A method and system for simulating road network operation under traffic events

Abstract

The embodiments of the present invention relate to the technical field of data analysis, and specifically, to a method and system for simulating road network operation under traffic events. Through a first traffic event description mining strategy and a second traffic event description mining strategy in a first road network state knowledge vector analysis component, event-road network linkage knowledge vector set mining and event state knowledge vector set mining are performed on the predicted road network operation data, which can fully consider the state characteristics of the traffic event itself and the road network impact characteristics caused by the traffic event, thereby enriching the processing basis for road network operation prediction, and by performing road network operation prediction on the event state knowledge vector set, the event-road network linkage knowledge vector set and the second road network state knowledge vector set, it is possible to consider characteristics of different dimensions as much as possible to improve the accuracy and credibility of road network operation trend data.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method and system for simulating road network operation under a traffic event. Background Art

[0002] As an emerging technology that is gaining increasing attention, road traffic system simulation can reproduce the current traffic operation status and complete the quantitative evaluation and analysis of traffic organization plans, thereby providing quantitative decision-making basis for traffic organization management, traffic incident warning and evaluation, traffic infrastructure planning and construction, and traffic policy feasibility analysis. As one of the tasks of road traffic system simulation, road network operation prediction is widely used in important transportation hubs such as airports and stations. However, traditional road network operation prediction technology has problems of low accuracy and reliability. Summary of the invention

[0003] In order to at least overcome the above-mentioned deficiencies in the prior art, one of the objectives of the present invention is to provide a method and system for simulating road network operation under traffic events.

[0004] The embodiment of the present invention provides a method for simulating road network operation under traffic events, which is applied to a road network operation simulation system. The method includes: Acquiring the road network operation data to be predicted, and performing knowledge mining on the road network operation data to be predicted to obtain a first road network state knowledge vector set of the road network operation data to be predicted; The first road network state knowledge vector set is loaded into the first road network state knowledge vector analysis component of the debugged road network operation simulation algorithm, and the event-road network linkage knowledge vector set in the road network operation data to be predicted is obtained through the first traffic event description mining strategy of the first road network state knowledge vector analysis component, and the event state knowledge vector set in the road network operation data to be predicted is obtained through the second traffic event description mining strategy of the first road network state knowledge vector analysis component; Performing feature downsampling on the first road network state knowledge vector set, the event-road network linkage knowledge vector set, and the event state knowledge vector set through a downsampling component of the road network operation simulation algorithm to obtain a second road network state knowledge vector set; The second road network state knowledge vector analysis component of the road network operation simulation algorithm processes the second road network state knowledge vector set and the set operation core, and aggregates the processed knowledge vector set with the first road network state knowledge vector set, the event-road network linkage knowledge vector set, and the event state knowledge vector set to obtain a target road network state knowledge vector set; Through the road network operation prediction component of the road network operation simulation algorithm, the road network operation prediction is performed on the target road network state knowledge vector set to obtain the road network operation trend data in the road network operation data to be predicted.

[0005] Optionally, the step of loading the first road network state knowledge vector set into a first road network state knowledge vector analysis component of a debugged road network operation simulation algorithm, obtaining an event-road network linkage knowledge vector set in the road network operation data to be predicted through a first traffic event description mining strategy of the first road network state knowledge vector analysis component, and obtaining an event state knowledge vector set in the road network operation data to be predicted through a second traffic event description mining strategy of the first road network state knowledge vector analysis component includes: By means of the dynamic reversible unit of the first road network state knowledge vector analysis component, the first road network state knowledge vector set is multiplied by the first importance list to obtain an event-road network linkage knowledge vector set; And through the static reversible unit of the first road network state knowledge vector analysis component, the first road network state knowledge vector set is multiplied by the second importance list to obtain an event state knowledge vector set.

[0006] Optionally, before the downsampling component of the simulation algorithm running through the road network performs feature downsampling on the first road network state knowledge vector set, the event-road network linkage knowledge vector set and the event state knowledge vector set to obtain the second road network state knowledge vector set, the method further includes: The event-road network linkage knowledge vector set and the event state knowledge vector set are summed up by the summation component of the first road network state knowledge vector analysis component to obtain a first aggregated knowledge vector set; Performing sliding averaging on the first aggregated knowledge vector set through a sliding averaging unit of the first road network state knowledge vector analysis component to obtain a processed first aggregated knowledge vector set; By using the knowledge compression component of the first road network state knowledge vector analysis component, the processed first aggregated knowledge vector set is compressed to obtain a second aggregated knowledge vector set; Determine a first characteristic variable set and a second characteristic variable set of the second aggregated knowledge vector set by disassembling components of the first road network state knowledge vector analysis component, wherein a result obtained by summing the first characteristic variable set and the second characteristic variable set is a set parameter set; The downsampling component of the road network simulation algorithm performs feature downsampling on the first road network state knowledge vector set, the event-road network linkage knowledge vector set, and the event state knowledge vector set to obtain a second road network state knowledge vector set, including: Through the downsampling component of the road network running simulation algorithm, feature downsampling is performed on the first road network state knowledge vector set, the event-road network linkage knowledge vector set, the event state knowledge vector set, the first feature variable set and the second feature variable set to obtain a second road network state knowledge vector set.

[0007] Optionally, the downsampling component of the road network simulation algorithm performs feature downsampling on the first road network state knowledge vector set, the event-road network linkage knowledge vector set, the event state knowledge vector set, the first feature variable set, and the second feature variable set to obtain a second road network state knowledge vector set, including: The first road network state knowledge vector set, the event-road network linkage knowledge vector set, the event state knowledge vector set, the first feature variable set and the second feature variable set are fused by the knowledge vector set fusion component of the downsampling component to obtain a third road network state knowledge vector set; The third road network state knowledge vector set is subjected to average feature downsampling by the execution unit of the downsampling component to obtain a second road network state knowledge vector set.

[0008] Optionally, the knowledge vector set fusion component of the downsampling component fuses the first road network state knowledge vector set, the event-road network linkage knowledge vector set, the event state knowledge vector set, the first feature variable set, and the second feature variable set to obtain a third road network state knowledge vector set, including: Performing a multiplication operation on the event-road network linkage knowledge vector set and the first characteristic variable set to obtain a first intermediate knowledge vector set; Performing a multiplication operation on the event state knowledge vector set and the second feature variable set to obtain a second intermediate knowledge vector set; The first intermediate knowledge vector set, the second intermediate knowledge vector set and the first road network state knowledge vector set are summed to obtain a third road network state knowledge vector set.

[0009] Optionally, the second road network state knowledge vector analysis component of the road network running simulation algorithm processes the second road network state knowledge vector set and the set operation core, and aggregates the processed knowledge vector set with the first road network state knowledge vector set, the event-road network linkage knowledge vector set, and the event state knowledge vector set to obtain a target road network state knowledge vector set, including: The first road network state knowledge vector set, the event-road network linkage knowledge vector set, the event state knowledge vector set and the second road network state knowledge vector set are merged by the first splicing component of the second road network state knowledge vector analysis component to obtain a third aggregated knowledge vector set; Performing sliding averaging on the second road network state knowledge vector set by a sliding averaging unit of the second road network state knowledge vector analysis component, and processing the second road network state knowledge vector set after sliding averaging by a computing core to obtain a target vector set; The third aggregated knowledge vector set, the target vector set and the second road network state knowledge vector set are aggregated by the second splicing component of the second road network state knowledge vector analysis component to obtain a target road network state knowledge vector set.

[0010] Optionally, the second concatenation component of the second road network state knowledge vector analysis component aggregates the third aggregated knowledge vector set, the target vector set, and the second road network state knowledge vector set to obtain a target road network state knowledge vector set, including: Performing a multiplication operation on the third aggregated knowledge vector set and the target vector set to obtain a third intermediate knowledge vector set; The third intermediate knowledge vector set and the second road network state knowledge vector set are summed to obtain a target road network state knowledge vector set.

[0011] An embodiment of the present invention also provides a road network operation simulation system, including a processor and a memory and a bus connected to the processor; wherein the processor and the memory communicate with each other through the bus; the processor is used to call program instructions in the memory to execute the above-mentioned road network operation simulation method under traffic events.

[0012] The embodiment of the present invention further provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the above-mentioned road network operation simulation method under traffic events is implemented.

[0013] Beneficial Effects The present invention provides a method and system for simulating road network operation under traffic events, which obtains road network operation data to be predicted, performs knowledge mining on the road network operation data to be predicted, and obtains a first road network state knowledge vector set of the road network operation data to be predicted; loads the first road network state knowledge vector set into a first road network state knowledge vector analysis component of a road network operation simulation algorithm that has been debugged, obtains an event-road network linkage knowledge vector set in the road network operation data to be predicted through a first traffic event description mining strategy of the first road network state knowledge vector analysis component, and obtains an event state knowledge vector set in the road network operation data to be predicted through a second traffic event description mining strategy of the first road network state knowledge vector analysis component; obtains a downsampling group of the road network operation simulation algorithm; The invention relates to a method for performing feature downsampling on the first road network state knowledge vector set, the event state knowledge vector set and the event-road network linkage knowledge vector set to obtain a second road network state knowledge vector set; the second road network state knowledge vector set and a set operation core are processed by a second road network state knowledge vector analysis component of the road network operation simulation algorithm, and the processed knowledge vector set is aggregated with the first road network state knowledge vector set, the event-road network linkage knowledge vector set and the event state knowledge vector set to obtain a target road network state knowledge vector set; the target road network state knowledge vector set is predicted by a road network operation prediction component of the road network operation simulation algorithm to obtain road network operation trend data in the road network operation data to be predicted.

[0014] It can be seen that through the first traffic event description mining strategy and the second traffic event description mining strategy in the first road network state knowledge vector analysis component, the event-road network linkage knowledge vector set mining and the event state knowledge vector set mining are carried out on the predicted road network operation data, which can fully consider the state characteristics of the traffic event itself and the road network impact characteristics caused by the traffic event, thereby enriching the processing basis for road network operation prediction, and by performing road network operation prediction on the event state knowledge vector set, the event-road network linkage knowledge vector set and the second road network state knowledge vector set, it is possible to consider the characteristics of different dimensions as much as possible to improve the accuracy and credibility of the road network operation trend data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are 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 creative work.

[0016] Figure 1The present invention provides a flow chart of a method for simulating road network operation in the event of a traffic incident.

[0017] Figure 2 A block diagram of a road network operation simulation system provided by an embodiment of the present invention.

[0018] icon: 100-Road network operation simulation system; 101 - processor; 102 - memory; 103 - bus. DETAILED DESCRIPTION

[0019] The exemplary embodiments disclosed in the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0020] In order to better understand the above technical scheme, the technical scheme of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical scheme of the present invention, rather than limitations on the technical scheme of the present invention. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0021] Figure 1 This is a flow chart of a method for simulating road network operation under traffic events according to an embodiment of the present invention, which is applied to a road network operation simulation system and includes S110-S150.

[0022] S110: Acquire the road network operation data to be predicted, and perform knowledge mining on the road network operation data to be predicted to obtain a first road network state knowledge vector set of the road network operation data to be predicted.

[0023] The road network operation data to be predicted may be the road network operation data for the airport area, and knowledge mining is used to extract characteristic information of the road network operation data to be predicted. Based on this, the road network state knowledge vector set can be understood as a road network state characteristic matrix.

[0024] S120. Load the first road network state knowledge vector set into the first road network state knowledge vector analysis component of the debugged road network operation simulation algorithm, obtain the event-road network linkage knowledge vector set in the road network operation data to be predicted through the first traffic event description mining strategy of the first road network state knowledge vector analysis component, and obtain the event state knowledge vector set in the road network operation data to be predicted through the second traffic event description mining strategy of the first road network state knowledge vector analysis component.

[0025] Among them, the road network operation simulation algorithm can be a long short-term memory neural network, the road network state knowledge vector analysis component can be understood as a road network state feature calculation subnet, and the traffic event description mining strategy can be understood as a mining rule for traffic event features. On this basis, the event-road network linkage knowledge vector set can be understood as an event-road network interaction feature matrix, and the event state knowledge vector set can be understood as a state feature matrix of traffic events. For example, a traffic event can be a PRT smart car docking event, an airport vehicle entrance and exit congestion event, a traffic control event around the airport, etc.

[0026] S130. Perform feature downsampling on the first road network state knowledge vector set, the event-road network linkage knowledge vector set and the event state knowledge vector set through the downsampling component of the road network simulation algorithm to obtain a second road network state knowledge vector set.

[0027] S140. Process the second road network state knowledge vector set and the set operation core through the second road network state knowledge vector analysis component of the road network running simulation algorithm, and aggregate the processed knowledge vector set with the first road network state knowledge vector set, the event-road network linkage knowledge vector set, and the event state knowledge vector set to obtain the target road network state knowledge vector set.

[0028] The set operation kernel may be an activation operator or an activation function. Therefore, the second road network state knowledge vector analysis component of the road network operation simulation algorithm may be used to perform nonlinear operation processing on the second road network state knowledge vector set and the set operation kernel, and obtain the target road network state knowledge vector set through feature fusion.

[0029] S150. Performing road network operation prediction on the target road network state knowledge vector set through the road network operation prediction component of the road network operation simulation algorithm to obtain road network operation trend data in the road network operation data to be predicted.

[0030] Among them, the target road network state knowledge vector set can be processed through the road network operation prediction layer of the road network operation simulation algorithm, thereby realizing road network operation prediction to obtain road network operation trend data.

[0031] In summary, S110-S150 are applied to obtain the road network operation data to be predicted, and knowledge mining is performed on the road network operation data to be predicted to obtain a first road network state knowledge vector set of the road network operation data to be predicted; the first road network state knowledge vector set is loaded into the first road network state knowledge vector analysis component of the debugged road network operation simulation algorithm, and the event-road network linkage knowledge vector set in the road network operation data to be predicted is obtained through the first traffic event description mining strategy of the first road network state knowledge vector analysis component, and the event state knowledge vector set in the road network operation data to be predicted is obtained through the second traffic event description mining strategy of the first road network state knowledge vector analysis component; the downsampling component of the road network operation simulation algorithm is used to mine the The first road network state knowledge vector set, the event state knowledge vector set and the event-road network linkage knowledge vector set are feature down-sampled to obtain a second road network state knowledge vector set; the second road network state knowledge vector set and the set operation core are processed by the second road network state knowledge vector analysis component of the road network operation simulation algorithm, and the processed knowledge vector set is aggregated with the first road network state knowledge vector set, the event-road network linkage knowledge vector set and the event state knowledge vector set to obtain a target road network state knowledge vector set; the target road network state knowledge vector set is predicted by the road network operation prediction component of the road network operation simulation algorithm to obtain the road network operation trend data in the road network operation data to be predicted.

[0032] It can be seen that by using the first traffic event description mining strategy and the second traffic event description mining strategy in the first road network state knowledge vector analysis component, the event-road network linkage knowledge vector set mining and the event state knowledge vector set mining are performed on the predicted road network operation data, which can fully consider the state characteristics of the traffic event itself and the road network impact characteristics caused by the traffic event, thereby enriching the processing basis for road network operation prediction, and by performing road network operation prediction on the event state knowledge vector set, the event-road network linkage knowledge vector set and the second road network state knowledge vector set, it is possible to consider the characteristics of different dimensions as much as possible, so as to improve the accuracy and credibility of the road network operation trend data. In some examples, the first road network state knowledge vector set is loaded into the first road network state knowledge vector analysis component of the debugged road network operation simulation algorithm, and the event-road network linkage knowledge vector set in the road network operation data to be predicted is obtained through the first traffic event description mining strategy of the first road network state knowledge vector analysis component, and the event state knowledge vector set in the road network operation data to be predicted is obtained through the second traffic event description mining strategy of the first road network state knowledge vector analysis component, that is, S120, including S121 and S122.

[0033] S121. Using the dynamic reversible unit of the first road network state knowledge vector analysis component, multiply the first road network state knowledge vector set by the first importance list to obtain an event-road network linkage knowledge vector set.

[0034] S122. Using the static reversible unit of the first road network state knowledge vector analysis component, multiply the first road network state knowledge vector set by the second importance list to obtain an event state knowledge vector set.

[0035] Among them, the dynamic reversible unit and the static reversible unit can be understood as an adjustable residual unit and a fixed residual unit, respectively, and the importance list can be understood as a weight matrix. On this basis, the event-road network linkage knowledge vector set and the event state knowledge vector set can be accurately determined through different reversible units and different importance lists.

[0036] In some optional embodiments, before the downsampling component of the simulation algorithm running through the road network performs feature downsampling on the first road network state knowledge vector set, the event-road network linkage knowledge vector set and the event state knowledge vector set to obtain the second road network state knowledge vector set, that is, before S130, the method also includes S210-S240.

[0037] S210. The event-road network linkage knowledge vector set and the event state knowledge vector set are summed up by the summation component of the first road network state knowledge vector analysis component to obtain a first aggregated knowledge vector set.

[0038] S220. Perform sliding averaging on the first aggregated knowledge vector set through a sliding averaging unit of the first road network state knowledge vector analysis component to obtain a processed first aggregated knowledge vector set.

[0039] S230. Perform knowledge compression on the processed first aggregated knowledge vector set through the knowledge compression component of the first road network state knowledge vector analysis component to obtain a second aggregated knowledge vector set.

[0040] S240. Determine a first characteristic variable set and a second characteristic variable set of the second aggregated knowledge vector set by disassembling components of the first road network state knowledge vector analysis component, wherein a result obtained by summing the first characteristic variable set and the second characteristic variable set is a set parameter set.

[0041] On the basis of the above, the downsampling component of the road network running simulation algorithm in S130 performs feature downsampling on the first road network state knowledge vector set, the event-road network linkage knowledge vector set and the event state knowledge vector set to obtain a second road network state knowledge vector set, including S131: performing feature downsampling on the first road network state knowledge vector set, the event-road network linkage knowledge vector set, the event state knowledge vector set, the first feature variable set and the second feature variable set through the downsampling component of the road network running simulation algorithm to obtain a second road network state knowledge vector set.

[0042] In this way, feature downsampling can be used to ensure that the feature representation performance and scale of the second road network state knowledge vector set are minimized, thereby improving the timeliness of subsequent road network operation predictions.

[0043] In some possible embodiments, the downsampling component of the simulation algorithm running through the road network performs feature downsampling on the first road network state knowledge vector set, the event-road network linkage knowledge vector set, the event state knowledge vector set, the first feature variable set and the second feature variable set to obtain a second road network state knowledge vector set, i.e., S131, including S1311 and S1312.

[0044] S1311. Through the knowledge vector set fusion component of the downsampling component, the first road network state knowledge vector set, the event-road network linkage knowledge vector set, the event state knowledge vector set, the first feature variable set and the second feature variable set are fused to obtain a third road network state knowledge vector set.

[0045] S1312: Perform average feature downsampling on the third road network state knowledge vector set through the execution unit of the downsampling component (which can be understood as an average pooling layer) to obtain a second road network state knowledge vector set.

[0046] In this way, the feature representation performance and scale of the second road network state knowledge vector set can be minimized by averaging feature downsampling, thereby improving the timeliness of subsequent road network operation predictions.

[0047] In some preferred embodiments, the knowledge vector set fusion component of the downsampling component fuses the first road network state knowledge vector set, the event-road network linkage knowledge vector set, the event state knowledge vector set, the first feature variable set and the second feature variable set to obtain a third road network state knowledge vector set, that is, S1311, including: multiplying the event-road network linkage knowledge vector set and the first feature variable set to obtain a first intermediate knowledge vector set; multiplying the event state knowledge vector set and the second feature variable set to obtain a second intermediate knowledge vector set; summing the first intermediate knowledge vector set, the second intermediate knowledge vector set and the first road network state knowledge vector set to obtain a third road network state knowledge vector set. With such a design, the third road network state knowledge vector set can be obtained completely and accurately.

[0048] In some exemplary embodiments, the second road network state knowledge vector analysis component of the road network simulation algorithm processes the second road network state knowledge vector set and the set operation core, and aggregates the processed knowledge vector set with the first road network state knowledge vector set, the event-road network linkage knowledge vector set, and the event state knowledge vector set to obtain the target road network state knowledge vector set, that is, S140, including S141-S143.

[0049] S141. Through the first splicing component of the second road network state knowledge vector analysis component, the first road network state knowledge vector set, the event-road network linkage knowledge vector set, the event state knowledge vector set and the second road network state knowledge vector set are merged to obtain a third aggregated knowledge vector set.

[0050] S142. Perform sliding averaging on the second road network state knowledge vector set through the sliding averaging unit of the second road network state knowledge vector analysis component, and process the second road network state knowledge vector set after sliding averaging through the operation core to obtain a target vector set.

[0051] S143. Aggregate the third aggregated knowledge vector set, the target vector set and the second road network state knowledge vector set through the second splicing component of the second road network state knowledge vector analysis component to obtain a target road network state knowledge vector set.

[0052] It can be seen that based on S141-S143, the aggregation of knowledge vectors of different dimensions can be achieved, thereby ensuring the characteristic representation performance of the target road network state knowledge vector set for the road network state.

[0053] Furthermore, the second splicing component of the second road network state knowledge vector analysis component aggregates the third aggregated knowledge vector set, the target vector set and the second road network state knowledge vector set to obtain a target road network state knowledge vector set, that is, S143, including: performing multiplication processing on the third aggregated knowledge vector set and the target vector set to obtain a third intermediate knowledge vector set; summing the third intermediate knowledge vector set and the second road network state knowledge vector set to obtain a target road network state knowledge vector set.

[0054] The embodiment of the present invention provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the method for simulating road network operation under a traffic event is implemented.

[0055] An embodiment of the present invention provides a processor, which is used to run a program, wherein the program executes the traffic event road network operation simulation method when running.

[0056] In the embodiment of the present invention, Figure 2 As shown, the road network operation simulation system 100 includes at least one processor 101, and at least one memory 102 and a bus 103 connected to the processor 101; wherein the processor 101 and the memory 102 communicate with each other through the bus 103; the processor 101 is used to call the program instructions in the memory 102 to execute the above-mentioned road network operation simulation method under traffic events.

[0057] The present invention is described with reference to the flowcharts and / or block diagrams of the method, the road network operation simulation system (system), and the computer program product according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the process in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0058] In a typical configuration, the road network operation simulation system includes one or more processors (CPU), memory and bus. The road network operation simulation system may also include input / output interface, network interface and the like.

[0059] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.

[0060] Computer readable media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage computer readable storage media or any other non-transmission media that can be used to store information that can be accessed by the road network operation simulation system. As defined in this article, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0061] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or computer-readable storage medium that includes a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also includes elements inherent to such process, method, commodity or computer-readable storage medium. In the absence of further restrictions, the elements defined by the sentence "comprises a ..." do not exclude the presence of other identical elements in the process, method, commodity or computer-readable storage medium that includes the elements.

[0062] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] The above are only embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent substitution, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A method for simulating road network operation under traffic events, characterized in that: Applied to a road network operation simulation system, the method comprises: Acquiring the road network operation data to be predicted, and performing knowledge mining on the road network operation data to be predicted to obtain a first road network state knowledge vector set of the road network operation data to be predicted; The first road network state knowledge vector set is loaded into the first road network state knowledge vector analysis component of the debugged road network operation simulation algorithm, and the event-road network linkage knowledge vector set in the road network operation data to be predicted is obtained through the first traffic event description mining strategy of the first road network state knowledge vector analysis component, and the event state knowledge vector set in the road network operation data to be predicted is obtained through the second traffic event description mining strategy of the first road network state knowledge vector analysis component; Performing feature downsampling on the first road network state knowledge vector set, the event-road network linkage knowledge vector set, and the event state knowledge vector set through a downsampling component of the road network operation simulation algorithm to obtain a second road network state knowledge vector set; The second road network state knowledge vector analysis component of the road network operation simulation algorithm processes the second road network state knowledge vector set and the set operation core, and aggregates the processed knowledge vector set with the first road network state knowledge vector set, the event-road network linkage knowledge vector set, and the event state knowledge vector set to obtain a target road network state knowledge vector set; Through the road network operation prediction component of the road network operation simulation algorithm, the road network operation prediction is performed on the target road network state knowledge vector set to obtain the road network operation trend data in the road network operation data to be predicted.

2. The method for simulating road network operation under traffic events according to claim 1, characterized in that: The method of loading the first road network state knowledge vector set into the first road network state knowledge vector analysis component of the debugged road network operation simulation algorithm, obtaining the event-road network linkage knowledge vector set in the road network operation data to be predicted through the first traffic event description mining strategy of the first road network state knowledge vector analysis component, and obtaining the event state knowledge vector set in the road network operation data to be predicted through the second traffic event description mining strategy of the first road network state knowledge vector analysis component, comprises: By means of the dynamic reversible unit of the first road network state knowledge vector analysis component, the first road network state knowledge vector set is multiplied by the first importance list to obtain an event-road network linkage knowledge vector set; And through the static reversible unit of the first road network state knowledge vector analysis component, the first road network state knowledge vector set is multiplied by the second importance list to obtain an event state knowledge vector set.

3. The method for simulating road network operation under traffic events according to claim 1, characterized in that: Before the downsampling component of the road network simulation algorithm performs feature downsampling on the first road network state knowledge vector set, the event-road network linkage knowledge vector set and the event state knowledge vector set to obtain a second road network state knowledge vector set, the method further includes: The event-road network linkage knowledge vector set and the event state knowledge vector set are summed up by the summation component of the first road network state knowledge vector analysis component to obtain a first aggregated knowledge vector set; Performing sliding averaging on the first aggregated knowledge vector set through a sliding averaging unit of the first road network state knowledge vector analysis component to obtain a processed first aggregated knowledge vector set; By using the knowledge compression component of the first road network state knowledge vector analysis component, the processed first aggregated knowledge vector set is compressed to obtain a second aggregated knowledge vector set; By disassembling the components of the first road network state knowledge vector analysis component, a first characteristic variable set and a second characteristic variable set of the second aggregated knowledge vector set are determined, wherein the result obtained by summing the first characteristic variable set and the second characteristic variable set is a set parameter set.

4. The method for simulating road network operation under traffic events according to claim 3 is characterized in that: The downsampling component of the road network simulation algorithm performs feature downsampling on the first road network state knowledge vector set, the event-road network linkage knowledge vector set, and the event state knowledge vector set to obtain a second road network state knowledge vector set, including: Through the downsampling component of the road network running simulation algorithm, feature downsampling is performed on the first road network state knowledge vector set, the event-road network linkage knowledge vector set, the event state knowledge vector set, the first feature variable set and the second feature variable set to obtain a second road network state knowledge vector set.

5. The method for simulating road network operation under traffic events according to claim 3, characterized in that: The downsampling component of the road network simulation algorithm performs feature downsampling on the first road network state knowledge vector set, the event-road network linkage knowledge vector set, the event state knowledge vector set, the first feature variable set, and the second feature variable set to obtain a second road network state knowledge vector set, including: The first road network state knowledge vector set, the event-road network linkage knowledge vector set, the event state knowledge vector set, the first feature variable set and the second feature variable set are fused by the knowledge vector set fusion component of the downsampling component to obtain a third road network state knowledge vector set; The third road network state knowledge vector set is subjected to average feature downsampling by the execution unit of the downsampling component to obtain a second road network state knowledge vector set.

6. The method for simulating road network operation under traffic events according to claim 5, characterized in that: The knowledge vector set fusion component of the downsampling component fuses the first road network state knowledge vector set, the event-road network linkage knowledge vector set, the event state knowledge vector set, the first feature variable set and the second feature variable set to obtain a third road network state knowledge vector set, including: Performing a multiplication operation on the event-road network linkage knowledge vector set and the first characteristic variable set to obtain a first intermediate knowledge vector set; Performing a multiplication operation on the event state knowledge vector set and the second feature variable set to obtain a second intermediate knowledge vector set; The first intermediate knowledge vector set, the second intermediate knowledge vector set and the first road network state knowledge vector set are summed to obtain a third road network state knowledge vector set.

7. The method for simulating road network operation under traffic events according to claim 1, characterized in that: The second road network state knowledge vector analysis component of the road network simulation algorithm processes the second road network state knowledge vector set and the set operation core, and aggregates the processed knowledge vector set with the first road network state knowledge vector set, the event-road network linkage knowledge vector set, and the event state knowledge vector set to obtain a target road network state knowledge vector set, including: The first road network state knowledge vector set, the event-road network linkage knowledge vector set, the event state knowledge vector set and the second road network state knowledge vector set are merged by the first splicing component of the second road network state knowledge vector analysis component to obtain a third aggregated knowledge vector set; Performing sliding averaging on the second road network state knowledge vector set by a sliding averaging unit of the second road network state knowledge vector analysis component, and processing the second road network state knowledge vector set after sliding averaging by a computing core to obtain a target vector set; The third aggregated knowledge vector set, the target vector set and the second road network state knowledge vector set are aggregated by the second splicing component of the second road network state knowledge vector analysis component to obtain a target road network state knowledge vector set.

8. The method for simulating road network operation under traffic events according to claim 7, characterized in that: The second concatenation component of the second road network state knowledge vector analysis component aggregates the third aggregated knowledge vector set, the target vector set and the second road network state knowledge vector set to obtain a target road network state knowledge vector set, including: Performing a multiplication operation on the third aggregated knowledge vector set and the target vector set to obtain a third intermediate knowledge vector set; The third intermediate knowledge vector set and the second road network state knowledge vector set are summed to obtain a target road network state knowledge vector set.

9. A road network operation simulation system, characterized in that: It comprises a processor, a memory and a bus connected to the processor; wherein the processor and the memory communicate with each other through the bus; the processor is used to call the program instructions in the memory to execute the road network operation simulation method under traffic events as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: A program is stored thereon, and when the program is executed by a processor, the method for simulating road network operation under a traffic event as described in any one of claims 1 to 8 is implemented.

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