CN112036755A - Supervision method and system for building engineering quality detection - Google Patents
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Abstract
The invention discloses a supervision method and a supervision system for building engineering quality detection. When the scheme is applied, firstly, a construction site image set of each area is obtained, secondly, data conversion is carried out on the construction site image set, further, preliminary raw material detection is carried out on the building material quality data, whether the obtained first target detection result meets the preset building material quality information or not is judged, if yes, the building engineering site safety data are inquired in the construction site image set, the change degree between the building engineering site safety data and the historical safety data is obtained, and the change ratio is determined according to the change degree. Furthermore, target building information is collected, and a construction progress and progress value are obtained. And finally, counting the preliminary detection information, the variation ratio and the progress value, comparing the statistical result with a preset standard value and judging whether the statistical result is matched with the preset standard value. By the design, the construction engineering site can be comprehensively supervised, and the engineering quality can be guaranteed.
Description
Technical Field
The disclosure relates to the technical field of engineering quality detection, in particular to a supervision method and a supervision system for building engineering quality detection.
Background
Along with the rapid development of urban modern construction in China, the structure of a building is more and more complex, however, in the construction, the engineering quality detection is an important link of the construction engineering, the engineering quality detection is mainly carried out through manpower and equipment at present, but along with the continuous increase of engineering projects, the detection is not in place through the manpower detection, and thus the engineering quality cannot be guaranteed.
Disclosure of Invention
In order to solve the technical problems in the related art, the present disclosure provides a supervision method and system for building engineering quality detection.
The invention provides a supervision method for detecting the quality of construction engineering, which is applied to supervision equipment and comprises the following steps:
acquiring a construction site image set of each area shot by a camera device in a construction engineering site within a preset historical time period;
aiming at one area, carrying out data conversion on a construction site image set of the area to obtain an engineering quality data set; the engineering quality data set comprises building material quality data, real-time data of construction progress and construction engineering site safety data;
performing preliminary raw material detection on the building material quality data included in the engineering quality data set to obtain a first target detection result including preliminary detection information and a detection list corresponding to the preliminary detection information;
judging whether the first target detection result meets preset building material quality information or not, and if not, sending an alarm signal; if yes, inquiring the construction project site safety data in a construction site image set based on the first target detection result, acquiring the variation degree between the construction project site safety data and the historical safety data, and determining a variation ratio according to the variation degree;
acquiring target building information in the process of shooting the construction project site, and acquiring the construction progress of the target building information in the construction project site;
acquiring a progress value of the target building information within a preset time period by using the construction progress of the shot target building image in the target building information;
and counting the preliminary detection information, the variation ratio and the progress value to obtain a statistical result, comparing the statistical result with a preset standard value, judging whether the statistical result is matched with the preset standard value, if not, sending an alarm prompt by the camera device to remind a worker to perform secondary detection, and if so, judging that the quality of the building engineering is qualified.
In an alternative embodiment, data converting the set of job site images for the area into a project quality data set includes:
classifying the construction site image set of the region according to types to obtain a classification feature list;
performing data feature extraction on classification parameters matched with classification data in a preset classification data thread according to a plurality of mutually matched classification features in the classification feature list to obtain classification feature information and corresponding classification state information;
analyzing the image types in the construction site image set according to the classification characteristic information, screening classification state information corresponding to the classification characteristic information of the image types to form a classification characteristic curve graph, and acquiring a parameter distribution trace graph of each type of parameter read into the classification characteristic curve graph;
comparing the similarity of the obtained parameter distribution track graph with a preset parameter graph, and integrating the classification characteristics which are matched with each other and correspond to the parameter distribution track with the similarity consistent with the preset parameter graph to obtain an image type classification sequence;
and sequentially carrying out data conversion on the key images in the construction site image set according to the sequence size in the image type classification sequence to obtain an engineering quality data set.
In an alternative embodiment, performing a preliminary raw material test on the building material quality data included in the engineering quality data set to obtain a first target test result including preliminary test information and a test list corresponding to the preliminary test information includes:
acquiring at least one type of building material quality data and parameter information corresponding to the at least one type of building material quality data; wherein the parameter information is used for characterizing material component proportions in the building material quality data;
carrying out binary processing on the at least one type of building material quality data to obtain at least two types of target building material quality data corresponding to the building material quality data;
de-noising the at least one building material quality data and the at least two target building material quality data to obtain at least two current building material quality data with different weight coefficients corresponding to each building material quality data and the target building material quality data;
performing parameter calculation on the at least two types of current building material quality data with different weight coefficients according to a preset calculation mode to obtain a raw material detection template; the raw material detection template comprises at least two raw material databases with different mapping weight coefficients;
acquiring building material quality data to be detected, mapping at least two types of noise removal processing data obtained through noise removal processing to the raw material detection template after the noise removal processing is carried out on the building material quality data, and respectively carrying out primary raw material detection processing on the at least two types of noise removal processing data through at least two raw material databases with different mapping weight coefficients in the raw material detection template to obtain at least two pieces of primary detection information respectively corresponding to the at least two types of noise removal processing data;
and under the condition that the at least two pieces of preliminary detection information are matched with a preset detection result, identifying the building material quality data as a first target detection result of a detection list corresponding to the preliminary detection information.
In an alternative embodiment, the determining whether the first target detection result meets preset building material quality information includes:
acquiring a detection parameter sequence of at least one detection item corresponding to the first target detection result; wherein, the sequence value of the detection parameter sequence of the detection item is matched with the item type of the detection item;
determining a target detection item of the first target detection result from the at least one detection item based on the detection parameter sequence of the at least one detection item;
and judging whether the detection result of the target detection item meets the preset building material quality information or not according to a preset judgment standard.
In an alternative embodiment, querying the constructional engineering site safety data in the construction site image set based on the first target detection result, acquiring a variation degree between the constructional engineering site safety data and historical safety data, and determining a variation ratio according to the variation degree includes:
determining construction site operation information, finished construction quantity data in a preset time period and quality control data in the construction site image set;
determining quality control data in the completed construction amount data within a preset time period based on the construction site operation information;
inquiring the construction project site safety data in the construction site image according to the quality control data, carrying out safety accuracy verification on the construction project site safety data, and judging that the construction project site is in a safe state when the construction quantity data corresponding to the construction project site safety data is consistent with the finished construction quantity data in a preset time period;
and obtaining the change degree between the on-site safety data and the historical safety data of the constructional engineering based on the safety data corresponding to the safety state obtained by verification, and determining a change ratio according to the change degree.
The invention also provides a monitoring system for detecting the quality of the building engineering, which comprises monitoring equipment and a camera device, wherein the monitoring equipment is communicated with the camera device;
the supervisory device is to:
acquiring a construction site image set of each area shot by a camera device in a construction engineering site within a preset historical time period;
the camera device is to communicate:
the construction site image sets of all the areas shot in a preset historical time period;
the supervisory device is to:
aiming at one area, carrying out data conversion on a construction site image set of the area to obtain an engineering quality data set; the engineering quality data set comprises building material quality data, real-time data of construction progress and construction engineering site safety data;
performing preliminary raw material detection on the building material quality data included in the engineering quality data set to obtain a first target detection result including preliminary detection information and a detection list corresponding to the preliminary detection information;
judging whether the first target detection result meets preset building material quality information or not, and if not, sending an alarm signal; if yes, inquiring the construction project site safety data in a construction site image set based on the first target detection result, acquiring the variation degree between the construction project site safety data and the historical safety data, and determining a variation ratio according to the variation degree;
acquiring target building information in the process of shooting the construction project site, and acquiring the construction progress of the target building information in the construction project site;
acquiring a progress value of the target building information within a preset time period by using the construction progress of the shot target building image in the target building information;
and counting the preliminary detection information, the variation ratio and the progress value to obtain a statistical result, comparing the statistical result with a preset standard value, judging whether the statistical result is matched with the preset standard value, if not, sending an alarm prompt by the camera device to remind a worker to perform secondary detection, and if so, judging that the quality of the building engineering is qualified.
In an alternative embodiment, the supervising device is configured to:
classifying the construction site image set of the region according to types to obtain a classification feature list;
performing data feature extraction on classification parameters matched with classification data in a preset classification data thread according to a plurality of mutually matched classification features in the classification feature list to obtain classification feature information and corresponding classification state information;
analyzing the image types in the construction site image set according to the classification characteristic information, screening classification state information corresponding to the classification characteristic information of the image types to form a classification characteristic curve graph, and acquiring a parameter distribution trace graph of each type of parameter read into the classification characteristic curve graph;
comparing the similarity of the obtained parameter distribution track graph with a preset parameter graph, and integrating the classification characteristics which are matched with each other and correspond to the parameter distribution track with the similarity consistent with the preset parameter graph to obtain an image type classification sequence;
and sequentially carrying out data conversion on the key images in the construction site image set according to the sequence size in the image type classification sequence to obtain an engineering quality data set.
In an alternative embodiment, the supervising device is configured to:
acquiring at least one type of building material quality data and parameter information corresponding to the at least one type of building material quality data; wherein the parameter information is used for characterizing material component proportions in the building material quality data;
carrying out binary processing on the at least one type of building material quality data to obtain at least two types of target building material quality data corresponding to the building material quality data;
de-noising the at least one building material quality data and the at least two target building material quality data to obtain at least two current building material quality data with different weight coefficients corresponding to each building material quality data and the target building material quality data;
performing parameter calculation on the at least two types of current building material quality data with different weight coefficients according to a preset calculation mode to obtain a raw material detection template; the raw material detection template comprises at least two raw material databases with different mapping weight coefficients;
acquiring building material quality data to be detected, mapping at least two types of noise removal processing data obtained through noise removal processing to the raw material detection template after the noise removal processing is carried out on the building material quality data, and respectively carrying out primary raw material detection processing on the at least two types of noise removal processing data through at least two raw material databases with different mapping weight coefficients in the raw material detection template to obtain at least two pieces of primary detection information respectively corresponding to the at least two types of noise removal processing data;
and under the condition that the at least two pieces of preliminary detection information are matched with a preset detection result, identifying the building material quality data as a first target detection result of a detection list corresponding to the preliminary detection information.
In an alternative embodiment, the supervising device is configured to:
acquiring a detection parameter sequence of at least one detection item corresponding to the first target detection result; wherein, the sequence value of the detection parameter sequence of the detection item is matched with the item type of the detection item;
determining a target detection item of the first target detection result from the at least one detection item based on the detection parameter sequence of the at least one detection item;
and judging whether the detection result of the target detection item meets the preset building material quality information or not according to a preset judgment standard.
In an alternative embodiment, the supervising device is configured to:
determining construction site operation information, finished construction quantity data in a preset time period and quality control data in the construction site image set;
determining quality control data in the completed construction amount data within a preset time period based on the construction site operation information;
inquiring the construction project site safety data in the construction site image according to the quality control data, carrying out safety accuracy verification on the construction project site safety data, and judging that the construction project site is in a safe state when the construction quantity data corresponding to the construction project site safety data is consistent with the finished construction quantity data in a preset time period;
and obtaining the change degree between the on-site safety data and the historical safety data of the constructional engineering based on the safety data corresponding to the safety state obtained by verification, and determining a change ratio according to the change degree.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects.
The invention provides a monitoring method and a system for detecting the quality of constructional engineering, which comprises the steps of firstly obtaining a construction site image set of each area, secondly carrying out data conversion on the construction site image set to obtain an engineering quality data set, further carrying out primary raw material detection on the construction material quality data included in the engineering quality data set, judging whether the obtained first target detection result accords with the preset construction material quality information, if not, sending an alarm signal, if so, inquiring the construction engineering site safety data in the construction site image set based on the first target detection result, obtaining the change degree between the construction engineering site safety data and the historical safety data, and determining the change ratio according to the change degree. Furthermore, in the process of shooting the construction engineering site, target building information is collected, and the construction progress and the progress value are obtained. And finally, counting the preliminary detection information, the variation ratio and the progress value to obtain a statistical result, comparing the statistical result with a preset standard value and judging whether the statistical result is matched with the preset standard value, if not, sending an alarm prompt through a camera device to remind a worker to carry out secondary detection, and if so, judging that the quality of the building engineering is qualified. By the design, the construction engineering site can be comprehensively supervised, and the engineering quality can be guaranteed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a schematic communication architecture diagram of a monitoring system for building engineering quality detection according to an embodiment of the present invention.
Fig. 2 is a flowchart of a supervision method for building engineering quality detection according to an embodiment of the present invention.
Fig. 3 is a block diagram of a monitoring device for detecting quality of construction engineering according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a hardware structure of a monitoring device according to an embodiment of the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
To solve the above problem, the present disclosure provides a communication architecture diagram of a supervision system 100 for building engineering quality detection as shown in fig. 1. Wherein the supervision system 100 for the quality detection of the construction project comprises the supervision apparatus 200 and the camera 300. In this embodiment, the monitoring device 200 may be a desktop computer, a mobile phone, or the like, and the camera 300 may be a camera or the like.
Fig. 2 is a flow chart of a supervision method of construction engineering quality detection according to the present disclosure, which may be applied to a supervision device, and which may include the following:
step S110, acquiring a construction site image set of each area shot by a camera device in a construction engineering site within a preset historical time period;
step S120, aiming at one area, carrying out data conversion on a construction site image set of the area to obtain an engineering quality data set; the engineering quality data set comprises building material quality data, real-time data of construction progress and construction engineering site safety data;
step S130, performing preliminary raw material detection on the building material quality data included in the engineering quality data set to obtain a first target detection result including preliminary detection information and a detection list corresponding to the preliminary detection information;
step S140, judging whether the first target detection result meets preset building material quality information or not, and if not, sending an alarm signal; if yes, inquiring the construction project site safety data in a construction site image set based on the first target detection result, acquiring the variation degree between the construction project site safety data and the historical safety data, and determining a variation ratio according to the variation degree;
step S150, in the process of shooting the construction project site, acquiring target construction information and acquiring the construction progress of the target construction information in the construction project site;
step S160, acquiring a progress value of the target building information within a preset time period by using the construction progress of the shot target building image in the target building information;
step S170, counting the preliminary detection information, the change ratio and the progress value to obtain a statistical result, comparing the statistical result with a preset standard value and judging whether the statistical result is matched with the preset standard value, if not, sending an alarm prompt through the camera device to remind a worker to carry out secondary detection, and if so, judging that the quality of the building engineering is qualified.
Executing the content described in the steps S110 to S170, firstly obtaining a construction site image set of each area, secondly performing data conversion on the construction site image set to obtain an engineering quality data set, further performing preliminary raw material detection on the building material quality data included in the engineering quality data set, and judging whether the obtained first target detection result meets the preset building material quality information, if not, sending an alarm signal, if so, inquiring the building engineering site safety data in the construction site image set based on the first target detection result, obtaining the change degree between the building engineering site safety data and the historical safety data, and determining the change ratio according to the change degree. Furthermore, in the process of shooting the construction engineering site, target building information is collected, and the construction progress and the progress value are obtained. And finally, counting the preliminary detection information, the variation ratio and the progress value to obtain a statistical result, comparing the statistical result with a preset standard value and judging whether the statistical result is matched with the preset standard value, if not, sending an alarm prompt through a camera device to remind a worker to carry out secondary detection, and if so, judging that the quality of the building engineering is qualified. By the design, the construction engineering site can be comprehensively supervised, and the engineering quality can be guaranteed.
In a specific implementation, in order to obtain the engineering quality data set quickly and accurately, the data conversion of the construction site image set of the area to obtain the engineering quality data set described in step S120 may specifically include the following contents described in sub-steps S1201-S1205:
step S1201, classifying the construction site image set of the region according to types to obtain a classification feature list;
step S1202, performing data feature extraction on classification parameters matched with classification data in a preset classification data thread according to a plurality of mutually matched classification features in the classification feature list to obtain classification feature information and corresponding classification state information;
step S1203, analyzing the image types in the construction site image set according to the classification characteristic information, screening classification state information corresponding to the classification characteristic information of the image types to form a classification characteristic curve graph, and collecting a parameter distribution trace graph of each type of parameter in the classification parameters, which is read into the classification characteristic curve graph;
step S1204, comparing the similarity of the obtained parameter distribution track graph with a preset parameter graph, and integrating the classification characteristics which are matched with each other and correspond to the parameter distribution track with the similarity consistent with the preset parameter graph to obtain an image type classification sequence;
and S1205, sequentially performing data conversion on the key images in the construction site image set according to the sequence size in the image type classification sequence to obtain an engineering quality data set.
Executing the content described in the steps S1201-S1205, classifying the construction site image set according to types to obtain a classification feature list, performing data feature extraction on classification parameters, further analyzing the image types in the construction site image set according to the obtained classification feature information and screening classification state information to form a classification feature curve graph, acquiring a parameter distribution track graph of each type of parameter read into the classification feature curve graph, performing similarity comparison between the parameter distribution track graph and a preset parameter graph, integrating the classification features with consistent similarity to obtain an image type classification sequence, and sequentially performing data conversion on key images in the construction site image set according to the sequence size in the image type classification sequence to obtain an engineering quality data set. Therefore, the engineering quality data set can be obtained quickly and accurately.
In specific implementation, in order to avoid a delay of a construction project due to a quality problem of a construction material, the preliminary raw material detection on the construction material quality data included in the construction quality data set described in step S130 to obtain a first target detection result including preliminary detection information and a detection list corresponding to the preliminary detection information may specifically include the contents described in the following substeps 1301-substep S1306:
step S1301, acquiring at least one type of building material quality data and parameter information corresponding to the at least one type of building material quality data;
in this embodiment, the parameter information is used to characterize the material composition ratio in the building material quality data.
Step S1302, performing binary processing on the at least one building material quality data to obtain at least two kinds of target building material quality data corresponding to the building material quality data;
step S1303, performing denoising processing on the at least one building material quality data and the at least two kinds of target building material quality data to obtain at least two kinds of current building material quality data with different weighting coefficients corresponding to each kind of building material quality data and the target building material quality data;
step S1304, performing parameter calculation on at least two kinds of current building material quality data with different weight coefficients according to a preset calculation mode to obtain a raw material detection template; the raw material detection template comprises at least two raw material databases with different mapping weight coefficients;
step S1305, acquiring building material quality data to be detected, performing denoising processing on the building material quality data, mapping at least two types of denoising processing data obtained through the denoising processing to the raw material detection template, and performing preliminary raw material detection processing on the at least two types of denoising processing data through at least two raw material databases having different mapped weight coefficients in the raw material detection template, to obtain at least two pieces of preliminary detection information corresponding to the at least two types of denoising processing data, respectively;
step S1306, under the condition that the at least two pieces of preliminary detection information match preset detection results, identifying the building material quality data as a first target detection result of a detection list corresponding to the preliminary detection information.
Executing the contents described in the steps S1301 to S1306, first obtaining at least one kind of building material quality data and corresponding parameter information, and performing binary processing on the building material quality data to obtain at least two kinds of corresponding target building material quality data. And secondly, denoising the at least one building material quality data and the at least two target building material quality data, and performing parameter calculation on the two current building material quality data with different weight coefficients to obtain a raw material detection template. And finally, acquiring the quality data of the building material to be detected, mapping at least two types of noise-removed processed data subjected to noise removal into a raw material detection template for detection, and further identifying the quality data of the building material as a first target detection result of a detection list corresponding to the preliminary detection information under the condition that at least two corresponding pieces of preliminary detection information are matched with a preset detection result. Therefore, the building material quality is detected, and the building engineering delay caused by the quality problem of the building material can be avoided.
In a specific implementation, the determining whether the first target detection result meets the preset building material quality information described in step S140 may specifically include the following steps described in sub-steps S1401 to S1403:
step 1401, acquiring a detection parameter sequence of at least one detection item corresponding to the first target detection result;
in this embodiment, the sequence value of the detection parameter sequence of the detection item and the item type of the detection item are matched with each other.
Step S1402, determining a target detection item of the first target detection result from the at least one detection item based on the detection parameter sequence of the at least one detection item;
step S1403, determining whether the detection result of the target detection item meets preset building material quality information according to a preset determination criterion.
In a specific implementation, in order to quickly determine the safety of the construction engineering site, the method described in step S140 may further include the following sub-steps S1404 to S1407, in which the first target detection result is used to query the construction engineering site safety data in the construction site image set, the degree of change between the construction engineering site safety data and the historical safety data is obtained, and the change ratio is determined according to the degree of change:
step S1404, determining construction site operation information, completed construction amount data within a preset time period and quality control data in the construction site image set;
step S1405, determining quality control data among the completed construction amount data within a preset time period based on the job site operation information;
step S1406, according to the quality control data, inquiring the construction project site safety data in the construction site image in a centralized manner, carrying out safety accuracy verification on the construction project site safety data, and when the construction quantity data corresponding to the construction project site safety data is consistent with the finished construction quantity data in a preset time period, judging that the construction project site is in a safe state;
step S1407, obtaining the variation degree between the construction project site safety data and the historical safety data based on the safety data corresponding to the safety state obtained by verification, and determining the variation ratio according to the variation degree.
By executing the contents described in steps S1404 to S1407, the safety of the construction site can be quickly judged.
In specific implementation, in order to avoid being tampered by third party information during the detection of the quality of the architectural engineering and further accurately determine the engineering quality condition of the architectural engineering, the statistics of the preliminary detection information, the change ratio and the progress value described in step S170 is performed to obtain a statistical result, and the statistical result is compared with a preset standard value and is determined to be matched, which may specifically include the contents described in the following substeps S1701 and substep S1702:
step S1701, performing statistics on the preliminary detection information, the variation ratio, and the progress value according to any one of a plurality of preset statistical methods to obtain a statistical result, and performing regression analysis calculation on the obtained statistical result to obtain a regression equation;
step 1702, comparing the statistical result with a preset standard value according to the regression equation, and determining whether the statistical result is matched with the preset standard value.
The contents described in step S1701 and step S1702 are compared and determined by using the statistical result and the preset standard value, so that tampering by third party information during detection of the construction quality can be avoided, and further the construction quality condition of the construction can be accurately determined.
Based on the same inventive concept, the present disclosure also provides a monitoring system for building engineering quality detection, comprising a monitoring device and a camera device, wherein the monitoring device is communicated with the camera device;
the supervisory device is to:
acquiring a construction site image set of each area shot by a camera device in a construction engineering site within a preset historical time period;
the camera device is to communicate:
the construction site image sets of all the areas shot in a preset historical time period;
the supervisory device is to:
aiming at one area, carrying out data conversion on a construction site image set of the area to obtain an engineering quality data set; the engineering quality data set comprises building material quality data, real-time data of construction progress and construction engineering site safety data;
performing preliminary raw material detection on the building material quality data included in the engineering quality data set to obtain a first target detection result including preliminary detection information and a detection list corresponding to the preliminary detection information;
judging whether the first target detection result meets preset building material quality information or not, and if not, sending an alarm signal; if yes, inquiring the construction project site safety data in a construction site image set based on the first target detection result, acquiring the variation degree between the construction project site safety data and the historical safety data, and determining a variation ratio according to the variation degree;
acquiring target building information in the process of shooting the construction project site, and acquiring the construction progress of the target building information in the construction project site;
acquiring a progress value of the target building information within a preset time period by using the construction progress of the shot target building image in the target building information;
and counting the preliminary detection information, the variation ratio and the progress value to obtain a statistical result, comparing the statistical result with a preset standard value, judging whether the statistical result is matched with the preset standard value, if not, sending an alarm prompt by the camera device to remind a worker to perform secondary detection, and if so, judging that the quality of the building engineering is qualified.
In an alternative embodiment, the supervising device is configured to:
classifying the construction site image set of the region according to types to obtain a classification feature list;
performing data feature extraction on classification parameters matched with classification data in a preset classification data thread according to a plurality of mutually matched classification features in the classification feature list to obtain classification feature information and corresponding classification state information;
analyzing the image types in the construction site image set according to the classification characteristic information, screening classification state information corresponding to the classification characteristic information of the image types to form a classification characteristic curve graph, and acquiring a parameter distribution trace graph of each type of parameter read into the classification characteristic curve graph;
comparing the similarity of the obtained parameter distribution track graph with a preset parameter graph, and integrating the classification characteristics which are matched with each other and correspond to the parameter distribution track with the similarity consistent with the preset parameter graph to obtain an image type classification sequence;
and sequentially carrying out data conversion on the key images in the construction site image set according to the sequence size in the image type classification sequence to obtain an engineering quality data set.
In an alternative embodiment, the supervising device is configured to:
acquiring at least one type of building material quality data and parameter information corresponding to the at least one type of building material quality data; wherein the parameter information is used for characterizing material component proportions in the building material quality data;
carrying out binary processing on the at least one type of building material quality data to obtain at least two types of target building material quality data corresponding to the building material quality data;
de-noising the at least one building material quality data and the at least two target building material quality data to obtain at least two current building material quality data with different weight coefficients corresponding to each building material quality data and the target building material quality data;
performing parameter calculation on the at least two types of current building material quality data with different weight coefficients according to a preset calculation mode to obtain a raw material detection template; the raw material detection template comprises at least two raw material databases with different mapping weight coefficients;
acquiring building material quality data to be detected, mapping at least two types of noise removal processing data obtained through noise removal processing to the raw material detection template after the noise removal processing is carried out on the building material quality data, and respectively carrying out primary raw material detection processing on the at least two types of noise removal processing data through at least two raw material databases with different mapping weight coefficients in the raw material detection template to obtain at least two pieces of primary detection information respectively corresponding to the at least two types of noise removal processing data;
and under the condition that the at least two pieces of preliminary detection information are matched with a preset detection result, identifying the building material quality data as a first target detection result of a detection list corresponding to the preliminary detection information.
In an alternative embodiment, the supervising device is configured to:
acquiring a detection parameter sequence of at least one detection item corresponding to the first target detection result; wherein, the sequence value of the detection parameter sequence of the detection item is matched with the item type of the detection item;
determining a target detection item of the first target detection result from the at least one detection item based on the detection parameter sequence of the at least one detection item;
and judging whether the detection result of the target detection item meets the preset building material quality information or not according to a preset judgment standard.
In an alternative embodiment, the supervising device is configured to:
determining construction site operation information, finished construction quantity data in a preset time period and quality control data in the construction site image set;
determining quality control data in the completed construction amount data within a preset time period based on the construction site operation information;
inquiring the construction project site safety data in the construction site image according to the quality control data, carrying out safety accuracy verification on the construction project site safety data, and judging that the construction project site is in a safe state when the construction quantity data corresponding to the construction project site safety data is consistent with the finished construction quantity data in a preset time period;
and obtaining the change degree between the on-site safety data and the historical safety data of the constructional engineering based on the safety data corresponding to the safety state obtained by verification, and determining a change ratio according to the change degree.
On the basis, please refer to fig. 3 in combination, the present invention provides a monitoring apparatus 400 for detecting quality of construction engineering, which is applied to monitoring equipment.
The image set acquisition module 410 is used for acquiring a construction site image set of each area shot by a camera device in a construction project site within a preset historical time period.
The image set conversion module 420 is configured to perform data conversion on a construction site image set of one of the regions to obtain an engineering quality data set; the engineering quality data set comprises building material quality data, real-time data of construction progress and construction engineering site safety data.
And the building material detection module 430 is configured to perform preliminary raw material detection on the building material quality data included in the engineering quality data set to obtain a first target detection result including preliminary detection information and a detection list corresponding to the preliminary detection information.
The safety data query module 440 is configured to determine whether the first target detection result meets preset building material quality information, and if not, send an alarm signal; if yes, inquiring the construction project site safety data in the construction site image set based on the first target detection result, acquiring the variation degree between the construction project site safety data and the historical safety data, and determining the variation ratio according to the variation degree.
The construction progress acquiring module 450 is configured to acquire target building information during shooting of the construction project site, and acquire a construction progress of the target building information in the construction project site.
The progress value obtaining module 460 is configured to obtain a progress value of the target building information within a preset time period by using a construction progress of the photographed target building image in the target building information.
And the engineering quality judging module 470 is used for counting the preliminary detection information, the variation ratio and the progress value to obtain a statistical result, comparing the statistical result with a preset standard value and judging whether the statistical result is matched, if not, sending an alarm prompt by the camera device to remind a worker to perform secondary detection, and if so, judging that the quality of the building engineering is qualified.
On the basis of the above, please refer to fig. 4 in combination, there is provided a monitoring apparatus 200, which includes a processor 211, and a memory 212 and a bus 213 connected to the processor 211; wherein, the processor 211 and the memory 212 complete the communication with each other through the bus 213; the processor 211 is configured to call the program instructions in the memory 212 to execute the above-mentioned method.
Further, a readable storage medium is provided, on which a program is stored, which when executed by a processor implements the method described above.
It will be understood that the invention is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (10)
1. A supervision method for building engineering quality detection is applied to supervision equipment, and comprises the following steps:
acquiring a construction site image set of each area shot by a camera device in a construction engineering site within a preset historical time period;
aiming at one area, carrying out data conversion on a construction site image set of the area to obtain an engineering quality data set; the engineering quality data set comprises building material quality data, real-time data of construction progress and construction engineering site safety data;
performing preliminary raw material detection on the building material quality data included in the engineering quality data set to obtain a first target detection result including preliminary detection information and a detection list corresponding to the preliminary detection information;
judging whether the first target detection result meets preset building material quality information or not, and if not, sending an alarm signal; if yes, inquiring the construction project site safety data in a construction site image set based on the first target detection result, acquiring the variation degree between the construction project site safety data and the historical safety data, and determining a variation ratio according to the variation degree;
acquiring target building information in the process of shooting the construction project site, and acquiring the construction progress of the target building information in the construction project site;
acquiring a progress value of the target building information within a preset time period by using the construction progress of the shot target building image in the target building information;
and counting the preliminary detection information, the variation ratio and the progress value to obtain a statistical result, comparing the statistical result with a preset standard value, judging whether the statistical result is matched with the preset standard value, if not, sending an alarm prompt by the camera device to remind a worker to perform secondary detection, and if so, judging that the quality of the building engineering is qualified.
2. The method of claim 1, wherein data converting the set of job site images for the area into a project quality data set comprises:
classifying the construction site image set of the region according to types to obtain a classification feature list;
performing data feature extraction on classification parameters matched with classification data in a preset classification data thread according to a plurality of mutually matched classification features in the classification feature list to obtain classification feature information and corresponding classification state information;
analyzing the image types in the construction site image set according to the classification characteristic information, screening classification state information corresponding to the classification characteristic information of the image types to form a classification characteristic curve graph, and acquiring a parameter distribution trace graph of each type of parameter read into the classification characteristic curve graph;
comparing the similarity of the obtained parameter distribution track graph with a preset parameter graph, and integrating the classification characteristics which are matched with each other and correspond to the parameter distribution track with the similarity consistent with the preset parameter graph to obtain an image type classification sequence;
and sequentially carrying out data conversion on the key images in the construction site image set according to the sequence size in the image type classification sequence to obtain an engineering quality data set.
3. The method according to claim 1, wherein performing a preliminary raw material test on the building material quality data included in the engineering quality data set to obtain a first target test result including preliminary test information and a test list corresponding to the preliminary test information comprises:
acquiring at least one type of building material quality data and parameter information corresponding to the at least one type of building material quality data; wherein the parameter information is used for characterizing material component proportions in the building material quality data;
carrying out binary processing on the at least one type of building material quality data to obtain at least two types of target building material quality data corresponding to the building material quality data;
de-noising the at least one building material quality data and the at least two target building material quality data to obtain at least two current building material quality data with different weight coefficients corresponding to each building material quality data and the target building material quality data;
performing parameter calculation on the at least two types of current building material quality data with different weight coefficients according to a preset calculation mode to obtain a raw material detection template; the raw material detection template comprises at least two raw material databases with different mapping weight coefficients;
acquiring building material quality data to be detected, mapping at least two types of noise removal processing data obtained through noise removal processing to the raw material detection template after the noise removal processing is carried out on the building material quality data, and respectively carrying out primary raw material detection processing on the at least two types of noise removal processing data through at least two raw material databases with different mapping weight coefficients in the raw material detection template to obtain at least two pieces of primary detection information respectively corresponding to the at least two types of noise removal processing data;
and under the condition that the at least two pieces of preliminary detection information are matched with a preset detection result, identifying the building material quality data as a first target detection result of a detection list corresponding to the preliminary detection information.
4. The method of claim 1, wherein determining whether the first target detection result meets preset building material quality information comprises:
acquiring a detection parameter sequence of at least one detection item corresponding to the first target detection result; wherein, the sequence value of the detection parameter sequence of the detection item is matched with the item type of the detection item;
determining a target detection item of the first target detection result from the at least one detection item based on the detection parameter sequence of the at least one detection item;
and judging whether the detection result of the target detection item meets the preset building material quality information or not according to a preset judgment standard.
5. The method of claim 1, wherein querying the set of construction work site safety data in a construction site image based on the first target detection result, and obtaining a degree of change between the construction work site safety data and historical safety data, and determining a change ratio according to the degree of change comprises:
determining construction site operation information, finished construction quantity data in a preset time period and quality control data in the construction site image set;
determining quality control data in the completed construction amount data within a preset time period based on the construction site operation information;
inquiring the construction project site safety data in the construction site image according to the quality control data, carrying out safety accuracy verification on the construction project site safety data, and judging that the construction project site is in a safe state when the construction quantity data corresponding to the construction project site safety data is consistent with the finished construction quantity data in a preset time period;
and obtaining the change degree between the on-site safety data and the historical safety data of the constructional engineering based on the safety data corresponding to the safety state obtained by verification, and determining a change ratio according to the change degree.
6. A supervision system for detecting the quality of construction engineering is characterized by comprising supervision equipment and a camera device, wherein the supervision equipment is communicated with the camera device;
the supervisory device is to:
acquiring a construction site image set of each area shot by a camera device in a construction engineering site within a preset historical time period;
the camera device is to communicate:
the construction site image sets of all the areas shot in a preset historical time period;
the supervisory device is to:
aiming at one area, carrying out data conversion on a construction site image set of the area to obtain an engineering quality data set; the engineering quality data set comprises building material quality data, real-time data of construction progress and construction engineering site safety data;
performing preliminary raw material detection on the building material quality data included in the engineering quality data set to obtain a first target detection result including preliminary detection information and a detection list corresponding to the preliminary detection information;
judging whether the first target detection result meets preset building material quality information or not, and if not, sending an alarm signal; if yes, inquiring the construction project site safety data in a construction site image set based on the first target detection result, acquiring the variation degree between the construction project site safety data and the historical safety data, and determining a variation ratio according to the variation degree;
acquiring target building information in the process of shooting the construction project site, and acquiring the construction progress of the target building information in the construction project site;
acquiring a progress value of the target building information within a preset time period by using the construction progress of the shot target building image in the target building information;
and counting the preliminary detection information, the variation ratio and the progress value to obtain a statistical result, comparing the statistical result with a preset standard value, judging whether the statistical result is matched with the preset standard value, if not, sending an alarm prompt by the camera device to remind a worker to perform secondary detection, and if so, judging that the quality of the building engineering is qualified.
7. The system of claim 6, wherein the supervisory device is configured to:
classifying the construction site image set of the region according to types to obtain a classification feature list;
performing data feature extraction on classification parameters matched with classification data in a preset classification data thread according to a plurality of mutually matched classification features in the classification feature list to obtain classification feature information and corresponding classification state information;
analyzing the image types in the construction site image set according to the classification characteristic information, screening classification state information corresponding to the classification characteristic information of the image types to form a classification characteristic curve graph, and acquiring a parameter distribution trace graph of each type of parameter read into the classification characteristic curve graph;
comparing the similarity of the obtained parameter distribution track graph with a preset parameter graph, and integrating the classification characteristics which are matched with each other and correspond to the parameter distribution track with the similarity consistent with the preset parameter graph to obtain an image type classification sequence;
and sequentially carrying out data conversion on the key images in the construction site image set according to the sequence size in the image type classification sequence to obtain an engineering quality data set.
8. The system of claim 6, wherein the supervisory device is configured to:
acquiring at least one type of building material quality data and parameter information corresponding to the at least one type of building material quality data; wherein the parameter information is used for characterizing material component proportions in the building material quality data;
carrying out binary processing on the at least one type of building material quality data to obtain at least two types of target building material quality data corresponding to the building material quality data;
de-noising the at least one building material quality data and the at least two target building material quality data to obtain at least two current building material quality data with different weight coefficients corresponding to each building material quality data and the target building material quality data;
performing parameter calculation on the at least two types of current building material quality data with different weight coefficients according to a preset calculation mode to obtain a raw material detection template; the raw material detection template comprises at least two raw material databases with different mapping weight coefficients;
acquiring building material quality data to be detected, mapping at least two types of noise removal processing data obtained through noise removal processing to the raw material detection template after the noise removal processing is carried out on the building material quality data, and respectively carrying out primary raw material detection processing on the at least two types of noise removal processing data through at least two raw material databases with different mapping weight coefficients in the raw material detection template to obtain at least two pieces of primary detection information respectively corresponding to the at least two types of noise removal processing data;
and under the condition that the at least two pieces of preliminary detection information are matched with a preset detection result, identifying the building material quality data as a first target detection result of a detection list corresponding to the preliminary detection information.
9. The system of claim 6, wherein the supervisory device is configured to:
acquiring a detection parameter sequence of at least one detection item corresponding to the first target detection result; wherein, the sequence value of the detection parameter sequence of the detection item is matched with the item type of the detection item;
determining a target detection item of the first target detection result from the at least one detection item based on the detection parameter sequence of the at least one detection item;
and judging whether the detection result of the target detection item meets the preset building material quality information or not according to a preset judgment standard.
10. The system of claim 6, wherein the supervisory device is configured to:
determining construction site operation information, finished construction quantity data in a preset time period and quality control data in the construction site image set;
determining quality control data in the completed construction amount data within a preset time period based on the construction site operation information;
inquiring the construction project site safety data in the construction site image according to the quality control data, carrying out safety accuracy verification on the construction project site safety data, and judging that the construction project site is in a safe state when the construction quantity data corresponding to the construction project site safety data is consistent with the finished construction quantity data in a preset time period;
and obtaining the change degree between the on-site safety data and the historical safety data of the constructional engineering based on the safety data corresponding to the safety state obtained by verification, and determining a change ratio according to the change degree.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112990711A (en) * | 2021-03-19 | 2021-06-18 | 云南建投第九建设有限公司 | Aluminum alloy formwork construction monitoring method and system based on site construction |
| CN113192042A (en) * | 2021-05-12 | 2021-07-30 | 白楠 | Engineering main body structure construction progress identification method based on opencv |
| CN113379323A (en) * | 2021-07-06 | 2021-09-10 | 山东电力工程咨询院有限公司 | Construction site quality supervision method and system |
| CN113393342A (en) * | 2021-03-30 | 2021-09-14 | 浙江绿城房屋服务系统有限公司 | Building decoration project overall process management method and system |
| CN113935605A (en) * | 2021-09-30 | 2022-01-14 | 海南电网有限责任公司 | Electric power civil engineering quality rapid detection and alarm method and system |
| CN113960071A (en) * | 2021-10-23 | 2022-01-21 | 朗升柯式印刷(深圳)有限公司 | Quality detection process and detection system for color printing products |
| CN115309303A (en) * | 2022-08-09 | 2022-11-08 | 福建汇川物联网技术科技股份有限公司 | Control method and device for construction area, electronic equipment and readable storage medium |
| CN116630752A (en) * | 2023-07-25 | 2023-08-22 | 广东南方电信规划咨询设计院有限公司 | Construction site target object identification method and device based on AI algorithm |
| CN117291329A (en) * | 2023-08-29 | 2023-12-26 | 质点数字科技有限公司 | Anomaly monitoring method, device, equipment and medium based on high-level reconstruction project |
| CN117592950A (en) * | 2024-01-19 | 2024-02-23 | 湖南长劲鹿网络科技有限公司 | Inspection detection management platform and data processing method thereof |
| CN117949048A (en) * | 2024-03-27 | 2024-04-30 | 山东晟旸建筑科技有限责任公司 | Assembly type building quality monitoring method and system based on wireless sensor technology |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004078987A (en) * | 2003-10-20 | 2004-03-11 | Jutaku Shizai Ryutsu Center:Kk | Verification method of building construction progress status |
| JP2015118435A (en) * | 2013-12-17 | 2015-06-25 | 三谷セキサン株式会社 | Construction quality management system |
| CN105912909A (en) * | 2016-04-20 | 2016-08-31 | 孙雨 | Monitoring and management system and method for construction project quality detection |
| CN108960668A (en) * | 2018-07-19 | 2018-12-07 | 上海小蚁科技有限公司 | The early warning and reminding method and device of construction quality monitoring, calculate equipment at storage medium |
| CN109214679A (en) * | 2018-08-31 | 2019-01-15 | 王岩峰 | A kind of projects management system based on Building Information Model |
| CN109461290A (en) * | 2018-11-13 | 2019-03-12 | 机械工业第六设计研究院有限公司 | A kind of monitoring management system and equipment of civil construction |
| CN109784758A (en) * | 2019-01-30 | 2019-05-21 | 北京筑业志远软件开发有限公司 | Construction quality supervision early warning system and method based on BIM model |
| CN111046075A (en) * | 2019-12-20 | 2020-04-21 | 广东海外建设咨询有限公司 | Project supervision informatization construction and management method and device |
| CN111539488A (en) * | 2020-05-27 | 2020-08-14 | 北京理工大学 | Imaging distortion resisting target classification method for ultra-narrow pulse radar under complex dynamic track |
-
2020
- 2020-09-02 CN CN202010911754.8A patent/CN112036755B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004078987A (en) * | 2003-10-20 | 2004-03-11 | Jutaku Shizai Ryutsu Center:Kk | Verification method of building construction progress status |
| JP2015118435A (en) * | 2013-12-17 | 2015-06-25 | 三谷セキサン株式会社 | Construction quality management system |
| CN105912909A (en) * | 2016-04-20 | 2016-08-31 | 孙雨 | Monitoring and management system and method for construction project quality detection |
| CN108960668A (en) * | 2018-07-19 | 2018-12-07 | 上海小蚁科技有限公司 | The early warning and reminding method and device of construction quality monitoring, calculate equipment at storage medium |
| CN109214679A (en) * | 2018-08-31 | 2019-01-15 | 王岩峰 | A kind of projects management system based on Building Information Model |
| CN109461290A (en) * | 2018-11-13 | 2019-03-12 | 机械工业第六设计研究院有限公司 | A kind of monitoring management system and equipment of civil construction |
| CN109784758A (en) * | 2019-01-30 | 2019-05-21 | 北京筑业志远软件开发有限公司 | Construction quality supervision early warning system and method based on BIM model |
| CN111046075A (en) * | 2019-12-20 | 2020-04-21 | 广东海外建设咨询有限公司 | Project supervision informatization construction and management method and device |
| CN111539488A (en) * | 2020-05-27 | 2020-08-14 | 北京理工大学 | Imaging distortion resisting target classification method for ultra-narrow pulse radar under complex dynamic track |
Non-Patent Citations (3)
| Title |
|---|
| WU JIAN等: "Study on management information system for construction engineering supervision based on ubiquitous environment", 《IEEE》 * |
| 李堂: "建筑工程施工现场工程监理质量控制", 《建材与装饰》 * |
| 马建梅: "基于物联网的建设工程质量检测监管信息系统的实践与应用", 《建筑监督检测与造价》 * |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112990711A (en) * | 2021-03-19 | 2021-06-18 | 云南建投第九建设有限公司 | Aluminum alloy formwork construction monitoring method and system based on site construction |
| CN112990711B (en) * | 2021-03-19 | 2023-11-17 | 云南建投第九建设有限公司 | Aluminum alloy template construction monitoring method and system based on site construction |
| CN113393342A (en) * | 2021-03-30 | 2021-09-14 | 浙江绿城房屋服务系统有限公司 | Building decoration project overall process management method and system |
| CN113192042A (en) * | 2021-05-12 | 2021-07-30 | 白楠 | Engineering main body structure construction progress identification method based on opencv |
| CN113379323A (en) * | 2021-07-06 | 2021-09-10 | 山东电力工程咨询院有限公司 | Construction site quality supervision method and system |
| CN113379323B (en) * | 2021-07-06 | 2023-06-16 | 山东电力工程咨询院有限公司 | Construction site quality supervision method and system |
| CN113935605A (en) * | 2021-09-30 | 2022-01-14 | 海南电网有限责任公司 | Electric power civil engineering quality rapid detection and alarm method and system |
| CN113960071A (en) * | 2021-10-23 | 2022-01-21 | 朗升柯式印刷(深圳)有限公司 | Quality detection process and detection system for color printing products |
| CN115309303B (en) * | 2022-08-09 | 2023-12-15 | 福建汇川物联网技术科技股份有限公司 | Construction area management and control method and device, electronic equipment and readable storage medium |
| CN115309303A (en) * | 2022-08-09 | 2022-11-08 | 福建汇川物联网技术科技股份有限公司 | Control method and device for construction area, electronic equipment and readable storage medium |
| CN116630752A (en) * | 2023-07-25 | 2023-08-22 | 广东南方电信规划咨询设计院有限公司 | Construction site target object identification method and device based on AI algorithm |
| CN116630752B (en) * | 2023-07-25 | 2023-11-17 | 广东南方电信规划咨询设计院有限公司 | Construction site target object identification method and device based on AI algorithm |
| CN117291329A (en) * | 2023-08-29 | 2023-12-26 | 质点数字科技有限公司 | Anomaly monitoring method, device, equipment and medium based on high-level reconstruction project |
| CN117291329B (en) * | 2023-08-29 | 2024-03-19 | 质点数字科技有限公司 | Anomaly monitoring method, device, equipment and medium based on high-level reconstruction project |
| CN117592950A (en) * | 2024-01-19 | 2024-02-23 | 湖南长劲鹿网络科技有限公司 | Inspection detection management platform and data processing method thereof |
| CN117592950B (en) * | 2024-01-19 | 2024-03-26 | 湖南长劲鹿网络科技有限公司 | Inspection detection management platform and data processing method thereof |
| CN117949048A (en) * | 2024-03-27 | 2024-04-30 | 山东晟旸建筑科技有限责任公司 | Assembly type building quality monitoring method and system based on wireless sensor technology |
| CN117949048B (en) * | 2024-03-27 | 2024-05-31 | 山东晟旸建筑科技有限责任公司 | Assembly type building quality monitoring method and system based on wireless sensor technology |
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