CN113255989A - Persistent service resource allocation method based on limited service range in road network environment - Google Patents
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Abstract
The invention discloses a persistent service resource allocation method based on a limited service range in a road network environment, which comprises the following steps: performing initial allocation on the initial position of the client; judging the movement direction of the client according to the position data at the last moment; predicting the motion range of the client according to the motion direction and the speed; obtaining a bipartite graph for prediction by combining the service range of the server and the motion range of the client; checking and adjusting the capacity of the service end according to the bipartite graph, so that all the capacities of the service end meet the capacity limit; checking and adjusting the connection condition of the client and the server according to the bipartite graph, so that one client is only correspondingly connected with one server; the final assignment relationship is obtained by adjusting the prediction result using the bipartite graph updated in step S6. The invention utilizes the pre-calculation of the motion characteristics of the client, reduces the calculation time of the distribution updating and can meet the requirement of continuous service resource distribution under the scene of quick updating.
Description
Technical Field
The invention relates to a method for realizing service resource allocation, in particular to a persistent service resource allocation method based on a limited service range in a road network environment.
Background
With the development and maturity of pervasive computing technology, positioning technology and wireless communication technology, the problem of resource allocation in a mobile computing scene becomes a new research hotspot, and the method is widely applied to the fields of communication resource scheduling, emergency response, public resource planning and the like. The allocation problem is an optimization problem for allocating resource providers (service terminals) to resource users (client terminals), and common problems include facility site selection, task allocation, resource scheduling and the like. In the service resource allocation problem, the distance is an important factor affecting the quality of service between the client and the server. For example, when a mobile phone is connected to WIFI, the closer the mobile phone is to the WIFI transmitter, the better the signal is, and the higher the service quality received by the mobile phone is, so the allocation problem is actually an optimization problem for optimizing the overall service quality.
The existing distribution method converts the relationship between the server and the client into a bipartite graph, and if the distribution relationship may exist between the server and the client, an edge exists between nodes representing the server and the client in the bipartite graph. Then, the continuous shortest path algorithm proposed by Derigs is executed in the bipartite graph to find the optimal distribution relation. The input to the continuous shortest path algorithm is a flow graph, and then γ iterations are performed on the flow graph to output all the reverse edges. In each iteration, the shortest path from the source p to the sink q is calculated and then the path is inverted for augmentation. After the last iteration, each reverse edge on the flow graph from a point in U to a point in S represents one of the best matches.
In urban geospaties, a large number of clients in a mobile computing environment are constantly moving in road networks and remain in communication with servers. Under the influence of the change of the client position, the current distribution result cannot ensure that the optimal service effect is continuously and effectively, and the server needs to quickly make adjustment according to the change condition of the client position to calculate the optimal distribution relation according with the current condition. However, the traditional service resource allocation method for the static scenario is not suitable for the persistent computation problem in the dynamic scenario, and is mainly embodied in the following three aspects:
real-time performance: due to the fact that the position of the client is frequently updated, the current distribution relation needs to be rapidly obtained according to the distribution situation of the whole user. Most of the current static algorithms have higher time complexity and cannot realize quick response.
Universality: from the urban perspective, the number and distribution of users in different areas are severely uneven in different time periods. The existing allocation algorithm can not perform effective resource allocation according to different client distribution conditions, and resource allocation imbalance is easily caused.
Updating the distribution relation: user movement is limited by a road network, so that links exist between adjacent moments when updating is frequently performed, the links can be used for helping updating the distribution relationship, existing algorithms ignore the links between the adjacent moments, a large amount of repeated and unnecessary calculation is performed, and the efficiency is low.
Due to the development of mobile terminals, new mobile scenes are continuously appearing, and allocation results need to be rapidly calculated according to the relation between a client and a server after movement, which undoubtedly provides great challenges for the current service resource allocation technology.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the persistent service resource allocation method based on the limited service range in the road network environment is provided, the pre-calculation of the motion characteristics of the client is utilized, the calculation time for allocating and updating is reduced, and the persistent service resource allocation requirement in a fast updating scene can be met.
The technical scheme is as follows: in order to achieve the above object, the present invention provides a persistent service resource allocation method based on a limited service range in a road network environment, comprising the following steps:
s1: performing initial allocation on the initial position of the client;
s2: judging the movement direction of the client according to the position data at the last moment;
s3: predicting the motion range of the client according to the motion direction and the speed of the client;
s4: obtaining a bipartite graph for prediction by combining the service range of the server and the motion range of the client;
s5: checking and adjusting the capacity of the service end according to the bipartite graph, so that all the capacities of the service end meet the capacity limit;
s6: checking and adjusting the connection condition of the client and the server according to the bipartite graph, so that one client is only correspondingly connected with one server;
s7: the final assignment relationship is obtained by adjusting the prediction result using the bipartite graph updated in step S6.
Further, the method for determining the moving direction of the client in step S2 is to use the position difference between two moments to obtain the current moving direction.
Further, the calculation of the current moving direction of the client in step S2 includes two cases, which are as follows:
case 1: two points at adjacent moments are positioned on the same edge, the offset is directly subtracted, if the difference value is greater than 0, the movement direction is along the edge, otherwise, the movement direction is reverse;
case 2: two points at adjacent moments are positioned on different edges, the intersection point of the current edge and the edge at the previous moment is calculated, and the intersection point is judged to be the starting point or the end point of the current edge, so that the current movement direction of the intersection point is obtained.
Further, the method for predicting the motion range of the client in step S3 is as follows:
a1: calculating a moving distance: the offset d' of the current moment is used for subtracting the offset d of the previous moment to obtain the moving distance delta d of the previous moment, then the range of the moving distance of the next moment is assumed to be between 0.5 delta d and 1.5 delta d, namely the farthest distance of the movement is 1.5 delta d, the nearest distance is 0.5 delta d, and the moving range is added to the current offset along the moving direction to obtain the client position range of the next moment, namely USi.l＝USi-1.l+Δd；
A2: and predicting the motion range of the client by combining the motion direction and the moving distance of the client.
Further, the client location range at the next time in step a1 includes two cases, which are respectively as follows:
case 1: adding the current offset and the moving range to obtain the client position range at the next moment if the current offset plus the moving range is less than or equal to the length of the current edge;
case 2: and if the current offset plus the moving range is greater than the length of the current edge, subtracting the edge length from the current offset plus the moving range to obtain an overflow value, and finding adjacent edges according to the end points of the edges, wherein the range of each adjacent edge from the starting point is the client position range at the next moment.
Further, the method for acquiring the bipartite graph in step S4 includes: because the position of the server is fixed and unchangeable, an index is established for each edge in the road network in the preprocessing process, elements in an index list of each edge are the server on the edge, the server is ordered according to the offset of the server on the current edge from small to large, the index on the right is the index of each edge in a broken line frame on the left, then an empty bipartite graph is newly established, a client list is traversed, the ID of the edge where the client is located is found for each client, an inverted list of the client is searched, a corresponding server set is found according to the range, the server set and the edge between the client corresponding to the server set are added into the bipartite graph, and the predicted bipartite graph is obtained.
Further, the method for adjusting the server side exceeding the capacity limit in step S5 is as follows: maintaining the connection of the server by adopting a maximum heap, taking an edge at the top of the heap every time, checking whether the edge is the minimum cost edge or the only edge of the corresponding client, if not, deleting the edge until the number of the remaining edges meets the upper limit of the capacity or traversing the connection of the whole server, and if the edge is the only connection edge or the minimum cost edge of the corresponding client, continuously popping the edge from the top of the heap; and adopting a second traversal by utilizing a heuristic algorithm, deleting the edge as long as the connection is not the only connection edge of the corresponding client during the second traversal, and meeting the capacity limit of all the servers after the two traversals.
Further, the method for adjusting the connection between the client and the server in step S6 includes: and for the conflict of more than one server connected by the client, sequencing the connected edges, wherein the server already meets the capacity limit, so that only the connecting edge with the minimum cost is reserved for each client, and the conflict of the client is solved, namely the conflict with the minimum cost is distributed.
Further, the method for obtaining the final assignment relationship in step S7 is as follows: after the client snapshot at the current moment is updated, converting the relationship between the client and the server in the snapshot at the current moment into a bipartite graph; the bipartite graph at the current time is still a many-to-many bipartite graph, but the conflicts existing in the bipartite graph are not needed to be solved one by one, because a prediction-based distribution already exists in the previous step, next, the predicted distribution is traversed, whether each distribution edge of the distribution edge exists in the bipartite graph representing the relationship between the client and the server at the current time is checked, if the distribution edge exists, namely the prediction result is reasonable for the current time, the edge is deleted from the bipartite graph at the current time, the client connected with the edge is deleted in the bipartite graph at the current time, the capacity of the corresponding server is also reduced by 1, after the predicted distribution is traversed, the correct prediction result is also obtained, only the part which is not predicted correctly remains at the time, the correct prediction edge is deleted, and the capacity of the corresponding server is also reduced by the corresponding number of clients, for the remainder, the assignment of the remainder is calculated using a classical continuous shortest path algorithm.
The invention adopts a method of first prediction and second correction, firstly, the initial position of a client is initially distributed by adopting a classical continuous shortest path algorithm, then the range which is possible to appear in the next snapshot is predicted according to the moving direction of the client on a road network, all distribution relations which are possible to exist in the range are analyzed, then a heuristic algorithm is adopted to obtain a predicted optimal distribution, the predicted correct rate is checked according to the updated position of the client and the relation between the client and a service end after the client snapshot is updated at the next moment, the correct distribution is directly pruned for the predicted distribution, and the residual continuous shortest path algorithm is executed to obtain the final distribution.
Has the advantages that: compared with the prior art, the invention has the following advantages:
1. the road network information and the motion information of the client are used for pre-calculation to obtain the predicted distribution condition, so that the calculation cost after data updating is reduced.
2. And a new heuristic method is adopted to replace the iterative computation of the existing method to increase the path solution, and the relation between adjacent moments is applied to distribution, so that the time consumption of a conflict solution stage is greatly saved, and the distribution efficiency is improved.
3. The method is suitable for all scenes needing to distribute service resources, such as WIFI resource distribution, electric vehicle charging pile distribution, base station signal distribution and the like, has a wide application range, and solves the problem that the existing distribution algorithm cannot effectively distribute resources according to different client distribution conditions and easily causes resource distribution unbalance.
4. The method can meet the requirement of low delay of persistent allocation under the condition of frequent updating at a high updating speed, solves the problems that most of the current static algorithms have high time complexity and cannot realize quick response, and has good market application value.
Drawings
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic diagram of a bipartite graph;
FIG. 3 is a comparison graph of the computation time of the method and the classical method (continuous shortest path method) when the capacity of the server side changes;
FIG. 4 is a graph of update interval versus runtime;
FIG. 5 is a graph of service scope versus runtime;
FIG. 6 is a graph of client count versus runtime;
FIG. 7 is a diagram of the relationship between server number and runtime;
FIG. 8 is a graph of data size versus run time for the process of the present invention.
Detailed Description
The present invention is further illustrated by the following figures and specific examples, which are to be understood as illustrative only and not as limiting the scope of the invention, which is to be given the full breadth of the appended claims and any and all equivalent modifications thereof which may occur to those skilled in the art upon reading the present specification.
As shown in fig. 1, the present invention provides a persistent service resource allocation method based on a limited service range in a road network environment, which includes the following steps:
s1: performing initial allocation on the initial position of the client;
s2: judging the movement direction of the client according to the position data at the last moment;
s3: predicting the motion range of the client according to the motion direction and the speed of the client;
s4: obtaining a bipartite graph for prediction by combining the service range of the server and the motion range of the client;
s5: checking and adjusting the capacity of the service end according to the bipartite graph, so that all the capacities of the service end meet the capacity limit;
s6: checking and adjusting the connection condition of the client and the server according to the bipartite graph, so that one client is only correspondingly connected with one server;
s7: the final assignment relationship is obtained by adjusting the prediction result using the bipartite graph updated in step S6.
In this embodiment, the method for determining the moving direction of the client in step S2 is to use the position difference between two time instants to obtain the current moving direction. The calculation of the current motion direction of the client includes two cases, which are respectively as follows:
case 1: two points at adjacent moments are positioned on the same edge, the offset is directly subtracted, if the difference value is greater than 0, the movement direction is along the edge, otherwise, the movement direction is reverse;
case 2: two points at adjacent moments are positioned on different edges, the intersection point of the current edge and the edge at the previous moment is calculated, and the intersection point is judged to be the starting point or the end point of the current edge, so that the current movement direction of the intersection point is obtained.
The method for predicting the motion range of the client in step S3 in this embodiment is as follows:
a1: calculating a moving distance: the offset d' of the current moment is used for subtracting the offset d of the previous moment to obtain the moving distance delta d of the previous moment, then the range of the moving distance of the next moment is assumed to be between 0.5 delta d and 1.5 delta d, namely the farthest distance of the movement is 1.5 delta d, the nearest distance is 0.5 delta d, and the moving range is added to the current offset along the moving direction to obtain the client position range of the next moment, namely USi.l＝USi-1.l+Δd；
A2: and predicting the motion range of the client by combining the motion direction and the moving distance of the client.
The client location range at the next time in step a1 includes two cases, which are respectively as follows:
case 1: adding the current offset and the moving range to obtain the client position range at the next moment if the current offset plus the moving range is less than or equal to the length of the current edge;
case 2: and if the current offset plus the moving range is greater than the length of the current edge, subtracting the edge length from the current offset plus the moving range to obtain an overflow value, and finding adjacent edges according to the end points of the edges, wherein the range of each adjacent edge from the starting point is the client position range at the next moment.
The method for acquiring the bipartite graph in step S4 in this embodiment is as follows: because the position of the server is fixed and unchanged, an index is established for each edge in the road network in the preprocessing process, elements in an index list of each edge are the server on the edge, and are sorted from small to large according to the offset of the current edge, as shown in fig. 2, the index on the right is the index of each edge in a broken line frame on the left, then an empty bipartite graph is newly established, a client list is traversed, the ID of the edge where each client is located is found for each client, an inverted list of the client is found, a corresponding server set is found according to the range, the edge between the server set and the corresponding client is added into the bipartite graph, and the predicted bipartite graph is obtained.
In step S5 of this embodiment, the method for adjusting the server that exceeds the capacity limit includes: maintaining the connection of the server by adopting a maximum heap, taking an edge at the top of the heap every time, checking whether the edge is the minimum cost edge or the only edge of the corresponding client, if not, deleting the edge until the number of the remaining edges meets the upper limit of the capacity or traversing the connection of the whole server, and if the edge is the only connection edge or the minimum cost edge of the corresponding client, continuously popping the edge from the top of the heap; after the first traversal, there still may exist a server exceeding the capacity, for example, if all connections of the server are minimum-cost connections corresponding to the client, the server cannot be deleted during the first traversal, a heuristic algorithm is used to perform a second traversal, and during the second traversal, as long as the connection is not the only connection edge corresponding to the client, the edge is deleted, after the two traversals, all the servers have satisfied the capacity limit, and the conflict that the server exceeds the capacity limit is resolved.
In this embodiment, the method for checking the connection between the client and the server in step S6 includes: since a client can only receive the services provided by one server, it is not desirable if more than one server is connected to a client. The client list needs to be traversed to check whether more than one server is connected to any client.
In this embodiment, the method for adjusting the connection between the client and the server in step S6 includes: and for the conflict of more than one server connected by the client, sequencing the connected edges, wherein the server already meets the capacity limit, so that only the connecting edge with the minimum cost is reserved for each client, and the conflict of the client is solved, namely the conflict with the minimum cost is distributed.
The method for acquiring the final assignment relationship in step S7 in this embodiment is as follows: after the client snapshot at the current moment is updated, converting the relationship between the client and the server in the snapshot at the current moment into a bipartite graph; the bipartite graph at the current time is still a many-to-many bipartite graph, but the conflicts existing in the bipartite graph are not needed to be solved one by one, because a prediction-based distribution already exists in the previous step, next, the predicted distribution is traversed, whether each distribution edge of the distribution edge exists in the bipartite graph representing the relationship between the client and the server at the current time is checked, if the distribution edge exists, namely the prediction result is reasonable for the current time, the edge is deleted from the bipartite graph at the current time, the client connected with the edge is deleted in the bipartite graph at the current time, the capacity of the corresponding server is also reduced by 1, after the predicted distribution is traversed, the correct prediction result is also obtained, only the part which is not predicted correctly remains at the time, the correct prediction edge is deleted, and the capacity of the corresponding server is also reduced by the corresponding number of clients, for the remainder, the assignment of the remainder is calculated using a classical continuous shortest path algorithm.
The embodiment also provides a persistent service resource allocation system based on a limited service range in a road network environment, which comprises a network interface, a memory and a processor; the network interface is used for receiving and sending signals in the process of receiving and sending information with other external network elements; a memory for storing computer program instructions executable on the processor; a processor for, when executing the computer program instructions, performing the steps of the consensus method described above.
The present embodiment also provides a computer storage medium storing a computer program that when executed by a processor can implement the method described above. The computer-readable medium may be considered tangible and non-transitory. Non-limiting examples of a non-transitory tangible computer-readable medium include a non-volatile memory circuit (e.g., a flash memory circuit, an erasable programmable read-only memory circuit, or a mask read-only memory circuit), a volatile memory circuit (e.g., a static random access memory circuit or a dynamic random access memory circuit), a magnetic storage medium (e.g., an analog or digital tape or hard drive), and an optical storage medium (e.g., a CD, DVD, or blu-ray disc), among others. The computer program includes processor-executable instructions stored on at least one non-transitory tangible computer-readable medium. The computer program may also comprise or rely on stored data. The computer programs may include a basic input/output system (BIOS) that interacts with the hardware of the special purpose computer, a device driver that interacts with specific devices of the special purpose computer, one or more operating systems, user applications, background services, background applications, and the like.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Based on the technical solution provided above, in order to verify the effect of the method of the present invention, the above solution is applied as an example in this embodiment, which is specifically as follows:
in the experimental verification, a real data set is adopted for carrying out an experiment, wherein client data is Shanghai taxi GPS track data, 13000 taxies in Shanghai are used for generating real GPS track data in No. 4/1 of 2015, and the sampling frequency is once every 10 seconds. The server data set extracts the position information of public facilities such as subway stations on the OSM map, and the number of the server data sets is 200. The road network information is real road network data of Shanghai city on the OSM map.
The distribution is carried out by respectively utilizing the method of the invention and a classical method (a continuous shortest path method), and the specific experimental data integration result is as follows:
as can be seen from fig. 3, the running time of the method of the present invention is much smaller than that of the classical method, and although the server capacity is changing, the total allocation number is not changing, so that the running time of the algorithm is not affected.
As can be seen from fig. 4, in order to study the influence of the update frequency on the algorithm, the original data is extracted in the embodiment, the time intervals of the location update are respectively set to 10 to 50 seconds, and the location of the client is sampled once at regular time intervals, so that the method disclosed by the invention is superior to the classical method under different update frequencies.
As can be seen from fig. 5, as the service scope increases, both the algorithm running time and the allocation cost increase, because as the service scope increases, the server can cover more clients that were not within any server scope before, so that the clients are included in the overall allocation, and therefore the remaining allocation quantity also increases gradually, which results in the increase of the algorithm running time and the allocation cost, but the increase of the running time of the method of the present invention is significantly smaller than that of the classical method, so the allocation cost is also smaller than that of the classical method.
As can be seen from fig. 6 and 7, as the number of clients and the number of servers increase, the running time of the method of the present invention increases by a much smaller magnitude than that of the classical method, and both of them are within ten seconds, which is enough to show that the performance of the method of the present invention is higher than that of the classical allocation method.
Fig. 8 is a comparison graph of the running times of the various stages of the method of the present invention, and it can be seen that the step with the highest proportion of time consumption is the predicted allocation stage, and the prediction range and the adjustment allocation stage are both much shorter than the predicted allocation stage, which cannot be realized by the classical method.
Claims (9)
1. The persistent service resource allocation method based on the limited service range under the road network environment is characterized by comprising the following steps of:
s1: performing initial allocation on the initial position of the client;
s2: judging the movement direction of the client according to the position data at the last moment;
s3: predicting the motion range of the client according to the motion direction and the speed of the client;
s4: obtaining a bipartite graph for prediction by combining the service range of the server and the motion range of the client;
s5: checking and adjusting the capacity of the service end according to the bipartite graph, so that all the capacities of the service end meet the capacity limit;
s6: checking and adjusting the connection condition of the client and the server according to the bipartite graph, so that one client is only correspondingly connected with one server;
s7: the final assignment relationship is obtained by adjusting the prediction result using the bipartite graph updated in step S6.
2. The method for allocating continuous service resources based on limited service scope in road network environment according to claim 1, wherein the method for determining the moving direction of the client in step S2 is to use the position difference between two time points to obtain the current moving direction.
3. The method for allocating continuous service resources based on limited service scope in road network environment according to claim 2, wherein the calculation of the current moving direction of the client in step S2 includes two cases, which are as follows:
case 1: two points at adjacent moments are positioned on the same edge, the offset is directly subtracted, if the difference value is greater than 0, the movement direction is along the edge, otherwise, the movement direction is reverse;
case 2: two points at adjacent moments are positioned on different edges, the intersection point of the current edge and the edge at the previous moment is calculated, and the intersection point is judged to be the starting point or the end point of the current edge, so that the current movement direction of the intersection point is obtained.
4. The method for allocating continuous service resources based on limited service scope in road network environment according to claim 1, wherein the method for predicting the client' S motion scope in step S3 is:
a1: calculating a moving distance: the offset d' of the current moment is used for subtracting the offset d of the previous moment to obtain the moving distance delta d of the previous moment, then the range of the moving distance of the next moment is assumed to be between 0.5 delta d and 1.5 delta d, namely the farthest distance of the movement is 1.5 delta d, the nearest distance is 0.5 delta d, and the moving range is added to the current offset along the moving direction to obtain the client position range of the next moment, namely USi.l＝USi-1.l+Δd；
A2: and predicting the motion range of the client by combining the motion direction and the moving distance of the client.
5. The method for allocating continuous service resources based on limited service scope in road network environment according to claim 4, wherein the client location scope at the next time in step A1 includes two cases, which are as follows:
case 1: adding the current offset and the moving range to obtain the client position range at the next moment if the current offset plus the moving range is less than or equal to the length of the current edge;
case 2: and if the current offset plus the moving range is greater than the length of the current edge, subtracting the edge length from the current offset plus the moving range to obtain an overflow value, and finding adjacent edges according to the end points of the edges, wherein the range of each adjacent edge from the starting point is the client position range at the next moment.
6. The method for allocating continuous service resources based on limited service scope in road network environment according to claim 1, wherein the acquisition method of the bipartite graph in step S4 is as follows: establishing an index for each edge in the road network in the preprocessing process, wherein elements in an index list of each edge are service ends on the edge and are sorted from small to large according to the offset of the current edge, the index on the right is the index of each edge in a broken line frame on the left, then establishing an empty bipartite graph, traversing a client list, searching for an ID (identity) of the edge of each client, searching for an inverted list of the client, searching for a corresponding service end set according to the range, and adding the service end set and the edge between the corresponding clients into the bipartite graph to obtain a predicted bipartite graph.
7. The method for allocating continuous service resources based on limited service scope in road network environment according to claim 1, wherein the method for adjusting the service end exceeding the capacity limit in step S5 is as follows: maintaining the connection of the server by adopting a maximum heap, taking an edge at the top of the heap every time, checking whether the edge is the minimum cost edge or the only edge of the corresponding client, if not, deleting the edge until the number of the remaining edges meets the upper limit of the capacity or traversing the connection of the whole server, and if the edge is the only connection edge or the minimum cost edge of the corresponding client, continuously popping the edge from the top of the heap; and adopting a second traversal by utilizing a heuristic algorithm, deleting the edge as long as the connection is not the only connection edge of the corresponding client during the second traversal, and meeting the capacity limit of all the servers after the two traversals.
8. The method for allocating continuous service resources based on limited service scope in road network environment according to claim 1, wherein the method for adjusting the connection between the client and the server in step S6 comprises: and for the conflict of more than one server connected by the client, sequencing the connected edges, wherein the server already meets the capacity limit, so that only the connecting edge with the minimum cost is reserved for each client, and the conflict of the client is solved, namely the conflict with the minimum cost is distributed.
9. The method for allocating persistent service resources based on limited service scope in road network environment according to claim 1, wherein the method for obtaining the final allocation relationship in step S7 is as follows: after the client snapshot at the current moment is updated, converting the relationship between the client and the server in the snapshot at the current moment into a bipartite graph; traversing the predicted distribution, checking whether each distribution edge exists in a bipartite graph representing the relationship between the client and the server at the current time, if so, namely the predicted result is reasonable for the current time, deleting the edge from the bipartite graph at the current time, deleting the client connected with the edge in the bipartite graph at the current time, reducing the capacity of the corresponding server by 1, obtaining the correct predicted result after traversing the predicted distribution, only leaving the part without correct prediction in the size of the bipartite graph at the moment, deleting the correct predicted edge, simultaneously reducing the capacity of the corresponding server by the number of the corresponding clients, and calculating the distribution of the rest part by adopting a classical continuous shortest path algorithm for the rest part.
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