Pressure-bearing equipment real-time risk early warning system and method based on big data analysis

Abstract

The invention discloses a pressure-bearing equipment real-time risk early warning system and method based on big data analysis, and relates to the field of pressure-bearing equipment risk management and control, and the early warning method comprises the following steps: S1, screening safety state parameters reflecting the risk of pressure-bearing equipment through a cloud model; s2, constructing a risk early warning index system, determining an index system construction principle, and determining an index weight; s3, obtaining an equipment risk early warning grading model based on the equipment risk early warning theoretical model, the index system and the weight; and S4, after a risk value is obtained through the risk early warning model, a risk grading standard is determined according to a two-eight rule, and a risk grade of the equipment is obtained. The system is based on the big data technology and depends on the Internet, automatic collection, transmission, processing, calculation and output of multi-source safety risk parameters are achieved, and the macroscopic risk real-time monitoring and early warning technology of the mobile pressure-bearing equipment is achieved.

Description

Technical Field

[0001] The present invention relates to the field of risk control of pressure-bearing equipment, and in particular to a real-time risk warning system and method for pressure-bearing equipment based on big data analysis. Background Art

[0002] Pressure-bearing equipment includes boilers, pressure vessels, industrial pipelines, etc., which are basic equipment in industries such as petroleum, chemical, electric power, and energy, and the consequences of accidents are huge. On the one hand, pressure-bearing equipment is constantly developing towards large-scale and high-parameter directions, with a large number of new materials and new structures being applied, and the use environment is more severe and complex; on the other hand, as the number of overdue service equipment increases, the safety risks gradually increase. Many gaseous and liquid hazardous substances are transported by mobile pressure-bearing equipment. Compared with atmospheric pressure equipment, the transportation medium of pressure-bearing equipment is large in quantity and the working conditions are harsh, further highlighting the public safety risks along the way. In short, the risk control of pressure-bearing equipment has become a hot topic in the fields of public safety and production safety.

[0003] How to timely obtain the measured values of each risk index in the risk prediction and warning of pressure-bearing equipment has always been a difficult point in the research of risk warning. Summary of the Invention

[0004] To solve the above problems, the present invention provides a real-time risk warning system and method for pressure-bearing equipment based on big data analysis. Based on big data technology and relying on the Internet, it realizes the automatic collection, transmission, processing, calculation, and output of multi-source safety risk parameters, realizes the real-time monitoring and warning technology for the risks of mobile pressure-bearing equipment, comprehensively considers the coupling effects of various risk factors such as personnel, equipment, medium, road, meteorology, and sensitive dates, is conducive to promoting the modernization of the safety supervision / management mode of mobile pressure-bearing equipment, and significantly improves the supervision efficiency.

[0005] To achieve the above object, the present invention provides a real-time risk warning method for pressure-bearing equipment based on big data analysis, which specifically includes the following steps:

[0006] S1: Screening the safety state parameters reflecting the risks of pressure-bearing equipment through a cloud model;

[0007] S2: Constructing a risk warning index system, clarifying the principles for constructing the index system and determining the index weights;

[0008] S3: Obtaining an equipment risk warning classification model based on the equipment risk warning theoretical model, index system, and weights;

[0009] S4: After obtaining the risk value through the risk warning model, determining the risk classification standard according to the "80 / 20 rule" to obtain the risk level of the equipment.

[0010] Preferably, in step S1, the cloud model is implemented by a forward cloud generator and a reverse cloud generator;

[0011] The forward cloud generator is a mapping from qualitative to quantitative. The inputs are the three numerical characteristics of the cloud, namely the expected value Ex, entropy En, hyperentropy He, and the number of cloud droplets N, and the outputs are the quantitative positions of the N cloud droplets in the numerical domain space and the degree of certainty of the concepts represented by the cloud droplets.

[0012] The input of the one-dimensional forward cloud generator is the numerical characteristics (Ex, En, He) reflecting the qualitative concept of weight and the number N of generated cloud droplets, and the output is N cloud droplets X i and the membership degree of each cloud droplet to the concept. The specific operation process is as follows:

[0013] In the first step, generate a normal random number En' with En as the expected value and He as the standard deviation. i ’.

[0014] In the second step, generate a normal random number X with Ex as the expected value and En' as the standard deviation. i ’.

[0015] In the third step, calculate the membership degree of X belonging to the concept: i

[0016]

[0017] In the fourth step, repeat steps one to three until N cloud droplets are generated;

[0018] The reverse cloud generator takes the weight value as the sample value X = Xi, where i = 1, 2,..., n; the output is the numerical characteristics (Ex, En, He) reflecting the qualitative concept of the parameter weight; i

[0019] Calculate the sample mean according to Xi: i

[0020]

[0021] where

[0022] The screening process of the safety state parameters is as follows:

[0023] S11: Evaluate the importance of the parameters. The language scale for evaluation is divided into 9 levels: {extremely unimportant, very unimportant, unimportant, relatively unimportant, average, relatively important, important, very important, extremely important}. The interval numbers corresponding to the 9 levels are {[1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7], [6, 8], [7, 9], [8, 9]}. Convert the interval numbers of the 9 uncertain language evaluation scales on the domain [1, 9] into 9 one-dimensional normal clouds;

[0024] S12: The scoring set for parameter importance is {V1, V2, …, V 10}, using the score set of parameters as the evaluation samples, generating the digital characteristics of the weight factor evaluation set by using the inverse cloud generator, and obtaining the evaluation cloud diagram of the parameters by using the forward cloud generator;

[0025] S13: Repeat step S12 to obtain the evaluation cloud models and parameter cloud characteristics of all parameters;

[0026] S14: Using the nine one-dimensional normal cloud diagrams generated by the evaluation language scale as the evaluation criteria, compare the evaluation cloud diagrams of the initial parameters with them one by one;

[0027] S15: According to the screening criteria in S14, screen out the safety status parameters.

[0028] Preferably, in step S2, the construction principles of the index system include the systematic principle, the consistency principle, the independence principle, the plasticity principle, the scientific principle, and the comparability principle;

[0029] Systematic principle - The indicators complement each other and comprehensively reflect the essential characteristics and overall performance of the evaluation object; The index system has clear levels, reasonable structure, mutual correlation, and coordination;

[0030] Consistency principle - The evaluation index system should be consistent with the evaluation goal. The selected indicators should reflect both the direct results and the indirect effects, and the indicators that have nothing to do with the evaluation object and evaluation content must be excluded;

[0031] Independence principle - The indicators at the same level should minimize their inclusion and cross relationships to ensure that the indicators reflect the actual situation of the evaluation object from different angles;

[0032] Measurability principle - The indicators can be measured or quantified;

[0033] Scientific principle - Guided by scientific theories, starting from the objective elements and their essential connections within the system, combining qualitative and quantitative analysis, correctly reflecting the quantitative characteristics of the overall system and its internal mutual relations;

[0034] Comparability principle - Objectively and realistically formulate rating indicators and evaluation criteria. The stronger the comparability of the index system, the greater the credibility of the evaluation results;

[0035] Process the recovered data and calculate the importance coefficient of the indicators according to the following formula:

[0036]

[0037] where m is the total number of survey experts; a ij is the evaluation score of the j-th expert for the i-th indicator; RFi is the importance coefficient of the i-th indicator; RF i ≥4.5; CV i is the coefficient of variation of the i-th indicator; CV i <25%; σ i is the sample standard deviation of the i-th indicator.

[0038] Preferably, in step S2, determining the index weight specifically includes the following steps:

[0039] S21: Construct a judgment matrix, use the analytic hierarchy process to determine the weight of the indicators, select the paired comparison method to determine the influence ratio between the indicators, take two indicators Ci and Cj, and use a ij to represent the ratio of the influence of Ci and Cj on the upper-level indicator. Represent all the comparison results with the matrix A=(a ij ) n×n (a ij >0) indicates:

[0040]

[0041] a ii =1, A is an n-order positive reciprocal matrix;

[0042] ΔRF = RF i -RF j (-8 ≤ ΔRF ≤ 8); Corresponding the value of ΔRF to the 9-point scale method, judging the values of the elements in the judgment matrix, and making pairwise comparisons of the importance data of "possibility", "severity" and "sensitivity" and all the lower-level secondary indicators respectively, the following 3 judgment matrices can be obtained. Matrix A1 is the judgment matrix of the "possibility" indicator, matrix A2 is the judgment matrix of the "severity" indicator, and matrix A3 is the judgment matrix of the "sensitivity" indicator;

[0043]

[0044] S22: The steps to determine the index weight coefficient by the sum-product method are as follows:

[0045] ① Normalize the elements of the judgment matrix A by column to obtain the matrix B=(b ij ) n×n , where

[0046]

[0047] ② Add the elements in the matrix B by row to obtain the vector Z=(z1, z2,..., z n ) T , where

[0048]

[0049] ③ Normalize the vector Z to obtain the eigenvector W = (w1, w2, …, w n ) T , where

[0050]

[0051] Using the above method to calculate the matrices A1, A2, and A3, the weights of "equipment inspection status", "equipment inherent reliability", "container working parameters", "continuous driving duration", "vehicle speed", "local weather", and "current road type" for the "possibility" index are: 0.1527, 0.0605, 0.2441, 0.2037, 0.1030, 0.1571, 0.0788; the weights of "medium property" and "medium storage" for the "severity" index are: 0.7500, 0.2500; the weights of "sensitive date", "surrounding social environment", and "surrounding natural environment" for the "sensitivity" index are: 0.2519, 0.5889, 0.1592;

[0052] S23: Conduct a consistency test, calculate the maximum eigenvalue λ of the judgment matrix max , and then calculate the consistency index n represents the order of the judgment matrix;

[0053] If CI = 0, it indicates that the judgment matrix has perfect consistency;

[0054] If CI ≠ 0, then the random consistency ratio needs to be calculated;

[0055] where RI is the average random consistency index of the judgment matrix, and the value of RI is related to the order of the matrix;

[0056] After calculation, it can be obtained that for the judgment matrix A1, CR = 0.0811 < 0.1, for the judgment matrix A2, CR = 0.0000 < 0.1, and for A3, CR = 0.0465 < 0.1, all of which pass the consistency test;

[0057] Finally, it is obtained that:

[0058] The weights ω of the possibility influencing factor indicators P = (0.1527, 0.0605, 0.2441, 0.2037, 0.1030, 0.1571, 0.0788);

[0059] The weights ω of the severity influencing factor indicators L = (0.7500, 0.2500);

[0060] Sensitivity influencing factor index weight ω S =(0.2519, 0.5889, 0.1592).

[0061] Preferably, in step S3, the equipment risk early warning classification model is as follows:

[0062]

[0063] Where: R represents the risk score; d represents the index score; ω represents the corresponding weight of the index.

[0064] A system for a real-time risk early warning method of pressure-bearing equipment based on big data analysis includes a risk map display module, a risk information query module, a historical data analysis module, and a system management module;

[0065] By calling the emergency platform, weather interface, holiday interface, vehicle-mounted equipment detection system interface, and Baidu Map API to obtain the required raw data parameter information, the risk level information is calculated through the risk early warning model based on the obtained raw data information, and the data information is transmitted to the front-end display interface and then locally backed up and stored in the MySQL database.

[0066] Preferably, the risk map display module reads the real-time risk level data of the equipment and section units from the database, marks them with different colors on the map, and displays them to the user in real time.

[0067] Preferably, the risk information query module reads the specific parameter data of the equipment and section units from the database, displays them to the user in the form of a pop-up window on the page, and the user can query the specific information of the equipment or section unit by clicking on the equipment code or section code; or query its specific information by entering the specified equipment or section unit in the search box.

[0068] Preferably, the historical data analysis module reads the database table for storing the risk calculation results of the equipment and section units, takes the equipment, enterprise, and section unit as the main bodies respectively, uses the risk index model for risk statistical analysis, and outputs the calculation results to the front-end interface, and displays its historical risk status to the user in the form of a line chart and a bar chart.

[0069] Preferably, the system management module includes user management, equipment inherent information, and sensitive date maintenance and update.

[0070] Therefore, the present invention adopts the above real-time risk early warning system and method for pressure-bearing equipment based on big data analysis, and has the following beneficial effects:

[0071] (1) Based on big data technology and relying on the Internet, the present invention realizes automatic collection, transmission, processing, calculation, and output of multi-source security risk parameters, and realizes the macro risk real-time monitoring and early warning technology of mobile pressure-bearing equipment.

[0072] (2) The present invention comprehensively considers the coupling effects of various risk factors such as personnel, equipment, media, roads, meteorology, sensitive dates, etc., and breaks through the single-index two-state early warning technology that only considers speed or position in the safety supervision of "two types of passenger vehicles and one type of dangerous goods vehicle".

[0073] (3) The research results of the present invention are conducive to promoting the modernization of the safety supervision / management mode of mobile pressure-bearing equipment and significantly improving the supervision efficiency.

[0074] The technical solutions of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 One-dimensional normal evaluation standard cloud map of a real-time risk early warning method for pressure-bearing equipment based on big data analysis according to the present invention;

[0076] Figure 2 Vehicle speed parameter cloud map of a real-time risk early warning method for pressure-bearing equipment based on big data analysis according to the present invention;

[0077] Figure 3 Driving age parameter cloud map in an embodiment of the present invention;

[0078] Figure 4 Equipment safety risk early warning index system diagram in an embodiment of the present invention;

[0079] Figure 5 System data flow chart of an implementation risk early warning method for pressure-bearing equipment based on big data analysis according to the present invention;

[0080] Figure 6 Equipment safety risk display effect diagram in an embodiment of the present invention;

[0081] Figure 7 Query interface diagram of the risk information query module in an embodiment of the present invention;

[0082] Figure 8 Historical data statistical analysis diagram in the historical risk data statistics module in an embodiment of the present invention;

[0083] Figure 9 System management module in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0084] The technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0085] Unless otherwise defined, the technical terms or scientific terms used in the present invention shall have the ordinary meanings understood by those of ordinary skill in the art to which the present invention pertains.

[0086] In the present invention, words such as "including" or "comprising" and the like are intended to mean that the elements before this word are covered by the elements listed after this word, and it does not exclude the possibility of also covering other elements. The orientation or positional relationship indicated by terms such as "inside", "outside", "above", "below", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present invention. When the absolute position of the object being described changes, the relative positional relationship may also change accordingly. In the present invention, unless otherwise clearly specified and defined, terms such as "attached" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can be directly connected, or indirectly connected through an intermediate medium. It can be the communication inside two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0087] Embodiment

[0088] A real-time risk warning method for pressure-bearing equipment based on big data analysis specifically includes the following steps:

[0089] S1: Screening the safety state parameters reflecting the risk of pressure-bearing equipment through the cloud model;

[0090] In step S1, the cloud model is implemented by a forward cloud generator and a reverse cloud generator;

[0091] The forward cloud generator is a mapping from qualitative to quantitative. The inputs are the three digital characteristics of the cloud, namely the expectation Ex, entropy En, and hyperentropy He, as well as the number of cloud droplets N. The output is the quantitative positions of N cloud droplets in the number domain space and the degree of certainty of the concepts represented by the cloud droplets;

[0092] The input of the one-dimensional forward cloud generator is the digital characteristics (Ex, En, He) reflecting the weighted qualitative concept and the number of generated cloud droplets N, and the output is N cloud droplets X i and the membership degree of each cloud droplet to the concept. The specific operation process is as follows:

[0093] The first step is to generate a normal random number En' with En as the expectation value and He as the standard deviation; i ’.

[0094] The second step is to generate a normal random number X with Ex as the expectation value and En' as the standard deviation; i ’;

[0095] The third step is to calculate the membership degree of X belonging to the concept: i ;

[0096]

[0097] In the fourth step, repeat Steps 1 to 3 until N cloud droplets are generated;

[0098] The reverse cloud generator uses the weight value as the sample value X = X i , as the input, where i = 1, 2, …, n; and outputs the digital features (Ex, En, He) that reflect the qualitative concept of the parameter weight;

[0099] Based on X i Calculate the sample mean:

[0100]

[0101] where

[0102] The screening process of the safety state parameters is as follows:

[0103] S11: 30 experts who are familiar with and understand the connotation of the parameters evaluate the importance of the parameters. The language scale for evaluation is divided into 9 levels: {extremely unimportant, very unimportant, unimportant, relatively unimportant, average, relatively important, important, very important, extremely important}. Convert the language scale evaluated by the experts into the corresponding interval numbers. The interval numbers corresponding to the 9 levels are {[1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7], [6, 8], [7, 9], [8, 9]}. Convert the interval numbers of the 9 uncertain language evaluation scales on the domain [1, 9] into 9 one-dimensional normal clouds. The specific method is as follows (assuming He0 = 0.01):

[0104] Convert the central interval number [a0, b0] = [4, 6] into an approximate one-dimensional normal cloud. From the formula a = μ - kσ, b = μ + kσ (x ~ N(μ, σ 2 ), k = 2), we can get:

[0105]

[0106] Therefore, Ex0 = 5, En0 = 0.5, He0 = 0.01, that is, the one-dimensional normal cloud corresponding to the interval number [4, 6] is C0(5, 0.5, 0.01);

[0107] Similarly, we can get Ex +1 = 6, En +1 = 0.5. By the approximate method of the golden ratio, we can get:

[0108]

[0109] The one-dimensional normal cloud corresponding to the interval number [5, 7] is C +1(6, 0.5, 0.016).

[0110] After calculation, the 9 one-dimensional normal clouds are as follows:

[0111] C -4 (Ex -4 , En -4 , He -4 ) = C -4 (1.5, 0.25, 0.068);

[0112] C -3 (Ex -3 , En -3 , He -3 ) = C -3 (2, 0.5, 0.042);

[0113] C -2 (Ex -2 , En -2 , He -2 ) = C -2 (3, 0.5, 0.026);

[0114] C -1 (Ex -1 , En -1 , He -1 ) = C -1 (4, 0.5, 0.016);

[0115] C0(Ex0, En0, He0) = C0(5, 0.5, 0.01);

[0116] C +1 (Ex +1 , En +1 , He +1 ) = C +1 (6, 0.5, 0.016);

[0117] C +2 (Ex +2 , En +2 , He +2 ) = C +2 (7, 0.5, 0.026);

[0118] C +3 (Ex +3 , En +3 , He +3 ) = C +3 (8, 0.5, 0.042);

[0119] C +4 (Ex +4, English +4 , He +4 ) = C +4 (8.5, 0.25, 0.068);

[0120] The cloud diagrams of 9 one - dimensional normal evaluation standard clouds are as Figure 1 shown.

[0121] S12: Taking the parameter u1 as an example, the scoring set of 30 experts on the importance of parameter u1 to the parameter is {V1, V2, …, V 10}. Using the score set of the parameter as the evaluation sample, the digital characteristics C1’(7.300, 1.512, 0.261) of the weight factor evaluation set are generated by using the inverse cloud generator, and the evaluation cloud diagram of the parameter is obtained by using the forward cloud generator;

[0122] S13: Repeat step S12 to obtain the evaluation cloud models and parameter cloud characteristics of all parameters; 56 initial parameter cloud characteristics are shown in Table 1.

[0123] Table 1

[0124]

[0125]

[0126] S14: Using the 9 one - dimensional normal cloud diagrams generated by the evaluation language scale as the evaluation criteria, compare the evaluation cloud diagrams of the initial parameters with them one by one; Since the evaluation cloud model reflects the importance of the initial parameters to the evaluation target, and there may be differences in the experts' understanding of the parameter importance, it may lead to poor cohesion of the cloud diagram and a fog - like distribution. Therefore, take Ex≥5 in the order of importance from high to low, and combine with the cohesion distribution of the cloud diagram to screen out appropriate initial parameters as safety - state parameters from the evaluation cloud models of all factors. After calculation, the cloud diagrams of each initial parameter are obtained, and the cloud diagrams and cloud - droplet morphologies of each initial parameter are observed. If the cloud - droplet dispersion is small and the overall cloud diagram shows a linear shape, as Figure 2 shown, it indicates that the experts have a relatively unified understanding of the importance of this initial parameter; if the cloud - droplet dispersion is large and the overall cloud diagram shows a fog - like shape, as Figure 3 shown, it indicates that the experts have not yet formed a unified understanding of the importance of this initial parameter.

[0127] S15: According to the screening criteria in S14, 29 safety - state parameters are screened out. Table 2 is the description table of 29 equipment safety - state parameters.

[0128] Table 2

[0129]

[0130]

[0131]

[0132] S2: Construct a risk early warning index system, clarify the principles for constructing the index system and determine the index weights;

[0133] In step S2, the principles for constructing the index system include the systematic principle, the consistency principle, the independence principle, the plasticity principle, the scientific principle and the comparability principle;

[0134] Systematic principle - The indicators complement each other and comprehensively reflect the essential characteristics and overall performance of the evaluation object; The index system has clear levels, reasonable structure, is interrelated and coordinated;

[0135] Consistency principle - The evaluation index system should be consistent with the evaluation objective. The selected indicators should reflect both the direct results and the indirect effects, and the indicators that have nothing to do with the evaluation object and evaluation content must be excluded;

[0136] Independence principle - For indicators at the same level, their inclusion and cross - relationship should be minimized to ensure that the indicators reflect the actual situation of the evaluation object from different angles;

[0137] Measurability principle - The indicators can be measured or quantified;

[0138] Scientific principle - Guided by scientific theories, starting from the objective elements and their essential connections within the system, combining qualitative and quantitative analysis, correctly reflecting the quantitative characteristics of the overall system and the internal inter - relationships;

[0139] Comparability principle - Objectively and realistically formulate rating indicators and evaluation criteria. The stronger the comparability of the index system, the greater the credibility of the evaluation results;

[0140] According to the principles to be followed, convert the safety state parameters into measurable and meaningful indicators, analyze the 29 selected safety state parameters, and make appropriate modifications or combinations to form real - time risk early warning indicators for mobile pressure - bearing equipment. The processing process and results are shown in Table 3.

[0141] Table 3

[0142]

[0143] Further test the importance of the indicators. Use the risk importance evaluation method to test the indicators, design an expert opinion scoring form for the importance of the indicators, evaluate the importance of the indicators, and assign values to the importance according to Table 4:

[0144] Table 4

[0145]

[0146] 70 copies of expert scoring forms were distributed. The surveyed experts included the Special Inspection Institute, equipment operation units, LanKai High-Tech, Shougang Group, as well as scientific research institutes such as the Academy of Safety Science and Technology and universities. A total of 66 valid questionnaires were recovered. The statistical results of some questionnaires are shown in Table 5.

[0147] Table 5

[0148] Initial selection indicators Expert 1 Expert 2 Expert 3 Expert 4 Expert 5 Expert 6 Expert 7 Expert 8 Equipment inspection status 7 9 9 9 9 9 7 9 Equipment inherent reliability 7 9 9 9 5 9 5 9 Container operating parameters 7 9 9 9 5 9 9 5 Medium properties 9 7 5 7 5 7 5 7 Medium storage 7 1 1 3 5 5 5 5 Continuous driving duration 7 7 9 9 5 7 5 7 Vehicle speed 9 5 7 9 7 9 7 9 Local meteorology 5 5 5 7 1 3 1 3 Current road type 5 5 5 7 1 3 1 3 Sensitive date 1 3 3 3 1 1 1 1 Surrounding social environment 1 5 3 3 1 1 1 1 Surrounding natural environment 3 3 1 3 5 1 5 1

[0149] The recovered data was processed, and the importance coefficient of the index was calculated according to the following formula:

[0150]

[0151] Among them, m is the total number of surveyed experts; a ij is the evaluation score of the jth expert for the ith index; RF i is the importance coefficient of the ith index; RF i ≥4.5; CV i is the coefficient of variation of the ith index; CV i <25%; σ i is the sample standard deviation of the ith index.

[0152] It is stipulated that the importance coefficient RF of each index i ≥4.5 (4.5 is the 50% level value of the 9-point scoring form), and the coefficient of variation CV i <25%. The indexes that do not meet the requirements are removed. The RF and CV values of each index are shown in Table 6. After inspection, all indexes pass the inspection and are retained.

[0153] Table 6

[0154] Initial selection indicators RF CV Equipment inspection status 7.05 15.94％ Equipment inherent reliability 6.16 18.87％ Container operating parameters 7.69 23.54％ Medium properties 8 17.53％ Medium storage 6.95 18.76％ Continuous driving duration 7.46 22.79％ Vehicle speed 6.85 14.35％ Local meteorology 6.9 19.88％ Current road type 6.31 20.46％ Sensitive date 6.59 23.54％ Surrounding social environment 7.72 14.47％ Surrounding natural environment 6.18 21.83％

[0155] The macro safety risk warning index system of pressure-bearing equipment is constructed as Figure 4 shown. According to scientific principles, standards and regulations, expert opinions, etc., the evaluation criteria of each index are determined, as shown in Table 7.

[0156] Table 7

[0157]

[0158]

[0159]

[0160]

[0161] Explanation of the grading of some indexes:

[0162] ① Equipment inspection status: According to relevant regulations, vehicles need to be inspected regularly. If it meets the regulations and is within the inspection period, it is assigned 1 point; if it has not been inspected overdue, it is assigned 4 points.

[0163] ② Current road type: Considering the actual situation, during the data processing of the system, the road type is divided into ordinary roads and highways. Because the vehicle speed is slower and the risk value is lower during driving on ordinary roads; while driving on highways, the vehicle speed is fast and the risk value is higher. Curved road sections are classified as special sections and assigned 4 points. If further detailed classification is carried out, there are many road types, which is not conducive to classification and grading, and will greatly increase the technical difficulty of software development. Therefore, the road type is only divided into two levels.

[0164] ③ Sensitive date: According to the designed system database, there are multiple sensitive dates stored in the database. During the vehicle driving process, the current date will be compared with the sensitive dates in the database. If they coincide, it is a higher risk; otherwise, it is a lower risk.

[0165] ④ Surrounding natural environment: During the vehicle driving process, the natural environment is more complex than the social environment, including plains, mountains, rivers, etc., and it is not easy to carry out detailed classification. The system compares the current position coordinates of the vehicle with the coordinates of rivers, lakes, etc. in the database. If the coordinates coincide, it means that the vehicle has driven to a sensitive location and the risk value is high, assigned 4 points; otherwise, the risk value is low, assigned 1 point.

[0166] In step S2, determining the index weights specifically includes the following steps:

[0167] S21: Construct a judgment matrix, use the analytic hierarchy process to determine the weights of the indicators, select the pairwise comparison method to determine the influence ratio between the indicators, take two indicators Ci and Cj, and use a ij to represent the influence ratio of Ci and Cj on the upper-level indicator. Represent all the comparison results with the matrix A = (a ij ) n×n (a ij > 0) indicates:

[0168]

[0169] a ii = 1, A is an n-order positive reciprocal matrix;

[0170] ΔRF = RF i - RF j (-8 ≤ ΔRF ≤ 8); Corresponding the value of ΔRF to the 9-point scale method, as shown in Table 8, the values of the elements in the judgment matrix can be judged.

[0171] Table 8

[0172] ΔRF 0 (0,1] (1,2] (2,3] (3,4] (4,5] (5,6] (6,7] (7,8] Scale 1 2 3 4 5 6 7 8 9 ΔRF 0 [-1,0) [-2,-1) [-3,-2) [-4,-3) [-5,-4) [-6,-5) [-7,-6) [-8,-7) Scale 1 1 / 2 1 / 3 1 / 4 1 / 5 1 / 6 1 / 7 1 / 8 1 / 9

[0173] The importance data of "possibility", "severity" and "sensitivity" and all the secondary indicators at the lower level are compared pairwise to obtain the following three judgment matrices. Matrix A1 is the judgment matrix for the "possibility" indicator, matrix A2 is the judgment matrix for the "severity" indicator, and matrix A3 is the judgment matrix for the "sensitivity" indicator;

[0174]

[0175] S22: The steps to determine the index weight coefficient using the sum-product method are as follows:

[0176] ① Normalize the elements of judgment matrix A by column to obtain matrix B = (b ij ) n×n , where

[0177]

[0178] ② Add the elements in matrix B by row to obtain vector Z = (z1, z2,..., z n ) T , where

[0179]

[0180] ③ Normalize vector Z to obtain the eigenvector W = (w1, w2,..., w n ) T , where

[0181]

[0182] Using the above method to calculate matrices A1, A2 and A3, the weights of "equipment inspection status", "equipment inherent reliability", "container working parameters", "continuous driving duration", "vehicle speed", "local weather", "current road type" for the "possibility" indicator are: 0.1527, 0.0605, 0.2441, 0.2037, 0.1030, 0.1571, 0.0788; the weights of "medium property", "medium storage" for the "severity" indicator are: 0.7500, 0.2500; the weights of "sensitive date", "surrounding social environment", "surrounding natural environment" for the "sensitivity" indicator are: 0.2519, 0.5889, 0.1592;

[0183] S23: Conduct a consistency test, calculate the maximum eigenvalue λ max of the judgment matrix, and then calculate the consistency index n represents the order of the judgment matrix;

[0184] If CI = 0, it indicates that the judgment matrix has perfect consistency;

[0185] If CI ≠ 0, it is necessary to calculate the random consistency ratio ;

[0186] Among them, RI is the average random consistency index of the judgment matrix, and the value of RI is related to the matrix order; if CR < 0.1, it is considered that the consistency of the judgment matrix and the single-layer sorting result is acceptable. The RI corresponding to matrices of order 1 to 10 is shown in Table 9.

[0187] Table 9

[0188]

[0189] After calculation, it can be obtained that the CR corresponding to the judgment matrix A1 is 0.0811 < 0.1, the CR corresponding to the judgment matrix A2 is 0.0000 < 0.1, and the CR corresponding to A3 is 0.0465 < 0.1, all of which pass the consistency test;

[0190] Finally, it is obtained that:

[0191] The weight ω of the possibility influencing factor index P = (0.1527, 0.0605, 0.2441, 0.2037, 0.1030, 0.1571, 0.0788);

[0192] The weight ω of the severity influencing factor index L = (0.7500, 0.2500);

[0193] The weight ω of the sensitivity influencing factor index S = (0.2519, 0.5889, 0.1592).

[0194] S3: Based on the equipment risk early warning theoretical model, index system and weights, an equipment risk early warning classification model is obtained;

[0195] In step S3, the equipment risk early warning classification model is as follows:

[0196]

[0197]

[0198] Among them: R represents the risk score; d represents the index score; ω represents the weight corresponding to the index.

[0199] S4: After obtaining the risk value through the risk early warning model, according to the "Pareto principle", the risk classification standard is determined, as shown in Table 10, and the risk level of the equipment is obtained.

[0200] Table 10

[0201]

[0202] A system for real-time risk warning of pressure equipment based on big data analysis, such as Figure 5 As shown, it includes risk map display module, risk information query module, historical data analysis module and system management module;

[0203] The required raw data parameter information is obtained by calling the emergency platform, weather interface, holiday interface, vehicle equipment detection system interface and Baidu map API. The risk level information is calculated based on the acquired raw data information through the risk warning model. The data information is transmitted to the front display interface and then backed up locally and saved in the MySQL database.

[0204] Baidu Map API Service

[0205] Baidu Map API is provided free of charge to developers to Figure 2 This is a set of application interfaces developed for the first time. Developers can use the provided JavaScript API, Internet of Vehicles API, Android SDK, etc. to achieve browser-based development on multiple devices such as PC, mobile, and server. Among them, JavaScript API is a set of application program interfaces written in JavaScript. Using the open source library provided to users, the classes and methods therein are directly called, which is conducive to simplifying development and quickly realizing basic map display, positioning, reverse / geocoding and retrieval and other highly interactive functions on the website.

[0206] MySQL database management system

[0207] Common database management systems include MySQL, ORACLE, SQL Server, ACCESS, etc.

[0208] Among them, MySQL is the most widely used in the development of small and medium-sized websites due to its advantages in cost and development difficulty. However, MySQL itself does not support the Windows graphical interface and can only operate and manage the database in code under MS-DOS, which brings inconvenience to developers. In order to solve this problem, Navicat is needed to provide a graphical user interface to operate MySQL. It can simplify the management cost of the database and system, and enable users to create, organize, access and share information in a safe and simple way.

[0209] The basic operations on database tables are divided into four types: insertion, deletion, modification, and query, which are usually performed through SQL statements. SQL is the statement for operating on data in a database, and it will vary slightly in different databases, such as MySQL, Oracle, SQL Server, SQLite, etc. However, the basic statements of SQL, namely the SELECT, INSERT, UPDATE, and DELETE statements, are the same. These four statements are the foundation of SQL basics, and other complex statements are combinations of these four statements. Based on these four basic operations, combined with the WHERE filtering query statement, the ORDER BY ascending and descending sorting statements, the AND and OR statements, etc., various complex operations can be performed on the data table to meet the command requirements.

[0210] The risk map display module reads the real-time risk level data of devices and road sections from the database, marks them with different colors on the map, and displays them to users in real time. This module introduces Baidu Map into the system interface through the API provided by the Baidu Map Open Platform and constructs a map application according to its development documentation to achieve functions such as the display of the risk status and risk warning required by the system.

[0211] ① Display of device risk map

[0212] The main function of this part is to display the real-time risk status of devices. After the system background obtains the relevant index parameter information of each device and calculates information such as the vehicle risk level through the risk warning model, the data is transmitted to the emergency platform in JSON format. The platform parses the position and risk level information of the vehicles in it and displays them on the constructed Baidu Map. On the map, devices are represented by vehicle icons, and different icon colors represent devices with different risk levels. Red represents devices with level I risk, orange represents devices with level II risk, yellow represents devices with level III risk, and blue represents devices with level IV risk, as Figure 6 shown.

[0213] In addition, the emergency platform counts the total number of currently operating devices and the number of devices at each risk level from the transmitted JSON data and displays them in real time in the interface border. When it is necessary to query the risk level status of a specified vehicle, enter the license plate number of the specified vehicle in the search box of the interface, and the position of the vehicle and its risk level information can be marked on the map.

[0214] ② Risk warning

[0215] Trigger condition: When the risk level of a single device is level I, a risk warning will be triggered. Warning form: Sound warning, when the risk level of the device reaches level I, the system emits an alarm sound warning; Color warning, when the risk level is level I, the device icon is displayed in red; Flashing dynamic warning, when the risk level of the device reaches level I, the device icon flashes for dynamic warning.

[0216] The risk information query module reads the specific parameter data of devices and road section units from the database and displays it to users in the form of a pop-up box on the page. Users can query the specific information of a device or road section unit by clicking on the device code or road section code; or by entering a specified device or road section unit in the search box to query its specific information.

[0217] Through the risk map display module, the overall supervision of hazardous chemical transportation can be carried out from a macroscopic perspective. If further detailed information on hazardous chemical transportation is required, the risk information query module is needed. This module can provide real-time information on all parameters of the device, so as to facilitate supervisors to take targeted measures to reduce risks.

[0218] Risk information query refers to querying and displaying the real-time values of various parameters of a specified device, mainly including two categories. One category is the inherent parameters of the vehicle, that is, the relatively fixed attributes of the vehicle itself, including vehicle license plate number, vehicle type, manufacturing unit, affiliated unit, using unit, vehicle inspection unit, registration number of the tank (cylinder), last inspection date of the tank (cylinder), chassis type, safety relief device, tank insulation method, volume of the tank (cylinder), designed speed of the vehicle, name of the transported medium, designed storage capacity of the tank (cylinder), designed temperature of the tank, etc.; the other category is real-time changing parameters, mainly including transportation volume, real-time temperature of the cylinder (tank), local meteorology, methane concentration, real-time position coordinates of the vehicle, vehicle speed, road type and speed limit information, etc., as Figure 7 shown.

[0219] The historical data analysis module reads the database table for storing the risk calculation results of devices and road section units, and takes devices, enterprises, and road section units as the main bodies respectively, uses the risk index model to conduct risk statistical analysis, and outputs the calculation results to the front-end interface, and displays its historical risk status to users in the form of line charts and bar charts.

[0220] After the risk warning system has been running for a period of time, a large amount of historical risk data will be accumulated in the background database. Based on these data, the safety status of the device during a certain period can be quantitatively represented through statistics. For the historical data analysis module, the device risk index selects the device risk index model per kilometer of driving mileage:

[0221]

[0222] Among them, R e1 is the device risk index per kilometer of driving mileage; R i is the score of the device in the i-level risk; s is the mileage traveled per transportation.

[0223] Quantitatively statistically analyze the safe operation status of the device during a certain period through the risk index value, and display the results in the form of a line chart, asFigure 8 As shown Figure 8 In it, the abscissa is the date and time of statistical analysis, and the ordinate is the risk index value. By clicking on the risk value of a certain month in the device risk index model, a bar chart of the contribution degree of early warning indicators of the device can be obtained, so as to analyze the risk level of each indicator in the early warning model of a single device within a certain period. Conduct annual risk data analysis on the device to identify its overall safety status, risk level of each month and the change trend with months in that year, providing a basis for improving the supervision level of safety supervision departments, strengthening internal assessment management of enterprises and improving the safety level of drivers, etc. Analyze the past risk status through the change trend of the broken line, predict the future risk level, and provide a basis for the next step of safety supervision work.

[0224] The system management module includes user management, equipment inherent information and sensitive date maintenance and update. As Figure 9 shown, user management can realize user addition and deletion, permission allocation, etc., limit the resources that users can access, and prevent information leakage; the equipment inherent information and sensitive date update function allows administrators to add new equipment to the system database and update its data, add sensitive dates to the sensitive date sensitivedate table in the database, better adapt to the actual usage situation, and extend the system life cycle.

[0225] Field research

[0226] Empirical research was carried out in Hainan, Shenyang, Shanghai and other places. A number of long-tube trailers and cryogenic tank trucks were selected. Taking the long-tube trailer as an example, the introduction is as follows:

[0227] The long-tube trailer, the last inspection time (during the empirical study) was June 5, 2018. It is a semi-trailer chassis with a rupture disc, a designed speed of 80, the manufacturing unit is Shijiazhuang Anruike Gas Machinery Co., Ltd., the affiliated unit and the using unit are Hainan Branch of China National Petroleum Transportation Co., Ltd., the inspection unit is Hebei Special Equipment Supervision and Inspection Institute, the registration number is 18GC184A-17, the designed temperature is 50°C, and the medium is CNG.

[0228] The long-tube trailer carried sensors and worked normally from 9:36 on September 24, 2019 to 14:15 on September 26, 2019. A total of 18,825 data were collected by the system background. After automatic calculation by the system, the risk level of this equipment always remained at the fourth-level risk or the third-level risk. Some of the collected data and calculation results are shown in Table 11 below.

[0229] Table 11

[0230]

[0231] Based on the safety risk theory and quantitative model, the three major elements of safety state parameters - possibility, severity, and sensitivity - are clarified. Through primary parameter selection and cloud model screening, 29 safety state parameters are determined, and they are processed into indicators. An equipment risk early warning indicator system is established, a grading standard for the indicators is proposed, the indicator weights are determined by the analytic hierarchy process, and an equipment safety risk early warning evaluation model and grading standard are established.

[0232] Using the Java language, B / S architecture, and MySQL database technology, and relying on the Baidu Map API service, a safety risk early warning system with modules such as risk map display, risk information query, historical risk data statistics, and system management is developed. An empirical study is conducted on the risk early warning model, and the effect feedback is good.

[0233] Therefore, the present invention adopts the above-mentioned real-time risk early warning system and method for pressure-bearing equipment based on big data analysis. Based on big data technology and relying on the Internet, it realizes the automatic collection, transmission, processing, calculation, and output of multi-source safety risk parameters, and realizes the real-time monitoring and early warning technology for the macroscopic risks of mobile pressure-bearing equipment.

[0234] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements do not make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A real-time risk early warning method for pressure-bearing equipment based on big data analysis, characterized in that: Specifically, it includes the following steps: S1: Screen the safety state parameters reflecting the risks of pressure-bearing equipment through the cloud model; S2: Construct a risk warning index system, clarify the construction principles of the index system and determine the index weights; S3: Based on the equipment risk warning theoretical model, index system and weights, obtain the equipment risk warning classification model; S4: After obtaining the risk value through the risk warning model, determine the risk classification standard according to the "Pareto principle" to obtain the risk level of the equipment.

2. The real-time risk early warning method for pressure-bearing equipment based on big data analysis according to claim 1, wherein: In step S1, the cloud model is implemented by a forward cloud generator and a reverse cloud generator; The forward cloud generator is a mapping from qualitative to quantitative. The inputs are the three digital characteristics of the cloud, namely the expectation Ex, entropy En, and hyperentropy He, as well as the number of cloud droplets N. The output is the quantitative positions of N cloud droplets in the number domain space and the certainty degrees of the concepts represented by the cloud droplets; The input of the one-dimensional forward cloud generator is the digital characteristics (Ex, En, He) reflecting the qualitative concept of weight and the number N of generated cloud droplets, and the output is N cloud droplets X i and the membership degree of each cloud droplet to the concept. The specific operation process is as follows: First step, generate a normal random number En with En as the expected value and He as the standard deviation i ’. Step 2: Generate a normal random number X with an expected value of Ex and a standard deviation of En i ’. Step 3: Calculate X i Membership degree belonging to the concept: Fourthly, repeat steps one to three until N cloud droplets are generated; The reverse cloud generator takes the weight value as the sample value \(X = X\) i , as the input, where \(i = 1, 2, \ldots, n\); and outputs the digital features \((Ex, En, He)\) that reflect the qualitative concept of the parameter weight. According to X i Calculate the sample mean: wherein The screening process of the safety state parameters is as follows: S11: Evaluate the importance of the parameters. The language scales for evaluation are divided into 9 levels: {extremely unimportant, very unimportant, unimportant, less important, average, more important, important, very important, extremely important}. The interval numbers corresponding to the 9 levels are {[1,2], [1,3], [2,4], [3,5], [4,6], [5,7], [6,8], [7,9], [8,9]}. Convert the interval numbers of the 9 uncertain language evaluation scales on the domain [1,9] into 9 one-dimensional normal clouds; S12: The scoring set of parameter importance is {V1, V2, …, V 10}, using the score set of parameters as the evaluation sample, generating the digital characteristics of the weight factor evaluation set by using the inverse cloud generator, and obtaining the evaluation cloud diagram of the parameters by using the forward cloud generator; S13: Repeat step S12 to obtain the evaluation cloud models and cloud characteristics of all parameters; S14: Use the 9 one-dimensional normal cloud charts generated by the evaluation language scales as the evaluation criteria, and compare the evaluation cloud charts of the initial parameters with them one by one; S15: Screen out the safety state parameters according to the screening criteria in S14.

3. A real-time risk warning method for pressure-bearing equipment based on big data analysis according to claim 1, characterized in that: In step S2, the construction principles of the index system include the systematic principle, the consistency principle, the independence principle, the plasticity principle, the scientific principle, and the comparability principle; Systematic principle - The indicators complement each other and comprehensively reflect the essential characteristics and overall performance of the evaluation object; the index system is clear in level, reasonable in structure, interrelated, and coordinated; Consistency principle - The evaluation index system should be consistent with the evaluation goal. The selected indicators should reflect both the direct results and the indirect effects, and the indicators irrelevant to the evaluation object and evaluation content must be excluded; Independence principle - The indicators at the same level should minimize their inclusion and cross relationships to ensure that the indicators reflect the actual situation of the evaluation object from different angles; Measurability principle - The indicators can be measured or quantified; Scientific principle - Guided by scientific theories, starting from the objective elements and their essential connections within the system, combining qualitative and quantitative analysis, correctly reflecting the quantitative characteristics of the overall system and the internal mutual relationships; Comparability principle - Objectively and realistically formulate rating indicators and evaluation criteria. The stronger the comparability of the index system, the greater the credibility of the evaluation results; Process the recovered data and calculate the importance coefficient of the indicators according to the following formula: Among them, m is the total number of investigation experts; a ij is the evaluation score of the j-th expert for the i-th index; RF i is the importance coefficient of the i-th index; RF i ≥ 4.5; CV i is the coefficient of variation of the i-th index; CV i < 25%; σ i is the sample standard deviation of the i-th index.

4. A real-time risk warning system and method for pressure-bearing equipment based on big data analysis according to claim 3, characterized in that: In step S2, determining the index weights specifically includes the following steps: S21: Construct a judgment matrix, use the analytic hierarchy process to determine the weights of the indicators, select the paired comparison method to determine the influence ratio between the indicators. Take two indicators Ci and Cj, and use a ij to represent the ratio of the influence of Ci and Cj on the upper-level indicator. Represent all the comparison results with the matrix A=(a ij ) n×n (a ij >0) indicates: a ii = 1, A is a positive reciprocal matrix of order n; ΔRF = RF i -RF j (-8 ≤ ΔRF ≤ 8); The value of ΔRF is corresponded to the 9-point scale method to judge the value of the elements in the matrix. By pairwise comparing the data of "possibility", "severity", "sensitivity" and the importance of all lower-level secondary indicators respectively, the following 3 judgment matrices can be obtained. Matrix A1 is the judgment matrix of the "possibility" index, matrix A2 is the judgment matrix of the "severity" index, and matrix A3 is the judgment matrix of the "sensitivity" index; S22: The steps to determine the index weight coefficient by the sum-product method are as follows: ① Normalize the elements of the judgment matrix A column by column to obtain the matrix B = (b ij ) n×n , where ② Add the elements in matrix B row by row to obtain vector Z = (z1, z2, …, z n ) T , where ③Normalize the vector Z to obtain the eigenvector W = (w1, w2, …, w n ) T , where Using the above method to calculate matrices A1, A2, and A3, the weights of "equipment inspection status", "equipment inherent reliability", "container working parameters", "continuous driving duration", "vehicle speed", "local weather", and "current road type" for the "possibility" index are respectively: 0.1527, 0.0605, 0.2441, 0.2037, 0.1030, 0.1571, 0.0788; the weights of "medium property" and "medium storage" for the "severity" index are respectively: 0.7500, 0.2500; the weights of "sensitive date", "surrounding social environment", and "surrounding natural environment" for the "sensitivity" index are respectively: 0.2519, 0.5889, 0.1592; S23: Conduct consistency test and calculate the maximum eigenvalue λ of the judgment matrix max , and then calculate the consistency index n represents the order of the judgment matrix; If CI = 0, it indicates that the judgment matrix has perfect consistency; If CI ≠ 0, it is necessary to calculate the random consistency ratio . where RI is the average random consistency index of the judgment matrix, and the value of RI is related to the matrix order; After calculation, it can be obtained that for judgment matrix A1, CR = 0.0811 < 0.1, for judgment matrix A2, CR = 0.0000 < 0.1, and for A3, CR = 0.0465 < 0.1, all passing the consistency test; Finally, it is obtained that: Possibility influencing factor index weight ω P =(0.1527, 0.0605, 0.2441, 0.2037, 0.1030, 0.1571, 0.0788); Severity impact factor index weight ω L =(0.7500, 0.2500); Sensitivity influencing factor index weight ω S = (0.2519, 0.5889, 0.1592).

5. The real-time risk early warning system and method for pressure-bearing equipment based on big data analysis according to claim 4, characterized in that: In step S3, the equipment risk warning classification model is as follows: Where: R represents the risk score; d represents the index score; ω represents the weight corresponding to the index.

6. The system of a real-time risk warning method for pressure-bearing equipment based on big data analysis according to any one of claims 1-5, characterized in that: It includes a risk map display module, a risk information query module, a historical data analysis module, and a system management module; By calling the emergency platform, weather interface, holiday interface, vehicle-mounted equipment detection system interface, and Baidu Map API to obtain the required original data parameter information, the risk level information is calculated through the risk warning model based on the obtained original data information, and the data information is transmitted to the front-end display interface and then locally backed up and stored in the MySQL database.

7. The system of a real-time risk warning method for pressure-bearing equipment based on big data analysis according to claim 6, wherein: The risk map display module reads the real-time risk level data of equipment and road sections from the database, marks them with different colors on the map, and displays them to the user in real time.

8. The system of a real-time risk warning method for pressure-bearing equipment based on big data analysis according to claim 6, wherein: The risk information query module reads the specific parameter data of equipment and road sections from the database and displays them to the user in the form of a pop-up box on the page. The user can click on the equipment code or road section code to query the specific information of the equipment or road section; or enter the specified equipment or road section in the search box to query its specific information.

9. The system of a real-time risk warning method for pressure-bearing equipment based on big data analysis according to claim 6, characterized in that: The historical data analysis module reads the database table used to store the risk calculation results of equipment and road sections, takes equipment, enterprises, and road sections as the main bodies respectively, uses the risk index model for risk statistical analysis, and outputs the calculation results to the front-end interface, and displays its historical risk status to the user in the form of line charts and bar charts.

10. The system of a real-time risk warning method for pressure-bearing equipment based on big data analysis according to claim 6, wherein: The system management module includes user management, equipment inherent information, and sensitive date maintenance and update.

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