CN117197770B - Inspection complete flow data supervision system and method based on Internet of things - Google Patents
Inspection complete flow data supervision system and method based on Internet of things Download PDFInfo
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Abstract
The invention discloses a patrol total-flow data supervision system and method based on the Internet of things, comprising the following steps: acquiring a three-dimensional scene of a target inspection area, making an inspection route by combining the demand information with the three-dimensional scene, dividing the target inspection area, extracting area characteristics, and distributing corresponding inspection routes for inspection personnel and robots according to the area characteristics and the position information; judging the matching degree of the real-time position information of the inspection personnel and the robot and the inspection route, setting the data requirement of the inspection data, and judging whether the perceived inspection data meets the corresponding data requirement; and importing the inspection data meeting the requirements into a cloud database for integration, and identifying an abnormal state by utilizing the integrated multi-source data sequence. The invention guides the whole flow of the inspection task in an informatization way, improves the inspection efficiency, greatly improves the reliability and usability of inspection data, realizes the visualization of abnormal states and operation and maintenance conditions, and improves the inspection quality.
Description
Technical Field
The invention relates to the technical field of inspection, in particular to an inspection whole-flow data supervision system and method based on the Internet of things.
Background
The coming of the 'Internet plus' age further promotes the development quality of the information society, an intelligent control system based on a big data processing technology, an Internet of things technology and a computer network technology is generated, and the intelligent control system is permeated into various fields and becomes an important carrier for promoting the industrial development and improving the life quality of the whole people. Because of the overall social progress, the requirements on power transmission are continuously improved, the intelligent power transmission inspection work is comprehensively realized, and the power utilization safety and stability can be further improved. The optimization and upgrading of the power grid management mode are important works for continuously meeting the social development electricity demand, and more loopholes are easy to occur in the process of optimizing and controlling.
The power grid inspection refers to inspection and check of fixed points of facilities and equipment, and the phenomenon of frequent equipment missing inspection due to complex data acquisition modes in the process of inspecting equipment in a power plant; the on-site operators have inconvenience in the process of monitoring data; the environment where the power grid equipment is located is relatively complex, part of equipment facility information is not clear enough, and power grid management staff cannot confirm the inspection state and progress of inspection staff, so that inspection in-place conditions and inspection quality supervision are not facilitated, and false alarm, missing alarm and other conditions are easy to occur in the large-scale inspection process; in addition, the method and the mode for monitoring the real-time operation condition of the equipment are lacking, and the operation state of the equipment is difficult to monitor and evaluate. Therefore, in the power grid management, how to formulate a high-efficiency inspection method based on the internet of things and manage inspection data is a problem that needs to be solved at present.
Disclosure of Invention
In order to solve the technical problems, the invention provides a patrol total-flow data supervision system and method based on the Internet of things.
The invention provides a patrol total-flow data supervision method based on the Internet of things, which comprises the following steps:
acquiring a three-dimensional scene of a target inspection area, and making an inspection route by combining the requirement information of a current inspection task with the three-dimensional scene to acquire the position information of inspection personnel and inspection robots in the target inspection area;
dividing a target inspection area through the three-dimensional scene, extracting area characteristics, and distributing corresponding inspection routes according to the area characteristics and the position information;
judging the matching degree of the inspection route according to the real-time position information of the inspection personnel and the inspection robot, setting the data requirement of the inspection data according to the requirement information and the regional characteristics, and judging whether the inspection data collected by the inspection personnel and the inspection robot meet the corresponding data requirement;
judging whether the inspection data meet the preset standard or not based on the matching degree and the data requirement, importing the inspection data meeting the preset standard into a cloud database for integration, and identifying an abnormal state by utilizing the integrated multi-source data sequence;
And displaying the abnormal state in the three-dimensional scene, and scheduling the inspection robot to the abnormal part for real-time monitoring.
In the scheme, a three-dimensional scene of a target inspection area is acquired, an inspection route is formulated by combining the requirement information of a current inspection task with the three-dimensional scene, and the method specifically comprises the following steps:
collecting image information and laser point cloud data of a target inspection area, preprocessing the image information and the laser point cloud data, and eliminating miscellaneous points of the laser point cloud data by utilizing the preprocessed image information;
splicing and converting the laser point cloud data, and carrying out three-dimensional reconstruction by combining the laser point cloud data after coordinate conversion with environment data to generate three-dimensional data of a target inspection area;
acquiring power equipment and an assembly relation in a target inspection area, combining a three-dimensional model corresponding to the power equipment according to the spatial position and the assembly relation of the power equipment, and performing calibration adjustment according to historical operation data of the target inspection area to construct a digital twin model of the power equipment;
combining the three-dimensional data of the target inspection area with a digital twin model of the power equipment to obtain a three-dimensional scene of the target inspection area, and extracting a road topological graph according to the three-dimensional scene;
And acquiring the demand information of the current inspection task in the target inspection area, setting inspection point distribution according to the historical inspection data, and planning an inspection route according to the path minimum principle.
In this scheme, divide the target inspection area through three-dimensional scene, extract regional characteristic, according to regional characteristic combines the corresponding route of patrolling and examining of positional information distribution, specifically do:
acquiring position information of patrol personnel and patrol robots in a target patrol area through data perception, and feeding the position information back to a three-dimensional scene to be marked by combining basic information of the patrol personnel and the patrol robots;
dividing a three-dimensional scene into a plurality of subareas by utilizing grid division, acquiring historical abnormal data of the subareas and complexity of power equipment and corresponding pipelines, acquiring geographic information and trafficability of the subareas to obtain safety degree, and generating regional characteristics of the subareas according to the historical abnormal data, the complexity and the safety degree;
setting two area labels of manual inspection and machine inspection, determining the cluster number as 2, and acquiring the corresponding relation between each sub-area and the area label by using a clustering algorithm;
distributing the patrol personnel and the patrol robot to the subareas closest to the regional labels according to the position information of the patrol personnel and the patrol robot, and adjusting the patrol route corresponding to the subareas according to the current position information;
And sending the adjusted routing inspection route to an inspection personnel and an inspection robot according to a preset mode.
In this scheme, judge and verify whether the inspection data satisfies the preset standard based on the matching degree and data requirement, specifically:
reading real-time position information of patrol personnel and patrol robots in each subarea to generate a position sequence as track information, and calculating the matching degree of the track information and the patrol route of each subarea by using a dynamic time warping algorithm;
historical inspection data corresponding to the demand information is obtained through data retrieval, the historical inspection data meeting preset standards is screened, integrated and extracted, the data requirements of inspection tasks can be met, the data requirements are corrected through adjustment coefficients generated according to the regional characteristics of each subarea, and the data requirements of each subarea are used for collecting the inspection data;
acquiring corresponding task workflow according to the requirement information of the inspection task, generating inspection task guide through the task workflow, establishing a data abnormal supervision task according to the inspection task guide, and extracting previous inspection data as a data reference;
judging whether the deviation between the inspection data acquired under the guidance of the current inspection task and the data reference is larger than a preset deviation threshold value, if so, regarding the current inspection data as invalid data, reminding an inspection person or an inspection robot to acquire the data again, and if the acquired inspection data are consistent with the first inspection data, transmitting the data to a cloud database to generate abnormal state early warning;
And generating a patrol data sequence with the matching degree and the data specification according to the patrol task guide, respectively calculating the deviation of the matching degree and the data specification from a preset standard, and judging whether the data of each time stamp in the patrol data sequence meets the preset standard or not.
In this scheme, generate inspection task guide through the task workflow, still include:
monitoring the inspection quality and the inspection completion degree through the inspection task guidance, generating a next inspection task guidance when the inspection data is imported under the current inspection task guidance, and generating corresponding duration labels under each inspection task guidance according to the time consumption of the inspection operation of staff;
when the operation time length of the patrol personnel under the guidance of the current patrol task is longer than the corresponding time length label, generating reminding information, sending the reminding information to the corresponding patrol personnel, presetting a waiting time threshold, and when the corresponding patrol personnel does not feed back in the waiting time threshold, generating a safety early warning;
and preferentially sending the safety early warning combined with the basic information to the nearest patrol personnel or patrol robots, and synchronously sending the safety early warning combined with the basic information to a supervision center.
In the scheme, the inspection data meeting the preset standard is imported into the cloud database for integration, and the integrated multi-source data sequence is utilized to identify abnormal states, specifically:
Acquiring inspection data meeting preset standards, introducing the inspection data into a cloud database in combination with position labels, classifying the data in the cloud database, generating a multi-source data sequence after standardized processing of different data types, and comparing the multi-source data sequence with a multi-source data sequence corresponding to a previous inspection task to acquire a key data sequence;
constructing an identification training set according to the historical abnormal state corresponding to the historical inspection data, constructing an abnormal identification model based on a convolutional neural network, performing model training by using the identification training set, and importing a key data sequence into the abnormal identification model;
extracting multidimensional features in the key data sequence through a convolution layer, introducing a time attention method to obtain the time sequence relativity of the features, and setting time attention weight to fuse the multidimensional features in the key data sequence;
identifying abnormal states and potential anomalies in the target inspection area through multidimensional fusion features, generating an operation and maintenance work order by the abnormal states and the potential anomaly matching position labels, and transmitting the operation and maintenance work order according to a preset mode;
and visually displaying the operation and maintenance information in a three-dimensional scene, and when the abnormal state is greater than a preset serious threshold value, scheduling the idle inspection robot for real-time monitoring before operation and maintenance.
The second aspect of the invention also provides a system for supervising the whole inspection process data based on the Internet of things, which comprises: the system comprises a memory and a processor, wherein the memory comprises a patrol total flow data supervision method program based on the Internet of things, and the patrol total flow data supervision method program based on the Internet of things realizes the following steps when being executed by the processor:
a three-dimensional scene of a target inspection area is obtained, an inspection route is formulated by combining the requirement information of the current inspection task with the three-dimensional scene, acquiring position information of a patrol personnel and a patrol robot in a target patrol area;
dividing a target inspection area through the three-dimensional scene, extracting area characteristics, and distributing corresponding inspection routes according to the area characteristics and the position information;
judging the matching degree of the inspection route according to the real-time position information of the inspection personnel and the inspection robot, setting the data requirement of the inspection data according to the requirement information and the regional characteristics, and judging whether the inspection data collected by the inspection personnel and the inspection robot meet the corresponding data requirement;
judging whether the inspection data meet the preset standard or not based on the matching degree and the data requirement, importing the inspection data meeting the preset standard into a cloud database for integration, and identifying an abnormal state by utilizing the integrated multi-source data sequence;
And displaying the abnormal state in the three-dimensional scene, and scheduling the inspection robot to the abnormal part for real-time monitoring.
The invention discloses a patrol total-flow data supervision system and method based on the Internet of things, comprising the following steps: acquiring a three-dimensional scene of a target inspection area, making an inspection route by combining the demand information with the three-dimensional scene, dividing the target inspection area, extracting area characteristics, and distributing corresponding inspection routes for inspection personnel and robots according to the area characteristics and the position information; judging the matching degree of the real-time position information of the inspection personnel and the robot and the inspection route, setting the data requirement of the inspection data, and judging whether the perceived inspection data meets the corresponding data requirement; and importing the inspection data meeting the requirements into a cloud database for integration, and identifying an abnormal state by utilizing the integrated multi-source data sequence. The invention guides the whole flow of the inspection task in an informatization way, improves the inspection efficiency, greatly improves the reliability and usability of inspection data, realizes the visualization of abnormal states and operation and maintenance conditions, and improves the inspection quality.
Drawings
Fig. 1 shows a flowchart of a patrol total-process data supervision method based on the internet of things;
FIG. 2 is a flow chart of a method of the present invention for assigning a routing inspection route based on regional characteristics in combination with location information;
FIG. 3 is a flow chart of a method of verifying that the inspection data meets preset criteria in accordance with the present invention;
fig. 4 shows a block diagram of a patrol total-flow data supervision system based on the internet of things.
Detailed Description
In order that the above-recited objects, features and advantages of the present invention will be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
Fig. 1 shows a flowchart of a patrol total-process data supervision method based on the internet of things.
As shown in fig. 1, the first aspect of the present invention provides a method for supervising inspection complete flow data based on internet of things, including:
S102, acquiring a three-dimensional scene of a target inspection area, and making an inspection route by combining the requirement information of a current inspection task with the three-dimensional scene to acquire the position information of inspection personnel and inspection robots in the target inspection area;
s104, dividing a target inspection area through the three-dimensional scene, extracting area characteristics, and distributing corresponding inspection routes according to the area characteristics and the position information;
s106, judging the matching degree of the inspection route according to the real-time position information of the inspection personnel and the inspection robot, setting the data requirement of the inspection data according to the requirement information and the regional characteristics, and judging whether the inspection data collected by the inspection personnel and the inspection robot meet the corresponding data requirement;
s108, judging whether the inspection data meet the preset standard or not based on the matching degree and the data requirement, importing the inspection data meeting the preset standard into a cloud database for integration, and identifying an abnormal state by utilizing the integrated multi-source data sequence;
s110, displaying the abnormal state in the three-dimensional scene, and scheduling the inspection robot to the abnormal part for real-time monitoring.
The three-dimensional scene is constructed according to the physical environment of the target inspection area and the deployment conditions of various electric equipment, a user can acquire inspection data in the three-dimensional scene in real time, the abnormal condition of the target inspection area and the places needing operation and maintenance are clear, and the position information and the state information of inspection personnel and inspection robots are mastered.
Collecting image information and laser point cloud data of a target inspection area, preprocessing the image information and the laser point cloud data, and eliminating miscellaneous points of the laser point cloud data by utilizing the preprocessed image information; splicing and converting laser point cloud data, carrying out three-dimensional reconstruction by combining the laser point cloud data after coordinate conversion with environment data, carrying out group denoising and simplification on the point cloud data, packaging according to image information, and filling gaps after packaging to generate three-dimensional data of a target inspection area; acquiring power equipment and an assembly relation in a target inspection area, combining a three-dimensional model corresponding to the power equipment according to the spatial position and the assembly relation of the power equipment, and performing calibration adjustment according to historical operation data of the target inspection area to construct a digital twin model of the power equipment; combining the three-dimensional data of the target inspection area with a digital twin model of the power equipment to obtain a three-dimensional scene of the target inspection area, and extracting a road topological graph according to the three-dimensional scene; the method comprises the steps of obtaining demand information of a current patrol task in a target patrol area, setting patrol point distribution according to historical patrol data, obtaining historical abnormal data of all subareas under the current demand information through the historical patrol data, wherein the more the historical abnormal data are, the more the subareas are the high-frequency abnormal areas, the more patrol points of the corresponding subareas are, planning a patrol route according to a path minimum principle, and planning a preferred patrol route can be achieved through methods such as a genetic algorithm, an A-type algorithm, a particle swarm algorithm and the like.
FIG. 2 is a flow chart of a method of allocating a patrol route according to the present invention based on regional characteristics in combination with location information.
According to the embodiment of the invention, the target inspection area is divided through the three-dimensional scene, the area characteristics are extracted, and the corresponding inspection route is distributed according to the area characteristics and the position information, specifically:
s202, acquiring position information of a patrol personnel and a patrol robot in a target patrol area through data perception, and feeding the position information back to a three-dimensional scene to be marked by combining basic information of the patrol personnel and the patrol robot;
s204, dividing the three-dimensional scene into a plurality of subareas by utilizing grating division, acquiring historical abnormal data of the subareas and complexity of the power equipment and corresponding pipelines, acquiring geographic information and trafficability of the subareas to obtain safety degree, and generating regional characteristics of the subareas according to the historical abnormal data, the complexity and the safety degree;
s206, setting two area labels of manual inspection and machine inspection, determining the cluster number as 2, and acquiring the corresponding relation between each sub-area and the area label by using a clustering algorithm;
s208, distributing the patrol personnel and the patrol robot to the subareas closest to the patrol personnel and the patrol robot according to the position information of the patrol personnel and the patrol robot, and adjusting the patrol route corresponding to the subareas according to the current position information;
S210, sending the adjusted inspection route to inspection personnel and inspection robots according to a preset mode.
It should be noted that, the position information of the patrol personnel and the patrol robot can be obtained from the terminal equipment and the own IMU, and because the power plant or the transformer substation has a plurality of areas with complex equipment and narrow channels, the patrol personnel has difficulty in patrol, and the patrol robot provides a better solution for the detection work problem of the areas. And generating regional characteristics of the subareas according to the historical abnormal data, the complexity and the safety degree, evaluating the regional characteristics of each subarea, and if the greater the number of the historical abnormal data of a certain subarea is, indicating that the abnormal occurrence frequency of the subarea is high, the heavy the inspection task is, and inspecting the subareas with the large number of the historical abnormal data, the high complexity and the low safety degree by using an inspection robot.
FIG. 3 is a flow chart of a method of verifying that the inspection data meets the preset criteria according to the present invention.
According to the embodiment of the invention, whether the inspection data meets the preset standard is judged and verified based on the matching degree and the data requirement, specifically:
s302, reading real-time position information of patrol personnel and patrol robots in each subarea to generate a position sequence as track information, and calculating the matching degree of the track information and the patrol route of each subarea by using a dynamic time warping algorithm;
S304, historical inspection data corresponding to the demand information is obtained through data retrieval, the historical inspection data meeting preset standards is screened for integration and extraction, the data requirements of inspection tasks can be met, adjustment coefficients are generated according to the regional characteristics of each subarea to correct the data requirements, and the data requirements of each subarea are utilized for collecting the inspection data;
s306, acquiring corresponding task workflow according to the requirement information of the inspection task, generating inspection task guide through the task workflow, establishing a data abnormal supervision task according to the inspection task guide, and extracting the previous inspection data as a data reference;
s308, judging whether the deviation between the inspection data acquired under the guidance of the current inspection task and the data reference is larger than a preset deviation threshold, if so, regarding the current inspection data as invalid data, reminding an inspection person or an inspection robot to acquire the data again, and when the acquired inspection data is consistent with the first inspection data, transmitting the data to a cloud database to generate abnormal state early warning;
s310, generating a patrol data sequence with the matching degree and the data specification according to the patrol task guide, respectively calculating the deviation of the matching degree and the data specification from a preset standard, and judging whether the data of each time stamp in the patrol data sequence meets the preset standard or not.
The method is characterized in that data requirements meeting the requirements of the inspection task are obtained according to historical inspection data, sharing of the data requirements of different subareas is achieved by using the similarity among the subareas, and inspection efficiency is improved; generating an adjustment coefficient according to the regional characteristics of each subarea to correct the data requirement, for example, when the complexity of the pipeline in the subarea is higher, acquiring image information with higher resolution; and acquiring data according to the data requirements to obtain inspection data, judging the data specification of the inspection data, and eliminating abnormal inspection data which does not meet the data requirements in the inspection data. For the guidance of a specific inspection task, introducing data abnormal supervision, for example, regarding key parameters such as pressure, temperature, voltage, current and the like, taking the previous inspection data as a data reference, if the deviation between the previous inspection data and the data reference is too large, indicating that the data is invalid, detecting again, and when the inspection data is consistent for two times, indicating that an abnormal state exists at the position, sending the abnormal state to a cloud database, and generating early warning.
It is to be noted that, the inspection quality and the inspection completion degree are monitored through the inspection task guidance, when the inspection data is imported under the current inspection task guidance, the next inspection task guidance is generated, and the corresponding duration labels under each inspection task guidance are generated according to the time consumption of the inspection operation of the staff; when the operation time length of the patrol personnel under the guidance of the current patrol task is longer than the corresponding time length label, generating reminding information, sending the reminding information to the corresponding patrol personnel, presetting a waiting time threshold, and when the corresponding patrol personnel does not feed back in the waiting time threshold, generating a safety early warning; and preferentially sending the safety early warning combined with the basic information to the nearest patrol personnel or patrol robots, and synchronously sending the safety early warning combined with the basic information to a supervision center. Acquiring the state of the inspection robot through a three-dimensional scene, acquiring the inspection progress of each manual inspection subarea in the neighborhood subarea when the inspection robot is in an idle state, reading the residual inspection task quantity of each subarea according to the inspection progress, and calculating the predicted working time of the residual inspection task quantity according to the average operation time of the inspection robot under the guidance of each inspection task; acquiring the correlation degree of the manual inspection time length and the electric quantity of the inspection robot, acquiring the working time length corresponding to the electric quantity value of the idle inspection robot, comparing the working time length with the expected working time length of each manual inspection subarea, acquiring the manual inspection subarea with the smallest deviation for manual-machine combined inspection, extracting the inspection route of the combined inspection area, and taking the end point of the inspection route as the inspection start point of the inspection robot; and extracting the original data requirement of the data requirement covering the inspection robot in the combined inspection area.
It should be noted that, the inspection data meeting the preset standard is acquired and combined with the position label to be imported into the cloud database, the data classification is performed in the cloud database, the multi-source data sequence including parameters of image information, pressure, temperature, voltage, current and the like is generated after the standardization processing of different data types, and the parameters are determined by the sensor corresponding to the requirement information. The cloud database is subjected to centralized classification processing and stored in the cloud database, and data in the cloud database can provide relevant basis for the next inspection task. Comparing the multi-source data sequence with a multi-source data sequence corresponding to a previous inspection task to obtain a key data sequence; constructing an identification training set according to the historical abnormal state corresponding to the historical inspection data, constructing an abnormal identification model based on a convolutional neural network, performing model training by using the identification training set, and importing a key data sequence into the abnormal identification model; extracting multidimensional features in the key data sequence through a convolution layer, introducing a time attention method to obtain the time sequence relativity of the features, and setting time attention weight to fuse the multidimensional features in the key data sequence; identifying abnormal states and potential anomalies in the target inspection area through multidimensional fusion features, generating an operation and maintenance work order by the abnormal states and the potential anomaly matching position labels, and transmitting the operation and maintenance work order according to a preset mode; and visually displaying the operation and maintenance information in a three-dimensional scene, and when the abnormal state is greater than a preset serious threshold value, scheduling the idle inspection robot for real-time monitoring before operation and maintenance.
Fig. 4 shows a block diagram of a patrol total-flow data supervision system based on the internet of things.
The second aspect of the present invention also provides a system 4 for supervising inspection whole-flow data based on the internet of things, the system comprising: the storage 41 and the processor 42, wherein the storage comprises a patrol total flow data supervision method program based on the internet of things, and the patrol total flow data supervision method program based on the internet of things realizes the following steps when being executed by the processor:
acquiring a three-dimensional scene of a target inspection area, and making an inspection route by combining the requirement information of a current inspection task with the three-dimensional scene to acquire the position information of inspection personnel and inspection robots in the target inspection area;
dividing a target inspection area through the three-dimensional scene, extracting area characteristics, and distributing corresponding inspection routes according to the area characteristics and the position information;
judging the matching degree of the inspection route according to the real-time position information of the inspection personnel and the inspection robot, setting the data requirement of the inspection data according to the requirement information and the regional characteristics, and judging whether the inspection data collected by the inspection personnel and the inspection robot meet the corresponding data requirement;
Judging whether the inspection data meet the preset standard or not based on the matching degree and the data requirement, importing the inspection data meeting the preset standard into a cloud database for integration, and identifying an abnormal state by utilizing the integrated multi-source data sequence;
and displaying the abnormal state in the three-dimensional scene, and scheduling the inspection robot to the abnormal part for real-time monitoring.
The three-dimensional scene is constructed according to the physical environment of the target inspection area and the deployment conditions of various electric equipment, a user can acquire inspection data in the three-dimensional scene in real time, the abnormal condition of the target inspection area and the places needing operation and maintenance are clear, and the position information and the state information of inspection personnel and inspection robots are mastered.
Collecting image information and laser point cloud data of a target inspection area, preprocessing the image information and the laser point cloud data, and eliminating miscellaneous points of the laser point cloud data by utilizing the preprocessed image information; splicing and converting laser point cloud data, carrying out three-dimensional reconstruction by combining the laser point cloud data after coordinate conversion with environment data, carrying out group denoising and simplification on the point cloud data, packaging according to image information, and filling gaps after packaging to generate three-dimensional data of a target inspection area; acquiring power equipment and an assembly relation in a target inspection area, combining a three-dimensional model corresponding to the power equipment according to the spatial position and the assembly relation of the power equipment, and performing calibration adjustment according to historical operation data of the target inspection area to construct a digital twin model of the power equipment; combining the three-dimensional data of the target inspection area with a digital twin model of the power equipment to obtain a three-dimensional scene of the target inspection area, and extracting a road topological graph according to the three-dimensional scene; the method comprises the steps of obtaining demand information of a current patrol task in a target patrol area, setting patrol point distribution according to historical patrol data, obtaining historical abnormal data of all subareas under the current demand information through the historical patrol data, wherein the more the historical abnormal data are, the more the subareas are the high-frequency abnormal areas, the more patrol points of the corresponding subareas are, planning a patrol route according to a path minimum principle, and planning a preferred patrol route can be achieved through methods such as a genetic algorithm, an A-type algorithm, a particle swarm algorithm and the like.
According to the embodiment of the invention, the target inspection area is divided through the three-dimensional scene, the area characteristics are extracted, and the corresponding inspection route is distributed according to the area characteristics and the position information, specifically:
acquiring position information of patrol personnel and patrol robots in a target patrol area through data perception, and feeding the position information back to a three-dimensional scene to be marked by combining basic information of the patrol personnel and the patrol robots;
dividing a three-dimensional scene into a plurality of subareas by utilizing grid division, acquiring historical abnormal data of the subareas and complexity of power equipment and corresponding pipelines, acquiring geographic information and trafficability of the subareas to obtain safety degree, and generating regional characteristics of the subareas according to the historical abnormal data, the complexity and the safety degree;
setting two area labels of manual inspection and machine inspection, determining the cluster number as 2, and acquiring the corresponding relation between each sub-area and the area label by using a clustering algorithm;
distributing the patrol personnel and the patrol robot to the subareas closest to the regional labels according to the position information of the patrol personnel and the patrol robot, and adjusting the patrol route corresponding to the subareas according to the current position information;
And sending the adjusted routing inspection route to an inspection personnel and an inspection robot according to a preset mode.
It should be noted that, the position information of the patrol personnel and the patrol robot can be obtained from the terminal equipment and the own IMU, and because the power plant or the transformer substation has a plurality of areas with complex equipment and narrow channels, the patrol personnel has difficulty in patrol, and the patrol robot provides a better solution for the detection work problem of the areas. And generating regional characteristics of the subareas according to the historical abnormal data, the complexity and the safety degree, evaluating the regional characteristics of each subarea, and if the greater the number of the historical abnormal data of a certain subarea is, indicating that the abnormal occurrence frequency of the subarea is high, the heavy the inspection task is, and inspecting the subareas with the large number of the historical abnormal data, the high complexity and the low safety degree by using an inspection robot.
According to the embodiment of the invention, whether the inspection data meets the preset standard is judged and verified based on the matching degree and the data requirement, specifically:
reading real-time position information of patrol personnel and patrol robots in each subarea to generate a position sequence as track information, and calculating the matching degree of the track information and the patrol route of each subarea by using a dynamic time warping algorithm;
Historical inspection data corresponding to the demand information is obtained through data retrieval, the historical inspection data meeting preset standards is screened, integrated and extracted, the data requirements of inspection tasks can be met, the data requirements are corrected through adjustment coefficients generated according to the regional characteristics of each subarea, and the data requirements of each subarea are used for collecting the inspection data;
acquiring corresponding task workflow according to the requirement information of the inspection task, generating inspection task guide through the task workflow, establishing a data abnormal supervision task according to the inspection task guide, and extracting previous inspection data as a data reference;
judging whether the deviation between the inspection data acquired under the guidance of the current inspection task and the data reference is larger than a preset deviation threshold value, if so, regarding the current inspection data as invalid data, reminding an inspection person or an inspection robot to acquire the data again, and if the acquired inspection data are consistent with the first inspection data, transmitting the data to a cloud database to generate abnormal state early warning;
and generating a patrol data sequence with the matching degree and the data specification according to the patrol task guide, respectively calculating the deviation of the matching degree and the data specification from a preset standard, and judging whether the data of each time stamp in the patrol data sequence meets the preset standard or not.
The method is characterized in that data requirements meeting the requirements of the inspection task are obtained according to historical inspection data, sharing of the data requirements of different subareas is achieved by using the similarity among the subareas, and inspection efficiency is improved; generating an adjustment coefficient according to the regional characteristics of each subarea to correct the data requirement, for example, when the complexity of the pipeline in the subarea is higher, acquiring image information with higher resolution; and acquiring data according to the data requirements to obtain inspection data, judging the data specification of the inspection data, and eliminating abnormal inspection data which does not meet the data requirements in the inspection data. For the guidance of a specific inspection task, introducing data abnormal supervision, for example, regarding key parameters such as pressure, temperature, voltage, current and the like, taking the previous inspection data as a data reference, if the deviation between the previous inspection data and the data reference is too large, indicating that the data is invalid, detecting again, and when the inspection data is consistent for two times, indicating that an abnormal state exists at the position, sending the abnormal state to a cloud database, and generating early warning.
It is to be noted that, the inspection quality and the inspection completion degree are monitored through the inspection task guidance, when the inspection data is imported under the current inspection task guidance, the next inspection task guidance is generated, and the corresponding duration labels under each inspection task guidance are generated according to the time consumption of the inspection operation of the staff; when the operation time length of the patrol personnel under the guidance of the current patrol task is longer than the corresponding time length label, generating reminding information, sending the reminding information to the corresponding patrol personnel, presetting a waiting time threshold, and when the corresponding patrol personnel does not feed back in the waiting time threshold, generating a safety early warning; and preferentially sending the safety early warning combined with the basic information to the nearest patrol personnel or patrol robots, and synchronously sending the safety early warning combined with the basic information to a supervision center.
It is to be noted that, the inspection data meeting the preset standard is acquired and is imported into a cloud database in combination with a position label, data classification is carried out in the cloud database, a multi-source data sequence is generated after standardization processing of different data types, and a key data sequence is acquired based on comparison of the multi-source data sequence and a multi-source data sequence corresponding to the previous inspection task; constructing an identification training set according to the historical abnormal state corresponding to the historical inspection data, constructing an abnormal identification model based on a convolutional neural network, performing model training by using the identification training set, and importing a key data sequence into the abnormal identification model; extracting multidimensional features in the key data sequence through a convolution layer, introducing a time attention method to obtain the time sequence relativity of the features, and setting time attention weight to fuse the multidimensional features in the key data sequence; identifying abnormal states and potential anomalies in the target inspection area through multidimensional fusion features, generating an operation and maintenance work order by the abnormal states and the potential anomaly matching position labels, and transmitting the operation and maintenance work order according to a preset mode; and visually displaying the operation and maintenance information in a three-dimensional scene, and when the abnormal state is greater than a preset serious threshold value, scheduling the idle inspection robot for real-time monitoring before operation and maintenance.
The third aspect of the present invention also provides a computer readable storage medium, where the computer readable storage medium includes an inspection complete flow data supervision method program based on the internet of things, and when the inspection complete flow data supervision method program based on the internet of things is executed by a processor, the steps of the inspection complete flow data supervision method based on the internet of things described in any one of the above are implemented.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above described device embodiments are only illustrative, e.g. the division of the units is only one logical function division, and there may be other divisions in practice, such as: multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or units, whether electrically, mechanically, or otherwise.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units; can be located in one place or distributed to a plurality of network units; some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, or each unit may be separately used as one unit, or two or more units may be integrated in one unit; the integrated units may be implemented in hardware or in hardware plus software functional units.
Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), a magnetic disk or an optical disk, or the like, which can store program codes.
Alternatively, the above-described integrated units of the present invention may be stored in a computer-readable storage medium if implemented in the form of software functional modules and sold or used as separate products. Based on such understanding, the technical solutions of the embodiments of the present invention may be embodied in essence or a part contributing to the prior art in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, ROM, RAM, magnetic or optical disk, or other medium capable of storing program code.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. The inspection full-flow data supervision method based on the Internet of things is characterized by comprising the following steps of:
acquiring a three-dimensional scene of a target inspection area, and making an inspection route by combining the requirement information of a current inspection task with the three-dimensional scene to acquire the position information of inspection personnel and inspection robots in the target inspection area;
dividing a target inspection area through the three-dimensional scene, extracting area characteristics, and distributing corresponding inspection routes according to the area characteristics and the position information;
judging the matching degree of the inspection route according to the real-time position information of the inspection personnel and the inspection robot, setting the data requirement of the inspection data according to the requirement information and the regional characteristics, and judging whether the inspection data collected by the inspection personnel and the inspection robot meet the corresponding data requirement;
Judging whether the inspection data meet the preset standard or not based on the matching degree and the data requirement, importing the inspection data meeting the preset standard into a cloud database for integration, and identifying an abnormal state by utilizing the integrated multi-source data sequence;
displaying the abnormal state in the three-dimensional scene, and scheduling the inspection robot to the abnormal part for real-time monitoring;
judging and verifying whether the inspection data meets preset standards based on the matching degree and the data requirement, wherein the method specifically comprises the following steps:
reading real-time position information of patrol personnel and patrol robots in each subarea to generate a position sequence as track information, and calculating the matching degree of the track information and the patrol route of each subarea by using a dynamic time warping algorithm;
historical inspection data corresponding to the demand information is obtained through data retrieval, the historical inspection data meeting preset standards is screened, integrated and extracted, the data requirements of inspection tasks can be met, the data requirements are corrected through adjustment coefficients generated according to the regional characteristics of each subarea, and the data requirements of each subarea are used for collecting the inspection data;
acquiring corresponding task workflow according to the requirement information of the inspection task, generating inspection task guide through the task workflow, establishing a data abnormal supervision task according to the inspection task guide, and extracting previous inspection data as a data reference;
Judging whether the deviation between the inspection data acquired under the guidance of the current inspection task and the data reference is larger than a preset deviation threshold value, if so, regarding the current inspection data as invalid data, reminding an inspection person or an inspection robot to acquire the data again, and if the acquired inspection data are consistent with the first inspection data, transmitting the data to a cloud database to generate abnormal state early warning;
generating a patrol data sequence with the matching degree and the data specification according to the patrol task guide, respectively calculating the deviation of the matching degree and the data specification from a preset standard, and judging whether the data of each time stamp in the patrol data sequence meets the preset standard or not;
generating inspection task guidance through the task workflow, further comprising:
monitoring the inspection quality and the inspection completion degree through the inspection task guidance, generating a next inspection task guidance when the inspection data is imported under the current inspection task guidance, and generating corresponding duration labels under each inspection task guidance according to the time consumption of the inspection operation of staff;
when the operation time length of the patrol personnel under the guidance of the current patrol task is longer than the corresponding time length label, generating reminding information, sending the reminding information to the corresponding patrol personnel, presetting a waiting time threshold, and when the corresponding patrol personnel does not feed back in the waiting time threshold, generating a safety early warning;
And preferentially sending the safety early warning combined with the basic information to the nearest patrol personnel or patrol robots, and synchronously sending the safety early warning combined with the basic information to a supervision center.
2. The method for supervising the whole inspection process data based on the internet of things according to claim 1, wherein the method is characterized in that a three-dimensional scene of a target inspection area is obtained, an inspection route is formulated by combining the requirement information of a current inspection task with the three-dimensional scene, and the method is specifically as follows:
collecting image information and laser point cloud data of a target inspection area, preprocessing the image information and the laser point cloud data, and eliminating miscellaneous points of the laser point cloud data by utilizing the preprocessed image information;
splicing and converting the laser point cloud data, and carrying out three-dimensional reconstruction by combining the laser point cloud data after coordinate conversion with environment data to generate three-dimensional data of a target inspection area;
acquiring power equipment and an assembly relation in a target inspection area, combining a three-dimensional model corresponding to the power equipment according to the spatial position and the assembly relation of the power equipment, and performing calibration adjustment according to historical operation data of the target inspection area to construct a digital twin model of the power equipment;
combining the three-dimensional data of the target inspection area with a digital twin model of the power equipment to obtain a three-dimensional scene of the target inspection area, and extracting a road topological graph according to the three-dimensional scene;
And acquiring the demand information of the current inspection task in the target inspection area, setting inspection point distribution according to the historical inspection data, and planning an inspection route according to the path minimum principle.
3. The method for supervising the whole inspection process data based on the internet of things according to claim 1, wherein the method is characterized in that a target inspection area is divided through the three-dimensional scene, area characteristics are extracted, and corresponding inspection routes are distributed according to the area characteristics in combination with the position information, specifically:
acquiring position information of patrol personnel and patrol robots in a target patrol area through data perception, and feeding the position information back to a three-dimensional scene to be marked by combining basic information of the patrol personnel and the patrol robots;
dividing a three-dimensional scene into a plurality of subareas by utilizing grid division, acquiring historical abnormal data of the subareas and complexity of power equipment and corresponding pipelines, acquiring geographic information and trafficability of the subareas to obtain safety degree, and generating regional characteristics of the subareas according to the historical abnormal data, the complexity and the safety degree;
setting two area labels of manual inspection and machine inspection, determining the cluster number as 2, and acquiring the corresponding relation between each sub-area and the area label by using a clustering algorithm;
Distributing the patrol personnel and the patrol robot to the subareas closest to the regional labels according to the position information of the patrol personnel and the patrol robot, and adjusting the patrol route corresponding to the subareas according to the current position information;
and sending the adjusted routing inspection route to an inspection personnel and an inspection robot according to a preset mode.
4. The inspection full-flow data supervision method based on the internet of things according to claim 1, wherein the inspection data meeting the preset standard is imported into a cloud database for integration, and the integrated multi-source data sequence is used for identifying abnormal states, specifically:
acquiring inspection data meeting preset standards, introducing the inspection data into a cloud database in combination with position labels, classifying the data in the cloud database, generating a multi-source data sequence after standardized processing of different data types, and comparing the multi-source data sequence with a multi-source data sequence corresponding to a previous inspection task to acquire a key data sequence;
constructing an identification training set according to the historical abnormal state corresponding to the historical inspection data, constructing an abnormal identification model based on a convolutional neural network, performing model training by using the identification training set, and importing a key data sequence into the abnormal identification model;
Extracting multidimensional features in the key data sequence through a convolution layer, introducing a time attention method to obtain the time sequence relativity of the features, and setting time attention weight to fuse the multidimensional features in the key data sequence;
identifying abnormal states and potential anomalies in the target inspection area through multidimensional fusion features, generating an operation and maintenance work order by the abnormal states and the potential anomaly matching position labels, and transmitting the operation and maintenance work order according to a preset mode;
and visually displaying the operation and maintenance information in a three-dimensional scene, and when the abnormal state is greater than a preset serious threshold value, scheduling the idle inspection robot for real-time monitoring before operation and maintenance.
5. The utility model provides a patrol and examine full flow data supervisory systems based on thing networking which characterized in that, this system includes: the system comprises a memory and a processor, wherein the memory comprises an inspection full-flow data supervision method program based on the Internet of things, and the inspection full-flow data supervision method program based on the Internet of things realizes the following steps when being executed by the processor:
acquiring a three-dimensional scene of a target inspection area, and making an inspection route by combining the requirement information of a current inspection task with the three-dimensional scene to acquire the position information of inspection personnel and inspection robots in the target inspection area;
Dividing a target inspection area through the three-dimensional scene, extracting area characteristics, and distributing corresponding inspection routes according to the area characteristics and the position information;
judging the matching degree of the inspection route according to the real-time position information of the inspection personnel and the inspection robot, setting the data requirement of the inspection data according to the requirement information and the regional characteristics, and judging whether the inspection data collected by the inspection personnel and the inspection robot meet the corresponding data requirement;
judging whether the inspection data meet the preset standard or not based on the matching degree and the data requirement, importing the inspection data meeting the preset standard into a cloud database for integration, and identifying an abnormal state by utilizing the integrated multi-source data sequence;
displaying the abnormal state in the three-dimensional scene, and scheduling the inspection robot to the abnormal part for real-time monitoring;
judging and verifying whether the inspection data meets preset standards based on the matching degree and the data requirement, wherein the method specifically comprises the following steps:
reading real-time position information of patrol personnel and patrol robots in each subarea to generate a position sequence as track information, and calculating the matching degree of the track information and the patrol route of each subarea by using a dynamic time warping algorithm;
Historical inspection data corresponding to the demand information is obtained through data retrieval, the historical inspection data meeting preset standards is screened, integrated and extracted, the data requirements of inspection tasks can be met, the data requirements are corrected through adjustment coefficients generated according to the regional characteristics of each subarea, and the data requirements of each subarea are used for collecting the inspection data;
acquiring corresponding task workflow according to the requirement information of the inspection task, generating inspection task guide through the task workflow, establishing a data abnormal supervision task according to the inspection task guide, and extracting previous inspection data as a data reference;
judging whether the deviation between the inspection data acquired under the guidance of the current inspection task and the data reference is larger than a preset deviation threshold value, if so, regarding the current inspection data as invalid data, reminding an inspection person or an inspection robot to acquire the data again, and if the acquired inspection data are consistent with the first inspection data, transmitting the data to a cloud database to generate abnormal state early warning;
generating a patrol data sequence with the matching degree and the data specification according to the patrol task guide, respectively calculating the deviation of the matching degree and the data specification from a preset standard, and judging whether the data of each time stamp in the patrol data sequence meets the preset standard or not;
Generating inspection task guidance through the task workflow, further comprising:
monitoring the inspection quality and the inspection completion degree through the inspection task guidance, generating a next inspection task guidance when the inspection data is imported under the current inspection task guidance, and generating corresponding duration labels under each inspection task guidance according to the time consumption of the inspection operation of staff;
when the operation time length of the patrol personnel under the guidance of the current patrol task is longer than the corresponding time length label, generating reminding information, sending the reminding information to the corresponding patrol personnel, presetting a waiting time threshold, and when the corresponding patrol personnel does not feed back in the waiting time threshold, generating a safety early warning;
and preferentially sending the safety early warning combined with the basic information to the nearest patrol personnel or patrol robots, and synchronously sending the safety early warning combined with the basic information to a supervision center.
6. The inspection full-flow data supervision system based on the internet of things according to claim 5, wherein the inspection data meeting the preset standard is imported into a cloud database for integration, and the integrated multi-source data sequence is used for identifying abnormal states, specifically:
acquiring inspection data meeting preset standards, introducing the inspection data into a cloud database in combination with position labels, classifying the data in the cloud database, generating a multi-source data sequence after standardized processing of different data types, and comparing the multi-source data sequence with a multi-source data sequence corresponding to a previous inspection task to acquire a key data sequence;
Constructing an identification training set according to the historical abnormal state corresponding to the historical inspection data, constructing an abnormal identification model based on a convolutional neural network, performing model training by using the identification training set, and importing a key data sequence into the abnormal identification model;
extracting multidimensional features in the key data sequence through a convolution layer, introducing a time attention method to obtain the time sequence relativity of the features, and setting time attention weight to fuse the multidimensional features in the key data sequence;
identifying abnormal states and potential anomalies in the target inspection area through multidimensional fusion features, generating an operation and maintenance work order by the abnormal states and the potential anomaly matching position labels, and transmitting the operation and maintenance work order according to a preset mode;
and visually displaying the operation and maintenance information in a three-dimensional scene, and when the abnormal state is greater than a preset serious threshold value, scheduling the idle inspection robot for real-time monitoring before operation and maintenance.
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