CN111967882B - Method and device for verifying validity of vehicle type combination - Google Patents
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Abstract
The invention aims to provide a method and a device for verifying the legitimacy of a vehicle model combination. The method according to the invention comprises the following steps: acquiring a plurality of feature groups for verifying the validity of the vehicle type combination of the target vehicle, wherein each feature group corresponds to at least one option value; grouping according to the association relation among the feature families to obtain a plurality of feature family sets which are not associated with each other; for each feature group set, generating a corresponding first sample by permutation and combination of a predetermined number of feature groups having a higher priority order in the feature group set; performing rule checking on the first sample, and taking one or more combinations which are not verified as second samples; and removing the vehicle model combination containing the second sample in the sample to be verified, so as to perform validity verification based on the remaining vehicle model combination in the sample to be verified.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method for verifying the legitimacy of a vehicle model combination.
Background
In the prior art, in order to predict sales and prepare materials in advance, the automobile manufacturing industry generally exhausts all vehicle type combinations with different configurations in enterprises in advance, and because some technical and market constraints exist, for example, a remote control function must match with a voice recognition function, a ZF8AT gearbox must be equipped with an electronic gear shift, an automatic parking and gear shift indicator, etc., each combination needs to be checked regularly, and unsatisfied parts are removed. However, such verification is usually extremely time-consuming and labor-consuming, and is calculated by taking an average of one second per vehicle model combination, and the vehicle model combination range of one vehicle model of a traditional vehicle enterprise is about millions of magnitude, which requires 12 days of operation.
However, based on the scheme in the prior art, the verification manner of the vehicle model combination is simpler, for example, all the options under the characteristic groups are firstly exhausted to obtain N arrangements, then each arrangement is handed to the rule engine to verify the validity, and finally the vehicle model combination passing the verification is left. Since this exhaustion produces a cartesian product of all properties, the amount of data can be very large, and the checking activity of rule constraints itself is also very complex, resulting in insufficient computing resources and inefficiency. With the advent of the 5G age, the personalized demand of the product is stronger and stronger, and some Internet vehicle enterprises develop more and more characteristics for consumers to select, so that the vehicle model combination is rapidly increased to billions or even billions. In the face of the increasing amount of computation, many enterprises have difficulty in having enough computing resources to cope with this scenario.
Disclosure of Invention
The invention aims to provide a method and a device for verifying the legitimacy of a vehicle model combination.
According to an aspect of the present invention, there is provided a method for validity verification of a vehicle model combination, wherein the method includes:
acquiring a plurality of feature groups for verifying the validity of the vehicle type combination of the target vehicle, wherein each feature group corresponds to at least one option value;
grouping according to the association relation among the feature groups to obtain a plurality of feature group sets which are not associated with each other, wherein each feature group set corresponds to a sample to be verified, and the sample to be verified is a combination of all vehicle types obtained based on the feature groups contained in the feature group set;
for each feature group set, generating a corresponding first sample by permutation and combination of a predetermined number of feature groups having a higher priority order in the feature group set;
performing rule checking on the first sample, and taking one or more combinations which are not verified as second samples;
and removing the vehicle model combination containing the second sample in the sample to be verified, so as to perform validity verification based on the remaining vehicle model combination in the sample to be verified.
According to an aspect of the present invention, there is provided a verification apparatus for validity verification of a vehicle model combination, wherein the validity verification includes:
a feature acquisition unit configured to acquire a plurality of feature groups for validity verification of a vehicle type combination for a target vehicle, wherein each feature group corresponds to at least one option value;
the feature grouping unit is used for grouping according to the association relation among the feature groups to obtain a plurality of feature group sets which are not associated with each other, wherein each feature group set corresponds to a sample to be verified, and the sample to be verified is a combination of all vehicle types obtained based on the feature groups contained in the feature group set;
the sample generation unit is used for generating a corresponding first sample by arranging and combining a preset number of feature families with higher priority orders in each feature family set;
a sample verification unit, configured to perform rule verification on the first sample, and take one or more combinations that are not verified as a second sample;
a sample removing unit for removing the vehicle model combination containing the second sample in the sample to be verified, so as to perform validity verification based on the remaining vehicle model combination in the sample to be verified
According to one aspect of the present invention, there is provided a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of the embodiments of the present invention when executing the program.
According to an aspect of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements a method of an embodiment of the present invention.
Compared with the prior art, the invention has the following advantages: according to the embodiment of the invention, the legality verification feature groups for carrying out vehicle type combination on the target vehicle are grouped to obtain a plurality of feature group sets, verification is carried out continuously on the basis of each feature group set, the use of Cartesian products for calculating the number of vehicle type combinations to be verified is avoided, for example, the number of vehicle type combinations to be verified is converted from a form of M x N to a form of M+N, so that the number of vehicle type combinations to be verified is greatly reduced, calculation resources are saved, and efficiency is improved; and, according to the embodiment of the invention, the feature groups in each set are ordered according to the priority thereof, and the combination of the feature groups with higher priority is taken as a sample space for verification, so that the number of samples to be verified in the whole sample space is sharply reduced; moreover, according to the embodiment of the invention, the sample space is sampled for multiple times in an iterative mode, so that the number of samples to be verified can be further reduced, and good performance can be maintained when an oversized data set is encountered.
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Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the accompanying drawings in which:
FIG. 1 illustrates a flow chart of a method for validity verification of a combination of vehicle models in accordance with the present invention;
fig. 2 is a schematic diagram showing the structure of a verification device for verifying the validity of a vehicle model combination according to the present invention;
fig. 3 illustrates a schematic diagram of an exemplary feature family set in accordance with the present invention.
The same or similar reference numbers in the drawings refer to the same or similar parts.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings.
Fig. 1 illustrates a flow chart of a method for verifying the validity of a vehicle model combination according to the invention.
The method according to the invention is implemented, among other things, by an authentication device comprised in a computer device. The computer device comprises an electronic device capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and the hardware of the electronic device comprises, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a programmable gate array (FPGA), a digital processor (DSP), an embedded device and the like. The computer device comprises a network device and/or a user device. Wherein the network device includes, but is not limited to, a single network server, a server group composed of a plurality of network servers, or a Cloud based Cloud Computing (Cloud Computing) composed of a large number of hosts or network servers, wherein Cloud Computing is one of distributed Computing, and is a super virtual computer composed of a group of loosely coupled computer sets. The user equipment includes, but is not limited to, any electronic product that can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device, for example, a personal computer, a tablet computer, a smart phone, a PDA, a game console, an IPTV, or the like. The network where the user equipment and the network equipment are located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a VPN network, and the like.
It should be noted that the user equipment, the network equipment and the network are merely examples, and other user equipment, network equipment and network that may be present in the present invention or may appear in the future are applicable to the present invention, and are also included in the scope of the present invention and are incorporated herein by reference.
Referring to fig. 1, in step S1, the verification apparatus acquires a plurality of feature groups for validity verification of a vehicle type combination for a target vehicle.
Wherein each feature family corresponds to at least one option value. The feature group is used to represent a certain type of feature of the vehicle, for example, for a steering system, a transmission, an interior style, and the like of the vehicle, and may be respectively used as one feature group, so that classification and management are performed based on data of the feature group. Wherein the option value of each feature family is used to represent the type of feature family available for selection. For example, the user selectable feature families include 2 electronic shifts, 3 transmissions, 2 steering systems, and then corresponding feature families are established and corresponding option values are generated based on "electronic shifts", "transmissions", and "steering systems", respectively, where "electronic shifts" include two option values, "transmissions" include three option values, and "steering systems" include two option values.
Wherein the option value comprises various kinds of information that can be used to uniquely identify a certain feature group, e.g. a number consisting of letters or numbers, etc.
According to one embodiment, the verification device obtains predetermined constraint information. Next, the verification device uses a plurality of feature groups related to the constraint information as feature groups for verifying the validity of the vehicle model combination based on the constraint information.
Wherein the constraint information includes various preset rules or conditions. The plurality of feature families associated with constraint information refer to feature families that appear in the constraint information.
According to a first example of the present invention, a verification apparatus is included in an apparatus for verifying legitimacy of a vehicle type combination for a target vehicle of a certain vehicle enterprise. The characteristics of the vehicle enterprise which are open to the user for selection comprise 6 interior styles, 2 exterior styles, 2 power, 2 steering systems, 2 electronic gear shifting and the like. The verification means generates a corresponding feature group based on each of the characteristics, respectively, and uses a predetermined number to represent the option value contained in each feature group.
Wherein the predetermined constraint rules and corresponding feature expressions are shown in table 1 below:
TABLE 1
The verification means uses the feature group associated with the constraint rule as a feature group for verifying the validity of the vehicle model combination based on the constraint rule shown in the table. Specifically, taking the first rule a101& B101& a201< = > C101 in table 1 as an example, there are referred to 4 feature groups, A1, B1, A2, C1, and C1 is affected by the first three feature groups. Using the graph algorithm, A1, B1, A2, C1 are used as vertices on the graph, and the dependency abstraction can be expressed as A1- > C1, B1- > C1, A2- > C1 as three edges. Similarly, three vertices B1, B2, and ZA, and two edges B1- > ZA, B2- > ZA are identified by the second rule in table 1. Wherein vertex B1 already appears in the first rule, thus multiplexing the B1 vertices.
All the remaining rules are abstracted into vertexes and edges according to the same logic, and then are loaded into a memory graph by using a graph algorithm. By traversing the vertices of the graph, feature groups that do not appear in the graph can be considered as irrelevant to the constraint rules shown in table 1, so that a plurality of feature groups relevant to the constraint rules are used as feature groups for validity verification of vehicle model combinations.
Finally, the verification device determines in step S1 that the constraint rule relates to 21 feature groups and that the overall feature group has 25 feature groups, then 4 feature groups that can participate in validity verification of the vehicle model combination are removed, and the number of choices under the removed 4 feature groups is 6, 3, and 2, respectively, and the number of permutations of the generated vehicle model combination is 6×3×3×2=108. That is, the verification apparatus reduces the total number of permutations required for validity verification of the vehicle model combination by 108 times through the above steps.
Continuing with the description of fig. 1, in step S2, the verification device groups the feature groups according to the association relationship between the feature groups, and obtains a plurality of feature group sets that have no association relationship with each other.
Each feature group set corresponds to a sample to be verified, and the sample to be verified is all vehicle type combinations obtained based on the feature groups contained in the feature group set.
The association relation is used for indicating whether the feature groups are closely related technically.
For example, for the construction of a whole vehicle, it is composed of a plurality of subsystems, such as power, body, chassis, interior and exterior trim, electrical and electronic appliances, etc., each of which is organised in a different business sector, not all of which are closely related technically, such as the interior and exterior trim is obviously associated with the body, but may not be so much associated with the chassis.
After a plurality of feature group sets which are not associated with each other are obtained, the verification device selects one of the feature group sets to verify the validity of the vehicle type combination generated in the feature group set. After the verification is completed, verifying the next feature group set until the verification of all feature group sets is completed. Assuming that 4 feature family sets are obtained and include M, N, X and Y option values, the model combination to be verified can be converted from m+n+x+y by step S2.
Continuing with the first example described above, the association between the 21 selected feature groups (denoted as A1, A2, A3, B1, B2, B3, C1, C3, D1, D2, D3, E1, E2, E3, E4, F2, G1, H2, Z1, and ZA, respectively) is shown in fig. 3. Referring to FIG. 3, from the association relationship shown in FIG. 3To two feature group sets which are not related to each other, the left feature set set_1 includes 18 feature groups (A1, A2, A3, B1, B2, C1, D2, E1, E2, E3, E4, F2, G1, H2, Z1, and ZA), and the right feature set set_2 includes 3 feature groups (B3, C3, D3). The number of permutation and combination generated by the feature sets set_1 and set_2 is 2 respectively 18 =2626144 and 2 3 ＝8。
Continuing with the description of fig. 1, in step S3, the verification apparatus generates, for each feature group set, a corresponding first sample by permutation and combination of a predetermined number of feature groups having a higher priority order in the feature group set.
According to one embodiment, step S3 further comprises step S301 and step S302.
In step S301, the verification apparatus prioritizes, for each feature group set, the feature groups in the feature group set based on the option values included in the feature groups.
In step S302, the verification device selects a predetermined number of feature groups with higher priority orders for permutation and combination according to the sorting result, and generates a corresponding first sample.
Preferably, a corresponding constraint may be set for the number of first samples, for example, the first sample space is set to 1% of the total samples, and so on.
Preferably, the method prioritizes the respective feature families by a web page ranking algorithm.
In step S4, the verification device performs a rule check on the first sample, and takes one or more combinations in which verification is not passed as a second sample.
In step S5, the verification device removes the vehicle model combination including the second sample in the sample to be verified, so as to perform validity verification based on the remaining vehicle model combination in the sample to be verified.
Continuing with the first example, the feature family set set_1 is set to 262626144 as the number of samples currently to be verified. The verification device performs, in step S301, ranking of the respective feature groups based on the web page ranking (PageRank) algorithm based on the option values included in the respective feature groups in the feature group set_1, and the ranking results are shown in table 2 below:
TABLE 2
| Priority level | Family of features | Option value | Number of options |
| 1 | B1 | B101、B102、B103、B104 | 4 |
| 2 | A1 | A101、A102、A103 | 3 |
| 3 | B2 | B201、B202、B203 | 3 |
| 4 | F2 | F201、F202、F203 | 3 |
| 5 | ZA | ZA01、ZA02、ZA03 | 3 |
| 6 | C1 | C101、C102、C103 | 3 |
| 7 | H1 | H101、H102 | 2 |
| 8 | A2 | A201、A202 | 2 |
| … | … |
As shown in table 2, feature group B1 has four options of B101, B102, B103, and B104, feature group A1 has three options of a101, a102, and a103, and feature group B2 has three options of B201, B202, and B203. If verification of these high priority ranked advanced traffic pattern combinations proves that some of them are not legal, they are of course not legal to a greater extent, reducing the number of verifications.
The total number of combinations of the feature groups B1, A1, B2 is 4×3×3=36, and the total number of combinations of B1, A1, B2, F2 is 4×3×3×3=108. The sample space is assumed to be defined as 1% of the total sample, but not more than 1000 samples per sampling to avoid that 1% is still a not small number when the total sample size is too large. The comparison means selects the first 4 feature groups B1, A1, B2, F2 of the priority ranking according to the ranking result shown in table 2 to perform permutation and combination in step S302, and generates a corresponding first sample.
Next, the verification device performs rule verification on the first sample in step S4, and takes one or more combinations in which verification is not passed as a second sample.
Assuming that the combination of B101& a101& B201& F201 is found not to satisfy a predetermined constraint rule, these combinations of natural B101& a101& B201& F201& ZA01 &..and B101& a101& B201& F201& ZA02&... The authentication apparatus removes a combination of B101& a101& B201& F201& ZA01& gt. And B101& a101& B201& F201& ZA02& gt. And the like which do not satisfy the constraint rule in the sample to be authenticated, and performing validity verification based on the remaining vehicle model combinations in the sample to be verified.
Through the processing, the number of the samples to be verified corresponding to the feature group set set_1 is reduced from 2626144 to less than fifty thousand. And then, the verification device performs validity verification of the vehicle type combination through a preset rule engine based on the reduced sample to be verified.
Finally, the rule engine can calculate 32384 and 6 of the feature family sets set_1 and set_2, respectively, that truly satisfy the constraint in tens of minutes. Combining the isolated characteristic groups, namely the characteristic groups which are not related to constraint rules and are excluded in the step S1, so as to obtain the combination common for the actual producibility of the model of the target vehicle
32384*6*(226492416/2097152)＝20984832。
According to one embodiment, the method further comprises step S6 and step S7 after step S1 to step S5.
The process of steps S1 to S5 is not described here.
In step S6, the verification device replaces the selected feature group with the higher priority order to perform permutation and combination, and generates new first samples and second samples, thereby further reducing the number of samples to be verified.
Wherein the verification means may replace the selected feature family based on various rules. For example, assuming that the selected feature group is the top 5 feature group, the verification device may replace the 5 th feature group with the 6 th feature group, or the verification device may randomly choose to replace one or more of the 5 feature groups.
In step S7, the verification device repeatedly performs step S6 in an iterative manner until the number of samples to be verified is less than a predetermined threshold.
Continuing with the first example described above, the rules for verifying the predetermined replacement feature families of the device are: and according to the priority ranking result, sequentially replacing and ranking the last feature group according to the order of the priorities from high to low. For the feature groups B1, A1, B2, F2 of which the priority ranking has been previously selected for the first 4, the verification device replaces the last feature group F2 with the feature group ZA based on the rule and the ranking result shown in table 2 above in step S6, so that the permutation and combination is performed based on the feature groups B1, A1, B2 and ZA, generating new first samples and second samples, thereby further reducing the number of samples to be verified. Next, the verification device replaces the feature group ZA in the feature groups B1, A1, B2, and ZA with the feature group C1, thereby performing permutation and combination based on the feature groups B1, A1, B2, and C1, and generating new first and second samples again. The verification device repeatedly performs the above arrangement in an iterative manner until the total number of combinations of samples to be verified is less than 10 ten thousand.
According to the method provided by the embodiment of the invention, the legality verification feature groups for carrying out vehicle type combination on the target vehicle are grouped to obtain a plurality of feature group sets, verification is carried out continuously on the basis of each feature group set, the use of Cartesian products for calculating the number of vehicle type combinations to be verified is avoided, for example, the number of vehicle type combinations to be verified is converted from a form of M x N to a form of M+N, so that the number of vehicle type combinations to be verified is greatly reduced, the calculation resources are saved, and the efficiency is improved; and, according to the embodiment of the invention, the feature groups in each set are ordered according to the priority thereof, and the combination of the feature groups with higher priority is taken as a sample space for verification, so that the number of samples to be verified in the whole sample space is sharply reduced; moreover, according to the embodiment of the invention, the sample space is sampled for multiple times in an iterative mode, so that the number of samples to be verified can be further reduced, and good performance can be maintained when an oversized data set is encountered.
Fig. 2 is a schematic diagram showing the structure of a verification device for verifying the validity of a vehicle model combination according to the present invention. The verification apparatus includes a feature acquisition unit 1, a feature grouping unit 2, a sample generation unit 3, a sample verification unit 4, and a sample removal unit 5.
Referring to fig. 2, in step S1, the feature acquisition unit 1 acquires a plurality of feature groups for validity verification of a vehicle type combination for a target vehicle.
Wherein each feature family corresponds to at least one option value.
The feature group is used to represent a certain type of feature of the vehicle, for example, for a steering system, a transmission, an interior style, and the like of the vehicle, and may be respectively used as one feature group, so that classification and management are performed based on data of the feature group. Wherein the option value of each feature family is used to represent the type of feature family available for selection. For example, the user selectable feature families include 2 electronic shifts, 3 transmissions, 2 steering systems, and then corresponding feature families are established and corresponding option values are generated based on "electronic shifts", "transmissions", and "steering systems", respectively, where "electronic shifts" include two option values, "transmissions" include three option values, and "steering systems" include two option values.
Wherein the option value comprises various kinds of information that can be used to uniquely identify a certain feature group, e.g. a number consisting of letters or numbers, etc.
According to one embodiment, the feature acquisition unit comprises a constraint acquisition unit and a feature selection unit.
The constraint acquisition unit acquires predetermined constraint information. Next, the feature selection unit uses a plurality of feature groups related to the constraint information as feature groups for performing validity verification of the vehicle type combination based on the constraint information.
Wherein the constraint information includes various preset rules or conditions. The plurality of feature families associated with constraint information refer to feature families that appear in the constraint information.
The feature grouping unit 2 groups the feature groups according to the association relation between the feature groups to obtain a plurality of feature group sets which have no association relation with each other.
Each feature group set corresponds to a sample to be verified, and the sample to be verified is all vehicle type combinations obtained based on the feature groups contained in the feature group set.
The association relation is used for indicating whether the feature groups are closely related technically.
For example, for the construction of a whole vehicle, it is composed of a plurality of subsystems, such as power, body, chassis, interior and exterior trim, electrical and electronic appliances, etc., each of which is organised in a different business sector, not all of which are closely related technically, such as the interior and exterior trim is obviously associated with the body, but may not be so much associated with the chassis.
After a plurality of feature group sets which are not associated with each other are obtained, the verification device selects one of the feature group sets to verify the validity of the vehicle type combination generated in the feature group set. After the verification is completed, verifying the next feature group set until the verification of all feature group sets is completed. Assuming that 4 feature family sets are obtained and each feature family set includes M, N, X and Y option values, the model combination to be verified can be converted from m+n+x+y by the operation of the feature grouping unit 2.
The sample generation unit 3 generates, for each feature group set, a corresponding first sample by permutation and combination of a predetermined number of feature groups having a higher priority order in the feature group set.
According to one embodiment, the sample generation unit 3 further comprises.
The ranking unit ranks, for each feature family set, the feature families based on the option values that are contained in the feature families in the feature family set.
And the sub-generation unit selects a preset number of characteristic families with higher priority orders for permutation and combination according to the sequencing result, and generates a corresponding first sample.
Preferably, a corresponding constraint may be set for the number of first samples, for example, the first sample space is set to 1% of the total samples, and so on.
Preferably, the ranking unit prioritizes the respective feature families by a web page ranking algorithm.
The sample verification unit performs regular verification on the first sample, and takes one or more combinations which are not verified as second samples.
The sample removing unit 5 removes the combination of vehicle models including the second sample in the sample to be verified, thereby performing validity verification based on the combination of vehicle models remaining in the sample to be verified.
According to one embodiment, the verification device further comprises a sample exchange unit.
The sample replacing unit replaces the selected characteristic family with higher priority order to perform permutation and combination, and generates new first samples and second samples, thereby further reducing the number of samples to be verified.
Wherein the sample replacement unit may replace the selected feature family based on various rules. For example, assuming that the selected feature group is the top 5 feature group, the verification device may replace the 5 th feature group with the 6 th feature group, or the verification device may randomly choose to replace one or more of the 5 feature groups.
The sample replacing unit repeatedly performs the operations of replacing the selected feature group with the higher priority order to perform permutation and combination in an iterative manner, and generating new first samples and second samples until the number of samples to be verified is smaller than a predetermined threshold.
According to the scheme of the embodiment of the invention, the legality verification feature groups for carrying out vehicle type combination on the target vehicle are grouped to obtain a plurality of feature group sets, verification is carried out continuously on the basis of each feature group set, the use of Cartesian products for calculating the number of vehicle type combinations to be verified is avoided, for example, the number of vehicle type combinations to be verified is converted from a form of M x N to a form of M+N, so that the number of vehicle type combinations to be verified is greatly reduced, the calculation resources are saved, and the efficiency is improved; and, according to the embodiment of the invention, the feature groups in each set are ordered according to the priority thereof, and the combination of the feature groups with higher priority is taken as a sample space for verification, so that the number of samples to be verified in the whole sample space is sharply reduced; moreover, according to the embodiment of the invention, the sample space is sampled for multiple times in an iterative mode, so that the number of samples to be verified can be further reduced, and good performance can be maintained when an oversized data set is encountered.
The software program of the present invention may be executed by a processor to perform the steps or functions described above. Likewise, the software programs of the present invention (including associated data structures) may be stored on a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. In addition, some of the steps or functions of the present invention may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various functions or steps.
Furthermore, portions of the present invention may be implemented as a computer program product, such as computer program instructions, which when executed by a computer, may invoke or provide methods and/or techniques in accordance with the present invention by way of operation of the computer. Program instructions for invoking the inventive methods may be stored in fixed or removable recording media and/or transmitted via a data stream in a broadcast or other signal bearing medium and/or stored within a working memory of a computer device operating according to the program instructions. An embodiment according to the invention comprises an apparatus comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein the computer program instructions, when executed by the processor, trigger the apparatus to operate a method and/or a solution according to the embodiments of the invention as described above.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms first, second, etc. are used to denote a name, but not any particular order.
Claims (10)
1. A method for validity verification of a combination of vehicle models, wherein the method comprises: acquiring a plurality of feature groups for verifying the validity of the vehicle type combination of the target vehicle, wherein each feature group corresponds to at least one option value; grouping according to the association relation among the feature groups to obtain a plurality of feature group sets which are not associated with each other, wherein each feature group set corresponds to a sample to be verified, and the sample to be verified is a combination of all vehicle types obtained based on the feature groups contained in the feature group set; based on the option values contained in each feature group in the feature group set, priority ranking is carried out on each feature group, and according to ranking results, a preset number of feature groups are sequentially selected to be ranked and combined according to the order of the priority from high to low, so that a corresponding first sample is generated; performing rule checking on the first sample, and taking one or more combinations which are not verified as second samples; and removing the vehicle model combination containing the second sample in the sample to be verified, so as to perform validity verification based on the remaining vehicle model combination in the sample to be verified, wherein the association relationship is used for indicating the technical close association between the feature groups.
2. The method of claim 1, wherein the method further comprises: changing the selected characteristic family to perform permutation and combination, and generating a new first sample and a new second sample, thereby further reducing the number of samples to be verified; repeating the above steps in an iterative manner until the number of samples to be verified is less than a predetermined threshold.
3. The method according to claim 1 or 2, wherein the step of acquiring a plurality of feature families for performing validity verification of a vehicle model combination includes: acquiring preset constraint information; based on the constraint information, a plurality of feature groups related to constraint relations are used as feature groups for verifying the legality of the vehicle model combination.
4. The method of claim 1, wherein the method prioritizes the respective feature families by a web page ranking algorithm.
5. A verification apparatus for verifying validity of a vehicle model combination, wherein the validity verification apparatus comprises:
a feature acquisition unit configured to acquire a plurality of feature groups for validity verification of a vehicle type combination for a target vehicle, wherein each feature group corresponds to at least one option value;
the feature grouping unit is used for grouping according to the association relation among feature groups, wherein the association relation is used for indicating the feature groups to be closely associated technically to obtain a plurality of feature group sets without association relation, each feature group set corresponds to a sample to be verified, and the sample to be verified is a combination of all vehicle types obtained based on the feature groups contained in the feature group set;
the sample generation unit includes: a ranking unit, configured to, for each feature group set, prioritize each feature group based on an option value contained in each feature group in the feature group set; the sub-generation unit is used for sequentially selecting a predetermined number of characteristic families from high to low according to the sequence of the priority to be arranged and combined according to the sequencing result, and generating a corresponding first sample;
a sample verification unit, configured to perform rule verification on the first sample, and take one or more combinations that are not verified as a second sample;
the sample removing unit is used for removing the vehicle model combination containing the second sample in the sample to be verified, so that validity verification is performed based on the remaining vehicle model combination in the sample to be verified.
6. The authentication device of claim 5, wherein the authentication device further comprises: the sample replacing unit is used for replacing the selected characteristic groups to perform permutation and combination, and generating new first samples and second samples, so that the number of samples to be verified is further reduced; the sample exchange unit repeats the above steps in an iterative manner until the number of samples to be verified is less than a predetermined threshold.
7. The authentication apparatus according to claim 5 or 6, wherein the feature acquisition unit includes: a constraint acquisition unit configured to acquire predetermined constraint information; and the feature selection unit is used for taking a plurality of feature groups related to the constraint relation as feature groups for verifying the legality of the vehicle type combination based on the constraint information.
8. The authentication device of claim 5, wherein the authentication device prioritizes the respective feature families by a web page ranking algorithm.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 4 when the program is executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any one of claims 1 to 4.
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