A method, device, equipment and storage medium for automatically iterating benchmark models

Abstract

The present invention discloses a method, device, equipment and storage medium for automatically iterating a benchmark model, and relates to the field of computer science and technology. The method includes constructing a benchmark model, and constructing a key variable parameter library based on the key variable parameters of each sub-model of the benchmark model, and constructing a coefficient list based on the coefficients corresponding to the key variable parameters; creating a benchmark model application platform, and realizing the collection of corresponding data of the benchmark model based on the created benchmark model application platform; establishing a benchmark model digital iteration platform to realize the update of coefficients in the coefficient list in combination with the collected data. This application can provide more accurate and sufficient digital services for the budget of R&D expenses, reduce the maintenance workload of standards and improve the maintenance efficiency of standards.

Description

Technical Field

[0001] The present application relates to the field of computer science and technology, and in particular to a method, apparatus, device and storage medium for automatically iterating a benchmark model. Background Art

[0002] In the field of automobile manufacturing and application, various standards are published and used in the form of databases, and the updating and maintenance of standards are all direct operations of the database by users or maintainers.

[0003] It can be seen that the timeliness of standard maintenance depends on the subjective initiative of the maintainer, and the accuracy of standard maintenance depends on the professional quality of the maintainer. Relying on manual methods to maintain standards is both cumbersome and difficult to sustain. Summary of the invention

[0004] The present application provides a method, apparatus, device and storage medium for automatically iterating a benchmark model, which can provide more accurate and comprehensive digital services for R&D expense budgeting, reduce the workload of standard maintenance and improve the efficiency of standard maintenance.

[0005] In a first aspect, an embodiment of the present application provides a method for automatically iterating a benchmark model, the method for automatically iterating a benchmark model comprising:

[0006] Build a benchmark model, and build a key variable parameter library based on the key variable parameters of each sub-model of the benchmark model, and build a coefficient list based on the coefficients corresponding to the key variable parameters;

[0007] Create a benchmark model application platform, and collect corresponding data of the benchmark model based on the created benchmark model application platform;

[0008] Establish a digital iteration platform for benchmark models to update the coefficients in the coefficient list based on the collected data.

[0009] In combination with the first aspect, in one implementation,

[0010] The benchmark models include a road test benchmark model, a bench test benchmark model and a trial-production special parts procurement benchmark model, and each benchmark model includes a plurality of sub-models, and each sub-model includes key variable parameters and coefficients for calculating the key variable parameters;

[0011] The key variable parameters include fixed parameters and random parameters;

[0012] The fixed parameters are individual parameters of the function CASE, and the fixed parameters are bound to the function CASE in a table form;

[0013] The random parameters are common parameters of the functional CASE and can be increased or decreased randomly.

[0014] In combination with the first aspect, in one implementation, after a key variable parameter library is constructed based on key variable parameters of each sub-model of the benchmark model, and a coefficient list is constructed based on coefficients corresponding to the key variable parameters, the method further includes:

[0015] Build a platform for expanding the scope of application in multiple dimensions. In the calculation standard interface of the road test benchmark model, build sub-interfaces for road condition types, sample vehicle types, and fuel types, and increase or decrease key variable parameters in the sub-interfaces;

[0016] In the category rate association interface of the bench test benchmark model, multiple general stages are randomly matched to combine and generate different bench test categories;

[0017] In the input interface of the functional CASE of the benchmark model for trial-production of special parts procurement, the mathematical calculation model is input based on the preset logic;

[0018] Construct a sub-model hierarchical update platform to update the sub-models of the bench test benchmark model;

[0019] Construct an overall replaceable interface for the model, and after the sub-model of the trial-production special parts procurement benchmark model is created, calculate the benchmark value of the specified functional CASE.

[0020] In combination with the first aspect, in one implementation, the creation of a benchmark model application platform and the collection of corresponding data of the benchmark model based on the created benchmark model application platform are specifically:

[0021] Build a project budget establishment platform, establish project budgets based on grid management, and locate project budgets on the detailed expenses of specific business of the department;

[0022] Build a budget execution application platform to collect the frequency and budget amount of functional cases;

[0023] Build a budget write-off application platform to collect the amount of functional CASE write-off.

[0024] In combination with the first aspect, in one implementation, the establishment of a digital and intelligent iteration platform for the benchmark model is to update the coefficients in the coefficient list in combination with the collected data, specifically:

[0025] Based on the selected write-off time period and key variable parameters corresponding to the functional CASE, search the execution application database and establish the first functional CASE collection library;

[0026] Based on the selected write-off time period and key variable parameters corresponding to the functional CASE, search the write-off application database and establish a second functional CASE collection library;

[0027] Perform correlation analysis on the data of the first function CASE collection library and the second function CASE collection library, and index them according to the selected key variable parameters to obtain the data within the specified time range;

[0028] The trend line is determined based on the data within the specified time range, and the slope of the trend line is the updated and optimized coefficient of the benchmark model.

[0029] In combination with the first aspect, in one implementation,

[0030] Different benchmark models correspond to different key variable parameters;

[0031] The key variable parameters corresponding to the functional CASE of the road test benchmark model include road conditions, mileage, sample vehicle type, number of axles, tonnage, and fuel type;

[0032] The key variable parameters corresponding to the functional CASE of the bench test benchmark model include test category and engine model.

[0033] In combination with the first aspect, in one implementation, the trend line is determined based on the data obtained within the specified time range, and the slope of the trend line is the coefficient after the benchmark model is updated and optimized, specifically:

[0034] The data obtained within the specified time range is plotted on the coordinate axis and the trend line is determined;

[0035] Eliminate the abnormal points on both sides of the trend line so that all analysis points are evenly distributed on both sides of the trend line;

[0036] Based on the slope of the trend line, the updated and optimized coefficients of the benchmark model are obtained.

[0037] In a second aspect, an embodiment of the present application provides a device for automatically iterating a benchmark model, the device for automatically iterating a benchmark model comprising:

[0038] A construction module is used to construct a benchmark model, and to construct a key variable parameter library based on the key variable parameters of each sub-model of the benchmark model, and to construct a coefficient list based on the coefficients corresponding to the key variable parameters;

[0039] A creation module, which is used to create a benchmark model application platform and realize the collection of corresponding data of the benchmark model based on the created benchmark model application platform;

[0040] The update module is used to establish a digital iteration platform for the benchmark model to update the coefficients in the coefficient list in combination with the collected data.

[0041] In a third aspect, an embodiment of the present application provides a device for automatically iterating a benchmark model, wherein the device for automatically iterating a benchmark model comprises a processor, a memory, and a program for automatically iterating a benchmark model stored in the memory and executable by the processor, wherein when the program for automatically iterating a benchmark model is executed by the processor, the steps of the method for automatically iterating a benchmark model described above are implemented.

[0042] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which is stored a program for automatically iterating a benchmark model. When the program for automatically iterating a benchmark model is executed by a processor, the steps of the method for automatically iterating a benchmark model described above are implemented.

[0043] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:

[0044] By constructing a benchmark model, and building a key variable parameter library based on the key variable parameters of each sub-model of the benchmark model, and building a coefficient list based on the coefficients corresponding to the key variable parameters, and then creating a benchmark model application platform, and based on the created benchmark model application platform, the corresponding data of the benchmark model is collected, and finally a benchmark model digital iteration platform is established to combine the collected data to realize the update of the coefficients in the coefficient list, so that the optimized benchmark model can provide more accurate and comprehensive digital services for the R&D cost budget, reduce the standard maintenance workload and improve the standard maintenance efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 A flowchart of a method for automatically iterating a benchmark model for this application;

[0046] Figure 2 A schematic diagram of the system architecture corresponding to the method of automatically iterating the benchmark model;

[0047] Figure 3 A schematic diagram of the structure of a device for automatically iterating a benchmark model for this application;

[0048] Figure 4 Schematic diagram of the hardware structure of the device for automatically iterating the benchmark model for this application. DETAILED DESCRIPTION

[0049] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0050] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0051] On the first aspect, an embodiment of the present application provides a method for automatically iterating a benchmark model by constructing a benchmark model, and constructing a key variable parameter library based on the key variable parameters of each sub-model of the benchmark model, and constructing a coefficient list based on the coefficients corresponding to the key variable parameters, and then creating a benchmark model application platform, and based on the created benchmark model application platform, the collection of corresponding data of the benchmark model is realized, and finally, a benchmark model digital iteration platform is established to update the coefficients in the coefficient list in combination with the collected data, so that the optimized benchmark model can provide more accurate and sufficient digital services for the R&D cost budget, reduce the standard maintenance workload and improve the standard maintenance efficiency.

[0052] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of the method for automatically iterating the benchmark model in this application. Figure 1 As shown, the method of automatically iterating the benchmark model includes:

[0053] S1: Build a benchmark model, and build a key variable parameter library based on the key variable parameters of each sub-model of the benchmark model, and build a coefficient list based on the coefficients corresponding to the key variable parameters;

[0054] It should be noted that the benchmark model includes the road test benchmark model, the bench test benchmark model and the trial production special parts procurement benchmark model, and each benchmark model includes multiple sub-models, each sub-model includes key variable parameters and coefficients for calculating the key variable parameters. That is, each benchmark model is a combination of multiple sub-models, and the sub-model can be expressed as Fi = u*i+C, where Fi represents the sub-model, i represents the key variable parameters, u represents the coefficient, and C represents the adjustment constant.

[0055] Key variable parameters include fixed parameters and random parameters; fixed parameters are individual parameters of functional CASE (example), and fixed parameters are bound to functional CASE in table form; random parameters are common parameters of functional CASE, which can be increased or decreased randomly.

[0056] Building a benchmark model means building a coefficient list and a key variable parameter library for the model. According to business segmentation, the built benchmark models can include 29 types, such as 4 road test benchmark models (standard-self-made-common, standard-self-made-temporary, standard-outsourced-user environment, standard-outsourced-other), 3 bench test benchmark models (standard-self-made-common, standard-self-made-temporary, standard-outsourced), 6 trial-production special parts procurement benchmark models (standard-special, standard-sample prototype, standard-commissioned processing, non-standard-special parts, non-standard-tooling fixtures, non-standard-gauging fixtures), etc.

[0057] The coefficient set under multiple seed models constitutes the coefficient list. The structure of the coefficient list: the row fields and column fields are composed of key variable parameters, and the cell value corresponds to the coefficient. The optimization of the existing benchmark model is to update the coefficient list.

[0058] Furthermore, in one embodiment, the maintenance of the benchmark model includes, in addition to the optimization iteration of the coefficients in the coefficient list, all-round maintenance of the key variable parameters in the benchmark model. Specifically, after the key variable parameter library is constructed based on the key variable parameters of each sub-model of the benchmark model, and the coefficient list is constructed based on the coefficients corresponding to the key variable parameters, it also includes:

[0059] S101: Build a platform for expanding the scope of application in multiple dimensions. In the calculation standard interface of the road test benchmark model, build sub-interfaces for road condition types, sample vehicle types, and fuel types. Increase or decrease key variable parameters in the sub-interfaces, and simulate new road test business types accordingly.

[0060] S102: In the category rate association interface of the bench test benchmark model, randomly matching multiple general stages to combine and generate different bench test categories;

[0061] S103: In the input interface of the functional CASE of the trial-production special parts procurement benchmark model, a mathematical calculation model is input based on a preset logic;

[0062] S104: construct a sub-model hierarchical update platform to update the sub-model of the bench test benchmark model;

[0063] That is, in the bench test benchmark model, there are three interfaces: default, customization, and review, and the sub-model is updated progressively. Default link - in the formula input tab page, the sub-model is specified in the configuration item and the system defaults; Customization link - if some materials need to apply special calculation rules, they are specified after the materials, and the system replaces the default items; Review link - use the coefficient to adjust the sub-model, but it is only valid for the current reviewed material.

[0064] S105: Construct the overall replaceable interface of the model, and after the sub-model of the trial-production special parts procurement benchmark model is created, calculate the benchmark value of the specified functional CASE. That is, the trial-production special parts procurement benchmark model, in the process procurement configuration interface, the procurement sub-benchmark models of various trial-production processes, after being entered according to the Excel rules, can be called by the system in real time to calculate the benchmark value of the specified functional CASE.

[0065] S2: Create a benchmark model application platform, and collect corresponding data of the benchmark model based on the created benchmark model application platform;

[0066] Furthermore, in one embodiment, a benchmark model application platform is created, and based on the created benchmark model application platform, data corresponding to the benchmark model is collected, specifically:

[0067] S201: Build a project budget establishment platform, establish project budgets based on the grid management method, and locate project budgets on the detailed expenses of specific business of the department;

[0068] S202: Build a budget execution application platform to collect the frequency and budget amount of functional cases, so as to standardize and clean the data;

[0069] In the actual operation process, check the execution project and compare it with its budget balance to ensure that the execution amount does not exceed the budget; compare it with the project to ensure that no items are mixed; compare it with the expense details to ensure that no subjects are mixed.

[0070] S203: Build a budget write-off application platform to collect the amount of functional case write-offs. That is, collect the amount of functional case write-offs and the key write-off parameters associated with the execution, and then compare the execution amount to control the detailed expenses not to exceed the execution budget amount.

[0071] S3: Establish a digital iteration platform for benchmark models to update the coefficients in the coefficient list based on the collected data.

[0072] Furthermore, in one embodiment, a benchmark model digitalization iteration platform is established to update the coefficients in the coefficient list in combination with the collected data, specifically:

[0073] S301: Based on the selected write-off time period and key variable parameters corresponding to the functional CASE, search the execution application database to establish a first functional CASE collection library;

[0074] That is, in the execution application database, according to the write-off time period selected by the analyst and the key variable parameters corresponding to the functional CASE, a search operation is performed to obtain corresponding data to establish a first functional CASE collection library.

[0075] Different benchmark models correspond to different key variable parameters. The key variable parameters corresponding to the functional case of the road test benchmark model include road conditions, mileage, sample vehicle type, number of axles, tonnage, and fuel type; the key variable parameters corresponding to the functional case of the bench test benchmark model include test category and engine model.

[0076] S302: Based on the selected write-off time period and key variable parameters corresponding to the functional CASE, searching the write-off application database, and establishing a second functional CASE collection library;

[0077] That is, in the write-off application database, according to the write-off time period selected by the analyst and the key variable parameters corresponding to the functional case, a search operation is performed to obtain the corresponding data to establish the second functional case collection library.

[0078] S303: performing correlation analysis on the data of the first function CASE collection library and the second function CASE collection library, indexing according to the selected key variable parameters, and obtaining data within a specified time range;

[0079] S304: Determine a trend line based on the obtained data within the specified time range, and the slope of the trend line is the coefficient of the benchmark model after update and optimization.

[0080] That is, the data of the first function CASE collection library and the second function CASE collection library are correlated and analyzed, and indexed according to the selected key variable parameters to collect all the data within the specified time range. Find the trend line in a large amount of related data, and the slope of the trend line is the coefficient after the benchmark model is optimized.

[0081] Furthermore, in one embodiment, a trend line is determined based on the data obtained within a specified time range, and the slope of the trend line is the coefficient of the benchmark model after update and optimization, specifically:

[0082] S3041: Plot the data within the specified time range onto the coordinate axis and determine the trend line; that is, plot the key variable parameters at different times and the corresponding sub-model calculation results onto the coordinate axis, and the slope of the trend line obtained is the coefficient corresponding to the key variable parameter, thereby realizing the update of the coefficient in the sub-model.

[0083] S3042: Remove abnormal points on both sides of the trend line to make all analysis points evenly distributed on both sides of the trend line; that is, click to remove points far from both sides of the trend line (abnormal points) to make all analysis points evenly distributed on both sides of the trend line. Abnormal points are manifestations of business abnormalities. Click details to trace back to the execution application form and the cancellation application form to find out the causes of business abnormalities and improvement plans.

[0084] S3043: Based on the slope of the trend line, the updated and optimized coefficients of the benchmark model are obtained. In the actual application process, by clicking the "Optimize button", the system will automatically proceed with the approval process with the analysis legend and automatically update the corresponding coefficients in the benchmark model coefficient list.

[0085] It should be noted that the coefficient list for the road test benchmark model includes a fuel consumption coefficient list, a maintenance coefficient list, a road and bridge crossing coefficient list, a speed coefficient list, a site rental coefficient list, a loading and unloading number coefficient list, a fuel ratio coefficient list, etc.

[0086] See also Figure 2 The figure shows the system architecture corresponding to the method of automatically iterating benchmark models. In the system architecture, corresponding functional platforms are built for the establishment, maintenance, application and iteration of 29 benchmark models related to direct R&D costs. The benchmark model construction and maintenance platform, benchmark model application platform, and benchmark model digital iteration platform are closely linked to each other.

[0087] For the benchmark model construction and maintenance platform, a coefficient list and key variable parameter library of the benchmark model were constructed, and a multi-faceted manual maintenance interface was opened for the key variable parameters, including: an interface for expanding the scope of application in multiple dimensions (such as in the road test benchmark model, tab pages such as road condition type, sample vehicle type, fuel type, etc. are opened, and the test type can be increased or decreased by randomly increasing or decreasing parameters), an interface for layered updates of sub-models (such as in the bench test benchmark model, three interfaces of default, customization, and review are set up to progressively update sub-models), and an interface for overall model replacement (such as for trial-produced outsourced benchmark models, the replacement model is entered through the front-end human-computer interaction interface).

[0088] The benchmark model application platform is used for budget management of direct R&D expenses. Through the grid-based management and control of project budget execution applications and write-off applications, the pre-realized data of functional CASE standardization is collected and accumulated, and massive data is collected for the optimization and iteration of the benchmark model.

[0089] For the benchmark model digital iteration platform, the system automatically optimizes and iterates the benchmark model coefficients under the preset logic. After using the platform to collect massive standardized data, the iteration of all benchmark model coefficients can be triggered at any time on the iteration platform. On the visual interface, human-computer interaction is achieved and the optimization effect is displayed. After the optimization proposal is adopted, the system replaces the individual coefficients in the various coefficient lists one by one.

[0090] The three platforms promote each other: the benchmark model construction and maintenance platform timely maintains key variable parameters; the benchmark model application platform, in addition to providing unified standards for R&D, also regularly collects massive data to provide an optimization basis for the benchmark model digital iteration platform; the optimized benchmark model provides more accurate and comprehensive digital services for the R&D cost budget.

[0091] For the method of automatically iterating the benchmark model of this application, the construction of the benchmark model is the construction of two types of databases, that is, the construction of the benchmark model is the construction of the coefficient list and the key variable parameter library. Under the premise of accurately expressing the cost details, different benchmark models have different coefficient lists and key variable parameters.

[0092] The maintenance of benchmark models is divided into two paths: conventional and digital. The maintenance of key variable parameters is suitable for manual daily maintenance of the system front-end interface, and the scope of application of the benchmark model is expanded through the human-computer interaction interface; the maintenance of the coefficient list is suitable for the digital update of the system back-end preset logic, which is an optimization of the existing benchmark model.

[0093] The massive data required for the iteration of the benchmark model is collected by the system application platform. The collection of massive data required for the iteration of the model within the system can better solve the problems of data logic, standardization, uniformity, accuracy, etc., and can also discover and trace the business drawbacks revealed by abnormal data.

[0094] The method for automatically iterating a benchmark model in an embodiment of the present application constructs a benchmark model, and constructs a key variable parameter library based on the key variable parameters of each sub-model of the benchmark model, and a coefficient list based on the coefficients corresponding to the key variable parameters. Then, a benchmark model application platform is created, and the corresponding data of the benchmark model is collected based on the created benchmark model application platform. Finally, a benchmark model digital iteration platform is established to update the coefficients in the coefficient list in combination with the collected data, so that the optimized benchmark model can provide more accurate and comprehensive digital services for the R&D cost budget, reduce the standard maintenance workload and improve the standard maintenance efficiency.

[0095] In a second aspect, an embodiment of the present application also provides a device for automatically iterating a benchmark model.

[0096] In one embodiment, referring to Figure 3 , FIG. 1 is a schematic diagram of the functional modules of the device for automatically iterating the benchmark model of the present application. Figure 3 As shown, the device for automatically iterating the benchmark model includes a construction module, a creation module and an update module.

[0097] The construction module is used to construct a benchmark model, and to construct a key variable parameter library based on the key variable parameters of each sub-model of the benchmark model, and to construct a coefficient list based on the coefficients corresponding to the key variable parameters; the creation module is used to create a benchmark model application platform, and to collect corresponding data for the benchmark model based on the created benchmark model application platform; the update module is used to establish a digital iteration platform for the benchmark model, so as to update the coefficients in the coefficient list in combination with the collected data.

[0098] In a third aspect, an embodiment of the present application provides a device for automatically iterating a benchmark model. The device for automatically iterating a benchmark model may be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0099] Reference Figure 4 , Figure 4 Schematic diagram of the hardware structure of the device for automatically iterating the benchmark model involved in the embodiment of the present application. In the embodiment of the present application, the device for automatically iterating the benchmark model may include a processor, a memory, a communication interface and a communication bus.

[0100] The communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0101] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces, which are used to interconnect devices within the device for implementing the automatic iteration benchmark model, and interfaces for interconnecting the device for implementing the automatic iteration benchmark model with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber optic interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0102] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0103] The processor may be a general-purpose processor, and the general-purpose processor may call the program of the automatic iteration benchmark model stored in the memory, and execute the method of the automatic iteration benchmark model provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the program of the automatic iteration benchmark model is called may refer to the various embodiments of the method of the automatic iteration benchmark model of the present application, which will not be repeated here.

[0104] Those skilled in the art will understand that Figure 4The hardware structure shown in the figure does not constitute a limitation on the present application, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0105] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0106] The computer-readable storage medium of the present application stores a program for automatically iterating a benchmark model, wherein when the program for automatically iterating a benchmark model is executed by a processor, the steps of the method for automatically iterating a benchmark model as described above are implemented.

[0107] Among them, the method implemented when the program of automatically iterating the benchmark model is executed can refer to the various embodiments of the method of automatically iterating the benchmark model of the present application, and will not be repeated here.

[0108] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0109] The terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0110] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0111] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0112] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0113] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0114] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for automatically iterating benchmark models, It is characterized in that The method for automatically iterating the benchmark model includes: Build a benchmark model, and build a key variable parameter library based on the key variable parameters of each sub-model of the benchmark model, and build a coefficient list based on the coefficients corresponding to the key variable parameters; Create a benchmark model application platform, and collect corresponding data of the benchmark model based on the created benchmark model application platform; Establish a digital and intelligent iteration platform for benchmark models to update the coefficients in the coefficient list based on the collected data; in, The benchmark models include a road test benchmark model, a bench test benchmark model and a trial-production special parts procurement benchmark model, and each benchmark model includes a plurality of sub-models, and each sub-model includes key variable parameters and coefficients for calculating the key variable parameters; The key variable parameters include fixed parameters and random parameters; The fixed parameters are individual parameters of the function CASE, and the fixed parameters are bound to the function CASE in a table form; The random parameters are common parameters of the functional CASE and can be increased or decreased randomly; The establishment of a digital and intelligent iteration platform for the benchmark model is to update the coefficients in the coefficient list in combination with the collected data, specifically: Based on the selected write-off time period and key variable parameters corresponding to the functional CASE, search the execution application database and establish the first functional CASE collection library; Based on the selected write-off time period and key variable parameters corresponding to the functional CASE, search the write-off application database and establish a second functional CASE collection library; Perform correlation analysis on the data of the first function CASE collection library and the second function CASE collection library, and index them according to the selected key variable parameters to obtain the data within the specified time range; The trend line is determined based on the data within the specified time range, and the slope of the trend line is the updated and optimized coefficient of the benchmark model.

2. A method for automatically iterating a benchmark model according to claim 1, It is characterized in that After building a key variable parameter library based on the key variable parameters of each sub-model of the benchmark model, and building a coefficient list based on the coefficients corresponding to the key variable parameters, it also includes: Build a platform for expanding the scope of application in multiple dimensions. In the calculation standard interface of the road test benchmark model, build sub-interfaces for road condition types, sample vehicle types, and fuel types, and increase or decrease key variable parameters in the sub-interfaces; In the category rate association interface of the bench test benchmark model, multiple general stages are randomly matched to combine and generate different bench test categories; In the input interface of the functional CASE of the benchmark model for trial-production of special parts procurement, the mathematical calculation model is input based on the preset logic; Construct a sub-model hierarchical update platform to update the sub-models of the bench test benchmark model; Construct an overall replaceable interface for the model, and after the sub-model of the trial-production special parts procurement benchmark model is created, calculate the benchmark value of the specified functional CASE.

3. A method for automatically iterating a benchmark model according to claim 1, It is characterized in that The said creation of a benchmark model application platform and the collection of corresponding data of the benchmark model based on the created benchmark model application platform are specifically as follows: Build a project budget establishment platform, establish project budgets based on grid management, and locate project budgets on the detailed expenses of specific business of the department; Build a budget execution application platform to collect the frequency and budget amount of functional cases; Build a budget write-off application platform to collect the amount of functional CASE write-off.

4. A method for automatically iterating a benchmark model as claimed in claim 3, Features: Different benchmark models correspond to different key variable parameters; The key variable parameters corresponding to the functional CASE of the road test benchmark model include road conditions, mileage, sample vehicle type, number of axles, tonnage, and fuel type; The key variable parameters corresponding to the functional CASE of the bench test benchmark model include test category and engine model.

5. A method for automatically iterating a benchmark model as claimed in claim 3, It is characterized in that The trend line is determined based on the data obtained within the specified time range, and the slope of the trend line is the coefficient of the benchmark model after update and optimization, specifically: The data obtained within the specified time range is plotted on the coordinate axis and the trend line is determined; Eliminate the abnormal points on both sides of the trend line so that all analysis points are evenly distributed on both sides of the trend line; Based on the slope of the trend line, the updated and optimized coefficients of the benchmark model are obtained.

6. A device for automatically iterating a benchmark model, It is characterized in that The device for automatically iterating the benchmark model includes: A construction module is used to construct a benchmark model, and to construct a key variable parameter library based on the key variable parameters of each sub-model of the benchmark model, and to construct a coefficient list based on the coefficients corresponding to the key variable parameters; A creation module, which is used to create a benchmark model application platform and realize the collection of corresponding data of the benchmark model based on the created benchmark model application platform; An update module is used to establish a digital and intelligent iteration platform for benchmark models to update the coefficients in the coefficient list in combination with the collected data; in, The benchmark models include a road test benchmark model, a bench test benchmark model and a trial-production special parts procurement benchmark model, and each benchmark model includes a plurality of sub-models, and each sub-model includes key variable parameters and coefficients for calculating the key variable parameters; The key variable parameters include fixed parameters and random parameters; The fixed parameters are individual parameters of the function CASE, and the fixed parameters are bound to the function CASE in a table form; The random parameters are common parameters of the functional CASE and can be increased or decreased randomly; The establishment of a digital and intelligent iteration platform for the benchmark model is to update the coefficients in the coefficient list in combination with the collected data, specifically: Based on the selected write-off time period and key variable parameters corresponding to the functional CASE, search the execution application database and establish the first functional CASE collection library; Based on the selected write-off time period and key variable parameters corresponding to the functional CASE, search the write-off application database and establish a second functional CASE collection library; Perform correlation analysis on the data of the first function CASE collection library and the second function CASE collection library, and index them according to the selected key variable parameters to obtain the data within the specified time range; The trend line is determined based on the data within the specified time range, and the slope of the trend line is the updated and optimized coefficient of the benchmark model.

7. A device for automatically iterating the benchmark model, It is characterized in that The device for automatically iterating the benchmark model includes a processor, a memory, and a program for automatically iterating the benchmark model stored in the memory and executable by the processor, wherein when the program for automatically iterating the benchmark model is executed by the processor, the steps of the method for automatically iterating the benchmark model as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium, It is characterized in that The computer-readable storage medium stores a program for automatically iterating a benchmark model, wherein when the program for automatically iterating a benchmark model is executed by a processor, the steps of the method for automatically iterating a benchmark model as claimed in any one of claims 1 to 5 are implemented.

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