A production planning and scheduling method and system based on order data analysis

Abstract

The present invention discloses a production planning and scheduling method and system based on order data analysis, which belongs to the technical field of general control or regulation systems. The present invention imports order data into a commodity hot-selling analysis model to analyze the hot-selling situation of commodities, sorts the commodities in descending order according to the hot-selling situation of commodities and obtains commodity information of commodities within a set sequence number; imports order data into a commodity demand prediction model to predict commodity demand; imports commodity information of commodities within a set sequence number into a production line matching degree analysis model to analyze the matching situation of commodities and production lines; imports commodity demand and the matching degree of commodities and production lines into a comprehensive selection index analysis model to analyze the comprehensive situation of commodities, and schedules the commodities according to the comprehensive selection index of commodities; the present invention predicts demand by the hot-selling situation of commodities, and then analyzes the comprehensive selection index in combination with the matching degree of production lines, thereby more accurately judging the scheduling priority of commodities and improving production efficiency.

Description

Technical Field

[0001] The present invention belongs to the general field of control or regulation system technology, and specifically relates to a production planning and scheduling method and system based on order data analysis. Background Art

[0002] Production scheduling, or production planning, refers to the process of organizing, planning, allocating and coordinating various tasks in the production process based on factors such as market demand, order status, and production capacity. Production scheduling involves the rational allocation of production resources to ensure the smooth progress of the production process and meet customer needs.

[0003] Most of the existing technologies for production planning and scheduling are based on real-time order data or production equipment operating data to adjust the production plan. There is a lack of predicting the demand for goods according to the hot sales of goods, and then combining the demand with the matching degree of the production line to determine the selection index of the goods. As a result, the production order of the goods cannot be accurately determined, resulting in reduced production efficiency;

[0004] For example, a Chinese patent with application publication number CN115933573A discloses a virtual simulation test method and system in which the production rhythm of an extrusion production line can be controlled on demand. The invention relates to the field of virtual simulation technology of production rhythm, and includes the following steps: S1. Constructing a Petri net virtual simulation model based on the extrusion production line; S2. Obtaining real-time order data of production tasks, and then transmitting it to the Petri net virtual simulation model for analysis to obtain the optimal order scheduling sequence; S3. Obtaining the working condition data of each production equipment on the extrusion production line, and then transmitting it to the Petri net virtual simulation model for simulation test analysis, and generating instructions according to the optimal order scheduling sequence and sending them to each production equipment on the extrusion production line to control the operation of each production equipment. The present invention can more effectively simulate the dynamic characteristics of production resources on the extrusion production line during operation, and adjust the production orders and verify the effects of the adjusted orders in the absence of physical objects, which can truly reflect the actual situation of the extrusion production line, improve the production efficiency of the entire extrusion production line, and improve work efficiency;

[0005] For example, a Chinese patent with authorization announcement number CN107168263B discloses a knitting MES production planning and scheduling method based on big data mining. The invention method includes the following steps: S1. Establishing a multidimensional knitting production data model; S2. Building a big data analysis platform based on the Hadoop distributed platform; S3. Using the Apriori association rule mining algorithm under the MapReduce framework to mine the constraints of planning and scheduling; S4. Analyzing the order scheduling priority according to contract requirements; S5. Combining the scheduling priority with the constraints of planning and scheduling to obtain the order scheduling Gantt chart; S6. Real-time monitoring of ERP and ZigBee data, and dynamically adjusting the production plan when abnormal events such as order delivery changes, weaving process updates, and sudden loom failures are found.

[0006] The above patent has the problems raised by the background technology: the existing technology mostly adjusts the production plan based on real-time order data or production equipment working condition data, and lacks the ability to predict the demand for goods according to the hot sales of goods, and then combine the demand with the matching degree of the production line to determine the selection index of the goods, so it is impossible to accurately determine the production order of the goods, resulting in reduced production efficiency;

[0007] In order to solve the above problems, the present invention designs a production planning and scheduling method and system based on order data analysis. Summary of the invention

[0008] In view of the deficiencies in the prior art, the present invention proposes a production planning and scheduling method and system based on order data analysis, which imports order data into a commodity hot-selling analysis model to analyze the hot-selling situation of commodities, sorts the commodities in descending order according to the hot-selling situation of commodities and obtains commodity information of commodities within a set sequence number; imports order data into a commodity demand prediction model to predict commodity demand; imports commodity information of commodities within a set sequence number into a production line matching analysis model to analyze the matching situation of commodities and production lines; imports commodity demand and the matching situation of commodities and production lines into a comprehensive selection index analysis model to analyze the comprehensive situation of commodities, and schedules the commodities according to the comprehensive selection index of commodities; the present invention predicts demand by the hot-selling situation of commodities, and then analyzes the comprehensive selection index in combination with the matching situation of the production line, thereby more accurately judging the scheduling priority of commodities and improving production efficiency.

[0009] To achieve the above object, the present invention provides the following technical solutions:

[0010] A production planning method based on order data analysis includes the following specific steps:

[0011] S1. Collect order data within a set period, import the order data into a product hot-selling analysis model to analyze the hot-selling situation of the products, sort the products in descending order according to the hot-selling situation of the products, and obtain product information of the products within the set sequence number;

[0012] S2, importing the order data of the goods with the set serial number within the set period into the goods demand prediction model to predict the demand for the goods;

[0013] S3, collect production line data, import the product information of the products in the set sequence number into the production line matching analysis model to analyze the matching between the products and the production line;

[0014] S4. Import the demand for goods and the matching degree between goods and production lines into the comprehensive selection index analysis model to analyze the comprehensive situation of goods, and schedule the production of goods according to the comprehensive selection index of goods.

[0015] Specifically, S1 includes the following specific steps:

[0016] S11, collecting order data within a set period, wherein the order data includes order completion time, ordered goods, unit price of goods and ordered quantity;

[0017] S12, importing the order completion time, ordered goods, unit price of goods and ordered quantity into a formula for calculating the popularity score of goods to calculate the popularity score of goods, wherein the formula for calculating the popularity score of goods is:

[0018] ;

[0019] in, Indicates the order completion time of the n+1th order of the product. Indicates the order completion time of the nth order of the product. Indicates the time interval between the completion of the n+1th order and the nth order of the product. Indicates the total number of orders for this product. Indicates the unit price of the product in the nth order. Indicates the order quantity of the product in the nth order. Indicates the sales volume of the product in the nth order. Indicates the standard order completion time interval for this product. Indicates the standard sales amount of a single order of the product;

[0020] S13. Sort the products in descending order according to their popularity and obtain product information of the products within a set serial number range, wherein the product information includes the number of equipment types required to produce the products, product raw materials, and product sizes.

[0021] Specifically, S2 includes the following specific steps:

[0022] The order data of the goods with the set serial number within the set period is imported into the goods demand calculation formula to calculate the goods demand. The goods demand calculation formula is:

[0023] ;

[0024] in, Indicates the basic demand for the product. Indicates the standard rating value of hot-selling products. Represents the hot-selling index adjustment factor.

[0025] Specifically, S3 includes the following specific steps:

[0026] S31, collecting production line data, the production line data including the number of equipment, commodity raw material inventory, production line size and the time it takes for the production line to produce a single commodity;

[0027] S32, importing the number of devices into the device number impact value calculation formula to calculate the device number impact value, the device number impact value calculation formula is: ;in, A collection representing the number of equipment types required to produce the product. A collection representing the number of equipment types currently in the production line;

[0028] S33, importing the commodity raw material inventory into the raw material inventory impact value calculation formula to calculate the raw material inventory impact value, the raw material inventory impact value calculation formula is: ;in, Indicates the inventory of the mth raw material of the product, represents the demand for the mth raw material to produce a single commodity, Indicates the number of raw material types required for the product;

[0029] S34, importing the production line size into the size impact value calculation formula to calculate the impact value of the production line size, the size impact value calculation formula is: ;in, Indicates the width of the production line, Indicates the maximum length of the product;

[0030] S35. Import the production time of a single commodity into the production time impact value calculation formula to calculate the production time impact value. The production time impact value calculation formula is: ;in, It indicates the time it takes for a production line to produce a single product. Indicates the daily working time of the production line. Indicates the set production cycle;

[0031] S36. Import the equipment quantity impact value, raw material inventory impact value, size impact value and production time impact value into the production line matching value calculation formula to calculate the matching value between the product and the production line. The production line matching value calculation formula is:

[0032] ;

[0033] in, Indicates the coefficient of the number of devices. represents the raw material inventory ratio, represents the production line size ratio, represents the production time ratio, , , and are all positive and sum to 1.

[0034] Specifically, S4 includes the following specific steps:

[0035] S41. The demand for the commodity and the matching degree between the commodity and the production line are introduced into a comprehensive selection index value calculation formula to calculate the comprehensive selection index value of the commodity. The comprehensive selection index value calculation formula is:

[0036] ;

[0037] in, represents the commodity demand ratio, Indicates the matching value ratio between products and production lines, and are all positive and sum to 1;

[0038] S42. Sort the commodities in descending order according to their comprehensive selection indexes, and schedule the production of the commodities according to the sequence.

[0039] A production planning and scheduling system based on order data analysis, used to implement the above-mentioned production planning and scheduling method based on order data analysis, including a control module, a data acquisition module, a hot sale analysis module, a demand forecasting module, a matching analysis module, a comprehensive analysis module and a scheduling module;

[0040] Specifically, the data collection module is used to collect order data, production line data and commodity information of commodities within a set period; the hot-selling analysis module is used to import order data into a commodity hot-selling analysis model to analyze the hot-selling situation of commodities; the demand forecasting module is used to import order data of commodities within a set period containing commodities within a set sequence number into a commodity demand quantity forecasting model to predict commodity demand; the matching analysis module is used to import commodity information of commodities within a set sequence number into a production line matching degree analysis model to analyze the matching situation between commodities and production lines; the comprehensive analysis module is used to import the demand for commodities and the matching degree between commodities and production lines into a comprehensive selection index analysis model to analyze the comprehensive situation of commodities; the production scheduling module is used to schedule the production of commodities according to the comprehensive selection index of commodities;

[0041] Specifically, the control module is used to control the operation of the data acquisition module, the hot-selling analysis module, the demand forecasting module, the matching analysis module, the comprehensive analysis module and the production scheduling module.

[0042] Compared with the prior art, the present invention has the following beneficial effects:

[0043] The present invention imports order data into a commodity hot-selling analysis model to analyze the hot-selling situation of commodities, sorts the commodities in descending order according to the hot-selling situation of commodities and obtains commodity information of commodities within a set sequence number; imports order data into a commodity demand prediction model to predict commodity demand; imports commodity information of commodities within a set sequence number into a production line matching degree analysis model to analyze the matching situation of commodities and production lines; imports commodity demand and the matching degree of commodities and production lines into a comprehensive selection index analysis model to analyze the comprehensive situation of commodities, and schedules production of commodities according to the comprehensive selection index of commodities; the present invention predicts demand by the hot-selling situation of commodities, and then analyzes the comprehensive selection index in combination with the matching degree of the production line, thereby more accurately judging the scheduling priority of commodities and improving production efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A flowchart of a production planning and scheduling method based on order data analysis according to the present invention;

[0046] Figure 2 This is a flow chart of step S1 in a production planning and scheduling method based on order data analysis of the present invention;

[0047] Figure 3 This is a flow chart of step S3 in a production planning and scheduling method based on order data analysis of the present invention;

[0048] Figure 4 This is a flow chart of step S4 in a production planning and scheduling method based on order data analysis of the present invention;

[0049] Figure 5 It is a schematic diagram of the overall framework of a production planning and scheduling system based on order data analysis of the present invention. DETAILED DESCRIPTION

[0050] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present invention will be more comprehensive and complete and fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0051] Example 1

[0052] See also Figure 1-Figure 4 , an embodiment provided by the present invention: a production planning and scheduling method based on order data analysis, comprising the following specific steps:

[0053] S1. Collect order data within a set period, import the order data into a product hot-selling analysis model to analyze the hot-selling situation of the products, sort the products in descending order according to the hot-selling situation of the products, and obtain product information of the products within the set sequence number;

[0054] In this embodiment, S1 includes the following specific steps:

[0055] S11. Collect order data within a set period, where the order data includes order completion time, ordered goods, unit price of goods, and ordered quantity. The period is set according to industry standards or seasonal demand changes, and the unit can be weekly, monthly, quarterly, etc. The order data is generally stored in a database, and data is extracted by identifying the fields of order completion time, ordered goods, unit price of goods, and ordered quantity;

[0056] S12. Import the order completion time, ordered goods, unit price of goods and ordered quantity into the formula for calculating the popularity score of goods to calculate the popularity score of goods. The formula for calculating the popularity score of goods is:

[0057] ;

[0058] in, Indicates the order completion time of the n+1th order of the product. Indicates the order completion time of the nth order of the product. Indicates the time interval between the completion of the n+1th order and the nth order of the product. The smaller the value, that is, the shorter the time interval between the completion of two orders, the more popular the product is. Indicates the total number of orders for this product. Indicates the unit price of the product in the nth order. Indicates the order quantity of the product in the nth order. Indicates the sales volume of the product in the nth order. The larger the value, that is, the larger the order sales, the more popular the product is. Indicates the standard order completion time interval for this product. Indicates the standard sales of a single order of the product, where the standard order completion time interval of the product And the standard sales volume of a single order of this product The steps for obtaining the value of are as follows: randomly select 100 orders containing the product and successfully traded, obtain the order completion time, product unit price and order quantity, calculate the order completion time interval between every two orders and add them up to obtain the average value as the standard order completion time interval for the product, calculate the sales of each order and add them up to obtain the average value as the standard sales of a single order of the product;

[0059] S13. Sort the products in descending order according to their popularity and obtain product information of the products within a set serial number range, wherein the product information includes the number of types of equipment required to produce the products, product raw materials and product sizes, wherein the serial number range is set according to production or service capacity; product raw materials are obtained through the ingredients marked on the label or instructions or through the production batch records of the products, and product sizes are obtained through the product specification or directly measured. If the product is irregular, the maximum length is measured.

[0060] S2, importing the order data of the goods with the set serial number within the set period into the goods demand prediction model to predict the demand for the goods;

[0061] In this embodiment, S2 includes the following specific steps:

[0062] Import the order data of the products with the set serial number in the set period into the product demand calculation formula to calculate the product demand. The product demand calculation formula is:

[0063] ;

[0064] in, Indicates the basic demand for the product. Indicates the standard rating value of hot-selling products. It represents the hot-selling index adjustment factor. The commodity demand is the sum of the basic demand and the hot-selling increase demand. The steps for obtaining the basic demand of the commodity are as follows: usually, the average sales volume in the cycle is calculated based on the historical order data as the basic demand. If necessary, seasonal factors, holidays, promotional activities and other factors that may affect sales are considered to determine the basic demand of the commodity.

[0065] The steps to obtain the standard rating value of hot-selling products are as follows:

[0066] Set scoring criteria: obtain the historical order data of the product, and determine the scoring dimensions based on the product characteristics, such as unit price, sales volume, sales speed, customer reviews, etc.; assign corresponding weights based on the importance of each dimension; and take the weighted average of the data of each dimension to obtain the standard scoring value of the hot-selling product.

[0067] S3, collect production line data, import the product information of the products in the set sequence number into the production line matching analysis model to analyze the matching between the products and the production line;

[0068] In this embodiment, S3 includes the following specific steps:

[0069] S31. Collect production line data. Production line data includes the number of equipment, commodity raw material inventory, production line size, and the time it takes for a production line to produce a single commodity. The commodity raw material inventory is obtained by retrieving inventory records; the production line size is obtained by measuring the width of the production line; the time it takes for a production line to produce a single commodity is obtained through historical production process records. The production process includes multiple links such as raw material preparation, processing and manufacturing, quality inspection, and packaging. The time required for each process step is recorded, including machine running time, manual operation time, transfer time, etc. In special cases, the impact of factors such as equipment capacity, worker skills, and raw material supply on production time must also be considered;

[0070] S32, importing the number of devices into the calculation formula of the impact value of the number of devices to calculate the impact value of the number of devices, the calculation formula of the impact value of the number of devices is: ;in, A collection representing the number of equipment types required to produce the product. A collection representing the number of equipment types currently in the production line. Indicates the number of equipment types in the production line that meet the production requirements; for example, Z(A)={0081:2,0134:4,3076:1}, where 0081 before : indicates the equipment type number, and 2 after : indicates the number of equipment types required;

[0071] S33. Import the commodity raw material inventory into the raw material inventory impact value calculation formula to calculate the raw material inventory impact value. The raw material inventory impact value calculation formula is: ;in, Indicates the inventory of the mth raw material of the product, represents the demand for the mth raw material to produce a single commodity, Indicates the number of raw material types required for the product. It indicates the total demand for the mth raw material by the predicted commodity demand. It indicates the remaining inventory of the mth raw material after the predicted demand for the product is produced. The larger the value, that is, the more remaining inventory, the less raw material pressure there is in production, which means that the product and production line are more compatible;

[0072] S34, the production line size is imported into the size impact value calculation formula to calculate the impact value of the production line size, and the size impact value calculation formula is: ;in, Indicates the width of the production line, Indicates the maximum length of the product. Indicates the remaining width after placing the product on the production line. The larger the value, the more remaining width, which means that the production line and the product size are more closely matched;

[0073] S35. Import the production time of a single commodity into the production time impact value calculation formula to calculate the production time impact value. The production time impact value calculation formula is: ;in, It indicates the time it takes for a production line to produce a single product. It represents the total time for the production line to produce the predicted demand for goods. Indicates the daily working time of the production line. Indicates the set production cycle, It indicates the total working time of the production line in the production cycle, where the daily working time of the production line is obtained based on the daily equipment working records; the set production cycle is determined based on the product delivery date.

[0074] S36. Import the equipment quantity impact value, raw material inventory impact value, size impact value and production time impact value into the production line matching value calculation formula to calculate the matching value between the product and the production line. The production line matching value calculation formula is:

[0075] ;

[0076] in, Indicates the coefficient of the number of devices. represents the raw material inventory ratio, represents the production line size ratio, represents the production time ratio, , , and are all positive and sum to 1.

[0077] S4. Import the demand for the product and the matching degree between the product and the production line into the comprehensive selection index analysis model to analyze the comprehensive situation of the product, and schedule the production of the product according to the comprehensive selection index of the product;

[0078] In this embodiment, S4 includes the following specific steps:

[0079] S41. The demand for the commodity and the matching degree between the commodity and the production line are introduced into the comprehensive selection index value calculation formula to calculate the comprehensive selection index value of the commodity. The comprehensive selection index value calculation formula is:

[0080] ;

[0081] in, represents the commodity demand ratio, Indicates the matching value ratio between products and production lines, and are all positive and sum to 1;

[0082] S42. Sort the commodities in descending order according to their comprehensive selection indexes, and schedule the production of the commodities according to the sequence.

[0083] Among them, the hot selling index adjustment factor , equipment number ratio , Raw material inventory ratio , production line size ratio , production time ratio , commodity demand ratio , the product and production line matching value ratio The steps of obtaining the value are as follows: randomly select 1,000 successful transaction orders, collect order data and import them into the product popularity score calculation formula to calculate the product popularity score, import order data into the product demand calculation formula to calculate the product demand, import product information and production line data into the production line matching value calculation formula to calculate the matching value between the product and the production line, and finally import the product demand and the matching degree between the product and the production line into the comprehensive selection index value calculation formula to analyze the comprehensive selection index of the product, schedule the production of the products according to the comprehensive selection index, and at the same time, ask relevant professional experts to analyze the orders to predict the demand of hot-selling products and analyze the optimal product production sequence in combination with the production line data, import the priority rankings obtained by the above two different methods into the fitting software for matching, and screen out a group of hot-selling index adjustment factors with the highest similarity. , equipment number ratio , Raw material inventory ratio , production line size ratio , production time ratio , commodity demand ratio , the product and production line matching value ratio The value of .

[0084] Example 2

[0085] See also Figure 5 , a production planning and scheduling system based on order data analysis, used to implement the above-mentioned production planning and scheduling method based on order data analysis, including a control module, a data acquisition module, a hot-selling analysis module, a demand forecasting module, a matching analysis module, a comprehensive analysis module and a scheduling module;

[0086] In this embodiment, the data acquisition module is used to collect order data, production line data and commodity information of commodities within a set period; the hot-selling analysis module is used to import the order data into the commodity hot-selling analysis model to analyze the hot-selling situation of the commodity; the demand forecasting module is used to import the order data of commodities within the set period containing the set serial number into the commodity demand forecasting model to predict the demand for the commodity; the matching analysis module is used to import the commodity information of the commodity within the set serial number into the production line matching analysis model to analyze the matching situation between the commodity and the production line; the comprehensive analysis module is used to import the demand for the commodity and the matching degree between the commodity and the production line into the comprehensive selection index analysis model to analyze the comprehensive situation of the commodity; the production scheduling module is used to schedule the production of the commodity according to the comprehensive selection index of the commodity.

[0087] In this embodiment, the control module is used to control the operation of the data acquisition module, the hot-selling analysis module, the demand forecasting module, the matching analysis module, the comprehensive analysis module and the production scheduling module.

[0088] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0089] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0090] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0091] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of units is only one way. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0092] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units, and some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0093] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0094] In the description of this specification, the descriptions with reference to the terms "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example, and the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples.

[0095] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can well understand and use the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A production planning method based on order data analysis, characterized in that: The specific steps include: S1. Collect order data within a set period, import the order data into a product hot-selling analysis model to analyze the hot-selling situation of the product, sort the products in descending order according to the hot-selling situation of the product, and obtain product information of the product within the set sequence number; S2, importing the order data of the goods with the set serial number within the set period into the goods demand prediction model to predict the demand for the goods; S3, collect production line data, import the product information of the products in the set sequence number into the production line matching analysis model to analyze the matching between the products and the production line; S4. Import the demand for the product and the matching degree between the product and the production line into the comprehensive selection index analysis model to analyze the comprehensive situation of the product, and schedule the production of the product according to the comprehensive selection index of the product; The S1 comprises the following specific steps: S11, collecting order data within a set period, wherein the order data includes order completion time, ordered goods, unit price of goods and ordered quantity; S12, importing the order completion time, ordered goods, unit price of goods and ordered quantity into a formula for calculating the popularity score of goods to calculate the popularity score of goods, the formula for calculating the popularity score of goods is: ； in, Indicates the order completion time of the n+1th order of the product. Indicates the order completion time of the nth order of the product. Indicates the time interval between the completion of the n+1th order and the nth order of the product. Indicates the total number of orders for this product. Indicates the unit price of the product in the nth order. Indicates the order quantity of the product in the nth order. Indicates the sales volume of the product in the nth order. Indicates the standard order completion time interval for this product. Indicates the standard sales amount of a single order of the product; S13, sorting the commodities in descending order according to the popularity of the commodities and obtaining commodity information of the commodities within a set serial number range, wherein the commodity information includes the number of types of equipment required to produce the commodities, commodity raw materials, and commodity sizes; The S2 comprises the following specific steps: The order data of the goods with the set serial number within the set period is imported into the goods demand calculation formula to calculate the goods demand. The goods demand calculation formula is: ； in, Indicates the basic demand for the product. Indicates the standard rating value of hot-selling products. represents the hot selling index adjustment factor; The S3 includes the following specific steps: S31, collecting production line data, the production line data including the number of equipment, commodity raw material inventory, production line size and the time it takes for the production line to produce a single commodity; S32, importing the number of devices into the device number impact value calculation formula to calculate the device number impact value, the device number impact value calculation formula is: ;in, A collection representing the number of equipment types required to produce the product. Indicates the number of equipment types in the production line that meet the production requirements; S33, importing the commodity raw material inventory into the raw material inventory impact value calculation formula to calculate the raw material inventory impact value, the raw material inventory impact value calculation formula is: ;in, Indicates the inventory of the mth raw material of the product, represents the demand for the mth raw material to produce a single commodity, Indicates the number of raw material types required for the product; The S3 also includes the following specific steps: S34, importing the production line size into the size impact value calculation formula to calculate the impact value of the production line size, the size impact value calculation formula is: ;in, Indicates the width of the production line, Indicates the maximum length of the product; S35. Import the production time of a single commodity into the production time impact value calculation formula to calculate the production time impact value. The production time impact value calculation formula is: ;in, It indicates the time it takes for a production line to produce a single product. Indicates the daily working time of the production line. Indicates the set production cycle; S36. Import the equipment quantity impact value, raw material inventory impact value, size impact value and production time impact value into the production line matching value calculation formula to calculate the matching value between the product and the production line. The production line matching value calculation formula is: ； in, Indicates the coefficient of the number of devices. represents the raw material inventory ratio, represents the production line size ratio, represents the production time ratio, , , and are all positive and sum to 1.

2. A production planning and scheduling method based on order data analysis as claimed in claim 1, characterized in that: The S4 comprises the following specific steps: S41. The demand for the commodity and the matching degree between the commodity and the production line are introduced into a comprehensive selection index value calculation formula to calculate the comprehensive selection index value of the commodity. The comprehensive selection index value calculation formula is: ； in, represents the commodity demand ratio, Indicates the matching value ratio between products and production lines, and are all positive and sum to 1; S42. Sort the commodities in descending order according to their comprehensive selection indexes, and schedule the production of the commodities according to the sequence.

3. A production planning and scheduling system based on order data analysis, used to implement the production planning and scheduling method based on order data analysis as claimed in any one of claims 1 to 2, characterized in that: It includes control module, data acquisition module, hot sale analysis module, demand forecasting module, matching analysis module, comprehensive analysis module and production scheduling module; The data collection module is used to collect order data, production line data and commodity information of commodities within a set sequence number within a set period; The hot-selling analysis module is used to import order data into the commodity hot-selling analysis model to analyze the hot-selling situation of the commodity; The demand forecasting module is used to import the order data of the goods with the set serial number within the set period into the goods demand forecasting model to predict the goods demand; The matching analysis module is used to import the commodity information of the commodity in the set serial number into the production line matching analysis model to analyze the matching situation between the commodity and the production line; The comprehensive analysis module is used to import the demand for goods and the matching degree between goods and production lines into the comprehensive selection index analysis model to analyze the comprehensive situation of goods; The production scheduling module is used to schedule the production of commodities according to the comprehensive selection index of the commodities.

4. A production planning and scheduling system based on order data analysis as claimed in claim 3, characterized in that: The control module is used to control the operation of the data acquisition module, the hot-selling analysis module, the demand forecasting module, the matching analysis module, the comprehensive analysis module and the production scheduling module.

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