A production optimization method and system for washing machine drum stamping parts based on big data

Abstract

The present application relates to an optimization method and system for the production of washing machine drum stamping parts based on big data, belonging to the field of intelligent production technology. The method includes obtaining external detailed orders; determining the required production type and the corresponding required production quantity according to the external detailed orders; determining the layout planning area corresponding to the required production type according to the preset area matching relationship; performing layout simulation according to the required production quantity and the layout planning area to determine the cutting combination used, and processing the raw materials according to the cutting combination used. The present application has the effect of reducing the waste of raw materials for stamping part production and thus reducing the production cost of stamping parts.

Description

Technical Field

[0001] The present application relates to the field of intelligent production technology, and in particular, to a production optimization method and system for washing machine drum stamping parts based on big data. Background Art

[0002] Washing machine drum stamping parts refer to the parts used for manufacturing washing machine drums processed by stamping technology, mainly including the outer shell and inner cylinder of the washing machine drum.

[0003] Before producing washing machine drum stamping parts, it is necessary to perform layout cutting on the sheet metal to obtain raw material sheets that can meet the requirements of stamping production. Currently, generally, the staff will first determine the layout of the raw material sheets for various types of stamping parts. When stamping parts need to be processed, only the sheet metal needs to be processed according to the processing volume.

[0004] In the above related technologies, generally, when receiving external orders, there are various types of stamping parts that need to be produced. At this time, the layout cutting of the sheets for each type is independent of each other. There may be a situation where the surplus materials of the sheets for some types of stamping parts can be used for the production of other types of stamping parts, that is, there is waste of raw materials at this time, resulting in an increase in the production cost of stamping parts, and there is still room for improvement. Summary of the Invention

[0005] In order to reduce the waste of raw materials for stamping part production and reduce the production cost of stamping parts, the present application provides a production optimization method and system for washing machine drum stamping parts based on big data.

[0006] In a first aspect, the present application provides a production optimization method for washing machine drum stamping parts based on big data, adopting the following technical solutions:

[0007] A production optimization method for washing machine drum stamping parts based on big data includes:

[0008] Obtain external detailed orders;

[0009] Determine the required production types and the corresponding required production quantities according to the external detailed orders;

[0010] Determine the layout planning area corresponding to the required production type according to the preset area matching relationship;

[0011] Randomly select any number of required production types to construct a product type combination, and randomly generate the simulated type quantity according to the required production types in the product type combination;

[0012] Randomly arrange according to the number of simulation types and the corresponding layout planning areas on the preset original sheet area to output a type layout plan, and define the type layout plan in which all sheet planning areas are within the original sheet area and the sheet planning areas do not overlap as the effective layout plan for this product type combination;

[0013] Randomly generate the number of plan selections according to each effective layout plan, and combine according to the effective layout plan and the corresponding number of plan selections to generate an effective cutting combination;

[0014] Determine the simulated production quantity of each required production type according to the number of plan selections and the number of simulation types of each required production type in the effective layout plan;

[0015] Define the effective cutting combination when the simulated production quantity of each required production type is not less than the corresponding required production quantity, and perform a summation calculation according to the number of plan selections in the reasonable cutting combination to determine the original required quantity;

[0016] Determine the original required quantity with the smallest value according to the preset sorting rule, and define the reasonable cutting combination corresponding to the original required quantity as the used cutting combination, and process the raw materials according to the used cutting combination.

[0017] Optionally, the steps of randomly arranging according to the number of simulation types and the corresponding layout planning areas on the preset original sheet area to output a type layout plan include:

[0018] Determine the regional feature points corresponding to the layout planning area according to the preset feature matching relationship;

[0019] Determine the point-to-point separation distance according to the regional feature points and the contour points on the contour line of the layout planning area, and define the point-to-point separation distance with the smallest value as the reference separation distance;

[0020] Randomly select a layout planning area to arrange the regional feature points on the original sheet area, and delimit a no-disturbance area according to the regional feature points and the reference separation distance;

[0021] Update the area on the original sheet area except the no-disturbance area to a new original sheet area, and re-select the layout planning area for arrangement according to the new original sheet area until all layout planning areas are arranged to output a type layout plan.

[0022] Optionally, the steps of randomly selecting a layout planning area to arrange the regional feature points on the original sheet area include:

[0023] Randomly generate a virtual position point on the original sheet area, and determine the virtual interval distance according to the virtual position point and the contour points of the contour line of the original sheet area;

[0024] Define the virtual position points with the virtual interval distance not less than the reference interval distance of the current layout planning area as valid position points;

[0025] Form a valid position set according to all the valid position points, and arrange the regional feature points of the layout planning area only within the valid position set.

[0026] Optionally, after determining the valid layout plan, the production optimization method for the washing machine drum stamping parts based on big data further includes:

[0027] Define the valid layout plans with the same required production type and the corresponding number of simulation types in each valid layout plan as similar layout plans;

[0028] Determine the regional gap distance according to each layout planning area in the similar layout plans, and calculate the mean value according to all the regional gap distances to determine the regional mean distance;

[0029] Determine the regional mean distance with the largest value according to the sorting rule, and delete the remaining similar layout plans other than the valid layout plan corresponding to the regional mean distance.

[0030] Optionally, after determining the original required quantity, the production optimization method for the washing machine drum stamping parts based on big data further includes:

[0031] Judge whether there are at least two reasonable cutting combinations with the same and minimum original required quantity;

[0032] If there are not at least two reasonable cutting combinations with the same and minimum original required quantity, define the reasonable cutting combination corresponding to the minimum original required quantity as the used cutting combination;

[0033] If there are at least two reasonable cutting combinations with the same and minimum original required quantity, define the reasonable cutting combination corresponding to the minimum original required quantity as the alternative cutting combination;

[0034] Calculate the difference between the simulated production quantity and the corresponding required production quantity of each required production type in the alternative cutting combination to determine the excess production quantity;

[0035] Construct a historical interval with the current time point as the end point and a width of the preset historical duration on the preset time axis, and determine the historical production quantity according to the current required production type in the historical interval;

[0036] Calculate based on each historical production quantity to determine the historical production proportion of each demand production type;

[0037] Calculate based on the historical production proportion and the current corresponding excess production quantity to determine the monomer excess parameter, and sum all the monomer excess parameters to determine the combined excess parameter;

[0038] Determine the combined excess parameter with the largest value according to the sorting rule, and determine the alternative cutting combination corresponding to the combined excess parameter as the cutting combination to be used.

[0039] Optionally, after the combined excess parameter is determined, the production optimization method for the washing machine drum stamping parts based on big data further includes:

[0040] Judge whether there are at least two alternative cutting combinations with the same and largest combined excess parameter;

[0041] If there are not at least two alternative cutting combinations with the same and largest combined excess parameter, determine the alternative cutting combination corresponding to the largest combined excess parameter as the cutting combination to be used;

[0042] If there are at least two alternative cutting combinations with the same and largest combined excess parameter, define the alternative cutting combination corresponding to the largest combined excess parameter as the waiting cutting combination;

[0043] Determine the number of single-board types according to the demand production types in each effective layout plan in the waiting cutting combination;

[0044] Calculate the average value based on all the number of single-board types to determine the average type number, and determine the waiting cutting combination corresponding to the smallest average type number as the cutting combination to be used.

[0045] In a second aspect, the present application provides a production optimization system for washing machine drum stamping parts based on big data, and adopts the following technical solutions:

[0046] A production optimization system for washing machine drum stamping parts based on big data, including:

[0047] An acquisition module, used to acquire external detailed orders;

[0048] A processing module, connected to the acquisition module and the judgment module, and used for information storage and processing;

[0049] A judgment module, connected to the acquisition module and the processing module, and used for information judgment;

[0050] The processing module determines the demand production type and the corresponding demand production quantity according to the external detailed order;

[0051] The processing module determines the typesetting planning area corresponding to the required production type according to the preset area matching relationship;

[0052] The processing module randomly selects any number of required production types to construct a product type combination, and randomly generates the number of simulation types according to the required production types in the product type combination;

[0053] The processing module randomly arranges on the preset original sheet area according to the number of simulation types and the corresponding typesetting planning area to output a type layout plan, and defines the type layout plan in which all sheet planning areas are within the original sheet area and the sheet planning areas do not overlap as the effective layout plan of the product type combination;

[0054] The processing module randomly generates the number of plan selections according to each effective layout plan, and combines according to the effective layout plan and the corresponding number of plan selections to generate an effective cutting combination;

[0055] The processing module determines the simulated production quantity of each required production type according to the number of plan selections and the number of simulation types of each required production type in the effective layout plan;

[0056] The processing module defines the effective cutting combination when the simulated production quantity of each required production type judged by the judgment module is not less than the corresponding required production quantity, and sums up according to the number of plan selections in the reasonable cutting combination to determine the original required quantity;

[0057] The processing module determines the original required quantity with the smallest value according to the preset sorting rule, defines the reasonable cutting combination corresponding to the original required quantity as the used cutting combination, and processes the raw materials according to the used cutting combination.

[0058] In a third aspect, the present application provides a computer storage medium that can store corresponding programs, which has the characteristics of reducing the waste of raw materials for stamping part production and reducing the production cost of stamping parts, and adopts the following technical solutions:

[0059] A computer-readable storage medium stores a computer program that can be loaded and executed by a processor to perform any one of the above-mentioned washing machine drum stamping part production optimization methods based on big data.

[0060] In summary, the present application includes at least one of the following beneficial technical effects:

[0061] 1. During the production of washing machine drum stamping parts, it is possible to reasonably typeset the raw material sheets according to the actual order situation, so as to minimize the raw material sheets called while meeting the production requirements, reduce the waste of raw materials and reduce the production cost;

[0062] 2. The production situation of various types of stamping parts in historical orders can be used to reasonably select the solution combination. Description of the Drawings

[0063] Figure 1 It is a flowchart of an optimization method for the production of washing machine drum stamping parts based on big data.

[0064] Figure 2 It is a module flowchart of an optimization method for the production of washing machine drum stamping parts based on big data. Detailed Implementation Manner

[0065] In order to make the purpose, technical solution and advantages of the present application clearer, the following will further describe the present application in detail in combination with Figure 1 - Figure 2 and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0066] The following will further describe the embodiments of the present application in detail with reference to the accompanying drawings of the specification.

[0067] The embodiments of the present application disclose an optimization method for the production of washing machine drum stamping parts based on big data. Referring to Figure 1 , the method flow of the optimization method for the production of washing machine drum stamping parts based on big data includes the following steps:

[0068] Step S100: Obtain external detailed orders.

[0069] The external detailed order is an order received externally that requires the production and manufacturing of washing machine drum stamping parts.

[0070] Step S101: Determine the required production type and the corresponding required production quantity according to the external detailed order.

[0071] The required production type is the type of stamping parts that need to be produced, and the required production quantity is the quantity of each type of stamping parts that need to be produced.

[0072] Step S102: Determine the layout planning area corresponding to the required production type according to the preset area matching relationship.

[0073] The layout planning area is the area that the stamping parts of the required production type need to occupy on the original sheet. This area includes the size and the outer contour. Since the sizes and other shapes of the stamping parts of different required production types are different, the corresponding layout planning areas are also different. The area matching relationship between the two can be determined and input by the staff in advance.

[0074] Step S103: Randomly select any number of required production types to construct a product type combination, and randomly generate the simulated type quantity according to the required production types in the product type combination.

[0075] The product type combination is a combination composed of randomly selected demand production types. The number of simulation types is the number of each randomly selected demand production type. For example, there are two demand production types A and B. At this time, the number of simulation types can be 2 As and 0 Bs, or 3 As and 1 B. There are countless combination schemes, and random selection can be made at this time. In order to ensure that the selected combination can make subsequent cutting possible as much as possible, the number of simulation types selected cannot be higher than a preset fixed number. The fixed number is also the maximum number of stamping part materials of this demand production type that can be cut out from one original sheet. Therefore, the fixed numbers corresponding to different demand production types are different, and are specifically entered by the staff according to the actual situation.

[0076] Step S104: Randomly arrange according to the number of simulation types and the corresponding layout planning area on the preset original sheet area to output a type layout plan, and define the type layout plan in which all sheet planning areas are within the original sheet area and the sheet planning areas do not overlap as the effective layout plan for this product type combination.

[0077] The original sheet area is the entire area of the original sheet that has not been cut. By placing the layout planning areas of each demand production type in the product type combination in the original sheet area according to the number of simulation types, the simulation of the sheet layout situation can be realized. The type layout plan is the layout plan obtained after layout; when all sheet planning areas are within the original sheet area and the sheet planning areas do not overlap, it means that according to the type layout plan, the original sheet can be better cut into the original materials for stamping parts that meet each demand production type. At this time, it is defined as an effective layout plan to distinguish different type layout plans and facilitate subsequent analysis.

[0078] Step S105: Randomly generate the number of scheme selections according to each effective layout plan, and combine according to the effective layout plan and the corresponding number of scheme selections to generate an effective cutting combination.

[0079] The number of scheme selections is the number of a single selected effective layout plan, that is, the simulation selection of the original sheet of a single effective layout plan; the effective cutting combination is a scheme combination formed by combining the effective layout plan and the corresponding number of scheme selections for unified processing of the entire original sheet.

[0080] Step S106: Determine the simulated production quantity of each demand production type according to the number of scheme selections and the number of simulation types of each demand production type in the effective layout plan.

[0081] The stamping parts materials cut from the original sheet corresponding to a single effective layout plan are different. At this time, by multiplying the number of plan selections by the number of simulation types of each required production type in the plan and summing them all, the number of materials that can be obtained for the stamping parts of a required production type can be obtained, that is, the simulated production quantity. For example, there are two effective layout plans. The first is 3 As and 1 B, and the second is 3 As and 1 C. At this time, the number of plan selections for the first effective layout plan is 5, and the number of plan selections for the second effective layout plan is 10. Then the materials that can be obtained for the stamping parts of the required production type A are 45, the materials that can be obtained for the stamping parts of the required production type B are 5, and the materials that can be obtained for the stamping parts of the required production type C are also 5.

[0082] Step S107: Define the effective cutting combinations when the simulated production quantities of each required production type are not less than the corresponding required production quantities as reasonable cutting combinations, and sum up the number of plan selections within the reasonable cutting combinations to determine the original required quantity.

[0083] When the simulated production quantities of each required production type are not less than the corresponding required production quantities, it means that the raw materials of each type of stamping parts can meet the requirements. At this time, define the corresponding effective cutting combinations as reasonable cutting combinations to distinguish different effective cutting combinations, which is convenient for subsequent analysis; the original required quantity is the quantity of the original sheets needed. One effective layout plan corresponds to one original sheet. Therefore, the original required quantity can be determined by adding up the number of plan selections for each plan.

[0084] Step S108: Determine the original required quantity with the smallest value according to the preset sorting rule, define the reasonable cutting combination corresponding to this original required quantity as the used cutting combination, and process the raw materials according to the used cutting combination.

[0085] The sorting rule is a method set by the staff to sort the numerical values, such as the bubble sort method. Through the sorting rule, the original required quantity with the smallest value can be determined, that is, the quantity of the original sheets needed while meeting the production requirements is the least at this time. Therefore, just define the corresponding reasonable cutting combination as the used cutting combination for raw material processing, which reduces the use of the original sheets, thereby reducing the production cost and realizing the optimization of the production cost of the washing machine drum stamping parts.

[0086] The steps of randomly arranging according to the number of simulation types and the corresponding layout planning area on the preset original sheet area to output the type layout plan include:

[0087] Step S200: Determine the area feature points corresponding to the layout planning area according to the preset feature matching relationship.

[0088] The regional feature point is a fixed position point on the layout planning area. Generally, this regional feature point is the center point of the layout planning area, and the specific position is determined by the staff in advance and the feature matching relationship is constructed.

[0089] Step S201: Determine the distance between points according to the regional feature point and the contour points on the contour line of the layout planning area, and define the smallest distance between points as the reference distance.

[0090] The distance between points is the straight-line distance between the regional feature point and the contour points on the contour line of the layout planning area. Defining the reference distance is convenient for distinguishing different distances between points and facilitating subsequent analysis.

[0091] Step S202: Randomly select a layout planning area to arrange the regional feature point on the original sheet area, and delimit a no-disturbance area according to the regional feature point and the reference distance.

[0092] By arranging the regional feature point on the original sheet area, the layout simulation of the layout planning area can be realized. At this time, the no-disturbance area delimited with the regional feature point as the center and the reference distance as the radius is the area where the regional feature points of the other layout planning areas cannot be placed, because when the regional feature points of the other areas are within the no-disturbance area, there will definitely be an area overlap situation, and the obtained solution must not be an effective layout solution at this time.

[0093] Step S203: Update the area on the original sheet area except the no-disturbance area as the new original sheet area, and re-select the layout planning area for arrangement according to the new original sheet area until all layout planning areas are arranged to output the type layout solution.

[0094] By continuously updating the original sheet area, the effective arrangement of each layout planning area can be realized, so as to minimize the arrangement times of the layout planning area as much as possible, reduce the data processing volume and improve the overall operation efficiency.

[0095] The steps of randomly selecting a layout planning area to arrange the regional feature point on the original sheet area include:

[0096] Step S300: Randomly generate a virtual position point on the original sheet area, and determine the virtual interval distance according to the virtual position point and the contour points of the contour line of the original sheet area.

[0097] The virtual position point is a position point on the original sheet area, and the virtual interval distance is the distance value between the virtual position point and the contour points of the contour line of the original sheet area.

[0098] Step S301: Define the virtual position points with a virtual interval distance not less than the reference interval distance of the current layout planning area as valid position points.

[0099] When the virtual interval distance is not less than the reference interval distance of the current layout planning area, it indicates that there will be a situation where two layout planning areas do not overlap when placing the layout planning area under this virtual position point. At this time, define it as a valid position point for identification, which is convenient for subsequent analysis.

[0100] Step S302: Based on all the valid position points, form a valid position set, and arrange the regional feature points of the layout planning area only within the valid position set.

[0101] The valid position set is the set composed of all the determined valid position points. By arranging the regional feature points only within the valid position set, the number of arrangement times of the layout planning area can be reduced, and the overall operation efficiency can be improved.

[0102] After determining the valid layout plan, the production optimization method of the washing machine drum stamping parts based on big data further includes:

[0103] Step S400: In each valid layout plan, define the valid layout plans with the same required production type and the corresponding number of simulation types as similar layout plans.

[0104] Define similar layout plans to determine the valid layout plans that can produce the same effect, which is convenient for subsequent analysis.

[0105] Step S401: In the similar layout plans, determine the regional gap distance according to each layout planning area, and calculate the average value based on all the regional gap distances to determine the regional average distance.

[0106] The regional gap distance is the distance value between a single layout planning area and the other layout planning areas. In the current situation, the regional gap distance of a single layout planning area can be determined by calculating the average value of all the distance values between a single layout planning area and the other layout planning areas; the regional average distance is the average value of all the regional gap distances.

[0107] Step S402: Determine the regional average distance with the largest value according to the sorting rule, and delete the other similar layout plans except the valid layout plan corresponding to this regional average distance.

[0108] Through the sorting rule, the regional average distance with the largest value can be determined, that is, the gap between each layout planning area is relatively large at this time, which is convenient for subsequent cutting processing. Therefore, only retain this valid layout plan to reduce the occurrence of repeated processing of data that can achieve the same effect subsequently.

[0109] After the original demand quantity is determined, the production optimization method for washing machine drum stampings based on big data further includes:

[0110] Step S500: Determine whether there are at least two reasonable cutting combinations with the same and minimum original demand quantity.

[0111] The purpose of the determination is to find out whether there are multiple reasonable cutting combinations that meet the requirements, so as to determine the unique cutting combination for use.

[0112] Step S5001: If there are not at least two reasonable cutting combinations with the same and minimum original demand quantity, define the reasonable cutting combination corresponding to the minimum original demand quantity as the cutting combination for use.

[0113] When there are not at least two reasonable cutting combinations with the same and minimum original demand quantity, it means there is only one reasonable cutting combination that meets the requirements. At this time, it can be determined as the cutting combination for use.

[0114] Step S5002: If there are at least two reasonable cutting combinations with the same and minimum original demand quantity, define the reasonable cutting combination corresponding to the minimum original demand quantity as the alternative cutting combination.

[0115] When there are at least two reasonable cutting combinations with the same and minimum original demand quantity, it means there are multiple reasonable cutting combinations that meet the requirements. At this time, define them as alternative cutting combinations to distinguish different reasonable cutting combinations for subsequent analysis.

[0116] Step S501: Calculate the difference between the simulated production quantity of each demand production type in the alternative cutting combination and the corresponding demand production quantity to determine the excess production quantity.

[0117] The excess production quantity is the excess material quantity that will be obtained after cutting according to the layout on the basis of the plan, and is determined by subtracting the corresponding demand production quantity from the simulated production quantity.

[0118] Step S502: Construct a historical interval on the preset time axis with the current time point as the end point and a width of the preset historical duration, and determine the historical production quantity according to the current demand production type in the historical interval.

[0119] The time axis is an axis formed by combining each time point. This axis points from the passed time points to the unarrived time points. Among them, the passed time points are on the left side of the time axis, and the left side of the time axis is defined as the front; the historical duration is the duration set by the staff for obtaining data on the historical order situation of stamping parts. This duration is a fixed value duration. By constructing a historical interval, it is convenient to obtain and analyze the data within the historical duration; the historical production quantity is the total quantity of stamping parts of a single demand production type produced within the historical interval.

[0120] Step S503: Calculate based on each historical production quantity to determine the historical production proportion of each demand production type.

[0121] The historical production proportion is the production ratio of the stamping parts of a single demand production type to the stamping parts of all demand production types, which is determined by dividing the single historical production quantity by the sum of each historical production quantity. The larger this value, the more stamping parts of this type are produced in the historical situation, that is, the most popular. At this time, the possibility of using up the extra-produced stamping parts subsequently is greater.

[0122] Step S504: Calculate based on the historical production proportion and the current corresponding extra production quantity to determine the single extra parameter, and sum up all the single extra parameters to determine the combined extra parameter.

[0123] The single extra parameter is the value obtained by multiplying the historical production proportion by the extra production quantity, and the combined extra parameter is the sum of all single extra parameters.

[0124] Step S505: Determine the combined extra parameter with the largest value according to the sorting rule, and determine the alternative cutting combination corresponding to this combined extra parameter as the used cutting combination.

[0125] Through the sorting rule, the combined extra parameter with the largest value can be determined, that is, the possibility of using up the extra-produced stamping parts subsequently is the highest at this time. Therefore, it is only necessary to determine the alternative cutting combination corresponding to it as the used cutting combination.

[0126] After the combined extra parameter is determined, the production optimization method for the washing machine drum stamping parts based on big data further includes:

[0127] Step S600: Determine whether there are at least two alternative cutting combinations with the same and largest combined extra parameters.

[0128] The purpose of the determination is to find out whether there are multiple alternative cutting combinations that meet the requirements of the combined extra parameter, so as to determine the only used cutting combination.

[0129] Step S6001: If there is no alternative cutting combination with at least two identical and maximum combined redundant parameters, then determine the alternative cutting combination corresponding to the maximum combined redundant parameter as the cutting combination to be used.

[0130] When there is no alternative cutting combination with at least two identical and maximum combined redundant parameters, it means there is only a single alternative cutting combination that meets the requirements. In this case, simply determine it as the cutting combination to be used.

[0131] Step S6002: If there is at least two alternative cutting combinations with identical and maximum combined redundant parameters, then define the alternative cutting combination corresponding to the maximum combined redundant parameter as the waiting cutting combination.

[0132] When there is at least two alternative cutting combinations with identical and maximum combined redundant parameters, it means there are multiple alternative cutting combinations that meet the requirements. In this case, define a waiting cutting combination to distinguish different alternative cutting combinations, facilitating subsequent analysis.

[0133] Step S601: Determine the number of veneer types according to the required production types in each effective layout plan among the waiting cutting combinations.

[0134] The number of veneer types is the number of types of required production types in a single effective layout plan.

[0135] Step S602: Calculate the average value based on all the numbers of veneer types to determine the average number of types, and determine the waiting cutting combination corresponding to the average number of types with the smallest value as the cutting combination to be used.

[0136] The average number of types is the average value of all the numbers of veneer types. The average number of types with the smallest value indicates that the number of types of stamping part materials to be cut out from a single original board is the least, which means it is the most convenient for processing. Therefore, simply determine the corresponding waiting cutting combination as the cutting combination to be used.

[0137] Refer to Figure 2 , based on the same inventive concept, an embodiment of the present invention provides an optimization system for the production of washing machine drum stamping parts based on big data, including:

[0138] An acquisition module, configured to acquire external detailed orders;

[0139] A processing module, connected to the acquisition module and the judgment module, and configured to store and process information;

[0140] A judgment module, connected to the acquisition module and the processing module, and configured to judge information;

[0141] The processing module determines the required production types and the corresponding required production quantities according to the external detailed orders;

[0142] The processing module determines the typesetting planning area corresponding to the demand production type according to the preset area matching relationship;

[0143] The processing module randomly selects any number of demand production types to construct a product type combination, and randomly generates the number of simulation types according to the demand production types in the product type combination;

[0144] The processing module randomly arranges on the preset original sheet area according to the number of simulation types and the corresponding typesetting planning area to output a type layout plan, and defines the type layout plan in which all sheet planning areas are within the original sheet area and the sheet planning areas do not overlap as the effective layout plan of the product type combination;

[0145] The processing module randomly generates the number of plan selections according to each effective layout plan, and combines according to the effective layout plan and the corresponding number of plan selections to generate an effective cutting combination;

[0146] The processing module determines the simulated production quantity of each demand production type according to the number of plan selections and the number of simulation types of each demand production type in the effective layout plan;

[0147] The processing module defines the effective cutting combination when the simulated production quantity of each demand production type judged by the judgment module is not less than the corresponding demand production quantity, and sums up according to the number of plan selections in the reasonable cutting combination to determine the original demand quantity;

[0148] The processing module determines the original demand quantity with the smallest value according to the preset sorting rule, defines the reasonable cutting combination corresponding to the original demand quantity as the used cutting combination, and processes the raw materials according to the used cutting combination;

[0149] The type layout plan determination module is used to determine the type layout plan;

[0150] The area feature point arrangement module is used to determine the specific arrangement of area feature points;

[0151] The effective layout plan selection module is used to select and process some identical effective layout plans;

[0152] The reasonable cutting combination screening module is used to screen and process multiple reasonable cutting combinations that meet the requirements;

[0153] The alternative cutting combination screening module is used to screen and process multiple alternative cutting combinations that meet the requirements.

[0154] Those skilled in the art can clearly understand that, for the convenience and simplicity of description, only the above division of each functional module is used as an example. In actual applications, the above functions can be allocated to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. For the specific working processes of the systems, devices, and units described above, reference can be made to the corresponding processes in the foregoing method embodiments, which will not be elaborated herein.

[0155] An embodiment of the present invention provides a computer-readable storage medium storing a computer program that can be loaded and executed by a processor to perform an optimization method for the production of washing machine drum stamping parts based on big data.

[0156] Computer storage media include, for example, various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs.

Claims

1. An optimization method for the production of washing machine drum stamping parts based on big data, characterized in that, Including: Obtain external detailed orders; Determine the required production type and the corresponding required production quantity according to the external detailed order; Determine the typesetting planning area corresponding to the required production type according to the preset area matching relationship; Randomly select any number of required production types to construct a product type combination, and randomly generate the simulated type quantity according to the required production types in the product type combination; Randomly arrange on the preset original sheet area according to the simulated type quantity and the corresponding typesetting planning area to output a type layout plan, and define the type layout plan in which all sheet planning areas are within the original sheet area and the sheet planning areas do not overlap as the effective layout plan for this product type combination; Randomly generate the plan selection quantity according to each effective layout plan, and combine according to the effective layout plan and the corresponding plan selection quantity to generate an effective cutting combination; Determine the simulated production quantity of each required production type according to the plan selection quantity and the simulated type quantity of each required production type in the effective layout plan; Define the effective cutting combination when the simulated production quantity of each required production type is not less than the corresponding required production quantity, and perform a summation calculation according to the plan selection quantity in the effective cutting combination to determine the original required quantity; Determine the original required quantity with the smallest value according to the preset sorting rule, define the reasonable cutting combination corresponding to this original required quantity as the used cutting combination, and process the raw materials according to the used cutting combination.

2. The production optimization method of the washing machine drum stamping parts based on big data according to claim 1, characterized in that, The step of randomly arranging on the preset original sheet area according to the simulated type quantity and the corresponding typesetting planning area to output a type layout plan includes: Determine the area feature points corresponding to the typesetting planning area according to the preset feature matching relationship; Determine the point interval distance according to the area feature points and the contour points on the contour line of the typesetting planning area, and define the point interval distance with the smallest value as the reference interval distance; Randomly select a typesetting planning area to arrange the area feature points on the original sheet area, and delimit a no-disturbance area according to the area feature points and the reference interval distance; Update the area on the original sheet area except the no-disturbance area to a new original sheet area, and select the typesetting planning area again for arrangement according to the new original sheet area until all typesetting planning areas are arranged to output a type layout plan.

3. The production optimization method of the washing machine drum stamping part based on big data according to claim 2, characterized in that The step of randomly selecting a typesetting planning area to arrange the area feature points on the original sheet area includes: Randomly generate a virtual position point on the original sheet area, and determine the virtual interval distance according to the virtual position point and the contour points of the contour line of the original sheet area; Define the virtual position point with the virtual interval distance not less than the reference interval distance of the current typesetting planning area as the effective position point; Constitute an effective position set according to all the effective position points, and arrange the area feature points of the typesetting planning area only within the effective position set.

4. The production optimization method of the washing machine drum stamping part based on big data according to claim 1, characterized in that After determining the effective layout plan, the production optimization method for the washing machine drum stamping parts based on big data further includes: In each valid layout plan, valid layout plans with the same demand production type and the same corresponding number of simulation types are defined as similar layout plans; In similar layout plans, determine the regional gap distance according to each layout planning area, and calculate the average value based on all the regional gap distances to determine the regional average distance; Determine the regional average distance with the largest value according to the sorting rule, and delete the remaining similar layout plans other than the valid layout plan corresponding to this regional average distance.

5. The production optimization method of the washing machine drum stamping parts based on big data according to claim 1, characterized in that, After determining the original demand quantity, the production optimization method for the washing machine drum stamping parts based on big data further includes: Judge whether there are at least two reasonable cutting combinations with the same and smallest original demand quantity; If there are not at least two reasonable cutting combinations with the same and smallest original demand quantity, define the reasonable cutting combination corresponding to the smallest original demand quantity as the used cutting combination; If there are at least two reasonable cutting combinations with the same and smallest original demand quantity, define the reasonable cutting combination corresponding to the smallest original demand quantity as the alternative cutting combination; Calculate the difference between the simulated production quantity of each demand production type in the alternative cutting combination and the corresponding demand production quantity to determine the excess production quantity; Construct a historical interval on the preset time axis with the current time point as the rear end point and a width of the preset historical duration, and determine the historical production quantity according to the current demand production type in the historical interval; Calculate according to each historical production quantity to determine the historical production proportion of each demand production type; Calculate according to the historical production proportion and the current corresponding excess production quantity to determine the single excess parameter, and sum up all the single excess parameters to determine the combined excess parameter; Determine the combined excess parameter with the largest value according to the sorting rule, and determine the alternative cutting combination corresponding to this combined excess parameter as the used cutting combination.

6. The production optimization method of the washing machine drum stamping parts based on big data according to claim 5, characterized in that, After determining the combined excess parameter, the production optimization method for the washing machine drum stamping parts based on big data further includes: Judge whether there are at least two alternative cutting combinations with the same and largest combined excess parameter; If there are not at least two alternative cutting combinations with the same and largest combined excess parameter, determine the alternative cutting combination corresponding to the largest combined excess parameter as the used cutting combination; If there are at least two alternative cutting combinations with the same and largest combined excess parameter, define the alternative cutting combination corresponding to the largest combined excess parameter as the waiting cutting combination; Determine the number of single board types according to the demand production type in each valid layout plan in the waiting cutting combination; Calculate the average value based on all the numbers of single board types to determine the average type number, and determine the waiting cutting combination corresponding to the smallest average type number as the used cutting combination.

7. A production optimization system for washing machine drum stamping parts based on big data, characterized in that, Including: An acquisition module, used to acquire external detailed orders; A processing module, connected to the acquisition module and the judgment module, used for information storage and processing; A judgment module, connected to the acquisition module and the processing module, used for information judgment; The processing module determines the demand production type and the corresponding demand production quantity according to the external detailed order; The processing module determines the layout planning area corresponding to the required production type according to the preset area matching relationship; The processing module randomly selects any number of required production types to construct a product type combination, and randomly generates the number of simulation types according to the required production types in the product type combination; The processing module randomly arranges on the preset original sheet area according to the number of simulation types and the corresponding layout planning area to output a type layout plan, and defines the type layout plan in which all sheet planning areas are within the original sheet area and the sheet planning areas do not overlap as the effective layout plan of the product type combination; The processing module randomly generates the number of plan selections according to each effective layout plan, and combines according to the effective layout plan and the corresponding number of plan selections to generate an effective cutting combination; The processing module determines the simulated production quantity of each required production type according to the number of plan selections and the number of simulation types of each required production type in the effective layout plan; The processing module defines the effective cutting combination when the simulated production quantity of each required production type judged by the judgment module is not less than the corresponding required production quantity, and sums up the number of plan selections in the reasonable cutting combination to determine the original required quantity; The processing module determines the original required quantity with the smallest value according to the preset sorting rule, defines the reasonable cutting combination corresponding to the original required quantity as the used cutting combination, and processes the raw materials according to the used cutting combination.

8. A computer-readable storage medium, characterized in that, A computer program is stored that can be loaded and executed by a processor to perform the production optimization method for washing machine drum stampings based on big data according to any one of claims 1 to 6.

Images