CN114034770A - Data detection method and system based on construction dam mechanics big data - Google Patents

Data detection method and system based on construction dam mechanics big data Download PDF

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CN114034770A
CN114034770A CN202111344938.1A CN202111344938A CN114034770A CN 114034770 A CN114034770 A CN 114034770A CN 202111344938 A CN202111344938 A CN 202111344938A CN 114034770 A CN114034770 A CN 114034770A
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CN114034770B (en
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杨明珠
李铮
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Jinling Institute of Technology
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    • G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G—PHYSICS
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Abstract

The application relates to a data detection method and a data detection system based on construction dam mechanics big data, which can determine the structural transmission influence of a plurality of groups of mechanics big data under different operation load states by means of the defect description of the mechanics big data of the plurality of groups of mechanics big data under different operation load states, the structural transmission influence is beneficial to mining and obtaining the defect description of the mechanical big data which is as complete, abundant and accurate as possible, further, the structural transmission influence between the mechanical big data can be comprehensively analyzed by combining the structural transmission influence information of the mechanical big data which is configured in advance, so that the defect description of the mechanical big data which is obtained by mining is as accurate and credible as possible, therefore, the accuracy and the reliability of the determined final defect detection result are guaranteed, the overhaul and the maintenance of the construction dam can be realized through the final defect detection result, and the production accident is avoided.

Description

Data detection method and system based on construction dam mechanics big data
Technical Field
The application relates to the technical field of construction dams and mechanics big data detection, in particular to a data detection method and system based on mechanics big data of a construction dam.
Background
The dam (dam) refers to a weir for intercepting river water, a water-intercepting dam of reservoir, river, etc. The common reservoir dam mainly comprises a main dam, an auxiliary dam, a gravity dam, a normal spillway, an emergency spillway, a smart main canal culvert and a power station. The dam has the functions of flood control, power generation and the like, and is an important branch in the capital construction project. With the continuous deterioration of the environment, the structural damage of the existing dam (such as an arch dam or a gravity dam) in the use process has a high probability, and if the dam is not accurately and reliably detected for subsequent overhaul and maintenance, serious production accidents can be caused.
Disclosure of Invention
In a first aspect, an embodiment of the present application provides a data detection method based on large mechanical data of a construction dam, which is applied to a data detection system. The method at least comprises the following steps: acquiring an initial stress condition description label of a plurality of groups of mechanical big data of a construction dam to be subjected to defect detection in a target defect detection mapping list; determining the defect descriptions of the mechanical big data obtained by pairing the multiple groups of mechanical big data in the multiple groups of target construction dam operation records respectively according to the initialized stress condition description label; the operation records of the multiple groups of target construction dams are obtained by testing the construction dams to be subjected to defect detection under the operating load state; and determining a final defect detection result of the construction dam to be subjected to defect detection according to the structural transmission influence information of the mechanical big data which is configured in advance and corresponds to the construction dam to be subjected to defect detection and the mechanical big data defect description of the plurality of groups of mechanical big data in the target construction dam operation records corresponding to the plurality of groups of operation load states.
By the embodiment, on the basis of obtaining the initialized stress condition description labels of the multiple groups of mechanical big data of the construction dam to be subjected to defect detection in the target defect detection mapping list, the defect description of the multiple groups of mechanical big data obtained by respectively matching the multiple groups of mechanical big data in the running records of the multiple groups of target construction dams can be determined based on the initialized stress condition description labels, and finally, the final defect detection result of the construction dam to be subjected to defect detection is determined based on the structural transmission influence information of the mechanical big data which is configured in advance and corresponds to the construction dam to be subjected to defect detection and the mechanical big data defect description of the multiple groups of mechanical big data in the running records of the target construction dams corresponding to the multiple groups of running load states. Therefore, the structural transmission influence of the multiple groups of mechanical big data under different operation load states can be determined by means of the mechanical big data defect description of the multiple groups of mechanical big data under different operation load states, the structural transmission influence is beneficial to mining to obtain the mechanical big data defect description which is complete, abundant and accurate as far as possible, further, the structural transmission influence between the mechanical big data can be comprehensively analyzed by combining the mechanical big data structural transmission influence information which is configured in advance, the mechanical big data defect description obtained by mining is accurate and reliable as far as possible, the accuracy and the reliability of the determined final defect detection result are guaranteed, the overhaul and the maintenance of the construction dam can be realized through the final defect detection result, and the production accident is avoided.
Under some design ideas which can be independently implemented, determining, according to the initialized stress condition description label, a defect description of the mechanical big data obtained by pairing the multiple sets of mechanical big data in the multiple sets of target construction dam operation records respectively includes: according to the initialized stress condition description label, determining staged matching item information of the multiple groups of mechanical big data in the multiple groups of target construction dam operation records respectively, and extracting the respective corresponding significant construction dam operation descriptions of the multiple groups of target construction dam operation records; extracting mechanical big data defect descriptions corresponding to the mechanical big data from the significance construction dam operation descriptions corresponding to the multiple groups of target construction dam operation records according to the staged matching item information of the mechanical big data in the multiple groups of target construction dam operation records; and determining the extracted mechanical big data defect description corresponding to the mechanical big data as the mechanical big data defect description obtained by matching in the plurality of groups of target construction dam operation records.
By the embodiment, the defect description of the mechanical big data corresponding to the mechanical big data can be accurately and efficiently determined based on the matching condition between the staged matching item information of the mechanical big data in the multiple groups of target construction dam operation records and the operation description of the significant construction dam.
Under some design ideas which can be independently implemented, the stage matching item information comprises construction dam operation distribution information of the stage matching items; the step of extracting the mechanical big data defect description corresponding to the mechanical big data from the significance construction dam operation description corresponding to each of the plurality of groups of target construction dam operation records according to the staged matching item information of the mechanical big data in the plurality of groups of target construction dam operation records comprises the following steps: for each group of target construction dam operation records in the multiple groups of target construction dam operation records, extracting significant construction dam operation descriptions corresponding to the construction dam operation distribution information from significant construction dam operation descriptions corresponding to the target construction dam operation records according to construction dam operation distribution information of staged matching items of the mechanical big data in the multiple groups of target construction dam operation records; and determining the extracted significant construction dam operation description corresponding to the construction dam operation distribution information as the mechanical big data defect description corresponding to the mechanical big data.
Under some design ideas which can be independently implemented, determining a final defect detection result of the construction dam to be subjected to defect detection according to the mechanics big data structural transmission influence information which corresponds to the construction dam to be subjected to defect detection and the mechanics big data defect description of a plurality of groups of mechanics big data which are respectively recorded in the operation of the target construction dam corresponding to the plurality of groups of operation load states, wherein the method comprises the following steps: for each mechanical big data in the multiple groups of mechanical big data, determining defect descriptions of the adjusted mechanical big data of the mechanical big data in different operation load states according to defect descriptions of the mechanical big data in different operation load states and defect descriptions of the mechanical big data of the associated mechanical big data linked with the mechanical big data; and determining a final defect detection result of the construction dam to be subjected to the defect detection according to the adjusted defect description of the mechanical big data corresponding to the multiple groups of mechanical big data and the structural transmission influence information of the mechanical big data which is configured in advance and corresponds to the construction dam to be subjected to the defect detection.
Through the embodiment, the defect description adjustment of the mechanical big data defect description of the mechanical big data can be carried out by means of the mechanical big data defect description of each mechanical big data in different operation load states and the mechanical big data defect description of the associated mechanical big data linked with the mechanical big data, the adjusted mechanical big data defect description covers the defect description of the associated mechanical big data in one operation load state under some conditions, and also covers the defect description of the mechanical big data among different operation load states, so that the defect description of the mechanical big data tends to be complete and accurate as much as possible, and the determined final defect detection result is accurate and reliable as much as possible.
Under some design ideas which can be independently implemented, determining adjusted mechanical big data defect descriptions of the mechanical big data under different operation load states according to the mechanical big data defect descriptions of the mechanical big data under different operation load states and the mechanical big data defect descriptions of the associated mechanical big data linked with the mechanical big data includes: taking each operation load state in the multiple groups of operation load states as a target operation load state, and sequentially implementing the following steps: according to the mechanical big data defect description of the mechanical big data under different operation load states and the first structural transmission influence among all the staged matching items of the mechanical big data under different operation load states, performing first round defect description adjustment on the mechanical big data defect description of the mechanical big data under different operation load states to obtain the mechanical big data defect description after the first round defect description adjustment; performing second round defect description adjustment on the mechanical big data defect description of the mechanical big data in the target operation load state according to the mechanical big data defect description of the mechanical big data in the target operation load state and the mechanical big data defect description of the associated mechanical big data which corresponds to the target operation load state and has second structural transmission influence with the mechanical big data, so as to obtain second round defect description of the mechanical big data after the second round defect description adjustment; and determining the adjusted mechanical big data defect description of the mechanical big data in the target running load state according to the mechanical big data defect description adjusted by the first round of defect description and the mechanical big data defect description adjusted by the second round of defect description.
Under some design ideas which can be independently implemented, determining a final defect detection result of the construction dam to be subjected to defect detection according to the mechanics big data structural transmission influence information which corresponds to the construction dam to be subjected to defect detection and the mechanics big data defect description of a plurality of groups of mechanics big data which are respectively recorded in the operation of the target construction dam corresponding to the plurality of groups of operation load states, wherein the method comprises the following steps: for each mechanical big data in the multiple groups of mechanical big data, sorting the mechanical big data defect description of the mechanical big data under different operation load states to obtain sorted mechanical big data defect description corresponding to the mechanical big data; and determining a final defect detection result of the construction dam to be subjected to defect detection according to the preset mechanical big data structural transmission influence information corresponding to the construction dam to be subjected to defect detection and the sorted mechanical big data defect descriptions corresponding to the multiple groups of mechanical big data.
Through the embodiment, the defect descriptions of different operation load states can be considered by the determined sorted mechanical big data defect descriptions through the sorting operation of the mechanical big data defect descriptions under different operation load states, and the accuracy of the final defect detection result is further improved.
Under some independently implementable design ideas, the mechanical big data defect description comprises multiple groups of mechanical big data quantitative description indexes in the concerned aspects; the sorting of the mechanical big data defect description of the mechanical big data under different operation load states to obtain the sorted mechanical big data defect description corresponding to the mechanical big data comprises the following steps: for each concerned aspect in the plurality of groups of concerned aspects, determining a plurality of groups of quantized mechanical big data description indexes corresponding to the concerned aspects of the mechanical big data in different running load states, and determining the sorted quantized mechanical big data description indexes corresponding to the concerned aspects by combining the determined plurality of groups of quantized mechanical big data description indexes; and determining the sorted mechanical big data defect description corresponding to the mechanical big data according to the sorted mechanical big data quantitative description indexes corresponding to the plurality of groups of concerned aspects.
Under some design ideas which can be independently implemented, the step of determining the sorted mechanical big data quantitative descriptor corresponding to the concerned aspect in combination with the multiple groups of determined mechanical big data quantitative descriptors is implemented by any one of the following steps: determining a mechanical big data quantitative description index with the highest index value from the multiple groups of mechanical big data quantitative description indexes, and taking the mechanical big data quantitative description index as a sorted mechanical big data quantitative description index corresponding to the concerned aspect; taking a centralized index of the multiple groups of mechanical big data quantitative description indexes as a sorted mechanical big data quantitative description index corresponding to the concerned aspect; obtaining confidence coefficients corresponding to the multiple groups of mechanical big data quantitative description indexes, and determining the sorted mechanical big data quantitative description indexes corresponding to the concerned aspect according to the calculation results between the multiple groups of mechanical big data quantitative description indexes and the confidence coefficients corresponding to the multiple groups of mechanical big data quantitative description indexes.
Through the embodiment, the precision and the reliability of the mechanical big data quantitative description indexes after arrangement can be guaranteed.
Under some design ideas which can be independently implemented, determining a final defect detection result of the construction dam to be subjected to defect detection according to the structural transmission influence information of the previously configured mechanics big data corresponding to the construction dam to be subjected to defect detection and the sorted mechanics big data defect descriptions corresponding to the multiple groups of mechanics big data respectively, wherein the final defect detection result comprises the following steps: performing defect description adjustment on the sorted mechanical big data defect descriptions corresponding to the multiple groups of mechanical big data respectively according to a third structural transmission influence among all the mechanical big data included in the mechanical big data structural transmission influence information which is configured in advance and corresponds to the construction dam to be subjected to defect detection, so as to obtain the sorted mechanical big data defect descriptions after the defect description adjustment; and determining a final defect detection result of the construction dam to be subjected to defect detection according to the adjusted big mechanical data defect description.
Through the embodiment, defect description adjustment can be performed on the sorted mechanical big data defect descriptions corresponding to multiple groups of mechanical big data respectively based on the third structural transmission influence among the mechanical big data included in the mechanical big data structural transmission influence information which is configured in advance, so that the sorted mechanical big data defect descriptions after the defect description adjustment are obtained, in other words, the sorted mechanical big data defect descriptions can be corrected by the third structural transmission influence which is configured in advance, and the determined global defect item is accurate and reliable as much as possible.
Under some design ideas which can be independently implemented, each mechanical big data in the multiple groups of mechanical big data of the construction dam to be subjected to defect detection is used as a first mechanical big data, and each mechanical big data in each mechanical big data with the third structural transmission influence is used as a second mechanical big data; the second mechanical big data is big data of stress load of the construction dam; the first mechanics big data comprises at least one of construction dam stress load big data and construction dam structure identification big data.
Under some design ideas which can be independently implemented, determining a final defect detection result of the construction dam to be subjected to defect detection according to the sorted mechanics big data defect description after the defect description adjustment includes: loading the sorted mechanics big data defect description after the defect description adjustment into a target intelligent defect detection thread which is successfully configured in advance to obtain defect item error information; the defect item error information aims to reflect the error condition between the current defect item and the initialized defect item of the construction dam to be subjected to defect detection; and determining the corrected stress condition description labels of the multiple groups of mechanical big data of the construction dam to be subjected to the defect detection in the target defect detection mapping list according to the defect item error information and the initialized stress condition description labels of the multiple groups of mechanical big data of the construction dam to be subjected to the defect detection in the target defect detection mapping list, and determining the final defect detection result of the construction dam to be subjected to the defect detection according to the corrected stress condition description labels.
Under some design ideas which can be independently implemented, the initialization stress condition description label of a plurality of groups of mechanical big data of the construction dam to be subjected to defect detection in a target defect detection mapping list is obtained through one of the following steps: obtaining a plurality of groups of target construction dam operation records obtained by testing the construction dam to be subjected to defect detection under a plurality of groups of operation load states, and determining initialization stress condition description labels of a plurality of groups of mechanical big data of the construction dam to be subjected to defect detection in the target defect detection mapping list according to the plurality of groups of target construction dam operation records; and acquiring reflection-type ultrasonic waves corresponding to a plurality of groups of detection-type ultrasonic waves sent by a structural flaw detection terminal of the construction dam respectively, and determining an initialization stress condition description label of a plurality of groups of mechanical big data of the construction dam to be subjected to flaw detection in the target flaw detection mapping list according to the reflection-type ultrasonic waves.
Under some design ideas which can be independently implemented, each group of target construction dam operation records in the obtained multiple groups of target construction dam operation records is used as a first target construction dam operation record, and each group of target construction dam operation records in the multiple groups of target construction dam operation records used for mechanical big data pairing is used as a second target construction dam operation record; a part of the operation records of the first target construction dam are the same as the operation records of the second target construction dam; or the first target construction dam operation record and the second target construction dam operation record do not have a consistent construction dam operation record.
In a second aspect, the embodiment of the present application further provides a data detection system based on mechanics big data of a construction dam, including a processing engine, a network module and a memory, where the processing engine and the memory communicate through the network module, and the processing engine is configured to read a computer program from the memory and operate the computer program, so as to implement the above method.
The method is applied to the technical scheme, on the basis of obtaining the initialized stress condition description labels of a plurality of groups of mechanical big data of the construction dam to be subjected to defect detection in the target defect detection mapping list, the defect description of the mechanical big data obtained by respectively matching the plurality of groups of mechanical big data in the operation records of the plurality of groups of target construction dams can be determined based on the initialized stress condition description labels, and finally, the final defect detection result of the construction dam to be subjected to defect detection is determined based on the structural transmission influence information of the mechanical big data which is matched in advance and corresponds to the construction dam to be subjected to defect detection and the mechanical big data defect description of the plurality of groups of mechanical big data in the operation records of the target construction dams corresponding to the plurality of groups of operation load states. Therefore, the structural transmission influence of the multiple groups of mechanical big data under different operation load states can be determined by means of the mechanical big data defect description of the multiple groups of mechanical big data under different operation load states, the structural transmission influence is beneficial to mining to obtain the mechanical big data defect description which is complete, abundant and accurate as far as possible, further, the structural transmission influence between the mechanical big data can be comprehensively analyzed by combining the mechanical big data structural transmission influence information which is configured in advance, the mechanical big data defect description obtained by mining is accurate and reliable as far as possible, the accuracy and the reliability of the determined final defect detection result are guaranteed, the overhaul and the maintenance of the construction dam can be realized through the final defect detection result, and the production accident is avoided.
In the description that follows, additional features will be set forth, in part, in the description. These features will be in part apparent to those skilled in the art upon examination of the following and the accompanying drawings, or may be learned by production or use. The features of the present application may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations particularly pointed out in the detailed examples that follow.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required 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 application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
The methods, systems, and/or processes of the figures are further described in accordance with the exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments in which reference numerals represent similar mechanisms throughout the various views of the drawings.
Fig. 1 is a block diagram illustrating an application scenario of an exemplary construction dam mechanics big data based data detection method according to some embodiments of the present application.
FIG. 2 is a diagram illustrating hardware and software components in an exemplary data detection system according to some embodiments of the present application.
FIG. 3 is a flow diagram of an exemplary construction dam mechanics big data based data detection method and/or process, according to some embodiments of the present application.
Detailed Description
In order to better understand the technical solutions, the technical solutions of the present application are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.
In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant guidance. It will be apparent, however, to one skilled in the art that the present application may be practiced without these specific details. In other instances, well-known methods, procedures, systems, compositions, and/or circuits have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present application.
These and other features, functions, methods of execution, and combination of functions and elements of related elements in the structure and economies of manufacture disclosed in the present application may become more apparent upon consideration of the following description with reference to the accompanying drawings, all of which form a part of this application. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the application. It should be understood that the drawings are not to scale. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the application. It should be understood that the drawings are not to scale.
Flowcharts are used herein to illustrate the implementations performed by systems according to embodiments of the present application. It should be expressly understood that the processes performed by the flowcharts may be performed out of order. Rather, these implementations may be performed in the reverse order or simultaneously. In addition, at least one other implementation may be added to the flowchart. One or more implementations may be deleted from the flowchart.
Fig. 1 is a block diagram of an exemplary construction dam mechanics big data based data detection system 300 according to some embodiments of the present application, where the construction dam mechanics big data based data detection system 300 may include a data detection system 100 and a construction dam structural inspection terminal 200.
In some embodiments, as shown in FIG. 2, the data detection system 100 may include a processing engine 110, a network module 120, and a memory 130, the processing engine 110 and the memory 130 communicating through the network module 120.
Processing engine 110 may process the relevant information and/or data to perform one or more of the functions described herein. For example, in some embodiments, processing engine 110 may include at least one processing engine (e.g., a single core processing engine or a multi-core processor). By way of example only, the Processing engine 110 may include a Central Processing Unit (CPU), an Application-Specific Integrated Circuit (ASIC), an Application-Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof.
Network module 120 may facilitate the exchange of information and/or data. In some embodiments, the network module 120 may be any type of wired or wireless network or combination thereof. Merely by way of example, the Network module 120 may include a cable Network, a wired Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a Wireless personal Area Network, a Near Field Communication (NFC) Network, and the like, or any combination thereof. In some embodiments, the network module 120 may include at least one network access point. For example, the network module 120 may include wired or wireless network access points, such as base stations and/or network access points.
The Memory 130 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 130 is used for storing a program, and the processing engine 110 executes the program after receiving the execution instruction.
It will be appreciated that the configuration shown in FIG. 2 is merely illustrative and that data detection system 100 may include more or fewer components than shown in FIG. 2 or have a different configuration than shown in FIG. 2. The components shown in fig. 2 may be implemented in hardware, software, or a combination thereof.
Fig. 3 is a flowchart illustrating an exemplary method and/or process for data detection based on mechanical big data of a construction dam according to some embodiments of the present disclosure, where the method for data detection based on mechanical big data of a construction dam is applied to the data detection system 100 in fig. 1, and may further include the following technical solutions.
Step 100, acquiring an initialized stress condition description label of a plurality of groups of mechanical big data of a construction dam to be subjected to defect detection in a target defect detection mapping list; and determining the defect descriptions of the mechanical big data obtained by pairing the multiple groups of mechanical big data in the multiple groups of target construction dam operation records respectively according to the initialized stress condition description labels.
In this embodiment of the application, the construction dam to be subjected to defect detection is an arch dam or a gravity dam, and the multiple sets of target construction dam operation records are target construction dam operation records (for example, flow records or visual records) obtained by testing the construction dam to be subjected to defect detection under multiple sets of operation load states (for example, different water storage pressure loads, wind loads, and the like).
Further, the stress situation description label may be coordinate information or stress subject information of the construction dam to be subjected to defect detection in a target defect detection mapping list (BIM model space). The defect description of the mechanical big data can be connected into defect feature information corresponding to the mechanical big data, and the expression form of the defect feature information can be a feature vector or a feature map.
For some design ideas that can be implemented independently, the initialization stress condition description tag described in step 100, which is used for obtaining multiple sets of mechanical big data of a construction dam to be subjected to defect detection in a target defect detection mapping list, can be implemented through the following step 100a or step 100 b.
Step 100a, obtaining multiple groups of target construction dam operation records obtained by testing the construction dam to be subjected to defect detection under multiple groups of operation load states, and determining an initialization stress condition description label of multiple groups of mechanical big data of the construction dam to be subjected to defect detection in the target defect detection mapping list according to the multiple groups of target construction dam operation records.
And step 100b, obtaining reflection-type ultrasonic waves (such as reflected waves) corresponding to a plurality of groups of detection-type ultrasonic waves (such as flaw detection ultrasonic waves) sent by a structural flaw detection terminal of the construction dam respectively, and determining an initialization stress condition description label of a plurality of groups of mechanical big data of the construction dam to be subjected to flaw detection in the target flaw detection mapping list according to the reflection-type ultrasonic waves.
In some possible examples, each of the obtained plurality of sets of target construction dam operation records is used as a first target construction dam operation record, and each of the plurality of sets of target construction dam operation records for the mechanical big data pairing is used as a second target construction dam operation record; a part of the operation records of the first target construction dam are consistent with a part of the operation records of the second target construction dam; or the first target construction dam operation record and the second target construction dam operation record do not have a consistent construction dam operation record.
For some embodiments that can be implemented independently, the defect description of the mechanical big data obtained by determining that the multiple sets of mechanical big data are paired in the multiple sets of target construction dam operation records according to the initialized stress condition description label described in step 100 can be implemented by the following technical solution.
Step 110, according to the initialized stress condition description label, determining staged matching item information of the multiple groups of mechanical big data in the multiple groups of target construction dam operation records respectively, and extracting the significance construction dam operation descriptions corresponding to the multiple groups of target construction dam operation records respectively.
For example, the staged pairing transaction information may be understood as related information of local pairing transactions (such as pairing situations of various key structure regions).
And 120, extracting mechanical big data defect descriptions corresponding to the mechanical big data from the significant construction dam operation descriptions corresponding to the multiple groups of target construction dam operation records according to the staged matching item information of the mechanical big data in the multiple groups of target construction dam operation records.
In some possible embodiments, the staged mating event information includes a location of the construction dam operation distribution information (staged mating event) of the staged mating event in the construction dam operation record. Based on this, the step 120 of extracting the mechanical big data defect description corresponding to the mechanical big data from the significant construction dam operation descriptions corresponding to the multiple sets of target construction dam operation records according to the staged matching item information of the mechanical big data in the multiple sets of target construction dam operation records can be realized by the technical solutions described in the steps 121 and 122.
And step 121, for each group of target construction dam operation records in the plurality of groups of target construction dam operation records, extracting the significance construction dam operation description corresponding to the construction dam operation distribution information from the significance construction dam operation description corresponding to the target construction dam operation records according to the construction dam operation distribution information of the staged matching items of the mechanical big data in the target construction dam operation records.
And step 122, determining the extracted significant construction dam operation description corresponding to the construction dam operation distribution information as a mechanical big data defect description corresponding to the mechanical big data.
By the design, the defect description of the large mechanical data corresponding to the large mechanical data can be completely and accurately determined.
And step 130, determining the extracted mechanical big data defect description corresponding to the mechanical big data as the mechanical big data defect description obtained by matching in the plurality of groups of target construction dam operation records.
200, determining a final defect detection result of the construction dam to be subjected to defect detection according to the structural transmission influence information of the mechanical big data which is configured in advance and corresponds to the construction dam to be subjected to defect detection and the mechanical big data defect description of the multiple groups of mechanical big data which are recorded in the operation of the target construction dam corresponding to the multiple groups of operation load states respectively.
In the embodiment of the application, the structural transmission influence information of the mechanical big data includes the mutual influence or interconnection between different mechanical big data, and can be used as a basis from local analysis to overall analysis. Based on this, the integrity of the obtained final defect detection result can be guaranteed.
For some possible embodiments, the determining of the final defect detection result of the construction dam to be subjected to defect detection according to the structural transmission influence information of the previously configured mechanical big data corresponding to the construction dam to be subjected to defect detection described in step 200 and the mechanical big data defect description of the operation record of the multiple sets of mechanical big data in the target construction dam corresponding to the multiple sets of operation load states respectively may be implemented by the technical solutions described in step 210 and step 220.
Step 210, for each mechanical big data in the multiple sets of mechanical big data, determining adjusted mechanical big data defect descriptions of the mechanical big data in different operation load states according to the mechanical big data defect descriptions of the mechanical big data in different operation load states and the mechanical big data defect descriptions of the associated mechanical big data linked with the mechanical big data.
For some design ideas that can be implemented independently, the determining an adjusted mechanical big data defect description of the mechanical big data in different operating load states according to the mechanical big data defect description of the mechanical big data in different operating load states and the mechanical big data defect description of the associated mechanical big data linked to the mechanical big data, which is described in step 210, may include the following: the following embodiments a to C are sequentially performed with each of the plurality of sets of operating load states as a target operating load state.
According to the defect description of the mechanical big data under different operating load states and the first structural transmission influence between the staged matching items of the mechanical big data under different operating load states, performing first round defect description adjustment on the defect description of the mechanical big data under different operating load states to obtain the defect description of the mechanical big data after the first round defect description adjustment.
And according to the mechanical big data defect description of the mechanical big data in the target running load state and the mechanical big data defect description of the associated mechanical big data, which corresponds to the target running load state and has a second structural transmission influence with the mechanical big data, performing second round defect description adjustment on the mechanical big data defect description of the mechanical big data in the target running load state to obtain the mechanical big data defect description after the second round defect description adjustment.
And determining the adjusted mechanical big data defect description of the mechanical big data in the target running load state according to the adjusted mechanical big data defect description of the first round of defect description and the adjusted mechanical big data defect description of the second round of defect description.
Step 220, determining a final defect detection result of the construction dam to be subjected to defect detection according to the adjusted defect descriptions of the mechanical big data corresponding to the multiple groups of mechanical big data and the structural transmission influence information of the mechanical big data which is configured in advance and corresponds to the construction dam to be subjected to defect detection.
It can be understood that the defect description of the adjusted mechanical big data of the mechanical big data under different operation load states is determined firstly, and then the structural transmission influence information of the mechanical big data is combined to carry out comprehensive analysis, so that the final defect detection result can reflect the structural damage of the construction dam from the whole layer, and the integrity and the reliability of the final defect detection result are further ensured.
Under other design considerations, the final defect detection result can be realized by other embodiments. Based on this, the step 200 of determining the final defect detection result of the construction dam to be subjected to defect detection according to the structural transmission influence information of the previously configured mechanical big data corresponding to the construction dam to be subjected to defect detection and the mechanical big data defect descriptions of the multiple groups of mechanical big data respectively in the target construction dam operation records corresponding to the multiple groups of operation load states can be realized by the technical scheme described in the step 200a and the step 200 b.
Step 200a, sorting the mechanical big data defect description of each mechanical big data in the multiple groups of mechanical big data under different operation load states to obtain the sorted mechanical big data defect description corresponding to the mechanical big data.
In the embodiment of the application, the defect descriptions of the mechanical big data under different operation load states can be fused, so that the global defect description of the sorted mechanical big data is obtained. In some possible embodiments, the mechanical big data defect description includes a plurality of sets of mechanical big data quantitative descriptors (eigenvalues or descriptors) of the aspect (dimension) of interest. Based on this, the sorting of the mechanical big data defect descriptions of the mechanical big data in different operation load states described in step 200a to obtain the sorted mechanical big data defect descriptions corresponding to the mechanical big data may include the technical solutions described in step 200a1 and step 200a 2.
Step 200a1, for each focus aspect in the multiple focus aspects, determining multiple sets of quantitative description indexes of the mechanical big data corresponding to the focus aspect in different operating load states, and determining a sorted quantitative description index of the mechanical big data corresponding to the focus aspect by combining the determined multiple sets of quantitative description indexes of the mechanical big data.
For some possible embodiments, the determining, by combining the multiple sets of mechanical big data quantization descriptors determined in step 200a1, the sorted mechanical big data quantization descriptor corresponding to the concerned aspect may be implemented by one of the following three technical solutions.
According to the first technical scheme, the mechanical big data quantitative description index with the highest index value is determined from the multiple groups of mechanical big data quantitative description indexes and serves as the sorted mechanical big data quantitative description index corresponding to the concerned aspect.
And secondly, taking the centralized index of the multiple groups of mechanical big data quantitative description indexes as the sorted mechanical big data quantitative description index corresponding to the concerned aspect.
Obtaining confidence coefficients corresponding to the multiple groups of mechanical big data quantitative description indexes, and determining the sorted mechanical big data quantitative description indexes corresponding to the concerned aspects according to the multiple groups of mechanical big data quantitative description indexes and calculation results of the confidence coefficients corresponding to the multiple groups of mechanical big data quantitative description indexes.
In the third technical solution, the confidence coefficient may be understood as a weight value, and the calculation result may be a weighted sum result.
Step 200a2, determining the sorted mechanical big data defect description corresponding to the mechanical big data according to the sorted mechanical big data quantitative description indexes corresponding to the plurality of groups of attention aspects.
Therefore, by considering the quantitative description indexes of the mechanical big data of different attention layers, the accurate fusion of the defect descriptions of the mechanical big data of different attention layers can be realized, and errors in the fusion process are avoided.
200b, determining a final defect detection result of the construction dam to be subjected to defect detection according to the structural transmission influence information of the mechanical big data which is configured in advance and corresponds to the construction dam to be subjected to defect detection and the sorted mechanical big data defect descriptions which correspond to the multiple groups of mechanical big data respectively.
Under some design ideas which can be independently implemented, the step 200b of determining the final defect detection result of the construction dam to be subjected to defect detection according to the structural transmission influence information of the previously configured mechanical big data corresponding to the construction dam to be subjected to defect detection and the sorted mechanical big data defect descriptions corresponding to the multiple sets of mechanical big data can be implemented by the technical schemes described in the steps 200b1 and 200b 2.
Step 200b1, according to a third structural transmission influence between each mechanical big data included in the mechanical big data structural transmission influence information which is configured in advance and corresponds to the construction dam to be subjected to defect detection, performing defect description adjustment on the sorted mechanical big data defect description corresponding to each of the multiple groups of mechanical big data, and obtaining the sorted mechanical big data defect description after defect description adjustment.
Step 200b2, determining the final defect detection result of the construction dam to be subjected to defect detection according to the adjusted big mechanical data defect description.
In some examples, each of the multiple sets of mechanical big data of the construction dam to be subjected to defect detection is used as first mechanical big data, and each of the mechanical big data having the third structural transmission influence is used as second mechanical big data; the second mechanical big data is big data of stress load of the construction dam; the first mechanics big data comprises at least one of construction dam stress load big data and construction dam structure identification big data. Based on this, the step 200b2 of determining the final defect detection result of the construction dam to be subjected to defect detection according to the sorted mechanical big data defect description after the defect description is adjusted may be implemented by: loading the sorted mechanics big data defect description after the defect description adjustment into a target intelligent defect detection thread which is successfully configured in advance to obtain defect item error information; the defect item error information aims to reflect the error condition between the current defect item and the initialized defect item of the construction dam to be subjected to defect detection; and determining the corrected stress condition description labels of the multiple groups of mechanical big data of the construction dam to be subjected to the defect detection in the target defect detection mapping list according to the defect item error information and the initialized stress condition description labels of the multiple groups of mechanical big data of the construction dam to be subjected to the defect detection in the target defect detection mapping list, and determining the final defect detection result of the construction dam to be subjected to the defect detection according to the corrected stress condition description labels.
By way of example, the target intelligent defect detection thread may be understood as a neural network model (such as CNN, RNN or LSTM, etc.) with defect detection functionality. This enables accurate and reliable determination of the final defect detection result.
In summary, on the basis of obtaining the initialized stress condition description labels of the multiple sets of mechanical big data of the construction dam to be subjected to defect detection in the target defect detection mapping list, the defect descriptions of the multiple sets of mechanical big data obtained by respectively matching the multiple sets of mechanical big data in the multiple sets of target construction dam operation records can be determined based on the initialized stress condition description labels, and finally, the final defect detection result of the construction dam to be subjected to defect detection is determined based on the structural transmission influence information of the mechanical big data which is configured in advance and corresponds to the construction dam to be subjected to defect detection, and the mechanical big data defect descriptions of the multiple sets of mechanical big data in the target construction dam operation records corresponding to the multiple sets of operation load states. Therefore, the structural transmission influence of the multiple groups of mechanical big data under different operation load states can be determined by means of the mechanical big data defect description of the multiple groups of mechanical big data under different operation load states, the structural transmission influence is beneficial to mining to obtain the mechanical big data defect description which is complete, abundant and accurate as far as possible, further, the structural transmission influence between the mechanical big data can be comprehensively analyzed by combining the mechanical big data structural transmission influence information which is configured in advance, the mechanical big data defect description obtained by mining is accurate and reliable as far as possible, the accuracy and the reliability of the determined final defect detection result are guaranteed, the overhaul and the maintenance of the construction dam can be realized through the final defect detection result, and the production accident is avoided.
The skilled person can unambiguously determine some preset, reference, predetermined, set and target technical features/terms, such as threshold values, threshold intervals, threshold ranges, etc., from the above disclosure. For some technical characteristic terms which are not explained, the technical solution can be clearly and completely implemented by those skilled in the art by reasonably and unambiguously deriving the technical solution based on the logical relations in the previous and following paragraphs. Prefixes of unexplained technical feature terms, such as "first", "second", "previous", "next", "current", "history", "latest", "best", "target", "specified", and "real-time", etc., can be unambiguously derived and determined from the context. Suffixes of technical feature terms not to be explained, such as "list", "feature", "sequence", "set", "matrix", "unit", "element", "track", and "list", etc., can also be derived and determined unambiguously from the foregoing and the following.
The above disclosure of the embodiments of the present application will be apparent to those skilled in the art from the above disclosure. It should be understood that the process of deriving and analyzing technical terms, which are not explained, by those skilled in the art based on the above disclosure is based on the contents described in the present application, and thus the above contents are not an inventive judgment of the overall scheme.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.
Also, this application uses specific terminology to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of at least one embodiment of the present application may be combined as appropriate.
In addition, those skilled in the art will recognize that the various aspects of the application may be illustrated and described in terms of several patentable species or contexts, including any new and useful combination of procedures, machines, articles, or materials, or any new and useful modifications thereof. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as a "unit", "component", or "system". Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in at least one computer readable medium.
A computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, and the like, or any suitable combination. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code on a computer readable signal medium may be propagated over any suitable medium, including radio, electrical cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the execution of aspects of the present application may be written in any combination of one or more programming languages, including object oriented programming, such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, or similar conventional programming languages, such as the "C" programming language, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, dynamic programming languages, such as Python, Ruby, and Groovy, or other programming languages. The programming code may execute entirely on the user's computer, as a stand-alone software package, partly on the user's computer, partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as 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), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order of the process elements and sequences described herein, the use of numerical letters, or other designations are not intended to limit the order of the processes and methods unless otherwise indicated in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it should be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware means, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
It should also be appreciated that in the foregoing description of embodiments of the present application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of at least one embodiment of the invention. However, this method of disclosure is not intended to require more features than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Claims (10)

1. A data detection method based on construction dam mechanics big data is characterized by being applied to a data detection system, and at least comprising the following steps:
acquiring an initial stress condition description label of a plurality of groups of mechanical big data of a construction dam to be subjected to defect detection in a target defect detection mapping list; determining the defect descriptions of the mechanical big data obtained by pairing the multiple groups of mechanical big data in the multiple groups of target construction dam operation records respectively according to the initialized stress condition description label; the construction dam to be subjected to defect detection is an arch dam or a gravity dam, and the multiple groups of target construction dam operation records are target construction dam operation records obtained by testing the construction dam to be subjected to defect detection under the multiple groups of operation load states;
and determining a final defect detection result of the construction dam to be subjected to defect detection according to the structural transmission influence information of the mechanical big data which is configured in advance and corresponds to the construction dam to be subjected to defect detection and the mechanical big data defect description of the plurality of groups of mechanical big data in the target construction dam operation records corresponding to the plurality of groups of operation load states.
2. The method of claim 1, wherein the determining, according to the initialized stress situation description label, the mechanical big data defect description obtained by respectively pairing the multiple sets of mechanical big data in the multiple sets of target construction dam operation records comprises:
according to the initialized stress condition description label, determining staged matching item information of the multiple groups of mechanical big data in the multiple groups of target construction dam operation records respectively, and extracting the respective corresponding significant construction dam operation descriptions of the multiple groups of target construction dam operation records;
extracting mechanical big data defect descriptions corresponding to the mechanical big data from the significance construction dam operation descriptions corresponding to the multiple groups of target construction dam operation records according to the staged matching item information of the mechanical big data in the multiple groups of target construction dam operation records;
and determining the extracted mechanical big data defect description corresponding to the mechanical big data as the mechanical big data defect description obtained by matching in the plurality of groups of target construction dam operation records.
3. The method of claim 2, wherein the staging mating information includes construction dam operational distribution information for the staging mating;
the step of extracting the mechanical big data defect description corresponding to the mechanical big data from the significance construction dam operation description corresponding to each of the plurality of groups of target construction dam operation records according to the staged matching item information of the mechanical big data in the plurality of groups of target construction dam operation records comprises the following steps:
for each group of target construction dam operation records in the multiple groups of target construction dam operation records, extracting significant construction dam operation descriptions corresponding to the construction dam operation distribution information from significant construction dam operation descriptions corresponding to the target construction dam operation records according to construction dam operation distribution information of staged matching items of the mechanical big data in the target construction dam operation records;
and determining the extracted significant construction dam operation description corresponding to the construction dam operation distribution information as the mechanical big data defect description corresponding to the mechanical big data.
4. The method according to any one of claims 1 to 3, wherein the determining of the final defect detection result of the construction dam to be subjected to defect detection according to the structural transmission influence information of the previously configured mechanical big data corresponding to the construction dam to be subjected to defect detection and the mechanical big data defect descriptions of the multiple sets of mechanical big data respectively in the target construction dam operation records corresponding to the multiple sets of operation load states comprises:
for each mechanical big data in the multiple groups of mechanical big data, determining defect descriptions of the adjusted mechanical big data of the mechanical big data in different operation load states according to defect descriptions of the mechanical big data in different operation load states and defect descriptions of the mechanical big data of the associated mechanical big data linked with the mechanical big data;
and determining a final defect detection result of the construction dam to be subjected to the defect detection according to the adjusted defect description of the mechanical big data corresponding to the multiple groups of mechanical big data and the structural transmission influence information of the mechanical big data which is configured in advance and corresponds to the construction dam to be subjected to the defect detection.
5. The method of claim 4, wherein the determining the adjusted mechanical big data defect description of the mechanical big data under different operating load states according to the mechanical big data defect description of the mechanical big data under different operating load states and the mechanical big data defect description of the associated mechanical big data linked with the mechanical big data comprises:
taking each operation load state in the multiple groups of operation load states as a target operation load state, and sequentially implementing the following steps:
according to the mechanical big data defect description of the mechanical big data under different operation load states and the first structural transmission influence among all the staged matching items of the mechanical big data under different operation load states, performing first round defect description adjustment on the mechanical big data defect description of the mechanical big data under different operation load states to obtain the mechanical big data defect description after the first round defect description adjustment;
performing second round defect description adjustment on the mechanical big data defect description of the mechanical big data in the target operation load state according to the mechanical big data defect description of the mechanical big data in the target operation load state and the mechanical big data defect description of the associated mechanical big data which corresponds to the target operation load state and has second structural transmission influence with the mechanical big data, so as to obtain second round defect description of the mechanical big data after the second round defect description adjustment;
and determining the adjusted mechanical big data defect description of the mechanical big data in the target running load state according to the mechanical big data defect description adjusted by the first round of defect description and the mechanical big data defect description adjusted by the second round of defect description.
6. The method as claimed in claim 1, wherein the determining of the final defect detection result of the construction dam to be defect-detected according to the mechanics big data structural transmission influence information of the pre-configuration corresponding to the construction dam to be defect-detected and the mechanics big data defect description of the operation record of the target construction dam corresponding to the plurality of sets of operation load states of the mechanics big data respectively comprises: for each mechanical big data in the multiple groups of mechanical big data, sorting the mechanical big data defect description of the mechanical big data under different operation load states to obtain sorted mechanical big data defect description corresponding to the mechanical big data; determining a final defect detection result of the construction dam to be subjected to defect detection according to the preset mechanical big data structural transmission influence information corresponding to the construction dam to be subjected to defect detection and the sorted mechanical big data defect descriptions corresponding to the multiple groups of mechanical big data;
the mechanical big data defect description comprises a plurality of groups of mechanical big data quantitative description indexes in the concerned aspect; the sorting of the mechanical big data defect description of the mechanical big data under different operation load states to obtain the sorted mechanical big data defect description corresponding to the mechanical big data comprises the following steps: for each concerned aspect in the plurality of groups of concerned aspects, determining a plurality of groups of quantized mechanical big data description indexes corresponding to the concerned aspects of the mechanical big data in different running load states, and determining the sorted quantized mechanical big data description indexes corresponding to the concerned aspects by combining the determined plurality of groups of quantized mechanical big data description indexes; determining the sorted mechanical big data defect description corresponding to the mechanical big data according to the sorted mechanical big data quantitative description indexes corresponding to the plurality of groups of concerned aspects;
wherein, the step of determining the sorted mechanical big data quantitative description index corresponding to the concerned aspect in combination with the determined multiple groups of mechanical big data quantitative description indexes is realized by any one of the following steps: determining a mechanical big data quantitative description index with the highest index value from the multiple groups of mechanical big data quantitative description indexes, and taking the mechanical big data quantitative description index as a sorted mechanical big data quantitative description index corresponding to the concerned aspect; taking a centralized index of the multiple groups of mechanical big data quantitative description indexes as a sorted mechanical big data quantitative description index corresponding to the concerned aspect; obtaining confidence coefficients corresponding to the multiple groups of mechanical big data quantitative description indexes, and determining the sorted mechanical big data quantitative description indexes corresponding to the concerned aspect according to the calculation results between the multiple groups of mechanical big data quantitative description indexes and the confidence coefficients corresponding to the multiple groups of mechanical big data quantitative description indexes.
7. The method as claimed in claim 6, wherein the determining a final defect detection result of the construction dam to be defect-detected according to the structurally transmitted influence information of the previously configured mechanical big data corresponding to the construction dam to be defect-detected and the sorted mechanical big data defect descriptions corresponding to the multiple sets of mechanical big data includes: performing defect description adjustment on the sorted mechanical big data defect descriptions corresponding to the multiple groups of mechanical big data respectively according to a third structural transmission influence among all the mechanical big data included in the mechanical big data structural transmission influence information which is configured in advance and corresponds to the construction dam to be subjected to defect detection, so as to obtain the sorted mechanical big data defect descriptions after the defect description adjustment; determining a final defect detection result of the construction dam to be subjected to defect detection according to the adjusted big mechanical data defect description;
each mechanical big data in the multiple groups of mechanical big data of the construction dam to be subjected to defect detection is used as first mechanical big data, and each mechanical big data in each mechanical big data with the third structural transmission influence is used as second mechanical big data; the second mechanical big data is big data of stress load of the construction dam; the first mechanics big data comprises at least one of construction dam stress load big data and construction dam structure identification big data;
determining a final defect detection result of the construction dam to be subjected to defect detection according to the adjusted mechanical big data defect description, wherein the final defect detection result comprises the following steps: loading the sorted mechanics big data defect description after the defect description adjustment into a target intelligent defect detection thread which is successfully configured in advance to obtain defect item error information; the defect item error information aims to reflect the error condition between the current defect item and the initialized defect item of the construction dam to be subjected to defect detection; and determining the corrected stress condition description labels of the multiple groups of mechanical big data of the construction dam to be subjected to the defect detection in the target defect detection mapping list according to the defect item error information and the initialized stress condition description labels of the multiple groups of mechanical big data of the construction dam to be subjected to the defect detection in the target defect detection mapping list, and determining the final defect detection result of the construction dam to be subjected to the defect detection according to the corrected stress condition description labels.
8. The method as claimed in claim 1, wherein the obtaining of the initialized stress situation description label of the multiple sets of mechanical big data of the construction dam to be subjected to defect detection in the target defect detection mapping list is implemented by one of the following steps:
obtaining a plurality of groups of target construction dam operation records obtained by testing the construction dam to be subjected to defect detection under a plurality of groups of operation load states, and determining initialization stress condition description labels of a plurality of groups of mechanical big data of the construction dam to be subjected to defect detection in the target defect detection mapping list according to the plurality of groups of target construction dam operation records;
and acquiring reflection-type ultrasonic waves corresponding to a plurality of groups of detection-type ultrasonic waves sent by a structural flaw detection terminal of the construction dam respectively, and determining an initialization stress condition description label of a plurality of groups of mechanical big data of the construction dam to be subjected to flaw detection in the target flaw detection mapping list according to the reflection-type ultrasonic waves.
9. The method of claim 8, wherein each of the obtained plurality of sets of target construction dam operation records is used as a first target construction dam operation record, and each of the plurality of sets of target construction dam operation records for the mechanical big data pairing is used as a second target construction dam operation record; a part of the operation records of the first target construction dam are consistent with a part of the operation records of the second target construction dam; or the first target construction dam operation record and the second target construction dam operation record do not have a consistent construction dam operation record.
10. A data detection system based on mechanical big data of a construction dam, which is characterized by comprising a processing engine, a network module and a memory, wherein the processing engine and the memory are communicated through the network module, and the processing engine is used for reading a computer program from the memory and running the computer program to realize the method of any one of claims 1-9.
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