CN115269663A - Method, device and equipment for processing static load test data and readable medium - Google Patents
Method, device and equipment for processing static load test data and readable medium Download PDFInfo
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- CN115269663A CN115269663A CN202210816007.5A CN202210816007A CN115269663A CN 115269663 A CN115269663 A CN 115269663A CN 202210816007 A CN202210816007 A CN 202210816007A CN 115269663 A CN115269663 A CN 115269663A
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Abstract
The application discloses a method, a device, equipment and a readable medium for processing static load test data, wherein the method comprises the following steps: inquiring historical data of static load test data which are uploaded by the acquisition terminal; selecting a plurality of static load test data in the historical data as a data group to be analyzed according to the change condition of the inquired historical data; converting a data set to be analyzed into a single machine input matrix, and inputting the single machine input matrix into a terminal data analysis model so that the terminal data analysis model outputs a single machine characteristic value and a single machine confidence coefficient which represent whether the building structure detected by the acquisition terminal meets the design requirement; and judging whether the single machine confidence coefficient is greater than a single machine confidence coefficient threshold value, and if so, taking the characteristic value of the single machine for acquiring as the current state value of the single machine of the acquisition terminal. The application has the advantages that: the processing method, the device, the equipment and the readable medium for the static load test data can analyze the collected static load test data in real time to quickly judge whether the static load test data meet the design requirements.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a method, a device, equipment and a readable medium for processing static test data.
Background
In the existing construction foundation grouting reinforcement engineering, the bearing capacity of the pile foundation after grouting reinforcement is judged by adopting a traditional pile foundation static load test method, and whether the bearing capacity of the pile foundation after reinforcement meets the design requirements is detected by arranging a reaction frame, a reference beam and a jack for loading.
The traditional pile foundation static load test detection method has certain defects, for example, a datum point cannot guarantee no deformation, and once the datum beam deforms, the detected data has no reference value; if the reaction frame is arranged, the reaction frame is anchored on a structural member of the existing building, and the anchoring and the application force can cause certain damage to the original structure; for the bearing capacity after grouting reinforcement or foundation treatment of the large-area basement pile foundation, if an overlong and overlarge reference beam and a reaction frame need to be customized according to a traditional pile foundation static load test method, great difficulty is added to a static load test, and the feasibility of implementation is low; especially, in the static load test process, an operator is required to continuously measure the sedimentation amount, so that the waste of human resources is caused, and the effect of real-time monitoring cannot be achieved.
In the related technology, a stress sensor, a collecting instrument and the like can be adopted to overcome the defects of the traditional pile foundation static load test, and particularly the static load test after the building structure of the existing building is reinforced. However, in the related art, a method for concentrating data of a stress sensor to a field acquisition instrument and then summarizing data of all the acquisition instruments in a building by a detector to judge whether the building meets the strength requirement after being reinforced has the problem that when a load is applied under static load, if a reinforced structure fails, early warning is not provided, so that potential safety risk is caused.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
Some embodiments of the present application provide a method, an apparatus, a device and a readable medium for processing static test data, so as to solve the technical problems mentioned in the above background.
As a first aspect of the present application, some embodiments of the present application provide a method for processing static test data, including: responding to static load test data uploaded by an acquisition terminal, and inquiring historical data of the static load test data uploaded by the acquisition terminal; selecting a plurality of static load test data in the historical data as a data group to be analyzed according to the change condition of the inquired historical data; converting a data set to be analyzed into a single machine input matrix, and inputting the single machine input matrix into a terminal data analysis model so that the terminal data analysis model outputs a single machine characteristic value and a single machine confidence coefficient which represent whether the building structure detected by the acquisition terminal meets the design requirement; and judging whether the single machine confidence coefficient is greater than a single machine confidence coefficient threshold value, and if so, taking the characteristic value of the single machine for acquiring as the current state value of the single machine of the acquisition terminal.
As a second aspect of the present application, some embodiments of the present application provide a device for processing dead load test data, including: the query module is used for responding to the static load test data uploaded by one acquisition terminal and querying historical data of the static load test data uploaded by the acquisition terminal; the selection module is used for selecting a plurality of static load test data in the historical data as a data group to be analyzed according to the change condition of the inquired historical data; the analysis module is used for converting the data set to be analyzed into a single machine input matrix and inputting the single machine input matrix into a terminal data analysis model so that the terminal data analysis model outputs a single machine characteristic value and a single machine confidence coefficient which represent whether the building structure detected by the acquisition terminal meets the design requirement or not; and the judging module is used for judging whether the single machine confidence coefficient is greater than the single machine confidence coefficient threshold value, and if so, the characteristic value of the single machine for acquiring the information is used as the current state value of the single machine of the acquisition terminal.
As a third aspect of the present application, some embodiments of the present application provide an electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method described in any of the implementations of the first aspect.
As a fourth aspect of the present application, some embodiments of the present application provide a storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect.
The beneficial effect of this application lies in: the processing method, the device, the equipment and the readable medium for the static load test data can analyze the collected static load test data in real time to quickly judge whether the static load test data meet the design requirements.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, serve to provide a further understanding of the application and to enable other features, objects, and advantages of the application to be more apparent. The drawings and their description illustrate the embodiments of the invention and do not limit it.
Further, throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic and that elements and elements are not necessarily drawn to scale.
In the drawings:
FIG. 1 is a schematic diagram of an application scenario of a method for processing dead load test data according to an embodiment of the present application;
FIG. 2 is a schematic diagram of the main steps of a method of processing dead load test data according to one embodiment of the present application;
FIG. 3 is a diagram illustrating a specific step of step S2 in a method for processing static test data according to an embodiment of the present application;
FIG. 4 is a diagram illustrating a specific step of step S3 in a method for processing static test data according to an embodiment of the present application;
FIG. 5 is a diagram illustrating a specific step of step S5 in a method for processing static test data according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a static test data processing apparatus according to an embodiment of the present application;
FIG. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
FIG. 8 is a schematic diagram of a single-machine input matrix according to one embodiment of the present application;
FIG. 9 is a schematic diagram of a synthetic input matrix according to one embodiment of the present application.
The reference numerals have the meanings:
101. a server; 102. collecting a terminal; 103. a stress sensor;
20. a processing device for static load test data;
21. a query module; 22. selecting a module; 23. an analysis module; 24. and a judging module.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the disclosure are shown in the drawings, it is to be understood that the disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings. The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
As shown in fig. 1, in one application scenario of the present application, a plurality of stress sensors 103 are arranged at a plurality of monitoring locations to collect static test data; the acquisition terminal 102 is connected with the stress sensor 103 by wire or wireless connection, and uploads the acquired static load test data to the server 101. Specifically, the stress sensor 103 of the present application may be a patch type stress sensor 103.
As shown in fig. 2, the processing method of the dead load test data of the present application is mainly executed by the server 101 shown in fig. 1, and the processing method mainly includes the following main steps:
s1: responding to static load test data uploaded by one acquisition terminal, and inquiring historical data of the static load test data uploaded by the acquisition terminal.
S2: and selecting a plurality of static load test data in the historical data as a data group to be analyzed according to the change condition of the inquired historical data.
S3: and converting the data group to be analyzed into a single machine input matrix, and inputting the single machine input matrix into a terminal data analysis model so that the terminal data analysis model outputs a single machine characteristic value and a single machine confidence coefficient which represent whether the building structure detected by the acquisition terminal meets the design requirement.
S4: and judging whether the single machine confidence coefficient is greater than a single machine confidence coefficient threshold value, and if so, taking the characteristic value of the single machine for acquiring as the current state value of the single machine of the acquisition terminal. The current state value of the single machine can be a number of 0, 1, 3, 4, etc., which represents different results of satisfying the load test, for example, 0 represents no overload risk, and the remaining numbers represent risk levels according to size.
S5: and forming a comprehensive input matrix by historical data of single machine current state values of a plurality of acquisition terminals of a building into an integral data analysis model so that the integral data analysis model outputs a comprehensive characteristic value and a comprehensive confidence coefficient which represent whether the multiple building structures detected by the plurality of acquisition terminals comprehensively meet the design requirements.
S6: and judging whether the comprehensive confidence coefficient is larger than a comprehensive confidence coefficient threshold value or not, and if so, taking the comprehensive characteristic value of the credit as the integral comprehensive current state value of the multiple building structures detected by the multiple acquisition terminals.
Specifically, the static load test data of one acquisition terminal comprises stress data of a plurality of monitoring positions. Namely, in the actual test, a plurality of monitoring positions can be set according to the actual situation, and stress sensors are arranged at the monitoring positions, and as a specific scheme, the stress sensors can be patch type stress sensors. Further, when detecting whether a building meets the design requirements, multiple building structures may need to be detected, that is, there are multiple collecting terminals to detect the whole building to obtain an overall detection result.
As a specific scheme, the static load test data of one acquisition terminal comprises load data of the building structure detected by the acquisition terminal. The load data referred to herein is the load applied to the building structure, for example, by filling water in a water bag, and generally speaking, the load data is a pressure data, which may be in Mpa, measured as the load strength applied to the building structure for the static load test. It should be noted that the applied load may be gradually increased in the static load test.
Step S1 is equivalent to triggering a subsequent processing program after receiving static load test data, and all the static load test data are added with ID information data of an acquisition terminal, so that a server can know the data source and is convenient for subsequent query.
As shown in fig. 3, as a specific solution, the step S2 specifically includes the following steps:
s21: and calculating the ratio of the stress data to the load data of each monitoring position, and defining the ratio as a stress-load ratio.
S22: and calculating the slope of the current on-load ratio relative to the last on-load ratio, judging whether the slope is greater than a slope threshold value, and if so, selecting the previous N times of static load test data containing the current time as a data group to be analyzed.
Here, the slope is a slope with time as a horizontal axis, that is, a slope of the duty ratio value with respect to time. In actual operation, because the time periods of data acquisition are the same, the slope can be directly obtained by adopting a mode of making difference by applying a load ratio value.
The purpose of selecting the data group to be analyzed by adopting the slope is to reduce the analysis burden, not to analyze all data, only to analyze when the data fluctuation is large, and meanwhile, the quantitative acquisition also facilitates the regular degree of the matrix during the later-stage neural network model training and analysis. Preferably, N is 5 to 10.
As shown in fig. 4 again, as a specific solution, the step S3 specifically includes the following steps:
s31: and taking the stress-bearing ratio values of the plurality of monitoring positions specified in the static load test data as different columns of the single-machine input matrix.
S32: and taking static load test data of different acquisition times as different rows of the single-machine input matrix.
Fig. 8 shows an example of a single-machine input matrix, in which each row represents the static test data at different acquisition times, that is, the ratio of the applied load of the static test data at different acquisition times is divided into different rows, and each column corresponds to a different monitoring position. In practical applications, the monitoring position may vary according to specific situations, in order to normalize the single-machine input matrix so as to make the training of the trained terminal data analysis model easy to converge. The first L stress-carrying ratios from large to small in all monitoring positions can be used as different columns, so that the model training difficulty can be reduced, and the model can be suitable for most scenes.
As shown in fig. 5, as a specific solution, step S5 specifically includes the following steps:
s51: and acquiring the median of the corresponding load ratio of the current static load test data of each acquisition terminal.
S52: and comparing the median of the corresponding load ratio of the current static load test data of each acquisition terminal to select the largest first M medians and the acquisition terminals corresponding to the M medians.
S53: and sorting the selected acquisition terminals according to the magnitude of the median value of the onload ratio and acquiring the current state value of the single machine corresponding to the acquisition terminals according to the sorting.
S54: and forming a comprehensive input matrix by the acquired single-machine current state values of the corresponding acquisition terminals.
Specifically, as shown in fig. 9, rows of the integrated input matrix represent different collection terminals, and different columns represent different current state values of a single machine, which are sorted by size by a collection terminal.
By adopting the scheme, whether the comprehensive condition of the building meets the design requirement can be effectively analyzed, so that dynamic and unmanned monitoring is realized.
As a preferred scheme, both the terminal data analysis model and the whole data analysis model can adopt a convolutional neural network, and the structure and training of the convolutional neural network are common technical means in the field and are not described herein again.
As a further alternative, the above neural network model may be trained using historical data in a previous database to construct training set data of the above input data and output data to construct the neural network model.
As shown in fig. 6, the static load test data processing apparatus 20 according to the present application includes: the query module 21 is configured to query historical data of the static load test data uploaded by an acquisition terminal in response to the static load test data uploaded by the acquisition terminal; the selecting module 22 is used for selecting a plurality of static load test data in the historical data as a data group to be analyzed according to the change condition of the inquired historical data; the analysis module 23 is configured to convert the data set to be analyzed into a single-machine input matrix and input the single-machine input matrix to a terminal data analysis model, so that the terminal data analysis model outputs a single-machine characteristic value and a single-machine confidence coefficient that represent whether the building structure detected by the acquisition terminal meets the design requirements; and the judging module 24 is used for judging whether the single machine confidence coefficient is greater than the single machine confidence coefficient threshold value, and if so, the single machine characteristic value is taken as the current state value of the single machine of the acquisition terminal.
As shown in fig. 7, an electronic device 800 may include a processing means (e.g., central processing unit, graphics processor, etc.) 801 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 802 or a program loaded from a storage means 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data necessary for the operation of the electronic apparatus 800 are also stored. The processing device 801, the ROM802, and the RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
Generally, the following devices may be connected to the I/O interface 805: input devices 806 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.: output devices 807 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, or the like; storage 808 including, for example, magnetic tape, hard disk, etc.: and a communication device 809. The communication means 809 may allow the electronic device 800 to communicate wirelessly or by wire with other devices to exchange data. While fig. 7 illustrates an electronic device 800 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 7 may represent one device or may represent multiple devices as desired.
In particular, according to some embodiments of the present disclosure, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, some embodiments of the present disclosure include a computer program product comprising a computer program carried on a storage medium, the computer program containing program code for performing the method illustrated in the flow chart. In some such embodiments, the computer program may be downloaded and installed from a network through communications device 809, or installed from storage device 808, or installed from ROM 802. The computer program, when executed by the processing apparatus 801, performs the above-described functions defined in the methods of some embodiments of the present disclosure.
It should be noted that the storage medium described above in some embodiments of the disclosure may be a computer-readable signal medium or a storage medium or any combination of the two. A storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of storage media may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
In some embodiments of the disclosure, a storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In some embodiments of the present disclosure, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any storage medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP, and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks, as well as any currently known or future developed networks.
The storage medium may be one contained in the electronic device: or may be separate and not incorporated into the electronic device. The storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: responding to static load test data uploaded by an acquisition terminal, and inquiring historical data of the static load test data uploaded by the acquisition terminal; selecting a plurality of static load test data in the historical data as a data group to be analyzed according to the change condition of the inquired historical data; converting a data set to be analyzed into a single machine input matrix, and inputting the single machine input matrix into a terminal data analysis model so that the terminal data analysis model outputs a single machine characteristic value and a single machine confidence coefficient which represent whether the building structure detected by the acquisition terminal meets the design requirement; and judging whether the single machine confidence coefficient is greater than the single machine confidence coefficient threshold value, and if so, taking the characteristic value of the single machine for collecting the information as the current state value of the single machine of the collecting terminal.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C + +, or the like, including the conventional procedural programming languages: such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. 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.
The units described in some embodiments of the present disclosure may be implemented by software, and may also be implemented by hardware. The described units may also be provided in a processor, the names of which units do not in some cases constitute a limitation of the unit itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combinations of the above-mentioned features, and other embodiments in which the above-mentioned features or their equivalents are combined arbitrarily without departing from the spirit of the invention are also encompassed. For example, the above features and (but not limited to) the features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (10)
1. A method for processing static test data comprises the following steps:
responding to static load test data uploaded by an acquisition terminal, and inquiring historical data of the static load test data uploaded by the acquisition terminal;
selecting a plurality of static load test data in the historical data as a data group to be analyzed according to the change condition of the inquired historical data;
converting the data set to be analyzed into a single machine input matrix and inputting the single machine input matrix into a terminal data analysis model so that the terminal data analysis model outputs a single machine characteristic value and a single machine confidence coefficient, wherein the single machine characteristic value represents whether the building structure detected by the acquisition terminal meets the design requirements;
and judging whether the single machine confidence coefficient is greater than a single machine confidence coefficient threshold value, and if so, adopting the single machine characteristic value as the current single machine state value of the acquisition terminal.
2. The method for processing the static test data according to claim 1, wherein:
the processing method of the static load test data further comprises the following steps:
forming a comprehensive input matrix by historical data of single machine current state values of a plurality of acquisition terminals of a building into an integral data analysis model so that the integral data analysis model outputs a comprehensive characteristic value and a comprehensive confidence coefficient, wherein the comprehensive characteristic value and the comprehensive confidence coefficient represent whether the multiple building structures detected by the plurality of acquisition terminals meet the design requirements or not;
and judging whether the comprehensive confidence coefficient is larger than a comprehensive confidence coefficient threshold value or not, and if so, acquiring the comprehensive characteristic value as the comprehensive current state value of the whole of the plurality of building structures detected by the plurality of acquisition terminals.
3. The method for processing the static test data according to claim 2, wherein:
the static load test data of one acquisition terminal comprises stress data of a plurality of monitoring positions.
4. The method for processing the static test data according to claim 3, wherein:
the static load test data of one of the acquisition terminals comprises load data of the building structure detected by the acquisition terminal.
5. The method for processing the static test data according to claim 4, wherein:
wherein, the selecting a plurality of static test data in the historical data as a data group to be analyzed according to the change condition of the inquired historical data comprises:
calculating the ratio of the stress data to the load data of each monitoring position, and defining the ratio as a corresponding load ratio;
and calculating the slope of the current load ratio relative to the last load ratio, judging whether the slope is greater than a slope threshold value, and if so, selecting the previous N times of static load test data containing the current time as the data group to be analyzed.
6. The method for processing the static test data according to claim 5, wherein:
converting the data group to be analyzed into a single machine input matrix, and inputting the single machine input matrix into a terminal data analysis model so as to enable the terminal data analysis model to output a single machine characteristic value and a single machine confidence coefficient which represent whether the building structure detected by the acquisition terminal meets the design requirements, wherein the single machine confidence coefficient comprises the following steps:
taking the stress-load ratios of a plurality of specified monitoring positions in the static load test data as different columns of the single-machine input matrix;
and taking the static load test data with different acquisition times as different rows of the single-machine input matrix.
7. The method for processing the static test data according to claim 6, wherein:
wherein, the step of forming a comprehensive input matrix from historical data of single machine current state values of a plurality of acquisition terminals of a building into an integral data analysis model so as to enable the integral data analysis model to output a comprehensive characteristic value and a comprehensive confidence coefficient for representing whether a plurality of building structures detected by the plurality of acquisition terminals comprehensively meet design requirements comprises the steps of:
acquiring a median of the current stress load ratio of the static load test data of each acquisition terminal;
comparing the median of the corresponding load ratio of the current static load test data of each acquisition terminal to select the maximum first M medians and the acquisition terminals corresponding to the M medians;
sorting the selected acquisition terminals according to the magnitude of the median value of the ratio to be loaded and acquiring the current state value of the single machine corresponding to the acquisition terminals according to the sorting;
and forming the comprehensive input matrix by the acquired single-machine current state values corresponding to the acquisition terminals.
8. A device for processing static test data, comprising:
the query module is used for responding to the static load test data uploaded by one acquisition terminal and querying the historical data of the static load test data uploaded by the acquisition terminal;
the selection module is used for selecting a plurality of static load test data in the historical data as a data group to be analyzed according to the change condition of the inquired historical data;
the analysis module is used for converting the data set to be analyzed into a single-machine input matrix and inputting the single-machine input matrix into a terminal data analysis model so that the terminal data analysis model outputs a single-machine characteristic value and a single-machine confidence coefficient, wherein the single-machine characteristic value represents whether the building structure detected by the acquisition terminal meets the design requirements;
and the judging module is used for judging whether the stand-alone confidence coefficient is greater than a stand-alone confidence coefficient threshold value, and if so, the stand-alone characteristic value is adopted as the stand-alone current state value of the acquisition terminal.
9. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
the one or more programs, when executed by the one or more processors, cause the processors to perform the method recited in any of claims 1-7.
10. A storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method of any one of claims 1 to 7.
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| CN202210816007.5A Pending CN115269663A (en) | 2022-07-12 | 2022-07-12 | Method, device and equipment for processing static load test data and readable medium |
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