] According to formula (5), the probability of each risk level can be predicted, and according to formula (6), the probability of the gas station having risk can be predicted.

[0108] This embodiment provides a technical solution for security situation prediction based on Hidden Markov Model HMM. First, the risk level observation sequence O = {O1, O2, ..., O T}After multiple iterations, the hidden Markov model of the gas station state is obtained. The Baum-Welch algorithm is an unsupervised learning algorithm that can estimate the parameters (state transfer matrix A) of the hidden Markov model HMM when there is only observation data but no corresponding hidden state data. Figure 3 As shown. In Python, the statsmodels library can be used to implement the Baum-Welch algorithm for HMM training. Then, based on the estimated HMM model parameters, the forward algorithm is used to calculate the probability α of the gas station at each risk level. t+1 (i), as shown in formula (5); by calculating the α of each risk level T (i) The sum is used to obtain the probability that the gas station has risk, as shown in formula (6).

[0109] As an optional embodiment, the method further includes: establishing a time series of monitoring signals, decomposing the time series into a trend series, a periodic series and a residual series, and predicting the monitoring signals in the future.

[0110] This embodiment establishes a time series of monitoring signals, analyzes the changing rules of monitoring signals over time, and on this basis predicts monitoring signals in the future. The time series of monitoring signals can be obtained according to the timestamp of the monitoring signal acquisition data. This embodiment first uses the time series decomposition method STL (Seasonal-Trend decomposition using LOESS) to decompose the time series of monitoring signals into three parts: trend series, seasonal series and residual series. Then perform trend analysis, seasonal analysis and residual analysis. Trend analysis: shows the long-term changes in data, whether the equipment performance gradually improves or decreases; seasonal analysis: shows the periodic changes in data, which helps to identify which months or quarters the equipment performance is usually better or worse; residual analysis: is the remaining part of the original data after removing the trend and seasonal components, which helps to identify outliers or random noise. This implementation combines the trend series and seasonal series obtained by STL decomposition, and constructs a prediction model to predict the changes in monitoring signals (equipment parameters and maintenance data) in the future.

[0111] As an optional embodiment, the monitoring signal of the future time is predicted based on the SARIMA (Seasonal Autoregressive Integrated Moving Average) model.

[0112] This embodiment predicts the monitoring signal of the future time by establishing a SARIMA model. The SARIMA model consists of two parts: an autoregressive model (AR) and a moving average model (MA). The AR model is a function that predicts the variable by its past observations, and the MA model is a function that predicts the variable by its past prediction errors.

[0113] Specifically, the SARIMA model is established by following the steps below:

[0114] SS1. Determine the seasonal variation period s of the time series.

[0115] Draw the time series curve y t (The horizontal axis is time t, the vertical axis is time series y t For monthly data (data collected once a month), if the seasonal change repeats once a year, the seasonal change cycle s = 12 (months).

[0116] SS2. Through data stabilization processing, the non-seasonal difference number d and the seasonal difference number D are obtained.

[0117] The statistical properties of non-stationary time series (such as mean and variance) change over time, which complicates the direct analysis of non-stationary time series. Non-stationary time series usually need to be converted into stationary time series through difference transformation. Unit root test can be used to check the stationarity of time series.

[0118] If the time series is non-stationary, first perform first-order or second-order differences, for example, the time series y t The first-order difference of: Δ=(1-L)y t =y t -y t-1 , until the data becomes stable, and the number of non-seasonal differences is d. If the seasonal component is still obvious, perform multiple seasonal differences on the series until the data becomes stable, and the number of seasonal differences is D. t The seasonal difference of is: Δ s =(1-L s )y t =y t -y t-s .

[0119] SS3. Determine the order p of the non-seasonal autoregressive term, the order q of the moving average term, as well as the order P of the seasonal autoregressive term and the order Q of the moving average term.

[0120] SS3.1. Draw the ACF and PACF diagrams of the time series after difference processing.

[0121] ACF is the autocorrelation function, and PACF is the partial autocorrelation function. The horizontal axis of the ACF graph represents the lag order, and the vertical axis represents the correlation coefficient between the corresponding lag sequence and the original sequence. The horizontal axis of the PACF graph represents the lag order, and the vertical axis represents the partial autocorrelation coefficient. Figure 4 shown.

[0122] SS3.2. Identify q, Q, p, and P from the ACF and PACF plots as follows:

[0123] Autoregressive term (p): Determined based on the lag value (horizontal axis value) corresponding to the first significant difference in the correlation coefficient in the PACF diagram.

[0124] Moving average term (q): determined according to the lag value (horizontal axis value) corresponding to the first significant difference in the autocorrelation coefficient of the ACF graph.

[0125] Seasonal autoregressive term (P): Determined by observing the significant lag values ​​(horizontal axis values) of the ACF graph at multiples of the seasonal cycle.

[0126] Seasonal moving average term (Q): Determined by observing the significant lag values ​​(horizontal axis values) of the PACF plot at multiples of the seasonal period.

[0127] SS4. Get the value z of the time series at time t by model fitting t :

[0128]

[0129] In the formula, v t is the noise at time t, A i , B i , Φ i , Θ i are the coefficients to be fitted.

[0130] By replacing t in formula (7) with t+n, the value of the time series at any future time can be predicted by recursion, where n=1, 2, ...

[0131] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A method for assessing the risk level of a gas station, characterized in that: It includes the following steps: Conduct a structural division of the gas station to determine the equipment and components that make up the gas station; Determine the signal monitoring points of key equipment, and obtain monitoring signals in real time from the sensors installed at the signal monitoring points; Estimate the risk level of the gas station based on the monitoring signals and equipment parameters, and conduct safety situation prediction by establishing a Hidden Markov Model.

2. The gas station risk level assessment method according to claim 1 is characterized in that: The gas station includes pipelines, valves, pressure regulators, filters, relief pipes, and gas-using equipment.

3. The method for assessing the risk level of a gas station according to claim 1, characterized in that: The signal monitoring points are set at the inlets and outlets of key equipment, important nodes of pipelines, and equally divided points in the pipeline length direction.

4. The method for assessing the risk level of a gas station according to claim 1, characterized in that: The method for estimating the risk level of the gas station includes: Calculate the process processing degree E, which is used to characterize the comprehensive ability of the gas station to process, transform, and handle gas; Calculate the layout density F, which is used to characterize the strength of the association and cooperation between one signal monitoring point and other signal monitoring points; Calculate the risk level of the gas station based on the process processing degree E and the layout density F.

5. The method for assessing the risk level of a gas station according to claim 4, characterized in that: The method for calculating the risk level of the gas station based on E and F includes: Set the process processing degree threshold E0 and the layout density threshold F0; If E ≤ E0 and F ≤ F0, the risk level is level 1; If E > E0 and F ≤ F0, the risk level is level 2; If E ≤ E0 and F > F0, the risk level is level 3; If E > E0 and F > F0, the risk level is level 4.

6. The method for assessing the risk level of a gas station according to claim 4, characterized in that: The calculation formula for the layout density F is: F = k1F1 + k2F2 (1) In the formula, k1 and k2 are weighting coefficients, 0 < k1 < 1, 0 < k2 < 1, k1 + k2 = 1, F1 is the normalized value of the average spacing of on-site equipment, which is equal to the ratio of the average value of the adjacent equipment spacing to the maximum spacing, F2 is the equipment layout efficiency index, and the calculation formula for F2 is: F2=S v / S z ×(1-ε) (2) In the formula, S v is the actual floor space occupied by all equipment, S z is the area occupied by the equipment, ε is the equipment complexity coefficient, which is equal to the mean of all equipment complexity coefficients. The complexity coefficient of a single device is determined by the complexity of the device shape and takes a value between 0 and 1. The larger the value, the more complex the device.

7. The method for assessing the risk level of a gas station according to claim 4, characterized in that: The calculation formula for the process processing degree E is: E = E1 × E2 × E3 × E4 (3) In the formula, E1 is the normalized value of the number of key equipment, E2 is the normalized value of the average value of equipment performance parameters, E3 is the equipment operation status index, and E4 is the synergy coefficient.

8. The method for assessing the risk level of a gas station according to claim 5, characterized in that: The method for conducting safety situation prediction based on the Hidden Markov Model includes: S1, based on the risk level observation sequence O = {O t }Establish hidden Markov model: λ=[π,A,B]; Among them, O t is the observed value of the risk level at the t-th moment, t=1,2,......,T, T is the current moment; the i-th risk level S i The value of is i, i = 1, 2, 3, 4; π is the initial state probability vector, π = {π i }, π i The risk level at the first moment is S i The probability of state transition; A is the state transition probability matrix, denoted by A[a ij ], a ij The risk level S at the t-1th moment i Transfer to the risk level S at the tth moment j The probability of; B is the output observation probability matrix, denoted as B[b ij ], b ij The risk level is S i When the risk level observation value is S j probability; S2. Initialization: α1(i)=π i b i (O1) (4) In the formula, α1(i) is the risk level S at the first moment i The probability of b i (O1) = b ij , O1=S j , i=1,2,3,4; S3. Iterative calculation: In the formula, α t+1 (i) The risk level at the t+1th moment is S i The probability of i (O t+1 )=b ij , O t+1 =S j , i=1,2,3,4, t=1,2,...,T-1; S4. Output the probability of the existence of risks in the gas station: According to formula (5), the probability of each risk level can be predicted, and according to formula (6), the probability of the existence of risks in the gas station can be predicted.

9. The method for assessing the risk level of a gas station according to claim 1, characterized in that: The method also includes: establishing a time series of monitoring signals, decomposing the time series into a trend series, a periodic series, and a residual series, and predicting the monitoring signals at future times.

10. The method for assessing the risk level of a gas station according to claim 1, characterized in that: Predict the monitoring signals at future times based on the SARIMA model.

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