CN117405124A - Path planning method and system based on big data - Google Patents
Path planning method and system based on big data Download PDFInfo
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- CN117405124A CN117405124A CN202311703748.3A CN202311703748A CN117405124A CN 117405124 A CN117405124 A CN 117405124A CN 202311703748 A CN202311703748 A CN 202311703748A CN 117405124 A CN117405124 A CN 117405124A
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
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Abstract
The invention is applicable to the field of computers, and provides a path planning method and a system based on big data, wherein the method comprises the following steps: acquiring preset planning paths of a plurality of moving parts, and reading marked risk sites in the preset planning paths, wherein the preset planning paths comprise a plurality of staggered first planning paths, a plurality of cross sites are formed among the staggered first planning paths, and the plurality of moving parts comprise target moving parts and non-target moving parts; acquiring the current position of the target moving part, and screening a successful pass record of a first risk site if two subsequent continuous sites at the current position are a first cross site and a first risk site respectively; the technical scheme of the embodiment of the application can ensure the traffic fluency and the traffic efficiency of the target moving piece.
Description
Technical Field
The invention belongs to the field of computers, and particularly relates to a path planning method and system based on big data.
Background
The path planning is one of main research contents of the motion planning, the motion planning is composed of the path planning and the track planning, a sequence point or curve connecting a starting point position and an end point position is called a path, and a strategy for forming the path is called the path planning.
The existing path planning usually utilizes a preset path planning algorithm, such as an a-algorithm, a D-algorithm, a Dijkstra algorithm, etc., to plan one or more preset paths from a departure place to a destination, however, after the preset paths are planned, flexible changes are difficult to be performed according to environment changes, such as a certain station in the preset paths may have traffic risks, but for a target moving part (such as a target vehicle), the arrival of the target moving part may result in that the station is not found to be in traffic, thereby affecting traffic smoothness and traffic efficiency.
Disclosure of Invention
The embodiment of the invention aims to provide a path planning method and system based on big data, which aim to solve the problems in the background technology.
The embodiment of the invention is realized in such a way that, on the one hand, a path planning method based on big data comprises the following steps:
acquiring preset planning paths of a plurality of moving parts, and reading marked risk sites in the preset planning paths, wherein the preset planning paths comprise a plurality of staggered first planning paths, a plurality of cross sites are formed among the staggered first planning paths, and the plurality of moving parts comprise target moving parts and non-target moving parts;
acquiring the current position of the target moving part, and screening a successful pass record of a first risk site if two subsequent continuous sites at the current position are a first cross site and a first risk site respectively;
acquiring traffic record data corresponding to a certain non-target moving part in a successful traffic record, wherein the certain non-target moving part is one of the non-target moving parts of a first risk site which is successfully passed through, and the traffic record data comprises passing distance sensing data;
acquiring a passing requirement condition of a target mobile part, judging whether a first risk station has a successful passing condition of the target mobile part according to passing record data of a certain non-target mobile part and the passing requirement condition of the target mobile part, and obtaining a judging result, wherein the judging result comprises judging that the first risk station has the successful passing condition of the target mobile part and judging that the first risk station does not have the successful passing condition of the target mobile part;
and updating the passing path of the target moving piece according to the judging result and the first cross station.
As an alternative solution of the present invention, before acquiring preset planned paths of the plurality of moving parts and reading marked risk sites in the preset planned paths, the method further includes:
the method comprises the steps of periodically obtaining corresponding website passing failure records when a plurality of moving parts pass according to respective preset planning paths;
extracting a corresponding site of the site passing failure record to obtain an extraction result;
and marking the extraction result as a new risk site.
As a still further alternative of the present invention, the screening the successful pass records of the first risk site includes:
acquiring a passing video of a first risk site, wherein the passing video comprises a plurality of passing sub-videos arranged in sequence according to time;
sequentially identifying a plurality of traffic sub-videos according to the sequence from the near to the far from the current moment;
when the passing picture of the non-target moving part in a certain passing sub-video is identified to meet the preset passing judgment condition, judging that the non-target moving part in the certain passing sub-video successfully passes, and stopping identifying the subsequent passing sub-video, wherein the successful passing record comprises the certain passing sub-video corresponding to the successful passing of the non-target moving part, and the preset passing judgment condition comprises the successful passing condition set along the advancing direction.
As a still further alternative of the present invention, the acquiring traffic record data corresponding to a non-target moving part in the successful traffic record includes:
extracting a time stamp of a passing picture meeting preset passing judgment conditions in a certain passing sub-video;
and extracting the passing distance sensing data containing the time stamp to obtain passing record data, wherein the passing distance sensing data comprises distance data which is acquired perpendicular to the travelling direction of a certain non-target moving part, and the distance data corresponds to the side part of the certain non-target moving part.
As an optional solution of the present invention, the determining whether the first risk station has a successful passing condition of the target moving object according to the passing record data of the certain non-target moving object and the passing requirement condition of the target moving object, and obtaining the determination result includes:
screening the minimum distance value in the distance data, acquiring the target size of a certain non-target moving part at the acquisition position of the minimum distance value, and calculating the maximum allowed passing distance according to the minimum distance value and the target size;
reading the minimum passing requirement distance in the passing requirement condition of the target moving piece;
judging whether the maximum allowed passing distance is larger than a minimum passing required distance or not;
if yes, judging that the first risk site has the successful passing condition of the target moving part, otherwise, judging that the first risk site does not have the successful passing condition of the target moving part.
As an optional solution of the present invention, according to the determination result and the first intersection, updating the traffic path of the target moving member includes:
when the first risk site is judged to have the successful passing condition of the target moving part, maintaining the planned road section from the first cross site to the first risk site unchanged;
and when the first risk site is judged to not have the successful passing condition of the target moving part, deleting the planned road section from the first intersection site to the first risk site, and planning the planned road section from the first intersection site to the non-first risk site.
As an alternative of the present invention, the non-first risk site is located in the first planned path.
In another aspect, a big data based path planning system, the system comprising:
the system comprises a reading module, a marking module and a judging module, wherein the reading module is used for acquiring preset planning paths of a plurality of moving parts, and reading marked risk sites in the preset planning paths, wherein the preset planning paths comprise a plurality of staggered first planning paths, a plurality of cross sites are formed among the staggered first planning paths, and the plurality of moving parts comprise target moving parts and non-target moving parts;
the screening module is used for acquiring the current position of the target moving part, and screening a successful passing record of the first risk site if two subsequent continuous sites at the current position are the first cross site and the first risk site respectively;
the extraction module is used for acquiring traffic record data corresponding to a certain non-target moving part in the successful traffic record, wherein the certain non-target moving part is one of the non-target moving parts of the first risk site which successfully passes through, and the traffic record data comprises passing distance sensing data;
the judging module is used for acquiring the passing requirement condition of the target moving piece, judging whether the first risk station has the successful passing condition of the target moving piece according to the passing record data of a certain non-target moving piece and the passing requirement condition of the target moving piece, and obtaining a judging result, wherein the judging result comprises judging that the first risk station has the successful passing condition of the target moving piece and judging that the first risk station does not have the successful passing condition of the target moving piece;
and the path updating module is used for updating the passing path of the target moving piece according to the judging result and the first cross station.
According to the route planning method and system based on big data, after the target moving piece is selected, for each first risk site, if the moving piece which passes successfully before exists, a judgment and calculation basis can be provided for the passing of the target moving piece with different types and loading conditions, so that the passing route of the target moving piece can be decided to avoid or pass through the first risk site in advance, and the passing fluency and passing efficiency of the target moving piece are ensured.
Drawings
Fig. 1 is a main flow chart of a path planning method based on big data.
Fig. 2 is a flow chart of marking the extraction result as a new risk site in a big data based path planning method.
Fig. 3 is a flowchart of screening successful traffic records of a first risk site in a big data based path planning method.
Fig. 4 is a flowchart of a method for determining whether a first risk site has a successful traffic condition of a target moving part in a path planning method based on big data.
Fig. 5 is a flowchart of updating a traffic path of a target moving member in a path planning method based on big data.
Fig. 6 is a main structural diagram of a path planning system based on big data.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Specific implementations of the invention are described in detail below in connection with specific embodiments.
The path planning method and system based on big data provided by the invention solve the technical problems in the background technology.
As shown in fig. 1, a main flow chart of a big data based path planning method according to an embodiment of the present invention includes:
step S10: acquiring preset planning paths of a plurality of moving parts, and reading marked risk sites in the preset planning paths, wherein the preset planning paths comprise a plurality of staggered first planning paths, a plurality of cross sites are formed among the staggered first planning paths, and the plurality of moving parts comprise target moving parts and non-target moving parts;
the risk site is marked in a preset planning path in advance, which means that the site has more or less unstable factors (such as obstacles, traffic width changes and the like), thereby causing the possibility of influencing the traffic of the moving parts;
for each moving part in the plurality of moving parts, a corresponding preset planning path (from a departure place to a destination) exists, and a plurality of first planning paths are staggered in the preset planning path, wherein the staggering means that at least one crossing exists between at least two planning paths, and the position of each crossing becomes a crossing station; the types and loading conditions of the moving parts are different, and the moving parts can be moving objects with autonomous moving capability or non-autonomous moving capability, such as a moving robot, a moving vehicle and the like; for a plurality of moving parts, when a certain moving part is selected as a target for planning, the moving part becomes a target moving part; the other moving parts are non-target moving parts;
step S11: acquiring the current position of the target moving part, and screening a successful pass record of a first risk site if two subsequent continuous sites at the current position are a first cross site and a first risk site respectively;
the current position is read from the positioning information (such as GPS positioning) of the target moving part; when two subsequent stations at the current position are respectively a first intersection station and a first risk station, namely, when a planned road section subsequent to the two subsequent stations sequentially comprises the first intersection station and the first risk station, taking the traffic situation of the first risk station into consideration, so that a successful traffic record of the first risk station is screened at the moment, and because the first risk station may be a passing station of other non-target moving parts, the successful traffic record may comprise a plurality of traffic (recorded) videos (of the non-target moving parts passing the station) arranged sequentially in time;
step S12: acquiring traffic record data corresponding to a certain non-target moving part in a successful traffic record, wherein the certain non-target moving part is one of the non-target moving parts of a first risk site which is successfully passed through, and the traffic record data comprises passing distance sensing data;
when the non-target moving part passes through the first risk site, the non-target moving part (such as a sensor at the side) collects traffic distance sensing data and reports the traffic distance sensing data; the distance sensing data also reflects the latest traffic situation of the first risk site, and mainly reflects the distance between the non-target moving part and the obstacle (and/or the site itself) when the non-target moving part passes, if the distance is larger, the non-target moving part is easier to pass, otherwise, the non-target moving part is harder to pass;
step S13: acquiring a passing requirement condition of a target mobile part, judging whether a first risk station has a successful passing condition of the target mobile part according to passing record data of a certain non-target mobile part and the passing requirement condition of the target mobile part, and obtaining a judging result, wherein the judging result comprises judging that the first risk station has the successful passing condition of the target mobile part and judging that the first risk station does not have the successful passing condition of the target mobile part;
the traffic demand conditions of the target moving member may include the size of the traffic space required by the target moving member, when judging whether the first risk site has the successful traffic condition of the target moving member, providing a reference for the traffic of the target moving member by using the distance sensing data, if the size of the traffic space required by the target moving member is 150cm in width, the traffic is not allowed if the sum of the distance sensing data and the width of the traffic space measured by a certain non-target moving member is 130cm when the traffic space required by the target moving member is 110cm in width; the moving parts realizing the prior successful passing can provide a judging and calculating basis for the passing of the target moving parts with different types and loading conditions;
step S14: and updating the passing path of the target moving piece according to the judging result and the first cross station.
When the successful passing condition of the target moving part is met, the first risk site can meet the successful passing condition of the target moving part, and the direct target moving part can directly pass through the first risk site from the current position to the first cross site;
otherwise, when the successful passing condition of the target moving part is not met, the first risk site is indicated to not meet the successful passing condition of the target moving part, the target moving part is required to pass through the first intersection difference site from the current position, the first risk site is avoided, other first planning paths are entered through the first intersection site, and finally the destination is reached.
According to the method and the system, after the target moving part is selected through combining with the preset planning road section, for each first risk site, if the moving part which successfully passes before exists, a judgment and calculation basis can be provided for the passing of the target moving part with different types and loading conditions, so that the passing path of the target moving part can be used for deciding to avoid or continue to pass through the first risk site in advance, and the passing fluency and the passing efficiency of the target moving part are guaranteed.
As shown in fig. 2, as a preferred embodiment of the present invention, before acquiring the preset planned paths of the plurality of moving parts and reading the marked risk sites in the preset planned paths, the method further includes:
step S20: the method comprises the steps of periodically obtaining corresponding website passing failure records when a plurality of moving parts pass according to respective preset planning paths;
step S21: extracting a corresponding site of the site passing failure record to obtain an extraction result;
step S22: and marking the extraction result as a new risk site.
It can be understood that the passing failure record of a site is reported by a plurality of moving parts in the plurality of moving parts, and the passing failure indicates that a site cannot pass when passing according to respective planned paths, and all sites in a preset planned road section can pass theoretically (without considering changes caused by subsequent environments and the like), so that if the site cannot pass, the site is indicated to have a new condition, such as a new obstacle, and the like, and therefore the site in the extraction result needs to be marked as a new risk site, and if the site is originally a risk site, the site is still marked as a risk site; the method can ensure the timely updating of the risk site.
As shown in fig. 3, as a preferred embodiment of the present invention, the screening the successful traffic records of the first risk site includes:
step S111: acquiring a passing video of a first risk site, wherein the passing video comprises a plurality of passing sub-videos arranged in sequence according to time;
the traffic video of the first risk site is panoramic traffic video recorded by monitoring equipment of the risk site; the plurality of traffic sub-videos are sequentially arranged according to time after being recorded; a plurality of traffic sub-videos form a (panoramic) traffic video;
step S112: sequentially identifying a plurality of traffic sub-videos according to the sequence from the near to the far from the current moment;
the more the time is, the more the sorting is before in the traffic videos, and the latest traffic condition of the first risk site needs to be acquired, so that a plurality of traffic sub videos are identified one by one according to the sequence from the near to the far from the current moment;
step S113: when the passing picture of the non-target moving part in a certain passing sub-video is identified to meet the preset passing judgment condition, judging that the non-target moving part in the certain passing sub-video successfully passes, and stopping identifying the subsequent passing sub-video, wherein the successful passing record comprises the certain passing sub-video corresponding to the successful passing of the non-target moving part, and the preset passing judgment condition comprises the successful passing condition set along the advancing direction.
The traffic sub-video is composed of a plurality of frames of pictures, and records the completion process from the start of the traffic risk site to the successful passing of the risk site of the non-target moving part (the success here includes the forced passing type, which can cause a certain risk, so that picture identification is required);
the passing picture accords with a preset passing judgment condition, and in order to identify that the multi-frame picture accords with the preset passing judgment condition, the multi-frame picture reflects whether the non-target moving part completely passes or not, wherein the specific steps of identification comprise: according to the travelling direction, identifying that the non-target moving part (at least comprising two sides, the top is generally not limited under a preset planning path) does not have any contact with the first risk site, namely that the shortest distance between the side of the non-target moving part and the first risk site is not less than a first set threshold value, and at least one of any loaded article, part of the non-target moving part and non-ground surface structure of the first risk site does not exist on the ground surface of the first risk site in the multi-frame picture; if the identification is successful, the non-target moving part is indicated to pass without problems, and the passing does not cause accidents such as scratch and the like (not forced passing); successfully passing the first risk site on behalf of the non-target mobile;
the distance recognition and the object recognition in the image can be respectively adopted to measure the distance between objects in the image by using an OpenCV algorithm, and target detection algorithms such as OverFeat, YOLOv1 and Yolov 2. The algorithm is a mature algorithm in the prior art.
As a preferred embodiment of the present invention, the acquiring traffic record data corresponding to a non-target mobile unit in a successful traffic record includes:
the steps are as follows: extracting a time stamp of a passing picture meeting preset passing judgment conditions in a certain passing sub-video;
the steps are as follows: and extracting the passing distance sensing data containing the time stamp to obtain passing record data, wherein the passing distance sensing data comprises distance data which is acquired perpendicular to the travelling direction of a certain non-target moving part, and the distance data corresponds to the side part of the certain non-target moving part.
The time stamp is a time stamp in a passing picture of the passing sub-video, and the time recorded on the video frame number can be read to obtain the time stamp as long as the passing picture belongs to which frame of the passing sub-video;
when the passing distance sensing data containing the time stamp is extracted, firstly determining a time period containing the time stamp, wherein the time period needs to meet the preset duration, extracting the passing distance sensing data meeting the preset duration after corresponding to the time stamp, and collecting and reporting the passing distance sensing data after a certain non-target moving part (through a sensor on the side part) which successfully passes through;
it should be appreciated that the traffic record data includes traffic distance sensing data, so that, for the distance data collected in the traveling direction of a non-target moving member, the distance data also reflects the latest traffic situation of the first risk station, and can provide reference and calculation reference for traffic determination of the target moving member.
As shown in fig. 4, as a preferred embodiment of the present invention, the determining whether the first risk station has the successful traffic condition of the target moving object according to the traffic record data of the non-target moving object and the traffic demand condition of the target moving object, and the obtaining the determination result includes:
step S131: screening the minimum distance value in the distance data, acquiring the target size of a certain non-target moving part at the acquisition position of the minimum distance value, and calculating the maximum allowed passing distance according to the minimum distance value and the target size;
the distance data is the distance from the acquisition position to the passing edge of the first risk site, which is detected by the side part of a certain non-target moving part, and as the non-target moving part is moving, all the distances which are equal to all the distances from the passing route to the passing edge of the first risk site are detected, the minimum distance is selected, and the acquisition positions are a plurality of the non-target moving parts which are distributed in sequence from top to bottom according to the vertical direction of the side part, so that the non-target moving part is suitable for the target moving part with a certain height; the target dimension of a non-target mover refers to the dimension of the non-target mover itself, such as the width between the two sides of the acquisition location in the loaded or non-loaded condition (e.g., the width of the target mover itself in the non-loaded condition, such dimension being known); the distance data is the sum of the distances acquired by the acquisition positions on the two sides;
step S132: reading the minimum passing requirement distance in the passing requirement condition of the target moving piece;
step S133: judging whether the maximum allowed passing distance is larger than a minimum passing required distance or not;
for example, three sets of distance values (in cm each) are obtained (20,30,22,18), (20.18,20,19), and (15, 18,16,15); and the target size is 120, and the maximum allowed passing distance is the sum of three groups of distance values and the target size;
according to the wooden barrel effect, the maximum allowed passing distance is 135, when the minimum passing required distance of the target moving piece is 140, the passing is not allowed, and when the minimum passing required distance of the target moving piece is 120, the passing is allowed;
whether the above maximum allowable passing distance is greater than the minimum passing required distance requires that the difference between the former and the latter is greater than a second set threshold, e.g., 10, which can be set according to practical experience, smaller values representing stronger passing ability under narrow passing conditions of the target moving member.
Step S134: if yes, judging that the first risk site has a successful passing condition of the target moving part;
step S135: otherwise, judging that the first risk site does not have the successful passing condition of the target moving piece.
Considering the traffic size difference between the moving parts, the traffic size difference is caused by the size of the moving parts or the cargo carrying size and the like; therefore, the passing requirement distances between different moving parts are different, and the maximum allowed passing distance is calculated by means of the minimum distance value and the target size, so that whether the failed target moving part can pass or not is evaluated according to the non-target moving part which passes successfully.
As shown in fig. 5, as a preferred embodiment of the present invention, the updating the traffic path of the target moving member according to the determination result and the first intersection includes:
step S141: when the first risk site is judged to have the successful passing condition of the target moving part, maintaining the planned road section from the first cross site to the first risk site unchanged;
step S142: when the first risk site is judged to not have the successful passing condition of the target moving part, deleting the planned road section from the first intersection site to the first risk site, and planning the planned road section from the first intersection site to the non-first risk site;
optionally, the non-first risk site is located in a first planned path.
It can be understood that when the successful passing condition of the target moving member is provided, the first risk site can meet the successful passing condition of the target moving member, and the target moving member can directly pass through the first risk site from the current position to the first intersection site at the moment, namely, the planned road section from the first intersection site to the first risk site is maintained unchanged;
it should be noted that, whether the first risk station has the successful passing condition of the target moving member or not, if the subsequent risk station needs to determine whether the first risk station has the successful passing condition of the target moving member or not, the subsequent risk station may determine the first risk station according to the same method as the above embodiment, which will not be described herein;
otherwise, when the successful passing condition of the target moving part is not met, the first risk site is indicated to not meet the successful passing condition of the target moving part, the target moving part is required to pass through the first intersection difference site from the current position, then passes through the non-first risk site, namely the first risk site is avoided, other first planning paths are entered through the first intersection site, and finally the destination is reached.
As another preferred embodiment of the present invention, as shown in fig. 6, in another aspect, a path planning system based on big data, the system comprising:
the reading module 100 is configured to obtain preset planned paths of a plurality of moving parts, and read marked risk sites in the preset planned paths, where the preset planned paths include a plurality of staggered first planned paths, a plurality of intersecting sites are formed between the plurality of staggered first planned paths, and the plurality of moving parts include target moving parts and non-target moving parts;
the screening module 200 is configured to obtain a current position of the target moving part, and screen a successful traffic record of a first risk site if two subsequent continuous sites at the current position are the first intersection site and the first risk site respectively;
the extraction module 300 is configured to obtain traffic record data corresponding to a non-target mobile part in a successful traffic record, where the non-target mobile part is one of the non-target mobile parts that successfully passes through the first risk site, and the traffic record data includes traffic distance sensing data;
the judging module 400 is configured to obtain a traffic demand condition of a target mobile unit, judge whether the first risk station has a successful traffic condition of the target mobile unit according to traffic record data of a non-target mobile unit and the traffic demand condition of the target mobile unit, and obtain a judging result, where the judging result includes judging that the first risk station has the successful traffic condition of the target mobile unit and judging that the first risk station does not have the successful traffic condition of the target mobile unit;
the path updating module 500 is configured to update a traffic path of the target moving object according to the determination result and the first intersection.
It should be noted that, referring to the description of the specific implementation of a path planning method based on big data in the foregoing embodiment, the system corresponds to the implementation method of the method completely, and will not be described herein.
According to the route planning method based on big data, the route planning system based on big data is provided based on the route planning method based on big data, after the target moving part is selected through combining with a preset planning road section, for each first risk site, if the moving part which is successfully passed before exists, a judgment and calculation basis can be provided for the passing of the target moving part with different types and loading conditions, so that the passing route of the target moving part can be decided to avoid or continue to pass through the first risk site in advance, and the passing fluency and passing efficiency of the target moving part are guaranteed.
In order to be able to load the method and system described above to function properly, the system may include more or less components than those described above, or may combine some components, or different components, in addition to the various modules described above, for example, may include input and output devices, network access devices, buses, processors, memories, and the like.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the above system, and various interfaces and lines are used to connect the various parts.
The memory may be used to store a computer and a system program and/or module, and the processor may perform the various functions described above by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as an information acquisition template presentation function, a product information distribution function, etc.), and the like. The storage data area may store data created according to the use of the berth status display system (e.g., product information acquisition templates corresponding to different product types, product information required to be released by different product providers, etc.), and so on. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (8)
1. A method for path planning based on big data, the method comprising:
acquiring preset planning paths of a plurality of moving parts, and reading marked risk sites in the preset planning paths, wherein the preset planning paths comprise a plurality of staggered first planning paths, a plurality of cross sites are formed among the staggered first planning paths, and the plurality of moving parts comprise target moving parts and non-target moving parts;
acquiring the current position of the target moving part, and screening a successful pass record of a first risk site if two subsequent continuous sites at the current position are a first cross site and a first risk site respectively;
acquiring traffic record data corresponding to a certain non-target moving part in a successful traffic record, wherein the certain non-target moving part is one of the non-target moving parts of a first risk site which is successfully passed through, and the traffic record data comprises passing distance sensing data;
acquiring a passing requirement condition of a target mobile part, judging whether a first risk station has a successful passing condition of the target mobile part according to passing record data of a certain non-target mobile part and the passing requirement condition of the target mobile part, and obtaining a judging result, wherein the judging result comprises judging that the first risk station has the successful passing condition of the target mobile part and judging that the first risk station does not have the successful passing condition of the target mobile part;
and updating the passing path of the target moving piece according to the judging result and the first cross station.
2. The big data based path planning method of claim 1, wherein before acquiring the preset planned path of the plurality of moving members and reading the marked risk sites in the preset planned path, the method further comprises:
the method comprises the steps of periodically obtaining corresponding website passing failure records when a plurality of moving parts pass according to respective preset planning paths;
extracting a corresponding site of the site passing failure record to obtain an extraction result;
and marking the extraction result as a new risk site.
3. The big data based path planning method of claim 1, wherein the screening the successful pass records of the first risk site comprises:
acquiring a passing video of a first risk site, wherein the passing video comprises a plurality of passing sub-videos arranged in sequence according to time;
sequentially identifying a plurality of traffic sub-videos according to the sequence from the near to the far from the current moment;
when the passing picture of the non-target moving part in a certain passing sub-video is identified to meet the preset passing judgment condition, judging that the non-target moving part in the certain passing sub-video successfully passes, and stopping identifying the subsequent passing sub-video, wherein the successful passing record comprises the certain passing sub-video corresponding to the successful passing of the non-target moving part, and the preset passing judgment condition comprises the successful passing condition set along the advancing direction.
4. The route planning method according to claim 3, wherein the obtaining traffic record data corresponding to a non-target moving part in the successful traffic record includes:
extracting a time stamp of a passing picture meeting preset passing judgment conditions in a certain passing sub-video;
and extracting the passing distance sensing data containing the time stamp to obtain passing record data, wherein the passing distance sensing data comprises distance data which is acquired perpendicular to the travelling direction of a certain non-target moving part, and the distance data corresponds to the side part of the certain non-target moving part.
5. The big data based path planning method according to claim 4, wherein the determining whether the first risk site has the successful passing condition of the target moving part according to the passing record data of the certain non-target moving part and the passing requirement condition of the target moving part, and the obtaining the determination result includes:
screening the minimum distance value in the distance data, acquiring the target size of a certain non-target moving part at the acquisition position of the minimum distance value, and calculating the maximum allowed passing distance according to the minimum distance value and the target size;
reading the minimum passing requirement distance in the passing requirement condition of the target moving piece;
judging whether the maximum allowed passing distance is larger than a minimum passing required distance or not;
if yes, judging that the first risk site has a successful passing condition of the target moving part;
otherwise, judging that the first risk site does not have the successful passing condition of the target moving piece.
6. The big data based path planning method of claim 1 or 5, wherein updating the traffic path of the target moving object according to the determination result and the first intersection comprises:
when the first risk site is judged to have the successful passing condition of the target moving part, maintaining the planned road section from the first cross site to the first risk site unchanged;
and when the first risk site is judged to not have the successful passing condition of the target moving part, deleting the planned road section from the first intersection site to the first risk site, and planning the planned road section from the first intersection site to the non-first risk site.
7. The big data based path planning method of claim 6, wherein the non-first risk site is located in a first planned path.
8. A big data based path planning system, the system comprising:
the system comprises a reading module, a marking module and a judging module, wherein the reading module is used for acquiring preset planning paths of a plurality of moving parts, and reading marked risk sites in the preset planning paths, wherein the preset planning paths comprise a plurality of staggered first planning paths, a plurality of cross sites are formed among the staggered first planning paths, and the plurality of moving parts comprise target moving parts and non-target moving parts;
the screening module is used for acquiring the current position of the target moving part, and screening a successful passing record of the first risk site if two subsequent continuous sites at the current position are the first cross site and the first risk site respectively;
the extraction module is used for acquiring traffic record data corresponding to a certain non-target moving part in the successful traffic record, wherein the certain non-target moving part is one of the non-target moving parts of the first risk site which successfully passes through, and the traffic record data comprises passing distance sensing data;
the judging module is used for acquiring the passing requirement condition of the target moving piece, judging whether the first risk station has the successful passing condition of the target moving piece according to the passing record data of a certain non-target moving piece and the passing requirement condition of the target moving piece, and obtaining a judging result, wherein the judging result comprises judging that the first risk station has the successful passing condition of the target moving piece and judging that the first risk station does not have the successful passing condition of the target moving piece;
and the path updating module is used for updating the passing path of the target moving piece according to the judging result and the first cross station.
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