Operation target generation method based on optimal curve search and related device

Abstract

The present application discloses a method for generating an operation target based on an optimal curve search, comprising: determining a fastest operation speed list and a slowest operation speed list based on a list of track sections between the train's arrival at the next stop and corresponding speed limit information; performing time prediction calculations based on the fastest operation speed list and the slowest operation speed list to obtain the fastest predicted operation time and the slowest predicted operation time; performing an optimal curve fuzzy search based on the obtained planned operation time, the fastest predicted operation time, and the slowest predicted operation time to obtain a fuzzy search result; performing an optimal curve precise search based on the planned operation time, the fastest predicted operation time, the slowest predicted operation time, and the fuzzy search result to obtain an operation target, thereby improving the accuracy of the operation target. The present application also discloses an operation target generation device, a computing device, and a computer-readable storage medium based on an optimal curve search, which have the above beneficial effects.

Description

Technical Field

[0001] The present application relates to the technical field of train operation control, and in particular to a method for generating an operation target based on optimal curve search, an apparatus for generating an operation target based on optimal curve search, a computing device, and a computer-readable storage medium. Background Art

[0002] To improve train operation efficiency, the ATO (Automatic Train Operation) subsystem in the CBTC (Communication based Train Control System) system needs to be able to respond to instructions from the ATS (Automatic Train Supervision) system to adjust the train's running time between stations.

[0003] In related technologies, the adjustment of inter-station running time is mainly based on the adjustment of running levels, that is, the maximum running speed between stations is divided into several levels, the most common of which are 5 levels. Then the ATS sends different running levels to the train before the train departs from a certain station. After the ATO obtains this level information, it adjusts the running curve during the journey from the station to the next station, and runs according to a certain percentage of the maximum speed limit on the corresponding track section. However, the running level is discrete information and the adjustment accuracy is low. In addition, the current running level adjustment only supports one adjustment before departure at the platform, because the pre-agreed level is based on the calculation from departure at the previous platform to parking at the next platform. Once the train starts, it cannot be adjusted in real time, which reduces the effect of the adjustment.

[0004] Therefore, how to improve the accuracy of train running routes is a key issue that technical personnel in this field are concerned about. Summary of the Invention

[0005] The purpose of this application is to provide a method for generating an operation target based on optimal curve search, an operation target generating device based on optimal curve search, a computing device and a computer-readable storage medium to improve the accuracy of the generated operation target.

[0006] To solve the above technical problems, the present application provides a method for generating an operation target based on optimal curve search, comprising:

[0007] Determine a fastest running speed list and a slowest running speed list based on a track section list between the train and the next stop and corresponding speed limit information;

[0008] Performing time prediction calculation based on the fastest running speed list and the slowest running speed list to obtain the fastest predicted running time and the slowest predicted running time;

[0009] Performing an optimal curve fuzzy search based on the acquired planned running time, the fastest predicted running time, and the slowest predicted running time to obtain a fuzzy search result;

[0010] Based on the planned running time, the fastest predicted running time, the slowest predicted running time and the fuzzy search results, an optimal curve precise search is performed to obtain an operating target.

[0011] Optionally, performing a time prediction calculation based on the fastest running speed list and the slowest running speed list to obtain the fastest predicted running time and the slowest predicted running time includes:

[0012] Performing a running time prediction calculation on the fastest running speed list based on a time prediction algorithm to obtain the fastest predicted running time;

[0013] A running time prediction calculation is performed on the slowest running speed list based on a time prediction algorithm to obtain the slowest predicted running time.

[0014] Optionally, performing an optimal curve fuzzy search based on the acquired planned running time, the fastest predicted running time, and the slowest predicted running time to obtain a fuzzy search result includes:

[0015] Obtaining the planned running time, the fastest predicted running time, and the slowest predicted running time;

[0016] An optimal curve fuzzy search is performed based on the planned running time, the fastest predicted running time, and the slowest predicted running time to obtain the fuzzy search result.

[0017] Optionally, based on the planned running time, the fastest predicted running time, the slowest predicted running time, and the fuzzy search results, performing an optimal curve precise search to obtain an operating target includes:

[0018] Obtaining the planned running time, the fastest predicted running time, the slowest predicted running time, and the fuzzy search result;

[0019] An optimal curve precise search operation is performed on the planned running time, the fastest predicted running time, the slowest predicted running time and the fuzzy search result to obtain the running target.

[0020] The present application also provides an operation target generation device based on optimal curve search, comprising:

[0021] A speed list acquisition module is used to determine a fastest running speed list and a slowest running speed list based on a track section list between the train and the next stop and corresponding speed limit information;

[0022] A running time prediction module, configured to perform time prediction calculation based on the fastest running speed list and the slowest running speed list to obtain the fastest predicted running time and the slowest predicted running time;

[0023] An optimal curve fuzzy search module is used to perform an optimal curve fuzzy search based on the acquired planned running time, the fastest predicted running time, and the slowest predicted running time to obtain a fuzzy search result;

[0024] The optimal curve precise search module is used to perform an optimal curve precise search based on the planned running time, the fastest predicted running time, the slowest predicted running time and the fuzzy search result to obtain the running target.

[0025] Optionally, the running time prediction module is specifically used to perform running time prediction calculation on the fastest running speed list based on a time prediction algorithm to obtain the fastest predicted running time; and perform running time prediction calculation on the slowest running speed list based on a time prediction algorithm to obtain the slowest predicted running time.

[0026] Optionally, the optimal curve fuzzy search module is specifically used to obtain the planned running time, the fastest predicted running time, and the slowest predicted running time; perform an optimal curve fuzzy search based on the planned running time, the fastest predicted running time, and the slowest predicted running time to obtain the fuzzy search result.

[0027] Optionally, the optimal curve precise search module is specifically used to obtain the planned running time, the fastest predicted running time, the slowest predicted running time and the fuzzy search result; perform the optimal curve precise search operation based on the planned running time, the fastest predicted running time, the slowest predicted running time and the fuzzy search result to obtain the running target.

[0028] The present application also provides a computing device, comprising:

[0029] memory for storing computer programs;

[0030] A processor is used to implement the steps of the above-mentioned operation target generation method when executing the computer program.

[0031] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of the operation target generation method described above are implemented.

[0032] The present application provides a method for generating an operation target based on an optimal curve search, comprising: determining a fastest operation speed list and a slowest operation speed list based on a track section list between the train and the next stop and corresponding speed limit information; performing time prediction calculation based on the fastest operation speed list and the slowest operation speed list to obtain the fastest predicted operation time and the slowest predicted operation time; performing an optimal curve fuzzy search based on the acquired planned operation time, the fastest predicted operation time, and the slowest predicted operation time to obtain a fuzzy search result; performing an optimal curve precise search based on the planned operation time, the fastest predicted operation time, the slowest predicted operation time, and the fuzzy search result to obtain an operation target.

[0033] By first predicting and calculating the fastest predicted running time and the slowest predicted running time, and then performing a fuzzy search for the optimal curve based on the calculated running time, a fuzzy search result is obtained. On this basis, a precise search for the optimal curve is performed to obtain the running target, achieving a more optimized curve search and improving the accuracy of the generated running target.

[0034] The present application also provides an operation target generation device, a computing device and a computer-readable storage medium based on optimal curve search, which have the above beneficial effects and will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0036] Figure 1 A flowchart of a method for generating an operation target based on optimal curve search provided in an embodiment of the present application;

[0037] Figure 2 A schematic structural diagram of an operation target generation device based on optimal curve search provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] The core of this application is to provide an operation target generation method based on optimal curve search, an operation target generation device based on optimal curve search, a computing device and a computer-readable storage medium to improve the accuracy of the generated operation target.

[0039] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0040] In related technologies, the adjustment of inter-station running time is mainly based on the adjustment of running levels, that is, the maximum running speed between stations is divided into several levels, the most common of which are 5 levels. Then the ATS sends different running levels to the train before the train departs from a certain station. After the ATO obtains this level information, it adjusts the running curve during the journey from the station to the next station, and runs according to a certain percentage of the maximum speed limit on the corresponding track section. However, the running level is discrete information and the adjustment accuracy is low. In addition, the current running level adjustment only supports one adjustment before departure at the platform, because the pre-agreed level is based on the calculation from departure at the previous platform to parking at the next platform. Once the train starts, it cannot be adjusted in real time, which reduces the effect of the adjustment.

[0041] Therefore, the present application provides a method for generating an operation target based on an optimal curve search, which first predicts and calculates the fastest predicted operation time and the slowest predicted operation time, and then performs a fuzzy search for the optimal curve based on the calculated operation time to obtain a fuzzy search result. On this basis, an accurate search for the optimal curve is performed to obtain the operation target, thereby achieving a more optimized curve search and improving the accuracy of the generated operation target.

[0042] The following describes an example of an operation target generation method based on optimal curve search provided by the present application.

[0043] Please refer to Figure 1 , Figure 1 A flowchart of a method for generating an operation target based on optimal curve search provided in an embodiment of the present application.

[0044] In this embodiment, the method may include:

[0045] S101, determining a fastest running speed list and a slowest running speed list based on a track section list between the train and the next stop and corresponding speed limit information;

[0046] It can be seen that this step aims to determine the fastest running speed list and the slowest running speed list based on the track section list between the train and the next stop and the corresponding speed limit information.

[0047] S102, performing time prediction calculation based on the fastest running speed list and the slowest running speed list to obtain the fastest predicted running time and the slowest predicted running time;

[0048] On the basis of S101, this step aims to perform time prediction calculation based on the fastest running speed list and the slowest running speed list to obtain the fastest predicted running time and the slowest predicted running time.

[0049] Among them, the time prediction calculation is based on the speed limit information of each section and the operation of the train to predict the running time of the train.

[0050] Furthermore, this step may include:

[0051] Step 1: Based on the time prediction algorithm, the fastest running speed list is used to predict the running time and obtain the fastest predicted running time;

[0052] Step 2: Based on the time prediction algorithm, a running time prediction calculation is performed on the slowest running speed list to obtain the slowest predicted running time.

[0053] As can be seen, this optional solution mainly performs run time calculation. In this optional solution, based on the time prediction algorithm, the run time prediction calculation is performed on the fastest run speed list to obtain the fastest predicted run time, and based on the time prediction algorithm, the run time prediction calculation is performed on the slowest run speed list to obtain the slowest predicted run time.

[0054] S103, performing an optimal curve fuzzy search based on the acquired planned running time, the fastest predicted running time, and the slowest predicted running time to obtain a fuzzy search result;

[0055] On the basis of S102, this step aims to perform an optimal curve fuzzy search based on the acquired planned running time, the fastest predicted running time, and the slowest predicted running time to obtain a fuzzy search result.

[0056] The optimal curve search algorithm is a multi-objective search algorithm in a limited space. Multi-objective refers to the search for the optimal combination of speed limits across multiple track sections. This algorithm uses a time prediction algorithm as an evaluation tool. All track sections are adjusted in a unified direction. The exit condition is that the difference between the time predicted based on a set of curves and the planned operating time is within a threshold range (plus or minus 3%).

[0057] Furthermore, this step may include:

[0058] Step 1: Get the planned running time, the fastest predicted running time, and the slowest predicted running time;

[0059] Step 2: The middle value between the speed limit corresponding to the fastest predicted running time and the speed limit corresponding to the slowest predicted running time is used as the search speed limit;

[0060] Step 3: Perform an optimal curve fuzzy search based on the planned running time, the fastest predicted running time, the slowest predicted running time, and the search speed limit to obtain a fuzzy search result.

[0061] This option primarily explains how to perform a fuzzy search. The planned run time, fastest predicted run time, and slowest predicted run time are obtained. The midpoint between the speed limits corresponding to the fastest and slowest predicted run times is used as the search speed limit. A fuzzy search is then performed on the optimal curve based on the planned run time, fastest predicted run time, slowest predicted run time, and the search speed limit to obtain the fuzzy search results.

[0062] S104: Based on the planned running time, the fastest predicted running time, the slowest predicted running time and the fuzzy search results, an optimal curve is precisely searched to obtain the running target.

[0063] Based on S103, this step aims to perform an accurate search for the optimal curve based on the planned running time, the fastest predicted running time, the slowest predicted running time and the fuzzy search results to obtain the running target.

[0064] The optimal curve search algorithm is a multi-objective search algorithm in a limited space. Multi-objective refers to searching for the optimal combination of speed limits across multiple track sections. This algorithm uses a time prediction algorithm as an evaluation method and can be further subdivided into fuzzy search and precise search.

[0065] Precise search is an effective complement to fuzzy search. Based on the fuzzy search results, it supports searching within the upper and lower limits of each track segment. If the results for the previous track segment have reached the adjusted upper limit but still do not meet the planned time requirements, the search continues to the next track segment. Precise search exits when the difference between the predicted time for a set of curves and the planned run time is within a precision range (plus or minus 3%).

[0066] Furthermore, this step may include:

[0067] Step 1: Obtain the planned running time, the fastest predicted running time, the slowest predicted running time, and the fuzzy search results;

[0068] Step 2: Based on the planned running time, the fastest predicted running time, the slowest predicted running time and the fuzzy search results, an optimal curve precise search operation is performed to obtain the running target.

[0069] This option primarily illustrates how to perform a precise search. In this option, the planned run time, the fastest predicted run time, the slowest predicted run time, and the fuzzy search results are obtained. Based on these results, a precise search for the optimal curve is performed to obtain the run target.

[0070] In summary, this embodiment first predicts and calculates the fastest predicted running time and the slowest predicted running time, and then performs a fuzzy search for the optimal curve based on the calculated running time to obtain a fuzzy search result. On this basis, a precise search for the optimal curve is performed to obtain the running target, thereby achieving a more optimized curve search and improving the accuracy of the generated running target.

[0071] The following is a specific example to further illustrate the method for generating an operation target based on optimal curve search provided by the present application.

[0072] In this embodiment, the method may include:

[0073] Step 1: Using the fastest running speed list and the slowest running speed list as input, call the time prediction algorithm to calculate the fastest predicted running time and the slowest predicted running time respectively;

[0074] Step 2: Obtain the planned running time, the fastest predicted running time between stations, and the slowest predicted running time between stations, and search for the optimal curve;

[0075] Step 3: For all track sections, the median of the speed limit corresponding to the fastest predicted time and the speed limit corresponding to the slowest predicted time is taken as the speed limit for this search;

[0076] Step 4: Determine whether the search here has not reached the upper limit and the exit flag is false; if so, execute step 5; if not, terminate the execution;

[0077] Step 5: Use the time prediction algorithm to predict the time of the speed limit list for this search;

[0078] Step 6: Determine whether the difference between the search prediction result and the planned time is within the accuracy range; if so, execute step 7; if not, execute step 8;

[0079] Step 7, set the exit flag to true;

[0080] Step 8: Determine whether the calculation result is slower than the planned time; if so, go to step 9; if not, go to step 10;

[0081] Step 9: Use the search results and the list corresponding to the fastest predicted time as the search space to further search upwards;

[0082] Step 10, determine whether the calculation result is faster than the planned time; if so, execute step 11;

[0083] Step 11: Use the search results and the list corresponding to the slowest predicted time as the search space to further search downward; and execute step 4.

[0084] Furthermore, the precise search process may include:

[0085] Step 1: Using the fastest running speed list and the slowest running speed list as input, call the time prediction algorithm to calculate the fastest predicted running time and the slowest predicted running time respectively;

[0086] Step 2: Using the planned running time, the search result when the fuzzy search exits, the fastest predicted running time between stations, and the slowest predicted running time between stations as input, search for the optimal curve;

[0087] Step 3: Determine whether the speed limit partition has not been traversed and the exit flag is false; if so, execute step 4; if not, terminate the execution;

[0088] Step 4: Get the upper and lower speed limits of a speed limit zone;

[0089] Step 5: Determine whether the last predicted time is slower than the planned time; if so, go to step 6; if not, go to step 7;

[0090] Step 6: Adjust the speed limit of the partition to the fastest speed limit of the partition; proceed to step 8;

[0091] Step 7: Adjust the speed limit of the partition to the slowest speed limit of the partition;

[0092] Step 8: Based on the results of the partition adjustment, the entire speed limit list is used to make a time prediction;

[0093] Step 9: Determine whether the adjusted prediction time of this partition is within the accuracy range; if so, execute step 10; if not, execute step 11;

[0094] Step 10, set the exit flag to true; and execute step 3;

[0095] Step 11: determine whether the difference between the adjusted predicted time and the planned time of the current partition has an opposite sign to the difference between the predicted time and the planned time after the last adjustment; if so, execute step 12; if not, execute step 13;

[0096] Step 12: Lock the current partition and perform a binary search until the predicted time meets the accuracy requirement, and then set the exit flag to true;

[0097] Step 13: Save the current partition adjustment result and continue to adjust the next partition.

[0098] It can be seen that this embodiment first predicts and calculates the fastest predicted running time and the slowest predicted running time, and then performs a fuzzy search for the optimal curve based on the calculated running time to obtain a fuzzy search result. On this basis, a precise search for the optimal curve is performed to obtain the running target, thereby achieving a more optimized curve search and improving the accuracy of the generated running target.

[0099] The following is an introduction to the operation target generation device provided in the embodiment of the present application. The operation target generation device described below and the operation target generation method described above can be referenced to each other.

[0100] Please refer to Figure 2 , Figure 2 A schematic structural diagram of an operation target generation device based on optimal curve search provided in an embodiment of the present application.

[0101] In this embodiment, the device may include:

[0102] The speed list acquisition module 100 is used to determine the fastest running speed list and the slowest running speed list based on the track section list between the train and the next stop and the corresponding speed limit information;

[0103] A running time prediction module 200 is used to perform time prediction calculation based on the fastest running speed list and the slowest running speed list to obtain the fastest predicted running time and the slowest predicted running time;

[0104] The optimal curve fuzzy search module 300 is used to perform an optimal curve fuzzy search based on the acquired planned running time, the fastest predicted running time, and the slowest predicted running time to obtain a fuzzy search result;

[0105] The optimal curve precise search module 400 is used to perform a precise search for the optimal curve based on the planned running time, the fastest predicted running time, the slowest predicted running time and the fuzzy search results to obtain the running target.

[0106] Optionally, the running time prediction module 200 is specifically used to perform running time prediction calculation on the fastest running speed list based on the time prediction algorithm to obtain the fastest predicted running time; and perform running time prediction calculation on the slowest running speed list based on the time prediction algorithm to obtain the slowest predicted running time.

[0107] Optionally, the optimal curve fuzzy search module 300 is specifically used to obtain the planned running time, the fastest predicted running time, and the slowest predicted running time; take the middle value between the speed limit corresponding to the fastest predicted running time and the speed limit corresponding to the slowest predicted running time as the search speed limit; perform an optimal curve fuzzy search based on the planned running time, the fastest predicted running time, the slowest predicted running time, and the search speed limit to obtain a fuzzy search result.

[0108] Optionally, the optimal curve precise search module 400 is specifically used to obtain the planned running time, the fastest predicted running time, the slowest predicted running time and the fuzzy search results; based on the planned running time, the fastest predicted running time, the slowest predicted running time and the fuzzy search results, perform the optimal curve precise search operation to obtain the running target.

[0109] The present application also provides a computing device, including:

[0110] memory for storing computer programs;

[0111] A processor is used to implement the steps of the operation target generation method as described in the above embodiment when executing the computer program.

[0112] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the operation target generation method described in the above embodiment are implemented.

[0113] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0114] Professionals may further 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 above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0115] The steps of the methods or algorithms described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0116] The above is a detailed introduction to the operation target generation method based on optimal curve search, the operation target generation device based on optimal curve search, the computing device and the computer-readable storage medium provided by the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. A method for generating an operation target based on optimal curve search, characterized in that: include: Determine a fastest running speed list and a slowest running speed list based on a track section list between the train and the next stop and corresponding speed limit information; Performing time prediction calculation based on the fastest running speed list and the slowest running speed list to obtain the fastest predicted running time and the slowest predicted running time; Performing an optimal curve fuzzy search based on the acquired planned running time, the fastest predicted running time, and the slowest predicted running time to obtain a fuzzy search result; The optimal curve fuzzy search is performed based on the acquired planned running time, the fastest predicted running time, and the slowest predicted running time to obtain fuzzy search results, including: Obtaining the planned running time, the fastest predicted running time, and the slowest predicted running time; The middle value between the speed limit corresponding to the fastest predicted running time and the speed limit corresponding to the slowest predicted running time is used as the search speed limit; Performing an optimal curve fuzzy search based on the planned running time, the fastest predicted running time, the slowest predicted running time, and the search speed limit to obtain the fuzzy search result; Based on the planned running time, the fastest predicted running time, the slowest predicted running time and the fuzzy search results, an optimal curve precise search is performed to obtain an operating target.

2. The operation target generation method according to claim 1, characterized in that: Performing a time prediction calculation based on the fastest running speed list and the slowest running speed list to obtain the fastest predicted running time and the slowest predicted running time includes: Performing a running time prediction calculation on the fastest running speed list based on a time prediction algorithm to obtain the fastest predicted running time; A running time prediction calculation is performed on the slowest running speed list based on a time prediction algorithm to obtain the slowest predicted running time.

3. The operation target generation method according to claim 1, characterized in that: Based on the planned running time, the fastest predicted running time, the slowest predicted running time, and the fuzzy search results, an optimal curve precise search is performed to obtain an operating target, including: Obtaining the planned running time, the fastest predicted running time, the slowest predicted running time, and the fuzzy search result; An optimal curve precise search operation is performed on the planned running time, the fastest predicted running time, the slowest predicted running time and the fuzzy search result to obtain the running target.

4. A running target generation device based on optimal curve search, characterized in that: include: A speed list acquisition module is used to determine a fastest running speed list and a slowest running speed list based on a track section list between the train and the next stop and corresponding speed limit information; A running time prediction module, configured to perform time prediction calculation based on the fastest running speed list and the slowest running speed list to obtain the fastest predicted running time and the slowest predicted running time; An optimal curve fuzzy search module is configured to perform an optimal curve fuzzy search based on the acquired planned running time, the fastest predicted running time, and the slowest predicted running time to obtain a fuzzy search result; wherein the optimal curve fuzzy search module is specifically configured to: obtain the planned running time, the fastest predicted running time, and the slowest predicted running time; use the intermediate value between the speed limit corresponding to the fastest predicted running time and the speed limit corresponding to the slowest predicted running time as the search speed limit; perform an optimal curve fuzzy search based on the planned running time, the fastest predicted running time, the slowest predicted running time, and the search speed limit to obtain the fuzzy search result; The optimal curve precise search module is used to perform an optimal curve precise search based on the planned running time, the fastest predicted running time, the slowest predicted running time and the fuzzy search result to obtain the running target.

5. The operation target generating device according to claim 4, characterized in that: The running time prediction module is specifically used to perform running time prediction calculation on the fastest running speed list based on a time prediction algorithm to obtain the fastest predicted running time; and perform running time prediction calculation on the slowest running speed list based on a time prediction algorithm to obtain the slowest predicted running time.

6. The operation target generating device according to claim 4, characterized in that: The optimal curve precise search module is specifically used to obtain the planned running time, the fastest predicted running time, the slowest predicted running time and the fuzzy search result; perform the optimal curve precise search operation based on the planned running time, the fastest predicted running time, the slowest predicted running time and the fuzzy search result to obtain the running target.

7. A computing device, characterized in that include: memory for storing computer programs; A processor, configured to implement the steps of the operation target generation method according to any one of claims 1 to 3 when executing the computer program.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the operation target generation method according to any one of claims 1 to 3.

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