WO2024045302A1 - 义齿分拣辅助方法、装置、计算机设备及可读存储介质 - Google Patents
义齿分拣辅助方法、装置、计算机设备及可读存储介质 Download PDFInfo
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- WO2024045302A1 WO2024045302A1 PCT/CN2022/127820 CN2022127820W WO2024045302A1 WO 2024045302 A1 WO2024045302 A1 WO 2024045302A1 CN 2022127820 W CN2022127820 W CN 2022127820W WO 2024045302 A1 WO2024045302 A1 WO 2024045302A1
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- denture
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- sintering
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- 238000000034 method Methods 0.000 title claims abstract description 55
- 238000003860 storage Methods 0.000 title claims description 25
- 238000005245 sintering Methods 0.000 claims abstract description 129
- 239000000463 material Substances 0.000 claims description 18
- 238000004364 calculation method Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 11
- 230000008859 change Effects 0.000 claims description 10
- 238000005520 cutting process Methods 0.000 claims description 10
- 238000004422 calculation algorithm Methods 0.000 abstract description 5
- MCMNRKCIXSYSNV-UHFFFAOYSA-N Zirconium dioxide Chemical compound O=[Zr]=O MCMNRKCIXSYSNV-UHFFFAOYSA-N 0.000 description 12
- 238000012545 processing Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000004891 communication Methods 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000010295 mobile communication Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007664 blowing Methods 0.000 description 1
- 238000012790 confirmation Methods 0.000 description 1
- 230000008094 contradictory effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 239000002241 glass-ceramic Substances 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000000843 powder Substances 0.000 description 1
- 230000008439 repair process Effects 0.000 description 1
- 230000011218 segmentation Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/16—Sorting according to weight
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/36—Sorting apparatus characterised by the means used for distribution
- B07C5/361—Processing or control devices therefor, e.g. escort memory
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
- G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
Definitions
- Embodiments of the present invention relate to the technical field of dental restoration, and in particular to a denture sorting auxiliary method, device, computer equipment and computer-readable storage medium.
- the denture materials widely used in clinical practice are zirconia and glass-ceramics.
- Zirconia occupies the vast majority of denture materials due to its high hardness and resistance to deformation. Among them, after the cutting of zirconia dentures is completed, they must be sintered to achieve the quality of the finished teeth. Zirconia sintering generally takes 8-10 hours. Due to the demand for denture production volume, existing denture manufacturers usually put multiple dentures into the same sintering furnace for simultaneous sintering, and then sort the dentures after the sintering is completed. Since the dental bridge (multiple connected teeth) has a certain degree of recognition, the rest of the teeth are not very different in shape.
- Sorting takes a long time and the sorting efficiency is low. Taking 30 dentures sintered at one time as an example, the first denture needs to be worn up to 30 times, the second denture needs to be worn up to 29 times, and so on, 30 dentures need to be worn up to 30+29+28+27+..
- embodiments of the present invention provide a denture sorting auxiliary method, device, computer equipment and computer-readable storage medium to solve the problems of low sorting efficiency and low accuracy of existing denture sorting methods.
- the denture sorting auxiliary system includes a user end and a server end.
- the user end includes a front end and a back end.
- the method include:
- the back-end receives the denture model data corresponding to at least one denture and the corresponding denture parameter data sent by the front-end, wherein the denture model data and the denture parameter data are generated by the front-end based on the user's input on the front-end. Data obtained by operations in the preset area;
- the denture model volume data corresponding to each denture is calculated
- the backend calculates the predicted weight of each denture after sintering based on the denture model volume data and corresponding denture parameter data of each denture, and sends the predicted weight of each denture after sintering. Sent to the said server;
- the predicted sintered weight and the actual sintered weight of each denture are received through the server, based on the predicted sintered weight and the actual sintered weight of each denture. , determine at least one candidate denture order data corresponding to each denture, and send the at least one candidate denture order data corresponding to each denture to the backend; and
- the backend determines the target denture order data corresponding to each denture from the at least one candidate denture order data corresponding to each denture according to the received at least one candidate denture order data corresponding to each denture. To achieve sorting of each denture.
- the backend calculates the denture model volume data corresponding to each denture based on the denture model data of each denture, including:
- the backend calculates the denture model volume data corresponding to each denture based on the multiple model patch data corresponding to each denture.
- the backend calculates the denture model volume data corresponding to each denture based on the multiple model patch data corresponding to each denture, including:
- the backend calculates the patch volume data corresponding to each model patch data based on the multiple model patch data corresponding to each denture.
- the patch volume data corresponding to each model patch data corresponding to each denture is combined respectively through the back end to obtain the denture model volume data corresponding to each denture.
- the denture parameter data includes the quantity contained by each denture, the weight before sintering, the steps experienced from cutting completion to the denture before sintering, and the material data selected for the denture.
- the back end calculates the predicted weight of each denture after sintering based on the denture model volume data and corresponding denture parameter data of each denture, including:
- the volume shrinkage ratio corresponding to each denture is obtained through the backend based on the denture model volume data of each denture;
- the predicted weight after sintering corresponding to each denture is calculated by the back end based on the denture model volume data of each denture, the volume shrinkage ratio, and the average weight change ratio.
- the predicted sintering weight and the actual sintering weight of each denture are received through the server, based on the predicted sintering weight and the actual sintering weight of each denture.
- Determine the actual sintered weight determine at least one candidate denture order data corresponding to each denture, and send the at least one candidate denture order data corresponding to each denture to the backend, including:
- the server compares the actual sintered weight of each denture with the predicted sintered weight of each denture to obtain at least one difference value corresponding to each denture;
- the server obtains the smallest difference value from at least one difference value corresponding to each denture, or sorts the at least one difference value corresponding to each denture in order according to the numerical value and obtains the top M values. Difference values, where the first difference value is the smallest difference value; and
- the server determines the denture order data corresponding to the smallest difference value corresponding to each denture as candidate denture order data, and sends at least one candidate denture order data corresponding to each denture to the backend, Or, determine the top M difference values corresponding to each denture as at least one candidate denture order data, and send the at least one candidate denture order data corresponding to each denture to the backend.
- One aspect of the present invention also provides a denture sorting auxiliary method, which is applied to the user end.
- the method includes:
- the denture model volume data corresponding to each denture is calculated
- the server Based on the predicted sintered weight of each denture and the actual sintered weight, at least one candidate denture order data corresponding to each denture is determined, and at least one candidate denture order data corresponding to each denture is sent.
- One aspect of the present invention also provides a denture sorting auxiliary device, which is applied to the user end.
- the device includes:
- a data receiving module configured to receive denture model data and corresponding denture parameter data corresponding to at least one denture input by the user
- the volume calculation module is used to calculate the volume data of the denture model corresponding to each denture based on the denture model data of each denture;
- a weight calculation module configured to calculate the predicted weight of each denture after sintering based on the denture model volume data and corresponding denture parameter data of each denture, and send the predicted weight of each denture after sintering. The weight is sent to the server;
- the weight acquisition module is used to obtain the actual sintering weight of each denture after each denture is put into the same sintering furnace for sintering, and send the actual sintering weight of each denture to the server to causing the server to: determine at least one candidate denture order data corresponding to each denture based on the predicted sintered weight and the actual sintered weight of each denture, and send each denture At least one candidate denture order data corresponding to each denture is sent to the user terminal; and
- An order receiving module configured to receive at least one candidate denture order data corresponding to each denture sent by the server;
- An order determination module configured to determine the target denture order corresponding to each denture from the at least one candidate denture order data corresponding to each denture based on the received at least one candidate denture order data corresponding to each denture. Data to enable sorting of each denture described.
- One aspect of the embodiment of the present invention further provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor.
- the processor executes the computer program, the above denture is implemented. Steps of the sorting aid method.
- One aspect of the embodiment of the present invention further provides a computer-readable storage medium, including a memory, a processor, and a computer program stored on the memory and executable on at least one processor, and the at least one processor executes the The computer program implements the steps of the above auxiliary method for sorting dentures.
- the denture sorting auxiliary method, device, computer equipment and computer-readable storage medium provided by the embodiment of the present invention receive at least one denture model data and corresponding denture parameter data corresponding to the denture sent by the front end through the back end, wherein , the denture model data and the denture parameter data are data obtained by the front end based on the user's operation in the preset area of the front end; through the back end, each denture is calculated according to the denture model data of each denture.
- Denture model volume data corresponding to the denture the backend calculates the predicted weight of each denture after sintering based on the denture model volume data and corresponding denture parameter data of each denture, and sends the The predicted weight of each denture after sintering is sent to the server; after each denture is put into the same sintering furnace for sintering, the actual weight of each denture after sintering is obtained through the back end, and the weight of each denture is sent to the server.
- the actual sintered weight of each denture is sent to the server; the predicted sintered weight and the actual sintered weight of each denture are received through the server, based on the sintered weight of each denture.
- Predicting the weight after sintering and the actual weight after sintering determining at least one candidate denture order data corresponding to each denture, and sending the at least one candidate denture order data corresponding to each denture to the backend ; and determining, through the backend, the target denture corresponding to each denture from the at least one candidate denture order data corresponding to each denture according to the received at least one candidate denture order data corresponding to each denture.
- order data to realize the sorting of each denture uses an algorithm to intelligently predict the weight of the denture after sintering, and quickly compares it with the actual weight of the denture after sintering, which can assist users to quickly sort the dentures. , effectively improve the sorting efficiency; combining the denture model volume data and denture parameter data to predict the weight of the denture after sintering and sorting can improve the accuracy of denture sorting.
- Figure 1 schematically shows an example flow chart of the denture sorting assistance method according to Embodiment 1 of the present invention
- Figure 2 schematically shows an example flow chart of the denture sorting assistance method according to Embodiment 1 of the present invention
- Figure 3 schematically shows an example flow chart of the denture sorting assistance method according to Embodiment 1 of the present invention
- Figure 4 schematically shows an example flow chart of the denture sorting assistance method according to Embodiment 1 of the present invention
- Figure 5 schematically shows an example flow chart of the denture sorting assistance method according to Embodiment 1 of the present invention
- Figure 6 schematically shows an example flow chart of the denture sorting assistance method according to Embodiment 1 of the present invention
- Figure 7 schematically shows an example flow chart of the denture sorting assistance method according to Embodiment 2 of the present invention.
- Figure 8 schematically shows a block diagram of a denture sorting auxiliary device according to Embodiment 3 of the present invention.
- FIG. 9 schematically shows a hardware architecture diagram of a computer device suitable for implementing an auxiliary method for denture sorting according to Embodiment 4 of the present invention.
- FIG. 1 shows a flow chart of a method for assisting denture sorting according to an embodiment of the present invention. It can be understood that the flow chart in this method embodiment is not used to limit the order of execution steps.
- the denture sorting auxiliary method is applied to the denture sorting auxiliary system.
- the denture sorting auxiliary system includes a user end and a server end.
- the user end includes a front end and a back end.
- the back end runs denture sorting auxiliary software or application.
- Program the front end displays the operation interface corresponding to the software or application program for the user to operate, the user terminal is connected to the server terminal, and the user terminal and the server terminal are connected through wired network connection, wireless network connection, Bluetooth connection, etc. Any method can be used to connect, as long as the data transmission between the two can be realized, there is no limitation here.
- the following is an exemplary description using the client and server in the denture sorting auxiliary system as the execution subjects. The details are as follows:
- the denture sorting assistance method may include steps S100 to S110, wherein:
- Step S100 Receive, through the back-end, denture model data corresponding to at least one denture and corresponding denture parameter data sent by the front-end, where the denture model data and the denture parameter data are generated by the front-end based on the location of the user. The data obtained by the operation of the preset area of the front end.
- the denture model data can be data transmitted by the user in an STL format file, or data transmitted by the user in a CAD format file, as long as it is a data format that the server can parse and recognize, and is not limited here.
- the denture parameter data includes but is not limited to the quantity contained in each denture, the weight before sintering, the steps the denture goes through from the completion of cutting to before sintering, and the denture selected material data, where the denture selected material data is the finished denture processing material.
- the accurate density corresponding to the denture material data selected or input by the user can be searched in the preset database.
- the density data corresponding to the finished denture materials of each manufacturer are all public data, which have been collected and verified in advance, and are associated and stored in a preset database.
- Step S102 Calculate the denture model volume data corresponding to each denture based on the denture model data of each denture through the backend.
- the design model of each denture is a closed body, and the backend can calculate the accurate denture model volume data corresponding to each denture through the built-in algorithm of the denture sorting auxiliary software.
- the step S102 of calculating the denture model volume data corresponding to each denture through the backend according to the denture model data of each denture may further include steps S200 to S204, wherein: step S200, Determine the model vertex data of each denture according to the denture model data of each denture through the back end; step S202, determine the model vertex data of each denture according to the denture model data and the model vertex data of each denture through the back end. , segment each denture to obtain multiple model patch data corresponding to each denture; and step S204, calculate through the backend based on the multiple model patch data corresponding to each denture. Denture model volume data corresponding to each denture is obtained.
- the multiple model patch data are multiple triangle patch data.
- the backend divides each denture according to the denture model data and model vertex data of each denture, and obtains multiple triangular patch data corresponding to each denture. Based on the triangular patch data obtained by segmentation, the corresponding triangle patch data of each denture is calculated. Denture model volume data.
- the step S204 of calculating the denture model volume data corresponding to each denture through the backend based on the multiple model patch data corresponding to each denture can also be obtained by the following operations: Step S300, use the back end to calculate the patch volume data corresponding to each model patch data according to the multiple model patch data corresponding to each denture; and step S302, use the back end to combine the data respectively.
- the patch volume data corresponding to each model patch data corresponding to each denture is described to obtain the denture model volume data corresponding to each denture.
- the back-end denture sorting auxiliary software obtains the first triangular patch by calling the function method of the math library in the Python standard library, and obtains the three vertices of the first triangular patch.
- the three vertices follow the inverse
- the three-dimensional coordinates corresponding to the order of the hour hand are (x 11 , y 11 , z 11 ), (x 12 , y 12 , z 12 ), (x 13 , y 13 , z 13 ), based on the first triangle patch
- V i
- the coordinate values of the three vertices in the data in the three-dimensional space coordinate system, and so on, are calculated to obtain the patch volume data corresponding to each triangular patch, and then the patches of all the triangular patches corresponding to each denture are calculated.
- the number of model vertices obtained is 27557.
- the number of model patches is 55110, and the three vertices of the first triangular patch are obtained.
- For the patch volume data sum up the patch volume data of all triangular patches to obtain the corresponding denture model volume data, for example, 413.79 cubic millimeters.
- Step S104 The backend calculates the predicted weight of each denture after sintering based on the denture model volume data and corresponding denture parameter data of each denture, and sends the predicted sintering weight of each denture. The final weight is sent to the server.
- the denture model volume data and corresponding denture parameter data of each denture are input into a preset denture weight prediction model, and the predicted weight of each denture after sintering is calculated.
- the denture parameter data includes the quantity contained by each denture, the weight before sintering, the steps experienced by the denture from cutting completion to sintering, and the selected material data of the denture.
- Inputting the denture model volume data and corresponding denture parameter data of each denture into the preset denture weight prediction model can be understood as separately including the quantity, weight before sintering, and The steps that the denture goes through before sintering, the selected material data (material brand) of the denture, and the denture model volume data are input into the preset denture weight prediction model weight, and the predicted weight of each denture after sintering is calculated. Further, the predicted weight of each denture after sintering is sent to the server, so that the server stores it in a preset database.
- step S104 may further include steps S400 to S404, wherein: step S400, obtain the volume shrinkage ratio corresponding to each denture based on the denture model volume data of each denture through the back end; step S402, obtain the volume shrinkage ratio corresponding to each denture through the back end.
- step S400 obtain the volume shrinkage ratio corresponding to each denture based on the denture model volume data of each denture through the back end
- step S402 obtain the volume shrinkage ratio corresponding to each denture through the back end.
- the back end obtains the said denture according to the quantity contained in each denture, the weight before sintering, the steps the denture has gone through from the completion of cutting to before sintering, and the selected material data of the denture.
- the predicted weight after sintering corresponding to each denture is calculated by combining the volume shrinkage ratio, average weight change ratio, industry experience value, and denture model volume data of each denture.
- the method further includes constructing a model to be trained, and training to obtain the preset denture weight prediction model based on the model to be trained; as shown in Figure 6, the details are as follows: Step S600, obtain multiple sintering Multiple sample data of multiple sample dentures that have been sintered in the furnace, wherein the multiple sample data include the quantity contained in the denture, the actual weight before sintering, the steps experienced by the denture from the completion of cutting to before sintering, and the selected material data of the denture.
- step S602 classify the multiple sample data of the multiple sample dentures based on the sintering time of the completed sintering and the sintering furnace, so as to obtain multiple groups of sample data of the sample dentures, wherein the same group The sample dentures are all sintered in the same sintering furnace at the same sintering time; step S604, input the sample data of each group of sample dentures into the model to be trained, and output the sample prediction corresponding to each denture in each group of samples.
- step S606 compare the predicted weight after sintering of the sample corresponding to each denture in each group of samples with the corresponding actual weight after sintering, to obtain the corresponding loss value; step S608, based on the corresponding loss value, adjust the model parameters in the model to be trained to obtain the trained model parameters; step S610, obtain the preset denture weight prediction model based on the trained model parameters.
- the denture weight prediction model can be understood as the weight change rule before and after denture sintering in different scenarios, which needs to be calculated through a large amount of test data. Therefore, it is necessary to collect a large amount of sample data of actually delivered dentures for repeated verification. and adjustments.
- Step S106 After each denture is put into the same sintering furnace for sintering, the actual sintering weight of each denture is obtained through the backend, and the actual sintering weight of each denture is sent to the server .
- the denture is placed on an electronic scale with an accuracy of 0.1mg.
- the electronic scale is connected to the user terminal.
- the user terminal will automatically obtain the counting change of the electronic scale to obtain the actual weight of the corresponding denture after sintering.
- the actual sintered weight of each denture is transmitted to the server.
- Step S108 receiving the predicted sintering weight and the actual sintering weight of each denture through the server, based on the predicted sintering weight and the actual sintering weight of each denture. determine the weight of at least one candidate denture order corresponding to each denture, and send the at least one candidate denture order data corresponding to each denture to the backend.
- the predicted post-sintered weight and the actual post-sintered weight of each denture are received through the server, based on the sum of the predicted post-sintered weight of each denture.
- the step S108 of determining the actual weight after sintering, determining at least one candidate denture order data corresponding to each denture, and sending the at least one candidate denture order data corresponding to each denture to the backend may further include Steps S500 to S504, wherein: Step S500, the server compares the actual sintered weight of each denture with the predicted sintered weight of each denture to obtain each denture.
- step S502 obtain the minimum difference value from the at least one difference value corresponding to each denture through the server, or separately obtain the minimum difference value corresponding to the at least one difference value corresponding to each denture. Sort in order according to the numerical value and obtain the top M difference values, wherein the first difference value is the minimum difference value; and step S504, use the server to obtain the denture corresponding to the minimum difference value corresponding to each denture.
- the multiple denture order data corresponding to the smallest difference value among the multiple difference values corresponding to each denture can be determined as the candidate denture order data; or, the multiple difference values corresponding to each denture can be determined as the smallest difference value. Sort to the maximum, and determine the denture order data corresponding to the first M difference values after sorting as candidate denture order data.
- Step S110 Determine the target corresponding to each denture from the at least one candidate denture order data corresponding to each denture through the backend based on the received at least one candidate denture order data corresponding to each denture. Denture order data to enable sorting of each denture.
- the server displays at least one candidate denture order data corresponding to each denture on the user's display interface. That is, the server directly displays the denture order data that is most similar to each denture to the user, and then assists the user in completing sorting.
- denture A the repair type is a full crown
- the number of dentures is 1 tooth
- the material brand is a certain brand of zirconia st
- denture experience from cutting completion to sintering The steps are just blowing powder as an example, the instructions are as follows:
- the average weight change ratio before and after sintering in the above example is 0.995469991.
- the density provided by a certain brand of zirconia st manufacturer is greater than 6.02g/cm3, and the corresponding industry experience value is: 6.07g/cm3.
- the volume shrinkage ratio after sintering is 1:1.25.
- the volume of denture A is 413.79 cubic millimeters.
- the predicted weight calculation method after sintering is:
- the embodiment of the present invention uses an algorithm to intelligently predict the weight of the denture after sintering, and quickly compares it with the actual weight of the denture after sintering, which can assist the user to quickly sort the dentures and effectively improve the sorting efficiency; combined with the denture model volume data of the dentures Using denture parameter data to predict the weight of dentures after sintering and sorting them can improve the accuracy of denture sorting.
- the present invention can quickly realize denture sorting. In the entire process, users only need to upload the denture model data once and select the denture parameter data of the finished denture material for each denture to achieve fast and accurate sorting, which can greatly improve the sorting efficiency and reduce the probability of errors. There is no need to print impressions anymore. There are no longer any requirements for the professionalism of technicians; it is a qualitative leap for denture factories.
- the denture weight prediction model and denture model volume algorithm can accurately predict the weight of dentures after sintering, effectively improve the sorting efficiency, and assist users to quickly sort dentures.
- FIG. 7 shows a step flow chart of the denture sorting assisting method according to the embodiment of the present invention. It can be understood that the flow chart in this method embodiment is not used to limit the order of execution steps.
- the denture sorting assistance method is applied to the user end. The following is an exemplary description using the client as the execution subject, as follows:
- the denture sorting assistance method may include steps S700 to S710, wherein:
- Step S700 Receive denture model data and corresponding denture parameter data corresponding to at least one denture input by the user;
- Step S702 Calculate the denture model volume data corresponding to each denture based on the denture model data of each denture;
- Step S704 Calculate the predicted weight of each denture after sintering based on the denture model volume data and corresponding denture parameter data of each denture, and send the predicted weight of each denture after sintering to Server;
- Step S706 After each denture is put into the same sintering furnace for sintering, the actual sintering weight of each denture is obtained, and the actual sintering weight of each denture is sent to the server, so that the Server: Based on the predicted weight after sintering and the actual weight after sintering of each denture, determine at least one candidate denture order data corresponding to each denture, and send the corresponding order data for each denture at least one candidate denture order data to the client; and
- Step S708 Receive at least one candidate denture order data corresponding to each denture sent by the server.
- Step S710 According to the received at least one candidate denture order data corresponding to each denture, determine the target denture order data corresponding to each denture from the at least one candidate denture order data corresponding to each denture to achieve Sorting of each denture described.
- FIG. 8 shows a schematic diagram of the program module of the denture sorting auxiliary device 80 according to the embodiment of the present invention.
- the denture sorting auxiliary device 80 may include or be divided into one or more program modules.
- the one or more program modules are stored in an embedded memory chip and executed by one or more processors. , to complete the present invention and realize the above auxiliary method for sorting dentures.
- the program module referred to in the embodiment of the present invention refers to a series of computer program instruction segments that can complete specific functions, and is more suitable for describing the execution process of the denture sorting auxiliary device 80 in the storage medium than the program itself. The following description will specifically introduce the functions of each program module in this embodiment:
- the device includes: data receiving module 800, volume calculation module 802, weight calculation module 804, weight acquisition module 806, order receiving module 808 and order confirmation module 810, wherein:
- the data receiving module 800 is used to receive the denture model data and corresponding denture parameter data corresponding to at least one denture input by the user;
- the volume calculation module 802 is used to calculate the denture model volume data corresponding to each denture based on the denture model data of each denture;
- the weight calculation module 804 is used to calculate the predicted weight of each denture after sintering based on the denture model volume data and corresponding denture parameter data of each denture, and send the predicted weight of each denture after sintering. The weight is sent to the server;
- the weight acquisition module 806 is used to obtain the actual sintering weight of each denture after each denture is put into the same sintering furnace for sintering, and send the actual sintering weight of each denture to the server, So that the server: determines at least one candidate denture order data corresponding to each denture based on the predicted sintered weight and the actual sintered weight of each denture, and sends the At least one candidate denture order data corresponding to each denture is sent to the user terminal; and
- the order receiving module 808 is used to receive at least one candidate denture order data corresponding to each denture sent by the server.
- the order determination module 810 is configured to determine the target denture corresponding to each denture from the at least one candidate denture order data corresponding to each denture according to the received at least one candidate denture order data corresponding to each denture. Order data to enable sorting of each denture described. 8
- FIG. 9 schematically shows a hardware architecture diagram of a computer device 10000 suitable for implementing a denture sorting assistance method according to Embodiment 4 of the present invention.
- the computer device 10000 is a device that can automatically perform score calculation and/or information processing according to preset or stored instructions.
- it can be a smartphone, tablet, laptop, desktop computer, rack server, blade server, tower server or cabinet server (including independent server, or server cluster composed of multiple servers), gateway wait.
- the computer device 10000 at least includes but is not limited to: a link memory 10010, a processor 10020, and a network interface 10030 that can communicate with each other through a system bus. in:
- the memory 10010 includes at least one type of computer-readable storage medium.
- the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disks, optical disks, etc.
- the memory 10010 may be an internal storage module of the computer device 10000, such as a hard disk or memory of the computer device 10000.
- the memory 10010 may also be an external storage device of the computer device 10000, such as a plug-in hard disk, a smart media card (SMC for short), or a secure digital (Secure) card equipped on the computer device 10000.
- the memory 10010 may also include both the internal storage modules of the computer device 10000 and its external storage devices.
- the memory 10010 is usually used to store the operating system and various application software installed on the computer device 10000, such as the program code of the denture sorting assistance method, etc.
- the memory 10010 can also be used to temporarily store various types of data that have been output or will be output.
- Processor 10020 may be a central processing unit (Central Processing Unit) in some embodiments. Processing Unit (referred to as CPU), controller, microcontroller, microprocessor, or other data processing chip.
- the processor 10020 is generally used to control the overall operation of the computer device 10000, such as performing control and processing related to data interaction or communication with the computer device 10000. In this embodiment, the processor 10020 is used to run the program code stored in the memory 10010 or process data.
- Network interface 10030 may include a wireless network interface or a wired network interface, and is typically used to establish communication links between computer device 10000 and other computer devices.
- the network interface 10030 is used to connect the computer device 10000 to an external terminal through a network, establish a data transmission channel and a communication link between the computer device 10000 and the external terminal, etc.
- the network can be an intranet, the Internet, or the Global System of Mobile Communications.
- Mobile communication GSM for short
- WCDMA Wideband Code Division Multiple Access
- 4G network 5G network
- Bluetooth Wi-Fi and other wireless or wired networks.
- FIG. 9 only shows a computer device having components 10010-10030, but it should be understood that implementation of all illustrated components is not required, and more or fewer components may be implemented instead.
- the denture sorting assistance method stored in the memory 10010 can also be divided into one or more program modules and executed by a processor (in this embodiment, the processor 10020) to complete the implementation of the present invention. example.
- the present invention also provides a computer-readable storage medium on which a computer program is stored.
- the computer program is executed by at least one processor, the steps of the denture sorting assisting method in the embodiment are implemented.
- the computer-readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disks, optical disks, etc.
- the computer-readable storage medium may be an internal storage unit of a computer device, such as a hard drive or memory of the computer device.
- the computer-readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a smart memory card (Smart Memory Card) equipped on the computer device.
- the computer-readable storage medium may also include both internal storage units of the computer device and external storage devices thereof.
- the computer-readable storage medium is usually used to store the operating system and various application software installed on the computer device, such as the program code of the denture sorting assistance method in the embodiment.
- the computer-readable storage medium can also be used to temporarily store various types of data that have been output or will be output.
- each module or each step of the above-mentioned embodiments of the present invention can be implemented by a general-purpose computing device. They can be concentrated on a single computing device, or distributed among multiple computing devices. on a network, optionally, they may be implemented in program code executable by a computing device, such that they may be stored in a storage device for execution by the computing device, and in some cases, may be implemented in a manner different from that described herein
- the steps shown or described are performed in sequence, or they are separately made into individual integrated circuit modules, or multiple modules or steps among them are made into a single integrated circuit module. As such, embodiments of the present invention are not limited to any specific combination of hardware and software.
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Abstract
一种义齿分拣辅助方法，包括根据每个义齿的义齿模型体积数据和对应的义齿参数数据，计算得到每个义齿预测烧结后的重量；待每个义齿放入同一烧结炉进行烧结后，获取每个义齿实际烧结后的重量；基于每个义齿的预测烧结后的重量和实际烧结后的重量，确定与每个义齿对应的候选义齿订单数据；从每个义齿的候选义齿订单数据中确定每个义齿的目标义齿订单数据以实现对每个义齿的分拣。通过算法智能预测义齿烧结后的重量，与义齿烧结后的实际重量进行快速比对，能够辅助用户实现对义齿的快速分拣，有效提高分拣效率；结合义齿的义齿模型体积数据和义齿参数数据预测义齿烧结后的重量并进行分115156拣，能够提高义齿的分拣准确性。
Description
本发明实施例涉及牙修复技术领域，尤其涉及一种义齿分拣辅助方法、装置、计算机设备及计算机可读存储介质。
目前临床上广泛使用的义齿材质为氧化锆和玻璃陶瓷，因氧化锆有硬度高、不易变形等优点，占据了其中绝大部分份额。其中，氧化锆义齿切削完成后，必须经过烧结才可达到成品牙的品质，氧化锆烧结一般需要8-10个小时。由于义齿制作量的需求，现有的义齿厂家通常将多颗义齿放入同一烧结炉中进行同时烧结，义齿烧结完成后进行分拣。由于除牙桥（连着的多颗牙齿）有一定的辨识度外，其余的牙齿形态差异并不大，只能将烧结好的义齿与患者牙模逐个试戴来寻找牙齿所在的订单（或者牙盒）。然而，上述将每颗义齿逐个试戴分拣的方法具有以下缺陷：（1）分拣花费时间长，分拣效率低。以一次性烧结30颗义齿为例，第一颗义齿最多需要佩戴30次，第二颗义齿最多需要佩戴29次，以此类推，30颗义齿最多需要佩戴30+29+28+27+........+1=465次，分拣效率低；（2）由于有些义齿的形态差异很小，经常会出现多个义齿都可正确佩戴牙模的情况，这样就需要依靠操作人员的专业性来判断，出错的概率较高，准确性差，对操作人员的专业性要求高。
有鉴于此，本发明实施例提供了一种义齿分拣辅助方法、装置、计算机设备及计算机可读存储介质，用于解决现有义齿分拣方法分拣效率低、准确率低的问题。
本发明实施例是通过下述技术方案来解决上述技术问题：
本发明的一个方面提供了一种义齿分拣辅助方法，应用于义齿分拣辅助系统，所述义齿分拣辅助系统包括用户端和服务端，所述用户端包括前端和后端，所述方法包括：
通过所述后端接收所述前端发送的至少一个义齿对应的义齿模型数据和对应的义齿参数数据，其中，所述义齿模型数据和所述义齿参数数据为所述前端基于用户在所述前端的预设区域的操作获取的数据；
通过所述后端根据每个义齿的义齿模型数据，计算得到每个义齿对应的义齿模型体积数据；
通过所述后端根据所述每个义齿的所述义齿模型体积数据和对应的义齿参数数据，计算得到所述每个义齿预测烧结后的重量，并发送所述每个义齿预测烧结后的重量发送至所述服务端；
待每个义齿放入同一烧结炉进行烧结后，通过所述后端获取所述每个义齿实际烧结后的重量，并发送所述每个义齿实际烧结后的重量至所述服务端；
通过所述服务端接收所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，基于所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，确定与所述每个义齿对应的至少一个候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述后端；及
通过所述后端根据接收到的所述每个义齿对应的至少一个候选义齿订单数据，从所述每个义齿对应的至少一个候选义齿订单数据中确定所述每个义齿对应的目标义齿订单数据以实现对所述每个义齿的分拣。
可选地，所述通过所述后端根据每个义齿的义齿模型数据，计算得到每个义齿对应的义齿模型体积数据，包括：
通过所述后端根据所述每个义齿的义齿模型数据确定每个义齿的模型顶点数据；
通过所述后端根据所述每个义齿的所述义齿模型数据和所述模型顶点数据，切分所述每个义齿，以得到所述每个义齿对应的多个模型面片数据；及
通过所述后端根据所述每个义齿对应的多个模型面片数据，计算得到所述每个义齿对应的义齿模型体积数据。
可选地，所述通过所述后端根据所述每个义齿对应的多个模型面片数据，计算得到所述每个义齿对应的义齿模型体积数据，包括：
通过所述后端根据所述每个义齿对应的多个模型面片数据，计算得到每个模型面片数据对应的面片体积数据；及
通过所述后端分别组合所述每个义齿对应的所述每个模型面片数据对应的面片体积数据，以得到所述每个义齿对应的义齿模型体积数据。
可选地，所述义齿参数数据包括每个义齿所包含的数量、烧结前的重量、切削完成至烧结前义齿经历的步骤和义齿所选材料数据。
可选地，所述通过所述后端根据所述每个义齿的所述义齿模型体积数据和对应的义齿参数数据，计算得到所述每个义齿预测烧结后的重量，包括：
通过所述后端基于所述每个义齿的义齿模型体积数据，获取所述每个义齿对应的体积收缩比；
通过所述后端根据所述每个义齿的所述每个义齿所包含的数量、所述烧结前的重量、所述切削完成至烧结前义齿经历的步骤和所述义齿所选材料数据，获取所述每个义齿对应的平均重量变化比；及
通过所述后端根据所述每个义齿的义齿模型体积数据、所述体积收缩比、所述平均重量变化比，计算得到所述每个义齿对应的预测烧结后的重量。
可选地，所述通过所述服务端接收所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，基于所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，确定与所述每个义齿对应的至少一个候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述后端，包括：
通过所述服务端基于所述每个义齿的实际烧结后的重量，分别与所述每个义齿的预测烧结后的重量进行对比，得到所述每个义齿对应的至少一个差异值；
通过所述服务端从所述每个义齿对应的至少一个差异值中获取最小的差异值，或，分别将所述每个义齿对应的至少一个差异值根据数值大小依次排序并获取排序前M个差异值，其中，第一个差异值为最小差异值；及
通过所述服务端将所述每个义齿对应的最小的差异值对应的义齿订单数据确定为候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述后端，或，将所述每个义齿对应的排序前M个差异值确定为至少一个候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述后端。
本发明的一个方面又提供了一种义齿分拣辅助方法，应用于用户端，所述方法包括：
接收用户输入的至少一个义齿对应的义齿模型数据和对应的义齿参数数据；
根据每个义齿的义齿模型数据，计算得到每个义齿对应的义齿模型体积数据；
根据所述每个义齿的所述义齿模型体积数据和对应的义齿参数数据，计算得到所述每个义齿预测烧结后的重量，并发送所述每个义齿预测烧结后的重量发送至服务端；
待每个义齿放入同一烧结炉进行烧结后，获取所述每个义齿实际烧结后的重量，并发送所述每个义齿实际烧结后的重量至所述服务端，以使所述服务端：基于所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，确定与所述每个义齿对应的至少一个候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述用户端；及
接收所述服务端发送的所述每个义齿对应的至少一个候选义齿订单数据；
根据接收到的所述每个义齿对应的至少一个候选义齿订单数据，从所述每个义齿对应的至少一个候选义齿订单数据中确定所述每个义齿对应的目标义齿订单数据以实现对所述每个义齿的分拣。
本发明的一个方面又提供了一种义齿分拣辅助装置，应用于用户端，所述装置包括：
数据接收模块，用于接收用户输入的至少一个义齿对应的义齿模型数据和对应的义齿参数数据；
体积计算模块，用于根据每个义齿的义齿模型数据，计算得到每个义齿对应的义齿模型体积数据；
重量计算模块，用于根据所述每个义齿的所述义齿模型体积数据和对应的义齿参数数据，计算得到所述每个义齿预测烧结后的重量，并发送所述每个义齿预测烧结后的重量发送至服务端；
重量获取模块，用于待每个义齿放入同一烧结炉进行烧结后，获取所述每个义齿实际烧结后的重量，并发送所述每个义齿实际烧结后的重量至所述服务端，以使所述服务端：基于所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，确定与所述每个义齿对应的至少一个候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述用户端；及
订单接收模块，用于接收所述服务端发送的所述每个义齿对应的至少一个候选义齿订单数据；及
订单确定模块，用于根据接收到的所述每个义齿对应的至少一个候选义齿订单数据，从所述每个义齿对应的至少一个候选义齿订单数据中确定所述每个义齿对应的目标义齿订单数据以实现对所述每个义齿的分拣。
本发明实施例的一个方面又提供了一种计算机设备，包括存储器、处理器以及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现如上述义齿分拣辅助方法的步骤。
本发明实施例的一个方面又提供了一种计算机可读存储介质，包括存储器、处理器以及存储在存储器上并可在至少一个处理器上运行的计算机程序，所述至少一个处理器执行所述计算机程序时实现如上述义齿分拣辅助方法的步骤。
本发明实施例提供的义齿分拣辅助方法、装置、计算机设备以及计算机可读存储介质，通过所述后端接收所述前端发送的至少一个义齿对应的义齿模型数据和对应的义齿参数数据，其中，所述义齿模型数据和所述义齿参数数据为所述前端基于用户在所述前端的预设区域的操作获取的数据；通过所述后端根据每个义齿的义齿模型数据，计算得到每个义齿对应的义齿模型体积数据；通过所述后端根据所述每个义齿的所述义齿模型体积数据和对应的义齿参数数据，计算得到所述每个义齿预测烧结后的重量，并发送所述每个义齿预测烧结后的重量发送至所述服务端；待每个义齿放入同一烧结炉进行烧结后，通过所述后端获取所述每个义齿实际烧结后的重量，并发送所述每个义齿实际烧结后的重量至所述服务端；通过所述服务端接收所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，基于所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，确定与所述每个义齿对应的至少一个候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述后端；及通过所述后端根据接收到的所述每个义齿对应的至少一个候选义齿订单数据，从所述每个义齿对应的至少一个候选义齿订单数据中确定所述每个义齿对应的目标义齿订单数据以实现对所述每个义齿的分拣；本发明实施例通过算法智能预测义齿烧结后的重量，与义齿烧结后的实际重量进行快速比对，能够辅助用户实现对义齿的快速分拣，有效提高分拣效率；结合义齿的义齿模型体积数据和义齿参数数据预测义齿烧结后的重量并进行分拣，能够提高义齿的分拣准确性。
以下结合附图和具体实施例对本发明进行详细描述，但不作为对本发明的限定。
图1示意性示出了本发明实施例一的义齿分拣辅助方法的示例流程图；
图2示意性示出了本发明实施例一的义齿分拣辅助方法的示例流程图；
图3示意性示出了本发明实施例一的义齿分拣辅助方法的示例流程图；
图4示意性示出了本发明实施例一的义齿分拣辅助方法的示例流程图；
图5示意性示出了本发明实施例一的义齿分拣辅助方法的示例流程图；
图6示意性示出了本发明实施例一的义齿分拣辅助方法的示例流程图；
图7示意性示出了本发明实施例二的义齿分拣辅助方法的示例流程图；
图8示意性示出了根据本发明实施例三的义齿分拣辅助装置的框图；及
图9示意性示出了根据本发明实施例四的适于实现义齿分拣辅助方法的计算机设备的硬件架构示意图。
为了使本发明的目的、技术方案及优点更加清楚明白，以下结合附图及实施例，对本发明进行进一步详细说明。应当理解，此处所描述的具体实施例仅用以解释本发明，并不用于限定本发明。基于本发明中的实施例，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，都属于本发明保护的范围。
需要说明的是，在本发明实施例中涉及“第一”、“第二”等的描述仅用于描述目的，而不能理解为指示或暗示其相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包括至少一个该特征。另外，各个实施例之间的技术方案可以相互结合，但是必须是以本领域普通技术人员能够实现为基础，当技术方案的结合出现相互矛盾或无法实现时应当认为这种技术方案的结合不存在，也不在本发明要求的保护范围之内。
在本发明的描述中，需要理解的是，步骤前的数字标号并不标识执行步骤的前后顺序，仅用于方便描述本发明及区别每一步骤，因此不能理解为对本发明的限制。
实施例一
请参阅图1，示出了本发明实施例之义齿分拣辅助方法的步骤流程图。可以理解，本方法实施例中的流程图不用于对执行步骤的顺序进行限定。所述义齿分拣辅助方法应用于义齿分拣辅助系统，所述义齿分拣辅助系统包括用户端和服务端，所述用户端包括前端和后端，后端运行有义齿分拣辅助软件或应用程序，前端显示该软件或应用程序对应的供用户进行操作的操作界面，所述用户端和所述服务端连接，用户端和服务端之间通过有线网络连接、无线网络连接、蓝牙连接等方式中任一种方式进行连接，只要能实现两者之间的数据传输的方式均可，在此不作限定。下面分别以义齿分拣辅助系统中的用户端和服务端为执行主体进行示例性描述，具体如下：
如图1所示，所述义齿分拣辅助方法可以包括步骤S100~步骤S110，其中：
步骤S100，通过所述后端接收所述前端发送的至少一个义齿对应的义齿模型数据和对应的义齿参数数据，其中，所述义齿模型数据和所述义齿参数数据为所述前端基于用户在所述前端的预设区域的操作获取的数据。
示例性的，义齿模型数据可以是用户以stl格式文件传输的数据，也可以是用户以cad格式文件传输的数据，只要是服务端能够解析识别的数据格式即可，在此不作限定。所述义齿参数数据包括但不限于每个义齿所包含的数量、烧结前的重量、切削完成至烧结前义齿经历的步骤和义齿所选材料数据，其中，义齿所选材料数据为义齿加工成品材料，在运行于预设的数据库中能够搜索到用户选择或者输入的义齿所选材料数据对应的准确密度。具体的，各厂商义齿加工成品材料对应的密度数据均为公开数据，已预先进行收集验证，并关联存储于预设的数据库中。
步骤S102，通过所述后端根据每个义齿的义齿模型数据，计算得到每个义齿对应的义齿模型体积数据。
首先，每个义齿的设计模型均是一个闭合体，后端可以通过义齿分拣辅助软件内置的算法计算出每个义齿对应的准确的义齿模型体积数据。
请参阅图2，所述通过所述后端根据每个义齿的义齿模型数据，计算得到每个义齿对应的义齿模型体积数据的步骤S102还可以进一步包括步骤S200~步骤S204，其中：步骤S200，通过所述后端根据所述每个义齿的义齿模型数据确定每个义齿的模型顶点数据；步骤S202，通过所述后端根据所述每个义齿的所述义齿模型数据和所述模型顶点数据，切分所述每个义齿，以得到所述每个义齿对应的多个模型面片数据；及步骤S204，通过所述后端根据所述每个义齿对应的多个模型面片数据，计算得到所述每个义齿对应的义齿模型体积数据。在本实施例中，多个模型面片数据为多个三角形面片数据。后端根据每个义齿的义齿模型数据和模型顶点数据，切分每个义齿，得到每个义齿对应的多个三角形面片数据，根据切分得到的三角形面片数据，计算得到每个义齿对应的义齿模型体积数据。
请参阅图3，所述通过所述后端根据所述每个义齿对应的多个模型面片数据，计算得到所述每个义齿对应的义齿模型体积数据的步骤S204还可以通过以下操作得到：步骤S300，通过所述后端根据所述每个义齿对应的多个模型面片数据，计算得到每个模型面片数据对应的面片体积数据；及步骤S302，通过所述后端分别组合所述每个义齿对应的所述每个模型面片数据对应的面片体积数据，以得到所述每个义齿对应的义齿模型体积数据。接上例，后端的义齿分拣辅助软件通过调用Python标准库中的math库的函数方法，获取第一个三角形面片，并获取第一个三角形面片的三个顶点，三个顶点按照逆时针的顺序对应的三维坐标，分别为（x
11，y
11，z
11）、（x
12，y
12，z
12）、（x
13，y
13，z
13），基于第一个三角形面片的三个顶点坐标和公式1：
V
i =
|(−x
i3y
i2z
i1+x
i2y
i3z
i1+x
i3y
i1z
i2−x
i1y
i3z
i2−x
i2y
i1z
i3+x
i1y
i2z
i3)*(1/6)|，计算得到第一个三角形面片的面片体积数据V1，其中，V
i表示第i个模型面片数据对应的面片体积数据，x、y、z分别表示模型面片数据中的三个顶点在三维空间坐标系中的坐标数值，以此类推，计算得到每个三角形面片对应的面片体积数据，再将每个义齿分别对应的所有的三角形面片的面片体积数据代入公式2：V
total=∑
iV
i，将一个义齿对应的所有三角形面片的面片体积数据进行求和，得到该义齿对应的义齿模型体积数据V
total。
举例而言，以某个义齿模型数据为例，获取到模型顶点数量为27557个，通过对义齿进行切分，得到模型面片数量为55110个，获取到第一个三角形面片的三个顶点的坐标值为x
11 = -1.680549，y
11 = -49.935905，z
11=
-2.455484，x
12 = -1.729336，y
12 = -50.001141，z
12
= -2.456306，x
13 = -1.731409，y
13 = -49.971203，z
13
= -2.481415；带入上述公式1计算得到第一个三角形面片的面片体积数据，依次类推，将所有的三角形面片数据带入公式1计算得到每个三角形面片对应的面片体积数据，求和所有的三角形面片的面片体积数据，得到对应的义齿模型体积数据，例如为413.79立方毫米。
步骤S104，通过所述后端根据所述每个义齿的所述义齿模型体积数据和对应的义齿参数数据，计算得到所述每个义齿预测烧结后的重量，并发送所述每个义齿预测烧结后的重量发送至所述服务端。
在本实施例中，将所述每个义齿的所述义齿模型体积数据和对应的义齿参数数据输入至预设的义齿重量预测模型中，计算得到每个义齿预测烧结后的重量。在示例性的实施例中， 所述义齿参数数据包括每个义齿所包含的数量、烧结前的重量、切削完成至烧结前义齿经历的步骤和义齿所选材料数据。将所述每个义齿的所述义齿模型体积数据和对应的义齿参数数据输入至预设的义齿重量预测模型中可以理解为是分别将所述每个义齿所包含的数量、烧结前的重量、切削完成至烧结前义齿经历的步骤和义齿所选材料数据（材料品牌）和所述义齿模型体积数据输入至预设的义齿重量预测模型重，计算得到每个义齿预测烧结后的重量。进一步地，将每个义齿预测烧结后的重量发送至服务端，以使服务端存储于预设的数据库中。
示例性的，请参阅图4，所述通过所述后端根据所述每个义齿的所述义齿模型体积数据和对应的义齿参数数据，计算得到所述每个义齿预测烧结后的重量的步骤S104还可以进一步包括步骤S400~步骤S404，其中：步骤S400，通过所述后端基于所述每个义齿的义齿模型体积数据，获取所述每个义齿对应的体积收缩比；步骤S402，通过所述后端根据所述每个义齿的所述每个义齿所包含的数量、所述烧结前的重量、所述切削完成至烧结前义齿经历的步骤和所述义齿所选材料数据，获取所述每个义齿对应的平均重量变化比；及步骤S404，通过所述后端根据所述每个义齿的义齿模型体积数据、所述体积收缩比、所述平均重量变化比，计算得到所述每个义齿对应的预测烧结后的重量。在本实施例中，结合每个义齿的体积收缩比、平均重量变化比、行业经验值、义齿模型体积数据，计算得到每个义齿对应的预测烧结后的重量。
在示例性的实施例中，所述方法还包括构建待训练模型，基于待训练模型训练得到所述预设的义齿重量预测模型；如图6所示，具体如下：步骤S600，获取多个烧结炉完成烧结的多个样本义齿的多个样本数据，其中，所述多个样本数据包括义齿所包含的数量、实际烧结前的重量、切削完成至烧结前义齿经历的步骤、义齿所选材料数据和实际烧结后的重量；步骤S602，将所述多个样本义齿的多个样本数据基于所完成烧结的烧结时间和所在烧结炉进行分类，以得到多组样本义齿的样本数据，其中，同一组的样本义齿均为在同一烧结时间下在同一烧结炉中进行烧结；步骤S604，分别将每组的样本义齿的样本数据输入至待训练模型中，输出每组样本中每个义齿对应的样本预测烧结后的重量；步骤S606，将每组样本中每个义齿对应的样本预测烧结后的重量与对应的实际烧结后的重量进行比对，以得到对应的损失值；步骤S608，基于对应的损失值，调整所述待训练模型中的模型参数，以得到训练后的模型参数；步骤S610，基于所述训练后的模型参数，得到所述预设的义齿重量预测模型。在本实施例中，义齿重量预测模型可以理解为是不同场景下义齿烧结前后重量变化规则，需要通过大量的试验数据计算得到，因此，需要收集大量已实际交付的义齿的样本数据进行重复的验证及调整。
步骤S106，待每个义齿放入同一烧结炉进行烧结后，通过所述后端获取所述每个义齿实际烧结后的重量，并发送所述每个义齿实际烧结后的重量至所述服务端。
在本实施例中，义齿烧结完成后，将义齿放到0.1mg精度的电子秤上，电子秤与用户端连接，用户端会自动获取电子秤的计数变化一得到相应义齿烧结后的实际重量，病将每个义齿实际烧结后的重量传输至服务端。
步骤S108，通过所述服务端接收所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，基于所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，确定与所述每个义齿对应的至少一个候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述后端。
请参阅图5，所述通过所述服务端接收所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，基于所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，确定与所述每个义齿对应的至少一个候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述后端的步骤S108还可以进一步包括步骤S500~步骤S504，其中：步骤S500，通过所述服务端基于所述每个义齿的实际烧结后的重量，分别与所述每个义齿的预测烧结后的重量进行对比，得到所述每个义齿对应的至少一个差异值；步骤S502，通过所述服务端从所述每个义齿对应的至少一个差异值中获取最小的差异值，或，分别将所述每个义齿对应的至少一个差异值根据数值大小依次排序并获取排序前M个差异值，其中，第一个差异值为最小差异值；及步骤S504，通过所述服务端将所述每个义齿对应的最小的差异值对应的义齿订单数据确定为候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述后端，或，将所述每个义齿对应的排序前M个差异值确定为至少一个候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述后端。在本实施例中，可以将每个义齿对应的多个差异值中最小的差异值对应的多个义齿订单数据确定为候选义齿订单数据；或者，可以将每个义齿对应的多个差异值从小至大进行排序，并将排序后排序前M个差异值对应的义齿订单数据确定为候选义齿订单数据。
步骤S110，通过所述后端根据接收到的所述每个义齿对应的至少一个候选义齿订单数据，从所述每个义齿对应的至少一个候选义齿订单数据中确定所述每个义齿对应的目标义齿订单数据以实现对所述每个义齿的分拣。
服务端将每个义齿对应的至少一个候选义齿订单数据在用户端的显示界面进行显示，即服务端将于每个义齿最相近的义齿订单数据直接显示给用户，继而辅助用户完成分拣。
为了更加容易理解本发明所述的义齿分拣辅助方法，以下以义齿A：修复类型为全冠、义齿数量为1颗牙、材料品牌为某品牌氧化锆st、切削完成至烧结前义齿经历的步骤为仅吹粉为例，说明如下：
通过计算得到上例的烧结前后的重量平均变化比为0.995469991，某品牌氧化锆st厂家提供的密度为大于6.02g/cm³，对应的行业经验值为：6.07g/cm³，烧结后体积的收缩比为1:1.25，根据以上数据，义齿A的体积为413.79立方毫米，预测烧结后的重量计算方法为：
413.79/1000/1.25*6.07*0.995469991=2.00026180191克；结合下表一：
表一：同一烧结炉中义齿的相关数据
根据以上计算结果，确定与义齿A最接近的义齿为 13号义齿。
本发明实施例通过算法智能预测义齿烧结后的重量，与义齿烧结后的实际重量进行快速比对，能够辅助用户实现对义齿的快速分拣，有效提高分拣效率；结合义齿的义齿模型体积数据和义齿参数数据预测义齿烧结后的重量并进行分拣，能够提高义齿的分拣准确性。
本发明实施例中的义齿分拣辅助的方案还至少具有以下有益效果：
（1）通过本发明可以快速的实现义齿分拣。整个过程用户只需要上传一次义齿模型数据、选择每个义齿的成品材料的义齿参数数据，即可实现快速精准分拣，可大幅提高分拣效率，降低出错概率，不再需要打印印模，对技师的专业性不再有任何要求；对义齿工厂来说是一个质的飞跃。
（2）通过义齿重量预测模型、义齿模型体积算法能够准确预测义齿烧结后的重量，有效提高分拣效率，辅助用户实现义齿的快速分拣。
实施例二
请参阅图7，示出了本发明实施例之义齿分拣辅助方法的步骤流程图。可以理解，本方法实施例中的流程图不用于对执行步骤的顺序进行限定。所述义齿分拣辅助方法应用于用户端。下面分别以用户端为执行主体进行示例性描述，具体如下：
如图7所示，所述义齿分拣辅助方法可以包括步骤S700~步骤S710，其中：
步骤S700，接收用户输入的至少一个义齿对应的义齿模型数据和对应的义齿参数数据；
步骤S702，根据每个义齿的义齿模型数据，计算得到每个义齿对应的义齿模型体积数据；
步骤S704，根据所述每个义齿的所述义齿模型体积数据和对应的义齿参数数据，计算得到所述每个义齿预测烧结后的重量，并发送所述每个义齿预测烧结后的重量发送至服务端；
步骤S706，待每个义齿放入同一烧结炉进行烧结后，获取所述每个义齿实际烧结后的重量，并发送所述每个义齿实际烧结后的重量至所述服务端，以使所述服务端：基于所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，确定与所述每个义齿对应的至少一个候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述用户端；及
步骤S708，接收所述服务端发送的所述每个义齿对应的至少一个候选义齿订单数据；及
步骤S710，根据接收到的所述每个义齿对应的至少一个候选义齿订单数据，从所述每个义齿对应的至少一个候选义齿订单数据中确定所述每个义齿对应的目标义齿订单数据以实现对所述每个义齿的分拣。
实施例三
请继续参阅图8，示出了本发明实施例之义齿分拣辅助装置80的程序模块示意图。在本实施例中，义齿分拣辅助装置80可以包括或被分割成一个或多个程序模块，一个或者多个程序模块被存储于嵌入式存储芯片中，并由一个或多个处理器所执行，以完成本发明，并可实现上述义齿分拣辅助方法。本发明实施例所称的程序模块是指能够完成特定功能的一系列计算机程序指令段，比程序本身更适合于描述所述义齿分拣辅助装置80在存储介质中的执行过程。以下描述将具体介绍本实施例各程序模块的功能：
所述装置包括：数据接收模块800、体积计算模块802、重量计算模块804、重量获取模块806、订单接收模块808以及订单确定模块810，其中：
数据接收模块800，用于接收用户输入的至少一个义齿对应的义齿模型数据和对应的义齿参数数据；
体积计算模块802，用于根据每个义齿的义齿模型数据，计算得到每个义齿对应的义齿模型体积数据；
重量计算模块804，用于根据所述每个义齿的所述义齿模型体积数据和对应的义齿参数数据，计算得到所述每个义齿预测烧结后的重量，并发送所述每个义齿预测烧结后的重量发送至服务端；
重量获取模块806，用于待每个义齿放入同一烧结炉进行烧结后，获取所述每个义齿实际烧结后的重量，并发送所述每个义齿实际烧结后的重量至所述服务端，以使所述服务端：基于所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，确定与所述每个义齿对应的至少一个候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述用户端；及
订单接收模块808，用于接收所述服务端发送的所述每个义齿对应的至少一个候选义齿订单数据；及
订单确定模块810，用于根据接收到的所述每个义齿对应的至少一个候选义齿订单数据，从所述每个义齿对应的至少一个候选义齿订单数据中确定所述每个义齿对应的目标义齿订单数据以实现对所述每个义齿的分拣。8
实施例四
图9示意性示出了根据本发明实施例四的适于实现义齿分拣辅助方法的计算机设备10000的硬件架构示意图。本实施例中，计算机设备10000是一种能够按照事先设定或者存储的指令，自动进行分数计算和/或信息处理的设备。例如，可以是智能手机、平板电脑、笔记本电脑、台式计算机、机架式服务器、刀片式服务器、塔式服务器或机柜式服务器（包括独立的服务器，或者多个服务器所组成的服务器集群）、网关等。如图9所示，计算机设备10000至少包括但不限于：可通过系统总线相互通信链接存储器10010、处理器10020、网络接口10030。其中：
存储器10010至少包括一种类型的计算机可读存储介质，可读存储介质包括闪存、硬盘、多媒体卡、卡型存储器（例如，SD或DX存储器等）、随机访问存储器（RAM）、静态随机访问存储器（SRAM）、只读存储器（ROM）、电可擦除可编程只读存储器（EEPROM）、可编程只读存储器（PROM）、磁性存储器、磁盘、光盘等。在一些实施例中，存储器10010可以是计算机设备10000的内部存储模块，例如该计算机设备10000的硬盘或内存。在另一些实施例中，存储器10010也可以是计算机设备10000的外部存储设备，例如该计算机设备10000上配备的插接式硬盘，智能存储卡（Smart Media Card，简称为SMC），安全数字（Secure Digital，简称为SD）卡，闪存卡（Flash Card）等。当然，存储器10010还可以既包括计算机设备10000的内部存储模块也包括其外部存储设备。本实施例中，存储器10010通常用于存储安装于计算机设备10000的操作系统和各类应用软件，例如义齿分拣辅助方法的程序代码等。此外，存储器10010还可以用于暂时地存储已经输出或者将要输出的各类数据。
处理器10020在一些实施例中可以是中央处理器（Central
Processing Unit，简称为CPU）、控制器、微控制器、微处理器、或其他数据处理芯片。该处理器10020通常用于控制计算机设备10000的总体操作，例如执行与计算机设备10000进行数据交互或者通信相关的控制和处理等。本实施例中，处理器10020用于运行存储器10010中存储的程序代码或者处理数据。
网络接口10030可包括无线网络接口或有线网络接口，该网络接口10030通常用于在计算机设备10000与其他计算机设备之间建立通信链接。例如，网络接口10030用于通过网络将计算机设备10000与外部终端相连，在计算机设备10000与外部终端之间的建立数据传输通道和通信链接等。网络可以是企业内部网（Intranet）、互联网（Internet）、全球移动通讯系统（Global System of
Mobile communication，简称为GSM）、宽带码分多址（Wideband Code Division Multiple Access，简称为WCDMA）、4G网络、5G网络、蓝牙（Bluetooth）、Wi-Fi等无线或有线网络。
需要指出的是，图9仅示出了具有部件10010-10030的计算机设备，但是应理解的是，并不要求实施所有示出的部件，可以替代的实施更多或者更少的部件。
在本实施例中，存储于存储器10010中的义齿分拣辅助方法还可以被分割为一个或者多个程序模块，并由处理器（本实施例为处理器10020）所执行，以完成本发明实施例。
实施例五
本发明还提供一种计算机可读存储介质，计算机可读存储介质其上存储有计算机程序，计算机程序被至少一个处理器执行时实现实施例中的义齿分拣辅助方法的步骤。
本实施例中，计算机可读存储介质包括闪存、硬盘、多媒体卡、卡型存储器（例如，SD或DX存储器等）、随机访问存储器（RAM）、静态随机访问存储器（SRAM）、只读存储器（ROM）、电可擦除可编程只读存储器（EEPROM）、可编程只读存储器（PROM）、磁性存储器、磁盘、光盘等。在一些实施例中，计算机可读存储介质可以是计算机设备的内部存储单元，例如该计算机设备的硬盘或内存。在另一些实施例中，计算机可读存储介质也可以是计算机设备的外部存储设备，例如该计算机设备上配备的插接式硬盘，智能存储卡（Smart
Media Card，简称为SMC），安全数字（Secure
Digital，简称为SD）卡，闪存卡（Flash
Card）等。当然，计算机可读存储介质还可以既包括计算机设备的内部存储单元也包括其外部存储设备。本实施例中，计算机可读存储介质通常用于存储安装于计算机设备的操作系统和各类应用软件，例如实施例中义齿分拣辅助方法的程序代码等。此外，计算机可读存储介质还可以用于暂时地存储已经输出或者将要输出的各类数据。
显然，本领域的技术人员应该明白，上述的本发明实施例的各模块或各步骤可以用通用的计算装置来实现，它们可以集中在单个的计算装置上，或者分布在多个计算装置所组成的网络上，可选地，它们可以用计算装置可执行的程序代码来实现，从而，可以将它们存储在存储装置中由计算装置来执行，并且在某些情况下，可以以不同于此处的顺序执行所示出或描述的步骤，或者将它们分别制作成各个集成电路模块，或者将它们中的多个模块或步骤制作成单个集成电路模块来实现。这样，本发明实施例不限制于任何特定的硬件和软件结合。
以上仅为本发明的优选实施例，并非因此限制本发明的专利范围，凡是利用本发明说明书及附图内容所作的等效结构或等效流程变换，或直接或间接运用在其他相关的技术领域，均同理包括在本发明的专利保护范围内。
Claims (10)
- 一种义齿分拣辅助方法，其特征在于，应用于义齿分拣辅助系统，所述义齿分拣辅助系统包括用户端和服务端，所述用户端包括前端和后端，所述方法包括：通过所述后端接收所述前端发送的至少一个义齿对应的义齿模型数据和对应的义齿参数数据，其中，所述义齿模型数据和所述义齿参数数据为所述前端基于用户在所述前端的预设区域的操作获取的数据；通过所述后端根据每个义齿的义齿模型数据，计算得到每个义齿对应的义齿模型体积数据；通过所述后端根据所述每个义齿的所述义齿模型体积数据和对应的义齿参数数据，计算得到所述每个义齿预测烧结后的重量，并发送所述每个义齿预测烧结后的重量发送至所述服务端；待每个义齿放入同一烧结炉进行烧结后，通过所述后端获取所述每个义齿实际烧结后的重量，并发送所述每个义齿实际烧结后的重量至所述服务端；通过所述服务端接收所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，基于所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，确定与所述每个义齿对应的至少一个候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述后端；及通过所述后端根据接收到的所述每个义齿对应的至少一个候选义齿订单数据，从所述每个义齿对应的至少一个候选义齿订单数据中确定所述每个义齿对应的目标义齿订单数据以实现对所述每个义齿的分拣。
- 根据权利要求1所述的义齿分拣辅助方法，其特征在于，所述通过所述后端根据每个义齿的义齿模型数据，计算得到每个义齿对应的义齿模型体积数据，包括：通过所述后端根据所述每个义齿的义齿模型数据确定每个义齿的模型顶点数据；通过所述后端根据所述每个义齿的所述义齿模型数据和所述模型顶点数据，切分所述每个义齿，以得到所述每个义齿对应的多个模型面片数据；及通过所述后端根据所述每个义齿对应的多个模型面片数据，计算得到所述每个义齿对应的义齿模型体积数据。
- 根据权利要求2所述的义齿分拣辅助方法，其特征在于，所述通过所述后端根据所述每个义齿对应的多个模型面片数据，计算得到所述每个义齿对应的义齿模型体积数据，包括：通过所述后端根据所述每个义齿对应的多个模型面片数据，计算得到每个模型面片数据对应的面片体积数据；及通过所述后端分别组合所述每个义齿对应的所述每个模型面片数据对应的面片体积数据，以得到所述每个义齿对应的义齿模型体积数据。
- 根据权利要求2所述的义齿分拣辅助方法，其特征在于，所述义齿参数数据包括每个义齿所包含的数量、烧结前的重量、切削完成至烧结前义齿经历的步骤和义齿所选材料数据。
- 根据权利要求4所述的义齿分拣辅助方法，其特征在于，所述通过所述后端根据所述每个义齿的所述义齿模型体积数据和对应的义齿参数数据，计算得到所述每个义齿预测烧结后的重量，包括：通过所述后端基于所述每个义齿的义齿模型体积数据，获取所述每个义齿对应的体积收缩比；通过所述后端根据所述每个义齿的所述每个义齿所包含的数量、所述烧结前的重量、所述切削完成至烧结前义齿经历的步骤和所述义齿所选材料数据，获取所述每个义齿对应的平均重量变化比；及通过所述后端根据所述每个义齿的义齿模型体积数据、所述体积收缩比、所述平均重量变化比，计算得到所述每个义齿对应的预测烧结后的重量。
- 根据权利要求1所述的义齿分拣辅助方法，其特征在于，所述通过所述服务端接收所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，基于所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，确定与所述每个义齿对应的至少一个候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述后端，包括：通过所述服务端基于所述每个义齿的实际烧结后的重量，分别与所述每个义齿的预测烧结后的重量进行对比，得到所述每个义齿对应的至少一个差异值；通过所述服务端从所述每个义齿对应的至少一个差异值中获取最小的差异值，或，分别将所述每个义齿对应的至少一个差异值根据数值大小依次排序并获取排序前M个差异值，其中，第一个差异值为最小差异值；及通过所述服务端将所述每个义齿对应的最小的差异值对应的义齿订单数据确定为候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述后端，或，将所述每个义齿对应的排序前M个差异值确定为至少一个候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述后端。
- 一种义齿分拣辅助方法，其特征在于，应用于用户端，所述方法包括：接收用户输入的至少一个义齿对应的义齿模型数据和对应的义齿参数数据；根据每个义齿的义齿模型数据，计算得到每个义齿对应的义齿模型体积数据；根据所述每个义齿的所述义齿模型体积数据和对应的义齿参数数据，计算得到所述每个义齿预测烧结后的重量，并发送所述每个义齿预测烧结后的重量发送至服务端；待每个义齿放入同一烧结炉进行烧结后，获取所述每个义齿实际烧结后的重量，并发送所述每个义齿实际烧结后的重量至所述服务端，以使所述服务端：基于所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，确定与所述每个义齿对应的至少一个候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述用户端；及接收所述服务端发送的所述每个义齿对应的至少一个候选义齿订单数据；根据接收到的所述每个义齿对应的至少一个候选义齿订单数据，从所述每个义齿对应的至少一个候选义齿订单数据中确定所述每个义齿对应的目标义齿订单数据以实现对所述每个义齿的分拣。
- 一种义齿分拣辅助装置，其特征在于，应用于用户端，所述装置包括：数据接收模块，用于接收用户输入的至少一个义齿对应的义齿模型数据和对应的义齿参数数据；体积计算模块，用于根据每个义齿的义齿模型数据，计算得到每个义齿对应的义齿模型体积数据；重量计算模块，用于根据所述每个义齿的所述义齿模型体积数据和对应的义齿参数数据，计算得到所述每个义齿预测烧结后的重量，并发送所述每个义齿预测烧结后的重量发送至服务端；重量获取模块，用于待每个义齿放入同一烧结炉进行烧结后，获取所述每个义齿实际烧结后的重量，并发送所述每个义齿实际烧结后的重量至所述服务端，以使所述服务端：基于所述每个义齿的所述预测烧结后的重量和所述实际烧结后的重量，确定与所述每个义齿对应的至少一个候选义齿订单数据，并发送所述每个义齿对应的至少一个候选义齿订单数据至所述用户端；及订单接收模块，用于接收所述服务端发送的所述每个义齿对应的至少一个候选义齿订单数据；及订单确定模块，用于根据接收到的所述每个义齿对应的至少一个候选义齿订单数据，从所述每个义齿对应的至少一个候选义齿订单数据中确定所述每个义齿对应的目标义齿订单数据以实现对所述每个义齿的分拣。
- 一种计算机设备，包括存储器、处理器以及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时用于实现权利要求1~6中任意一项所述的义齿分拣辅助方法的步骤。
- 一种计算机可读存储介质，其特征在于，其内存储有计算机程序，所述计算机程序可被至少一个处理器所执行，以使所述至少一个处理器执行权利要求1~6中任意一项所述的义齿分拣辅助方法的步骤。
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