CN117611582A - Urban river channel culvert intelligent inspection method, system and medium based on big data - Google Patents
Urban river channel culvert intelligent inspection method, system and medium based on big data Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 57
- 238000007689 inspection Methods 0.000 title claims abstract description 51
- 238000001514 detection method Methods 0.000 claims abstract description 75
- 238000012502 risk assessment Methods 0.000 claims abstract description 13
- 239000007789 gas Substances 0.000 claims description 75
- 239000002341 toxic gas Substances 0.000 claims description 12
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 8
- 239000001301 oxygen Substances 0.000 claims description 8
- 229910052760 oxygen Inorganic materials 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 6
- 230000011218 segmentation Effects 0.000 claims description 6
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 108
- 239000002351 wastewater Substances 0.000 description 24
- 239000004568 cement Substances 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 4
- 239000002912 waste gas Substances 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000000903 blocking effect Effects 0.000 description 2
- 210000004907 gland Anatomy 0.000 description 2
- 239000011796 hollow space material Substances 0.000 description 2
- 230000002452 interceptive effect Effects 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 1
- 238000003911 water pollution Methods 0.000 description 1
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- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
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Abstract
The embodiment of the application provides an intelligent city river culvert inspection method, system and medium based on big data. The method comprises the following steps: obtaining the darkness detection information, obtaining a darkness image, processing the darkness image to obtain an optimized darkness image, extracting characteristics of the optimized darkness image, comparing the characteristics of the optimized darkness image with preset standard darkness image characteristics to obtain a deviation rate, judging whether the deviation rate is larger than or equal to a preset deviation rate threshold value or not, if so, fusing the optimized darkness image with a prestoring darkness image set in a database to generate a first darkness detection report, and if so, directly sending the optimized darkness image to a terminal; and acquiring the obscuration gas information of a plurality of acquisition points in a preset time period, extracting gas concentration characteristic data, and processing through a preset gas risk assessment model according to a plurality of groups of corresponding gas concentration characteristic data in the preset time period to obtain a gas risk fluctuation coefficient, thereby providing powerful support for the guarantee of engineering quality.
Description
Technical Field
The application relates to the technical field of big data and culvert detection, in particular to an intelligent urban river culvert inspection method, system and medium based on big data.
Background
Along with the aggravation of the urban process, cement boards are artificially paved above a plurality of river channels, and then roads are repaired on the cement boards and buildings are built, so that more and more implications are necessarily generated. The environment is sealed for a long time, the culverts are hidden underground, huge safety influence can be caused to people and building groups at any time, dirt and scale are already hidden in the culverts, and in the water pollution treatment process, the culvert treatment is the ring with the greatest difficulty, and the internal environment of the culverts is mastered.
At present, the mode of the meaning detection mainly relies on manual operation equipment of personnel to collect various data, and particularly, the data are collected through several modes of ultrasonic detection, electromagnetic wave detection, sound vibration detection, ray detection and the like, and the data are seriously affected by the environment, so that the accuracy of the collected data is not high, and the collection technology has limitations. The lack of technical means that can carry out all-round detection and processing according to the whole bearing of meaning image and meaning internal gas information, quality of water information and water level information leads to operation complexity and implementation cost too high, and then leads to meaning detection unable popularization, can't provide powerful support for the guarantee of engineering quality, also can't guarantee personnel life and equipment safety.
In view of the above problems, an effective technical solution is currently needed.
Disclosure of Invention
The embodiment of the application aims to provide an intelligent inspection method, system and medium for urban river culvert based on big data, which can obtain a culvert image by acquiring culvert detection information, process the culvert image to obtain an optimized culvert image, extract the characteristics of the optimized culvert image, compare the characteristics of the optimized culvert image with the characteristics of a preset standard culvert image to obtain a deviation rate, judge whether the deviation rate is greater than or equal to a preset deviation rate threshold value or not, if the deviation rate is greater than or equal to the preset deviation rate threshold value, fuse the optimized culvert image with a prestored culvert image set in a database to generate a first culvert detection report, and if the deviation rate is less than the preset deviation rate threshold value, directly send the optimized culvert image to a terminal to provide powerful support for guaranteeing the engineering quality, and ensure the life and equipment safety of personnel.
The embodiment of the application provides an intelligent tour inspection method for urban river culvert based on big data, which comprises the following steps:
obtaining the culvert detection information;
obtaining a culvert image according to the culvert detection information, and processing the culvert image to obtain an optimized culvert image;
extracting the optimized dark image features, and comparing the optimized dark image features with preset standard dark image features to obtain a deviation rate;
Judging whether the deviation rate is larger than or equal to or smaller than a preset deviation rate threshold value;
if the optimized culvert image is greater than or equal to the preset culvert image set in the database, fusing the optimized culvert image with the preset culvert image set in the database, generating a first culvert detection report, and sending the first culvert detection report to the terminal;
if the optimized dark image is smaller than the optimized dark image, the optimized dark image is directly sent to the terminal.
Optionally, in the method for intelligent inspection of urban river culvert based on big data according to the embodiment of the present application, before the processing of the culvert image to obtain the optimized culvert image, the method further includes:
obtaining an edge line of the culvert structure according to the culvert image;
generating a structure area and a hollow area according to the edge line of the obscuration structure;
calculating the gray level of the structural region according to a preset gray level algorithm, comparing the gray level of the structural region with a preset first gray level threshold value to obtain a first gray level deviation and correcting the edge line of the structural region;
and calculating the gray level of the hollow region according to a preset gray level algorithm, comparing the gray level of the hollow region with a preset second gray level threshold value, obtaining second gray level deviation and correcting the edge line of the hollow region.
Optionally, in the method for intelligent inspection of urban river culvert based on big data according to the embodiment of the present application, the processing of the culvert image to obtain an optimized culvert image specifically includes:
Performing region segmentation on the implication image to obtain a plurality of sub-region images;
acquiring the brightness of each sub-region image to obtain a corresponding sub-region image brightness value;
comparing the brightness value of the sub-region image with a preset brightness threshold value to obtain a brightness deviation rate;
judging whether the brightness deviation rate is larger than or equal to a preset brightness deviation rate threshold value;
if the brightness of the sub-region image is greater than or equal to the brightness of the sub-region image, generating feedback information, and adjusting the brightness of the sub-region image;
and if the image is smaller than the preset value, obtaining an optimized dark image.
Optionally, in the method for intelligent inspection of urban river culvert based on big data in the embodiment of the present application, if the value is greater than or equal to the preset value, fusing the optimized culvert image with a preset culvert image set in a database, generating a first culvert detection report, and sending the first culvert detection report to a terminal, and further including:
acquiring a culvert history image;
obtaining a culvert image set according to the culvert historical image;
extracting the features of the dark culvert image set according to the dark culvert image set;
performing feature comparison on the optimized dark image features and the dark image set features to obtain feature similarity;
and if the feature similarity is greater than or equal to a preset similarity threshold, fusing the optimized culvert image with a culvert image set stored in a database, generating a first culvert detection report and sending the first culvert detection report to a terminal.
Optionally, in the method for intelligent inspection of urban river culvert based on big data according to the embodiment of the present application, the method further includes:
extracting a implication bearing value according to the implication detection information;
comparing the implication bearing value with a preset bearing threshold value to obtain a bearing difference value;
judging whether the bearing difference value is larger than or equal to a preset bearing difference threshold value or not;
if the value is greater than or equal to the preset value, sending out early warning and prohibiting the entry into a culvert;
if the value is smaller than the threshold value, the state is implicitly the accessible state.
Optionally, in the method for intelligent inspection of urban river culvert based on big data according to the embodiment of the present application, the method further includes:
extracting the obscuration gas information according to the obscuration detection information;
collecting the obscuration gas information of a plurality of collecting points in a preset time period, and extracting gas concentration characteristic data, wherein the gas concentration characteristic data comprise combustible gas content data, oxygen content data and toxic gas content data;
processing through a preset gas risk assessment model according to a plurality of groups of corresponding gas concentration characteristic data in the preset time period to obtain a gas risk fluctuation coefficient;
threshold value comparison is carried out on the gas risk fluctuation coefficient and a preset gas risk fluctuation threshold value, so that risk fluctuation deviation rate data are obtained;
Judging whether the risk fluctuation deviation rate data is larger than or equal to a preset risk fluctuation deviation rate threshold value or not;
if the air flow is greater than or equal to the air flow, giving out early warning and ventilating;
if the value is smaller than the preset value, the meaning is an accessible state;
the calculation formula of the gas risk assessment model is as follows:
；
wherein,is the gas risk fluctuation coefficient->、/>、/>Combustible gas content data, oxygen content data and toxic gas content data of the ith acquisition point respectively, n is the number of acquisition points in a preset time period, and +.>、/>Is a preset characteristic coefficient.
In a second aspect, an embodiment of the present application provides an intelligent inspection system for a blind meaning of a city river based on big data, the system including: the system comprises a memory and a processor, wherein the memory comprises a program of the intelligent urban river culvert inspection method based on big data, and the program of the intelligent urban river culvert inspection method based on big data realizes the following steps when being executed by the processor:
obtaining the culvert detection information;
obtaining a culvert image according to the culvert detection information, and processing the culvert image to obtain an optimized culvert image;
extracting the optimized dark image features, and comparing the optimized dark image features with preset standard dark image features to obtain a deviation rate;
Judging whether the deviation rate is larger than or equal to or smaller than a preset deviation rate threshold value;
if the optimized culvert image is greater than or equal to the preset threshold value, fusing the optimized culvert image with a culvert image set stored in a database, generating a first culvert detection report and sending the first culvert detection report to a terminal;
if the optimized dark image is smaller than the optimized dark image, the optimized dark image is directly sent to the terminal.
Optionally, in the intelligent inspection system for urban river culvert based on big data according to the embodiment of the present application, before the processing of the culvert image to obtain the optimized culvert image, the intelligent inspection system further includes:
obtaining an edge line of the culvert structure according to the culvert image;
generating a structure area and a hollow area according to the edge line of the obscuration structure;
calculating the gray level of the structural region, comparing the gray level of the structural region with a preset first gray level threshold value to obtain a first gray level deviation and correcting the edge line of the structural region;
and calculating the gray level of the hollow region, comparing the gray level of the hollow region with a preset second gray level threshold value to obtain second gray level deviation, and correcting the edge line of the hollow region.
Optionally, in the system for intelligent inspection of urban river culvert based on big data according to the embodiment of the present application, the processing of the culvert image to obtain an optimized culvert image specifically includes:
Performing region segmentation on the implication image to obtain a plurality of sub-region images;
acquiring the brightness of each sub-area image to obtain a sub-area image brightness value;
comparing the brightness value of the sub-region image with a preset brightness threshold value to obtain a brightness deviation rate;
judging whether the brightness deviation rate is larger than or equal to a preset brightness deviation rate threshold value;
if the brightness of the sub-region image is greater than or equal to the brightness of the sub-region image, generating feedback information, and adjusting the brightness of the sub-region image according to the feedback information;
and if the image is smaller than the preset value, obtaining an optimized dark image.
In a third aspect, an embodiment of the present application further provides a readable storage medium, where the readable storage medium includes a big data-based intelligent inspection method program for urban river culvert, and when the big data-based intelligent inspection method program for urban river culvert is executed by a processor, the steps of the big data-based intelligent inspection method for urban river culvert are implemented.
As can be seen from the above, according to the method, system and medium for intelligent inspection of urban river culvert based on big data provided in the embodiments of the present application, through obtaining the culvert detection information, obtaining the culvert image, processing the culvert image to obtain the optimized culvert image, extracting the characteristics of the optimized culvert image, comparing with the characteristics of the preset standard culvert image to obtain the deviation rate, judging whether the deviation rate is greater than or equal to or less than the preset deviation rate threshold, if so, fusing the optimized culvert image with the prestored culvert image set in the database to generate the first culvert detection report, if so, directly transmitting the optimized culvert image to the terminal; and acquiring the obscuration gas information of a plurality of acquisition points in a preset time period, extracting gas concentration characteristic data, processing through a preset gas risk assessment model according to a plurality of groups of corresponding gas concentration characteristic data in the preset time period to obtain a gas risk fluctuation coefficient, and realizing the technical means of carrying out omnibearing detection and processing on the whole load bearing of the obscuration and the gas information, the water quality information and the water level information in the obscuration, thereby providing powerful support for the guarantee of engineering quality and guaranteeing the life and equipment safety of personnel.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the application. The objects and other advantages of the present application may be realized and attained by the structure particularly pointed out in the written description and drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of an intelligent inspection method for urban river culvert based on big data provided in an embodiment of the present application;
fig. 2 is a flowchart of obtaining gray level deviation and correcting an edge line in the intelligent inspection method for urban river culvert based on big data provided in the embodiment of the present application;
fig. 3 is a flowchart of an obtaining optimized culvert image of an intelligent inspection method for urban river culvert based on big data according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that like reference numerals and letters refer to like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Referring to fig. 1, fig. 1 is a flowchart of an intelligent inspection method for urban river culvert based on big data in some embodiments of the present application. The intelligent tour inspection method for the urban river culvert based on the big data is used in terminal equipment, such as a computer, a mobile phone terminal and the like. The intelligent tour inspection method for the urban river culvert based on big data comprises the following steps:
s101, acquiring the inclusion detection information;
s102, obtaining a culvert image according to the culvert detection information, and processing the culvert image to obtain an optimized culvert image;
s103, extracting the optimized dark culvert image features, and comparing the optimized dark culvert image features with preset standard dark culvert image features to obtain a deviation rate;
s104, judging whether the deviation rate is larger than or equal to or smaller than a preset deviation rate threshold value;
s105, if the optimized culvert image is greater than or equal to the preset culvert image set in the database, fusing the optimized culvert image set with the preset culvert image set in the database, generating a first culvert detection report, and sending the first culvert detection report to the terminal;
and S106, if the optimized implication image is smaller than the preset threshold value, directly sending the optimized implication image to a terminal.
Firstly, obtaining the obscuration detection information, obtaining an obscuration image according to the obscuration detection information, and processing the obscuration image to obtain an optimized obscuration image, wherein before processing the obscuration image, edge lines of an obscuration structure can be obtained according to the obscuration image, a structure region and a hollow region can be generated according to the edge lines in the obscuration image so as to distinguish an obscuration main body structure from the hollow space and interference elements therein, further, calculating gray levels of the structure region and the central control region according to a preset gray algorithm, comparing gray levels of the structure region and the central control region with a preset first gray level threshold and a preset second gray level threshold respectively to obtain gray level deviation, and correcting the edge lines; dividing the implication image area into a plurality of sub-area images, acquiring the brightness of each sub-area image, obtaining brightness values of each sub-area, comparing the brightness values with a preset brightness threshold value to obtain a brightness deviation rate, comparing the brightness deviation rate with the preset brightness deviation rate threshold value, and adjusting or directly obtaining an optimized implication image if the deviation is larger; and extracting the optimized dark image features, comparing the optimized dark image features with preset standard dark image features to obtain a deviation rate, judging whether the deviation rate is larger than or equal to a preset deviation rate threshold value, if so, fusing the optimized dark image with a pre-stored dark image set in a database to generate a first dark detection report, and sending the first dark detection report to a terminal, and if so, directly sending the optimized dark image to the terminal.
Referring to fig. 2, fig. 2 is a flowchart of a method for obtaining gray scale deviation and correcting edge lines in an intelligent inspection method for urban river culvert based on big data in some embodiments of the present application. According to an embodiment of the present invention, before the processing of the culvert image to obtain the optimized culvert image, the method further includes:
s201, obtaining an edge line of the culvert structure according to the culvert image;
s202, generating a structure area and a hollow area according to the edge line of the culvert structure;
s203, calculating the gray level of the structural region according to a preset gray level algorithm, comparing the gray level of the structural region with a preset first gray level threshold value to obtain a first gray level deviation and correcting the edge line of the structural region;
s204, calculating the gray level of the hollow region according to a preset gray level algorithm, comparing the gray level of the hollow region with a preset second gray level threshold value to obtain second gray level deviation, and correcting the edge line of the hollow region.
For better displaying an image of the internal space of the culvert and distinguishing the culvert structure and the hollow region, identifying an interfering object positioned in the central control region, acquiring an edge line of the culvert structure according to the image of the culvert, producing the structural region and the central control region, and comparing the structural region and the central control region with a preset first gray threshold value and a preset second gray threshold value respectively according to a preset gray algorithm to obtain a first gray deviation and a second gray deviation, and further correcting the edge line of the corresponding region, thereby realizing accurate division of the internal structural region of the culvert and the hollow region.
Referring to fig. 3, fig. 3 is a flowchart of an optimized image of a city river culvert intelligent tour inspection method based on big data according to some embodiments of the present application. According to the embodiment of the invention, the optimized culvert image is obtained by processing the culvert image, specifically:
s301, carrying out region segmentation on the obscuration image to obtain a plurality of sub-region images;
s302, acquiring the brightness of each sub-region image to obtain a corresponding sub-region image brightness value;
s303, comparing the brightness value of the sub-region image with a preset brightness threshold value to obtain a brightness deviation rate;
s304, judging whether the brightness deviation rate is larger than or equal to a preset brightness deviation rate threshold value;
s305, if the brightness of the sub-region image is greater than or equal to the brightness of the sub-region image, generating feedback information, and adjusting the brightness of the sub-region image;
and S306, if the image is smaller than the preset threshold value, obtaining an optimized culvert image.
In order to enhance the recognition degree of the obscura image, people can more easily check the internal condition of the obscura, the obscura image can be segmented according to needs to obtain a plurality of sub-area images, the brightness value of each sub-area image is obtained, the brightness value of the area image is compared with a preset brightness threshold value to obtain a brightness deviation rate, whether the brightness deviation rate is larger than the preset brightness deviation rate threshold value is further judged, if so, the brightness of the corresponding sub-area image is adjusted according to the generated feedback information, and otherwise, the optimized obscura image is directly obtained.
According to an embodiment of the present invention, if the optimized culvert image is greater than or equal to the preset culvert image set in the database, the method further includes:
acquiring a culvert history image;
obtaining a culvert image set according to the culvert historical image;
extracting the features of the dark culvert image set according to the dark culvert image set;
performing feature comparison on the optimized dark image features and the dark image set features to obtain feature similarity;
and if the feature similarity is greater than or equal to a preset similarity threshold, fusing the optimized culvert image with a culvert image set stored in a database, generating a first culvert detection report and sending the first culvert detection report to a terminal.
The method comprises the steps of taking the fact that the period of the change of the internal structure and space of the culvert affected by the environment is long into consideration, obtaining a culvert historical image and obtaining a culvert image set for better reflecting the change condition of the culvert and avoiding sudden dangerous situations, extracting the characteristics of the culvert image set, comparing the characteristics of the optimized culvert image with the characteristics of the culvert image set to obtain the characteristic similarity, and fusing the optimized culvert image with the culvert image set stored in the database if the characteristic similarity is larger than or equal to a preset similarity threshold value, and generating a first culvert detection report and sending the first culvert detection report to a terminal.
According to an embodiment of the present invention, further comprising:
extracting a implication bearing value according to the implication detection information;
comparing the implication bearing value with a preset bearing threshold value to obtain a bearing difference value;
judging whether the bearing difference value is larger than or equal to a preset bearing difference threshold value or not;
if the value is greater than or equal to the preset value, sending out early warning and prohibiting the entry into a culvert;
if the value is smaller than the threshold value, the state is implicitly the accessible state.
The closed environment with long-term darkness and wetness can corrode the darkness main body and the cement board above the gland, the darkness bearing value is extracted and compared with a preset bearing threshold value to obtain a bearing difference value, whether the bearing difference value is larger than or equal to the preset bearing difference threshold value is further judged, whether the structure of the darkness main body is firm can be judged, and life danger caused by personnel entering the darkness operation is avoided.
According to an embodiment of the present invention, further comprising:
extracting the obscuration gas information according to the obscuration detection information;
collecting the obscuration gas information of a plurality of collecting points in a preset time period, and extracting gas concentration characteristic data, wherein the gas concentration characteristic data comprise combustible gas content data, oxygen content data and toxic gas content data;
processing through a preset gas risk assessment model according to a plurality of groups of corresponding gas concentration characteristic data in the preset time period to obtain a gas risk fluctuation coefficient;
Threshold value comparison is carried out on the gas risk fluctuation coefficient and a preset gas risk fluctuation threshold value, so that risk fluctuation deviation rate data are obtained;
judging whether the risk fluctuation deviation rate data is larger than or equal to a preset risk fluctuation deviation rate threshold value or not;
if the air flow is greater than or equal to the air flow, giving out early warning and ventilating;
if the value is smaller than the preset value, the meaning is an accessible state;
the calculation formula of the gas risk assessment model is as follows:
；
wherein,is the gas risk fluctuation coefficient->、/>、/>Combustible gas content data, oxygen content data and toxic gas content data of the ith acquisition point respectively, n is the number of acquisition points in a preset time period, and +.>、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained by inquiring a preset culvert environment monitoring database).
Wherein, the internal environment is occluded in a complex way, various waste gases and toxic gases are mixed in the internal environment, and the invention is collected. And processing through a preset gas risk assessment model according to a plurality of groups of corresponding gas concentration characteristic data in a preset time period to obtain a gas risk fluctuation coefficient, comparing the gas risk fluctuation coefficient with a preset gas risk fluctuation threshold value to obtain risk fluctuation deviation rate data, judging whether the risk fluctuation deviation rate data is greater than or equal to the preset risk fluctuation deviation rate threshold value, if so, indicating that waste gas and toxic gas in the culvert are out of standard, threatening the lives of personnel, and effectively ventilating before entering the culvert.
According to an embodiment of the present invention, further comprising:
acquiring the water quality information of the culvert;
extracting the content of wastewater according to the water quality information;
comparing the wastewater content with a preset wastewater content threshold value to obtain a wastewater content deviation rate;
judging whether the wastewater content deviation rate is larger than or equal to a preset wastewater content deviation rate threshold value;
if the water quality report is greater than or equal to the preset value, generating a water quality report and sending the water quality report to the terminal;
if the water quality is smaller than the preset value, the water quality of the culvert is qualified.
The method comprises the steps of obtaining water quality information of a culvert, extracting wastewater content by obtaining the water quality information of the culvert, comparing the wastewater content with a preset wastewater content threshold value to obtain a wastewater content deviation rate, judging whether the wastewater content deviation rate is greater than or equal to the preset wastewater content deviation rate threshold value, generating a water quality report, and helping people know the water quality condition inside the culvert.
According to an embodiment of the present invention, further comprising:
obtaining water level information according to the obscuration image;
extracting a water level height value according to the water level information;
comparing the water level height value with a preset water level threshold value, and if the water level height value is larger than or equal to the preset water level threshold value, exceeding the water level warning line and acquiring a water level height exceeding value;
Acquiring the times and the frequency of the water level exceeding the water level warning line in the preset time period, and processing according to the times and the frequency and the corresponding water level height exceeding value each time to acquire a water level risk warning coefficient in the preset time period;
comparing the water level risk alarm coefficient with a preset water level alarm threshold value;
if the content is greater than or equal to the content, sending the culvert dredging information to the terminal;
if the water content is smaller than the water content, the culvert does not need dredging;
the calculation formula of the water level risk alarm coefficient is as follows:
；
wherein,is a water level risk alarm coefficient->For the k-th occurrence of water level height exceeding the water level warning line by the corresponding water level height exceeding standard value, < >>For the number of times, h is frequency, < >>、/>、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained by inquiring a preset culvert environment monitoring database).
If the accumulated water in the culvert reaches a certain stock, huge risks are caused to operators and the structure of the culvert body, potential safety hazards exist, the technology can obtain water level information and extract a water level height value according to the culvert image, can visually see the influence of the accumulated water on the culvert, compares the water level height value with a preset water level threshold, if the water level height value exceeds the water level warning line, indicates that the water level height value exceeds the water level warning line, obtains the times and the frequency of exceeding the water level warning line in a preset time period, processes the water level height exceeding standard value corresponding to each time according to the times and the frequency, obtains a water level risk alarm coefficient in the preset time period, compares the water level risk alarm coefficient with the preset water level alarm threshold, and if the water level risk alarm coefficient is greater than or equal to the preset water level warning threshold, indicates that the accumulated water in the culvert is excessive, and the blocking risk exists, and the dredging work needs to be timely carried out.
The invention also discloses an urban river culvert intelligent inspection system based on big data, which comprises a memory and a processor, wherein the memory comprises an urban river culvert intelligent inspection method program based on big data, and the urban river culvert intelligent inspection method program based on big data is implemented when executed by the processor as follows:
obtaining the culvert detection information;
obtaining a culvert image according to the culvert detection information, and processing the culvert image to obtain an optimized culvert image;
extracting the optimized dark image features, and comparing the optimized dark image features with preset standard dark image features to obtain a deviation rate;
judging whether the deviation rate is larger than or equal to or smaller than a preset deviation rate threshold value;
if the optimized culvert image is greater than or equal to the preset culvert image set in the database, fusing the optimized culvert image with the preset culvert image set in the database, generating a first culvert detection report, and sending the first culvert detection report to the terminal;
if the optimized dark image is smaller than the optimized dark image, the optimized dark image is directly sent to the terminal.
Firstly, obtaining the obscuration detection information, obtaining an obscuration image according to the obscuration detection information, and processing the obscuration image to obtain an optimized obscuration image, wherein before processing the obscuration image, edge lines of an obscuration structure can be obtained according to the obscuration image, a structure region and a hollow region can be generated according to the edge lines in the obscuration image so as to distinguish an obscuration main body structure from the hollow space and interference elements therein, further, calculating gray levels of the structure region and the central control region according to a preset gray algorithm, comparing gray levels of the structure region and the central control region with a preset first gray level threshold and a preset second gray level threshold respectively to obtain gray level deviation, and correcting the edge lines; dividing the implication image area into a plurality of sub-area images, acquiring the brightness of each sub-area image, obtaining brightness values of each sub-area, comparing the brightness values with a preset brightness threshold value to obtain a brightness deviation rate, comparing the brightness deviation rate with the preset brightness deviation rate threshold value, and adjusting or directly obtaining an optimized implication image if the deviation is larger; and extracting the optimized dark image features, comparing the optimized dark image features with preset standard dark image features to obtain a deviation rate, judging whether the deviation rate is larger than or equal to a preset deviation rate threshold value, if so, fusing the optimized dark image with a pre-stored dark image set in a database to generate a first dark detection report, and sending the first dark detection report to a terminal, and if so, directly sending the optimized dark image to the terminal.
According to an embodiment of the present invention, before the processing of the culvert image to obtain the optimized culvert image, the method further includes:
obtaining an edge line of the culvert structure according to the culvert image;
generating a structure area and a hollow area according to the edge line of the obscuration structure;
calculating the gray level of the structural region according to a preset gray level algorithm, comparing the gray level of the structural region with a preset first gray level threshold value to obtain a first gray level deviation and correcting the edge line of the structural region;
and calculating the gray level of the hollow region according to a preset gray level algorithm, comparing the gray level of the hollow region with a preset second gray level threshold value, obtaining second gray level deviation and correcting the edge line of the hollow region.
For better displaying an image of the internal space of the culvert and distinguishing the culvert structure and the hollow region, identifying an interfering object positioned in the central control region, acquiring an edge line of the culvert structure according to the image of the culvert, producing the structural region and the central control region, and comparing the structural region and the central control region with a preset first gray threshold value and a preset second gray threshold value respectively according to a preset gray algorithm to obtain a first gray deviation and a second gray deviation, and further correcting the edge line of the corresponding region, thereby realizing accurate division of the internal structural region of the culvert and the hollow region.
According to the embodiment of the invention, the optimized culvert image is obtained by processing the culvert image, specifically:
performing region segmentation on the implication image to obtain a plurality of sub-region images;
acquiring the brightness of each sub-region image to obtain a corresponding sub-region image brightness value;
comparing the brightness value of the sub-region image with a preset brightness threshold value to obtain a brightness deviation rate;
judging whether the brightness deviation rate is larger than or equal to a preset brightness deviation rate threshold value;
if the brightness of the sub-region image is greater than or equal to the brightness of the sub-region image, generating feedback information, and adjusting the brightness of the sub-region image;
and if the image is smaller than the preset value, obtaining an optimized dark image.
In order to enhance the recognition degree of the obscura image, people can more easily check the internal condition of the obscura, the obscura image can be segmented according to needs to obtain a plurality of sub-area images, the brightness value of each sub-area image is obtained, the brightness value of the area image is compared with a preset brightness threshold value to obtain a brightness deviation rate, whether the brightness deviation rate is larger than the preset brightness deviation rate threshold value is further judged, if so, the brightness of the corresponding sub-area image is adjusted according to the generated feedback information, and otherwise, the optimized obscura image is directly obtained.
If the optimized culvert image is greater than or equal to the preset culvert image set in the database, fusing the optimized culvert image with the preset culvert image set in the database, generating a first culvert detection report, and sending the first culvert detection report to the terminal, and further comprising:
acquiring a culvert history image;
obtaining a culvert image set according to the culvert historical image;
extracting the features of the dark culvert image set according to the dark culvert image set;
performing feature comparison on the optimized dark image features and the dark image set features to obtain feature similarity;
and if the feature similarity is greater than or equal to a preset similarity threshold, fusing the optimized culvert image with a culvert image set stored in a database, generating a first culvert detection report and sending the first culvert detection report to a terminal.
The method comprises the steps of taking the fact that the period of the change of the internal structure and space of the culvert affected by the environment is long into consideration, obtaining a culvert historical image and obtaining a culvert image set for better reflecting the change condition of the culvert and avoiding sudden dangerous situations, extracting the characteristics of the culvert image set, comparing the characteristics of the optimized culvert image with the characteristics of the culvert image set to obtain the characteristic similarity, and fusing the optimized culvert image with the culvert image set stored in the database if the characteristic similarity is larger than or equal to a preset similarity threshold value, and generating a first culvert detection report and sending the first culvert detection report to a terminal.
According to an embodiment of the present invention, further comprising:
extracting a implication bearing value according to the implication detection information;
comparing the implication bearing value with a preset bearing threshold value to obtain a bearing difference value;
judging whether the bearing difference value is larger than or equal to a preset bearing difference threshold value or not;
if the value is greater than or equal to the preset value, sending out early warning and prohibiting the entry into a culvert;
if the value is smaller than the threshold value, the state is implicitly the accessible state.
The closed environment with long-term darkness and wetness can corrode the darkness main body and the cement board above the gland, the darkness bearing value is extracted and compared with a preset bearing threshold value to obtain a bearing difference value, whether the bearing difference value is larger than or equal to the preset bearing difference threshold value is further judged, whether the structure of the darkness main body is firm can be judged, and life danger caused by personnel entering the darkness operation is avoided.
According to an embodiment of the present invention, further comprising:
extracting the obscuration gas information according to the obscuration detection information;
collecting the obscuration gas information of a plurality of collecting points in a preset time period, and extracting gas concentration characteristic data, wherein the gas concentration characteristic data comprise combustible gas content data, oxygen content data and toxic gas content data;
processing through a preset gas risk assessment model according to a plurality of groups of corresponding gas concentration characteristic data in the preset time period to obtain a gas risk fluctuation coefficient;
Threshold value comparison is carried out on the gas risk fluctuation coefficient and a preset gas risk fluctuation threshold value, so that risk fluctuation deviation rate data are obtained;
judging whether the risk fluctuation deviation rate data is larger than or equal to a preset risk fluctuation deviation rate threshold value or not;
if the air flow is greater than or equal to the air flow, giving out early warning and ventilating;
if the value is smaller than the preset value, the meaning is an accessible state;
the calculation formula of the gas risk assessment model is as follows:
；
wherein,is the gas risk fluctuation coefficient->、/>、/>Combustible gas content data, oxygen content data and toxic gas content data of the ith acquisition point respectively, n is the number of acquisition points in a preset time period, and +.>、/>Is a preset characteristic coefficient (the characteristic coefficient is obtained by inquiring a preset culvert environment monitoring database).
Wherein, the internal environment is occluded in a complex way, various waste gases and toxic gases are mixed in the internal environment, and the invention is collected. And processing through a preset gas risk assessment model according to a plurality of groups of corresponding gas concentration characteristic data in a preset time period to obtain a gas risk fluctuation coefficient, comparing the gas risk fluctuation coefficient with a preset gas risk fluctuation threshold value to obtain risk fluctuation deviation rate data, judging whether the risk fluctuation deviation rate data is greater than or equal to the preset risk fluctuation deviation rate threshold value, if so, indicating that waste gas and toxic gas in the culvert are out of standard, threatening the lives of personnel, and effectively ventilating before entering the culvert.
According to an embodiment of the present invention, further comprising:
acquiring the water quality information of the culvert;
extracting the content of wastewater according to the water quality information;
comparing the wastewater content with a preset wastewater content threshold value to obtain a wastewater content deviation rate;
judging whether the wastewater content deviation rate is larger than or equal to a preset wastewater content deviation rate threshold value;
if the water quality report is greater than or equal to the preset value, generating a water quality report and sending the water quality report to the terminal;
if the water quality is smaller than the preset value, the water quality of the culvert is qualified.
The method comprises the steps of obtaining water quality information of a culvert, extracting wastewater content by obtaining the water quality information of the culvert, comparing the wastewater content with a preset wastewater content threshold value to obtain a wastewater content deviation rate, judging whether the wastewater content deviation rate is greater than or equal to the preset wastewater content deviation rate threshold value, generating a water quality report, and helping people know the water quality condition inside the culvert.
According to an embodiment of the present invention, further comprising:
obtaining water level information according to the obscuration image;
extracting a water level height value according to the water level information;
comparing the water level height value with a preset water level threshold value, and if the water level height value is larger than or equal to the preset water level threshold value, exceeding the water level warning line and acquiring a water level height exceeding value;
Acquiring the times and the frequency of the water level exceeding the water level warning line in the preset time period, and processing according to the times and the frequency and the corresponding water level height exceeding value each time to acquire a water level risk warning coefficient in the preset time period;
comparing the water level risk alarm coefficient with a preset water level alarm threshold value;
if the content is greater than or equal to the content, sending the culvert dredging information to the terminal;
if the water content is smaller than the water content, the culvert does not need dredging;
the calculation formula of the water level risk alarm coefficient is as follows:
；
wherein,is a water level risk alarm coefficient->For the k-th occurrence of water level height exceeding the water level warning line by the corresponding water level height exceeding standard value, < >>For the number of times, h is frequency, < >>、/>、/>For presetting characteristic coefficients (characteristicsThe coefficients are obtained by querying a preset implication environment monitoring database).
If the accumulated water in the culvert reaches a certain stock, huge risks are caused to operators and the structure of the culvert body, potential safety hazards exist, the technology can obtain water level information and extract a water level height value according to the culvert image, can visually see the influence of the accumulated water on the culvert, compares the water level height value with a preset water level threshold, if the water level height value exceeds the water level warning line, indicates that the water level height value exceeds the water level warning line, obtains the times and the frequency of exceeding the water level warning line in a preset time period, processes the water level height exceeding standard value corresponding to each time according to the times and the frequency, obtains a water level risk alarm coefficient in the preset time period, compares the water level risk alarm coefficient with the preset water level alarm threshold, and if the water level risk alarm coefficient is greater than or equal to the preset water level warning threshold, indicates that the accumulated water in the culvert is excessive, and the blocking risk exists, and the dredging work needs to be timely carried out.
The third aspect of the present invention also provides a readable storage medium, where the readable storage medium includes a big data-based intelligent inspection method program for urban river culvert, and when the big data-based intelligent inspection method program for urban river culvert is executed by a processor, the steps of the big data-based intelligent inspection method for urban river culvert are implemented.
The invention discloses a method, a system and a medium for intelligent inspection of the culvert of an urban river based on big data, which are used for acquiring the detection information of the culvert; obtaining a culvert image, processing the culvert image to obtain an optimized culvert image, extracting characteristics of the optimized culvert image, comparing the characteristics of the optimized culvert image with preset standard culvert image characteristics to obtain a deviation rate, judging whether the deviation rate is larger than or equal to a preset deviation rate threshold value, if so, fusing the optimized culvert image with a pre-stored culvert image set in a database to generate a first culvert detection report, and if so, directly transmitting the optimized culvert image to a terminal; and acquiring the obscuration gas information of a plurality of acquisition points in a preset time period, extracting gas concentration characteristic data, processing through a preset gas risk assessment model according to a plurality of groups of corresponding gas concentration characteristic data in the preset time period to obtain a gas risk fluctuation coefficient, and realizing the technical means of carrying out omnibearing detection and processing on the whole load bearing of the obscuration and the gas information, the water quality information and the water level information in the obscuration, thereby providing powerful support for the guarantee of engineering quality and guaranteeing the life and equipment safety of personnel.
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 readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a removable storage device, a read-only memory, a random access memory, a magnetic or optical disk, or other various media capable of storing program code.
Alternatively, the above-described integrated units of the present invention may be stored in a 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 solution 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.
Claims (10)
1. The intelligent city river culvert inspection method based on big data is characterized by comprising the following steps of:
obtaining the culvert detection information;
obtaining a culvert image according to the culvert detection information, and processing the culvert image to obtain an optimized culvert image;
extracting the optimized dark image features, and comparing the optimized dark image features with preset standard dark image features to obtain a deviation rate;
judging whether the deviation rate is larger than or equal to or smaller than a preset deviation rate threshold value;
if the optimized culvert image is greater than or equal to the preset culvert image set in the database, fusing the optimized culvert image with the preset culvert image set in the database, generating a first culvert detection report, and sending the first culvert detection report to the terminal;
if the optimized dark image is smaller than the optimized dark image, the optimized dark image is directly sent to the terminal.
2. The intelligent tour inspection method for the urban river culvert based on big data according to claim 1, wherein before the processing of the culvert image to obtain the optimized culvert image, the method further comprises:
obtaining an edge line of the culvert structure according to the culvert image;
generating a structure area and a hollow area according to the edge line of the obscuration structure;
calculating the gray level of the structural region according to a preset gray level algorithm, comparing the gray level of the structural region with a preset first gray level threshold value to obtain a first gray level deviation and correcting the edge line of the structural region;
And calculating the gray level of the hollow region according to a preset gray level algorithm, comparing the gray level of the hollow region with a preset second gray level threshold value, obtaining second gray level deviation and correcting the edge line of the hollow region.
3. The intelligent tour inspection method for the urban river culvert based on big data according to claim 2, wherein the processing of the culvert image is performed to obtain an optimized culvert image, specifically:
performing region segmentation on the implication image to obtain a plurality of sub-region images;
acquiring the brightness of each sub-region image to obtain a corresponding sub-region image brightness value;
comparing the brightness value of the sub-region image with a preset brightness threshold value to obtain a brightness deviation rate;
judging whether the brightness deviation rate is larger than or equal to a preset brightness deviation rate threshold value;
if the brightness of the sub-region image is greater than or equal to the brightness of the sub-region image, generating feedback information, and adjusting the brightness of the sub-region image;
and if the image is smaller than the preset value, obtaining an optimized dark image.
4. The intelligent tour inspection method for the urban river culvert based on big data according to claim 3, wherein if the optimized culvert image is greater than or equal to the preset culvert image set in the database, a first culvert detection report is generated and sent to a terminal, and the method further comprises:
Acquiring a culvert history image;
obtaining a culvert image set according to the culvert historical image;
extracting the features of the dark culvert image set according to the dark culvert image set;
performing feature comparison on the optimized dark image features and the dark image set features to obtain feature similarity;
and if the feature similarity is greater than or equal to a preset similarity threshold, fusing the optimized culvert image with a culvert image set stored in a database, generating a first culvert detection report and sending the first culvert detection report to a terminal.
5. The intelligent tour inspection method for the urban river culvert based on big data according to claim 1, further comprising:
extracting a implication bearing value according to the implication detection information;
comparing the implication bearing value with a preset bearing threshold value to obtain a bearing difference value;
judging whether the bearing difference value is larger than or equal to a preset bearing difference threshold value or not;
if the value is greater than or equal to the preset value, sending out early warning and prohibiting the entry into a culvert;
if the value is smaller than the threshold value, the state is implicitly the accessible state.
6. The intelligent tour inspection method for the urban river culvert based on big data according to claim 1, further comprising:
extracting the obscuration gas information according to the obscuration detection information;
Collecting the obscuration gas information of a plurality of collecting points in a preset time period, and extracting gas concentration characteristic data, wherein the gas concentration characteristic data comprise combustible gas content data, oxygen content data and toxic gas content data;
processing through a preset gas risk assessment model according to a plurality of groups of corresponding gas concentration characteristic data in the preset time period to obtain a gas risk fluctuation coefficient;
threshold value comparison is carried out on the gas risk fluctuation coefficient and a preset gas risk fluctuation threshold value, so that risk fluctuation deviation rate data are obtained;
judging whether the risk fluctuation deviation rate data is larger than or equal to a preset risk fluctuation deviation rate threshold value or not;
if the air flow is greater than or equal to the air flow, giving out early warning and ventilating;
if the value is smaller than the preset value, the meaning is an accessible state;
the calculation formula of the gas risk assessment model is as follows:
；
wherein,is the gas risk fluctuation coefficient->、/>、/>Combustible gas content data, oxygen content data and toxic gas content data of the ith acquisition point respectively, n is the number of acquisition points in a preset time period, and +.>、/>Is a preset characteristic coefficient.
7. The intelligent city river culvert inspection system based on the big data is characterized by comprising a memory and a processor, wherein the memory comprises an intelligent city river culvert inspection method program based on the big data, and the intelligent city river culvert inspection method program based on the big data is implemented when being executed by the processor as follows:
Obtaining the culvert detection information;
obtaining a culvert image according to the culvert detection information, and processing the culvert image to obtain an optimized culvert image;
extracting the optimized dark image features, and comparing the optimized dark image features with preset standard dark image features to obtain a deviation rate;
judging whether the deviation rate is larger than or equal to or smaller than a preset deviation rate threshold value;
if the optimized culvert image is greater than or equal to the preset culvert image set in the database, fusing the optimized culvert image with the preset culvert image set in the database, generating a first culvert detection report, and sending the first culvert detection report to the terminal;
if the optimized dark image is smaller than the optimized dark image, the optimized dark image is directly sent to the terminal.
8. The intelligent tour inspection system for the culvert of the urban river based on big data according to claim 7, wherein before the processing of the culvert image to obtain the optimized culvert image, the intelligent tour inspection system further comprises:
obtaining an edge line of the culvert structure according to the culvert image;
generating a structure area and a hollow area according to the edge line of the obscuration structure;
calculating the gray level of the structural region according to a preset gray level algorithm, comparing the gray level of the structural region with a preset first gray level threshold value to obtain a first gray level deviation and correcting the edge line of the structural region;
And calculating the gray level of the hollow region according to a preset gray level algorithm, comparing the gray level of the hollow region with a preset second gray level threshold value, obtaining second gray level deviation and correcting the edge line of the hollow region.
9. The intelligent tour inspection system for the urban river culvert based on big data according to claim 8, wherein the processing of the culvert image is performed to obtain an optimized culvert image, specifically:
performing region segmentation on the implication image to obtain a plurality of sub-region images;
acquiring the brightness of each sub-region image to obtain a corresponding sub-region image brightness value;
comparing the brightness value of the sub-region image with a preset brightness threshold value to obtain a brightness deviation rate;
judging whether the brightness deviation rate is larger than or equal to a preset brightness deviation rate threshold value;
if the brightness of the sub-region image is greater than or equal to the brightness of the sub-region image, generating feedback information, and adjusting the brightness of the sub-region image;
and if the image is smaller than the preset value, obtaining an optimized dark image.
10. A computer readable storage medium, wherein the computer readable storage medium includes a big data based intelligent inspection method, system and medium program for urban river culvert, and when the big data based intelligent inspection method, system and medium program are executed by a processor, the steps of the big data based intelligent inspection method for urban river culvert are implemented.
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