Multimodal order intelligent management system and method based on artificial intelligence

Abstract

The present invention provides a multimodal order intelligent management system and method based on artificial intelligence. The system includes a data acquisition module, an AI intelligent processing module, an order management module and a tracking and feedback module, which realizes the automatic allocation and dispatch of production tasks, ensuring that each order can be put into production in a timely manner and completed on time according to the contract delivery date. At the same time, it also realizes the overall management of order production tasks, can coordinate production cooperation between factories, realize the reasonable arrangement of resources, and maximize the realization of limited resources for customers, improve order production efficiency, and after the order is dispatched, use the tracking and feedback module to monitor and track the production status of each order throughout the entire process, which is conducive to timely discovery of production problems, and sends order feedback data to target customers and order-related responsible persons, realizing information synchronization among the three parties of production, customer and person in charge, and visualization of order change information, breaking the information barriers between supply chains.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent order dispatching and tracking, and in particular to an artificial intelligence-based multimodal order intelligent management system and method. Background Art

[0002] With the development of computer technology, more and more fields have begun to use intelligent information. However, the existing order management system mainly relies on text input to complete the collection of order information. The order management system leads to the problem of a single data collection method. Moreover, after the order is signed and entered into the system, before the order is put into production, it is still necessary to rely on the administrator to process it according to the current production situation of each manufacturer, determine the manufacturer capable of undertaking the production task of the new order, and then manually assign the new order to the corresponding manufacturer for production. This order allocation method mainly relies on the administrator's judgment to allocate orders, which makes order allocation efficiency low, orders cannot be put into production quickly, and it is also easy to cause uneven production distribution, resulting in low enterprise resource utilization.

[0003] Moreover, after the order is dispatched, the traditional order management system lacks tracking and monitoring of the order fulfillment process. If the customer wants to understand the order, he needs to contact the order manager first, and then the order manager will contact the manufacturer to understand the situation. Finally, the order manager will provide production progress feedback to the customer, resulting in a complicated and lengthy process of following up on the customer's order progress, which easily forms an information barrier between the manufacturer, the order manager and the customer. Summary of the Invention

[0004] The present invention provides an artificial intelligence-based multimodal order intelligent management system and method to solve the above problems.

[0005] The present invention provides an artificial intelligence-based multimodal order intelligent management system, comprising:

[0006] Data collection module, used to collect multimodal order data;

[0007] Among them, multimodal order data includes text order information, voice order information, and image order information;

[0008] AI intelligent processing module, used to analyze and process multimodal order data, determine the order dispatch priority of the current order, and obtain order dispatch data;

[0009] The order management module is used to allocate the current order to the corresponding order dispatch target based on the order dispatch data;

[0010] The tracking and feedback module is used to track the processing of current orders based on the order allocation results, determine the order processing progress and status, generate order feedback data, and send the order feedback data to the target customers and order-related responsible persons.

[0011] Preferably, in an artificial intelligence-based multimodal order intelligent management system, the data acquisition module includes:

[0012] Text entry unit, used to manually enter order data into the order information table;

[0013] The voice input unit is used to collect the order input voice of the order person in charge, convert the order voice into corresponding translated text, identify the translated text to determine the data attributes of the order content contained in the translated text, and generate the corresponding filled identification tag;

[0014] The image input unit is used to recognize the order voucher image uploaded by the project leader, obtain text data, and identify the text data to determine the data attributes of the order content corresponding to each text item, and generate the corresponding filled identification label;

[0015] The automatic filling unit is used to automatically fill the translated text or text data into the corresponding position in the order information table based on the preset mapping relationship between the filling identification tag and each position to be filled in the order information table.

[0016] Preferably, in an artificial intelligence-based multimodal order intelligent management system, the AI ​​intelligent processing module includes:

[0017] The analysis and evaluation unit is used to search based on order tags, obtain all multimodal order data of the current order, evaluate the order size and order urgency of the current order, and obtain the order evaluation result;

[0018] An order sorting unit, configured to determine the order priority of the current order based on the order evaluation result;

[0019] The intelligent dispatching unit is used to determine the result based on priority, combine static configuration rules and dynamic algorithms to determine the order dispatch target for the current order, and generate the corresponding optimal production plan;

[0020] Generate corresponding order dispatch data based on the optimal production plan.

[0021] Preferably, in an artificial intelligence-based multimodal order intelligent management system, the analysis and evaluation unit includes:

[0022] An intelligent retrieval unit is used to determine the order type of the current order based on the order tag, and to search based on the order type to obtain existing orders of the same type as the current order;

[0023] A comparison and analysis unit is used to sort the delivery dates of the current order type and similar existing orders according to the order delivery date to obtain a delivery date order sorting sequence, and determine the delivery urgency of the current order based on the delivery date order sorting sequence and the total number of delivered orders on the date corresponding to the current order delivery date;

[0024] The production forecasting unit is used to determine the order size of the current order based on the scheduled production quantity of the current order and the order size assessment rules. It also obtains the allocation and dispatch results of existing orders of the same type as the current order that are before the delivery date of the current order and predicts the earliest start time of production for the current order.

[0025] Determine the fastest delivery date for the current order based on the current order size and the earliest production start time, combined with completed orders of the same size and the current production capacity of each manufacturer, and compare the fastest delivery date with the current order delivery date to obtain the delivery date difference;

[0026] An order evaluation unit is used to determine an initial urgency coefficient based on the positive or negative difference in delivery dates, and to obtain the production urgency of the current order based on the initial urgency coefficient and the difference in delivery dates;

[0027] According to the delivery urgency and production urgency, combined with the preset weight distribution, the order urgency of the current order is obtained, and the order evaluation result is generated based on the order urgency and order size of the current order.

[0028] Preferably, in an artificial intelligence-based multimodal order intelligent management system, the order sorting unit includes:

[0029] An insertion interval determination unit, configured to determine an order dispatch insertion interval based on a delivery date order sorting sequence and a preset date span;

[0030] an order dispatch position determination unit, configured to evaluate the order urgency of each existing order of the same type within the order dispatch insertion interval based on the analysis and evaluation unit, and compare the evaluation results with the order evaluation results corresponding to the current order, to determine the order dispatch insertion position of the current order within the order dispatch insertion interval;

[0031] The priority determination unit is used to generate a new dispatch sequence based on the order dispatch insertion position and the total order sequence of all existing orders of the same type, and determine the order priority of the current order based on the dispatch sequence.

[0032] Preferably, in an artificial intelligence-based multimodal order intelligent management system, the intelligent dispatching unit includes:

[0033] A static configuration screening unit is used to determine the dispatchable manufacturer for the current order based on the priority determination result, order production product information, and order customer information, combined with static configuration rules;

[0034] The dynamic analysis and dispatching unit is used to analyze and screen the actual production status of each dispatchable manufacturer based on a dynamic algorithm, determine the order dispatching target of the current order and its corresponding optimal production plan, and produce the corresponding order according to the order dispatching target and its corresponding order dispatching data.

[0035] Preferably, in an artificial intelligence-based multimodal order intelligent management system, the dynamic analysis and dispatching unit includes:

[0036] An order classification subunit, configured to determine an upper priority order set and a lower priority order set of a current order based on a priority determination result;

[0037] The initial selection sub-unit is used to retrieve information about the superior priority order, determine the actual completion date of the superior priority order corresponding to each dispatchable manufacturer, and determine whether there is a target manufacturer that can complete the production task of the current order before the current order reaches the delivery date;

[0038] The intelligent screening sub-unit is used to predict the production completion date of the current order based on the actual production capacity of each target manufacturer if there is a target manufacturer that can complete the production task of the current order before the delivery date of the current order, and obtain the original orders of each target manufacturer between the earliest production start time and the production completion date of the current order and the production scheduled quantity of each original order;

[0039] Based on the comparison between the latest delivery date corresponding to the original order and the dispatched predicted delivery date, if there is an urgent order in the original order, the target delivery date corresponding to the urgent order in the original order is obtained;

[0040] Based on the target manufacturer's total unfinished production volume and the sum of the current order production and quantity, combined with the target delivery date, the target manufacturer's daily productivity is adjusted to obtain the adjusted maximum daily production volume. The target manufacturer whose maximum daily production volume is less than or equal to the maximum productivity is used as the order dispatch target;

[0041] If there are multiple order dispatch targets, the maximum daily production volume is compared with the maximum productivity of each order dispatch target to obtain the daily production difference, and the order dispatch target with the largest daily production difference is used as the final order dispatch target;

[0042] Generate the optimal production plan and corresponding order dispatch data based on the current order product quantity and the corresponding maximum daily production volume.

[0043] Preferably, in an artificial intelligence-based multimodal order intelligent management system, the dynamic analysis and dispatching unit further includes:

[0044] The intelligent allocation sub-unit is used to obtain the adjustable production volume of each non-red marked manufacturer when there is no target manufacturer that can complete the production task of the current order before the current order reaches the delivery date or there is no order dispatch target.

[0045] Based on the delivery date of the current order, combined with the preset module, the minimum allocation dispatch strategy is produced, and the optimal production plan for the current order is generated according to the minimum dispatch strategy, the order dispatch quantity corresponding to each order dispatch target is determined, and the order dispatch data is generated.

[0046] Preferably, in an artificial intelligence-based multimodal order intelligent management system, the tracking and feedback module includes:

[0047] The order tracking unit is used to track the entire life cycle of the current order and collect change information of the current order in real time. The entire life cycle of the order includes order creation, order production, order delivery, order arrival and order closure.

[0048] During the order production process, based on the order allocation results of each order, a corresponding number of encrypted data trackers are generated to obtain the actual production status and production data of each order at the corresponding manufacturer;

[0049] And obtain the information of in-production orders and unproduced orders corresponding to each manufacturer respectively, and determine the target customers and order-related responsible persons corresponding to the in-production orders and unproduced orders;

[0050] Feedback notification unit, used to generate order feedback data based on the change information of the current order and send it to the target customer and the person in charge of the order;

[0051] The exception notification unit is used to determine whether an exception occurs in the current order processing based on the change information. If so, it sends the corresponding type of order exception notification to the target customer and the person in charge of the order according to the order exception stage;

[0052] Among them, the order anomalies include creation anomalies, production anomalies, delivery anomalies, and logistics anomalies;

[0053] The intelligent allocation unit is used to obtain the estimated production recovery time of the abnormal manufacturer when the manufacturer's production is abnormal, and determine whether the estimated production recovery time is greater than the threshold time;

[0054] If so, obtain the shelved orders within the expected production recovery time and send them to the AI ​​intelligent processing module for order secondary distribution. Based on the secondary distribution results, generate a reallocation notice and send it to the target customer and the person in charge of the order;

[0055] Otherwise, based on the expected production resumption time, the production rates of in-production orders and unfinished orders are adjusted, and the production plans corresponding to each order are synchronously updated based on the adjustment results;

[0056] The adjustment notification unit is used to send the latest production plan to the corresponding target customers and order-related responsible persons, and send a production resumption notification to the target customers and order-related responsible persons after the abnormal production manufacturer resumes production.

[0057] The present invention provides an artificial intelligence-based multimodal order intelligent management method, comprising:

[0058] Collect multimodal order data;

[0059] Among them, multimodal order data includes text order information, voice order information, and image order information;

[0060] Analyze and process multimodal order data, determine the order dispatch priority of the current order, and obtain order dispatch data;

[0061] Allocate current orders based on order dispatch data;

[0062] Based on the order allocation results, the current order is tracked, the order processing progress and status are determined, order feedback data is generated, and the order feedback data is sent to the target customer and the person in charge of the order.

[0063] Compared with the prior art, the present invention has at least the following beneficial effects:

[0064] The present invention adopts multiple modes to collect order data through the data collection module, provides multiple ways of data collection, and facilitates the input of order data. Then, the multimodal order data is analyzed and processed by the AI ​​intelligent processing module to determine the order dispatch priority of the current order. The order dispatch data is obtained to realize the automatic allocation of production tasks, ensuring that each order can be put into production in time and completed on time according to the contract delivery date. The order management module will allocate the current order to the corresponding order dispatch target based on the order dispatch data, realize the automatic dispatch of order production tasks and the overall management of order production tasks, and coordinate the production cooperation between factories. , realize the reasonable arrangement of resources, complete the maximum realization of limited resources for customers, improve order production efficiency, and track the current order based on the order allocation results through the tracking and feedback module, determine the order processing progress and status, generate order feedback data, and realize full-process monitoring and tracking of the production situation of each order, which is conducive to timely discovery of production problems, and send order feedback data to target customers and order-related responsible persons, realizing information synchronization among the three parties of production, customers and responsible persons, and visualization of order change information, breaking the information barriers between supply chains while also providing actual data support for customers and responsible persons to continue to follow up on orders.

[0065] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in this application document.

[0066] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0068] Figure 1 This is a structural diagram of an artificial intelligence-based multimodal order intelligent management system of the present invention;

[0069] Figure 2 This is a structural diagram of a data acquisition module for a multimodal order intelligent management system based on artificial intelligence in the present invention;

[0070] Figure 3 This is a structural diagram of the AI ​​intelligent processing module of a multimodal order intelligent management system based on artificial intelligence in the present invention;

[0071] Figure 4This is a structural diagram of a tracking and feedback module of a multimodal order intelligent management system based on artificial intelligence in the present invention;

[0072] Figure 5 This is a flow chart of an artificial intelligence-based multimodal order intelligent management method of the present invention. DETAILED DESCRIPTION

[0073] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0074] In embodiment 1:

[0075] The present invention provides a multi-modal order intelligent management system based on artificial intelligence, such as Figure 1 Shown, including:

[0076] Data collection module, used to collect multimodal order data;

[0077] Among them, multimodal order data includes text order information, voice order information, and image order information;

[0078] AI intelligent processing module, used to analyze and process multimodal order data, determine the order dispatch priority of the current order, and obtain order dispatch data;

[0079] The order management module is used to allocate the current order to the corresponding order dispatch target based on the order dispatch data;

[0080] The tracking and feedback module is used to track the processing of current orders based on the order allocation results, determine the order processing progress and status, generate order feedback data, and send the order feedback data to the target customers and order-related responsible persons.

[0081] In this embodiment, the manufacturer may be a factory or a workshop or a production line in the factory.

[0082] The beneficial effects of the above technical solution: The present invention adopts multiple modes to collect order data through the data collection module, provides multiple ways of data collection, and facilitates the input of order data. Then, the multimodal order data is analyzed and processed by the AI ​​intelligent processing module to determine the order dispatch priority of the current order. The order dispatch data is obtained to realize the automatic allocation of production tasks, ensuring that each order can be put into production in time and completed on time according to the contract delivery date. The order management module will allocate the current order to the corresponding order dispatch target based on the order dispatch data, realize the automatic dispatch of order production tasks and the overall management of order production tasks, and can coordinate the factories. It can realize the production cooperation between the two parties, realize the reasonable arrangement of resources, maximize the realization of limited resources for customers, improve the order production efficiency, and track the current order based on the order allocation result through the tracking and feedback module, determine the order processing progress and status, generate order feedback data, and realize the full-process monitoring and tracking of the production situation of each order, which is conducive to timely discovery of production problems and sending order feedback data to target customers and order-related responsible persons, realizing the information synchronization among the producers, customers and responsible persons and the visualization of order change information, breaking the information barriers between supply chains while also providing actual data support for customers and responsible persons to continue to follow up on orders.

[0083] Example 2:

[0084] Based on Example 1, the data acquisition module, such as Figure 2 As shown, including:

[0085] Text entry unit, used to manually enter order data into the order information table;

[0086] The voice input unit is used to collect the order input voice of the order person in charge, convert the order voice into corresponding translated text, identify the translated text to determine the data attributes of the order content contained in the translated text, and generate the corresponding filled identification tag;

[0087] The image input unit is used to recognize the order voucher image uploaded by the project leader, obtain text data, and identify the text data to determine the data attributes of the order content corresponding to each text item, and generate the corresponding filled identification label;

[0088] The automatic filling unit is used to automatically fill the translated text or text data into the corresponding position in the order information table based on the preset mapping relationship between the filling identification tag and each position to be filled in the order information table.

[0089] In this embodiment, the system allows people with the current order filling permission to fill in the same order data multiple times in a variety of ways:

[0090] If the project leader enters the same order information through different input methods, the system will automatically compare the input order content with each other. If the comparison structure is consistent, the system will control the automatic filling unit to fill in the data;

[0091] Otherwise, an order exception notification will be generated and sent to the business supervisor for verification;

[0092] Among them, there can be one or more project leaders, who complete the automatic comparison and verification of the same order data, which is conducive to timely detection of abnormal orders.

[0093] The beneficial effects of the above technical solution: The present invention realizes order data entry in various ways through a text entry unit, a voice entry unit and an image entry unit, and provides data collection in various ways, which not only facilitates the collection of order data but also provides a basis for the automatic dispatch and tracking of orders.

[0094] Example 3:

[0095] Based on Example 1, the AI ​​intelligent processing module, such as Figure 3 Shown, including:

[0096] The analysis and evaluation unit is used to search based on order tags, obtain all multimodal order data of the current order, evaluate the order size and order urgency of the current order, and obtain the order evaluation result;

[0097] An order sorting unit, configured to determine the order priority of the current order based on the order evaluation result;

[0098] The intelligent dispatching unit is used to determine the result based on priority, combine static configuration rules and dynamic algorithms to determine the order dispatch target for the current order, and generate the corresponding optimal production plan;

[0099] Generate corresponding order dispatch data based on the optimal production plan.

[0100] In this embodiment, order size refers to the size of the order. For example, an order for products less than or equal to 10,000 pieces is a small order, an order greater than 10,000 and less than or equal to 50,000 pieces is a medium order, an order greater than 50,000 and less than or equal to 100,000 pieces is a large order, and an order greater than 100,000 pieces is an extra-large order. The classification of order size, i.e., the order size assessment rules, varies depending on the product produced.

[0101] In this embodiment, order priority refers to the dispatching priority of each order among its corresponding orders of the same type. The higher the priority, the earlier it is dispatched.

[0102] In this embodiment, static configuration rules refer to pre-set order production allocation rules. For example, if the customer's delivery address or the customer requires overseas production, the order distribution target is all overseas manufacturers; if the customer's delivery address or the customer requires domestic production, the order distribution target is all domestic manufacturers.

[0103] The dynamic algorithm refers to analyzing the actual production situation and production capacity of the optional manufacturers, combining it with the customer's delivery location to determine the best delivery target for the current order.

[0104] In this embodiment, the optimal production plan includes product production volume and product production speed.

[0105] The beneficial effects of the above technical solution: the present invention first retrieves based on the order tag through the analysis and evaluation unit to obtain all multimodal order data of the current order, evaluates the order size and order urgency of the current order, and obtains the order evaluation result, and then determines the order priority of the current order based on the order evaluation result through the order sorting unit, realizes the automatic evaluation of undispatched orders and the automatic confirmation and update of the order dispatch sequence, and provides a basis for the automatic dispatch of the current order. Finally, the intelligent dispatch unit determines the result based on the priority, combines the static configuration rules and the dynamic algorithm to determine the order dispatch target of the current order, and generates the corresponding optimal production plan; based on the optimal production plan, the corresponding order dispatch data is generated, and by realizing the intelligent allocation and dispatch of orders while intelligently adjusting the daily production volume within the controllable production volume of the production and manufacturing, the production speed of the manufacturer is controlled, and the smooth delivery of the dispatched orders is guaranteed to the greatest extent, so as to maximize the realization of limited resources for customers.

[0106] Example 4:

[0107] On the basis of Example 3, the analysis and evaluation unit includes:

[0108] An intelligent retrieval unit is used to determine the order type of the current order based on the order tag, and to search based on the order type to obtain existing orders of the same type as the current order;

[0109] A comparison and analysis unit is used to sort the delivery dates of the current order type and similar existing orders according to the order delivery date to obtain a delivery date order sorting sequence, and determine the delivery urgency of the current order based on the delivery date order sorting sequence and the total number of delivered orders on the date corresponding to the current order delivery date;

[0110] The production forecasting unit is used to determine the order size of the current order based on the scheduled production quantity of the current order and the order size assessment rules. It also obtains the allocation and dispatch results of existing orders of the same type as the current order that are before the delivery date of the current order and predicts the earliest start time of production for the current order.

[0111] Determine the fastest delivery date for the current order based on the current order size and the earliest production start time, combined with completed orders of the same size and the current production capacity of each manufacturer, and compare the fastest delivery date with the current order delivery date to obtain the delivery date difference;

[0112] An order evaluation unit is used to determine an initial urgency coefficient based on the positive or negative difference in delivery dates, and to obtain the production urgency of the current order based on the initial urgency coefficient and the difference in delivery dates;

[0113] According to the delivery urgency and production urgency, combined with the preset weight distribution, the order urgency of the current order is obtained, and the order evaluation result is generated based on the order urgency and order size of the current order.

[0114] In this embodiment, the order sorting sequence of delivery date orders is obtained by sorting orders according to their delivery dates, wherein orders with the same delivery date have the same sequence order.

[0115] In this embodiment, the existing order of the same type refers to an order of the same type as the current order that has been dispatched and completed.

[0116] In this embodiment, the delivery urgency refers to the delivery urgency of the current order determined based on the order of the current order in the delivery date order sorting sequence and the total number of orders with the same delivery date as the current order (ie, the total number of delivered orders).

[0117] The earlier the current order is in the delivery date order sorting sequence, the greater the delivery urgency of the current order. The greater the total number of delivery orders corresponding to the current order delivery date, the greater the delivery urgency of the current order. The value range is [0, 1].

[0118] In this embodiment, the earliest production start time refers to the time corresponding to the order with the previous delivery date of the current order in the order sorting of the completed delivery dates of each factory.

[0119] In this embodiment, the delivery date difference refers to the difference between the current order delivery date and the earliest delivery date. If the delivery date difference is positive, the initial urgency coefficient ranges from [0, 0.2]. The closer the delivery date difference is to 0, the greater the initial urgency coefficient (making it easier to produce other orders from the manufacturer). If the delivery date difference is negative, the initial urgency coefficient ranges from [0.4, 1]. The closer the delivery date difference is to 0, the smaller the initial urgency coefficient. Production urgency is the product of the initial urgency coefficient and the absolute value of the delivery date difference.

[0120] In this embodiment, based on the delivery urgency and the production urgency, combined with the preset weight distribution, the production urgency needs to be normalized first in the process of obtaining the order urgency of the current order.

[0121] In this embodiment, order urgency refers to the urgency of dispatching the current order for production, including delivery urgency and production urgency, and the sum of the corresponding weight values ​​of the two is 1.

[0122] The beneficial effects of the above technical solution: the present invention determines the order type of the current order based on the order label through the intelligent retrieval unit, and searches according to the order type to obtain existing orders of the same type as the current order; then, the current order type and the delivery date of the existing orders of the same type are sorted according to the order delivery date by the comparison and analysis unit to obtain the delivery date order sorting sequence, and the delivery urgency of the current order is determined according to the delivery date order sorting sequence and the total number of delivery orders on the date corresponding to the delivery date of the current order; then, the production forecasting unit determines the order size of the current order based on the product production schedule of the current order and the order size assessment rules, and simultaneously obtains the allocation and dispatch results of the existing orders of the same type as the current order before the delivery date of the current order, and predicts the earliest start time of production for the current order; according to the current order size and the earliest start time , combined with the completed orders of the same scale and the current productivity of each manufacturer, determine the fastest delivery date of the current order, compare the fastest delivery date with the current order delivery date, and obtain the delivery date difference; finally, the order evaluation unit determines the initial urgency coefficient based on the positive and negative of the delivery date difference, and based on the initial urgency coefficient, combined with the delivery date difference, obtain the production urgency of the current order; according to the delivery urgency and production urgency, combined with the preset weight distribution, obtain the order urgency of the current order, generate the order evaluation result based on the order urgency and order size of the current order, and comprehensively evaluate the order urgency through the two aspects of order delivery date and order scheduled production volume, which provides a basis for the dispatch of the current order, and also provides a basis for the regulation of production resources of each manufacturer, effectively improving the probability of on-time delivery of the current order and improving the credibility of the enterprise in the minds of customers.

[0123] Example 5:

[0124] Based on Example 4, the order sorting unit includes:

[0125] An insertion interval determination unit, configured to determine an order dispatch insertion interval based on a delivery date order sorting sequence and a preset date span;

[0126] an order dispatch position determination unit, configured to evaluate the order urgency of each existing order of the same type within the order dispatch insertion interval based on the analysis and evaluation unit, and compare the evaluation results with the order evaluation results corresponding to the current order, to determine the order dispatch insertion position of the current order within the order dispatch insertion interval;

[0127] The priority determination unit is used to generate a new dispatch sequence based on the order dispatch insertion position and the total order sequence of all existing orders of the same type, and determine the order priority of the current order based on the dispatch sequence.

[0128] In this embodiment, the order dispatch insertion interval refers to the interval consisting of all orders within the preset date span (range [0, 5]) centered around the current order's delivery date in the order order sorting sequence. If the preset date span is 2, the order dispatch insertion interval includes orders with five delivery dates corresponding to the current order's delivery date.

[0129] The beneficial effects of the above technical solution: the present invention shortens the insertable range of the current order by first inserting the interval determination unit, greatly reducing the amount of calculation in the order dispatching process while minimizing the impact of the current order production on the production of the allocated orders, so that the allocated orders can be successfully completed when the delivery date arrives, and then the order dispatch position determination unit determines the order dispatch insertion position of the current order in the order dispatch insertion interval according to the comparison of the order urgency, and finally the priority determination unit produces a new order dispatch sequence based on the order dispatch insertion position and the total order sequence of all existing orders of the same type, thereby realizing the intelligent update of orders stored in the system, ensuring that the archived orders are consistent with the actual production order, and improving the consistency of order data in the supply chain, and then based on the order dispatch sequence, determining the order priority of the current order, providing a basis for the dispatch of the current order.

[0130] Example 6:

[0131] Based on Example 3, the intelligent dispatching unit includes:

[0132] A static configuration screening unit is used to determine the dispatchable manufacturer of the current order based on the priority determination result, order production product information and order customer information, combined with static configuration rules;

[0133] The dynamic analysis and dispatching unit is used to analyze and screen the actual production status of each dispatchable manufacturer based on a dynamic algorithm, determine the order dispatching target of the current order and its corresponding optimal production plan, and produce the corresponding order according to the order dispatching target and its corresponding order dispatching data.

[0134] The beneficial effects of the above technical solution: The present invention combines dynamic order allocation and dynamic analysis through static configuration rules and dynamic algorithms, and while realizing automatic order distribution and management, it improves the rationality of order distribution and provides guarantees for the completion of current orders and the original orders of the generated manufacturers.

[0135] Example 7:

[0136] Based on Example 6, the dynamic analysis dispatch unit includes:

[0137] An order classification subunit, configured to determine an upper priority order set and a lower priority order set of a current order based on a priority determination result;

[0138] The initial selection sub-unit is used to search based on the superior priority order, determine the actual completion date of the superior priority order corresponding to each manufacturer, and determine whether there is a target manufacturer that can complete the production task of the current order before the current order reaches the delivery date;

[0139] The intelligent screening sub-unit is used to predict the production completion date of the current order based on the actual production capacity of each target manufacturer if there is a target manufacturer that can complete the production task of the current order before the delivery date of the current order, and obtain the original orders of each target manufacturer between the earliest production start time and the production completion date of the current order and the production scheduled quantity of each original order;

[0140] Based on the comparison between the latest delivery date corresponding to the original order and the dispatched predicted delivery date, if there is an urgent order in the original order, the target delivery date corresponding to the urgent order in the original order is obtained;

[0141] Based on the target manufacturer's total unfinished production volume and the sum of the current order production and quantity, combined with the target delivery date, the target manufacturer's daily productivity is adjusted to obtain the adjusted maximum daily production volume. The target manufacturer whose maximum daily production volume is less than or equal to the maximum productivity is used as the order dispatch target;

[0142] If there are multiple order dispatch targets, the maximum daily production volume is compared with the maximum productivity of each order dispatch target to obtain the daily production difference, and the order dispatch target with the largest daily production difference is used as the final order dispatch target;

[0143] Generate the optimal production plan and corresponding order dispatch data based on the current order product quantity and the corresponding maximum daily production volume.

[0144] In this embodiment, the upper priority order set refers to existing orders of the same type before the current order in the dispatch order sequence, and the lower priority order set refers to existing orders of the same type after the current order in the dispatch order sequence.

[0145] In this embodiment, actual productivity refers to the daily production volume and consumption of production materials of a manufacturing plant.

[0146] In this embodiment, the earliest production start time refers to the time at which the target manufacturer can start production of the current order at the earliest.

[0147] In this embodiment, the maximum productivity refers to the maximum daily production volume and production material consumption of a manufacturing plant.

[0148] In this embodiment, the urgent order refers to an original order that needs to be delivered between the earliest production start time and the production completion date of the current order.

[0149] In this embodiment, the daily production difference refers to the ratio of the difference between the maximum productivity of each order dispatch target and the maximum daily production volume to the maximum productivity.

[0150] The beneficial effects of the above technical solution: The present invention conducts a preliminary screening of manufacturers based on the current productivity of each manufacturer, combined with the production schedule and delivery date of the current order, to determine the target manufacturer, and then uses the intelligent screening unit to conduct a secondary screening based on the assigned production tasks of the target manufacturer, to ensure that while completing the current order, the order distribution target of the target manufacturer's original production task can be not affected, thereby improving the rationality of order distribution and realizing the maximum realization of limited resources to customers.

[0151] Example 8:

[0152] Based on Example 7, the tracking feedback module, such as Figure 4 As shown, including:

[0153] A feedback mark warning unit is used to obtain the daily production volume corresponding to each manufacturer in real time, and add a red mark to the manufacturer when the daily production volume of the household reaches the maximum production capacity corresponding to the manufacturer;

[0154] According to the actual daily production volume and maximum productivity of the non-red-marked manufacturers, the adjustable production volume of the non-red-marked manufacturers is determined, displayed, and sent to the persons in charge of each order.

[0155] The beneficial effects of the above technical solution: the present invention uses a feedback mark early warning unit to obtain the daily production volume corresponding to each manufacturer in real time, and when the daily production volume of the household reaches the maximum productivity corresponding to the manufacturer, a red mark is added to the manufacturer; according to the actual daily production volume and maximum productivity of the manufacturer without red marks, the adjustable production volume of the manufacturer without red marks is determined and displayed and sent to each order-related person in charge, providing a reference basis for the order-related person in charge to determine the order delivery date, realizing information sharing between the production factory and the order-related person in charge, avoiding the problem of the company and the customer being unable to deliver the order on time due to the lag in the factory production information of the order-related person in charge, and effectively reducing the probability of the company's breach of contract loss.

[0156] Example 9:

[0157] Based on Example 8, the intelligent dispatching unit further includes:

[0158] The intelligent allocation unit is used to obtain the adjustable production volume of each non-red-marked manufacturer when there is no target manufacturer that can complete the production task of the current order before the current order reaches the delivery date or there is no order dispatch target.

[0159] Based on the delivery date of the current order and the preset allocation planning model, a minimum nearest allocation dispatch strategy is generated. Based on the minimum nearest dispatch strategy, the optimal production plan for the current order is generated, the order dispatch quantity corresponding to each order dispatch target is determined, and the order dispatch data is generated.

[0160] In this embodiment, the adjustable production volume refers to the difference between the maximum production capacity of the manufacturer not marked in red and the actual daily production volume.

[0161] In this embodiment, the preset allocation planning model refers to a pre-trained order segmentation and manufacturer selection model. This model can divide the current order into as few sub-orders as possible based on the adjustable production volume of each target manufacturer, the distance between the target manufacturer and the customer's delivery location, and the current order size. The sub-orders comply with the principle of nearby delivery.

[0162] In this embodiment, the order dispatch target refers to the manufacturer to whom the current order is ultimately dispatched.

[0163] The beneficial effects of the above technical solution: when there is no target manufacturer who can complete the production task of the current order before the current order arrives at the delivery date or there is no order dispatch target, the present invention obtains the adjustable production volume of each non-red-marked manufacturer through the intelligent allocation unit, generates a minimum nearest allocation and dispatch strategy based on the delivery date of the current order and in combination with a preset allocation planning model, and generates the best production plan for the current order according to the minimum nearest dispatch strategy, determines the order dispatch volume corresponding to each order dispatch target, and generates order dispatch data. When each manufacturer is unable to independently complete the current order while ensuring the smooth completion of the original order, the current order is divided into the minimum number of small orders to the maximum extent according to the current order, the maximum production volume that each manufacturer can currently bear and the customer's delivery address, and the reasonable allocation of the current order is completed without damaging the current interests of each manufacturer, thereby maximizing the use of limited resources.

[0164] Example 10:

[0165] Based on Example 1, the tracking feedback module, such as Figure 4 As shown, including:

[0166] The order tracking unit is used to track the entire life cycle of the current order and collect change information of the current order in real time. The entire life cycle of the order includes order creation, order production, order delivery, order arrival and order closure.

[0167] During the order production process, based on the order allocation results of each order, a corresponding number of encrypted data trackers are generated to obtain the actual production status and production data of each order at the corresponding manufacturer;

[0168] And obtain the information of in-production orders and unproduced orders corresponding to each manufacturer respectively, and determine the target customers and order-related responsible persons corresponding to the in-production orders and unproduced orders;

[0169] Feedback notification unit, used to generate order feedback data based on the change information of the current order and send it to the target customer and the person in charge of the order;

[0170] The exception notification unit is used to determine whether an exception occurs in the current order processing based on the change information. If so, it sends the corresponding type of order exception notification to the target customer and the person in charge of the order according to the order exception stage;

[0171] Among them, the order anomalies include creation anomalies, production anomalies, delivery anomalies, and logistics anomalies;

[0172] The intelligent allocation unit is used to obtain the estimated production recovery time of the abnormal manufacturer when the manufacturer encounters production abnormalities, and determine whether the estimated production recovery time is greater than the threshold time;

[0173] If so, obtain the shelved orders within the expected production recovery time and send them to the AI ​​intelligent processing module for order secondary distribution. Based on the secondary distribution results, generate a reallocation notice and send it to the target customer and the person in charge of the order;

[0174] Otherwise, based on the expected production resumption time, the production rates of in-production orders and unfinished orders are adjusted, and the production plans corresponding to each order are synchronously updated based on the adjustment results;

[0175] The adjustment notification unit is used to send the latest production plan to the corresponding target customers and order-related responsible persons, and send a production resumption notification to the target customers and order-related responsible persons after the abnormal production manufacturer resumes production.

[0176] In this embodiment, the shelved orders refer to orders that cannot be produced within the estimated production recovery time, and may include only production orders, and may also include some unproduced orders.

[0177] In this embodiment, the threshold time is determined based on the maximum productivity of each manufacturer. After the abnormal manufacturer resumes production, the production can be interrupted by continuing production according to the maximum productivity of the abnormal manufacturer to complete the order before the latest delivery date of the production order and the unproduced order.

[0178] The beneficial effects of the above technical solution are as follows: the present invention first tracks the entire life cycle of the current order through the order tracking unit, collects the change information of the current order in real time, realizes the full process tracking of the order, and generates a corresponding number of encrypted data trackers based on the order allocation result of each order during the order production process, obtains the actual production status and production data of each order at the corresponding manufacturer; and obtains the information of the in-production orders and unproduced orders corresponding to each manufacturer respectively, determines the target customers and order-related responsible persons corresponding to the in-production orders and unproduced orders; then, the feedback notification unit generates order feedback data based on the change information of the current order and sends it to the target customer and order-related responsible person, realizes the information synchronization of the three parties of the manufacturer, the customer and the responsible person, and the visualization of the order change information, breaks the information barriers between the supply chain, and also provides actual data support for the continuous follow-up of the order by the customer and the person in charge; at the same time, the abnormal notification unit will judge whether the current order processing is abnormal based on the change information. If so, the corresponding type of order abnormality notification is sent to the target customer and the person in charge according to the order abnormality stage, which is beneficial for the customer and the person in charge to understand the order abnormality in time; When a manufacturer encounters a production abnormality, the intelligent allocation unit obtains the estimated production recovery time of the abnormal manufacturer and determines whether the estimated production recovery time is greater than the threshold time. If so, the shelved orders within the estimated production recovery time are obtained and sent to the AI ​​intelligent processing module for secondary order distribution. Based on the secondary distribution results, a redistribution notification is generated and sent to the target customers and order-related responsible persons. Otherwise, based on the estimated production recovery time, the production rate of the in-production orders and unproduced orders is adjusted, and the production plan corresponding to each order is synchronously updated according to the adjustment result. The latest production plan is sent to the corresponding target customers and order-related responsible persons through the adjustment notification unit, and after the abnormal manufacturer resumes production, a production recovery notification is sent to the target customers and order-related responsible persons, realizing full-process monitoring and tracking of the production status of each order, which is conducive to timely discovery of production problems, and sending order feedback data to the target customers and order-related responsible persons, realizing information synchronization among the three parties of the manufacturer, customer and person in charge, and visualization of order change information, breaking down the information barriers between the supply chain and providing actual data support for the continuous follow-up of orders by customers and persons in charge.

[0179] Example 11:

[0180] The present invention provides a multi-modal order intelligent management method based on artificial intelligence, such as Figure 5 As shown, including:

[0181] Step 1: Collect multimodal order data;

[0182] Among them, multimodal order data includes text order information, voice order information, and image order information;

[0183] Step 2: Analyze and process the multimodal order data, determine the order dispatch priority of the current order, and obtain the order dispatch data;

[0184] Step 3: Allocate the current order based on the order dispatch data;

[0185] Step 4: Based on the order allocation results, track the current order processing, determine the order processing progress and status, generate order feedback data, and send the order feedback data to the target customer and order-related responsible persons.

[0186] The beneficial effects of the above technical solution are as follows: the present invention first collects order data using multiple modes, provides multiple methods of data collection, and facilitates the input of order data. Then, the multimodal order data is analyzed and processed to determine the order dispatch priority of the current order. The order dispatch data is obtained to realize the automatic allocation of production tasks, ensuring that each order can be put into production in a timely manner and completed on time according to the contract delivery date. Then, based on the order dispatch data, the current order is allocated to the corresponding order dispatch target, realizing the automatic dispatch of order production tasks and the overall management of order production tasks, which can coordinate production cooperation between factories, realize the rational allocation of resources, maximize the realization of limited resources for customers, and improve order production efficiency. Finally, based on the order allocation result, the current order is processed and tracked, the order processing progress and status are determined, and order feedback data is generated, realizing full-process monitoring and tracking of the production status of each order, which is conducive to timely detection of production problems. The order feedback data is sent to the target customer and the person in charge of the order, realizing information synchronization among the three parties of the manufacturer, the customer and the person in charge, and visualization of order change information, breaking down the information barriers between the supply chain and providing actual data support for the continuous follow-up of the customer and the person in charge on the order.

[0187] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A multimodal order intelligent management system based on artificial intelligence, characterized by: include: Data collection module, used to collect multimodal order data; Among them, multimodal order data includes text order information, voice order information, and image order information; AI intelligent processing module, used to analyze and process multimodal order data, determine the order dispatch priority of the current order, and obtain order dispatch data; The order management module is used to allocate the current order to the corresponding order dispatch target based on the order dispatch data; The tracking and feedback module is used to track the processing of current orders based on the order allocation results, determine the order processing progress and status, generate order feedback data, and send the order feedback data to the target customers and order-related responsible persons; Among them, the AI ​​intelligent processing module includes: The intelligent dispatching unit is used to determine the result based on priority, combine static configuration rules and dynamic algorithms to determine the order dispatch target for the current order, and generate the corresponding optimal production plan; Generate corresponding order dispatch data based on the optimal production plan, including: A static configuration screening unit is used to determine the dispatchable manufacturer for the current order based on the priority determination result, order production product information, and order customer information, combined with static configuration rules; The dynamic analysis and dispatch unit is used to analyze and screen the actual production status of each dispatchable manufacturer based on a dynamic algorithm, determine the order dispatch target and its corresponding optimal production plan for the current order, and generate corresponding order dispatch data based on the order dispatch target and its corresponding optimal production plan; Among them, the dynamic analysis and dispatch unit includes: An order classification subunit, configured to determine an upper priority order set and a lower priority order set of a current order based on a priority determination result; The initial selection sub-unit is used to retrieve information about the superior priority order, determine the actual completion date of the superior priority order corresponding to each dispatchable manufacturer, and determine whether there is a target manufacturer that can complete the production task of the current order before the current order reaches the delivery date; The intelligent screening sub-unit is used to predict the production completion date of the current order based on the actual production capacity of each target manufacturer if there is a target manufacturer that can complete the production task of the current order before the delivery date of the current order, and obtain the original orders of each target manufacturer between the earliest production start time and the production completion date of the current order and the production scheduled quantity of each original order; Based on the comparison between the latest delivery date corresponding to the original order and the dispatched predicted delivery date, if there is an urgent order in the original order, the target delivery date corresponding to the urgent order in the original order is obtained; Based on the target manufacturer's total unfinished production volume and the sum of the current order production and quantity, combined with the target delivery date, the target manufacturer's daily productivity is adjusted to obtain the adjusted maximum daily production volume. The target manufacturer whose maximum daily production volume is less than or equal to the maximum productivity is used as the order dispatch target; If there are multiple order dispatch targets, the maximum daily production volume is compared with the maximum productivity of each order dispatch target to obtain the daily production difference, and the order dispatch target with the largest daily production difference is used as the final order dispatch target; Generate the optimal production plan and corresponding order dispatch data based on the current order product quantity and the corresponding maximum daily production volume.

2. The multimodal order intelligent management system based on artificial intelligence according to claim 1 is characterized in that: Data acquisition module, including: Text entry unit, used to manually enter order data into the order information table; The voice input unit is used to collect the order input voice of the order person in charge, convert the order voice into corresponding translated text, identify the translated text to determine the data attributes of the order content contained in the translated text, and generate the corresponding filled identification tag; The image input unit is used to recognize the order voucher image uploaded by the project leader, obtain text data, and identify the text data to determine the data attributes of the order content corresponding to each text item, and generate the corresponding filled identification label; The automatic filling unit is used to automatically fill the translated text or text data into the corresponding position in the order information table based on the preset mapping relationship between the filling identification tag and each position to be filled in the order information table.

3. The multimodal order intelligent management system based on artificial intelligence according to claim 1 is characterized in that: AI intelligent processing module, including: The analysis and evaluation unit is used to search based on order tags, obtain all multimodal order data of the current order, evaluate the order size and order urgency of the current order, and obtain the order evaluation result; The order sorting unit is used to determine the order priority of the current order based on the order evaluation result.

4. The multimodal order intelligent management system based on artificial intelligence according to claim 3 is characterized in that: Analytical evaluation unit, including: An intelligent retrieval unit is used to determine the order type of the current order based on the order tag, and to search based on the order type to obtain existing orders of the same type as the current order; A comparison and analysis unit is used to sort the delivery dates of the current order type and similar existing orders according to the order delivery date to obtain a delivery date order sorting sequence, and determine the delivery urgency of the current order based on the delivery date order sorting sequence and the total number of delivered orders on the date corresponding to the current order delivery date; The production forecasting unit is used to determine the order size of the current order based on the scheduled production quantity of the current order and the order size assessment rules. It also obtains the allocation and dispatch results of existing orders of the same type as the current order that are before the delivery date of the current order and predicts the earliest start time of production for the current order. Determine the fastest delivery date for the current order based on the current order size and the earliest production start time, combined with completed orders of the same size and the current production capacity of each manufacturer, and compare the fastest delivery date with the current order delivery date to obtain the delivery date difference; An order evaluation unit is used to determine an initial urgency coefficient based on the positive or negative difference in delivery dates, and to obtain the production urgency of the current order based on the initial urgency coefficient and the difference in delivery dates; According to the delivery urgency and production urgency, combined with the preset weight distribution, the order urgency of the current order is obtained, and the order evaluation result is generated based on the order urgency and order size of the current order.

5. The multimodal order intelligent management system based on artificial intelligence according to claim 3 is characterized in that: Order sequencing unit, including: An insertion interval determination unit, configured to determine an order dispatch insertion interval based on a delivery date order sorting sequence and a preset date span; an order dispatch position determination unit, configured to evaluate the order urgency of each existing order of the same type within the order dispatch insertion interval based on the analysis and evaluation unit, and compare the evaluation results with the order evaluation results corresponding to the current order, to determine the order dispatch insertion position of the current order within the order dispatch insertion interval; The priority determination unit is used to generate a new dispatch sequence based on the order dispatch insertion position and the total dispatch sequence of all existing orders of the same type, and determine the order priority of the current order based on the dispatch sequence.

6. The multimodal order intelligent management system based on artificial intelligence according to claim 1, characterized in that: The dynamic analysis dispatch unit also includes: The intelligent allocation subunit is used to obtain the adjustable production capacity of each non-red-marked manufacturer when there is no target manufacturer that can complete the production task of the current order before the delivery date of the current order or there is no order dispatch target; Based on the delivery date of the current order and the preset allocation planning model, a minimum nearest allocation dispatch strategy is generated. Based on the minimum nearest allocation dispatch strategy, the optimal production plan for the current order is generated, the order dispatch quantity corresponding to each order dispatch target is determined, and the order dispatch data is generated.

7. The multimodal order intelligent management system based on artificial intelligence according to claim 1 is characterized in that: Tracking feedback module, including: The order tracking unit is used to track the entire life cycle of the current order and collect change information of the current order in real time. The entire life cycle of the order includes order creation, order production, order delivery, order arrival and order closure. During the order production process, based on the order allocation results of each order, a corresponding number of encrypted data trackers are generated to obtain the actual production status and production data of each order at the corresponding manufacturer; And obtain the information of in-production orders and unproduced orders corresponding to each manufacturer respectively, and determine the target customers and order-related responsible persons corresponding to the in-production orders and unproduced orders; Feedback notification unit, used to generate order feedback data based on the change information of the current order and send it to the target customer and the person in charge of the order; The exception notification unit is used to determine whether an exception occurs in the current order processing based on the change information. If so, it sends the corresponding type of order exception notification to the target customer and the person in charge of the order according to the order exception stage; Among them, the order anomalies include creation anomalies, production anomalies, delivery anomalies, and logistics anomalies; The intelligent deployment unit is used to obtain the estimated production recovery time of the abnormal manufacturer when the manufacturer has a production abnormality, and determine whether the estimated production recovery time is greater than a threshold time; If so, obtain the shelved orders within the expected production recovery time and send them to the AI ​​intelligent processing module for order secondary distribution. Based on the secondary distribution results, generate a reallocation notice and send it to the target customer and the person in charge of the order; Otherwise, based on the expected production resumption time, the production rates of in-production orders and unfinished orders are adjusted, and the production plans corresponding to each order are synchronously updated based on the adjustment results; The adjustment notification unit is used to send the latest production plan to the corresponding target customers and order-related responsible persons, and send a production resumption notification to the target customers and order-related responsible persons after the abnormal production manufacturer resumes production.

8. An artificial intelligence-based multimodal order intelligent management method, which is applied to an artificial intelligence-based multimodal order intelligent management system according to any one of claims 1 to 7, characterized in that: include: Collect multimodal order data; Among them, multimodal order data includes text order information, voice order information, and image order information; Analyze and process multimodal order data, determine the order dispatch priority of the current order, and obtain order dispatch data; Allocate current orders based on order dispatch data; Based on the order allocation results, the current order is tracked, the order processing progress and status are determined, order feedback data is generated, and the order feedback data is sent to the target customer and the person in charge of the order.

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