Disclosure of Invention
In order to realize efficient and accurate green vision rate data acquisition, the application provides a vehicle-mounted green vision rate acquisition control method and system based on a planned route.
In a first aspect, the application provides a vehicle-mounted green vision rate acquisition control method based on a planned route, which adopts the following technical scheme:
A vehicle-mounted green vision rate acquisition control method based on a planned route, which is applied to an image acquisition device installed on an acquisition vehicle, the acquisition control method comprising:
Acquiring a starting point coordinate of a road section to be acquired in a target green vision rate acquisition area;
Initializing exposure parameters and equipment control parameters of the image acquisition equipment;
Receiving the current position coordinates and the current speed data of the collected vehicle;
Calculating the distance between the current position coordinate and the starting point coordinate of the road section to be acquired to obtain a first distance;
Judging whether the first distance is larger than a first preset distance threshold value or not, if so, dynamically adjusting the corresponding calculation frequency of the first distance according to the first distance and the current speed data of the collected vehicle;
if not, sending an exposure starting control instruction to the image acquisition equipment, and determining the road section to be acquired as a current acquisition road section;
receiving road section image data acquired by the image acquisition equipment on the current acquisition road section in real time;
Calculating the distance between the current position coordinate and the end point coordinate of the current acquisition road section to obtain a second distance;
And if not, sending an exposure closing instruction to the image acquisition equipment, and marking the current acquisition road section as an acquired road section.
Through adopting above-mentioned technical scheme, based on the accurate calculation to the real-time state (including position and speed) of collection vehicle to and dynamic adjustment collection frequency and automatic exposure control, realized high-efficient, accurate green vision rate data acquisition, not only can reduce manual intervention in the complex environment flexible adaptation, can also ensure the accurate matching of collection image and target highway section, effectively optimize computational resource and collection time simultaneously, possess extensive practicality and reliability.
Optionally, before the step of acquiring the start point coordinates of the road segment to be acquired in the target green vision rate acquisition area, the method further includes:
Receiving coordinate data of a plurality of key nodes of the target green vision rate acquisition area;
Dividing a road among a plurality of key nodes into a plurality of road sections based on a preset route dividing rule, and generating corresponding planning route information;
and determining a plurality of road sections as road sections to be acquired based on the planned route information, and generating an acquisition task list.
By adopting the technical scheme, the key node data of the target area is received, the roads between the nodes are divided based on the preset rule, and the planned route information is generated. And then, by marking the road section to be acquired and dynamically extracting the starting point coordinates, the system can realize the fine planning and automatic execution of the large-scale acquisition task.
Optionally, the planned route information includes a number, a start point coordinate, and an end point coordinate of each road section.
Optionally, the exposure parameters include an exposure time, an ISO value, and an aperture size, and the device control parameters include the first preset distance threshold and the second preset distance threshold.
Optionally, the step of initializing exposure parameters of the image acquisition device includes:
acquiring current speed data and current environment illumination data of the collected vehicle in real time;
And determining corresponding exposure parameters according to the current vehicle speed data and the current environment illumination data based on a preset mapping relation.
By adopting the technical proposal, the speed data and the ambient light data of the collected vehicle are obtained in real time, and the exposure parameters (comprising the exposure time, the ISO value and the aperture) of the camera are determined by combining the preset mapping relation, the technical proposal realizes the intelligent adaptability adjustment of the image collecting equipment to the dynamic collecting environment, the method can effectively solve the problems of image blurring or darkness caused by speed change and various illumination conditions in the traditional green vision rate acquisition, simultaneously reduces the complexity of manual parameter setting, improves the acquisition efficiency, ensures the quality consistency of image acquisition in different environments, and provides reliable data support for green vision rate evaluation.
Optionally, after the step of marking the current collected road segment as a collected road segment, the method further includes:
Updating state information of the acquired road sections in the acquisition task list, and removing the acquired road sections from the acquisition task list;
according to the updated acquisition task list, loading the next road section data to be acquired in sequence and circularly executing the road section image data acquisition step until all the road sections to be acquired are marked as acquired road sections, so as to obtain the road section image data of all the road sections;
And classifying and storing the road section image data of all road sections according to the road section numbers to obtain a collected data report of the target green vision rate collecting area.
By adopting the technical scheme, the dynamic scheduling and efficient execution of the acquisition tasks are realized, the acquisition tasks are circularly executed from the removal of the acquired road sections to the sequential loading of the road sections to be acquired, and the accuracy and continuity of the tasks are ensured in each stage by the system. Finally, by means of dynamic updating of the acquisition task list and judgment of termination conditions, image acquisition of all road sections can be completed without human intervention, and a structured data report can be generated.
In a second aspect, the application provides a vehicle-mounted green vision rate acquisition control system based on a planned route, which adopts the following technical scheme:
an on-vehicle green vision rate acquisition control system based on a planned route, applied to an image acquisition device mounted on an acquisition vehicle, the acquisition control system comprising:
The acquisition module is used for acquiring the starting point coordinates of the road section to be acquired in the target green vision rate acquisition area;
The parameter initialization module is used for initializing the exposure parameters and the equipment control parameters of the image acquisition equipment;
The receiving module is used for receiving the current position coordinates and the current speed data of the collected vehicle;
the first distance calculation module is used for calculating the distance between the current position coordinate and the starting point coordinate of the road section to be acquired to obtain a first distance;
The first judging module is used for judging whether the first distance is larger than a first preset distance threshold value, outputting a first judging result if the first distance is larger than the first preset distance threshold value, and outputting a second judging result if the first distance is smaller than the first preset distance threshold value;
the calculation frequency adjustment module is used for responding to the first judgment result and dynamically adjusting the calculation frequency of the corresponding first distance according to the first distance and the current speed data of the collected vehicle;
The control module is used for responding to the second judging result, sending an exposure starting control instruction to the image acquisition equipment and determining the road section to be acquired as the current acquisition road section;
The receiving module is used for receiving the road section image data acquired by the image acquisition equipment on the current acquisition road section in real time;
The second distance calculation module is used for calculating the distance between the current position coordinate and the end point coordinate of the current acquisition road section to obtain a second distance;
The second judging module is used for judging whether the second distance is larger than a second preset distance threshold value or not, and outputting a third judging result if not;
and the control module is also used for responding to the third judging result, sending an exposure closing instruction to the image acquisition equipment and marking the current acquisition road section as an acquired road section.
Optionally, the acquisition control system further includes:
the coordinate data receiving module is used for receiving coordinate data of a plurality of key nodes of the target green vision rate acquisition area;
The route planning module is used for dividing the roads among the plurality of key nodes into a plurality of road sections based on a preset route dividing rule, and generating corresponding planning route information;
and the acquisition task list generation module is used for determining a plurality of road sections as road sections to be acquired based on the planned route information and generating an acquisition task list.
In a third aspect, the present application provides a computer device, which adopts the following technical scheme:
A computer device comprising a memory, a processor and a computer program stored on the memory, the processor executing the computer program to perform the steps of the method according to the first aspect.
In a fourth aspect, the present application provides a computer readable storage medium, which adopts the following technical scheme:
a computer readable storage medium storing a computer program capable of being loaded by a processor and executing any one of the methods of the first aspect.
In summary, the application has at least one of the following beneficial technical effects that the automatic exposure triggering and stopping of the vehicle-mounted image acquisition equipment in the running process of the vehicle are realized, the requirement of manual intervention is completely eliminated, the operating time and the labor cost of green vision rate acquisition are obviously reduced, the working efficiency is greatly improved, and the application is particularly suitable for application scenes in which road section data are required to be acquired in batches in a large-scale and multiple routes.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings 1 to 4 and examples. 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 application.
The embodiment of the application discloses a vehicle-mounted green vision rate acquisition control method based on a planned route.
Referring to fig. 1, a vehicle-mounted green vision rate acquisition control method based on a planned route is applied to an image acquisition device mounted on an acquisition vehicle, and the acquisition control method includes:
step S101, acquiring the starting point coordinates of a road section to be acquired in a target green vision rate acquisition area;
Specifically, the target green view rate acquisition area is usually preset through a planned route file, wherein the planned route file comprises starting point coordinates and end point coordinates of a plurality of road sections, and the system extracts starting point coordinates of the next road section to be acquired from the to-be-acquired list through loading the planned route file so as to determine the target road section of the current acquisition task and provide accurate starting point reference for subsequent automatic control.
Step S102, initializing exposure parameters and equipment control parameters of an image acquisition device;
The exposure parameters (such as ISO value, aperture size and shutter speed) are key to the image acquisition device to shoot clear images, and can be initially set according to current vehicle speed data and ambient illumination data, and the device control parameters comprise a starting point trigger distance (a first preset distance threshold) and an end point stop distance (a second preset distance threshold) and are used for defining starting and stopping conditions and logic of automatic control.
It can be appreciated that the initialization parameters can ensure that the device has proper shooting capability and control logic when the acquisition work is started, thereby improving the acquisition efficiency and the image quality.
It should be noted that the image capturing device needs to have high resolution, dynamic parameter adjustment capability and good environmental adaptability, and can implement accurate shooting in an environment where a vehicle runs at a high speed or illumination changes rapidly, so as to provide high-quality image data support for green vision rate collection in a planned route. In some embodiments, the image processing device may be a camera device with high resolution imaging capabilities, or may be a high definition industrial camera specially designed for on-board vehicles,
Step S103, receiving and collecting current position coordinates and current speed data of a vehicle;
the real-time coordinates and the vehicle speed of the current vehicle can be obtained through the GNSS module and the vehicle-mounted speed sensor and are periodically transmitted to the control system so as to facilitate subsequent distance calculation and logic judgment.
Step S104, calculating the distance between the current position coordinate and the starting point coordinate of the road section to be acquired to obtain a first distance;
The shortest path from the current position coordinates to the starting point of the road section to be collected can be found from the current position of the road section of the vehicle based on a path planning algorithm, and the path length is the first distance.
Step S105, judging whether the first distance is larger than a first preset distance threshold value, if so, jumping to step S106, and if not, jumping to step S107;
step S106, dynamically adjusting the calculation frequency of the corresponding first distance according to the first distance and the current speed data of the collected vehicle;
the dynamic adjustment calculation frequency is determined through a mapping relation between the first distance and current vehicle speed data, when the first distance is larger than the first preset distance threshold, the distance between the collected vehicle and the starting point of the road section is far, and at the moment, the distance calculation frequency can be flexibly adjusted by combining the first distance with the current vehicle speed of the vehicle, if the first distance is smaller and the current vehicle speed is faster, the set calculation frequency is higher.
The distance calculation frequency may be configured to be 5 seconds/time when the vehicle speed is below 30km/h, the distance calculation frequency is 3 seconds/time when the vehicle speed is in a 30-60km/h section, the distance calculation frequency is raised to 2 seconds/time when the vehicle speed exceeds 60km/h in order to more finely and timely monitor the position change condition of the vehicle during high-speed traveling, and in addition, the distance calculation frequency may be automatically adjusted to be 1 second/time when the first distance is close to a first preset distance threshold value, which indicates that the vehicle is relatively close to the starting point coordinates of the road section.
It can be understood that the above technical scheme can flexibly balance the calculation amount according to the actual speed of the vehicle while ensuring accurate grasp of the position of the vehicle relative to the planned route, and more finely monitor the position change condition of the vehicle in the high-speed running process when running at high speed, and save the calculation resource when running at low speed, reduce the unnecessary calculation amount, avoid excessive occupation of system resources, and simultaneously, can not have great influence on the overall route tracking accuracy, so that the whole operation process achieves good balance between high efficiency and accuracy.
Step S107, an exposure opening control instruction is sent to the image acquisition equipment, and a road section to be acquired is determined as a current acquisition road section;
when the first distance is smaller than or equal to a first preset distance threshold value, the fact that the distance between the acquisition vehicle and the starting point of the road section is relatively short is indicated, the system enables the image acquisition equipment to start exposure through the exposure starting control instruction, the number or the identification of the road section to be acquired is written into the current acquisition task list, image data are conveniently associated, and the matching performance of the image data and the road section is ensured.
Step S108, receiving road section image data acquired by the image acquisition equipment on the current acquisition road section in real time;
the road section image data are transmitted to the storage module in real time through the high-bandwidth interface, GNSS coordinates and time stamp information are added, efficient collection of images and data integrity guarantee are achieved, and a basis is provided for analysis of subsequent green vision rate data.
Step S109, calculating the distance between the current position coordinate and the end point coordinate of the current acquisition road section to obtain a second distance;
The shortest path from the current position coordinate to the end point of the current acquisition road section can be found from the current position of the road section of the vehicle based on a path planning algorithm, and the path length is the second distance.
Step S110, judging whether the second distance is larger than a second preset distance threshold, if not, jumping to step S111, and if so, not executing any operation;
step S111, an exposure closing instruction is sent to the image acquisition equipment, and the current acquired road section is marked as the acquired road section.
The second preset distance threshold defines a stopping condition of acquisition, and the current acquisition task is accurately ended by stopping the exposure of the image acquisition equipment, so that repeated acquisition is avoided, and the acquisition efficiency is improved. And meanwhile, marking the current acquired road section as the acquired road section, and removing the acquired road section from the task list.
In the embodiment, based on the accurate calculation of the real-time state (including the position and the speed) of the collected vehicle, and the dynamic adjustment of the collection frequency and the automatic exposure control, the efficient and accurate green vision rate data collection is realized, the flexible adaptation in a complex environment is realized, the manual intervention is reduced, the accurate matching of the collected image and the target road section can be ensured, meanwhile, the calculation resource and the collection time are effectively optimized, and the wide practicability and reliability are realized.
Referring to fig. 2, as a further embodiment of the acquisition control method, before the step of acquiring the start point coordinates of the road segment to be acquired of the target green vision rate acquisition area, further includes:
step S201, receiving coordinate data of a plurality of key nodes of a target green vision rate acquisition area;
The key nodes are geographic coordinate points used for calibrating regional boundaries or important positions in the target green vision rate acquisition region, such as intersections, road turning points or regional boundary points, and the coordinates of the points can be generated by manual setting, map data importing or planning software.
Step S202, dividing a road among a plurality of key nodes into a plurality of road sections based on a preset route dividing rule, and generating corresponding planning route information;
The method comprises the steps of planning route information, namely, acquiring the accurate coordinates (longitude and latitude coordinate forms) of a road section starting point and a road section ending point under a map coordinate system by means of a coordinate acquisition function of map software for each divided road section, automatically calling corresponding interfaces in a programming mode, and correspondingly recording the coordinate values in the fields of the road section starting point coordinate and the road section ending point coordinate of a corresponding road section file respectively, wherein the whole process does not need manual measurement and input, and efficiency and accuracy are greatly improved.
Specifically, the system defines roads between adjacent key nodes as independent road segments according to the order of the key nodes, the dividing rule can be based on a map topological structure, for example, the roads from the node 1 to the node 2 are taken as an independent road segment and marked in a numbering mode, and the numbers are sequentially generated according to a certain order rule (a numerical increasing mode) so as to ensure that each road segment can be accurately distinguished.
In addition, the route division rule may be optimized according to the length of the road segment or the topography (such as whether the road segment crosses the river), for example, if the road between two nodes is too long, a virtual node may be inserted in the middle to further subdivide the road segment.
It should be noted that the multiple segments in the planned route information may be discontinuous multiple segments, that is, there may be a certain interval between adjacent segments.
Step S203, determining the plurality of road segments as the road segments to be collected based on the planned route information and generating a collection task list.
The road sections to be acquired are road sections needing green vision rate data acquisition in the planned route, the system defaults to set all the planned road sections to be in a state to be acquired and generates an acquisition task list, and the acquisition task list can be screened or adjusted according to the user requirements.
It will be appreciated that the system allows a user to screen road segments in a planned route, for example, based on road segment length, acquisition priority of a target area, or excluding repeatedly acquired road segments, wherein the priority may be set based on specific mission requirements, for example, road segments of urban arterial roads may be prioritized as "high priority to be acquired".
In the above embodiment, the key node data of the target area is received, the roads between the nodes are divided based on the preset rule and the planned route information is generated, according to the technical scheme, a complex acquisition area is divided into a plurality of independent road sections, each road section is managed in a structured mode, and the starting point information and the end point information of a task are clearly acquired. And then, by marking the road section to be acquired and dynamically extracting the starting point coordinates, the system can realize the fine planning and automatic execution of the large-scale acquisition task.
Referring to fig. 3, as an embodiment of step S102, the step of initializing the exposure parameters of the image capturing apparatus includes:
Step S301, acquiring current speed data and current environment illumination data of a collected vehicle in real time;
The vehicle speed data of the collected vehicle is usually obtained through a vehicle-mounted speed sensor or a GNSS module, the vehicle-mounted speed sensor can directly measure the real-time speed of the vehicle, and the GNSS module calculates the vehicle speed based on continuous position information. The real-time performance of the vehicle speed is crucial to the subsequent adjustment of exposure parameters, and particularly when the vehicle runs at a high speed (such as >30 km/h), the vehicle moves fast, the scene changes frequently, and the exposure time needs to respond quickly.
Also, ambient lighting data is typically measured in real-time by lighting sensors mounted on the collection vehicle, with the lighting intensity expressed in "illuminance" units (e.g., lux). The real-time measurement of illumination intensity is used to determine the brightness of the current acquisition environment, for example, strong illumination (e.g., >1000 Lux) for outdoor sunny days, weak illumination (e.g., <200 Lux) for shaded areas or night environments.
It can be understood that by collecting the vehicle speed and the ambient light data in real time, necessary basic information is provided for dynamically adjusting the exposure parameters, and the collecting equipment is ensured to be capable of rapidly adapting to different running speeds and environmental changes.
Step S302, based on a preset mapping relation, corresponding exposure parameters are determined according to current vehicle speed data and current environment illumination data.
The mapping relation is generated according to preset rules or experience data and is used for mapping the vehicle speed data and the illumination data to corresponding exposure parameter combinations. The exposure parameters include exposure time (for controlling the light sensing time of the image sensor), ISO value (indicating the higher the light sensitivity of the camera, the stronger the sensitivity to light but the noise will also increase), and aperture (indicating the aperture size of the camera lens, controlling the amount of incoming light).
The system searches the corresponding parameter combination from the preset mapping table through the vehicle speed data and the illumination data which are input in real time. For example, when the vehicle speed is relatively slow (between 0 and 30 km/h), the exposure time is prolonged to be between 1/200 and 1/100 seconds according to the actual light condition on the premise of ensuring the image quality, and the exposure time is adjusted to be between 1/800 and 1/500 seconds according to the light change while the ISO value and the aperture size are correspondingly adjusted when the vehicle speed is relatively fast (above 30 km/h) to prevent the picture from being blurred due to the movement of the vehicle. For example, for a road section with good illumination condition, the ISO value is set at a lower level (100-400), so that the picture quality is fine and smooth, the noise is less, and for a road section with poor illumination condition, the ISO value is increased to 800-1200, the sensitivity of the camera to light is enhanced, and the problem of insufficient light is solved.
The higher the vehicle speed is, the shorter the exposure time is, so as to avoid the blurring of the picture caused by the movement of the vehicle, and the lower the ambient illumination intensity is, the higher the ISO value is, and the larger the aperture value is, so as to make up the condition of insufficient brightness.
It can be understood that in the running process of the vehicle, the exposure time, the ISO value and the aperture are comprehensively adjusted for different road segments, so that the image acquisition equipment can be quickly adapted to the environment under different running and illumination conditions, the balance of acquisition image quality and efficiency is realized, and the powerful guarantee is provided for the follow-up data statistics and analysis.
In the embodiment, the speed data and the ambient light data of the collected vehicle are obtained in real time, and the exposure parameters (including the exposure time, the ISO value and the aperture) of the camera are determined by combining the preset mapping relation, the technical scheme realizes the intelligent adaptive adjustment of the image collecting equipment to the dynamic collecting environment, the method can effectively solve the problems of image blurring or darkness caused by speed change and various illumination conditions in the traditional green vision rate acquisition, simultaneously reduces the complexity of manual parameter setting, improves the acquisition efficiency, ensures the quality consistency of image acquisition in different environments, and provides reliable data support for green vision rate evaluation.
Referring to fig. 4, as a further embodiment of the acquisition control method, after the step of marking the current acquisition road segment as an acquired road segment, further includes:
step S401, updating state information of the acquired road segments in the acquisition task list, and removing the acquired road segments from the acquisition task list;
the system stores all the road segments to be collected in a list form, and each road segment is recorded in a structured data record, including a road segment number, a starting point coordinate, an ending point coordinate and priority information (which can be pre-ordered according to the position or importance of each road segment). And, after each acquisition, the system will re-update the task list and remove the acquired road segments.
Illustratively, when a certain acquired road segment is marked as "acquired" state, the system removes it from the acquisition task list, examines the status field of each road segment by traversing the task list, and for a road segment with a state of "completed" (acquired), deletes the corresponding record from the task list.
Step S402, loading the next road section data to be acquired in turn and circularly executing the road section image data acquisition step according to the updated acquisition task list until all the road sections to be acquired are marked as acquired road sections, and obtaining the road section image data of all the road sections;
After the system completes the current road segment collection, the next road segment in the task list can be loaded as the current collected road segment, the road segment image data collection steps (step S101 to step S111) are repeatedly executed, the loaded road segment data comprises road segment numbers, starting point coordinates, end point coordinates and priority information, the system loads the road segments according to the priority, for example, the main road segment is collected preferentially, and the secondary road segment is collected later. When all road sections in the acquisition task list are marked as an acquired state, the system can judge that the acquisition task is completed.
It can be understood that the scheduling logic can be combined with a priority-based ordering algorithm to realize sequential loading according to the importance of the tasks, and the system can quickly switch to the next task after completing the current task by sequentially loading the road section data to be acquired, so that the acquisition task can be continuously and efficiently executed. Meanwhile, by combining priority scheduling, the method can adapt to the requirements of different acquisition scenes.
Step S403, classifying and storing the road section image data of all road sections according to road section numbers to obtain a collected data report of the target green vision rate collecting area.
After the collection of all road sections is completed, the system integrates all collected road section image data, including classified storage according to road section numbers, and metadata such as GNSS coordinates, time stamps and the like are added, and a data index file can be generated for subsequent retrieval and analysis.
In the embodiment, the dynamic scheduling and efficient execution of the acquisition tasks are realized, and the accuracy and the consistency of the tasks are ensured in each stage from the removal of the acquired road sections to the sequential loading of the road sections to be acquired and then to the cyclic execution of the acquisition tasks. Finally, by means of dynamic updating of the acquisition task list and judgment of termination conditions, image acquisition of all road sections can be completed without human intervention, and a structured data report can be generated.
The core technology of the application is a reasonable utilization and unique automatic control method of a planned route, and the pre-planned route provides definite targets and directions for acquisition control operation, so that the acquisition process is more accurate, and the image data of a key road section can be accurately captured. In addition, a set of unique automatic control method is formed by combining the technical processes of distance parameter, real-time positioning, dynamic camera parameter adjustment and automatic rejection of the acquired road sections, and the phenomena of repeated shooting and missing shooting are effectively avoided.
Compared with the prior art, the application has obvious advantages in the aspects of high efficiency, accuracy and reliability. Through automatic control, the system can complete efficient collection according to a preset route without manual operation of operators, and particularly can orderly execute tasks when covering a plurality of areas or long-distance routes. Accurate route planning combines dynamic distance parameters with real-time positioning, ensures the accuracy of acquisition, and does not miss critical positions or cause redundant acquisition. Through the carefully designed control logic, the error risk caused by manual operation is further reduced, the consistency and the integrity of the acquired data are ensured, and the stability and the reliability of the system in different scenes are enhanced.
The embodiment of the application also discloses a vehicle-mounted green vision rate acquisition control system based on the planned route.
A vehicle-mounted green vision rate acquisition control system based on a planned route is applied to image acquisition equipment installed on an acquisition vehicle, and the acquisition control system comprises:
The acquisition module is used for acquiring the starting point coordinates of the road section to be acquired in the target green vision rate acquisition area;
the parameter initialization module is used for initializing exposure parameters and equipment control parameters of the image acquisition equipment;
The receiving module is used for receiving and collecting the current position coordinates and the current speed data of the vehicle;
The first distance calculation module is used for calculating the distance between the current position coordinate and the starting point coordinate of the road section to be acquired to obtain a first distance;
The first judging module is used for judging whether the first distance is larger than a first preset distance threshold value, outputting a first judging result if the first distance is larger than the first preset distance threshold value, and outputting a second judging result if the first distance is smaller than the first preset distance threshold value;
The calculation frequency adjustment module is used for responding to the first judgment result and dynamically adjusting the corresponding calculation frequency of the first distance according to the first distance and the current speed data of the collected vehicle;
The control module is used for responding to the second judging result, sending an exposure starting control instruction to the image acquisition equipment and determining the road section to be acquired as the current acquisition road section;
the receiving module is used for receiving the road section image data acquired by the image acquisition equipment on the current acquisition road section in real time;
The second distance calculation module is used for calculating the distance between the current position coordinate and the end point coordinate of the current acquisition road section to obtain a second distance;
the second judging module is used for judging whether the second distance is larger than a second preset distance threshold value or not, and outputting a third judging result if not;
And the control module is also used for responding to the third judging result, sending an exposure closing instruction to the image acquisition equipment and marking the current acquisition road section as the acquired road section.
As a further embodiment of the acquisition control system, further comprising:
the coordinate data receiving module is used for receiving coordinate data of a plurality of key nodes of the target green vision rate acquisition area;
The route planning module is used for dividing the roads among the plurality of key nodes into a plurality of road sections based on a preset route dividing rule, and generating corresponding planning route information;
and the acquisition task list generation module is used for determining a plurality of road sections as road sections to be acquired based on the planned route information and generating an acquisition task list.
The vehicle-mounted green vision rate acquisition control system based on the planned route can realize any one of the acquisition control methods, and the specific working process of each module in the acquisition control system can refer to the corresponding process in the method embodiment.
In several embodiments provided by the present application, it should be understood that the methods and systems provided may be implemented in other ways. For example, the system embodiments described above are merely illustrative, e.g., the partitioning of a module is merely a logical function partitioning, and there may be additional partitioning in actual implementation, e.g., multiple modules may be combined or integrated into another system, or some features may be omitted, or not performed.
The embodiment of the application also discloses computer equipment.
The computer equipment comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, and the processor realizes the vehicle-mounted green vision rate acquisition control method based on the planned route when executing the computer program.
The embodiment of the application also discloses a computer readable storage medium.
A computer readable storage medium storing a computer program capable of being loaded by a processor and executing any one of the above-described on-board green vision rate collection control methods based on a planned route.
Wherein the computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device, the program code contained on the computer readable medium can be transmitted over any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
In the foregoing embodiments, the descriptions of the embodiments are focused on, and for those portions of one embodiment that are not described in detail, reference may be made to the related descriptions of other embodiments.
The foregoing description of the preferred embodiments of the application is not intended to limit the scope of the application in any way, including the abstract and drawings, in which case any feature disclosed in this specification (including abstract and drawings) may be replaced by alternative features serving the same, equivalent purpose, unless expressly stated otherwise. That is, each feature is one example only of a generic series of equivalent or similar features, unless expressly stated otherwise.