CN111105151A - Air conditioner material prediction method and system and storage medium - Google Patents
Air conditioner material prediction method and system and storage medium Download PDFInfo
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- CN111105151A CN111105151A CN201911260647.7A CN201911260647A CN111105151A CN 111105151 A CN111105151 A CN 111105151A CN 201911260647 A CN201911260647 A CN 201911260647A CN 111105151 A CN111105151 A CN 111105151A
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Abstract
The invention provides an air conditioner material prediction method, an air conditioner material prediction system and a storage medium, wherein the method comprises the following steps: acquiring the respective predicted material quantity of a plurality of materials required by manufacturing the air conditioner; determining at least one historical model which is the same as the type of the air conditioner and has the same matching number, and acquiring the respective historical material quantity of multiple materials for manufacturing the at least one historical model; determining the same materials of the air conditioner and at least one historical machine type, and respectively acquiring a data set aiming at each same material, wherein the data set comprises the historical material quantity and the predicted material quantity aiming at the same material; estimating the reference material quantity corresponding to the same material by adopting a specified algorithm according to the data set; and obtaining a prediction material score related to the air conditioner according to the reference material quantity corresponding to each same material, and adjusting the prediction material quantity according to the prediction material score. The method and the device enable the quantity of the air conditioner materials to be accurate, save the estimation cost and improve the estimation efficiency.
Description
Technical Field
The invention relates to the technical field of air conditioner materials, in particular to an air conditioner material prediction method, an air conditioner material prediction system and a storage medium.
Background
Air conditioner manufacturing design is difficult to manage in terms of air conditioner design materials due to most of attention on design forming, and evaluation of the set of process materials is difficult. The structure of the air conditioner includes: the compressor, the condenser, the evaporimeter, the four-way valve, check valve capillary tube subassembly etc.. The material consumption conditions of all parts are different, and in most cases, the specific material consumption conditions of all parts are difficult to determine, so that a large amount of cost funds are needed to buy enough raw materials, waste of some raw materials is caused, and the cost cannot be reduced to the maximum extent.
Disclosure of Invention
The invention aims to ensure that the quantity of the preset materials of the air conditioner is accurate in the air conditioner design stage and solve the problem that the quantity of the process design materials is difficult to evaluate.
In order to solve the above problems, the present invention provides an air conditioner material prediction method, system and storage medium.
In a first aspect, the present invention provides a method for predicting air conditioner material, comprising the following steps: acquiring the respective predicted material quantity of a plurality of materials required by manufacturing the air conditioner; determining at least one historical model which is the same as the type of the air conditioner and has the same matching number, and acquiring the respective historical material quantity of multiple materials for manufacturing the at least one historical model; determining the same materials of the air conditioner and at least one historical machine type, and respectively acquiring a data set aiming at each same material, wherein the data set comprises the historical material quantity and the predicted material quantity aiming at the same material; estimating the reference material quantity corresponding to the same material by adopting a specified algorithm according to the data set; and obtaining a prediction material score related to the air conditioner according to the reference material quantity corresponding to each same material, and adjusting the prediction material quantity according to the prediction material score.
Preferably, the specifying algorithm includes: a curve fitting algorithm or a positive-Tailored distribution algorithm.
Preferably, obtaining a predictive material score associated with the air conditioner comprises: acquiring the weight of each same material, wherein the sum of the weights of the same materials is 1; acquiring the sum of the products of the reference material quantity corresponding to each same material and the corresponding weight; and adjusting the average value of the sum value to obtain the predicted material score related to the air conditioner.
Preferably, adjusting the number of the predicted materials according to the score of the predicted materials comprises: judging whether the score of the prediction material is smaller than a preset score threshold value or not; and when the predicted material score is smaller than a preset score threshold value, taking the reference material quantity corresponding to the same material as the material quantity of the air conditioner aiming at the same material.
Preferably, when a curve fitting algorithm is used, estimating the reference material quantity corresponding to the same material comprises: creating a coordinate system with the refrigerating capacity of the air conditioner as a horizontal coordinate and the material quantity as a vertical coordinate; performing curve fitting on each data in the data set in the coordinate system to form a fitted curve related to the material quantity of the same material; and in the coordinate system, obtaining a point on the fitting curve with the same refrigerating capacity as the air conditioner, and taking the ordinate of the point as the reference material quantity corresponding to the same material.
Preferably, when the positive power distribution algorithm is adopted, estimating the reference material quantity corresponding to the same material comprises: according to each data in the data set, drawing a positive-Taiwanese distribution curve related to the material quantity of the same material; and taking the material quantity corresponding to the peak value of the positive-too distribution curve as the reference material quantity corresponding to the same material.
Preferably, obtaining a predicted material score related to the air conditioner according to the reference material amount corresponding to each of the same materials includes: and obtaining a prediction material grade related to the air conditioner according to the difference value between the reference material quantity and the prediction material quantity corresponding to the same material.
Preferably, the type of the air conditioner is the same, including the same series of models as the air conditioner.
Preferably, the type of the air conditioner is the same as that of the air conditioner, and the air conditioner is a household type, a commercial type or an industrial type.
In a second aspect, the present invention provides an air conditioner material prediction system comprising a processor and a memory, the memory storing program instructions, which when executed by the processor, implement the method as described above.
In a third aspect, the present invention provides a storage medium storing program instructions which, when executed by a processor, implement a method as described above.
Compared with the prior art, the invention has the following advantages or beneficial effects:
according to the method and the device, the prediction material and the prediction material quantity of the air conditioner are integrally evaluated through the historical material and the historical material quantity of the historical machine type similar to the air conditioner, and the material quantity of the air conditioner is adjusted according to the evaluation result, so that the air conditioner material quantity is accurate and reasonable, the prediction cost is saved, and the prediction efficiency is improved. The invention can more accurately position the quantity of materials, thereby improving the process design efficiency and reducing the process design cost.
Drawings
The scope of the present disclosure may be better understood by reading the following detailed description of exemplary embodiments in conjunction with the accompanying drawings, in which:
fig. 1 is a flowchart of an air conditioner material prediction method according to a first embodiment of the present invention.
FIG. 2 is a flow chart for estimating the reference charge amount corresponding to the same charge using a curve fitting algorithm.
FIG. 3 is a flow chart for obtaining a predicted material score and adjusting the amount of predicted material.
Fig. 4 is a flow chart for estimating the reference material amount corresponding to the same material by using a positive-power distribution algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, embodiments of the present invention are described in detail below with reference to the accompanying drawings and examples, so that how to apply technical means to solve technical problems and achieve a technical effect can be fully understood and implemented.
The invention has the idea that the prediction material of the air conditioner is evaluated according to the prediction material of the air conditioner and the historical material of similar machine types, and the material of the air conditioner is adjusted according to the evaluation result value.
Example one
Fig. 1 is a flowchart of an air conditioner material prediction method according to a first embodiment of the present invention, and each step of the first embodiment of the present invention is described in detail below with reference to fig. 1.
As shown in fig. 1, the present invention provides a method for predicting air conditioner material, comprising the following steps:
s1: acquiring the respective predicted material quantity of a plurality of materials required by manufacturing the air conditioner;
the manufacture of an air conditioner requires a large number of materials, for example: screw (70), sponge (18), damping piece (4), bundle of wires (20), magnetic ring (1), controller (1), motor (1), WIFI module (1), electric heater (1), reactor (2), wiring board (1), thermal bulb (1), power cord (1), rubber sleeve patchcord (1), inter-plate line (1), built-in wiring (1), pressure sensor (1), pressure switch (1), CBB electric capacity (a plurality of, indefinite), compressor and accessory (1), condenser subassembly (1), filter (1), electronic expansion valve (1), four-way valve (1), check valve (1), stop valve (1), muffler (1), valve support (1), electrical apparatus box (1), radiator (1), soundproof cotton (16), Motor support (1), axial fan blade (1), grid (1), chassis (1), condenser sideboard (1), screen panel (1).
When the types of the air conditioners are different, the materials and the number of the materials of the air conditioners are different. In this embodiment, in the design stage of the air conditioner, various materials and the predicted material quantity required by the air conditioner may be obtained through the interactive interface.
S2: determining at least one historical model which is the same as the type of the air conditioner and has the same matching number, and acquiring the respective historical material quantity of multiple materials for manufacturing the at least one historical model;
and matching the models similar to the air conditioner according to the type and the number of the air conditioner. In the present embodiment, when the air conditioners belong to the same series, the types of the air conditioners may be considered to be the same; alternatively, the type of air conditioner may be classified into a home machine, a commercial machine, and an industrial machine. And determining historical machine types which are the same as the types and the matching numbers of the air conditioners according to the storage scale of a database, wherein the database stores the configuration of various historical machine types. The number of the models matched with the air conditioner can be various, and in practice, the number of the models is more than 100. In the air conditioner matched with the air conditioner, the matched air conditioner has the same type, and the materials of the matched air conditioner are similar to a great extent. For example, screws, sponges, damping blocks, wire ties, etc. are used, differing only in the number used. In the present embodiment, the historical material amount refers to the material amount of the material of the historical model.
S3: determining the same materials of the air conditioner and at least one historical machine type, and respectively acquiring a data set aiming at each same material, wherein the data set comprises the historical material quantity and the predicted material quantity aiming at the same material;
specifically, due to the similarity of models, the materials of the air conditioner can be similar to those of historical models to a large extent, that is, there is a little difference. In step S3, the same material of the air conditioner and the plurality of historical models is first determined. For example, four-way valves, check valves, shut valves, silencers, and the like are used. And secondly, acquiring a data set of various same materials, wherein the data set comprises the predicted material quantity of the air conditioner and the historical material quantity of the historical model. For example, a data set for the same material screw may include [ 65 (the air conditioner), 70 (historical model one), 72 (historical model two),...., 63 (historical model N). Since the air conditioner may have a plurality of same materials as the historical model, the data set may also have a plurality of data sets. For example, data sets for sponges, damping blocks or wire ties, respectively, are included.
S4: estimating the reference material quantity corresponding to the same material by adopting a specified algorithm according to the data set;
in the present embodiment, the designated algorithm is a curve fitting algorithm. FIG. 2 is a flow chart for estimating the reference charge amount corresponding to the same charge using a curve fitting algorithm. As shown in fig. 2, when the curve fitting algorithm is adopted, estimating the reference material amount corresponding to the same material includes:
s41: creating a coordinate system with the refrigerating capacity of the air conditioner as a horizontal coordinate and the material quantity as a vertical coordinate;
s42: performing curve fitting on each data in the data set in the coordinate system to form a fitted curve related to the material quantity of the same material;
for example, a data set for a screw may include [ 60, 56, 78, 98, 47, 65, 58, 63, 68, 80, 86, 46, 76, 69, 43, 77, 83, etc. ] plotting the data into a coordinate system and connecting the points, forming a fitted curve for the screw.
S43: and in the coordinate system, obtaining a point on the fitting curve with the same refrigerating capacity as the air conditioner, and taking the ordinate of the point as the reference material quantity corresponding to the same material.
And when the refrigerating capacity is the same, namely the abscissa is the same, taking the ordinate of a point on a fitting curve with the same abscissa as the reference material quantity. In this embodiment, for example, when the fitted curve is a fitted curve for a screw, the obtained reference material quantity is the reference material quantity of the air conditioner for the screw. Since the same material used by the air conditioner and the historical model may be various as described above, in step S43, reference material quantities for various same materials may be obtained. The reference material quantity can play a guiding role in the material of the air conditioner.
S5: and obtaining a prediction material score related to the air conditioner according to the reference material quantity corresponding to each same material, and adjusting the prediction material quantity according to the prediction material score.
Fig. 3 is a flowchart of obtaining the score of the predicted material and adjusting the amount of the predicted material, as shown in fig. 3, step S5 includes:
s51: acquiring the weight of each same material, wherein the sum of the weights of the same materials is 1;
the air conditioner and historical models can be made of various same materials, and different weights are given to the same materials according to actual conditions or practical requirements. For example, the same material one (weight 20%), the same material two (weight 40%), the same material three (weight 15%), the same material four (weight 25%). The sum of the weights of all the same materials is 1.
S52: acquiring the sum of the products of the reference material quantity corresponding to each same material and the corresponding weight;
in step S4, the reference material quantity of each identical material is known, and the weight of each identical material obtained in step S51 is combined. The calculation of the summation may be: (quantity of same material one, weight of same material one) + (quantity of same material two, weight of same material two) +.
S53: and adjusting the average value of the sum value to obtain the predicted material score related to the air conditioner.
In this embodiment, the adjusting process specifically includes: and introducing an intervention value in a concept parallel to the average value, respectively giving the average value and the intervention value respective weights, and adjusting the average value in a mode of (average value weight one) + (intervention value weight two) to obtain the prediction material score of the air conditioner. In this embodiment, the intervention value may be a value related to a difference between the prediction material quantity and the reference material quantity of each of the same materials; alternatively, the intervention value may be an experience value of the operator, which is given by experience accumulation. The predicted material grade represents an overall grade given whether the predicted material quantity of each material of the air conditioner is proper or not. In this embodiment, the prediction of the air conditioner material that does not meet the rating requirement may be prompted to adjust the material strategy.
S54: judging whether the score of the prediction material is smaller than a preset score threshold value or not;
s55: and when the predicted material score is smaller than a preset score threshold value, taking the reference material quantity corresponding to the same material as the material quantity of the air conditioner aiming at the same material.
In the embodiment, the prediction material and the prediction material quantity of the air conditioner are integrally evaluated through the historical material and the historical material quantity of the historical type similar to the air conditioner, and the material quantity of the air conditioner is adjusted according to the evaluation result, so that the air conditioner material quantity is accurate and reasonable, the prediction cost is saved, and the prediction efficiency is improved.
Example two
Different from the first embodiment, in the present embodiment, a positive distribution algorithm is used to estimate the reference material amount corresponding to the same material. Fig. 4 is a flow chart for estimating the reference material amount corresponding to the same material by using a positive-power distribution algorithm. As shown in fig. 4, when the positive distribution algorithm is adopted, estimating the reference material amount corresponding to the same material includes:
s41': according to each data in the data set, drawing a positive-Taiwanese distribution curve related to the material quantity of the same material;
for example, the data set for a screw may include [ 60, 56, 78, 98, 47, 65, 58, 63, 68, 80, 86, 46, 76, 69, 43, 77, 83, etc. ]. Drawing a positive-Tai distribution curve by a positive-Tai distribution formula, wherein f (x) represents a positive-Tai distribution curve function of each material parameter in the data set, x represents each material parameter in the data set, mu represents an average value of each material parameter in the data set, sigma represents a standard deviation of each material parameter in the data set, and sigma represents a standard deviation of each material parameter in the data set2Representing the variance of each material parameter in the data set.
Variance σ2The calculation formula of (a) is as follows:
wherein x represents each material parameter in the data set, M represents an average value of each material parameter in the data set, and n represents the number of material parameters in the data set.
S42': and taking the material quantity corresponding to the peak value of the positive-too distribution curve as the reference material quantity corresponding to the same material.
In this example, the peak of the positive-tai distribution curve represents the number of materials that have the highest utilization rate in the individual model for the same material.
Next, in this embodiment, a predicted material score related to the air conditioner may also be obtained according to a difference between a reference material quantity and a predicted material quantity corresponding to the same material. The difference between the reference material quantity and the predicted material quantity can represent the deviation of the predicted material quantity from the reference material quantity, and the larger the difference is, the more inaccurate the predicted material quantity is, so that whether the predicted material quantity is accurate as a whole can be evaluated by a plurality of differences related to the same material, and in the embodiment, the differences are expressed in the form of the predicted material score.
EXAMPLE III
The invention also provides an air conditioner material prediction system which comprises a processor and a memory, wherein the memory stores program instructions, and when the program instructions are executed by the processor, the air conditioner material prediction method in the embodiment can be realized, so that the prediction material and the prediction material quantity of the air conditioner can be integrally evaluated, and the material quantity of the air conditioner can be adjusted according to the evaluation result when the evaluation scoring colleagues are given, so that the air conditioner material quantity is accurate and reasonable, the prediction cost is saved, and the prediction efficiency is improved.
The invention also provides a storage medium, which stores program instructions, and when the program instructions are executed by a processor, the method for predicting the air conditioner material can be realized.
While, for purposes of simplicity of explanation, the methodologies are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance with one or more embodiments, occur in different orders and/or concurrently with other acts from that shown and described herein or not shown and described herein, as would be understood by one skilled in the art.
The above embodiments are only specific embodiments of the present invention. It is obvious that the invention is not limited to the above embodiments, but that many variations are possible. All modifications attainable by one versed in the art from the present disclosure within the scope and spirit of the present invention are to be considered as within the scope and spirit of the present invention.
Claims (11)
1. An air conditioner material prediction method is characterized by comprising the following steps:
acquiring the respective predicted material quantity of a plurality of materials required by manufacturing the air conditioner;
determining at least one historical model which is the same as the type of the air conditioner and has the same matching number, and acquiring the respective historical material quantity of multiple materials for manufacturing the at least one historical model;
determining the same materials of the air conditioner and at least one historical machine type, and respectively acquiring a data set aiming at each same material, wherein the data set comprises the historical material quantity and the predicted material quantity aiming at the same material;
estimating the reference material quantity corresponding to the same material by adopting a specified algorithm according to the data set;
and obtaining a prediction material score related to the air conditioner according to the reference material quantity corresponding to each same material, and adjusting the prediction material quantity according to the prediction material score.
2. The air conditioner material prediction method according to claim 1, wherein the specifying algorithm includes: a curve fitting algorithm or a positive-Tailored distribution algorithm.
3. The air conditioner material prediction method according to claim 1, characterized in that: obtaining a predictive material score associated with the air conditioner, comprising:
acquiring the weight of each same material, wherein the sum of the weights of the same materials is 1;
acquiring the sum of the products of the reference material quantity corresponding to each same material and the corresponding weight;
and adjusting the average value of the sum value to obtain the predicted material score related to the air conditioner.
4. The air conditioner material prediction method of claim 1, wherein adjusting the predicted material quantity according to the predicted material score comprises:
judging whether the score of the prediction material is smaller than a preset score threshold value or not;
and when the predicted material score is smaller than a preset score threshold value, taking the reference material quantity corresponding to the same material as the material quantity of the air conditioner aiming at the same material.
5. The air conditioner material prediction method according to claim 2, characterized in that: when a curve fitting algorithm is adopted, estimating the reference material quantity corresponding to the same material, including:
creating a coordinate system with the refrigerating capacity of the air conditioner as a horizontal coordinate and the material quantity as a vertical coordinate;
performing curve fitting on each data in the data set in the coordinate system to form a fitted curve related to the material quantity of the same material;
and in the coordinate system, obtaining a point on the fitting curve with the same refrigerating capacity as the air conditioner, and taking the ordinate of the point as the reference material quantity corresponding to the same material.
6. The air conditioner material prediction method according to claim 2, characterized in that: when a positive-Taiwan distribution algorithm is adopted, estimating the reference material quantity corresponding to the same material, including:
according to each data in the data set, drawing a positive-Taiwanese distribution curve related to the material quantity of the same material;
and taking the material quantity corresponding to the peak value of the positive-too distribution curve as the reference material quantity corresponding to the same material.
7. The air conditioner material prediction method according to claim 1, characterized in that: obtaining a predicted material score associated with the air conditioner based on the reference material quantities corresponding to each of the same materials, comprising: and obtaining a prediction material grade related to the air conditioner according to the difference value between the reference material quantity and the prediction material quantity corresponding to the same material.
8. The air conditioner material prediction method according to claim 1, characterized in that: the type of the air conditioner is the same as that of the air conditioner, and the air conditioner are of the same series of models.
9. The air conditioner material prediction method according to claim 1, characterized in that: the type of the air conditioner is the same as that of the air conditioner, and the air conditioner are both a household type, a commercial type or an industrial type.
10. An air conditioner material prediction system comprising a processor and a memory, wherein the memory stores program instructions that, when executed by the processor, implement the method of any one of claims 1-9.
11. A storage medium storing program instructions which, when executed by a processor, implement the method of any one of claims 1 to 9.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010033536A (en) * | 2007-12-20 | 2010-02-12 | Nippon Steel Corp | Method, device for predicting product material value, method, program for determining handling condition, and computer readable recording medium |
| US20130272829A1 (en) * | 2010-10-29 | 2013-10-17 | Chris Innes | Method and system for tracking material |
| CN106295995A (en) * | 2016-08-10 | 2017-01-04 | 曼威网络科技（上海）有限公司 | A kind of data analysing method for determining the production schedule and equipment |
| CN106636616A (en) * | 2016-12-20 | 2017-05-10 | 中国地质大学（武汉） | Sintered ore yield prediction method based on bellows waste gas temperature |
| CN106897803A (en) * | 2017-04-27 | 2017-06-27 | 云南中烟工业有限责任公司 | A kind of method based on combination grey model prediction management redried leaf tobacco raw materials requirement |
| CN110210000A (en) * | 2019-04-18 | 2019-09-06 | 贵州大学 | The identification of industrial process efficiency and diagnostic method based on Multiple Non Linear Regression |
-
2019
- 2019-12-10 CN CN201911260647.7A patent/CN111105151B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2010033536A (en) * | 2007-12-20 | 2010-02-12 | Nippon Steel Corp | Method, device for predicting product material value, method, program for determining handling condition, and computer readable recording medium |
| US20130272829A1 (en) * | 2010-10-29 | 2013-10-17 | Chris Innes | Method and system for tracking material |
| CN106295995A (en) * | 2016-08-10 | 2017-01-04 | 曼威网络科技（上海）有限公司 | A kind of data analysing method for determining the production schedule and equipment |
| CN106636616A (en) * | 2016-12-20 | 2017-05-10 | 中国地质大学（武汉） | Sintered ore yield prediction method based on bellows waste gas temperature |
| CN106897803A (en) * | 2017-04-27 | 2017-06-27 | 云南中烟工业有限责任公司 | A kind of method based on combination grey model prediction management redried leaf tobacco raw materials requirement |
| CN110210000A (en) * | 2019-04-18 | 2019-09-06 | 贵州大学 | The identification of industrial process efficiency and diagnostic method based on Multiple Non Linear Regression |
Non-Patent Citations (2)
| Title |
|---|
| 王宏志 等: "工业大数据分析综述：模型与算法", 《大数据》 * |
| 蒋辉等: "基于案例推理的软件工作量估算的分析与设计", 《科技信息》 * |
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