CN115271490A - Engineering completion acceptance monitoring and evaluating management system based on big data analysis - Google Patents
Engineering completion acceptance monitoring and evaluating management system based on big data analysis Download PDFInfo
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
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Abstract
The invention discloses a construction completion acceptance monitoring and evaluating management system based on big data analysis, which comprises an acceptance process monitoring module, an acceptance process analysis module, a beam column acceptance information acquisition module, a beam column acceptance quality analysis module, a comprehensive acceptance quality analysis and confirmation module, an early warning terminal and an acceptance management database.
Description
Technical Field
The invention belongs to the technical field of project acceptance monitoring, and relates to a project completion acceptance monitoring and evaluation management system based on big data analysis.
Background
The beam column is one of main structures of an engineering building, the quality of the beam column affects the stability of the whole building, and the engineering quality of the beam column needs to be checked and analyzed before the building is put into use, so that the qualification of building construction and the safety in the subsequent use process are guaranteed, and therefore, the quality of the beam column is very important to be analyzed.
At present, the beam column acceptance quality analysis mode is mainly used for comparing the collected acceptance data with standard data thereof by an acceptance person, and the acceptance quality of the beam column is analyzed in the mode, and obviously, the analysis mode has the following problems:
1. the current acceptance item and the acceptance tool that do not have the checking personnel of checking and accepting monitor and analyze, the qualified circumstances of the acceptance item and the qualified circumstances of acceptance tool use can't audio-visually be known, and then can't ensure to check and accept data acquisition's authenticity and accuracy, also can't provide reliable data for follow-up beam column quality analysis of checking and accepting simultaneously, thereby reliability and accuracy that can't ensure beam column quality result of checking and accepting, also can't ensure the quality in the beam column use, and still can't reduce the risk that follow-up building takes place the dangerous accident.
2. The current analytic mode that carries out the data contrast through the person of checking up the receipts belongs to the analytic mode of crude formula, and there is the problem that intellectuality and automation level are not high, can't ensure data analysis's comprehensiveness and comprehensiveness, also can't ensure the accurate nature of checking up the data analysis result simultaneously, and the artifical process that carries out the data contrast is too loaded down with trivial details and complicated, increase the error rate of analytic result easily, the while has also increased the work burden of checking up the personnel, and then lead to the person of checking up the receipts analytical efficiency to reduce, on the other hand, the artifical flexibility that carries out the data contrast is relatively poor, thereby can't ensure the rationality of data analysis result.
Disclosure of Invention
The invention aims to provide a completion acceptance monitoring and evaluating management system based on big data analysis, which solves the problems in the background art.
The purpose of the invention can be realized by the following technical scheme:
a completion acceptance monitoring and evaluation management system based on big data analysis comprises: the system comprises an acceptance process monitoring module, an acceptance process analysis module, a beam column acceptance information acquisition module, a beam column acceptance quality analysis module, a comprehensive acceptance quality analysis and confirmation module, an early warning terminal and an acceptance management database;
the acceptance process monitoring module is used for carrying out video monitoring on an acceptance process corresponding to a target acceptance person in an appointed project to obtain an acceptance process video corresponding to the target acceptance person;
the acceptance process monitoring and analyzing module is used for analyzing an acceptance process corresponding to the target acceptance staff according to an acceptance process video corresponding to the target acceptance staff, and comprises the steps of analyzing an acceptance item of the target acceptance staff and analyzing an acceptance tool of the target acceptance staff;
the beam column acceptance information acquisition module is used for acquiring acceptance information corresponding to target acceptance personnel, wherein the acceptance information comprises the number of the beam columns, the verticality, the flatness, the cross section area and the number of cracks corresponding to each beam column and the size corresponding to each crack;
the beam column acceptance quality analysis module is used for analyzing the acceptance quality of the beam column according to the acceptance information corresponding to the target acceptance personnel;
the comprehensive acceptance quality analysis and confirmation module is used for analyzing and confirming the comprehensive acceptance quality of the beam column;
when the comprehensive acceptance quality of the beam column corresponding to the target acceptance personnel is unqualified, the early warning terminal sends the name of the target acceptance personnel and the position corresponding to the acceptance beam column of the target acceptance personnel to an engineering management center and carries out early warning prompt;
the acceptance management database is used for storing names of target acceptance persons, positions corresponding to the acceptance beams and columns and contact ways of an engineering management center, storing the number of the initially set beams and columns corresponding to the designated engineering, the verticality, the flatness and the cross section area corresponding to each beam and column, storing the number of target acceptance items corresponding to the beams and columns, the standard acceptance times and the standard acceptance visual angle number corresponding to each target acceptance item corresponding to the beams and columns, and storing standard placement information corresponding to tools in each acceptance item and each associated acceptance tool type corresponding to each acceptance item, wherein the standard placement information comprises a standard placement distance and a standard placement height.
Optionally, the acceptance items corresponding to the target acceptance staff are analyzed, and the specific analysis process is as follows:
dividing the acceptance process video corresponding to the target acceptance personnel into all acceptance item video segments according to the acceptance items, counting the number of the acceptance items corresponding to the target acceptance personnel, and positioning the number of the acceptance times and the number of the acceptance visual angles corresponding to the acceptance items from all the acceptance item video segments;
numbering each acceptance item according to a preset sequence, wherein the number is 1,2, i.i.n;
substituting the number of acceptance items corresponding to the target acceptance personnel, the acceptance times corresponding to the number of each acceptance item and the number of acceptance views into a calculation formula
In the method, the acceptance item conformity index corresponding to the target acceptance personnel is obtained
Wherein N represents the number of acceptance items corresponding to the target acceptance staff, C i 、J i Respectively representing the number of times of acceptance and the number of acceptance views corresponding to the ith acceptance item, wherein N ' is the number of target acceptance items, C ', corresponding to the beam column stored in the acceptance management database ' i 、J′ i The standard monitoring times, the number of standard acceptance visual angles, epsilon, corresponding to the ith target acceptance item stored in the acceptance management database 1 、ε 2 、ε 3 The weighting factors are respectively corresponding to the set number of acceptance items, the number of times of acceptance and the number of acceptance visual angles, i is a number corresponding to each acceptance item, and i =1,2.
Optionally, the acceptance tool corresponding to the target acceptance staff is analyzed, and a specific analysis process is as follows:
positioning the number corresponding to the acceptance tools corresponding to each acceptance item from each acceptance item video segment corresponding to each acceptance item corresponding to the target acceptance person, extracting images corresponding to each acceptance tool, further performing matching comparison on the images corresponding to each acceptance tool in each acceptance item and each acceptance tool image corresponding to each set acceptance tool type, screening to obtain the type corresponding to each acceptance tool in each acceptance item, performing matching comparison on the type corresponding to each acceptance tool in each acceptance item and each associated acceptance tool type corresponding to each acceptance item stored in an acceptance management database, and taking the type of the acceptance tool in the acceptance item as a standard acceptance tool type if the type of a certain acceptance tool in a certain acceptance item is matched with a certain associated acceptance tool in the acceptance item stored in the acceptance management database, and further counting the number of the standard acceptance tool types corresponding to each acceptance item;
dividing each acceptance item video segment corresponding to each acceptance item into each acceptance picture, positioning a position corresponding to each acceptance tool and a position corresponding to each beam column from the acceptance pictures corresponding to each acceptance item, further obtaining the horizontal distance between each acceptance tool and each beam column, obtaining the average horizontal distance between each acceptance tool and each beam column through mean value calculation, and taking the average horizontal distance as the placing distance corresponding to each acceptance tool;
meanwhile, the placing height corresponding to each acceptance tool is obtained from an acceptance picture corresponding to each acceptance item;
numbering the checking and accepting tools according to a preset sequence, wherein the checking and accepting tools are numbered as 1,2, j.
Substituting the number of types of standard acceptance tools corresponding to each acceptance item, the placement distance and the placement height corresponding to each acceptance tool into a calculation formula
Obtaining a corresponding acceptance tool conformity index alpha of the target person, wherein Q i Indicates the number of types of standard acceptance tools corresponding to the ith acceptance item, L j 、H j Respectively represents the placement distance, the placement height, Q 'corresponding to the jth acceptance tool' i Number of types of acceptance tools, L ', initially set for the set i-th acceptance item' j 、H′ j A standard placement distance, a standard placement height, γ, corresponding to the jth acceptance tool stored in the acceptance management database 1 、γ 2 、γ 3 The weight factors are respectively corresponding to the type number, the placing distance and the placing height of the set acceptance tools, j is a number corresponding to each acceptance tool, and j =1,2.
Optionally, the acceptance process corresponding to the target acceptance staff is analyzed, and the specific analysis process is as follows:
conforming the acceptance items corresponding to the target acceptance personnel to the index
Substituting acceptance tool conformity index alpha corresponding to target person into calculation formula
Obtaining a corresponding acceptance process conformity index beta of the target acceptance staff, wherein eta 1 、η 2 And the weight factors are respectively corresponding to the set acceptance item conformity index and the acceptance tool conformity index.
Optionally, the analyzing the acceptance quality of the beam column specifically includes analyzing apparent information of the beam column and analyzing breakage information of the beam column.
Optionally, the analysis is performed on the apparent information of the beam column, and the specific analysis process is as follows:
numbering the checking and accepting beam columns according to a preset sequence, wherein the checking and accepting beam columns are numbered as 1,2 in sequence;
substituting the number of the checked beams and columns, and the corresponding verticality, flatness and cross-sectional area of each beam and column into a calculation formula
Obtaining a beam column apparent mass conformity index phi, wherein R represents the number of the acceptance beam columns, R' is the number of the initial setting beam columns stored in the acceptance management database, and z k 、p k 、s k Respectively is the corresponding verticality, flatness and section area z 'of the kth beam column' k 、p′ k 、s′ k Verticality, flatness, cross-sectional area, lambda, of the kth beam column initial setting stored in the acceptance management database 1 、λ 2 、λ 3 、λ 4 And the weight factors are respectively corresponding to the set beam column number, verticality, flatness and section area, e represents a natural constant, k represents a number corresponding to each beam column, and k =1,2.f。
Optionally, the cracks on the beams and the columns are numbered according to a preset sequence, and the number of the cracks is 1,2,. D.. V;
substituting the number of cracks on each beam column and the corresponding size of each crack into a calculation formula
In the method, the corresponding damage coincidence index of the beam column is obtained
Wherein, W k Is the number of cracks on the kth beam, W' is the set number of cracks on the allowable beam,
is the size corresponding to the d-th crack on the k-th beam column, r' is the set allowable crack size, mu 1 、μ 2 The weight factor is corresponding to the set number of cracks and the size of the cracks, theta is a correction factor corresponding to the set damage conformity index, d represents the number corresponding to each crack, and d =1,2.
Optionally, the quality of acceptance of the beam column is analyzed by the following specific analysis process:
the beam column apparent mass coincidence index phi and the corresponding damage coincidence index of the beam column
Substituting into a calculation formula
And obtaining a beam column acceptance quality coincidence index psi, wherein tau 1 and tau 2 are weight factors corresponding to the set beam column apparent quality coincidence index and the beam column damage coincidence index respectively.
Optionally, the beam-column comprehensive acceptance quality corresponding to the target acceptance staff is analyzed, and the specific analysis process is as follows:
substituting the acceptance process conformity index beta and the beam column acceptance quality conformity index psi corresponding to the target acceptance staff into the meterFormula of calculation
And obtaining a comprehensive acceptance quality conformity index of the beam column corresponding to the target acceptance staff, wherein zeta 1 and zeta 2 are weight factors corresponding to the set acceptance process conformity index and the beam column acceptance quality conformity index respectively.
Optionally, the comprehensive acceptance quality of the beam column corresponding to the target acceptance staff is confirmed, and the specific confirmation process is as follows:
and comparing the comprehensive acceptance quality conformity index of the beam column corresponding to the target acceptance person with the set standard comprehensive acceptance quality conformity index of the beam column, if the comprehensive acceptance quality conformity index of the beam column corresponding to the target acceptance person is greater than or equal to the standard comprehensive acceptance quality conformity index of the beam column, judging that the comprehensive acceptance quality of the beam column corresponding to the target acceptance person is qualified, otherwise, judging that the comprehensive acceptance quality of the beam column corresponding to the target acceptance person is unqualified.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the project completion acceptance monitoring and evaluation management system based on big data analysis, provided by the invention, the acceptance qualification of the acceptance project of the acceptance personnel, the standard of the use of an acceptance tool and the qualification of the acceptance data are analyzed, and then the acceptance result of a beam column is analyzed and confirmed, so that the problems of intelligentization and low automation level of the current beam column acceptance are solved, the multi-dimensional analysis of the beam column acceptance is realized, the authenticity and reliability of the acceptance data are effectively ensured, the accuracy of the acceptance data analysis result is also ensured, the service life of the subsequent beam column after being put into use is also effectively ensured, the stability and the safety of a building in the subsequent use process are greatly increased, on the other hand, the problem that the current manual data processing is rough is solved by automatically processing and analyzing the acceptance data, the accuracy of the acceptance data analysis process is realized, the efficiency and the effect of the acceptance data analysis process are also increased, and the workload of the data processing of the acceptance personnel is greatly reduced.
2. The invention monitors the acceptance process of the acceptance personnel by the monitoring and analyzing module in the acceptance process, further analyzes the acceptance items and acceptance tools of the acceptance personnel, visually displays the qualification of the acceptance items of the acceptance personnel and the standard property of the acceptance tools in the use process, further provides reliable data guarantee for the subsequent acceptance data analysis, ensures the authenticity of the acceptance data analysis and results, and improves the quality and the effect of the acceptance personnel in the acceptance process to a certain extent.
3. According to the invention, the acceptance data of the acceptance personnel is analyzed in the beam column acceptance quality analysis module, so that the work burden of data processing of the acceptance personnel is effectively reduced, the work efficiency of the acceptance personnel is greatly increased, the problem that the manual data processing process is too complicated and complicated is solved, the efficiency of the acceptance data processing process is effectively ensured, and the accuracy of the analysis result is greatly increased, so that the reasonability and flexibility of the analysis of the acceptance data are ensured.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic view of a module connection structure according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a completion acceptance monitoring and evaluation management system based on big data analysis includes: the system comprises an acceptance process monitoring module, an acceptance process analysis module, a beam column acceptance information acquisition module, a beam column acceptance quality analysis module, a comprehensive acceptance quality analysis and confirmation module, an early warning terminal and an acceptance management database;
the checking and accepting process analysis module is respectively connected with the checking and accepting process monitoring module, the comprehensive checking and accepting quality analysis and confirmation module and the checking and accepting management database, the beam column checking and accepting quality analysis module is respectively connected with the beam column checking and accepting information acquisition module, the comprehensive checking and accepting quality analysis and confirmation module and the checking and accepting management database, and the early warning terminal is respectively connected with the comprehensive checking and accepting quality analysis and confirmation module and the checking and accepting management database.
The acceptance process monitoring module is used for carrying out video monitoring on an acceptance process corresponding to the target acceptance staff in the designated project to obtain an acceptance process video corresponding to the target acceptance staff;
the acceptance process monitoring and analyzing module is used for analyzing the acceptance process corresponding to the target acceptance staff according to the acceptance process video corresponding to the target acceptance staff, wherein the analysis comprises the analysis of the acceptance items of the target acceptance staff and the analysis of the acceptance tools of the target acceptance staff;
in a specific embodiment, the analysis of the acceptance items corresponding to the target acceptance personnel is performed in the following specific analysis process:
dividing the acceptance process video corresponding to the target acceptance personnel into acceptance item video segments according to the acceptance items, counting the number of the acceptance items corresponding to the target acceptance personnel, and further positioning the number of acceptance times and the number of acceptance visual angles corresponding to the acceptance items from the acceptance item video segments;
numbering all acceptance items according to a preset sequence, wherein the number is 1,2,. I.n;
substituting the number of acceptance items corresponding to the target acceptance personnel, the acceptance times corresponding to the number of each acceptance item and the number of acceptance views into a calculation formula
In the middle, get the meshAcceptance item conformity index corresponding to standard acceptance personnel
Wherein N represents the number of acceptance items corresponding to the target acceptance staff, C i 、J i Respectively expressed as the number of times of acceptance and the number of acceptance views corresponding to the ith acceptance item, and N ' is the number of target acceptance items C ' corresponding to the beam column stored in the acceptance management database ' i 、J′ i The standard monitoring times, the number of standard acceptance visual angles, epsilon, corresponding to the ith target acceptance item stored in the acceptance management database 1 、ε 2 、ε 3 The weighting factors are respectively corresponding to the set number of acceptance items, the number of times of acceptance and the number of acceptance visual angles, i is a number corresponding to each acceptance item, and i =1,2.
In a particular embodiment, one acceptance item corresponds to one acceptance item video segment.
In a specific embodiment, the higher the acceptance item conformity index corresponding to the target acceptance staff is, the higher the accuracy of the acceptance item corresponding to the target acceptance staff is, the higher the authenticity of the acceptance data is, and the higher the reference value of the acceptance data is.
In another specific embodiment, the acceptance tool corresponding to the target acceptance staff is analyzed, and the specific analysis process is as follows:
positioning the number corresponding to the acceptance tools corresponding to each acceptance item from each acceptance item video segment corresponding to each acceptance item corresponding to the target acceptance staff, extracting images corresponding to each acceptance tool, further matching and comparing the images corresponding to each acceptance tool in each acceptance item with the images corresponding to each set acceptance tool type, screening to obtain the type corresponding to each acceptance tool in each acceptance item, matching and comparing the type corresponding to each acceptance tool in each acceptance item with each associated acceptance tool type corresponding to each acceptance item stored in an acceptance management database, and taking the acceptance tool type in the acceptance item as a standard acceptance tool type if the type of a certain acceptance tool in a certain acceptance item is matched and consistent with a certain associated acceptance tool in the acceptance item stored in the acceptance management database, and further counting the number of the standard acceptance tool types corresponding to each acceptance item;
dividing each acceptance item video segment corresponding to each acceptance item into each acceptance picture, positioning a position corresponding to each acceptance tool and a position corresponding to each beam column from the acceptance pictures corresponding to each acceptance item, further obtaining the horizontal distance between each acceptance tool and each beam column, obtaining the average horizontal distance between each acceptance tool and each beam column through mean value calculation, and taking the average horizontal distance as the placing distance corresponding to each acceptance tool;
meanwhile, the placing height corresponding to each acceptance tool is obtained from an acceptance picture corresponding to each acceptance item;
numbering the checking and accepting tools according to a preset sequence, wherein the numbers are 1,2,. J.. M;
substituting the number of types of standard acceptance tools corresponding to each acceptance item, the placement distance and the placement height corresponding to each acceptance tool into a calculation formula
Obtaining a corresponding acceptance tool conformity index alpha of the target person, wherein Q i Indicates the number of types of standard acceptance tools corresponding to the ith acceptance item, L j 、H j Respectively indicates a placement distance, a placement height, Q 'corresponding to the jth acceptance tool' i Number of types of acceptance tools, L ', initially set for the set i-th acceptance item' j 、H′ j A standard placement distance, a standard placement height, gamma, corresponding to the jth acceptance tool stored in the acceptance management database 1 、γ 2 、γ 3 And the weight factors are respectively corresponding to the type number, the placing distance and the placing height of the set acceptance tools, j is a number corresponding to each acceptance tool, and j =1,2.
In a specific embodiment, the higher the acceptance tool compliance index corresponding to the target person is, which indicates that the higher the normative of the target person using the acceptance tool in the acceptance process is, the higher the accuracy of the measured acceptance data is, and the higher the rationality and reliability of the acceptance data is.
In another specific embodiment, the acceptance process corresponding to the target acceptance staff is analyzed, and the specific analysis process is as follows:
conforming the acceptance items corresponding to the target acceptance personnel to the index
Substituting acceptance tool conformity index alpha corresponding to target person into calculation formula
Obtaining a corresponding acceptance process conformity index beta of the target acceptance staff, wherein eta 1 、η 2 And the weight factors are respectively corresponding to the set acceptance item conformity index and the acceptance tool conformity index.
According to the embodiment of the invention, the acceptance item and the acceptance tool of the acceptance staff are analyzed by monitoring the acceptance process of the acceptance staff, so that the qualification of the acceptance item of the acceptance staff and the standard property of the acceptance tool in the use process are visually displayed, reliable data guarantee is provided for the subsequent acceptance data analysis, the authenticity of the acceptance data analysis and result is ensured, and the quality and effect of the acceptance staff in the acceptance process are improved to a certain extent.
In a specific embodiment, the higher the acceptance process conformity index corresponding to the target acceptance person is, the more standard the behavior of the acceptance person in the acceptance process is, and the higher the rationality of the acceptance data is.
The beam and column acceptance information acquisition module is used for acquiring acceptance information corresponding to target acceptance personnel, wherein the acceptance information comprises the number of the beams and columns, the verticality, the flatness, the cross section area and the number of cracks corresponding to each beam and column and the size corresponding to each crack;
the beam column acceptance quality analysis module is used for analyzing the acceptance quality of the beam column according to the acceptance information corresponding to the target acceptance staff;
illustratively, analyzing the acceptance quality of the beam column specifically includes analyzing the appearance information of the beam column and analyzing the breakage information of the beam column.
Specifically, the apparent information of the beam column is analyzed, and the specific analysis process is as follows:
numbering the checking and accepting beam columns according to a preset sequence, wherein the checking and accepting beam columns are numbered as 1,2 in sequence;
substituting the number of the checked beams and columns, and the corresponding verticality, flatness and cross-sectional area of each beam and column into a calculation formula
Obtaining a beam column apparent mass conformity index phi, wherein R represents the number of the acceptance beam columns, R' is the number of the initially set beam columns stored in an acceptance management database, and z k 、p k 、s k Respectively is the corresponding verticality, flatness and section area z 'of the kth beam column' k 、p′ k 、s′ k The verticality, the flatness, the cross section area and the lambda of the kth beam column initial setting stored in the acceptance management database are respectively 1 、λ 2 、λ 3 、λ 4 And the weight factors are respectively corresponding to the set beam column number, verticality, flatness and section area, e represents a natural constant, k represents a number corresponding to each beam column, and k =1,2.
In a specific embodiment, the higher the apparent mass of the beam-column meets the index, the higher the corresponding verticality and flatness of each beam-column, and the more the cross-sectional area meets the standard.
In another embodiment, the damage information of the beam column is analyzed, and the specific analysis process is as follows:
numbering the cracks on each beam column according to a preset sequence, wherein the cracks are numbered as 1,2, a.
Substituting the number of cracks on each beam column and the corresponding size of each crack into a calculation formula
In the method, the corresponding damage coincidence index of the beam column is obtained
Wherein, W k Is the number of cracks on the kth beam, W' is the set number of cracks on the allowable beam,
is the size corresponding to the d-th crack on the k-th beam column, r' is the set allowable crack size, mu 1 、μ 2 The weight factor is corresponding to the set number of cracks and the size of the cracks, theta is a correction factor corresponding to the set damage conformity index, d represents the number corresponding to each crack, and d =1,2.
In a specific embodiment, the higher the corresponding breakage compliance index of the beam-column, meaning that the fewer the number of cracks in the beam-column, the smaller the size of the cracks, and the fewer the breakage of the beam-column.
In one embodiment, the proof mass of the beam column is analyzed as follows:
the beam column apparent mass coincidence index phi and the corresponding damage coincidence index of the beam column
Substituting into a calculation formula
And obtaining a beam column acceptance quality coincidence index psi, wherein tau 1 and tau 2 are weight factors corresponding to the set beam column apparent quality coincidence index and the beam column damage coincidence index respectively.
In a specific embodiment, the higher the acceptance quality index of the beam column is, the higher the construction quality of the beam column is, the higher the stability in the subsequent using process is, and the longer the service life is guaranteed.
According to the embodiment of the invention, the acceptance data of the acceptance personnel is analyzed, so that the work burden of data processing of the acceptance personnel is effectively reduced, the work efficiency of the acceptance personnel is greatly increased, the problem that the manual data processing process is too complicated and complicated is solved, the efficiency of the acceptance data processing process is effectively ensured, and the accuracy of the analysis result is greatly increased, so that the reasonability and flexibility of the analysis of the acceptance data are ensured.
The comprehensive acceptance quality analysis and confirmation module is used for analyzing and confirming the comprehensive acceptance quality of the beam column;
in a specific embodiment, the analysis of the comprehensive acceptance quality of the beam column corresponding to the target acceptance staff is performed by the following specific analysis process:
substituting the acceptance process conformity index beta and the beam column acceptance quality conformity index psi corresponding to the target acceptance staff into a calculation formula
And obtaining a comprehensive acceptance quality conformity index of the beam column corresponding to the target acceptance staff, wherein zeta 1 and zeta 2 are weight factors corresponding to the set acceptance process conformity index and the beam column acceptance quality conformity index respectively.
In another specific embodiment, the comprehensive acceptance quality of the beam column corresponding to the target acceptance staff is confirmed by the following specific confirmation process:
and comparing the comprehensive acceptance quality conformity index of the beam column corresponding to the target acceptance person with the set standard comprehensive acceptance quality conformity index of the beam column, if the comprehensive acceptance quality conformity index of the beam column corresponding to the target acceptance person is greater than or equal to the standard comprehensive acceptance quality conformity index of the beam column, judging that the comprehensive acceptance quality of the beam column corresponding to the target acceptance person is qualified, otherwise, judging that the comprehensive acceptance quality of the beam column corresponding to the target acceptance person is unqualified.
According to the embodiment of the invention, the acceptance qualification of the acceptance project of the acceptance personnel, the standard of the use of an acceptance tool and the qualification of the acceptance data are analyzed, and then the acceptance result of the beam column is analyzed and confirmed, so that the problems of intellectualization and low automation level of the current beam column acceptance are solved, the multidimensional analysis of the beam column acceptance is realized, the authenticity and reliability of the acceptance data are effectively ensured, the accuracy of the analysis result of the acceptance data is also ensured, the service life of the subsequent beam column after being put into use is also effectively ensured, the stability and the safety of the building in the subsequent use process are greatly increased, on the other hand, the problem of relatively rough current manual data processing is solved by automatically processing and analyzing the acceptance data, the accuracy of the analysis process of the acceptance data is realized, the efficiency and the effect of the analysis process of the acceptance data are also increased, and the workload of the data processing of the acceptance personnel is also greatly reduced.
The early warning terminal is used for sending the name of the target acceptance person and the position corresponding to the beam column accepted by the target acceptance person to the engineering management center and carrying out early warning prompt when the comprehensive acceptance quality of the beam column corresponding to the target acceptance person is unqualified;
the checking and accepting management database is used for storing names of target checking and accepting personnel, positions corresponding to checking and accepting beams, contact ways of an engineering management center, the number of monitoring standard beams, the number of target monitoring items corresponding to the beams, the number of standard monitoring times and standard monitoring visual angles corresponding to each target monitoring item corresponding to the beams, standard placement information corresponding to tools in each checking and accepting monitoring item and each associated monitoring equipment type corresponding to each monitoring item, wherein the standard placement information comprises a standard placement distance and a standard placement height.
The foregoing is merely illustrative and explanatory of the present invention and various modifications, additions or substitutions may be made to the specific embodiments described by those skilled in the art without departing from the scope of the invention as defined in the accompanying claims.
Claims (10)
1. A completion acceptance monitoring and evaluation management system based on big data analysis is characterized by comprising: the system comprises an acceptance process monitoring module, an acceptance process analysis module, a beam column acceptance information acquisition module, a beam column acceptance quality analysis module, a comprehensive acceptance quality analysis and confirmation module, an early warning terminal and an acceptance management database;
the acceptance process monitoring module is used for carrying out video monitoring on an acceptance process corresponding to a target acceptance person in an appointed project to obtain an acceptance process video corresponding to the target acceptance person;
the acceptance process monitoring and analyzing module is used for analyzing the acceptance process corresponding to the target acceptance staff according to the acceptance process video corresponding to the target acceptance staff, wherein the analysis comprises the analysis of the acceptance items of the target acceptance staff and the analysis of the acceptance tools of the target acceptance staff;
the beam and column acceptance information acquisition module is used for acquiring acceptance information corresponding to target acceptance personnel, wherein the acceptance information comprises the number of accepted beams and columns, the corresponding verticality, flatness, cross section area, crack number and the corresponding size of each crack;
the beam column acceptance quality analysis module is used for analyzing the acceptance quality of the beam column according to the acceptance information corresponding to the target acceptance staff;
the comprehensive acceptance quality analysis and confirmation module is used for analyzing and confirming the comprehensive acceptance quality of the beam column;
when the comprehensive acceptance quality of the beam column corresponding to the target acceptance personnel is unqualified, the early warning terminal sends the name of the target acceptance personnel and the position corresponding to the acceptance beam column of the target acceptance personnel to an engineering management center and carries out early warning prompt;
the acceptance management database is used for storing names of target acceptance persons, positions corresponding to the acceptance beams and columns and contact ways of an engineering management center, storing the number of the initially set beams and columns corresponding to the designated engineering, the verticality, the flatness and the cross section area corresponding to each beam and column, storing the number of target acceptance items corresponding to the beams and columns, the standard acceptance times and the standard acceptance visual angle number corresponding to each target acceptance item corresponding to the beams and columns, and storing standard placement information corresponding to tools in each acceptance item and each associated acceptance tool type corresponding to each acceptance item, wherein the standard placement information comprises a standard placement distance and a standard placement height.
2. The as-built acceptance monitoring and evaluation management system based on big data analysis as claimed in claim 1, wherein: the acceptance items corresponding to the target acceptance personnel are analyzed, and the specific analysis process is as follows:
dividing the acceptance process video corresponding to the target acceptance personnel into acceptance item video segments according to the acceptance items, counting the number of the acceptance items corresponding to the target acceptance personnel, and further positioning the number of acceptance times and the number of acceptance visual angles corresponding to the acceptance items from the acceptance item video segments;
numbering each acceptance item according to a preset sequence, wherein the number is 1,2, i.i.n;
substituting the number of acceptance items corresponding to the target acceptance personnel, the acceptance times corresponding to the number of each acceptance item and the number of acceptance views into a calculation formula
In the method, the acceptance item conformity index corresponding to the target acceptance personnel is obtained
Wherein N represents the number of acceptance items corresponding to the target acceptance personnel, C i 、J i Respectively representing the number of times of acceptance and the number of acceptance views corresponding to the ith acceptance item, wherein N ' is the number of target acceptance items, C ', corresponding to the beam column stored in the acceptance management database ' i 、J′ i The standard monitoring times, the number of standard acceptance visual angles, epsilon, corresponding to the ith target acceptance item stored in the acceptance management database 1 、ε 2 、ε 3 The weighting factors are respectively corresponding to the set number of acceptance items, the number of times of acceptance and the number of acceptance visual angles, i is a number corresponding to each acceptance item, and i =1,2.
3. The as-built acceptance monitoring and evaluation management system based on big data analysis as claimed in claim 2, wherein: the acceptance tool corresponding to the target acceptance staff is analyzed, and the specific analysis process is as follows:
positioning the number corresponding to the acceptance tools corresponding to each acceptance item from each acceptance item video segment corresponding to each acceptance item corresponding to the target acceptance person, extracting images corresponding to each acceptance tool, further performing matching comparison on the images corresponding to each acceptance tool in each acceptance item and each acceptance tool image corresponding to each set acceptance tool type, screening to obtain the type corresponding to each acceptance tool in each acceptance item, performing matching comparison on the type corresponding to each acceptance tool in each acceptance item and each associated acceptance tool type corresponding to each acceptance item stored in an acceptance management database, and taking the type of the acceptance tool in the acceptance item as a standard acceptance tool type if the type of a certain acceptance tool in a certain acceptance item is matched with a certain associated acceptance tool in the acceptance item stored in the acceptance management database, and further counting the number of the standard acceptance tool types corresponding to each acceptance item;
dividing each acceptance item video segment corresponding to each acceptance item into each acceptance picture, positioning a position corresponding to each acceptance tool and a position corresponding to each beam column from the acceptance pictures corresponding to each acceptance item, further obtaining the horizontal distance between each acceptance tool and each beam column, obtaining the average horizontal distance between each acceptance tool and each beam column through mean value calculation, and taking the average horizontal distance as the placing distance corresponding to each acceptance tool;
meanwhile, the placing height corresponding to each acceptance tool is obtained from the acceptance picture corresponding to each acceptance project;
numbering the checking and accepting tools according to a preset sequence, wherein the numbers are 1,2,. J.. M;
substituting the number of types of standard acceptance tools corresponding to each acceptance item, the placement distance and the placement height corresponding to each acceptance tool into a calculation formula
Obtaining a corresponding acceptance tool conformity index alpha of the target person, wherein Q i Indicates the number of types of standard acceptance tools corresponding to the ith acceptance item, L j 、H j Respectively represents the corresponding placement distance, placement height and Q of the jth acceptance tool i 'number of types of acceptance tools initially set for set i-th acceptance item, L' j 、H′ j A standard placement distance, a standard placement height, gamma, corresponding to the jth acceptance tool stored in the acceptance management database 1 、γ 2 、γ 3 And the weight factors are respectively corresponding to the type number, the placing distance and the placing height of the set acceptance tools, j is a number corresponding to each acceptance tool, and j =1,2.
4. The as-built acceptance monitoring and evaluation management system based on big data analysis as claimed in claim 3, wherein: the acceptance process corresponding to the target acceptance staff is analyzed, and the specific analysis process is as follows:
conforming the acceptance items corresponding to the target acceptance personnel to the index
Substituting acceptance tool conformity index alpha corresponding to target person into calculation formula
Obtaining a corresponding acceptance process conformity index beta of the target acceptance staff, wherein eta 1 、η 2 And the weight factors are respectively corresponding to the set acceptance item conformity index and the acceptance tool conformity index.
5. The as-built acceptance monitoring and evaluation management system based on big data analysis as claimed in claim 4, wherein: the analyzing of the acceptance quality of the beam column specifically comprises analyzing the apparent information of the beam column and analyzing the damage information of the beam column.
6. The as-built acceptance monitoring and evaluation management system based on big data analysis as claimed in claim 5, wherein: the method for analyzing the apparent information of the beam column comprises the following specific analysis processes:
numbering the checking and accepting beam columns according to a preset sequence, wherein the checking and accepting beam columns are numbered as 1,2 in sequence;
number of beams and columns to be checked and acceptedSubstituting the verticality, the flatness and the cross-sectional area corresponding to each beam column into a calculation formula
Obtaining a beam column apparent mass conformity index phi, wherein R represents the number of the acceptance beam columns, R' is the number of the initial setting beam columns stored in the acceptance management database, and z k 、p k 、s k Is the corresponding verticality, flatness and cross-sectional area z 'of the kth beam column respectively' k 、p′ k 、s′ k The verticality, the flatness, the cross section area and the lambda of the kth beam column initial setting stored in the acceptance management database are respectively 1 、λ 2 、λ 3 、λ 4 And the weight factors are respectively corresponding to the set beam column number, verticality, flatness and section area, e represents a natural constant, k represents a number corresponding to each beam column, and k =1,2.
7. The as-built acceptance monitoring and evaluation management system based on big data analysis as claimed in claim 6, wherein: the method for analyzing the damage information of the beam column comprises the following specific analysis processes:
numbering the cracks on each beam column according to a preset sequence, wherein the cracks are numbered as 1,2, a.
Substituting the number of cracks on each beam column and the corresponding size of each crack into a calculation formula
In the method, the corresponding damage coincidence index of the beam column is obtained
Wherein, W k The number of cracks on the kth beam column, W' is the set number of cracks on the allowable beam column,
is the size corresponding to the d crack on the k beam column, and r' is a set numberSize of crackable,. Mu. 1 、μ 2 The weight factor is corresponding to the set number of cracks and the size of the cracks, theta is a correction factor corresponding to the set damage conformity index, d represents the number corresponding to each crack, and d =1,2.
8. The as-built acceptance monitoring and evaluation management system based on big data analysis as claimed in claim 7, wherein: the quality of acceptance of the beam column is analyzed, and the specific analysis process is as follows:
the beam column apparent mass coincidence index phi and the corresponding damage coincidence index of the beam column
Substituting into a calculation formula
And obtaining a beam column acceptance quality conformity index psi, wherein tau 1 and tau 2 are weight factors corresponding to the set beam column apparent quality conformity index and the set beam column breakage conformity index respectively.
9. The as-built acceptance monitoring and evaluation management system based on big data analysis as claimed in claim 8, wherein: the beam column comprehensive acceptance quality corresponding to the target acceptance staff is analyzed, and the specific analysis process is as follows:
substituting the acceptance process conformity index beta and the beam column acceptance quality conformity index psi corresponding to the target acceptance staff into a calculation formula
And obtaining a comprehensive acceptance quality conformity index of the beam column corresponding to the target acceptance staff, wherein zeta 1 and zeta 2 are weight factors corresponding to the set acceptance process conformity index and the beam column acceptance quality conformity index respectively.
10. The as-built acceptance monitoring and evaluation management system based on big data analysis as claimed in claim 9, wherein: the comprehensive acceptance quality of the beam column corresponding to the target acceptance staff is confirmed, and the specific confirmation process is as follows:
and comparing the comprehensive acceptance quality conformity index of the beam column corresponding to the target acceptance staff with the set standard comprehensive acceptance quality conformity index of the beam column, if the comprehensive acceptance quality conformity index of the beam column corresponding to the target acceptance staff is greater than or equal to the standard comprehensive acceptance quality conformity index of the beam column, judging that the comprehensive acceptance quality of the beam column corresponding to the target acceptance staff is qualified, otherwise, judging that the comprehensive acceptance quality of the beam column corresponding to the target acceptance staff is unqualified.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116934179A (en) * | 2023-09-15 | 2023-10-24 | 菏泽建工建筑设计研究院 | Building engineering quality detection data analysis management system based on big data |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116934179A (en) * | 2023-09-15 | 2023-10-24 | 菏泽建工建筑设计研究院 | Building engineering quality detection data analysis management system based on big data |
| CN116934179B (en) * | 2023-09-15 | 2023-12-01 | 菏泽建工建筑设计研究院 | Building engineering quality detection data analysis management system based on big data |
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