A water affairs abnormal event early warning method and system

Abstract

The present invention discloses a water supply abnormal event early warning method and system, which relates to the field of water supply abnormality early warning technology, including a preset defect level generation module, a defect sequence number establishment module, an early warning level acquisition and judgment module, a historical defect data acquisition module, a transfer frequency acquisition module, a defect water pressure interval acquisition module and a predicted defect sequence number marking module. By analyzing the water pressure data of each node in the water supply network of the water supply station, combining historical defect data and real-time monitoring information, it is possible to predict in advance the most likely future defect state of the water supply network of the water supply station and the development trend of abnormal events, realize active early warning and trend prediction of abnormal events in the pipeline network, thereby improving the accuracy and response efficiency of water management, providing operation and maintenance personnel with sufficient time to take preventive measures and reduce losses, providing water management personnel with a scientific decision-making basis, and helping to take measures in time to prevent and deal with water accidents and ensure water supply safety.

Description

Technical Field

[0001] The present invention belongs to the technical field of water affairs abnormality early warning, and in particular relates to a water affairs abnormality event early warning method and system. Background Art

[0002] In the field of water management, the stable operation of water supply networks is of great significance to ensuring the lives of residents and industrial production. During the actual operation of water supply networks, various factors such as pipeline aging and blockage may cause abnormal water pressure, thereby affecting the water supply quality and system safety, causing safety hazards in the use of water supply networks. Traditional water monitoring systems mainly rely on regular inspections and manual analysis, which have certain limitations.

[0003] Patent publication number CN116631162A discloses a water equipment abnormality warning method and device based on digital twins. The method includes obtaining product data of each water equipment, constructing a digital twin corresponding to each water equipment based on the product data; establishing a data channel between the water equipment and the digital twin; when there is target dynamic data reaching a first warning threshold and the change trend of the target dynamic data approaches a second warning threshold, determining the calculation result of the target digital twin, and sending a warning message to the target digital twin corresponding to the object to be notified. When the present application determines that a certain dynamic data has reached the first warning threshold and is still changing towards the second warning threshold, it will send a warning to the maintenance personnel and surrounding residents in advance, so that the surrounding residents can avoid passing through the corresponding dangerous area, and the maintenance personnel can go to the corresponding place for inspection and maintenance. It can timely discover the abnormality of the water equipment and notify the maintenance personnel to handle it. The abnormality warning processing efficiency is high and no additional manpower costs are consumed.

[0004] However, when monitoring the water supply network of a water supply station, abnormal water pressure defects may occur simultaneously in multiple network management nodes during operation. If these defects are not discovered and handled in time, they may turn into serious water pressure abnormal defects, causing huge economic losses and social impacts. However, traditional water monitoring methods mainly rely on manual inspections and regular maintenance, and cannot predict future defects based on the real-time water pressure information of the water supply network of the water supply station. It is difficult to detect potential defects in time and provide operation and maintenance personnel with sufficient time to take preventive measures. Based on this, a water abnormal event early warning method and system are proposed. Summary of the Invention

[0005] The purpose of the present invention is to provide a water supply abnormal event early warning method and system, which solves the technical problem that traditional water supply monitoring methods cannot predict future defects based on the real-time water pressure information of the water supply network of the water supply station, and it is difficult to detect potential defects in a timely manner.

[0006] A water affairs abnormal event early warning system, comprising:

[0007] A preset defect level generation module generates multiple preset defect levels and corresponding defect labels;

[0008] The defect serial number establishment module numbers the network management nodes and generates different defect serial numbers for the water supply network of the water supply station based on the defect labels of the preset defect levels;

[0009] The warning level acquisition and determination module calculates the defect coefficient of each defect sequence number according to the number of each defect number in each defect sequence number, and obtains the warning level corresponding to each defect sequence number according to the defect coefficient;

[0010] The historical defect data acquisition module acquires multiple historical defect data of the water supply network of the water supply station. The historical defect data includes the timestamp of each time a defect is detected in the water supply network of the water supply station and the test water pressure data;

[0011] The transfer frequency acquisition module analyzes the timestamp and water pressure data of each defect detection to obtain the transfer sequence number corresponding to each defect sequence number and the transfer frequency corresponding to each transfer sequence number;

[0012] The defect water pressure interval acquisition module analyzes the water pressure data detected each time a defect is detected to obtain the defect water pressure interval corresponding to each defect serial number;

[0013] The predicted defect sequence number marking module obtains the real-time defect sequence number of the water supply network of the water station according to the real-time water pressure value of each network management node. Combined with the transfer frequency of each transfer sequence number corresponding to the real-time defect sequence number and the defect water pressure range, it obtains the predicted defect sequence number and corresponding warning level corresponding to the water supply network of the water station and outputs them.

[0014] As a further solution of the present invention, a specific method of generating multiple preset defect levels and corresponding defect labels is as follows:

[0015] The defect level when the absolute value of the difference between the water pressure value and the standard water pressure is less than the preset value threshold Y1 is marked as the first defect level, the defect level when the absolute value of the difference between the water pressure and the standard water pressure is greater than or equal to the preset value threshold Y1 and less than the preset value threshold Y2 is marked as the second defect level, and the defect level when the absolute value of the difference between the water pressure and the standard water pressure is greater than or equal to the preset value threshold Y3 is marked as the third defect level. At the same time, the defect numbers of different preset defect levels are marked as n, where n refers to different defect levels, n=1, 2, 3, and Y3>Y2>Y1>0.

[0016] As a further solution of the present invention, the specific method of generating different defect serial numbers of the water supply network of the water station is:

[0017] Obtain the node number j of each network management node, combine the node number j with different defect numbers n, and generate defect numbers j•n corresponding to different network management nodes. Sort the defect numbers j•n corresponding to each network management node in ascending order according to the numerical value corresponding to the node number j, and use the symbol ※ to separate every two defect numbers j•n, so as to obtain different defect serial numbers Bc corresponding to the water supply network of the water station, where j=1, 2, ..., a, a refers to the total number of network management nodes, a is a positive integer, and a satisfies a≥2, c refers to different defect serial numbers, and the value of c is 1, 2, 3, ..., e, where e refers to the number of defect serial numbers, and the specific value of the number e of defect serial numbers Ba is e=ba.

[0018] As a further solution of the present invention, the specific method of obtaining the warning level corresponding to each defect serial number is as follows:

[0019] The number of each defect number in each defect sequence number is marked as R1j, R2j, and R3j, respectively, where R1j is the number of network management nodes with a first-level defect level in each defect number, R2j is the number of network management nodes with a second-level defect level in each defect number, and R3j is the number of network management nodes with a third-level defect level in each defect number. The sum of the products of R1j, R2j, and R3j with preset proportional coefficients β1, β2, and β3, respectively, is taken as the defect coefficient Xj corresponding to each defect sequence number, where 1=β1+β2+β3 and β1<β2<β3. The warning level of a defect sequence number whose defect coefficient Xj is less than a preset threshold Y4 is marked as a mild warning sequence number, the warning level of a defect sequence number whose defect coefficient Xj is greater than or equal to a preset threshold Y5 and less than a preset threshold Y6 is marked as a moderate warning sequence number, and the warning level of a defect sequence number whose defect coefficient Xj is greater than or equal to a preset threshold Y6 is marked as a severe warning sequence number, where Y6>Y5>Y4>0.

[0020] As a further solution of the present invention, the specific method of obtaining the transfer sequence number corresponding to each defect sequence number and the transfer frequency corresponding to each transfer sequence number is as follows:

[0021] Obtain the timestamp of each time a defect is detected from multiple historical defect data, thereby obtaining multiple defect timestamps and the defect sequence number corresponding to each defect timestamp for the entire water supply network;

[0022] S1: Randomly select a defective serial number from all defective serial numbers without replacement as the analysis serial number;

[0023] S2: Obtain each defect sequence number whose defect timestamp is after the defect timestamp of the analysis sequence number, and mark them as the transfer sequence number corresponding to the analysis sequence number, obtain the total number of times A that the analysis sequence number appears in multiple historical defect data, and at the same time mark the number of times each transfer sequence number of the analysis sequence number appears in the historical defect data as Bf, where f refers to different transfer sequence numbers corresponding to the analysis sequence number, f=1, 2,..., h, h is a positive integer, and h refers to the number of transfer sequence numbers corresponding to the analysis sequence number, calculate the ratio between the number of times Bf that each transfer sequence number appears in multiple historical defect data and the total number of times A that the analysis sequence number appears in the historical defect data, and mark them as the transfer frequency of different transfer sequence numbers corresponding to the analysis sequence number as Ef, and bind them to the analysis sequence number to generate a transfer sequence frequency comparison table corresponding to the analysis sequence number.

[0024] As a further solution of the present invention, the specific method of obtaining the defect water pressure interval corresponding to each defect serial number is:

[0025] S01: Select the same defective serial number as that in step S1 from among the defective serial numbers as the target serial number;

[0026] Obtain the mean of the detection water pressure data corresponding to each network management node each time the defect sequence number is the target sequence number from multiple historical defect data, and use it as the defect water pressure Qr corresponding to the water supply network of the water supply station each time the defect sequence number is the target sequence number. Calculate the discrete value U and mean Qp of the defect water pressure Qr, use the sum of the mean Qp and the discrete value U as the upper limit of the defect water pressure interval of the target sequence number, and use the difference between the mean Qp and the discrete value U as the lower limit of the defect water pressure interval of the target sequence number, and then establish the defect water pressure interval K1[Qp-U, Qp+U] of the target sequence number, where r is a different defect water pressure, r=1, 2, ..., g, g refers to the total number of defect water pressures, g is a positive integer, and g satisfies g≥2;

[0027] S02: Repeat the above step S01 to obtain the defect water pressure range Kc corresponding to each defect serial number.

[0028] As a further solution of the present invention, the specific method of obtaining the predicted defect serial number and the corresponding warning level corresponding to the water supply network of the water station is:

[0029] According to the real-time water pressure values of each network management node in the water supply network of the water supply station, the real-time defect sequence number of the water supply network of the water supply station is obtained, and the average of the real-time water pressures of each network management node in the water supply network of the water supply station is used as the calibration water pressure. The calibration water pressure and the defect water pressure intervals corresponding to each transfer sequence number of the real-time defect sequence number are compared and analyzed one by one to obtain the selected coefficients corresponding to each transfer sequence number. For the transfer sequence number whose calibration water pressure is within the corresponding defect water pressure interval, the product between its corresponding transfer frequency and the preset value is used as the selected coefficient of the corresponding transfer sequence number. For the transfer sequence number whose calibration water pressure is not within the corresponding defect water pressure interval, its transfer frequency is directly used as the selected coefficient of the corresponding transfer sequence number. The transfer sequence number corresponding to the maximum selected coefficient is marked as the predicted defect sequence number and output, and the warning level corresponding to the predicted defect sequence number is output synchronously.

[0030] A water affairs abnormal event early warning method, which is implemented by a water affairs abnormal event early warning system, specifically comprises the following steps:

[0031] Step 1: Generate multiple preset defect levels and corresponding defect labels;

[0032] Step 2: Number the network management nodes and generate different defect serial numbers for the water supply network of the water supply station based on the defect labels of the preset defect levels;

[0033] Step 3: Calculate the defect coefficient of each defect sequence number based on the number of each defect number in each defect sequence number, and obtain the warning level corresponding to each defect sequence number based on the defect coefficient;

[0034] Step 4: Obtain the timestamp and water pressure data of each time a defect is detected in the water supply network of the water supply station;

[0035] Step 5: Analyze the timestamp and water pressure data of each defect detection to obtain the transfer sequence number corresponding to each defect sequence number and the transfer frequency corresponding to each transfer sequence number;

[0036] Step 6: Analyze the water pressure data each time a defect is detected to obtain the defect water pressure range corresponding to each defect serial number;

[0037] Step 7: Obtain the real-time defect sequence number of the water supply network of the water station based on the real-time water pressure value of each network management node. Combined with the transfer frequency of each transfer sequence number corresponding to the real-time defect sequence number and the defect water pressure range, obtain the predicted defect sequence number and corresponding warning level of the water supply network of the water station and output them.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention realizes timely discovery of water anomalies, graded warning and future defect prediction of water supply anomalies by analyzing water pressure data of each node in the water supply network of the water station, combining historical defect data and real-time monitoring information. It performs defect prediction in combination with real-time water pressure information, and can predict in advance the most likely defect state of the water supply network of the water station in the future, as well as the development trend of abnormal events, and realize active warning and trend prediction of abnormal events in the network, thereby improving the accuracy and response efficiency of water management, providing operation and maintenance personnel with sufficient time to take preventive measures and reduce losses, and providing water management personnel with a scientific decision-making basis, which helps to take measures in time to prevent and deal with water accidents and ensure water supply safety. While improving the efficiency and accuracy of water management, it has significant economic and social benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Schematic diagram of the system framework structure of the present invention;

[0041] Figure 2 Schematic diagram of the framework structure of the method of the present invention. DETAILED DESCRIPTION

[0042] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0043] Example 1: Please refer to Figure 1 ,This application provides a water affairs abnormal event early warning system, including;

[0044] The preset defect level generation module is used to classify the defect levels according to the water pressure data of each network management node, and then generate multiple preset defect levels. The specific method is as follows:

[0045] The water pressure data of each network management node is obtained in real time through the sensor network. The defect level when the absolute value of the difference between the water pressure value and the standard water pressure is less than the preset threshold value Y1 is marked as the first defect level. The defect level when the absolute value of the difference between the real-time water pressure and the standard water pressure is greater than or equal to the preset threshold value Y1 and less than the preset threshold value Y2 is marked as the second defect level. The defect level when the absolute value of the difference between the real-time water pressure and the standard water pressure is greater than or equal to the preset threshold value Y3 is marked as the third defect level.

[0046] According to the preset defect level, the defect number corresponding to the different preset defect levels is marked as n, where n refers to different defect levels, n=1, 2, 3, that is, the number of preset defect levels is three;

[0047] It should be noted that the higher the defect level, the higher the corresponding defect severity. That is, the defect severity of the third-level defect is higher than the second-level defect, and the defect severity of the second-level defect is higher than the first-level defect.

[0048] The specific values of the preset thresholds Y1, Y2, and Y3 are all formulated by relevant staff based on actual needs, and Y3>Y2>Y1>0. At the same time, the standard water pressure is the water pressure value corresponding to the water supply network of the water supply station working under standard conditions. The specific values are obtained by relevant personnel from the water supply network operation manual of the water supply station, so no further details are given here.

[0049] The defect serial number establishment module numbers each network management node in the water supply network of the water supply station, and obtains different defect serial numbers corresponding to the water supply network of the water supply station according to the preset defect level. The specific method is as follows:

[0050] Obtain the node number j corresponding to each network management node, where j represents a different network management node and also a different node number, j = 1, 2, ..., a, where a represents the total number of network management nodes, a is a positive integer, and a satisfies a ≥ 2;

[0051] Combine the node number j with different defect numbers to generate defect numbers j•n corresponding to different network management nodes;

[0052] The defect numbers j•n corresponding to each network management node are sorted in ascending order according to the value corresponding to the node number j. A ※ symbol is used between each two defect numbers j•n to obtain the different defect sequence numbers Bc corresponding to the water supply network of the water supply station. c represents different defect sequence numbers. The values of c are 1, 2, 3, ..., e, where e represents the number of defect sequence numbers.

[0053] The specific value of the number e of defect serial number Ba is, e=b a ;

[0054] For example, there are three different network management nodes, that is, m=3, and the corresponding node numbers j are 1, 2, and 3 respectively; there are two different defect numbers, that is, k=2, which are 1 and 2 respectively. The defect numbers j•n corresponding to the different network management nodes are 1•1, 1•2, 2•1, 2•2, 3•1, and 3•2 respectively. According to the numerical value corresponding to the node number j, they are sorted in ascending order. The resulting multiple defect sequence numbers Ba are as follows: 1•1※2•1※3•1, 1•1※2•1※3•2, 1•1※2•2※3•1, 1•1※2•2※3•2, 1•2※2•2※3•1, 1•2※2•2※3•2, 1•2※2•1※3•1, 1•2※2•1※3•2, and so on.

[0055] Sort the defect sequence number Bc in ascending order according to the corresponding values of the node number j and the defect number n to generate a defect comparison list;

[0056] The warning level acquisition and determination module calculates the defect coefficient corresponding to each defect serial number based on the number of defect labels in the defect numbers that make up each defect serial number, and obtains the warning level corresponding to each defect serial number based on the defect coefficient. The specific method is as follows:

[0057] The number of defect numbers corresponding to each defect number in the defect number that constitutes each defect sequence number is marked as R1j, R2j, and R3j, respectively, where R1j is the number of network management nodes with a level 1 defect in each defect number, R2j is the number of network management nodes with a level 2 defect in each defect number, and R3j is the number of network management nodes with a level 3 defect in each defect number.

[0058] The defect coefficient Xj corresponding to each defect serial number is calculated using the formula: Xj=R1j×β1+R2j×β2+R3j×β3, where β1, β2, and β3 are preset proportional coefficients, 1=β1+β2+β3, and β1<β2<β3;

[0059] The warning level of the defect serial number whose defect coefficient Xj is less than the preset threshold value Y4 is marked as a mild warning serial number, the warning level of the defect serial number whose defect coefficient Xj is greater than or equal to the preset threshold value Y5 and less than the preset threshold value Y6 is marked as a moderate warning serial number, and the warning level of the defect serial number whose defect coefficient Xj is greater than or equal to the preset threshold value Y6 is marked as a severe warning serial number;

[0060] It should be noted that the warning level of the severe warning sequence number is higher than that of the moderate warning sequence number, and the warning level of the moderate warning sequence number is higher than that of the mild warning sequence number. Among them, the specific values of the preset thresholds Y4, Y5 and Y6 are formulated by relevant staff according to actual needs, and Y6>Y5>Y4>0;

[0061] The historical defect data acquisition module acquires multiple historical defect data of the water supply network of the water supply station. The historical defect data includes the accurate timestamp of each defect detection and the test water pressure data;

[0062] The transfer frequency acquisition module analyzes multiple historical defect data of the water supply network of the water supply station to obtain the transfer sequence number corresponding to each defect sequence number and the transfer frequency corresponding to the transfer sequence number of each defect sequence number. The specific method is as follows:

[0063] Obtain the exact timestamp of each defect detection from multiple historical defect data, and then obtain multiple defect timestamps. It should be noted that when recording the defect timestamp each time a defect is detected, the timestamp must be accurate to the second level to facilitate subsequent analysis of how the defect changes over time. For example, if a defect is detected on a node at 10:30:00 on March 1, 2025, this time is recorded as the defect timestamp of the corresponding node.

[0064] At the same time, the defect sequence number corresponding to each defect time stamp of the entire water supply network is obtained from multiple historical defect data. As described in the defect sequence number establishment module, the defect sequence number is generated by combining the node number and the defect level and sorted according to specific rules, for example, the defect sequence number is "1•2※2•1※3•1";

[0065] S1: Randomly select a defective serial number from all defective serial numbers without replacement as the analysis serial number;

[0066] S2: Obtain each defect sequence number whose defect timestamp is after the defect timestamp of the analysis sequence number, and mark them as the transfer sequence number corresponding to the analysis sequence number;

[0067] Obtain the total number of times A that the analysis sequence number appears in multiple historical defect data, and mark the number of times each transfer sequence number of the analysis sequence number appears in the historical defect data as Bf, where f refers to the different transfer sequence numbers corresponding to the analysis sequence number, f = 1, 2, ..., h, h is a positive integer, and h refers to the number of transfer sequence numbers corresponding to the analysis sequence number;

[0068] The ratios of the number of times each transfer sequence number appears in multiple historical defect data (Bf) to the total number of times the analysis sequence number appears in the historical defect data (A) are respectively marked as the transfer frequencies of different transfer sequence numbers corresponding to the analysis sequence number (Ef), and the frequencies are bound to the analysis sequence number to generate a transfer sequence frequency comparison table corresponding to the analysis sequence number.

[0069] S3: Repeat steps S1-S2 above to obtain a transfer sequence frequency comparison table corresponding to each defect sequence number;

[0070] By analyzing historical defect data, the transfer frequency between each defect serial number is determined, providing a basis for predicting future defects.

[0071] The predicted defect sequence number marking module is used to obtain the real-time defect sequence number of the water supply network of the water supply station based on the real-time water pressure values of each network management node in the water supply network of the water supply station, and obtain the predicted defect sequence number corresponding to the water supply network of the water supply station based on the transfer sequence frequency comparison table corresponding to the real-time defect sequence number of the water supply network of the water supply station, and output it;

[0072] The transfer sequence number corresponding to the maximum transfer frequency in the transfer sequence frequency comparison table corresponding to the real-time defect sequence number is marked as the predicted defect sequence number and output, and the warning level corresponding to the predicted defect sequence number is output synchronously;

[0073] Generate real-time defect serial numbers based on real-time water pressure data, and predict future defect serial numbers and their warning levels based on transfer frequency and output them.

[0074] Embodiment 2: As the embodiment 2 of the present invention, when the present application is specifically implemented, compared with the embodiment 1, the technical solution of this embodiment differs from that of the embodiment 1 only in that this embodiment further includes a defect water pressure interval acquisition module;

[0075] The defect water pressure interval acquisition module obtains and analyzes the test water pressure data corresponding to each defect serial number from multiple historical defect data of the water supply network of the water supply station, and obtains the defect water pressure interval corresponding to each defect serial number based on the analysis results. The specific method is as follows:

[0076] S01: Select the same defective serial number as that in step S1 from among the defective serial numbers as the target serial number;

[0077] Obtain the mean of the detected water pressure data corresponding to each network management node each time the defect sequence number is the target sequence number from multiple historical defect data, and use it as the defect water pressure Qr corresponding to the water supply network of the water supply station each time the defect sequence number is the target sequence number, where r is a different defect water pressure, r = 1, 2, ..., g, g refers to the total number of defect water pressures, g is a positive integer, and g satisfies g ≥ 2;

[0078] By formula , calculate the discrete value U of the defect water pressure Qr, where Qp is the mean value of Qr, and Qq is any one of Qr; the sum of the mean value Qp and the discrete value U is used as the upper limit of the defect water pressure interval of the target serial number, and the difference between the mean value Qp and the discrete value U is used as the lower limit of the defect water pressure interval of the target serial number, thereby establishing the defect water pressure interval K1[Qp-U,Qp+U] of the target serial number;

[0079] S02: Repeat the above step S01 to obtain the defect water pressure range Kc corresponding to each defect serial number;

[0080] Based on Example 1, a defect water pressure interval acquisition module is added to analyze historical data to obtain the water pressure interval corresponding to each defect serial number, quantify the correlation between the defect and the specific water pressure range, and combine the real-time water pressure to more accurately predict future defects, enhance the physical basis of the prediction, and improve the accuracy of the early warning.

[0081] Example 3: As Example 3 of the present invention, when this application is specifically implemented, compared with Example 1 and Example 2, the technical solution of this embodiment is to combine the solutions of Example 1 and Example 2. The difference between the technical solution of this embodiment and Example 1 and Example 2 is only that in this embodiment, the predicted defect sequence number corresponding to the real-time defect sequence number is determined in combination with the real-time water of each network management node in the water supply network of the water station. The specific method is as follows:

[0082] The real-time water pressure of each network management node in the water supply network of the water supply station is obtained in real time, and its mean is obtained as the calibration water pressure BD. The transfer sequence frequency comparison table of the real-time defect sequence number of the water supply network of the water supply station is obtained, and the defect water pressure range of each transfer sequence number corresponding to the real-time defect sequence number is obtained. The calibration water pressure BD is compared with the defect water pressure range of each transfer sequence number one by one to obtain the selected coefficient corresponding to each transfer sequence number. According to the selected coefficient, the predicted defect sequence number corresponding to the water supply network of the water supply station is marked and output. The specific method is as follows:

[0083] The calibration water pressure BD is compared and analyzed with the defective water pressure interval of each transfer sequence number one by one. For the transfer sequence number whose calibration water pressure BD is within the corresponding defective water pressure interval, the product of its corresponding transfer frequency and the preset value Y7 is used as the selection coefficient of the corresponding transfer sequence number. For the transfer sequence number whose calibration water pressure BD is not within the corresponding defective water pressure interval, its transfer frequency is directly used as the selection coefficient of the corresponding transfer sequence number. The transfer sequence number corresponding to the maximum selected coefficient is marked as the predicted defective sequence number and output. At the same time, the warning level corresponding to the predicted defective sequence number is output synchronously. Among them, the specific value of the preset value Y7 is formulated by relevant staff according to actual needs, and 1.5>Y7>1;

[0084] Calculate the transfer frequency of defect serial numbers based on historical data and combine it with real-time water pressure information to predict defects. This can predict the development trend of abnormal events in advance, providing operation and maintenance personnel with sufficient time to take preventive measures and reduce losses.

[0085] By analyzing the water pressure data of each node in the water supply network of the water station, combining historical defect data and real-time monitoring information, timely discovery of water anomalies, graded warning and future defect prediction are achieved. Defect prediction combined with real-time water pressure information can predict in advance the most likely future defect status of the water supply network of the water station, as well as the development trend of abnormal events, and realize active warning and trend prediction of abnormal events in the network, thereby improving the accuracy and response efficiency of water management, providing operation and maintenance personnel with sufficient time to take preventive measures and reduce losses, and providing water management personnel with a scientific decision-making basis, which helps to take timely measures to prevent and deal with water accidents and ensure water supply safety. This solution has significant economic and social benefits while improving the efficiency and accuracy of water management.

[0086] Example 4: Please refer to Figure 2 As shown, as a fourth embodiment of the present invention, a water affairs abnormal event early warning method is further provided. The method is implemented by the aforementioned water affairs abnormal event early warning system, and specifically includes the following steps:

[0087] Step 1: Generate multiple preset defect levels and corresponding defect labels;

[0088] Step 2: Number the network management nodes and generate different defect serial numbers for the water supply network of the water supply station based on the defect labels of the preset defect levels;

[0089] Step 3: Calculate the defect coefficient of each defect sequence number based on the number of each defect number in each defect sequence number, and obtain the warning level corresponding to each defect sequence number based on the defect coefficient;

[0090] Step 4: Obtain the timestamp and water pressure data of each time a defect is detected in the water supply network of the water supply station;

[0091] Step 5: Analyze the timestamp and water pressure data of each defect detection to obtain the transfer sequence number corresponding to each defect sequence number and the transfer frequency corresponding to each transfer sequence number;

[0092] Step 6: Analyze the water pressure data each time a defect is detected to obtain the defect water pressure range corresponding to each defect serial number;

[0093] Step 7: Obtain the real-time defect sequence number of the water supply network of the water station based on the real-time water pressure value of each network management node. Combined with the transfer frequency of each transfer sequence number corresponding to the real-time defect sequence number and the defect water pressure range, obtain the predicted defect sequence number and corresponding warning level of the water supply network of the water station and output them.

[0094] Example 5: As Example 5 of the present invention, when this application is specifically implemented, compared with Example 1, Example 2, Example 3 and Example 4, the technical solution of this embodiment is to combine and implement the solutions of the above-mentioned Example 1, Example 2, Example 3 and Example 4.

[0095] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0096] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A water affairs abnormal event early warning system, characterized in that: include; A preset defect level generation module is configured to mark a defect level where the absolute value of the difference between the water pressure value and the standard water pressure is less than a preset threshold value Y1 as a first-level defect level, mark a defect level where the absolute value of the difference between the water pressure value and the standard water pressure is greater than or equal to the preset threshold value Y1 and less than a preset threshold value Y2 as a second-level defect level, and mark a defect level where the absolute value of the difference between the water pressure value and the standard water pressure is greater than or equal to the preset threshold value Y3 as a third-level defect level, thereby generating multiple preset defect levels and corresponding defect labels n, where n=1, 2, and 3, and Y3>Y2>Y1>0; The defect sequence number establishment module obtains the node number j of each network management node, combines the node number j with different defect numbers n, generates defect numbers j·n corresponding to different network management nodes, and sorts the defect numbers j·n corresponding to each network management node in ascending order according to the numerical value corresponding to the node number j, and uses the symbol ※ to separate every two defect numbers j·n, thereby obtaining different defect sequence numbers Bc corresponding to the water supply network of the water station, where j=1, 2, ..., a, a refers to the total number of network management nodes, a is a positive integer, and a satisfies a≥2, c refers to different defect sequence numbers, and the value of c is 1, 2, 3..., e, where e refers to the number of defect sequence numbers, and the specific value of the number e of defect sequence numbers Ba is e=b a ; The warning level acquisition and determination module marks the number of each defect number in each defect sequence number as R1j, R2j, and R3j, respectively, where R1j is the number of network management nodes with a first-level defect level in each defect number, R2j is the number of network management nodes with a second-level defect level in each defect number, and R3j is the number of network management nodes with a third-level defect level in each defect number. The sum of the products of R1j, R2j, and R3j with preset proportional coefficients β1, β2, and β3, respectively, is used as the defect coefficient Xj corresponding to each defect sequence number, where 1 = β1 + β2 + β3 and β1 < β2 < β3. The warning level corresponding to each defect sequence number is obtained based on the defect coefficient. The historical defect data acquisition module acquires multiple historical defect data of the water supply network of the water supply station. The historical defect data includes the timestamp of each time a defect is detected in the water supply network of the water supply station and the test water pressure data; The transfer frequency acquisition module analyzes the timestamp and water pressure data of each defect detection to obtain the transfer sequence number corresponding to each defect sequence number and the transfer frequency corresponding to each transfer sequence number; The defect water pressure interval acquisition module analyzes the water pressure data detected each time a defect is detected to obtain the defect water pressure interval corresponding to each defect serial number; The predicted defect sequence number marking module obtains the real-time defect sequence number of the water supply network of the water station according to the real-time water pressure value of each network management node. Combined with the transfer frequency of each transfer sequence number corresponding to the real-time defect sequence number and the defect water pressure range, it obtains the predicted defect sequence number and corresponding warning level corresponding to the water supply network of the water station and outputs them.

2. The water affairs abnormal event early warning system according to claim 1, characterized in that: The specific method for obtaining the warning level corresponding to each defect serial number is as follows: The warning level of the defect serial number whose defect coefficient Xj is less than the preset threshold value Y4 is marked as a mild warning serial number, the warning level of the defect serial number whose defect coefficient Xj is greater than or equal to the preset threshold value Y5 and less than the preset threshold value Y6 is marked as a moderate warning serial number, and the warning level of the defect serial number whose defect coefficient Xj is greater than or equal to the preset threshold value Y6 is marked as a severe warning serial number, where Y6>Y5>Y4>0.

3. The water affairs abnormal event early warning system according to claim 2, characterized in that: The specific method of obtaining the transfer sequence number corresponding to each defect sequence number and the transfer frequency corresponding to each transfer sequence number is as follows: Obtain the timestamp of each time a defect is detected from multiple historical defect data, thereby obtaining multiple defect timestamps and the defect sequence number corresponding to each defect timestamp for the entire water supply network; S1: Randomly select a defective serial number from all defective serial numbers without replacement as the analysis serial number; S2: Obtain each defect sequence number whose defect timestamp is after the defect timestamp of the analysis sequence number, and mark them as the transfer sequence number corresponding to the analysis sequence number, obtain the total number of times A that the analysis sequence number appears in multiple historical defect data, and at the same time mark the number of times each transfer sequence number of the analysis sequence number appears in the historical defect data as Bf, where f refers to different transfer sequence numbers corresponding to the analysis sequence number, f=1, 2,..., h, h is a positive integer, and h refers to the number of transfer sequence numbers corresponding to the analysis sequence number, calculate the ratio between the number of times Bf that each transfer sequence number appears in multiple historical defect data and the total number of times A that the analysis sequence number appears in the historical defect data, and mark them as the transfer frequency of different transfer sequence numbers corresponding to the analysis sequence number as Ef, and bind them to the analysis sequence number to generate a transfer sequence frequency comparison table corresponding to the analysis sequence number.

4. The water affairs abnormal event early warning system according to claim 3, characterized in that: The specific method of obtaining the defect water pressure range corresponding to each defect serial number is as follows: S01: Select the same defective serial number as that in step S1 from among the defective serial numbers as the target serial number; Obtain the mean of the detection water pressure data corresponding to each network management node each time the defect sequence number is the target sequence number from multiple historical defect data, and use it as the defect water pressure Qr corresponding to the water supply network of the water supply station each time the defect sequence number is the target sequence number. Calculate the discrete value U and mean Qp of the defect water pressure Qr, use the sum of the mean Qp and the discrete value U as the upper limit of the defect water pressure interval of the target sequence number, and use the difference between the mean Qp and the discrete value U as the lower limit of the defect water pressure interval of the target sequence number, and then establish the defect water pressure interval K1[Qp-U, Qp+U] of the target sequence number, where r is a different defect water pressure, r=1, 2, ..., g, g refers to the total number of defect water pressures, g is a positive integer, and g satisfies g≥2; S02: Repeat the above step S01 to obtain the defect water pressure range Kc corresponding to each defect serial number.

5. The water affairs abnormal event early warning system according to claim 4, characterized in that: The specific method to obtain the predicted defect serial number and corresponding warning level corresponding to the water supply network of the water station is as follows: According to the real-time water pressure values of each network management node in the water supply network of the water supply station, the real-time defect serial number of the water supply network of the water supply station is obtained, and the average of the real-time water pressures of each network management node in the water supply network of the water supply station is used as the calibration water pressure. The calibration water pressure is compared and analyzed with the defect water pressure intervals corresponding to each transfer serial number of the real-time defect serial number, and the selected coefficient corresponding to each transfer serial number is obtained. According to the selected coefficient, the predicted defect serial number and the corresponding warning level of the water supply network of the water supply station are obtained and output.

6. The water affairs abnormal event early warning system according to claim 5, characterized in that: The specific method of obtaining the predicted defect serial number and the corresponding warning level of the water supply network of the water station according to the selected coefficient is as follows: For the transfer sequence number whose calibrated water pressure is within the corresponding defect water pressure range, the product between its corresponding transfer frequency and the preset value is used as the selection coefficient of the corresponding transfer sequence number. For the transfer sequence number whose calibrated water pressure is not within the corresponding defect water pressure range, its transfer frequency is directly used as the selection coefficient of the corresponding transfer sequence number. The transfer sequence number corresponding to the maximum selected coefficient is marked as the predicted defect sequence number and output, and the warning level corresponding to the predicted defect sequence number is output synchronously.

7. A water affairs abnormal event early warning method, characterized in that: The method is implemented by a water affairs abnormal event early warning system according to any one of claims 1 to 6, and specifically comprises the following steps: Step 1: Generate multiple preset defect levels and corresponding defect labels; Step 2: Number the network management nodes and generate different defect serial numbers for the water supply network of the water supply station based on the defect labels of the preset defect levels; Step 3: Calculate the defect coefficient of each defect sequence number based on the number of each defect number in each defect sequence number, and obtain the warning level corresponding to each defect sequence number based on the defect coefficient; Step 4: Obtain the timestamp and water pressure data of each time a defect is detected in the water supply network of the water supply station; Step 5: Analyze the timestamp and water pressure data of each defect detection to obtain the transfer sequence number corresponding to each defect sequence number and the transfer frequency corresponding to each transfer sequence number; Step 6: Analyze the water pressure data each time a defect is detected to obtain the defect water pressure range corresponding to each defect serial number; Step 7: Obtain the real-time defect sequence number of the water supply network of the water station based on the real-time water pressure value of each network management node. Combined with the transfer frequency of each transfer sequence number corresponding to the real-time defect sequence number and the defect water pressure range, obtain the predicted defect sequence number and corresponding warning level of the water supply network of the water station and output them.

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