Methods and systems for assessing pipeline leakage points

Abstract

This application discloses a method and system for assessing pipeline network leakage points, comprising: constructing an evaluation decision matrix; normalizing the evaluation decision matrix to obtain standardized values ​​for each evaluation indicator; calculating a dynamic adjustment factor based on real-time and historical pipeline network data, updating the weights of the evaluation indicators, and performing sensitivity verification; applying the weight values ​​that pass the sensitivity verification as the final weights of the evaluation indicators; performing weighted normalization on the final weights using the standardized values ​​to obtain the indicator values; constructing an ideal solution for the pipeline network leakage points based on the indicator values, calculating the distance between the pipeline network leakage points and the ideal solution, and obtaining the relative proximity of the pipeline network leakage points based on the calculation results; obtaining the relative proximity of each pipeline network leakage point, and outputting a priority ranking result based on the magnitude of the relative proximity. This application can adaptively adjust the weights of the evaluation indicators according to real-time operating conditions, improving the accuracy of priority judgment for pipeline network leakage points.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pipe network, in particular to a pipe network leakage point evaluation method and system. BACKGROUND

[0002] Urban water supply is the lifeline of the city, which is directly related to the stability of society and the development of economy. At present, water supply pipeline leakage is the core problem in urban water management, and water supply pipeline leakage is mainly caused by problems in planning and design, pipeline material and construction quality. If not properly handled, it will bring a series of direct and indirect economic losses and negative effects.

[0003] The leakage risk evaluation of water supply pipeline is the premise of completing leakage control. The traditional pipeline leakage detection method cannot respond to the real-time working condition change of the pipeline, and the misjudgment rate of the priority of the leakage point is high, which leads to poor timeliness in dealing with high-risk leakage points and easy occurrence of secondary disasters. SUMMARY

[0004] In order to solve the problems in the prior art, the technical scheme adopted by the present application is as follows:

[0005] The present application provides a pipe network leakage point evaluation method, which comprises the following steps:

[0006] Obtain the historical working condition data of the pipe network, construct an evaluation decision matrix about the pipe network leakage point, the evaluation decision matrix comprising the original value of the pipe network leakage point on each evaluation index, normalize the evaluation decision matrix to obtain the standardized value of each evaluation index of the pipe network leakage point;

[0007] Obtain the real-time working condition data of the pipe network, calculate a dynamic adjustment factor based on the real-time working condition data and the historical working condition data, dynamically update the initial weight of the evaluation index through the dynamic adjustment factor to obtain the updated weight of the evaluation index, and verify the sensitivity of the updated weight of the evaluation index. The weight value passing the sensitivity verification is applied as the final weight of the evaluation index;

[0008] The final weight is used for weighted normalization processing of the normalized evaluation decision matrix to obtain the index value of the evaluation index;

[0009] Based on the index value, an ideal solution of the pipe network leakage point is constructed, the distance between the pipe network leakage point and the ideal solution is calculated, and the relative closeness of the pipe network leakage point is obtained according to the calculation result;

[0010] The relative closeness of each pipe network leakage point is obtained, and the priority ranking result of the pipe network leakage point is output based on the size of the relative closeness.

[0011] In summary, the application provides a pipe network leakage point evaluation method, which calculates a dynamic adjustment factor of an evaluation index through pipe network operation data, adjusts the weight of each evaluation index, and further adjusts the influence of each evaluation index on the quantification of pipe network leakage point priority, so as to timely respond to the real-time working condition changes of the pipe network, improve the accuracy of the judgment of the pipe network leakage point priority, and build an ideal solution of the pipe network leakage point, obtain the relative closeness of the pipe network leakage point based on the distance between the pipe network leakage point and the ideal solution, realize intelligent priority sorting of the pipe network leakage point, improve the response sensitivity to high-risk leakage points, so that the maintenance personnel can prioritize the high-risk leakage points for processing according to the priority sorting, reduce resource waste and reduce the risk of secondary disasters.

[0012] Further, the ideal solution includes a positive ideal solution and a negative ideal solution, the positive ideal solution is a vector composed of the maximum values of the index values of the evaluation indexes, and the negative ideal solution is a vector composed of the minimum values of the index values of the evaluation indexes.

[0013] The distance between the pipe network leakage point and the positive ideal solution and the distance between the pipe network leakage point and the negative ideal solution are calculated respectively, and the relative closeness is calculated according to the distance between the pipe network leakage point and the positive ideal solution and the distance between the pipe network leakage point and the negative ideal solution.

[0014] Further, the distance between the pipe network leakage point and the negative ideal solution is calculated by the following formula:

[0015] ;

[0016] In the formula, wherein d i represents the distance between the i th pipe network leakage point and the negative ideal solution, and n represents the number of evaluation indexes, represents the index value of the j th evaluation index of the i th pipe network leakage point, represents the minimum value of the j th evaluation index.

[0017] Further, the evaluation method further comprises:

[0018] A penalty term is introduced when calculating the distance between the pipe network leakage point and the positive ideal solution, the penalty term is used to weight the influence of the abnormal index on the priority of the pipe network leakage point, and the abnormal index at least includes pressure drop or leakage alarm.

[0019] Further, the distance between the pipe network leakage point and the positive ideal solution is calculated by the following formula:

[0020] ;

[0021] In the formula, represents the distance between the i-th pipe network leakage point and the positive ideal solution, n represents the number of evaluation indexes, represents the index value of the j-th evaluation index of the i-th pipe network leakage point, represents the maximum value of the j-th evaluation index, represents a penalty term.

[0022] Further, the sensitivity verification of the updated weight of the evaluation index comprises:

[0023] The distance between the pipe network leakage point and the ideal solution is calculated based on the updated weight of the evaluation index, the first relative closeness of the pipe network leakage point is obtained based on the calculation result, a preset floating value is superimposed on the updated weight of the evaluation index, the second relative closeness of the pipe network leakage point is recalculated based on the weight after superimposing the preset floating value, and the change rate between the first relative closeness and the second relative closeness is calculated;

[0024] If the change rate is less than a preset change threshold, the updated weight is applied as the final weight of the evaluation index;

[0025] If the change rate is greater than the preset change threshold, the dynamic adjustment factor is returned to be calculated, and the weight of the evaluation index is updated again.

[0026] Further, the evaluation method further comprises:

[0027] It is judged whether the data fluctuation of the evaluation index exceeds a preset fluctuation range, if the data fluctuation of the evaluation index does not exceed the preset fluctuation range, the weight of the evaluation index is not updated;

[0028] If the data fluctuation of the evaluation index exceeds the preset fluctuation range, the dynamic adjustment factor corresponding to the evaluation index is calculated to update the weight of the evaluation index.

[0029] Further, the calculation of the dynamic adjustment factor based on the real-time working condition data and the historical working condition data comprises:

[0030] The standard deviation of the evaluation index is calculated based on the real-time working condition data, the historical mean of the evaluation index is calculated based on the historical working condition data, and the ratio result between the standard deviation and the historical mean is calculated and obtained;

[0031] If the ratio result is greater than 0.5, the dynamic adjustment factor is 0.5; if the ratio result is less than 0.5, the dynamic adjustment factor is the ratio result.

[0032] Further, the evaluation method further comprises:

[0033] The normalization processing adopts a vector normalization method, and performs positive index processing on the evaluation decision matrix based on original values of the evaluation indexes to obtain standardized values of the evaluation indexes.

[0034] In a second aspect, the application further provides a pipe network leakage point evaluation system, which applies the pipe network leakage point evaluation method described above. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 A step flowchart of the pipe network leakage point evaluation method provided by an embodiment of the application is shown in

[0036] Figure 2 A flowchart of the sensitivity verification in the pipe network leakage point evaluation method provided by an embodiment of the application is shown in

[0037] Figure 3 A flowchart of the calculation of the relative closeness of the pipe network leakage point in the pipe network leakage point evaluation method provided by an embodiment of the application is shown in DETAILED DESCRIPTION

[0038] The application will be described in detail below with reference to the specific embodiments shown in the drawings, but these embodiments do not limit the application, and the structural, method, or functional changes made by those skilled in the art based on these embodiments are included in the protection scope of the application.

[0039] In the conventional pipe network leakage detection method, the importance of the evaluation indexes is assigned by using fixed weights, and the method cannot dynamically respond to real-time working condition changes of the pipe network, which leads to poor timeliness of leakage treatment and a long average repair response time of 7.2 hours, and easily causes the occurrence of secondary disasters. In order to solve the problems of the prior art, in a first aspect, an embodiment of the application provides a pipe network leakage point evaluation method, as shown in Figure 1 The evaluation method includes the following steps:

[0040] In step S11, historical working condition data of the pipe network is acquired, an evaluation decision matrix about the pipe network leakage point is constructed, the evaluation decision matrix includes original values of the pipe network leakage point on each evaluation index, normalization processing is performed on the evaluation decision matrix, and standardized values of each evaluation index of the pipe network leakage point are obtained.

[0041] In step S12, real-time working condition data of the pipe network is acquired, a dynamic adjustment factor is calculated based on the real-time working condition data and the historical working condition data, the initial weight of the evaluation index is dynamically updated through the dynamic adjustment factor, the updated weight of the evaluation index is obtained, and sensitivity verification is performed on the updated weight of the evaluation index. The weight value that passes the sensitivity verification is applied as the final weight of the evaluation index.

[0042] Step S13: Use the final weights to perform weighted normalization on the normalized evaluation decision matrix to obtain the index values ​​of the evaluation indicators.

[0043] Step S14: Construct an ideal solution for the pipeline leakage point based on the index value, calculate the distance between the pipeline leakage point and the ideal solution, and obtain the relative proximity of the pipeline leakage point based on the calculation results.

[0044] Step S15: Obtain the relative proximity of each pipeline leakage point, and output the priority ranking result of the pipeline leakage points based on the relative proximity.

[0045] Specifically, historical operating data of the pipeline network is acquired. This data includes various assessment indicators related to leakage risk, such as: current status parameters of pipeline materials (e.g., real-time monitoring data on material aging and corrosion), leakage duration, historical leakage frequency, geographical location sensitivity (e.g., population density and commercial activity in the area where the current leakage point is located), water pressure fluctuations, pipe diameter (the diameter of the pipeline at the leakage point), burial depth (the depth to which the pipeline is buried underground), and the impact of surrounding facilities (e.g., road conditions around the leakage point, and the distribution of underground cables or other pipelines). By comprehensively covering the operational status of the pipeline network through multi-dimensional assessment indicators, a holistic consideration of pipeline leakage points can be achieved.

[0046] Based on pipeline network operating data, an evaluation decision matrix for pipeline leakage points is constructed. Assuming m leakage points are detected in the current pipeline network, and each leakage point corresponds to n evaluation indicators, the evaluation decision matrix can be an m x n matrix, expressed as: x = [ ], i=(1,2…m), j=(1,2…n); the value of each cell in the evaluation decision matrix. This represents the original value of the i-th leakage point on the j-th evaluation indicator. For example, if the first leakage point is located in a commercial area, has a pipe diameter of 1000mm, and the leakage duration is 2 hours, then in the evaluation decision matrix, the corresponding data values ​​should be filled into the cells of the evaluation indicators such as "geographical location sensitivity", "pipe diameter", and "leakage duration" for this leakage point.

[0047] Different evaluation indicators have different data units and numerical ranges. Normalizing the evaluation decision matrix converts the data values ​​of all evaluation indicators to a uniform numerical range (such as 0 to 1). Normalization can eliminate the influence of differences in the dimensions and numerical ranges between different evaluation indicators, so that all evaluation indicators are on the same order of magnitude, thereby obtaining standardized values ​​for each evaluation indicator of pipeline leakage points.

[0048] After obtaining standardized values ​​for various evaluation indicators related to pipeline leakage points, in step S12, real-time operating data of the pipeline network is acquired. A dynamic adjustment factor is calculated based on the real-time and historical operating data. This dynamic adjustment factor quantifies the degree of deviation between real-time and historical operating conditions and converts this deviation into the basis for adjusting the weights of the evaluation indicators, allowing the weights of the evaluation indicators to adaptively adjust with changes in the pipeline network's operating status. To further illustrate this, an example of calculating the dynamic adjustment factor for an evaluation indicator is provided below: For the water pressure fluctuation indicator, the historical average of this evaluation indicator is statistically analyzed from historical operating data. The real-time water pressure fluctuation data is compared with this historical average to calculate the real-time deviation rate. The real-time deviation rate is then compared with a preset value, and the minimum value is taken as the dynamic adjustment factor for the "water pressure fluctuation" indicator.

[0049] The weights of the evaluation indicators are dynamically updated by using initial weights and dynamic adjustment factors. The update logic is as follows: based on the initial weights, the initial weights are corrected by incorporating the dynamic adjustment factors to obtain the updated weights. In one embodiment, the weights can be updated using the following formula:

[0050] ;

[0051] In the formula, This represents the updated weight of the j-th evaluation indicator at time t. This represents the initial weight of the j-th evaluation indicator. This represents the dynamic adjustment factor of the j-th evaluation indicator at time t.

[0052] Furthermore, sensitivity verification is performed on the updated weights of the evaluation indicators to improve the robustness of the system. Sensitivity verification typically involves simulating small changes in weights (such as increasing or decreasing them by a certain percentage), recalculating the priority of leakage points, and observing the degree of change in the recalculated priority of leakage points. If the rate of change is within an acceptable range, the weight adjustment is considered reasonable; if the change is too large, the dynamic adjustment factor needs to be recalculated. Applying the weights that have passed sensitivity verification as the final weights of the evaluation indicator effectively avoids misjudgments caused by improper weight settings, thereby improving the robustness of the evaluation system.

[0053] Furthermore, the normalized evaluation decision matrix is ​​weighted and normalized using the final weights obtained in the preceding steps to obtain the index value of each evaluation indicator in the evaluation. The index value represents the magnitude of the influence of each evaluation indicator on the priority of pipeline leakage points. In one embodiment, the index value of the evaluation indicator can be calculated using the following formula:

[0054] ;

[0055] In the formula, This represents the value of the j-th evaluation index at the i-th pipeline leakage point. This represents the standardized value of the j-th evaluation index at the i-th pipeline leakage point.

[0056] After obtaining the values ​​of each evaluation index, in step S14, an ideal solution for the pipeline network leakage points is constructed based on the index values. The ideal solution provides a state reference for each actual leakage point. By calculating the difference between the actual leakage point and this state reference, the priority of the pipeline network leakage point is determined. The distance between the pipeline network leakage point and the ideal solution is calculated to quantify the difference. Further, the relative proximity of the pipeline network leakage point is calculated based on the distance between the pipeline network leakage point and the ideal solution. The relative proximity value ranges from 0 to 1; the closer the relative proximity is to 1, the higher the priority of the pipeline network leakage point; the closer the relative proximity is to 0, the lower the priority of the pipeline network leakage point.

[0057] Using the above method, the relative proximity of all leakage points in the pipeline network is calculated, and they are sorted from high to low relative proximity. The leakage point with the highest relative proximity is considered the most urgent leakage point and needs to be dealt with first; the leakage points with lower relative proximity are dealt with second.

[0058] Based on the above description, the pipeline network leakage point assessment method provided in this application calculates dynamic adjustment factors for assessment indicators using pipeline network operation data, adjusts the weights of each assessment indicator, and thus adjusts the impact of each assessment indicator on the priority of quantified pipeline network leakage points. This allows for timely response to real-time changes in pipeline network operating conditions, improving the accuracy of priority judgment for pipeline network leakage points. Furthermore, by constructing an ideal solution for pipeline network leakage points and obtaining the relative proximity of the leakage points based on the distance between the leakage points and the ideal solution, intelligent priority ranking of pipeline network leakage points is achieved. This improves the response sensitivity to high-risk leakage points, enabling maintenance personnel to prioritize and handle high-risk leakage points according to priority ranking, reducing resource waste and lowering the risk of secondary disasters.

[0059] As an optional implementation, in step S11, the evaluation decision matrix is ​​normalized using a vector normalization method. Based on the original values ​​of the evaluation indicators, the average decision matrix is ​​positively oriented to obtain standardized values ​​for the evaluation indicators. The vector normalization method correlates the original value of each evaluation indicator with the overall situation of the original values ​​of all leakage points under that evaluation indicator, obtaining the standardized value of each leakage point on the corresponding evaluation indicator. The vector normalization method can be expressed by the following formula:

[0060] ;

[0061] In the formula, This represents the standardized value of the j-th evaluation index at the i-th pipeline leakage point. represents the original value of the i-th leakage point on the j-th evaluation index, and m represents the number of leakage points in the pipeline network.

[0062] After obtaining the standardized values ​​of the evaluation metrics, as an optional implementation, the evaluation method also determines whether the evaluation metrics need to be updated in terms of weights through the following methods:

[0063] Determine whether the data fluctuation of the evaluation indicator exceeds the preset fluctuation range. If the data fluctuation of the evaluation indicator does not exceed the preset fluctuation range, the weight of the evaluation indicator will not be updated. If the data fluctuation of the evaluation indicator exceeds the preset fluctuation range, the dynamic adjustment factor corresponding to the evaluation indicator will be calculated to update the weight of the evaluation indicator.

[0064] Specifically, the preset fluctuation range is formulated based on historical operating data of the pipeline network over a long period of time, leakage risk control standards in the industry, and the experience of experts in the field, to ensure the rationality of the fluctuation judgment and avoid misjudgment due to differences in the attributes of the assessment indicators.

[0065] If the data fluctuation of the assessment indicator does not exceed the preset fluctuation range, it indicates that the current state of the assessment indicator is basically consistent with the historical norm. In this case, the weight of the assessment indicator is not updated to reduce management costs. When the data fluctuation exceeds the preset range, it indicates that the current state of the assessment indicator has deviated from the historical norm. At this time, a dynamic adjustment factor is calculated and the weight of the assessment indicator is updated. By changing the weight of the assessment indicator, its risk influence in the priority assessment of leakage points is strengthened, thereby enabling timely identification and priority handling of high-risk leakage points. This avoids the defect of traditional static weights not responding to changes in operating conditions and prevents assessment instability caused by oversensitivity, thus achieving precise weight management.

[0066] Furthermore, for evaluation metrics that require weight updates, as an optional implementation method, in step S12, the dynamic adjustment factor is calculated in the following way:

[0067] The standard deviation of the evaluation index is calculated based on real-time operating data, and the historical mean of the evaluation index is calculated based on historical operating data. The ratio between the standard deviation and the historical mean is then calculated. If the ratio is greater than 0.5, the dynamic adjustment factor is 0.5. If the ratio is less than 0.5, the dynamic adjustment factor is the ratio.

[0068] The deviation of the current operating condition from historical operating conditions is quantified by calculating the ratio between the standard deviation of the evaluation indicator and its historical mean. A larger ratio indicates a more abnormal data point for the current evaluation indicator, and a more significant fluctuation compared to the normal level. A preset threshold is introduced for comparison. If the ratio is greater than the preset threshold, the dynamic adjustment factor is equal to the preset threshold; if the ratio is less than the preset threshold, the dynamic adjustment factor is equal to the ratio.

[0069] The preset threshold can limit the amplitude. Even if a certain evaluation indicator fluctuates extremely abnormally, by limiting the maximum value of the dynamic adjustment factor to the preset threshold, it can prevent the weight from changing drastically due to a single extreme event or sensor false alarm, thus maintaining the stability and robustness of the evaluation system and avoiding abnormal jumps in the evaluation results.

[0070] In one embodiment, the dynamic adjustment factor for the evaluation index can be calculated using the following formula:

[0071] ;

[0072] In the formula, This represents the dynamic adjustment factor for the j-th evaluation indicator at time t. It represents the standard deviation of the j-th evaluation indicator at time t.

[0073] After updating the weights of the evaluation metrics, as an optional implementation method, such as... Figure 2 As shown, sensitivity verification is performed on the updated weights of the evaluation metrics, including:

[0074] The distance between the pipeline leakage point and the ideal solution is calculated based on the updated weights of the evaluation indicators. The first relative proximity of the pipeline leakage point is obtained based on the calculation results. A preset floating value is added to the updated weights of the evaluation indicators. The second relative proximity of the pipeline leakage point is recalculated based on the added weights. The rate of change between the first relative proximity and the second relative proximity is calculated.

[0075] If the rate of change is less than the preset change threshold, the updated weights are applied as the final weights of the evaluation indicators; if the rate of change is greater than the preset change threshold, the calculation of the dynamic adjustment factor is returned, and the weights of the evaluation indicators are updated again.

[0076] Specifically, based on the updated weights of the evaluation indicators, the first relative proximity of the pipeline leakage points is calculated. Then, a preset floating value is added to the updated weight values. The relative proximity is recalculated based on the weights after adding the preset floating value to obtain the second relative proximity of the pipeline leakage points. The rate of change between the first and second relative proximity is calculated. If this rate of change is less than a preset threshold, it indicates that the updated weights of the evaluation indicators have good stability, and these updated weights are applied as the final weights of the evaluation indicators. If the rate of change is greater than the preset threshold, it indicates that the weights of the evaluation indicators are too sensitive to small fluctuations, which may lead to unreliable ranking results. In this case, the evaluation system returns to recalculate the dynamic adjustment factor and re-updates the weights of the evaluation indicators to ensure the robustness of the evaluation method.

[0077] To further illustrate, the following is an example of sensitivity verification: Set a preset fluctuation value of ±10% and a preset change threshold of 5%. For the updated evaluation index weights, after adding the preset fluctuation value, if the rate of change between the first relative closeness and the second relative closeness is less than 5%, then the updated weights are applied as the final weights of the evaluation indexes; if the rate of change between the first relative closeness and the second relative closeness is greater than 5%, then the dynamic adjustment factor is recalculated, and the weights of the evaluation indexes are updated again.

[0078] After obtaining the final weights of the evaluation indicators, the standardized values ​​of the evaluation indicators are used to perform weighted normalization on the final weight pairs to obtain the indicator values. Based on these indicator values, an ideal solution for pipeline leakage points is constructed. As an optional implementation method, such as... Figure 3 As shown, the ideal solution includes positive ideal solutions and negative ideal solutions. Positive ideal solutions are vectors composed of the maximum values ​​of the evaluation indicators, and negative ideal solutions are vectors composed of the minimum values ​​of the evaluation indicators.

[0079] Calculate the distance between the leakage point in the pipeline network and the positive ideal solution, and the distance between the leakage point in the pipeline network and the negative ideal solution, respectively. Calculate the relative proximity based on the distance between the leakage point in the pipeline network and the positive ideal solution, and the distance between the leakage point in the pipeline network and the negative ideal solution.

[0080] Specifically, the positive ideal solution consists of the optimal value of each evaluation index, and the positive ideal solution represents the best leakage point state in the ideal way; the negative ideal solution consists of the worst value of each evaluation index, and the negative ideal solution represents the least ideal leakage point state.

[0081] In one embodiment, the ideal solution can be expressed by the following formula:

[0082] ;

[0083] In the formula, This represents the ideal solution. This represents the value of the j-th evaluation index at the i-th pipeline leakage point.

[0084] The negative ideal solution can be expressed by the following formula:

[0085] ;

[0086] In the formula, This represents a negative ideal solution. This represents the value of the j-th evaluation index at the i-th pipeline leakage point.

[0087] Calculate the distance between each leak point and both the positive and negative ideal solutions. The closer the leak point is to the positive ideal solution, the better its condition; the closer it is to the negative ideal solution, the worse its condition. Based on the distance calculations, calculate the relative proximity of each leak point. The greater the relative proximity, the higher the priority of addressing the leak point. Sort the leak points according to their relative proximity from largest to smallest, and output the priority ranking of the network leak points. Optionally, the priority ranking of the network leak points can also generate corresponding treatment plans.

[0088] In one embodiment, the relative proximity can be calculated using the following formula:

[0089] ;

[0090] In the formula, This represents the relative proximity of the i-th pipeline leakage point. This represents the distance between the i-th leakage point in the pipeline and the negative ideal solution. This represents the distance between the i-th leakage point in the pipeline and the ideal solution.

[0091] In one embodiment, the distance between the pipeline leakage point and the negative ideal solution can be calculated using the following formula:

[0092] ;

[0093] In the formula, This represents the distance between the i-th pipeline leakage point and the negative ideal solution, where n represents the number of evaluation indicators. This represents the value of the j-th evaluation index at the i-th pipeline leakage point. This represents the minimum value of the j-th index.

[0094] Furthermore, as an optional implementation, a penalty term is introduced when calculating the distance between the pipeline leakage point and the ideal solution. The penalty term is used to weight the impact of abnormal indicators on the priority of pipeline leakage points. Abnormal indicators include at least a sudden drop in pressure or a leakage alarm.

[0095] Specifically, sudden abnormal events in pipeline network operation, such as a sudden drop in water pressure at a certain point within a very short time, or receiving an automatic leakage alarm signal from that area, may indicate a serious accident such as a pipe burst. A penalty term is introduced into the distance calculation between the leakage point and the ideal solution. If a sudden event such as a pressure drop or leakage alarm is detected at a leakage point, the penalty term calculation for that leakage point is activated. By changing the distance between the leakage point and the ideal solution through the penalty term, the calculation result of the relative proximity of the pipeline network leakage point is affected. By introducing the penalty term mechanism, the evaluation system can proactively identify and respond to sudden abnormal events, improve the sensitivity to high-risk leakage points, and significantly enhance the sensitivity of priority ranking.

[0096] In one embodiment, the distance between the pipeline leakage point and the ideal solution can be calculated using the following formula:

[0097] ;

[0098] In the formula, Let represent the distance between the i-th pipeline leakage point and the ideal solution, and n represent the number of evaluation indicators. This represents the value of the j-th evaluation index at the i-th pipeline leakage point. This represents the maximum value of the j-th evaluation indicator. This indicates a penalty.

[0099] Based on the above description, the pipeline network leakage point assessment method provided in this application calculates dynamic adjustment factors for assessment indicators using pipeline network operation data, adjusts the weights of each assessment indicator, and thus adjusts the impact of each assessment indicator on the priority of quantified pipeline network leakage points. This allows for timely response to real-time changes in pipeline network operating conditions and improves the accuracy of prioritizing pipeline network leakage points. Furthermore, by constructing positive and negative ideal solutions for pipeline network leakage points and obtaining the relative proximity of the leakage points based on the distance between the leakage points and the positive and negative ideal solutions, intelligent priority ranking of pipeline network leakage points is achieved, significantly improving the accuracy of judging leakage point priority. Moreover, a penalty term mechanism is introduced when calculating the distance between the pipeline network leakage point and the positive ideal solution, enhancing the response sensitivity to high-risk leakage points. This enables maintenance personnel to prioritize and handle high-risk leakage points based on priority ranking, reducing resource waste and lowering the risk of secondary disasters.

[0100] Secondly, based on the same inventive concept, this application also provides a pipeline leakage point assessment system. This assessment system applies the pipeline leakage point assessment method described above. The assessment system can adaptively adjust the weight of each assessment index according to real-time operating conditions, thereby improving the accuracy of priority judgment of pipeline leakage points.

[0101] It is understood that the term "exemplary" as used herein means "as an example, illustration, or description." Any embodiment described as "exemplary" is not necessarily preferred or superior to other embodiments and / or does not exclude features in combination with other embodiments. It should be understood that certain features of this application described in the context of a single embodiment for clarity may also be provided in combination in a single embodiment. Conversely, various features of this application described in the context of a single embodiment for clarity may also be provided individually or in any suitable combination or as part of any other described embodiment of this application.

[0102] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0103] The above-disclosed embodiments are merely preferred embodiments of this application, but are not intended to limit the scope of this application. Those skilled in the art will understand that any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and scope of this application and the appended claims are equivalent substitutions and still fall within the scope of this application.

Claims

1. A method of assessing a leak point in a pipe network, characterised by, The method comprises the following steps: obtaining historical working condition data of the pipe network, constructing an evaluation decision matrix about the pipe network leakage points, the evaluation decision matrix comprising original values of the pipe network leakage points on each evaluation index, performing normalization processing on the evaluation decision matrix to obtain standardized values of each evaluation index about the pipe network leakage points; obtaining real-time working condition data of the pipe network, calculating a dynamic adjustment factor based on the real-time working condition data and the historical working condition data, dynamically updating initial weights of the evaluation indexes through the dynamic adjustment factor to obtain updated weights of the evaluation indexes, and performing sensitivity verification on the updated weights of the evaluation indexes, applying the weights passing the sensitivity verification as final weights of the evaluation indexes; performing weighted normalization processing on the normalized evaluation decision matrix by using the final weights to obtain index values of the evaluation indexes; constructing an ideal solution of the pipe network leakage points based on the index values, calculating distances between the pipe network leakage points and the ideal solution, and obtaining relative closeness degrees of the pipe network leakage points according to the calculation results; obtaining the relative closeness degrees of each pipe network leakage point, and outputting a priority ranking result of the pipe network leakage points based on the sizes of the relative closeness degrees.

2. The evaluation method according to claim 1, characterized in that The ideal solution comprises a positive ideal solution and a negative ideal solution, the positive ideal solution is a vector composed of maximum values of the index values of the evaluation indexes, and the negative ideal solution is a vector composed of minimum values of the index values of the evaluation indexes; distances between the pipe network leakage points and the positive ideal solution and distances between the pipe network leakage points and the negative ideal solution are calculated respectively, and the relative closeness degrees are calculated according to the distances between the pipe network leakage points and the positive ideal solution and the distances between the pipe network leakage points and the negative ideal solution.

3. The evaluation method according to claim 2, characterized in that The distance between the pipe network leakage point and the negative ideal solution is calculated by the following formula: ； In the formula, represents the distance between the i-th pipe network leakage point and the negative ideal solution, n represents the number of evaluation indexes, represents the index value of the j-th evaluation index of the i-th pipe network leakage point, represents the minimum value of the j-th index.

4. The evaluation method according to claim 2, characterized in that The evaluation method further comprises: a penalty term is introduced when calculating the distance between the pipe network leakage point and the positive ideal solution, the penalty term is used to weight the influence of an abnormal index on the priority of the pipe network leakage point, and the abnormal index at least comprises a pressure sudden drop or a leakage alarm.

5. The evaluation method according to claim 4, characterized in that The distance between the pipe network leakage point and the positive ideal solution is calculated by the following formula: ； In the formula, represents the distance between the i-th pipe network leakage point and the positive ideal solution, n represents the number of evaluation indexes, represents the index value of the j-th evaluation index of the i-th pipe network leakage point, represents the maximum value of the j-th evaluation index, represents the penalty term.

6. The evaluation method according to claim 1, characterized in that The sensitivity verification on the updated weights of the evaluation indexes comprises: distances between the pipe network leakage points and the ideal solution are calculated based on the updated weights of the evaluation indexes, first relative closeness degrees of the pipe network leakage points are obtained based on the calculation results, preset floating values are superimposed on the updated weights of the evaluation indexes, second relative closeness degrees of the pipe network leakage points are recalculated based on the weights superimposed with the preset floating values, and a change rate between the first relative closeness degrees and the second relative closeness degrees is calculated; if the change rate is less than a preset change threshold, the updated weights are applied as the final weights of the evaluation indexes; if the change rate is greater than the preset change threshold, the dynamic adjustment factor is returned to be calculated, and the weights of the evaluation indexes are updated again.

7. The evaluation method according to claim 1, characterized in that The evaluation method further comprises: determining whether the data fluctuation of the evaluation index exceeds a preset fluctuation range, and if the data fluctuation of the evaluation index does not exceed the preset fluctuation range, not updating the weight of the evaluation index; if the data fluctuation of the evaluation index exceeds the preset fluctuation range, calculating the dynamic adjustment factor corresponding to the evaluation index to update the weight of the evaluation index.

8. The evaluation method according to claim 1, characterized in that The calculation of the dynamic adjustment factor based on the real-time working condition data and the historical working condition data comprises: calculating the standard deviation of the evaluation index based on the real-time working condition data, calculating the historical mean of the evaluation index based on the historical working condition data, and calculating the ratio between the standard deviation and the historical mean; if the ratio is greater than 0.5, the dynamic adjustment factor is 0.5; if the ratio is less than 0.5, the dynamic adjustment factor is the ratio.

9. The evaluation method according to claim 1, characterized in that The evaluation method further comprises: The normalization processing adopts a vector normalization method, and the evaluation decision matrix is subjected to positive index processing based on the original value of the evaluation index to obtain the standardized value of the evaluation index.

10. A system for assessing leakage points in a pipeline network, characterized in that, The evaluation system applies the evaluation method of the leakage point of the pipe network as claimed in any one of claims 1 to 9.

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