Production scheduling data grouping method, device and storage medium based on APS system

Abstract

The present invention relates to the field of data processing, and discloses a production scheduling data grouping method, device, and storage medium based on an APS system. The method comprises: receiving a production scheduling data table; calculating the unique field name ratio and the average field name size according to a matching set and the total number of production scheduling records; calculating the unique field name ratio by a proportion score according to a preset proportion algorithm to obtain a unique proportion score; calculating the average field name size by a size score according to a preset name size algorithm to obtain a field size score; calculating a grouping quality score according to the unique proportion score and the field size score; screening out the optimal grouping field corresponding to the production scheduling data table according to the grouping quality score; and performing grouping processing on the production scheduling data table according to the optimal grouping field to generate a production scheduling grouping table. In an embodiment of the present invention, the visualization of the production scheduling table is improved, the complexity of data adjustment by the production scheduler is reduced, and the clarity of the production scheduling is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a production scheduling data grouping method, device and storage medium based on an APS system. Background Art

[0002] The APS system (Advanced Planning and Scheduling) is a multi-agent system that plans and schedules the allocation and utilization of enterprise resources, including production, logistics, and equipment, based on the company's constraints and optimization goals, by comprehensively balancing limited resources. It also evaluates the results of these plans using advanced algorithms and models, combined with information such as a company's production capacity, process flow, and material requirements, to conduct comprehensive production planning and resource scheduling.

[0003] The APS system primarily uses web-based spreadsheets for production scheduling, but web-based spreadsheets lack Excel features (such as formulas and freeze windows), making it difficult for schedulers accustomed to Excel scheduling to get started. Furthermore, traditional Excel export methods fail to intelligently merge related data, disparate information, and make it difficult to visually distinguish between different production batches or process groups, complicating the scheduler's work. Therefore, to address the technical issue of the APS system's scheduling data tables making it difficult to visually distinguish between different production batches or process groups, a new technology was needed to address this issue. Summary of the Invention

[0004] The main purpose of the present invention is to solve the technical problem that in the production scheduling process of the APS system, the production scheduling data table is difficult to intuitively distinguish different production batches or process groups.

[0005] A first aspect of the present invention provides a method for grouping production scheduling data based on an APS system, comprising the steps of:

[0006] Receive production scheduling data sheet;

[0007] Perform name matching processing on the production scheduling data table according to the preset grouping field name to obtain a matching set;

[0008] Analyze the number of records in the production scheduling data table to obtain the total number of production scheduling records;

[0009] Calculate the unique field name ratio and the average field name size according to the matching set and the total number of production scheduling records;

[0010] Calculating the proportion score of the unique field name proportion according to a preset proportion algorithm to obtain a unique proportion score;

[0011] Calculating a size score for the average size of the field names according to a preset name size algorithm to obtain a field size score;

[0012] Calculating a packet quality score based on the unique proportion score and the field size score;

[0013] Filtering the optimal grouping field corresponding to the production scheduling data table according to the grouping quality score;

[0014] The production scheduling data table is grouped according to the optimal grouping field to generate a production scheduling grouping table.

[0015] Optionally, in a first implementation of the first aspect of the present invention, calculating the unique field name ratio and the average field name size based on the matching set and the total number of production scheduling records includes:

[0016] Counting the number of elements in the matching set to obtain a unique field statistical value;

[0017] Divide the unique field statistics by the total number of production scheduling records to obtain the unique field name ratio;

[0018] The total number of production scheduling records is divided by the unique field statistics to obtain the average size of the field name.

[0019] Optionally, in a second implementation of the first aspect of the present invention, calculating the proportion score of the unique field name proportion according to a preset proportion algorithm to obtain the unique proportion score includes:

[0020] RS=1.0-|a-DR_f|;

[0021] Among them, RS is the unique proportion score, a is the ideal proportion parameter, and DR_f is the unique field name ratio.

[0022] Optionally, in a third implementation of the first aspect of the present invention, calculating the size score of the average size of the field names according to a preset name size algorithm to obtain the field size score includes:

[0023] Determining whether the average size of the field names is greater than or equal to a preset ideal grouping size parameter;

[0024] If it is greater than or equal to the preset ideal group size parameter, the field size fraction is assigned a value of min(1.0, IS_i / IS_f), where IS_i is the ideal group size parameter and IS_f is the average size of the field name;

[0025] When it is not greater than or equal to the preset ideal group size parameter, the field size fraction is assigned as IS_f / IS_i.

[0026] Optionally, in a fourth implementation manner of the first aspect of the present invention, calculating the packet quality score according to the unique proportion score and the field size score includes:

[0027] fS=RS*w1+gS*w2;

[0028] Among them, fS is the group quality score, RS is the unique proportion score, gS is the field size score, w1 is the first proportion weight, and w2 is the second proportion weight.

[0029] Optionally, in a fifth implementation of the first aspect of the present invention, screening out the optimal grouping field corresponding to the production scheduling data table according to the grouping quality score includes:

[0030] Read N grouping fields of the production scheduling data table, where N is a positive integer;

[0031] Calculating the group quality scores corresponding to the N group fields to obtain a group quality score set;

[0032] Extracting a maximum value from the set of group quality scores to obtain a maximum group quality score;

[0033] The grouping field corresponding to the maximum grouping quality score is determined to be the optimal grouping field corresponding to the production scheduling data table.

[0034] Optionally, in a sixth implementation of the first aspect of the present invention, grouping the production scheduling data table according to the optimal grouping field to generate a production scheduling grouping table includes:

[0035] Generating a grouping tree according to the optimal grouping field;

[0036] Based on the grouping tree, the production scheduling data table is matched row by row to generate a production scheduling grouping table.

[0037] Optionally, in a seventh implementation manner of the first aspect of the present invention, after grouping the production scheduling data table according to the optimal grouping field to generate a production scheduling grouping table, the method further includes:

[0038] Parsing the group data of the production scheduling group table to obtain M group features, where M is a positive integer;

[0039] Based on a preset coloring algorithm, color matching calculation is performed on the M grouping features to obtain coloring strategies corresponding to the M grouping features;

[0040] The grouping cells of the production scheduling grouping table are dyed according to the dyeing strategies corresponding to the M grouping features to obtain a dyed production scheduling grouping table.

[0041] The second aspect of the present invention provides a production scheduling data grouping device based on the APS system, comprising: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected through a line; the at least one processor calls the instructions in the memory so that the production scheduling data grouping device based on the APS system executes the above-mentioned production scheduling data grouping method based on the APS system.

[0042] A third aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned production scheduling data grouping method based on the APS system.

[0043] In this embodiment of the present invention, data analysis is performed on online production scheduling data tables, grouping field names and group sizes to suit business scenarios, and quality scores for data visualization under different grouping conditions are obtained. Using the grouping quality scores, the production scheduling data tables are effectively reorganized and grouped, improving the visualization of the production scheduling tables, reducing the complexity of data adjustments for schedulers, and effectively enhancing the clarity of the schedules. This resolves the technical issue of production scheduling data tables being unable to intuitively distinguish between different batches or process groups. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 A schematic diagram of an embodiment of a method for grouping production scheduling data based on an APS system in an embodiment of the present invention;

[0045] Figure 2 104 is a schematic diagram of an embodiment of the method for grouping production scheduling data based on the APS system in an embodiment of the present invention;

[0046] Figure 3 106 is a schematic diagram of an embodiment of the method for grouping production scheduling data based on the APS system in an embodiment of the present invention;

[0047] Figure 4 108 is a schematic diagram of an embodiment of the method for grouping production scheduling data based on the APS system in an embodiment of the present invention;

[0048] Figure 5 This is a schematic diagram of an embodiment of a production scheduling data grouping device based on an APS system in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The embodiment of the present invention provides a production scheduling data grouping method, device and storage medium based on an APS system.

[0050] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0051] In the description of the embodiments disclosed herein, the term "including" and similar terms should be understood as open inclusion, i.e., "including but not limited to." The term "based on" should be understood as "based, at least in part, on." The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment." The terms "first," "second," etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0052] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 In one embodiment of the present invention, a method for grouping production scheduling data based on an APS system includes:

[0053] 101. Receive production scheduling data sheet;

[0054] In this embodiment, the APS system can receive Excel spreadsheets uploaded by users or directly pull web spreadsheets from a network database. The production scheduling data sheet contains the products to be produced, and a product bill of materials exists under the product catalog. The production scheduling data sheet contains a list of materials and products required for the production process, as well as the production schedule date for the product list.

[0055] Specifically, after receiving the production scheduling data table, you can pre-process the production scheduling data table, build multiple headers within the designed date range, and design a data structure method. First, build a multi-level header, map data values ​​based on the multi-level header, and generate a dynamic column structure. The specific implementation method can refer to the following pseudo code:

[0056] #Generate a date sequence, the date range is specified by date_range

[0057] date_list = generate_date_sequence(date_range)

[0058] # Create column headers, each header is in tuple form, containing "Daily Plan" and the specific date

[0059] column_headers = [("Daily Plan", date) for date in date_list]

[0060] # Build a value mapping dictionary. The key of the dictionary is the row number, the value is a list, and the elements in the list are the values ​​of the corresponding data items on different dates. D is the production scheduling data table

[0061] value_map = {

[0062] row_num: [get_value(data_item, date) for date in date_list]

[0063] for row_num, data_item in enumerate(D)

[0064] }

[0065] # Returns a DynamicStructure object containing column headers and value mapping dictionaries for organizing and displaying data

[0066] return DynamicStructure(column_headers, value_map).

[0067] 102. Perform name matching processing on the production scheduling data table according to the preset grouping field names to obtain a matching set;

[0068] In this embodiment, some grouping field names can be pre-set. The grouping field names can be set in certain fields in the background database, and the production scheduling data table is matched to obtain a KV key value matching set of the matching grouping field names.

[0069] 103. Analyze the number of records in the production scheduling data table to obtain the total number of production scheduling records;

[0070] In this embodiment, the size() function can be used to directly analyze the number of records in the production scheduling data table.

[0071] 104. Calculate the unique field name ratio and the average field name size based on the matching set and the total number of production scheduling records;

[0072] In this embodiment, all matching field names exist in the matching set, and the total number of production schedule records is the total number of occurrences of all field names. Based on the field names, the proportion of a single field name in the entire production schedule records and the data size of each field name can be calculated.

[0073] For details, please refer to Figure 2 , Figure 2 This is an embodiment of step 104 of the production scheduling data grouping method based on the APS system in an embodiment of the present invention. Step 104 includes the following specific implementation methods:

[0074] 1041. Count the number of elements in the matching set to obtain a unique field statistical value.

[0075] 1042. Divide the unique field statistics by the total number of production scheduling records to obtain a unique field name ratio;

[0076] 1043. Divide the total number of production scheduling records by the unique field statistics to obtain the average size of the field name.

[0077] In steps 1041-1043, the number of all elements in the matching set is counted to obtain the unique field statistics of the entire matching set, and the size() function is used to analyze the number of elements in the matching set to obtain the unique field statistics.

[0078] Then, the unique field name ratio and the average field name size are calculated based on the following method:

[0079] DR_f = uC / tR / / Calculate the ratio of unique field names

[0080] IS_f = tR / uC / / Calculate the average size of the field name

[0081] Where DR_f is the ratio of unique field names, uC is the unique field statistics, tR is the total number of production records, and IS_f is the average size of the field name.

[0082] 105. Calculate a proportion score for the unique field name proportion according to a preset proportion algorithm to obtain a unique proportion score;

[0083] In this embodiment, to achieve balanced discrimination, a good grouping field should have appropriate discrimination. If the discrimination is too high (each record has a unique value), the grouping loses its meaning; if the discrimination is too low (all records have the same value), the grouping becomes worthless. Therefore, a pre-set ratio algorithm is used to evaluate the ratio of unique field names to obtain a unique ratio score.

[0084] Specifically, step 105 includes the following specific implementation methods:

[0085] RS=1.0-|a-DR_f|;

[0086] Among them, RS is the unique proportion score, a is the ideal proportion parameter, DR_f is the unique field name ratio, a is the ideal proportion parameter, which can be set to 0.2 in advance.

[0087] 106. Calculate a size score for the average size of the field name according to a preset name size algorithm to obtain a field size score;

[0088] In this embodiment, to ensure that the grouped data size is moderate, the ideal grouping method is to divide the data into groups of appropriate size. Groups that are too large will lose their distinguishing function, while groups that are too small (especially a large number of groups with only one element) will have little meaning. Therefore, a name size algorithm is used to calculate a score based on the average size of the field name and calculate the group size score of the field name. This allows the group size to be limited to ensure that each group size is neither too large nor too small.

[0089] For details, please refer to Figure 3 , Figure 3 This is an embodiment of step 106 of the production scheduling data grouping method based on the APS system in an embodiment of the present invention. Step 106 includes the following specific implementation methods:

[0090] 1061. Determine whether the average size of the field name is greater than or equal to a preset ideal grouping size parameter;

[0091] 1062. When it is greater than or equal to the preset ideal group size parameter, the field size fraction is assigned a value of min(1.0, IS_i / IS_f), where IS_i is the ideal group size parameter and IS_f is the average size of the field name;

[0092] 1063. When it is not greater than or equal to the preset ideal group size parameter, the field size fraction is assigned as IS_f / IS_i.

[0093] In steps 1061-1063, it is first necessary to determine whether the average size of the field name is greater than or equal to the ideal grouping size parameter. If so, the field size score is assigned a value of min (1.0, IS_i / IS_f), where IS_i is the ideal grouping size parameter and IS_f is the average size of the field name.

[0094] If the requirement is not met, the field size fraction is assigned to IS_f / IS_i. This assignment logic can be implemented using an if-else statement as follows:

[0095] if (IS_f>= IS_ideal):

[0096] groupSizeScore = min(1.0, IS_ideal / IS_f)

[0097] else:

[0098] groupSizeScore = IS_f / IS_ideal

[0099] The above logic can be implemented.

[0100] 107. Calculate a packet quality score based on the unique proportion score and the field size score;

[0101] In this embodiment, the unique ratio score and the field size score may be multiplied or divided to calculate the packet quality score.

[0102] Specifically, step 107 includes the following specific embodiments:

[0103] fS=RS*w1+gS*w2;

[0104] Among them, fS is the group quality score, RS is the unique proportion score, gS is the field size score, w1 is the first proportion weight, and w2 is the second proportion weight. By multiplying the weights, the weight adjustment can be used to control the preference for finer-grained grouping or coarser-grained grouping.

[0105] 108. Filter out the optimal grouping field corresponding to the production scheduling data table according to the grouping quality score;

[0106] In this embodiment, the above steps 101-107 can be encapsulated as follows:

[0107] “Function calculateGroupingQualityScore(dataset D, field name f):”

[0108] Calculate the various grouping fields of the production scheduling data table in turn to obtain the corresponding grouping quality scores, and based on the quality scores, screen out the optimal grouping fields corresponding to the production scheduling data table.

[0109] For details, please refer to Figure 4 , Figure 4 This is an embodiment of step 108 of the production scheduling data grouping method based on the APS system in an embodiment of the present invention. Step 108 includes the following specific implementation methods:

[0110] 1081. Read N grouping fields of the production scheduling data table, where N is a positive integer;

[0111] 1082. Calculate the packet quality scores corresponding to the N packet fields to obtain a packet quality score set;

[0112] 1083. Extract the maximum value in the group quality score set to obtain a maximum group quality score;

[0113] 1084. Determine the grouping field corresponding to the maximum grouping quality score in the production scheduling data table as the optimal grouping field.

[0114] In steps 1081-1084, the above steps 101-107 can be encapsulated as follows:

[0115] “Function calculateGroupingQualityScore(dataset D, field name f):”

[0116] Then, create a function and reference the above function calculateGroupingQualityScore() to first obtain N possible grouping fields of the production scheduling data table. Then, for each grouping field, determine whether it has an annotation or conforms to the grouping naming pattern. If so, first create a new literal "criterion" and reference it as one of the N grouping fields. Then, reference the encapsulated function to calculate the score of the literal "criterion". The score is set as an attribute of the literal "criterion". Then, the score attribute of the literal "criterion" is set in the grouping quality score set, and the grouping quality score set is sorted in descending order by score.

[0117] Determine whether the grouping quality score set is an empty set. If it is not an empty set, obtain the first sorted grouping field and determine it as the optimal grouping field, that is, the grouping field corresponding to the maximum grouping quality score, and determine that the production scheduling data table corresponds to the optimal grouping field.

[0118] 109. Group the production scheduling data table according to the optimal grouping field to generate a production scheduling grouping table.

[0119] In this embodiment, based on selecting the optimal grouping field criterion, the production scheduling data table D is grouped to obtain a production scheduling grouping table.

[0120] Specifically, step 109 includes the following specific implementation methods:

[0121] 1091. Generate a grouping tree according to the optimal grouping field;

[0122] 1092. Based on the grouping tree, perform row-by-row matching processing on the production scheduling data table to generate a production scheduling grouping table.

[0123] In steps 1091-1092, the optimal grouping field is introduced into a "groupMap" literal, and the optimal grouping field is combined into a grouping tree with the grouping value and the row index. At the same time, the index is set to the initial row index as the row index of the optimal grouping field.

[0124] Then perform the following process for each data item i in the scheduling data table:

[0125] groupValue = getFieldValue(i, bestField.name) / / Get the grouped field values ​​row by row

[0126] if groupValue is not empty:

[0127] / / Analyze the data of "groupMap" literal to generate the production group table

[0128] groupMap.computeIfAbsent(groupValue,k->New ArrayList()).add(rowNum)

[0129] rowNum++

[0130] Perform data KV matching row by row and build a new table until all row indexes are built and a production scheduling grouping table is generated.

[0131] Specifically, after step 109, the following specific implementation methods are also included:

[0132] 1093. Parse the group data of the production scheduling group table to obtain M group features, where M is a positive integer.

[0133] 1094. Perform color matching calculation on the M grouping features based on a preset coloring algorithm to obtain coloring strategies corresponding to the M grouping features.

[0134] 1095. Perform dyeing processing on the grouping cells of the production scheduling grouping table according to the dyeing strategies corresponding to the M grouping features to obtain a dyed production scheduling grouping table.

[0135] In steps 1093-1095, each grouping data in the production scheduling grouping table is first parsed, and then the grouping data of each row is parsed in turn to obtain the grouping features corresponding to each row, which total M.

[0136] Based on the attributes such as the score and data size corresponding to the grouping feature, each threshold is used to filter out the color matching types and obtain the coloring strategy corresponding to each grouping feature.

[0137] Then, based on the coloring strategy corresponding to each grouping feature, the group cells of the production scheduling group table are colored to obtain a colored production group table. The specific implementation can be referred to the following pseudo code:

[0138] function ApplyIntelligentColors(G, S): / / Build a function

[0139] / / 1. Analyze grouping characteristics

[0140] for (g in G):

[0141] g.size = g.rows.number

[0142] g.importance = Calculate importance score (g)

[0143] / / 2. Select a color scheme

[0144] for (g in G):

[0145] if g.importance>1:

[0146] g.colorScheme = "Accent color scheme"

[0147] elif g.size>5:

[0148] g.colorScheme = "gradient color scheme"

[0149] else:

[0150] g.colorScheme = "Color scheme"

[0151] / / Check for color conflicts in adjacent groups

[0152] Check and resolve color conflicts with adjacent groups (g, G)

[0153] / / 3. Apply color strategy

[0154] for (g in G):

[0155] colors = Get color list (g.colorScheme)

[0156] strategy = new color strategy (colors, g.rows, g.colorScheme)

[0157] / / 4. Apply color to cells

[0158] for (r in g.rows):

[0159] row = S.getRow(r)

[0160] color = strategy.getColorForRow(r)

[0161] for (fixedColumnCount to row.lastCell):

[0162] cell = getOrCreateCell(row, columnIndex)

[0163] Apply color style (cell, color)

[0164] return S

[0165] In this embodiment of the present invention, data analysis is performed on online production scheduling data tables, grouping field names and group sizes to suit business scenarios, and quality scores for data visualization under different grouping conditions are obtained. Using the grouping quality scores, the production scheduling data tables are effectively reorganized and grouped, improving the visualization of the production scheduling tables, reducing the complexity of data adjustments for schedulers, and effectively enhancing the clarity of the schedules. This resolves the technical issue of production scheduling data tables being unable to intuitively distinguish between different batches or process groups.

[0166] Figure 5 This is a schematic diagram of the structure of a production scheduling data grouping device based on an APS system, provided by an embodiment of the present invention. This APS-based production scheduling data grouping device 500 may vary significantly depending on configuration or performance. It may include one or more central processing units (CPUs) 510 (e.g., one or more processors), a memory 520, and one or more storage media 530 (e.g., one or more mass storage devices) storing application programs 533 or data 532. The memory 520 and storage medium 530 may be either transient or persistent storage. The program stored in the storage medium 530 may include one or more modules (not shown), each of which may include a series of instruction operations within the APS-based production scheduling data grouping device 500. Furthermore, the processor 510 may be configured to communicate with the storage medium 530, executing the series of instruction operations stored in the storage medium 530 on the APS-based production scheduling data grouping device 500.

[0167] The production scheduling data grouping device 500 based on the APS system may further include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input and output interfaces 560, and / or one or more operating systems 531, such as Windows Server, Mac OS X, Unix, Linux, Free BSD, etc. It will be understood by those skilled in the art that Figure 5 The structure of the production scheduling data grouping device based on the APS system shown does not constitute a limitation of the production scheduling data grouping device based on the APS system, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0168] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of the production scheduling data grouping method based on the APS system.

[0169] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0170] In addition, although adopting specific order to describe each operation, this should be understood as requiring such operation to be carried out in the specific order shown or in sequential order, or requiring that all illustrated operations should be carried out to obtain desired results. Under certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although comprising some specific implementation details in the above discussion, these should not be interpreted as limiting the scope of the present disclosure. Some features described in the context of separate embodiment can also be implemented in a single implementation in combination. On the contrary, the various features described in the context of a single implementation also can be implemented in a plurality of implementations individually or in the mode of any suitable subcombination.

[0171] Although the subject matter has been described in language specific to structural features and / or methodological logical acts, it should be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are merely example forms of implementing the claims.

Claims

1. A production scheduling data grouping method based on the APS system, characterized in that: Including steps: Receive production scheduling data sheet; Perform name matching processing on the production scheduling data table according to the preset grouping field name to obtain a matching set; Analyze the number of records in the production scheduling data table to obtain the total number of production scheduling records; Calculate the unique field name ratio and the average field name size according to the matching set and the total number of production scheduling records; Calculating the proportion score of the unique field name proportion according to a preset proportion algorithm to obtain a unique proportion score; Calculating a size score for the average size of the field names according to a preset name size algorithm to obtain a field size score; Calculating a packet quality score based on the unique proportion score and the field size score; Filtering the optimal grouping field corresponding to the production scheduling data table according to the grouping quality score; performing grouping processing on the production scheduling data table according to the optimal grouping field to generate a production scheduling grouping table; The calculation of the unique field name ratio using a preset ratio algorithm to obtain the unique ratio score includes: RS=1.0-|a-DR_f|; Among them, RS is the unique proportion score, a is the ideal proportion parameter, and DR_f is the unique field name ratio; The size score is calculated based on the preset name size algorithm for the average size of the field names to obtain the field size score, including: Determining whether the average size of the field names is greater than or equal to a preset ideal grouping size parameter; If it is greater than or equal to the preset ideal group size parameter, the field size fraction is assigned a value of min(1.0, IS_i / IS_f), where IS_i is the ideal group size parameter and IS_f is the average size of the field name; When it is not greater than or equal to the preset ideal group size parameter, the field size fraction is assigned as IS_f / IS_i.

2. The production scheduling data grouping method based on the APS system according to claim 1, characterized in that: Calculating the unique field name ratio and the average field name size based on the matching set and the total number of production scheduling records includes: Counting the number of elements in the matching set to obtain a unique field statistical value; Divide the unique field statistics by the total number of production scheduling records to obtain the unique field name ratio; The total number of production scheduling records is divided by the unique field statistics to obtain the average size of the field name.

3. The production scheduling data grouping method based on the APS system according to claim 1, characterized in that: Calculating the packet quality score according to the unique proportion score and the field size score includes: fS=RS*w1+gS*w2; Among them, fS is the group quality score, RS is the unique proportion score, gS is the field size score, w1 is the first proportion weight, and w2 is the second proportion weight.

4. The production scheduling data grouping method based on the APS system according to claim 1, characterized in that: The step of filtering out the optimal grouping fields corresponding to the production scheduling data table according to the grouping quality score includes: Read N grouping fields of the production scheduling data table, where N is a positive integer; Calculating the group quality scores corresponding to the N group fields to obtain a group quality score set; Extracting a maximum value from the set of group quality scores to obtain a maximum group quality score; The grouping field corresponding to the maximum grouping quality score is determined to be the optimal grouping field corresponding to the production scheduling data table.

5. The production scheduling data grouping method based on the APS system according to claim 1, characterized in that: The grouping process of the production scheduling data table according to the optimal grouping field to generate a production scheduling grouping table includes: Generating a grouping tree according to the optimal grouping field; Based on the grouping tree, the production scheduling data table is matched row by row to generate a production scheduling grouping table.

6. The production scheduling data grouping method based on the APS system according to claim 1, characterized in that: After grouping the production scheduling data table according to the optimal grouping field to generate a production scheduling grouping table, the method further includes: Parsing the group data of the production scheduling group table to obtain M group features, where M is a positive integer; Based on a preset coloring algorithm, color matching calculation is performed on the M grouping features to obtain coloring strategies corresponding to the M grouping features; The grouping cells of the production scheduling grouping table are dyed according to the dyeing strategies corresponding to the M grouping features to obtain a dyed production scheduling grouping table.

7. A production scheduling data grouping device based on the APS system, characterized in that: The production scheduling data grouping device based on the APS system includes: a memory and at least one processor, wherein the memory stores instructions, and the memory and the at least one processor are interconnected via a line; The at least one processor calls the instructions in the memory to enable the APS system-based production scheduling data grouping device to execute the APS system-based production scheduling data grouping method according to any one of claims 1 to 6.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the production scheduling data grouping method based on the APS system according to any one of claims 1 to 6 is implemented.

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