Pipe explosion analysis method and system based on water source point position

Abstract

The invention belongs to the technical field of water supply network management. The invention provides a pipe explosion analysis method and system based on a water source point position. The method comprises the steps that a resource center list in a pipe network is traversed, and a resource center closest to a pipe explosion point is found according to the geographic position of each resource center and the geographic position of the pipe explosion point; using a Dijkstra algorithm to search for a shortest pipeline path communicating the pipe explosion point with the nearest resource center; determining a shortest path valve set: searching all valves on the shortest path in the pipe network according to the obtained shortest pipeline path to obtain the shortest path valve set; traversing the obtained shortest path valve set, and obtaining a valve closest to the pipe explosion point as a closed valve; and according to the obtained shortest pipeline path, obtaining all equipment sets on the shortest pipeline path as all influenced equipment sets after the pipe explosion event occurs. The method has higher accuracy and efficiency, and is particularly suitable for a large complex pipe network system.

Description

Technical Field

[0001] The present invention relates to the technical field of water supply network management, and in particular to a pipe burst analysis method and system based on water source point locations. Background Art

[0002] The water supply network is an important part of the city's lifeline project. Once a pipe burst occurs, it will not only cause a large amount of water waste, but may also cause social problems such as traffic paralysis. Traditional pipe burst analysis algorithms perform well when the pipe network structure is less complex, but their accuracy and efficiency are reduced in complex pipe network structures. Therefore, how to quickly and accurately find the closed valve closest to the pipe burst point has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] In this regard, the present invention provides a pipe burst analysis method, system, electronic device, computer storage medium and computer program product based on water source point location to solve the above technical problems.

[0004] The present invention discloses a pipe burst analysis method based on the location of a water source point, the method comprising the following steps:

[0005] S1. Determine the resource center: traverse the list of resource centers in the pipe network, and find the resource center closest to the pipe burst point according to the geographical location of each resource center and the geographical location of the pipe burst point;

[0006] S2. Find the shortest path: Use the Dijkstra algorithm to find the shortest pipeline path connecting the burst point and the nearest resource center;

[0007] S3, determining the shortest path valve set: according to the shortest pipeline path obtained in step S2, searching for all valves on the shortest path in the pipeline network to obtain the shortest path valve set;

[0008] S4, confirming the closed valve: traverse the shortest path valve set obtained in step S3, and obtain the valve closest to the pipe burst point as the closed valve;

[0009] S5. Analysis of the impact range of pipe burst: According to the shortest pipeline path obtained in step S2, a collection of all equipment on the shortest pipeline path is obtained as a collection of all equipment affected after the pipe burst event occurs.

[0010] Optionally, in step S1, the finding of the resource center closest to the pipe burst point is specifically: finding the resource center closest to the pipe burst point in a straight line distance.

[0011] Optionally, in step S2, the use of the Dijkstra algorithm to find the shortest pipeline path connecting the burst point and the nearest resource center specifically includes:

[0012] Suppose G = (V, E) is a weighted directed graph. Divide the vertex set V in the graph into two groups. The first group is the vertex set S for which the shortest path has been found. The second group is the vertex set U for which the shortest path has not been determined. Add the vertices in the vertex set U, i.e., the resource centers, to S in ascending order of the shortest path length.

[0013] During the adding process, the shortest path length from the source point v, i.e. the burst point, to each vertex in S is always kept no longer than the shortest path length from the source point v to any vertex in U; ​​and each vertex corresponds to a distance, the distance of the vertex in the vertex set S is the shortest path length from v to this vertex, and the distance of the vertex in the vertex set U is the current shortest path length from v to this vertex that only includes vertices in S as intermediate vertices.

[0014] Optionally, in step S1, traversing the resource center list in the network further includes:

[0015] Extracting upstream pipe network structure data, downstream pipe network structure data and water user data of the target area where the pipe burst point is located according to the pipe network structure map; wherein the upstream pipe network structure data and the downstream pipe network structure data are both located in the target area;

[0016] The complexity of the pipe network is obtained by evaluating the upstream pipe network structure data and the downstream pipe network structure data, and the user complexity is obtained by evaluating the water user data;

[0017] A comprehensive complexity is calculated according to the network complexity and the user complexity, a traversal range is determined according to the comprehensive complexity, and a list of resource centers in the network is traversed within the traversal range.

[0018] Optionally, evaluating and obtaining the pipe network complexity according to the upstream pipe network structure data and the downstream pipe network structure data, and evaluating and obtaining the user complexity according to the water user data, include:

[0019] Using a convolutional network to extract features from the upstream pipe network structure data and the downstream pipe network structure data to obtain a first feature, and using a convolutional network to extract features from the water user data to obtain a second feature;

[0020] The first evaluation model is used to evaluate the first feature to obtain the network complexity, and the second evaluation model is used to evaluate the second feature to obtain the user complexity; wherein the first evaluation model and the second evaluation model are both built based on the BERT large model.

[0021] Optionally, the calculating the comprehensive complexity according to the pipe network complexity and the user complexity includes:

[0022] Determine the user activity in the target area, determine a first weight of the user complexity according to the user activity, and calculate a second weight of the pipe network complexity according to the first weight;

[0023] The comprehensive complexity is calculated based on the network complexity and the second weight, and the user complexity and the first weight.

[0024] The present invention also discloses a pipe burst analysis system based on the location of a water source point. The system includes a processor and a memory. The processor calls and executes a computer code in the memory to implement the following steps:

[0025] S1. Determine the resource center: traverse the list of resource centers in the pipe network, and find the resource center closest to the pipe burst point according to the geographical location of each resource center and the geographical location of the pipe burst point;

[0026] S2. Find the shortest path: Use the Dijkstra algorithm to find the shortest pipeline path connecting the burst point and the nearest resource center;

[0027] S3, determining the shortest path valve set: according to the shortest pipeline path obtained in step S2, searching for all valves on the shortest path in the pipeline network to obtain the shortest path valve set;

[0028] S4, confirming the closed valve: traverse the shortest path valve set obtained in step S3, and obtain the valve closest to the pipe burst point as the closed valve;

[0029] S5. Analysis of the impact range of pipe burst: According to the shortest pipeline path obtained in step S2, a collection of all equipment on the shortest pipeline path is obtained as a collection of all equipment affected after the pipe burst event occurs.

[0030] The present invention also discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement any of the above methods.

[0031] The present invention also discloses a computer storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement any of the above methods.

[0032] The present invention also discloses a computer program product, wherein the computer program product includes a computer program, and the computer program is executed by a processor of an electronic device to implement any of the above methods.

[0033] The present invention innovatively uses the location of the resource center to obtain the closed valve set and the range of affected equipment after the pipe burst event. Through the method of the present invention, the closed valve closest to the pipe burst point can be quickly and accurately found in a complex pipe network structure, effectively controlling the disaster and reducing resource waste. Compared with traditional methods, the present invention has higher accuracy and efficiency, and is particularly suitable for large and complex pipe network systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0035] Figure 1 It is a flow chart of a pipe burst analysis method based on water source point location disclosed in an embodiment of the present invention;

[0036] Figure 2 The invention discloses a pipe burst analysis system based on the location of a water source point according to an embodiment of the invention. DETAILED DESCRIPTION

[0037] The following is a description of the implementation of the present application by specific specific embodiments. People familiar with the technology can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0038] In addition, the technical features involved in the different embodiments of the present application described below can be combined with each other as long as they do not conflict with each other.

[0039] like Figure 1 As shown, an embodiment of the present invention discloses a pipe burst analysis method based on the location of a water source point, and the method comprises the following steps:

[0040] S1. Determine the resource center: traverse the list of resource centers in the pipe network, and find the resource center closest to the pipe burst point according to the geographical location of each resource center and the geographical location of the pipe burst point;

[0041] S2. Find the shortest path: Use the Dijkstra algorithm to find the shortest pipeline path connecting the burst point and the nearest resource center;

[0042] S3, determining the shortest path valve set: according to the shortest pipeline path obtained in step S2, searching for all valves on the shortest path in the pipeline network to obtain the shortest path valve set;

[0043] S4, confirming the closed valve: traverse the shortest path valve set obtained in step S3, and obtain the valve closest to the pipe burst point as the closed valve;

[0044] S5. Analysis of the impact range of pipe burst: According to the shortest pipeline path obtained in step S2, a collection of all equipment on the shortest pipeline path is obtained as a collection of all equipment affected after the pipe burst event occurs.

[0045] The present invention innovatively uses the location of the resource center to obtain the closed valve set and the range of affected equipment after the pipe burst event. Through the method of the present invention, the closed valve closest to the pipe burst point can be quickly and accurately found in a complex pipe network structure, effectively controlling the disaster and reducing resource waste. Compared with traditional methods, the present invention has higher accuracy and efficiency, and is particularly suitable for large and complex pipe network systems.

[0046] It is worth mentioning that the resource center is usually equipped with necessary emergency repair equipment and personnel and is an important response point after a pipe burst incident.

[0047] Optionally, in step S1, the finding of the resource center closest to the pipe burst point is specifically: finding the resource center closest to the pipe burst point in a straight line distance.

[0048] Optionally, in step S2, the use of the Dijkstra algorithm to find the shortest pipeline path connecting the burst point and the nearest resource center specifically includes:

[0049] Suppose G = (V, E) is a weighted directed graph. Divide the vertex set V in the graph into two groups. The first group is the vertex set S for which the shortest path has been found (initially, there is only one source point in S. Each time a shortest path is found, it is added to the set S until all vertices are added to S, and the algorithm ends). The second group is the remaining vertex set U for which the shortest path has not been determined. Add the vertices in the vertex set U (i.e., resource centers) to S in ascending order of the shortest path length.

[0050] During the adding process, the shortest path length from the source point v to each vertex in S is always kept no greater than the shortest path length from the source point v to any vertex in U; ​​and, each vertex corresponds to a distance, the distance of the vertex in the vertex set S is the shortest path length from v to this vertex, and the distance of the vertex in the vertex set U is the current shortest path length from v to this vertex that only includes vertices in S as intermediate vertices.

[0051] The specific steps of the algorithm are as follows:

[0052] (1) Initially, the vertex set S contains only the source point v (i.e., the burst point), and the distance of v is 0; the vertex set U contains all vertices except v, and the distance of vertex u in the vertex set U is the weight on the edge (if v and u have an edge or if u is not an out-edge adjacent point of v);

[0053] (2) Select a vertex k from the vertex set U that is the shortest distance from the source point v, and add vertex k to the vertex set S (the selected distance is the shortest path length from v to k);

[0054] (3) Take vertex k as the new intermediate point and modify the distances of each vertex in the vertex set U; if the distance from the source point v to the vertex u (through vertex k) is shorter than the original distance (not through vertex k), modify the distance value of vertex u, and the modified distance value is the distance of vertex k plus the weight of the edge;

[0055] (4) Repeat steps (2) and (3) until all vertices are included in the vertex set S.

[0056] Optionally, in step S1, traversing the resource center list in the network further includes:

[0057] Extracting upstream pipe network structure data, downstream pipe network structure data and water user data of the target area where the pipe burst point is located according to the pipe network structure map; wherein the upstream pipe network structure data and the downstream pipe network structure data are both located in the target area;

[0058] The complexity of the pipe network is obtained by evaluating the upstream pipe network structure data and the downstream pipe network structure data, and the user complexity is obtained by evaluating the water user data;

[0059] A comprehensive complexity is calculated according to the network complexity and the user complexity, a traversal range is determined according to the comprehensive complexity, and a list of resource centers in the network is traversed within the traversal range.

[0060] In this embodiment, the resource center list includes all resource centers in the control area. If all resource centers in the resource center list are traversed, it will obviously consume a lot of computing power and the traversal efficiency is extremely low. In this regard, the present invention sets a method for local traversal of resource centers in the resource center list, that is, by limiting the traversal range to reduce the number of resource centers that need to be traversed. Specifically:

[0061] The pipe network structure map stores the layout structure of the pipe network in each water supply area. From the pipe network structure map, the upstream pipe network structure data, the downstream pipe network structure data and the water user data in the target area where the pipe burst point is located can be extracted. Among them, the upstream pipe network structure data and the downstream pipe network structure data are both located in the target area, and the water user data refers to the types of water users in the target area within a predetermined size range (e.g., 2km) where the pipe burst point is located, such as factory types such as chemical plants and paper mills, residential types, and office types such as office buildings.

[0062] The complexity of the pipe network can be obtained by evaluating the complexity of the upstream pipe network structure data and the downstream pipe network structure data, and the user complexity can be obtained by evaluating the water user data. The comprehensive complexity of the pipe network complexity and the user complexity is then analyzed and calculated to obtain the comprehensive complexity, which is used to characterize the water supply complexity of the area where the pipe burst point is located. The traversal range is determined based on the comprehensive complexity, and then the list of resource centers in the pipe network is traversed within the traversal range. The higher the comprehensive complexity, the larger the corresponding traversal range, so that a more suitable resource center can be found within a larger traversal range.

[0063] Optionally, evaluating and obtaining the pipe network complexity according to the upstream pipe network structure data and the downstream pipe network structure data, and evaluating and obtaining the user complexity according to the water user data, include:

[0064] Using a convolutional network to extract features from the upstream pipe network structure data and the downstream pipe network structure data to obtain a first feature, and using a convolutional network to extract features from the water user data to obtain a second feature;

[0065] The first evaluation model is used to evaluate the first feature to obtain the network complexity, and the second evaluation model is used to evaluate the second feature to obtain the user complexity; wherein the first evaluation model and the second evaluation model are both built based on the BERT large model.

[0066] In this embodiment, the present invention constructs a first evaluation model and a second evaluation model based on the BERT large model, and uses the first evaluation model to perform a deep analysis on the first features of the upstream pipe network structure data and the downstream pipe network structure data, thereby obtaining the pipe network complexity; and uses the second evaluation model to perform a deep analysis on the second features of the water user data, thereby obtaining the user complexity. Among them, the first feature and the second feature are both extracted through a convolutional network, and the convolutional network can be the same network or different convolutional networks.

[0067] It is worth noting that the more complex the pipe network structure upstream and downstream of the burst point, the higher the corresponding pipe network complexity, and vice versa. User complexity is related to the types and number of water users within a predetermined size range. The more types of water users and the more users there are, the higher the user complexity, and vice versa.

[0068] Optionally, the calculating the comprehensive complexity according to the pipe network complexity and the user complexity includes:

[0069] Determine the user activity in the target area, determine a first weight of the user complexity according to the user activity, and calculate a second weight of the pipe network complexity according to the first weight;

[0070] The comprehensive complexity is calculated based on the network complexity and the second weight, and the user complexity and the first weight.

[0071] In this embodiment, the comprehensive complexity in the present invention is calculated by weighting the network complexity and the user complexity. The network complexity and the user complexity are both numerical values ​​used to characterize the complexity. The larger the numerical value, the higher the corresponding complexity. Among them, the first weight of the user complexity is determined based on the user activity in the target area, and the user activity is determined based on the total water consumption of each user in the target area within a preset statistical period. The larger the total water consumption, the higher the user activity of the corresponding user, and vice versa. In addition, the sum of the first weight and the second weight is 1. After the first weight is determined, the second weight can be calculated.

[0072] like Figure 2 As shown, an embodiment of the present invention further discloses a pipe burst analysis system based on the location of a water source point, the system comprising a processor and a memory, the processor calls and executes a computer code in the memory to implement the following steps:

[0073] S1. Determine the resource center: traverse the list of resource centers in the pipe network, and find the resource center closest to the pipe burst point according to the geographical location of each resource center and the geographical location of the pipe burst point;

[0074] S2. Find the shortest path: Use the Dijkstra algorithm to find the shortest pipeline path connecting the burst point and the nearest resource center;

[0075] S3, determining the shortest path valve set: according to the shortest pipeline path obtained in step S2, searching for all valves on the shortest path in the pipeline network to obtain the shortest path valve set;

[0076] S4, confirming the closed valve: traverse the shortest path valve set obtained in step S3, and obtain the valve closest to the pipe burst point as the closed valve;

[0077] S5. Analysis of the impact range of pipe burst: According to the shortest pipeline path obtained in step S2, a collection of all equipment on the shortest pipeline path is obtained as a collection of all equipment affected after the pipe burst event occurs.

[0078] An embodiment of the present invention further discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method described in the above embodiment.

[0079] An embodiment of the present invention further discloses a computer storage medium, wherein the computer storage medium stores a computer program, and the computer program is executed by a processor to implement the method described in the above embodiment.

[0080] An embodiment of the present invention further discloses a computer program product, wherein the computer program product includes a computer program, and the computer program is executed by a processor of an electronic device to implement any of the above methods.

[0081] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0082] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0083] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, or can be electrical, mechanical or other forms of connection.

[0084] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0085] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0086] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-On lyMemory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0087] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A pipe burst analysis method based on the location of a water source point, characterized in that: The method comprises the following steps: S1. Determine the resource center: traverse the list of resource centers in the pipe network, and find the resource center closest to the pipe burst point according to the geographical location of each resource center and the geographical location of the pipe burst point; S2. Find the shortest path: Use the Dijkstra algorithm to find the shortest pipeline path connecting the burst point and the nearest resource center; S3, determining the shortest path valve set: according to the shortest pipeline path obtained in step S2, searching for all valves on the shortest path in the pipeline network to obtain the shortest path valve set; S4, confirming the closed valve: traverse the shortest path valve set obtained in step S3, and obtain the valve closest to the pipe burst point as the closed valve; S5. Analysis of the impact range of pipe burst: According to the shortest pipeline path obtained in step S2, a collection of all equipment on the shortest pipeline path is obtained as a collection of all equipment affected after the pipe burst event occurs.

2. A pipe burst analysis method based on water source location according to claim 1, characterized in that: In step S1, the finding of the resource center closest to the pipe burst point is specifically: finding the resource center closest to the pipe burst point in a straight line.

3. A pipe burst analysis method based on water source location according to claim 2, characterized in that: In step S2, the Dijkstra algorithm is used to find the shortest pipeline path connecting the burst point and the nearest resource center, specifically including: Suppose G = (V, E) is a weighted directed graph. Divide the vertex set V in the graph into two groups. The first group is the vertex set S for which the shortest path has been found. The second group is the vertex set U for which the shortest path has not been determined. Add the vertices in the vertex set U, i.e., the resource centers, to S in ascending order of the shortest path length. During the adding process, the shortest path length from the source point v, i.e. the burst point, to each vertex in S is always kept no longer than the shortest path length from the source point v to any vertex in U; ​​and each vertex corresponds to a distance, the distance of the vertex in the vertex set S is the shortest path length from v to this vertex, and the distance of the vertex in the vertex set U is the current shortest path length from v to this vertex that only includes vertices in S as intermediate vertices.

4. A pipe burst analysis method based on water source location according to claim 3, characterized in that: In step S1, the traversal of the resource center list in the network also includes: Extracting upstream pipe network structure data, downstream pipe network structure data and water user data of the target area where the pipe burst point is located according to the pipe network structure map; wherein the upstream pipe network structure data and the downstream pipe network structure data are both located in the target area; The complexity of the pipe network is obtained by evaluating the upstream pipe network structure data and the downstream pipe network structure data, and the user complexity is obtained by evaluating the water user data; A comprehensive complexity is calculated according to the network complexity and the user complexity, a traversal range is determined according to the comprehensive complexity, and a list of resource centers in the network is traversed within the traversal range.

5. The pipe burst analysis method based on water source location according to claim 4 is characterized by: The pipe network complexity is obtained by evaluating the upstream pipe network structure data and the downstream pipe network structure data, and the user complexity is obtained by evaluating the water user data, including: Using a convolutional network to extract features from the upstream pipe network structure data and the downstream pipe network structure data to obtain a first feature, and using a convolutional network to extract features from the water user data to obtain a second feature; The first evaluation model is used to evaluate the first feature to obtain the network complexity, and the second evaluation model is used to evaluate the second feature to obtain the user complexity; wherein the first evaluation model and the second evaluation model are both built based on the BERT large model.

6. A pipe burst analysis method based on water source location according to claim 5, characterized in that: The comprehensive complexity is calculated according to the pipe network complexity and the user complexity, including: Determine the user activity in the target area, determine a first weight of the user complexity according to the user activity, and calculate a second weight of the pipe network complexity according to the first weight; The comprehensive complexity is calculated based on the network complexity and the second weight, and the user complexity and the first weight.

7. A pipe burst analysis system based on water source location, the system comprising a processor and a memory, characterized in that: The processor calls and executes the computer code in the memory to implement the following steps: S1. Determine the resource center: traverse the list of resource centers in the pipe network, and find the resource center closest to the pipe burst point according to the geographical location of each resource center and the geographical location of the pipe burst point; S2. Find the shortest path: Use the Dijkstra algorithm to find the shortest pipeline path connecting the burst point and the nearest resource center; S3, determining the shortest path valve set: according to the shortest pipeline path obtained in step S2, searching for all valves on the shortest path in the pipeline network to obtain the shortest path valve set; S4, confirming the closed valve: traverse the shortest path valve set obtained in step S3, and obtain the valve closest to the pipe burst point as the closed valve; S5. Analysis of the impact range of pipe burst: According to the shortest pipeline path obtained in step S2, a collection of all equipment on the shortest pipeline path is obtained as a collection of all equipment affected after the pipe burst event occurs.

8. An electronic device comprising: At least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method according to any one of claims 1 to 6.

9. A computer storage medium storing a computer program, characterized in that: The computer program is executed by a processor to implement the method according to any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that: The computer program is executed by a processor of an electronic device to implement the method according to any one of claims 1 to 6.

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