CN113408916B - Fire-fighting facility detection and field acceptance assessment system based on intelligent AI and mobile APP - Google Patents
Fire-fighting facility detection and field acceptance assessment system based on intelligent AI and mobile APP Download PDFInfo
- Publication number
- CN113408916B CN113408916B CN202110716728.4A CN202110716728A CN113408916B CN 113408916 B CN113408916 B CN 113408916B CN 202110716728 A CN202110716728 A CN 202110716728A CN 113408916 B CN113408916 B CN 113408916B
- Authority
- CN
- China
- Prior art keywords
- point
- data
- fire
- index
- standard
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 46
- 238000012545 processing Methods 0.000 claims abstract description 27
- 238000011156 evaluation Methods 0.000 claims abstract description 23
- 230000005540 biological transmission Effects 0.000 claims abstract description 8
- 238000000034 method Methods 0.000 claims description 40
- 230000008569 process Effects 0.000 claims description 19
- 230000007246 mechanism Effects 0.000 claims description 16
- 238000010606 normalization Methods 0.000 claims description 10
- 238000004364 calculation method Methods 0.000 claims description 9
- 238000011282 treatment Methods 0.000 claims description 9
- 238000005259 measurement Methods 0.000 claims description 7
- 238000012797 qualification Methods 0.000 claims description 4
- 238000013507 mapping Methods 0.000 claims description 3
- 238000010845 search algorithm Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 4
- 238000009434 installation Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000003672 processing method Methods 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000001680 brushing effect Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013479 data entry Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000008520 organization Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
- G06F16/252—Integrating or interfacing systems involving database management systems between a Database Management System and a front-end application
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Databases & Information Systems (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Alarm Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to a fire-fighting equipment detection and field acceptance assessment system based on intelligent AI and mobile APP, which is realized based on intelligent AI and mobile APP; the detection system comprises a data acquisition system, a mobile terminal system and a background server, wherein the mobile terminal system is connected with the background server and comprises: an account login module; a GPS module; the data input module is used for inputting the acquired data of the data acquisition module to the mobile terminal system; the data processing module is used for processing the acquired data; the evaluation module is used for comparing the processed data with the standard data, evaluating the processed data and obtaining an evaluation result; the wireless transmission module is used for uploading the assessed data to the background server; the evaluation system can detect the fire-fighting facilities and check and evaluate fire-fighting sites, thereby not only avoiding the influence of artificial factors, but also improving the detection accuracy.
Description
Technical Field
The invention relates to the technical field of fire control acceptance based on intelligent AI, in particular to a fire control facility detection and field acceptance assessment system based on intelligent AI and mobile APP.
Background
Acceptance of fire facilities is one of the important links in relation to the life and property safety of people. From the 90 th century of 20 th, china starts to detect building fire-fighting facilities, so that the quality of construction and installation of the building interior automatic fire-fighting facilities is greatly improved, the reliability of facility operation is obviously improved, the building fire-fighting facilities are installed, the building fire-fighting facilities are prevented from being damaged, and the life and property safety of people is better guaranteed.
However, at present, the fire-fighting facilities in some large buildings still have the conditions that the fire-fighting facilities cannot timely and effectively control the disaster condition due to the fact that the quality is not too close and the maintenance is not in place, so that the acceptance of the fire-fighting facilities is particularly important.
In the prior art, when fire-fighting equipment is tested, the fire-fighting equipment is mainly subjected to safety assessment by depending on the observation and recording of technical service personnel, but because the service personnel are not comprehensive in connection with the states of fire-fighting equipment in a building, the overall safety condition of the building and the existing fire hidden danger cannot be identified, the technical service personnel are not known about fire-fighting laws and regulations and technical standards, the fire-fighting equipment instrument is unfamiliar, the detection quality is reduced, and some fire-fighting equipment instruments which are not aligned and fail are even used, so that the detection quality and the service level are low finally, and the detection result of the fire-fighting equipment instrument is lack of public confidence in authenticity and accuracy, so that the detection quality of the fire-fighting equipment of the building is directly influenced.
Disclosure of Invention
The invention aims to provide a fire-fighting equipment detection and on-site acceptance assessment system based on intelligent AI and mobile APP, which is used for solving the problem that the detection quality of the existing on-site acceptance assessment system for the building fire-fighting equipment is inaccurate due to human or instrument and other reasons.
The invention provides a technical scheme of a fire-fighting equipment detection and field acceptance assessment system based on intelligent AI and mobile APP, wherein the detection system comprises a data acquisition system, a mobile terminal system and a background server, the mobile terminal system is connected with the background server, and the mobile terminal system comprises:
the account number is logged in to the module,
the GPS module is used for positioning the position of the acceptance person;
the account login module comprises: the registration module is used for the fire control detection mechanism and the acceptance mechanism to apply for registration and warehouse entry in the system; the identity authentication module is used for checking qualification information, personnel certificates and personnel identity card information of the fire detection mechanism and the acceptance mechanism and recording; an account number distribution module and a login module;
the mobile terminal system further includes:
the data input module is used for inputting the acquired data of the data acquisition module to the mobile terminal system;
the data processing module is used for processing the acquired data;
the evaluation module is used for comparing the processed data with the standard data, evaluating the processed data and obtaining an evaluation result;
and the wireless transmission module is used for uploading the assessed data to the background server.
Further, the data processing module processes the acquired data as follows:
step 1, acquiring acquisition data of a fire-fighting facility, wherein the acquisition data comprise images and measurement data of an electronic fence area;
step 2, carrying out three-dimensional reconstruction on the image of the electronic fence area to obtain point cloud data of the fire-fighting facility;
step 3, clustering the point cloud data to obtain a plurality of point clusters, and obtaining a minimum external bounding box of the point clusters;
calculating probability density and direction uniformity indexes of the point clusters according to the minimum external bounding box;
according to the probability density and the direction uniformity index of the point clusters, calculating the uniformity index of the point clusters, and obtaining the average uniformity index of the fire-fighting equipment;
step 4, calculating the difference value between the average consistency index and the standard consistency index of the fire-fighting facilities, and calculating the integrity index of each fire-fighting facility;
step 5, acquiring acceptance rules according to fire-fighting acceptance criteria, selecting corresponding key point clusters according to the acceptance rules, extracting mass centers from the key point clusters, acquiring K nearest point clouds of the mass centers through a nearest neighbor search algorithm, acquiring mass centers of the point clusters and key point sets of the corresponding point clouds, acquiring the key point cluster according to the key point sets, and calculating the distance between any two key point clusters;
respectively carrying out difference calculation on the distances and a set standard value, and summing the absolute values of the differences to obtain a standardability index of the fire-fighting facility;
step 6, calculating the difference evaluation index according to the integrity index and the normalization index; and when the difference index is smaller than the set standard and the measured data is similar to the set standard data, the fire-fighting facility is qualified.
Further, in step 2, the calculation process of the direction uniformity index is as follows:
performing primary gridding treatment on the minimum external bounding box, counting the number of normal vector points of each grid, forming a grid bounding box according to the number of normal vector points of each grid, and calculating the volume of the grid bounding box;
and calculating the direction uniformity index of each grid according to the number of the grid normal vector points and the volume, and obtaining the direction uniformity index of the point cluster.
Further, in the step 2, the probability density of the point cluster is obtained by performing a first gridding treatment on the minimum external bounding box, obtaining three-dimensional grids, calculating the probability density of each three-dimensional grid, and calculating the average value of the probability densities of all the three-dimensional grids;
the calculation process of the direction uniformity index comprises the following steps: performing second meshing processing on grids obtained after the first meshing processing, counting the number of normal vector points of each second grid, forming a second grid bounding box according to the number of the normal vector points, and calculating the volume of the second grid bounding box;
and calculating the direction uniformity index of each second grid according to the number of the grid normal vector points and the volume, and obtaining the direction uniformity index of the point cluster.
In step 2, before the clustering process, the method further includes gridding the point cloud data of the electronic fence area.
Further, the standard consistency index is to perform clustering processing on the point cloud data of the standard library according to the point cloud data of the pre-established standard library to obtain a plurality of standard point clusters, and obtain a minimum external bounding box of the standard point clusters;
calculating probability density and direction uniformity indexes of the standard point clusters according to the minimum external bounding box;
according to the probability density and the direction uniformity index of the standard point cluster, calculating the uniformity index of the standard point cluster, and obtaining the standard uniformity index of the fire-fighting equipment;
the point clusters in the step 2 are in one-to-one correspondence with the standard point clusters.
Further, the integrity index of the fire-fighting facility is: GM 2= |gm-GM 0 Wherein GM is the average consistency index of the point clusters, GM 0 Is a standard consistency index of fire-fighting facilities.
Further, the standard distance is obtained by calculating the chamfer distance between any two standard key point clusters according to the point cloud data of the standard library and then summing.
Further, the normalization index is:
wherein,setting standard value corresponding to a test rule a, < ->And the distance between any two key point clusters corresponding to the a-th acceptance rule is obtained.
Further, the normalization index is:
wherein q is the number of acceptance rules, n1 is the number of standard point clusters, n is the number of point clusters,mean probability density representing the j-th cluster of standard points,/->Average probability density of the respective first cluster.
Further, the evaluation module compares the differential index with a set standard, and compares the measurement data with the set standard, and when the differential index is smaller than the set standard and the measurement data is similar to the set standard, the fire-fighting facility is qualified, and an evaluation result of the fire-fighting facility is obtained.
The beneficial effects of the invention are as follows:
the data processing module arranged in the field acceptance assessment system can process acquired acquisition data, particularly an image of an electronic fence area, namely, the image data are converted into point cloud data to acquire consistency indexes, and the influence of human factors can be avoided during detection; meanwhile, from the integrity and standardization of the fire-fighting equipment, the fire-fighting equipment is detected, and the detection accuracy is improved.
According to the method, the two gridding treatments are respectively carried out on the plurality of point clusters, the point cloud uniformity of the first grid is reflected by using the direction uniformity index of the second grid, and the spatial distribution of the point cloud in the grid can be effectively and accurately reflected from the view of local details.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present invention, the following briefly describes the drawings that are required to be used in the embodiments:
FIG. 1 is a block diagram of a fire protection facility detection and field acceptance assessment system based on intelligent AI and mobile APP of the present invention;
FIG. 2 is a block diagram of a mobile end system in a smart AI and mobile APP based fire protection equipment detection and field acceptance assessment system of the present invention;
FIG. 3 is a method flow diagram of a fire protection facility detection and field acceptance assessment method based on intelligent AI and mobile APP of the present invention;
FIG. 4 is a flow chart of a data processing method of a data processing module in the intelligent AI and mobile APP based fire protection equipment detection and field acceptance assessment system of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings in the embodiments of the present invention.
FIG. 1 shows a fire protection facility detection and field acceptance assessment system based on intelligent AI and mobile APP, which comprises a data acquisition system, a mobile terminal system and a background server.
The data acquisition system is an artificial intelligent tool box for acquiring data of fire-fighting equipment, and the artificial intelligent tool box comprises an image acquisition device, an illuminometer, a sound level meter, a range finder, a tape measure, an anemometer, a digital micropressure meter and the like, and specific other relevant detection tools are not described in detail herein, and reference is made to the detection tools disclosed in the prior art.
The image acquisition device is mainly used for acquiring image information of fire fighting equipment, analyzing the fire fighting equipment, and rapidly processing a plurality of images, so that the efficiency of acquiring the image information is improved. While other illuminometers, sound level meters, rangefinders, tape measures, etc. are used to measure the measurable data of the degree of illumination of an object, the noise of a fire-fighting installation, the fire-fighting installation distance, height, width, length, area, thickness, etc., respectively.
It should be noted that, the artificial intelligence tool box is an independent hardware device, for the image acquisition device, it can transmit the acquired image data to the mobile terminal system, the transmission can be wire transmission, it can also set the image acquisition device on the mobile terminal system directly, and when the mobile terminal system is a mobile phone system, the image acquisition device can be a camera of the mobile phone; the data collected by tools such as an illuminometer, a sound level meter and the like can be directly manually recorded into the mobile terminal system.
In addition, the arrow in the structural block diagram of fig. 1 indicates the relationship of data transmission among the data acquisition system, the mobile terminal system and the background server, and does not fully represent the connection relationship among the three; in practice, the data acquisition system is not connected with the mobile terminal system, and after the data acquisition system acquires data, the acceptance personnel records the acquired related data into the mobile terminal system; and the mobile terminal system is in wireless connection with the background server.
As shown in fig. 2, the mobile terminal system comprises an account login module, a GPS module, a data entry module, a data processing module, an assessment module and a wireless transmission module;
the account login module comprises:
the registration module is used for the fire control detection mechanism and the acceptance mechanism to apply for registration and warehouse entry in the system;
the identity authentication module is used for checking qualification information, personnel certificates and personnel identity card information of the fire detection mechanism and the acceptance mechanism and recording;
the account number distribution module is used for randomly establishing a temporary account number and distributing the temporary account number to corresponding tasks of technical service personnel after the record is made; it should be noted that, the account number with authority may be automatically established by a program based on uuid, and the specific method is the prior art, which is not described herein in detail.
And the login module is used for logging in by using the temporary account number.
And the GPS module is used for positioning the position of the acceptance person, judging whether the acceptance person reaches the vicinity of the accepted fire-fighting facility, and reminding the acceptance person to carry out APP login.
The data input module is used for inputting the data acquired by the data acquisition system into the mobile terminal system.
The data processing module is used for processing the acquired data acquired by the data acquisition system, such as three-dimensional reconstruction of the acquired image, acquisition of image characteristics and acceptance analysis of the fire-fighting facility.
And the evaluation module is used for comparing the processed data with the standard data and evaluating the processed data.
And the wireless transmission module is used for uploading the assessed data to a background server, and can be a GPRS module or a WIFI module.
The handheld terminal system is basic acceptance equipment, is carried by an acceptance person, and can input acceptance data into the handheld terminal system when the acceptance person reaches the position of fire-fighting equipment to be accepted.
The handheld terminal system in this embodiment may be a mobile terminal App.
And the background server forms an acceptance evaluation table according to the received evaluated data and stores the related data.
The inspection and acceptance personnel (detection mechanism and acceptance mechanism) need to apply for registration and warehousing on the platform, and the operations of qualification information, personnel certificates, personnel identity card record and the like are required.
When the fire-fighting equipment is tested, the fire-fighting equipment detection mechanism and the fire-fighting acceptance assessment mechanism need to conduct contract recording; after the proposal, the platform returns a temporary account number to the organization company, the temporary account number is distributed to the task of the acceptance person and establishes the temporary account number of the acceptance person.
The position information is whether the acceptance person is located in an area near the fire-fighting facility to be accepted or not, after the acceptance person enters the corresponding area, the temporary account number can be used for logging in the APP to carry out face brushing real-name authentication, and the mobile terminal APP and the artificial intelligent terminal toolbox are used for fire-fighting detection/assessment.
Based on the evaluation system for fire-fighting equipment field acceptance, the invention provides an embodiment of a data processing process suitable for a data processing module in the evaluation system, and the embodiment is specifically introduced below by taking mobile APP as an example and combining with specific application scenes.
As shown in fig. 3, the acceptance assessment method of the fire-fighting equipment detection and field acceptance assessment system based on intelligent AI and mobile APP of the invention comprises the following steps:
1) Acquiring the position information of the acceptance person, judging whether the position information is in an electronic fence area, if so, acquiring the identity information of the acceptance person, and logging in the mobile terminal; the identity information comprises a login account number and a corresponding login password of the acceptance person; the position information is that the acceptance person is located in a fire-fighting facility detection area;
2) The mobile terminal system acquires the uploaded acquisition data of the fire-fighting facility in real time, processes the acquisition data, compares the processed acquisition data with set standard data, generates an acceptance result according to a comparison result, and carries out acceptance assessment on the fire-fighting facility according to the acceptance result.
The electronic fence area in the step 2) is an area in the working site, which needs to be subjected to fire-fighting facility detection and fire-fighting acceptance assessment.
As other embodiments, the mobile terminal APP background further includes detection of a technical service person, and performs behavior detection on a person in the electronic fence area by using an on-site intelligent camera, and when the behavior is abnormal, the request is fed back to the terminal to perform authentication.
In this embodiment, the data processing module processes the collected data, and the processing procedure thereof, as shown in fig. 4, includes the following steps:
step 1, acquiring acquisition data of a fire-fighting facility, wherein the acquisition data comprise images and measurement data of an electronic fence area;
the acquired data of the fire fighting equipment in the step comprises two groups of data, wherein one group of data is measurable data and the other group of data is detectable data; the measurable indexes are measured on site by corresponding measuring tools in the artificial intelligent terminal tool box, such as the distance, the height, the width, the length, the area, the thickness, the air speed and the like of the fire-fighting equipment. Since the measurable indicators are data measured directly by the corresponding tools, the present invention will not be discussed in detail.
The detectable index is the detection of image data by the image acquisition device, and mainly realizes the recording of the measurable index of the fire-fighting equipment and the acquisition of the image of the fire-fighting equipment, so as to obtain the detected/rated data.
Step 2, carrying out three-dimensional reconstruction on the image of the electronic fence area to obtain point cloud data of the fire-fighting facility;
the image of the electronic fence area in this embodiment is measured on site by using an artificial intelligent terminal toolbox, specifically, the electronic fence area is scanned by using the artificial intelligent terminal toolbox, and a three-dimensional representation of the area is obtained, wherein a sensor device, preferably a depth camera, is embedded in the artificial intelligent terminal toolbox. The three-dimensional representation of the region is preferably a point cloud representation. The image acquired by the depth camera is a depth image. And then carrying out three-dimensional reconstruction on the scanned image of the electronic fence area to obtain point cloud data.
Wherein the three-dimensional reconstruction generally comprises the steps of:
a) Extracting image features (e.g., SIFT, SURF, etc.);
b) Calculating feature matching between images by using the features;
c) Performing sparse reconstruction based on the matched features to obtain camera pose and sparse feature point cloud (SfM) of each image;
d) Dense reconstruction is performed based on the camera pose, resulting in dense point clouds (PMVS/CMVS).
Because the three-dimensional reconstruction is mature in the field of computer vision and application thereof, specific details of the three-dimensional reconstruction are not described here.
Further, fire-fighting equipment target recognition is performed based on point cloud data, a three-dimensional object detection technology based on deep learning is adopted in a preferred technology, a three-dimensional object detector takes point cloud of a scene as input, and an oriented three-dimensional bounding box is generated around each detection object, and three-dimensional representation of each fire-fighting equipment is finally obtained by a method based on Reion Proposal and a method based on Single Shot.
Step 3, clustering the point cloud data to obtain a plurality of point clusters, and obtaining a minimum external bounding box of the point clusters; calculating probability density and direction uniformity indexes of the point clusters according to the minimum external bounding box;
according to the probability density and the direction uniformity index of the point clusters, calculating the uniformity index of the point clusters, and obtaining the average uniformity index of the fire-fighting equipment;
the method for calculating the probability density of the dot cluster in the embodiment includes:
counting point clouds in the point clusters, calculating the mean value and covariance of the point cloud geometry, and calculating the probability density of each point cluster;
density of point cloud:
mean value of point cloud:
covariance of point cloud:
the probability density of the cluster of points is:
wherein p is i For the density of the ith point cloud in the point cluster, n is the number of point clouds in the point cluster,average probability density of the respective first cluster.
As another embodiment, in order to facilitate the clustering of fire-fighting equipment components, the present application performs a meshing process before performing the clustering process on the point cloud data.
The calculation process of the direction uniformity index in the above embodiment is:
performing primary gridding treatment on the minimum external bounding box, counting the number of normal vector points of each grid, forming a grid bounding box according to the number of normal vector points of each grid, and calculating the volume of the grid bounding box;
and calculating the direction uniformity index of each grid according to the number of the grid normal vector points and the volume, and obtaining the direction uniformity index of the point cluster.
The direction uniformity index of each grid is as follows:
wherein,gridN b respectively representing the average number of the normal vector points of the grid and the number of the normal vector points in the b-th grid; />gridV b The average volume of the 3D minimum circumscribed bounding box of the grid normal vector points and the volume of the 3D minimum circumscribed bounding box of the normal vector points in the B-th grid are respectively represented, and B is the number of grids, and in this embodiment, B is 8.
In this embodiment, UD b The larger the value, the more uneven the directional distribution.
The point cloud consistency index for each point cluster is:
wherein w1 and w2 are respectively corresponding weight mapping values, which are respectively 4 and 0.2.
The average uniformity index for the cluster of points is:
wherein n is the number of dot clusters, M l And the point cloud consistency index is the point cloud consistency index of the first point cluster.
It should be noted that, the present invention considers that different fire-fighting equipment components may exist, so that DBSCAN density clustering is performed on the point cloud in each grid in the standard point cloud to obtain a plurality of point clusters, where one point cluster is considered to be a component, and the DBSCAN density clustering usually needs to be manually performed for parameter adjustment; for a cluster of points, there is typically a threshold G1, ensuring that each cluster of points has at least G1 point cloud composition, with an empirical value of 50.
In this embodiment, after the probability density of the point cluster is calculated, statistics of the point clouds of the point cluster in the direction is also required to be considered, so that the normal vector of each point cloud in the grid after the grid treatment is estimated, where the normal vector estimation method includes K neighbor estimation, radius neighbor estimation, hybrid search estimation and the like, and the specifically adopted method can be selected according to the actual situation, and finally the normal vector point of each point cloud is obtained.
According to the invention, the spatial distribution of the point clouds in the grids can be effectively reflected through the average probability density distribution and the direction uniformity index of each grid, so that the method is further used for detecting the integrity of facilities, and meanwhile, the problem of accuracy reduction caused by inconsistency of the point clouds (because the point clouds are three-dimensionally reconstructed point clouds, even if the same equipment is positioned at the same position, the reconstructed point clouds are possibly inconsistent) is further solved by considering the volume and the quantity of the minimum external bounding box formed by each second grid.
Step 4, calculating the difference value between the average consistency index and the standard consistency index of the electronic fence area, and calculating the integrity index of each fire-fighting facility of the electronic fence area;
the integrity index of the fire protection facility in this embodiment is:
GM2＝|GM-GM 0 |
wherein GM is the average consistency index of the point clusters, and GM 0 Is a standard consistency index.
The standard consistency index in the embodiment is that clustering processing is carried out on the point cloud data of a standard library according to the point cloud data of the pre-established standard library, so as to obtain a plurality of standard point clusters, and a minimum external bounding box of the standard point clusters is obtained; calculating probability density and direction uniformity indexes of the standard point clusters according to the minimum external bounding box;
according to probability density and direction uniformity indexes of the standard point clusters, calculating uniformity indexes of the standard point clusters, namely obtaining standard uniformity indexes of the fire-fighting equipment;
it should be noted that, in the embodiment, the average consistency index and the standard consistency index of the fire protection facility are mapped one by one, that is, the point cluster set of the three-dimensional reconstructed point cloud is obtained by mapping the point cluster set of the standard point cloud, firstly, the minimum circumscribed bounding box of each point cluster in the standard point cloud is obtained, and then, the point cloud in the minimum circumscribed bounding box at the same position in the reconstructed point cloud is obtained.
Step 5, acquiring acceptance rules according to fire-fighting acceptance criteria, selecting corresponding key point clusters according to the acceptance rules, extracting mass centers from the key point clusters, acquiring K nearest point clouds of the mass centers through a nearest neighbor search algorithm, acquiring mass centers of the point clusters and key point sets of the corresponding point clouds, acquiring the key point cluster according to the key point sets, and calculating the distance between any two key point clusters;
respectively carrying out difference calculation on the distances and a set standard value, and summing the absolute values of the differences to obtain a standardability index of the fire-fighting facility;
the calculated chamfering distances of all the key point clusters are as follows:
in the formula, S k 、S t The key point sets are respectively the kth key point cluster and the kth key point cluster; x is S k Any coordinate in the set, y is S t Any coordinate in the set; the first term represents S k Any point of (3) to S t The second term then represents S t Any point of (3) to S k Average minimum distance of (c).
The normalization index is as follows:
wherein,setting standard value corresponding to a test rule a, < ->And (3) the distance between every two key point clusters corresponding to the a-th acceptance rule.
The set standard value in the embodiment can be obtained by calculating the distance between any two standard key point clusters according to the point cloud data of the standard library and then summing; it should be clear that the set standard value at this time corresponds to the calculated distance between any two key point clusters one by one. That is, the standard values of the settings corresponding to the different acceptance rules are different, and the settings may be directly set according to the actual situation.
In the above embodiment, the inconsistency of the point clouds at the spatial positions is considered, so that the 3D point cloud chamfering distance is used to evaluate the distance of the nearest neighbor point cloud set at the same position, thereby more accurately evaluating the normalization of the facility and avoiding the inconsistency of the point clouds at the spatial positions and the error of the distance caused by the three-dimensional reconstruction error of the point clouds.
As a further implementation mode, the method also considers the influence of the probability density of the point cluster on the normalization index, and the formula is as follows:
wherein q is the number of acceptance rules, n1 is the number of standard point clusters, n is the number of point clusters,mean probability density representing the j-th cluster of standard points,/->Average probability of the respective first point clusterDensity.
In this embodiment, the larger the GM5 difference, the less standard the facility; the method is characterized in that the difference value between the standard point cluster probability density sum and the detection point cluster probability density sum is obtained, wherein the larger difference value is the larger the average probability density of the point clusters is changed, namely, the more or larger sundries are present or the part is absent.
It should be noted that, the acceptance rules in this embodiment are formulated according to fire-fighting acceptance criteria, different acceptance criteria correspond to different acceptance rules, and key point clusters corresponding to different acceptance rules are different, that is, the meaning of the calculated chamfer distance is also different. The specific acceptance rules can be one or a plurality of, and the set of the key point clusters corresponding to each acceptance rule is different.
For example, fire-fighting acceptance criteria for fire hydrant boxes are:
1) The center of the valve is 140mm from the side surface of the box, 100mm from the rear inner surface of the box, and the allowable deviation is +/-5 mm;
2) The allowable deviation of the verticality of the fire hydrant box body installation is 3mm.
And the acceptance rule is that the positions of the center of the hydrant valve and the side face of the tank are key points, and the center point cluster and the point cluster of the side face of the tank are selected correspondingly to calculate the chamfering distance.
And step 6, calculating the difference evaluation index according to the integrity index and the normalization index.
Wherein the difference evaluation index is:
Diff＝GM2*GM5
the above formula is the most preferable mode of the invention, and of course, the Diff can be calculated by multiplying GM2 and GM 4; for Diff, the larger the value, the greater the device is considered to be different from the standard library device.
The scheme of the invention can solve the problems of inaccurate and unreliable acceptance assessment caused by inconsistent standards and strong subjectivity of technical service personnel in the part of inspection items in the acceptance assessment of the fire protection.
The setting standard in this embodiment is; since the fire-fighting facilities are different, standards of the different fire-fighting facilities are also different, and thus, the fire-fighting facilities can be set according to actual conditions.
And comparing the differential evaluation index with a set standard, measuring data and set standard data by an evaluation module according to the differential evaluation index obtained in the steps, and if the differential index is smaller than the set standard and the measuring data is similar to the set standard data, qualifying the fire-fighting facility and obtaining the acceptance result of the fire-fighting facility.
The acceptance result can be the result of whether the fire-fighting facility is qualified or not, and can also be a directly generated evaluation list; of course, the rating table may also be generated in a background server.
According to the mobile APP, the acquired acceptance result is uploaded to a background server for storage through the wireless communication module.
As other implementation manners, the invention also provides another embodiment of the data processing method of the data processing module.
Specifically, in this embodiment, two gridding processes are performed on a plurality of point clusters respectively, the first gridding process is performed on the point clusters, different first three-dimensional grids are obtained, and probability density of each first three-dimensional grid is calculated; performing secondary gridding treatment on each three-dimensional grid, calculating a direction uniformity index of the second three-dimensional grid, and reflecting the uniformity of the first three-dimensional grid by using the direction uniformity index of the second three-dimensional grid;
specifically, the method for calculating the consistency index of the point cluster in this embodiment is as follows:
1. calculating the probability density of the first three-dimensional grid:
1) Performing gridding treatment on a plurality of point clusters to obtain a three-dimensional grid;
the empirical size of the grid division in this embodiment is:
l'＝L/4
w'＝W/2
h'＝H/6
l, W, H is length, width, height, l ', w' of the minimum external surrounding frame of the fire-fighting facility respectively,
h' are the length, width and height of the grid respectively.
Taking a hydrant as an example, the fire hydrant can be divided into 48 three-dimensional grids according to the grids.
2) Counting the point clouds in the three-dimensional grid, calculating the mean and covariance of the point cloud geometry, and
calculating the probability density of the three-dimensional grid;
wherein p is I The probability density of the I-th point cloud in the grid is obtained, and N is the number of the grids in the point cluster.
The calculation of the probability density of the point cloud in the above formula is not described in detail here, since a specific method is already given in the first embodiment.
2. Calculating a direction uniformity index:
1) Further dividing the first three-dimensional grids into h small grids, wherein each first three-dimensional grid is called a second three-dimensional grid, and the length, the width and the height of the second three-dimensional grid are half of those of the original grid;
2) Counting the number of normal vector points of each second three-dimensional grid, constructing a 3D minimum external bounding box according to the number, calculating the volume of the bounding box, and calculating the direction uniformity index of each second three-dimensional grid according to the number and the volume;
wherein the direction uniformity index of each grid:
wherein,gridN r respectively representing the average number of normal vector points of the second three-dimensional grid, the number of normal vector points in the (r) th second three-dimensional grid,/or%>gridV r The average volume of the 3D minimum circumscribed bounding box of the normal vector points of the second three-dimensional grid and the volume of the 3D minimum circumscribed bounding box of the normal vector points in the r second grid are respectively represented.
It should be noted that, in this embodiment, only the number difference in each large direction is known only by the statistical number, and the directional distribution in each large direction (i.e., each quadrant) cannot be known, so here, the distribution of directions is reflected by the volume of the 3D minimum circumscribed bounding box of the normal vector point, and a larger value means a more uniform directional distribution. And extracting a 3D minimum external bounding box for normal vector points in each second grid, and then reflecting the directional uniformity distribution of the second grid by using the volume of the minimum external bounding box.
3. And calculating the consistency index of the point cloud in each first three-dimensional grid, and further obtaining the average consistency index of the point clusters.
According to the method, the spatial distribution of the point clouds in the grids can be effectively reflected through the average probability density distribution and the direction uniformity index of each grid, so that the method is used for detecting the integrity of facilities, and meanwhile, the problem of accuracy reduction caused by inconsistency of the point clouds (because the point clouds are three-dimensionally reconstructed point clouds, even if the same equipment is located at the same position, the reconstructed point clouds are possibly inconsistent) is further solved by considering the volume and the quantity of the minimum external bounding boxes formed by each second grid.
In this embodiment, since the number of reconstructed grid point clouds is not necessarily consistent with that of the point clouds of the standard library, the average probability density of the calculated grid represents the probability that the position in the grid is occupied, and the distribution of the point clouds in the grid can be represented.
The statistics can represent the spatial distribution of the point clouds in the grid and lack of statistics of the point clouds in the direction, so that further, the normal vector of each point cloud in the grid is estimated, and the normal vector estimation method such as K neighbor estimation, radius neighbor estimation, mixed search estimation and the like can be freely selected by an implementer, and finally, the normal vector point of each point cloud is obtained, and the normal vector point is also in the grid.
It should be noted that while the preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
Claims (5)
1. Fire protection facility detection and on-site acceptance assessment system based on intelligent AI and mobile APP, wherein the system comprises a data acquisition system, a mobile terminal system and a background server, the mobile terminal system is connected with the background server, and the mobile terminal system comprises:
the account number is logged in to the module,
the GPS module is used for positioning the position of the acceptance person;
the account login module comprises: the registration module is used for the fire control detection mechanism and the acceptance mechanism to apply for registration and warehouse entry in the system; the identity authentication module is used for checking qualification information, personnel certificates and personnel identity card information of the fire detection mechanism and the acceptance mechanism and recording; an account number distribution module and a login module;
wherein the mobile terminal system further comprises:
the data input module is used for inputting the acquired data of the data acquisition module to the mobile terminal system;
the data processing module is used for processing the acquired data;
the evaluation module is used for comparing the processed data with the standard data, evaluating the processed data and obtaining an evaluation result;
the wireless transmission module is used for uploading the assessed data to the background server;
the data processing module processes the acquired data, and the process of processing the acquired data comprises the following steps:
step 1, acquiring acquisition data of a fire-fighting facility, wherein the acquisition data comprise images and measurement data of an electronic fence area;
step 2, carrying out three-dimensional reconstruction on the image of the electronic fence area to obtain point cloud data of the fire-fighting facility;
step 3, clustering the point cloud data to obtain a plurality of point clusters, and obtaining a minimum external bounding box of the point clusters;
calculating probability density and direction uniformity indexes of the point clusters according to the minimum external bounding box;
according to the probability density and the direction uniformity index of the point clusters, calculating the uniformity index of the point clusters, and obtaining the average uniformity index of the fire-fighting equipment;
step 4, calculating the difference value between the average consistency index and the standard consistency index of the fire-fighting facilities, and calculating the integrity index of each fire-fighting facility;
step 5, acquiring acceptance rules according to fire-fighting acceptance criteria, selecting corresponding key point clusters according to the acceptance rules, extracting mass centers from the key point clusters, acquiring K nearest point clouds of the mass centers through a nearest neighbor search algorithm, acquiring mass centers of the point clusters and key point sets of the corresponding point clouds, acquiring the key point cluster according to the key point sets, and calculating the distance between any two key point clusters;
respectively carrying out difference calculation on the distances and a set standard value, and summing the absolute values of the differences to obtain a standardability index of the fire-fighting facility;
step 6, calculating a difference evaluation index according to the product of the integrity index and the normalization index;
step 3, before the clustering process, the method further comprises the step of carrying out gridding process on the point cloud data of the electronic fence area;
the probability density of the point cluster is obtained by performing first gridding treatment on the minimum external bounding box, obtaining three-dimensional grids, calculating the probability density of each three-dimensional grid, and calculating the average value of the probability densities of all the three-dimensional grids;
counting point clouds in the point clusters, calculating the mean value and covariance of the point cloud geometry, and calculating the probability density of each point cluster:
density of point cloud:
mean value of point cloud:
covariance of point cloud:
the probability density of the cluster of points is:
wherein,for the density of the ith point cloud in the point cluster, n is the number of point clouds in the point cluster,/for the point cloud>An average probability density for the first cluster of points;
the calculation process of the direction uniformity index comprises the following steps:
performing primary gridding treatment on the minimum external bounding box, counting the number of normal vector points of each grid, forming a grid bounding box according to the number of normal vector points of each grid, and calculating the volume of the grid bounding box;
according to the number of the grid normal vector points and the volume, calculating the direction uniformity index of each grid, and obtaining the direction uniformity index of the point cluster;
the direction uniformity index of each grid is:
wherein,、/>respectively representing the average number of the normal vector points of the grid and the number of the normal vector points in the b-th grid; />、/>The average volume of the 3D minimum circumscribed bounding box of the grid normal vector points and the volume of the 3D minimum circumscribed bounding box of the normal vector points in the B-th grid are respectively represented, and B is the number of the grids;
the point cloud consistency index of each point cluster is as follows:
wherein w1 and w2 are respectively corresponding weight mapping values;
the average uniformity index for the cluster of points is:
wherein n is the number of the dot clusters,is the firstlA point cloud consistency index of each point cluster;
in step 4, the standard consistency index is that clustering processing is carried out on the point cloud data of the standard library according to the point cloud data of the pre-established standard library, so as to obtain a plurality of standard point clusters, and a minimum external bounding box of the standard point clusters is obtained;
calculating probability density and direction uniformity indexes of the standard point clusters according to the minimum external bounding box;
according to the probability density and the direction uniformity index of the standard point cluster, calculating the uniformity index of the standard point cluster, and obtaining the standard uniformity index of the fire-fighting equipment;
the point clusters are in one-to-one correspondence with the standard point clusters;
and the evaluation module is used for comparing the differential index with a set standard and comparing the measurement data with the set standard data respectively, and if the differential index is smaller than the set standard and the measurement data is similar to the set standard data, the fire-fighting facility is qualified and an evaluation result of the fire-fighting facility is obtained.
2. The fire protection facility detection and field acceptance assessment system based on intelligent AI and mobile APP according to claim 1, wherein in step 3, the calculation process of the direction uniformity index is: performing second meshing processing on grids obtained after the first meshing processing, counting the number of normal vector points of each second grid, forming a second grid bounding box according to the number of the normal vector points, and calculating the volume of the second grid bounding box; and calculating the direction uniformity index of each second grid according to the number of the grid normal vector points and the volume, and obtaining the direction uniformity index of the point cluster.
3. The fire protection facility detection and field acceptance assessment system based on intelligent AI and mobile APP according to claim 1, wherein the standard distance is obtained by calculating a chamfer distance between any two standard key point clusters according to point cloud data of the standard library and summing.
4. The intelligent AI and mobile APP based fire protection facility detection and field acceptance assessment system of claim 3, wherein the normative index is:
wherein,setting standard value corresponding to a test rule a, < ->Rule pair for a-th acceptance ruleThe distance between every two corresponding key point clusters.
5. The intelligent AI and mobile APP based fire protection facility detection and field acceptance assessment system of claim 4, wherein the impact of the probability density of the cluster of points on the normalization index is considered, the normalization index being:
wherein q is the number of acceptance rules, n1 is the number of standard point clusters, n is the number of point clusters,mean probability density representing the j-th cluster of standard points,/->Respectively->Average probability density of individual clusters of points.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110716728.4A CN113408916B (en) | 2021-06-28 | 2021-06-28 | Fire-fighting facility detection and field acceptance assessment system based on intelligent AI and mobile APP |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110716728.4A CN113408916B (en) | 2021-06-28 | 2021-06-28 | Fire-fighting facility detection and field acceptance assessment system based on intelligent AI and mobile APP |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113408916A CN113408916A (en) | 2021-09-17 |
| CN113408916B true CN113408916B (en) | 2023-12-29 |
Family
ID=77679593
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110716728.4A Active CN113408916B (en) | 2021-06-28 | 2021-06-28 | Fire-fighting facility detection and field acceptance assessment system based on intelligent AI and mobile APP |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113408916B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116631536A (en) * | 2023-07-25 | 2023-08-22 | 安徽省交通规划设计研究总院股份有限公司 | Method for calculating permeable concrete aggregate uniformity index in image processing view |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106127250A (en) * | 2016-06-24 | 2016-11-16 | 深圳市唯特视科技有限公司 | A kind of face method for evaluating quality based on three dimensional point cloud |
| CN106595525A (en) * | 2016-12-26 | 2017-04-26 | 中国矿业大学(北京) | Three-dimensional-point-cloud-data-based prevention method of coal mine roof disaster |
| CN106960468A (en) * | 2017-04-12 | 2017-07-18 | 武汉理工大学 | A kind of 3 D laser scanning point cloud Accuracy Assessment |
| CN109379220A (en) * | 2018-10-10 | 2019-02-22 | 太原理工大学 | The method that complex network key node cluster based on Combinatorial Optimization excavates |
| CN110047131A (en) * | 2019-04-02 | 2019-07-23 | 中科恒运股份有限公司 | A kind of point cloud fusion method that human body three-dimensional is rebuild |
| CN110223377A (en) * | 2019-05-28 | 2019-09-10 | 上海工程技术大学 | One kind being based on stereo visual system high accuracy three-dimensional method for reconstructing |
| CN110414941A (en) * | 2019-08-01 | 2019-11-05 | 河南诺盾消防安全评估有限公司 | Fire control acceptance professional standard application system based on APP |
| CN110574071A (en) * | 2017-01-27 | 2019-12-13 | Ucl商业有限公司 | Device, method and system for aligning 3D data sets |
| CN111340457A (en) * | 2020-03-12 | 2020-06-26 | 辽宁云盾网力科技有限公司 | Building fire-fighting examination and acceptance platform system based on artificial intelligence and method thereof |
| CN112802071A (en) * | 2021-01-22 | 2021-05-14 | 北京农业智能装备技术研究中心 | Three-dimensional reconstruction effect evaluation method and system |
| CN112825791A (en) * | 2020-12-25 | 2021-05-25 | 河南科技大学 | Milk cow body condition scoring method based on deep learning and point cloud convex hull characteristics |
| CN112862878A (en) * | 2021-02-07 | 2021-05-28 | 浙江工业大学 | Mechanical arm trimming method based on 3D vision |
| CN112977358A (en) * | 2021-02-24 | 2021-06-18 | 河南唐都科技有限公司 | Parking device for lateral parking space in smart city and vehicle adopting same |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109325998B (en) * | 2018-10-08 | 2023-06-30 | 香港理工大学 | Indoor 3D modeling method, system and related device based on point cloud data |
| GB2589751B (en) * | 2019-01-23 | 2023-05-17 | Motional Ad Llc | Automatically choosing data samples for annotation |
| US11816762B2 (en) * | 2019-05-27 | 2023-11-14 | Zhejiang University | Three-dimensional reconstruction method based on half-peak probability density distribution |
-
2021
- 2021-06-28 CN CN202110716728.4A patent/CN113408916B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106127250A (en) * | 2016-06-24 | 2016-11-16 | 深圳市唯特视科技有限公司 | A kind of face method for evaluating quality based on three dimensional point cloud |
| CN106595525A (en) * | 2016-12-26 | 2017-04-26 | 中国矿业大学(北京) | Three-dimensional-point-cloud-data-based prevention method of coal mine roof disaster |
| CN110574071A (en) * | 2017-01-27 | 2019-12-13 | Ucl商业有限公司 | Device, method and system for aligning 3D data sets |
| CN106960468A (en) * | 2017-04-12 | 2017-07-18 | 武汉理工大学 | A kind of 3 D laser scanning point cloud Accuracy Assessment |
| CN109379220A (en) * | 2018-10-10 | 2019-02-22 | 太原理工大学 | The method that complex network key node cluster based on Combinatorial Optimization excavates |
| CN110047131A (en) * | 2019-04-02 | 2019-07-23 | 中科恒运股份有限公司 | A kind of point cloud fusion method that human body three-dimensional is rebuild |
| CN110223377A (en) * | 2019-05-28 | 2019-09-10 | 上海工程技术大学 | One kind being based on stereo visual system high accuracy three-dimensional method for reconstructing |
| CN110414941A (en) * | 2019-08-01 | 2019-11-05 | 河南诺盾消防安全评估有限公司 | Fire control acceptance professional standard application system based on APP |
| CN111340457A (en) * | 2020-03-12 | 2020-06-26 | 辽宁云盾网力科技有限公司 | Building fire-fighting examination and acceptance platform system based on artificial intelligence and method thereof |
| CN112825791A (en) * | 2020-12-25 | 2021-05-25 | 河南科技大学 | Milk cow body condition scoring method based on deep learning and point cloud convex hull characteristics |
| CN112802071A (en) * | 2021-01-22 | 2021-05-14 | 北京农业智能装备技术研究中心 | Three-dimensional reconstruction effect evaluation method and system |
| CN112862878A (en) * | 2021-02-07 | 2021-05-28 | 浙江工业大学 | Mechanical arm trimming method based on 3D vision |
| CN112977358A (en) * | 2021-02-24 | 2021-06-18 | 河南唐都科技有限公司 | Parking device for lateral parking space in smart city and vehicle adopting same |
Non-Patent Citations (4)
| Title |
|---|
| 基于快速点特征直方图的特征点云迭代插值配准算法;陆军;彭仲涛;;国防科技大学学报(第06期);15-20 * |
| 基于改进NDT算法的城市场景三维点云配准;赵凯;朱愿;王任栋;;军事交通学院学报(第03期);84-88 * |
| 改进泊松算法的图像三维重建点云模型网格化;黄明伟;方莉娜;唐丽玉;王思洁;;测绘科学(04);23-28+38 * |
| 露天矿边坡影像三维重建技术;刘军;王鹤;李峰;刘小阳;;金属矿山(04);259-261 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113408916A (en) | 2021-09-17 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110246112B (en) | Laser scanning SLAM indoor three-dimensional point cloud quality evaluation method based on deep learning | |
| CN106950614B (en) | A kind of region automatic weather station hour rainfall data method of quality control | |
| CN107392247B (en) | Real-time detection method for ground object safety distance below power line | |
| CN109708688A (en) | A kind of monitoring of history culture building safety and early warning system and method | |
| CN116070887B (en) | Intelligent analysis management system for land mapping data | |
| CN111458721A (en) | Exposed garbage identification and positioning method, device and system | |
| CN113344745A (en) | Engineering project intelligent construction site data acquisition and analysis integrated cloud platform based on remote video monitoring and cloud computing | |
| CN113869629A (en) | Laser point cloud-based power transmission line safety risk analysis, judgment and evaluation method | |
| CN113408916B (en) | Fire-fighting facility detection and field acceptance assessment system based on intelligent AI and mobile APP | |
| CN116308958A (en) | Carbon emission online detection and early warning system and method based on mobile terminal | |
| CN105866732B (en) | The mixing indoor orientation method that a kind of improvement MK models and WKNN algorithms are combined | |
| CN108801221B (en) | Quick and fine dereferencing method for surface mine slope rock mass joint scale based on digital photogrammetry | |
| CN108981616A (en) | A method of by unmanned plane laser radar inverting artificial forest effective leaf area index | |
| CN108896021A (en) | Method based on aerophotogrammetry data reduction plantation stand structural parameters | |
| CN111461487B (en) | Indoor decoration engineering wisdom management system based on BIM | |
| CN114492984A (en) | Method, device, equipment and storage medium for predicting time-space distribution of dust concentration | |
| CN113155109B (en) | Supervision control method, system, equipment and medium for super high-rise building engineering measurement | |
| CN114254267A (en) | Building elevation calculation device and calculation method based on urban space planning | |
| CN114022035A (en) | Method for evaluating carbon emission of building in urban heat island effect | |
| CN116399305B (en) | ADCP (automatic dependent control protocol) current measurement result on-site warehouse entry and self-correction method based on cloud platform | |
| CN117350974A (en) | Engineering quality determining method, device, equipment and medium | |
| CN112836942A (en) | Bridge engineering project cost evaluation intelligent management system based on big data and cloud computing | |
| CN115983796A (en) | Power transmission line construction progress supervision and early warning method and system | |
| CN115481941A (en) | Multifunctional area combined intelligent security management method and system | |
| CN112033335B (en) | Intelligent monitoring and early warning system and method for railway gauging rule |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |