Order printing intelligent integrated management system based on big data

Abstract

The invention belongs to the technical field of order management, and particularly discloses an order printing intelligent integrated management system based on big data, which comprises an order data acquisition module, an order printing processing module, an equipment information import module, an order printing analysis module, a database and an order printing execution terminal. According to the invention, the order information of each collected order is analyzed carefully to confirm whether the order printing mode of each order account is conventional printing, split printing or combined printing, so that the problem of insufficient attention to the order printing mode at present is effectively solved, targeted printing distribution of orders is realized, and the printing efficiency is improved. The order printing management method and system provide powerful guarantee for smooth processing and delivery of orders, meanwhile, order arrangement index analysis is carried out on each order placing account, printing task distribution is carried out according to the order arrangement index, integrated management of order printing is achieved, order printing management rationality and reliability are improved, and consumption experience of consumers is guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical field of order management, and in particular, relates to an intelligent integrated management system for order printing based on big data. Background Art

[0002] With the rapid rise of e-commerce, online shopping has become one of the mainstream consumption methods. E-commerce platforms generate a large number of orders every day. The complexity and rapid growth of orders have put forward higher requirements for the efficiency, accuracy and timeliness of order printing. In this context, the requirements for order printing management are also getting higher and higher.

[0003] Prior art, such as a 3D printing order analysis management method and system disclosed in a Chinese invention patent application with application number 201710083313.1, wherein the method includes: generating an order, storing the customer information in the order in a first database, and storing the 3D model ID in the order in a second database. Obtain the 3D model ID to be printed from the second database, assign the 3D model to be printed to the corresponding 3D printer for printing, and the 3D printer uploads the printing progress to the server in real time. Extract the customer order terminal type, the customer's location, the customer's order amount, and the number of customer orders from the customer information in the first database and perform statistical analysis, and form corresponding charts at the terminal according to user needs. The 3D printer printing progress received by the server is transmitted to the terminal in real time for display. It can automatically analyze and count customer information and display it in the terminal in the form of charts. The 3D printer printing progress is transmitted to the terminal in real time for display, which is easy to use, more intelligent, and highly automated.

[0004] With regard to the above technical solutions, it is obvious that the current order printing management still has the following deficiencies: 1. Insufficient attention is paid to the order printing mode, and no specific analysis such as order splitting and merging is performed, which affects the smooth processing and delivery of orders. At the same time, unreasonable splitting or merging may occur, and consumers' consumption experience cannot be guaranteed.

[0005] 2. The data processing depth is insufficient. Currently, it only conducts statistical analysis and generates charts on basic information such as the customer's order terminal type, city of location, order amount, number of orders, etc.

[0006] 3. The efficiency of order printing management has not been significantly improved. Currently, there is no further detailed analysis of the allocation of order printing management, and the consideration is not comprehensive enough, which to a certain extent limits its applicability and processing efficiency in different application scenarios. Summary of the invention

[0007] In view of this, in order to solve the problems raised in the above background technology, an intelligent integrated management system for order printing based on big data is proposed.

[0008] The purpose of the present invention can be achieved through the following technical solutions: The present invention provides an intelligent integrated management system for order printing based on big data, and an order data collection module for collecting real-time order data from different channels, obtaining each collected order under each order account, and recording the order information of each collected order.

[0009] The order printing processing module is used to confirm the order printing mode of each ordering account, including regular printing mode, split printing mode and merge printing mode, and sort the orders of each ordering account based on the order printing mode.

[0010] The device information import module is used to import the rated printing speed, current planned printing data and printing tracking data of each current printing device.

[0011] The order printing analysis module is used to analyze the order scheduling indicators of each order-placing account, obtain the order scheduling indicators, and allocate printing tasks accordingly.

[0012] The database is used to store the packaging method of each numbered product and the transportation method of each attribute product.

[0013] The order printing execution terminal is used to start the printing device corresponding to the task assignment according to the result of the printing task assignment and execute the corresponding printing instruction.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention conducts a detailed analysis of the order information of each collected order to confirm whether the order printing mode of each ordering account is regular printing, split printing or merge printing, thereby effectively solving the current problem of insufficient attention to order printing modes, realizing the specific analysis of order splitting and merging modes and targeted printing allocation of orders, providing a strong guarantee for the smooth processing and delivery of orders, while also preventing the occurrence of unreasonable splitting or merging events, and ensuring the consumer experience.

[0015] (2) When confirming the order printing mode of each ordering account, the present invention conducts a detailed analysis of the order's delivery address, product attributes, product packaging, product transportation and other information, thereby effectively solving the current problem of insufficient data processing depth, avoiding the current limitation of only conducting statistical analysis and forming charts on basic information such as the customer's order terminal type, city, order amount, number of orders, etc., thereby improving the rationality of order printing management.

[0016] (3) The present invention analyzes the order scheduling indicators of each ordering account and allocates printing tasks according to the order scheduling indicators, thereby effectively making up for the lack of obvious improvement in the current order printing management efficiency, realizing further detailed analysis of order printing management allocation and integrated management of order printing, improving the comprehensiveness of order management considerations, and further improving the applicability and processing efficiency of order printing management in different application scenarios.

[0017] (4) The present invention determines the comprehensive score of the printing status of the printing device based on the current planned printing data and printing tracking data of the printing device, and performs a comprehensive score analysis of the order characteristics by counting the three order scheduling indicators of the order expedited ratio, order priority ratio and order complexity ratio of each order account, and then performs printing task allocation, which intuitively displays the real-time status and performance characteristics of each device, facilitates the precise matching of tasks to the most suitable device, avoids the situation where some devices are excessively idle while other devices have a backlog of tasks, thereby improving the overall utilization rate of the printing equipment, and at the same time improves the rationality of task allocation, so that equipment resources can be fully and effectively utilized, and can ensure that the order can complete the printing link quickly, thereby reducing the time of the entire order processing process. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0019] Figure 1 It is a schematic diagram of the connection of the system modules of the present invention.

[0020] Figure 2 The present invention is a schematic flow chart of the steps for implementing the method.

[0021] Figure 3 This is a schematic diagram of the order sorting process for the ordering account of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] See also Figure 1 and Figure 2As shown, the present invention provides an intelligent integrated management system for order printing based on big data, which includes: an order data collection module, an order printing processing module, a device information import module, an order printing analysis module, a database and an order printing execution terminal.

[0024] In the above, the order printing processing module is connected to the order data collection module, the order printing analysis module and the database respectively, and the order printing analysis module is also connected to the device information import module and the order printing execution terminal respectively.

[0025] The order data collection module is used to collect real-time order data from different channels, obtain each collected order under each order account, and record the order information of each collected order.

[0026] Specifically, the order information includes the order generation time, order type, delivery address, number of items, and the item number and item attributes of each item.

[0027] It should be added that different channels include multiple e-commerce platforms, self-built e-commerce websites, offline store sales systems and other channels.

[0028] The order printing processing module is used to confirm the order printing mode of each ordering account, including a regular printing mode, a split printing mode and a merge printing mode, and at the same time, organize the orders of each ordering account based on the order printing mode.

[0029] Specifically, confirming the order printing mode of each ordering account includes: S1, extracting the order generation time, delivery address, number of goods, and product number and product attributes of each product from the order information.

[0030] S2. Compare the delivery addresses of the collected orders under the same order account, and count the consistency of the order addresses of each order account.

[0031] S3. Compare the product numbers and product attributes of each collected order under each ordering account, and count the consistency of product information of each ordering account.

[0032] S4. Filter out the earliest order generation time from the order generation time of each collected order, and build a same batch evaluation time window under each order placing account based on the preset same batch order evaluation interval.

[0033] It should be added that the specific value of the pre-set interval for evaluating the same batch of orders can be 24 hours. For example, assuming that the earliest order generation time of a certain order account is 8:30 on January 2, 24, then the time window for evaluating the same batch under the order account can be from 8:30 on January 1, 24 to 8:30 on January 3, 24.

[0034] S5. Record the collected orders whose order generation time is within the corresponding batch evaluation time window as concurrent orders.

[0035] S6. Count the number of orders and the number of collected orders under each order account during the same period, make a comparison, and use the ratio as the order ratio during the same period.

[0036] S7. The order ratio of the same period exceeds the set reference order ratio of the same period and the order address consistency and product information consistency are greater than the corresponding set reference values ​​as the merging conditions. If an order account meets the merging conditions, the order printing mode of the order account is the merged printing mode. If the order account does not meet the merging conditions, the order account is recorded as an analysis account, and the feasibility of order splitting of the analysis account is statistically analyzed.

[0037] S8. If the feasibility of order splitting of the analysis account is greater than or equal to the set value, the order printing mode of the analysis account is the split printing mode. Otherwise, the order printing mode of the analysis account is the regular printing mode. In this way, the order printing mode of each ordering account is obtained in turn.

[0038] It should be added that, in order to improve the accuracy and efficiency of logistics, reasonable splitting or merging of orders can better plan the delivery route and reduce transportation costs and time. For example, merging multiple orders in the same area can reduce logistics costs, and for special goods or shipments from different warehouses, order splitting can be more targeted for delivery. Therefore, based on this background, the present invention considers the order printing mode.

[0039] For example, suppose an order contains two products, A and B, and the properties of product A are significantly different from those of product B. Then it may be necessary to split the order into two sub-orders and ship them from the corresponding warehouses to ensure that the products can be shipped in time. If some products are out of stock, but other products are in stock, the order may also be split. For example, there are 10 items in an order, but only 2 items of one of them are left in stock. In order to ship the items in stock first, the order will be split into two parts.

[0040] In a specific embodiment, the corresponding reference values ​​of the order address consistency and the product information consistency can both be 0.9.

[0041] Understandably, the regular printing mode means that a collection order is a single printed page. Generally, if the goods in the order can be delivered from the same place, the interval between delivery times is long, and there is no special splitting or merging requirements, if the customer does not make special requirements and the product attributes do not require special processing, then it can be determined as the regular printing mode. For example, if the order details show that all goods are in sufficient stock and located in the same warehouse, it can be preliminarily determined to be the regular printing mode.

[0042] The embodiment of the present invention effectively solves the problem of insufficient data processing depth by performing a detailed analysis of various information such as the order's delivery address, product attributes, product packaging, and product transportation when confirming the order printing mode of each ordering account, avoids the current limitation of only performing statistical analysis and forming charts on basic information such as the customer's order terminal type, city, order amount, and number of orders, and improves the rationality of order printing management.

[0043] Furthermore, in step S2, the order address consistency of each ordering account is counted, including: S21. If the delivery addresses of each collected order under a certain ordering account are the same address, 1 is used as the order address consistency of the ordering account.

[0044] S22. If the delivery addresses of the collected orders under a certain order account are not the same address, compare the distances between the delivery addresses of the collected orders and select the maximum distance, which is recorded as ,Will As the order address consistency of the order account, record it as , To set the reference interval distance, so as to obtain the consistency of the order addresses of each order account.

[0045] It should be added that the consistency of the order address corresponding to each order account is or one of 1, where , the specific reference interval distance can be set to 3 kilometers.

[0046] Furthermore, in step S3, the consistency of product information of each ordering account is counted, including: S31, for each collected order under each ordering account, the ratio of the number of products of each product attribute is counted, and the maximum value is selected from the ratio of the number of products of each product attribute as the reference attribute consistency of each collected order under each ordering account, and the average product attribute consistency under each ordering account is obtained by mean calculation, which is recorded as , Indicates the order account number. .

[0047] In a specific embodiment, when comparing product attributes, if the product attributes are stored in the form of structured data such as key-value pairs, compare whether the values ​​corresponding to each key are exactly the same. If the product attributes are in the form of text descriptions, text similarity calculation may be required, and the calculation results are used as comparison. For example, some text processing libraries, such as NLTK in Python or the text processing module in Scikit-learn, can be used to calculate the similarity scores of product attribute descriptions of the same product number in different orders. Common methods include cosine similarity, etc.

[0048] It should be added that when the product attributes are in the form of text descriptions, when the similarity value reaches 0.85, it can be determined that the product attributes are the same.

[0049] S32. All items with the same product attributes corresponding to different collected orders under the same order account are sorted to obtain the products with the same product attributes under each order account. The number of products with the same product attributes under the same order account is divided by the total number of products under the same order account to obtain the ratio of the number of products with the same product attributes under each order account, and the maximum value is selected as the overall product attribute consistency under each order account, which is recorded as .

[0050] S33, extracting from the database the packaging method and transportation method of the product number of each product corresponding to each collected order under each ordering account.

[0051] S34: For each collected order under each order account, the number of items in each packaging method and each transportation method is counted, and the ratio is calculated according to and The statistical method is to count the average product packaging consistency, overall product packaging consistency, average product transportation consistency and overall transportation consistency under each order account in turn, and record them as , , and .

[0052] S35. Count the consistency of product information of each ordering account , , , and They are the weight coefficients of the set product attributes, packaging methods and transportation methods. .

[0053] It should be added that the product attribute information must be accurately presented in the order printing content, such as product name, specifications, model, etc. This helps warehouse staff to accurately select the corresponding products for packaging and delivery. Therefore, the more consistent the product attributes are, the more likely they are to merge orders. Order printing may involve information indicating packaging requirements, such as whether special packaging such as fragile product marking, waterproof packaging, etc. is required. The demand and significance of merging orders with consistent packaging will also increase accordingly. Transportation method information such as conventional express delivery, logistics distribution, refrigerated express delivery, logistics distribution, etc. The more similar the transportation methods are, the higher the possibility of merging. Because product attributes also indirectly affect the subsequent packaging and transportation method settings, the weight coefficient of product attributes is set to the largest. Packaging determines the scheduling of subsequent orders, so the weight coefficient of packaging method is second, and the weight coefficient of transportation method is relatively the smallest. For example, , and The possible values ​​are 0.5, 0.3 and 0.2 respectively.

[0054] Furthermore, in step S9, the feasibility of order splitting of the account is statistically analyzed, including: comparing the product attributes of each product corresponding to the same collection order, counting the number of different product attributes under the same collection order, and dividing it by the number of items in the order, and taking the ratio as the attribute difference.

[0055] The packaging method and transportation method of the product number of each product corresponding to the same collection order are compared respectively, and the packaging difference and transportation difference are calculated in the same way as the attribute difference.

[0056] The attribute difference, packaging difference and transportation difference of each collected order under the analysis account are recorded as , and , Indicates the collection order number. , calculate and analyze the feasibility of order splitting of the account , .

[0057] It should be added that when the attributes of a product are greatly different from those of other products, or the packaging is greatly different or the transportation is greatly different, the product cannot be packaged and transported in the same batch, so the order needs to be split. Therefore, two maximum operation functions are used to calculate the feasibility of order splitting, and the three parameters of attribute difference, packaging difference and transportation difference are selected as the main measurement parameters.

[0058] The embodiment of the present invention conducts a detailed analysis of the order information of each collected order to confirm whether the order printing mode of each ordering account is regular printing, split printing or merge printing, thereby effectively solving the current problem of insufficient attention to order printing modes, and realizing specific analysis of modes such as order splitting and merging and targeted printing allocation of orders, thereby providing strong guarantees for the smooth processing and delivery of orders, and also preventing the occurrence of unreasonable splitting or merging events, thereby ensuring the consumer experience.

[0059] More specifically, see Figure 3 As shown, the orders of each ordering account are sorted, including: importing the order printing mode of each ordering account.

[0060] When the order printing mode of a certain ordering account is the regular printing mode, all the collected orders under the ordering account are sorted in order of generation time to generate a print order list.

[0061] When the order printing mode of a certain ordering account is the combined printing mode, for all collected orders under the ordering account, an information list of each order is constructed, including the delivery address, the packaging method of each item, the transportation method and the product attributes.

[0062] A similarity assessment algorithm is used to evaluate the similarity between different orders. If the similarity between two orders exceeds a preset reference value, the two orders are merged and the earliest generation time is selected as the generation time of the merged order.

[0063] Mark all orders that are not involved in the merger as original orders, sort the merged orders and original orders in order of generation time, and generate a print order list.

[0064] When the order printing mode of a certain ordering account is the split printing mode, for all collected orders under the ordering account, an information list of each item in each order is constructed, and the similarity between each item is calculated using a similarity assessment algorithm.

[0065] If the similarity between a certain product and any other product is less than a preset reference value, the product will be recorded as a split product, and the generation time and delivery address of the original collection order will be used to construct the split order.

[0066] The remaining collection orders after the split and the collection orders that did not participate in the split are recorded as original orders, the split orders and the original orders are sorted in order of generation time, and a print order list is generated.

[0067] In a specific embodiment, the above-mentioned similarity algorithm may specifically use the Jaccard similarity coefficient, and specifically, the similarity corresponds to a preset reference value having a value of 0.85.

[0068] The device information importing module is used to import the current rated printing speed of each printing device, the current planned printing data and the printing tracking data.

[0069] Specifically, the currently planned printing data includes the remaining number of orders to be printed and the remaining estimated total completion time, etc., and the printing tracking data includes the average printing speed, failure rate and paper jam rate, etc.

[0070] The order printing analysis module is used to analyze the order scheduling indicators of each order-placing account, obtain each order scheduling indicator, and allocate printing tasks based on the indicators.

[0071] Specifically, an order scheduling indicator analysis is performed on each ordering account, including: extracting the order type from the order information, counting the number of collection orders whose order type is marked as expedited, and dividing it by the number of collection orders under the corresponding ordering account to obtain the order expediting ratio of each ordering account.

[0072] Integrate the collected orders of each ordering account to obtain the cumulative collected orders, sort the cumulative collected orders in chronological order of order production time, count the number of collected orders before the middle position in the corresponding sorting position, and divide it by the number of collected orders of each ordering account to obtain the order priority ratio of each ordering account.

[0073] The order address consistency and product information consistency of each order account are recorded as and , calculate the order complexity ratio of each order account , , and are the weight coefficients of the address consistency and product information consistency, respectively. .

[0074] In a specific embodiment, for order processing, processing efficiency is the main consideration and the factor that mainly affects order complexity. The consistency of product information reflects the consistency of product information of multiple orders under the same order account. The worse the consistency, the greater the difference between the number of orders, and the higher the processing complexity. Therefore, the weight coefficient of product information consistency is set to be greater than the weight coefficient of address consistency. For example, and The values ​​can be 0.4 and 0.6 respectively.

[0075] It should be added that before allocating each order account to each printing device in sequence, it is necessary to compare the difference between the order account and the number of printing devices. If the number of order accounts is greater than the number of printing devices, the order accounts are allocated cyclically. For example, there are 100 order accounts and 10 printing devices. The order accounts ranked from the first to the tenth are first allocated to the printing devices ranked from the first to the tenth in sequence. After the first round of allocation is completed, the order accounts ranked from the eleventh to the twentieth are allocated to the printing devices ranked from the first to the tenth in sequence, and the allocation is cyclically performed according to this rule. If the number of order accounts is less than or equal to the number of printing devices, the order account is allocated to the printing device with the same ranking position.

[0076] The embodiment of the present invention analyzes the order scheduling indicators of each order account and allocates printing tasks according to the order scheduling indicators, thereby effectively making up for the lack of obvious improvement in the current order printing management efficiency, realizing further detailed analysis of order printing management allocation and integrated management of order printing, improving the comprehensiveness of order management considerations, and thereby improving the applicability and processing efficiency of order printing management in different application scenarios.

[0077] In yet another specific embodiment, the printing task allocation includes: confirming the comprehensive score of the printing status of each printing device based on the current planned printing data and the printing tracking data.

[0078] The order expediting ratio, order priority ratio and order complexity ratio of each ordering account are recorded as , and .

[0079] Calculate the comprehensive score of order characteristics of each ordering account, recorded as , , , and They represent the weight coefficients of order expedited ratio, order priority ratio and order complexity ratio respectively. , To set the score.

[0080] The printing devices are sorted from large to small according to their comprehensive printing status scores, and the order accounts are sorted similarly. Based on the sorting, the order accounts are sequentially allocated to the printing devices.

[0081] It should be added that in general business scenarios, the three factors of order expedited ratio, order priority ratio and order complexity ratio are usually considered comprehensively, and the weight coefficients are relatively balanced, but may be slightly emphasized according to the specific circumstances of the business. For example, if the business hopes to handle expedited orders in a timely manner, take into account the priority orders of important customers, and also need to deal with a certain number of complex orders, then the weight coefficients can be determined based on actual operating experience and data analysis. For example, the weight coefficient of order expedited ratio in general business scenarios is Set to 0.35, the weight coefficient of order priority ratio Set to 0.35, the weight coefficient of the order complexity ratio Set to 0.3.

[0082] Further, the comprehensive score of the printing status of each printing device is determined, including: extracting the remaining number of orders to be printed and the remaining estimated total completion time from the current planned printing data of each printing device, and recording them as and , Indicates the printing device number. , count the idleness of each printing device , , The number of printing devices.

[0083] The average printing speed, failure rate and paper jam rate are extracted from the printing tracking data of each printing device and recorded as , and , count the printing consistency of each printing device, recorded as .

[0084] Calculate the comprehensive score of the printing status of each printing device , , is a natural constant.

[0085] Furthermore, the printing consistency of each printing device is counted, including: recording the rated printing speed of each printing device as .

[0086] Statistics on the printing consistency of each printing device , , and Set the reference failure rate and paper jam rate respectively.

[0087] It should be added that the printing speed of the printing device will gradually become slower as it is used. Theoretically, the average printing speed will tend to be less than the rated printing speed and will not exceed the rated printing speed. The value interval of is (0, 1], while the failure rate and paper jam rate are compared with the reference value, and the uncertainty of the value interval increases. In order to ensure the consistency of data analysis, when the failure rate and paper jam rate are less than the set reference failure rate and paper jam rate, the printing device is more consistent with the initial state. At this time, the value is assigned to 1, that is, the independent variable is assigned to 0.

[0088] It should also be added that, in a specific embodiment, The specific value can be 0.03. Mainly based on the type of printing device, for example, when the printing device is a small printing device, its paper jam rate is generally expected to be controlled at 1% per 100 pages, and when the printing device is a medium-sized printing device, its paper jam rate is generally expected to be controlled at 2% per 100 pages.

[0089] The embodiment of the present invention confirms the comprehensive score of the printing status of the printing device based on the current planned printing data and printing tracking data of the printing device, and performs a comprehensive score analysis of the order characteristics by counting the three order scheduling indicators of the order expedited ratio, order priority ratio and order complexity ratio of each order account, and then performs printing task allocation, which intuitively displays the real-time status and performance characteristics of each device, facilitates the accurate matching of tasks to the most suitable device, avoids the situation where some devices are excessively idle while other devices have a backlog of tasks, thereby improving the utilization rate of the overall printing equipment, and at the same time improves the rationality of task allocation, so that equipment resources can be fully and effectively utilized, and can ensure that the order can complete the printing link quickly, thereby reducing the time of the entire order processing flow.

[0090] The database is used to store the packaging method of each numbered commodity and the transportation method of each attribute commodity.

[0091] The order printing execution terminal is used to start the printing device corresponding to the task assignment according to the result of the printing task assignment, and execute the corresponding printing instruction.

[0092] The above contents are merely examples and explanations of the concept of the present invention. Those skilled in the art may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, they shall all fall within the protection scope of the present invention.

Claims

1. An intelligent integrated management system for order printing based on big data, It is characterized in that include: The order data collection module is used to collect real-time order data from different channels, obtain the collected orders under each order account, and record the order information of each collected order; The order printing processing module is used to confirm the order printing mode of each order account, including the regular printing mode, the split printing mode and the merge printing mode, and sort the orders of each order account based on the order printing mode; The device information import module is used to import the rated printing speed, current planned printing data and printing tracking data of each printing device; The order printing analysis module is used to analyze the order scheduling indicators of each order account, obtain the order scheduling indicators, and allocate printing tasks accordingly; A database for storing the packaging method of each numbered product and the transportation method of each attributed product; The order printing execution terminal is used to start the printing device corresponding to the task assignment according to the result of the printing task assignment and execute the corresponding printing instruction.

2. An intelligent integrated management system for order printing based on big data as claimed in claim 1, Features: The confirmation of the order printing mode of each ordering account includes: Extracting the order generation time, delivery address, number of items, and item number and item attributes of each item from the order information; Compare the delivery addresses of each collected order under the same order account, and count the consistency of the order addresses of each order account; Compare the product IDs and product attributes of each collected order under each ordering account, and count the consistency of product information of each ordering account; Filter out the earliest order generation time from the order generation time of each collected order, and build the same batch evaluation time window under each order account based on the preset same batch order evaluation interval; The collection orders whose order generation time is within the corresponding batch evaluation time window are recorded as the same period orders; Count the number of orders and the number of collected orders under each order account during the same period, and compare them, and use the ratio as the order ratio during the same period; The merging condition is that the order ratio of the same period exceeds the set reference order ratio of the same period and the order address consistency and product information consistency are greater than the corresponding set reference values. If an order account meets the merging condition, the order printing mode of the order account is the merged printing mode. If the order account does not meet the merging condition, the order account is recorded as an analysis account, and the feasibility of order splitting of the analysis account is statistically analyzed; If the feasibility of order splitting of the analysis account is greater than or equal to the set value, the order printing mode of the analysis account is the split printing mode. Otherwise, the order printing mode of the analysis account is the regular printing mode. In this way, the order printing mode of each ordering account is obtained in turn.

3. An intelligent integrated management system for order printing based on big data as claimed in claim 2, Features: The statistics of order address consistency of each order account include: If the delivery addresses of all collected orders under a certain order account are the same, 1 is used as the order address consistency of the order account; If the delivery addresses of the collected orders under a certain order account are not the same address, compare the distances between the delivery addresses of the collected orders and select the maximum distance, which is recorded as ,Will As the order address consistency of the order account, record it as , To set the reference interval distance, so as to obtain the consistency of the order addresses of each order account.

4. The intelligent integrated management system for order printing based on big data as claimed in claim 2, Features: The statistical consistency of product information of each ordering account includes: For each collected order under each order account, the ratio of the number of products of each product attribute is counted, and the maximum value is selected from the ratio of the number of products of each product attribute as the reference attribute consistency of each collected order under each order account. The average product attribute consistency under each order account is obtained by mean calculation, which is recorded as , Indicates the order account number. ; The items with the same attributes corresponding to different collected orders under the same order account are sorted out to obtain the items with the same attributes under each order account. The number of items with the same attributes under the same order account is divided by the total number of items under the same order account to obtain the ratio of the number of items with the same attributes under each order account. The maximum value is selected as the overall consistency of the product attributes under each order account, which is recorded as ; Extract the packaging method and transportation method of each item number of each collected order under each order account from the database; For each order placed by each account, the number of items in each packaging method and each transportation method is counted. and The statistical method is to count the average product packaging consistency, overall product packaging consistency, average product transportation consistency and overall transportation consistency under each order account in turn, and record them as , , and ; Statistics on the consistency of product information of each ordering account , , , and They are the weight coefficients of the set product attributes, packaging methods and transportation methods. .

5. An intelligent integrated management system for order printing based on big data as claimed in claim 4, Features: The statistical analysis of the feasibility of order splitting of the account includes: Compare the product attributes of each product in the same collection order, count the number of different product attributes under the same collection order, divide it by the number of products in the order, and use the ratio as the attribute difference; Compare the packaging methods and transportation methods of the product numbers of each product in the same collection order, and calculate the packaging difference and transportation difference in the same way as the attribute difference. The attribute difference, packaging difference and transportation difference of each collected order under the analysis account are recorded as , and , Indicates the collection order number. , calculate and analyze the feasibility of order splitting of the account , 。 6. An intelligent integrated management system for order printing based on big data as claimed in claim 4, Features: The order arrangement for each ordering account includes: Import the order printing mode of each ordering account; When the order printing mode of a certain order account is the regular printing mode, all the collected orders under the order account are sorted in order of generation time to generate a print order list; When the order printing mode of a certain order account is the combined printing mode, for all the collected orders under the order account, a list of information for each order is constructed, including the delivery address, packaging method of each item, transportation method and item attributes; Use a similarity assessment algorithm to evaluate the similarity between different orders. If the similarity between two orders exceeds a preset reference value, the two orders are merged, and the earliest generation time is selected as the generation time of the merged order. Mark all orders not involved in the merger as original orders, sort the merged orders and original orders in order of generation time, and generate a print order list; When the order printing mode of a certain order account is the split printing mode, for all the collected orders under the order account, a list of information of each item in each order is constructed, and the similarity between each item is calculated using a similarity evaluation algorithm; If the similarity between a certain product and any other product is less than a preset reference value, the product will be recorded as a split product, and the generation time and delivery address of the original collection order will be used to construct a split order; The remaining collection orders after the split and the collection orders that did not participate in the split are recorded as original orders, the split orders and the original orders are sorted in order of generation time, and a print order list is generated.

7. According to claim 4, an intelligent integrated management system for order printing based on big data, Features: The order-queuing indicator analysis of each order-placing account includes: Extracting the order type from the order information, counting the number of collection orders whose order type is marked as expedited, and dividing the number by the number of collection orders under the corresponding order account, to obtain the order expedited ratio of each order account; Integrate the collected orders of each ordering account to obtain the accumulated collected orders, sort the accumulated collected orders according to the order production time, count the number of collected orders before the middle position in the corresponding sorting position, and divide it by the number of collected orders of each ordering account to obtain the order priority ratio of each ordering account; The order address consistency and product information consistency of each order account are recorded as and , calculate the order complexity ratio of each order account , , and are the weight coefficients of the address consistency and product information consistency, respectively. ; The order expediting ratio, order priority ratio and order complexity ratio of each ordering account are used as order scheduling indicators.

8. The intelligent integrated management system for order printing based on big data as claimed in claim 4, Features: The printing task allocation includes: Determining a comprehensive score of the printing status of each printing device based on the currently planned printing data and the printing tracking data; The order expediting ratio, order priority ratio and order complexity ratio of each ordering account are recorded as , and ; Calculate the comprehensive score of order characteristics of each ordering account, recorded as , , , and They represent the weight coefficients of order expedited ratio, order priority ratio and order complexity ratio respectively. , To set the score; The printing devices are sorted from large to small according to their comprehensive printing status scores, and the order accounts are sorted similarly. Based on the sorting, the order accounts are sequentially allocated to the printing devices.

9. The intelligent integrated management system for order printing based on big data as claimed in claim 8, Features: The determination of the comprehensive score of the printing status of each printing device includes: Extract the remaining order quantity to be printed and the remaining estimated total completion time from the current planned printing data of each printing device, and record them as and , Indicates the printing device number. , count the idleness of each printing device , , is the number of printing devices; The average printing speed, failure rate and paper jam rate are extracted from the printing tracking data of each printing device and recorded as , and , count the printing consistency of each printing device, recorded as ; Calculate the comprehensive score of the printing status of each printing device , , is a natural constant.

10. An intelligent integrated management system for order printing based on big data as claimed in claim 9, Features: The counting of the printing consistency of each printing device includes: The rated printing speed of each printing device is recorded as ; Statistics on the printing consistency of each printing device , , and Set the reference failure rate and paper jam rate respectively.

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