CN-122022206-A - Digital supervision and traceability management method and system for pressure-bearing special equipment

CN122022206ACN 122022206 ACN122022206 ACN 122022206ACN-122022206-A

Abstract

The invention relates to the technical field of data management, in particular to a method and a system for digitally supervising and tracing management of pressure-bearing special equipment, wherein a multidimensional real-time operation time sequence of the special equipment is constructed by collecting a pressure signal at an interface and a surface temperature signal of a pressure-bearing container body, and the time sequence is mapped and compared with a standard test operation time sequence, the grid path difference matrix is further constructed, comprehensive morphological difference information corresponding to continuous grid paths is extracted, dynamic quantitative supervision of the running state of the pressure-bearing special equipment is realized, and the function of converting real-time running deviation from discrete monitoring values into comparable and determinable supervision results is achieved. The invention also realizes the through treatment from the abnormal operation identification to the life cycle reason tracing of the pressure-bearing special equipment by introducing the material thickness record in the manufacturing stage, the welding temperature record in the installation stage and the environment humidity record in the using stage.

Inventors

Zeng Bangxiong
LI CHAOSONG
XU WEIBIN
CHEN ZHIWEI
HUANG HAODONG

Assignees

福建陆源智能科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260415

Claims (10)

1. The digital supervision and traceability management method for the pressure-bearing special equipment is characterized by comprising the following steps of: S1, acquiring a temperature signal and a pressure signal of a pressure-bearing container body, determining a temperature value, a pressure value and a corresponding time stamp, and generating a multidimensional real-time operation time sequence of special equipment; s2, acquiring a standard test run time sequence of the special equipment, and comparing the standard test run time sequence with the multidimensional real-time run time sequence of the special equipment to construct a grid path difference matrix; s3, judging comprehensive morphological difference information of the special equipment according to the grid path difference matrix, and generating a digital supervision result of the pressure-bearing special equipment according to the multi-type comprehensive morphological difference information; s4, acquiring a reason node state parameter of the pressure-bearing special equipment, and determining the causal association probability of the life cycle of the equipment according to the reason node state parameter and the digital supervision result of the pressure-bearing special equipment; S5, comparing the life cycle causal association probability of the equipment with a preset responsibility tracing judgment threshold value, determining a target posterior probability inferred value, and obtaining a source tracing and responsibility returning result of the pressure-bearing special equipment, wherein the source node state parameter corresponds to the target posterior probability inferred value.
2. The method for digitally supervising and tracing the pressure-bearing special equipment according to claim 1, wherein the multi-dimensional real-time operation time sequence of the special equipment comprises a real-time temperature value, a real-time pressure value and a corresponding timestamp, the grid path difference matrix comprises a standard test operation time sequence comparison difference value and a multi-dimensional real-time operation time sequence comparison difference value, the digital supervision result of the pressure-bearing special equipment comprises a comprehensive morphology difference judgment item and a comprehensive morphology difference type, the causal association probability of the life cycle of the equipment comprises a causal node state parameter association probability and a posterior probability inference value, and the tracing and responsibility returning result of the pressure-bearing special equipment comprises a target posterior probability inference positioning item and a corresponding causal node state parameter.
3. The method for digitally supervising and tracing the pressure-bearing special equipment according to claim 1, wherein the step S1 is specifically: S101, acquiring big data of a pressure-bearing container body surface temperature sensor voltage signal in a real-time operation period of pressure-bearing special equipment, inputting the voltage signal into an analog-to-digital converter to perform conversion, outputting a temperature value, acquiring a pressure transmitter current signal at an interface of the pressure-bearing container body in the same real-time operation period, performing numerical conversion on the current signal, outputting a pressure value, and acquiring equipment multidimensional operation parameters; S102, extracting a timestamp corresponding to a temperature value and a pressure value based on the multidimensional operation parameters of the equipment, and generating a parameter time sequence pairing set by pairing and combining the temperature value and the pressure value corresponding to the same occurrence time based on the timestamp; and S103, performing permutation and splicing on each paired data in the parameter time sequence pairing set according to the time stamp sequence, and establishing a multidimensional real-time operation time sequence of the special equipment.
4. The method for digitally supervising and tracing the pressure-bearing special equipment according to claim 1, wherein the step S2 is specifically: S201, collecting a test temperature value and a test pressure value in a test operation period of the pressure-bearing special equipment in the initial installation finishing test operation stage, arranging the test temperature value and the test pressure value into a special equipment standard test operation time sequence according to time sequence, combining the special equipment multi-dimensional real-time operation time sequence, respectively calculating the ratio between the temperature value and the pressure value and the corresponding preset temperature range reference value and the corresponding preset pressure range reference value to obtain a first temperature mapping ratio and a first pressure mapping ratio, and simultaneously respectively calculating the ratio between the test temperature value and the test pressure value and the corresponding preset temperature range reference value and the corresponding preset pressure range reference value to obtain a second temperature mapping ratio and a second pressure mapping ratio; S202, calculating a first characteristic deviation absolute value between the first temperature mapping proportion and the second temperature mapping proportion, simultaneously calculating a second characteristic deviation absolute value between the first pressure mapping proportion and the second pressure mapping proportion, and summing the first characteristic deviation absolute value and the second characteristic deviation absolute value to obtain a multidimensional comprehensive characteristic deviation absolute value; S203, combining all the multidimensional comprehensive characteristic deviation absolute values in the same time range to construct a grid path difference matrix.
5. The method for digitally supervising and tracing the pressure-bearing special equipment according to claim 4, wherein the step S3 is specifically: S301, extracting a plurality of continuous grid paths of the grid path difference matrix, and respectively calculating the sum of all the multidimensional comprehensive characteristic deviation absolute values of each continuous grid path to obtain accumulated path distances of a plurality of pressure-bearing containers; s302, comparing the numerical value parameters among the accumulated path distances of the pressure-bearing containers, screening out the minimum numerical value parameters from the numerical value parameters, and setting the minimum numerical value parameters as the comprehensive morphological difference value of special equipment; S303, comparing the comprehensive morphology difference value of the special equipment with a preset morphology difference safety threshold, and extracting corresponding comprehensive morphology difference information of the special equipment under the condition that the comprehensive morphology difference value of the special equipment is larger than the morphology difference safety threshold, so as to generate a digital supervision result of the pressure-bearing special equipment.
6. The method for digitally supervising and tracing the pressure-bearing special equipment according to claim 1, wherein the step S4 is specifically: S401, collecting the material thickness record of the pressure-bearing container in the manufacturing stage of the pressure-bearing special equipment, the welding temperature record of the pressure-bearing container in the installation stage and the big data of the humidity record of the running environment of the special equipment in the using stage, setting the big data as a reason node state parameter, setting the digital supervision result of the pressure-bearing special equipment as a result node state parameter, and merging the result node state parameter into a network model node parameter set; S402, inputting the network model node parameter set into a Bayesian network model, and calculating node joint distribution probability between a reason node state parameter and a result node state parameter; S403, according to the node joint distribution probability, the posterior probability inferred values between the performance cause node state parameters and the result node state parameters are deduced, all posterior probability inferred values in the Bayesian network model are extracted, the parameter combination operation is executed, and the equipment life cycle causal association probability is established.
7. The method for digitally supervising and tracing the pressure-bearing special equipment according to claim 1, wherein the step S5 is specifically: s501, extracting a plurality of posterior probability inferred values in the causal association probability of the life cycle of the equipment, calculating the ratio between the sum of the posterior probability inferred values and the total number of the posterior probability inferred values to obtain average probability distribution, and setting the average probability distribution as a responsibility tracing judgment threshold; S502, screening posterior probability inferred values larger than the responsibility tracing judgment threshold value from a plurality of posterior probability inferred values to obtain a target posterior probability inferred value; And S503, addressing and mapping out a reason node state parameter matching item corresponding to the target posterior probability inferred value from the reason node state parameters, extracting the reason node state parameters positioned at the position corresponding to the target posterior probability inferred value, and generating a tracing and responsibility returning result of the pressure-bearing special equipment.
8. The method for digitally supervising and tracing management of pressure-bearing special equipment according to claim 5, wherein the special equipment comprehensive morphology difference information comprises a temperature thermodynamic drift difference parameter mapped by a continuous grid path generating a minimum numerical parameter, a medium pressure distortion difference parameter mapped by a continuous grid path generating a minimum numerical parameter, and a numerical value out-of-range offset amplitude parameter corresponding to a morphology difference safety threshold part.
9. The method for digitally supervising and tracing the pressure-bearing special equipment according to claim 6, wherein the bayesian network model is constructed according to network directed graph node parameters formed by source node state parameters and result node state parameters in a network model node parameter set, network directed graph associated edge structure parameters pointed to the result node state parameters by the source node state parameters and set node initial condition probability distribution parameters.
10. The digital supervision and traceability management system for the pressure-bearing special equipment is characterized by being used for realizing the digital supervision and traceability management method for the pressure-bearing special equipment according to any one of claims 1-9, and comprises the following steps: The sequence generation module is used for acquiring a temperature signal and a pressure signal of the pressure-bearing container body, determining a temperature value, a pressure value and a corresponding timestamp, and generating a multidimensional real-time operation time sequence of the special equipment; the matrix construction module is used for acquiring a standard test run time sequence of the special equipment, comparing the standard test run time sequence with the multidimensional real-time run time sequence of the special equipment and constructing a grid path difference matrix; the monitoring result generation module is used for judging comprehensive form difference information of the special equipment according to the grid path difference value matrix and generating a digital monitoring result of the pressure-bearing special equipment according to the multi-type comprehensive form difference information; the probability determining module is used for acquiring the reason node state parameters of the pressure-bearing special equipment and determining the causal association probability of the life cycle of the equipment according to the reason node state parameters and the digital supervision result of the pressure-bearing special equipment; And the traceability result generation module is used for comparing the life cycle causality association probability of the equipment with a preset responsibility tracing judgment threshold value, determining a target posterior probability inferred value, and obtaining a causal node state parameter corresponding to the target posterior probability inferred value, thereby obtaining the traceability and responsibility returning result of the pressure-bearing special equipment.

Description

Digital supervision and traceability management method and system for pressure-bearing special equipment Technical Field The invention relates to the technical field of data management, in particular to a digital supervision and traceability management method and system for pressure-bearing special equipment. Background The technical field of data management refers to a technical system surrounding data acquisition, storage, processing, analysis and application expansion, relates to information platform construction, data standardization processing, data security and sharing mechanism and the like, is mainly used for improving the organization and utilization level of information resources in various industries, and is widely applied to the fields of industrial manufacturing, public safety, energy management, equipment operation and maintenance and the like. The digital supervision and traceability management method for the pressure-bearing special equipment is a management method for recording, supervising and tracing related data through informatization means in the whole life cycle process of designing, manufacturing, installing, checking, using, maintaining and the like of the pressure-bearing special equipment such as boilers, pressure vessels, pressure pipelines and the like, and generally uniformly managing and inquiring equipment information by means of database management technology, information acquisition technology and network communication technology so as to realize systematic management of equipment state and historical information. The traditional pressure-bearing special equipment management mode can uniformly record and inquire basic information, state information and history information of equipment such as boilers, pressure vessels, pressure pipelines and the like by means of database management technology, information acquisition technology and network communication technology, but under the conditions that the equipment is continuously operated, test operation state comparison analysis and abnormal state fine judgment are carried out, the problem that monitoring data stay on a static ledger management layer, multidimensional operation parameters such as temperature, pressure and the like lack of correlation analysis after timestamp pairing, the operation sequence and standard test operation sequence lack of quantitative comparison paths, and the problem that through mapping is difficult to form between equipment abnormal results and manufacturing, installation and use stage reason data exists. Although the traditional tracing mode can carry out post-finding according to the inspection record, maintenance record or single detection result, under the condition that the pressure-bearing container has thermal drift, pressure distortion or comprehensive morphology difference overrun, the problems of thicker abnormal recognition granularity, stay of the supervision result on a phenomenon level, dependence on manual turning and checking of multi-stage record in reason positioning, dispersion of responsibility-returning basis and insufficient relevance exist. Disclosure of Invention In order to solve the technical problems in the prior art, the embodiment of the invention provides a digital supervision and traceability management method for pressure-bearing special equipment, which comprises the following steps: In order to achieve the above purpose, the present invention adopts the following technical scheme: The digital supervision and traceability management method for the pressure-bearing special equipment comprises the following steps: S1, acquiring a temperature signal and a pressure signal of a pressure-bearing container body, determining a temperature value, a pressure value and a corresponding time stamp, and generating a multidimensional real-time operation time sequence of special equipment; s2, acquiring a standard test run time sequence of the special equipment, and comparing the standard test run time sequence with the multidimensional real-time run time sequence of the special equipment to construct a grid path difference matrix; s3, judging comprehensive morphological difference information of the special equipment according to the grid path difference matrix, and generating a digital supervision result of the pressure-bearing special equipment according to the multi-type comprehensive morphological difference information; s4, acquiring a reason node state parameter of the pressure-bearing special equipment, and determining the causal association probability of the life cycle of the equipment according to the reason node state parameter and the digital supervision result of the pressure-bearing special equipment; S5, comparing the life cycle causal association probability of the equipment with a preset responsibility tracing judgment threshold value, determining a target posterior probability inferred value, and obtaining a source tracing and responsibility returning result of