Urban drainage pipe network endoscopic detection optimization method and device, equipment and medium

Abstract

The invention discloses an urban drainage network endoscopic detection optimization method and device, equipment and a medium. The method comprises the following steps: acquiring CCTV detection historical result data of each street detection unit and each batch in each water quality purification plant service range in a to-be-detected working range; when the real-time image detection of the street detection unit judges that the street detection unit meets the CCTV sampling inspection rechecking condition, the sampling inspection proportion is determined; determining a to-be-reinspected pipeline in the randomly determined detection points according to the sampling inspection proportion in the street detection unit, and performing CCTV detection processing on the to-be-reinspected pipeline to obtain a pipeline defect sampling inspection result; by taking a water quality purification plant as a comparison unit, carrying out comparison statistics according to the pipeline defect sampling inspection result and the CCTV detection historical result data on the basis of a preset comparison principle to obtain a comparison result; and generating a sampling inspection comparison report according to the pipeline defect sampling inspection result and the comparison result. And reliable reference can be provided for the next step of work, so that detection resources are saved.

Description

Technical Field

[0001] The present application relates to the technical field of urban drainage pipe network detection, and in particular to an urban drainage pipe network endoscopic detection optimization method and its device, equipment, and medium. Background Art

[0002] CCTV (Closed-Circuit Television) detection system is a core technology used in drainage pipeline safety status detection. The system first appeared in the 1950s, was introduced to my country in the 1990s and began to be used in some large cities. It is currently the most advanced and effective means for drainage pipeline status detection in the world. It has the characteristics of intuitive detection, easy interpretation, and strong adaptability. It can effectively identify the structural and functional defects of the pipeline and can be widely used in pipelines of different materials and uses, such as cement pipes, PE pipes and PVC pipes. Even if the water level reaches 20% of the pipe diameter, more than 90% of the defects can be effectively found. Among them, the CCTV detection system is mainly composed of a control platform, a crawler, a camera, a cable reel and a recorder. The detection system uses a crawler vehicle equipped with a high-frequency camera to enter the interior of the pipeline to record the rust layer, scaling, corrosion, perforation, cracks and other conditions inside the pipeline, obtain high-definition image information inside the pipeline, use computer technology to automatically identify and interpret the images, and then evaluate them according to the detection technical regulations. Ultimately, relevant information that objectively reflects the health status of the interior of the pipeline is obtained, the health status of the drainage pipe is evaluated, and basic data and basis are provided for the maintenance of the pipe network.

[0003] At present, CCTV detection systems are often used as the main means to investigate and inspect the entire urban drainage network. However, in actual use, as the detection area expands, the CCTV detection system consumes a long time and the detection cost is high; and the pipeline conditions are complicated, which can easily lead to various unexpected situations during the endoscopic detection process, resulting in various detection quality problems and waste of detection resources; in addition, with the diversification of defect types and the expansion of the detection area, the amount of data is becoming larger and more complex. If the network is investigated indiscriminately based on CCTV detection, it is easy to cause a waste of investment. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art.

[0005] To this end, the present application proposes an optimization method for endoscopic detection of urban drainage pipe networks and its devices, equipment, and media, which can compare and analyze the defects of the sampled pipes, provide a reliable reference for the next step of work, and save detection resources.

[0006] In a first aspect, an embodiment of the present application provides an optimization method for endoscopic detection of urban drainage pipe networks, comprising:

[0007] Determine the scope of work to be tested;

[0008] Obtain the historical results of CCTV inspections carried out by each street inspection unit and each batch within the service scope of each water purification plant in the work scope to be inspected; the street inspection unit includes multiple defective pipeline locations that have been endoscopically inspected;

[0009] Acquire a live image of the street detection unit;

[0010] When it is determined through the live image detection that the street detection unit meets the CCTV random inspection and review conditions, determining the random inspection ratio of the street detection unit that meets the CCTV random inspection and review conditions;

[0011] Randomly determine a detection point in the street detection unit, determine a pipeline to be re-inspected in the detection point according to the sampling ratio, perform CCTV detection on the pipeline to be re-inspected, and obtain a pipeline defect sampling inspection result;

[0012] The water purification plant in the working range to be inspected is used as a comparison unit, and in the comparison unit, based on a preset comparison principle, comparison statistics are performed according to the pipeline defect sampling inspection results and the CCTV inspection historical results data to obtain a comparison result;

[0013] A sampling inspection comparison report is generated according to the pipeline defect sampling inspection results and the comparison results.

[0014] According to some embodiments of the present application, the comparison unit performs comparative statistics based on the pipeline defect sampling results and the CCTV detection historical results data based on a preset comparison principle to obtain a comparison result, including:

[0015] Determining structural defects from the pipeline defect sampling results;

[0016] Compare the historical defect positions in the CCTV detection historical results data with the current defect positions of the detected structural defects one by one, divide the structural defects into original defects and newly added defects, and determine the total number of compared defects;

[0017] Comparing the historical defect types in the CCTV inspection historical results data with the types of the original defects detected, and obtaining the number of defect type non-conformities by counting;

[0018] The percentage value of the non-conforming number of the defect type to the total number of the compared defects is determined as the unit non-conforming rate.

[0019] According to some embodiments of the present application, the historical defect positions in the CCTV detection historical results data and the current defect positions of the detected structural defects are compared one by one to divide the structural defects into original defects and newly added defects, including:

[0020] When the position error between the historical defect position and the current defect position is less than or equal to the error threshold, the defect at the current defect position is determined to be an original defect;

[0021] When the position error between the historical defect position and the current defect position is greater than an error threshold, the defect at the current defect position is determined to be a newly added defect, and the number of the newly added defects is counted.

[0022] According to some embodiments of the present application, after generating the sampling inspection comparison report, the method further includes:

[0023] Obtaining the unit non-conformity rate of the plurality of comparison units from the sampling comparison report;

[0024] Screening the comparison units whose unit non-compliance rate is greater than the preset non-compliance rate threshold as target retest objects;

[0025] Conduct a comprehensive retest on the items implemented for the batch to which the target retest subjects belong.

[0026] According to some embodiments of the present application, the historical defect types in the CCTV detection historical results data are compared with the types of the original defects detected, and the number of defect types that do not meet the requirements is obtained by counting, including:

[0027] When the historical defect type is different from the type of the original defect detected, it is determined that the defect type does not match;

[0028] When the historical defect type is the same as the type of the original defect detected, it is determined that the defect type is consistent;

[0029] After the comparison is completed, the number of defect types that do not meet the requirements is statistically obtained.

[0030] According to some embodiments of the present application, determining the sampling ratio of the street detection units that meet the CCTV sampling review conditions includes:

[0031] Obtain the area of ​​the street detection unit that meets the CCTV random inspection and review conditions;

[0032] Determine the sampling ratio of the street detection units whose area is greater than or equal to a preset area threshold as a first ratio value;

[0033] The sampling ratio of the street detection units whose area is smaller than a preset area threshold is determined as a second ratio value; wherein the first ratio value is greater than the second ratio value.

[0034] According to some embodiments of the present application, acquiring the live image of the street detection unit includes:

[0035] Acquire the live image of the detection area by means of drone inspection;

[0036] Alternatively, the live image of the detection area is obtained through a city monitoring system;

[0037] Alternatively, the live image is obtained by on-site survey personnel.

[0038] In the second aspect, an embodiment of the present application provides an urban drainage pipe network endoscopic detection optimization device, comprising at least one processor and a memory for communicating with the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the urban drainage pipe network endoscopic detection optimization method as described in any one of the embodiments of the first aspect.

[0039] In a third aspect, an embodiment of the present application provides an electronic device, comprising the urban drainage network endoscopic detection optimization device as described in the embodiment of the second aspect.

[0040] In a fourth aspect, a storage medium is provided, wherein the storage medium is a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, wherein the computer-executable instructions are used to enable a computer to execute the urban drainage network endoscopic detection optimization method as described in any one of the embodiments of the third aspect.

[0041] The embodiment of the present application includes: first, determining the working scope to be inspected; then, obtaining the CCTV inspection historical results data of each street inspection unit and each batch within the service scope of each water purification plant in the working scope to be inspected; the street inspection unit includes multiple defective pipeline locations that have been endoscopically inspected; then, obtaining the live image of the street inspection unit; then, when the street inspection unit is judged to meet the CCTV random inspection and review conditions through live image detection, the sampling ratio of the street inspection unit that meets the CCTV random inspection and review conditions is determined; then, the inspection point is randomly determined in the street inspection unit, and the pipeline to be re-inspected is determined in the inspection point according to the sampling ratio, and CCTV inspection is performed on the pipeline to be re-inspected to obtain the pipeline defect sampling inspection result; then, the water purification plant in the working scope to be inspected is used as the comparison unit, and based on the preset comparison principle, the pipeline defect sampling inspection result and the CCTV inspection historical results data are compared and counted in the comparison unit to obtain the comparison result; finally, according to the pipeline defect sampling inspection result and the comparison result, a sampling inspection comparison report is generated; so as to provide a reliable reference for the next step of work and save detection resources. That is to say, the embodiment of the present application can compare and analyze the defects of the sampled pipelines, provide a reliable reference for the next step of work, and save detection resources.

[0042] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained through the structures particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0044] Figure 1 It is a flow chart of an optimization method for endoscopic detection of urban drainage pipe networks provided by an embodiment of the present application;

[0045] Figure 2 Some embodiments of the present application provide Figure 1 Specific flow diagram of step S160;

[0046] Figure 3 It is a structural schematic diagram of an urban drainage network endoscopic detection optimization device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0048] It should be understood that in the description of the present application, the orientation descriptions, such as up, down, front, back, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0049] It should be noted that although a logical order is shown in the flowchart in the description of the present application, in some cases, the steps shown or described may be performed in an order different from that in the flowchart. In the description of the present application, a number of means one or more, and a plurality of means two or more. The description of "first" and "second" is only used for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0051] The present application discloses a method for optimizing endoscopic detection of urban drainage pipe networks, an optimization device for endoscopic detection of urban drainage pipe networks, an electronic device, and a computer-readable storage medium. The method includes: obtaining the historical CCTV detection results of each street detection unit and each batch within the service range of each water purification plant in the working range to be detected; when the street detection unit's real-time image detection determines that the street detection unit meets the CCTV random inspection and review conditions, the sampling ratio is determined; in the street detection unit, according to the sampling ratio, the pipeline to be re-inspected is determined in the randomly determined detection points, and CCTV detection is performed on the pipeline to be re-inspected to obtain the pipeline defect sampling inspection result; taking the water purification plant as the comparison unit, based on the preset comparison principle, the pipeline defect sampling inspection results and the CCTV detection historical results are compared and counted to obtain the comparison result; and a sampling inspection comparison report is generated according to the pipeline defect sampling inspection results and the comparison results. It can provide a reliable reference for the next step of work to save detection resources. The following is a further elaboration of the embodiments of the present application in conjunction with the accompanying drawings.

[0052] First, as Figure 1 As shown, an embodiment of the present application provides an optimization method for endoscopic detection of urban drainage pipe networks, including but not limited to steps S110 to S170.

[0053] Step S110: Determine the working range to be detected.

[0054] Step S120: Obtain historical CCTV inspection results data of each street inspection unit and each batch within the service range of each water purification plant in the work range to be inspected; the street inspection unit includes multiple defective pipeline locations that have been endoscopically inspected.

[0055] Step S130: Acquire a live image of the street detection unit.

[0056] Step S140: When it is determined through live image detection that the street detection unit meets the CCTV random inspection and review conditions, the random inspection ratio of the street detection units that meet the CCTV random inspection and review conditions is determined.

[0057] Step S150: randomly determine a detection point in the street detection unit, determine the pipeline to be re-inspected among the detection points according to the sampling ratio, perform CCTV detection on the pipeline to be re-inspected, and obtain the pipeline defect sampling inspection result.

[0058] Step S160: Taking the water purification plant within the working range to be inspected as a comparison unit, in the comparison unit, based on the preset comparison principle, comparative statistics are performed according to the pipeline defect sampling results and the CCTV inspection historical results data to obtain the comparison results.

[0059] Step S170: Generate a sampling inspection comparison report based on the pipeline defect sampling inspection results and the comparison results.

[0060] Step S110 is further described. The determined working range to be inspected is the required inspection area. The work content required to be performed in the working range to be inspected is: through CCTV inspection, find out the structural defects (such as rupture, leakage, misalignment, deformation, disconnection, etc.) and functional defects (such as sedimentation, obstacles, etc.) in the pipeline, evaluate its structural condition and functional status, and conduct comparative analysis on the structural defects sampled.

[0061] It is understandable that after determining the scope of work to be tested, the sampling inspection implementation process is determined. The CCTV sampling inspection and verification process provided in the embodiment of the present application mainly includes the steps of obtaining information, preparing a sampling inspection and verification plan, on-site investigation, confirming random sampling points, on-site CCTV testing, data processing, and preparing a sampling inspection and verification report (specifically as shown in steps S120 to S170).

[0062] To further illustrate step S120, specifically, the CCTV detection historical results data can be quickly obtained from the database.

[0063] It is understandable that before step S130, it also includes: preparing a random inspection and review plan. The random inspection and review plan includes the random inspection and review ratio, review workload, review assessment method, workflow, etc. The random inspection work will be carried out based on the pre-prepared random inspection and review plan.

[0064] According to some embodiments of the present application, step S130 is further described. Step S130: obtaining the real-time image of the street detection unit includes but is not limited to: obtaining the real-time image of the detection area by drone inspection; or, obtaining the real-time image of the detection area through a city monitoring system; or, obtaining the real-time image transmitted back by on-site survey personnel.

[0065] In one embodiment, step S130 further includes: after acquiring the live image, determining whether the street detection unit meets the CCTV random inspection and review conditions.

[0066] Specifically, through step S130, for municipal roads with existing CCTV detection data, high-altitude visible ground surveys are conducted in combination with drone inspections, urban monitoring, manual feedback, etc. to obtain real-time images; detection and judgment are performed based on the real-time images; CCTV random inspection and review conditions include but are not limited to: the well location is not covered or occupied, the pipe section is not blocked, etc. In addition, for places that are not visible from the air, manual field surveys can be conducted to understand whether the conditions for conducting CCTV random inspections and reviews are met, such as whether the well location is covered or occupied, the water level and blockage of the pipe section, etc.

[0067] According to some embodiments of the present application, step S140 is further described, wherein determining the sampling ratio of street detection units that meet the CCTV sampling and review conditions includes but is not limited to: obtaining the area of ​​the street detection units that meet the CCTV sampling and review conditions; determining the sampling ratio of street detection units whose area area is greater than or equal to a preset area threshold as a first ratio value; determining the sampling ratio of street detection units whose area area is less than the preset area threshold as a second ratio value; wherein the first ratio value is greater than the second ratio value.

[0068] Specifically, the first ratio value is 20%, and the second ratio value is 10%.

[0069] To further explain step S150, first, a detection point is randomly determined in the street detection unit, and the detection point is specifically some streets in the street detection unit. Then, the pipeline to be re-inspected is determined in the detection point according to the sampling ratio, that is, the pipeline is the sampling object. Finally, CCTV detection processing is performed on the pipeline to be re-inspected to obtain the pipeline defect sampling inspection result.

[0070] It can be understood that step S150 implements the work contents of random confirmation of sampling points, on-site CCTV detection, and data processing.

[0071] Specifically, randomly confirming the inspection points includes: randomly determining the inspection points in the street inspection unit and filling in and generating a CCTV review random sampling record form.

[0072] Specifically, according to the inspection objects determined by the CCTV review random sampling record form, the CCTC inspection system is controlled to carry out on-site CCTV inspection.

[0073] Specifically, data processing includes: collecting CCTV detection images after on-site CCTV detection, processing the CCTV detection images based on image recognition algorithms or manual verification, and determining pipeline defect sampling results. Determining pipeline defect sampling results includes but is not limited to: determining the type, type and level of defects, as well as the repair or maintenance level of the pipe section, etc.

[0074] The embodiment of the present application implements the sampling detection process through steps S140 to S150, and further explains the sampling principles involved in the sampling detection process. The sampling principles mainly include: sampling content, sampling method, and sampling ratio. Among them, the sampling content is: CCTV has been implemented to detect pipeline defects in the municipal pipeline network. The sampling method is: taking the street as the detection unit, without clearly defining the category of pipeline defects, randomly sampling from the unit in proportion. If a certain sample site does not have CCTV detection conditions, other samples in the unit are selected to keep the number of samples in each unit in accordance with the sampling ratio requirements. Sampling ratio: The sampling rate for larger areas is 20%, and the sampling rate for smaller areas is 10%.

[0075] According to some embodiments of the present application, Figure 2 As shown, step S160 is further described. Step S160: in the comparison unit, based on the preset comparison principle, comparative statistics are performed according to the pipeline defect sampling results and the CCTV detection historical results data to obtain a comparison result, including but not limited to steps S161 to S164.

[0076] Step S161: Determine structural defects from pipeline defect sampling results.

[0077] Step S162: Compare the historical defect positions in the CCTV detection history results data with the current defect positions of the detected structural defects one by one, divide the structural defects into original defects and new defects, and determine the total number of compared defects.

[0078] Step S163: Compare the historical defect types in the CCTV detection historical results data with the types of original defects detected, and obtain the number of defect types that do not meet the requirements.

[0079] Step S164: Determine the percentage value of the number of defect type non-conformance to the total number of compared defects as the unit non-conformance rate.

[0080] It should be noted that in step S161, the pipeline defect sampling results also include: functional defects; however, the embodiment of the present application only compares and counts the detected structural defects, and does not compare and count the original functional defects. Specifically, structural defects refer to defects that damage the pipeline structure itself and affect the strength, rigidity and service life. Functional defects refer to defects that cause changes in the water-passing section of the pipeline and affect the smooth performance.

[0081] According to some embodiments of the present application, step S162 is further described, wherein the structural defects are divided into original defects and newly added defects based on the historical defect positions in the CCTV detection history results data and the current defect positions of the detected structural defects. The steps include: when the position error between the historical defect position and the current defect position is less than or equal to the error threshold, the defect at the current defect position is judged to be an original defect; when the position error between the historical defect position and the current defect position is greater than the error threshold, the defect at the current defect position is judged to be a newly added defect, and the number of newly added defects is counted.

[0082] According to some embodiments of the present application, step S163 is further described, and the historical defect types in the CCTV detection history results data are compared with the types of the original defects detected, and the number of defect types that do not conform is obtained by counting, including: when the historical defect type is different from the type of the original defect detected, it is judged that the defect type does not conform; when the historical defect type is the same as the type of the original defect detected, it is judged that the defect type conforms; after the comparison is completed, the number of defect types that do not conform is obtained by counting.

[0083] Through step S161 to step S164, a comparative analysis is performed based on the historical CCTV detection results and the pipeline defect sampling results, and a comparative result is obtained, which provides a reliable reference for subsequent work.

[0084] The embodiment of the present application implements the review and evaluation process through step S160. To further explain the review and evaluation process, according to the Technical Regulations for Inspection and Evaluation of Urban Drainage Pipelines (CJJ 181-2012), the review and evaluation process involves comparison principles and comparison methods.

[0085] Specifically, the comparison principles are as follows: (1) only the detected structural defects are compared and counted, and the original functional defects are not compared and counted; (2) the detected structural defects are divided into two categories: original defects and new defects. Only the original defects are compared, and the new defects are only counted in number without comparison; (3) taking the water purification plant as the unit, the two factors of defect location and defect type are mainly selected for comparison.

[0086] Specifically, the comparison method involves: (1) Determination of existing defects and newly added defects. Using the historical CCTV inspection results data as the standard, the structural defects in the standard are compared with the structural defects in this inspection one by one, and the defects with the closest positions in the two inspections are defined as existing defects, and defects other than existing defects are defined as newly added defects. (2) Determination of defect non-compliance rate. Using the historical CCTV inspection results data as the standard, the defect types in the standard are considered to be compliant if they are the same as the defect types in this random inspection, otherwise they are considered to be non-compliant. The unit defect type non-compliance rate F l is calculated according to the following formula. F l = (number of defect type non-compliance ÷ total number of compared defects) × 100.

[0087] It can be understood that, through step S170, after all CCTV random inspections are completed, a random inspection comparison report is compiled and submitted; the random inspection comparison report includes: pipeline defect random inspection results and comparison results, the pipeline defect random inspection results are random inspection results based on streets, and the comparison results are comparison results based on water purification plants.

[0088] According to some embodiments of the present application, further, in step S170: after generating the sampling comparison report, the urban drainage pipe network endoscopic detection optimization method also includes: obtaining the unit non-conformity rate of multiple comparison units from the sampling comparison report; screening the comparison units whose unit non-conformity rate is greater than the preset non-conformity rate threshold as the target retest objects; and performing a comprehensive retest on the projects implemented in the batch to which the target retest object belongs. Comprehensive retesting is performed based on the sampling comparison analysis results, thereby saving detection resources.

[0089] Specifically, the preset non-compliance rate threshold is 10%. When the non-compliance rate of the sampled part reaches more than 10%, a comprehensive retest of the items implemented for the batch will be carried out.

[0090] Through steps S110 to S170 provided in the embodiment of the present application, first, the working scope to be inspected is determined; then, the historical CCTV inspection results data of each street inspection unit and each batch within the service scope of each water purification plant in the working scope to be inspected are obtained; the street inspection unit includes multiple defective pipeline locations that have been endoscopically inspected; then, the live image of the street inspection unit is obtained; then, when the street inspection unit is judged to meet the CCTV random inspection and review conditions through live image detection, the sampling ratio of the street inspection unit that meets the CCTV random inspection and review conditions is determined; then, the inspection point is randomly determined in the street inspection unit, and the pipeline to be re-inspected is determined in the inspection point according to the sampling ratio, and CCTV inspection is performed on the pipeline to be re-inspected to obtain the pipeline defect sampling inspection result; then, the water purification plant in the working scope to be inspected is used as the comparison unit, and based on the preset comparison principle, the pipeline defect sampling inspection result and the CCTV inspection historical results data are compared and counted in the comparison unit to obtain the comparison result; finally, according to the pipeline defect sampling inspection result and the comparison result, a sampling inspection comparison report is generated; so as to provide a reliable reference for the next step of work and save detection resources. Therefore, the embodiment of the present application can compare and analyze the defects of the sampled pipelines, provide a reliable reference for the next step of work, and save detection resources.

[0091] In some embodiments, the embodiments of the present application also perform endoscopic inspection quality control. Specifically, in order to avoid the situation where the endoscopic inspection data does not meet the requirements of the specifications and improve the quality of on-site photography and internal data of pipeline endoscopic inspection, according to the characteristics of pipeline endoscopic inspection and combined with the specific situation of the Longgang District drainage system quality improvement and efficiency investigation work, quality control is performed from the aspects of technical training and disclosure, quality inspection, quality correction, etc.

[0092] In some embodiments, the urban drainage pipe network endoscopic inspection optimization method also includes: for pipe networks that have not undergone endoscopic inspection, obtaining external water flow and pollutant concentration data through water quality and quantity balance analysis and calculation; when the external water flow increases and the inlet water concentration decreases, it is judged that the pipeline has defects.

[0093] Among them, the water quality and quantity balance analysis and calculation include water quantity balance method and water quality balance method.

[0094] Specifically, the water balance method includes: First, determine the domestic water consumption and industrial production water consumption through water supply analysis. The domestic water consumption of industrial plants is included in the domestic water consumption. Secondly, determine the domestic sewage and industrial wastewater through sewage analysis. The domestic water pollution coefficient is 0.90, and the industrial wastewater pollution coefficient is 0.80. Then, calculate the external water volume. The external water volume is equal to the difference between the total water volume entering the plant and the comprehensive sewage volume. The specific formula is:

[0095] Q 外水 =Q 进厂-Q 生活污水 -Q 工业废水 ; Among them, Q 生活污水 is the theoretical sewage generation, Q 工业废水 is the theoretical industrial wastewater generation, Q 外水 The theoretical external water volume.

[0096] Specifically, the water quality balance method includes: first, investigating the background value of sewage. Secondly, based on the sewage volume analysis and background value investigation, and taking into account 20% of the inseparable groundwater infiltration and 10% of the pipeline sedimentation and degradation, determine the theoretical inlet concentration. The calculation formula for the theoretical inlet concentration is:

[0097]

[0098] Among them, Q 生活污水 is the theoretical sewage generation, Q 工业废水 is the theoretical industrial wastewater generation, C 生活污水 is the theoretical domestic sewage background value, C 工业废水 It is the background value of industrial wastewater.

[0099] Finally, according to the following pollutant balance equation:

[0100] (Q 生活污水 +Q 工业废水 )C 理论进厂浓度 +Q 外水 C 外水

[0101] =(Q 生活污水 +Q 工业废水 +Q 外水 )C 实际进厂浓度 ;

[0102] The calculation formula of theoretical external water volume is:

[0103]

[0104] Among them, Q 外水 is the theoretical external water volume, C 外水 is the background value of clean external water, C 实际进厂浓度 is the theoretical background value of sewage entering the plant, C 理论进厂浓度 It is the theoretical background value of the concentration entering the factory.

[0105] It can be understood that by calculating the external water flow rate and pollutant concentration data, when there is a defect in the pipeline, water outside the pipeline will enter, causing the water volume to increase and the concentration of water entering the plant to decrease. The approximate defect problem area or problem point of the pipeline can be found according to the changes in the external water volume data, which is conducive to identifying the defective pipeline as the target pipeline to be inspected by CCTV.

[0106] In summary, the method of the embodiment of the present application systematically solves the problems encountered by the CCTV system in actual use, is suitable for solving the municipal pipe network detection problem, and can provide a reference solution for the wide application of the CCTV system.

[0107] like Figure 3 As shown, the present invention also provides an urban drainage network endoscopic detection optimization device, and the urban drainage network endoscopic detection optimization device 300 includes:

[0108] The processor 301 can be implemented by a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solution provided in the embodiment of the present application.

[0109] The memory 302 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 302 can store an operating system and other application programs. When the technical solution provided in the embodiment of this specification is implemented by software or firmware, the relevant program code is stored in the memory 302, and the processor 301 calls and executes the urban drainage pipe network endoscopy detection optimization method of the embodiment of this application.

[0110] The input / output interface 303 is used to implement information input and output.

[0111] The communication interface 304 is used to realize communication interaction between the apparatus and other devices. Communication can be realized through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WI FI, Bluetooth, etc.).

[0112] The bus 305 transmits information between various components of the device (eg, the processor 301 , the memory 302 , the input / output interface 303 , and the communication interface 304 ).

[0113] The processor 301 , the memory 302 , the input / output interface 303 and the communication interface 304 are connected to each other in communication within the device via the bus 305 .

[0114] According to the urban drainage network endoscopic detection optimization device provided in the second aspect of the embodiment of the present application, it is possible to compare and analyze the defects of the sampled pipes, provide a reliable reference for the next step of work, and save detection resources.

[0115] In a third aspect, an embodiment of the present application provides an electronic device, including an urban drainage network endoscopic detection optimization device as described in the above embodiment.

[0116] The electronic device provided in the embodiment of the third aspect of the present application can perform comparative analysis on the defects of the sampled pipelines, provide a reliable reference for the next step of work, and save detection resources.

[0117] In a fourth aspect, an embodiment of the present application provides a storage medium, which is a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute an urban drainage network endoscopic detection optimization method such as any one of the above embodiments.

[0118] As a non-transient computer-readable storage medium, the memory can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage devices. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and are implemented to be located in one place, or may also be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment.

[0119] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically include computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0120] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the above-mentioned implementation mode. Technical personnel familiar with the field can also make various equivalent deformations or substitutions without violating the spirit of the present application. These equivalent deformations or substitutions are all included in the scope defined by the present application.

Claims

1. A method for optimizing endoscopic detection of urban drainage pipe networks, characterized in that: include: Determine the scope of work to be tested; Obtain the historical results of CCTV inspections carried out by each street inspection unit and each batch within the service scope of each water purification plant in the work scope to be inspected; the street inspection unit includes multiple defective pipeline locations that have been endoscopically inspected; Acquire a live image of the street detection unit; When it is determined through the live image detection that the street detection unit meets the CCTV random inspection and review conditions, determining the random inspection ratio of the street detection unit that meets the CCTV random inspection and review conditions; Randomly determine a detection point in the street detection unit, determine a pipeline to be re-inspected in the detection point according to the sampling ratio, perform CCTV detection on the pipeline to be re-inspected, and obtain a pipeline defect sampling inspection result; The water purification plant in the working range to be inspected is used as a comparison unit, and in the comparison unit, based on a preset comparison principle, comparison statistics are performed according to the pipeline defect sampling inspection results and the CCTV inspection historical results data to obtain a comparison result; A sampling inspection comparison report is generated according to the pipeline defect sampling inspection results and the comparison results.

2. The urban drainage network endoscopic detection optimization method according to claim 1 is characterized in that: The comparison unit performs comparison statistics based on the preset comparison principle according to the pipeline defect sampling inspection results and the CCTV detection historical results data to obtain the comparison results, including: Determining structural defects from the pipeline defect sampling results; Compare the historical defect positions in the CCTV detection historical results data with the current defect positions of the detected structural defects one by one, divide the structural defects into original defects and newly added defects, and determine the total number of compared defects; Comparing the historical defect types in the CCTV inspection historical results data with the types of the original defects detected, and obtaining the number of defect type non-conformities by counting; The percentage value of the non-conforming number of the defect type to the total number of the compared defects is determined as the unit non-conforming rate.

3. The urban drainage network endoscopic detection optimization method according to claim 2 is characterized in that: The step of comparing the historical defect positions in the CCTV detection historical results data with the current defect positions of the detected structural defects one by one, and dividing the structural defects into original defects and newly added defects, includes: When the position error between the historical defect position and the current defect position is less than or equal to the error threshold, the defect at the current defect position is determined to be an original defect; When the position error between the historical defect position and the current defect position is greater than an error threshold, the defect at the current defect position is determined to be a newly added defect, and the number of the newly added defects is counted.

4. The urban drainage network endoscopic detection optimization method according to claim 2 is characterized in that: After generating the sampling inspection comparison report, the method further includes: Obtaining the unit non-conformity rate of the plurality of comparison units from the sampling comparison report; Screening the comparison units whose unit non-compliance rate is greater than the preset non-compliance rate threshold as target retest objects; Conduct a comprehensive retest on the items implemented for the batch to which the target retest subjects belong.

5. The urban drainage network endoscopic detection optimization method according to claim 2 is characterized in that: The comparison between the historical defect types in the CCTV detection historical results data and the types of the original defects detected, and the statistically obtained number of defect types that do not meet the requirements, includes: When the historical defect type is different from the type of the original defect detected, it is determined that the defect type does not match; When the historical defect type is the same as the type of the original defect detected, it is determined that the defect type is consistent; After the comparison is completed, the number of defect types that do not meet the requirements is statistically obtained.

6. The urban drainage network endoscopic detection optimization method according to claim 1 is characterized in that: The determining of the sampling ratio of the street detection units that meet the CCTV sampling inspection and review conditions includes: Obtain the area of ​​the street detection unit that meets the CCTV random inspection and review conditions; Determine the sampling ratio of the street detection units whose area is greater than or equal to a preset area threshold as a first ratio value; The sampling ratio of the street detection units whose area is smaller than a preset area threshold is determined as a second ratio value; wherein the first ratio value is greater than the second ratio value.

7. The urban drainage network endoscopic detection optimization method according to claim 1 is characterized in that: The acquiring of the live image of the street detection unit comprises: Acquire the live image of the detection area by means of drone inspection; Alternatively, the live image of the detection area is obtained through a city monitoring system; Alternatively, the live image is obtained by on-site survey personnel.

8. An urban drainage network endoscopic detection optimization device, characterized in that: It includes at least one processor and a memory for communicating with the at least one processor; the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the urban drainage pipe network endoscopic detection optimization method as described in any one of claims 1 to 7.

9. An electronic device, characterized in that: It includes the urban drainage pipe network endoscopic detection optimization device as described in claim 8.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the urban drainage pipe network endoscopic detection optimization method as described in any one of claims 1 to 7.

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