CN115609924A - Management method and system based on distributed 3D printing equipment - Google Patents

Management method and system based on distributed 3D printing equipment Download PDF

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Publication number
CN115609924A
CN115609924A CN202211554467.1A CN202211554467A CN115609924A CN 115609924 A CN115609924 A CN 115609924A CN 202211554467 A CN202211554467 A CN 202211554467A CN 115609924 A CN115609924 A CN 115609924A
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printing
user
model
distributed
type
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CN115609924B (en
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张胜哲
曾维棋
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Shenzhen Intelligent Technology Co ltd
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Shenzhen Intelligent Technology Co ltd
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
    • B29C64/00—Additive manufacturing, i.e. manufacturing of three-dimensional [3D] objects by additive deposition, additive agglomeration or additive layering, e.g. by 3D printing, stereolithography or selective laser sintering
    • B29C64/30—Auxiliary operations or equipment
    • B29C64/386—Data acquisition or data processing for additive manufacturing
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B33—ADDITIVE MANUFACTURING TECHNOLOGY
    • B33Y—ADDITIVE MANUFACTURING, i.e. MANUFACTURING OF THREE-DIMENSIONAL [3-D] OBJECTS BY ADDITIVE DEPOSITION, ADDITIVE AGGLOMERATION OR ADDITIVE LAYERING, e.g. BY 3-D PRINTING, STEREOLITHOGRAPHY OR SELECTIVE LASER SINTERING
    • B33Y50/00—Data acquisition or data processing for additive manufacturing
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/10—Protocols in which an application is distributed across nodes in the network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • 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

Abstract

The invention discloses a management method based on distributed 3D printing equipment, wherein a front-end server acquires pre-uploaded data of a user, the pre-uploaded data are a pre-uploaded 3D model to be printed and position information of the user, the front-end server evaluates the 3D model to be printed uploaded by the user, a first type of distributed 3D printing cluster points are distributed according to the printing difficulty and/or the size of the model, a platform server is connected with the front-end server, the platform server receives demand parameters of the user on the precision and the tensile strength of the model, and suggested printing cluster points are pushed according to the demand of the user. 2 different servers are arranged for protecting classified storage and calculation of user privacy, the cost and maintenance difficulty of equipment construction are greatly reduced at the same time of enhancing the system security, meanwhile, the requirements corresponding to different users are executed by different servers, and the different users can be more conveniently distinguished during statistics.

Description

Management method and system based on distributed 3D printing equipment
Technical Field
The invention relates to the technical field of 3D printing, in particular to a management method and a management system based on distributed 3D printing equipment.
Background
In recent years, 3D printers have rapidly spread. The 3D printer itself has existed for a long time, but due to recent technological innovations, various modeling methods such as fused deposition modeling (FDM method), stereolithography, powder sintering lamination, gypsum powder lamination, and inkjet method have been adopted, which are designed and put into practical use. Further, among users who use 3D printers, there are users who own 3D printers of a plurality of modeling methods and correctly use the 3D printers according to contents of modeling objects (modeling accuracy, production cost, and the like).
However, many users who can use 3D printing devices only have one printing apparatus or fail to own private printing devices, and therefore, in order to solve the problem, the prior art provides a cluster 3D printing platform for providing relevant 3D printing to users, however, for the cluster 3D printing devices, the devices that can be printed corresponding to different models are different, as one of the biggest differences between 3D printing and general document printing is that one 3D printing device cannot meet all printing requirements, each 3D printing device has different parameters for material, model size and fineness, and the requirements of each different type of 3D printing device for the environment are also different, so how to deliver 3D printed products meeting the user requirements is a problem that is difficult to solve.
In the related art, for example, japanese patent JP2015030578A provides an information processing device for efficiently delivering a modeling object. The solution is as follows: when the user transmits a modeling object that can be output by a 3D printer to an arbitrary location, the information processing apparatus will input 3D model data and a delivery destination of the modeling object, and operate the 3D printer provided in each distribution base. Based on the situation, the time required for output, the operating state of the transfer apparatus, and the transfer time from each allocation pool to the transfer destination, an apparatus for determining an effective distribution pool for transferring the modeling object is provided. That is, the distribution of 3D products and the distribution of printing devices in the prior art are not considered the benefits of the 3D devices themselves, and can not optimize the process links from production to distribution to the greatest extent.
Disclosure of Invention
The present invention improves upon the problems of one or more of the above-described problems in the hopes of addressing user suggestions for materials, fill-in miscelections, and optimal printing equipment. The present invention is directed to solving at least one of the problems of the prior art.
To this end, the invention discloses a management method based on a distributed 3D printing device, which comprises the following steps:
step 1, a user firstly communicates with a front-end server through a client, after the identity authentication of the user client is carried out, the front-end server acquires pre-uploaded data of the user, wherein the pre-uploaded data are pre-uploaded 3D models to be printed and position information of the user, and the pre-uploaded data are in a well-agreed data format;
step 2, the front-end server evaluates the 3D model to be printed uploaded by the user, distributes distributed 3D printing cluster points of a first type according to the printing difficulty and/or size of the model, and sorts a plurality of distributed 3D printing cluster points of the first type according to the reachable distance according to the position information of the user to obtain a first sequence, wherein the distributed 3D printing cluster points of the first type closest to the user are arranged at a first position;
step 3, a platform server is connected with the front server, the platform server receives parameters required by a user for the precision and tensile strength of the model, when the requirement of the user for the 3D printing work is smaller than the precision and tensile strength which can be provided by default printing parameters of the first type of 3D printing equipment, a first sequence is sent to a client, and the user only allows to select a 3D printing cluster point in the first sequence;
and 4, when the requirement of the user on the 3D printing works exceeds the default printing parameters of the first type of 3D printing equipment, changing the model filling rate and the selected material type of the model, judging whether the printing products with the changed model filling rate and the selected material type meet the requirements of the user on the precision and the tensile strength of the model, if so, sending the first sequence to a client, allowing the 3D printing cluster points to be selected inside or outside the first sequence, and when the 3D printing cluster points are selected outside, directly evaluating whether the equipment of the externally selected 3D printing cluster points can meet the requirements by the platform server, and pushing suggested printing cluster points according to the requirements of the user.
Furthermore, the pre-uploaded data is in a well-agreed data format, and the method further comprises the steps of encrypting the position information provided by the user through a confusion encryption algorithm, and encrypting and compressing the 3D model data provided by the user through a compression algorithm, wherein the confusion encryption algorithm and a corresponding secret key of the compression algorithm are generated according to the information provided by the user during registration.
Furthermore, the difficulty evaluation model of the model is used for grading the flatness in the printing model in a preset area, the flatter the model is, the lower the score is, and the flatter the corresponding model is, the higher the score is, wherein the preset area is a plurality of model areas, different weights are set for different areas, the comprehensive printing difficulty of the model is a weighted average value of each area, the different weighted average values correspond to different types of 3D printing equipment, namely, the type of the 3D electric equipment and the weighted average value have a first mapping relation.
Still further, the assigning distributed 3D printing cluster points of the first type according to the printing difficulty and/or size of the model further comprises: and distributing the types of the distributed 3D printing equipment according to the sizes of the printing models to enable the size information corresponding to different printing models and the 3D printing equipment of different types to have a second mapping relation, and distributing the types of the printing equipment of the pre-printing models by the front-end server according to the second mapping relation.
Furthermore, the method further comprises a step 5 of changing the model filling rate and the selected material type of the model when the requirement of the user on the 3D printing works exceeds the default printing parameters of the first type of 3D printing equipment, judging whether the printing product with the changed model filling rate and the selected material type meets the requirement of the user on the precision and the tensile strength of the model, and if not, directly recommending the 3D printing cluster points by the platform server.
Furthermore, the security level of the front-end server is higher than that of the platform server, the judgment of the front-end server needs to be carried out by combining with the private information of the user, the geographical location information, the upper cost limit and the time requirement of the user are considered when the recommended 3D printing cluster point is generated, the platform server generates the recommended 3D printing cluster point according to the requirement of the user and does not consider the private information of the user, and if the default parameters of the 3D printing equipment can meet the printing requirement of the user, the recommendation is directly carried out.
The invention also discloses a management system based on the distributed 3D printing equipment, the system comprises a client, a front server and a plurality of types of distributed 3D printing cluster points, wherein the printing equipment adopted by any one cluster point is of the same type, and the system comprises an authentication communication part: the method comprises the steps that a user firstly communicates with a front-end server through a client, after identity authentication of the user client is carried out, the front-end server obtains pre-uploaded data of the user, wherein the pre-uploaded data are pre-uploaded 3D models to be printed and position information of the user, and the pre-uploaded data are in a well-agreed data format;
the pre-evaluation part is used for evaluating a 3D model to be printed uploaded by a user through the pre-server, distributing distributed 3D printing cluster points of a first type according to the printing difficulty and/or the size of the model, and sequencing a plurality of distributed 3D printing cluster points of the first type according to the position information of the user to obtain a first sequence according to the reachable distance, wherein the distributed 3D printing cluster points of the first type closest to the user are arranged at a first position;
the first judgment recommendation part is realized by connecting a platform server with the front server, receiving the parameters of the precision and the tensile strength of the model required by the user, and when the requirement of the user on the 3D printing work is smaller than the precision and the tensile strength which can be provided by the default printing parameters of the first type of 3D printing equipment, sending a first sequence to a client, wherein the user only allows to select a 3D printing cluster point in the first sequence;
a second judgment recommendation part, which is implemented by judging whether a printed product with the changed model filling rate and the selected material type meets the requirements of the user on the precision and the tensile strength of the model when the requirement of the user on the 3D printed product exceeds the default printing parameters of the first type of 3D printing equipment, sending a first sequence to a client if the requirement is met, allowing the 3D printing cluster points to be selected in the first sequence or outside the first sequence, directly evaluating whether the externally selected equipment for the 3D printing cluster points can meet the requirement by the platform server when the externally selected 3D printing cluster points are selected, and pushing the suggested printing cluster points according to the requirements of the user;
and the third judgment and recommendation part is realized by changing the model filling rate and the selected material type of the model when the requirement of the user on the 3D printing works exceeds the default printing parameters of the first type of 3D printing equipment, judging whether the printing product with the changed model filling rate and the selected material type meets the requirements of the user on the precision and the tensile strength of the model, and if not, directly recommending the 3D printing cluster points by the platform server.
Furthermore, the pre-uploaded data is in a well-agreed data format, and the method further comprises the steps of encrypting the position information provided by the user through a confusion encryption algorithm, and encrypting and compressing the 3D model data provided by the user through a compression algorithm, wherein the confusion encryption algorithm and a corresponding secret key of the compression algorithm are generated according to the information provided by the user during registration.
Furthermore, the security level of the front-end server is higher than that of the platform server, the judgment of the front-end server needs to be carried out by combining with the private information of the user, the geographical location information, the upper cost limit and the time requirement of the user are considered when the recommended 3D printing cluster point is generated, the platform server generates the recommended 3D printing cluster point according to the requirement of the user and does not consider the private information of the user, and if the default parameters of the 3D printing equipment can meet the printing requirement of the user, the recommendation is directly carried out.
Furthermore, the difficulty evaluation model of the model is to grade the flatness in the printing model in a preset region, the flatter the model is, the lower the score is, the flatter the model is, the higher the score is, the more the corresponding model is, wherein the preset region is a plurality of model regions, different weights are set for different regions, the comprehensive printing difficulty of the model is a weighted average value of each region, the different weighted average values correspond to different types of 3D printing equipment, that is, there is a first mapping relationship between the type of the 3D electrical equipment and the weighted average value, a first type of distributed 3D printing cluster point is distributed according to the printing difficulty and/or the size of the model, and the method further comprises the following steps: and distributing the types of the distributed 3D printing equipment according to the sizes of the printing models to enable the size information corresponding to different printing models and the 3D printing equipment of different types to have a second mapping relation, and distributing the types of the printing equipment of the pre-printing models by the front-end server according to the second mapping relation.
Compared with the prior art, the method has the beneficial effects that: after the existing cluster 3D printing reaches a certain scale, a cluster point is convenient to manage and maintain, 3D printing equipment with the same model and type is often adopted for printing, and the printing effect and the economy are generally considered to be printed according to default standard parameters.
Drawings
The invention will be further understood from the following description in conjunction with the accompanying drawings. The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principles of the embodiments. In the drawings, like reference numerals designate corresponding parts throughout the different views.
Fig. 1 is a flowchart of a management method based on distributed 3D printing devices according to the present invention.
Detailed Description
Example one
A management method based on distributed 3D printing devices as shown in fig. 1, the method comprising the steps of:
step 1, a user firstly communicates with a front-end server through a client, after the identity authentication of the user client is carried out, the front-end server acquires pre-uploaded data of the user, wherein the pre-uploaded data are a pre-uploaded 3D model to be printed and position information of the user, and the pre-uploaded data are in a well-agreed data format;
step 2, the front-end server evaluates the 3D model to be printed uploaded by the user, distributes distributed 3D printing cluster points of a first type according to the printing difficulty and/or size of the model, and sorts a plurality of distributed 3D printing cluster points of the first type according to the reachable distance according to the position information of the user to obtain a first sequence, wherein the distributed 3D printing cluster points of the first type closest to the user are arranged at a first position;
step 3, a platform server is connected with the front server, the platform server receives parameters required by a user for the precision and the tensile strength of the model, when the requirement of the user for the 3D printing work is smaller than the precision and the tensile strength which can be provided by default printing parameters of the first type of 3D printing equipment, a first sequence is sent to a client, and the user only allows to select a 3D printing cluster point in the first sequence;
and 4, when the requirement of the user on the 3D printing works exceeds the default printing parameters of the first type of 3D printing equipment, changing the model filling rate and the selected material type of the model, judging whether the printing product with the changed model filling rate and the selected material type meets the requirements of the user on the precision and the tensile strength of the model, if so, sending the first sequence to the client, allowing the 3D printing cluster points to be selected inside or outside the first sequence, and when the 3D printing cluster points are selected outside, directly evaluating whether the equipment of the externally selected 3D printing cluster points can meet the requirements by the platform server, and pushing the suggested printing cluster points according to the requirements of the user.
Furthermore, the pre-uploaded data is in an agreed data format, and the method further comprises the steps of encrypting the position information provided by the user through a confusion encryption algorithm, and encrypting and compressing the 3D model data provided by the user through a compression algorithm, wherein corresponding keys of the confusion encryption algorithm and the compression algorithm are generated according to the information provided by the user during registration.
Furthermore, the difficulty evaluation model of the model is used for grading the flatness in the printing model in a preset region, the more the model is flat, the lower the score is, and the more the corresponding model is flat, the higher the score is, wherein the preset region is a plurality of model regions, different weights are set for different regions, the comprehensive printing difficulty of the model is a weighted average value of each region, the different weighted average values correspond to different types of 3D printing equipment, that is, the type of the 3D electrical equipment and the weighted average value have a first mapping relation.
Still further, the assigning distributed 3D printing cluster points of the first type according to the printing difficulty and/or size of the model further comprises: and distributing the types of the distributed 3D printing equipment to have second mapping relations for the size information corresponding to different printing models and the 3D printing equipment of different types according to the sizes of the printing models, and distributing the types of the printing equipment of the pre-printing models by the front-end server according to the second mapping relations.
Furthermore, the method further comprises a step 5 of changing the model filling rate and the selected material type of the model when the requirement of the user on the 3D printing works exceeds the default printing parameters of the first type of 3D printing equipment, judging whether the printing product with the changed model filling rate and the selected material type meets the requirement of the user on the precision and the tensile strength of the model, and if not, directly recommending the 3D printing cluster points by the platform server.
Furthermore, the security level of the front-end server is higher than that of the platform server, the judgment of the front-end server needs to be carried out by combining with the private information of the user, the geographical location information, the upper cost limit and the time requirement of the user are considered when the recommended 3D printing cluster point is generated, the platform server generates the recommended 3D printing cluster point according to the requirement of the user and does not consider the private information of the user, and if the default parameters of the 3D printing equipment can meet the printing requirement of the user, the recommendation is directly carried out.
Example two
The embodiment describes the inventive concept of the invention from the viewpoint of hardware construction, and discloses a management system based on distributed 3D printing equipment, which comprises a client, a front-end server and multiple types of distributed 3D printing cluster points, wherein the printing equipment adopted by any one cluster point is of the same type, the system comprises an authentication communication part, the implementation mode is that a user firstly communicates with the front-end server through the client, after the identity authentication of the user client is carried out, the front-end server acquires the pre-uploaded data of the user, the pre-uploaded data is the pre-uploaded 3D model to be printed and the position information of the user, and the pre-uploaded data is an agreed data format;
the pre-evaluation part is used for evaluating a 3D model to be printed uploaded by a user through the pre-server, distributing distributed 3D printing cluster points of a first type according to the printing difficulty and/or the size of the model, and sequencing a plurality of distributed 3D printing cluster points of the first type according to the position information of the user to obtain a first sequence according to the reachable distance, wherein the distributed 3D printing cluster points of the first type closest to the user are arranged at a first position;
the first judgment recommendation part is realized by connecting a platform server with the front server, receiving the parameters of the precision and the tensile strength of the model required by the user, and when the requirement of the user on the 3D printing work is smaller than the precision and the tensile strength which can be provided by the default printing parameters of the first type of 3D printing equipment, sending a first sequence to a client, wherein the user only allows to select a 3D printing cluster point in the first sequence;
a second judgment recommendation part, which is implemented by judging whether a printed product with the changed model filling rate and the selected material type meets the requirements of the user on the precision and the tensile strength of the model when the requirement of the user on the 3D printed product exceeds the default printing parameters of the first type of 3D printing equipment, sending a first sequence to a client if the requirement is met, allowing the 3D printing cluster points to be selected in the first sequence or outside the first sequence, directly evaluating whether the externally selected equipment for the 3D printing cluster points can meet the requirement by the platform server when the externally selected 3D printing cluster points are selected, and pushing the suggested printing cluster points according to the requirements of the user;
and the third judgment and recommendation part is realized by changing the model filling rate and the selected material type of the model when the requirement of the user on the 3D printing works exceeds the default printing parameters of the first type of 3D printing equipment, judging whether the printing product with the changed model filling rate and the selected material type meets the requirements of the user on the precision and the tensile strength of the model, and if not, directly recommending the 3D printing cluster points by the platform server.
Furthermore, the pre-uploaded data is in an agreed data format, and the method further comprises the steps of encrypting the position information provided by the user through a confusion encryption algorithm, and encrypting and compressing the 3D model data provided by the user through a compression algorithm, wherein corresponding keys of the confusion encryption algorithm and the compression algorithm are generated according to the information provided by the user during registration.
Furthermore, the security level of the front-end server is higher than that of the platform server, the judgment of the front-end server needs to be carried out by combining with the private information of the user, the geographical location information, the upper cost limit and the time requirement of the user are considered when the recommended 3D printing cluster point is generated, the platform server generates the recommended 3D printing cluster point according to the requirement of the user and does not consider the private information of the user, and if the default parameters of the 3D printing equipment can meet the printing requirement of the user, the recommendation is directly carried out.
Furthermore, the difficulty evaluation model of the model is to grade the flatness in the printing model in a preset region, the flatter the model is, the lower the score is, the flatter the model is, the higher the score is, the flatter the corresponding model is, wherein the preset region is a plurality of model regions, different weights are set for different regions, the comprehensive printing difficulty of the model is a weighted average value of each region, the different weighted average values correspond to different types of 3D printing devices, that is, there is a first mapping relationship between the type of the 3D electrical device and the weighted average value, the first type of distributed 3D printing cluster points are distributed according to the printing difficulty and/or the size of the model, and the method further includes: and distributing the types of the distributed 3D printing equipment into different types of 3D printing equipment and size information corresponding to different printing models according to the sizes of the printing models.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of other like elements in a process, method, article, or apparatus comprising the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or 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, and so forth) having computer-usable program code embodied therein.
Although the invention has been described above with reference to various embodiments, it should be understood that many changes and modifications can be made without departing from the scope of the invention. It is therefore intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that it is the following claims, including all equivalents, that are intended to define the spirit and scope of this invention. The above examples are to be construed as merely illustrative and not limitative of the remainder of the disclosure. After reading the description of the present invention, the skilled person can make various changes or modifications to the invention, and these equivalent changes and modifications also fall into the scope of the invention defined by the claims.

Claims (10)

1. A management method based on distributed 3D printing equipment is characterized by comprising the following steps:
step 1, a user firstly communicates with a front-end server through a client, after the identity authentication of the user client is carried out, the front-end server acquires pre-uploaded data of the user, wherein the pre-uploaded data are pre-uploaded 3D models to be printed and position information of the user, and the pre-uploaded data are in a well-agreed data format;
step 2, the front-end server evaluates the 3D model to be printed uploaded by the user, distributes distributed 3D printing cluster points of a first type according to the printing difficulty and/or size of the model, and sorts a plurality of distributed 3D printing cluster points of the first type according to the reachable distance according to the position information of the user to obtain a first sequence, wherein the distributed 3D printing cluster points of the first type closest to the user are arranged at a first position;
step 3, a platform server is connected with the front server, the platform server receives parameters required by a user for the precision and tensile strength of the model, when the requirement of the user for the 3D printing work is smaller than the precision and tensile strength which can be provided by default printing parameters of the first type of 3D printing equipment, a first sequence is sent to a client, and the user only allows to select a 3D printing cluster point in the first sequence;
and 4, when the requirement of the user on the 3D printing works exceeds the default printing parameters of the first type of 3D printing equipment, changing the model filling rate and the selected material type of the model, judging whether the printing product with the changed model filling rate and the selected material type meets the requirements of the user on the precision and the tensile strength of the model, if so, sending the first sequence to the client, allowing the 3D printing cluster points to be selected inside or outside the first sequence, and when the 3D printing cluster points are selected outside, directly evaluating whether the equipment of the externally selected 3D printing cluster points can meet the requirements by the platform server, and pushing the suggested printing cluster points according to the requirements of the user.
2. The management method based on the distributed 3D printing device as claimed in claim 1, wherein the pre-uploaded data is in a well-agreed data format, and further comprising encrypting the position information provided by the user through an obfuscated encryption algorithm, and encrypting and compressing the 3D model data provided by the user through a compression algorithm, wherein corresponding keys of the obfuscated encryption algorithm and the compression algorithm are generated according to information provided when the user registers.
3. The management method based on the distributed 3D printing equipment as claimed in claim 1, wherein the model for evaluating the difficulty level of the model is to grade the flatness in the printing model in a preset area, the more flat the model, the lower the score, and the more uneven the corresponding model, the higher the score, wherein the preset area is a plurality of model areas, different weights are set for different areas, the integrated printing difficulty level of the model is a weighted average value of each area, and the different weighted average values correspond to different types of 3D printing equipment, that is, the type and the weighted average value of the 3D printing equipment have a first mapping relation.
4. The distributed 3D printing device-based management method according to claim 3, wherein the first type of distributed 3D printing cluster points are allocated according to the printing difficulty and/or the size of the model, and further comprising: and distributing the types of the distributed 3D printing equipment according to the sizes of the printing models to enable the size information corresponding to different printing models and the 3D printing equipment of different types to have a second mapping relation, and distributing the types of the printing equipment of the pre-printing models by the front-end server according to the second mapping relation.
5. The management method based on the distributed 3D printing device as claimed in claim 1, further comprising a step 5 of, when the user's requirement for the 3D printing product exceeds the default printing parameters of the first type of 3D printing device, changing the model filling rate and the selected material type of the model, determining whether the printing product with the changed model filling rate and the selected material type meets the user's requirements for the accuracy and tensile strength of the model, and if not, directly recommending the 3D printed cluster points by the platform server.
6. The management method based on the distributed 3D printing equipment as claimed in claim 5, wherein the security level of the front server is higher than that of the platform server, the judgment of the front server needs to be carried out in combination with the private information of the user, the geographical location information, the upper cost limit and the time requirement of the user are considered when the recommended 3D printing cluster point is generated, the platform server generates the recommended 3D printing cluster point according to the requirement of the user without considering the private information of the user, and the recommendation is directly carried out if the default parameters of the 3D printing equipment can meet the printing requirement of the user.
7. A management system based on distributed 3D printing equipment is characterized by comprising a client, a front-end server and multiple types of distributed 3D printing cluster points, wherein the printing equipment adopted by any one cluster point is of the same type, a user firstly communicates with the front-end server through the client, after the identity authentication of the user client is carried out, the front-end server acquires pre-uploaded data of the user, the pre-uploaded data are a pre-uploaded 3D model to be printed and position information of the user, and the pre-uploaded data are in a well-agreed data format;
the pre-evaluation part is used for evaluating a 3D model to be printed uploaded by a user through the pre-server, distributing distributed 3D printing cluster points of a first type according to the printing difficulty and/or the size of the model, and sequencing a plurality of distributed 3D printing cluster points of the first type according to the position information of the user to obtain a first sequence according to the reachable distance, wherein the distributed 3D printing cluster points of the first type closest to the user are arranged at a first position;
the first judgment recommendation part is realized by connecting a platform server with the front server, receiving the parameters of the precision and the tensile strength of the model required by the user, and when the requirement of the user on the 3D printing work is smaller than the precision and the tensile strength which can be provided by the default printing parameters of the first type of 3D printing equipment, sending a first sequence to a client, wherein the user only allows to select a 3D printing cluster point in the first sequence;
a second judgment recommendation part, which is implemented by judging whether a printed product with the changed model filling rate and the selected material type meets the requirements of the user on the precision and the tensile strength of the model when the requirement of the user on the 3D printed product exceeds the default printing parameters of the first type of 3D printing equipment, sending a first sequence to a client if the requirement is met, allowing the 3D printing cluster points to be selected in the first sequence or outside the first sequence, directly evaluating whether the externally selected equipment for the 3D printing cluster points can meet the requirement by the platform server when the externally selected 3D printing cluster points are selected, and pushing the suggested printing cluster points according to the requirements of the user;
and the third judgment and recommendation part is realized by changing the model filling rate and the selected material type of the model when the requirement of the user on the 3D printing works exceeds the default printing parameters of the first type of 3D printing equipment, judging whether the printing product with the changed model filling rate and the selected material type meets the requirements of the user on the precision and the tensile strength of the model, and if not, directly recommending the 3D printing cluster points by the platform server.
8. The management system of claim 7, wherein the pre-uploaded data is in a well-agreed data format, further comprising encrypting location information provided by the user through an obfuscated encryption algorithm, and encrypting and compressing 3D model data provided by the user through a compression algorithm, wherein corresponding keys of the obfuscated encryption algorithm and the compression algorithm are generated according to information provided by the user during registration.
9. The management system based on the distributed 3D printing device according to claim 8, wherein the security level of the front server is higher than that of the platform server, the judgment of the front server needs to be performed in combination with the private information of the user, the geographical location information, the upper cost limit and the time requirement of the user are considered when the recommended 3D printing cluster point is generated, the platform server generates the recommended 3D printing cluster point according to the requirement of the user without considering the private information of the user, and the recommendation is directly performed if the default parameters of the 3D printing device can meet the printing requirement of the user.
10. The management system of claim 9, wherein the model for evaluating the difficulty level of the model is to score the flatness of the printing model in a preset region, the flatter the model the lower the score, and the flatter the model the higher the score, wherein the preset region is a plurality of model regions, different weights are set for different regions, the integrated printing difficulty level of the model is a weighted average value of the regions, the different weighted average values correspond to different types of 3D printing devices, that is, there is a first mapping relationship between the types and the weighted average values of the 3D printing devices, and the first type of distributed 3D printing cluster points are assigned according to the printing difficulty level and/or the size of the model, further comprising: and distributing the types of the distributed 3D printing equipment according to the sizes of the printing models to enable the size information corresponding to different printing models and the 3D printing equipment of different types to have a second mapping relation, and distributing the types of the printing equipment of the pre-printing models by the front-end server according to the second mapping relation.
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