Method and device for determining water diversion project recovery scheme, and electronic equipment

Abstract

The present application relates to the technical field of hydraulic engineering, and discloses a method and device for determining a water transfer project recovery scheme, and an electronic device, the method comprising: obtaining a water transfer physical network and multiple sets of fault data; based on multiple preset repair strategies, determining a repair order of each to-be-repaired directed edge under a repair condition to generate a candidate recovery scheme corresponding to each preset repair strategy, the repair condition comprising a difference between a water head of a superior node of the to-be-repaired directed edge and a water head of an inferior node of the to-be-repaired directed edge being greater than or equal to a minimum water head difference required to maintain a design flow, the superior node being a node that transfers water to the inferior node, the candidate recovery scheme comprising repair state data, the repair state data representing a degree of hydraulic recovery and an estimated repair time for achieving the degree of hydraulic recovery in a repair process of the water transfer physical network; and based on the repair state data, determining a target recovery scheme from the multiple sets of candidate recovery schemes.

Description

Technical Field

[0001] This invention relates to the field of water conservancy engineering technology, specifically to methods, apparatus, and electronic equipment for determining restoration schemes for water diversion projects. Background Technology

[0002] In related technologies, graph theory is generally used to treat water supply networks as undirected graphs, and then the repair order of each location to be repaired in the project is determined based on topological connectivity. However, considering only the continuity of the channel segments corresponding to each edge in the undirected graph cannot achieve effective water delivery. Summary of the Invention

[0003] This invention provides a method, apparatus, and electronic equipment for determining a water diversion project restoration plan, in order to solve the problem that simply judging the continuity of a channel section is insufficient to achieve effective water conveyance.

[0004] In a first aspect, the present invention provides a method for determining a restoration scheme for a water diversion project, comprising: acquiring a water diversion physical network and multiple sets of fault data, wherein the water diversion physical network includes source nodes and branch nodes, and the nodes are connected by directed edges or directed edges to be repaired, wherein the directed edges represent the direction of water flow in the water diversion project, and the fault data corresponds to at least one directed edge to be repaired; determining the repair order of each directed edge to be repaired under the condition of satisfying multiple preset repair strategies, so as to generate candidate restoration schemes corresponding to each preset repair strategy, wherein the repair conditions include that the difference between the head of the upper-level node of the directed edge to be repaired and the head of the lower-level node of the directed edge to be repaired is greater than or equal to the minimum head difference required to maintain the design flow rate, wherein the upper-level node is a node that supplies water to the lower-level node, and the candidate restoration scheme includes restoration status data, wherein the restoration status data represents the degree of hydraulic recovery during the restoration process of the water diversion physical network and the expected restoration time to reach the degree of hydraulic recovery; and determining a target restoration scheme from multiple sets of candidate restoration schemes based on the restoration status data.

[0005] According to embodiments of the present invention, by acquiring the water source nodes, branch nodes, and directed edges in the water diversion physical network, multiple candidate restoration schemes that meet hydraulic requirements are determined based on the water diversion physical network and fault data. Thus, when determining the restoration scheme, hydraulic continuity is taken into consideration as a parameter of the restoration scheme, and only canal sections with actual water conveyance capacity are included in the restoration scheme, making the evaluation results closer to reality. Furthermore, since the water diversion physical network is a directed network, the water flow direction is logically bound to the repair sequence of the canal sections corresponding to the directed edges. This at least partially solves the problem in related technologies where ignoring the water flow direction leads to invalid repair states in the restoration scheme, thereby providing a decision-making basis that conforms to actual hydraulic conditions.

[0006] In one optional implementation, the above-mentioned determination of the repair order of each directed edge to be repaired that satisfies the above-mentioned repair conditions based on multiple preset repair strategies, the above-mentioned proximity group and / or the above-mentioned multiple sets of fault data further includes: when the preset repair strategy is the first repair strategy, determining the repair order of each directed edge to be repaired that satisfies the above-mentioned repair conditions based on the above-mentioned proximity group.

[0007] In one optional implementation, the fault data includes the expected repair time range corresponding to the directed edges to be repaired. The determination of the repair order of each directed edge to be repaired that satisfies the repair conditions based on multiple preset repair strategies, the proximity group, and / or the multiple sets of fault data further includes: when the preset repair strategy is a second repair strategy, for each directed edge to be repaired, using a Monte Carlo simulation algorithm to extract a target repair time from the expected repair time range to obtain a target repair time group; determining a priority repair index for each directed edge to be repaired based on the target repair time group and the proximity group; and determining the repair order of each directed edge to be repaired that satisfies the repair conditions based on the priority repair index.

[0008] In an optional implementation, the candidate recovery scheme is determined through the following steps: removing the directed edges to be repaired from the water diversion physical network to obtain a physical network to be repaired; performing a repair simulation on the physical network to be repaired based on the repair order of each directed edge to be repaired and the various preset repair strategies to obtain multiple sets of repair physical networks, where any branch node in the repair physical network is topologically connected to the water source node; determining the repair status data for any repair physical network based on the fault data; and determining the multiple sets of repair physical networks and the repair status data corresponding to each repair physical network as the candidate recovery scheme.

[0009] In one optional implementation, determining the target recovery scheme from multiple sets of candidate recovery schemes based on the repair status data includes: for each candidate recovery scheme corresponding to a preset repair strategy, determining the recovery time range of the candidate recovery scheme under at least one preset recovery degree threshold, wherein the recovery time range is the difference between the maximum and minimum values ​​of the time required to reach the preset recovery degree threshold; and determining the target recovery scheme from the multiple sets of candidate recovery schemes based on the recovery time range.

[0010] In an optional implementation, the fault data further includes the failure type corresponding to the directed edge to be repaired, and the failure type has a corresponding relationship with the repair time probability distribution function. The above-mentioned extraction of the target repair time from the expected repair time range using the Monte Carlo simulation algorithm includes: determining the target repair time probability distribution function based on the failure type of the directed edge to be repaired; and randomly extracting the target repair time of the directed edge to be repaired based on the target repair time probability distribution function.

[0011] In one optional implementation, the repair status data includes the degree of hydraulic recovery, which is characterized by a hydraulically effective giant connected component, as shown in the following formula:

[0012] Among them, Q network,t For the hydraulically effective giant connected component of the water transfer network at time t, N GCC,t Let N be the number of nodes in the largest connected component of the water diversion physical network at time t. GCC,baseline This represents the number of nodes in the largest connected component of the water diversion physical network.

[0013] Secondly, the present invention provides an apparatus for determining a restoration scheme for a water diversion project, comprising: an acquisition module for acquiring a water diversion physical network and multiple sets of fault data, wherein the water diversion physical network includes source nodes and branch nodes, and the nodes are connected by directed edges or directed edges to be repaired, wherein the directed edges represent the direction of water flow in the water diversion project, and the fault data corresponds to at least one directed edge to be repaired; a first determination module for determining the repair order of each directed edge to be repaired under the condition of satisfying multiple preset repair strategies, so as to generate candidate restoration schemes corresponding to each preset repair strategy, wherein the repair conditions include that the difference between the head of the upper-level node of the directed edge to be repaired and the head of the lower-level node of the directed edge to be repaired is greater than or equal to the minimum head difference required to maintain the design flow rate, wherein the upper-level node is a node that supplies water to the lower-level node, and the candidate restoration scheme includes restoration status data, wherein the restoration status data represents the degree of hydraulic recovery during the restoration process of the water diversion physical network and the expected restoration time to reach the degree of hydraulic recovery; and a second determination module for determining a target restoration scheme from multiple sets of candidate restoration schemes based on the restoration status data.

[0014] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the method for determining the water diversion project restoration scheme as described in the first aspect or any corresponding embodiment.

[0015] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the method for determining a water diversion project restoration scheme as described in the first aspect or any corresponding embodiment thereof.

[0016] Fifthly, the present invention provides a computer program product, including computer instructions, which are used to cause the computer to execute the method for determining the water diversion project restoration scheme of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is an application scenario diagram of the method and apparatus for determining the water diversion project restoration scheme according to an embodiment of the present invention; Figure 2 This is a flowchart of a method for determining a water diversion project restoration plan according to an embodiment of the present invention; Figure 3 This is a schematic diagram of a water regulation physical network according to an embodiment of the present invention; Figure 4 This is a flowchart of a method for determining another water diversion project restoration scheme according to an embodiment of the present invention; Figure 5 This is a structural block diagram of a device for determining a water diversion project restoration scheme according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0021] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0022] Figure 1 This is an application scenario diagram of the method and apparatus for determining the water diversion project restoration scheme according to an embodiment of the present invention.

[0023] like Figure 1 As shown, application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 serves as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0024] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0025] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0026] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0027] It should be noted that the method for determining the water diversion project restoration plan provided in this embodiment of the invention can generally be executed by server 105. Correspondingly, the device for determining the water diversion project restoration plan provided in this embodiment of the invention can generally be located in server 105. The method for determining the water diversion project restoration plan provided in this embodiment of the invention can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the device for determining the water diversion project restoration plan provided in this embodiment of the invention can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105.

[0028] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0029] In related technologies, existing graph theory methods only consider topological connectivity, i.e., "whether a path exists between two nodes." However, the water conveyance function of water transfer projects requires not only physical connectivity of the channel sections but also the fulfillment of hydraulic continuity conditions—upstream nodes must have sufficient head for water to flow through downstream channel sections, and upstream and downstream flow rates must match. A physically repaired channel section cannot achieve effective water conveyance if there is no water or insufficient head upstream. Existing methods cannot identify this situation and will overestimate the actual restoration effect. Furthermore, since water transfer projects are unidirectional water flow systems (such as the South-to-North Water Transfer Project, which conveys water from south to north), they are fundamentally different from the ring-shaped bidirectional flow of water supply networks. Related technologies treat the water conveyance network as an undirected graph to calculate the shortest path, which fails to accurately reflect the crucial constraint that "upstream restoration is a prerequisite for downstream restoration."

[0030] In view of this, the present invention provides a method for determining a restoration scheme for a water diversion project. The method includes: acquiring a water diversion physical network and multiple sets of fault data. The water diversion physical network includes source nodes and branch nodes, and each node is connected by a directed edge or a directed edge to be repaired. The directed edge represents the direction of water flow in the water diversion project, and the fault data corresponds to at least one directed edge to be repaired. Based on multiple preset repair strategies, the repair order of each directed edge to be repaired is determined under the condition that the repair is satisfied, so as to generate candidate restoration schemes corresponding to each preset repair strategy. The repair conditions include that the difference between the head of the upper-level node of the directed edge to be repaired and the head of the lower-level node of the directed edge to be repaired is greater than or equal to the minimum head difference required to maintain the design flow rate. The upper-level node is a node that supplies water to the lower-level node. The candidate restoration schemes include restoration status data, which represents the degree of hydraulic recovery during the restoration process of the water diversion physical network and the expected restoration time to reach the degree of hydraulic recovery. Based on the restoration status data, a target restoration scheme is determined from multiple sets of candidate restoration schemes.

[0031] According to an embodiment of the present invention, a method for determining a water diversion project restoration scheme is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0032] This embodiment provides a method for determining a water diversion project restoration plan, which can be used in the aforementioned server. Figure 2 This is a flowchart of a method for determining a water diversion project restoration plan according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain the water diversion physical network and multiple sets of fault data.

[0033] The water transfer physical network includes source nodes and branch nodes. The nodes are connected by directed edges or directed edges to be repaired. The directed edges represent the direction of water flow in the water transfer project. Fault data corresponds to at least one directed edge to be repaired.

[0034] The complex physical network of a water diversion project can be abstracted into a directed graph (water diversion physical network) G(N, L, H, Q) that can be processed by a computer. Here, N is the set of nodes, including water source nodes, control gate nodes, water distribution nodes, tunnel inlet and outlet nodes, etc.; L is the set of directed edges connecting the nodes, representing channel sections, tunnel sections, and aqueduct sections, with the direction consistent with the direction of water flow; H is the design water level (or head) value of each node, in meters; and Q is the design flow rate value of each channel section, in cubic meters per second.

[0035] Figure 3This is a schematic diagram of a water regulation physical network according to an embodiment of the present invention.

[0036] like Figure 3 As shown, there can be multiple water source nodes and branch nodes in the water transfer physical network. Directed edges can correspond to multiple water conveyance channels of different levels. Water flows from the water source area corresponding to the water source node through the main canal, regulating reservoir, branch canal and branch canal, and finally reaches multiple water receiving area nodes (corresponding to branch nodes).

[0037] Step S202: Based on multiple preset repair strategies, determine the repair order of each directed edge to be repaired under the repair conditions, so as to generate candidate recovery schemes corresponding to each preset repair strategy.

[0038] The repair conditions include that the difference between the head of the upper-level node of the directed edge to be repaired and the head of the lower-level node of the directed edge to be repaired is greater than or equal to the minimum head difference required to maintain the design flow rate. The upper-level node is the node that supplies water to the lower-level node. The candidate recovery scheme includes repair status data, which characterizes the degree of hydraulic recovery during the repair process of the water diversion physical network and the expected repair time to reach the degree of hydraulic recovery.

[0039] In feasible embodiments of the present invention, various preset repair strategies can be defined based on the characteristics of water diversion projects (linear distribution, hierarchical management, and single water source direction). The core of the repair strategy is to determine the restoration order of the channel segments corresponding to the directed edges to be repaired after a sudden event (channel segments that have failed due to damage to structures, flooding, water pollution, freezing blockage, etc.). When determining the order of channels to be repaired, the following must be considered: proximity to the water source, channel level (main canal, branch canal, or classified according to the importance of the water receiving area), and the time required to repair the channels.

[0040] For example, the preset repair strategies may include the following repair directions: Strategy 1 (based on proximity): prioritize repairing the channel segment corresponding to the directed edge to be repaired that is closest to the water source node; Strategy 2 (based on proximity and channel level): prioritize repairing the channel with the highest level, and sort by proximity within the same level; Strategy 3 (based on proximity and repair time): prioritize repairing the channel segment that is both close to the water source node and has a short repair time; Strategy 4 (dynamic sorting based on "repair time"): sort entirely according to the estimated repair time.

[0041] It is important to note that in each repair step of the candidate recovery scheme, each node in the repaired water diversion physical network must satisfy topological continuity.

[0042] Step S203: Based on the repair status data, determine the target recovery scheme from multiple candidate recovery schemes.

[0043] In one feasible embodiment of the present invention, the repair status data may include, for example, the hydraulic effective recovery rate, the hydraulic effective recovery efficiency, and the recovery confidence level. The hydraulic effective recovery rate represents the average time required for the entire water diversion physical network to be restored; the hydraulic effective recovery efficiency is the average time required for the water diversion physical network to achieve a repair level of 25%, 50%, and 75%. If a strategy requires significantly less time in the early stages (e.g., 25% recovery), it indicates higher recovery efficiency and the ability to restore emergency water supply to cities along the route more quickly. The recovery confidence level represents the width of the confidence interval for recovery time at each stage. The robustness of each strategy is evaluated by comparing the minimum and maximum recovery time ranges (i.e., the confidence interval width in the simulation results) for each recovery stage (e.g., 25%, 50%, 75%). A narrower range indicates that the strategy is less affected by uncertainty and the results are more reliable. Therefore, a target recovery scheme can be selected from multiple candidate recovery schemes corresponding to various preset repair strategies based on the repair status data. For example, the candidate recovery scheme with the fastest hydraulic effective recovery rate can be selected as the target recovery scheme.

[0044] According to embodiments of the present invention, by acquiring the water source nodes, branch nodes, and directed edges in the water diversion physical network, multiple candidate restoration schemes that meet hydraulic requirements are determined based on the water diversion physical network and fault data. Thus, when determining the restoration scheme, hydraulic continuity is taken into consideration as a parameter of the restoration scheme, and only canal sections with actual water conveyance capacity are included in the restoration scheme, making the evaluation results closer to reality. Furthermore, since the water diversion physical network is a directed network, the water flow direction is logically bound to the repair sequence of the canal sections corresponding to the directed edges. This at least partially solves the problem in related technologies where ignoring the water flow direction leads to invalid repair states in the restoration scheme, thereby providing a decision-making basis that conforms to actual hydraulic conditions.

[0045] According to embodiments of the present invention, based on multiple preset repair strategies, the repair order of each directed edge to be repaired under the condition of satisfying repair is determined, including: for each directed edge to be repaired, based on the water diversion physical network, the proximity degree between the directed edge to be repaired and the water source node is determined to obtain a proximity degree group, wherein the proximity degree characterizes the water conveyance distance between the directed edge to be repaired and the water source node; based on multiple preset repair strategies, proximity degree group and / or multiple sets of fault data, the repair order of each directed edge to be repaired under the condition of satisfying repair is determined.

[0046] According to an embodiment of the present invention, the repair order of each directed edge to be repaired that meets the repair conditions is determined based on multiple preset repair strategies, proximity groups and / or multiple sets of fault data. The method further includes: when the preset repair strategy is a first repair strategy, determining the repair order of each directed edge to be repaired that meets the repair conditions based on the proximity group.

[0047] For example, there are three directed edges A, B, and C to be repaired, where the water conveyance distance of A from the water source node is 10km, the water conveyance distance of B from the water source node is 20km, and the water conveyance distance of C from the water source node is 30km. Therefore, it can be assumed that the repair order is to repair the channel segment corresponding to the directed edge A first, then repair the channel segment corresponding to the directed edge B, and finally repair the channel segment corresponding to the directed edge C.

[0048] According to an embodiment of the present invention, the fault data includes the expected repair time range corresponding to the directed edges to be repaired. Based on multiple preset repair strategies, proximity groups, and / or multiple sets of fault data, the repair order of each directed edge to be repaired that meets the repair conditions is determined. The method further includes: when the preset repair strategy is the second repair strategy, for each directed edge to be repaired, using a Monte Carlo simulation algorithm, extracting a target repair time from the expected repair time range to obtain a target repair time group; determining a priority repair index for each directed edge to be repaired based on the target repair time group and the proximity group; and determining the repair order of each directed edge to be repaired that meets the repair conditions based on the priority repair index.

[0049] The priority repair index is a value calculated by combining the proximity information of the directed edges to be repaired and the target repair time information extracted from Monte Carlo simulation. This value is used to sort the directed edges to be repaired that meet the repair conditions; the higher the index, the higher the repair order. The priority index can be determined by weighting the target repair time and the proximity, or other methods can be used to obtain the priority repair index; this invention does not limit this method.

[0050] Taking directed edges A, B, and C to be repaired as an example, their proximity degrees are 3, 2, and 1, respectively. The expected repair time range for A is [1,3]; for B it is [2,5]; and for C it is [4,8]. The sampled target repair times are A: 2.5 hours, B: 3.2 hours, and C: 5.6 hours. Assuming that the priority repair index is determined by dividing the proximity degree by the target repair time, the priority repair indices are A: 0.8, B: 0.9376, and C: 0.1786, respectively. Therefore, the repair order is B, A, C.

[0051] Furthermore, this invention proposes the Hydraulically Effective Giant Connected Component (H-GCC) as a core performance indicator, which needs to satisfy two conditions: first, topological connectivity: that is, starting from the water source node, there exists a path consisting of restored directed edges to reach that node; second, hydraulic feasibility: along this path, the actual head at each node meets the water conveyance requirements of the downstream channel section, that is, the actual head difference between upstream and downstream nodes for each edge must satisfy H.i -H j ≥△H min , where △H min To maintain the minimum head difference required for the design flow rate, H i H is the upstream node. j For downstream nodes. H-GCC is defined as the proportion of nodes in the largest connected subgraph that satisfies the above two conditions to the total number of nodes in the network.

[0052] According to a feasible embodiment of the present invention, the H-GCC of the water diversion physical network can be calculated through the following steps. In step one: An initial state is constructed, i.e., the actual situation. The directed edges to be repaired are removed from the water diversion physical network, a new physical network to be repaired is constructed, and the H-GCC at this point is calculated. In step two: Following a preset repair strategy, only one directed edge to be repaired is repaired at a time, and hydraulic feasibility is verified. If the H-GCC is satisfied... i -H j ≥△H min Then, it is added back to the physical network to be repaired. In step three, the H-GCC of the network is recalculated for each damaged channel segment repaired. In step four, dynamic plotting is performed, that is, connecting the time point after each repair with the corresponding H-GCC value to form an "H-GCC-time" recovery curve, which clearly shows the change of connectivity over time.

[0053] Furthermore, the repair status data includes the degree of hydraulic recovery, which is characterized by the hydraulically effective giant connected component. In order to eliminate the influence of the size of the water transfer physical network in different repair steps and to allow water transfer physical networks of different scales to be compared, the H-GCC can also be normalized as shown in the following formula (1).

[0054] (1) Among them, Q network,t N is the normalized parameter of the hydraulically effective giant connected component of the water transfer network at time t. GCC,t Let N be the number of nodes in the largest connected component of the water diversion physical network at time t. GCC,baseline This represents the number of nodes in the largest connected component (i.e., the total number of nodes) in the water transfer physical network.

[0055] According to an embodiment of the present invention, candidate recovery schemes are determined through the following steps: removing directed edges to be repaired from the water diversion physical network to obtain a physical network to be repaired; performing repair simulation on the physical network to be repaired based on the repair order of each directed edge to be repaired and multiple preset repair strategies to obtain multiple sets of repair physical networks, wherein any branch node in the repair physical network is topologically connected to the water source node; determining repair status data based on fault data for any repair physical network; and determining the multiple sets of repair physical networks and the repair status data corresponding to each repair physical network as candidate recovery schemes.

[0056] According to an embodiment of the present invention, the method of determining a target recovery scheme from multiple candidate recovery schemes based on repair status data includes: for each candidate recovery scheme corresponding to a preset repair strategy, determining the recovery time range of the candidate recovery scheme under at least one preset recovery degree threshold, wherein the recovery time range is the difference between the maximum and minimum values ​​of the time required to reach the preset recovery degree threshold; and determining the target recovery scheme from multiple candidate recovery schemes based on the recovery time range.

[0057] According to an embodiment of the present invention, the fault data also includes the failure type corresponding to the directed edge to be repaired. The failure type has a corresponding relationship with the repair time probability distribution function. Using the Monte Carlo simulation algorithm, the target repair time is extracted from the expected repair time range, including: determining the target repair time probability distribution function based on the failure type of the directed edge to be repaired; and randomly extracting the target repair time of the directed edge to be repaired based on the target repair time probability distribution function.

[0058] Monte Carlo simulations can reflect the uncertainty of the recovery time for each directed edge to be repaired. Although the upper and lower limits of the water supply restoration time for each canal segment corresponding to the directed edge to be repaired can be determined by field visits, uncertainty still exists regarding the final time required to restore water supply. Therefore, the recovery time for each strategy can be calculated using the simulation results of the Monte Carlo model.

[0059] Different probability density functions can be used to sample the time variable of a single strategy, and 10,000 iterations can be performed for each strategy. In each iteration, the repair time is randomly selected according to the probability distribution of each failed node, the recovery order is generated according to the strategy rules, and the H-GCC change curve with cumulative time is calculated. Through 10,000 simulations, the probability distribution, mean, and confidence interval of the recovery time of each strategy are obtained.

[0060] Taking Strategy 1 above as an example, Monte Carlo simulation can be performed through the following steps.

[0061] In step one, the repair order of each directed edge to be repaired is determined based on proximity, and the repaired directed edges are added to the physical network to be repaired according to the repair order; in step two, random samples (representing the water supply recovery time of the damaged channel) are plotted using the target repair time probability distribution function; in step three, it is determined whether the H-GCC satisfies the two conditions of topological connectivity and hydraulic feasibility; in step four, the cumulative recovery time is evaluated, and steps one to three are repeated 10,000 times.

[0062] The Monte Carlo simulation corresponding to Strategy 4 above can be performed through the following steps.

[0063] In step one, a random sample is plotted to represent the water supply restoration time of the damaged canal segment; in step two, the repair order of the damaged canal segments is sorted in ascending order of restoration time to determine the sequence; in step three, water supply restoration canal segments are added according to the sorted order; in step four, it is determined whether the H-GCC meets the two conditions of topological connectivity and hydraulic feasibility; in step five, the cumulative restoration time is evaluated; in step six, steps one to five are repeated 10,000 times.

[0064] Different types of emergencies exist in water diversion projects. This invention defines failure types and corresponding target repair time probability distribution functions based on the types of emergencies.

[0065] For example, in the event of sudden damage to the physical structure of a water conveyance structure requiring structural repair, it can be known from actual engineering surveys that the repair time under this failure mode is long (therefore, a triangular distribution function can be used). The formula for the triangular distribution function is shown in formula (2) below.

[0066] (2) Where a is the minimum number of days, b is the most likely number of days, and c is the maximum number of days.

[0067] For sudden events such as flood damage, i.e., channel destruction or siltation, dredging and slope repair are required. According to actual engineering surveys, the repair time under this failure mode is moderate (therefore, a uniform distribution function can be used). The formula for the uniform distribution function is shown in formula (3) below.

[0068] (3) Where a is the minimum number of days and b is the maximum number of days.

[0069] For sudden water pollution events, i.e., water bodies are polluted and the area becomes unusable for water intake, resulting in functional loss but intact physical structure, according to actual engineering surveys, the water supply recovery time under this failure mode is short (therefore, a truncated normal distribution function can be used). The formula for the truncated normal distribution function is shown in formula (4) below (assuming the original normal distribution is X~N(μ,σ2), and the truncated interval is [a,b], i.e., the upper and lower limits of the repair days, ( ∞≤a <b≤∞))。

[0070] (4) in, It is the distribution function of the standard normal distribution. It is the cumulative distribution function of the standard distribution.

[0071] In the event of sudden freezing in winter, ice blockage can lead to a decrease or interruption of water supply capacity, requiring ice breaking operations. According to actual engineering surveys, the water supply recovery time is short under this failure mode (therefore, a uniform distribution function can be used).

[0072] This invention establishes differentiated failure models for water conveyance structures at different times, such as damage, floods, pollution, and freezing, adapting to various repair scenarios.

[0073] Furthermore, by conducting 1000 simulations, the target repair time can be randomly selected under the target repair time probability distribution function simulation, thereby quantifying the impact of repair time uncertainty on the recovery process. This allows the system to evaluate the robustness of various repair strategies in an uncertain environment, thereby determining the optimal target recovery scheme.

[0074] In related technologies, the Monte Carlo method only evaluates the initial state after repair without simulating the recovery process. This invention is the first to combine Monte Carlo simulation with topology-hydraulic coupling simulation to simulate the dynamic process of the failure channel segment being gradually repaired over time. It can quantify the speed difference of system function recovery under different recovery strategies, thereby providing a data basis for determining the target recovery scheme.

[0075] In one feasible embodiment of the present invention, a specific implementation plan of the present invention is illustrated using a certain channel section of a certain project as an example. The channel section includes: nodes (Taocha headwater source node with a water level of 140m, 15 control gate nodes with design water levels at each node, and 20 water distribution nodes) and directed edges (220 channel sections, each recording the start and end nodes, design flow rate, length, and design head loss), to construct a directed graph G(N,L,H,Q) corresponding to the water diversion physical network.

[0076] Assume a combined disaster causes five functional failures: Failure point A (damaged aqueduct structure, 80km from the water source, repair time 30-45 days, triangular distribution); Failure points B and C (flooding, canal section damage, 120km and 200km from the water source, repair time 10-20 days, uniform distribution); Failure point D (water pollution, 50km from the water source, treatment time 5-10 days, truncated normal distribution); Failure point E (freezing, inverted siphon ice blockage, 250km from the water source, ice breaking time 2-5 days, uniform distribution). Pre-set repair strategies include Strategy 1 (proximity), Strategy 2 (proximity and canal grade), Strategy 3 (proximity and repair time), and Strategy 4 (repair time).

[0077] Taking Strategy 1 as an example, the failure points are sorted by proximity as D (50km) → A (80km) → B (120km) → C (200km) → E (250km). Then, each failure point is restored and verified: After restoring D, the water source → D topologically connects. The head is calculated: the water source level is 140m, and after a 50km channel head loss of approximately 5m, the water level at node D reaches 135m, satisfying the downstream water conveyance conditions, and the H-GCC is updated. After restoring A, the water source → D → A topologically connects, the water level at node D is 135m, and after a 30km channel head loss of approximately 3m, the water level at node A reaches 132m, satisfying the conditions, and the H-GCC is updated. This process continues until all failure points are restored and the H-GCC reaches 1.

[0078] Furthermore, Monte Carlo simulations were performed on the candidate recovery schemes corresponding to each strategy for 10,000 iterations. In each iteration, the repair time was randomly selected based on the probability distribution of each failure point, the recovery order was determined according to the strategy rules, and topology-hydraulic coupling simulation was performed to record the time for H-GCC to reach each stage.

[0079] Simulation results show that, in terms of full recovery time, Strategy 2 performs best, requiring an average of 32 days to achieve 100% H-GCC; Strategy 1 performs worst, requiring an average of 38.5 days; Strategy 3 and Strategy 4 are in the middle, requiring averages of 34.2 days and 36.8 days, respectively. This indicates that Strategy 2, which prioritizes channel levels (repairing the main canal first) and considers proximity, has the greatest advantage in achieving full system recovery speed.

[0080] In terms of recovery efficiency, Strategy 4 (ranked by repair time) performed best in the early recovery phase: it only took an average of 10.5 days to restore H-GCC to 50% (i.e., half of the hydraulically effective water conveyance capacity). In comparison, Strategy 3 took an average of 12.8 days, Strategy 2 took an average of 15.2 days, and Strategy 1 took an average of 22.5 days. This indicates that if the primary objective of decision-makers is to restore some water conveyance capacity as quickly as possible after a disaster to ensure emergency water supply, Strategy 4 is the optimal choice.

[0081] Regarding the uncertainty of the recovery process, Strategy 2 exhibits the most stable recovery time: at the stage where H-GCC reaches 50%, its recovery time ranges from 13 to 18 days, with a fluctuation of only 5 days. Strategy 1 has a slightly wider uncertainty range, from 18 to 28 days. Strategies 3 and 4 have greater uncertainty, with Strategy 3's recovery time ranging from 10 to 16 days (fluctuating by 6 days) and Strategy 4's from 7 to 15 days (fluctuating by 8 days). This indicates that while Strategy 4 has the highest early recovery efficiency, its outcome is also most affected by the randomness of the repair time; while Strategy 2 trades slightly lower early efficiency for greater robustness in the recovery process.

[0082] Therefore, compared with related technologies that only consider topological connectivity (restoring 50% of the failed edges (i.e., 3 edges) takes only 5 to 8 days on average, because the failure points E (freezing), D (pollution), and B (flood) with short repair times are repaired first, but the upstream critical failure point A is not repaired, so the actual hydraulic effective water conveyance function is not restored to 50%), the method proposed in this invention reveals the upstream dependence law of hydraulics, that is, the restoration of downstream channels can only take effect after the upstream hydraulic conditions are met. This is something that related technologies cannot provide at all, and it has important value for emergency decision-making in water diversion projects.

[0083] Figure 4 This is a flowchart of a method for determining another water diversion project restoration scheme according to an embodiment of the present invention.

[0084] like Figure 4 As shown, this invention first establishes a directed graph model through GIS layer construction, basic data processing, failure modeling, and repair time investigation. Based on this, four recovery strategies are identified (based on proximity, proximity and channel level, proximity and repair time, and dynamic ranking of pure repair time, respectively). Then, hydraulically effective connectivity components (H-GCC) are used as performance metrics, and the recovery water supply time of different strategies is evaluated and compared through Monte Carlo simulation and topology-hydraulic coupling simulation, thereby determining the optimal repair sequence decision method.

[0085] This embodiment also provides a device for determining a water diversion project restoration plan. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0086] This embodiment provides a device for determining a water diversion project restoration plan, such as... Figure 5 As shown, it includes: The acquisition module 501 is used to acquire the water transfer physical network and multiple sets of fault data. The water transfer physical network includes water source nodes and branch nodes. Each node is connected by a directed edge or a directed edge to be repaired. The directed edge represents the direction of water flow in the water transfer project. The fault data corresponds to at least one of the directed edges to be repaired. The first determining module 502 is used to determine the repair order of each directed edge to be repaired under the condition of satisfying a variety of preset repair strategies, so as to generate candidate recovery schemes corresponding to each preset repair strategy. The repair conditions include that the difference between the head of the upper-level node of the directed edge to be repaired and the head of the lower-level node of the directed edge to be repaired is greater than or equal to the minimum head difference required to maintain the design flow. The upper-level node is the node that supplies water to the lower-level node. The candidate recovery scheme includes repair status data, which characterizes the degree of hydraulic recovery during the repair process of the water diversion physical network and the expected repair time to reach the degree of hydraulic recovery. The second determining module 503 is used to determine the target recovery scheme from the multiple candidate recovery schemes based on the above-mentioned repair status data.

[0087] In some optional implementations, the first determining module 502 includes: a proximity group determining submodule, used to determine the proximity between the directed edge to be repaired and the water source node based on the above-mentioned water diversion physical network for each directed edge to be repaired, so as to obtain a proximity group, wherein the proximity represents the water conveyance distance between the directed edge to be repaired and the water source node; and an order determining submodule, used to determine the repair order of each directed edge to be repaired that meets the above-mentioned repair conditions based on multiple preset repair strategies, the above-mentioned proximity group and / or the above-mentioned multiple sets of fault data.

[0088] In some optional implementations, the order determination submodule includes: a proximity order determination unit, used to determine the repair order of each directed edge to be repaired that satisfies the above repair conditions based on the above proximity group when the preset repair strategy is the first repair strategy.

[0089] In some optional implementations, the fault data includes the expected repair time range corresponding to the directed edge to be repaired, and the order determination submodule includes: The extraction unit is used to extract the target repair time from the above-mentioned expected repair time range for each directed edge to be repaired, using the Monte Carlo simulation algorithm, when the preset repair strategy is the second repair strategy, so as to obtain the target repair time group. The priority index determination unit is used to determine the priority repair index of each of the above-mentioned directed edges to be repaired based on the above-mentioned target repair time group and the above-mentioned proximity group. The order determination unit is used to determine the repair order of each directed edge to be repaired that satisfies the above repair conditions based on the above priority repair index.

[0090] In some alternative embodiments, the above-described apparatus further includes: The removal module is used to remove the directed edges to be repaired from the above water diversion physical network to obtain the physical network to be repaired. The simulation module is used to simulate the repair of the physical network to be repaired based on the repair order of each of the above-mentioned directed edges to be repaired and the above-mentioned multiple preset repair strategies, so as to obtain multiple sets of repair physical networks, in which any branch node of the above-mentioned repair physical network is topologically connected to the above-mentioned water source node. The status determination module is used to determine the repair status data based on the above fault data for any repaired physical network. The scheme determination module is used to determine the above-mentioned multiple sets of repair physical networks and the repair status data corresponding to each repair physical network as the above-mentioned multiple sets of candidate recovery schemes.

[0091] In some alternative implementations, the second determining module 503 includes: The time range determination submodule is used to determine the recovery time range of the candidate recovery schemes corresponding to each preset repair strategy under at least one preset recovery degree threshold. The recovery time range is the difference between the maximum and minimum values ​​of the time required to reach the preset recovery degree threshold. The target determination submodule is used to determine the target recovery scheme from the multiple candidate recovery schemes based on the above recovery time range.

[0092] In some optional implementations, the fault data further includes failure types corresponding to the directed edges to be repaired, and the failure types correspond to the repair time probability distribution function. The extraction unit includes: The function determines the sub-unit, which is used to determine the target repair time probability distribution function based on the failure type of the directed edge to be repaired mentioned above; Randomly select sub-units to randomly select the target repair time of the directed edge to be repaired based on the target repair time probability distribution function mentioned above.

[0093] In some optional implementations, the aforementioned repair status data includes the degree of hydraulic recovery, which is characterized by a hydraulically effective giant connected component, as shown in the following formula:

[0094] Among them, Q network,t For the hydraulically effective giant connected component of the water transfer network at time t, N GCC,t Let N be the number of nodes in the largest connected component of the water diversion physical network at time t. GCC,baselineThis represents the number of nodes in the largest connected component of the water diversion physical network.

[0095] The device for determining the water diversion project restoration plan provided in this embodiment of the invention can execute the method for determining the water diversion project restoration plan provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as in the corresponding embodiments described above, and will not be repeated here.

[0096] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0097] The following is a detailed reference. Figure 6 This diagram illustrates a suitable structural design for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of the electronic device. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0098] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0099] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in the method for determining the water diversion project restoration scheme according to embodiments of the present invention.

[0100] Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0101] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the method for determining the water diversion project restoration scheme shown in the above embodiments is implemented.

[0102] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0103] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for determining a restoration plan for a water diversion project, characterized in that, The method includes: The water diversion physical network and multiple sets of fault data are obtained. The water diversion physical network includes water source nodes and branch nodes. Each node is connected by a directed edge or a directed edge to be repaired. The directed edge represents the direction of water flow in the water diversion project. The fault data corresponds to at least one of the directed edges to be repaired. Based on multiple preset repair strategies, the repair order of each directed edge to be repaired is determined under the condition that the repair is met, so as to generate candidate recovery schemes corresponding to each preset repair strategy. The repair conditions include that the difference between the head of the upper-level node of the directed edge to be repaired and the head of the lower-level node of the directed edge to be repaired is greater than or equal to the minimum head difference required to maintain the design flow. The upper-level node is the node that supplies water to the lower-level node. The candidate recovery scheme includes repair status data, which characterizes the degree of hydraulic recovery during the repair process of the water diversion physical network and the expected repair time to reach the degree of hydraulic recovery. Based on the repair status data, a target recovery scheme is determined from multiple sets of candidate recovery schemes.

2. The method according to claim 1, characterized in that, The process of determining the repair order of each directed edge to be repaired under the condition of satisfying multiple preset repair strategies includes: For each directed edge to be repaired, based on the water diversion physical network, the proximity degree between the directed edge to be repaired and the water source node is determined to obtain a proximity degree group. The proximity degree characterizes the water conveyance distance between the directed edge to be repaired and the water source node. Based on multiple preset repair strategies, the proximity group, and / or the multiple sets of fault data, the repair order of each directed edge to be repaired that meets the repair conditions is determined.

3. The method according to claim 2, characterized in that, The step of determining the repair order of each directed edge to be repaired that meets the repair conditions based on multiple preset repair strategies, the proximity group, and / or the multiple sets of fault data further includes: When the preset repair strategy is the first repair strategy, the repair order of each directed edge to be repaired that satisfies the repair conditions is determined based on the proximity group.

4. The method according to claim 2, characterized in that, The fault data includes the estimated repair time range corresponding to the directed edges to be repaired. The step of determining the repair order of each directed edge to be repaired that meets the repair conditions, based on multiple preset repair strategies, the proximity group, and / or the multiple sets of fault data, further includes: When the preset repair strategy is the second repair strategy, for each directed edge to be repaired, the Monte Carlo simulation algorithm is used to extract the target repair time from the expected repair time range to obtain the target repair time group. Based on the target repair time group and the proximity group, the priority repair index of each directed edge to be repaired is determined; Based on the priority repair index, the repair order of each directed edge to be repaired that meets the repair conditions is determined.

5. The method according to any one of claims 1 to 4, characterized in that, The candidate recovery scheme was determined through the following steps: Remove the directed edges to be repaired from the water diversion physical network to obtain the physical network to be repaired; Based on the repair order of each directed edge to be repaired and the multiple preset repair strategies, the physical network to be repaired is simulated to obtain multiple sets of repaired physical networks. Any branch node in the repaired physical network is topologically connected to the water source node. For any repaired physical network, the repair status data is determined based on the fault data; The multiple sets of repair physical networks and the repair status data corresponding to each repair physical network are determined as the candidate recovery schemes.

6. The method according to claim 1, characterized in that, The step of determining the target recovery plan from multiple candidate recovery plans based on the repair status data includes: For each preset repair strategy, a candidate recovery scheme is determined, and the recovery time range of the candidate recovery scheme is determined under at least one preset recovery degree threshold. The recovery time range is the difference between the maximum and minimum values ​​of the time required to reach the preset recovery degree threshold. Based on the recovery time range, the target recovery scheme is determined from multiple sets of candidate recovery schemes.

7. The method according to claim 4, characterized in that, The fault data also includes the failure type corresponding to the directed edge to be repaired, and the failure type has a corresponding relationship with the repair time probability distribution function. The step of extracting the target repair time from the expected repair time range using the Monte Carlo simulation algorithm includes: Based on the failure type of the directed edge to be repaired, determine the target repair time probability distribution function; Based on the target repair time probability distribution function, the target repair time of the directed edge to be repaired is randomly selected.

8. The method according to claim 1, characterized in that, The repair status data includes the degree of hydraulic recovery, which is characterized by a hydraulically effective giant connected component, and the hydraulically effective giant connected component is expressed by the following formula: Among them, Q network,t For the hydraulically effective giant connected component of the water transfer network at time t, N GCC,t Let N be the number of nodes in the largest connected component of the water diversion physical network at time t. GCC,baseline This represents the number of nodes in the largest connected component of the water diversion physical network.

9. A device for determining a water diversion project restoration plan, characterized in that, The device includes: The acquisition module is used to acquire the water diversion physical network and multiple sets of fault data. The water diversion physical network includes water source nodes and branch nodes. Each node is connected by a directed edge or a directed edge to be repaired. The directed edge represents the direction of water flow in the water diversion project. The fault data corresponds to at least one of the directed edges to be repaired. The first determining module is used to determine the repair order of each directed edge to be repaired under the condition of satisfying multiple preset repair strategies, so as to generate candidate recovery schemes corresponding to each preset repair strategy. The repair conditions include that the difference between the head of the upper-level node of the directed edge to be repaired and the head of the lower-level node of the directed edge to be repaired is greater than or equal to the minimum head difference required to maintain the design flow. The upper-level node is the node that supplies water to the lower-level node. The candidate recovery scheme includes repair status data, which characterizes the degree of hydraulic recovery during the repair process of the water diversion physical network and the expected repair time to reach the degree of hydraulic recovery. The second determining module is used to determine the target recovery scheme from multiple sets of candidate recovery schemes based on the repair status data.

10. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the method for determining the water diversion project restoration scheme as described in any one of claims 1 to 8.

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