A water supply network risk assessment method, device and medium

Abstract

Embodiments of the present invention provide a risk assessment method, device, and medium for a water supply network. The assessment method includes: obtaining historical accident data and basic network information for the water supply network; dividing the water supply network into multiple independent pipe sections based on the basic network information, and determining the same-characteristic pipe sections for each independent pipe section; determining the number of accidents that occurred in each independent pipe section based on the historical accident data; determining the number of accidents in the same-characteristic pipe section based on the number of accidents in all independent pipe sections under the same-characteristic pipe section; and determining the risk level of the same-characteristic pipe section based on the basic network information and the number of accidents in the same-characteristic pipe section. Embodiments of the present invention can accurately assess the accident risk of each pipe section in the water supply network.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of data information processing of water supply networks, and in particular to a risk assessment method, device, and medium for water supply networks. Background Art

[0002] Current risk assessments for water supply networks primarily consider the impact of factors like pipe material and age on accidents. This lacks comprehensive utilization of historical accident data, making it difficult to accurately reflect the actual risk profile of the network. Existing technologies struggle with matching and analysis efficiency when processing large amounts of historical accident data, failing to quickly and effectively link historical accidents to specific pipeline sections. Accident risk predictions often rely on empirical judgment and lack scientific quantitative models. Summary of the Invention

[0003] The technical problem to be solved by the embodiments of the present invention is to provide a risk assessment method, device and medium for a water supply network, which can accurately assess the accident risk of each pipe section of the water supply network.

[0004] To solve the above technical problems, the technical solutions of the embodiments of the present invention are as follows:

[0005] A risk assessment method for a water supply network, comprising:

[0006] Obtain historical accident data and basic information of the water supply network;

[0007] Dividing the water supply network into a plurality of independent pipe sections according to the basic information of the pipe network, and determining pipe sections with the same characteristics of each independent pipe section;

[0008] Determine the number of accidents that occurred in each independent pipe section based on the historical accident data;

[0009] Determine the number of accidents of the pipe section with the same characteristics according to the number of accidents of all independent pipe sections under the pipe section with the same characteristics;

[0010] The risk level of the pipe section with the same characteristics is determined based on the basic information of the pipe network and the number of accidents in the pipe section with the same characteristics.

[0011] Optionally, the water supply network is divided into independent pipe sections according to the basic pipe network information, and pipe sections with the same characteristics of each independent pipe section are determined, including:

[0012] Dividing the water supply network into independent pipe sections using the construction unit and construction time of the pipe sections in the basic information of the pipe network as identifiers;

[0013] Independent pipe segments with the same radius and pipe material are identified as pipe segments with the same characteristics.

[0014] Optionally, determining the number of accidents occurring in each independent pipe section based on the historical accident data includes:

[0015] Correcting and analyzing the address of the accident site to obtain the coordinates of the accident site;

[0016] According to the radius of the pipe section where the accident occurred and the pipe material of the pipe section where the accident occurred, determining a pipe section with the same characteristics and matching radius and pipe material;

[0017] generating a representative point group for each independent pipe segment of the matched pipe segments with the same characteristics, wherein the representative point group includes at least one representative point;

[0018] Determining the shortest distance between each representative point and the coordinates of the accident point according to the coordinates of the accident point;

[0019] Taking the shortest distance between each representative point and the coordinates of the accident point as a radius, the independent pipe sections with the same characteristics that match within the radius are determined as candidate independent pipe sections;

[0020] The candidate independent pipe section closest to the coordinates of the accident point is used as the independent pipe section where the accident occurred;

[0021] The number of accidents matched to each independent pipe section is counted to obtain the number of accidents occurring in each independent pipe section.

[0022] Optionally, determining the number of accidents of the pipe section with the same characteristics according to the number of accidents of all independent pipe sections under the pipe section with the same characteristics includes:

[0023] The number of accidents of all independent pipe sections under the pipe section with the same characteristics is added together to obtain the number of accidents of the pipe section with the same characteristics.

[0024] Optionally, determining the risk level of the pipe section with the same characteristics according to the basic information of the pipe network and the number of accidents in the pipe section with the same characteristics includes:

[0025] according to , determine the total length of the pipe section with the same characteristics;

[0026] Where L is the total length of the pipe section with the same characteristics, L i is the length of each independent pipe segment of the same characteristic pipe segment, i=1, 2, 3, ..., n, n is the number of each independent pipe segment of the same characteristic pipe segment;

[0027] According to D=m / L, determine the number of accidents that occur in a set time period and per unit length of pipe section with the same characteristics;

[0028] Where D is the number of accidents that occur in a set time period and per unit length of the pipe section with the same characteristics, and m is the number of accidents in the pipe section with the same characteristics;

[0029] If D>D1, the risk level of the pipe section with the same characteristics is a high-risk pipe section;

[0030] If D2<D≤D1, the risk level of the pipe section with the same characteristics is medium-high risk;

[0031] If D3<D≤D2, the risk level of the pipe section with the same characteristics is a medium-risk pipe section;

[0032] If D4<D≤D3, the risk level of the pipe section with the same characteristics is a medium-low risk pipe section;

[0033] If D < D4, the risk level of the pipe section with the same characteristics is a low-risk pipe section;

[0034] Wherein, D1 is the first threshold, D2 is the second threshold, D3 is the third threshold, and D4 is the fourth threshold.

[0035] Optionally, the above method further includes:

[0036] The accident probability of each independent pipe section is determined based on the basic information of the pipe network and the number of accidents in the pipe sections with the same characteristics.

[0037] Optionally, determining the accident probability of each independent pipe section based on the basic information of the pipe network and the total number of accidents in pipe sections with the same characteristics includes:

[0038] According to λ=D / T, determine the number of accidents per unit time and per unit length of the pipe section with the same characteristics;

[0039] Where λ is the number of accidents per unit time and per unit length of the same characteristic pipe section, D is the number of accidents per unit length of the same characteristic pipe section in the set time period, and T is the set time period;

[0040] according to , determine the accident probability of each independent pipe section;

[0041] Where P(X=k) is the probability of k accidents occurring per unit time for an independent pipe section, k=0, 1, 2, ..., and λ is the number of accidents occurring per unit time and per unit length for the pipe section with the same characteristics corresponding to the independent pipe section; Represents the factorial of k.

[0042] An embodiment of the present invention further provides a risk assessment device for a water supply network, comprising:

[0043] Acquisition module, used to obtain historical accident data and basic information of the water supply network;

[0044] A processing module is used to divide the water supply network into multiple independent pipe sections based on the basic information of the pipe network, and determine the same-characteristic pipe sections of each independent pipe section; determine the number of accidents that have occurred in each independent pipe section based on the historical accident data; determine the number of accidents in the same-characteristic pipe section based on the number of accidents in all independent pipe sections under the same-characteristic pipe section; and determine the risk level of the same-characteristic pipe section based on the basic information of the pipe network and the number of accidents in the same-characteristic pipe section.

[0045] An embodiment of the present invention also provides a computing device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described above.

[0046] An embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when executed on a computer, enable the computer to execute the method described above.

[0047] The above solution of the embodiment of the present invention has at least the following beneficial effects:

[0048] The above-mentioned solution of the embodiment of the present invention deeply associates historical accident data with pipe section characteristics through the process of "obtaining historical accident data, matching independent pipe sections, and counting the total number of accidents in pipe sections with the same characteristics". Risks are analyzed directly based on real accident records rather than relying solely on theoretical attribute inferences. This can more accurately reflect the actual risk status of the pipeline network and avoid assessment deviations caused by the disconnection between static attributes and actual failure patterns. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 4 is a flow chart of a risk assessment method for a water supply network according to an embodiment of the present invention.

[0050] Figure 2 3 is a schematic diagram of a module of a risk assessment device for a water supply network according to an embodiment of the present invention. DETAILED DESCRIPTION

[0051] Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0052] like Figure 1 As shown, an embodiment of the present invention provides a risk assessment method for a water supply network, comprising:

[0053] Step 11: Obtain historical accident data and basic information of the water supply network;

[0054] Step 12: Divide the water supply network into multiple independent pipe sections based on the basic pipe network information, and determine pipe sections with the same characteristics for each independent pipe section; specifically, the pipe sections with the same characteristics are a collection of multiple independent pipe sections with the same attribute characteristics;

[0055] Step 13: determining the number of accidents occurring in each independent pipe section based on the historical accident data;

[0056] Step 14, determining the number of accidents in the pipe section with the same characteristics according to the number of accidents in all independent pipe sections under the pipe section with the same characteristics;

[0057] Step 15: Determine the risk level of the pipe section with the same characteristics based on the basic information of the pipe network and the number of accidents in the pipe section with the same characteristics.

[0058] In this embodiment, by obtaining historical accident data, matching independent pipe sections, and counting the number of accidents in pipe sections with the same characteristics, historical accident data is deeply associated with pipe section characteristics. Risk analysis is directly based on actual accident records rather than relying solely on theoretical attribute inferences. This can more accurately reflect the actual risk status of the pipeline network and avoid assessment deviations caused by the disconnection between static attributes and actual failure patterns.

[0059] In an optional embodiment of the present invention, in step 11, the historical accident data of the water supply network includes at least one of: a radius of the pipe section where the accident occurred, a pipe material of the pipe section where the accident occurred, an address of the accident occurrence point, and a time when the accident occurred;

[0060] The basic information of the pipe network includes at least one of the construction unit, construction time, radius, pipe material and length information of the pipe section.

[0061] In this embodiment, the specific definition of historical accident data and basic information of the pipeline network in step 11 provides complete and accurate data support for subsequent pipeline segmentation, accident matching and risk quantification analysis.

[0062] In an optional embodiment of the present invention, in step 12, the water supply network is divided into independent pipe sections according to the basic information of the pipe network, and the pipe sections with the same characteristics of each independent pipe section are determined, including:

[0063] Step 121: Divide the water supply network into independent pipe sections using the construction units and construction times of the pipe sections in the basic information of the pipe network as identifiers;

[0064] In step 122 , independent pipe segments having the same radius and pipe material are determined as pipe segments with the same characteristics.

[0065] In this embodiment, the pipe sections constructed by the same construction unit in the same time period usually have consistent construction standards, material batches, and installation processes. Their potential risk characteristics (such as leakage hazards caused by construction quality) are common. This is used as the basis for dividing independent pipe sections, ensuring that the boundaries of each independent pipe section are clear and the properties are unified.

[0066] This division method provides a basic unit for the accurate matching of historical accidents and pipeline sections (each accident can be clearly attributed to an independent pipeline section), avoiding the "unclear attribution of accidents" caused by ambiguous pipeline section division.

[0067] The radius (pipe diameter) determines the pipeline's operating parameters such as stress characteristics and water flow velocity, while the pipe material determines the pipeline's material properties such as corrosion resistance and aging rate. Both are core physical properties that affect the risk of pipeline accidents.

[0068] By classifying based on these two key characteristics, pipe sections with similar risk characteristics can be aggregated for analysis (for example, all DN300 cast iron pipes are classified into one category), so that subsequent accident statistics and risk level classification can focus more on "commonalities of attributes" and avoid risk assessment bias caused by missing feature dimensions.

[0069] In an optional embodiment of the present invention, in step 13, determining the number of accidents occurring in each independent pipe section based on the historical accident data includes:

[0070] Step 131, correcting and parsing the address of the accident point to obtain the coordinates of the accident point;

[0071] Step 132: Determine a pipe section with the same characteristics and matching radius and material based on the radius and material of the pipe section where the accident occurred.

[0072] Step 133 , generating a representative point group for each of the matched independent pipe segments with the same characteristics, wherein the representative point group includes at least one representative point (including the start point, end point, midpoint, and equidistantly distributed points of the independent pipe segment);

[0073] Step 134, based on the coordinates of the accident point, determines the shortest distance between each representative point and the coordinates of the accident point. Specifically, this may include:

[0074] Step 1341, according to ,

[0075] Determine the distance between each representative point and the coordinates of the accident point;

[0076] Where, d(Q,R j ) is the distance between each representative point and the coordinates of the accident point, Q (x q ,y q ) is the coordinate of the accident point, Rj (x j ,y j ) are the coordinates of each representative point, j=1,2,3, e, e is the number of each representative point;

[0077] Step 1342, according to d0 min[ ], determine the shortest distance between each representative point and the coordinates of the accident point;

[0078] Wherein, d0 is the shortest distance between each representative point and the coordinates of the accident point;

[0079] Step 135 , using the shortest distance between each representative point and the coordinates of the accident point as a radius, and determining the independent pipe sections with the same characteristics that match within the radius as candidate independent pipe sections;

[0080] Step 1351, if d(Q,R j )≤d0, the corresponding independent pipe section is determined as a candidate independent pipe section;

[0081] Step 136 , taking the candidate independent pipe section closest to the coordinates of the accident point as the independent pipe section where the accident occurred;

[0082] Step 1361, according to ,

[0083] Determining the distance between each representative point of the candidate independent pipe section and the coordinates of the accident point;

[0084] Where, d(Q,R sg ) is the distance between each representative point of the candidate independent pipe segment and the coordinates of the accident point, R sg (x sg ,y sg ) are the coordinates of each representative point of the candidate independent pipe segment, s=1,2,3,u,u is the number of candidate independent pipe segments, g=1,2,3,o,o is the number of representative points of each candidate independent pipe segment;

[0085] Step 137 , counting the number of accidents matched to each independent pipe section, and obtaining the number of accidents occurring in each independent pipe section.

[0086] In this embodiment, step 131 converts the ambiguous text address into precise latitude and longitude coordinates by "correcting and parsing the address of the accident site" (combining ambiguity processing with the BERT model and reverse parsing coordinates with the map API), thereby solving the problem of "matching failure caused by unclear address" from the source, providing a precise spatial positioning basis for subsequent pipe section matching, ensuring that each accident can be mapped to a specific geographic point, and avoiding risk statistical deviations caused by address errors.

[0087] Step 132 uses the "radius and material of the pipe segment where the accident occurred" to first identify matching pipe segments with the same characteristics, rather than blindly searching across all pipe segments. This avoids redundant calculations that would require traversing the entire pipe network, making the matching process more targeted and significantly improving data processing speed.

[0088] Step 133 specifies that the representative point group includes "starting point, end point, midpoint, and equidistant points". The spatial position of the pipe section is represented by a limited number of representative points (rather than all points along the entire pipe section), avoiding the direct calculation of the complex distance from the accident point to the pipe section (such as point-by-point distance measurement of a curved pipe section). The end point ensures that the pipe section boundary is covered, the midpoint reflects the central area of ​​the pipe section, and the equidistant points avoid the loss of representative points due to the long distance in the long pipe section, ensuring that there are sufficient "spatial samples" along the entire pipe section to reduce missed judgments due to insufficient representative points.

[0089] Steps 134-136 implement a progressive matching process of coarse screening, fine screening, and confirmation through three levels of screening: the nearest representative point, the candidate pipe section range, and the nearest actual pipe section. First, the direction of the potentially associated pipe section is quickly locked through the nearest representative point. Then, the candidate range is defined with the distance to the representative point as the radius to filter out obviously irrelevant pipe sections. Finally, the actual shortest distance from the accident point to the candidate pipe section is calculated to determine the final independent pipe section (rather than relying solely on the representative point judgment), completely solving the problem of "mismatching caused by representative point deviation."

[0090] Step 137 directly obtains the “actual number of accidents occurring in each pipe section” by counting the number of matching accidents for each independent pipe section.

[0091] In an optional embodiment of the present invention, in step 14, determining the number of accidents of the pipe section with the same characteristics based on the number of accidents of all independent pipe sections under the pipe section with the same characteristics includes:

[0092] Step 141, the number of accidents under the same characteristic pipe section all independent pipe sections are added to obtain the number of accidents with the same characteristic pipe section; specifically, may include;

[0093] according to , determine the number of accidents in the pipe section with the same characteristics;

[0094] Among them, m is the number of accidents in the same characteristic pipe section, m α is the number of accidents in each independent pipe section with the same characteristic, α=1,2,…,β, β is the number of independent pipe sections with the same characteristic.

[0095] In this embodiment, step 141 converts the scattered independent pipe section accident data into group data with common characteristics through combined statistics.

[0096] In an optional embodiment of the present invention, in step 15, determining the risk level of the pipe section with the same characteristics based on the basic information of the pipe network and the number of accidents in the pipe section with the same characteristics includes:

[0097] Step 151, according to , determine the total length of the pipe section with the same characteristics;

[0098] Where L is the total length of the pipe section with the same characteristics, L i is the length of each independent pipe segment of the same characteristic pipe segment, i=1, 2, 3, ..., n, n is the number of each independent pipe segment of the same characteristic pipe segment;

[0099] Step 152: Determine the number of accidents occurring per unit length of a pipe section with the same characteristics within a set time period according to D=m / L.

[0100] Where D is the number of accidents that occur in a set time period and per unit length of the pipe section with the same characteristics, and m is the number of accidents in the pipe section with the same characteristics;

[0101] Step 153: If D>D1, the risk level of the pipe section with the same characteristics is a high-risk pipe section;

[0102] Step 154: If D2<D≤D1, the risk level of the pipe section with the same characteristics is a medium-high risk pipe section;

[0103] Step 155: If D3 < D ≤ D2, the risk level of the pipe section with the same characteristics is a medium-risk pipe section;

[0104] Step 156: If D4<D≤D3, the risk level of the pipe section with the same characteristics is a medium-low risk pipe section;

[0105] Step 157: If D < D4, the risk level of the pipe section with the same characteristics is a low-risk pipe section;

[0106] Wherein, D1 is the first threshold, D2 is the second threshold, D3 is the third threshold, and D4 is the fourth threshold.

[0107] In this embodiment, step 15 combines quantitative calculations with grading thresholds to determine the risk level of pipeline sections with similar characteristics, achieving significant results. By calculating the number of accidents per unit length and correlating the number of accidents with the total length of the pipeline section, we can avoid misjudgments caused by focusing solely on the number of accidents. The introduction of a four-level threshold to categorize risk into five levels unifies standards, replaces empirical judgment, and eliminates subjective differences. Furthermore, the thresholds can be dynamically adjusted, achieving both universal and targeted application. This grading system clearly identifies high-risk pipeline sections, facilitating maintenance priorities, while also reducing maintenance frequency for low-risk sections, thereby improving resource allocation efficiency.

[0108] In an optional embodiment of the present invention, the method further comprises:

[0109] Step 16, determining the accident probability of each independent pipe section based on the basic information of the pipe network and the number of accidents in the pipe section with the same characteristics, specifically, may include:

[0110] Step 161, according to λ=D / T, determine the number of accidents per unit time and per unit pipe length for the pipe section with the same characteristics;

[0111] Where λ is the number of accidents per unit time and per unit length of the same characteristic pipe section, D is the number of accidents per unit length of the same characteristic pipe section in the set time period, and T is the set time period;

[0112] Step 162, according to , determine the accident probability of each independent pipe section;

[0113] Where P(X=k) is the probability of k accidents occurring per unit time for an independent pipe section, k=0, 1, 2, ..., and λ is the number of accidents occurring per unit time and per unit length for the pipe section with the same characteristics corresponding to the independent pipe section; Represents the factorial of k.

[0114] Step 162, you can also , determine the accident probability of each independent pipe section;

[0115] Where P(X≥k) is the probability of k or more accidents occurring in an independent pipe section per unit time, k = 0, 1, 2, ..., w, where w is the maximum value of k.

[0116] In this embodiment, the risk level of pipeline sections with the same characteristics (such as "high risk" and "medium risk") is a macro-qualitative description, while the accident probability of an independent pipeline section (such as "the probability of two accidents occurring in the next year is 30%) is a micro-quantitative result.

[0117] The overall risk characteristics of pipeline sections with the same characteristics (reflecting the average level within a set time period) are converted into risk intensity per unit time, and then the probability is calculated based on the inherent properties of the independent pipeline section (such as length). This allows the risk assessment to be refined from "common risks of a category of pipeline sections" to "specific risks of individual pipeline sections", solving the problem of "it is impossible to distinguish the priorities of pipeline sections with the same level of risk".

[0118] The dynamic update mechanism enables risk assessment to continuously adapt to the actual operating status of the pipeline network, avoiding the problem of static assessment results being out of touch with actual risks.

[0119] Example 1

[0120] For a water supply network that requires accident risk analysis and contains a large number of pipe sections, we will take two types of pipe sections with the same characteristics as an example:

[0121] Featured Pipeline Section A: DN300 cast iron pipe, constructed by Unit A in 2010, consisting of three independent pipe sections (A1, A2, and A3), with lengths of 2 km, 3 km, and 5 km respectively;

[0122] Featured pipe section B: DN200 plastic pipe, constructed by Unit B in 2015, consisting of two independent pipe sections (B1 and B2), with lengths of 4 km and 6 km respectively;

[0123] Example 1 provides a risk assessment method for a water supply network, including:

[0124] Step 21, data acquisition:

[0125] Historical accident data: Feature pipe section A had 12 accidents in the past five years (A1: 3 times, A2: 4 times, A3: 5 times); Feature pipe section B had 5 accidents in the past five years (B1: 2 times, B2: 3 times);

[0126] Pipeline network basic information: pipe section construction unit, time, radius, pipe material, and length are the same as above;

[0127] Step 22, pipe segment division:

[0128] Independent pipe sections: A1, A2, A3 (Unit A built in 2010); B1, B2 (Unit B built in 2015);

[0129] Pipe sections with the same characteristics: A (DN300 cast iron pipe), B (DN200 plastic pipe);

[0130] Step 23, statistics of accidents in independent pipe sections:

[0131] The accident point address is corrected and parsed into coordinates. By matching representative points (such as the starting point and midpoint of A1), it is confirmed that A1 corresponds to three accidents, and so on;

[0132] Step 24: Merge the same characteristic pipe section accidents:

[0133] Number of accidents in characteristic pipe section A: 3+4+5=12 times; number of accidents in characteristic pipe section B: 2+3=5 times;

[0134] Step 25, risk level classification:

[0135] Total length: A=2+3+5=10km; B=4+6=10km;

[0136] Accident frequency per unit length (D): A=12 / 10=1.2 times / km (5 years); B=5 / 10=0.5 times / km (5 years);

[0137] If thresholds D1 = 1.0 and D2 = 0.6, then A > D1 (high risk) and B is between D2 and D1 (medium-high risk).

[0138] Step 26: Prediction of accident probability of independent pipe sections:

[0139] Number of accidents per unit time (year) and per unit length of characteristic pipe section A: λ = 1.2 / 5 = 0.24 times / (km per year);

[0140] Characteristic pipe section B: λ = 0.5 / 5 = 0.1 times / (km per year);

[0141] Accident probability calculation: The probability of an accident occurring in A1 (length 2 km) in the next year is: P(X=1)≈0.48×0.618≈29.6%;

[0142] The probability of an accident occurring in B1 (length 4 km) in the next year is: P(X=1)≈0.4×0.670≈26.8%.

[0143] This invention clarifies the collection dimensions of historical accident data (radius, pipe material, location, and time) and basic pipeline network information (construction unit, time, radius, pipe material, and length), laying a solid foundation for subsequent analysis. This precise data item covers key attributes throughout the entire pipeline lifecycle, comprehensively linking everything from accident-causing conditions to inherent pipeline characteristics, ensuring accurate "accident-pipeline segment" matching and risk calculation.

[0144] Using the construction unit + construction time as the identifier, grasp the commonalities of the pipe section construction stage (construction standards, material batches, and processes), so that the boundaries of independent pipe sections are clear and the attributes are unified, becoming the "basic unit" for accurate attribution of accidents.

[0145] Based on two core physical attributes—radius (affecting stress and flow) and pipe material (affecting corrosion and aging)—we group pipe sections with similar risk characteristics. This allows subsequent statistics and grading to focus on "attribute commonalities," avoiding risk misjudgments due to missed features and allowing analysis to better focus on the essential risk factors of the pipeline network.

[0146] Through the sophisticated process of "address correction and analysis, locking pipe sections with the same characteristics, generating representative points, and three-level screening and matching", the difficulty of matching accidents with pipe sections is overcome.

[0147] Address resolution converts ambiguous text into precise coordinates, resolving the root cause of "inaccurate addresses"; first, it locks onto pipe sections with the same characteristics, narrowing the search scope and improving matching efficiency; representative point groups (starting point, end point, midpoint, and equidistant points) use limited points to cover the pipe section space, simplifying calculations while ensuring coverage; three-layer screening (coarse screening - fine screening - confirmation) progressive matching is fast and accurate, completely resolving "mismatches" and enabling each accident to accurately correspond to an independent pipe section, providing reliable data for risk statistics.

[0148] By combining the number of accidents in individual pipeline sections and the total length to calculate the number of accidents per unit length, we then use four thresholds to categorize the risk into five levels. This quantification of the correlation between the number of accidents and the total length of the pipeline section avoids the bias of focusing solely on the number of accidents while ignoring the size of the pipeline section. The thresholds replace empirical judgments with unified, dynamically adjustable standards, making them universally applicable and adaptable to the characteristics of diverse pipeline networks. The grading results clearly distinguish between high-, medium-, and low-risk pipeline sections, prioritizing the allocation of O&M resources to high-risk areas and improving allocation efficiency.

[0149] Based on data from pipeline sections with the same characteristics, qualitative ratings are refined into quantitative probabilities, resolving the issue of varying risks within pipeline sections of the same rating (e.g., for medium-risk pipeline sections, the probability is 30% for pipeline section A and 50% for pipeline section B). This allows risk analysis to move from macro-level grading to micro-level probabilities, supporting more accurate O&M decisions (e.g., prioritizing pipeline sections of the same rating with higher probabilities).

[0150] like Figure 2 As shown, an embodiment of the present invention further provides a risk assessment device 20 for a water supply network, comprising:

[0151] Acquisition module 21, used to obtain historical accident data and basic information of the water supply network;

[0152] The processing module 22 is used to divide the water supply network into multiple independent pipe sections according to the basic information of the pipe network, and determine the same-characteristic pipe sections of each independent pipe section; determine the number of accidents that have occurred in each independent pipe section according to the historical accident data; determine the number of accidents in the same-characteristic pipe section according to the number of accidents in all independent pipe sections under the same-characteristic pipe section; and determine the risk level of the same-characteristic pipe section according to the basic information of the pipe network and the number of accidents in the same-characteristic pipe section.

[0153] Optionally, the historical accident data of the water supply network includes at least one of the radius of the pipe section where the accident occurred, the pipe material of the pipe section where the accident occurred, the address of the accident location, and the time when the accident occurred;

[0154] The basic information of the pipe network includes at least one of the construction unit, construction time, radius, pipe material and length information of the pipe section.

[0155] Optionally, the water supply network is divided into independent pipe sections according to the basic pipe network information, and pipe sections with the same characteristics of each independent pipe section are determined, including:

[0156] Dividing the water supply network into independent pipe sections using the construction unit and construction time of the pipe sections in the basic information of the pipe network as identifiers;

[0157] Independent pipe segments with the same radius and pipe material are identified as pipe segments with the same characteristics.

[0158] Optionally, determining the number of accidents occurring in each independent pipe section based on the historical accident data includes:

[0159] Correcting and analyzing the address of the accident site to obtain the coordinates of the accident site;

[0160] According to the radius of the pipe section where the accident occurred and the pipe material of the pipe section where the accident occurred, determining a pipe section with the same characteristics and matching radius and pipe material;

[0161] generating a representative point group for each independent pipe segment of the matched pipe segments with the same characteristics, wherein the representative point group includes at least one representative point;

[0162] Determining the shortest distance between each representative point and the coordinates of the accident point according to the coordinates of the accident point;

[0163] Taking the shortest distance between each representative point and the coordinates of the accident point as a radius, the independent pipe sections with the same characteristics that match within the radius are determined as candidate independent pipe sections;

[0164] The candidate independent pipe section closest to the coordinates of the accident point is used as the independent pipe section where the accident occurred;

[0165] The number of accidents matched to each independent pipe section is counted to obtain the number of accidents occurring in each independent pipe section.

[0166] Optionally, determining the number of accidents of the pipe section with the same characteristics according to the number of accidents of all independent pipe sections under the pipe section with the same characteristics includes:

[0167] The number of accidents of all independent pipe sections under the pipe section with the same characteristics is added together to obtain the number of accidents of the pipe section with the same characteristics.

[0168] Optionally, determining the risk level of the pipe section with the same characteristics according to the basic information of the pipe network and the number of accidents in the pipe section with the same characteristics includes:

[0169] according to , determine the total length of the pipe section with the same characteristics;

[0170] Where L is the total length of the pipe section with the same characteristics, L i is the length of each independent pipe segment of the same characteristic pipe segment, i=1, 2, 3, ..., n, n is the number of each independent pipe segment of the same characteristic pipe segment;

[0171] According to D=m / L, determine the number of accidents that occur in a set time period and per unit length of pipe section with the same characteristics;

[0172] Where D is the number of accidents that occur in a set time period and per unit length of the pipe section with the same characteristics, and m is the number of accidents in the pipe section with the same characteristics;

[0173] If D>D1, the risk level of the pipe section with the same characteristics is a high-risk pipe section;

[0174] If D2<D≤D1, the risk level of the pipe section with the same characteristics is medium-high risk;

[0175] If D3<D≤D2, the risk level of the pipe section with the same characteristics is a medium-risk pipe section;

[0176] If D4<D≤D3, the risk level of the pipe section with the same characteristics is a medium-low risk pipe section;

[0177] If D < D4, the risk level of the pipe section with the same characteristics is a low-risk pipe section;

[0178] Wherein, D1 is the first threshold, D2 is the second threshold, D3 is the third threshold, and D4 is the fourth threshold.

[0179] Optionally, the processing module 22 is further configured to determine the accident probability of each independent pipe section according to the basic information of the pipe network and the number of accidents in pipe sections with the same characteristics.

[0180] Optionally, determining the accident probability of each independent pipe section based on the basic information of the pipe network and the total number of accidents in pipe sections with the same characteristics includes:

[0181] According to λ=D / T, determine the number of accidents per unit time and per unit length of the pipe section with the same characteristics;

[0182] Where λ is the number of accidents per unit time and per unit length of the same characteristic pipe section, D is the number of accidents per unit length of the same characteristic pipe section in the set time period, and T is the set time period;

[0183] according to , determine the accident probability of each independent pipe section;

[0184] Where P(X=k) is the probability of k accidents occurring per unit time for an independent pipe section, k=0, 1, 2, ..., and λ is the number of accidents occurring per unit time and per unit length for the pipe section with the same characteristics corresponding to the independent pipe section; Represents the factorial of k.

[0185] It should be noted that this device is a device corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0186] An embodiment of the present invention further provides a computing device comprising: one or more processors; and a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0187] An embodiment of the present invention further provides a computing device readable storage medium storing instructions that, when executed on a computing device, cause the computing device to execute the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0188] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computing device software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0189] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0190] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0191] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0192] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0193] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a storage medium readable by a computing device. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computing device software product is stored in a storage medium and includes a number of instructions for enabling a computing device (which can be a personal computing device, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard drive, ROM, RAM, a magnetic disk, or an optical disk.

[0194] In addition, it should be pointed out that in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent solutions of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but they do not necessarily need to be performed in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it can be understood that all or any steps or components of the method and apparatus of the present invention can be implemented in hardware, firmware, software or a combination thereof in any computing device (including a processor, storage medium, etc.) or a network of computing devices. This can be achieved by those of ordinary skill in the art using basic programming skills after reading the description of the present invention.

[0195] Therefore, the purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the purpose of the present invention can also be achieved simply by providing a program product containing program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but do not necessarily need to be performed in chronological order. Certain steps can be performed in parallel or independently of each other.

[0196] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A risk assessment method for a water supply network, characterized in that: include: Obtain historical accident data and basic information of the water supply network; Dividing the water supply network into a plurality of independent pipe sections according to the basic information of the pipe network, and determining pipe sections with the same characteristics of each independent pipe section; Determine the number of accidents that occurred in each independent pipe section based on the historical accident data; Determine the number of accidents of the pipe section with the same characteristics according to the number of accidents of all independent pipe sections under the pipe section with the same characteristics; Determining the risk level of the pipe section with the same characteristics based on the basic information of the pipe network and the number of accidents in the pipe section with the same characteristics; The number of accidents occurring in each independent pipe section is determined based on the historical accident data, including: Correcting and analyzing the address of the accident site to obtain the coordinates of the accident site; According to the radius of the pipe section where the accident occurred and the pipe material of the pipe section where the accident occurred, determining a pipe section with the same characteristics and matching radius and pipe material; generating a representative point group for each independent pipe segment of the matched pipe segments with the same characteristics, wherein the representative point group includes at least one representative point; Determining the shortest distance between each representative point and the coordinates of the accident point according to the coordinates of the accident point; Taking the shortest distance between each representative point and the coordinates of the accident point as a radius, the independent pipe sections with the same characteristics that match within the radius are determined as candidate independent pipe sections; The candidate independent pipe section closest to the coordinates of the accident point is used as the independent pipe section where the accident occurred; The number of accidents matched to each independent pipe section is counted to obtain the number of accidents occurring in each independent pipe section.

2. The risk assessment method for a water supply network according to claim 1, characterized in that: According to the basic information of the pipe network, the water supply pipe network is divided into independent pipe sections, and pipe sections with the same characteristics of each independent pipe section are determined, including: Dividing the water supply network into independent pipe sections using the construction unit and construction time of the pipe sections in the basic information of the pipe network as identifiers; Independent pipe segments with the same radius and pipe material are identified as pipe segments with the same characteristics.

3. The risk assessment method for a water supply network according to claim 2, characterized in that: Determining the number of accidents of the pipe section with the same characteristics according to the number of accidents of all independent pipe sections under the pipe section with the same characteristics includes: The number of accidents of all independent pipe sections under the pipe section with the same characteristics is added together to obtain the number of accidents of the pipe section with the same characteristics.

4. The risk assessment method for a water supply network according to claim 1, characterized in that: Determine the risk level of the pipe section with the same characteristics based on the basic information of the pipe network and the number of accidents in the pipe section with the same characteristics, including: according to , determine the total length of the pipe section with the same characteristics; Where L is the total length of the pipe section with the same characteristics, L i is the length of each independent pipe segment of the same characteristic pipe segment, i=1, 2, 3, ..., n, n is the number of each independent pipe segment of the same characteristic pipe segment; According to D=m / L, determine the number of accidents that occur in a set time period and per unit length of pipe section with the same characteristics; Where D is the number of accidents that occur in a set time period and per unit length of the pipe section with the same characteristics, and m is the number of accidents in the pipe section with the same characteristics; If D>D1, the risk level of the pipe section with the same characteristics is a high-risk pipe section; If D2<D≤D1, the risk level of the pipe section with the same characteristics is medium-high risk; If D3<D≤D2, the risk level of the pipe section with the same characteristics is a medium-risk pipe section; If D4<D≤D3, the risk level of the pipe section with the same characteristics is a medium-low risk pipe section; If D < D4, the risk level of the pipe section with the same characteristics is a low-risk pipe section; Wherein, D1 is the first threshold, D2 is the second threshold, D3 is the third threshold, and D4 is the fourth threshold.

5. The risk assessment method for a water supply network according to claim 4, characterized in that: Also includes: The accident probability of each independent pipe section is determined based on the basic information of the pipe network and the number of accidents in the pipe sections with the same characteristics.

6. The risk assessment method for a water supply network according to claim 5, characterized in that: Based on the basic information of the pipeline network and the total number of accidents in pipeline sections with the same characteristics, the accident probability of each independent pipeline section is determined, including: According to λ=D / T, determine the number of accidents per unit time and per unit length of the pipe section with the same characteristics; Where λ is the number of accidents per unit time and per unit length of the same characteristic pipe section, D is the number of accidents per unit length of the same characteristic pipe section in the set time period, and T is the set time period; according to , determine the accident probability of each independent pipe section; Where P(X=k) is the probability of k accidents occurring per unit time in an independent pipe section, k=0, 1, 2..., λ is the number of accidents per unit time and per unit length in the pipe section with the same characteristics corresponding to the independent pipe section, Represents the factorial of k.

7. A risk assessment device for a water supply network, characterized in that: include: Acquisition module, used to obtain historical accident data and basic information of the water supply network; a processing module configured to divide the water supply network into a plurality of independent pipe sections based on the basic pipe network information, and determine pipe sections with the same characteristics for each independent pipe section; determine the number of accidents that occurred in each independent pipe section based on the historical accident data; determine the number of accidents in the pipe section with the same characteristics based on the number of accidents in all independent pipe sections under the pipe section with the same characteristics; and determine the risk level of the pipe section with the same characteristics based on the basic pipe network information and the number of accidents in the pipe section with the same characteristics; The number of accidents occurring in each independent pipe section is determined based on the historical accident data, including: Correcting and analyzing the address of the accident site to obtain the coordinates of the accident site; According to the radius of the pipe section where the accident occurred and the pipe material of the pipe section where the accident occurred, determining a pipe section with the same characteristics and matching radius and pipe material; generating a representative point group for each independent pipe segment of the matched pipe segments with the same characteristics, wherein the representative point group includes at least one representative point; Determining the shortest distance between each representative point and the coordinates of the accident point according to the coordinates of the accident point; Taking the shortest distance between each representative point and the coordinates of the accident point as a radius, the independent pipe sections with the same characteristics that match within the radius are determined as candidate independent pipe sections; The candidate independent pipe section closest to the coordinates of the accident point is used as the independent pipe section where the accident occurred; The number of accidents matched to each independent pipe section is counted to obtain the number of accidents occurring in each independent pipe section.

8. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The device stores instructions, which, when executed on a computer, cause the computer to execute the method according to any one of claims 1 to 6.

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