CN113515506A - LDAR (laser direct reflectance assessment) system and method based on big data mining analysis - Google Patents
LDAR (laser direct reflectance assessment) system and method based on big data mining analysis Download PDFInfo
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Abstract
The embodiment of the invention provides an LDAR (laser direct reflectance) evaluation system based on big data mining analysis, which comprises: the data storage layer is used for storing original data for leak detection and repair; the data application layer is used for providing data analysis application for the data storage layer and comprises a data analysis module and a project auditing module; the data analysis module is used for responding to data analysis operation of a user to obtain the association between the leakage detection and the repaired original data and form decision knowledge; the project auditing module is used for establishing an auditing project for leakage detection and repair, auditing the compliance condition of the auditing project and outputting an evaluation report. Meanwhile, the LDAR assessment method based on big data mining analysis is also provided. The technical scheme of the invention realizes the rapid evaluation of the operation effect of the LDAR device, and is beneficial to the formulation and implementation of key control measures for the unorganized emission of equipment.
Description
Technical Field
The invention relates to the technical field of environment, in particular to an LDAR (laser direct reflectance assessment) assessment system based on big data mining analysis, an LDAR assessment method based on big data mining analysis and a corresponding storage medium.
Background
LDAR (leak Detection And repair): the leakage detection and repair is to detect potential leakage points of the refining device, find components with leakage phenomena in time, repair or replace the components, and further reduce leakage emission.
Equipment leakage is an important source item of the unorganized emission of VOCs, the requirements of developing leakage detection and LDAR restoration are provided by an atmospheric pollution control method and the like, the unorganized leakage of VOCs from equipment and pipeline components such as a pump, a compressor, a valve, a flange and the like is controlled, and the leakage control of the equipment and the pipeline components is enhanced. The equipment and the pipeline subassembly that carry gaseous state, liquid VOCs material in the enterprise, sealed some quantity more than or equal to 2000 should develop LDAR work as required.
With the implementation of LDAR system, the LDAR controlled sealing points of a petrochemical enterprise are in the range of tens of thousands to hundreds of thousands, and at least 2-4 times per year according to the national standard. And each set of device has a continuous operation period of 3-6 years, so that the LDAR data of one petrochemical enterprise is hundreds of thousands to millions in each operation period. In view of the current status of LDAR operation management, how to evaluate the equipment leakage control effect of enterprises, and how to optimize and improve LDAR performance of enterprises or third parties are the problems of great concern to the enterprises and government environmental protection departments with internal related VOCs. However, as many as 70 thousands of sealing points of a large petrochemical enterprise are needed, 2 thousands of sealing points are needed to be tested in a pumping mode according to the principle, a large amount of labor and time are needed to be spent on the pumping test, the feasibility is not strong, and the comprehensive development of the auditing work is not facilitated.
Disclosure of Invention
The embodiment of the invention aims to provide an LDAR (laser direct reflectance assessment) assessment system and method based on big data mining analysis, so as to at least solve the problem of low assessment efficiency caused by large data scale of the existing LDAR.
To achieve the above object, in a first aspect of the present invention, there is provided a LDAR assessment system based on big data mining analysis, the system comprising:
the data access layer is used for providing original data of the LDAR project to be analyzed; the original data comprises historical original data and real-time original data;
the data storage layer is used for storing the original data of the LDAR project to be analyzed;
and the data application layer is used for providing data analysis application to the data storage layer.
Optionally, the data access layer includes a data acquisition module, a field inspection detection module, and an interface module;
the data acquisition module is used for responding to user data acquisition operation, extracting data from historical original data of the LDAR project to be analyzed, and storing the data into the data storage layer;
the field inspection detection module is used for responding to the field inspection detection operation of a user, detecting extracted devices, groups and sealing points by field inspection, and storing the generated real-time original data into the data storage layer;
the interface module is used for responding to reading and storing operations of user data and respectively connecting the data acquisition module with historical original data of an LDAR project to be analyzed and the data storage layer; and simultaneously, connecting the field inspection detection module with a data storage layer to finish original data transmission.
Optionally, the data application layer includes a data analysis module and a project audit module;
the data analysis module is used for responding to data analysis operation of a user to acquire the association between the original data of the LDAR project to be analyzed and form decision-making knowledge;
the project auditing module is used for establishing auditing of the LDAR project, evaluating compliance conditions of the LDAR project and outputting an evaluation report.
Optionally, the data acquisition module includes: a project establishing data acquisition submodule, a detection and repair data acquisition submodule and an operation and management data acquisition submodule;
the project establishment data acquisition submodule is used for acquiring project establishment related data from historical original data of the LDAR project to be analyzed and storing the project establishment related data in the data storage layer;
the detection and repair data acquisition submodule is used for acquiring detection and repair related data from historical original data of the LDAR project to be analyzed and storing the data into the data storage layer;
the operation and management data acquisition submodule is used for acquiring operation and management related data from historical original data of the LDAR project to be analyzed and storing the operation and management related data into the data storage layer.
Optionally, the field inspection detection module includes: the system comprises a field sampling submodule, a project establishing field inspection submodule, a detection and repair field detection submodule and an operation and management field inspection submodule;
the field sampling submodule is used for extracting devices, groups and sealing points from the LDAR project to be analyzed to form device samples, group samples and sealing point samples;
the project establishment field inspection submodule is used for on-site inspection of consistency of relevant data of the device samples, the group samples and the sealing point samples and on-site actual conditions, and comprises the following steps: the controlled device processes the components and the concentrations (quality) of raw materials, additives, intermediate products and products, the components and the concentrations (quality) of equipment and pipeline contact materials, group codes, group position information, sealing point codes, sealing point position information, sealing point types and material forms;
the detection and repair field inspection submodule is used for carrying out field inspection on the compliance of the detection and repair related operation of the sealing point sample; the method comprises the steps of instrument calibration, background detection, detection distance, detection speed, detection reading, instrument drift check, leakage sealing point identification, delayed repair leakage sealing point identification and delayed repair reason;
the detection and repair field detection submodule is used for detecting the sealing point sample in field and comprises a gas leakage detection instrument based on hydrogen flame ions and/or gas optical imaging.
The operation and management field inspection submodule is used for carrying out field inspection on the actual operation condition of the LDAR on the sealed point sample, and comprises detection task allocation, detection task downloading, detection path management, detection data uploading, calibration management, leakage identification and emission calculation.
Optionally, the interface module includes: a historical original data transmission interface submodule, a project-established field inspection data transmission interface submodule, a detection and repair field detection data transmission interface submodule and an operation and management field inspection data transmission interface submodule;
the historical original data transmission interface submodule is used for connecting the data acquisition module, and the data acquisition module accesses and reads historical original data of an LDAR project to be analyzed through the historical original data transmission interface submodule and stores related data into a data storage layer;
and the field inspection data transmission interface submodule established by the project is connected with the project establishment field inspection submodule, and relevant real-time data generated by the project establishment field inspection submodule is stored in the data storage layer through the field inspection data transmission interface submodule established by the project.
The detection and repair field inspection data transmission interface sub-module is used for being connected with the detection and repair field inspection sub-module, and relevant real-time data generated by the detection and repair field inspection sub-module is stored in the data storage layer through the detection and repair field inspection data transmission interface sub-module.
The detection and repair field detection data transmission interface sub-module is used for being connected with the detection and repair field detection sub-module, and relevant real-time data generated by the detection and repair field detection sub-module is transmitted to the data storage layer through the detection and repair field detection data transmission interface sub-module.
The operation and management field inspection data transmission interface submodule is used for being connected with the operation and management field inspection submodule, and relevant real-time data of the operation and management field inspection submodule is transmitted to the data storage layer through the operation and management field inspection data transmission interface submodule.
The data analysis module is used for responding to data analysis operation of a user to acquire the association between the original data of the LDAR and form decision-making knowledge;
the project auditing module is used for establishing an LDAR auditing project, auditing the compliance condition of the auditing project and outputting an evaluation report.
Optionally, the data analysis module includes: the LDAR data extraction submodule, the LDAR data preprocessing submodule, the LDAR data mining analysis submodule and the LDAR decision knowledge application submodule;
the LDAR data extraction submodule is used for extracting original data to be analyzed from the data storage layer;
the LDAR data preprocessing submodule is used for carrying out data cleaning on the original data to be analyzed to obtain cleaned data;
the LDAR data mining analysis submodule is used for forming decision-making knowledge according to the cleaned data and a data analysis algorithm configured by a user; and
the LDAR decision knowledge application submodule is used for forming a plurality of measures and suggestions according to the decision knowledge.
Optionally, the item auditing module includes: a project establishment condition submodule, a detection and repair condition submodule, an operation and management condition submodule and an evaluation report generating submodule;
the project establishment condition submodule is used for checking whether project establishment related data of the LDAR project to be analyzed are in compliance;
the detection and repair condition submodule is used for checking whether the detection and repair related data of the LDAR item to be analyzed are in compliance;
the operation and management condition submodule is used for checking whether the operation and management related data of the LDAR project to be analyzed is in compliance;
and the evaluation report generation submodule is used for generating an evaluation report of the LDAR project to be analyzed according to an evaluation result.
Optionally, the project establishment related data of the LDAR project to be analyzed includes: controlled range, base data;
the controlled ranges include: the controlled device processes the components and the concentrations (quality) of raw materials, auxiliary agents, intermediate products and products, and the components and the concentrations (quality) of equipment and pipeline contact materials;
the basic data includes: group code, group position information, sealing point code, sealing point position information, sealing point type and material form.
Optionally, the data related to detection and repair of the to-be-analyzed LDAR item includes: detecting related data and repairing the related data;
the detection-related data includes: calibrating an instrument, detecting a background, detecting a distance, detecting a speed, detecting data, detecting frequency and verifying instrument drift;
the repair-related data includes: time of first repair and repair of a leaking seal, retest data, and delayed repair of the leaking seal cause.
Optionally, the operation and management related data to be analyzed includes: running related data and managing related data;
the operation-related data includes: group data uploading and/or changing, seal point data uploading and/or changing, detection task allocation, detection task downloading, detection path management, detection data uploading, calibration management, leakage identification and emission calculation, maintenance task allocation and repair record uploading;
the management-related data includes: the system LDAR regulation, the system emission calculation method and the pollution discharge permission threshold value.
In a second aspect of the present invention, there is also provided a method for LDAR assessment based on big data mining analysis, the method comprising:
the data acquisition step is used for acquiring related data from historical original data of the LDAR project to be analyzed through an interface according to the user operation and storing the related data in a data storage layer;
the on-site inspection detection step is used for carrying out inspection and/or detection on the devices, groups and sealing points extracted on site according to the user operation, and storing the generated related real-time original data into the data storage layer through an interface;
the data analysis step is used for acquiring the association between the original data of the LDAR according to the user operation and forming decision knowledge;
and the item auditing step is used for establishing an LDAR auditing item according to the user operation, auditing the compliance condition of the LDAR item and outputting an evaluation report.
Optionally, the data acquisition step includes the following substeps: a project establishing data acquisition sub-step, a detection and repair data acquisition sub-step and an operation and management data acquisition sub-step;
the project establishment data acquisition substep is used for acquiring project establishment related data from historical original data of the LDAR project to be analyzed and storing the project establishment related data in the data storage layer;
the detection and repair data acquisition substep is used for acquiring detection and repair related data from historical original data of the LDAR project to be analyzed and storing the data into the data storage layer;
and the operation and management data acquisition sub-step is used for acquiring operation and management related data from historical original data of the LDAR project to be analyzed and storing the operation and management related data into the data storage layer.
Optionally, the field inspection detecting step includes the following sub-steps: the method comprises a field sampling sub-step, a project establishing field inspection sub-step, a detection and repair field detection sub-step and an operation and management field inspection sub-step;
the in-situ sampling sub-step is used for extracting devices, groups and sealing points from the LDAR project to be analyzed to form device samples, group samples and sealing point samples;
the project establishment field inspection sub-step is used for checking whether the relevant data established by the projects of the device sample, the group sample and the sealing point sample are consistent in actual field conditions or not, and comprises the following steps: the controlled device processes the components and the concentrations (quality) of raw materials, additives, intermediate products and products, the components and the concentrations (quality) of equipment and pipeline contact materials, group codes, group position information, sealing point codes, sealing point position information, sealing point types and material forms;
the detection and repair field inspection substep is used for checking whether the detection and repair related operation of the sealing point sample is in compliance in field; the method comprises the steps of instrument calibration, background detection, detection distance, detection speed, detection reading, instrument drift check, leakage sealing point identification, delayed repair leakage sealing point identification and delayed repair reason;
the detection and repair field detection substep is used for detecting the sealing point sample in field; the inspection and repair field inspection substep is used to inspect the extracted seal points using, but not limited to, hydrogen flame ion and/or gas optical imaging based gas leak detection equipment.
The operation and management field inspection substep is used for field inspection of compliance of the actual operation condition of the LDAR carried out on the sealed point sample, and comprises detection task allocation, detection task downloading, detection path management, detection data uploading, calibration management, leakage identification and emission calculation.
And carrying out project analysis based on the LDAR original data in the data storage layer.
Responding to user operation, and performing analysis application on the data of the data storage layer, wherein the analysis application comprises data analysis and project audit;
the data analysis is used for acquiring the association between the original data of the LDAR according to the user operation and forming decision knowledge;
and the item audit is used for establishing an LDAR audit item according to the user operation, auditing the compliance condition of the audit item and outputting an evaluation report.
Optionally, the data analysis includes the following sub-steps: the method comprises an LDAR data extraction sub-step, an LDAR data preprocessing sub-step, an LDAR data mining analysis sub-step and an LDAR decision knowledge application sub-step;
the LDAR data extraction sub-step is used for extracting original data to be analyzed from the data storage layer;
the LDAR data preprocessing substep is used for carrying out data cleaning on the original data to be analyzed to obtain cleaned data;
the LDAR data mining and analyzing substep is used for forming decision-making knowledge according to the cleaned data and a data analysis algorithm configured by a user; and
the LDAR decision knowledge application sub-step is used for forming a plurality of measures and suggestions according to the decision knowledge.
Optionally, the item audit includes: the method comprises an LDAR project establishment condition auditing substep, a detection and maintenance condition auditing substep, an LDAR operation and management condition auditing substep and an evaluation report generating substep;
the LDAR project establishment condition auditing substep is used for checking whether project establishment related data of the LDAR project to be analyzed are in compliance;
the detection and maintenance condition auditing substep is used for checking whether the detection and repair related data of the LDAR project to be analyzed are in compliance;
the LDAR operation and management condition auditing sub-step is used for checking whether the operation and management related data of the LDAR project to be analyzed are in compliance;
the evaluation report generating sub-step is used for generating an evaluation report of the LDAR project to be analyzed according to the checking result.
Optionally, the project establishment related data of the LDAR project to be analyzed includes: controlled range, base data;
the controlled ranges include: the controlled device processes the components and the concentrations (quality) of raw materials, auxiliary agents, intermediate products and products, and the components and the concentrations (quality) of equipment and pipeline contact materials;
the basic data includes: group code, group position information, sealing point code, sealing point position information, sealing point type and material form.
Optionally, the data related to detection and repair of the LDAR item to be analyzed includes: detecting related data and repairing the related data;
the detection-related data includes: calibrating an instrument, detecting a background, detecting speed, detecting data, detecting frequency and verifying instrument drift;
the repair-related data includes: time of first repair and repair of a leaking seal, retest data, and delayed repair of the leaking seal cause.
Optionally, the operation and management related data to be analyzed includes: running related data and managing related data;
the operation-related data includes: group data uploading and/or changing, seal point data uploading and/or changing, detection task allocation, detection task downloading, detection path management, detection data uploading, calibration management, leakage identification, emission calculation, maintenance task allocation and repair record uploading;
the management-related data includes: the system LDAR regulation, the system emission calculation method and the pollution discharge permission threshold value.
Optionally, the evaluation report of the LDAR project to be analyzed includes: an assessment summary, an enterprise summary, an LDAR project summary to be analyzed, an assessment method and process, and an assessment conclusion;
the evaluation summary comprises: evaluation basis, evaluation range and evaluation program;
the enterprise profile comprises: the method mainly comprises the steps of production process, capacity and/or processing capacity and geographical position;
the LDAR project summary to be analyzed comprises: the number and the operating condition of the controlled devices, the number of the sealing points, the number of the unreachable points, the initial time of the LDAR project to be analyzed and the operating mode;
the evaluation method and process comprises the following steps: estimating starting and finishing dates, field sampling modes and results, detection instruments and operation processes used for field inspection and detection, and a data analysis mining method;
the evaluation conclusion comprises: project establishment condition compliance, detection and repair condition compliance, operation and management condition compliance and project promotion suggestions.
In a third aspect of the present invention, there is also provided a server, on which the aforementioned LDAR evaluation system based on big data mining analysis is loaded.
In a fourth aspect of the present invention, there is also provided a machine-readable storage medium having stored thereon instructions, which when run on a computer, cause the computer to perform the aforementioned LDAR assessment method based on big data mining analysis.
The technical scheme of the invention provides the LDAR assessment system and method based on big data mining analysis, so that the rapid assessment of the LDAR operation effect of the device is realized, the optimization measures of equipment leakage management and control are favorably formulated and implemented, the unorganized emission of VOCs is reduced, and the social benefit and the economic benefit of the LDAR are improved.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the embodiments of the invention without limiting the embodiments of the invention. In the drawings:
FIG. 1 is a block diagram of a LDAR assessment system based on big data mining analysis provided by an embodiment of the present invention;
FIG. 2 is a block diagram and a data collection flow diagram of a data collection module according to an alternative embodiment of the present invention;
FIG. 3 is a block diagram of a field check test module provided in an alternative embodiment of the present invention;
FIG. 4 is a block diagram of a data analysis module provided in an alternative embodiment of the present invention;
FIG. 5 is a block diagram of a project review module provided in an alternative embodiment of the present invention;
FIG. 6 is a flowchart of a LDAR assessment method based on big data mining analysis according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
In the embodiments of the present invention, unless otherwise specified, the use of directional terms such as "upper, lower, top, and bottom" is generally used with respect to the orientation shown in the drawings or the positional relationship of the components with respect to each other in the vertical, or gravitational direction.
FIG. 1 is a block diagram of an LDAR assessment system based on big data mining analysis according to an embodiment of the present invention. As shown in fig. 1, an embodiment of the present invention provides a system for evaluating an LDAR based on big data mining analysis, where the system includes:
the system comprises a data access layer and a data analysis layer, wherein the data access layer is used for providing historical original data and real-time original data of an LDAR project to be analyzed, and comprises a data acquisition module, a field inspection detection module and an interface module;
the data storage layer is used for storing the original data of the LDAR;
the data application layer is used for providing data analysis application for the data storage layer and comprises a data analysis module and a project auditing module;
the data acquisition module is used for responding to user data acquisition operation, extracting data from historical original data of the LDAR project to be analyzed, and storing the data into the data storage layer;
the field inspection detection module is used for responding to the field inspection detection operation of a user, detecting extracted devices, groups and sealing points by field inspection, and storing the generated real-time original data into the data storage layer;
the interface module is used for responding to reading and storing operations of user data and respectively connecting the data acquisition module with historical original data of an LDAR project to be analyzed and the data storage layer; meanwhile, the field inspection detection module is connected with a data storage layer to complete original data transmission;
the data analysis module is used for responding to data analysis operation of a user to acquire the association between the original data of the LDAR and form decision-making knowledge;
the project auditing module is used for establishing an LDAR auditing project, auditing the compliance condition of the auditing project and outputting an evaluation report.
Therefore, the data access layer transmits the historical original data of the LDAR to be analyzed and the real-time data of the field spot check and spot test controlled sealing point to the data storage layer through the interface module. The data storage layer stores the original data of the LDAR project to be analyzed, and the data application layer provides an external data analysis application function, so that the structure of the whole big data LDAR evaluation system is clear, and data interaction among modules is simplified. The data storage layer manages the data uniformly, so that the maintenance cost of the LDAR data is reduced; the free configuration of the service layer module enriches the functions of the system. The implementation mode provided by the invention realizes the rapid evaluation of the LDAR operation effect of the device, is beneficial to making and implementing the optimization measure of equipment leakage control, and reduces the unorganized emission of VOCs.
Specifically, the data access layer is used for collecting historical original data of an LDAR project and real-time original data of a field spot check sampling controlled sealing point; the historical raw data refers to the operation data of the LDAR project to be analyzed in the time period to be evaluated before evaluation. The real-time raw data refers to relevant data generated by operation and production of some devices, groups and sealing points extracted by an evaluator in the evaluation process, and the devices, groups and sealing points are inspected and detected on site. The LDAR historical original data is connected through an interface and transmitted to a data storage layer; further, the method is simple. And connecting the detection instrument, and transmitting the real-time original data to the data storage layer.
The data storage layer is used for storing the original data of the LDAR; the original data mainly refers to massive historical original data accumulated in LDAR operation and real-time original data of sampling measurement, such as seal point codes, seal point types, material forms, detection data, background records, quality control records, repair records and the like, so that the data can be collected by a software system and stored in a database, and the integrity of the operation data of the LDAR evaluation system can be protected more safely.
The data application layer is used for providing data analysis application for the data storage layer and comprises a data analysis module and a project auditing module; the data analysis module is used for responding to data analysis operation of a user to acquire the association between the original data of the LDAR and form decision-making knowledge; the project auditing module is used for establishing an LDAR auditing project, auditing the compliance of the auditing project and outputting an evaluation report. Data analysis and project review are two major functions provided by the present embodiment. The data analysis can obtain the overall condition and characteristics of equipment leakage of the device through data mining, so that the current situation of VOCs (volatile organic chemicals) emission of the device can be rapidly evaluated, and the lifting direction of equipment leakage management and control is determined. The project audit reduces the manual evaluation cost to a certain extent, and improves the evaluation accuracy of the existing LDAR management system.
The data acquisition module, the field inspection detection module, the data analysis module and the project audit module of the data access layer are respectively described below.
Fig. 2 is a structural diagram and a data acquisition flow chart of a data acquisition module according to an alternative embodiment of the present invention, and as shown in fig. 2, the data acquisition module includes: a project establishing data acquisition submodule, a detection and repair data acquisition submodule and an operation and management data acquisition submodule;
the project establishment data acquisition submodule is used for acquiring project establishment related data from historical original data of the LDAR project to be analyzed, the detection and repair data acquisition submodule is used for acquiring detection and repair related data from the historical original data of the LDAR project to be analyzed, and the operation and management data acquisition submodule is used for acquiring operation and management related data from the historical original data of the LDAR project to be analyzed. The data acquisition module is connected with an LDAR project operation platform to be analyzed through a historical original data transmission interface submodule, a database in which enterprise LDAR operation records are stored can be accessed through the LDAR project operation platform, and acquired data are stored in the data storage layer. The method comprises the following specific steps:
(1) and a project establishment data acquisition submodule. The project establishment data acquisition submodule is used for extracting the components and the concentrations (quality) of processing raw materials, auxiliaries, intermediate products and products of the controlled device, the components and the concentrations (quality) of equipment and pipeline contact materials, group codes, group position information, sealing point codes, sealing point position information, sealing point categories, material forms and other data from the LDAR project operation platform and the database through interfaces;
(2) and a detection and repair data acquisition submodule. The detection and repair data acquisition submodule is used for extracting data of instrument calibration, background detection, detection data, instrument drift check, first maintenance time and repair time of a leakage sealing point, retest data, reasons for delaying repair of the leakage sealing point and the like in a time period to be evaluated from the LDAR project operation platform and the database through an interface;
(3) and the operation and management data acquisition submodule. The operation and management data acquisition submodule is used for extracting the operation and management related data of a time period to be evaluated from an LDAR project operation platform and a database through an interface, and comprises: group data uploading and/or changing, sealing point data uploading and/or changing, detection task allocation, detection task downloading, detection path management, detection data uploading, calibration management, leakage identification and emission calculation, maintenance task allocation, repair record uploading, system LDAR regulations, system emission calculation methods, pollution discharge permission thresholds and the like.
Fig. 3 is a structural diagram of an on-site inspection module according to an alternative embodiment of the present invention, and as shown in fig. 3, the on-site inspection module includes: the system comprises a field sampling submodule, a project establishing field inspection submodule, a detection and repair field detection submodule and an operation and management field inspection submodule;
the field sampling submodule is used for extracting devices, groups and sealing points from the LDAR project to be analyzed to form device samples, group samples and sealing point samples; the project establishment field inspection submodule is used for carrying out field inspection on the consistency of relevant data established by projects of the device samples, the group samples and the sealing point samples and the actual situation on the spot; the detection and repair field inspection submodule is used for carrying out field inspection on the compliance of the detection and repair related operation of the sealing point sample; the detection and repair field detection submodule is used for detecting the sealing point sample in a field; the operation and management field inspection submodule is used for carrying out field inspection on the actual operation condition of LDAR on the sealing point sample; the method comprises the following specific steps:
(1) and a field sampling submodule. The field sampling submodule is used for randomly extracting devices, groups and sealing points from an LDAR project operation platform or \ and PFD or \ and P & ID or \ and device field. The sealing points can be totally sampled by adopting simple random sampling or \ and system sampling or \ and layered random sampling and the like. The layered random sampling can be layered based on the device or/and sealing point type or/and material form, and can also be layered in a combined mode of (and (indicated by) ', ' or (indicated by) ', by adopting the device, the sealing point type and the material form, for example, a gas valve and a light liquid valve of an atmospheric and vacuum device can be represented as follows: atmospheric and vacuum equipment (gas + light liquid) valve; the gas flange and the gas connecting piece of the atmospheric and vacuum device can be expressed as follows: atmospheric and vacuum devices gas (flange + connection); constructing a device sample, a cluster sample, and a seal point sample;
(2) and (4) a project building field inspection submodule. The project establishment field inspection submodule is used for inspecting components and concentrations of processing raw materials, auxiliaries, intermediate products and products which are contacted with the device sample, the group sample and the sealing point sample, and checking whether the controlled range of the LDAR project to be analyzed is in compliance; checking the accuracy and the uniqueness of codes of the position descriptions of the group samples and the sealing point samples, and the consistency of the types and the material forms of the sealing point samples with the site;
(3) and a detection and repair field inspection submodule. The detection and repair field inspection submodule is used for inspecting the compliance of an LDAR implementation party to the field operation of the sealing point sample; the method comprises the steps of instrument calibration, background detection, detection distance, detection speed, detection reading and instrument drift check; checking the compliance of the leakage sealing point identification, the delayed repair leakage sealing point identification and the delayed repair reason;
(4) and a detection and repair field detection submodule. And the detection and repair field detection submodule is used for detecting the sealing point sample in field. The method comprises the step of detecting the extracted sealing point by using a hydrogen flame ionization gas leakage detection instrument (FID) or/and a gas optical imaging gas leakage detection instrument (OGI). Recording detection data or/and video according to standard requirements, and providing real-time original data for comparison with historical original data;
(5) and operating and managing a field inspection submodule. The operation and management field inspection submodule is used for inspecting the actual operation condition of an LDAR project operation platform in the process of carrying out an LDAR project on the sealing point sample; the method comprises the steps of detection task allocation, detection task downloading, detection path management, detection data uploading, calibration management, leakage identification and emission calculation;
fig. 4 is a structural diagram of a data analysis module according to an alternative embodiment of the present invention, and as shown in fig. 4, the data analysis module includes: the LDAR data extraction submodule, the LDAR data preprocessing submodule, the LDAR data mining analysis submodule and the LDAR decision knowledge application submodule;
the LDAR data extraction submodule is used for extracting original data to be analyzed from the data storage layer; the LDAR data preprocessing submodule is used for carrying out data cleaning on the original data to be analyzed to obtain cleaned data; the LDAR data mining analysis submodule is used for forming decision-making knowledge according to the cleaned data and a data analysis algorithm configured by a user; and the LDAR decision knowledge application submodule is used for forming a plurality of measures and suggestions according to the decision knowledge. The method comprises the following specific steps:
(1) LDAR historical raw data and LDAR real-time raw data. The LDAR historical original data mainly refers to massive information accumulated by LDAR operation in a time period to be evaluated, and includes but is not limited to:
a) the project establishes the relevant data. The controlled device processes the components and the concentrations (quality) of raw materials, additives, intermediate products and products, the components and the concentrations (quality) of equipment and pipeline contact materials, group codes, group position information, sealing point codes, sealing point position information, sealing point types and material forms;
b) data relating to the repair is detected. Calibrating an instrument, detecting a background, detecting speed, detecting data, detecting frequency, checking instrument drift, checking the first maintenance time and repair time of a leakage sealing point, re-testing data and delaying repair of the reason of the leakage sealing point;
c) running and managing related data. Group data uploading and/or changing, sealing point data uploading and/or changing, detection task allocation, detection task downloading, detection path management, detection data uploading, calibration management, maintenance task allocation, repair record uploading, system LDAR regulations, system emission calculation method and pollution discharge permission threshold;
LDAR real-time raw data refers primarily to data generated by performing field examination tests on the device samples, cluster samples, and seal point samples during evaluation of LDAR projects. Including but not limited to:
a) the project establishes the relevant data. The controlled device processes the components and the concentrations (quality) of raw materials, additives, intermediate products and products, the components and the concentrations (quality) of equipment and pipeline contact materials, group codes, group position information, sealing point codes, sealing point position information, sealing point types and material forms;
b) data relating to the repair is detected. Calibrating an instrument, detecting a background, detecting a distance, detecting a speed, detecting a reading, checking instrument drift, identifying a leakage sealing point, identifying a delayed repair leakage sealing point and delaying repair reasons;
c) running and managing related data. Detection task allocation, detection task downloading, detection path management, detection data uploading, calibration management and system emission calculation;
(2) and preprocessing LDAR data. The LDAR original data has a lot of noise information, such as net detection value <0 mu mol/mol or \ and inconsistency of material state with national standard or ecological environment department specification or \ inconsistency of sealing type with national standard or ecological environment department file, etc. The data quality is further improved by methods such as data cleaning and sorting, data preprocessing can be used for carrying out consistency operation on data generated by LDAR operation of different devices, units or equipment, sparse attributes in the data can be reduced, the attributes have low contribution or no contribution or interference on evaluation, and the accuracy of LDAR data mining is improved;
(3) LDAR data mining analysis. After the imported data are preprocessed, data mining technologies can be used for mining and analyzing the data, the embodiment of the invention adopts data mining technologies such as a K-means algorithm, a support vector machine, a BP neural network and a genetic algorithm to analyze the incidence relation of material form, sealing form, detection data, leakage frequency and maintenance effect, and a mode of data centralized hiding is obtained to form decision-making knowledge;
(4) LDAR decision knowledge application. The decision-making knowledge can predict the leakage risk and development trend of the sealing points. Setting a risk threshold, and if the sealing point leakage risk exceeds the threshold, deriving early warning indication and treatment suggestion of detection and evaluation. Meanwhile, the enterprise is guided to carry out detection based on leakage risk, the leakage duration is shortened, and the unorganized emission of VOCs is greatly reduced; in addition, the adaptability level of some equipment or sealing, process and environment can be found, an iterative optimization mode of the equipment and the sealing, the process and the environment of the equipment is formed, and the reliability of long-period operation of the equipment is improved.
Fig. 5 is a block diagram of a project audit module according to an alternative embodiment of the present invention, and as shown in fig. 5, the project audit module includes: a project establishment condition submodule, a detection and repair condition submodule, an operation and management condition submodule and an evaluation report generating submodule;
the project establishment condition submodule is used for checking whether project establishment related data of the LDAR project to be analyzed are in compliance; the detection and repair condition submodule is used for checking whether the relevant data of the LDAR item to be analyzed, which is detected and repaired, is in compliance; the operation and management condition submodule is used for checking whether the operation and management related data of the LDAR project to be analyzed is in compliance; and the evaluation report generation submodule is used for generating an evaluation report of the LDAR project to be analyzed according to an evaluation result. The method comprises the following specific steps:
(1) project establishment sub-module
The project establishment condition submodule is used for checking whether project establishment related data of the LDAR project to be analyzed are in compliance, and the specific steps are as follows:
a) scope integrity was enforced. An analysis device operation process manual, a process principle flow chart (PFD), and the components and concentrations (quality) of materials such as processing raw materials, additives, intermediate products, products and the like of the analysis device, wherein the controlled device to be incorporated into the LDAR is determined according to the mass concentration of the VOCs components of the materials and the comparison with a threshold set by a relevant standard or/and a specification; furthermore, from the controlled device, a pipeline instrument diagram (P & ID) is extracted by adopting the modes of simple random sampling or \ and system sampling or \ and layered random sampling and the like, the components and the concentrations (quality) of contact materials of the equipment and the pipeline are analyzed, and the contact materials are compared with the threshold value set by the relevant standard or \ and the specification, so that the controlled equipment and the pipeline which are required to be incorporated into the LDAR are determined; and extracting a sealing point sample from the sealing point of the equipment and the pipeline by adopting a simple random sampling mode or/and systematic sampling mode or/and layered random sampling mode and the like, analyzing the components and the concentration (mass) of the contact materials of the sealing point sample, and determining that the contact materials are contained in the LDAR controlled sealing point. And evaluating the misjudgment condition of the LDAR implementation range, and calculating the controlled misjudgment rate of the sealing point.
Wherein: deltai-a class i seal point controlled false positive rate; n isio-the number of missing class i controlled seal points during the project building process; n isisThe number of i-type uncontrolled seal points which are mistakenly included in the controlled range in the project establishing process; n is a radical ofi-total number of i-type controlled sealing points.
b) The group, seal point base data describes the normalcy. And (3) evaluating whether the group and the sealing point description are standard or not by adopting a mode of combining data analysis and device field survey, wherein the method comprises the following steps: uniqueness of group coding and seal point coding; material form standardization (gas, light liquid or volatile organic liquid, heavy liquid); seal point class standardization (pump, compressor, stirrer, valve, pressure relief device, sampling connection system, open valve or open piping, flange, connection, others); group and seal point location description (including photo, P & ID multiple profiling modes) accuracy; the sealing point can reach the point.
(2) Detection and repair situation submodule
The detection and repair condition submodule is used for checking whether the relevant data of the LDAR item detection and repair to be analyzed is in compliance, and the relevant data of the LDAR item detection and repair comprises the following steps: detecting related data and repairing the related data; the method comprises the following specific steps:
a) detecting relevant data compliance
The following data were evaluated for compliance: calibration of a detection instrument (comprising standard gas type, concentration, relative uncertainty of extension, effective period, instrument model and number, indication error, calibration time and calibration personnel), background detection (comprising detection time, detection concentration, detection position, instrument model and number for detection and detection personnel), detection distance (in a detection and repair field detection submodule, the distance between a sampling port based on a hydrogen flame ion detection instrument and a detected sealing point), detection speed (time difference of detection readings of two adjacent sealing points or/and the movement detection speed of a sampling port based on a hydrogen flame ion gas leakage detection instrument of the detection and repair field detection submodule on the surface of the detected sealing point), detection data (comprising sealing point code, detection time, detection concentration, instrument model and number for detection and detection personnel), the detection frequency (the time difference between two adjacent detections at the same sealing point), and the instrument drift check (including standard gas type, concentration, relative uncertainty of extension, effective period, instrument model and number, instrument drift error, check time and checker).
b) Repairing relevant data compliance
The following data were evaluated for compliance: the first maintenance time and repair time of the leakage sealing point, retest data (including sealing point code, retest time, retest concentration, retest instrument model and serial number, retest personnel), and the reason for delayed repair of the leakage sealing point.
(3) LDAR operation and management submodule
And the operation and management condition submodule is used for checking whether the operation and management related data of the LDAR project to be analyzed is in compliance. Described below, respectively:
a) compliance of operation-related data
Analyzing feasibility and compliance of processes such as group data uploading and/or changing, sealing point data uploading and/or changing, detection task allocation, detection task downloading, detection path management, detection data uploading, calibration management, leakage identification and emission calculation, maintenance task allocation, repair record uploading and the like.
b) Managing applicability of related data
And calling management related data through an enterprise LDAR project operation platform, and analyzing the applicability of system LDAR regulations, a system emission amount calculation method and a pollution discharge permission threshold value.
(4) Evaluation report generation submodule
The evaluation report generation submodule is used for forming an evaluation report of the LDAR project to be analyzed, and comprises the following steps: an assessment summary, an enterprise summary, an LDAR project summary to be analyzed, an assessment method and process, and an assessment conclusion;
the evaluation summary comprises: evaluation basis, evaluation range and evaluation program; the enterprise profile comprises: the method mainly comprises the steps of production process, capacity and/or processing capacity and geographical position; the LDAR project summary to be analyzed comprises: the number and the operating condition of the controlled devices, the number of the sealing points, the number of the unreachable points, the initial time of the LDAR project to be analyzed and the operating mode; the evaluation method and process comprises the following steps: estimating starting and finishing dates, field sampling modes and results, detection instruments and operation processes used for field inspection and detection, and a data analysis mining method; the evaluation conclusion comprises: project establishment condition compliance, detection and repair condition compliance, operation and management condition compliance and project promotion suggestions.
Fig. 6 is a flowchart of an LDAR evaluation method based on big data mining analysis according to an embodiment of the present invention, as shown in fig. 6: in an embodiment provided by the present invention, there is also provided a method for evaluating an LDAR based on big data mining analysis, the method including:
storing LDAR raw data (including historical raw data and real-time raw data) to a data storage layer;
responding to user operation, and performing analysis application on the data of the data storage layer, wherein the analysis application comprises data analysis and project audit;
the data analysis is used for acquiring the association between the original data of the LDAR according to the user operation and forming decision knowledge;
and the item audit is used for establishing an LDAR audit item according to the user operation, auditing the compliance condition of the audit item and outputting an evaluation report.
In an alternative embodiment, the data analysis comprises the following sub-steps: the method comprises an LDAR data extraction sub-step, an LDAR data preprocessing sub-step, an LDAR data mining analysis sub-step and an LDAR decision knowledge application sub-step;
the LDAR data extraction sub-step is used for extracting original data to be analyzed from the data storage layer;
the LDAR data preprocessing substep is used for carrying out data cleaning on the original data to be analyzed to obtain cleaned data;
the LDAR data mining and analyzing substep is used for forming decision-making knowledge according to the cleaned data and a data analysis algorithm configured by a user; and
the LDAR decision knowledge application sub-step is used for forming a plurality of measures and suggestions according to the decision knowledge.
In an alternative embodiment, the project review includes: the method comprises an LDAR project establishment condition auditing substep, a detection and maintenance condition auditing substep, an LDAR operation and management condition auditing substep and an evaluation report generating substep;
the LDAR project establishment condition auditing substep is used for checking whether project establishment related data of the LDAR project to be analyzed are in compliance;
the detection and maintenance condition auditing substep is used for checking whether the detection and repair related data of the LDAR project to be analyzed are in compliance;
the LDAR operation and management condition auditing sub-step is used for checking whether the operation and management related data of the LDAR project to be analyzed are in compliance;
the evaluation report generating sub-step is used for generating an evaluation report of the LDAR project to be analyzed according to the checking result.
In an optional embodiment, the relevant data of the LDAR project establishment review sub-step includes: controlled range, base data;
the controlled ranges include: the controlled device processes the components and the concentrations (quality) of raw materials, auxiliary agents, intermediate products and products, and the components and the concentrations (quality) of equipment and pipeline contact materials;
the basic data includes: group codes, group position information, sealing point codes, sealing point position information, sealing point types and material forms;
in an alternative embodiment, the detecting the relevant data of the repair situation auditing sub-step includes: detecting related data and repairing the related data;
the detecting the relevant data comprises: calibrating an instrument, detecting a background, detecting a distance, detecting a speed, detecting data, detecting frequency and verifying instrument drift;
the repair-related data includes: time of first repair and repair of a leaking seal, retest data, and delayed repair of the leaking seal cause.
In an alternative embodiment, the LDAR runs the relevant data of the management situation auditing sub-step, and comprises: running related data and managing related data;
the operation-related data includes: group data uploading and/or changing, seal point data uploading and/or changing, detection task allocation, detection task downloading, detection path management, detection data uploading, calibration management, maintenance task allocation and repair record uploading;
the management-related data includes: system LDAR regulations, system emission calculation methods and pollution discharge permission threshold values;
in an alternative embodiment, evaluating the relevant content of the report generation substep comprises: an assessment summary, an enterprise summary, an LDAR project summary to be analyzed, an assessment method and process, and an assessment conclusion;
the evaluation summary comprises: evaluation basis, evaluation range and evaluation program;
the enterprise profile comprises: the method mainly comprises the steps of production process, capacity and/or processing capacity and geographical position;
the LDAR project summary to be analyzed comprises: the number and the operating condition of the controlled devices, the number of the sealing points, the number of the unreachable points, the initial time of the LDAR project to be analyzed and the operating mode;
the evaluation method and process comprises the following steps: estimating starting and finishing dates, field sampling modes and results, detection instruments and operation processes used for field inspection and detection, and a data analysis mining method;
the evaluation conclusion comprises: project establishment condition compliance, detection and repair condition compliance, operation and management condition compliance and project promotion suggestions.
The method provided by the embodiments herein is referred to in the technical details of the aforementioned system and will not be repeated here.
In an embodiment of the present invention, a server is further provided, and the LDAR evaluation system based on big data mining analysis as described above is loaded on the server. The server comprises a processing unit and a storage system, wherein the processing unit is preferably a server group or a server group cluster, and load sharing and service response requirements under large data volume are mainly considered. Which provides a service response through network access. The storage system comprises a physical medium for storing LDAR data and a medium driver, and the physical medium which is commonly used at present is a disk array.
In an embodiment of the present invention, there is also provided a machine-readable storage medium having stored thereon instructions, which when run on a computer, cause the computer to perform the aforementioned big data mining analysis-based LDAR evaluation method.
Therefore, the embodiment of the invention increases LDAR big data mining, can discover the development trend of degradation and leakage of mass equipment seal, and can provide active reference for enterprise to optimize equipment model selection, installation, operation and maintenance in the next step.
While the embodiments of the present invention have been described in detail with reference to the accompanying drawings, the embodiments of the present invention are not limited to the details of the above embodiments, and various simple modifications can be made to the technical solution of the embodiments of the present invention within the technical idea of the embodiments of the present invention, and the simple modifications are within the scope of the embodiments of the present invention.
It should be noted that the various features described in the above embodiments may be combined in any suitable manner without departing from the scope of the invention. In order to avoid unnecessary repetition, the embodiments of the present invention will not be described separately for the various possible combinations.
Those skilled in the art will appreciate that all or part of the steps in the method for implementing the above embodiments may be implemented by a program, which is stored in a storage medium and includes several instructions to enable a single chip, a chip, or a processor (processor) to execute all or part of the steps in the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In addition, any combination of the various embodiments of the present invention is also possible, and the same should be considered as disclosed in the embodiments of the present invention as long as it does not depart from the spirit of the embodiments of the present invention.
Claims (23)
1. An LDAR assessment system based on big data mining analysis, the system comprising:
the data access layer is used for providing original data of the LDAR project to be analyzed; the original data comprises historical original data and real-time original data;
the data storage layer is used for storing the original data of the LDAR project to be analyzed;
and the data application layer is used for providing data analysis application to the data storage layer.
2. The system of claim 1, wherein the data access layer comprises a data acquisition module, a field inspection detection module, and an interface module;
the data acquisition module is used for responding to user data acquisition operation, extracting data from historical original data of the LDAR project to be analyzed, and storing the data into the data storage layer;
the field inspection detection module is used for responding to the field inspection detection operation of a user, detecting extracted devices, groups and sealing points by field inspection, and storing the generated real-time original data into the data storage layer;
the interface module is used for responding to reading and storing operations of user data and respectively connecting the data acquisition module with historical original data of an LDAR project to be analyzed and the data storage layer; and simultaneously, connecting the field inspection detection module with a data storage layer to finish original data transmission.
3. The system of claim 2, wherein the data application layer comprises a data analysis module and a project review module;
the data analysis module is used for responding to data analysis operation of a user to acquire the association between the original data of the LDAR project to be analyzed and form decision-making knowledge;
the project auditing module is used for establishing auditing of the LDAR project, evaluating compliance conditions of the LDAR project and outputting an evaluation report.
4. The system of claim 2, wherein the data acquisition module comprises: a project establishing data acquisition submodule, a detection and repair data acquisition submodule and an operation and management data acquisition submodule;
the project establishment data acquisition submodule is used for acquiring project establishment related data from historical original data of the LDAR project to be analyzed and storing the project establishment related data in the data storage layer;
the detection and repair data acquisition submodule is used for acquiring detection and repair related data from historical original data of the LDAR project to be analyzed and storing the data into the data storage layer;
and the operation and management data acquisition submodule is used for acquiring operation and management related data from historical original data of the LDAR project to be analyzed and storing the operation and management related data into the data storage layer.
5. The system of claim 4, wherein the field check detection module comprises: the system comprises a field sampling submodule, a project establishing field inspection submodule, a detection and repair field detection submodule and an operation and management field inspection submodule;
the field sampling submodule is used for extracting devices, groups and sealing points from the LDAR project to be analyzed to form device samples, group samples and sealing point samples;
the project establishment field inspection submodule is used for carrying out field inspection on the consistency of relevant data established by projects of the device samples, the group samples and the sealing point samples and the actual situation on the spot;
the detection and repair field inspection submodule is used for carrying out field inspection on the compliance of the detection and repair related operation of the sealing point sample;
the detection and repair field detection submodule is used for detecting the sealing point sample in a field;
and the operation and management field inspection submodule is used for carrying out field inspection on the actual operation condition of the LDAR on the sealing point sample.
6. The system of claim 5, wherein the interface module comprises:
the historical original data transmission interface submodule is connected with the data acquisition module, and the data acquisition module accesses and reads a historical original database of the LDAR project to be analyzed through the historical original data transmission interface submodule and stores related data into a data storage layer;
the project establishment field inspection data transmission interface submodule is connected with the project establishment field inspection submodule, and relevant real-time data generated by the project establishment field inspection submodule is stored in the data storage layer through the project establishment field inspection data transmission interface submodule;
the detection and repair field inspection data transmission interface sub-module is connected with the detection and repair field inspection sub-module, and relevant real-time data generated by the detection and repair field inspection sub-module is stored in the data storage layer through the detection and repair field inspection data transmission interface sub-module;
the detection and repair field detection data transmission interface sub-module is connected with the detection and repair field detection sub-module, and relevant real-time data generated by the detection and repair field detection sub-module is transmitted to the data storage layer through the detection and repair field detection data transmission interface sub-module;
and the operation and management field inspection data transmission interface submodule is connected with the operation and management field inspection submodule, and relevant real-time data of the operation and management field inspection submodule is transmitted to the data storage layer through the operation and management field inspection data transmission interface submodule.
7. The system of claim 3, wherein the data analysis module comprises: the LDAR data extraction submodule, the LDAR data preprocessing submodule, the LDAR data mining analysis submodule and the LDAR decision knowledge application submodule;
the LDAR data extraction submodule is used for extracting original data to be analyzed from the data storage layer;
the LDAR data preprocessing submodule is used for carrying out data cleaning on the original data to be analyzed to obtain cleaned data;
the LDAR data mining analysis submodule is used for forming decision-making knowledge according to the cleaned data and a data analysis algorithm configured by a user; and
the LDAR decision knowledge application submodule is used for forming a plurality of measures and suggestions according to the decision knowledge.
8. The system of claim 7, wherein the project review module comprises: a project establishment condition submodule, a detection and repair condition submodule, an operation and management condition submodule and an evaluation report generating submodule;
the project establishment condition submodule is used for checking whether project establishment related data of the LDAR project to be analyzed are in compliance;
the detection and repair condition submodule is used for checking whether the detection and repair related data of the LDAR item to be analyzed are in compliance;
the operation and management condition submodule is used for checking whether the operation and management related data of the LDAR project to be analyzed is in compliance;
and the evaluation report generation submodule is used for generating an evaluation report of the LDAR project to be analyzed according to an evaluation result.
9. The system according to claim 8, wherein the project establishment status submodule is configured to detect project establishment related data in project establishment related data of the LDAR project to be analyzed, and comprises: controlled range and base data;
the controlled ranges include: the controlled device processes the components and concentrations of the raw materials, the auxiliary agents, the intermediate products and the products, and the components and concentrations of the equipment and the pipeline contact materials;
the basic data includes: group code, group position information, sealing point code, sealing point position information, sealing point type and material form.
10. The system of claim 8, wherein the detecting repair-related data comprises: detecting related data and repairing related data;
the detecting the relevant data comprises: calibrating an instrument, detecting a background, detecting a distance, detecting a speed, detecting data, detecting frequency and verifying instrument drift;
the repair-related data includes: time of first repair and repair of a leaking seal, retest data, and delayed repair of the leaking seal cause.
11. The system of claim 8, wherein the operation and management related data comprises: running related data and managing related data;
the operation-related data includes: group data uploading and/or changing, seal point data uploading and/or changing, detection task allocation, detection task downloading, detection path management, detection data uploading, calibration management, leakage identification and emission calculation, maintenance task allocation and repair record uploading;
the management-related data includes: system LDAR regulations, system emissions calculation methods, and emissions permit thresholds.
12. The system of claim 8, wherein the assessment report of the LDAR project to be analyzed comprises: an assessment summary, an enterprise summary, a LDAR project summary to be analyzed, assessment methods and procedures, and assessment conclusions;
the evaluation summary comprises: evaluation basis, evaluation range and evaluation program;
the enterprise profile comprises: the method mainly comprises the steps of production process, capacity and/or processing capacity and geographical position;
the LDAR project summary to be analyzed comprises: the number and the operating condition of the controlled devices, the number of the sealing points, the number of the unreachable points, the initial time of the LDAR project to be analyzed and the operating mode;
the evaluation method and process comprises the following steps: estimating starting and finishing dates, field sampling modes and results, detection instruments and operation processes used for field inspection and detection, and a data analysis mining method;
the evaluation conclusion comprises: project establishment condition compliance, detection and repair condition compliance, operation and management condition compliance and project promotion suggestions.
13. An LDAR assessment method based on big data mining analysis, the method comprising:
a data acquisition step, which is used for acquiring related data from historical original data of an LDAR project to be analyzed through an interface according to user operation and storing the related data into a data storage layer;
the field inspection detection step is used for carrying out inspection and/or detection on the field extracted devices, groups and sealing points according to user operation and storing the generated related real-time original data into the data storage layer through an interface;
a data analysis step, which is used for obtaining the correlation between the original data of the LDAR according to the user operation and forming decision knowledge;
and a project auditing step, which is used for establishing an LDAR auditing project according to the user operation, auditing the compliance condition of the LDAR project and outputting an evaluation report.
14. The method of claim 13, wherein the data acquisition step comprises the sub-steps of:
a project establishment data acquisition substep, which is used for acquiring project establishment related data from historical original data of the LDAR project to be analyzed and storing the project establishment related data in the data storage layer;
a detection and repair data acquisition substep, which is used for acquiring detection and repair related data from historical original data of the LDAR project to be analyzed and storing the data into the data storage layer;
and the operation and management data acquisition sub-step is used for acquiring operation and management related data from historical original data of the LDAR project to be analyzed and storing the operation and management related data into the data storage layer.
15. The method of claim 13, wherein the field inspection testing step comprises the substeps of:
an in-situ sampling sub-step, which is used for extracting devices, groups and sealing points from the LDAR project to be analyzed to form device samples, group samples and sealing point samples;
a project establishment field inspection substep, which is used for checking whether the relevant data established by the projects of the device sample, the group sample and the sealing point sample are consistent in actual field conditions;
a detection and repair field inspection sub-step for inspecting in situ whether the operations related to the detection and repair of the seal point sample are compliant;
a detection and repair field detection substep for detecting the seal point sample in situ;
and the operation and management field inspection substep is used for carrying out field inspection on the compliance of the actual operation condition of the LDAR on the sealing point sample.
16. The method of claim 13, wherein the data analysis step comprises the sub-steps of:
an LDAR data extraction sub-step for extracting original data to be analyzed from the data storage layer;
the LDAR data preprocessing substep is used for carrying out data cleaning on the original data to be analyzed to obtain cleaned data;
an LDAR data mining and analyzing sub-step, which is used for forming decision knowledge according to the cleaned data and a data analysis algorithm configured by a user; and
an LDAR decision knowledge application sub-step for forming a plurality of measures and suggestions according to the decision knowledge.
17. The method of claim 13, wherein the project review step comprises:
the LDAR project establishment condition examination sub-step is used for examining whether project establishment related data of the LDAR project to be analyzed are in compliance;
a detection and maintenance condition auditing substep, which is used for checking whether the detection and repair related data of the LDAR project to be analyzed are in compliance;
the LDAR operation and management condition auditing substep is used for checking whether the operation and management related data of the LDAR project to be analyzed are in compliance;
and an evaluation report generation sub-step, which is used for generating an evaluation report of the LDAR project to be analyzed according to the checking result.
18. The method of claim 17, wherein the project building correlation data of the LDAR project to be analyzed comprises: controlled range and base data;
the controlled ranges include: the controlled device processes the components and concentrations of the raw materials, the auxiliary agents, the intermediate products and the products, and the components and concentrations of the equipment and the pipeline contact materials;
the basic data includes: group code, group position information, sealing point code, sealing point position information, sealing point type and material form.
19. The method of claim 17, wherein the data related to the detection and repair of the LDAR items to be analyzed comprises: detecting related data and repairing related data;
the detecting the relevant data comprises: calibrating an instrument, detecting a background, detecting a distance, detecting a speed, detecting data, detecting frequency and verifying instrument drift;
the repair-related data includes: time of first repair and repair of a leaking seal, retest data, and delayed repair of the leaking seal cause.
20. The method of claim 17, wherein the operating and managing related data comprises: running related data and managing related data;
the operation-related data includes: group data uploading and/or changing, seal point data uploading and/or changing, detection task allocation, detection task downloading, detection path management, detection data uploading, calibration management, leakage identification and emission calculation, maintenance task allocation and repair record uploading;
the management-related data includes: the system LDAR regulation, the system emission calculation method and the pollution discharge permission threshold value.
21. The method of claim 17, wherein the report of the evaluation of the LDAR project to be analyzed comprises: an assessment summary, an enterprise summary, an LDAR project summary to be analyzed, an assessment method and process, and an assessment conclusion;
the evaluation summary comprises: evaluation basis, evaluation range and evaluation program;
the enterprise profile comprises: the method mainly comprises the steps of production process, capacity and/or processing capacity and geographical position;
the LDAR project summary to be analyzed comprises: the number of controlled devices, the operating condition, the number of sealing points and the number of inaccessible points; starting time and operation mode of an LDAR project to be analyzed;
the evaluation method and process comprises the following steps: estimating starting and finishing dates, field sampling modes and results, detection instruments and operation processes used for field inspection and detection, and a data analysis mining method;
the evaluation conclusion comprises: project establishment condition compliance, detection and repair condition compliance, operation and management condition compliance and project promotion suggestions.
22. A server, wherein the LDAR evaluation system based on big data mining analysis of any claim 1 to 12 is loaded on the server.
23. A machine-readable storage medium having stored thereon instructions which, when executed on a computer, cause the computer to perform the method for LDAR assessment based on big data mining analysis of any of claims 13 to 21.
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