CN113240475A - Cost prediction method and device, electronic equipment and storage medium - Google Patents
Cost prediction method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application provides a cost prediction method, a device, an electronic device and a storage medium, wherein the cost prediction method comprises the following steps: acquiring product information of a product to be predicted; and obtaining a predicted cost according to the product information and a cost analysis model, wherein the cost analysis model comprises the component composition of a sample product and/or the manufacturing process of the sample product, and the sample product comprises the product to be predicted. According to the method and the device, the product information of the product to be predicted is processed through the cost analysis model, and the accuracy of the predicted cost is improved.
Description
Technical Field
The present application relates to the field of data analysis technologies, and in particular, to a cost prediction method, apparatus, electronic device, and storage medium.
Background
The automobile industry is increasingly competitive, and various types of cost analysis and control activities are developed for a plurality of automobile enterprises for maintaining the competitiveness of samples.
At present, the cost analysis and control activities are mostly performed by using an empirical estimation method, in this way, an evaluator predicts the production cost of a product to be predicted according to experience, and then controls the production condition of the product to be predicted according to a prediction result, so that the accuracy of a cost prediction result obtained by the empirical estimation method is low due to the uneven experience of the evaluator.
Disclosure of Invention
An object of the embodiments of the present application is to provide a cost prediction method, a cost prediction apparatus, an electronic device, and a storage medium, which are used to solve the problem of low accuracy of a cost prediction result in cost analysis and control activities.
In a first aspect, an embodiment of the present application provides a cost prediction method, including:
acquiring product information of a product to be predicted;
and obtaining a predicted cost according to the product information and a cost analysis model, wherein the cost analysis model comprises the component composition of a sample product and/or the manufacturing process of the sample product, and the sample product comprises the product to be predicted.
Optionally, before obtaining the predicted cost according to the product information and the cost analysis model, the method further includes:
acquiring a material information set, an equipment information set and a process information set;
wherein the material information set includes raw material information of the sample product, and the device information set includes manufacturing device information of the sample product; the set of process information includes manufacturing process information for the sample product;
classifying the material information set by taking the raw material information as a classification basis to generate a first data table, wherein the raw material information comprises the material, manufacturer and price of raw materials;
classifying the equipment information set by taking the manufacturing equipment information as a classification basis to generate a second data table, wherein the manufacturing equipment information comprises the price, the model and the labor cost of manufacturing equipment;
classifying the process information set by taking the manufacturing process information as a classification basis to generate a third data table, wherein the manufacturing process information comprises the sample product, a plurality of parts forming the sample product, a production process for producing the parts and a plurality of production procedures forming the production process; the production process comprises a plurality of first tags and a plurality of second tags, wherein the first tags are used for associating elements in the first data table, and the second tags are used for associating elements in the second data table;
and constructing the cost analysis model according to the first data table, the second data table and the third data table.
Optionally, after obtaining the predicted cost according to the product information and the cost analysis model, the method further includes:
acquiring the actual cost of the product to be predicted;
and optimizing the cost analysis model according to the predicted cost and the actual cost.
Optionally, the optimizing the cost analysis model according to the predicted cost and the actual cost includes:
obtaining a cost deviation according to the predicted cost and the actual cost;
according to the cost analysis model, obtaining the prediction cost detail information of the product to be predicted;
acquiring actual cost detail information of the product to be predicted;
determining deviation reason information causing the cost deviation according to the predicted cost detail information and the actual cost detail information;
and if the cost deviation is greater than a deviation threshold value and the deviation reason information meets a preset condition, adjusting the cost analysis model.
Optionally, after obtaining the predicted cost according to the product information and the cost analysis model, the method further includes:
acquiring raw material fluctuation information, wherein the raw material fluctuation information comprises price fluctuation information of raw materials in unit time;
and adjusting the cost analysis model according to the raw material fluctuation information and a preset fluctuation coefficient.
In a second aspect, an embodiment of the present application provides a cost prediction apparatus, including:
the system comprises an acquisition module, a prediction module and a prediction module, wherein the acquisition module is used for acquiring product information of a product to be predicted;
the prediction module is used for obtaining the prediction cost according to the product information and a cost analysis model, the cost analysis model comprises the component composition of a sample product and/or the manufacturing process of the sample product, and the sample product comprises the product to be predicted.
Optionally, the apparatus further comprises a modeling module, and the modeling module comprises:
acquiring a material information set, an equipment information set and a process information set before acquiring a predicted cost according to the product information and the cost analysis model;
wherein the material information set includes raw material information of the sample product, and the device information set includes manufacturing device information of the sample product; the set of process information includes manufacturing process information for the sample product;
classifying the material information set by taking the raw material information as a classification basis to generate a first data table, wherein the raw material information comprises the material, manufacturer and price of raw materials;
classifying the equipment information set by taking the manufacturing equipment information as a classification basis to generate a second data table, wherein the manufacturing equipment information comprises the price, the model and the labor cost of manufacturing equipment;
classifying the process information set by taking the manufacturing process information as a classification basis to generate a third data table, wherein the manufacturing process information comprises the sample product, a plurality of parts forming the sample product, a production process for producing the parts and a plurality of production procedures forming the production process; the production process comprises a plurality of first tags and a plurality of second tags, wherein the first tags are used for associating elements in the first data table, and the second tags are used for associating elements in the second data table;
and constructing the cost analysis model according to the first data table, the second data table and the third data table.
Optionally, the apparatus further includes an optimization module, where the optimization module includes:
acquiring the actual cost of the product to be predicted after obtaining the predicted cost according to the product information and the cost analysis model;
and optimizing the cost analysis model according to the predicted cost and the actual cost.
In a third aspect, an embodiment of the present application provides an electronic device, including:
a processor, a memory and a program or instructions stored on the memory and executable on the processor, the program or instructions, when executed by the processor, implementing the steps in the cost prediction method as described above in the first aspect.
In a fourth aspect, embodiments of the present application provide a readable storage medium, on which a program or instructions are stored, and when executed by a processor, the program or instructions implement the steps in the cost prediction method according to the first aspect.
In the technical scheme provided by the embodiment of the application, the cost prediction method utilizes a mode of processing the product information of the product to be predicted by using the cost analysis model, so that the accuracy of the cost prediction result can be improved.
Drawings
FIG. 1 is a flow chart of a cost prediction method provided by an embodiment of the present application;
FIG. 2 is another flow chart of a cost prediction method provided by an embodiment of the present application;
FIG. 3 is a flowchart of a cost prediction method provided in an embodiment of the present application;
FIG. 4 is a schematic structural diagram of a cost prediction apparatus according to an embodiment of the present disclosure;
FIG. 5 is a schematic structural diagram of another cost prediction apparatus according to an embodiment of the present disclosure;
FIG. 6 is a schematic diagram of another structure of a cost prediction apparatus according to an embodiment of the present disclosure;
FIG. 7 is a schematic diagram of another structure of a cost prediction apparatus according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Referring to fig. 1, fig. 1 is a flowchart of a cost prediction method provided in an embodiment of the present application, where the method may be executed by a cost prediction apparatus, and the cost prediction apparatus may be composed of hardware and/or software and may be generally integrated in a device with a cost prediction function, where the device may be an electronic device such as a server, a mobile terminal, or a server cluster. As shown in fig. 1, the cost prediction method includes the following steps:
And 102, obtaining the predicted cost according to the product information and the cost analysis model.
Wherein the cost analysis model comprises a component composition of a sample product and/or a manufacturing process of the sample product, and the sample product comprises the product to be predicted.
In recent years, the competition of the automobile industry is unprecedented, and various cost reduction and efficiency improvement measures are adopted for maintaining the market competitiveness of a plurality of automobile enterprises, and the main purpose is to regulate and control the cost expenditure in the production process through the analysis of the production cost so as to achieve the purpose of reducing the production cost.
In practice, enterprises often use an experience estimation algorithm to analyze the production cost, that is, an evaluator predicts the production cost of a product to be predicted according to experience, and the experience of the evaluator is uneven, so that the accuracy of the cost prediction result obtained by the experience estimation algorithm is low.
Based on this, the embodiment of the application provides a cost prediction method, which performs systematic analysis on the component composition and the manufacturing process of a product to be predicted through a cost analysis model, can effectively avoid error interference caused by human factors, and obtains a prediction cost (namely a cost prediction result) with high accuracy.
It should be noted that the manufacturing process of the sample product includes an assembling process of the sample product and a production process of the parts of the sample product.
In addition, the application of the predicted cost obtained in step 102 includes at least one of:
and displaying through the electronic equipment, sending to other equipment and printing.
For example, a cost index of the product to be predicted is set according to the predicted cost obtained in step 102, so that a user can complete production of the product to be predicted according to the cost index; or, comparing the predicted cost obtained in the step 102 with the actual cost of the competitive products of the product to be predicted, so that the user can regulate and control the production flow of the product to be predicted according to the comparison result.
Optionally, before obtaining the predicted cost according to the product information and the cost analysis model, the method further includes:
acquiring a material information set, an equipment information set and a process information set;
wherein the material information set includes raw material information of the sample product, and the device information set includes manufacturing device information of the sample product; the set of process information includes manufacturing process information for the sample product;
classifying the material information set by taking the raw material information as a classification basis to generate a first data table, wherein the raw material information comprises the material, manufacturer and price of raw materials;
classifying the equipment information set by taking the manufacturing equipment information as a classification basis to generate a second data table, wherein the manufacturing equipment information comprises the price, the model and the labor cost of manufacturing equipment;
classifying the process information set by taking the manufacturing process information as a classification basis to generate a third data table, wherein the manufacturing process information comprises the sample product, a plurality of parts forming the sample product, a production process for producing the parts and a plurality of production procedures forming the production process; the production process comprises a plurality of first tags and a plurality of second tags, wherein the first tags are used for associating elements in the first data table, and the second tags are used for associating elements in the second data table;
and constructing the cost analysis model according to the first data table, the second data table and the third data table.
The raw materials include production raw materials for representing shape and configuration, etc., which are changed during production and assembly, such as plastic particles, steel materials, etc.; the shaping raw material is used for representing a raw material which does not change in shape and structure during production and assembly, such as a rearview mirror, a lamp, an electric appliance element and the like.
The materials of the raw materials include a major material class and a minor material class, wherein the major material class can be divided into eight types, namely plastics, chemicals, rubber, metal, fabrics, composite materials, surface treatment materials and integer materials, the first seven types correspond to the production raw materials, the last type corresponds to the integer raw materials, the minor material class is used for further subdividing the eight material classes, and the minor material class taken as the plastic class can be divided into a PP (Polypropylene), a PA (Polyamide ), a PC (polycarbonate, Polycarbonates), a PE (Polyethylene ), a PVC (Polyvinyl Chloride), a PS (Polystyrene ) and the like. In practice, the materials can be adaptively deleted or modified based on the needs of the enterprise, and the specific material category and the material category are not limited in the embodiments of the present application.
The price of the raw material includes a unit price corresponding to the raw material, a unit price period and the like, wherein the unit price period refers to a time period during which the unit price of the raw material is effective, for example, producer a produces PP plastic, the unit price is 1 ten thousand yuan per ton, the unit price period is 1 month 2020 to 12 months 2020, this means that the time period of the producer a in 1 month 2020 to 12 months 2020 is explained, and the unit price of the sold PP plastic is 1 ten thousand yuan per ton.
In practical applications, the raw material information may further include performance indexes of the raw material (such as an elasticity index, a hardness index, a strength index, a plasticity index, a toughness index, fatigue performance, and the like), types of applicable parts (such as an engine, a compressor, a reducer, and the like), and the specific raw material information is not limited in the embodiments of the present application.
The price of the manufacturing equipment is the use cost of the manufacturing equipment for processing one part of raw material, for example, if the purchase cost of a certain punching lathe is 5 ten thousand yuan, the total maintenance cost is 1 thousand yuan, the rated service life is 10 years, and the total parts of the raw material processed every year is 5100 parts, the use cost of the punching lathe for processing one part of raw material is 1 yuan.
The labor cost of the manufacturing equipment is the expenditure of labor cost for the manufacturing equipment to process one raw material, for example, if a certain numerically controlled lathe can process 30 raw materials in unit time, and the payroll of the corresponding type of the numerically controlled lathe in unit time is 900 yuan, the expenditure of labor cost for the numerically controlled lathe to process one raw material is 30 yuan.
The manufacturing equipment information may include a manufacturer of the manufacturing equipment, a purchase date, a use power, a type of applicable material, and the like, and specific manufacturing equipment information is not limited in the embodiment of the present application.
For the manufacturing process information, for example, if a certain sample product is a slush molding instrument panel assembly, the slush molding instrument panel assembly includes an upper instrument panel body, a left instrument panel cover, a right instrument panel cover, and a center control panel (i.e., a plurality of components constituting the sample product), the upper instrument panel body is manufactured by a slush molding process (i.e., a production process for producing the components), the slush molding process includes six steps of mold closing, heating, molding, mold splitting, cooling, and part taking (i.e., a plurality of production steps constituting the production process), raw materials required by the mold closing step are PVC-based plastics (i.e., a first label), and equipment required by the mold closing step is a mold closing machine (i.e., a second label).
The sample product is divided into a plurality of parts through sorting the material information set, the equipment information set and the process information set, a production process corresponding to each part and a plurality of production processes included in the production process are obtained, and the cost calculation logic of the sample product is combed from top to bottom with the help of the equipment information and the material information associated with each production process, so that the cost analysis model is constructed and obtained.
And as for the process of applying the cost analysis model, the product information can be obtained through the related operation instruction, the related data in the cost analysis model is called according to the product information, and the predicted cost is obtained in a bottom-up mode.
The product information at least comprises the names, the size requirements, the net weights and the materials of all parts of the product to be predicted, taking the slush molding instrument panel assembly in the above description as an example, the product information at this time comprises the size requirements, the net weights and the materials of the instrument panel upper body, the instrument panel left end cover, the instrument panel right end cover and the central control panel respectively;
as for the process of obtaining the predicted cost according to the product information and the cost analysis model, the process is as follows:
inputting first data including a dimensional requirement, a net weight, and a material usage of the on-dash body into the cost analysis model to obtain a first part cost including a raw material cost, an equipment cost, and a labor cost of the on-dash body;
inputting second data comprising the size requirement, the net weight, and the material usage of the instrument panel left end cap into the cost analysis model to obtain a second part cost comprising the raw material cost, the equipment cost, and the labor cost of the instrument panel left end cap;
inputting third data including the dimensional requirements, the net weight, and the material usage of the instrument panel right end cover into the cost analysis model to obtain third part costs including raw material costs, equipment costs, and labor costs of the instrument panel right end cover;
inputting fourth data comprising the size requirement, the net weight and the material of the central control panel into the cost analysis model to obtain fourth part cost comprising the raw material cost, the equipment cost and the labor cost of the central control panel;
obtaining the predicted cost based on the first part cost, the second part cost, the third part cost, and the fourth part cost.
It should be noted that, in the process of obtaining the predicted cost, when the parts (in the example, the upper dashboard body, the left dashboard cover, the right dashboard cover, and the center control panel) can be manufactured by different production processes, a certain production process can be selected based on the user's needs; similarly, in practical applications, the production processes may also be adjusted based on user requirements, for example, one or more production processes in a certain production process are removed, the order of a plurality of production processes in a certain production process is adjusted, and the tempo of a certain production process is adjusted.
Optionally, after obtaining the predicted cost according to the product information and the cost analysis model, the method further includes:
103, obtaining the actual cost of the product to be predicted, and optimizing the cost analysis model according to the predicted cost and the actual cost.
As shown in fig. 2, under the influence of market variation, the relevant data stored in the cost analysis model may have a large error with the actual data, for example, when the unit price of the PP-based plastic is set to be 1 ten thousand yuan per ton and the unit price of the actual PP-based plastic is set to be 1.5 ten thousand yuan per ton in the cost analysis model, based on this, the actual cost of the product to be predicted needs to be correspondingly obtained in the process of completing the production of the product to be predicted according to the predicted cost, and then the predicted cost and the actual cost are compared, and whether the cost analysis model needs to be optimized is determined according to the comparison result.
In the process of applying the cost analysis model, the actual cost of the product to be predicted is calculated to search the market change condition (namely, the predicted cost and the actual cost are compared), and then the cost analysis model is adaptively adjusted according to the market change condition, so that the reliability of the cost analysis model is improved.
Further, the optimizing the cost analysis model according to the predicted cost and the actual cost includes:
obtaining a cost deviation according to the predicted cost and the actual cost;
according to the cost analysis model, obtaining the prediction cost detail information of the product to be predicted;
acquiring actual cost detail information of the product to be predicted;
determining deviation reason information causing the cost deviation according to the predicted cost detail information and the actual cost detail information;
and if the cost deviation is greater than a deviation threshold value and the deviation reason information meets a preset condition, adjusting the cost analysis model.
The predicted cost detail information is used to represent a data source and a calculation rule of the predicted cost, and referring to the foregoing example, when the product to be predicted is a slush molding instrument panel assembly, the predicted cost detail information correspondingly shows a correlation between the predicted cost of the slush molding instrument panel assembly and the first part cost, the second part cost, the third part cost and the fourth part cost (i.e. component components of the product to be predicted), and a calculation process and a data source of the first part cost/the second part cost/the third part cost/the fourth part cost (i.e. a manufacturing process of the sample product).
As for the actual cost detail information, a data source and calculation rules for representing the actual cost are available during the actual production of the product to be predicted.
By comparing the predicted cost detail information with the actual cost detail information, the deviation cause information can be obtained, for example:
if the predicted cost detail information comprises data PP-1 (the unit price of PP plastics is 1 ten thousand yuan per ton) and data PE-2 (the unit price of PE plastics is 1 ten thousand yuan per ton);
if the actual cost detail information comprises data PP-0.5 (the unit price of PP plastics is 0.5 ten thousand yuan per ton) and data PE-2 (the unit price of PE plastics is 1 ten thousand yuan per ton);
the deviation cause information will include data PP-1-0.5 (meaning that the predicted unit price for PP-type plastics is 1 ten thousand dollars per ton and that for PP-type plastics is 0.5 ten thousand dollars per ton).
In addition, the preset condition is used to determine whether the deviation cause information is an extreme condition, if the cost deviation is greater than a deviation threshold, the deviation cause information still includes the data PP-1-0.5 in the above example, and the actual cost detail information still includes the data PP-0.5;
when the data PP-0.5 is average data of multiple plastic manufacturers, judging that the deviation reason information does not belong to an extreme condition, wherein the deviation reason information meets the preset condition, so that the cost analysis model needs to be adjusted, namely the data PP-1 is replaced through the data PP-0.5;
and when the data PP-0.5 is the personal data of a certain plastic manufacturer, judging that the deviation reason information belongs to an extreme condition, wherein the deviation reason information does not meet the preset condition, so that the cost analysis model does not need to be adjusted.
In general, the extreme cases include a case where a process of a certain producer lags/leads the overall process of the market, a case where a certain producer adopts low price competition based on business policy, and the like.
Furthermore, when the cost bias is less than or equal to the bias threshold, no adjustment to the cost analysis model is required.
It should be noted that the deviation threshold may be adaptively adjusted based on actual requirements, and the embodiment of the present application does not limit the specific deviation threshold.
Optionally, after obtaining the predicted cost according to the product information and the cost analysis model, the method further includes:
and 104, acquiring raw material fluctuation information, and adjusting the cost analysis model according to the raw material fluctuation information and a preset fluctuation coefficient.
Wherein the raw material fluctuation information includes price fluctuation information of raw materials per unit time;
as shown in fig. 3, in practical applications, in addition to passively adjusting the cost analysis model according to the deviation cause information, the cost analysis model may also be actively adjusted according to the market fluctuation situation, that is, data in the cost analysis model is adjusted by periodically (e.g. 1 day, 3 days, 7 days, etc.) acquiring raw material prices and human prices in the market, and the specific adjustment process may be:
and acquiring raw material fluctuation information, wherein the raw material fluctuation information comprises data PP-1-1.2-0.2 (the price of PP plastics is 1.2 ten thousand yuan per ton in the current time period, the price of PP plastics is 1 ten thousand yuan per ton in the previous time period, and the rise amplitude of the price in unit time is 0.2 ten thousand yuan per ton).
If the fluctuation coefficient is 0.5, obtaining the actual price of the PP plastics in the market to be 1.1 ten thousand yuan per ton according to the raw material fluctuation information and the fluctuation coefficient;
and adjusting the cost analysis model according to the actual price.
Wherein the actual price is calculated by the following steps: 1+0.2 × 0.5, namely, the market price fluctuation (namely, the value 0.2 in the example) of the raw material is multiplied by the fluctuation coefficient (namely, the value 0.5 in the example) corresponding to the raw material to obtain the actual price fluctuation (namely, the value 0.1 in the example) of the raw material, and then an adjusted price (namely, the value 1.1 in the example) is obtained according to the data (namely, the value 1 in the example) of the cost analysis model according to the actual price fluctuation, and the cost analysis model is adjusted according to the adjusted price.
The fluctuation coefficient is used for expressing the risk distribution proportion between the enterprise and the raw material producer, namely, the enterprise and the raw material producer share the price fluctuation condition caused by market change by deepening the cooperative relationship between the enterprise and the raw material producer, when the market price of the raw material is reduced, the enterprise and the raw material producer share the profit brought by the interest condition, when the market price of the raw material is increased, the enterprise and the raw material producer share the profit brought by the crisis condition, and the value range of the fluctuation coefficient is (0, 1).
It should be noted that, in practical applications, the cost analysis model may be actively adjusted directly according to the raw material fluctuation information, in addition to the active adjustment of the cost analysis model according to the raw material fluctuation information and a preset fluctuation coefficient.
Referring to fig. 4, fig. 4 is a cost prediction apparatus according to some embodiments of the present application, the apparatus including:
an obtaining module 201, where the obtaining module 201 is configured to obtain product information of a product to be predicted;
a prediction module 202, wherein the prediction module 202 is configured to obtain a predicted cost according to the product information and a cost analysis model, and the cost analysis model includes a component composition of a sample product and/or a manufacturing process of the sample product, and the sample product includes the product to be predicted.
Optionally, as shown in fig. 5, the apparatus further includes a modeling module 203, where the modeling module 203 includes:
acquiring a material information set, an equipment information set and a process information set before acquiring a predicted cost according to the product information and the cost analysis model;
wherein the material information set includes raw material information of the sample product, and the device information set includes manufacturing device information of the sample product; the set of process information includes manufacturing process information for the sample product;
classifying the material information set by taking the raw material information as a classification basis to generate a first data table, wherein the raw material information comprises the material, manufacturer and price of raw materials;
classifying the equipment information set by taking the manufacturing equipment information as a classification basis to generate a second data table, wherein the manufacturing equipment information comprises the price, the model and the labor cost of manufacturing equipment;
classifying the process information set by taking the manufacturing process information as a classification basis to generate a third data table, wherein the manufacturing process information comprises the sample product, a plurality of parts forming the sample product, a production process for producing the parts and a plurality of production procedures forming the production process; the production process comprises a plurality of first tags and a plurality of second tags, wherein the first tags are used for associating elements in the first data table, and the second tags are used for associating elements in the second data table;
and constructing the cost analysis model according to the first data table, the second data table and the third data table.
Optionally, as shown in fig. 6, the apparatus further includes an optimization module 204, where the optimization module 204 includes:
acquiring the actual cost of the product to be predicted after obtaining the predicted cost according to the product information and the cost analysis model;
and optimizing the cost analysis model according to the predicted cost and the actual cost.
Further, the optimization module 204 includes an adjustment submodule, which is specifically configured to:
obtaining a cost deviation according to the predicted cost and the actual cost;
according to the cost analysis model, obtaining the prediction cost detail information of the product to be predicted;
acquiring actual cost detail information of the product to be predicted;
determining deviation reason information causing the cost deviation according to the predicted cost detail information and the actual cost detail information;
and if the cost deviation is greater than a deviation threshold value and the deviation reason information meets a preset condition, adjusting the cost analysis model.
Optionally, as shown in fig. 7, the apparatus further includes an updating module 205, where the updating module 205 is specifically configured to:
obtaining raw material fluctuation information after obtaining a predicted cost according to the product information and a cost analysis model, wherein the raw material fluctuation information comprises price fluctuation information of raw materials in unit time;
and adjusting the cost analysis model according to the raw material fluctuation information and a preset fluctuation coefficient.
The cost prediction device in the embodiment of the present application may be a device, or may be a component, an integrated circuit, or a chip in an electronic apparatus.
Referring to fig. 8, fig. 8 is a structural diagram of an electronic device according to an embodiment of the present disclosure, and as shown in fig. 5, the electronic device 300 includes: a memory 301, a processor 302, and a program or instructions stored on the memory 301 and executable on the processor 302, which when executed by the processor 302, implement the steps in the cost prediction method described above.
The embodiment of the present application further provides a readable storage medium, where a program or an instruction is stored on the readable storage medium, and when the program or the instruction is executed by a processor, the program or the instruction implements each process of the embodiment of the cost prediction method, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
While the present embodiments have been described with reference to the accompanying drawings, it is to be understood that the invention is not limited to the precise embodiments described above, which are meant to be illustrative and not restrictive, and that various changes may be made therein by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method of cost prediction, the method comprising:
acquiring product information of a product to be predicted;
and obtaining a predicted cost according to the product information and a cost analysis model, wherein the cost analysis model comprises the component composition of a sample product and/or the manufacturing process of the sample product, and the sample product comprises the product to be predicted.
2. The method of claim 1, wherein prior to obtaining a predicted cost based on the product information and a cost analysis model, the method further comprises:
acquiring a material information set, an equipment information set and a process information set;
wherein the material information set includes raw material information of the sample product, and the device information set includes manufacturing device information of the sample product; the set of process information includes manufacturing process information for the sample product;
classifying the material information set by taking the raw material information as a classification basis to generate a first data table, wherein the raw material information comprises the material, manufacturer and price of raw materials;
classifying the equipment information set by taking the manufacturing equipment information as a classification basis to generate a second data table, wherein the manufacturing equipment information comprises the price, the model and the labor cost of manufacturing equipment;
classifying the process information set by taking the manufacturing process information as a classification basis to generate a third data table, wherein the manufacturing process information comprises the sample product, a plurality of parts forming the sample product, a production process for producing the parts and a plurality of production procedures forming the production process; the production process comprises a plurality of first tags and a plurality of second tags, wherein the first tags are used for associating elements in the first data table, and the second tags are used for associating elements in the second data table;
and constructing the cost analysis model according to the first data table, the second data table and the third data table.
3. The method of claim 1, wherein after obtaining a predicted cost based on the product information and a cost analysis model, the method further comprises:
acquiring the actual cost of the product to be predicted;
and optimizing the cost analysis model according to the predicted cost and the actual cost.
4. The method of claim 3, wherein optimizing the cost analysis model based on the predicted cost and the actual cost comprises:
obtaining a cost deviation according to the predicted cost and the actual cost;
according to the cost analysis model, obtaining the prediction cost detail information of the product to be predicted;
acquiring actual cost detail information of the product to be predicted;
determining deviation reason information causing the cost deviation according to the predicted cost detail information and the actual cost detail information;
and if the cost deviation is greater than a deviation threshold value and the deviation reason information meets a preset condition, adjusting the cost analysis model.
5. The method of claim 1, wherein after obtaining a predicted cost based on the product information and a cost analysis model, the method further comprises:
acquiring raw material fluctuation information, wherein the raw material fluctuation information comprises price fluctuation information of raw materials in unit time;
and adjusting the cost analysis model according to the raw material fluctuation information and a preset fluctuation coefficient.
6. A cost prediction apparatus, the apparatus comprising:
the system comprises an acquisition module, a prediction module and a prediction module, wherein the acquisition module is used for acquiring product information of a product to be predicted;
the prediction module is used for obtaining the prediction cost according to the product information and a cost analysis model, the cost analysis model comprises the component composition of a sample product and/or the manufacturing process of the sample product, and the sample product comprises the product to be predicted.
7. The cost prediction device of claim 6, further comprising a modeling module, the modeling module comprising:
acquiring a material information set, an equipment information set and a process information set before acquiring a predicted cost according to the product information and the cost analysis model;
wherein the material information set includes raw material information of the sample product, and the device information set includes manufacturing device information of the sample product; the set of process information includes manufacturing process information for the sample product;
classifying the material information set by taking the raw material information as a classification basis to generate a first data table, wherein the raw material information comprises the material, manufacturer and price of raw materials;
classifying the equipment information set by taking the manufacturing equipment information as a classification basis to generate a second data table, wherein the manufacturing equipment information comprises the price, the model and the labor cost of manufacturing equipment;
classifying the process information set by taking the manufacturing process information as a classification basis to generate a third data table, wherein the manufacturing process information comprises the sample product, a plurality of parts forming the sample product, a production process for producing the parts and a plurality of production procedures forming the production process; the production process comprises a plurality of first tags and a plurality of second tags, wherein the first tags are used for associating elements in the first data table, and the second tags are used for associating elements in the second data table;
and constructing the cost analysis model according to the first data table, the second data table and the third data table.
8. The cost prediction device of claim 6, further comprising an optimization module, the optimization module comprising:
acquiring the actual cost of the product to be predicted after obtaining the predicted cost according to the product information and the cost analysis model;
and optimizing the cost analysis model according to the predicted cost and the actual cost.
9. An electronic device comprising a processor, a memory, and a program or instructions stored on the memory and executable on the processor, the program or instructions when executed by the processor implementing the steps of the method according to any one of claims 1-5.
10. A readable storage medium, characterized in that it has stored thereon a program or instructions which, when executed by a processor, carry out the steps of the method according to any one of claims 1-5.
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