Vehicle rapid arrangement method and device

Abstract

The invention relates to a rapid vehicle arrangement method and device, and belongs to the technical field of automobile manufacturing, and the method comprises the steps: building a chunk unit library according to the correlation coefficient of historical system chunks and vehicle types, fully utilizing the existing data to provide data for the building of vehicles, and saving the capital cost; further, the mapping relation of the chunk unit library is searched through the demand data of the vehicle, a plurality of initial system chunks and the matching capacity of each attribute of each initial system chunk are obtained, and therefore according to the correlation coefficient and the matching capacity of each attribute corresponding to each historical system chunk in the chunk unit library, the matching capacity of each attribute corresponding to each historical system chunk in the chunk unit library is obtained. Obtaining a comprehensive influence coefficient of each initial system block; therefore, the target system block can be determined according to the vehicle type coefficient interval and the comprehensive influence coefficient, and the vehicle can be rapidly arranged according to the target system block without building each functional module from zero, so that the time cost is saved.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile manufacturing, and in particular to a method and device for rapid vehicle arrangement. Background Art

[0002] After a certain model of automobile is developed, there will be many different external interfaces or slightly changed sub-modules with the same functions. These modules reflect the function of the device externally, and may have different interface parameters (such as different bolt lengths, different installation hole sizes, etc.), different performance parameters (such as different radiator cooling core sizes, different heat dissipation capabilities, different frame installation dimensions), and different integration methods (such as overall structure or split combination structure, etc.). There is no corresponding or related relationship within these data, which is not convenient for the selection and utilization of design data. In order to fully and quickly utilize these large amounts of data for design and development work, associations can be established within the data module, and different associations represent different matching degrees. Therefore, it is necessary to establish an association mapping relationship within the module and quantify the matching degree, which helps to make full use of existing data to quickly arrange new models.

[0003] The existing mainstream vehicle manufacturers generally adopt the traditional model of designing from scratch in the development of new models. In this model, the design team needs to build each functional module from scratch, repeatedly investing a lot of design work, which is inefficient. Since the existing data cannot be fully utilized, a lot of development funds and time costs are consumed.

[0004] Therefore, it is urgent to propose a method and device for rapid vehicle layout to solve the technical problem in the prior art that when building vehicle modules, existing data cannot be fully utilized, resulting in a large amount of development funds and time costs. Summary of the invention

[0005] In view of this, it is necessary to provide a vehicle rapid layout method and device to solve the technical problem in the prior art that when building vehicle modules, existing data cannot be fully utilized, resulting in a large amount of development funds and time costs.

[0006] In order to solve the above problems, the present invention provides a vehicle rapid arrangement method, comprising: Acquire vehicle demand data and a block unit library; the demand data includes a vehicle model coefficient interval, and the block unit library includes a mapping relationship between each historical system block and a correlation coefficient of each attribute of each historical system block; Searching the mapping relationship of the block unit library according to the demand data to obtain a plurality of initial system blocks and a matching capability of each attribute of each initial system block; Obtaining a comprehensive influence coefficient of each initial system block according to the matching capability and the correlation coefficient; According to the vehicle type coefficient interval and the comprehensive influence coefficient, a target system block is determined, and the vehicle is quickly arranged according to the target system block.

[0007] In a possible implementation, before obtaining the vehicle demand data and the block unit library, the process further includes: Dividing the historical system blocks according to the functions of each historical system block to obtain a primary classification data unit library; The first-level classification data unit library is divided into two levels according to the category of each historical system block to obtain a second-level classification data unit library.

[0008] In a possible implementation, the first-level classification data unit library includes a general block and a dedicated block; the first-level classification data unit library is divided into two levels according to the category of each historical system block to obtain a second-level classification data unit library, including: Performing a secondary division on the system blocks in the general blocks according to the category of each historical system block, to obtain a secondary classification general data unit library; The system blocks in the dedicated blocks are divided into two levels according to the category of each historical system block to obtain a two-level classified dedicated data unit library.

[0009] In a possible implementation, searching the mapping relationship of the block unit library according to the demand data to obtain a plurality of initial system blocks and a matching capability of each attribute of each initial system block includes: Searching the mapping relationship of the block unit library according to the demand data to obtain a plurality of initial system blocks; Determine all properties of each initial system block according to the requirement data; Perform matching judgment on the attributes of the initial system blocks that have a mapping relationship between each other, and obtain the matching result of each attribute; According to the matching result, the matching capability of each initial system block corresponding to each attribute is determined.

[0010] In a possible implementation, obtaining the comprehensive influence coefficient of each initial system block according to the matching capability and the correlation coefficient includes: Calculating the matching capability and the correlation coefficient of each attribute of each initial system block to obtain a matching coefficient corresponding to each attribute; A comprehensive influence coefficient is obtained according to the matching coefficients of all the attributes of each initial system block.

[0011] In a possible implementation, determining the target system block according to the vehicle type coefficient interval and the comprehensive influence coefficient includes: Determining the comprehensive influence coefficient of each of the initial system blocks respectively to determine whether the comprehensive influence coefficient is within the vehicle model coefficient range; If so, the corresponding initial system block is determined as the target system block.

[0012] In a possible implementation manner, after determining the corresponding initial system block as the target system block, the method further includes: Determine whether different initial system blocks with the same function and the comprehensive influence coefficient being a preset threshold are included; If so, a target system block is determined according to a preset rule, and the block unit library is updated according to the target system block.

[0013] In a possible implementation, the method further includes: According to the comprehensive influence coefficient, a mapping probability of the initial system block in the vehicle model coefficient interval is obtained; Obtaining conditional probability according to the primary classification data unit library and the secondary classification data unit library; According to the mapping probability and the conditional probability, a comprehensive matching degree of each system block in the secondary classification data unit library is obtained, and then a comprehensive matching degree matrix is ​​obtained; Determine the matching scheme corresponding to the maximum value in the comprehensive matching degree matrix as the best matching scheme; According to the best matching solution, a target system block is determined.

[0014] In a possible implementation, obtaining the comprehensive matching degree of each system block in the secondary classification data unit library according to the mapping probability and the conditional probability includes: Obtaining a feature deviation norm according to the demand data, the primary classification data unit library, and the secondary classification data unit library; According to the mapping probability and the feature deviation norm, the comprehensive matching degree of each system block in the secondary classification data unit library is obtained.

[0015] On the other hand, the present invention also provides a vehicle rapid deployment device, comprising: A data acquisition module, used to acquire vehicle demand data and a block unit library; the demand data includes a vehicle model coefficient interval, and the block unit library includes a mapping relationship between each historical system block and a correlation coefficient of each attribute of each historical system block; A block search module, configured to search the mapping relationship of the block unit library according to the demand data, and obtain a plurality of initial system blocks and a matching capability of each attribute of each initial system block; A coefficient calculation module, used for obtaining a comprehensive influence coefficient of each initial system block according to the matching capability and the correlation coefficient; The block determination module is used to determine the target system block according to the vehicle type coefficient interval and the comprehensive influence coefficient, and quickly arrange the vehicle according to the target system block.

[0016] The beneficial effects of the present invention are as follows: a block unit library is constructed according to the correlation coefficient between historical system blocks and vehicle models, and existing data is fully utilized to provide data for vehicle construction, thereby saving capital costs; further, the mapping relationship of the block unit library is searched through the vehicle demand data to obtain multiple initial system blocks and the matching ability of each attribute of each initial system block, so as to obtain the comprehensive influence coefficient of each initial system block according to the correlation coefficient and matching ability of each attribute of each historical system block in the block unit library; and then the target system block can be determined according to the vehicle model coefficient interval and the comprehensive influence coefficient, and the vehicle can be quickly arranged according to the target system block, without building each functional module from scratch, thus saving time costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic flow chart of an embodiment of the vehicle rapid arrangement method provided by the present invention; Figure 2 For the present invention Figure 1 A schematic flow chart of an embodiment of step S102; Figure 3 For the present invention Figure 1 A schematic flow chart of an embodiment of step S104; Figure 4 A schematic diagram of a flow chart of an embodiment of determining the best matching solution provided by the present invention; Figure 5 A schematic structural diagram of an embodiment of a vehicle rapid deployment device provided by the present invention; Figure 6 A schematic structural diagram of an embodiment of an electronic device provided by the present invention. DETAILED DESCRIPTION

[0018] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0019] like Figure 1As shown, a specific embodiment of the present invention discloses a vehicle rapid deployment method, comprising: S101, obtaining vehicle demand data and a block unit library; the demand data includes a vehicle model coefficient interval, and the block unit library includes a mapping relationship between each historical system block and a correlation coefficient of each attribute of each historical system block; S102, searching the mapping relationship of the block unit library according to the demand data to obtain a plurality of initial system blocks and a matching capability of each attribute of each initial system block; S103, obtaining a comprehensive influence coefficient of each initial system block according to the matching capability and the correlation coefficient; S104: Determine the target system block according to the vehicle model coefficient range and the comprehensive influence coefficient, and quickly arrange the vehicle according to the target system block.

[0020] It should be understood that the historical system blocks obtained in step S101 may be a set of historical system blocks obtained from vehicle equipment, or a set of historical system blocks stored historically in a storage medium.

[0021] In a specific embodiment of the present invention, the historical system blocks may include an engine, a gearbox, an amphibious module, a firepower system module, an individual system module, etc. The mapping relationship may include a mapping relationship between each historical system block, such as a mapping relationship between a gearbox and an engine, a linear continuous mapping relationship of a power transmission system, etc. The attributes of each historical system block may be different depending on the block. For example, when the historical system block is an engine, the attributes may be parameters such as torque and power. When the historical system block is a firepower system module, the attributes may be parameters such as range, accuracy, rate of fire, and power. The vehicle functions may also be classified according to different functions and structures. Vehicle models with different functions such as transport vehicles and communication vehicles are established. The component units of these series of vehicles with the same functions are roughly the same, the required powertrain characteristics are roughly the same, and the required functional modules are roughly the same. The correlation coefficient of each attribute of each historical system block can also be set according to the vehicle model. For example, when the vehicle model is a transport vehicle, the transmission torque of the transmission is 0.5, the size of the gearbox housing is 0.4, and the output flange structure is 0.1. When the vehicle model is a communication vehicle, the transmission torque of the transmission is 0.4, the size of the gearbox housing is 0.4, and the output flange structure is 0.2. The specific historical system blocks, parameters, association relationships and the correlation coefficient of each attribute can be set according to actual conditions, and the embodiments of the present invention are not limited here.

[0022] The vehicle demand data can be obtained. The demand data can be the data of the system blocks required when the vehicle is built. It can include the data of all modules (i.e., all system blocks) from the inside to the outside of the vehicle, such as bolt length, installation hole size, heat dissipation capacity, overall structure, engine, gearbox, etc. The vehicle model can be determined according to the vehicle demand data, such as a transport vehicle or a communication vehicle. All vehicle models can also be divided into forward engineering prototypes and forward principle prototypes. Among them, the comprehensive influence coefficient of the production of various forward prototypes cannot be lower than 0.6, so the model coefficient interval can be determined as [1, 0.6]. The mapping relationship of the block unit library can be searched according to the demand data to obtain multiple initial system blocks with associated relationships. The demand data can also be matched to obtain the matching ability of each initial system block corresponding to each attribute, so that the comprehensive influence coefficient of each initial system block can be obtained according to the matching ability and the correlation coefficient of each attribute of each historical system block in the block unit library. Then, the target system block can be determined according to the judgment result of the model coefficient interval and the comprehensive influence coefficient of each initial system block, so that the vehicle can be quickly arranged according to the target system block.

[0023] Furthermore, a mapping relationship between vehicles and historical system blocks can be established. Now, vehicles with different functions are mapped to common blocks such as engines; the gearbox is mapped to the engine, and so on, the power transmission system is continuously associated; special blocks or block units that have no association relationship with assemblies such as chassis engines are associated with vehicles, that is, the vehicles are connected in parallel or in series to establish an associated mapping relationship with historical system blocks.

[0024] Compared with the prior art, the present embodiment provides a method of constructing a block unit library based on the correlation coefficients between historical system blocks and vehicle models, making full use of existing data to provide data for vehicle construction, thereby saving capital costs; further, the mapping relationship of the block unit library is searched through the vehicle demand data to obtain multiple initial system blocks and the matching capability of each attribute of each initial system block, thereby obtaining the comprehensive influence coefficient of each initial system block based on the correlation coefficient and matching capability of each attribute of each historical system block in the block unit library; and then, the target system block can be determined based on the vehicle model coefficient interval and the comprehensive influence coefficient, and the vehicle can be quickly arranged based on the target system block, without the need to build each functional module from scratch, thus saving time costs.

[0025] In some embodiments of the present invention, before step S101, the method further includes: Divide the historical system blocks according to their functions to obtain a primary classification data unit library; The first-level classification data unit library is divided into two levels according to the category of each historical system block to obtain a second-level classification data unit library.

[0026] In a specific embodiment of the present invention, the modules for designing the vehicle in the historical data are divided into the smallest unit block structure. Specifically, the blocks with the same functions in the historical system blocks are aggregated into a first-level classified data unit library, and the blocks of the same category of the historical system blocks in the first-level classified data unit library are aggregated into a sub-unit library of the first-level classified data unit library, that is, the second-level classified data unit library.

[0027] In some embodiments of the present invention, the primary classification data unit library includes a general block and a dedicated block; the primary classification data unit library is divided into two levels according to the category of each historical system block to obtain a secondary classification data unit library, including: According to the category of each historical system block, the system blocks in the general block are divided into two levels to obtain a two-level classification general data unit library; According to the category of each historical system block, the system blocks in the dedicated blocks are divided into two levels to obtain a two-level classified dedicated data unit library.

[0028] In a specific embodiment of the present invention, blocks with the same function are divided into general blocks and special blocks according to the composition of the vehicle, that is, the first-level classification data unit library can include general blocks and special blocks. General blocks can include engine, gearbox and other block units that are necessary for the vehicle; special blocks can include special function blocks that are not necessary for the vehicle, such as amphibious modules, firepower system modules, individual system modules, etc. The blocks of the same category as the engine are collected into different second-level classification function units to form a second-level classification general data unit library for engine and gearbox functions. The blocks of the same category as amphibious are collected into different second-level classification special function units to form a second-level classification special data unit library for amphibious and firepower system functions.

[0029] In some embodiments of the present invention, Figure 2 As shown, step S102 includes: S201, searching the mapping relationship of the block unit library according to the demand data to obtain a plurality of initial system blocks; S202, determining all attributes of each initial system block according to the demand data; S203, performing matching judgment on the attributes of the initial system blocks that have a mapping relationship between each other, and obtaining a matching result for each attribute; S204: Determine the matching capability of each initial system block corresponding to each attribute according to the matching result.

[0030] In a specific embodiment of the present invention, after the block unit library is constructed, the block unit library can be used, so that the vehicle's demand data can be brought into the block unit library for search, so that multiple initial system blocks required by the vehicle can be matched according to the mapping relationship. However, these initial system blocks only represent the needs of the vehicle, and do not represent the match with the vehicle's demand data. Therefore, it is necessary to determine all the attributes of each initial system block according to the demand data. For example, some attribute parameters of the engine need to be matched with the transport vehicle, while the communication vehicle does not need to be matched with the key attribute parameters. The communication vehicle needs to match other attribute parameters with the key attribute parameters. For example, the parameter attributes of the gearbox include the highest gear ratio, the lowest gear ratio, the transmission torque capacity, the gearbox housing size, the output flange structure, etc. When the vehicle is determined to be a transport vehicle according to the demand data, the transmission torque, the gearbox housing size and the output flange structure of the transmission can be determined. Then, the attributes of the initial system blocks that have a mapping relationship between each other can be matched and judged to obtain the matching result of each attribute. Because there is a mapping relationship between the gearbox and the engine, the transmission is based on the gearbox. Taking the gearbox and engine as an example, the transmission torque is 0.5, the gearbox housing size is 0.4, and the output flange structure is 0.1. The engine output torque and the gearbox transmission torque can be matched and judged to be less than or equal to the gearbox transmission torque, or the engine output torque is greater than the gearbox transmission torque by 20%, or the engine output torque is greater than the gearbox transmission torque by 40%. When the matching result of the transmission torque is that the engine output torque is less than or equal to the gearbox transmission torque, the matching capacity of the transmission torque of the gearbox is 1, when the matching result of the transmission torque is that the engine output torque is greater than the gearbox transmission torque by 20%, the matching capacity of the transmission torque of the gearbox is 0.6, when the matching result of the transmission torque is that the engine output torque is greater than the gearbox transmission torque by 40%, the matching capacity of the transmission torque of the gearbox is 0.3, and the size of the gearbox housing can also be judged. When the gearbox is No. 1 shell, the matching capacity is 1, when the gearbox is No. 2 shell, the matching capacity is 0.5, and when the gearbox is No. 3 shell, the matching capacity is 0. The other attribute parameters are similar, and the corresponding matching capacity can be obtained. The specific value of the matching capability can be set according to the specific matching situation, and the embodiment of the present invention is not limited here.

[0031] In some embodiments of the present invention, step S103 includes: The matching ability and correlation coefficient of each attribute of each initial system block are calculated to obtain the matching coefficient corresponding to each attribute; According to the matching coefficients of all attributes of each initial system block, the comprehensive influence coefficient is obtained.

[0032] In a specific embodiment of the present invention, after obtaining the matching capability of each attribute of each initial system block, calculation can be performed according to the correlation coefficient to obtain the matching coefficient corresponding to each attribute. For example, the demand end is the engine, the matching end is the gearbox, the attributes of the engine are the transmission torque, the housing size and the output flange structure, and the transmission torque of the gearbox is 0.5, the gearbox housing size is 0.4, and the output flange structure is 0.1. When the matching capability of the transmission torque of the gearbox is 1, the matching coefficient is 0.5×1=0.5. When the matching capability of the transmission torque of the gearbox is 0.6, the matching coefficient is 0.3. When the matching capability of the transmission torque of the gearbox is 0.3, the matching coefficient is 0.5×0.3=0.15. When the matching capability of the gearbox housing size is 1, The matching coefficient is 0.4×1=0.4. When the matching capacity of the gearbox housing size is 0, the matching coefficient is 0.4×0.5=0.2. When the matching capacity of the gearbox housing size is 0, the matching coefficient is 0.4×0=0. Similarly, the output flange structure can obtain the corresponding matching coefficient. For example, the matching coefficient calculation result is 0.1. Then, the matching coefficients of the transmission torque, the gearbox housing size and the output flange structure of the gearbox can be added to obtain the comprehensive influence coefficient. For example, the matching coefficient of the transmission torque is 0.5, the matching coefficient of the gearbox housing size is 0.2, and the matching coefficient of the output flange structure is 0.1. Then the comprehensive influence coefficient is 0.5+0.2+0.1=0.8. The other initial system blocks are similar. The embodiments of the present invention are not repeated here. Among them, the parameter attributes of the engine set the requirements in advance, and the matching end automatically displays the comprehensive influence coefficient with the demand end when selecting.

[0033] In some embodiments of the present invention, Figure 3 As shown, step S104 includes: S301, respectively judging the comprehensive influence coefficient of each initial system block to determine whether the comprehensive influence coefficient is within the vehicle model coefficient range; S302: If yes, determine the corresponding initial system block as the target system block.

[0034] In a specific embodiment of the present invention, the comprehensive influence coefficient of each initial system block can be judged to determine whether it is in the vehicle model coefficient range. For example, the comprehensive influence coefficient of the transmission is 0.8, which belongs to the vehicle model coefficient range, and the transmission can be determined as the target system block. The other initial system blocks are similar. Those that meet the conditions are determined as target system blocks, and those that do not meet the conditions are abandoned, thereby completing the criticism process of the vehicle's system blocks. Among them, when the forward engineering prototype is produced, only the blocks with a block mapping relationship of a coefficient of 1 can be selected for design selection. When the forward principle prototype is produced, the blocks with a block mapping relationship of a coefficient of 0.99-0.6 can be selected for design selection (matching can be achieved after modification). When various forward prototypes are produced, the system data cannot select blocks with a block mapping relationship of a coefficient of 0.59-0 for design selection to avoid design errors caused by unreasonable matching design.

[0035] In some embodiments of the present invention, after step S302, the method further includes: Determine whether different initial system blocks with the same functions and a comprehensive impact coefficient of a preset threshold are included; If so, the target system block is determined according to a preset rule, and the block unit library is updated according to the target system block.

[0036] In a specific embodiment of the present invention, as the design data increases, the matching relationships between components will also increase, the comprehensive influence coefficients between components will change from a single state to more states, and there will be more states with high matching degree that can be selected between data, which will further optimize the data matching state and improve the design matching and selection accuracy of the data. At the same time, if the correlation coefficient of the system block mapping relationship is 1 (preset threshold) and increases, that is, A1 maps B to 1, it is convenient to check whether the system blocks B1, B2, B3, etc. based on the A1 block (that is, the functions of the system blocks B1, B2, B3, etc. are the same, and the comprehensive influence coefficients are all 1 (preset threshold)) are repeated. If so, one of B1, B2, and B3 can be determined as the target system block according to the preset rules. The specific preset rules can be selected by engineers according to actual conditions and experience to determine the target system block. The results selected by different engineers may be different. Different universal blocks with the same functions can also be established according to the selection of engineers. Based on this, the repeated data of the block unit library can be optimized to achieve the optimal state of the block unit library data.

[0037] In some embodiments of the present invention, Figure 4 As shown, the method also includes: S401, obtaining a mapping probability of an initial system block in a vehicle model coefficient interval according to a comprehensive influence coefficient; S402, obtaining conditional probability according to the primary classification data unit library and the secondary classification data unit library; S403, according to the mapping probability and the conditional probability, the comprehensive matching degree of each system block in the secondary classification data unit library is obtained, and then the comprehensive matching degree matrix is ​​obtained; S404, determining the matching scheme corresponding to the maximum value in the comprehensive matching degree matrix as the best matching scheme; S405: Determine the target system block according to the best matching solution.

[0038] In a specific embodiment of the present invention, a block unit library model can be constructed based on the block unit library, so that the vehicle demand data can be input into the block unit library model, and the block unit library model can output the best matching solution for the vehicle, 0 where M is the number of vehicle types, and N is the number of categories in the primary classification data unit library; C={c1, c2, ...cm} represents a set of M vehicle types; P={p1, p2, ...pn} represents a set of categories in N primary classification data unit libraries; Qi={q1, q2, ...qni} represents a set of system blocks in ni secondary classification data unit libraries under the i-th primary block pi. The specific steps of the block unit library model are as follows: the first step is to construct a mapping matrix. Define Y(MxN) as the mapping matrix between the vehicle and the primary block, where yi,j∈{0, 1}, yi,j=1 indicates that there is a mapping association between the vehicle ci and the primary block pj, that is, the mapping probability. The second step is to calculate the conditional probability between the primary classification data unit library and the secondary classification data unit library. Define P(qk|pj) as the conditional probability of selecting the corresponding secondary block qk when a given primary block pj is given, and its value is the initial matching coefficient between the two. The third step is to calculate the comprehensive matching degree between the vehicle and each secondary system block based on the mapping probability and the conditional probability. Define X(MxNxSum(ni)) as the comprehensive matching degree matrix of the comprehensive matching degree between the vehicle and all secondary system blocks, where xi,j,k=yi,j×P(qk|pj). Finally, traverse the comprehensive matching degree matrix and select the maximum value xi,j,k as the best matching solution between the vehicle ci and the secondary system block qk. In this way, the system block in the best matching solution can be determined as the target system block.

[0039] In some embodiments of the present invention, step S403 includes: According to the demand data, the primary classification data unit library and the secondary classification data unit library, a feature deviation norm is obtained; According to the mapping probability and feature deviation norm, the comprehensive matching degree of each system block in the secondary classification data unit library is obtained.

[0040] In a specific embodiment of the present invention, the block unit library model can also be improved to incorporate multidimensional constraints such as price and performance. First, the multidimensional feature vector ci of the vehicle and each level of blocks is defined to represent (λci1, λci2, ..., λciL) representing the L features of the vehicle ci; pj represents (λpj1, λpj2, ..., λpjL) representing the L features of the first-level block pj; qk represents (λqk1, λqk2, ..., λqkL) representing the L features of the second-level block qk. Secondly, a feature matching vector space V is constructed, and the vector norm ||v|| is introduced as a matching metric for any ci and pj. Vi,j=(λci1-λpj1, λci2-λpj2, ..., λciL-λpjL)∈V is defined, then ||vi,j|| = sqrt(Σ(λcil-λpjl)^2) is the feature deviation norm of the two. Finally, the conditional probability is replaced by the feature deviation norm, and the comprehensive matching degree is corrected as shown in Formula 1: Xi,j,k=yi,j×(1-||vi,j-vj,k|| / Dj,k) (1) Among them, Dj,k is the upper limit of the feature allowable deviation norm of pj and qk.

[0041] This not only takes into account the direct mapping relationship, but also incorporates multi-dimensional constraints, making the vehicle module layout plan more reasonable and feasible.

[0042] The above calculation process and multi-constraint model construction can be realized automatically through algorithms, and the layout structure and process information can be visualized.

[0043] Finally, combined with application scenario 1 and application scenario 2, the specific application of the embodiment of the present invention in new product development and prototype vehicle resource utilization is described in detail.

[0044] Application scenario 1: Using sample vehicle resources to quickly deploy new products. Taking the "worthless" sample vehicle generated during the trial production process as an example, the embodiment of the present invention can sort the sample vehicle modules into a block library according to the above steps, establish a mapping matrix, and calculate the matching degree between each module. Then, the block combination with a matching coefficient above 0.6 is selected. After in-depth analysis and optimization, the best module reorganization scheme can be obtained. This scheme can not only fully reuse the residual value assets in the sample vehicle, but also greatly reduce the reorganization cost, making it possible to quickly build new products.

[0045] Specifically, for worthless prototype modules zj (j=1, 2, ..., Z), they are divided into different primary / secondary block categories according to their functions, and their affiliations pzi, qzi in the block library are obtained. Next, for each reusable zj, all matching vehicle types ci and corresponding matching values ​​xi, zi are listed: {(ci,xi,zi)|xi,zi=yi,pzi×P(qzi|pzi)>0.6}, where the condition 0.6 is the modified matching threshold set according to step 5. For prototype modules with low matching (<0.6), they can be temporarily shelved.

[0046] All (ci,xi,zi) triplets that meet the conditions are sorted from high to low by matching degree, and their corresponding total cost in vehicle construction is calculated, thereby obtaining multiple candidate solutions. Further considering the actual constraints such as module function coverage and supply cycle, the final output is a low-cost and better prototype vehicle module reuse solution.

[0047] This application scenario makes full use of idle resources within the enterprise, significantly reduces the cost of new product research and development and pilot production, and has important economic value.

[0048] Application scenario 2: quickly set target prices for new products. In the early stages of new product development, it is often impossible to obtain all component cost information in advance, but customers have clear requirements for the target price of the product. Traditionally, the vehicle design needs to be completed first, and then the price is determined. If there is a deviation from the customer's expected price, a large-scale redesign is required, resulting in huge repeated waste.

[0049] By using the technical solution of the embodiment of the present invention, we can start from customer needs and functional indicators, select components with higher matching coefficients from the existing block library for simulated assembly, and determine the approximate configuration of the vehicle. Then, we can add up and estimate the cost of these known components to get the preliminary product sales target price range.

[0050] More specifically, let the cost of module pi be Cpi, and for any vehicle ci, the module set it has is Mi={pi|yi,j=1}. Then the approximate cost estimate of this vehicle is: Costi = Σpi∈Mi (Cpi) + Kci, where Kci is the other fixed cost of vehicle ci.

[0051] By comparing the Costi values ​​of all vehicle ci with the current prices of similar products in the market, we can evaluate their rationality range. For solutions that deviate too much from market expectations, we can make timely adjustments and select other more reasonable modules for re-simulation and assembly.

[0052] This solution can significantly improve the accuracy and pre-emptiveness of new product pricing, avoiding the risk of major rework caused by pricing deviations in the later stages. Therefore, it can significantly shorten the new product design cycle and reduce R&D costs.

[0053] In order to better implement the vehicle rapid deployment method in the embodiment of the present invention, based on the vehicle rapid deployment method, the embodiment of the present invention also provides a vehicle rapid deployment device, such as Figure 5 As shown, the vehicle rapid deployment device 500 includes: The data acquisition module 501 is used to acquire the vehicle demand data and the block unit library; the demand data includes the vehicle model coefficient interval, and the block unit library includes the mapping relationship between each historical system block and the correlation coefficient of each attribute of each historical system block; The block search module 502 is used to search the mapping relationship of the block unit library according to the demand data to obtain a plurality of initial system blocks and the matching capability of each attribute of each initial system block; The coefficient calculation module 503 is used to obtain the comprehensive influence coefficient of each initial system block according to the matching ability and the correlation coefficient; The block determination module 504 is used to determine the target system block according to the vehicle type coefficient interval and the comprehensive influence coefficient, and quickly arrange the vehicle according to the target system block.

[0054] The vehicle rapid deployment device 500 provided in the above embodiment can implement the technical solution described in the above vehicle rapid deployment method embodiment. The specific implementation principles of the above modules or units can refer to the corresponding contents in the above vehicle rapid deployment method embodiment, which will not be repeated here.

[0055] like Figure 6 As shown, the present invention also provides an electronic device 600. The electronic device 600 includes a processor 601, a memory 602 and a display 603. Figure 6 Only some components of the electronic device 600 are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0056] In some embodiments, the memory 602 may be an internal storage unit of the electronic device 600, such as a hard disk or memory of the electronic device 600. In other embodiments, the memory 602 may also be an external storage device of the electronic device 600, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the electronic device 600.

[0057] Furthermore, the memory 602 may include both an internal storage unit of the electronic device 600 and an external storage device. The memory 602 is used to store application software installed in the electronic device 600 and various data.

[0058] In some embodiments, the processor 601 may be a central processing unit (CPU), a microprocessor or other data processing chip, and is used to run program codes or process data stored in the memory 602, such as the vehicle rapid deployment method of the present invention.

[0059] In some embodiments, the display 603 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 603 is used to display information of the electronic device 600 and to display a visual user interface. The components 601-603 of the electronic device 600 communicate with each other through a system bus.

[0060] In some embodiments of the present invention, when the processor 601 executes the vehicle quick arrangement program in the memory 602, the following steps may be implemented: Obtaining vehicle demand data and a block unit library; the demand data includes a vehicle model coefficient interval, and the block unit library includes a mapping relationship between each historical system block and a correlation coefficient of each attribute of each historical system block; Searching the mapping relationship of the block unit library according to the demand data to obtain a plurality of initial system blocks and the matching capability of each attribute of each initial system block; According to the matching ability and correlation coefficient, the comprehensive influence coefficient of each initial system block is obtained; According to the vehicle model coefficient range and comprehensive impact coefficient, the target system blocks are determined, and the vehicles are quickly arranged according to the target system blocks.

[0061] It should be understood that: when the processor 601 executes the vehicle rapid arrangement program in the memory 602, in addition to the above functions, other functions can also be implemented. For details, please refer to the description of the corresponding method embodiment above.

[0062] Furthermore, the embodiment of the present invention does not specifically limit the type of the electronic device 600 mentioned, and the electronic device 600 may be a portable electronic device such as a mobile phone, a tablet computer, a personal digital assistant (PDA), a wearable device, a laptop computer, etc. Exemplary embodiments of portable electronic devices include but are not limited to portable electronic devices equipped with IOS, Android, Microsoft or other operating systems. The above-mentioned portable electronic devices may also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 600 may not be a portable electronic device, but a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0063] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, the vehicle rapid arrangement method steps or functions provided in the above-mentioned method embodiments can be implemented.

[0064] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0065] The method and device for rapid arrangement of vehicles provided by the present invention are introduced in detail above. Specific examples are used herein to illustrate the principle and implementation mode of the present invention. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for rapid vehicle deployment, characterized in that: include: Obtain vehicle demand data and block unit library; The demand data includes a vehicle type coefficient interval, and the block unit library includes a mapping relationship between each historical system block and a correlation coefficient of each attribute of each historical system block; Searching the mapping relationship of the block unit library according to the demand data to obtain a plurality of initial system blocks and a matching capability of each attribute of each initial system block; Obtaining a comprehensive influence coefficient of each initial system block according to the matching capability and the correlation coefficient; According to the vehicle type coefficient interval and the comprehensive influence coefficient, a target system block is determined, and the vehicle is quickly arranged according to the target system block.

2. The vehicle rapid arrangement method according to claim 1, characterized in that: Before obtaining the vehicle demand data and the block unit library, the method further includes: Dividing the historical system blocks according to the functions of each historical system block to obtain a primary classification data unit library; The first-level classification data unit library is divided into two levels according to the category of each historical system block to obtain a second-level classification data unit library.

3. The vehicle rapid arrangement method according to claim 2, characterized in that: The first-level classification data unit library includes a general block and a special block; the first-level classification data unit library is divided into two levels according to the category of each historical system block to obtain a second-level classification data unit library, including: Performing a secondary division on the system blocks in the general blocks according to the category of each historical system block, to obtain a secondary classification general data unit library; The system blocks in the dedicated blocks are divided into two levels according to the category of each historical system block to obtain a two-level classified dedicated data unit library.

4. The vehicle rapid arrangement method according to claim 1, characterized in that: The searching the mapping relationship of the block unit library according to the demand data to obtain a plurality of initial system blocks and a matching capability of each attribute of each initial system block includes: Searching the mapping relationship of the block unit library according to the demand data to obtain a plurality of initial system blocks; Determine all properties of each initial system block according to the requirement data; Perform matching judgment on the attributes of the initial system blocks that have a mapping relationship between each other, and obtain the matching result of each attribute; According to the matching result, the matching capability of each initial system block corresponding to each attribute is determined.

5. The vehicle rapid arrangement method according to claim 4, characterized in that: The step of obtaining the comprehensive influence coefficient of each initial system block according to the matching capability and the correlation coefficient includes: Calculating the matching capability and the correlation coefficient of each attribute of each initial system block to obtain a matching coefficient corresponding to each attribute; A comprehensive influence coefficient is obtained according to the matching coefficients of all the attributes of each initial system block.

6. The vehicle rapid arrangement method according to claim 2, characterized in that: The step of determining the target system block according to the vehicle type coefficient interval and the comprehensive influence coefficient includes: Determining the comprehensive influence coefficient of each of the initial system blocks respectively to determine whether the comprehensive influence coefficient is within the vehicle model coefficient range; If so, the corresponding initial system block is determined as the target system block.

7. The vehicle rapid arrangement method according to claim 6, characterized in that: After determining the corresponding initial system block as the target system block, the method further includes: Determine whether different initial system blocks with the same function and the comprehensive influence coefficient being a preset threshold are included; If so, a target system block is determined according to a preset rule, and the block unit library is updated according to the target system block.

8. The vehicle rapid arrangement method according to claim 6, characterized in that: The method further comprises: According to the comprehensive influence coefficient, a mapping probability of the initial system block in the vehicle model coefficient interval is obtained; Obtaining conditional probability according to the primary classification data unit library and the secondary classification data unit library; According to the mapping probability and the conditional probability, a comprehensive matching degree of each system block in the secondary classification data unit library is obtained, and then a comprehensive matching degree matrix is ​​obtained; Determine the matching scheme corresponding to the maximum value in the comprehensive matching degree matrix as the best matching scheme; According to the best matching solution, a target system block is determined.

9. The vehicle rapid arrangement method according to claim 8, characterized in that: The step of obtaining the comprehensive matching degree of each system block in the secondary classification data unit library according to the mapping probability and the conditional probability includes: Obtaining a feature deviation norm according to the demand data, the primary classification data unit library, and the secondary classification data unit library; According to the mapping probability and the feature deviation norm, the comprehensive matching degree of each system block in the secondary classification data unit library is obtained.

10. A vehicle rapid deployment device, characterized in that: include: A data acquisition module, used to acquire the vehicle's demand data and block unit library; The demand data includes a vehicle type coefficient interval, and the block unit library includes a mapping relationship between each historical system block and a correlation coefficient of each attribute of each historical system block; A block search module, configured to search the mapping relationship of the block unit library according to the demand data, and obtain a plurality of initial system blocks and a matching capability of each attribute of each initial system block; A coefficient calculation module, used for obtaining a comprehensive influence coefficient of each initial system block according to the matching capability and the correlation coefficient; The block determination module is used to determine the target system block according to the vehicle type coefficient interval and the comprehensive influence coefficient, and quickly arrange the vehicle according to the target system block.

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