CN115685825A - Building load settlement point monitoring system and method - Google Patents
Building load settlement point monitoring system and method Download PDFInfo
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- CN115685825A CN115685825A CN202211315064.1A CN202211315064A CN115685825A CN 115685825 A CN115685825 A CN 115685825A CN 202211315064 A CN202211315064 A CN 202211315064A CN 115685825 A CN115685825 A CN 115685825A
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Abstract
The invention provides a building load settlement point monitoring system and a method, belonging to the technical field of building settlement point monitoring, wherein the building load settlement point monitoring system comprises a first acquisition module, a model training module, a second acquisition module, a model creating module and a judging module; the first acquisition module is used for receiving and preprocessing input historical monitoring parameters to obtain historical classification parameters; the model training module is used for training the historical classification parameters to obtain a monitoring model; the second acquisition module is used for receiving the input real-time monitoring parameters and inputting the real-time monitoring parameters into the monitoring model to obtain real-time suspicious parameters or real-time abnormal parameters; the model creating module is used for creating a current BIM three-dimensional model according to the real-time monitoring parameters; after the current BIM three-dimensional model is compared with the historical three-dimensional model, the real-time suspicious parameters can be further analyzed, the real-time suspicious parameters are qualitative, the early warning information can be accurately determined, and the danger can be reduced to the minimum.
Description
Technical Field
The invention belongs to the technical field of building settlement point monitoring, and particularly relates to a building load settlement point monitoring system and method.
Background
With the development of the industrial and civil building industry, various complex and large engineering buildings are increasing, the construction of the engineering buildings changes the original state of the ground, and certain pressure is applied to the foundation of the buildings, which inevitably causes the deformation of the foundation and the surrounding stratum. The necessity and importance of building settlement observation are more obvious in order to guarantee the normal service life and the safety of the building and provide reliable data and corresponding settlement parameters for future investigation and design construction. Current regulations also stipulate that settlement observation is required for high-rise buildings, high-rise structures, important historic buildings, continuous production facility foundations, power equipment foundations, landslide monitoring and the like, and should be performed during construction and for a certain period after completion. When the settlement observation is actually carried out, observation is carried out according to observation points arranged on a building (structure) and fixed measurement points (also called permanent leveling points).
However, after the current monitoring system obtains real-time parameters of the settlement point, there are two extremes. Although the result can be very accurately identified for both the normal parameter and the abnormal parameter, the identification accuracy rate is extremely reduced for the suspicious parameter between the normal parameter and the abnormal parameter, which is not beneficial to monitoring the settlement point, and further increases the danger of the building.
Disclosure of Invention
The embodiment of the invention provides a building load settlement point monitoring system and a building load settlement point monitoring method, and aims to solve the problem that the accuracy of suspicious parameter identification between normal parameters and abnormal parameters of the existing monitoring system is reduced.
In view of the above problems, the technical solution proposed by the present invention is:
in a first aspect, the present invention provides a building load settlement point monitoring system, comprising:
the first acquisition module is used for receiving and preprocessing input historical monitoring parameters to obtain historical classification parameters;
the model training module is used for training the historical classification parameters to obtain a monitoring model;
the second acquisition module is used for receiving the input real-time monitoring parameters and inputting the real-time monitoring parameters into the monitoring model to obtain real-time suspicious parameters or real-time abnormal parameters;
the model creating module is used for creating a current BIM three-dimensional model according to the real-time monitoring parameters;
and the judging module is used for comparing the current BIM three-dimensional model with the historical three-dimensional model and determining early warning information.
As a preferred technical solution of the present invention, the first obtaining module includes:
the device comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for receiving input historical monitoring parameters;
the classification unit is used for classifying the historical monitoring parameters to obtain the historical classification parameters;
and the identification unit is used for marking the historical classification parameters with classification identifications.
As a preferred technical solution of the present invention, the historical classification parameters include historical normal parameters, historical suspicious parameters, and historical abnormal parameters.
As a preferred technical solution of the present invention, the model training module includes:
and the arrangement unit is used for arranging the historical normal parameters, the historical suspicious parameters and the historical abnormal parameters to respectively obtain a training sample, a verification sample and a test sample.
And the training unit is used for training the training sample, the verification sample and the test sample by using an artificial intelligence algorithm to obtain the monitoring model.
As a preferred technical solution of the present invention, the second obtaining module includes:
the second acquisition unit is used for receiving the input real-time monitoring parameters;
the first comparison unit is used for inputting the real-time monitoring parameters into the monitoring model for comparison to obtain a comparison result;
and the first extraction unit is used for extracting real-time suspicious parameters and/or real-time abnormal parameters from the real-time monitoring parameters according to the comparison result.
As a preferred technical solution of the present invention, after the real-time suspicious parameters and/or the real-time abnormal parameters are extracted from the real-time monitoring parameters according to the comparison result, the severity level information is determined according to the real-time abnormal parameters and is output.
As a preferred technical solution of the present invention, the model creation module includes:
the second extraction unit is used for screening key point parameters from the real-time monitoring parameters and associating the key point parameters with three-dimensional coordinates;
and the creating unit is used for creating the current BIM three-dimensional model according to the key point parameters.
As a preferred technical solution of the present invention, the judging module includes:
the second comparison unit is used for comparing the current BIM three-dimensional model with a historical three-dimensional model to obtain displacement variation;
the judging unit is used for judging whether the displacement variation exceeds a preset variation to obtain a judgment result;
and the determining unit is used for determining early warning information according to the judgment result.
As a preferred technical solution of the present invention, the determining the early warning information according to the judgment result specifically includes: if the displacement variation exceeds a first preset threshold value of preset variation, marking a first abnormal mark on the real-time suspicious parameter, and determining and outputting the real-time suspicious parameter as light grade information;
and if the displacement variation exceeds a second preset threshold value of the preset variation, marking a second abnormal mark on the real-time suspicious parameter, and determining and outputting the real-time suspicious parameter as medium-grade information.
In a second aspect, an embodiment of the present invention provides a building load settlement point monitoring method, including the following steps:
s1, receiving and preprocessing input historical monitoring parameters to obtain historical classification parameters;
s2, training the historical classification parameters to obtain a monitoring model;
s3, receiving the input real-time monitoring parameters and inputting the real-time monitoring parameters into the monitoring model to obtain real-time suspicious parameters or real-time abnormal parameters;
s4, creating a current BIM three-dimensional model according to the real-time monitoring parameters;
and S5, comparing the current BIM three-dimensional model with the historical three-dimensional model and determining early warning information.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
(1) Classifying the obtained historical monitoring parameters to obtain historical classification parameters, and training the historical classification parameters to obtain a monitoring model; furthermore, in the monitoring process, the obtained real-time monitoring parameters can be directly input into the monitoring model, and further abnormity can be rapidly judged, so that the judgment efficiency can be improved.
(2) After the current BIM three-dimensional model is compared with the historical three-dimensional model, the real-time suspicious parameters can be further analyzed, and are qualitative, so that the early warning information can be accurately determined, and the danger can be reduced to the minimum.
The above description is only an overview of the technical solutions of the present invention, and the present invention can be implemented in accordance with the content of the description so as to make the technical means of the present invention more clearly understood, and the above and other objects, features, and advantages of the present invention will be more clearly understood.
Drawings
FIG. 1 is a schematic view of a building load settlement point monitoring system as disclosed herein;
fig. 2 is a flow chart of the building load settlement point monitoring method disclosed by the invention.
Description of reference numerals: 100. a first acquisition module; 110. a first acquisition unit; 120. a classification unit; 130. an identification unit; 200. a model training module; 210. a finishing unit; 220. a training unit; 300. a second acquisition module; 310. a second acquisition unit; 320. a first comparing unit; 330. a first extraction unit; 400. a model creation module; 410. a second extraction unit; 420. a creating unit; 500. a judgment module; 510. a second comparing unit; 520. a judgment unit; 530. a determination unit.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Example one
Referring to the attached figure 1, the invention provides a technical scheme: the building load settlement point monitoring system comprises a first acquisition module 100, a model training module 200, a second acquisition module 300, a model creation module 400 and a judgment module 500;
the first obtaining module 100 is configured to receive and preprocess an input historical monitoring parameter to obtain a historical classification parameter;
the model training module 200 is configured to train the historical classification parameters to obtain a monitoring model;
the second obtaining module 300 is configured to receive the input real-time monitoring parameters and input the real-time monitoring parameters into the monitoring model to obtain real-time suspicious parameters or real-time abnormal parameters;
the model creating module 400 is used for creating a current BIM three-dimensional model according to the real-time monitoring parameters;
the judging module 500 is configured to compare the current BIM three-dimensional model with a historical three-dimensional model and determine early warning information.
In the invention, firstly, after obtaining historical monitoring parameters and classifying the historical monitoring parameters to obtain historical classification parameters, training the historical classification parameters to obtain a monitoring model; furthermore, in the monitoring process, the obtained real-time monitoring parameters can be directly input into the monitoring model, and further abnormity can be rapidly judged, so that the judgment efficiency can be improved. In general, although the abnormality of the real-time monitoring parameters is not obvious, after the current BIM is compared with the historical three-dimensional model, the real-time suspicious parameters can be further analyzed, the real-time suspicious parameters can be determined qualitatively, and the early warning information can be accurately determined and the danger can be reduced to the minimum.
In an embodiment of the present invention, the first obtaining module 100 includes:
a first obtaining unit 110, configured to receive an input historical monitoring parameter;
a classifying unit 120, configured to classify the historical monitoring parameters to obtain the historical classification parameters; the historical classification parameters comprise historical normal parameters, historical suspicious parameters and historical abnormal parameters.
An identification unit 130 for labeling the historical classification parameters with classification identifications.
Specifically, in the classification process, the historical monitoring parameters are classified based on the historical normal parameters, the historical suspicious parameters and the historical abnormal parameters. However, in the process of labeling the identifiers, the historical normal parameters, the historical suspicious parameters and the historical abnormal parameters all have corresponding identifiers, wherein the identifiers can be colors, words, codes or the like; for example, the identifier of the historical normal parameter is historical normal, and so on, the identifiers of the historical suspicious parameter and the historical abnormal parameter are historical suspicious and historical abnormal, respectively. The training sample sorting process can be more convenient, the sorting time of the monitoring system can be shortened, and therefore working efficiency is improved.
Further, the model training module 200 includes:
a sorting unit 210, configured to sort the historical normal parameters, the historical suspicious parameters, and the historical abnormal parameters to obtain a training sample, a verification sample, and a test sample, respectively.
A training unit 220, configured to train the training sample, the verification sample, and the test sample by using an artificial intelligence algorithm, so as to obtain the monitoring model.
Specifically, the historical normal parameters, the historical suspicious parameters and the historical abnormal parameters are provided with corresponding training samples, verification samples and test samples, so that the judgment can be carried out more quickly in use, and the judgment result is more credible; further, more detailed judgment is provided for the real-time suspicious parameters subsequently, so that the judgment accuracy, authenticity and effectiveness are guaranteed.
In this embodiment, the artificial intelligence algorithm includes, but is not limited to, a neural network algorithm, a machine learning algorithm, or a deep learning algorithm.
In an embodiment of the present invention, the second obtaining module 300 includes:
a second obtaining unit 310, configured to receive an input real-time monitoring parameter;
a first comparing unit 320, configured to input the real-time monitoring parameter into the monitoring model for comparison, so as to obtain a comparison result;
a first extracting unit 330, configured to extract real-time suspicious parameters and/or real-time abnormal parameters from the real-time monitoring parameters according to the comparison result.
Specifically, the parameters of the real-time monitoring parameters have been identified as normal, suspicious and abnormal in real time according to the monitoring model, i.e. the results are compared. And then, the real-time normal parameters can be eliminated, and real-time suspicious parameters and/or real-time abnormal parameters are extracted for further processing.
Further, after the real-time suspicious parameters and/or the real-time abnormal parameters are extracted from the real-time monitoring parameters according to the comparison result, determining and outputting the severity grade information according to the real-time abnormal parameters.
In this embodiment, since the real-time abnormal parameter can be directly determined by the monitoring model, the determination result of the real-time abnormal parameter has higher reliability. Then in the process, the real-time abnormal parameter can be directly determined as the weight grade information and can be output together with the emphasized red mark.
In one embodiment of the present invention, the model creation module 400 includes:
a second extracting unit 410, configured to screen a keypoint parameter from the real-time monitoring parameters, and associate the keypoint parameter with a three-dimensional coordinate;
and a creating unit 420 for creating a current BIM three-dimensional model according to the key point parameters.
Specifically, repeated and redundant parameters exist in the real-time monitoring parameters, so that key point parameters need to be screened out, the key point parameters can be integrated and calculated to obtain three-dimensional coordinates to be modeled through the association of the key point parameters and the three-dimensional coordinates, and then the current BIM three-dimensional model can be created, so that the current BIM three-dimensional model is ensured to be more practical.
In an embodiment of the present invention, the determining module 500 includes:
a second comparing unit 510, configured to compare the current BIM three-dimensional model with a historical three-dimensional model to obtain a displacement variation;
a determining unit 520, configured to determine whether the displacement variation exceeds a preset variation to obtain a determination result;
a determining unit 530, configured to determine early warning information according to the determination result.
Particularly, through comparing the displacement variation of the current BIM and the historical three-dimensional model, and through comparing the displacement variation with the preset variation, not only can be qualitative to real-time suspicious parameters, but also qualitative credibility can be guaranteed, so that the manager can make related measures.
Further, the determining the early warning information according to the judgment result specifically includes: if the displacement variation exceeds a first preset threshold value of preset variation, marking a first abnormal mark on the real-time suspicious parameter, and determining and outputting the real-time suspicious parameter as light grade information;
and if the displacement variation exceeds a second preset threshold value of the preset variation, marking a second abnormal mark on the real-time suspicious parameter, and simultaneously determining the real-time suspicious parameter as medium-grade information and outputting the medium-grade information.
In the embodiment, the light grade information and the middle grade information can be subjected to color identification before being output; for example, the light level information is a blue mark, and the middle level information is a yellow mark.
Example two
The embodiment of the invention also discloses a method for monitoring the settlement point of the building load, which is shown by referring to the attached figure 2 and comprises the following steps:
s1, receiving and preprocessing input historical monitoring parameters to obtain historical classification parameters;
s2, training the historical classification parameters to obtain a monitoring model;
s3, receiving the input real-time monitoring parameters and inputting the real-time monitoring parameters into the monitoring model to obtain real-time suspicious parameters or real-time abnormal parameters;
s4, creating a current BIM three-dimensional model according to the real-time monitoring parameters;
and S5, comparing the current BIM three-dimensional model with the historical three-dimensional model and determining early warning information.
It should be understood that the specific order or hierarchy of steps in the processes disclosed is an example of exemplary approaches. Based upon design preferences, it is understood that the specific order or hierarchy of steps in the processes may be rearranged without departing from the scope of the present disclosure. The accompanying method claims present elements of the various steps in a sample order, and are not meant to be limited to the specific order or hierarchy presented.
In the foregoing detailed description, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the subject matter require more features than are expressly recited in each claim. Rather, as the following claims reflect, invention lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.
Those of skill would further appreciate that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. Of course, the processor and the storage medium may reside as discrete components in a user terminal.
For a software implementation, the techniques described herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in memory units and executed by processors. The memory unit may be implemented within the processor or external to the processor, in which case it can be communicatively coupled to the processor via various means as is known in the art.
What has been described above includes examples of one or more embodiments. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the aforementioned embodiments, but one of ordinary skill in the art may recognize that many further combinations and permutations of various embodiments are possible. Accordingly, the embodiments described herein are intended to embrace all such alterations, modifications and variations that fall within the scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim. Furthermore, any use of the term "or" in the specification of the claims is intended to mean a "non-exclusive or".
Claims (10)
1. Building load settlement point monitoring system, its characterized in that includes:
the first acquisition module is used for receiving and preprocessing input historical monitoring parameters to obtain historical classification parameters;
the model training module is used for training the historical classification parameters to obtain a monitoring model;
the second acquisition module is used for receiving the input real-time monitoring parameters and inputting the real-time monitoring parameters into the monitoring model to obtain real-time suspicious parameters or real-time abnormal parameters;
the model creating module is used for creating a current BIM three-dimensional model according to the real-time monitoring parameters;
and the judging module is used for comparing the current BIM three-dimensional model with the historical three-dimensional model and determining early warning information.
2. The building load settlement point monitoring system of claim 1, wherein the first acquisition module comprises:
the device comprises a first acquisition unit, a second acquisition unit and a control unit, wherein the first acquisition unit is used for receiving input historical monitoring parameters;
the classification unit is used for classifying the historical monitoring parameters to obtain the historical classification parameters;
and the identification unit is used for marking the historical classification parameters with classification identifications.
3. The building load settlement point monitoring system of claim 2, wherein the historical classification parameters comprise historical normal parameters, historical suspect parameters, and historical abnormal parameters.
4. The building load settlement point monitoring system of claim 3, wherein the model training module comprises:
and the sorting unit is used for sorting the historical normal parameters, the historical suspicious parameters and the historical abnormal parameters to respectively obtain training samples, verification samples and test samples.
And the training unit is used for training the training sample, the verification sample and the test sample by using an artificial intelligence algorithm to obtain the monitoring model.
5. The building load settlement point monitoring system of claim 1, wherein the second acquisition module comprises:
the second acquisition unit is used for receiving the input real-time monitoring parameters;
the first comparison unit is used for inputting the real-time monitoring parameters into the monitoring model for comparison to obtain a comparison result;
and the first extraction unit is used for extracting real-time suspicious parameters and/or real-time abnormal parameters from the real-time monitoring parameters according to the comparison result.
6. The building load settlement point monitoring system according to claim 5, wherein after the real-time suspicious parameters and/or the real-time abnormal parameters are extracted from the real-time monitoring parameters according to the comparison result, the severity grade information is determined according to the real-time abnormal parameters and is output.
7. The building load settlement point monitoring system of claim 6, wherein the model creation module comprises:
the second extraction unit is used for screening key point parameters from the real-time monitoring parameters and associating the key point parameters with three-dimensional coordinates;
and the creating unit is used for creating the current BIM three-dimensional model according to the key point parameters.
8. The building load settlement point monitoring system of claim 7, wherein the determining module comprises:
the second comparison unit is used for comparing the current BIM three-dimensional model with a historical three-dimensional model to obtain displacement variation;
the judging unit is used for judging whether the displacement variation exceeds a preset variation to obtain a judgment result;
and the determining unit is used for determining early warning information according to the judgment result.
9. The building load settlement point monitoring system according to claim 8, wherein the determining of the early warning information according to the determination result specifically comprises: if the displacement variation exceeds a first preset threshold value of preset variation, marking a first abnormal mark on the real-time suspicious parameter, and determining and outputting the real-time suspicious parameter as light grade information;
and if the displacement variation exceeds a second preset threshold value of the preset variation, marking a second abnormal mark on the real-time suspicious parameter, and simultaneously determining the real-time suspicious parameter as medium-grade information and outputting the medium-grade information.
10. The building load settlement point monitoring method is applied to the building load settlement point monitoring system of any one of claims 1 to 9, and is characterized by comprising the following steps of:
s1, receiving and preprocessing input historical monitoring parameters to obtain historical classification parameters;
s2, training the historical classification parameters to obtain a monitoring model;
s3, receiving the input real-time monitoring parameters and inputting the real-time monitoring parameters into the monitoring model to obtain real-time suspicious parameters or real-time abnormal parameters;
s4, creating a current BIM three-dimensional model according to the real-time monitoring parameters;
and S5, comparing the current BIM three-dimensional model with the historical three-dimensional model and determining early warning information.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116523181A (en) * | 2023-05-22 | 2023-08-01 | 中国标准化研究院 | Intelligent green energy monitoring and analyzing method and system based on big data |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116523181A (en) * | 2023-05-22 | 2023-08-01 | 中国标准化研究院 | Intelligent green energy monitoring and analyzing method and system based on big data |
| CN116523181B (en) * | 2023-05-22 | 2024-01-26 | 中国标准化研究院 | Intelligent green energy monitoring and analyzing method and system based on big data |
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