Intelligent community integrated comprehensive management platform based on Internet of Things

Abstract

The invention relates to the technical field of Internet of Things, in particular to an Internet of Things-based intelligent community integrated comprehensive management platform, which comprises a number setting module, an influence monitoring and analysis module, a pipe wall monitoring and analysis module, a circulation monitoring and analysis module, an abnormality judgment module and a display terminal, according to the invention, the underground pipeline of the community is subjected to monitoring area division, an area numbering system is established, environmental parameters such as temperature, humidity and corrosivity are collected, quantitative environmental influence values are formed, and multi-dimensional monitoring analysis of pipe wall abnormal distribution, corrosion area and depth and intelligent monitoring analysis of water flow velocity and sound characteristics are combined. And the health degree grade of the pipeline in the abnormal monitoring area is comprehensively output, so that refined monitoring and intelligent evaluation of the underground pipeline state are realized, and the safety and the operation and maintenance management efficiency of a pipeline system are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to an integrated comprehensive management platform for smart communities based on the Internet of Things. Background Art

[0002] Residential plumbing facilities are crucial infrastructure for ensuring the normal operation of residents' daily lives. They carry out key functions such as water supply and drainage. The continuity and safety of their operation are directly related to the stable operation of the community and the improvement of residents' quality of life. However, deep underground pipeline systems are exposed to complex and changing environments for a long time, facing numerous operational and management challenges.

[0003] First, underground spaces are permanently closed and susceptible to a variety of environmental factors, including temperature and humidity fluctuations and soil acidity and alkalinity corrosion. The combined effects of these factors exacerbate the aging and damage of pipeline structural materials, significantly increasing the risk of system failure. Second, most residential pipe networks currently lack a systematic, digital, and unified management mechanism. Pipelines lack unified numbering and zoning information, making it difficult to quickly locate problem points during subsequent maintenance and inspections, leading to delayed responses and even the spread of localized faults.

[0004] More importantly, existing underground pipeline network monitoring methods still rely primarily on manual inspections or periodic on-site monitoring. While these methods are practical to a certain extent, they suffer from low efficiency, limited coverage, and poor real-time performance. They make it difficult to achieve continuous online monitoring of key nodes, and often struggle to detect even minor anomalies during operation, thus posing safety risks.

[0005] In order to solve the above-mentioned defects, a technical solution is now provided. Summary of the Invention

[0006] In response to the above-mentioned shortcomings of the existing technology, the present invention provides an integrated comprehensive management platform for smart communities based on the Internet of Things, which can effectively solve the problems in the existing technology that lack real-time and systematic pipeline management mechanisms and cannot achieve timely positioning and early warning of abnormalities.

[0007] To achieve the above objectives, the present invention can be implemented through the following technical solutions:

[0008] The present invention provides an integrated management platform for smart communities based on the Internet of Things, including:

[0009] The number setting module is used to divide and analyze the monitoring areas of the underground pipelines in the target community to obtain the monitoring areas;

[0010] The impact monitoring and analysis module is used to monitor the environmental status parameters of the pipelines in each monitoring area, obtain the ambient temperature impact value, ambient humidity impact value and ambient corrosion impact value, and determine the environmental impact value of the pipelines in each monitoring area based on this;

[0011] The pipe wall monitoring and analysis module is used to monitor the pipe wall status parameters of the pipelines in each monitoring area, obtain the abnormal distribution value, abnormal area value and abnormal corrosion depth value, and then determine the pipe wall status assessment value of the pipelines in each monitoring area based on the environmental impact value;

[0012] Among them, the abnormal distribution value is determined by the maximum distance value, minimum distance value and average distance value in the distance matrix;

[0013] The flow monitoring and analysis module is used to monitor the flow status parameters of the pipelines in each monitoring area, obtain the water flow velocity fluctuation value and the water flow sound fluctuation value, and then determine the flow status assessment value of the pipelines in each monitoring area based on the environmental impact value;

[0014] Among them, the water flow velocity fluctuation value is determined by the rising angle value of each changing line segment;

[0015] The water flow sound fluctuation value is determined by the waveform overlap length, the maximum peak difference and the maximum trough difference in the water flow sound waveform overlap diagram;

[0016] The abnormality determination module is used to receive the pipe wall status evaluation value and the flow status evaluation value of the pipeline in each monitoring area, and determine the health level of the pipeline in each abnormal monitoring area based on the received value.

[0017] Furthermore, the specific process of demarcating each monitoring area is as follows:

[0018] Obtain a digital map of the underground pipelines in the target community and divide the pipelines into three categories based on their service range and connection structure: main pipeline end, building end, and user end.

[0019] Obtain the corresponding working surface lengths of the above three types of pipelines respectively, set a comparative reference interval for the working surface lengths of each type of pipeline, and conduct a comparative analysis between the working surface lengths of each type of pipeline and their comparative reference intervals;

[0020] When the working face length is greater than the maximum value of the comparison reference interval, it is determined to be Class A length grade, and the working face length of the pipeline is equally divided into K1 monitoring areas according to the Class A length grade;

[0021] When the working face length is within the comparison reference interval, it is determined to be Class B length grade, and the working face length of the pipeline is equally divided into K2 monitoring areas according to Class B length grade;

[0022] When the working face length is less than the minimum value of the comparison reference interval, it is determined to be Class C length grade, and the working face length of the pipeline is equally divided into K3 monitoring areas according to the Class C length grade;

[0023] Thus, each monitoring area is obtained and a unique number is assigned to each monitoring area.

[0024] Furthermore, the specific process of solving the ambient temperature influence value and the ambient humidity influence value is as follows:

[0025] Collect the ambient temperature data of the pipelines in each monitoring area during the current monitoring period, remove the maximum and minimum ambient temperatures from the ambient temperature data, and calculate the average of the remaining ambient temperature data to obtain the average ambient temperature as the ambient temperature impact value of the pipelines in each monitoring area during the current monitoring period;

[0026] Collect the ambient humidity data of the pipelines in each monitoring area during the current monitoring period, calculate the difference between the ambient humidity at adjacent monitoring time points to obtain the ambient humidity difference, and then calculate the average of all the obtained ambient humidity differences to obtain the average ambient humidity difference, which is used as the ambient humidity impact value of the pipelines in each monitoring area during the current monitoring period.

[0027] Furthermore, the specific process of solving the ring corrosion impact value is as follows:

[0028] Collect the types of components in the environment of the pipelines in each monitoring area during the current monitoring period, match the types of components in the environment of the pipelines in each monitoring area during the current monitoring period with the set types of corrosion components, obtain the corrosion components in the environment of the pipelines in each monitoring area during the current monitoring period, count the concentrations of the corrosion components, and add up the concentrations of the corrosion components to obtain the total concentration of the corrosion components, which is used as the environmental corrosion impact value of the pipelines in each monitoring area during the current monitoring period.

[0029] Furthermore, the specific formula for solving the environmental impact value is:

[0030] Extract the values ​​of the ambient temperature influence value Hwz, ambient humidity influence value Hsz and ambient corrosion influence value Hfz of the pipeline in each monitoring area during the current monitoring period and perform normalization processing according to the formula:

[0031] Calculate the environmental impact value θ of the pipeline in each monitoring area, where Hwz * 、Hsz * and Hfz * They represent the set reference ambient temperature influence value, reference ambient humidity influence value and reference ambient corrosion influence value respectively, λ1, λ2 and λ3 represent the weight coefficients of the ambient temperature influence value, ambient humidity influence value and ambient corrosion influence value respectively, and λ1>λ2>λ3.

[0032] Furthermore, the specific process of solving the abnormal distribution value is as follows:

[0033] Collect pipe wall images of each monitoring area during the current monitoring period, pre-process the pipe wall images, extract the grayscale value corresponding to each pixel in the processed pipe wall images, and compare and analyze them with the preset grayscale threshold. If the grayscale value corresponding to a pixel is greater than the preset grayscale threshold, the pixel is judged to be normal; otherwise, the pixel is judged to be abnormal.

[0034] All pixels judged as abnormal are spatially clustered and integrated to form abnormal areas;

[0035] Construct a two-dimensional space for each abnormal area and extract the geometric center of gravity coordinates corresponding to each abnormal area;

[0036] According to the geometric centroid coordinates corresponding to each abnormal area, the distance value between each abnormal area is calculated, and the distance values ​​between each abnormal area are used to form a distance matrix. The maximum distance value, minimum distance value and average distance value are extracted from the distance matrix to determine the abnormal distribution value.

[0037] Furthermore, the specific process of solving the abnormal area value and abnormal corrosion depth value is as follows:

[0038] Extract the physical area corresponding to each abnormal area from the pipe wall image of each monitoring area in the current monitoring period as the area value of each abnormal area, and calculate the average value to obtain the abnormal area value;

[0039] The average grayscale value corresponding to each abnormal area is extracted from the pipe wall image of each monitoring area during the current monitoring period, and the average grayscale value corresponding to each abnormal area is subtracted from the preset average grayscale threshold to obtain the average grayscale difference corresponding to each abnormal area. The maximum value among all the average grayscale differences is selected as the abnormal corrosion depth value.

[0040] Furthermore, the specific process of solving the water flow velocity fluctuation value is as follows:

[0041] Collect the water velocity data of the pipelines in each monitoring area during the current monitoring period, and establish a dynamic water velocity coordinate system with the monitoring period as the horizontal coordinate and the water velocity as the vertical coordinate. Mark the water velocity data as data points on the dynamic water velocity coordinate system one by one, and then use line segments to connect the discrete data points in sequence, thereby obtaining a water velocity fluctuation diagram;

[0042] The water velocity difference corresponding to adjacent data points in the water velocity fluctuation graph is subtracted and the absolute value is taken to obtain the water velocity difference. If the water velocity difference is greater than zero, it means that the water velocity is changing. The line segment with the water velocity difference greater than zero is marked as a changing line segment, and then the angle between each changing line segment and the horizontal axis is obtained by calculating its slope. That is, the slope value is converted into an angle value through the inverse tangent function, thereby obtaining the rising angle value of each changing line segment, thereby determining the water velocity fluctuation value.

[0043] Furthermore, the specific process of solving the water flow sound fluctuation value is as follows:

[0044] The water flow sound waveform of the pipeline in each monitoring area during the current monitoring period is collected and compared with the reference water flow sound waveform to obtain the water flow sound waveform coincidence diagram. The waveform coincidence length, maximum peak difference and maximum trough difference are extracted from the water flow sound waveform coincidence diagram to determine the water flow sound fluctuation value.

[0045] Furthermore, the specific process of determining the health level of the pipeline in each abnormal monitoring area is as follows:

[0046] Extract the pipe wall condition assessment value and flow condition assessment value of the pipeline in each monitoring area and perform normalization processing to obtain the health value of the pipeline in each monitoring area;

[0047] Compare and analyze the health value of pipelines in each monitoring area with the preset reference comparison interval. If the health value of pipelines in a monitoring area is outside the preset reference comparison interval, the pipeline in the monitoring area is judged to be in an abnormal trend state. Otherwise, the pipeline in the monitoring area is judged to be in a normal trend state.

[0048] The pipelines in the monitoring area that are judged to be in an abnormal trend state are marked as pipelines in the abnormal monitoring area;

[0049] Then extract the health value of the pipeline in each abnormal monitoring area, and perform a difference analysis between the health value of the pipeline in each abnormal monitoring area and the preset health threshold to obtain the health level value of the pipeline in each abnormal monitoring area;

[0050] The health level of the pipeline in each abnormal monitoring area is matched and analyzed with the pre-stored health status table to obtain the health level of the pipeline in each abnormal monitoring area.

[0051] Compared with the prior art, the technical solution provided by the present invention has the following beneficial effects:

[0052] 1. This invention establishes a clear and orderly spatial organization of the pipe network by dividing and numbering monitoring areas for residential underground pipelines. This provides clear regional guidance for subsequent data collection, problem location, and maintenance warnings. Furthermore, it continuously collects environmental parameters such as temperature, humidity, and corrosion to form a quantified environmental impact value. Combined with multi-dimensional analysis of pipe wall abnormal distribution, corrosion area, and depth, and intelligent monitoring of water flow velocity and sound characteristics, this system provides reliable data support for pipeline health assessment. Furthermore, the results of pipe wall and flow status assessments are combined to form a quantified health grade, achieving comprehensive perception and accurate diagnosis of pipeline status. This promotes intelligent abnormality warnings and operation and maintenance decision-making, significantly improving the safety and timeliness of residential pipe systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 It is the overall module block diagram of the present invention. DETAILED DESCRIPTION

[0055] To make the purpose, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0056] like Figure 1 As shown, the integrated management platform for smart communities based on the Internet of Things includes: number setting module, impact monitoring and analysis module, pipe wall monitoring and analysis module, circulation monitoring and analysis module, abnormality judgment module and display terminal;

[0057] The number setting module is used to divide and analyze the monitoring areas of the underground pipelines in the target community. The specific operation process is as follows:

[0058] Obtain a digital map of the existing underground pipelines in the target community. Based on their service areas and connection structures, divide the pipelines into the following three categories: main pipeline end (responsible for the unified transportation of water or sewage from the entire community's main pipeline network), building end (connecting the main pipeline to a specific building and used to transport water to the residents of that building), and user end (connecting the building pipeline network to a specific unit and used to transport water or sewage to a single household).

[0059] For the above three types of pipelines, the corresponding working surface lengths are obtained, that is, the actual length used along the pipeline laying direction, which serves as the basis for the subsequent division of monitoring areas;

[0060] Set a comparison reference interval for the length of the working surface of each type of pipeline, and compare and analyze the working surface length of each type of pipeline with its comparison reference interval. When the working surface length is greater than the maximum value of the comparison reference interval, it is determined to be Class A length grade. According to the Class A length grade, the working surface length of the pipeline is equidistantly divided into K1 monitoring areas;

[0061] When the working face length is within the comparison reference interval, it is determined to be Class B length grade, and the working face length of the pipeline is equally divided into K2 monitoring areas according to Class B length grade;

[0062] When the working face length is less than the minimum value of the comparison reference interval, it is determined to be Class C length grade, and the working face length of the pipeline is equally divided into K3 monitoring areas according to the Class C length grade;

[0063] In this way, each monitoring area is obtained and assigned a unique number, which is graphically displayed on the platform's visual interface to assist in the rapid location and processing of pipeline anomalies in subsequent monitoring areas.

[0064] The impact monitoring and analysis module is used to monitor and analyze the environmental status parameters of the pipelines in each monitoring area, and obtain the environmental status parameters of the pipelines in each monitoring area. The environmental status parameters include the ambient temperature impact value, the ambient humidity impact value, and the ambient corrosion impact value. The specific analysis process is as follows:

[0065] The ambient temperature data of the pipelines in each monitoring area during the current monitoring period is collected by the temperature sensor to obtain the ambient temperature data of the pipelines in each monitoring area during the current monitoring period. The maximum ambient temperature and the minimum ambient temperature are removed from the ambient temperature data, and the remaining ambient temperature data are averaged to obtain the average ambient temperature as the ambient temperature impact value of the pipelines in each monitoring area during the current monitoring period.

[0066] The humidity sensor is used to collect the ambient humidity data of the pipelines in each monitoring area during the current monitoring period, and the ambient humidity data of the pipelines in each monitoring area during the current monitoring period are obtained. The ambient humidity of adjacent monitoring time points is calculated to obtain the ambient humidity difference. All the obtained ambient humidity differences are averaged to obtain the average ambient humidity difference, which is used as the ambient humidity impact value of the pipelines in each monitoring area during the current monitoring period.

[0067] The component types in the environment of the pipelines in each monitoring area during the current monitoring period are monitored by a component analyzer to obtain the component types in the environment of the pipelines in each monitoring area during the current monitoring period. The component types in the environment of the pipelines in each monitoring area during the current monitoring period are matched with the set corrosion component types to obtain the corrosion components in the environment of the pipelines in each monitoring area during the current monitoring period. The concentration content of the corrosion components in the environment of the pipelines in each monitoring area during the current monitoring period is counted, and the concentration content of the corrosion components is added up to obtain the total concentration content of the corrosion components as the environmental corrosion impact value of the pipelines in each monitoring area during the current monitoring period.

[0068] Based on this, the impact of the environment on the pipelines in each monitoring area is analyzed. The specific analysis process is as follows:

[0069] By extracting the ambient temperature impact value, ambient humidity impact value and ambient corrosion impact value of the pipeline in each monitoring area during the current monitoring period, and calibrating them as Hwz, Hsz and Hfz respectively, the values ​​of the three are extracted and normalized according to the formula:

[0070] Calculate the environmental impact value θ of the pipeline in each monitoring area, where Hwz * 、Hsz * and Hfz * They represent the set reference ambient temperature influence value, reference ambient humidity influence value and reference ambient corrosion influence value respectively, λ1, λ2 and λ3 represent the weight coefficients of the ambient temperature influence value, ambient humidity influence value and ambient corrosion influence value respectively, and λ1>λ2>λ3.

[0071] The pipe wall monitoring and analysis module is used to monitor the pipe wall status parameters of the pipelines in each monitoring area, thereby analyzing the pipe wall status of the pipelines in each monitoring area. The specific analysis process is as follows:

[0072] An X-ray detector is used to collect pipe wall images of the pipelines in each monitoring area during the current monitoring period to obtain pipe wall images of the pipelines in each monitoring area during the current monitoring period. The pipe wall images are preprocessed, including noise suppression (using Gaussian filtering to remove image noise), image enhancement (enhancing edge features to make abnormal areas clearer and more identifiable), and normalization (standardizing the image grayscale values ​​to unify the grayscale scale). The grayscale value corresponding to each pixel in the processed pipe wall image is extracted, and the grayscale value corresponding to each pixel in the processed pipe wall image is compared and analyzed with a preset grayscale threshold. If the grayscale value corresponding to a pixel in the processed pipe wall image is greater than the preset grayscale threshold, the pixel in the processed pipe wall image is determined to be normal. If the grayscale value corresponding to a pixel in the processed pipe wall image is less than or equal to the preset grayscale threshold, the pixel in the processed pipe wall image is determined to be abnormal.

[0073] All pixels judged to be normal are spatially clustered and integrated to form normal areas;

[0074] All pixels judged as abnormal are spatially clustered and integrated to form abnormal areas;

[0075] Mark each abnormal area as A i , i represents the number of each abnormal area, and i = 1, 2, 3...n; construct the two-dimensional space of each abnormal area, extract the geometric center of gravity of each abnormal area

[0076]

[0077] Mark (x i ,y i ), the calculation formula of its geometric center of gravity coordinates is:

[0078] Where (x, y) refers to the pixel coordinates, representing a certain pixel in the image, x represents the horizontal position, and y represents the vertical position;

[0079] According to the geometric center coordinates of each abnormal area, the distance value d between each abnormal area is calculated. ij , the distance value is calculated as follows: Among them, d ij Indicates abnormal area A i With abnormal area A j The distance between the abnormal area A and the abnormal area A is i The number of another abnormal area to be compared with the distance, and j≠i, (x i ,y i ) represents abnormal area A i The geometric center of gravity coordinates, (x j ,y j) represents abnormal area A j The geometric center of gravity coordinates of

[0080] The distance matrix is ​​formed by the distance values ​​between the two abnormal areas. The maximum distance value, the minimum distance value and the average distance value are extracted from the distance matrix and marked as d max d min and d avg , according to the formula: Calculate the abnormal distribution value fbz of the pipeline in each monitoring area during the current monitoring period. The larger the abnormal distribution value, the more discrete the abnormal area is distributed on the pipe wall, which is more likely to cause the risk of multiple points of corrosion, leakage or cracking at the same time, and is no longer limited to a single fault point. Among them, a1, a2 and a3 represent the weight factors corresponding to the set maximum average distance difference, minimum average distance difference and maximum small distance difference, respectively;

[0081] Extract the physical area corresponding to each abnormal area from the pipe wall image of each monitoring area during the current monitoring period as the area value of each abnormal area in the pipe wall image of each monitoring area during the current monitoring period, and calculate the mean of the area values ​​to obtain the abnormal area value of each monitoring area during the current monitoring period, and mark it as ymz;

[0082] Extract the average grayscale value corresponding to each abnormal area from the pipe wall image of each monitoring area during the current monitoring period, and subtract the average grayscale value corresponding to each abnormal area from the preset average grayscale threshold to obtain the average grayscale difference value corresponding to each abnormal area. Select the maximum value among all the average grayscale differences as the abnormal corrosion depth value of the pipeline in each monitoring area during the current monitoring period and mark it as yfz;

[0083] According to the formula: JKZ = (fbz × η1 + ymz × η2 + yfz × η3) × θ, the pipe wall condition assessment value JKZ of each monitoring area is calculated, where η1, η2, and η3 represent the weighting factors of the set abnormal distribution value, abnormal area value, and abnormal corrosion depth value, respectively;

[0084] The pipe wall status evaluation value of each monitoring area pipeline is sent to the abnormality judgment module.

[0085] The flow monitoring and analysis module is used to monitor the flow status parameters of the pipelines in each monitoring area, thereby analyzing the flow status of the pipelines in each monitoring area. The specific analysis process is as follows:

[0086] The water flow velocity data of the pipelines in each monitoring area during the current monitoring period is collected by a fluid spectrometer to obtain the water flow velocity data of the pipelines in each monitoring area during the current monitoring period. A dynamic water flow velocity coordinate system is established based on the monitoring period as the horizontal coordinate and the water flow velocity as the vertical coordinate. The water flow velocity data of the pipelines in each monitoring area during the current monitoring period are marked one by one as data points on the dynamic water flow velocity coordinate system. At the same time, the discrete data points are sequentially connected with line segments to obtain a water flow velocity fluctuation graph.

[0087] The water velocity difference corresponding to adjacent data points in the water velocity fluctuation graph is subtracted and the absolute value is taken to obtain the water velocity difference. If the water velocity difference is greater than zero, it means that the water velocity is changing. The line segment with the water velocity difference greater than zero is marked as a changing line segment. Then, by calculating its slope (that is, a quantitative representation of the degree of inclination of the line segment), the angle between each changing line segment and the horizontal axis is obtained. That is, the slope value is converted into an angle value through the inverse tangent function, thereby obtaining the rising angle value of each changing line segment and marking it as jz y , y represents the number of each change segment, and y = 1, 2, 3...m, m represents the total number of each change segment number, according to the formula: Get the water flow velocity fluctuation value sjz, where jz y-1 represents the rising angle value of the y-1th change line segment, and p1 represents the set correction factor, which is used to improve the accuracy of the calculation results. The specific setting of the correction factor is reasonably set by those skilled in the art according to actual conditions;

[0088] The sound sensor is used to collect the water flow sound signals of the pipelines in each monitoring area during the current monitoring period, and a specific software is used to generate a water flow sound waveform diagram of the pipelines in each monitoring area during the current monitoring period;

[0089] At the same time, the reference water flow sound waveform of the pipeline in each monitoring area is extracted from the storage, and the water flow sound waveform of the pipeline in each monitoring area in the current monitoring period is overlapped and compared with the reference water flow sound waveform to obtain the water flow sound waveform overlap diagram of the pipeline in each monitoring area in the current monitoring period. The waveform overlap length, maximum peak difference and maximum trough difference are extracted from the water flow sound waveform overlap diagram and calibrated as bc, bf and bg respectively;

[0090] According to the formula: Calculate the water flow sound fluctuation value slz, where p2, p3, and p4 represent the correction factors corresponding to the set waveform overlap length, maximum peak difference, and maximum trough difference, respectively;

[0091] It should be noted that the waveform overlap length refers to the total duration of the continuous segment where the water flow sound waveform and the reference water flow sound waveform have basically the same outline on the time axis. It can reflect the overall matching degree of the two waveforms. The larger the value, the more normal and stable the water flow state.

[0092] The maximum peak difference refers to the maximum amplitude difference at the corresponding peak positions of the two waveforms. It is used to measure the impact force of the water flow and the change of sudden flow. The larger the difference, the more likely there is foreign matter blocking the pipe or a sudden change in local pressure.

[0093] The maximum trough difference refers to the maximum amplitude difference between the corresponding trough positions of the two waveforms. The larger the difference, the more likely there is leakage, flow interruption or pressure backflow in the pipeline.

[0094] The flow status assessment value SLA of the pipeline in each monitoring area is calculated according to the formula: SLA = (sjz × η4 + slz × η5) × θ, where η4 and η5 represent the weighting factors of the set water flow velocity fluctuation value and water flow sound fluctuation value respectively;

[0095] The flow status evaluation value of the pipeline in each monitoring area is sent to the abnormality judgment module.

[0096] The abnormality determination module is used to receive the pipe wall condition evaluation value and the flow condition evaluation value of the pipeline in each monitoring area, and thus determine and analyze the health status of the pipeline in each monitoring area. The specific analysis process is as follows:

[0097] By extracting the values ​​of the pipe wall condition assessment value JKZ and the flow condition assessment value SLA of the pipeline in each monitoring area, normalization is performed according to the formula: Calculate the health value of the pipeline in each monitoring area, where e represents the set natural constant, and μ1 and μ2 represent the set correction coefficients;

[0098] Compare and analyze the health value of the pipeline in each monitoring area with the preset reference comparison interval. If the health value of the pipeline in a monitoring area is outside the preset reference comparison interval, the pipeline in the monitoring area is determined to be in an abnormal trend state. If the health value of the pipeline in a monitoring area is within the preset reference comparison interval, the pipeline in the monitoring area is determined to be in a normal trend state.

[0099] The pipelines in the monitoring area that are judged to be in an abnormal trend state are marked as pipelines in the abnormal monitoring area;

[0100] Then extract the health value of the pipeline in each abnormal monitoring area, and perform a difference analysis between the health value of the pipeline in each abnormal monitoring area and the preset health threshold to obtain the health level value of the pipeline in each abnormal monitoring area;

[0101] The health value of each abnormal monitoring area pipeline is matched and analyzed with the pre-stored health status table to obtain the health level of each abnormal monitoring area pipeline. The health value of each abnormal monitoring area pipeline corresponds to a health level. At the same time, it is matched with the abnormal management signal corresponding to the health level to obtain the abnormal management signal of each abnormal monitoring area pipeline and display it on the display terminal for notification;

[0102] Among them, the abnormal management signal refers to the multi-dimensional response signal generated according to the health level of the pipeline in the abnormal monitoring area. Specifically, it includes: abnormal level label, corresponding processing suggestions, specific location and notification path setting, so as to facilitate subsequent maintenance operations, abnormal handling or alarm notification processes, so as to achieve closed-loop management of pipeline health status.

[0103] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to specific embodiments. Obviously, many modifications and variations are possible based on the contents of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. Based on the integrated management platform of the Internet of Things smart community, it is characterized by: include: The number setting module is used to divide and analyze the monitoring areas of the underground pipelines in the target community to obtain the monitoring areas; The impact monitoring and analysis module is used to monitor the environmental status parameters of the pipelines in each monitoring area, obtain the ambient temperature impact value, ambient humidity impact value and ambient corrosion impact value, and determine the environmental impact value of the pipelines in each monitoring area based on this; The pipe wall monitoring and analysis module is used to monitor the pipe wall status parameters of the pipelines in each monitoring area, obtain the abnormal distribution value, abnormal area value and abnormal corrosion depth value, and then determine the pipe wall status assessment value of the pipelines in each monitoring area based on the environmental impact value; Among them, the abnormal distribution value is determined by the maximum distance value, minimum distance value and average distance value in the distance matrix; The flow monitoring and analysis module is used to monitor the flow status parameters of the pipelines in each monitoring area, obtain the water flow velocity fluctuation value and the water flow sound fluctuation value, and then determine the flow status assessment value of the pipelines in each monitoring area based on the environmental impact value; Among them, the water flow velocity fluctuation value is determined by the rising angle value of each changing line segment; The water flow sound fluctuation value is determined by the waveform overlap length, the maximum peak difference and the maximum trough difference in the water flow sound waveform overlap diagram; The abnormality determination module is used to receive the pipe wall status evaluation value and the flow status evaluation value of the pipeline in each monitoring area, and determine the health level of the pipeline in each abnormal monitoring area based on the received value.

2. The integrated management platform for smart communities based on the Internet of Things according to claim 1 is characterized in that: The specific process for delineating each monitoring area is as follows: Obtain a digital map of the underground pipelines in the target community and divide the pipelines into three categories based on their service range and connection structure: main pipeline end, building end, and user end. Obtain the corresponding working surface lengths of the above three types of pipelines respectively, set a comparative reference interval for the working surface lengths of each type of pipeline, and conduct a comparative analysis between the working surface lengths of each type of pipeline and their comparative reference intervals; When the working face length is greater than the maximum value of the comparison reference interval, it is determined to be Class A length grade, and the working face length of the pipeline is equally divided into K1 monitoring areas according to the Class A length grade; When the working face length is within the comparison reference interval, it is determined to be Class B length grade, and the working face length of the pipeline is equally divided into K2 monitoring areas according to Class B length grade; When the working face length is less than the minimum value of the comparison reference interval, it is determined to be Class C length grade, and the working face length of the pipeline is equally divided into K3 monitoring areas according to the Class C length grade; Thus, each monitoring area is obtained and a unique number is assigned to each monitoring area.

3. The integrated management platform for smart communities based on the Internet of Things according to claim 1 is characterized in that: The specific process of solving the ambient temperature influence value and the ambient humidity influence value is as follows: Collect the ambient temperature data of the pipelines in each monitoring area during the current monitoring period, remove the maximum and minimum ambient temperatures from the ambient temperature data, and calculate the average of the remaining ambient temperature data to obtain the average ambient temperature as the ambient temperature impact value of the pipelines in each monitoring area during the current monitoring period; Collect the ambient humidity data of the pipelines in each monitoring area during the current monitoring period, calculate the difference between the ambient humidity at adjacent monitoring time points to obtain the ambient humidity difference, and then calculate the average of all the obtained ambient humidity differences to obtain the average ambient humidity difference, which is used as the ambient humidity impact value of the pipelines in each monitoring area during the current monitoring period.

4. The integrated management platform for smart communities based on the Internet of Things according to claim 1 is characterized in that: The specific process of solving the ring corrosion impact value is as follows: Collect the types of components in the environment of the pipelines in each monitoring area during the current monitoring period, match the types of components in the environment of the pipelines in each monitoring area during the current monitoring period with the set types of corrosion components, obtain the corrosion components in the environment of the pipelines in each monitoring area during the current monitoring period, count the concentrations of the corrosion components, and add up the concentrations of the corrosion components to obtain the total concentration of the corrosion components, which is used as the environmental corrosion impact value of the pipelines in each monitoring area during the current monitoring period.

5. The integrated management platform for smart communities based on the Internet of Things according to claim 1 is characterized in that: The specific formula for solving the environmental impact value is: Extract the values ​​of the ambient temperature influence value Hwz, ambient humidity influence value Hsz and ambient corrosion influence value Hfz of the pipeline in each monitoring area during the current monitoring period and perform normalization processing according to the formula: Calculate the environmental impact value θ of the pipeline in each monitoring area, where Hwz * 、Hsz * and Hfz * They represent the set reference ambient temperature influence value, reference ambient humidity influence value and reference ambient corrosion influence value respectively, λ1, λ2 and λ3 represent the weight coefficients of the ambient temperature influence value, ambient humidity influence value and ambient corrosion influence value respectively, and λ1>λ2>λ3.

6. The integrated management platform for smart communities based on the Internet of Things according to claim 1 is characterized in that: The specific process of solving abnormal distribution values ​​is as follows: Collect pipe wall images of each monitoring area during the current monitoring period, pre-process the pipe wall images, extract the grayscale value corresponding to each pixel in the processed pipe wall images, and compare and analyze them with the preset grayscale threshold. If the grayscale value corresponding to a pixel is greater than the preset grayscale threshold, the pixel is judged to be normal; otherwise, the pixel is judged to be abnormal. All pixels judged as abnormal are spatially clustered and integrated to form abnormal areas; Construct a two-dimensional space for each abnormal area and extract the geometric center of gravity coordinates corresponding to each abnormal area; According to the geometric centroid coordinates corresponding to each abnormal area, the distance value between each abnormal area is calculated, and the distance values ​​between each abnormal area are used to form a distance matrix. The maximum distance value, minimum distance value and average distance value are extracted from the distance matrix to determine the abnormal distribution value.

7. The integrated management platform for smart communities based on the Internet of Things according to claim 6 is characterized in that: The specific process of solving the abnormal area value and abnormal corrosion depth value is as follows: Extract the physical area corresponding to each abnormal area from the pipe wall image of each monitoring area in the current monitoring period as the area value of each abnormal area, and calculate the average value to obtain the abnormal area value; The average grayscale value corresponding to each abnormal area is extracted from the pipe wall image of each monitoring area during the current monitoring period, and the average grayscale value corresponding to each abnormal area is subtracted from the preset average grayscale threshold to obtain the average grayscale difference corresponding to each abnormal area. The maximum value among all the average grayscale differences is selected as the abnormal corrosion depth value.

8. The integrated management platform for smart communities based on the Internet of Things according to claim 1 is characterized in that: The specific process of solving the water flow velocity fluctuation value is as follows: Collect the water velocity data of the pipelines in each monitoring area during the current monitoring period, and establish a dynamic water velocity coordinate system with the monitoring period as the horizontal coordinate and the water velocity as the vertical coordinate. Mark the water velocity data as data points on the dynamic water velocity coordinate system one by one, and then use line segments to connect the discrete data points in sequence, thereby obtaining a water velocity fluctuation diagram; The water velocity difference corresponding to adjacent data points in the water velocity fluctuation graph is subtracted and the absolute value is taken to obtain the water velocity difference. If the water velocity difference is greater than zero, it means that the water velocity is changing. The line segment with the water velocity difference greater than zero is marked as a changing line segment, and then the angle between each changing line segment and the horizontal axis is obtained by calculating its slope. That is, the slope value is converted into an angle value through the inverse tangent function, thereby obtaining the rising angle value of each changing line segment, thereby determining the water velocity fluctuation value.

9. The integrated management platform for smart communities based on the Internet of Things according to claim 1 is characterized in that: The specific process of solving the water flow sound fluctuation value is as follows: The water flow sound waveform of the pipeline in each monitoring area during the current monitoring period is collected and compared with the reference water flow sound waveform to obtain the water flow sound waveform coincidence diagram. The waveform coincidence length, maximum peak difference and maximum trough difference are extracted from the water flow sound waveform coincidence diagram to determine the water flow sound fluctuation value.

10. The integrated management platform for smart communities based on the Internet of Things according to claim 1 is characterized in that: The specific process of determining the health level of the pipeline in each abnormal monitoring area is as follows: Extract the pipe wall condition assessment value and flow condition assessment value of the pipeline in each monitoring area and perform normalization processing to obtain the health value of the pipeline in each monitoring area; Compare and analyze the health value of pipelines in each monitoring area with the preset reference comparison interval. If the health value of pipelines in a monitoring area is outside the preset reference comparison interval, the pipeline in the monitoring area is judged to be in an abnormal trend state. Otherwise, the pipeline in the monitoring area is judged to be in a normal trend state. The pipelines in the monitoring area that are judged to be in an abnormal trend state are marked as pipelines in the abnormal monitoring area; Then extract the health value of the pipeline in each abnormal monitoring area, and perform a difference analysis between the health value of the pipeline in each abnormal monitoring area and the preset health threshold to obtain the health level value of the pipeline in each abnormal monitoring area; The health level of the pipeline in each abnormal monitoring area is matched and analyzed with the pre-stored health status table to obtain the health level of the pipeline in each abnormal monitoring area.

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