CN111709557A - High-precision map data optimal production flow calculation method and device - Google Patents
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Abstract
The invention provides a high-precision map data optimal production flow calculation method and a device, wherein the method comprises the following steps: calculating the total accuracy of map data production according to the production efficiency, quality inspection efficiency and sampling inspection efficiency in the map data production process; calculating the unit workload of each link in the map data production process according to the total accuracy, the probability of inspection of each link in the map data production and the outflow product amount of each link, and calculating the total cost of the map data production by combining the unit production cost of each link in the map data production process; and solving the optimal production flow by an enumeration method and a quadratic programming method under the constraint conditions that the total accuracy of map data production meets the preset requirement and the total cost of map data production is minimum. The method solves the problem of resource waste in the existing map data production process, and the optimal process based on solving can effectively reduce the production cost, reduce the resource waste in the generation process and ensure the rationality of resource allocation.
Description
Technical Field
The invention relates to the field of map production, in particular to a high-precision map data optimal production flow calculation method and device.
Background
The high-precision map data are applied to the unmanned system, compared with the traditional electronic navigation map, the high-precision map has many contained elements and high quality requirement, and links such as quality inspection, acceptance inspection and the like are often involved in the data production process. Under the condition of large-scale mass production, the waste can be reduced by reasonable production flow and resource investment, and the cost is reduced.
Due to the influence of human factors and errors of data, the data quality needs to be ensured through operator self-checking, quality inspection by quality inspection personnel and subsequent spot check. Generally, in order to ensure the accuracy of high-precision map data production, the traditional quality inspection and spot inspection processes are configured with excessive human resources and hardware resources according to experience, so that the resources in the map production process are wasted.
Disclosure of Invention
In view of this, the embodiment of the present invention provides a method and an apparatus for calculating an optimal production process of high-precision map data, so as to solve the problem of resource waste in the existing production process of high-precision map data.
In a first aspect of the embodiments of the present invention, a method for calculating an optimal production process of high-precision map data is provided, including:
calculating the total accuracy of map data production according to the production efficiency, quality inspection efficiency and sampling inspection efficiency in the map data production process;
calculating the unit workload of each link in the map data production process according to the total accuracy, the probability of inspection of each link in the map data production, the outflow product quantity of each link, the quality inspection small group number and the sample sampling rate, and calculating the total cost of the map data production by combining the unit production cost of each link in the map data production process;
and solving the optimal production flow by an enumeration method and a quadratic programming method under the constraint conditions that the total accuracy of map data production meets the preset requirement and the total cost of map data production is minimum.
In a second aspect of the embodiments of the present invention, there is provided an apparatus for high-precision map data optimal production flow calculation, including:
the first calculation module is used for calculating the total accuracy of map data production according to the production efficiency, the quality inspection efficiency and the sampling inspection efficiency in the map data production process;
the second calculation module is used for calculating the unit workload of each link in the map data production process according to the total accuracy, the probability of inspection of each link in the map data production, the outflow product quantity of each link, the quality inspection small group number and the sample sampling rate, and calculating the total cost of the map data production by combining the unit production cost of each link in the map data production process;
and the solving module is used for solving the optimal production flow through an enumeration method and a quadratic programming method under the constraint conditions that the total accuracy of map data production meets the preset requirement and the total cost of map data production is minimum.
In a third aspect of the embodiments of the present invention, there is provided an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the steps of the method according to the first aspect of the embodiments of the present invention.
In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the method provided in the first aspect of the embodiments of the present invention.
In the embodiment of the invention, the total accuracy of map data production is calculated based on efficiency parameters given by production self-checking, quality checking and sampling checking links, then the unit workload of each link in the map data production process is calculated, the total cost of map data production is calculated by combining the unit production cost of each link in the map data production process, and on the premise that the total accuracy of map data meets the preset requirement, the corresponding optimal production flow under the condition that the total cost of map data production is minimum is solved by an enumeration method and a quadratic programming method. Therefore, the problem of resource waste in the production process of the high-precision map data is solved, reasonable allocation of resources in the high-precision map data inspection process is realized, and the production cost is effectively reduced. By establishing a mathematical computation model and combining enumeration and quadratic programming, the optimal configuration of personnel in the production flow can be realized and the most reasonable sampling rate can be set, so that the method has higher practical application value.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings described below are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a high-precision map data optimal production flow calculation method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an apparatus for calculating an optimal production process of high-precision map data according to an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons skilled in the art without any inventive work shall fall within the protection scope of the present invention, and the principle and features of the present invention shall be described below with reference to the accompanying drawings.
The terms "comprises" and "comprising," when used in this specification and claims, and in the accompanying drawings and figures, are intended to cover non-exclusive inclusions, such that a process, method or system, or apparatus that comprises a list of steps or elements is not limited to the listed steps or elements.
The high-precision map data production flow consists of 3 links of production self-checking, quality inspection and sampling inspection, and in the production flow: in the production link, a plurality of production groups are used for producing in parallel, the production groups can carry out self-inspection on production results, and in the production self-inspection link, the yield of the results can be defined as
I.e. production efficiency. After the production link, the produced products are passed through n quality inspection groups for quality inspection, if k (k is less than or equal to n) groups (or more groups) are simultaneously inspected without errors, the quality inspection link considers that the groups pass the inspection, wherein k and n respectively represent the number of groups passing the quality inspection and the total number of groups in the quality inspection link, and the probability of error discovery of each group in the quality inspection link is
Namely, the quality inspection efficiency and the quality inspection probability are independent. In the production process, the products are subjected to random inspection by a random inspection group, the random inspection rate is gamma, and the probability of error finding of each group in the process is
I.e. spot check efficiency. The ratio of the wrong product in the whole product after the product passes through the whole production process is called the total accuracy.
In actual production, generally obtained parameters are the accuracy of operation after each link is finished, but not the efficiency of each link, and under certain assumed conditions, the efficiency of each link can be determined by back-stepping through the accuracy of each link.
Referring to fig. 1, fig. 1 is a schematic flow chart of a high-precision map data optimal production flow calculation method according to an embodiment of the present invention, including:
s101, calculating the total accuracy of map data production according to the production efficiency, quality inspection efficiency and sampling inspection efficiency in the map data production process;
the production efficiency, the quality inspection efficiency and the sampling inspection efficiency can be respectively represented by the probability that the production result is qualified, the probability that a quality inspection group finds errors and the probability that a sampling inspection finds errors. The total accuracy can be used as a constraint condition of the optimal production process, and the current total accuracy can be calculated based on given production process parameters.
Specifically, the total accuracy of map data production is calculated according to the following formula:
wherein, acc represents the total accuracy, α represents the production efficiency, namely the qualified probability of the data in the production self-inspection step, β represents the quality inspection efficiency, namely the probability of the data error found in the quality inspection step, γ represents the sampling inspection efficiency, namely the probability of the data error found in the sampling inspection step, i is a counting variable, k is the number of small groups passed by the quality inspection, and n is the total number of small groups of the quality inspection.
S102, calculating unit workload of each link in the map data production process according to the total accuracy, the probability of inspection of each link in the map data production, the outflow product quantity of each link, the quality inspection small group number and the sample sampling rate, and calculating the total cost of the map data production by combining the unit production cost of each link in the map data production process;
the unit workload represents the workload required by the links of self-checking/quality inspection/sampling inspection when the unit product is produced, so that the cost of the production flow is determined, the resource ratio of each link can be guided, the working coordination among all links is ensured, and the vacancy of the database cost and the labor cost is avoided.
When an error data error is found in a certain process, the error data will be erased and the error segment will re-enter the production process, which will result in inconsistent workload of each production link in producing a unit product, and as a result, the unit workload is generally determined by the production parameters and the production process parameters. In the actual production process, the quality inspection can influence the unit working volume through the small group number, the quality inspection small group number and the sample sampling rate, and the resource allocation for reducing the production cost of the high-precision map data can be solved by restricting the quality inspection through three process parameters of the small group number, the quality inspection small group number and the sample sampling rate.
Specifically, the unit workload of each link in the map data production process is calculated according to the following formula:
wherein, Workload1 represents the unit Workload of the production self-checking link, acc represents the total correct rate, output represents the outflow of product data, α represents the production efficiency, namely the data qualification probability of the production self-checking link, β represents the quality inspection efficiency, namely the probability of data error found by the quality inspection link, Workload2 represents the unit Workload of the quality inspection link, Workload3 represents the unit Workload of the sampling inspection link, i is a counting variable, k is the number of subgroups passed by the quality inspection, n is the total number of subgroups of the quality inspection, and r is the sampling rate.
Further, according to the unit generation cost and the unit workload, the unit total cost is solved:
total_cost＝c1×workload1+c2×workload2+c3×workload3；
wherein c1, c2 and c3 respectively represent the unit working cost of the production self-checking, quality-checking and sampling links, and total _ cost represents the unit total cost.
And S103, solving the optimal production flow through an enumeration method and a quadratic programming method under the constraint conditions that the total accuracy of map data production meets the preset requirement and the total cost of map data production is minimum.
And setting the reference accuracy of the high-precision map data production, taking the accuracy as the accuracy of a preset requirement, and ensuring that the total accuracy obtained by calculation in the step S101 meets the requirement of the reference accuracy, namely the total accuracy is more than or equal to the reference accuracy.
Specifically, values of the number of passed subgroups of quality testing, the total number of subgroups of quality testing and the sampling rate are enumerated, and the values of the number of passed subgroups of quality testing, the total number of subgroups of quality testing and the sampling rate of the sample corresponding to the minimum production cost of map data are calculated when the total accuracy of map data production meets the preset requirement.
Optionally, under the constraint conditions that the total accuracy of map data production meets a preset requirement, the sample random inspection rate is in a preset value range, and the total cost of map data production is minimum, enumerating values of the quality inspection passing small groups, the total quality inspection small groups and the sample random inspection rate, and solving values of the quality inspection passing small groups, the total quality inspection small groups and the sample random inspection rate in the optimal production flow. Generally, in the sampling inspection link, the range of the sampling inspection rate is given, sampling inspection is performed in the range, and the reasonability of the sampling inspection data volume is guaranteed.
It should be noted that an enumeration method is adopted for discrete variables in the optimization parameters, and a sequential quadratic programming method is adopted for continuous variables. The enumeration method has the characteristics of high computing speed and high accuracy of a computer, and can completely check all current possible situations, thereby finding out values meeting requirements and ensuring high accuracy of solution results. The sequential quadratic programming rule is one of the most effective methods for solving the nonlinear constraint optimization problem, has the characteristics of good convergence, high calculation efficiency and strong boundary search capability compared with other optimization algorithms, and has higher practical value in the small nonlinear constraint optimization problem.
By the method provided by the embodiment, the values of the quality inspection passing small group number, the total quality inspection small group number and the sample sampling rate corresponding to the optimal flow in the production process of the high-precision map data can be calculated, the production cost of the map data is effectively reduced, the reasonable allocation of resources in the production flow is ensured, and the waste of the resources is avoided.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 2 is a schematic structural diagram of an apparatus for calculating an optimal production process of high-precision map data according to an embodiment of the present invention, where the apparatus includes:
the first calculating module 210 is configured to calculate a total accuracy of map data production according to production efficiency, quality inspection efficiency, and sampling inspection efficiency in a map data production process;
specifically, the total accuracy of map data production is calculated according to the following formula:
wherein, acc represents the total accuracy, α represents the production efficiency, namely the qualified probability of the data in the production self-inspection step, β represents the quality inspection efficiency, namely the probability of the data error found in the quality inspection step, γ represents the sampling inspection efficiency, namely the probability of the data error found in the sampling inspection step, i is a counting variable, k is the number of small groups passed by the quality inspection, and n is the total number of small groups of the quality inspection.
The second calculating module 220 is used for calculating the unit workload of each link in the map data production process according to the total accuracy, the probability of passing each link inspection in the map data production, the outflow product quantity of each link, the quality inspection small group number and the sample sampling rate, and calculating the total cost of the map data production by combining the unit production cost of each link in the map data production process;
specifically, the unit workload of each link in the map data production process is calculated according to the following formula:
wherein, Workload1 represents the unit Workload of the production self-checking link, acc represents the total correct rate, output represents the outflow of product data, α represents the production efficiency, namely the data qualification probability of the production self-checking link, β represents the quality inspection efficiency, namely the probability of data error found by the quality inspection link, Workload2 represents the unit Workload of the quality inspection link, Workload3 represents the unit Workload of the sampling inspection link, i is a counting variable, k is the number of subgroups passed by the quality inspection, n is the total number of subgroups of the quality inspection, and r is the sampling rate.
And the solving module 230 is configured to solve the optimal production process through an enumeration method and a quadratic programming method under the constraint conditions that the total accuracy of map data production meets a preset requirement and the total cost of map data production is minimum.
Optionally, under the constraint conditions that the total accuracy of map data production meets a preset requirement, the sample sampling rate is in a preset value range, and the total cost of map data production is minimum, the optimal production process is solved through an enumeration method and a quadratic programming method.
Specifically, values of the number of passed subgroups of quality testing, the total number of subgroups of quality testing and the sampling rate are enumerated, and the values of the number of passed subgroups of quality testing, the total number of subgroups of quality testing and the sampling rate of the sample corresponding to the minimum production cost of map data are calculated when the total accuracy of map data production meets the preset requirement.
It is understood that, in one embodiment, the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program performs the steps S101 to S103 in the first embodiment, and the processor implements the high-precision map data optimal production flow calculation when executing the computer program.
Those skilled in the art will understand that all or part of the steps in the method for implementing the above embodiments may be implemented by a program to instruct associated hardware, where the program may be stored in a computer-readable storage medium, and when executed, the program includes steps S101 to S103, where the storage medium includes, for example: ROM/RAM, magnetic disk, optical disk, etc.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A high-precision map data optimal production flow calculation method is characterized by comprising the following steps:
calculating the total accuracy of map data production according to the production efficiency, quality inspection efficiency and sampling inspection efficiency in the map data production process;
calculating the unit workload of each link in the map data production process according to the total accuracy, the probability of inspection of each link in the map data production, the outflow product quantity of each link, the quality inspection small group number and the sample sampling rate, and calculating the total cost of the map data production by combining the unit production cost of each link in the map data production process;
and solving the optimal production flow by an enumeration method and a quadratic programming method under the constraint conditions that the total accuracy of map data production meets the preset requirement and the total cost of map data production is minimum.
2. The method according to claim 1, wherein the calculating the total accuracy of the map data production according to the production efficiency, the quality inspection efficiency and the sampling inspection efficiency in the map data production process specifically comprises:
calculating the total accuracy of map data production according to equation (1):
wherein, acc represents the total accuracy, α represents the production efficiency, namely the qualified probability of the data in the production self-inspection step, β represents the quality inspection efficiency, namely the probability of the data error found in the quality inspection step, γ represents the sampling inspection efficiency, namely the probability of the data error found in the sampling inspection step, i is a counting variable, k is the number of small groups passed by the quality inspection, and n is the total number of small groups of the quality inspection.
3. The method according to claim 1, wherein the calculating the unit workload of each link in the map data production process according to the total accuracy, the probability of inspection of each link in the map data production, the outflow product quantity of each link, the quality inspection small group number and the sample sampling rate specifically comprises:
respectively calculating the unit workload of each link in the map data production process according to formulas (2), (3) and (4):
wherein, Workload1 represents the unit Workload of the production self-checking link, acc represents the total correct rate, output represents the outflow of product data, α represents the production efficiency, namely the data qualification probability of the production self-checking link, β represents the quality inspection efficiency, namely the probability of data error found by the quality inspection link, Workload2 represents the unit Workload of the quality inspection link, Workload3 represents the unit Workload of the sampling inspection link, i is a counting variable, k is the number of subgroups passed by the quality inspection, n is the total number of subgroups of the quality inspection, and r is the sampling rate.
4. The method according to claim 1, further comprising, under the constraint that the total accuracy of map data production meets a preset requirement and the total cost of map data production is minimal:
and under the constraint conditions that the total accuracy of map data production meets the preset requirement, the sample sampling rate is in the preset value range, and the total cost of map data production is preset to be minimum.
5. The method of claim 1, wherein solving the optimal production flow through an enumeration method and a quadratic programming method under the constraint condition that the total accuracy of map data production meets a preset requirement and the total cost of map data production is minimum comprises:
enumerating the values of the small group number passing the quality inspection, the total small group number passing the quality inspection and the sample sampling rate, and calculating the values of the small group number passing the quality inspection, the total small group number passing the quality inspection and the sample sampling rate corresponding to the minimum production cost of the map data when the total accuracy of the map data production meets the preset requirement.
6. An apparatus for high-precision map data optimal production flow calculation, comprising:
the first calculation module is used for calculating the total accuracy of map data production according to the production efficiency, the quality inspection efficiency and the sampling inspection efficiency in the map data production process;
the second calculation module is used for calculating the unit workload of each link in the map data production process according to the total accuracy, the probability of inspection of each link in the map data production, the outflow product quantity of each link, the quality inspection small group number and the sample sampling rate, and calculating the total cost of the map data production by combining the unit production cost of each link in the map data production process;
and the solving module is used for solving the optimal production flow through an enumeration method and a quadratic programming method under the constraint conditions that the total accuracy of map data production meets the preset requirement and the total cost of map data production is minimum.
7. The apparatus according to claim 6, wherein the calculating of the total accuracy of the map data production according to the production efficiency, the quality inspection efficiency and the sampling inspection efficiency in the map data production process specifically comprises:
calculating the total accuracy of map data production according to equation (1):
wherein, acc represents the total accuracy, α represents the production efficiency, namely the qualified probability of the data in the production self-inspection step, β represents the quality inspection efficiency, namely the probability of the data error found in the quality inspection step, γ represents the sampling inspection efficiency, namely the probability of the data error found in the sampling inspection step, i is a counting variable, k is the number of small groups passed by the quality inspection, and n is the total number of small groups of the quality inspection.
8. The apparatus according to claim 6, wherein the calculating the unit workload of each link in the map data production process according to the total accuracy, the probability of inspection of each link in the map data production, the outflow product amount of each link, the quality inspection group number and the sample sampling rate specifically comprises:
respectively calculating the unit workload of each link in the map data production process according to formulas (2), (3) and (4):
wherein, Workload1 represents the unit Workload of the production self-checking link, acc represents the total correct rate, output represents the outflow of product data, α represents the production efficiency, namely the data qualification probability of the production self-checking link, β represents the quality inspection efficiency, namely the probability of data error found by the quality inspection link, Workload2 represents the unit Workload of the quality inspection link, Workload3 represents the unit Workload of the sampling inspection link, i is a counting variable, k is the number of subgroups passed by the quality inspection, n is the total number of subgroups of the quality inspection, and r is the sampling rate.
9. An electronic device comprising a processor, a memory, and a computer program stored in and executed on the memory, wherein the processor implements the steps of the high-precision map data optimal production flow calculation method according to any one of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the steps of the high-precision map data optimal production flow calculation method according to any one of claims 1 to 5.
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