CN106997420B - Method and device for intelligently sampling and detecting map data - Google Patents
Method and device for intelligently sampling and detecting map data Download PDFInfo
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Abstract
The invention relates to the technical field of map data processing, and discloses a method and a device for intelligently sampling and detecting map data. The method comprises the following steps: receiving target parameters of a detection sample set; calculating an optimal ratio according to the target parameters and the current map data; traversing according to the priority and the connectivity of each road in the current map data, and selecting the roads meeting the optimal proportion and adding the roads into the detection sample set one by one; and carrying out field detection on the roads in the detection sample set after traversing. According to the method, the optimal sampling result/target is obtained through automatic overall calculation, and the result/target automatically deduces the road to be extracted, so that highly intelligent sampling detection is realized, the process is efficient and accurate, and the map data detection closest to the objective ideal state can be realized.
Description
Technical Field
The invention relates to the technical field of map data processing, in particular to a method and a device for intelligently sampling and detecting map data.
Background
Due to the insufficient resolution and image recognition capability of the current satellite images, the map data (including traditional paper maps and electronic maps) in the prior art still depends heavily on manual acquisition to a large extent. For example, in a complex environment of a city, in order to achieve ideal precision and recognition accuracy, the amount of data to be acquired is huge, and the data can be acquired only manually at present. A professional carries necessary equipment, rides a vehicle such as a vehicle or a ship, or walks on the road network, thereby collecting information such as the shape of the road network (via GPS points), traffic signs on the road, and road attributes.
In this case, the detection of map data with higher requirements for data quality can only be performed manually, and in the case of requiring manual detection of massive data, the quality of the entire data can only be evaluated from a small amount of data in a sampling manner. The quality of map data is guaranteed, a sampling link is very critical, certain requirements are placed on the proportion occupied by extraction proportion, various attributes of an extraction sample and level roads, and in order to guarantee efficiency, the extracted sample roads are communicated as far as possible, so that a supervisor can conveniently and rapidly perform manual detection on the extracted roads.
In the prior art, the sampling detection of map data is completely carried out manually, namely, a supervisor extracts a sample road to be supervised in a manual mode, and then the data quality is evaluated through on-site detection and comparison. Because of the huge data volume, manual sampling cannot intuitively and accurately judge whether the extracted result reaches the expectation or not, and cannot judge whether the current extracted sample is the optimal result, and whether the extracted result meets the extraction target or not can be artificially judged only by checking the road proportion of each grade of the extracted sample road through experience or statistics. Therefore, the sampling detection of the map data in the prior art has the defects of low efficiency, low accuracy, non-uniform standard/result and the like.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method and a device for intelligently sampling and detecting map data, so as to automatically and objectively realize the sampling detection of the map data.
In one aspect, the invention provides a method for intelligently sampling and detecting map data, which comprises the following steps:
receiving target parameters of a detection sample set;
calculating an optimal ratio according to the target parameters and the current map data;
traversing according to the priority and the connectivity of each road in the current map data, and selecting the roads meeting the optimal proportion and adding the roads into the detection sample set one by one;
and carrying out field detection on the roads in the detection sample set after traversing.
Optionally, the optimal matching ratio is a sample amount of roads at each level closest to the ideal matching ratio, which can be actually met in the current map data.
Optionally, the calculating the optimal proportion includes:
calculating ideal sample amount of each level of road according to the target parameters and the default ideal ratio;
and extracting according to the ideal sampling amount from top to bottom according to the road grade, and transferring incomplete index amounts between an upper grade and a lower grade to obtain the optimal proportion.
Optionally, the index amount that is not completed by transferring between upper and lower levels includes:
extracting from the highest level;
if the current grade has no finished indexes, transferring the unfinished index quantity to the next grade;
if the index of the current level is not completed, the index amount is provided to the subsequent level, and when the index of the next level is not completed, the index amount of the next level which is not completed is transferred to the previous level by priority by using the residual amount;
for the penultimate level, if it receives the incomplete index amount transferred by the upper level, it directly transfers all the incomplete index amount to the last level and ends the calculation when its current level has no complete index.
Optionally, the selecting the road meeting the optimal proportion includes:
when traversing a road, judging whether the current road meets the selection condition of the optimal proportion;
when the selection condition is met, further judging whether the corresponding grade of the current road is fully selected;
when the corresponding grade of the current road is not fully selected, the current road is directly selected and added into the detection sample set, and the next road is traversed and judged;
and when the corresponding grade of the current road is selected to be full, further judging whether the current road can replace the existing non-connected road with the same grade in the detection sample set, then replacing or abandoning, and traversing and judging the next road.
In addition, in order to realize the method, the invention also provides a device for intelligently sampling and detecting the map data, which comprises the following steps:
the parameter receiving module is used for receiving target parameters of the detection sample set;
the ratio calculation module is used for calculating the optimal ratio according to the target parameters and the current map data;
the traversal selection module is used for traversing according to the priority and the connectivity of each road in the current map data, and selecting the roads meeting the optimal proportion and adding the roads into the detection sample set one by one;
and the detection module is used for carrying out field detection on the roads in the detection sample set after traversal is finished.
Optionally, the optimal matching ratio is a sample amount of roads at each level closest to the ideal matching ratio, which can be actually met in the current map data.
Optionally, the ratio calculating module includes:
the ideal amount calculation module is used for calculating ideal sample amount of each level of roads according to the target parameters and the default ideal ratio;
and the index extraction module is used for extracting from top to bottom according to the ideal sampling amount and the optimal proportion is obtained by transferring incomplete index amounts between an upper level and a lower level.
Optionally, the index extraction module includes:
the first extraction module is used for extracting from the highest level;
the index downward moving module is used for transferring the unfinished index amount to the next grade when the current grade does not finish the index;
the index upward moving module is used for providing the surplus to the subsequent level when the index is remained after the index is completed in the current level, and preferentially transferring the unfinished index quantity of the lower level to the upper level by using the surplus to complete the process when the unfinished index exists in the subsequent level;
and the ending processing module is used for directly transferring all the unfinished index quantities to the last level and ending the calculation when the current level of the penultimate level does not have the finished index if the penultimate level receives the unfinished index quantities transferred by the upper level.
Optionally, the traversal selection module includes:
the condition judgment module is used for judging whether the current road meets the selection condition of the optimal proportion or not when the road is traversed;
the full-selection judging module is used for further judging whether the corresponding grade of the current road is fully selected when the selecting condition is met;
the direct selection module is used for directly selecting the current road to be added into the detection sample set when the corresponding grade of the current road is not fully selected, and traversing and judging the next road;
and the replacement selection module is used for further judging whether the current road can replace the existing non-communicated road with the same level in the detection sample set when the corresponding level of the current road is fully selected, and then replacing or abandoning the road and traversing and judging the next road.
In summary, the embodiment of the invention provides a method and a device for intelligently sampling and detecting map data, which obtain an optimal sampling result/target by automatically performing overall calculation, and automatically reversely deducing a road to be extracted from the result/target, thereby realizing highly intelligent sampling and detection, having efficient and accurate process, and realizing map data detection closest to an objective and ideal state.
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FIG. 1 is a schematic flow chart of a method for intelligently sampling map data in accordance with an embodiment of the present invention;
FIG. 2 is a schematic flow chart of an algorithm for calculating optimal matching according to an alternative embodiment of the present invention;
fig. 3 is a schematic composition diagram of an apparatus for intelligently sampling and detecting map data according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
The map data detection in the prior art completely depends on manual sampling and detection, repeated sampling, statistics, re-sampling and re-statistics … are needed if a very ideal result is achieved, the result is always considered to be close to the optimal result, the actual result cannot achieve the optimal result, and the sampling standards of each person are not consistent, so that the manual sampling has the natural defect that ① sampling efficiency is low, manual routing is low in efficiency, the manual routing is matched with statistics and repeated operation, the sampling process is low in efficiency, ② sampling is not accurate, manual sampling cannot carry out overall control on the whole data, the manual routing can only carry out iteration trying, the optimal result cannot be achieved, the sampling result cannot be controlled accurately, the sampling inspection effect is influenced, the data control capability is influenced, ③ standards/results are not uniform, the manual sampling process can be controlled completely manually, the sampling result is extracted in thousands of different levels, even if the same sampling result is extracted, the same data is extracted, the same, the sampling result is not identical, and the inspection result is judged to be identical, and the same.
Therefore, the embodiment of the invention provides a scheme for intelligently sampling and detecting map data, and automatic and objective sampling detection is realized by a certain algorithm. Because the scheme of the embodiment of the invention can carry out overall planning on the data (including the direct connection relation of roads, the quantity of each attribute and grade road) before sampling, the optimal sampling result/target (such as the quantity of a road with certain attribute to be sampled) is calculated according to the sampling standard (the total sampling target quantity, the sampling range, the sampling condition, the sampling proportion of the roads with various functional grades and the like) before sampling; then, the result/target reversely deduces the road to be extracted; therefore, the sampling detection process is efficient and accurate, and the map data detection closest to the objective ideal state can be realized.
As shown in fig. 1, the method for intelligently sampling and detecting map data provided by the embodiment of the present invention includes the steps of:
s1, receiving target parameters of the detection sample set;
s2, calculating an optimal ratio according to the target parameters and the current map data;
s3, traversing according to the priority and the connectivity of each road in the current map data, and selecting the roads meeting the optimal proportion and adding the roads into the detection sample set one by one;
and S4, performing field detection on the roads in the detection sample set after traversal is completed.
In the embodiment of the present invention, the target parameter may be a preset standard or may be adjusted by user input, and typical target parameters include a map data range, a target amount, a condition, and the like. The target parameters express the basic objective requirements for the test sample set, typically indicating in which map data the tests are currently required to what extent.
After the target is determined, the optimal matching needs to be calculated according to the actual situation of the map data. Under ideal conditions, it is optimal to perform road extraction directly according to the ideal ratio to obtain an ideal sample set according to the input target parameters. However, in practice, the stoichiometry is only an ideal constant, for example, it is desirable that the roads at each level in the sampled sample set have a stoichiometry (assuming x is a functional level of 1 and 21Percent; occupied x of 3 and 42Percent; occupied x of 53%) if the content of each grade road in the extracted sample set completely meets the proportion, the sampling result is considered as an ideal result (ideal sample set, and more ideally, the extracted road has an optimal detection path with mutual communication). However, because the content of roads in each level in the actual map data is highly random, the difference between the actual map data and the ideal ratio cannot be predicted before sampling, and sometimes some map data cannot achieve ideal results no matter how the actual map data is sampled, for example, it is unlikely that a certain amount of expressways are extracted in a remote area or a certain amount of four-level highways are extracted in a central urban area of a large city, and in such a case, the ideal ratio is completely relied on, so that the scheme cannot be implemented.
From the actual condition of the map data, it can be determined that each piece of the map data has an actual result closest to the above-described ideal result, and this actual result is generally referred to as "optimum result". That is, each piece of map data will have an optimal result, which may be the ideal result that has met the stoichiometry, or may be only a sample set of the closest ideal results that have the smallest difference from the stoichiometry. One of the most core parts in the technical scheme of the embodiment of the invention is to calculate the optimal matching with the minimum gap from the ideal matching according to the actual situation of the map data, so that the extracted sample set is the optimal sample set closest to the ideal result.
It should be noted that the default stoichiometry may be based on historical experience-preferred sampling criteria, and that this value may be pre-set or may be set by the user before each sampling based on actual conditions, and as mentioned above, this value is typically a desired criterion and may differ from actual conditions.
To further illustrate the calculation process of the optimal mixture ratio in the embodiment of the present invention, the following default ideal mixture ratio is set as an example:
a) the roads in the map data are respectively recorded according to 6 functional levels: FC1, FC2, FC3, FC4, FC5, FC 0;
b) the ideal ratio or the optimal ratio is the ratio of only extracting the FC 1-FC 5 road content in a sample road, the FC0 road is not a sampling key point and is only used for completing a sampling target after balance is achieved, and the ratio is not set;
c) the content percentages of roads at all levels in the ideal mixture ratio are assumed as follows:
the ideal percentages for FC1+ FC2 and FC5 are both p1％；
Ideal percentage of FC3+ FC4 is p2％；
Wherein p is1％+p2％+p1％＝100％；
d) Assuming that the sampling target amount in the target map data T is Total, the ideal sampling amount of each function level, that is, the target sampling amount, can be calculated according to the stoichiometric ratio, and is respectively written as:
T_FC1＝Total*p1％*0.5；
T_FC2＝Total*p1％*0.5；
T_FC3＝Total*p2％*0.5；
T_FC4＝Total*p2％*0.5；
T_FC5＝Total*p1percent; wherein T _ FC1+ T _ FC2+ … + T _ FC5 is Total.
As described in the above analysis, the ideal sampling amount is only an ideal state plan, and actually needs to be adjusted according to the actual map data, for example, the actual sampling amount of the road should be an integer, and the calculated decimal should be interpolated in detail. In addition, what is more critical is that the actual amount of the current map data to be detected does not necessarily satisfy the ideal sampling amount, and therefore, the sampling amount of each level of road with the minimum difference from the ideal sampling amount, which can be actually extracted, needs to be calculated on the basis of the ideal sampling amount, and this extraction manner can make the proportion of each level of road content in the actual extraction result closest to the stoichiometric ratio (i.e., the difference is minimum, the difference can be 0, and it is completely matched when the difference is 0), and therefore, the ratio is the optimal ratio.
According to the setting, the optimal proportioning algorithm has the approximate idea that the ideal sampling amount of FC 1-FC 5 is calculated firstly and is used as a sampling index, the sampling index is extracted from the high level firstly, and if the index is not finished in the previous level, the index is transferred to the next level; if the index is completed in the previous level and the index is remained, the index in the next level can be completed by transferring the lower level index to the upper level by using the remained index if the index is not completed; thus, until FC5, FC5 did not complete the upward transfer of metrics, but instead transferred the metrics in their entirety to FC 0.
As shown in fig. 2, the detailed algorithm implementation process of optimal matching is as follows:
① calculating the difference between the FC1 target quantity and the actual quantity, namely distanceFC1 is T _ FC1-R _ FC1, wherein R _ FC1 represents the actual quantity of FC1, namely the quantity which can be extracted, and R represents the current map data in the actual situation;
② judging whether DistanceFC1>0, if so, executing ③, otherwise, executing ④;
③ is greater than 0, which indicates that the target value of FC1 is greater than the actual value, so the amount of FC1 is extracted in its entirety and the index of the amount of distanceFC1 after all extractions is not completed, i.e. actual FC1 is R _ FC1, and at this time all incomplete indexes are recorded as totalMiss is distanceFC 1;
④ being less than or equal to 0 indicates that the amount of FC1 is sufficient to achieve the target amount, therefore, FC1 is first extracted by the target amount, i.e., FC1 is T _ FC1, at this time, the road of FC1 is not extracted, and the absolute value of distanceFC1 is left, i.e., the amount of Math.abs (distanceFC1) remains;
⑤ begins to calculate the amount of FC2 to be extracted, the target amount of FC2 is to make up the amount of FC1 that does not reach the standard from FC2, so the target amount of FC2, targetFC2 is T _ FC2+ totalMiss;
⑥ calculating the difference between the target and actual quantities of FC2, distanceFC2, targetFC2-R _ FC 2;
⑦ judging whether DistanceFC2>0, if so, executing ⑧, otherwise, executing ⑨;
⑧ being less than or equal to 0 indicates that the amount of FC2 is sufficient to achieve the target amount, so FC2 is extracted by the target amount, that is, FC2 equals targetFC2, and the amount totalMiss that FC1 and FC1 are not completed is equal to 0, at this time, the target amount of the collection of FC1 and FC2 reaches the standard, and the last paragraph is extracted by FC1 and FC 2;
⑨ is greater than 0, it shows that the target value of FC2 is more than the actual value, so the amount of FC2 to be awarded is all extracted, namely FC2 is R _ FC2, and the index of the amount of DistanceFC2 after all the extractions is not completed, and the unfinished index is recorded in totalMiss FC2, at this time, it shows that FC2 is not reaching the target, and it is necessary to see whether the FC1 has the amount which is not extracted for supplement, ⑩ is executed;
⑩ judgment distancef FC1<0; if less than 0, execute
If 0 or more indicates that FC1 has not been extracted yet for supplement, FC1 and FC2 extract the next paragraph;
The subsequent decimation process is essentially the same as the FC1 and FC2 logic, except that:
the FC3 extracts the amount of unfinished FC1 and FC2 to be transferred to FC 3; the FC3 does not transfer the incomplete index upwards, but transfers the incomplete index to the FC4 and then transfers the incomplete index upwards uniformly by the FC 4;
when FC4 extracts, if not completed, the remaining indicators are transferred to FC 3; if not, transfer to FC1 and FC 2;
when the FC5 is extracted, the unreachable scalar of FC 1-FC 4 is added to the self, and if the FC5 fails to complete the index of the self and the indexes of FC 1-FC 4, the unreachable indexes are directly transferred to the FC 0.
And finally, obtaining the final values of FC 1-FC 5 and FC0 after the algorithm is finished, namely the sampling amount of the optimal ratio.
After the sampling quantity with the optimal proportion is obtained, the road can be traversed in the current map data according to the value and the priority and the connectivity of the road, and the selection is carried out. In the actual working process, the optimal proportion is just like a box, and the selected data of roads of various levels is specified in the target sampling amount; the selection is to fill the empty space in a box (sample set) when a road is traversed, judge whether the current road meets the empty space filling condition, judge whether the box is full, fill the empty space when the box is not full, and traverse and judge the next road; and if the current road is full, judging whether the current road can replace the existing same-level unconnected road in the box, then replacing or abandoning the road, and traversing to judge the next road.
According to the technical scheme of the embodiment of the invention, the calculation of the optimal proportion ensures that the extracted road is accurate, the actual traversal extraction ensures that the extracted road is coherent, and the efficiency of a subsequent monitoring link can be improved.
As further shown in fig. 3, in one-to-one correspondence with the above method, an embodiment of the present invention also provides an apparatus 1 for intelligently sampling and detecting map data, including:
a parameter receiving module 101, configured to receive target parameters of a detection sample set;
the ratio calculation module 102 is used for calculating an optimal ratio according to the target parameters and the current map data;
a traversal selection module 103, configured to perform traversal according to the priority and connectivity of each road in the current map data, and select roads that meet the optimal matching ratio and add the selected roads into the detection sample set one by one;
and the detection module 104 is configured to perform on-site detection on the roads in the detection sample set after the traversal is completed.
The optimal matching is the sampling quantity of each level of roads which is closest to the ideal matching and can be actually met in the current map data.
Optionally, the ratio calculating module includes:
the ideal amount calculation module is used for calculating ideal sample amount of each level of roads according to the target parameters and the default ideal ratio;
and the index extraction module is used for extracting from top to bottom according to the ideal sampling amount and the optimal proportion is obtained by transferring incomplete index amounts between an upper level and a lower level.
Optionally, the index extraction module includes:
the first extraction module is used for extracting from the highest level;
the index downward moving module is used for transferring the unfinished index amount to the next grade when the current grade does not finish the index;
the index upward moving module is used for providing the surplus to the subsequent level when the index is remained after the index is completed in the current level, and preferentially transferring the unfinished index quantity of the lower level to the upper level by using the surplus to complete the process when the unfinished index exists in the subsequent level;
and the ending processing module is used for directly transferring all the unfinished index quantities to the last level and ending the calculation when the current level of the penultimate level does not have the finished index if the penultimate level receives the unfinished index quantities transferred by the upper level.
Optionally, the traversal selection module includes:
the condition judgment module is used for judging whether the current road meets the selection condition of the optimal proportion or not when the road is traversed;
the full-selection judging module is used for further judging whether the corresponding grade of the current road is fully selected when the selecting condition is met;
the direct selection module is used for directly selecting the current road to be added into the detection sample set when the corresponding grade of the current road is not fully selected, and traversing and judging the next road;
and the replacement selection module is used for further judging whether the current road can replace the existing non-communicated road with the same level in the detection sample set when the corresponding level of the current road is fully selected, and then replacing or abandoning the road and traversing and judging the next road.
Alternatively, the device for intelligently sampling and detecting map data may be a processing device, such as a cluster, a server, or a processing terminal; or may be a relatively independent functional unit, such as a processing unit, an independent chip, or enhanced software, and the sharpness enhancement is realized after the processing device is loaded. In practical applications, each module in the above-mentioned apparatus can be implemented by a Central Processing Unit (CPU), a microprocessor Unit (MPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or the like, which are located in the apparatus.
Because the monitoring efficiency and effect are affected by the problems of low efficiency, inaccuracy and non-uniformity of manual sampling, the technical scheme of the embodiment of the invention provides a method and a device for intelligently sampling and detecting map data, and the intelligent sampling detection is automatically realized according to a certain algorithm. Compared with the manual sampling mode in the prior art, the technical scheme of the embodiment of the invention has the following two biggest differences:
the way of extracting the road is different: according to the technical scheme of the embodiment of the invention, roads are directly and automatically selected from the map data, so that the efficiency can be greatly improved, and the standard is objective;
the extraction algorithm is logically different: according to the technical scheme of the embodiment of the invention, sampling is performed by summarizing and planning data, then the optimal result of data combination is calculated, and then the reverse-reasoning is performed according to the optimal result to extract roads; in the manual sampling in the prior art, roads are extracted firstly, then extraction results are calculated, whether the extracted results reach the standard or not is judged (artificial judgment), and if the extracted results do not reach the standard, the process is repeated until the extracted results reach the standard (the standard is not the optimal result, but the result is subjectively considered to meet the requirement).
The technical scheme of the embodiment of the invention solves the defect of manual sampling and has the following obvious advantages:
the embodiment of the invention provides a solution for intelligent sampling of map data, which hands the fussy sampling work to a computer, and implements the automation of sampling detection by a compiled sampling algorithm, thereby being more efficient compared with manual operation.
In the above embodiments of the present invention, before sampling, overall planning is performed on data, including direct road connectivity, and the quantity of each attribute and level road; then, an optimal sampling result/target is calculated according to sampling criteria (including information such as a total sampling target amount, a sampling range, sampling conditions, and sampling ratios of roads of various functional levels), and then the result/target is used to reversely deduce the road to be sampled. Because the intelligent sampling is that the optimal proportion of the extracted samples is calculated according to the overall result of actual data (the optimal sampling amount which can be reached by each road sample can be finally obtained, and the data can be ensured to be closest to an ideal result, so the extracted result is the optimal result; and human error is avoided, so that the method is more accurate compared with manual sampling.
In addition, in the sampling process in each embodiment of the invention, the same algorithm and the same judgment logic are adopted, so that the extraction result can be ensured to be completely unified with the standard; in addition, the support of some statistical charts is added, so that the operation is easier, the result is more intuitive, and the detection efficiency can be further improved.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (8)
1. A method for intelligently sampling map data, the method comprising:
receiving target parameters of a detection sample set, wherein the target parameters comprise a map data range, a target amount and a condition;
calculating an optimal matching according to the target parameters and the current map data, wherein the optimal matching is the sampling quantity of each level of roads which are closest to the ideal matching and can be actually met in the current map data, and the optimal matching is the matching with the minimum difference between the optimal matching and the ideal matching according to the actual condition of the current map data;
traversing according to the priority and the connectivity of each road in the current map data, and selecting the roads meeting the optimal proportion and adding the roads into the detection sample set one by one;
and carrying out field detection on the roads in the detection sample set after traversing.
2. The method of claim 1, wherein the calculating the optimal proportioning comprises:
calculating ideal sample amount of each level of roads according to the target parameters and the default ideal ratio;
and extracting from high to low according to the road grade according to the ideal sampling amount, and transferring incomplete index amounts between upper and lower grades to obtain the optimal proportion.
3. The method of claim 2, wherein the transferring the outstanding metric quantities between upper and lower levels comprises:
extracting from the highest level;
if the current grade has no finished indexes, transferring the unfinished index quantity to the next grade;
if the index of the current level is not completed, the index amount is provided to the subsequent level, and when the index of the next level is not completed, the index amount of the next level which is not completed is transferred to the previous level by priority by using the residual amount;
for the penultimate level, if it receives the incomplete index amount transferred by the upper level, it directly transfers all the incomplete index amount to the last level and ends the calculation when its current level has no complete index.
4. The method according to any one of claims 1 to 3, wherein the selecting the roads satisfying the optimal matching comprises:
when traversing a road, judging whether the current road meets the selection condition of the optimal proportion;
when the selection condition is met, further judging whether the corresponding grade of the current road is fully selected;
when the corresponding grade of the current road is not fully selected, the current road is directly selected and added into the detection sample set, and the next road is traversed and judged;
and when the corresponding grade of the current road is selected to be full, further judging whether the current road can replace the existing non-connected road with the same grade in the detection sample set, then replacing or abandoning, and traversing and judging the next road.
5. An apparatus for intelligently sampling map data, the apparatus comprising:
the parameter receiving module is used for receiving target parameters of the detection sample set, wherein the target parameters comprise a map data range, a target amount and conditions;
the matching calculation module is used for calculating the optimal matching according to the target parameters and the current map data, wherein the optimal matching is the sampling quantity of each level of roads which are closest to the ideal matching and can be actually met in the current map data, and the optimal matching is the matching with the minimum difference between the optimal matching and the ideal matching according to the actual condition of the current map data;
the traversal selection module is used for traversing according to the priority and the connectivity of each road in the current map data, and selecting the roads meeting the optimal proportion and adding the roads into the detection sample set one by one;
and the detection module is used for carrying out field detection on the roads in the detection sample set after traversal is finished.
6. The apparatus of claim 5, wherein the ratio calculation module comprises:
the ideal amount calculation module is used for calculating ideal sample amount of each level of roads according to the target parameters and the default ideal ratio;
and the index extraction module is used for extracting according to the ideal sampling amount and from high to low according to the road grade, and obtaining the optimal ratio by transferring the unfinished index amount between the upper grade and the lower grade.
7. The apparatus of claim 6, wherein the metric extraction module comprises:
the first extraction module is used for extracting from the highest level;
the index downward moving module is used for transferring the unfinished index amount to the next grade when the current grade does not finish the index;
the index upward moving module is used for providing the surplus to the subsequent level when the index is remained after the index is completed in the current level, and preferentially transferring the unfinished index quantity of the lower level to the upper level by using the surplus to complete the process when the unfinished index exists in the subsequent level;
and the ending processing module is used for directly transferring all the unfinished index quantities to the last level and ending the calculation when the current level of the penultimate level does not have the finished index if the penultimate level receives the unfinished index quantities transferred by the upper level.
8. The apparatus of any one of claims 5 to 7, wherein the traversal extraction module comprises:
the condition judgment module is used for judging whether the current road meets the selection condition of the optimal proportion or not when the road is traversed;
the full-selection judging module is used for further judging whether the corresponding grade of the current road is fully selected when the selecting condition is met;
the direct selection module is used for directly selecting the current road to be added into the detection sample set when the corresponding grade of the current road is not fully selected, and traversing and judging the next road;
and the replacement selection module is used for further judging whether the current road can replace the existing non-communicated road with the same level in the detection sample set when the corresponding level of the current road is fully selected, and then replacing or abandoning the road and traversing and judging the next road.
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