CN115081970B - System and method for analyzing and evaluating decoration data of hardcover real estate - Google Patents
System and method for analyzing and evaluating decoration data of hardcover real estate Download PDFInfo
- Publication number
- CN115081970B CN115081970B CN202211015395.3A CN202211015395A CN115081970B CN 115081970 B CN115081970 B CN 115081970B CN 202211015395 A CN202211015395 A CN 202211015395A CN 115081970 B CN115081970 B CN 115081970B
- Authority
- CN
- China
- Prior art keywords
- decoration
- data
- evaluation
- module
- layer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005034 decoration Methods 0.000 title claims abstract description 188
- 238000000034 method Methods 0.000 title claims abstract description 66
- 238000011156 evaluation Methods 0.000 claims abstract description 98
- 238000013441 quality evaluation Methods 0.000 claims abstract description 56
- 230000008569 process Effects 0.000 claims abstract description 36
- 238000004364 calculation method Methods 0.000 claims abstract description 35
- 238000007405 data analysis Methods 0.000 claims abstract description 29
- 230000036541 health Effects 0.000 claims abstract description 28
- 238000012545 processing Methods 0.000 claims abstract description 14
- 238000004458 analytical method Methods 0.000 claims abstract description 12
- 238000010276 construction Methods 0.000 claims abstract description 12
- 238000003062 neural network model Methods 0.000 claims abstract description 10
- 238000013480 data collection Methods 0.000 claims abstract description 9
- 230000005540 biological transmission Effects 0.000 claims description 15
- 238000013507 mapping Methods 0.000 claims description 14
- 239000003344 environmental pollutant Substances 0.000 claims description 13
- 231100000719 pollutant Toxicity 0.000 claims description 13
- 238000013528 artificial neural network Methods 0.000 claims description 12
- 238000004422 calculation algorithm Methods 0.000 claims description 9
- 239000000126 substance Substances 0.000 claims description 9
- 230000004913 activation Effects 0.000 claims description 8
- 238000007781 pre-processing Methods 0.000 claims description 8
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 239000000356 contaminant Substances 0.000 claims description 4
- 238000012797 qualification Methods 0.000 claims description 4
- 210000002569 neuron Anatomy 0.000 claims description 3
- 230000003213 activating effect Effects 0.000 claims description 2
- 238000003672 processing method Methods 0.000 claims description 2
- 230000029058 respiratory gaseous exchange Effects 0.000 claims description 2
- 238000012502 risk assessment Methods 0.000 claims description 2
- 238000007689 inspection Methods 0.000 description 14
- 238000011157 data evaluation Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 6
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 238000004590 computer program Methods 0.000 description 4
- 238000001514 detection method Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 3
- 230000010365 information processing Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000013139 quantization Methods 0.000 description 2
- 230000008439 repair process Effects 0.000 description 2
- 239000011449 brick Substances 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 238000012937 correction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06395—Quality analysis or management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/82—Energy audits or management systems therefor
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Educational Administration (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- Health & Medical Sciences (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Game Theory and Decision Science (AREA)
- General Health & Medical Sciences (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Medical Informatics (AREA)
- Epidemiology (AREA)
- Pathology (AREA)
- Databases & Information Systems (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of decoration, and discloses a system and a method for analyzing and evaluating decoration data of a hardback real estate. The system comprises: the system comprises a standard analysis module, a data collection module, a first data analysis module, an initial evaluation module, a second data analysis module, an intelligent evaluation module, a health risk evaluation module and a notification module. The method comprises the following steps: collecting decoration data of the hardcover building in the construction process, and carrying out standardized processing on the decoration data to obtain an initial value and a specific index value of a decoration quality evaluation index; and constructing a decoration evaluation neural network model, inputting a specific index value of a decoration quality evaluation index to obtain a corresponding decoration evaluation result, and performing health risk evaluation. The invention solves the problems that in the prior art, data recording is complicated, engineering supervision efficiency is low, intelligence degree is low, calculation complexity is high due to a large amount of data generated in a decoration process, data calculation capability is poor, and the actual quality level of decoration cannot be automatically reflected through objective data.
Description
Technical Field
The invention relates to the technical field of decoration, in particular to a decoration data analysis and evaluation system and an evaluation method for a hardcover real estate.
Background
With the requirement of green development of the building industry, the current real estate construction is gradually changed from traditional blank delivery into more energy-saving and environment-friendly hardcover delivery, and the hardcover house can avoid the quality of life reduction and resource waste caused by the alternative decoration of residents, promote the sustainable development of the ecological environment, and become the best choice of a plurality of house purchasers with large working pressure and short idle time.
At present, in the process of real estate refitting, a construction unit generally entrusts a supervision unit to supervise and manage the engineering quality, so that the problems of insufficient supervision personnel and insufficient supervision are solved, and the decoration quality cannot be guaranteed; and most of the existing checking of the decoration quality system of the hardcover real estate uses artificial reports for recording, so that the problems of complex data recording, low engineering supervision efficiency and low intelligent degree exist, and the use requirements of people cannot be met.
China patent application No. CN202111305933.8 discloses an intelligent evaluation method and system for home decoration quality detection, wherein the method comprises the following steps: firstly, decorating a house by a decorating worker according to the requirement of a user; secondly, informing users and quality inspection personnel after finishing decoration by decoration personnel, and performing quality inspection on the decorated house by the quality inspection personnel and generating a quality inspection report; thirdly, the decoration personnel modify according to the quality inspection report; and fourthly, repeating the second step and the third step until the quality inspection is passed. The system comprises a display layer module, an agent layer module, an access layer module, a service layer module and a data layer module. The invention supports the user-defined setting of each node in the decoration process; support intelligent allocation of detection personnel; supporting the online generation of a self-checking report aiming at the nodes in the set decoration process; and subsequent correction follow-up recording of the same node or different nodes is supported.
However, in the process of implementing the technical scheme of the invention, the technology at least has the following technical problems: the data recording is tedious, the project supervision efficiency is low, the intelligent degree is low, the calculation complexity is high and the data calculation capability is poor due to a large amount of data generated in the decoration process, and the actual quality level of decoration cannot be automatically reflected through objective data.
Disclosure of Invention
The invention provides a system and a method for analyzing and evaluating decoration data of a hardcover real estate, and solves the problems that in the prior art, data recording is complicated, engineering supervision efficiency is low, intelligence degree is low, calculation complexity is high due to a large amount of data generated in a decoration process, data operation capability is poor, and the actual quality level of decoration cannot be automatically reflected through objective data. Finally, the method can be simplified, the calculation complexity is reduced, the decoration data analysis and evaluation efficiency is improved, and an intelligent and automatic complete evaluation flow is formed.
The invention specifically comprises the following technical scheme:
a decoration data analysis and evaluation system for a hardback real estate comprises the following parts:
the system comprises a standard analysis module, a data collection module, a first data analysis module, an initial evaluation module, a second data analysis module, an intelligent evaluation module, a health risk evaluation module and a notification module;
the initial evaluation module is used for calculating an initial value of a decoration quality evaluation index from decoration data of each project according to an algorithm of empirical entropy, and the initial evaluation module is connected with the second data analysis module in a data transmission mode;
the second data analysis module is used for preprocessing the initial value of the decoration quality evaluation index according to the corresponding relation between the initial value of the decoration quality evaluation index and the weight coefficient of the decoration quality evaluation index, and is connected with the intelligent evaluation module in a data transmission mode;
the intelligent evaluation module is used for constructing a decoration evaluation neural network model, inputting specific index values of decoration quality evaluation indexes into the model and outputting corresponding decoration evaluation results, and the intelligent evaluation module is connected with the health risk evaluation module in a data transmission mode;
the health risk evaluation module is used for evaluating health risks of the house after decoration, evaluating safety risks generated by pollutants in the house by evaluating the concentration, the residence time and the residence frequency of the pollutants in the space where the human body is located, and is connected with the notification module in a data transmission mode.
A decoration data analysis and evaluation method for a hardcover real estate comprises the following steps:
s1, collecting decoration data of the hardcover building in the construction process, carrying out standardized processing on the decoration data, and obtaining an initial value and a specific index value of a decoration quality evaluation index through analysis and calculation;
s2, constructing a decoration evaluation neural network model, inputting specific index values of decoration quality evaluation indexes to obtain corresponding decoration evaluation results, further performing health risk evaluation, and comprehensively judging whether the decoration quality is qualified.
Further, the step S1 includes:
and calculating an initial value of the decoration quality evaluation index from the decoration data of each project according to an empirical entropy algorithm.
Further, the step S1 includes:
and preprocessing the initial value of the decoration quality evaluation index according to the corresponding relation between the initial value of the decoration quality evaluation index and the weight coefficient of the decoration quality evaluation index to obtain a specific index value representing the house decoration quality evaluation index.
Further, the step S2 includes:
and constructing a decoration evaluation neural network model, selecting specific index values of a preset number of decoration quality evaluation indexes as network input samples, and taking decoration evaluation results corresponding to the input samples as network output samples.
Further, the step S2 includes:
and evaluating the safety risk of pollutants in the house by evaluating the concentration, residence time and residence frequency of the pollutants in the space where the human body is positioned.
Further, the decoration evaluation neural network comprises an input layer, a fuzzy layer, a rule layer, a re-fuzzy layer, a mapping layer and a fault judgment layer.
Further, the fuzzy layer fuzzifies the data, the rule layer sets fuzzy control rules, control values are obtained according to the fuzzy control rules and are multiplied, after activation, the activation degree of each rule is output to the re-fuzzy layer, the re-fuzzy layer introduces a severe factor, the data are fuzzified again, the mapping layer is used for solving the discomfort of the inverse problem, the judgment layer converts the output of each neuron into an accurate value of an output variable, and judges whether the final data meet index reference data or not, so that the decoration evaluation result of the current input sample is obtained.
The invention has at least the following technical effects or advantages:
1. the method comprises the steps of determining quality evaluation indexes of house decoration by using an analytic hierarchy process, obtaining initial values and specific index values of the decoration quality evaluation indexes through analytical calculation, decomposing targets into a plurality of targets or criteria, further decomposing the targets into a plurality of layers of multi-index to form a multi-layer analytical structure model, and then solving a decision problem that multiple targets and multiple criteria are difficult to be subjected to all quantization processing into a multi-layer single-target problem, wherein the calculation is simple and convenient, the obtained result is simple and clear, and the data operation capability is greatly improved.
2. The decoration evaluation is characterized by being an automatic intelligent algorithm through a decoration evaluation neural network, so that the method has strong parallel distributed information processing, the problem of large-scale real-time calculation is solved, and the redundancy in parallel calculation can enable the system to have strong fault tolerance and robustness; the method can be used for carrying out self-adaptive adjustment on line, realizing intelligent evaluation of the decoration project of the hardcover real estate, and reflecting the actual quality level of decoration through objective data.
3. The technical scheme of the invention can effectively solve the problems of complex data recording, low engineering supervision efficiency, low intelligent degree, high calculation complexity and poor data calculation capability caused by a large amount of data generated in the decoration process, and incapability of automatically reflecting the actual decoration quality level through objective data. Moreover, the system or the method is subjected to a series of effect investigation, and finally can be simplified through verification, the calculation complexity is reduced, the decoration data analysis and evaluation efficiency is improved, and an intelligent and automatic complete evaluation flow is formed.
Drawings
FIG. 1 is a block diagram of a decoration data analysis and evaluation system for a hardbound property according to the present invention;
FIG. 2 is a flow chart of the method for analyzing and evaluating the finishing data of the hardbound real estate of the present invention.
Detailed Description
By providing the system and the method for analyzing and evaluating the decoration data of the hardcover real estate, the problems that in the prior art, data recording is complex, engineering supervision efficiency is low, intelligence degree is low, calculation complexity is high due to a large amount of data generated in the decoration process, data operation capacity is poor, and the actual quality level of decoration cannot be automatically reflected through objective data are solved.
In order to solve the above problems, the technical solution in the embodiments of the present application has the following general idea:
determining a quality evaluation index of house decoration by using an analytic hierarchy process, obtaining an initial value and a specific index value of the decoration quality evaluation index by analyzing and calculating, decomposing a target into a plurality of targets or criteria, further decomposing the targets into a plurality of layers of multi-index to form a multi-layer analysis structure model, and then solving a decision problem that all the multiple targets and the multi-criteria are difficult to quantize into a multi-layer single-target problem, wherein the calculation is simple and convenient, the obtained result is simple and clear, and the data calculation capability is greatly improved; the decoration evaluation is characterized by being an automatic intelligent algorithm through a decoration evaluation neural network, so that the method has strong parallel distributed information processing, the problem of large-scale real-time calculation is solved, and the redundancy in parallel calculation can enable the system to have strong fault tolerance and robustness; the method can perform self-adaptive adjustment on line, realize intelligent evaluation of the decoration project of the hardbound real estate, and reflect the actual quality level of decoration through objective data.
In order to better understand the technical scheme, the technical scheme is described in detail in the following with reference to the attached drawings of the specification and specific embodiments.
Referring to the attached figure 1, the decoration data analysis and evaluation system for the hardcover real estate comprises the following parts:
a normative analysis module 10, a data collection module 20, a first data analysis module 30, an initial evaluation module 40, a second data analysis module 50, an intelligent evaluation module 60, a health risk evaluation module 70, and a notification module 80.
The standard analysis module 10 is used for analyzing and mining the content in the 'quality acceptance standard of architectural decoration and finishing project' by using an analytic hierarchy process, determining the quality evaluation index of house decoration, extracting the files and recorded information to be checked and the given limit range in each project in the standard, and setting index reference data mapped correspondingly to the qualified condition of decoration quality, wherein the standard analysis module 10 is connected with the data collection module 20 and the intelligent evaluation module 60 in a data transmission mode;
the data collection module 20 is used for collecting corresponding data, namely, repair data, of the hardbound building in the construction process according to files to be checked and records extracted from the specifications, and the data collection module 20 is connected with the first data analysis module 30 in a data transmission mode;
the first data analysis module 30 is used for performing standardized processing on the decoration data, and the first data analysis module 30 is connected with the initial evaluation module 40 in a data transmission manner;
the initial evaluation module 40 is used for calculating an initial value of a decoration quality evaluation index from decoration data of each project according to an algorithm of empirical entropy, and the initial evaluation module 40 is connected with the second data analysis module 50 in a data transmission mode;
the second data analysis module 50 is used for preprocessing the decoration quality evaluation index initial value according to the corresponding relation between the decoration quality evaluation index initial value and the decoration quality evaluation index weight coefficient, and the second data analysis module 50 is connected with the intelligent evaluation module 60 in a data transmission mode;
the intelligent evaluation module 60 is used for constructing a decoration evaluation neural network model, inputting specific index values of decoration quality evaluation indexes into the model and outputting corresponding decoration evaluation results, and the intelligent evaluation module 60 is connected with the health risk evaluation module 70 in a data transmission mode;
the health risk evaluating module 70 is used for evaluating the health risk of the house after decoration, evaluating the safety risk generated by pollutants in the house by evaluating the concentration, the residence time and the residence frequency of the pollutants in the space where the human body is located, and the health risk evaluating module 70 is connected with the informing module 80 in a data transmission mode;
the notification module 80 is configured to obtain a notification that the evaluation is not qualified, and display the notification to the user.
Referring to the attached figure 2, the method for analyzing and evaluating the decoration data of the hardback real estate comprises the following steps:
s1, collecting decoration data of the hardcover building in the construction process, carrying out standardized processing on the decoration data, and obtaining an initial value and a specific index value of a decoration quality evaluation index through analysis and calculation.
According to the 'inspection and acceptance of quality of building decoration project' written by the national ministry of construction (hereinafter referred to as 'standard'), real estate decoration is divided into 9 project branches, which comprise: plastering engineering, door and window engineering, ceiling engineering, light partition wall engineering, veneer (brick) engineering, curtain wall engineering, finishing engineering, pasting and soft package engineering and detail engineering.
The specification analysis module 10 analyzes and mines the content in the "quality acceptance specification of architectural decoration and finishing project" by using an analytic hierarchy process, determines the quality evaluation index of house finishing, and extracts the files, recorded information and given limit range to be checked in each project in the specification. The quality evaluation index refers to the data characteristics of the minimum acceptance item specified in the specification.
It can be understood that each project is further divided into a master project and a general project, and the basic information of each project comprises project description, inspection method and data source; the item description refers to the effect to be achieved by the current item, such as: the variety and the performance of materials used in the decorative plastering engineering meet the design requirements; assigning a number as an item ID to each item; the inspection method is an inspection method of each item given in the specification, for example: checking a product qualification certificate, an entrance acceptance record, a re-inspection report and a construction record; the data source is a data source which can be used for project inspection and is obtained according to an inspection method of a current project, for example: the entrance acceptance record, the re-inspection report and the construction record; and acquiring decoration data of the current project according to the data source.
The data collection module 20 collects data corresponding to the hardbound building in the construction process, i.e. the repair data, according to the file to be checked and the record extracted from the specification. Representing fitment data as
Wherein, in the step (A),
representing finishing data of the 9 th project, finishing number of any projectAccording to the use of
It is shown that,
，
，
finishing data representing the nth project in the ith project, for any finishing data in any project
It is shown that the process of the present invention,
。
because the units of the decoration data are different, the first data analysis module 30 needs to firstly perform standardized processing on the decoration data, and the processing method is as follows:
wherein the content of the first and second substances,
in order to standardize the decoration data after the treatment,
is the average value of all decoration data in the jth project in the ith project,
and the standard deviation of all decoration data in the jth project in the ith project is shown.
The initial evaluation module 40 calculates an initial value of a decoration quality evaluation index from decoration data of each project according to an empirical entropy algorithm, wherein a calculation formula of the initial value of the decoration quality evaluation index is as follows:
wherein the content of the first and second substances,
an initial value of the fitment quality evaluation index is evaluated for the jth project in the ith project,
for the number of the decoration data in the jth project of the ith project,
and the total number of all decoration data in the ith project is shown.
Automatically setting a decoration quality evaluation index weight coefficient according to actual requirements
Establishing a corresponding relation between the initial value of the decoration quality evaluation index and the weight coefficient of the decoration quality evaluation index, and preprocessing the initial value of the decoration quality evaluation index by the second data analysis module 50 according to the corresponding relation, wherein the preprocessing method comprises the following steps:
wherein, the first and the second end of the pipe are connected with each other,
showing the decoration quality evaluation index obtained by pretreatment,
represents the maximum value in the decoration data of the jth project of the ith project,
and representing the minimum value in the decoration data of the jth project of the ith project. Thereby obtaining a specific index value representing the evaluation index of the house decoration quality.
The standard analysis module 10 sets the index reference data mapped correspondingly to the decoration quality qualification condition, and the index reference data can be set according to the limit range given in the standard or can be set according to the actual requirement.
The beneficial effects of the step S1 are as follows: the method comprises the steps of determining quality evaluation indexes of house decoration by using an analytic hierarchy process, obtaining initial values and specific index values of the decoration quality evaluation indexes through analytical calculation, decomposing targets into a plurality of targets or criteria, further decomposing the targets into a plurality of layers of multi-index to form a multi-layer analytical structure model, and then solving a decision problem that multiple targets and multiple criteria are difficult to be subjected to all quantization processing into a multi-layer single-target problem, wherein the calculation is simple and convenient, the obtained result is simple and clear, and the data operation capability is greatly improved.
S2, constructing a decoration evaluation neural network model, inputting specific index values of decoration quality evaluation indexes to obtain corresponding decoration evaluation results, further performing health risk evaluation, and comprehensively judging whether the decoration quality is qualified.
The intelligent evaluation module 60 constructs a decoration evaluation neural network model, selects specific index values of preset decoration quality evaluation indexes as network input samples, and takes decoration evaluation results corresponding to the input samples as network output samples. The decoration evaluation neural network comprises an input layer, a fuzzy layer, a rule layer, a re-fuzzy layer, a mapping layer and a fault judgment layer.
Inputting network input sample data into a decoration evaluation neural network input layer, and transmitting the data to a fuzzy layer by the input layer;
the fuzzification layer fuzzifies the data, and the fuzzification process comprises the following steps:
wherein the content of the first and second substances,
is the output of the layer of the blur,
is a parameter of a Gaussian,
is the variance. And the fuzzy layer sends the calculated result to the rule layer.
The rule layer sets fuzzy control rules, obtains control values according to the fuzzy control rules, multiplies the control values, and outputs the activation degree of each rule to the re-fuzzy layer after activation, wherein the calculation process of the rule layer is as follows:
wherein the content of the first and second substances,
which is indicative of the control value(s),
to represent
The 2-norm of (a) of (b),
to represent
The norm of (a) of (b),
it is shown that the activation function is,
representing the output of the rule layer.
Introduction of a Severe factor into the re-blurred layer
And performing fuzzification processing on the data again, wherein the fuzzification process comprises the following steps:
wherein, the first and the second end of the pipe are connected with each other,
the output of the re-blur layer is represented,
is an activating factor. The heavy paste layer is
And transmitting to the mapping layer.
The mapping layer is used for solving the ill-qualification of the inverse problem, and the calculation process of the mapping layer is as follows:
wherein the content of the first and second substances,
the output of the mapping layer is represented,
to represent
The mapping layer will
And transmitting to fault judgment.
And the judgment layer converts the output of each neuron into an accurate value of an output variable and judges whether the final data accords with index reference data or not, so that a decoration evaluation result of the current input sample is obtained. The specific calculation process of fault judgment is as follows:
wherein the content of the first and second substances,
the exact value after the conversion is represented,
in order to be a function of the conversion,
in order to evaluate the neural network output for decoration,
，
all the data are index reference data and represent upper and lower thresholds of a judgment range.
In order to further evaluate the decoration data, the health risk evaluation of the house after decoration needs to be performed, and the health risk evaluation module 70 evaluates the safety risk generated by the pollutants in the house by evaluating the concentration, residence time and residence frequency of the pollutants in the space where the human body is located. The contaminant concentration can be obtained by actual detection; the specific calculation process of the health risk evaluation is as follows:
wherein, the first and the second end of the pipe are connected with each other,
the result of the health risk assessment is represented,
which is indicative of the concentration of the contaminant,
which is indicative of the breathing rate of the human body,
the time of residence is indicated as such,
the frequency of the dwell is indicated as,
the weight of the patient is indicated by the weight,
it is indicated that the period of stay is,
representing a contaminant hazard factor.
After the decoration evaluation neural network outputs the current house decoration data to meet the index reference data range, health risk evaluation needs to be carried out, if the health risk evaluation result meets a preset threshold value, the current house decoration quality is qualified, and otherwise, a notification that evaluation is unqualified needs to be sent to the notification module 80.
The beneficial effects of the step S2 are as follows: the decoration evaluation is characterized by being an automatic intelligent algorithm through a decoration evaluation neural network, so that the method has strong parallel distributed information processing, the problem of large-scale real-time calculation is solved, and the redundancy in parallel calculation can enable the system to have strong fault tolerance and robustness; the method can be used for carrying out self-adaptive adjustment on line, realizing intelligent evaluation of the decoration project of the hardcover real estate, and reflecting the actual quality level of decoration through objective data.
In conclusion, the system and the method for analyzing and evaluating the decoration data of the hardback real estate are completed.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including the preferred embodiment and all changes and modifications that fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (2)
1. A method for analyzing and evaluating decoration data of a hardback real estate is characterized by being based on a hardback real estate decoration data analyzing and evaluating system, and the system comprises the following parts:
the system comprises a standard analysis module, a data collection module, a first data analysis module, an initial evaluation module, a second data analysis module, an intelligent evaluation module, a health risk evaluation module and a notification module;
the initial evaluation module is used for calculating an initial value of a decoration quality evaluation index from decoration data of each project according to an algorithm of empirical entropy, and the initial evaluation module is connected with the second data analysis module in a data transmission mode;
the second data analysis module is used for preprocessing the initial value of the decoration quality evaluation index according to the corresponding relation between the initial value of the decoration quality evaluation index and the weight coefficient of the decoration quality evaluation index, and is connected with the intelligent evaluation module in a data transmission mode;
the intelligent evaluation module is used for constructing a decoration evaluation neural network model, inputting specific index values of decoration quality evaluation indexes into the model and outputting corresponding decoration evaluation results, and the intelligent evaluation module is connected with the health risk evaluation module in a data transmission mode;
the health risk evaluating module is used for evaluating the health risk of the house after decoration, evaluating the safety risk generated by pollutants in the house by evaluating the concentration, the residence time and the residence frequency of the pollutants in the space where the human body is located, and is connected with the informing module in a data transmission mode;
the method for analyzing and evaluating the decoration data of the hardcover real estate comprises the following steps:
s1, collecting decoration data of the hardback building in the construction process, carrying out standardized processing on the decoration data, and obtaining an initial value and a specific index value of a decoration quality evaluation index through analysis and calculation;
s2, constructing a decoration evaluation neural network model, inputting a specific index value of a decoration quality evaluation index to obtain a corresponding decoration evaluation result, further performing health risk evaluation, and comprehensively judging whether the decoration quality is qualified;
the step S1 includes:
the data collection module collects corresponding data of the hardbound building in the construction process, namely, the mounting and repairing data according to files to be checked and records extracted from the specifications; representing fitment data as
Wherein, in the process,
representing finishing data of the 9 th project, for finishing data of any project
It is shown that,
，
，
represents the decoration data of the nth project in the ith project, and is used for any decoration data in any project
It is shown that,
；
the first data analysis module is used for carrying out standardized processing on decoration data, and the processing method comprises the following steps:
wherein, the first and the second end of the pipe are connected with each other,
in order to standardize the decoration data after the treatment,
is the average value of all decoration data in the jth project in the ith project,
the standard deviation of all decoration data in the jth project in the ith project is calculated;
the initial evaluation module calculates an initial value of a decoration quality evaluation index from decoration data of each project according to an empirical entropy algorithm, and a calculation formula of the initial value of the decoration quality evaluation index is as follows:
wherein, the first and the second end of the pipe are connected with each other,
an initial value of the fitment quality evaluation index is evaluated for the jth project in the ith project,
for the number of finishing data in the jth project of the ith project,
representing the total number of all decoration data in the ith project;
automatically setting a decoration quality evaluation index weight coefficient according to actual requirements
Establishing a corresponding relation between the initial value of the decoration quality evaluation index and the weight coefficient of the decoration quality evaluation index, and preprocessing the initial value of the decoration quality evaluation index by the second data analysis module according to the corresponding relation, wherein the preprocessing method comprises the following steps:
wherein, the first and the second end of the pipe are connected with each other,
showing the decoration quality evaluation index obtained by pretreatment,
represents the maximum value in the decoration data of the jth project of the ith project,
representing the minimum value in the decoration data of the jth project of the ith project; thereby obtaining a specific index value representing the evaluation index of the house decoration quality;
the step S2 includes:
the intelligent evaluation module constructs a decoration evaluation neural network model, selects specific index values of a preset number of decoration quality evaluation indexes as network input samples, and takes decoration evaluation results corresponding to the input samples as network output samples; the decoration evaluation neural network comprises an input layer, a fuzzy layer, a rule layer, a re-fuzzy layer, a mapping layer and a fault judgment layer;
inputting network input sample data into a decoration evaluation neural network input layer, and transmitting the data to a fuzzy layer by the input layer;
the fuzzification layer fuzzifies the data, and the fuzzification process comprises the following steps:
wherein the content of the first and second substances,
is the output of the layer of the blur,
is a parameter of a gaussian shape, and is,
is the variance; the fuzzy layer sends the calculated result to the rule layer;
the rule layer sets fuzzy control rules, obtains control values according to the fuzzy control rules, multiplies the control values, and outputs the activation degree of each rule to the re-fuzzy layer after activation, wherein the calculation process of the rule layer is as follows:
wherein, the first and the second end of the pipe are connected with each other,
which is indicative of the control value(s),
to represent
The 2-norm of (a) of (b),
represent
The norm of (a) of (b),
it is shown that the activation function is,
representing the output of the rule layer;
heavy blurring layer introduction of heavy factor
And performing fuzzification processing on the data again, wherein the fuzzification process comprises the following steps:
wherein, the first and the second end of the pipe are connected with each other,
the output of the re-blurred layer is represented,
is an activating factor; the heavy paste layer is
Transmitting to a mapping layer;
the mapping layer is used for solving the inverse problem of the ill-qualification, and the calculation process of the mapping layer is as follows:
wherein, the first and the second end of the pipe are connected with each other,
the output of the mapping layer is represented,
to represent
The mapping layer will
Transmitting to fault judgment;
the judging layer converts the output of each neuron into an accurate value of an output variable, and judges whether the final data accords with index reference data or not, so that a decoration evaluation result of the current input sample is obtained; the specific calculation process for fault judgment is as follows:
wherein the content of the first and second substances,
the exact value after the conversion is represented,
in order to be a function of the conversion,
in order to fit up and evaluate the output of the neural network,
all the data are index reference data and represent upper and lower thresholds of a judgment range.
2. The method for analyzing and evaluating finishing data of a hardback property according to claim 1, wherein the step S2 comprises:
the risk evaluation module evaluates the safety risk generated by pollutants in the house by evaluating the concentration, the residence time and the residence frequency of the pollutants in the space where the human body is located; the specific calculation process of the health risk evaluation is as follows:
wherein the content of the first and second substances,
the result of the health risk assessment is represented,
which is indicative of the concentration of the contaminant,
which is indicative of the breathing rate of the human body,
the time of the stay is shown as,
the frequency of the dwell is indicated as,
the weight of the body is represented by,
it is indicated that the period of stay is,
representing a pollutant hazard factor;
and after the decoration evaluation neural network outputs the current house decoration data to meet the index reference data range, performing health risk evaluation, and if the health risk evaluation result meets a preset threshold value, indicating that the current house decoration quality is qualified, otherwise, indicating that the current house decoration quality is unqualified.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211015395.3A CN115081970B (en) | 2022-08-24 | 2022-08-24 | System and method for analyzing and evaluating decoration data of hardcover real estate |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211015395.3A CN115081970B (en) | 2022-08-24 | 2022-08-24 | System and method for analyzing and evaluating decoration data of hardcover real estate |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115081970A CN115081970A (en) | 2022-09-20 |
| CN115081970B true CN115081970B (en) | 2022-11-15 |
Family
ID=83245063
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211015395.3A Active CN115081970B (en) | 2022-08-24 | 2022-08-24 | System and method for analyzing and evaluating decoration data of hardcover real estate |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115081970B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116090634B (en) * | 2023-01-09 | 2024-02-23 | 江苏悦达绿色建筑科技有限公司 | Engineering fine-packaging and repairing intelligent management platform and method based on Internet of things |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109146234A (en) * | 2018-07-04 | 2019-01-04 | 国网电动汽车服务有限公司 | A kind of the safety evaluating method and system of charging network access power distribution network |
| CN109493973A (en) * | 2018-11-22 | 2019-03-19 | 中国建筑设计研究院有限公司 | A kind of household residential air Environmental Health method for prewarning risk and system |
| US10558913B1 (en) * | 2018-10-24 | 2020-02-11 | Equifax Inc. | Machine-learning techniques for monotonic neural networks |
| CN111861239A (en) * | 2020-07-27 | 2020-10-30 | 东北财经大学 | Fire risk assessment method and device for large hotel and computer equipment |
| CN113935571A (en) * | 2021-09-01 | 2022-01-14 | 应急管理部通信信息中心 | Gas station security risk assessment grading method and system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112085384A (en) * | 2020-09-08 | 2020-12-15 | 华侨大学 | Mailing risk evaluation method and system based on combination of fuzzy reasoning and LSTM |
-
2022
- 2022-08-24 CN CN202211015395.3A patent/CN115081970B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109146234A (en) * | 2018-07-04 | 2019-01-04 | 国网电动汽车服务有限公司 | A kind of the safety evaluating method and system of charging network access power distribution network |
| US10558913B1 (en) * | 2018-10-24 | 2020-02-11 | Equifax Inc. | Machine-learning techniques for monotonic neural networks |
| CN109493973A (en) * | 2018-11-22 | 2019-03-19 | 中国建筑设计研究院有限公司 | A kind of household residential air Environmental Health method for prewarning risk and system |
| CN111861239A (en) * | 2020-07-27 | 2020-10-30 | 东北财经大学 | Fire risk assessment method and device for large hotel and computer equipment |
| CN113935571A (en) * | 2021-09-01 | 2022-01-14 | 应急管理部通信信息中心 | Gas station security risk assessment grading method and system |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115081970A (en) | 2022-09-20 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107315884B (en) | Building energy consumption modeling method based on linear regression | |
| CN107886235A (en) | A kind of Fire risk assessment method for coupling certainty and uncertainty analysis | |
| CN110046743A (en) | Energy Consumption of Public Buildings prediction technique and system based on GA-ANN | |
| CN115497272B (en) | Construction period intelligent early warning system and method based on digital construction | |
| CN106453293A (en) | Network security situation prediction method based on improved BPNN (back propagation neural network) | |
| WO2023193458A1 (en) | Digital twin-based production line optimization method and apparatus, electronic device, and medium | |
| CN115081970B (en) | System and method for analyzing and evaluating decoration data of hardcover real estate | |
| CN110610308A (en) | Method for evaluating environmental technology based on benchmarking method | |
| CN109657962A (en) | A kind of appraisal procedure and system of the volume assets of brand | |
| CN115407038A (en) | Urban water supply pipe network water quality monitoring method based on water quality early warning point site selection | |
| CN114862267A (en) | Evaluation method and system of oil and gas pipeline alarm management system | |
| CN113674846A (en) | Hospital intelligent service public opinion monitoring platform based on LSTM network | |
| CN109978396A (en) | A kind of early screening system and method for risk case | |
| CN116205544B (en) | Non-invasive load identification system based on deep neural network and transfer learning | |
| CN111126694A (en) | Time series data prediction method, system, medium and device | |
| CN110414047A (en) | A method of it is evaluated for telecommunication transmission equipment health status | |
| CN113449966B (en) | Gypsum board equipment inspection method and system | |
| Balabanova et al. | Voice control and management in smart home system by artificial intelligence | |
| CN110852597B (en) | Electricity consumption peak period resident load ratio calculation method based on generation of countermeasure network | |
| CN114154415A (en) | Equipment life prediction method and device | |
| CN109325704B (en) | Risk evaluation method for hazardous waste disposal process based on rough set-GRNN algorithm | |
| CN114358514A (en) | Fire safety risk quantification method and device and storage medium | |
| CN112116238A (en) | Satisfaction evaluation method based on index weight system design | |
| CN116109211B (en) | Equipment operation level analysis method and device based on equipment digitization | |
| US20230027774A1 (en) | Smart real estate evaluation system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |