A pipeline steel fracture safety analysis system and method based on multi-source data

Abstract

The invention discloses a pipeline steel fracture safety analysis system and method based on multi-source data, which relates to the technical field of safety analysis, and obtains usage data and strength data of pipeline steel pipes and welds on a transportation line; obtains fracture failure probability density functions of pipeline steel pipes and welds; determines fracture failure rates of pipeline steel pipes and welds on a transportation line according to the fracture failure probability density functions of pipeline steel pipes and welds; obtains the failure rate of a transportation line according to the fracture failure rate of pipeline steel pipes and welds, analyzes the available time of the transportation line, outputs the failure rate of the transportation line and issues risk reminders; is not limited to the strength of pipeline steel pipes and welds, and even pipeline steel pipes and welds put into use according to new specifications can be analyzed using a target curve chart; analyzes the safety of the transportation line, improves the accuracy and comprehensiveness of the analysis, and ensures long-term stable operation of the transportation line.

Description

Technical Field

[0001] The present invention relates to the technical field of safety analysis, and in particular to a pipeline steel fracture safety analysis system and method based on multi-source data. Background Art

[0002] Pipeline transportation is a mode of transportation that uses pipelines to transport fluid media. It is widely used in the transportation of fluids such as oil, natural gas, and chemical products. Pipeline steel is a high-quality steel specially used for manufacturing transportation pipelines. It has high strength, wear resistance, corrosion resistance, fatigue resistance and other characteristics. It can meet the needs of fluid transportation under complex working conditions such as long distances, large diameters, high pressures, and high flow rates. Transportation routes are usually assembled from single pipeline steel pipes connected by welds. The condition of pipeline steel pipes and welds directly affects the reliability of transportation pipelines. However, the transportation routes involve a large number of pipeline steel pipes and welds, which brings difficulties to the safety analysis of transportation routes. How to analyze the safety of transportation routes through the condition of pipeline steel pipes and welds has become an urgent problem to be solved. Summary of the invention

[0003] The object of the present invention is to provide a pipeline steel fracture safety analysis system and method based on multi-source data to solve the problems raised in the prior art.

[0004] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a pipeline steel fracture safety analysis system based on multi-source data, comprising a data acquisition module, a data storage module, a first fault data analysis module, a second fault data analysis module and an early warning module; the output end of the data acquisition module is connected to the input end of the data storage module, and is used to obtain the usage data of the pipeline steel pipe and the weld on the transportation line; the output end of the data storage module is connected to the input end of the first fault data analysis module and the second fault data analysis module, and is used to store the fracture fault data of the pipeline steel pipe and the weld; the output end of the first fault data analysis module is connected to the input end of the second fault data analysis module, and is used to analyze the fracture fault probability density function of the pipeline steel pipe and the weld, and obtain the fracture fault probability of the pipeline steel pipe and the weld; the output end of the second fault data analysis module is connected to the input end of the early warning module, and the failure probability of the transportation line is obtained according to the fracture fault probability of the pipeline steel pipe and the weld; the fracture fault probability density function of the pipeline steel pipe and the weld is used to obtain the fracture fault probability density function of the transportation line; the early warning module is used to remind the transportation line of risks.

[0005] The data acquisition module also includes an acquisition unit and an acquisition unit; the acquisition unit is used to acquire the strength data of the pipeline steel pipe and the weld; the acquisition unit is used to acquire the usage data of the pipeline steel pipe and the weld. The first fault data analysis module also includes a horizontal scaling transformation unit, a kernel density estimation unit and a first fault analysis unit; the horizontal scaling transformation unit is used to perform horizontal scaling transformation on the input of the pipeline steel pipe and the weld; the kernel density estimation unit is used to calculate the fracture failure probability density function of the pipeline steel pipe and the weld; the first fault analysis unit is used to calculate the fracture failure probability of the pipeline steel pipe and the weld. The second fault data analysis module also includes a convolution unit, a second fault analysis unit and an available time calculation unit; the convolution unit convolves the fracture failure probability density function of the pipeline steel pipe and the weld on the transportation route to obtain the failure probability density function of the transportation route; the second fault analysis unit is used to calculate the failure probability of the transportation route; the available time calculation unit is used to analyze the available time of the transportation route.

[0006] To achieve the above object, the present invention provides the following technical solution: a pipeline steel fracture safety analysis method based on multi-source data, comprising the following steps:

[0007] S11, obtaining usage data and strength data of pipeline steel pipes and welds on the transportation route;

[0008] S12, obtaining a fracture failure probability density function of the pipeline steel pipe and the weld according to historical fracture failure data of the pipeline steel pipe and the weld;

[0009] S13, determining the fracture failure rate of the pipeline steel pipe and the weld on the transportation route according to the fracture failure probability density function of the pipeline steel pipe and the weld;

[0010] S14, obtaining the failure rate of the transport line according to the fracture failure rate of the pipeline steel pipe and the weld, analyzing the available time of the transport line, outputting the failure rate of the transport line and issuing a risk reminder.

[0011] In step S12, the method of obtaining the fracture failure probability density function of the pipeline steel pipe and the weld according to the historical fracture failure data of the pipeline steel pipe and the weld also includes the following steps:

[0012] S21, obtain the usage data characteristics of the pipeline steel pipeline in the historical fracture fault data and record them as A1, A2, ..., An, where n is the number of usage data characteristics, and calculate the load X1 of the pipeline steel pipeline according to the usage data characteristics, X1 = [W1×F(A1)+W2×F(A2)+...+Wn×F(An)]×t, where t is the time when the pipeline steel pipeline is put into use, W1, W2, ..., Wn are weights, the sum of all weights is 1, F(A1), F(A2), ..., F(An) are the influence of the usage data characteristics on the pipeline steel pipeline, F(A2), ..., F(An) are analogized based on F(A1); sthr is the safety critical value of the usage data feature, which is determined by the properties of the pipeline steel; k1 and k2 are the loss rate of the pipeline steel pipeline using the usage data feature; the strength of the pipeline steel pipeline is obtained by weighted summation of the strength data feature of the pipeline steel pipeline; the ratio of the pipeline steel pipeline load to the strength is calculated to obtain the input of the pipeline steel pipeline;

[0013] S22, according to the historical fracture failure data, the fracture failure rate of the same pipeline steel pipe under different input strengths is obtained, and an input and fracture failure rate curve is generated, where the horizontal axis is the input and the vertical axis is the fracture failure rate, and the fracture failure rate curve of all pipeline steel pipes is obtained; the mode of the pipeline steel pipe strength is obtained, and the fracture failure rate curve corresponding to the mode is used as the initial standard curve;

[0014] S23, calculating the root mean square error between the fracture failure rate curve corresponding to the i-th pipeline steel pipeline strength and the vertical coordinate of the standard curve, and performing horizontal expansion and contraction transformation on the fracture failure rate curve corresponding to the i-th pipeline steel pipeline strength so that the root mean square error between the fracture failure rate curve corresponding to the i-th pipeline steel pipeline strength and the vertical coordinate of the standard curve is minimized; calculating the sum of the root mean square errors between the fracture failure rate curves corresponding to all pipeline steel pipeline strengths and the vertical coordinates of the standard curve;

[0015] S24, calculating the root mean square error between the fracture failure rate curve corresponding to the jth pipeline steel pipe strength after the horizontal expansion and contraction transformation and the fracture failure rate curves corresponding to other pipeline steel pipe strengths, and summing up to obtain the total root mean square error between the fracture failure rate curve corresponding to the jth pipeline steel pipe strength and the fracture failure rate curves corresponding to other pipeline steel pipe strengths; setting the total root mean square error as the optimization target, finding the pipeline steel pipe strength with the smallest total root mean square error and using it as the new standard curve;

[0016] S25, repeating steps S23 and S24 until the number of iterations reaches a set value, and obtaining the pipeline steel pipe strength corresponding to the standard curve when the optimization target is minimum and the fracture failure rate curve after horizontal expansion transformation corresponding to other pipeline steel pipe strengths;

[0017] S26, superimposing the standard curve graph and the fracture failure rate curve graph after horizontal expansion and contraction transformation corresponding to the strength of other pipeline steel pipes to obtain a target curve graph for the pipeline steel pipe;

[0018] S27, executing steps S21 to S26 on the usage data and strength data of the weld to obtain a target curve diagram of the weld.

[0019] Generally, the probability of a pipeline steel pipe breaking failure increases with the increase of load. In order to analyze the probability of a pipeline steel pipe breaking failure, it is necessary to obtain the probability distribution function of the pipeline steel pipe breaking failure. Due to the different strengths of pipeline steel pipes, the probability of a pipeline steel pipe breaking failure under the same load and different strengths is different. Therefore, the input is obtained according to the load and strength, and the input is horizontally scaled. Since the material and shape of the pipeline steel pipe are the same, the fracture failure rate curves of the pipeline steel pipe under different strengths are similar, and then the target curve of the pipeline steel pipe is obtained. It is not limited to pipeline steel pipes of the same strength. Even pipeline steel pipes put into use according to new specifications can be analyzed using the target curve. The target curve of the weld is the same as that of the pipeline steel pipe.

[0020] In step S12, the method of obtaining the fracture failure probability density function of the pipeline steel pipe and the weld according to the historical fracture failure data of the pipeline steel pipe and the weld also includes the following steps:

[0021] S31, obtaining a data sample from a target curve graph of a pipeline steel pipe;

[0022] S32, select a non-negative and symmetric function as the kernel function of kernel density estimation;

[0023] S33, bandwidth selection for kernel density estimation by cross-validation;

[0024] S34, calculating the contribution of each data point in the data sample, and adding the contributions of all data points to obtain the probability density function of the target curve graph;

[0025] S35, executing steps S31 to S34 on the target curve graph of the weld seam to obtain a probability density function of the target curve graph of the weld seam.

[0026] In step S13, the method of determining the fracture failure rate of the pipeline steel pipe and the weld on the transportation route according to the fracture failure probability density function of the pipeline steel pipe and the weld also includes the following steps:

[0027] S41, obtaining usage data and strength data of the pipeline steel pipeline on the current transportation route, and calculating the input of the pipeline steel pipeline on the current transportation route according to the usage data and strength data; according to the strength of the pipeline steel pipeline on the current transportation route, obtaining the fracture failure rate curve and horizontal expansion and contraction transformation method corresponding to the strength of the pipeline steel pipeline on the transportation route in step S26, and performing horizontal expansion and contraction transformation on the input of the pipeline steel pipeline on the current transportation route to obtain the value x of the input random variable X;

[0028] S42, let the probability density function of the target curve be f(u), calculate the fracture failure rate Pg of the pipeline steel pipe, Where F(x) is the distribution function of the target curve graph, and u is the integral variable;

[0029] S43, obtaining the usage data and strength data of the welds on the current transportation route, and executing steps S41 and S42 to obtain the fracture failure rate Ph of the welds.

[0030] In step S14, the method of obtaining the failure rate of the transportation line according to the fracture failure rate of the pipeline steel pipe and the weld also includes the following steps:

[0031] S51, obtaining the fracture failure rate of all pipeline steel pipes and welds on the transportation line, and calculating the first failure probability P1 on the transportation line, P1 = 1-Π(1-Ph)Π(1-Pg);

[0032] S52, obtain the probability density function of the target curve graph of all pipeline steel pipes and welds on the transportation route, perform convolution processing on the obtained probability density function to obtain the probability density function of the random variable Z of the transportation route, Z = ∑X + ∑Y, where Y is the input random variable Y obtained by horizontally scaling the input of the weld on the transportation route; obtain the distribution function of the transportation route according to the probability density function of the random variable Z of the transportation route, and obtain the expected number of failures E(Z) on the transportation route according to the current value z0 of the random variable Z, Where g(v) is the probability density function of the random variable Z of the transport line, and v is the integral variable. When the first failure probability and the expected number of failures on the transport line exceed the threshold, a risk warning is issued.

[0033] In step S14, the analysis of the available time of the transport route further includes the following steps:

[0034] In the case where no risk warning is issued, the expected number of failures on the transport line is recorded as TZ, according to the formula Get the critical value zm of the random variable Z; obtain the load change of all pipeline steel pipes and welds on the transportation line to generate a load growth function, the input of the load growth function is time, and the output is load; perform horizontal scaling transformation to obtain the transformed load growth function, add the load growth functions of all pipeline steel pipes and welds on the transportation line to obtain the load growth function of the transportation line, and let the value of the load growth function be zm to obtain the available time of the transportation line.

[0035] Compared with the prior art, the beneficial effects of the present invention are: not limited to the strength of pipeline steel pipes and welds, even pipeline steel pipes and welds put into use according to new specifications can be analyzed using the target curve chart; the safety of the transportation line is analyzed, the accuracy and comprehensiveness of the analysis are improved, and the long-term stable operation of the transportation line is ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 The present invention is a schematic structural diagram of a pipeline steel fracture safety analysis system based on multi-source data. DETAILED DESCRIPTION

[0037] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0038] Example: Figure 1 As shown, the present invention provides a technical solution, a pipeline steel fracture safety analysis system based on multi-source data, comprising a data acquisition module, a data storage module, a first fault data analysis module, a second fault data analysis module and an early warning module; the output end of the data acquisition module is connected to the input end of the data storage module, and is used to obtain the usage data of the pipeline steel pipe and the weld on the transportation line; the output end of the data storage module is connected to the input end of the first fault data analysis module and the second fault data analysis module, and is used to store the fracture fault data of the pipeline steel pipe and the weld; the output end of the first fault data analysis module is connected to the input end of the second fault data analysis module, and is used to analyze the fracture fault probability density function of the pipeline steel pipe and the weld, and obtain the fracture fault probability of the pipeline steel pipe and the weld; the output end of the second fault data analysis module is connected to the input end of the early warning module, and the failure probability of the transportation line is obtained according to the fracture fault probability of the pipeline steel pipe and the weld; the fracture fault probability density function of the pipeline steel pipe and the weld is obtained; the early warning module is used to remind the transportation line of risks.

[0039] The data acquisition module also includes an acquisition unit and an acquisition unit; the acquisition unit is used to acquire the strength data of the pipeline steel pipe and the weld; the acquisition unit is used to acquire the usage data of the pipeline steel pipe and the weld. The first fault data analysis module also includes a horizontal scaling transformation unit, a kernel density estimation unit and a first fault analysis unit; the horizontal scaling transformation unit is used to perform horizontal scaling transformation on the input of the pipeline steel pipe and the weld; the kernel density estimation unit is used to calculate the fracture failure probability density function of the pipeline steel pipe and the weld; the first fault analysis unit is used to calculate the fracture failure probability of the pipeline steel pipe and the weld. The second fault data analysis module also includes a convolution unit, a second fault analysis unit and an available time calculation unit; the convolution unit convolves the fracture failure probability density function of the pipeline steel pipe and the weld on the transportation route to obtain the failure probability density function of the transportation route; the second fault analysis unit is used to calculate the failure probability of the transportation route; the available time calculation unit is used to analyze the available time of the transportation route.

[0040] Embodiment: The present invention provides a technical solution, a pipeline steel fracture safety analysis method based on multi-source data, comprising the following steps:

[0041] S11, obtaining usage data and strength data of pipeline steel pipes and welds on the transportation route;

[0042] S12, obtaining a fracture failure probability density function of the pipeline steel pipe and the weld according to the historical fracture failure data of the pipeline steel pipe and the weld, including steps S31 to S35;

[0043] S31, obtaining a data sample from a target curve graph of a pipeline steel pipe;

[0044] S32, select a non-negative and symmetric function as the kernel function of kernel density estimation;

[0045] S33, bandwidth selection for kernel density estimation by cross-validation;

[0046] S34, calculating the contribution of each data point in the data sample, and adding the contributions of all data points to obtain the probability density function of the target curve graph;

[0047] S35, executing steps S31 to S34 on the target curve graph of the weld seam to obtain a probability density function of the target curve graph of the weld seam.

[0048] Commonly used kernel functions include uniform, triangular, biweight, triweight, Epa nechnikov, normal, etc.

[0049] The target curve graph of the pipeline steel pipe and the target curve graph of the weld are obtained through steps S21 to S27:

[0050] S21, obtain the usage data characteristics of the pipeline steel pipeline in the historical fracture fault data and record them as A1, A2, ..., An, where n is the number of usage data characteristics, and calculate the load X1 of the pipeline steel pipeline according to the usage data characteristics, X1 = [W1×F(A1)+W2×F(A2)+...+Wn×F(An)]×t, where t is the time when the pipeline steel pipeline is put into use, W1, W2, ..., Wn are weights, the sum of all weights is 1, F(A1), F(A2), ..., F(An) are the influence of the usage data characteristics on the pipeline steel pipeline, F(A2), ..., F(An) are analogized based on F(A1); sthr is the safety critical value of the usage data feature, which is determined by the properties of the pipeline steel; k1 and k2 are the loss rate of the pipeline steel pipeline using the usage data feature; the strength of the pipeline steel pipeline is obtained by weighted summation of the strength data feature of the pipeline steel pipeline; the ratio of the pipeline steel pipeline load to the strength is calculated to obtain the input of the pipeline steel pipeline;

[0051] The usage data characteristics of pipeline steel pipes and welds include but are not limited to the pressure they bear, the flow rate and temperature of the transportation line, etc.; for the pressure characteristic A1, when the pressure borne by the pipeline steel pipes and welds is within the safety critical value, the loss rate is k1, and when the pressure borne by the pipeline steel pipes and welds is higher than the safety critical value, the loss rate is k2; k1 is obtained based on the allowable service life of the pipeline steel pipes and welds. For example, if the allowable service life is 20 years, when the unit of t is year, k1 is 1 / 20, and when the unit of t is other, k1 is adjusted accordingly; the value of k2 is greater than k1; other characteristics such as the flow rate and temperature of the transportation line are analogous;

[0052] The strength data characteristics of pipeline steel pipes are used to calculate the strength of pipeline steel, including but not limited to pipe wall thickness, cross-sectional area, pipeline steel pipe length, etc. The strength data characteristics of welds include weld depth, weld shape factor and weld toughness, etc. The strength of pipeline steel pipes and welds is used to distinguish pipeline steel pipes and welds of different specifications. The strength of pipeline steel pipes or welds of the same specification is the same.

[0053] S22, according to the historical fracture failure data, the fracture failure rate of the same pipeline steel pipe under different input strengths is obtained, and an input and fracture failure rate curve is generated, where the horizontal axis is the input and the vertical axis is the fracture failure rate, and the fracture failure rate curve of all pipeline steel pipes is obtained; the mode of the pipeline steel pipe strength is obtained, and the fracture failure rate curve corresponding to the mode is used as the initial standard curve;

[0054] S23, calculating the root mean square error between the fracture failure rate curve corresponding to the i-th pipeline steel pipeline strength and the vertical coordinate of the standard curve, and performing horizontal expansion and contraction transformation on the fracture failure rate curve corresponding to the i-th pipeline steel pipeline strength so that the root mean square error between the fracture failure rate curve corresponding to the i-th pipeline steel pipeline strength and the vertical coordinate of the standard curve is minimized; calculating the sum of the root mean square errors between the fracture failure rate curves corresponding to all pipeline steel pipeline strengths and the vertical coordinates of the standard curve;

[0055] S24, calculating the root mean square error between the fracture failure rate curve corresponding to the jth pipeline steel pipe strength after the horizontal expansion and contraction transformation and the fracture failure rate curves corresponding to other pipeline steel pipe strengths, and summing up to obtain the total root mean square error between the fracture failure rate curve corresponding to the jth pipeline steel pipe strength and the fracture failure rate curves corresponding to other pipeline steel pipe strengths; setting the total root mean square error as the optimization target, finding the pipeline steel pipe strength with the smallest total root mean square error and using it as the new standard curve;

[0056] S25, repeating steps S23 and S24 until the number of iterations reaches a set value, and obtaining the pipeline steel pipe strength corresponding to the standard curve when the optimization target is minimum and the fracture failure rate curve after horizontal expansion transformation corresponding to other pipeline steel pipe strengths;

[0057] S26, superimposing the standard curve graph and the fracture failure rate curve graph after horizontal expansion and contraction transformation corresponding to the strength of other pipeline steel pipes to obtain a target curve graph for the pipeline steel pipe;

[0058] S27, executing steps S21 to S26 on the usage data and strength data of the weld to obtain a target curve diagram of the weld.

[0059] S13, determining the fracture failure rate of the pipeline steel pipe and the weld on the transportation line according to the fracture failure probability density function of the pipeline steel pipe and the weld:

[0060] S41, obtaining usage data and strength data of the pipeline steel pipeline on the current transportation route, and calculating the input of the pipeline steel pipeline on the current transportation route according to the usage data and strength data; according to the strength of the pipeline steel pipeline on the current transportation route, obtaining the fracture failure rate curve and horizontal expansion and contraction transformation method corresponding to the strength of the pipeline steel pipeline on the transportation route in step S26, and performing horizontal expansion and contraction transformation on the input of the pipeline steel pipeline on the current transportation route to obtain the value x of the input random variable X;

[0061] S42, let the probability density function of the target curve be f(u), calculate the fracture failure rate Pg of the pipeline steel pipe, Where F(x) is the distribution function of the target curve graph, and u is the integral variable;

[0062] S43, obtaining the usage data and strength data of the welds on the current transportation route, and executing steps S41 and S42 to obtain the fracture failure rate Ph of the welds.

[0063] S14, the failure rate of the transportation line is obtained based on the failure rate of the pipeline steel pipe and weld:

[0064] Obtain the fracture failure rate of all pipeline steel pipes and welds on the transportation route, and calculate the first failure probability P1 on the transportation route, P1 = 1-Π(1-Ph)Π(1-Pg);

[0065] Obtain the probability density function of the target curve graph of all pipeline steel pipes and welds on the transportation route, and perform convolution processing on the obtained probability density function to obtain the probability density function of the random variable Z of the transportation route, Z = ∑X + ∑Y, where Y is the input random variable Y obtained by horizontally scaling the input of the weld on the transportation route; obtain the distribution function of the transportation route based on the probability density function of the random variable Z of the transportation route, and obtain the expected number of failures E(Z) on the transportation route based on the current value z0 of the random variable Z, Where g(v) is the probability density function of the random variable Z of the transport line, and v is the integral variable. When the first failure probability and the expected number of failures on the transport line exceed the threshold, a risk warning is issued.

[0066] The convolution process can be performed in pairs. First, the input of the pipeline steel pipe on the transportation line is horizontally expanded and contracted to obtain the input random variables X1 and X2 for convolution. Then, the result after convolution is convolved with the random variable X3, and so on. In this process, the random variables X and Y are processed in the same way.

[0067] Analyze the available time of transport routes:

[0068] In the case where no risk warning is issued, the expected number of failures on the transport line is recorded as TZ, according to the formula Get the critical value zm of the random variable Z; obtain the load change of all pipeline steel pipes and welds on the transportation line to generate a load growth function, the input of the load growth function is time, and the output is load; perform horizontal scaling transformation to obtain the transformed load growth function, add the load growth functions of all pipeline steel pipes and welds on the transportation line to obtain the load growth function of the transportation line, and let the value of the load growth function be zm to obtain the available time of the transportation line.

[0069] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A pipeline steel fracture safety analysis method based on multi-source data, characterized in that: The following steps are involved: S11, obtaining usage data and strength data of pipeline steel pipes and welds on the transportation route; S12, obtaining the fracture failure probability density function of the pipeline steel pipe and the weld according to the historical fracture failure data of the pipeline steel pipe and the weld, including the following steps S21 to S27; S21, obtain the usage data characteristics of the pipeline steel pipeline in the historical fracture fault data and record them as A1, A2, ..., An, where n is the number of usage data characteristics, and calculate the load X1 of the pipeline steel pipeline according to the usage data characteristics, X1 = [W1×F(A1)+W2×F(A2)+...+Wn×F(An)]×t, where t is the time when the pipeline steel pipeline is put into use, W1, W2, ..., Wn are weights, the sum of all weights is 1, F(A1), F(A2), ..., F(An) are the influence of the usage data characteristics on the pipeline steel pipeline, F(A2), ..., F(An) are analogized based on F(A1); sthr is the safety critical value of the usage data feature, which is determined by the properties of the pipeline steel; k1 and k2 are the loss rate of the pipeline steel pipeline using the usage data feature; the strength of the pipeline steel pipeline is obtained by weighted summation of the strength data feature of the pipeline steel pipeline; the ratio of the pipeline steel pipeline load to the strength is calculated to obtain the input of the pipeline steel pipeline; S22, according to the historical fracture failure data, the fracture failure rate of the same pipeline steel pipe under different input strengths is obtained, and an input and fracture failure rate curve is generated, where the horizontal axis is the input and the vertical axis is the fracture failure rate, and the fracture failure rate curve of all pipeline steel pipes is obtained; the mode of the pipeline steel pipe strength is obtained, and the fracture failure rate curve corresponding to the mode is used as the initial standard curve; S23, calculating the root mean square error between the fracture failure rate curve corresponding to the i-th pipeline steel pipeline strength and the vertical coordinate of the standard curve, and performing horizontal expansion and contraction transformation on the fracture failure rate curve corresponding to the i-th pipeline steel pipeline strength so that the root mean square error between the fracture failure rate curve corresponding to the i-th pipeline steel pipeline strength and the vertical coordinate of the standard curve is minimized; calculating the sum of the root mean square errors between the fracture failure rate curves corresponding to all pipeline steel pipeline strengths and the vertical coordinates of the standard curve; S24, calculating the root mean square error between the fracture failure rate curve corresponding to the jth pipeline steel pipe strength and the fracture failure rate curves corresponding to other pipeline steel pipe strengths after the horizontal expansion and contraction transformation, and summing up to obtain the total root mean square error between the fracture failure rate curve corresponding to the jth pipeline steel pipe strength and the fracture failure rate curves corresponding to other pipeline steel pipe strengths; Let the sum of the root mean square error be the optimization target, find the pipeline steel pipe strength with the minimum sum of the root mean square error and use it as the new standard curve; S25, repeating steps S23 and S24 until the number of iterations reaches a set value, and obtaining the pipeline steel pipe strength corresponding to the standard curve when the optimization target is minimum and the fracture failure rate curve after horizontal expansion transformation corresponding to other pipeline steel pipe strengths; S26, superimposing the standard curve graph and the fracture failure rate curve graph after horizontal expansion and contraction transformation corresponding to the strength of other pipeline steel pipes to obtain a target curve graph for the pipeline steel pipe; S27, executing steps S21 to S26 on the usage data and strength data of the weld to obtain a target curve graph of the weld; S13, determining the fracture failure rate of the pipeline steel pipe and the weld on the transportation route according to the fracture failure probability density function of the pipeline steel pipe and the weld; S14, obtaining the failure rate of the transport line according to the fracture failure rate of the pipeline steel pipe and the weld, analyzing the available time of the transport line, outputting the failure rate of the transport line and issuing a risk reminder.

2. The pipeline steel fracture safety analysis method based on multi-source data according to claim 1 is characterized in that: In step S12, the method of obtaining the fracture failure probability density function of the pipeline steel pipe and the weld according to the historical fracture failure data of the pipeline steel pipe and the weld also includes the following steps: S31, obtaining a data sample from a target curve graph of a pipeline steel pipe; S32, select a non-negative and symmetric function as the kernel function of kernel density estimation; S33, bandwidth selection for kernel density estimation by cross-validation; S34, calculating the contribution of each data point in the data sample, and adding the contributions of all data points to obtain the probability density function of the target curve graph; S35, executing steps S31 to S34 on the target curve graph of the weld seam to obtain a probability density function of the target curve graph of the weld seam.

3. The pipeline steel fracture safety analysis method based on multi-source data according to claim 2 is characterized in that: In step S13, the method of determining the fracture failure rate of the pipeline steel pipe and the weld on the transportation route according to the fracture failure probability density function of the pipeline steel pipe and the weld also includes the following steps: S41, obtaining usage data and strength data of the pipeline steel pipeline on the current transportation route, and calculating the input of the pipeline steel pipeline on the current transportation route according to the usage data and strength data; according to the strength of the pipeline steel pipeline on the current transportation route, obtaining the fracture failure rate curve and horizontal expansion and contraction transformation method corresponding to the strength of the pipeline steel pipeline on the transportation route in step S26, and performing horizontal expansion and contraction transformation on the input of the pipeline steel pipeline on the current transportation route to obtain the value x of the input random variable X; S42, let the probability density function of the target curve be f(u), calculate the fracture failure rate Pg of the pipeline steel pipe, Where F(x) is the distribution function of the target curve graph, and u is the integral variable; S43, obtaining the usage data and strength data of the welds on the current transportation route, and executing steps S41 and S42 to obtain the fracture failure rate Ph of the welds.

4. The pipeline steel fracture safety analysis method based on multi-source data according to claim 3 is characterized in that: In step S14, the method of obtaining the failure rate of the transportation line according to the fracture failure rate of the pipeline steel pipe and the weld also includes the following steps: S51, obtaining the fracture failure rate of all pipeline steel pipes and welds on the transportation line, and calculating the first failure probability P1 on the transportation line, P1 = 1-Π(1-Ph)Π(1-Pg); S52, obtain the probability density function of the target curve graph of all pipeline steel pipes and welds on the transportation route, perform convolution processing on the obtained probability density function to obtain the probability density function of the random variable Z of the transportation route, Z = ∑X + ∑Y, where Y is the input random variable Y obtained by horizontally scaling the input of the weld on the transportation route; obtain the distribution function of the transportation route according to the probability density function of the random variable Z of the transportation route, and obtain the expected number of failures E(Z) on the transportation route according to the current value z0 of the random variable Z, Where g(v) is the probability density function of the random variable Z of the transport line, and v is the integral variable. When the first failure probability and the expected number of failures on the transport line exceed the threshold, a risk warning is issued.

5. The pipeline steel fracture safety analysis method based on multi-source data according to claim 4 is characterized in that: In step S14, the analysis of the available time of the transport route further includes the following steps: In the case where no risk warning is issued, the expected number of failures on the transport line is recorded as TZ, according to the formula Get the critical value zm of the random variable Z; obtain the load change of all pipeline steel pipes and welds on the transportation line to generate a load growth function, the input of the load growth function is time, and the output is load; perform horizontal scaling transformation to obtain the transformed load growth function, add the load growth functions of all pipeline steel pipes and welds on the transportation line to obtain the load growth function of the transportation line, and let the value of the load growth function be zm to obtain the available time of the transportation line.

6. A pipeline steel fracture safety analysis system based on multi-source data, using a pipeline steel fracture safety analysis method based on multi-source data as claimed in any one of claims 1 to 5, characterized in that: It includes a data acquisition module, a data storage module, a first fault data analysis module, a second fault data analysis module and an early warning module; the output end of the data acquisition module is connected to the input end of the data storage module, and is used to obtain the usage data of the pipeline steel pipes and welds on the transportation route; the output end of the data storage module is connected to the input ends of the first fault data analysis module and the second fault data analysis module, and is used to store the fracture fault data of the pipeline steel pipes and welds; the output end of the first fault data analysis module is connected to the input end of the second fault data analysis module, and is used to analyze the fracture fault probability density function of the pipeline steel pipes and welds to obtain the fracture fault probability of the pipeline steel pipes and welds; the output end of the second fault data analysis module is connected to the input end of the early warning module, and the failure probability of the transportation route is obtained according to the fracture fault probability of the pipeline steel pipes and welds; the fracture fault probability density function of the pipeline steel pipes and welds is used to obtain the fracture fault probability density function of the transportation route; the early warning module is used to remind the transportation route of risks.

7. The pipeline steel fracture safety analysis system based on multi-source data according to claim 6 is characterized in that: The data acquisition module also includes an acquisition unit and a collection unit; the acquisition unit is used to acquire strength data of pipeline steel pipes and welds; the collection unit is used to acquire usage data of pipeline steel pipes and welds.

8. The pipeline steel fracture safety analysis system based on multi-source data according to claim 6 is characterized in that: The first fault data analysis module also includes a horizontal scaling transformation unit, a kernel density estimation unit and a first fault analysis unit; the horizontal scaling transformation unit is used to perform horizontal scaling transformation on the input of pipeline steel pipes and welds; the kernel density estimation unit is used to calculate the fracture failure probability density function of pipeline steel pipes and welds; the first fault analysis unit is used to calculate the fracture failure probability of pipeline steel pipes and welds.

9. The pipeline steel fracture safety analysis system based on multi-source data according to claim 6, characterized in that: The second fault data analysis module also includes a convolution unit, a second fault analysis unit and an available time calculation unit; the convolution unit convolves the fracture failure probability density function of the pipeline steel pipe and the weld on the transportation line to obtain the failure probability density function of the transportation line; the second fault analysis unit is used to calculate the failure probability of the transportation line; the available time calculation unit is used to analyze the available time of the transportation line.

Images