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

Abstract

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

Description

Technical Field

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

[0002] The APS system (Advanced Planning and Scheduling) is an advanced planning and scheduling system. Through the comprehensive balance of limited resources, based on the enterprise's constraints and optimization goals, it is a multi-agent system that plans and real-time schedules the allocation and utilization of enterprise resources such as production, logistics, and equipment, and evaluates the planned results. It uses advanced algorithms and models, combines information such as the enterprise's production capacity, process flow, and material requirements, and conducts comprehensive production planning and resource scheduling.

[0003] The APS system mainly schedules production based on Web tables, but Web tables cannot use Excel table functions (such as formulas, freeze windows, etc.), which is difficult for personnel accustomed to Excel scheduling to start with. However, through traditional Excel export methods, relevant data cannot be intelligently merged, information is scattered, and it is difficult to intuitively distinguish different production batches or process groups, resulting in complicated work for scheduling personnel. Therefore, in order to solve the technical problem that it is difficult to intuitively distinguish different production batches or process groups in the scheduling data table during the scheduling process of the APS system, a new technology is needed to solve the current problem. Summary of the Invention

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

[0005] The first aspect of the present invention provides a scheduling data grouping method based on an APS system, including the steps of: Receiving a scheduling data table; Performing name matching processing on the scheduling data table according to a preset grouping field name to obtain a matching set; Analyzing the number of records in the scheduling data table to obtain the total number of scheduling records; Calculating the unique field name ratio and the average field name size according to the matching set and the total number of scheduling records; Calculating a unique ratio score by performing ratio score calculation on the unique field name ratio according to a preset ratio algorithm; Calculating a field size score by performing size score calculation on the average field name size according to a preset name size algorithm; Calculating a grouping quality score according to the unique ratio score and the field size score; Filter out the optimal grouping field corresponding to the production scheduling data table according to the grouping quality score. Group the production scheduling data table according to the optimal grouping field to generate a production scheduling grouping table.

[0006] Optionally, in the first implementation manner of the first aspect of the present invention, the calculating the unique field name ratio and the average field name size according to the matching set and the total number of production scheduling records includes: Count the number of elements in the matching set to obtain a unique field statistic value. Divide the unique field statistic value by the total number of production scheduling records to obtain the unique field name ratio. Divide the total number of production scheduling records by the unique field statistic value to obtain the average field name size.

[0007] Optionally, in the second implementation manner of the first aspect of the present invention, the calculating the unique ratio score by performing ratio fraction calculation on the unique field name ratio according to a preset ratio algorithm includes: RS = 1.0 - |a - DR_f|; where RS is the unique ratio score, a is the ideal ratio parameter, and DR_f is the unique field name ratio.

[0008] Optionally, in the third implementation manner of the first aspect of the present invention, the calculating the field size score by performing size fraction calculation on the average field name size according to a preset name size algorithm includes: Determine whether the average field name size is greater than or equal to a preset ideal grouping size parameter. When it is greater than or equal to the preset ideal grouping size parameter, assign the field size score to min(1.0, IS_i / IS_f), where IS_i is the ideal grouping size parameter and IS_f is the average field name size. When it is not greater than or equal to the preset ideal grouping size parameter, assign the field size score to IS_f / IS_i.

[0009] Optionally, in the fourth implementation manner of the first aspect of the present invention, the calculating the grouping quality score according to the unique ratio score and the field size score includes: fS = RS * w1 + gS * w2; where fS is the grouping quality score, RS is the unique ratio score, gS is the field size score, w1 is the first ratio weight, and w2 is the second ratio weight.

[0010] Optionally, in the fifth implementation manner of the first aspect of the present invention, the filtering out the optimal grouping field corresponding to the production scheduling data table according to the grouping quality score includes: Read N grouping fields of the scheduling data table, where N is a positive integer; Calculate the grouping quality scores corresponding to the N grouping fields to obtain a set of grouping quality scores; Extract the maximum value from the set of grouping quality scores to obtain the maximum grouping quality score; Determine the grouping field corresponding to the maximum grouping quality score as the optimal grouping field corresponding to the scheduling data table.

[0011] Optionally, in the sixth implementation manner of the first aspect of the present invention, the grouping the scheduling data table according to the optimal grouping field to generate a scheduling grouping table includes: Generate a grouping tree according to the optimal grouping field; Based on the grouping tree, perform a row-by-row matching process on the scheduling data table to generate a scheduling grouping table.

[0012] Optionally, in the seventh implementation manner of the first aspect of the present invention, after the grouping the scheduling data table according to the optimal grouping field to generate a scheduling grouping table, it further includes: Analyze the grouping data of the scheduling grouping table to obtain M grouping features, where M is a positive integer; Based on a preset coloring algorithm, perform color matching calculation on the M grouping features to obtain coloring strategies corresponding to the M grouping features; According to the coloring strategies corresponding to the M grouping features, perform coloring processing on the grouping cells of the scheduling grouping table to obtain a colored scheduling grouping table.

[0013] The second aspect of the present invention provides a scheduling data grouping device based on an APS system, including: a memory and at least one processor, instructions are stored in the memory, and the memory and the at least one processor are interconnected by a line; the at least one processor invokes the instructions in the memory so that the scheduling data grouping device based on the APS system executes the above-mentioned scheduling data grouping method based on the APS system.

[0014] The third aspect of the present invention provides a computer-readable storage medium, instructions are stored in the computer-readable storage medium, and when it runs on a computer, it causes the computer to execute the above-mentioned scheduling data grouping method based on the APS system.

[0015] In an embodiment of the present invention, by analyzing the grouped field names and grouped sizes of the online input scheduling data table according to the business scenario, the quality score of data visualization in different grouped states is obtained. Using the quality scores of the groups, the scheduling data table is effectively reorganized and grouped, improving the visualization of the scheduling table, reducing the complexity of data adjustment by the scheduling personnel, effectively enhancing the scheduling clarity, and solving the technical problem that it is difficult to visually distinguish different production batches or process groups in the scheduling data table. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 FIG. is a schematic diagram of an embodiment of a scheduling data grouping method based on an APS system in an embodiment of the present invention; Figure 2 FIG. is a schematic diagram of an embodiment of step 104 of a scheduling data grouping method based on an APS system in an embodiment of the present invention; Figure 3 FIG. is a schematic diagram of an embodiment of step 106 of a scheduling data grouping method based on an APS system in an embodiment of the present invention; Figure 4 FIG. is a schematic diagram of an embodiment of step 108 of a scheduling data grouping method based on an APS system in an embodiment of the present invention; Figure 5 FIG. is a schematic diagram of an embodiment of a scheduling data grouping device based on an APS system in an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The embodiments of the present invention provide a scheduling data grouping method, device, and storage medium based on an APS system.

[0018] The embodiments disclosed in the present invention will be described in more detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to more thoroughly and completely understand the present disclosure. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not used to limit the protection scope of the present invention.

[0019] In the description of the embodiments disclosed in the present invention, the term "including" and its like should be understood as an open inclusion, that is, "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". Terms such as "first", "second", etc. may refer to different or the same objects. There may also be other explicit and implicit definitions hereinafter.

[0020] For ease of understanding, the specific process of the embodiments of the present invention will be described below. Please refer toFigure 1 , an embodiment of the scheduling data grouping method based on the APS system in the embodiments of the present invention includes: 101. Receive a scheduling data table; In this embodiment, on the APS system, an Excel table uploaded by a user can be received, or a Web table can be directly pulled from a network database. The scheduling data table contains products to be produced, and a product bill of materials exists under the product list at the same time. The scheduling data table is the list of materials and products required in the production and manufacturing process, as well as the scheduling time and date for the product list.

[0021] Specifically, after receiving the scheduling data table, the scheduling data table can be preprocessed. Within the designed date range, multiple header forms are constructed, a data structure method is designed, a multi-level header is first constructed, and data value mapping is performed based on the multi-level header to generate a dynamic column structure. The specific implementation method can refer to the following pseudo-code: # Generate a date sequence, and the date range is specified by date_range date_list = generate_date_sequence(date_range) # Create column headers, and each header is in the form of a tuple, containing "daily plan" and the specific date column_headers = [("daily plan", date) for date in date_list] # Construct a value mapping dictionary, where the key of the dictionary is the row number, and the value is a list. The elements in the list are the values of the corresponding data items on different dates, and D is the scheduling data table value_map = { row_num: [get_value(data_item, date) for date in date_list] for row_num, data_item in enumerate(D) } # Return a DynamicStructure object, which contains column headers and a value mapping dictionary for organizing and displaying data return DynamicStructure(column_headers, value_map).

[0022] 102. According to the preset grouping field name, perform name matching processing on the scheduling data table to obtain a matching set; In this embodiment, some grouping field names can be preset. These grouping field names can be set in certain fields of the background database, and the scheduling data table is processed for matching to obtain a K-V key-value matching set of the matching grouping field names.

[0023] 103. Analyze the number of records in the scheduling data table to obtain the total number of scheduling records; In this embodiment, the size() function can be used to directly analyze the number of records in the scheduling data table.

[0024] 104. Calculate the unique field name ratio and the average field name size according to the matching set and the total number of scheduling records; In this embodiment, when there are all matching field names in the matching set, and the total number of scheduling records is the total number of occurrences of all field names, based on the field names, the ratio of a single field name in the entire total number of scheduling records and the data size of each field name can be calculated.

[0025] Specifically, please refer to Figure 2 , Figure 2 This is an embodiment of step 104 of the scheduling data grouping method based on the APS system in the embodiments of the present invention. Step 104 includes the following specific implementation manners: 1041. Count the number of elements in the matching set to obtain the unique field count value; 1042. Divide the unique field count value by the total number of scheduling records to obtain the unique field name ratio; 1043. Divide the total number of scheduling records by the unique field count value to obtain the average field name size.

[0026] In steps 1041 - 1043, count the number of all elements in the matching set to obtain the unique field count value of the entire matching set, and use the size() function to analyze the number of elements in the matching set to obtain the unique field count value.

[0027] Then, the unique field name ratio and the average field name size are obtained based on the following calculation methods: DR_f = uC / tR / / Calculate the unique field name ratio IS_f = tR / uC / / Calculate the average field name size Among them, DR_f is the unique field name ratio, uC is the unique field count value, tR is the total number of scheduling records, and IS_f is the average field name size.

[0028] 105. Calculate the proportional fraction of the unique field name ratio according to the preset proportional algorithm to obtain the unique proportional fraction; In this embodiment, for the balance of discrimination, a good grouping field should have appropriate discrimination. If the discrimination is too high (each record is a unique value), the meaning of grouping is lost; if the discrimination is too low (all record values are the same), the grouping is worthless. Therefore, through a preset ratio algorithm, a fractional evaluation is performed on the ratio of the unique field name ratio to obtain a unique ratio score.

[0029] Specifically, step 105 includes the following specific implementation manners: RS = 1.0 - |a - DR_f|; Where RS is the unique ratio score, a is the ideal ratio parameter, DR_f is the unique field name ratio, and a, as the ideal ratio parameter, can be set to 0.2 in advance.

[0030] 106. According to the preset name size algorithm, calculate the size score of the average size of the field name to obtain the field size score; In this embodiment, in order to make the grouped data size appropriate, an ideal grouping should divide the data into groups with appropriate sizes. If the grouping is too large, the discrimination function of the grouping will be lost; if the grouping is too small (especially a large number of groups with only 1 element), the meaning of grouping is not significant. Therefore, the name size algorithm is used to calculate the score of the average size of the field name to calculate the grouping size score of the field name, so as to limit the grouping size and ensure that the size of each grouping is not too large or too small.

[0031] Specifically, 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 the embodiment of the present invention. Step 106 includes the following specific implementation manners: 1061. Determine whether the average size of the field name is greater than or equal to the preset ideal grouping size parameter; 1062. When it is greater than or equal to the preset ideal grouping size parameter, assign the field size score as 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; 1063. When it is not greater than or equal to the preset ideal grouping size parameter, assign the field size score as IS_f / IS_i.

[0032] In steps 1061 - 1063, first determine whether the average size of the field name is greater than or equal to the ideal grouping size parameter. When it is satisfied, assign the field size score as 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.

[0033] If this requirement is not met, the field size score is assigned as IS_f / IS_i. This assignment logic can be implemented using an if-else statement as follows: if (IS_f>= IS_ideal): groupSizeScore = min(1.0, IS_ideal / IS_f) else: groupSizeScore = IS_f / IS_ideal The above can implement this logic.

[0034] 107. Calculate the grouping quality score based on the unique ratio score and the field size score; In this embodiment, the unique ratio score and the field size score can be multiplied or divided to calculate the grouping quality score.

[0035] Specifically, the following specific embodiments are included in step 107: fS=RS*w1+gS*w2; where fS is the grouping quality score, RS is the unique ratio score, gS is the field size score, w1 is the first ratio weight, and w2 is the second ratio weight. By using the method of multiplying weights, the preference for finer-grained grouping or coarser-grained grouping can be controlled through weight adjustment.

[0036] 108. Screen out the optimal grouping field corresponding to the scheduling data table according to the grouping quality score; In this embodiment, steps 101-107 above can be encapsulated as: "function calculateGroupingQualityScore(dataset D, field name f):" Calculate the corresponding grouping quality scores for multiple grouping fields of the scheduling data table in sequence, and based on the quality scores, screen out the optimal grouping field corresponding to the scheduling data table.

[0037] Specifically, please refer to Figure 4 , Figure 4 This is an embodiment of step 108 of the scheduling data grouping method based on the APS system in the embodiments of the present invention. The following specific implementation manners are included in step 108: 1081. Read N grouping fields of the scheduling data table, where N is a positive integer; 1082. Calculate the grouping quality scores corresponding to the N grouping fields to obtain a grouping quality score set; 1083. Extract the maximum value from the concentrated grouping quality scores to obtain the maximum grouping quality score; 1084. Determine the grouping field corresponding to the maximum grouping quality score, and determine that the corresponding scheduling data table is the optimal grouping field.

[0038] In steps 1081 - 1084, the above steps 101 - 107 can be encapsulated as: "Function calculateGroupingQualityScore(Dataset D, Field Name f):" Then, create a function that references the above function calculateGroupingQualityScore(). First, obtain N possible grouping fields of the scheduling data table, and then for each grouping field, determine whether there is an annotation or it 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" for this field, set this score as an attribute of the literal "criterion", then add the score attribute of the literal "criterion" to the grouping quality score set, and then sort the grouping quality score set in descending order of scores.

[0039] Judge whether the grouping quality score set is an empty set. If it is not an empty set, then 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 corresponding scheduling data table is the optimal grouping field.

[0040] 109. Group the scheduling data table according to the optimal grouping field to generate a scheduling grouping table.

[0041] In this embodiment, based on the selected optimal grouping field criterion, then group the scheduling data table D to obtain a scheduling grouping table.

[0042] Specifically, step 109 includes the following specific implementation manners: 1091. Generate a grouping tree according to the optimal grouping field; 1092. Based on the grouping tree, perform a row - by - row matching process on the scheduling data table to generate a scheduling grouping table.

[0043] In steps 1091 - 1092, introduce the optimal grouping field into a "groupMap" literal. The optimal grouping field forms a grouping tree with the grouping value and the row index, and at the same time set the index as the initial row index, which is the row index of the optimal grouping field.

[0044] Then, perform the following process on each data item i in the production scheduling data table: groupValue = getFieldValue(i, bestField.name) / / Obtain the grouped field value row by row if groupValue is not null: / / Analyze the data of the "groupMap" literal to generate the production scheduling grouping table groupMap.computeIfAbsent(groupValue, k -> new ArrayList()).add(rowNum) rowNum++ Perform data key-value matching row by row to construct a new table until all row indexes are constructed, generating the production scheduling grouping table.

[0045] Specifically, after step 109, the following specific implementation manners are further included: 1093. Analyze the grouped data of the production scheduling grouping table to obtain M grouped features, where M is a positive integer; 1094. Based on a preset coloring algorithm, perform color matching calculation on the M grouped features to obtain the coloring strategies corresponding to the M grouped features; 1095. According to the coloring strategies corresponding to the M grouped features, perform coloring processing on the grouped cells of the production scheduling grouping table to obtain the colored production scheduling grouping table.

[0046] In steps 1093 - 1095, first analyze the grouped data of each production scheduling grouping table, sequentially analyze the grouped data of each row to obtain the grouped features corresponding to each row, with a total of M.

[0047] Based on attributes such as the scores and data sizes corresponding to the grouped features, use various thresholds to screen out the color matching types to obtain the coloring strategies corresponding to each grouped feature.

[0048] Then, based on the coloring strategies corresponding to each grouped feature, perform coloring processing on the grouped cells of the production scheduling grouping table to obtain the colored production scheduling grouping table. The specific implementation can refer to the following pseudocode: function ApplyIntelligentColors(G, S): / / Construct a function / / 1. Analyze the grouped features for (g in G): g.size = g.rows.size g.importance = calculateImportanceScore(g) / / 2. Select the color matching scheme for (g in G): if g.importance>1: g.colorScheme = "Emphasis color scheme" elif g.size>5: g.colorScheme = "Gradient color scheme" else: g.colorScheme = "Complementary color scheme" / / Check for color conflicts with adjacent groups Check and resolve color conflicts with adjacent groups(g, G) / / 3. Apply color strategy for (g in G): colors = Get color list(g.colorScheme) strategy = New color change strategy(colors, g.rows, g.colorScheme) / / 4. Apply color to cells for (r in g.rows): row = S.getRow(r) color = strategy.getColorForRow(r) for (fixedColumnCount to row.lastCell): cell = getOrCreateCell(row, columnIndex) Apply color style(cell, color) return S In an embodiment of the present invention, by analyzing the online input scheduling data table, adapting to the business scenario to analyze the grouping field names and grouping sizes, the quality score of data visualization in different grouping states is obtained. Using the quality score of the grouping, the scheduling data table is effectively reorganized and grouped, improving the visualization of the scheduling table, reducing the complexity of data adjustment by scheduling personnel, effectively enhancing the scheduling clarity, and solving the technical problem that it is difficult to visually distinguish different production batches or process groups in the scheduling data table.

[0049] Figure 5FIG. 0 is a schematic structural diagram of a scheduling data grouping device based on an APS system provided by an embodiment of the present invention. The scheduling data grouping device 500 based on the APS system may vary greatly due to configuration or performance differences, and may include one or more processors (central processing units, CPUs) 510 (for example, one or more processors) and a memory 520, and one or more storage media 530 storing application programs 533 or data 532 (for example, one or more mass storage devices). Among them, the memory 520 and the storage media 530 may be transient storage or persistent storage. The program stored in the storage media 530 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the scheduling data grouping device 500 based on the APS system. Further, the processor 510 may be configured to communicate with the storage media 530 and execute a series of instruction operations in the storage media 530 on the scheduling data grouping device 500 based on the APS system.

[0050] The 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 / output interfaces 560, and / or one or more operating systems 531, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, and so on. Those skilled in the art can understand that Figure 5 The shown structural diagram of the scheduling data grouping device based on the APS system does not limit the scheduling data grouping device based on the APS system, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0051] 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. Instructions are stored in the computer-readable storage medium, and when the instructions are run on a computer, the computer is caused to execute the steps of the scheduling data grouping method based on the APS system.

[0052] 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 connection with an instruction execution system, apparatus, or device. 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, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0053] Moreover, although the operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed to achieve the desired result. In certain circumstances, multitasking and parallel processing may be advantageous. Similarly, although several specific implementation details are included in the foregoing discussion, these should not be construed as limitations on the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented separately or in any suitable subcombination in multiple implementations.

[0054] Although the subject matter has been described in language specific to structural features and / or methodological 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 scheduling data grouping method based on the APS system, characterized in that, Including the steps: Receiving a production scheduling data table; Performing name matching processing on the production scheduling data table according to a preset grouping field name to obtain a matching set; Analyzing the number of records in the production scheduling data table to obtain the total number of production scheduling records; Calculating the unique field name ratio and the average field name size according to the matching set and the total number of production scheduling records; Performing ratio score calculation on the unique field name ratio according to a preset ratio algorithm to obtain a unique ratio score; Performing size score calculation on the average field name size according to a preset name size algorithm to obtain a field size score; Calculating a grouping quality score according to the unique ratio score and the field size score; Filtering out the optimal grouping field corresponding to the production scheduling data table according to the grouping quality score; Grouping the production scheduling data table according to the optimal grouping field to generate a production scheduling grouping table.

2. The scheduling data grouping method based on the APS system according to claim 1, wherein The calculating the unique field name ratio and the average field name size according to 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 statistic value; Dividing the unique field statistic value by the total number of production scheduling records to obtain the unique field name ratio; Dividing the total number of production scheduling records by the unique field statistic value to obtain the average field name size.

3. The scheduling data grouping method based on the APS system according to claim 1, wherein The performing ratio score calculation on the unique field name ratio according to a preset ratio algorithm to obtain a unique ratio score includes: RS = 1.0 - |a - DR_f|; Where RS is the unique ratio score, a is the ideal ratio parameter, and DR_f is the unique field name ratio.

4. The scheduling data grouping method based on the APS system according to claim 1, wherein The performing size score calculation on the average field name size according to a preset name size algorithm to obtain a field size score includes: Judging whether the average field name size is greater than or equal to a preset ideal grouping size parameter; When it is greater than or equal to the preset ideal grouping size parameter, the field size score is assigned as min(1.0, IS_i / IS_f), where IS_i is the ideal grouping size parameter and IS_f is the average field name size; When it is not greater than or equal to the preset ideal grouping size parameter, the field size score is assigned as IS_f / IS_i.

5. The scheduling data grouping method based on the APS system according to claim 1, characterized in that The calculating the grouping quality score according to the unique ratio score and the field size score includes: fS = RS * w1 + gS * w2; Where fS is the grouping quality score, RS is the unique ratio score, gS is the field size score, w1 is the first ratio weight, and w2 is the second ratio weight.

6. The scheduling data grouping method based on the APS system according to claim 1, wherein The filtering out the optimal grouping field corresponding to the production scheduling data table according to the grouping quality score includes: Reading N grouping fields of the production scheduling data table, where N is a positive integer; Calculating the grouping quality scores corresponding to the N grouping fields to obtain a grouping quality score set; Extracting the maximum value in the grouping quality score set to obtain the maximum grouping quality score; Determining the grouping field corresponding to the maximum grouping quality score as the optimal grouping field corresponding to the production scheduling data table.

7. The scheduling data grouping method based on the APS system according to claim 1, wherein The grouping the production scheduling data table according to the optimal grouping field to generate a production scheduling grouping table includes: Generate a grouping tree according to the optimal grouping field; Based on the grouping tree, perform a row-by-row matching process on the scheduling data table to generate a scheduling grouping table.

8. The scheduling data grouping method based on the APS system according to claim 1, wherein After generating the scheduling grouping table by grouping the scheduling data table according to the optimal grouping field, the method further includes: Parse the grouping data of the scheduling grouping table to obtain M grouping features, where M is a positive integer; Based on a preset coloring algorithm, perform color matching calculation on the M grouping features to obtain coloring strategies corresponding to the M grouping features; According to the coloring strategies corresponding to the M grouping features, perform coloring processing on the grouping cells of the scheduling grouping table to obtain a colored scheduling grouping table.

9. An equipment for grouping scheduling data based on the APS system, characterized in that, The scheduling data grouping device based on the APS system includes: a memory and at least one processor, instructions are stored in the memory, and the memory and the at least one processor are interconnected by a line; The at least one processor invokes the instructions in the memory so that the scheduling data grouping device based on the APS system executes the scheduling data grouping method based on the APS system according to any one of claims 1-8.

10. A computer-readable storage medium, on which a computer program is stored, characterized in that, When the computer program is executed by a processor, it implements the scheduling data grouping method based on the APS system according to any one of claims 1-8.

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