WO2022228176A1 - 一种订单处理方法、设备及存储介质 - Google Patents

一种订单处理方法、设备及存储介质 Download PDF

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Publication number
WO2022228176A1
WO2022228176A1 PCT/CN2022/087365 CN2022087365W WO2022228176A1 WO 2022228176 A1 WO2022228176 A1 WO 2022228176A1 CN 2022087365 W CN2022087365 W CN 2022087365W WO 2022228176 A1 WO2022228176 A1 WO 2022228176A1
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order
orders
pending
processed
historical
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PCT/CN2022/087365
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English (en)
French (fr)
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薛圣国
张泽鹏
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阿里巴巴（中国）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/04—Manufacturing
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Definitions

  • the present application relates to the technical field of data processing, and in particular, to an order processing method, device and storage medium.
  • production scheduling is an important work in prenatal preparation, and production scheduling is the arrangement of production tasks.
  • the management of personnel, equipment, product transportation and other links all depend on the production scheduling results. Therefore, the production scheduling results directly affect the production cost and production efficiency.
  • Various aspects of the present application provide an order processing method, device and storage medium for scheduling efficiency and/or accuracy of orders.
  • the embodiment of the present application provides an order processing method, including:
  • the embodiment of the present application also provides an order processing method, including:
  • a historically similar order adapted to the to-be-processed order is determined based on the process feature, and the process feature similarity between the historically similar order and the to-be-processed order meets a preset condition.
  • Embodiments of the present application further provide a computing device, including a memory, a processor, and a communication component;
  • the memory for storing one or more computer instructions
  • the processor is coupled to the memory and the communication component for executing the one or more computer instructions for:
  • Embodiments of the present application further provide a computing device, including a memory, a processor, and a communication component;
  • the memory for storing one or more computer instructions
  • the processor is coupled to the memory and the communication component for executing the one or more computer instructions for:
  • a historically similar order adapted to the to-be-processed order is determined based on the process feature, and the process feature similarity between the historically similar order and the to-be-processed order meets a preset condition.
  • the embodiment of the present application also provides an order processing method, including:
  • the order information determine the historical similar order corresponding to the pending order
  • scheduling the to-be-processed order Based on the scheduling information of the historical similar orders, scheduling the to-be-processed order to generate the scheduling information of the to-be-processed order.
  • Embodiments of the present application also provide a computing device, including a memory and a processor;
  • the memory for storing one or more computer instructions
  • the processor is coupled to the memory for executing the one or more computer instructions for:
  • the order information determine the historical similar order corresponding to the pending order
  • scheduling the to-be-processed order Based on the scheduling information of the historical similar orders, scheduling the to-be-processed order to generate the scheduling information of the to-be-processed order.
  • Embodiments of the present application further provide a computer-readable storage medium storing computer instructions, which, when executed by one or more processors, cause the one or more processors to execute the aforementioned order processing method.
  • the production process accumulated in the production process is used as the driving force to recommend historically similar orders for the pending orders, wherein the production processes between the historically similar orders and the pending orders are similar.
  • historical similar orders can be used as a reference to schedule the production of the pending orders, so that the pending orders can be produced more efficiently and accurately.
  • similar historical orders can be added as the basis for scheduling the orders to be processed, thereby improving the scheduling efficiency and accuracy, thereby breaking through the bottleneck and uncertainty of production efficiency.
  • FIG. 1a is a schematic flowchart of an order processing method provided by an exemplary embodiment of the present application
  • FIG. 1b is a schematic flowchart of an order processing method provided by an exemplary embodiment of the present application.
  • FIG. 2 is a schematic logical diagram of a similarity judgment solution provided by an exemplary embodiment of the present application
  • FIG. 3 is a schematic flowchart of another order processing method provided by another exemplary embodiment of the present application.
  • FIG. 4 is a schematic flowchart of another order processing method provided by another exemplary embodiment of the present application.
  • FIG. 5 is a schematic structural diagram of a computing device according to another exemplary embodiment of the present application.
  • FIG. 6 is a schematic structural diagram of another computing device provided by another exemplary embodiment of the present application.
  • FIG. 7 is a schematic diagram of a result of yet another computing device provided by yet another exemplary embodiment of the present application.
  • the production process accumulated in the production process is used as the driving force to recommend historically similar orders for the pending orders, wherein the production processes between the historically similar orders and the pending orders are similar.
  • historical similar orders can be used as a reference to schedule the production of the pending orders, so that the pending orders can be produced more efficiently and accurately.
  • similar historical orders can be added as the basis for scheduling the orders to be processed, thereby improving the scheduling efficiency and accuracy, thereby breaking through the bottleneck and uncertainty of production efficiency.
  • FIG. 1a is a schematic flowchart of an order processing method provided by another exemplary embodiment of the present application
  • FIG. 1b is a schematic flowchart of an order processing method provided by an exemplary embodiment of the present application.
  • the method may be performed by an order processing apparatus, which may be implemented as a combination of software and/or hardware, which may be integrated in a computing device. Referring to Figure 1a, the method includes:
  • Step 100 obtaining the production process of the pending order
  • Step 101 extracting process features from the production process
  • Step 102 determining a historically similar order adapted to the pending order based on the process feature, and the similarity of the process feature between the historically similar order and the pending order conforms to a preset condition.
  • the order processing method provided in this embodiment can be applied to various production scenarios, for example, a garment production scenario, a mechanical parts production scenario, and the like, and the application scenario is not limited in this embodiment. In different application scenarios, the process content contained in the production process may not be exactly the same.
  • the production process of the order to be processed may be obtained.
  • the production process may be description data of a production scheme adopted to produce a production object.
  • the process information contained in the production process may be diverse.
  • the production process may contain not only process information, but also material information, man-hour information, style information, graphic information, etc. involved in the production process.
  • the process information may include, but is not limited to, process name, sequence, position, component, operation type, process difficulty, action, action code, action frequency, action duration, sewing length, and the like.
  • Material information may include but not limited to material name, color, purchaser, type, section, description, size, amount, width, unit, length, gram weight, etc.
  • the man-hour information may include, but is not limited to, the man-hours required for each of a plurality of process steps, and the like.
  • Style information may include, but is not limited to, style identification, stitching, shape features, and the like.
  • Graphical information may include, but is not limited to, drawing information in CAD drawings, and the like. It is worth noting that, in this embodiment, the types and quantities of process information included in the production process are not limited to this, and the types and quantities of information items under each process information are also not limited to this. The type of process information to be divided into is also not limited to this.
  • the production process of the order to be processed may be distributed in one or more data sources.
  • the above man-hour information may be distributed in the enterprise's GSD standard man-hour management system, while the material information may be distributed in the material management system medium.
  • process information can be acquired from one or more data sources in real time to combine into the production process of the to-be-processed order.
  • This acquisition method can ensure the production process accuracy.
  • the production process of the order to be processed can also be collected in advance from one or more data sources and stored.
  • the process information in the data source is updated, the stored production process needs to be updated in time. Synchronous update, this method can improve the convenience of use of the production process. It does not need to access the data source in real time, but it needs to synchronize the data in time to avoid the data consistency problem of the production process.
  • the production process of at least one historical order may also be acquired in advance as a basis for judging the similarity of the production process.
  • process characteristics may be extracted from the production process of the order to be processed.
  • the process features can be extracted for each of the aforementioned process information, so as to obtain the process features of the order to be processed under at least one process information dimension, and can also Process information is combined, and process feature extraction is performed for the combined process information to obtain richer and more dimensional process features. Therefore, in this embodiment, the process feature can reflect the parameter values of each information item under the corresponding process information dimension, and the process feature can comprehensively and accurately characterize the production process of the order to be processed.
  • process features may be extracted in advance from the production process of at least one historical order according to an extraction scheme consistent with the order to be processed. Among them, the extraction scheme of process features will be described in detail later.
  • the display format of the production process may be various, for example, the production process may be in a text format or an image format.
  • the production process can be structured by means of NER named entity recognition, algorithmic parsing of DXF drawing exchange files, and the like.
  • the production process can also be normalized to unify the representation of information items in different production processes. Of course, these processing links are not necessary and can be selected according to the actual situation.
  • a historically similar order adapted to the pending order may be determined based on the process characteristics, wherein the similarity of the process characteristics between the historically similar order and the pending order is consistent with preset conditions. That is, the similarity judgment of the process characteristics of the order to be processed and at least one historical order can be performed to mine historical similar orders adapted to the order to be processed.
  • the preset condition here may be that the similarity is the highest.
  • the number of historically similar orders determined in step 102 may be one or more. For example, from at least one historical order, K historical orders with the highest similarity in process characteristics with the order to be processed can be determined as historically similar orders, where K can be a positive integer.
  • the historical similar orders adapted to the pending order can be output to provide reference for the production processing process of the pending order, for example, to provide reference for the production processing links such as production scheduling, equipment debugging, and quotation of the pending order.
  • the process features can be comprehensively and accurately extracted from the production processes accumulated in the production process, and used as a basis for judging the similarity of the production processes, so that the pending orders can be more objectively and accurately determined.
  • Mining suitable historical similar orders no longer relying on manual experience to judge the similarity of the production process, which can effectively improve the efficiency and accuracy of the similarity judgment of the production process.
  • FIG. 2 is a schematic logical diagram of a similarity determination solution provided by an exemplary embodiment of the present application.
  • an exemplary process feature extraction solution may be to quantize the specified process information in the production process to obtain a process vector to characterize the process feature.
  • the production process can be structured to generate at least one type of structured process information, such as the style information and process information mentioned above.
  • structured process information such as the style information and process information mentioned above.
  • an information item can be used as a field in the data table, and a single type of process information can be used as a record in the data table, so that a single type of process information can contain one or more information items.
  • at least one process information that affects the similarity of production processes can be specified, and a process vector can be generated for the specified process information;
  • the post process information generates process vector and so on.
  • a single process vector will usually be multi-dimensional, and different dimensions may correspond to different information items.
  • At least one process vector can be generated to characterize the process features contained in the production process.
  • this embodiment is not limited to this, and the information item may also be used as a unit to generate a process vector or the like.
  • a process vector can be generated for an order to be processed and at least one historical order respectively.
  • the process vector generation process of at least one historical order is consistent with the process vector generation process of the order to be processed.
  • the process vector of the order to be processed and the process vector of at least one historical order can be aligned, and then Calculation records, of course, have different requirements for different distance calculation schemes, and alignment is not necessary.
  • the specified process information may be input into the pre-trained process vector generation model to convert the specified process information into a process vector.
  • the process vector generation model may be encapsulated as a vector generation service, and the process vector generation model includes a mapping relationship between process information and process vectors.
  • the similarity of the process characteristics can be determined by judging the similarity of the process vectors, and then the historical similar orders adapted to the orders to be processed can be determined.
  • the distance between the process vectors can be used to represent the similarity of process features. Therefore, in this embodiment, the process vector corresponding to the order to be processed can be determined from the process vectors corresponding to at least one historical order. The distance between the target process vectors that meet the preset conditions; the historical orders corresponding to the target process vectors are regarded as historical similar orders.
  • different process vector distance calculation schemes may be defined for different application scenarios.
  • expert experience can be used as a reference to sort out the comparison rules of the production process, so as to determine the distance calculation scheme.
  • the distance calculation scheme of the process vector is not limited to the traditional Euclidean distance, Manhattan distance, etc., and the distance calculation scheme can be customized as required, which is not enumerated here.
  • N is an integer
  • M process vectors with the closest distance to the process vector corresponding to the order to be processed are determined as target process vectors, where M is an integer.
  • an index may be implemented based on a PostgreSql database, and the built-in GIST index framework of the PostgreSql database may provide support for the retrieval solution in this embodiment.
  • the subspace can be a spherical space.
  • the process vector corresponding to at least one historical order can be allocated to at least one spherical subspace.
  • the subspace can be managed based on the distribution status of historical orders in the space:
  • the distance between the two process vectors with the farthest distance in the subspace can be determined, and the two process vectors are taken as the aggregation center, based on the The distance between the remaining process vectors in the subspace and the two cluster centers, the remaining process vectors are divided into 2 groups, and each group of process vectors forms a new spherical space, so as to realize the splitting of the subspace;
  • subspaces that can be merged may be selected from other subspaces around the subspace (for example, the process vectors contained in the same subspace are lower than the second preset number) etc.), the diameter of the new subspace is defined by the two process vectors with the farthest distance in the two subspaces, so as to realize the merging of the subspaces.
  • the craft vector corresponding to the newly added historical order it can be judged whether the distance from the center of the craft vector to the target spherical subspace is smaller than the radius of the target spherical subspace, and if so, the craft vector can be distributed to the target spherical subspace in space. If there are multiple target spherical subspaces that the process vector can be distributed to, the process vector can be added to the spherical subspace with the least cost by using the aforementioned distance from the center of the sphere as the cost to realize the new historical orders. The distribution of the corresponding process vectors.
  • the N spherical subspaces closest to the process vector of the order to be processed can be found first, so as to filter out other unqualified subspaces. Subspace, then, the distance calculation scheme can be run only in the found N spherical subspaces to determine the historically similar orders to which the pending order fits. This can greatly reduce the number of distance calculations, thereby improving the efficiency of determination.
  • the distance between the process vectors can be used to represent the similarity of the process vectors, so that the similarity between the production processes can be calculated objectively and quantitatively, and then the appropriateness of the order to be processed can be quickly and accurately determined. It no longer needs to rely on expert experience for manual search.
  • 10 information items can be selected from working hours information and physical information: stitches, movements, parts, process difficulty, standard duration, color, size, amount, length, gram weight.
  • the parameter values under the 10 information items may be, for example, three lines, copy, front piece, C, 5.3, red, XXL, 0.3, 0.5, and 0.04. Based on this, a process vector can be constructed based on the 10 information items.
  • the vector generation rules can be: text into word vectors, for example, 'three lines' are converted into vectors through word2vec; enumeration values are turned into vectors, such as 'C' and 'XXL' The enumeration value is represented by a number, and then the number is normalized (proportional enlargement and reduction) to a floating-point number between -1 and 1. It is worth noting that this is only an exemplary process vector generation scheme, and under a single order, other information item combination schemes can also be used to generate more process vectors.
  • a 10-dimensional vector can be generated for the current order and 4 historical orders respectively:
  • process vector retrieval and distance calculation solutions provided in the above embodiments can be implemented for this exemplary process vector.
  • process vector retrieval and distance calculation solutions provided in the above embodiments can also be implemented for other process vectors.
  • FIG. 3 is a schematic flowchart of another order processing method provided by another exemplary embodiment of the embodiment of the present application.
  • the method may be executed by an order processing apparatus, and the order processing apparatus may be implemented as a combination of software and/or hardware.
  • the processing device may be integrated in a computing device. Referring to Figure 3, the method includes:
  • Step 300 obtaining the production process of the pending order
  • Step 301 determining a historically similar order adapted to the pending order based on the production process, and the production process similarity between the historically similar order and the pending order conforms to a preset condition;
  • Step 302 using the scheduling information of similar historical orders as a reference, schedule the orders to be processed.
  • the order processing method provided in this embodiment can be applied to various order processing scenarios to provide production scheduling solutions for orders.
  • Scheduling can refer to making a production plan for an order, including but not limited to determining which production lines, which equipment, which operators, and when to produce the order, etc.
  • step 300 reference may be made to the relevant description in the embodiment associated with FIG. 1a, and details are not repeated here.
  • an adapted historical similar order may be determined for the pending order based on the production process of the pending order.
  • a process feature can be extracted from the production process of the order to be processed; the process feature of at least one historical order can be obtained; in the process feature of at least one historical order, the similarity with the process feature of the order to be processed can be determined
  • the target process characteristics that meet the preset conditions; the historical orders corresponding to the target process characteristics are regarded as historical similar orders.
  • the process features may include, but are not limited to, graphic features, material features, process features, man-hour features, or style features, and the like.
  • the specified process information in the production process of the order to be processed can be vectorized to obtain a process vector to represent the process feature.
  • the specific process vector generation scheme please refer to the foregoing.
  • the process vector corresponding to the order to be processed may be determined from the process vectors corresponding to at least one historical order. The distance between the target process vectors that meet the preset conditions.
  • N is an integer
  • M process vectors with the closest distance to the process vector corresponding to the order to be processed are determined as target process vectors, where M is an integer.
  • the subspace can be spherical.
  • the production scheduling information of the historical similar orders can be used as a reference, and the production scheduling of the to-be-processed order is performed.
  • the scheduling information of similar orders can be historically used as one of the scheduling basis for pending orders.
  • the production scheduling information may include limited operator information, equipment information, working hours information, and the like. It should be understood that, in this embodiment, the production scheduling basis of the pending order may not only include production scheduling information of similar historical orders, but may also include information of other dimensions such as production resource information, production capacity constraint information, etc. This is no longer exhaustive.
  • an optimization scheme can be selected from thousands or even millions of feasible schemes through algorithms and optimization and simulation techniques, and a scientific production scheduling scheme can be generated. . Among them, the reuse of production scheduling information of similar historical orders can save a lot of algorithm resources and effectively improve production scheduling efficiency and accuracy. Accordingly, in step 302, production scheduling information of the order to be processed may be generated.
  • the reference priority of the multiple historically similar orders can be determined based on the similarity of the production process between each of the multiple historically similar orders and the order to be processed. Orders with high historical similarity can be configured with higher reference priority. On this basis, the orders to be processed can be scheduled according to the reference priority of multiple historically similar orders and the production scheduling information of multiple historically similar orders. For example, the scheduling information of the historically similar orders with the highest priority can be directly reused as the basis for scheduling the orders to be processed.
  • weights can be configured for different historically similar orders according to the reference priority, and the project parameters can be weighted and summed under the specified items (such as working hours, etc.) in the production scheduling information to determine the reference under the specified item. parameters, and the reference parameters under other items can directly reuse the corresponding item parameters in the historical similar orders with the highest reference priority, so as to determine the reference parameters under each item in the scheduling information, as the basis for scheduling the orders to be processed .
  • these are only exemplary, and the present embodiment is not limited thereto.
  • the target equipment corresponding to the order to be processed can be determined after production scheduling; the equipment debugging parameters of similar historical orders can be reused to perform pre-production debugging on the target equipment.
  • Equipment debugging parameters may include but are not limited to equipment model, rotational speed, needle distance, etc. This can realize the processization and digitization of each link in the prenatal equipment debugging stage, and reasonably guide the prenatal mechanical repair, prenatal equipment debugging and other work.
  • quotation information corresponding to historical similar orders adapted to the pending order can also be obtained, and according to the quotation information, the price of the pending order can be estimated. Based on this, a quotation request for the pending order is received, and a quotation is made for the pending order according to the price estimation result of the pending order.
  • this embodiment can also provide reference for other order processing requirements based on historical similar orders, and this embodiment is not limited to this.
  • the production process accumulated in the production process is used as the driving force to recommend historically similar orders for the pending orders, wherein the production processes between the historically similar orders and the pending orders are similar.
  • historical similar orders can be used as a reference to schedule the production of the pending orders, so that the pending orders can be produced more efficiently and accurately.
  • similar historical orders can be added as the basis for scheduling the orders to be processed, thereby improving the scheduling efficiency and accuracy, thereby breaking through the bottleneck and uncertainty of production efficiency.
  • FIG. 4 is a schematic flowchart of another order processing method provided by another exemplary embodiment of the present application.
  • the method may be performed by an order processing apparatus, which may be implemented as a combination of software and/or hardware, which may be integrated in a computing device.
  • the method includes:
  • Step 400 in response to the production scheduling instruction, obtain order information of the order to be processed
  • Step 401 determine the historical similar order corresponding to the order to be processed
  • Step 402 based on the scheduling information of historical similar orders, schedule the order to be processed, so as to generate scheduling information of the order to be processed.
  • the order processing method provided in this embodiment can be applied to a scenario where an order needs to be scheduled.
  • the application scenarios are not limited in this embodiment.
  • step 400 order information of the order to be processed may be obtained in response to the production scheduling instruction.
  • pending orders are orders that need to be scheduled.
  • a production scheduling interface is displayed, and the production scheduling staff can perform human-computer interaction based on the production scheduling interface.
  • the production scheduling staff can perform scheduling trigger operations in the production scheduling interface. to generate scheduling instructions.
  • order information of the order to be processed may include but not limited to the production process, order number, product model number, and so on.
  • step 401 historically similar orders corresponding to the orders to be processed may be determined according to the order information.
  • historically similar orders adapted to the pending orders may be determined according to the production process of the pending orders.
  • the preset conditions are met, for example, the production process similarity between the two parties is higher than the preset threshold, etc.
  • the production process of the pending order to determine the implementation details of the historically similar orders adapted to the pending order, reference may be made to the relevant descriptions in the embodiments associated with FIG. 1a, FIG. 1b and FIG. 2 above. To save space, in This will not be repeated here.
  • the product model number can be used to identify the style of the product, for example, the model number 11-30-12 can be used to identify the short-light blue-denim jacket with pockets, and the model number 22-30-12 can be used to identify the long model - light blue - denim jacket with pockets, in this case, the two models can be determined as the matching model.
  • the exact same model number can also be determined as the matching model number. For example, if there is the same product model number between the order a initiated by company A and the order b initiated by company B, then Order a and order b are determined to be similar orders.
  • the production scheduling information of the to-be-processed order may be performed based on the production scheduling information of the historical similar orders, so as to generate production scheduling information of the to-be-processed order.
  • the production scheduling information may include limited operator information, equipment information, working hours information, and the like.
  • the scheduling information of similar historical orders can be used as one of the scheduling basis for pending orders.
  • the production scheduling basis of the pending order may not only include production scheduling information of similar historical orders, but may also include information of other dimensions such as production resource information, production capacity constraint information, etc. This is no longer exhaustive.
  • an optimization scheme can be selected from thousands or even millions of feasible schemes through algorithms and optimization and simulation techniques, and a scientific production scheduling scheme can be generated. .
  • the reuse of production scheduling information of similar historical orders can save a lot of algorithm resources and effectively improve production scheduling efficiency and accuracy. Based on this, scheduling information for pending orders can be generated.
  • the reference priorities of the multiple historically similar orders can be determined. For example, a historically similar order with a higher production process similarity can be configured with a higher reference priority. In practical applications, the reference priorities of the multiple historically similar orders may be determined based on the production process similarity between each of the multiple historically similar orders and the pending order. Of course, this embodiment is not limited to this. On this basis, the orders to be processed can be scheduled according to the reference priority of multiple historically similar orders and the production scheduling information of multiple historically similar orders. For example, the scheduling information of the historically similar orders with the highest priority can be directly reused as the basis for scheduling the orders to be processed.
  • weights can be configured for different historically similar orders according to the reference priority, and the project parameters can be weighted and summed under the specified items (such as working hours, etc.) in the production scheduling information to determine the reference under the specified item. parameters, and the reference parameters under other items can directly reuse the corresponding item parameters in the historical similar orders with the highest reference priority, so as to determine the reference parameters under each item in the scheduling information, as the basis for scheduling the orders to be processed .
  • these are only exemplary, and the present embodiment is not limited thereto.
  • similar orders in history can be used as a reference to schedule production of the to-be-processed order, so that the to-be-processed order can be produced more efficiently and accurately.
  • similar historical orders can be added as the basis for scheduling the orders to be processed, thereby improving the scheduling efficiency and accuracy, thereby breaking through the bottleneck and uncertainty of production efficiency.
  • a production scheduling interface may also be displayed; in response to an order input operation occurring in the production scheduling interface, order information of the order to be processed is configured.
  • the production scheduling staff can enter the order information of the pending order in the production scheduling interface, for example, enter the order number, production process, product model number and other information.
  • the scheduling staff can click the scheduling button to generate a scheduling instruction, which in turn triggers the aforementioned scheduling process.
  • the scheduling information of the pending order can also be displayed in the scheduling interface; in response to the adjustment operation for the scheduling information, the scheduling information of the pending order can be modified.
  • the scheduling staff may modify and/or confirm the scheduling information of the order to be processed, so as to ensure the accuracy of the scheduling information of the order to be processed.
  • the target equipment corresponding to the pending orders can be determined after scheduling; the equipment debugging parameters of similar historical orders can be sent to the target equipment, so that the target equipment can be adjusted according to the equipment debugging parameters.
  • Equipment debugging parameters may include but are not limited to equipment model, rotational speed, needle distance, etc. This can realize the processization and digitization of each link in the prenatal equipment debugging stage, and reasonably guide the prenatal mechanical repair, prenatal equipment debugging and other work.
  • the quotation information corresponding to the historically similar orders adapted to the pending order can also be obtained, and based on the quotation information, the price of the pending order can be estimated. Based on this, a quotation request for the pending order is received, and a quotation is made for the pending order according to the price estimation result of the pending order.
  • an order interface can also be provided, and the production process of the order to be processed is displayed in the order interface.
  • Scheduling staff can monitor the production progress of pending orders based on the production process and scheduling information of pending orders.
  • the production scheduling staff can also initiate a processing reminder for the target process link in the production process of the pending order to remind the operator of the target equipment corresponding to the target process link to start the target process link of the pending order in time to ensure production efficiency.
  • this embodiment can also provide reference for other order processing requirements based on historical similar orders, and this embodiment is not limited to this.
  • the execution subject of each step of the method provided in the above-mentioned embodiments may be the same device, or the method may also be executed by different devices.
  • the execution subject of steps 100 to 102 may be device A; for another example, the execution subject of steps 100 and 1012 may be device A, and the execution subject of step 102 may be device B; and so on.
  • FIG. 5 is a schematic structural diagram of a computing device according to another exemplary embodiment of the present application. As shown in FIG. 5 , the computing device includes: a memory 50 , a processor 51 and a communication component 52 .
  • a processor 51 coupled to the memory 50, executes a computer program in the memory 50 for:
  • the processor 51 determines a historically similar order adapted to the pending order based on the production process, the processor 51 is configured to:
  • the historical orders corresponding to the target process characteristics are regarded as historical similar orders.
  • the processor 51 when extracting process features from the production process of the order to be processed, is used to:
  • the specified process information in the production process of the order to be processed is vectorized, and a process vector is obtained to characterize the process characteristics.
  • the processor 51 when the processor 51 determines that the similarity with the process feature of the order to be processed meets the preset condition, the processor 51 is configured to:
  • a target process vector whose distance between the process vectors corresponding to the order to be processed conforms to the preset condition is determined.
  • the processor 51 determines that the distance between the process vectors corresponding to the order to be processed conforms to a target process vector with a preset condition, the processor 51 is configured to:
  • N is an integer
  • M process vectors with the closest distance to the process vector corresponding to the order to be processed are determined as target process vectors, where M is an integer.
  • the subspace is spherical.
  • the process features include one or more of graphic features, material features, process features, man-hour features or style features.
  • processor 51 is further configured to:
  • the equipment debugging parameters of similar orders in history are reused to perform pre-production debugging on the target equipment.
  • processor 51 is further configured to:
  • the reference priority of multiple historically similar orders is determined based on the production process similarity between the multiple historically similar orders and the pending order;
  • scheduling the pending orders including: scheduling pending orders according to the reference priority of multiple historical orders and the scheduling information of multiple historical orders.
  • processor 51 is further configured to:
  • the computing device further includes: a power supply component 53 and other components. Only some components are schematically shown in FIG. 5 , which does not mean that the computing device only includes the components shown in FIG. 5 .
  • the embodiments of the present application further provide a computer-readable storage medium storing a computer program, and when the computer program is executed, each step that can be executed by a computing device in the foregoing method embodiments can be implemented.
  • FIG. 6 is a schematic structural diagram of another computing device provided by yet another exemplary embodiment of the present application. As shown in FIG. 6 , the computing device includes: a memory 60 , a processor 61 and a communication component 62 .
  • a processor 61 coupled to the memory 60, executes a computer program in the memory 60 for:
  • a historically similar order adapted to the pending order is determined, and the similarity of the process characteristics between the historically similar order and the pending order meets a preset condition.
  • the processor 61 when extracting process features from the production process, is used for:
  • the specified process information in the production process is vectorized to obtain process vectors to characterize process characteristics.
  • the processor 61 determines a historically similar order adapted to the order to be processed based on the process characteristics, the processor 61 is configured to:
  • the historical order corresponding to the target process vector is regarded as the historical similar order.
  • the processor 61 determines that the distance between the process vectors corresponding to the order to be processed conforms to a target process vector with a preset condition, the processor 61 is configured to:
  • N is an integer
  • the subspace is spherical.
  • the process features include one or more of graphic features, material features, process features, man-hour features or style features.
  • the computing device further includes: a power supply component 63 and other components. Only some components are schematically shown in FIG. 6 , which does not mean that the computing device only includes the components shown in FIG. 6 .
  • the embodiments of the present application further provide a computer-readable storage medium storing a computer program, and when the computer program is executed, each step that can be executed by a computing device in the foregoing method embodiments can be implemented.
  • FIG. 7 is a schematic structural diagram of still another computing device according to still another exemplary embodiment of the present application.
  • the computing device includes: a memory 70 and a processor 71 .
  • a processor 71 coupled to the memory 70, executes a computer program in the memory 70 for:
  • the processor 71 is further configured to:
  • order information for pending orders is configured.
  • the processor 71 is further configured to:
  • the scheduling information of the pending order is modified.
  • the processor 71 is further configured to:
  • the scheduling information of the pending order determine the target equipment corresponding to the pending order after scheduling
  • the order information includes one or more of a production process, an order number or a product model number.
  • the processor 71 is further configured to:
  • the production process of the pending order is displayed.
  • the processor 71 is further configured to:
  • scheduling the pending orders includes: scheduling pending orders according to the reference priorities of multiple historical orders and the scheduling information of multiple historical orders.
  • the processor 71 is further configured to:
  • the computing device further includes: a communication component 72 , a power supply component 73 and other components. Only some components are schematically shown in FIG. 7 , which does not mean that the computing device only includes the components shown in FIG. 7 .
  • the embodiments of the present application further provide a computer-readable storage medium storing a computer program, and when the computer program is executed, each step that can be executed by a computing device in the foregoing method embodiments can be implemented.
  • the memory in the above-described FIGS. 5-7 is used to store computer programs and may be configured to store various other data to support operations on the computing platform. Examples of such data include instructions for any application or method operating on the computing platform, contact data, phonebook data, messages, pictures, videos, etc.
  • Memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Read Only Memory (PROM), Read Only Memory (ROM), Magnetic Memory, Flash Memory, Magnetic or Optical Disk.
  • SRAM Static Random Access Memory
  • EEPROM Electrically Erasable Programmable Read Only Memory
  • EPROM Erasable Programmable Read Only Memory
  • PROM Programmable Read Only Memory
  • ROM Read Only Memory
  • Magnetic Memory Flash Memory
  • Magnetic or Optical Disk Magnetic Disk
  • the above-mentioned communication components in FIGS. 5-7 are configured to facilitate wired or wireless communication between the device where the communication component is located and other devices.
  • the device where the communication component is located can access a wireless network based on a communication standard, such as WiFi, a mobile communication network such as 2G, 3G, 4G/LTE, 5G, or a combination thereof.
  • the communication component receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel.
  • the communication assembly further includes a near field communication (NFC) module to facilitate short-range communication.
  • the NFC module may be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
  • RFID radio frequency identification
  • IrDA infrared data association
  • UWB ultra-wideband
  • Bluetooth Bluetooth
  • a power supply assembly may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the equipment in which the power supply assembly is located.
  • the embodiments of the present application may be provided as a method, a system, or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.
  • computer-usable storage media including, but not limited to, disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions
  • the apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.
  • a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
  • processors CPUs
  • input/output interfaces network interfaces
  • memory volatile and non-volatile memory
  • Memory may include forms of non-persistent memory, random access memory (RAM) and/or non-volatile memory in computer readable media, such as read only memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
  • RAM random access memory
  • ROM read only memory
  • flash RAM flash memory
  • Computer-readable media includes both persistent and non-permanent, removable and non-removable media, and storage of information may be implemented by any method or technology.
  • Information may be computer readable instructions, data structures, modules of programs, or other data.
  • Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Flash Memory or other memory technology, Compact Disc Read Only Memory (CD-ROM), Digital Versatile Disc (DVD) or other optical storage, Magnetic tape cartridges, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
  • computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves.

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Abstract

本申请实施例提供一种订单处理方法、设备及存储介质。在本申请实施例中，以生产过程中积累的生产工艺为驱动，为待处理订单推荐历史相似订单，其中，历史相似订单与待处理订单之间的生产工艺相似。基于此，可以历史相似订单作为参考，对待处理订单进行排产，从而更加高效、准确地对待处理订单进行生产。据此，本申请实施例中，可增加历史相似订单作为对待处理订单进行排产的依据，从而提高排产效率和准确率，进而突破生产效率的瓶颈及不确定性。

Description

一种订单处理方法、设备及存储介质
本申请要求2021年04月25日递交的申请号为202110450028.5、发明名称为“一种订单处理方法、设备及存储介质”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及数据处理技术领域，尤其涉及一种订单处理方法、设备及存储介质。
背景技术
在服装生产领域，排产是产前准备的重要工作，排产即是对生产任务进行排位布局。在服装生产过程中，人员、设备、制品运输等环节的管理都依赖于排产结果，因此，排产结果直接影响生产成本和生产效率。
目前，通常需要依赖专家经验进行人工排产，排序效率和准确率不佳，进而影响生产效率和稳定性。
发明内容
本申请的多个方面提供一种订单处理方法、设备及存储介质，用以订单的排产效率和/或准确率。
本申请实施例提供一种订单处理方法，包括：
获取待处理订单的生产工艺；
基于所述生产工艺确定与所述待处理订单适配的历史相似订单，所述历史相似订单与所述待处理订单之间的生产工艺相似度符合预设条件；
以所述历史相似订单的排产信息作为参考，对所述待处理订单进行排产。
本申请实施例还提供一种订单处理方法，包括：
获取待处理订单的生产工艺；
从所述生产工艺中，提取工艺特征；
基于所述工艺特征确定与所述待处理订单适配的历史相似订单，所述历史相似订单与所述待处理订单之间的工艺特征相似度符合预设条件。
本申请实施例还提供一种计算设备，包括存储器、处理器和通信组件；
所述存储器用于存储一条或多条计算机指令；
所述处理器与所述存储器及所述通信组件耦合，用于执行所述一条或多条计算机指令，以用于：
通过所述通信组件获取待处理订单的生产工艺；
基于所述生产工艺确定与所述待处理订单适配的历史相似订单，所述历史相似订单与所述待处理订单之间的生产工艺相似度符合预设条件；
以所述历史相似订单的排产信息作为参考，对所述待处理订单进行排产。
本申请实施例还提供一种计算设备，包括存储器、处理器和通信组件；
所述存储器用于存储一条或多条计算机指令；
所述处理器与所述存储器及所述通信组件耦合，用于执行所述一条或多条计算机指令，以用于：
通过所述通信组件获取待处理订单的生产工艺；
从所述生产工艺中，提取工艺特征；
基于所述工艺特征确定与所述待处理订单适配的历史相似订单，所述历史相似订单与所述待处理订单之间的工艺特征相似度符合预设条件。
本申请实施例还提供一种订单处理方法，包括：
响应于排产指令，获取待处理订单的订单信息；
根据所述订单信息，确定所述待处理订单对应的历史相似订单；
基于所述历史相似订单的排产信息，对所述待处理订单进行排产，以生成所述待处理订单的排产信息。
本申请实施例还提供一种计算设备，包括存储器和处理器；
所述存储器用于存储一条或多条计算机指令；
所述处理器与所述存储器耦合，用于执行所述一条或多条计算机指令，以用于：
响应于排产指令，获取待处理订单的订单信息；
根据所述订单信息，确定所述待处理订单对应的历史相似订单；
基于所述历史相似订单的排产信息，对所述待处理订单进行排产，以生成所述待处理订单的排产信息。
本申请实施例还提供一种存储计算机指令的计算机可读存储介质，当所述计算机指令被一个或多个处理器执行时，致使所述一个或多个处理器执行前述的订单处理方法。
在本申请实施例中，以生产过程中积累的生产工艺为驱动，为待处理订单推荐历史相似订单，其中，历史相似订单与待处理订单之间的生产工艺相似。基于此，可以历史相似订单作为参考，对待处理订单进行排产，从而更加高效、准确地对待处理订单进行生产。据此，本申请实施例中，可增加历史相似订单作为对待处理订单进行排产的依据，从而提高排产效率和准确率，进而突破生产效率的瓶颈及不确定性。
附图说明
此处所说明的附图用来提供对本申请的进一步理解，构成本申请的一部分，本申请的示意性实施例及其说明用于解释本申请，并不构成对本申请的不当限定。在附图中：
图1a为本申请一示例性实施例提供的一种订单处理方法的流程示意图；
图1b为本申请一示例性实施例提供的一种订单处理方法的流程示意图；
图2为本申请一示例性实施例提供的一种相似性判断方案的逻辑示意图；
图3为本申请另一示例性实施例提供的另一种订单处理方法的流程示意图；
图4为本申请又一示例性实施例提供的又一种订单处理方法的流程示意图；
图5为本申请又一示例性实施例提供的一种计算设备的结构示意图；
图6为本申请又一示例性实施例提供的另一种计算设备的结构示意图；
图7为本申请又一示例性实施例提供的又一种计算设备的结果示意图。
具体实施方式
为使本申请的目的、技术方案和优点更加清楚，下面将结合本申请具体实施例及相应的附图对本申请技术方案进行清楚、完整地描述。显然，所描述的实施例仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
目前，在服装生产领域，需要依赖人工排产，排产效率和准确率不佳。为此，本申请的一些实施例中：以生产过程中积累的生产工艺为驱动，为待处理订单推荐历史相似订单，其中，历史相似订单与待处理订单之间的生产工艺相似。基于此，可以历史相似订单作为参考，对待处理订单进行排产，从而更加高效、准确地对待处理订单进行生产。据此，本申请实施例中，可增加历史相似订单作为对待处理订单进行排产的依据，从而提高排产效率和准确率，进而突破生产效率的瓶颈及不确定性。
以下结合附图，详细说明本申请各实施例提供的技术方案。
图1a为本申请另一示例性实施例提供的一种订单处理方法的流程示意图，图1b为本申请一示例性实施例提供的一种订单处理方法的流程示意图。该方法可由订单处理装置执行，该订单处理装置可实现为软件和/或硬件的结合，该订单处理装置可集成在计算设备中。参考图1a，该方法包括：
步骤100、获取待处理订单的生产工艺；
步骤101、从生产工艺中，提取工艺特征；
步骤102、基于工艺特征确定与待处理订单适配的历史相似订单，历史相似订单与待处理订单之间的工艺特征相似度符合预设条件。
本实施例提供的订单处理方法，可应用于各类生产场景中，例如、服装生产场景、机械零件生产场景等等，本实施例对应用场景不作限定。在不同的应用场景中，生产工艺中包含的工艺内容可能不完全相同。
参考图1a和图1b，在步骤100中，可获取待处理订单的生产工艺。其中，生产工艺可以是生产完成一款生产对象所采用的生产方案的描述数据。生产工艺中包含的工艺信息可以是多样的，例如，生产工艺中不仅可包含工序信息，还可包含生产过程中涉及到的物料信息、工时信息、款式信息、图形信息等等。例如，工序信息可以包括但不限于工序名称、顺序、部位、部件、操作类型、工序难度、动作、动作编码、动作频率、动作时长、车缝长度等。物料信息可包括但不限于物料名称、颜色、采购方、类型、工段、描述、尺寸、用量、幅宽、单位、长度、克重等。工时信息可包括但不限于多个工序步骤各自所需的工时等。款式信息可包括但不限款式标识、线迹、形状特征等。图形 信息可包括但不限于CAD图中的制图信息等。值得说明的是，本实施例中，生产工艺中包含的工艺信息的类型和数量并不限于此，每种工艺信息下的信息项的类型和数量也并不限于此，另外，各信息项所划分至的工艺信息的类型也并不限于此。
本实施例中，待处理订单的生产工艺可能分布在一个或多个数据源中，例如，上述的工时信息可能分布在企业的GSD标准工时管理系统中，而物料信息则可能分布在物料管理系统中等。优选地，本实施例中，可在需要对待处理订单进行处理时，实时地从一个或多个数据源中获取工艺信息，以组合为待处理订单的生产工艺，这种获取方式可保证生产工艺的准确性。当然，本实施例中，也可从一个或多个数据源中预先收集待处理订单的生产工艺并进行存储，但是，在数据源中的工艺信息发生更新时，需要及时对存储的生产工艺进行同步更新，这种方式可提高生产工艺的使用便捷性，不需要实时地访问数据源，但需要及时进行数据同步，以避免生产工艺的数据一致性问题，。
本实施例中，还可预先获取至少一个历史订单的生产工艺，作为生产工艺相似性判断的基础。
参考图1a和图1b，在步骤101中，可从待处理订单的生产工艺中提取工艺特征。与前述的生产工艺包含的工艺信息相对应的，本实施例中，可针对前述的各个工艺信息分别提取工艺特征，从而可获得待处理订单在至少一个工艺信息维度下的工艺特征，还可对工艺信息进行合并，并针对合并后的工艺信息进行工艺特征提取，以获得更加丰富、更多维度的工艺特征。因此，本实施例中，工艺特征可反映其对应的工艺信息维度下的各信息项的参数取值，通过工艺特征可全面、准确地表征待处理订单的生产工艺。对于至少一个历史订单，本实施例中，可按照与待处理订单一致的提取方案，预先从至少一个历史订单的生产工艺中提取出工艺特征。其中，工艺特征的提取方案将在后文中详述。
实际应用中，生产工艺的展示格式可能是多种多样的，例如，生产工艺可能是文本格式或图像格式等。为此，本实施例中，可通过NER命名实体识别、算法解析DXF绘图交换文件等方式，对生产工艺进行结构化处理。另外，还可对生产工艺进行归一化处理，以统一信息项在不同生产工艺中的表示。当然，这些处理环节并不是必须的，可根据实际情况进行选用。
在此基础上，参考图1a和图1b，在步骤102中，可基于工艺特征确定与待处理订单适配的历史相似订单，其中，历史相似订单与待处理订单之间的工艺特征相似度符合预设条件。也即是，可对待处理订单和至少一个历史订单进行工艺特征的相似性判断，以挖掘待处理订单适配的历史相似订单。可选地，此处的预设条件可以是相似度最高，当然，本实施例并不限于此。另外，实际应用中，步骤102中确定出的历史相似订单可以是一个或多个。例如，可从至少一个历史订单中，确定与待处理订单之间的工艺特征相似度最高的K个历史订单，作为历史相似订单，这里K可取正整数。
本实施例中，可将待处理订单适配的历史相似订单输出，以为待处理订单生产处理 过程提供参考，例如，为待处理订单的排产、设备调试、报价等生产处理环节提供参考等。
据此，本实施例中，可从生产过程中积累的生产工艺中，全面、准确地提取工艺特征，作为对生产工艺进行相似性判断的依据，从而可更加客观、更加准确地为待处理订单挖掘适配的历史相似订单，不再依赖人工经验进行生产工艺的相似性判断，这可有效提高生产工艺的相似性判断的效率和准确率。
图2为本申请一示例性实施例提供的一种相似性判断方案的逻辑示意图。参考图2，在上述或下述实施例中，一种示例性的工艺特征提取方案可以是，对生产工艺中的指定工艺信息进行向量化，获得工艺向量，以表征工艺特征。
实际应用中，可对生产工艺进行结构化，以产生至少一类结构化的工艺信息，例如前文提及的款式信息、工序信息等。以将生产工艺结构化成数据表为例，可以信息项作为数据表中的字段，以单类工艺信息作为数据表中的一条记录，这样，单类工艺信息下可包含一个或多个信息项。基于此，参考图2，在一种可选方案中，可指定影响生产工艺相似性的至少一个工艺信息，并针对指定工艺信息，分别生成工艺向量；还可将工艺信息进行合并，并为合并后的工艺信息生成工艺向量等。其中，单个工艺向量通常将是多维的，不同维度可对应不同的信息项。这种方案下，针对单个生产工艺，可生成至少一个工艺向量，以表征生产工艺中蕴含的工艺特征。当然，本实施例并不限于此，也可以信息项为单位，生成工艺向量等。
本实施例中，可为待处理订单和至少一个历史订单分别生成工艺向量。至少一个历史订单的工艺向量生成过程与待处理订单的工艺向量生成过程一致，可选地，在后续的历史相似订单确定过程中，可将待处理订单和至少一个历史订单的工艺向量对齐，进而计算记录，当然，不同的距离计算方案的要求不同，对齐并不是必须的。可选地，在确定出指定工艺信息后，可将指定工艺信息输入预训练的工艺向量生成模型中，以将指定工艺信息转换为工艺向量。其中，工艺向量生成模型可封装为向量生成服务，工艺向量生成模型中包含工艺信息与工艺向量之间的映射关系。
在此基础上，本实施例中，可通过判断工艺向量的相似性来确定工艺特征的相似性，进而确定待处理订单适配的历史相似订单。其中，本实施例中，可以工艺向量之间的距离来表征工艺特征相似性，为此，本实施例中，可从至少一个历史订单对应的工艺向量中，确定与待处理订单对应的工艺向量之间的距离符合预设条件的目标工艺向量；将目标工艺向量对应的历史订单，作为历史相似订单。
本实施例中，针对不同的应用场景，可定义不完全相同的工艺向量距离计算方案。实际应用中，可以专家经验作为参考，梳理生产工艺的比较规则，从而确定距离计算方案。据此，本实施例中，工艺向量的距离计算方案并不局限于传统的欧式距离、曼哈顿距离等，可根据需要自定义距离计算方案，在此不作枚举。
考虑到自定义距离计算方案通常相比于传统的距离计算方案更加复杂，本实施例中，还提出了一种新的工艺向量检索方案：
将至少一个历史订单对应的工艺向量分配到至少一个子空间中；
搜索与待处理订单对应的工艺向量距离最近的N个目标子空间，N为整数；
在N个目标子空间中，确定与待处理订单对应的工艺向量距离最近的M个工艺向量，作为目标工艺向量，M为整数。
可选地，在该检索方案中，可基于PostgreSql数据库实现索引，PostgreSql数据库内置的GIST索引框架可为本实施例中的检索方案提供支持。优选地，在该检索方案中，子空间可采用球型的空间。基于此，在该检索方案中，可将至少一个历史订单对应的工艺向量分配到至少一个球型子空间中。实际应用中，可基于历史订单在空间的中分布状态，对子空间的管理：
若单个子空间中工艺向量的数量超过第一预设数量，则可确定出该子空间中距离最远的两个工艺向量之间的距离，并以这两个工艺向量为聚集中心，基于该子空间中剩余的工艺向量到两个聚类中心的距离，将剩余的工艺向量分为2组，每组工艺向量组成一个新的球型空间，从而实现子空间的拆分；
若单个子空间中工艺向量的数量低于第二预设数量，则可从该子空间周围的其它子空间中选择可合并的子空间(例如，包含的工艺向量同一低于第二预设数量等)，以两个子空间中距离最远的两个工艺向量定义新的子空间的直径，从而实现子空间的合并。
对于新增的历史订单对应的工艺向量，可判断该工艺向量到目标球型子空间的球心距离是否小于目标球型子空间的半径，若是，则可将该工艺向量分布至目标球型子空间中。如果可该工艺向量可分布至的目标球型子空间有多个，则可以前述的球心距离作为代价，将该工艺向量加入到代价最小的球型子空间中，以实现新增的历史订单对应的工艺向量的分布。
基于此，在该检索方案中，在对待处理订单的工艺向量进行相似向量检索过程中，可先找到距离待处理订单的工艺向量最近的N个球型子空间，从而过滤掉其他不符合条件的子空间，之后，可仅在找到的N个球型子空间中运行距离计算方案，从而确定出待处理订单适配的历史相似订单。这可大大减少距离计算次数，从而提升确定效率。
据此，本实施例中，可以工艺向量之间的距离来表征工艺向量的相似性，从而可客观、定量地计算生产工艺之间的相似度，进而可快速、准确地确定出待处理订单适配的历史相似订单，不再需要依赖专家经验进行人工查找。
以下以服装生产订单为例，对本实施例提供的订单处理方案进行说明。
可从工时信息和物理信息中选取10个信息项：线迹、动作、部件、工序难度、标准时长、颜色、尺寸、用量、长度、克重。10个信息项下的参数取值例如可以是：三线、拷、前片、C、5.3、红色、XXL、0.3、0.5、0.04。基于此，可基于该10个信息项构建 工艺向量，向量生成规则可以是：文本转成词向量，例如‘三线’通过word2vec转成向量；枚举值转向量，例如‘C’和‘XXL’是枚举值用数字表示，然后将数字归一化(等比放大缩小)到-1～1之间的浮点数。值得是，这仅是一个示例性工艺向量的生成方案，单个订单下，还可采用其它信息项组合方案，生成更多的工艺向量。
据此，可按照上述示例性工艺向量的生成方案，针对当前订单和4个历史订单分别生成一个10维向量：
当前订单：
[0.005462087,-0.0100988615,-0.0040000733,0.0017905467,-0.012032813,-0.0026727377,0.0063928985,-0.0018172202,-0.004628964,0.005429865]；
4个历史订单：
[-0.43348294,-0.005309539,-0.16797422,-0.111347705,-0.302786,-0.21936299,-0.13580714,0.2827767,-0.12682997,0.40433213]；
[-0.0051557007,-0.007110257,-0.0056681074,-0.0010106985,-0.011476816,-0.0056232507,0.0054634362,0.0039934306,1.6836553e-05,0.009823399,-0.0049403217]；
[-0.6713871,-0.043004863,-0.4496542,-0.45228654,-0.7560295,-0.5478991,-0.089437805,0.47785676,-0.111996986,0.70546716]；
[-0.49872798,-0.04214027,-0.329916,-0.31877938,-0.55709445,-0.4010836,-0.089899585,0.37543714,-0.07700009,0.523293]。
在此基础上，可针对该示例性工艺向量，实施上述实施例中提供的工艺向量检索及距离计算方案，同样，还可针对其它工艺向量，也实施上述实施例中提供的工艺向量检索及距离计算方案；并可对当前订单对应的各个工艺向量下的距离计算结果进行加权求和，从而找到与待处理订单的工艺向量综合距离最近的N个历史订单，作为历史相似订单。
参考图1b，上述的订单处理方法，可为多种订单处理需求提供参考。图3为本申请实施例另一示例性实施例提供的另一种订单处理方法的流程示意图，该方法可由订单处理装置执行，该订单处理装置可实现为软件和/或硬件的结合，该订单处理装置可集成在计算设备中。参考图3，该方法包括：
步骤300、获取待处理订单的生产工艺；
步骤301、基于生产工艺确定与待处理订单适配的历史相似订单，历史相似订单与待处理订单之间的生产工艺相似度符合预设条件；
步骤302、以历史相似订单的排产信息作为参考，对待处理订单进行排产。
本实施例提供的订单处理方法可应用于各种订单处理场景中，为订单提供排产方案。排产可以是指为订单制定生产计划，包括但不限于确定将订单分配值哪些生产线、哪些设备、哪些操作人员、哪些时间来进行生产等。其中，步骤300可参考图1a所关联的实 施例中的相关描述，在此不再赘述。
在步骤301中，可基于待处理订单的生产工艺为待处理订单确定适配的历史相似订单。其中，为待处理订单确定历史相似订单的方案可参考图1a所关联实施例中提供的方案，以下仅对历史相似订单的确定方案进行简单描述。
本实施例中，可从待处理订单的生产工艺中，提取工艺特征；获取至少一个历史订单的工艺特征；在至少一个历史订单的工艺特征中，确定与待处理订单的工艺特征之间的相似度符合预设条件的目标工艺特征；将目标工艺特征对应的历史订单，作为历史相似订单。本实施例中，工艺特征可包括但不限于图形特征、物料特征、工序特征、工时特征或款式特征等。
其中，在从待处理订单的生产工艺中，提取工艺特征的过程中，可对待处理订单的生产工艺中的指定工艺信息进行向量化，获得工艺向量，以表征工艺特征。具体的工艺向量生成方案可参考前文。
其中，在确定与待处理订单的工艺特征之间的相似度符合预设条件的目标工艺特征的过程中，可从至少一个历史订单对应的工艺向量中，确定与待处理订单对应的工艺向量之间的距离符合预设条件的目标工艺向量。
本实施例中，可提供一种新的工艺向量检索方案：
将至少一个历史订单对应的工艺向量分配到至少一个子空间中；
搜索与待处理订单对应的工艺向量距离最近的N个目标子空间，N为整数；
在N个目标子空间中，确定与待处理订单对应的工艺向量距离最近的M个工艺向量，作为目标工艺向量，M为整数。其中，可选地，子空间可采用球型。
值得说明的是，上述仅针对历史相似订单的确定方案进行了简单描述，方案细节可参考前文。另外，本实施例中，还可采用其它实现方式来确定待处理订单适配的历史相似订单，例如，人工查找等，本实施例并不限于此。
在此基础上，步骤302中，可以历史相似订单的排产信息作为参考，对待处理订单进行排产。实际应用中，可以历史相似订单的排产信息，作为待处理订单的排产依据之一。其中，排产信息可包括限于操作人员信息、设备信息、工时信息等。应当理解的是，本实施例中待处理订单的排产依据除了可包含历史相似订单的排产信息，还可包含生产资源信息、生产能力约束信息等其它多种维度的信息，本实施例在此不再穷举，在这些信息的基础上，可通过算法以及优化、模拟技术，从成千上万、甚至上百万个可行方案中选出一套优化方案，并生成科学的排产方案。其中，对历史相似订单的排产信息的复用，可节省大量的算法资源，有效提高排产效率和准确率。据此，在步骤302中，可生成待处理订单的排产信息。
其中，待处理订单对应的历史订单可能是一个或多个。若确定出的历史相似订单为多个，则可基于多个历史相似订单各自与待处理订单之间的生产工艺相似度，确定多个 历史相似订单的参考优先级，例如，生产工艺相似度越高的历史相似订单可配置更高的参考优先级。在此基础上，可按照多个历史相似订单的参考优先级和多个历史相似订单的排产信息，对待处理订单进行排产。例如，可直接复用优选级最高的历史相似订单的排产信息，作为对待处理订单进行排产的依据。又例如，可将按照参考优先级，为不同的历史相似订单配置权重，在排产信息中的指定项目(如工时等)下，对项目参数进行加权求和，以确定出指定项目下的参考参数，其它项目下的参考参数则可直接复用参考优先级最高的历史相似订单中的相应项目参数，从而确定出排产信息中各项目下的参考参数，作为对待处理订单进行排产的依据。当然，这些仅是示例性的，本实施例并不限于此。
另外，参考图1b，本实施例中还可确定待处理订单经排产后对应的目标设备；复用历史相似订单的设备调试参数，对目标设备进行产前调试。设备调试参数可包括但不限于设备型号、转速、针距等。这可实现产前设备调试阶段各环节的流程化和数字化，合理指导产前机修、产前设备调试等工作。
再者，参考图1b，本实施例中，还可获取待处理订单适配的历史相似订单对应的报价信息，根据报价信息，对待处理订单进行价格预估。基于此，接收针对待处理订单的报价请求，按照对待处理订单的价格预估结果，对待处理订单进行报价。
当然，本实施例中还可基于历史相似订单，为其它订单处理需求提供参考，本实施例并不限于此。
据此，本实施例中，以生产过程中积累的生产工艺为驱动，为待处理订单推荐历史相似订单，其中，历史相似订单与待处理订单之间的生产工艺相似。基于此，可以历史相似订单作为参考，对待处理订单进行排产，从而更加高效、准确地对待处理订单进行生产。据此，本申请实施例中，可增加历史相似订单作为对待处理订单进行排产的依据，从而提高排产效率和准确率，进而突破生产效率的瓶颈及不确定性。
图4为本申请又一示例性实施例提供的又一种订单处理方法的流程示意图。该方法可由订单处理装置执行，该订单处理装置可实现为软件和/或硬件的结合，该订单处理装置可集成在计算设备中。参考图4，该方法包括：
步骤400、响应于排产指令，获取待处理订单的订单信息；
步骤401、根据订单信息，确定待处理订单对应的历史相似订单；
步骤402、基于历史相似订单的排产信息，对待处理订单进行排产，以生成待处理订单的排产信息。
本实施例提供的订单处理方法可应用于需要对订单进行排产的场景中。例如、服装生产管理场景中等，本实施例对应用场景不作限定。
在步骤400中，可响应于排产指令，获取待处理订单的订单信息。其中，待处理订单即为需要进行排产的订单。实际应用中，展示一排产界面，排产工作人员可基于排产 界面进行人机交互，相应地，排产工作人员可在排产界面中执行排产触发操作，例如，点击排产按钮，以产生排产指令。
其中，待处理订单的订单信息可包括但不限于生产工艺、订单编号、产品款号等等。
基于此，在步骤401中，可根据订单信息，确定待处理订单对应的历史相似订单。
在一种可选的实现方式中，可根据待处理订单的生产工艺，确定与待处理订单适配的历史相似订单，这种方式下，历史相似订单与待处理订单之间的生产工艺相似度符合预设条件，例如，双方的生产工艺相似度高于预设阈值等。其中，根据待处理订单的生产工艺，确定与待处理订单适配的历史相似订单的实现细节可参考前文中图1a、图1b和图2所关联实施例中的相关描述，为节省篇幅，在此不再赘述。
本实施例中，还可采用其它实现方式确定待处理订单对应的历史相似订单。例如，可与待处理订单之间存在向适配的产品款号的历史订单，确定为待处理订单对应的历史相似订单。实际应用中，产品款号可用于标识产品的款式，例如，款号11-30-12可用于标识短款-浅蓝色-带口袋的牛仔衣，款号22-30-12可用于标识长款-浅蓝色-带口袋的牛仔衣，这种情况下，可将这两个款号确定为相适配的款号。当然，在实践中，也可将完全相同的款号才确定为相适配的款号，例如，A公司发起的订单a和B公司发起的订单b之间存在相同的产品款号，则可将订单a和订单b确定为相似的订单。
以上仅是示例性的，本实施例中确定与待处理订单适配的历史相似订单的实现方式并不限于此。
在此基础上，步骤402中，可基于历史相似订单的排产信息，对待处理订单进行排产，以生成待处理订单的排产信息。其中，排产信息可包括限于操作人员信息、设备信息、工时信息等。
实际应用中，可将历史相似订单的排产信息，作为待处理订单的排产依据之一。应当理解的是，本实施例中待处理订单的排产依据除了可包含历史相似订单的排产信息，还可包含生产资源信息、生产能力约束信息等其它多种维度的信息，本实施例在此不再穷举，在这些信息的基础上，可通过算法以及优化、模拟技术，从成千上万、甚至上百万个可行方案中选出一套优化方案，并生成科学的排产方案。其中，对历史相似订单的排产信息的复用，可节省大量的算法资源，有效提高排产效率和准确率。据此，可生成待处理订单的排产信息。
另外，本实施例中，待处理订单对应的历史订单可能是一个或多个。若确定出的历史相似订单为多个，则可确定多个历史相似订单的参考优先级，例如，生产工艺相似度越高的历史相似订单可配置更高的参考优先级。实际应用中，可基于多个历史相似订单各自与待处理订单之间的生产工艺相似度，来确定多个历史相似订单的参考优先级，当然，本实施例并不限于此。在此基础上，可按照多个历史相似订单的参考优先级和多个历史相似订单的排产信息，对待处理订单进行排产。例如，可直接复用优选级最高的历 史相似订单的排产信息，作为对待处理订单进行排产的依据。又例如，可将按照参考优先级，为不同的历史相似订单配置权重，在排产信息中的指定项目(如工时等)下，对项目参数进行加权求和，以确定出指定项目下的参考参数，其它项目下的参考参数则可直接复用参考优先级最高的历史相似订单中的相应项目参数，从而确定出排产信息中各项目下的参考参数，作为对待处理订单进行排产的依据。当然，这些仅是示例性的，本实施例并不限于此。
据此，本实施例中，可以历史相似订单作为参考，对待处理订单进行排产，从而更加高效、准确地对待处理订单进行生产。据此，本申请实施例中，可增加历史相似订单作为对待处理订单进行排产的依据，从而提高排产效率和准确率，进而突破生产效率的瓶颈及不确定性。
在上述或下述实施例中，还可展示排产界面；响应于在排产界面中发生的订单输入操作，配置待处理订单的订单信息。这里，排产工作人员，可在排产界面中输入待处理订单的订单信息，例如，输入订单编号、生产工艺、产品款号等信息。之后，排产工作人员，可点击排产按钮，以生成排产指令，进而触发前述的排产过程。
在对待处理订单完成排产后，还可在排产界面中，展示待处理订单的排产信息；响应于针对排产信息的调整操作，修改待处理订单的排产信息。这里，排产工作人员可对待处理订单的排产信息进行修改和/或确认等操作，以保证待处理订单的排产信息的准确性。
在此基础上，还可根据待处理订单的排产信息，确定待处理订单经排产后对应的目标设备；将历史相似订单的设备调试参数发送至目标设备，以按照设备调试参数对目标设备进行产前调试。设备调试参数可包括但不限于设备型号、转速、针距等。这可实现产前设备调试阶段各环节的流程化和数字化，合理指导产前机修、产前设备调试等工作。
另外，还可获取待处理订单适配的历史相似订单对应的报价信息，根据报价信息，对待处理订单进行价格预估。基于此，接收针对待处理订单的报价请求，按照对待处理订单的价格预估结果，对待处理订单进行报价。
本实施例中，还可提供订单界面，在订单界面中展示待处理订单的生产工艺。排产工作人员可基于待处理订单的生产工艺和排产信息，监管待处理订单的生产进度等。排产工作人员还可针对待处理订单的生产工艺中的目标工艺环节发起处理提醒，以提醒目标工艺环节对应的目标设备的操作人员及时启动待处理订单的目标工艺环节，保证生产效率。
当然，本实施例中还可基于历史相似订单，为其它订单处理需求提供参考，本实施例并不限于此。
需要说明的是，上述实施例所提供方法的各步骤的执行主体均可以是同一设备，或者，该方法也由不同设备作为执行主体。比如，步骤100至步骤102的执行主体可以为 设备A；又比如，步骤100和1012的执行主体可以为设备A，步骤102的执行主体可以为设备B；等等。
另外，在上述实施例及附图中的描述的一些流程中，包含了按照特定顺序出现的多个操作，但是应该清楚了解，这些操作可以不按照其在本文中出现的顺序来执行或并行执行，操作的序号如101、102等，仅仅是用于区分开各个不同的操作，序号本身不代表任何的执行顺序。另外，这些流程可以包括更多或更少的操作，并且这些操作可以按顺序执行或并行执行。需要说明的是，本文中的“第一”、“第二”等描述，是用于区分不同的阈值、消息、模块等，不代表先后顺序，也不限定“第一”和“第二”是不同的类型。
图5为本申请又一示例性实施例提供的一种计算设备的结构示意图。如图5所示，该计算设备包括：存储器50、处理器51以及通信组件52。
处理器51，与存储器50耦合，用于执行存储器50中的计算机程序，以用于：
通过通信组件52获取待处理订单的生产工艺；
基于生产工艺确定与待处理订单适配的历史相似订单，历史相似订单与待处理订单之间的生产工艺相似度符合预设条件；
以历史相似订单的排产信息作为参考，对待处理订单进行排产。
在一可选实施例中，处理器51在基于生产工艺确定与待处理订单适配的历史相似订单时，用于：
从待处理订单的生产工艺中，提取工艺特征；
获取至少一个历史订单的工艺特征；
在至少一个历史订单的工艺特征中，确定与待处理订单的工艺特征之间的相似度符合预设条件的目标工艺特征；
将目标工艺特征对应的历史订单，作为历史相似订单。
在一可选实施例中，处理器51在从待处理订单的生产工艺中，提取工艺特征时，用于：
对待处理订单的生产工艺中的指定工艺信息进行向量化，获得工艺向量，以表征工艺特征。
在一可选实施例中，处理器51在确定与待处理订单的工艺特征之间的相似度符合预设条件的目标工艺特征时，用于：
从至少一个历史订单对应的工艺向量中，确定与待处理订单对应的工艺向量之间的距离符合预设条件的目标工艺向量。
在一可选实施例中，处理器51在确定与待处理订单对应的工艺向量之间的距离符合预设条件的目标工艺向量时，用于：
将至少一个历史订单对应的工艺向量分配到至少一个子空间中；
搜索与待处理订单对应的工艺向量距离最近的N个目标子空间，N为整数；
在N个目标子空间中，确定与待处理订单对应的工艺向量距离最近的M个工艺向量，作为目标工艺向量，M为整数。
在一可选实施例中，子空间采用球型。
在一可选实施例中，工艺特征包括图形特征、物料特征、工序特征、工时特征或款式特征中的一种或多种。
在一可选实施例中，处理器51还用于：
确定待处理订单经排产后对应的目标设备；
复用历史相似订单的设备调试参数，对目标设备进行产前调试。
在一可选实施例中，处理器51还用于：
若与待处理订单适配的历史相似订单为多个，则基于多个历史相似订单与待处理订单之间的生产工艺相似度，确定多个历史相似订单的参考优先级；
以历史相似订单的排产信息作为参考，对待处理订单进行排产，包括：按照多个历史订单的参考优先级和多个历史订单的排产信息，对待处理订单进行排产。
在一可选实施例中，处理器51还用于：
接收针对待处理订单的报价请求；
获取与待处理订单适配的历史相似订单对应的报价信息；
根据报价信息，对待处理订单进行报价。
进一步，如图5所示，该计算设备还包括：电源组件53等其它组件。图5中仅示意性给出部分组件，并不意味着计算设备只包括图5所示组件。
值得说明的是，上述关于计算设备各实施例中的技术细节，可参考图3相关实施例中提供的订单处理方法的描述，为节省篇幅，在此不再赘述，但这不应造成本申请保护范围的损失。
相应地，本申请实施例还提供一种存储有计算机程序的计算机可读存储介质，计算机程序被执行时能够实现上述方法实施例中可由计算设备执行的各步骤。
图6为本申请又一示例性实施例提供的另一种计算设备的结构示意图。如图6所示，该计算设备包括：存储器60、处理器61以及通信组件62。
处理器61，与存储器60耦合，用于执行存储器60中的计算机程序，以用于：
通过通信组件62获取待处理订单的生产工艺；
从生产工艺中，提取工艺特征；
基于工艺特征确定与待处理订单适配的历史相似订单，历史相似订单与待处理订单之间的工艺特征相似度符合预设条件。
在一可选实施例中，处理器61在从生产工艺中，提取工艺特征时，用于：
对生产工艺中的指定工艺信息进行向量化，获得工艺向量，以表征工艺特征。
在一可选实施例中，处理器61在基于工艺特征确定与待处理订单适配的历史相似订单时，用于：
从至少一个历史订单对应的工艺向量中，确定与待处理订单对应的工艺向量之间的距离符合预设条件的目标工艺向量；
将目标工艺向量对应的历史订单，作为历史相似订单。
在一可选实施例中，处理器61在确定与待处理订单对应的工艺向量之间的距离符合预设条件的目标工艺向量时，用于：
将至少一个历史订单对应的工艺向量分配到至少一个子空间中；
搜索与待处理订单对应的工艺向量距离最近的N个目标子空间，N为整数；
在N个目标子空间中，确定与待处理订单对应的工艺向量距离最近的M个工艺向量，作为目标工艺向量，M为整数；
在一可选实施例中，子空间采用球型。
在一可选实施例中，工艺特征包括图形特征、物料特征、工序特征、工时特征或款式特征中的一种或多种。
进一步，如图6所示，该计算设备还包括：电源组件63等其它组件。图6中仅示意性给出部分组件，并不意味着计算设备只包括图6所示组件。
值得说明的是，上述关于计算设备各实施例中的技术细节，可参考图1a相关实施例中提供的订单处理方法的描述，为节省篇幅，在此不再赘述，但这不应造成本申请保护范围的损失。
相应地，本申请实施例还提供一种存储有计算机程序的计算机可读存储介质，计算机程序被执行时能够实现上述方法实施例中可由计算设备执行的各步骤。
图7为本申请又一示例性实施例提供的又一种计算设备的结构示意图。该计算设备包括：存储器70和处理器71。
处理器71，与存储器70耦合，用于执行存储器70中的计算机程序，以用于：
响应于排产指令，获取待处理订单的订单信息；
根据订单信息，确定待处理订单对应的历史相似订单；
基于历史相似订单的排产信息，对待处理订单进行排产，以生成待处理订单的排产信息。
在一可选实施例中，处理器71还用于：
展示排产界面；
响应于在排产界面中发生的订单输入操作，配置待处理订单的订单信息。
在一可选实施例中，处理器71还用于：
在排产界面中，展示待处理订单的排产信息；
响应于针对排产信息的调整操作，修改待处理订单的排产信息。
在一可选实施例中，处理器71还用于：
根据待处理订单的排产信息，确定待处理订单经排产后对应的目标设备；
将历史相似订单的设备调试参数发送至目标设备，以按照设备调试参数对目标设备进行产前调试。
在一可选实施例中，订单信息包括生产工艺、订单编号或产品款号中的一种或多种。
在一可选实施例中，处理器71还用于：
展示订单界面；
在订单界面中，显示待处理订单的生产工艺。
在一可选实施例中，处理器71还用于：
若待处理订单对应的历史相似订单为多个，则确定多个历史相似订单的参考优先级；
基于历史相似订单的排产信息，对待处理订单进行排产，包括：按照多个历史订单的参考优先级和多个历史订单的排产信息，对待处理订单进行排产。
在一可选实施例中，处理器71还用于：
接收针对待处理订单的报价请求；
获取待处理订单对应的历史相似订单的报价信息；
根据报价信息，对待处理订单进行报价。
进一步，如图7所示，该计算设备还包括：通信组件72、电源组件73等其它组件。图7中仅示意性给出部分组件，并不意味着计算设备只包括图7所示组件。
值得说明的是，上述关于计算设备各实施例中的技术细节，可参考图4相关实施例中提供的订单处理方法的描述，为节省篇幅，在此不再赘述，但这不应造成本申请保护范围的损失。
相应地，本申请实施例还提供一种存储有计算机程序的计算机可读存储介质，计算机程序被执行时能够实现上述方法实施例中可由计算设备执行的各步骤。
上述图5-图7中的存储器，用于存储计算机程序，并可被配置为存储其它各种数据以支持在计算平台上的操作。这些数据的示例包括用于在计算平台上操作的任何应用程序或方法的指令，联系人数据，电话簿数据，消息，图片，视频等。存储器可以由任何类型的易失性或非易失性存储设备或者它们的组合实现，如静态随机存取存储器(SRAM)，电可擦除可编程只读存储器(EEPROM)，可擦除可编程只读存储器(EPROM)，可编程只读存储器(PROM)，只读存储器(ROM)，磁存储器，快闪存储器，磁盘或光盘。
上述图5-图7中的通信组件，被配置为便于通信组件所在设备和其他设备之间有线或无线方式的通信。通信组件所在设备可以接入基于通信标准的无线网络，如WiFi，2G、3G、4G/LTE、5G等移动通信网络，或它们的组合。在一个示例性实施例中，通信组件经由广播信道接收来自外部广播管理系统的广播信号或广播相关信息。在一个示例性实 施例中，所述通信组件还包括近场通信(NFC)模块，以促进短程通信。例如，在NFC模块可基于射频识别(RFID)技术，红外数据协会(IrDA)技术，超宽带(UWB)技术，蓝牙(BT)技术和其他技术来实现。
上述图5-图7中的电源组件，为电源组件所在设备的各种组件提供电力。电源组件可以包括电源管理系统，一个或多个电源，及其他与为电源组件所在设备生成、管理和分配电力相关联的组件。
本领域内的技术人员应明白，本申请的实施例可提供为方法、系统、或计算机程序产品。因此，本申请可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本申请可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本申请是参照根据本申请实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
在一个典型的配置中，计算设备包括一个或多个处理器(CPU)、输入/输出接口、网络接口和内存。
内存可能包括计算机可读介质中的非永久性存储器，随机存取存储器(RAM)和/或非易失性内存等形式，如只读存储器(ROM)或闪存(flash RAM)。内存是计算机可读介质的示例。
计算机可读介质包括永久性和非永久性、可移动和非可移动媒体可以由任何方法或技术来实现信息存储。信息可以是计算机可读指令、数据结构、程序的模块或其他数据。计算机的存储介质的例子包括，但不限于相变内存(PRAM)、静态随机存取存储器 (SRAM)、动态随机存取存储器(DRAM)、其他类型的随机存取存储器(RAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、快闪记忆体或其他内存技术、只读光盘只读存储器(CD-ROM)、数字多功能光盘(DVD)或其他光学存储、磁盒式磁带，磁带磁磁盘存储或其他磁性存储设备或任何其他非传输介质，可用于存储可以被计算设备访问的信息。按照本文中的界定，计算机可读介质不包括暂存电脑可读媒体(transitory media)，如调制的数据信号和载波。
还需要说明的是，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、商品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、商品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、商品或者设备中还存在另外的相同要素。
以上所述仅为本申请的实施例而已，并不用于限制本申请。对于本领域技术人员来说，本申请可以有各种更改和变化。凡在本申请的精神和原理之内所作的任何修改、等同替换、改进等，均应包含在本申请的保护范围之内。

Claims (28)

  1. 一种订单处理方法，包括：
    获取待处理订单的生产工艺；
    基于所述生产工艺确定与所述待处理订单适配的历史相似订单，所述历史相似订单与所述待处理订单之间的生产工艺相似度符合预设条件；
    以所述历史相似订单的排产信息作为参考，对所述待处理订单进行排产。
  2. 根据权利要求1所述的方法，所述基于所述生产工艺确定与所述待处理订单适配的历史相似订单，包括：
    从所述待处理订单的生产工艺中，提取工艺特征；
    获取至少一个历史订单的工艺特征；
    在所述至少一个历史订单的工艺特征中，确定与所述待处理订单的工艺特征之间的相似度符合所述预设条件的目标工艺特征；
    将所述目标工艺特征对应的历史订单，作为所述历史相似订单。
  3. 根据权利要求2所述的方法，所述从所述待处理订单的生产工艺中，提取工艺特征，包括：
    对所述待处理订单的生产工艺中的指定工艺信息进行向量化，获得工艺向量，以表征所述工艺特征。
  4. 根据权利要求3所述的方法，所述确定与所述待处理订单的工艺特征之间的相似度符合所述预设条件的目标工艺特征，包括：
    从所述至少一个历史订单对应的工艺向量中，确定与所述待处理订单对应的工艺向量之间的距离符合所述预设条件的目标工艺向量。
  5. 根据权利要求4所述的方法，所述确定与所述待处理订单对应的工艺向量之间的距离符合所述预设条件的目标工艺向量，包括：
    将所述至少一个历史订单对应的工艺向量分配到至少一个子空间中；
    搜索与所述待处理订单对应的工艺向量距离最近的N个目标子空间，N为整数；
    在所述N个目标子空间中，确定与所述待处理订单对应的工艺向量距离最近的M个工艺向量，作为所述目标工艺向量，M为整数。
  6. 根据权利要求5所述的方法，所述子空间采用球型。
  7. 根据权利要求2所述的方法，所述工艺特征包括图形特征、物料特征、工序特征、工时特征或款式特征中的一种或多种。
  8. 根据权利要求1所述的方法，还包括：
    确定所述待处理订单经排产后对应的目标设备；
    复用所述历史相似订单的设备调试参数，对所述目标设备进行产前调试。
  9. 根据权利要求1所述的方法，还包括：
    若与所述待处理订单适配的历史相似订单为多个，则基于所述多个历史相似订单与 所述待处理订单之间的生产工艺相似度，确定所述多个历史相似订单的参考优先级；
    所述以所述历史相似订单的排产信息作为参考，对所述待处理订单进行排产，包括：按照所述多个历史订单的参考优先级和所述多个历史订单的排产信息，对所述待处理订单进行排产。
  10. 根据权利要求1所述的方法，还包括：
    接收针对所述待处理订单的报价请求；
    获取与待处理订单适配的历史相似订单对应的报价信息；
    根据所述报价信息，对所述待处理订单进行报价。
  11. 一种订单处理方法，包括：
    获取待处理订单的生产工艺；
    从所述生产工艺中，提取工艺特征；
    基于所述工艺特征确定与所述待处理订单适配的历史相似订单，所述历史相似订单与所述待处理订单之间的工艺特征相似度符合预设条件。
  12. 根据权利要求11所述的方法，所述从所述生产工艺中，提取工艺特征，包括：
    对所述生产工艺中的指定工艺信息进行向量化，获得工艺向量，以表征所述工艺特征。
  13. 根据权利要求12所述的方法，所述基于所述工艺特征确定与所述待处理订单适配的历史相似订单，包括：
    从至少一个历史订单对应的工艺向量中，确定与所述待处理订单对应的工艺向量之间的距离符合所述预设条件的目标工艺向量；
    将所述目标工艺向量对应的历史订单，作为所述历史相似订单。
  14. 根据权利要求13所述的方法，所述确定与所述待处理订单对应的工艺向量之间的距离符合所述预设条件的目标工艺向量，包括：
    将所述至少一个历史订单对应的工艺向量分配到至少一个子空间中；
    搜索与所述待处理订单对应的工艺向量距离最近的N个目标子空间，N为整数；
    在所述N个目标子空间中，确定与所述待处理订单对应的工艺向量距离最近的M个工艺向量，作为所述目标工艺向量，M为整数。
  15. 根据权利要求14所述的方法，所述子空间采用球型。
  16. 根据权利要求11所述的方法，所述工艺特征包括图形特征、物料特征、工序特征、工时特征或款式特征中的一种或多种。
  17. 一种订单处理方法，包括：
    响应于排产指令，获取待处理订单的订单信息；
    根据所述订单信息，确定所述待处理订单对应的历史相似订单；
    基于所述历史相似订单的排产信息，对所述待处理订单进行排产，以生成所述待处理订单的排产信息。
  18. 根据权利要求17所述的方法，还包括：
    展示排产界面；
    响应于在所述排产界面中发生的订单输入操作，配置所述待处理订单的订单信息。
  19. 根据权利要求18所述的方法，还包括：
    在所述排产界面中，展示所述待处理订单的排产信息；
    响应于针对所述排产信息的调整操作，修改所述待处理订单的排产信息。
  20. 根据权利要求17所述的方法，还包括：
    根据所述待处理订单的排产信息，确定所述待处理订单经排产后对应的目标设备；
    将所述历史相似订单的设备调试参数发送至所述目标设备，以按照所述设备调试参数对所述目标设备进行产前调试。
  21. 根据权利要求17所述的方法，所述订单信息包括生产工艺、订单编号或产品款号中的一种或多种。
  22. 根据权利要求17所述的方法，还包括：
    展示订单界面；
    在所述订单界面中，显示所述待处理订单的生产工艺。
  23. 根据权利要求17所述的方法，还包括：
    若所述待处理订单对应的历史相似订单为多个，则确定所述多个历史相似订单的参考优先级；
    所述基于所述历史相似订单的排产信息，对所述待处理订单进行排产，包括：按照所述多个历史订单的参考优先级和所述多个历史订单的排产信息，对所述待处理订单进行排产。
  24. 根据权利要求17所述的方法，还包括：
    接收针对所述待处理订单的报价请求；
    获取所述待处理订单对应的历史相似订单的报价信息；
    根据所述报价信息，对所述待处理订单进行报价。
  25. 一种计算设备，包括存储器、处理器和通信组件；
    所述存储器用于存储一条或多条计算机指令；
    所述处理器与所述存储器及所述通信组件耦合，用于执行所述一条或多条计算机指令，以用于：
    通过所述通信组件获取待处理订单的生产工艺；
    基于所述生产工艺确定与所述待处理订单适配的历史相似订单，所述历史相似订单与所述待处理订单之间的生产工艺相似度符合预设条件；
    以所述历史相似订单的排产信息作为参考，对所述待处理订单进行排产。
  26. 一种计算设备，包括存储器、处理器和通信组件；
    所述存储器用于存储一条或多条计算机指令；
    所述处理器与所述存储器及所述通信组件耦合，用于执行所述一条或多条计算机指令，以用于：
    通过所述通信组件获取待处理订单的生产工艺；
    从所述生产工艺中，提取工艺特征；
    基于所述工艺特征确定与所述待处理订单适配的历史相似订单，所述历史相似订单与所述待处理订单之间的工艺特征相似度符合预设条件。
  27. 一种计算设备，包括存储器和处理器；
    所述存储器用于存储一条或多条计算机指令；
    所述处理器与所述存储器耦合，用于执行所述一条或多条计算机指令，以用于：
    响应于排产指令，获取待处理订单的订单信息；
    根据所述订单信息，确定所述待处理订单对应的历史相似订单；
    基于所述历史相似订单的排产信息，对所述待处理订单进行排产，以生成所述待处理订单的排产信息。
  28. 一种存储计算机指令的计算机可读存储介质，当所述计算机指令被一个或多个处理器执行时，致使所述一个或多个处理器执行权利要求1-24任一项所述的订单处理方法。
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