CN116307950B - Building quality intelligent management system and method based on multivariate information - Google Patents

Building quality intelligent management system and method based on multivariate information Download PDF

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CN116307950B
CN116307950B CN202310595605.9A CN202310595605A CN116307950B CN 116307950 B CN116307950 B CN 116307950B CN 202310595605 A CN202310595605 A CN 202310595605A CN 116307950 B CN116307950 B CN 116307950B
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CN116307950A (en
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严文荣
王丹
李敬贤
裴蕾
杜金苏
苏志强
杨晓月
郑学伟
张阳
王厚义
李可
潘登
金鑫
琚琳琳
李杰光
刘权
李世琦
李堃寰
刘爽
柯芬
郭静
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China Construction Industrial and Energy Engineering Group Co Ltd
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Abstract

The invention relates to the technical field of building management, in particular to a building quality intelligent management system and method based on multivariate information. When the quality of the petrochemical building is monitored, the influence of the environment where the petrochemical building is located and the risk sources in the building on the petrochemical building is considered, and the risk degrees corresponding to different risk sources in the petrochemical building are comprehensively evaluated; and the quality supervision priorities of different risk sources are managed according to the corresponding risk degrees, so that the quality of the petrochemical building is effectively managed.

Description

Building quality intelligent management system and method based on multivariate information
Technical Field
The invention relates to the technical field of building management, in particular to an intelligent building quality management system and method based on multivariate information.
Background
Explosion accidents are one of the common disasters in the petrochemical field, and prediction and protection of explosion injury are important contents of research in the petrochemical safety field at present; existing solutions generally appear as two types: one is to strengthen the on-line supervision of petrochemical equipment, and the other is to strengthen the protection of petrochemical buildings, and in the protection of the reinforced petrochemical buildings, an explosion-proof design is usually adopted for the buildings, or explosion-proof facilities (such as an explosion-proof wall) are added in the petrochemical buildings.
The existing building quality supervision system generally analyzes and supervises the structure and the aging state of the building, the object of the analysis is the building body, however, when the explosion accident occurs to the petrochemical building, only the building is not comprehensive, the influence of the environment where the petrochemical building is positioned and the risk source inside the building on the petrochemical building is not considered, the risk probability of the explosion accident of the petrochemical building and the influence degree of the explosion on the petrochemical building are directly influenced, and further the prior art has larger defects; meanwhile, when the quality of petrochemical buildings is monitored, if all petrochemical building areas are monitored, the cost of manpower and material resources is high due to the large monitoring range, so that the development of enterprises is not facilitated.
Disclosure of Invention
The invention aims to provide a building quality intelligent management system and method based on multivariate information, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: the intelligent building quality management method based on the multivariate information comprises the following steps:
s1, acquiring environment information corresponding to different time periods in a petrochemical building area to be detected in historical data, and obtaining an environment vector set corresponding to the petrochemical building area to be detected; acquiring risk information corresponding to different explosion risk sources in the petrochemical building area to be detected respectively, and constructing a risk information set of the petrochemical building area to be detected;
s2, building a building model corresponding to the petrochemical building area to be detected through a BIM technology, predicting a risk area corresponding to each explosion risk source in the petrochemical building area to be detected according to a risk information set of the petrochemical building area to be detected, combining the environment vector set corresponding to the petrochemical building area to be detected obtained in the S1, building explosion risk source association chains corresponding to the same reference sources respectively under different environment vectors, and summarizing the explosion risk source association chains corresponding to the same reference sources respectively into the same blank set to obtain a risk association chain set corresponding to the corresponding reference source, wherein the reference source is one explosion risk source in the petrochemical building area to be detected;
S3, analyzing the ratio of the comprehensive risk areas corresponding to each explosion risk source association chain in the risk association chain set corresponding to the same reference source to obtain a comprehensive risk influence value corresponding to each reference source;
s4, acquiring a leakage risk coefficient corresponding to each explosion risk source, generating a quality supervision priority sequence of each explosion risk source in the petrochemical building area to be tested by combining the analysis result in the S3, and monitoring and early warning the first m explosion risk sources in the obtained quality supervision priority sequence; and the m is a constant preset in a database.
Further, the environmental information in S1 includes an average wind power level and a corresponding wind direction in a corresponding time period, the wind direction in the corresponding time period is the same as a direction of a combined vector of wind power vectors corresponding to each time point in the corresponding time period, the wind power vector corresponding to each time point is equal to a vector which is the same as the wind direction monitored by the wind sensor at the corresponding time point and has a vector module length equal to the wind power level monitored by the wind sensor, and the environmental information is acquired once every a first preset unit time in the database;
the wind direction in the invention defaults to the direction on the horizontal plane, namely, the two endpoints in the wind power vector are on the same horizontal plane;
The number of elements in the environment vector set corresponding to the petrochemical building area to be detected is equal to the number of environment information corresponding to the historical data, each element in the environment vector set corresponds to a first wind power vector, the environment vector set corresponding to the petrochemical building area to be detected is marked as A, the ith element in the A is marked as Ai, the Ai is a vector with the vector direction being the same as the wind direction of the corresponding environment information, and the modular length is equal to the average wind power level in the corresponding environment information;
the explosion risk source in the S1 is a building area where equipment for storing combustible substances in the petrochemical building area to be detected belongs, and is preset in a database, and the risk information corresponding to the explosion risk source comprises the central position of the corresponding area, the total building volume of the corresponding area, the explosion-proof characteristic coefficient of the building in the corresponding area and the combustible substance quantity in the corresponding area.
The building explosion-proof characteristic coefficients in the corresponding areas are obtained through building data query preset in a database, the combustible material quantity in the corresponding areas is obtained through sensor acquisition, and the central positions of the corresponding areas and the areas of the corresponding areas are obtained through database query.
Further, the method for predicting the risk area corresponding to each explosion risk source in the petrochemical building area to be detected in S2 includes the following steps:
S201, building a building model corresponding to a petrochemical building area to be detected;
s202, acquiring a risk information set in a petrochemical building area to be detected, and marking corresponding position coordinates of a building model constructed in S201 at positions corresponding to each risk information in the risk information set;
s203, combining the total building volume of the corresponding region in each risk information in the risk information set, the building explosion-proof characteristic coefficient in the corresponding region and the combustible material quantity in the corresponding region to obtain a fluctuation risk distance corresponding to each risk information, marking the fluctuation risk distance corresponding to the jth risk information in the risk information set in the petrochemical building region to be detected as Lj, and constructing a correlation data pair (j, lj);
in a first preset form of the database, lj is a corresponding fluctuation risk distance value under the conditions that the building explosion-proof characteristic coefficient is bj and the average quantity of combustible substances is cj; bj represents a building explosion-proof characteristic coefficient in the j-th risk information in the risk information set in the petrochemical building area to be detected, and cj is equal to the ratio of the combustible material quantity of the corresponding area in the j-th risk information in the risk information set in the petrochemical building area to be detected to the total volume of the building in the corresponding area; the fluctuation risk distance value represents the maximum value of the distance between each position point and the corresponding building in the explosion wave range corresponding to each explosion event with the same combustible material quantity in unit volume in the building of the corresponding building explosion-proof characteristic coefficient under the windless environment; the distance between the position point in the explosion wave range and the corresponding building represents the minimum value in the corresponding distance between the corresponding position point and each building position point in the corresponding building;
S204, acquiring a building area corresponding to a j-th element of the risk information set in the petrochemical building area to be detected, marking the building area by adopting a first marking mode in a building model, marking the obtained marking range as Qj, acquiring a related data pair (j, lj),
and acquiring areas corresponding to all the position point sets with the distance Lj of each point in the Qj, and obtaining a prediction result of a risk area corresponding to a j-th explosion risk source in the petrochemical building area to be detected, wherein the prediction result is marked as QLj.
The invention predicts the risk area of each explosion risk source, which aims to analyze and judge the influence range of each explosion risk source and provides data reference for analyzing the association relationship between different explosion risk sources in the subsequent steps; building a building model, namely determining the position of each explosion risk source, and accurately judging whether influence exists among different explosion risk sources by combining the risk areas corresponding to each explosion risk source, so as to accurately obtain each explosion risk source association chain.
Further, the method for constructing the explosion risk source association chain corresponding to the same reference source under different environment vectors in S2 includes the following steps:
s211, acquiring a wind power influence fluctuation coefficient P corresponding to M levels of wind power in a database M ；
S212, obtaining a prediction result QLj of a risk area corresponding to a j-th explosion risk source in the petrochemical building area to be detected and an environment vector set A corresponding to the petrochemical building area to be detected;
s213, when the acquired environment vector is Ai, the risk calibration area corresponding to QLj is marked as WQLj,
acquiring a straight line passing through the center position of the jth explosion risk source corresponding to the building area in the horizontal plane and perpendicular to the Ai direction, marking as U, and marking the ratio of the lengths of the line segments passing through any point on the obtained straight line U and having the same direction as the Ai and respectively passing through WQLj and QLj as P M The straight line which passes through any point on the obtained straight line U and has the same direction as the Ai respectively passes through one end of a line segment in WQLj and QLj, which is close to the start point of the vector Ai, and coincides with one end of the line segment;
s214, acquiring a risk calibration area WQLj corresponding to each of the environment vectors Ai and j with different values in the building model corresponding to the petrochemical building area to be tested, if the jth explosion risk source is taken as a reference source, acquiring an explosion risk source association chain corresponding to the corresponding reference source with the environment vector Ai, marking as EAi,
the chain comprises one or more chain nodes, each chain node comprises two end points, namely a first end point and a second end point, and the nth chain node in EAi is marked as { EAi } （n，1） ，EAi （n，2） }, therein EAi （n，1） Representing the first endpoint in the nth chain node in EAi, EAi （n，2） Representing a second end point in an nth chain node in EAi, and a first end point in a 1 st chain node in EAi as a reference source, wherein the first end point and the second end point in each chain node correspond to one or more explosion risk sources, and the number of the explosion risk sources corresponding to the second end point is equal to the number of the link points adjacent to the corresponding link point; EAi, in any adjacent chain node, taking an explosion risk source corresponding to a second endpoint in the previous chain node as a first endpoint in the subsequent chain node;
when the second end point corresponding to each chain link point in EAi is obtained, the corresponding risk calibration area of the explosion risk source corresponding to the first end point of the corresponding chain node is obtained, whether the rest explosion risk sources exist in the obtained risk calibration area or not is judged, the explosion risk sources are not recorded in all the obtained chain nodes,
if the obtained risk calibration area does not contain other explosion risk sources, judging that the chain link point does not exist, and obtaining a complete EAi; if the rest of the explosion risk sources exist in the obtained risk calibration area and the explosion risk sources are not recorded in all the obtained chain nodes, the rest of the explosion risk sources which are not recorded in all the obtained chain nodes and exist in the obtained risk calibration area are taken as the second endpoint of the chain nodes.
Wind power influence fluctuation coefficient P corresponding to M-level wind power in database M The method is obtained according to analysis of petrochemical building explosion events in historical data, and comprises the following steps:
s211-1, acquiring each petrochemical building explosion event with M-level wind power in historical data, and marking an actual explosion risk area corresponding to the j1 st petrochemical building explosion event as DMj;
s211-2, acquiring risk information corresponding to the last acquired data before the occurrence of the j1 st petrochemical building explosion event, predicting a risk area corresponding to the j1 st petrochemical building explosion event according to the contents in S203 to S204, and marking as Q DMj1 ；
S211-3, acquiring the wind direction corresponding to the j1 st petrochemical building explosion event, marking the wind direction as Fj1, and combining DMj and Q DMj1 The building center points in (1) are overlapped, a straight line is made in the horizontal plane through the building center points and in the direction vertical to Fj1, and the straight line which is at any point on the obtained straight line and has the same pointing direction as Fj1 respectively passes through DMj and Q DMj1 The length of the line segments in (1) are respectively denoted as P1 DMj1 And P2Q DMj1 Obtaining P1 DMj1 And P2Q DMj1 If neither is 0, P1 DMj1 And P2Q DMj1 The corresponding quotient is denoted as P3, when P1 DMj1 Or P2Q DMj1 If the value is 0, judging that P3=1, taking the maximum value in P3 corresponding to any point in the obtained straight line as the wind power influence fluctuation coefficient corresponding to the j1 st petrochemical building explosion event,
And taking the average value of the wind power influence fluctuation coefficients respectively corresponding to each petrochemical building explosion event with the wind power of M levels in the database as the wind power influence fluctuation coefficient when the wind power is of M levels.
Further, when the comprehensive risk influence value corresponding to each reference source is obtained in S3, acquiring an explosion risk source risk associated chain set corresponding to the j-th explosion risk source as the reference source, and recording the comprehensive risk area ratio corresponding to the k-th explosion risk source associated chain in the explosion risk source risk associated chain set corresponding to the j-th explosion risk source as the reference source as Hjk, hjk =rwtjk/rwtzjk,
in the k-th explosion risk source associated chain in the explosion risk source associated chain set corresponding to the j-th explosion risk source as a reference source, each explosion risk source corresponds to a union of risk calibration areas under the environment vector respectively, WTZjk represents a union of risk calibration areas corresponding to all explosion risk sources in the petrochemical building area to be tested respectively under the environment vector corresponding to the k-th explosion risk source associated chain in the explosion risk source associated chain set corresponding to the j-th explosion risk source as the reference source, and R [ ] represents operation for obtaining the area of the corresponding area;
And obtaining a comprehensive risk influence value corresponding to the j-th explosion risk source as the reference source, and marking the comprehensive risk influence value as gj, wherein gj represents the maximum value of the ratio of the comprehensive risk areas corresponding to the explosion risk source association chains respectively in the explosion risk source risk association chain set corresponding to the j-th explosion risk source as the reference source.
The comprehensive risk influence value corresponding to the j-th explosion risk source is obtained by the method, and the maximum influence of the petrochemical building area to be tested is considered when the explosion event occurs in the explosion risk source corresponding to the reference source; the analysis result provides a data reference for acquiring quality supervision priority sequences of various explosion risk sources in the petrochemical building area to be tested in the subsequent steps.
Further, the method for generating the quality supervision priority sequence of each explosion risk source in the petrochemical building area to be tested in S4 includes the following steps:
s41, acquiring comprehensive risk influence values corresponding to each explosion risk source in the petrochemical building area to be tested;
s42, acquiring leakage risk information of each explosion risk source in a first preset time before the current time, and inquiring leakage risk coefficients corresponding to the corresponding leakage risk information through a second preset form in the database;
The leakage risk information comprises the number of times of occurrence of leakage events, the average time length of occurrence of primary leakage events and the average primary leakage combustible material amount, and is obtained through statistics through historical data;
s43, sequencing the explosion risk sources in the petrochemical building area to be tested according to the sequence from large to small of the products of the comprehensive risk influence values and the leakage risk coefficients, which correspond to the explosion risk sources, so as to obtain the quality supervision priority sequence of the explosion risk sources in the petrochemical building area to be tested.
Building quality intelligent management system based on multivariate information, wherein the system comprises the following modules:
the information acquisition module acquires environment information corresponding to different time periods in the petrochemical building area to be detected in the historical data to obtain an environment vector set corresponding to the petrochemical building area to be detected; acquiring risk information corresponding to different explosion risk sources in the petrochemical building area to be detected, and constructing a risk information set of the petrochemical building area to be detected;
the risk source association information analysis module is used for constructing a building model corresponding to a petrochemical building area to be detected through a BIM technology, predicting a risk area corresponding to each explosion risk source in the petrochemical building area to be detected according to a risk information set of the petrochemical building area to be detected, combining an environment vector set corresponding to each explosion risk source in the petrochemical building area to be detected obtained in the information acquisition module, constructing explosion risk source association chains respectively corresponding to the same reference source under different environment vectors, and summarizing each explosion risk source association chain respectively corresponding to the same reference source into the same blank set to obtain a risk association chain set corresponding to the corresponding reference source, wherein the reference source is one explosion risk source in the petrochemical building area to be detected;
The risk influence analysis module is used for analyzing the comprehensive risk area ratio corresponding to each explosion risk source association chain in the risk association chain set corresponding to the same reference source to obtain a comprehensive risk influence value corresponding to each reference source;
the risk supervision area locking module acquires leakage risk coefficients corresponding to each explosion risk source, combines analysis results in the risk influence analysis module, generates quality supervision priority sequences of the explosion risk sources in the petrochemical building area to be detected, and monitors and early warns the first m explosion risk sources in the obtained quality supervision priority sequences.
The monitoring and early warning of the invention can comprise two types: on one hand, the early warning can be directly carried out on an administrator, and the administrator is reminded that potential safety hazards exist in the results (the first m explosion risk sources) obtained by the administrator, so that the petrochemical building needs to be improved; on the other hand, the manager can be reminded to further and more carefully monitor the areas corresponding to the obtained results (the first m explosion risk sources) so as to prevent explosion events.
Further, the information acquisition module comprises an environment vector acquisition unit and a risk information acquisition unit,
The environment vector acquisition unit acquires environment information corresponding to different time periods in the petrochemical building area to be detected in the historical data, and an environment vector set corresponding to the petrochemical building area to be detected is obtained;
the risk information acquisition unit acquires risk information corresponding to different explosion risk sources in the petrochemical building area to be detected respectively, and builds a risk information set of the petrochemical building area to be detected;
the risk source associated information analysis module comprises a model construction unit, a risk area prediction unit and a risk associated analysis unit,
the model construction unit constructs a building model corresponding to the petrochemical building area to be detected through BIM technology,
the risk area prediction unit predicts the risk area corresponding to each explosion risk source in the petrochemical building area to be detected according to the risk information set of the petrochemical building area to be detected,
the risk association analysis unit combines the environment vector sets corresponding to the petrochemical building areas to be detected obtained in the information acquisition module, builds explosion risk source association chains corresponding to the same reference sources respectively under different environment vectors, and summarizes each explosion risk source association chain corresponding to the same reference sources respectively into the same blank set to obtain risk association chain sets corresponding to the corresponding reference sources.
Compared with the prior art, the invention has the following beneficial effects: when the quality of the petrochemical building is monitored, the influence of the environment where the petrochemical building is located and the risk sources in the building on the petrochemical building is considered, and the risk degrees corresponding to different risk sources in the petrochemical building are comprehensively evaluated; quality supervision priorities of different risk sources are managed according to corresponding risk degrees, so that effective management of petrochemical building quality is realized; meanwhile, on the basis of ensuring the supervision effect of petrochemical building quality, the supervision scope is reduced, and the cost of manpower and material resources input by enterprises is saved.
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The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow diagram of a method for intelligent management of building quality based on multiple information according to the present invention;
fig. 2 is a schematic structural diagram of the intelligent building quality management system based on multiple pieces of information.
Description of the embodiments
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, the present invention provides the following technical solutions: the intelligent building quality management method based on the multivariate information comprises the following steps:
s1, acquiring environment information corresponding to different time periods in a petrochemical building area to be detected in historical data, and obtaining an environment vector set corresponding to the petrochemical building area to be detected; acquiring risk information corresponding to different explosion risk sources in the petrochemical building area to be detected respectively, and constructing a risk information set of the petrochemical building area to be detected;
the environment information in the S1 comprises average wind power level and corresponding wind direction in a corresponding time period, the wind direction in the corresponding time period is the same as the direction of the combined vector of the wind power vectors corresponding to all time points in the corresponding time period, the wind power vector corresponding to each time point is equal to the vector which is the same as the wind direction monitored by the wind sensor at the corresponding time point and has the vector module length equal to the wind power level monitored by the wind sensor, and the environment information is acquired once every a first unit time preset in a database;
the number of elements in the environment vector set corresponding to the petrochemical building area to be detected is equal to the number of environment information corresponding to the historical data, each element in the environment vector set corresponds to a first wind power vector, the environment vector set corresponding to the petrochemical building area to be detected is marked as A, the ith element in the A is marked as Ai, the Ai is a vector with the vector direction being the same as the wind direction of the corresponding environment information, and the modular length is equal to the average wind power level in the corresponding environment information;
The explosion risk source in the S1 is a building area where equipment for storing combustible substances in the petrochemical building area to be detected belongs, and is preset in a database, and the risk information corresponding to the explosion risk source comprises the central position of the corresponding area, the total building volume of the corresponding area, the explosion-proof characteristic coefficient of the building in the corresponding area and the combustible substance quantity in the corresponding area.
In this embodiment, the building explosion-proof characteristic coefficients in the corresponding areas are obtained through building data query preset in the database, the combustible material amounts in the corresponding areas are obtained through sensor acquisition, and the central positions of the corresponding areas and the areas of the corresponding areas are obtained through database query.
S2, building a building model corresponding to the petrochemical building area to be detected through a BIM technology, predicting a risk area corresponding to each explosion risk source in the petrochemical building area to be detected according to a risk information set of the petrochemical building area to be detected, combining the environment vector set corresponding to the petrochemical building area to be detected obtained in the S1, building explosion risk source association chains corresponding to the same reference sources respectively under different environment vectors, and summarizing the explosion risk source association chains corresponding to the same reference sources respectively into the same blank set to obtain a risk association chain set corresponding to the corresponding reference source, wherein the reference source is one explosion risk source in the petrochemical building area to be detected;
The method for predicting the risk area corresponding to each explosion risk source in the petrochemical building area to be detected in the S2 comprises the following steps:
s201, building a building model corresponding to a petrochemical building area to be detected;
in this embodiment, when the three-dimensional building model in the petrochemical building area to be measured is constructed by the BIM technology, the construction is performed by the Revit software.
S202, acquiring a risk information set in a petrochemical building area to be detected, and marking corresponding position coordinates of a building model constructed in S201 at positions corresponding to each risk information in the risk information set;
s203, combining the total building volume of the corresponding region in each risk information in the risk information set, the building explosion-proof characteristic coefficient in the corresponding region and the combustible material quantity in the corresponding region to obtain a fluctuation risk distance corresponding to each risk information, marking the fluctuation risk distance corresponding to the jth risk information in the risk information set in the petrochemical building region to be detected as Lj, and constructing a correlation data pair (j, lj);
in a first preset form of the database, lj is a corresponding fluctuation risk distance value under the conditions that the building explosion-proof characteristic coefficient is bj and the average quantity of combustible substances is cj; bj represents a building explosion-proof characteristic coefficient in the j-th risk information in the risk information set in the petrochemical building area to be detected, and cj is equal to the ratio of the combustible material quantity of the corresponding area in the j-th risk information in the risk information set in the petrochemical building area to be detected to the total volume of the building in the corresponding area; the fluctuation risk distance value represents the maximum value of the distance between each position point and the corresponding building in the explosion wave range corresponding to each explosion event with the same combustible material quantity in unit volume in the building of the corresponding building explosion-proof characteristic coefficient under the windless environment; the distance between the position point in the explosion wave range and the corresponding building represents the minimum value in the corresponding distance between the corresponding position point and each building position point in the corresponding building;
S204, acquiring a building area corresponding to a j-th element of the risk information set in the petrochemical building area to be detected, marking the building area by adopting a first marking mode in a building model, marking the obtained marking range as Qj, acquiring a related data pair (j, lj),
and acquiring areas corresponding to all the position point sets with the distance Lj of each point in the Qj, and obtaining a prediction result of a risk area corresponding to a j-th explosion risk source in the petrochemical building area to be detected, wherein the prediction result is marked as QLj.
The method for constructing the explosion risk source association chains respectively corresponding to the same reference source under different environment vectors in the S2 comprises the following steps:
s211, acquiring a wind power influence fluctuation coefficient P corresponding to M levels of wind power in a database M ；
S212, obtaining a prediction result QLj of a risk area corresponding to a j-th explosion risk source in the petrochemical building area to be detected and an environment vector set A corresponding to the petrochemical building area to be detected;
s213, when the acquired environment vector is Ai, the risk calibration area corresponding to QLj is marked as WQLj,
acquiring a straight line passing through the center position of the jth explosion risk source corresponding to the building area in the horizontal plane and perpendicular to the Ai direction, marking as U, and marking the ratio of the lengths of the line segments passing through any point on the obtained straight line U and having the same direction as the Ai and respectively passing through WQLj and QLj as P M The straight line which passes through any point on the obtained straight line U and has the same direction as the Ai respectively passes through one end of a line segment in WQLj and QLj, which is close to the start point of the vector Ai, and coincides with one end of the line segment;
s214, acquiring a risk calibration area WQLj corresponding to each of the environment vectors Ai and j with different values in the building model corresponding to the petrochemical building area to be tested, if the jth explosion risk source is taken as a reference source, acquiring an explosion risk source association chain corresponding to the corresponding reference source with the environment vector Ai, marking as EAi,
the chain comprises one or more chain nodes, each chain node comprises two end points, namely a first end point and a second end point, and the nth chain node in EAi is marked as { EAi } （n，1） ，EAi （n，2） }, therein EAi （n，1） Representing the first endpoint in the nth chain node in EAi, EAi （n，2） Representing a second end point in an nth chain node in EAi, and a first end point in a 1 st chain node in EAi as a reference source, wherein the first end point and the second end point in each chain node correspond to one or more explosion risk sources, and the number of the explosion risk sources corresponding to the second end point is equal to the number of the link points adjacent to the corresponding link point; EAi, in any adjacent chain node, taking an explosion risk source corresponding to a second endpoint in the previous chain node as a first endpoint in the subsequent chain node;
In this embodiment, if when the link is associated with the explosion risk source, and when the 3 rd link node in Y is obtained, if the second endpoint includes 2 other explosion risk sources, the 1 st explosion risk source and the 4 th explosion risk source are respectively, the number of link points adjacent to the subsequent 3 rd link point is 2, and the explosion risk source corresponding to the first end point of one of the two link points is the 1 st explosion risk source, and the explosion risk source corresponding to the first end point of the other one of the two link points is the 4 th explosion risk source;
when the second end point corresponding to each chain link point in EAi is obtained, the corresponding risk calibration area of the explosion risk source corresponding to the first end point of the corresponding chain node is obtained, whether the rest explosion risk sources exist in the obtained risk calibration area or not is judged, the explosion risk sources are not recorded in all the obtained chain nodes,
if the obtained risk calibration area does not contain other explosion risk sources, judging that the chain link point does not exist, and obtaining a complete EAi; if the rest of the explosion risk sources exist in the obtained risk calibration area and the explosion risk sources are not recorded in all the obtained chain nodes, the rest of the explosion risk sources which are not recorded in all the obtained chain nodes and exist in the obtained risk calibration area are taken as the second endpoint of the chain nodes.
Wind power influence fluctuation coefficient P corresponding to M-level wind power in database M The method is obtained according to analysis of petrochemical building explosion events in historical data, and comprises the following steps:
s211-1, acquiring each petrochemical building explosion event with M-level wind power in historical data, and marking an actual explosion risk area corresponding to the j1 st petrochemical building explosion event as DMj;
s211-2, acquiring risk information corresponding to the last acquired data before the occurrence of the j1 st petrochemical building explosion event, predicting a risk area corresponding to the j1 st petrochemical building explosion event according to the contents in S203 to S204, and marking as Q DMj1 ；
S211-3, acquiring the wind direction corresponding to the j1 st petrochemical building explosion event, marking the wind direction as Fj1, and combining DMj and Q DMj1 The building center points in (1) are overlapped, a straight line is made in the horizontal plane through the building center points and in the direction vertical to Fj1, and the straight line which is at any point on the obtained straight line and has the same pointing direction as Fj1 respectively passes through DMj and Q DMj1 The length of the line segments in (1) are respectively denoted as P1 DMj1 And P2Q DMj1 Obtaining P1 DMj1 And P2Q DMj1 If neither is 0, P1 DMj1 And P2Q DMj1 The corresponding quotient is denoted as P3, when P1 DMj1 Or P2Q DMj1 If the value is 0, judging that P3=1, taking the maximum value in P3 corresponding to any point in the obtained straight line as the wind power influence fluctuation coefficient corresponding to the j1 st petrochemical building explosion event,
And taking the average value of the wind power influence fluctuation coefficients respectively corresponding to each petrochemical building explosion event with the wind power of M levels in the database as the wind power influence fluctuation coefficient when the wind power is of M levels.
S3, analyzing the ratio of the comprehensive risk areas corresponding to each explosion risk source association chain in the risk association chain set corresponding to the same reference source to obtain a comprehensive risk influence value corresponding to each reference source;
when the comprehensive risk influence value corresponding to each reference source is obtained in S3, acquiring an explosion risk source risk associated chain set corresponding to the j-th explosion risk source as the reference source, and recording the comprehensive risk area ratio corresponding to the k-th explosion risk source associated chain in the explosion risk source risk associated chain set corresponding to the j-th explosion risk source as Hjk, hjk =rwtjk/rwtzjk,
in the k-th explosion risk source associated chain in the explosion risk source associated chain set corresponding to the j-th explosion risk source as a reference source, each explosion risk source corresponds to a union of risk calibration areas under the environment vector respectively, WTZjk represents a union of risk calibration areas corresponding to all explosion risk sources in the petrochemical building area to be tested respectively under the environment vector corresponding to the k-th explosion risk source associated chain in the explosion risk source associated chain set corresponding to the j-th explosion risk source as the reference source, and R [ ] represents operation for obtaining the area of the corresponding area;
And obtaining a comprehensive risk influence value corresponding to the j-th explosion risk source as the reference source, and marking the comprehensive risk influence value as gj, wherein gj represents the maximum value of the ratio of the comprehensive risk areas corresponding to the explosion risk source association chains respectively in the explosion risk source risk association chain set corresponding to the j-th explosion risk source as the reference source.
S4, acquiring a leakage risk coefficient corresponding to each explosion risk source, generating a quality supervision priority sequence of each explosion risk source in the petrochemical building area to be tested by combining the analysis result in the S3, and monitoring and early warning the first m explosion risk sources in the obtained quality supervision priority sequence;
the method for generating the quality supervision priority sequence of each explosion risk source in the petrochemical building area to be detected in the S4 comprises the following steps:
s41, acquiring comprehensive risk influence values corresponding to each explosion risk source in the petrochemical building area to be tested;
s42, acquiring leakage risk information of each explosion risk source in a first preset time before the current time, and inquiring leakage risk coefficients corresponding to the corresponding leakage risk information through a second preset form in the database;
s43, sequencing the explosion risk sources in the petrochemical building area to be tested according to the sequence from large to small of the products of the comprehensive risk influence values and the leakage risk coefficients, which correspond to the explosion risk sources, so as to obtain the quality supervision priority sequence of the explosion risk sources in the petrochemical building area to be tested.
The leakage risk information in this embodiment includes the number of times of occurrence of leakage events, the average duration of occurrence of one leakage event, and the average amount of combustible materials leaked once;
in this embodiment, if the value of m is 3, if the product of the comprehensive risk influence value corresponding to the explosion risk source and the leakage risk coefficient corresponding to the first 3 later explosion risk sources in the obtained quality supervision priority sequence is equal to the product of the comprehensive risk influence value corresponding to the 3 rd element in the corresponding priority sequence and the leakage risk coefficient, the value of m is updated, and at this time, the value of m is equal to the number of all explosion risk sources in the obtained quality supervision priority sequence, where the product of the comprehensive risk influence value corresponding to the 3 rd element in the corresponding priority sequence and the product of the leakage risk coefficient are greater than or equal to the product of the comprehensive risk influence value corresponding to the 3 rd element in the corresponding priority sequence and the leakage risk coefficient;
building quality intelligent management system based on multivariate information, wherein the system comprises the following modules:
the information acquisition module acquires environment information corresponding to different time periods in the petrochemical building area to be detected in the historical data to obtain an environment vector set corresponding to the petrochemical building area to be detected; acquiring risk information corresponding to different explosion risk sources in the petrochemical building area to be detected, and constructing a risk information set of the petrochemical building area to be detected;
The risk source association information analysis module is used for constructing a building model corresponding to a petrochemical building area to be detected through a BIM technology, predicting a risk area corresponding to each explosion risk source in the petrochemical building area to be detected according to a risk information set of the petrochemical building area to be detected, combining an environment vector set corresponding to each explosion risk source in the petrochemical building area to be detected obtained in the information acquisition module, constructing explosion risk source association chains respectively corresponding to the same reference source under different environment vectors, and summarizing each explosion risk source association chain respectively corresponding to the same reference source into the same blank set to obtain a risk association chain set corresponding to the corresponding reference source, wherein the reference source is one explosion risk source in the petrochemical building area to be detected;
the risk influence analysis module is used for analyzing the comprehensive risk area ratio corresponding to each explosion risk source association chain in the risk association chain set corresponding to the same reference source to obtain a comprehensive risk influence value corresponding to each reference source;
the risk supervision area locking module acquires leakage risk coefficients corresponding to each explosion risk source, combines analysis results in the risk influence analysis module, generates quality supervision priority sequences of the explosion risk sources in the petrochemical building area to be detected, and monitors and early warns the first m explosion risk sources in the obtained quality supervision priority sequences.
The information acquisition module comprises an environment vector acquisition unit and a risk information acquisition unit,
the environment vector acquisition unit acquires environment information corresponding to different time periods in the petrochemical building area to be detected in the historical data, and an environment vector set corresponding to the petrochemical building area to be detected is obtained;
the risk information acquisition unit acquires risk information corresponding to different explosion risk sources in the petrochemical building area to be detected respectively, and builds a risk information set of the petrochemical building area to be detected;
the risk source associated information analysis module comprises a model construction unit, a risk area prediction unit and a risk associated analysis unit,
the model construction unit constructs a building model corresponding to the petrochemical building area to be detected through BIM technology,
the risk area prediction unit predicts the risk area corresponding to each explosion risk source in the petrochemical building area to be detected according to the risk information set of the petrochemical building area to be detected,
the risk association analysis unit combines the environment vector sets corresponding to the petrochemical building areas to be detected obtained in the information acquisition module, builds explosion risk source association chains corresponding to the same reference sources respectively under different environment vectors, and summarizes each explosion risk source association chain corresponding to the same reference sources respectively into the same blank set to obtain risk association chain sets corresponding to the corresponding reference sources.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (4)

1. The intelligent building quality management method based on the multivariate information is characterized by comprising the following steps of:
s1, acquiring environment information corresponding to different time periods in a petrochemical building area to be detected in historical data, and obtaining an environment vector set corresponding to the petrochemical building area to be detected; acquiring risk information corresponding to different explosion risk sources in the petrochemical building area to be detected respectively, and constructing a risk information set of the petrochemical building area to be detected;
s2, building a building model corresponding to the petrochemical building area to be detected through a BIM technology, predicting a risk area corresponding to each explosion risk source in the petrochemical building area to be detected according to a risk information set of the petrochemical building area to be detected, combining the environment vector set corresponding to the petrochemical building area to be detected obtained in the S1, building explosion risk source association chains corresponding to the same reference sources respectively under different environment vectors, and summarizing the explosion risk source association chains corresponding to the same reference sources respectively into the same blank set to obtain a risk association chain set corresponding to the corresponding reference source, wherein the reference source is one explosion risk source in the petrochemical building area to be detected;
s3, analyzing the ratio of the comprehensive risk areas corresponding to each explosion risk source association chain in the risk association chain set corresponding to the same reference source to obtain a comprehensive risk influence value corresponding to each reference source;
S4, acquiring a leakage risk coefficient corresponding to each explosion risk source, generating a quality supervision priority sequence of each explosion risk source in the petrochemical building area to be tested by combining the analysis result in the S3, and monitoring and early warning the first m explosion risk sources in the obtained quality supervision priority sequence;
the environment information in the S1 comprises average wind power level and corresponding wind direction in a corresponding time period, the wind direction in the corresponding time period is the same as the direction of the combined vector of the wind power vectors corresponding to all time points in the corresponding time period, the wind power vector corresponding to each time point is equal to the vector which is the same as the wind direction monitored by the wind sensor at the corresponding time point and has the vector module length equal to the wind power level monitored by the wind sensor, and the environment information is acquired once every a first unit time preset in a database;
the number of elements in the environment vector set corresponding to the petrochemical building area to be detected is equal to the number of environment information corresponding to the historical data, each element in the environment vector set corresponds to a first wind power vector, the environment vector set corresponding to the petrochemical building area to be detected is marked as A, the ith element in the A is marked as Ai, the Ai is a vector with the vector direction being the same as the wind direction of the corresponding environment information, and the modular length is equal to the average wind power level in the corresponding environment information;
The explosion risk source in the S1 is a building area where equipment for storing combustible substances in a petrochemical building area to be detected belongs, and is preset in a database, and the risk information corresponding to the explosion risk source comprises the central position of a corresponding area, the total building volume of the corresponding area, the explosion-proof characteristic coefficient of the building in the corresponding area and the combustible substance quantity in the corresponding area;
the method for predicting the risk area corresponding to each explosion risk source in the petrochemical building area to be detected in the S2 comprises the following steps:
s201, building a building model corresponding to a petrochemical building area to be detected;
s202, acquiring a risk information set in a petrochemical building area to be detected, and marking corresponding position coordinates of a building model constructed in S201 at positions corresponding to each risk information in the risk information set;
s203, combining the total building volume of the corresponding region in each risk information in the risk information set, the building explosion-proof characteristic coefficient in the corresponding region and the combustible material quantity in the corresponding region to obtain a fluctuation risk distance corresponding to each risk information, marking the fluctuation risk distance corresponding to the jth risk information in the risk information set in the petrochemical building region to be detected as Lj, and constructing a correlation data pair (j, lj);
In a first preset form of the database, lj is a corresponding fluctuation risk distance value under the conditions that the building explosion-proof characteristic coefficient is bj and the average quantity of combustible substances is cj; bj represents a building explosion-proof characteristic coefficient in the j-th risk information in the risk information set in the petrochemical building area to be detected, and cj is equal to the ratio of the combustible material quantity of the corresponding area in the j-th risk information in the risk information set in the petrochemical building area to be detected to the total volume of the building in the corresponding area; the fluctuation risk distance value represents the maximum value of the distance between each position point and the corresponding building in the explosion wave range corresponding to each explosion event with the same combustible material quantity in unit volume in the building of the corresponding building explosion-proof characteristic coefficient under the windless environment; the distance between the position point in the explosion wave range and the corresponding building represents the minimum value in the corresponding distance between the corresponding position point and each building position point in the corresponding building;
s204, acquiring a building area corresponding to a j-th element of the risk information set in the petrochemical building area to be detected, marking the building area by adopting a first marking mode in a building model, marking the obtained marking range as Qj, acquiring a related data pair (j, lj),
Obtaining areas corresponding to all position point sets with the distance Lj of each point in the Qj, obtaining a prediction result of a risk area corresponding to a j-th explosion risk source in the petrochemical building area to be detected, and marking the prediction result as QLj;
the method for constructing the explosion risk source association chains respectively corresponding to the same reference source under different environment vectors in the S2 comprises the following steps:
s211, acquiring a wind power influence fluctuation coefficient P corresponding to M levels of wind power in a database M ；
S212, obtaining a prediction result QLj of a risk area corresponding to a j-th explosion risk source in the petrochemical building area to be detected and an environment vector set A corresponding to the petrochemical building area to be detected;
s213, when the acquired environment vector is Ai, the risk calibration area corresponding to QLj is marked as WQLj,
acquiring a straight line passing through the center position of the jth explosion risk source corresponding to the building area in the horizontal plane and perpendicular to the Ai direction, marking as U, and marking the ratio of the lengths of the line segments passing through any point on the obtained straight line U and having the same direction as the Ai and respectively passing through WQLj and QLj as P M The straight line which passes through any point on the obtained straight line U and has the same direction as the Ai respectively passes through one end of a line segment in WQLj and QLj, which is close to the start point of the vector Ai, and coincides with one end of the line segment;
s214, acquiring a risk calibration area WQLj corresponding to each of the environment vectors Ai and j with different values in the building model corresponding to the petrochemical building area to be tested, if the jth explosion risk source is taken as a reference source, acquiring an explosion risk source association chain corresponding to the corresponding reference source with the environment vector Ai, marking as EAi,
The chain comprises one or more chain nodes, each chain node comprises two end points, namely a first end point and a second end point, and the nth chain node in EAi is marked as { EAi } （n，1） ，EAi （n，2） }, therein EAi （n，1） Representing the first endpoint in the nth chain node in EAi, EAi （n，2） Representing a second endpoint in the nth chain node in EAi, a first endpoint in the 1 st chain node in EAiAs a reference source, the first end point and the second end point in each chain node correspond to one or more explosion risk sources, and the number of the explosion risk sources corresponding to the second end point is equal to the number of the link points adjacent to the corresponding link points; EAi, in any adjacent chain node, taking an explosion risk source corresponding to a second endpoint in the previous chain node as a first endpoint in the subsequent chain node;
when the second end point corresponding to each chain link point in EAi is obtained, the corresponding risk calibration area of the explosion risk source corresponding to the first end point of the corresponding chain node is obtained, whether the rest explosion risk sources exist in the obtained risk calibration area or not is judged, the explosion risk sources are not recorded in all the obtained chain nodes,
if the obtained risk calibration area does not contain other explosion risk sources, judging that the chain link point does not exist, and obtaining a complete EAi; if the rest of explosion risk sources exist in the obtained risk calibration area and are not recorded in all obtained chain nodes, taking the rest of explosion risk sources which are not recorded in all obtained chain nodes and exist in the obtained risk calibration area as a second endpoint of the chain link point;
When the comprehensive risk influence value corresponding to each reference source is obtained in S3, acquiring an explosion risk source risk associated chain set corresponding to the j-th explosion risk source as the reference source, and recording the comprehensive risk area ratio corresponding to the k-th explosion risk source associated chain in the explosion risk source risk associated chain set corresponding to the j-th explosion risk source as Hjk, hjk =rwtjk/rwtzjk,
in the k-th explosion risk source associated chain in the explosion risk source associated chain set corresponding to the j-th explosion risk source as a reference source, each explosion risk source corresponds to a union of risk calibration areas under the environment vector respectively, WTZjk represents a union of risk calibration areas corresponding to all explosion risk sources in the petrochemical building area to be tested respectively under the environment vector corresponding to the k-th explosion risk source associated chain in the explosion risk source associated chain set corresponding to the j-th explosion risk source as the reference source, and R [ ] represents operation for obtaining the area of the corresponding area;
and obtaining a comprehensive risk influence value corresponding to the j-th explosion risk source as the reference source, and marking the comprehensive risk influence value as gj, wherein gj represents the maximum value of the ratio of the comprehensive risk areas corresponding to the explosion risk source association chains respectively in the explosion risk source risk association chain set corresponding to the j-th explosion risk source as the reference source.
2. The intelligent building quality management method based on multiple information according to claim 1, wherein: the method for generating the quality supervision priority sequence of each explosion risk source in the petrochemical building area to be detected in the S4 comprises the following steps:
s41, acquiring comprehensive risk influence values corresponding to each explosion risk source in the petrochemical building area to be tested;
s42, acquiring leakage risk information of each explosion risk source in a first preset time before the current time, and inquiring leakage risk coefficients corresponding to the corresponding leakage risk information through a second preset form in the database;
s43, sequencing the explosion risk sources in the petrochemical building area to be tested according to the sequence from large to small of the products of the comprehensive risk influence values and the leakage risk coefficients, which correspond to the explosion risk sources, so as to obtain the quality supervision priority sequence of the explosion risk sources in the petrochemical building area to be tested.
3. A building quality intelligent management system based on multiple information applying the building quality intelligent management method based on multiple information according to any one of claims 1-2, characterized in that the system comprises the following modules:
The information acquisition module acquires environment information corresponding to different time periods in the petrochemical building area to be detected in the historical data to obtain an environment vector set corresponding to the petrochemical building area to be detected; acquiring risk information corresponding to different explosion risk sources in the petrochemical building area to be detected, and constructing a risk information set of the petrochemical building area to be detected;
the risk source association information analysis module is used for constructing a building model corresponding to a petrochemical building area to be detected through a BIM technology, predicting a risk area corresponding to each explosion risk source in the petrochemical building area to be detected according to a risk information set of the petrochemical building area to be detected, combining an environment vector set corresponding to each explosion risk source in the petrochemical building area to be detected obtained in the information acquisition module, constructing explosion risk source association chains respectively corresponding to the same reference source under different environment vectors, and summarizing each explosion risk source association chain respectively corresponding to the same reference source into the same blank set to obtain a risk association chain set corresponding to the corresponding reference source, wherein the reference source is one explosion risk source in the petrochemical building area to be detected;
the risk influence analysis module is used for analyzing the comprehensive risk area ratio corresponding to each explosion risk source association chain in the risk association chain set corresponding to the same reference source to obtain a comprehensive risk influence value corresponding to each reference source;
The risk supervision area locking module acquires leakage risk coefficients corresponding to each explosion risk source, combines analysis results in the risk influence analysis module, generates quality supervision priority sequences of the explosion risk sources in the petrochemical building area to be detected, and monitors and early warns the first m explosion risk sources in the obtained quality supervision priority sequences.
4. The intelligent building quality management system based on multivariate information according to claim 3, wherein: the information acquisition module comprises an environment vector acquisition unit and a risk information acquisition unit,
the environment vector acquisition unit acquires environment information corresponding to different time periods in the petrochemical building area to be detected in the historical data, and an environment vector set corresponding to the petrochemical building area to be detected is obtained;
the risk information acquisition unit acquires risk information corresponding to different explosion risk sources in the petrochemical building area to be detected respectively, and builds a risk information set of the petrochemical building area to be detected;
the risk source associated information analysis module comprises a model construction unit, a risk area prediction unit and a risk associated analysis unit,
the model construction unit constructs a building model corresponding to the petrochemical building area to be detected through BIM technology,
The risk area prediction unit predicts the risk area corresponding to each explosion risk source in the petrochemical building area to be detected according to the risk information set of the petrochemical building area to be detected,
the risk association analysis unit combines the environment vector sets corresponding to the petrochemical building areas to be detected obtained in the information acquisition module, builds explosion risk source association chains corresponding to the same reference sources respectively under different environment vectors, and summarizes each explosion risk source association chain corresponding to the same reference sources respectively into the same blank set to obtain risk association chain sets corresponding to the corresponding reference sources.
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