CN112116268A - Method, device and system for processing construction materials - Google Patents
Method, device and system for processing construction materials Download PDFInfo
- Publication number
- CN112116268A CN112116268A CN202011029360.6A CN202011029360A CN112116268A CN 112116268 A CN112116268 A CN 112116268A CN 202011029360 A CN202011029360 A CN 202011029360A CN 112116268 A CN112116268 A CN 112116268A
- Authority
- CN
- China
- Prior art keywords
- attribute
- feature
- queue
- list
- correlation degree
- 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.)
- Withdrawn
Links
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
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Physics & Mathematics (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Game Theory and Decision Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The method, the device and the system for processing the construction materials, provided by the invention, obtain the characteristic correlation of the attribute characteristics in the material attribute list after the material attribute list of the two material analysis reports in the process of comparing whether the first material evaluation index and the second material evaluation index in the two material analysis reports are the same index, obtain the correlation distribution list, so that the two material analysis reports are not divided into independent lists to be compared in the comparison process, but the characteristics of the two material analysis reports are fused to be compared, after the correlation distribution list is obtained, a target characteristic queue is obtained by adjusting the correlation distribution list, and the target characteristic queue is processed by a preset identification thread, so that the material analysis comparison result is obtained, thereby realizing the effects of improving the accuracy and the comprehensiveness of the material analysis comparison result, accurate comparison of the material analysis reports can be ensured from multiple dimensions, and the consistency of the material analysis reports can be accurately determined.
Description
Technical Field
The invention relates to the technical field of material quality analysis, in particular to a method, a device and a system for processing construction materials.
Background
The material analysis technology can analyze and evaluate the performance and quality of the construction material in various aspects, so as to determine the building environment or climate condition suitable for different construction materials, thereby ensuring the use safety of subsequent materials.
However, in practical applications, when different material analysis reports are analyzed, it is difficult to ensure accurate alignment of the material analysis reports from multiple dimensions, which makes it difficult to determine the consistency of the material analysis reports.
Disclosure of Invention
To improve the above problems, the present invention provides a method, apparatus and system for treating construction materials.
The embodiment of the invention provides a method for processing a construction material, which comprises the following steps:
after a first material analysis report and a second material analysis report are obtained, obtaining a first material attribute list of the first material analysis report and a second material attribute list of the second material analysis report, wherein the first material analysis report comprises a first material evaluation index, and the second material analysis report comprises a second material evaluation index;
acquiring each attribute feature in the first material attribute list and each attribute feature in the second material attribute list to obtain an attribute feature queue; determining the feature correlation between any two attribute features in the attribute feature queue to obtain a correlation distribution list; adjusting the feature correlation degree smaller than the preset correlation degree in the correlation degree distribution list to be the preset correlation degree to obtain a target feature queue;
and processing the target feature queue to obtain a material analysis comparison result, wherein the material analysis comparison result is used for indicating that the first material evaluation index and the second material evaluation index are the same index or different indexes.
Preferably, the determining a feature correlation between any two attribute features in the attribute feature queue to obtain a correlation distribution list includes:
determining each attribute feature in the attribute feature queue as a current attribute feature, and executing the following steps until the attribute feature queue is traversed: and calculating the feature correlation degree of the current attribute feature and each attribute feature in the attribute feature queue, and determining a plurality of calculated feature correlation degrees as a correlation degree set in the correlation degree distribution list.
Preferably, determining a feature correlation between two of said attribute features comprises: calculating multi-dimensional characteristic clustering indexes of the two attribute characteristics to obtain a calculation result; determining the calculation result as the feature correlation degree between the two attribute features.
Preferably, the adjusting the feature correlation degree smaller than the preset correlation degree in the correlation degree distribution list to the preset correlation degree to obtain the target feature queue includes:
determining each feature relevance in the relevance distribution list as a current relevance, and executing the following steps until the relevance distribution list is traversed: acquiring the current correlation; under the condition that the current correlation degree is smaller than the preset correlation degree, adjusting the current correlation degree to the preset correlation degree; and after the traversal is completed, determining the adjusted relevancy distribution list as the target feature queue.
Preferably, the processing the target feature queue to obtain a material analysis comparison result includes:
converting the target feature queue into a structured queue;
inputting the target feature queue, the structured queue, the first material attribute list and the second material attribute list into a preset convolutional neural network model to obtain a comparison list of the first material analysis report and the second material analysis report;
and identifying the comparison list by using a preset identification thread to obtain the material analysis comparison result.
The embodiment of the invention also provides a device for processing the construction material, which comprises:
the system comprises a list acquisition module, a first analysis module and a second analysis module, wherein the list acquisition module is used for acquiring a first material attribute list of a first material analysis report and a second material attribute list of a second material analysis report after acquiring the first material analysis report and the second material analysis report, the first material analysis report comprises a first material evaluation index, and the second material analysis report comprises a second material evaluation index;
the queue adjusting module is used for acquiring each attribute feature in the first material attribute list and each attribute feature in the second material attribute list to obtain an attribute feature queue; determining the feature correlation between any two attribute features in the attribute feature queue to obtain a correlation distribution list; adjusting the feature correlation degree smaller than the preset correlation degree in the correlation degree distribution list to be the preset correlation degree to obtain a target feature queue;
and the analysis comparison module is used for processing the target feature queue to obtain a material analysis comparison result, wherein the material analysis comparison result is used for indicating that the first material evaluation index and the second material evaluation index are the same index or different indexes.
Preferably, the queue adjusting module is configured to:
determining each attribute feature in the attribute feature queue as a current attribute feature, and executing the following steps until the attribute feature queue is traversed: and calculating the feature correlation degree of the current attribute feature and each attribute feature in the attribute feature queue, and determining a plurality of calculated feature correlation degrees as a correlation degree set in the correlation degree distribution list.
Preferably, the queue adjusting module is configured to:
determining each feature relevance in the relevance distribution list as a current relevance, and executing the following steps until the relevance distribution list is traversed: acquiring the current correlation; under the condition that the current correlation degree is smaller than the preset correlation degree, adjusting the current correlation degree to the preset correlation degree; and after the traversal is completed, determining the adjusted relevancy distribution list as the target feature queue.
Preferably, the analysis and alignment module is configured to:
converting the target feature queue into a structured queue;
inputting the target feature queue, the structured queue, the first material attribute list and the second material attribute list into a preset convolutional neural network model to obtain a comparison list of the first material analysis report and the second material analysis report;
and identifying the comparison list by using a preset identification thread to obtain the material analysis comparison result.
The embodiment of the invention also provides a system for processing the construction materials, which comprises a server and a material analysis terminal, wherein the server and the material analysis terminal are communicated with each other; wherein the server is configured to:
after a first material analysis report and a second material analysis report uploaded by the material analysis terminal are obtained, a first material attribute list of the first material analysis report and a second material attribute list of the second material analysis report are obtained, wherein the first material analysis report comprises a first material evaluation index, and the second material analysis report comprises a second material evaluation index;
acquiring each attribute feature in the first material attribute list and each attribute feature in the second material attribute list to obtain an attribute feature queue; determining the feature correlation between any two attribute features in the attribute feature queue to obtain a correlation distribution list; adjusting the feature correlation degree smaller than the preset correlation degree in the correlation degree distribution list to be the preset correlation degree to obtain a target feature queue;
and processing the target feature queue to obtain a material analysis comparison result, wherein the material analysis comparison result is used for indicating that the first material evaluation index and the second material evaluation index are the same index or different indexes.
By applying the method, the device and the system, after a first material analysis report and a second material analysis report are obtained, a first material attribute list of the first material analysis report and a second material attribute list of the second material analysis report are obtained, wherein the first material analysis report comprises a first material evaluation index, and the second material analysis report comprises a second material evaluation index; acquiring each attribute feature in the first material attribute list and each attribute feature in the second material attribute list to obtain an attribute feature queue; determining the feature correlation between any two attribute features in the attribute feature queue to obtain a correlation distribution list; adjusting the feature correlation degree smaller than the preset correlation degree in the correlation degree distribution list to be the preset correlation degree to obtain a target feature queue; and processing the target characteristic queue to obtain a material analysis comparison result, wherein the material analysis comparison result is used for indicating a method that the first material evaluation index and the second material evaluation index are the same index or different indexes.
In the method, in the process of comparing whether the first material evaluation index and the second material evaluation index in the two material analysis reports are the same index, after the material attribute lists of the two material analysis reports are obtained, the characteristic correlation degree of the attribute characteristics in the material attribute lists is obtained to obtain the correlation degree distribution list, so that the two material analysis reports are not split into independent lists to be compared in the comparison process, but the characteristics of the two material analysis reports are fused to be compared to obtain the correlation degree distribution list, the correlation degree distribution list is adjusted to obtain the target characteristic queue, and the target characteristic queue is processed through the preset identification thread to obtain the material analysis comparison result, so that the effects of improving the accuracy and the comprehensiveness of the material analysis comparison result are realized, and the accurate comparison of the material analysis reports can be ensured from multiple dimensions, and then the consistency of the material analysis report is accurately determined.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of a method for processing a construction material according to an embodiment of the present invention.
Fig. 2 is a functional block diagram of an apparatus for processing construction materials according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a system for processing construction materials according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a hardware structure of a server according to an embodiment of the present invention.
Detailed Description
In order to better understand the technical solutions of the present invention, the following detailed descriptions of the technical solutions of the present invention are provided with the accompanying drawings and the specific embodiments, and it should be understood that the specific features in the embodiments and the examples of the present invention are the detailed descriptions of the technical solutions of the present invention, and are not limitations of the technical solutions of the present invention, and the technical features in the embodiments and the examples of the present invention may be combined with each other without conflict.
Referring to fig. 1, a method for processing a construction material is shown, which includes the following steps S11-S13.
Step S11, after a first material analysis report and a second material analysis report are obtained, obtaining a first material property list of the first material analysis report and a second material property list of the second material analysis report, where the first material analysis report includes a first material evaluation index, and the second material analysis report includes a second material evaluation index.
Step S12, obtaining each attribute feature in the first material attribute list and each attribute feature in the second material attribute list to obtain an attribute feature queue; determining the feature correlation between any two attribute features in the attribute feature queue to obtain a correlation distribution list; and adjusting the feature correlation degree smaller than the preset correlation degree in the correlation degree distribution list to be the preset correlation degree to obtain a target feature queue.
Step S13, processing the target feature queue to obtain a material analysis comparison result, where the material analysis comparison result is used to indicate that the first material evaluation index and the second material evaluation index are the same index or different indexes.
Based on the above steps S11-S13, in the process of comparing whether the first material evaluation index and the second material evaluation index in the two material analysis reports are the same index, the feature correlation of the attribute features in the material attribute list after the material attribute list of the two material analysis reports is obtained to obtain the correlation distribution list, so that in the comparison process, the two material analysis reports are not divided into independent lists to be compared, but the features of the two material analysis reports are fused to be compared, after the correlation distribution list is obtained, the target feature queue is obtained by adjusting the correlation distribution list, and the target feature queue is processed by the preset identification thread to obtain the material analysis comparison result, thereby achieving the effect of improving the accuracy and the comprehensiveness of the material analysis comparison result, and ensuring the accurate comparison of the material analysis reports from multiple dimensions, and then the consistency of the material analysis report is accurately determined.
Optionally, in step S12, the determining a feature correlation between any two attribute features in the attribute feature queue to obtain a correlation distribution list includes:
determining each attribute feature in the attribute feature queue as a current attribute feature, and executing the following steps until the attribute feature queue is traversed: and calculating the feature correlation degree of the current attribute feature and each attribute feature in the attribute feature queue, and determining a plurality of calculated feature correlation degrees as a correlation degree set in the correlation degree distribution list.
Further, determining a feature correlation between two of the attribute features comprises: calculating multi-dimensional characteristic clustering indexes of the two attribute characteristics to obtain a calculation result; determining the calculation result as the feature correlation degree between the two attribute features.
Optionally, in step S12, the adjusting the feature correlation degree smaller than the preset correlation degree in the correlation degree distribution list to the preset correlation degree to obtain the target feature queue includes:
determining each feature relevance in the relevance distribution list as a current relevance, and executing the following steps until the relevance distribution list is traversed: acquiring the current correlation; under the condition that the current correlation degree is smaller than the preset correlation degree, adjusting the current correlation degree to the preset correlation degree; and after the traversal is completed, determining the adjusted relevancy distribution list as the target feature queue.
Optionally, in step S13, the processing the target feature queue to obtain a material analysis comparison result includes:
converting the target feature queue into a structured queue;
inputting the target feature queue, the structured queue, the first material attribute list and the second material attribute list into a preset convolutional neural network model to obtain a comparison list of the first material analysis report and the second material analysis report;
and identifying the comparison list by using a preset identification thread to obtain the material analysis comparison result.
Referring to fig. 2 in combination, there is shown an apparatus 200 for treating construction material, comprising:
the list obtaining module 210 is configured to obtain a first material attribute list of a first material analysis report and a second material attribute list of a second material analysis report after obtaining the first material analysis report and the second material analysis report, where the first material analysis report includes a first material evaluation index, and the second material analysis report includes a second material evaluation index;
a queue adjusting module 220, configured to obtain each attribute feature in the first material attribute list and each attribute feature in the second material attribute list, so as to obtain an attribute feature queue; determining the feature correlation between any two attribute features in the attribute feature queue to obtain a correlation distribution list; adjusting the feature correlation degree smaller than the preset correlation degree in the correlation degree distribution list to be the preset correlation degree to obtain a target feature queue;
an analysis and comparison module 230, configured to process the target feature queue to obtain a material analysis and comparison result, where the material analysis and comparison result is used to indicate that the first material evaluation index and the second material evaluation index are the same index or different indexes.
Optionally, the queue adjusting module 220 is configured to:
determining each attribute feature in the attribute feature queue as a current attribute feature, and executing the following steps until the attribute feature queue is traversed: and calculating the feature correlation degree of the current attribute feature and each attribute feature in the attribute feature queue, and determining a plurality of calculated feature correlation degrees as a correlation degree set in the correlation degree distribution list.
Optionally, the queue adjusting module 220 is configured to:
determining each feature relevance in the relevance distribution list as a current relevance, and executing the following steps until the relevance distribution list is traversed: acquiring the current correlation; under the condition that the current correlation degree is smaller than the preset correlation degree, adjusting the current correlation degree to the preset correlation degree; and after the traversal is completed, determining the adjusted relevancy distribution list as the target feature queue.
Optionally, the analysis and alignment module 230 is configured to:
converting the target feature queue into a structured queue;
inputting the target feature queue, the structured queue, the first material attribute list and the second material attribute list into a preset convolutional neural network model to obtain a comparison list of the first material analysis report and the second material analysis report;
and identifying the comparison list by using a preset identification thread to obtain the material analysis comparison result.
Referring now to FIG. 3, a system 100 for processing construction material is shown, including a server 110 and a material analysis terminal 120 in communication with each other; wherein the server 110 is configured to:
after a first material analysis report and a second material analysis report uploaded by the material analysis terminal are obtained, a first material attribute list of the first material analysis report and a second material attribute list of the second material analysis report are obtained, wherein the first material analysis report comprises a first material evaluation index, and the second material analysis report comprises a second material evaluation index;
acquiring each attribute feature in the first material attribute list and each attribute feature in the second material attribute list to obtain an attribute feature queue; determining the feature correlation between any two attribute features in the attribute feature queue to obtain a correlation distribution list; adjusting the feature correlation degree smaller than the preset correlation degree in the correlation degree distribution list to be the preset correlation degree to obtain a target feature queue;
and processing the target feature queue to obtain a material analysis comparison result, wherein the material analysis comparison result is used for indicating that the first material evaluation index and the second material evaluation index are the same index or different indexes.
Referring to fig. 4, a hardware block diagram of the server 110 is provided.
Fig. 4 is a block diagram illustrating a server 110 according to an embodiment of the present invention. The server 110 in the embodiment of the present invention may be a server with data storage, transmission, and processing functions, as shown in fig. 4, the server 110 includes: memory 111, processor 112, network module 113, and apparatus 200 for processing construction material.
The memory 111, the processor 112, and the network module 113 are electrically connected directly or indirectly to enable transmission or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 111 stores therein an apparatus 200 for processing construction materials, the apparatus 200 for processing construction materials includes at least one software functional module which can be stored in the memory 111 in the form of software or firmware (firmware), and the processor 112 executes various functional applications and data processing by running a software program and a module stored in the memory 111, such as the apparatus 200 for processing construction materials in the embodiment of the present invention, so as to implement the method for processing construction materials in the embodiment of the present invention.
The Memory 111 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like. The memory 111 is used for storing a program, and the processor 112 executes the program after receiving the execution instruction.
The processor 112 may be an integrated circuit chip having data processing capabilities. The Processor 112 may be a general-purpose Processor including a Central Processing Unit (CPU), a Network Processor (NP), and the like. The various methods, steps and logic blocks disclosed in embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The network module 113 is used for establishing communication connection between the server 110 and other communication terminal devices through a network, and implementing transceiving operation of network signals and data. The network signal may include a wireless signal or a wired signal.
It will be appreciated that the configuration shown in fig. 4 is merely illustrative and that server 110 may include more or fewer components than shown in fig. 4 or have a different configuration than shown in fig. 4. The components shown in fig. 4 may be implemented in hardware, software, or a combination thereof.
An embodiment of the present invention also provides a computer-readable storage medium, which includes a computer program. The computer program when executed controls the server 110 where the readable storage medium is located to perform the following method of processing a construction material.
In summary, by applying the method, the apparatus and the system, in the process of comparing whether the first material evaluation index and the second material evaluation index in the two material analysis reports are the same index, the feature correlation of the attribute features in the material attribute list after the material attribute list of the two material analysis reports is obtained to obtain the correlation distribution list, so that the two material analysis reports are not split into independent lists to be compared but are fused with the features of the two material analysis reports to be compared in the comparison process, after the correlation distribution list is obtained, the target feature queue is obtained by adjusting the correlation distribution list, and the target feature queue is processed by the preset identification thread to obtain the material analysis comparison result, thereby achieving the effects of improving the accuracy and comprehensiveness of the material analysis comparison result, and ensuring the accurate comparison of the material analysis reports from multiple dimensions, and then the consistency of the material analysis report is accurately determined.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention or a part thereof, which essentially contributes to the prior art, can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, an electronic device 10, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method of treating a construction material, comprising:
after a first material analysis report and a second material analysis report are obtained, obtaining a first material attribute list of the first material analysis report and a second material attribute list of the second material analysis report, wherein the first material analysis report comprises a first material evaluation index, and the second material analysis report comprises a second material evaluation index;
acquiring each attribute feature in the first material attribute list and each attribute feature in the second material attribute list to obtain an attribute feature queue; determining the feature correlation between any two attribute features in the attribute feature queue to obtain a correlation distribution list; adjusting the feature correlation degree smaller than the preset correlation degree in the correlation degree distribution list to be the preset correlation degree to obtain a target feature queue;
and processing the target feature queue to obtain a material analysis comparison result, wherein the material analysis comparison result is used for indicating that the first material evaluation index and the second material evaluation index are the same index or different indexes.
2. The method according to claim 1, wherein the determining a feature correlation between any two attribute features in the attribute feature queue to obtain a correlation distribution list comprises:
determining each attribute feature in the attribute feature queue as a current attribute feature, and executing the following steps until the attribute feature queue is traversed: and calculating the feature correlation degree of the current attribute feature and each attribute feature in the attribute feature queue, and determining a plurality of calculated feature correlation degrees as a correlation degree set in the correlation degree distribution list.
3. The method of claim 2, wherein determining a feature correlation between two of the attribute features comprises: calculating multi-dimensional characteristic clustering indexes of the two attribute characteristics to obtain a calculation result; determining the calculation result as the feature correlation degree between the two attribute features.
4. The method according to claim 1, wherein the adjusting the feature correlation degree smaller than the preset correlation degree in the correlation degree distribution list to the preset correlation degree to obtain a target feature queue comprises:
determining each feature relevance in the relevance distribution list as a current relevance, and executing the following steps until the relevance distribution list is traversed: acquiring the current correlation; under the condition that the current correlation degree is smaller than the preset correlation degree, adjusting the current correlation degree to the preset correlation degree; and after the traversal is completed, determining the adjusted relevancy distribution list as the target feature queue.
5. The method of claim 1, wherein the processing the target feature queue to obtain a material analysis comparison comprises:
converting the target feature queue into a structured queue;
inputting the target feature queue, the structured queue, the first material attribute list and the second material attribute list into a preset convolutional neural network model to obtain a comparison list of the first material analysis report and the second material analysis report;
and identifying the comparison list by using a preset identification thread to obtain the material analysis comparison result.
6. An apparatus for processing construction materials, comprising:
the system comprises a list acquisition module, a first analysis module and a second analysis module, wherein the list acquisition module is used for acquiring a first material attribute list of a first material analysis report and a second material attribute list of a second material analysis report after acquiring the first material analysis report and the second material analysis report, the first material analysis report comprises a first material evaluation index, and the second material analysis report comprises a second material evaluation index;
the queue adjusting module is used for acquiring each attribute feature in the first material attribute list and each attribute feature in the second material attribute list to obtain an attribute feature queue; determining the feature correlation between any two attribute features in the attribute feature queue to obtain a correlation distribution list; adjusting the feature correlation degree smaller than the preset correlation degree in the correlation degree distribution list to be the preset correlation degree to obtain a target feature queue;
and the analysis comparison module is used for processing the target feature queue to obtain a material analysis comparison result, wherein the material analysis comparison result is used for indicating that the first material evaluation index and the second material evaluation index are the same index or different indexes.
7. The apparatus of claim 6, wherein the queue adjustment module is configured to:
determining each attribute feature in the attribute feature queue as a current attribute feature, and executing the following steps until the attribute feature queue is traversed: and calculating the feature correlation degree of the current attribute feature and each attribute feature in the attribute feature queue, and determining a plurality of calculated feature correlation degrees as a correlation degree set in the correlation degree distribution list.
8. The apparatus of claim 6, wherein the queue adjustment module is configured to:
determining each feature relevance in the relevance distribution list as a current relevance, and executing the following steps until the relevance distribution list is traversed: acquiring the current correlation; under the condition that the current correlation degree is smaller than the preset correlation degree, adjusting the current correlation degree to the preset correlation degree; and after the traversal is completed, determining the adjusted relevancy distribution list as the target feature queue.
9. The apparatus of claim 6, wherein the analysis alignment module is configured to:
converting the target feature queue into a structured queue;
inputting the target feature queue, the structured queue, the first material attribute list and the second material attribute list into a preset convolutional neural network model to obtain a comparison list of the first material analysis report and the second material analysis report;
and identifying the comparison list by using a preset identification thread to obtain the material analysis comparison result.
10. A system for processing construction materials, comprising a server and a material analysis terminal in communication with each other; wherein the server is configured to:
after a first material analysis report and a second material analysis report uploaded by the material analysis terminal are obtained, a first material attribute list of the first material analysis report and a second material attribute list of the second material analysis report are obtained, wherein the first material analysis report comprises a first material evaluation index, and the second material analysis report comprises a second material evaluation index;
acquiring each attribute feature in the first material attribute list and each attribute feature in the second material attribute list to obtain an attribute feature queue; determining the feature correlation between any two attribute features in the attribute feature queue to obtain a correlation distribution list; adjusting the feature correlation degree smaller than the preset correlation degree in the correlation degree distribution list to be the preset correlation degree to obtain a target feature queue;
and processing the target feature queue to obtain a material analysis comparison result, wherein the material analysis comparison result is used for indicating that the first material evaluation index and the second material evaluation index are the same index or different indexes.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011029360.6A CN112116268A (en) | 2020-09-27 | 2020-09-27 | Method, device and system for processing construction materials |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011029360.6A CN112116268A (en) | 2020-09-27 | 2020-09-27 | Method, device and system for processing construction materials |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112116268A true CN112116268A (en) | 2020-12-22 |
Family
ID=73797968
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011029360.6A Withdrawn CN112116268A (en) | 2020-09-27 | 2020-09-27 | Method, device and system for processing construction materials |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112116268A (en) |
-
2020
- 2020-09-27 CN CN202011029360.6A patent/CN112116268A/en not_active Withdrawn
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108446210B (en) | System performance measurement method, storage medium and server | |
| CN111190792A (en) | Log storage method and device, electronic equipment and readable storage medium | |
| CN113408828A (en) | Production line optimization method and device based on intelligent manufacturing and server | |
| CN110737650A (en) | Data quality detection method and device | |
| US20220166702A1 (en) | Fuzzing preprocessing apparatus and method for automating smart network fuzzing | |
| CN114531341A (en) | Block chain network parameter adjusting method and device and server | |
| CN112416782A (en) | Test result verification method and device and electronic equipment | |
| CN112306041A (en) | Vehicle configuration information writing method and device and electronic equipment | |
| CN112116268A (en) | Method, device and system for processing construction materials | |
| CN112256660B (en) | Cast iron production safety monitoring method and device and server | |
| CN110968835A (en) | Approximate quantile calculation method and device | |
| CN113282677A (en) | Intelligent traffic data processing method, device and system based on big data | |
| CN112231194B (en) | Index abnormity root analysis method and device and computer readable storage medium | |
| CN113568815A (en) | Industrial manufacturing equipment fault diagnosis method and device based on big data and electronic equipment | |
| CN113190255A (en) | Big data online topic service upgrading method, server and storage medium | |
| CN112508656A (en) | Guest-obtaining information processing method and device | |
| CN112258056A (en) | Cast iron production line adjusting method and device and server | |
| CN110618906B (en) | Missing detection interface detection method and device, network equipment and storage medium | |
| CN113902139A (en) | Self-service borrowing and returning machine operation and maintenance control method and device based on Internet of things and server | |
| CN115718680B (en) | Data reading method, system, computer and readable storage medium | |
| CN110837469B (en) | Data testing method and device, testing server and storage medium | |
| CN116068111B (en) | Chromatographic data analysis method, chromatographic data analysis device, chromatographic data analysis equipment and chromatographic data analysis computer medium | |
| CN116401113B (en) | Environment verification method, device and medium for heterogeneous many-core architecture acceleration card | |
| CN116437363A (en) | Analysis accuracy evaluation method, device and system, and storage medium | |
| CN112116269A (en) | Material screening method and device based on multi-dimensional analysis and electronic equipment |
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 | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20201222 |
|
| WW01 | Invention patent application withdrawn after publication |