Three-dimensional printing model evaluation method and system for historical printing data

Abstract

The invention relates to the technical field of three-dimensional printing, and particularly discloses a three-dimensional printing model evaluation method and system for historical printing data, and the method comprises the steps: obtaining the printing complexity of modeling data when the modeling data is received, and selecting an electric control element according to the printing complexity; obtaining operation parameters of the selected electric control element according to a preset frequency, normalizing the operation parameters, inserting the operation parameters into a table, and after printing is completed, obtaining an operation table of modeling data; performing data compression on the operation table, and connecting modeling data with the operation table after data compression to serve as reference data; for modeling data being printed, reference data is matched according to the modeling data, the running table is verified based on the reference data, and the stability is generated as an evaluation result. Only a data transmission module needs to be additionally arranged, an external high-cost sensor and an external high-cost recognition algorithm are not needed, the accuracy is high, and the cost is low.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of three-dimensional printing, and in particular to a three-dimensional printing model evaluation method and system for historical printing data. BACKGROUND

[0002] 3D printing, also known as additive manufacturing, is a technology that builds objects layer by layer based on digital models. Compared with traditional manufacturing processes, 3D printing has higher flexibility and personalized customization capabilities.

[0003] The 3D printing process requires a large amount of time, and once an error occurs during the printing process, the best way is to stop. Existing error detection methods mostly rely on sensors. When these sensors identify larger errors, they will report errors and stop printing. To improve detection accuracy, the accuracy of the sensors needs to be improved, and the accuracy of the sensors is closely related to their cost. The higher the accuracy is improved, the faster the cost is increased. How to provide a low-cost and accurate printing process identification method is the technical problem that the present application technical solution wants to solve. SUMMARY

[0004] The present application aims to provide a three-dimensional printing model evaluation method and system for historical printing data to solve the problems raised in the background.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0006] A three-dimensional printing model evaluation method for historical printing data, the method comprising:

[0007] Obtaining an electric control element in a three-dimensional printer, and establishing a table with a column number equal to the total number of electric control elements; wherein the electric control element contains a level label for indicating the importance of the electric control element in the printing process;

[0008] When receiving modeling data, obtaining the printing complexity of the modeling data, and selecting an electric control element according to the printing complexity;

[0009] According to a preset frequency, obtaining the running parameters of the selected electric control element, normalizing the running parameters, inserting the table, and obtaining a running table of the modeling data after printing is completed;

[0010] Data compression is performed on the running table, and the modeling data and the data-compressed running table are connected as reference data;

[0011] For the modeling data being printed, the reference data is matched according to the modeling data, the running table is verified based on the reference data, a stability degree is generated as an evaluation result.

[0012] As a further scheme of the present application: the step of obtaining the printing complexity of the modeling data when the modeling data is received, and selecting the electric control element according to the printing complexity comprises:

[0013] When the modeling data is received, the edge line in the modeling data is located, and the total length of the edge line is calculated;

[0014] The fitting density of each component in the modeling data is obtained; the fitting density is the ratio of the actual volume of the component to the overall volume; the overall volume is the volume of the area surrounded by the edge line of the component;

[0015] The printing complexity of each component is determined according to the total length of the edge line and the fitting density;

[0016] The reference level is determined according to the printing complexity of all components, and the electric control element with a level greater than the reference level is selected in the table;

[0017] The printing complexity is directly proportional to the total length of the edge line and inversely proportional to the fitting density.

[0018] As a further scheme of the present application: the step of obtaining the running parameter of the selected electric control element according to the preset frequency, normalizing the running parameter, inserting the table, and obtaining the running table of the modeling data after printing comprises:

[0019] The user-set frequency is received, and the running parameter of the selected electric control element is obtained at the frequency;

[0020] For each electric control element, the running parameter at each time is converted into a percentage according to the historical end value parameter; the historical end value parameter represents the boundary condition, including the maximum value and the minimum value;

[0021] The percentage corresponding to each selected electric control element at each time is obtained, the percentage is inserted into the corresponding column, and the table is extended;

[0022] When the printing completion signal is detected, the name of the modeling data is queried, the name is inserted into the table, and the table is output as the running table of the modeling data.

[0023] As a further scheme of the present application: the step of performing data compression on the running table, connecting the modeling data and the data-compressed running table, and taking the data as reference data comprises:

[0024] For a certain running table, the abnormality degree of each data is calculated based on column data;

[0025] For any row data, the level of the corresponding electric control element of the row is queried, the weight is determined according to the level, and the abnormality degrees of the data in the same row are accumulated based on the weight;

[0026] Extracting a preset number of rows in the running table according to the accumulated abnormality degree as reference data;

[0027] The abnormality degree is: The accumulation process is: In the formula, D ij is the abnormality degree of the data in the i-th row and the j-th column of the running table, x ij is the data in the i-th row and the j-th column of the running table, E j is the mean value of the data in the j-th column, L i is the accumulated abnormality degree of the data in the i-th row, M is the total number of columns of the running table, and α j is the weight of the j-th column of the running table.

[0028] As a further scheme of the present application, the step of generating a stability degree as an evaluation result based on the reference data by verifying the running table according to the modeling data being printed and matching the reference data according to the modeling data being printed comprises:

[0029] For each modeling data, edge line points are selected on all edge lines according to a preset step length;

[0030] Connecting the preset origin and the edge line points to obtain vectors;

[0031] Arranging the vectors according to a preset edge line point sequence to obtain a vector cluster;

[0032] For the modeling data being printed, matching target modeling data based on the vector cluster, and reading reference data of the target modeling data;

[0033] Obtaining a running table of the modeling data being printed, reading tail data of the table at a timing, verifying the tail data of the table based on the reference data, and obtaining a stability degree as an evaluation result.

[0034] As a further scheme of the present application, the comparison process of the vector cluster is:

[0035] For any two vector clusters, a preset default vector is used to fill in a vector cluster with a small number of vectors, and a matrix similarity of the two vector clusters after vector filling is calculated as a comparison result;

[0036] The generation process of the stability degree is: In the formula, W is the stability degree, A is the tail data, B is the reference data, N(A∩B) is the number of the intersection of A and B, and N(A) is the total number of elements of the tail data.

[0037] The present application also provides a three-dimensional printing model evaluation system for historical printing data, which comprises:

[0038] The table creating module is configured to acquire electric control elements in the three-dimensional printer and create a table with a column number equal to the total number of the electric control elements, wherein the electric control elements have level labels for indicating the importance of the electric control elements in the printing process.

[0039] The electric control element selecting module is configured to acquire a printing complexity of the modeling data when receiving the modeling data and select the electric control elements according to the printing complexity.

[0040] The normalization module is configured to acquire operation parameters of the selected electric control elements according to a preset frequency, normalize the operation parameters, insert the table, and obtain an operation table of the modeling data after printing.

[0041] The reference data generating module is configured to compress data of the operation table, connect the modeling data and the operation table after data compression, and serve as reference data.

[0042] The stability calculating module is configured to match the modeling data with the reference data, verify the operation table based on the reference data, generate a stability as an evaluation result for the modeling data being printed.

[0043] As a further scheme of the present application, the electric control element selecting module comprises:

[0044] The edge line positioning unit is configured to position edge lines in the modeling data when receiving the modeling data and calculate a total length of the edge lines.

[0045] The density fitting unit is configured to acquire fitting densities of components in the modeling data, wherein the fitting density is a ratio of an actual volume of the component to an overall volume, and the overall volume is a volume of an area surrounded by the edge lines of the component.

[0046] The complexity calculating unit is configured to determine printing complexities of the components according to the total length of the edge lines and the fitting densities.

[0047] The selecting executing unit is configured to determine a reference level according to the printing complexities of all the components and select electric control elements with a level greater than the reference level in the table.

[0048] The printing complexity is directly proportional to the total length of the edge lines and inversely proportional to the fitting density.

[0049] As a further scheme of the present application, the normalization module comprises:

[0050] The parameter acquiring unit is configured to receive a frequency set by a user and acquire operation parameters of the selected electric control elements at the frequency.

[0051] A parameter conversion unit is configured to convert the operation parameter of each time into a percentage according to a historical end value parameter for each electric control element, and the historical end value parameter represents a boundary condition including a maximum value and a minimum value.

[0052] A table extension unit is configured to obtain the percentage corresponding to each selected electric control element at each time, insert the percentage into a corresponding column, and extend the table.

[0053] A name insertion unit is configured to query the name of the modeling data when a printing completion signal is detected, insert the name into the table, and output the table as an operation table of the modeling data.

[0054] As a further scheme of the present application, the reference data generation module comprises:

[0055] An abnormality degree calculation unit is configured to calculate the abnormality degree of each data based on column data for a certain operation table.

[0056] An abnormality degree accumulation unit is configured to query the level of the electric control element corresponding to a row for any row data, determine a weight according to the level, and accumulate the abnormality degrees of the data in the same row based on the weight.

[0057] A row extraction unit is configured to extract a preset number of rows from the operation table according to the accumulated abnormality degrees as reference data.

[0058] The abnormality degree is: The accumulation process is: In the formula, D ij is the abnormality degree of the data in the i-th row and the j-th column of the operation table, x ij is the data in the i-th row and the j-th column of the operation table, E j is the mean value of the data in the j-th column, L i is the accumulated abnormality degree of the data in the i-th row, M is the total number of columns of the operation table, and a j is the weight of the j-th column of the operation table.

[0059] Compared with the prior art, the present application has the beneficial effects that the present application records the operation parameters of the model and the electric control elements in each printing process in real time, compares the model when a new model is received, queries the most matched model, reads the operation parameters of the matched model as reference data, and evaluates the printing process of the new model based on the reference data. Only a data transmission module needs to be added, without the need for external high-cost sensors and identification algorithms, so that the accuracy is high and the cost is low. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to make the technical solutions in the embodiments of the present application clearer, the drawings needed to be used in the embodiments or prior art description will be briefly introduced as follows. Obviously, the drawings in the following description only represent some of the embodiments of the present application.

[0061] Figure 1 Flow chart of three-dimensional printing model evaluation method for historical printing data.

[0062] Figure 2 First sub-flow chart of three-dimensional printing model evaluation method for historical printing data.

[0063] Figure 3 Second sub-flow chart of three-dimensional printing model evaluation method for historical printing data.

[0064] Figure 4 Third sub-flow chart of three-dimensional printing model evaluation method for historical printing data.

[0065] Figure 5 Fourth sub-flow chart of three-dimensional printing model evaluation method for historical printing data.

[0066] Figure 6 Structure block diagram of three-dimensional printing model evaluation system for historical printing data. DETAILED DESCRIPTION

[0067] In order to make the technical problems, technical solutions and beneficial effects of the present application clearer, the present application will be further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0068] Figure 1 Flow chart of three-dimensional printing model evaluation method for historical printing data, in the embodiments of the present application, a three-dimensional printing model evaluation method for historical printing data, the method comprises:

[0069] Step S100: obtaining an electric control element in a three-dimensional printer, and establishing a table with column number being total number of electric control elements; wherein the electric control element contains a level label for indicating importance of the electric control element in a printing process;

[0070] The three-dimensional printing process is an automatic working process, in which the built-in elements of the three-dimensional printer identify the model by themselves, determine the printing process, and then control the printing execution element to perform the printing work. In this process, many electric control elements are involved. All electric control elements (elements that have influence on the printing process) are counted, and a table is established, each column corresponding to an electric control element.

[0071] On this basis, the level label is introduced to obtain the importance of each electric control element in the printing process, and then the level is determined, the higher the importance, the higher the level. For example, the position adjusting part of the printing nozzle belongs to the basic function, and the level is the highest. In order to facilitate processing, when building the table, the columns are sorted in descending order of level, that is, the column corresponding to the electric control element with higher level is earlier.

[0072] Step S200: When receiving the modeling data, the printing complexity of the modeling data is obtained, and the electric control element is selected according to the printing complexity;

[0073] When receiving the modeling data, the printing complexity of the modeling data is obtained, and the electric control element is selected according to the printing complexity; The selection process is selected in order, such as selecting the first five columns, the higher the printing complexity, the more columns selected; On the basis of having sorted the columns according to the importance of the electric control element, this order selection scheme is sufficient to select the important electric control element according to the complexity of the modeling data.

[0074] Step S300: Obtain the running parameter of the selected electric control element according to the preset frequency, normalize the running parameter, insert the table, and obtain the running table of the modeling data after printing is completed;

[0075] Under the condition of the preset frequency, the running parameter of the selected electric control element is obtained, the running parameter is normalized, that is, it has the same format, and then it is inserted into the table; The insertion process is to insert the normalized data into the column corresponding to the electric control element in time sequence; With the passage of time, the rows in the table will be more and more; The table after inserting the data, each row represents the data of all selected electric control elements at a certain time, and each table represents the data of a selected electric control element at different times; The frequency is how long the running parameter of the electric control element is collected every time, the more the amount of resources for monitoring, the higher the frequency, the more the collected data; In practical application, the monitoring process itself is an auxiliary function, and a too high frequency is generally not adopted.

[0076] Step S400: Data compression is performed on the running table, and the modeling data and the data compressed running table are connected as reference data;

[0077] The three-dimensional printing process is a stable process, and the running parameters are relatively stable data. The performance price ratio of global analysis is not high, therefore, the data compression process is introduced in the application, the running table is first data compressed (a simplified scheme), so that the running table containing a large amount of data is simplified to reference data with only a few rows of data, that is, a small table; It should be noted that the data compression process only removes the rows, and the number of columns remains unchanged.

[0078] Step S500: For the modeling data being printed, matching the reference data according to the modeling data, verifying the running table based on the reference data, generating the stability, as the evaluation result;

[0079] Step S100 and step S400 occur in each printing process, after printing is completed, the running table and the reference data extracted based on the running table become historical data; Step S500 is the application stage, for the modeling data being printed, the most similar modeling data is matched according to the modeling data, the reference data of the most similar modeling data is queried, the running table of the modeling data being printed is verified based on the reference data, whether the printing process is in a stable state can be judged, which is represented by the stability parameter, and the printing process is actually evaluated.

[0080] It is worth mentioning that the modeling data involved in the present application is a to-be-printed file, and the to-be-printed file is generally a three-dimensional model, such as an stp file.

[0081] Figure 2 The first sub-flow block diagram of the three-dimensional printing model evaluation method for historical printing data, when the modeling data is received, the printing complexity of the modeling data is obtained, and the step of selecting the electric control element according to the printing complexity comprises:

[0082] Step S201: When the modeling data is received, the edge line in the modeling data is located, and the total length of the edge line is calculated;

[0083] Step S202: Obtain the fitting density of each component in the modeling data; the fitting density is the ratio of the actual volume of the component to the overall volume; the overall volume is the volume of the area surrounded by the component edge line;

[0084] Step S203: Determine the printing complexity of each component according to the total length of the edge line and the fitting density;

[0085] Step S204: Determine the reference level according to the printing complexity of all components, and select the electric control element with a level greater than the reference level in the table;

[0086] Among them, the printing complexity is proportional to the total length of the edge line, and inversely proportional to the fitting density.

[0087] The above describes the selection process of the electric control element. When receiving the modeling data, the edge line in the modeling data is located, and the total length of the edge line is calculated. The longer the total length of the edge line, the more complex the three-dimensional model, and the higher the printing complexity. On this basis, each component in the modeling data is obtained, the material of each component is set to be the same by default, the ratio of the actual volume of any component to the overall volume is calculated, and the ratio is at most one. The smaller the ratio, the more hollow, and the higher the printing complexity. The printing complexity of all components is counted to obtain the total printing complexity. Based on the total printing complexity, the reference level is determined. Generally, the higher the total printing complexity, the smaller the reference level, and the more rear the located reference column. All columns before the reference column are selected, and the electric control elements corresponding to the columns are selected as the selected electric control elements.

[0088] Figure 3 The second sub-flow chart of the three-dimensional printing model evaluation method for historical printing data comprises the steps of obtaining the running parameters of the selected electric control element according to the preset frequency, normalizing the running parameters, and inserting the table. After printing is completed, the running table of the modeling data is obtained.

[0089] Step S301: receiving a frequency set by a user, and obtaining the running parameters of the selected electric control element at the frequency;

[0090] Step S302: for each electric control element, converting the running parameters at each time into a percentage according to historical end value parameters; the historical end value parameters represent boundary conditions, including a maximum value and a minimum value;

[0091] Step S303: obtaining the percentage corresponding to all selected electric control elements at each time, inserting the percentage into the corresponding column, and extending the table;

[0092] Step S304: when a printing completion signal is detected, querying the name of the modeling data, inserting the name into the table, and outputting the table as the running table of the modeling data.

[0093] Steps S301 to S304 specifically describe the generation process of the running table. The running parameters of the selected electric control element are obtained at a frequency set by a user. The running parameters are converted according to boundary values to obtain percentages. One conversion method is to calculate the difference between the running parameters and the minimum value, divide the difference by the difference between the maximum value and the minimum value, multiply the ratio by 100%, and obtain the percentage.

[0094] The percentage corresponding to all selected electric control elements at each time is obtained, the column corresponding to the electric control element is queried, the percentage is inserted into the corresponding column, and the number of rows of the table increases with time. This is the extension process of the table.

[0095] When the printing completion signal is detected, the name of the modeling data is queried, the name is taken as an index of a table, and a running table of the modeling data is obtained.

[0096] Figure 4 The third sub-flow block diagram of the three-dimensional printing model evaluation method for historical printing data, the step of data compression on the running table, connecting the modeling data and the data compressed running table as reference data, comprises:

[0097] Step S401: For a running table, the abnormality degree of each data is calculated based on column data;

[0098] Step S402: For any row data, the level of the corresponding electric control element is queried, the weight is determined according to the level, and the abnormality degrees of the data in the same row are accumulated based on the weight;

[0099] Step S403: According to the accumulated abnormality degree, a preset number of rows in the running table are extracted as reference data;

[0100] The above provides a data compression process of a running table, which has the function of simplifying the number of rows. First, for each column data, the abnormality degree of any data in the column is calculated based on the whole column data, and the importance of each data can be judged according to the abnormality degree. Then, for each row data, the calculated abnormality degrees are accumulated to obtain the overall importance of the row data. Finally, the row data whose overall importance reaches a preset threshold can be selected as reference data.

[0101] Wherein, the abnormality degree is: The accumulation process is: In the formula, D ij is the abnormality degree of the data in the i-th row and the j-th column of the running table, x ij is the data in the i-th row and the j-th column of the running table, E j is the mean value of the j-th column data, L i is the cumulative abnormality degree of the i-th row data, M is the total column number of the running table, and a j is the weight of the j-th column of the running table.

[0102] The calculation principle of the abnormality degree is very simple. The greater the difference between a data in a column and the mean value of the column data, the greater the abnormality degree. The accumulation process is to add all the abnormality degrees of a row of data, wherein a weight term is introduced. The later the column number is, the smaller the weight is. The calculated weight is in the range of 0 to 1.

[0103] Figure 5A fourth sub-flow block diagram of the three-dimensional printing model evaluation method for historical printing data, the step of generating a stability degree as an evaluation result based on reference data verification of tail data of a running table of modeling data being printed, according to matching of the modeling data being printed with reference data, includes:

[0104] Step S501: For each modeling data, edge line points are selected on all edge lines according to a preset step length;

[0105] Step S502: Connecting the preset origin and the edge line points to obtain vectors;

[0106] Step S503: Arranging the vectors according to a preset edge line point sequence to obtain a vector cluster;

[0107] Step S504: For modeling data being printed, matching target modeling data based on the vector cluster, and reading reference data of the target modeling data;

[0108] Step S505: Obtaining a running table of the modeling data being printed, reading tail data of the table at a timing, verifying the tail data based on the reference data to obtain a stability degree as an evaluation result.

[0109] The above provides a specific running verification process based on reference data. First, for modeling data being printed, reference data most matched therewith is queried in historical data. This process involves a three-dimensional model comparison process. The three-dimensional model comparison process provided by the present application is that points, referred to as edge line points, are selected on all edge lines according to a preset step length, and vectors are obtained by connecting the edge line points based on the same origin. At this time, the three-dimensional model is converted into a set of vectors. In order to make the comparison process more accurate, the present application further introduces an edge line point sequence. The edge line point sequence is numbered when the edge line points are determined, and generally uses an increasing order of distance from the origin. The vector cluster containing the sequence can be used to compare the three-dimensional model, and the most similar three-dimensional model is queried in the historical data.

[0110] Then, the reference data of the most similar three-dimensional model is read, the tail data of the running table of the modeling data being printed is verified based on the reference data, and a numerical value for representing a stability degree, referred to as a stability value, can be obtained. After the stability value is generated, it is used as an evaluation result.

[0111] The comparison process of the vector cluster is as follows:

[0112] For any two vector clusters, a preset default vector is used to supplement vectors of a vector cluster with a smaller number of vectors, and a matrix similarity of the two vector clusters after vector supplementing is calculated as a comparison result;

[0113] The generation process of the stability degree is as follows: In the formula, W is stability, A is tail data, B is reference data, N(A intersection B) is the number of the intersection of A and B, and N(A) is the total number of elements of the tail data.

[0114] The alignment process of the vector cluster and the generation process of the stability are essentially the alignment process of the matrix, the alignment process of the vector cluster is based on the longer vector cluster, and the data of the shorter vector cluster is padded, and then the matrix similarity, such as the structural similarity or the cosine similarity, is calculated; and the generation process of the stability is based on the tail data (shorter data), and the reason for generating this difference is that if the lengths of the two vector clusters are different, the difference is considered to be large, and if the tail data is all present in the reference data, the stability is considered to be the highest.

[0115] It is worth mentioning that, the calculation process of the intersection of A and B, the data in A can be selected in sequence, and then the data with the smallest difference in B is queried, if the smallest difference is small enough, it can be considered as the intersection data, which reduces the judgment condition of the intersection and improves the robustness.

[0116] Figure 6 The three-dimensional printing model evaluation system for historical printing data is a three-dimensional printing model evaluation system for historical printing data, and the system 10 comprises:

[0117] The table creation module 11 is used for acquiring the electric control elements in the three-dimensional printer, and establishing a table with the column number being the total number of the electric control elements; wherein the electric control elements contain level labels, which are used for indicating the importance of the electric control elements in the printing process;

[0118] The electric control element selection module 12 is used for acquiring the printing complexity of the modeling data when receiving the modeling data, and selecting the electric control elements according to the printing complexity;

[0119] The normalization module 13 is used for acquiring the running parameters of the selected electric control elements according to the preset frequency, normalizing the running parameters, inserting the table, and obtaining the running table of the modeling data after printing is completed;

[0120] The reference data generation module 14 is used for data compression on the running table, and connecting the modeling data and the running table after data compression as the reference data;

[0121] The stability calculation module 15 is used for matching the reference data according to the modeling data for the modeling data being printed, verifying the running table based on the reference data, generating the stability as the evaluation result.

[0122] Further, the electric control element selection module 12 comprises:

[0123] An edge line positioning unit is configured to position edge lines in the modeling data when the modeling data is received, and calculate a total length of the edge lines;

[0124] A density fitting unit is configured to obtain a fitting density of each component in the modeling data; the fitting density is a ratio of an actual volume of the component to an overall volume; the overall volume is a volume of an area enclosed by the edge lines of the component;

[0125] A complexity calculation unit is configured to determine a printing complexity of each component according to the total length of the edge lines and the fitting density;

[0126] A selection execution unit is configured to determine a reference level according to the printing complexities of all the components, and select electric control elements with levels greater than the reference level in the table;

[0127] The printing complexity is proportional to the total length of the edge lines and inversely proportional to the fitting density.

[0128] Specifically, the normalization module 13 includes:

[0129] A parameter acquisition unit is configured to receive a frequency set by a user, and obtain operation parameters of the selected electric control elements at the frequency;

[0130] A parameter conversion unit is configured to convert the operation parameters at each time into percentages according to historical end value parameters for each electric control element; the historical end value parameters represent boundary conditions, including maximum values and minimum values;

[0131] A table extension unit is configured to obtain percentages corresponding to all the selected electric control elements at each time, insert the percentages into corresponding columns, and extend the table;

[0132] A name insertion unit is configured to query a name of the modeling data when a printing completion signal is detected, insert the name into the table, and output the table as an operation table of the modeling data.

[0133] In addition, the reference data generation module 14 includes:

[0134] An abnormality degree calculation unit is configured to calculate an abnormality degree of each data based on column data for an operation table;

[0135] An abnormality degree accumulation unit is configured to query a level of an electric control element corresponding to a row for any row data, determine a weight according to the level, and accumulate abnormality degrees of data in the same row based on the weight;

[0136] A row extraction unit is configured to extract a preset number of rows in the operation table as reference data according to the accumulated abnormality degrees;

[0137] The abnormality degree is: The accumulation process is: where D ij is the abnormality degree of the data in the i-th row and j-th column of the running table, x ij is the data in the i-th row and j-th column of the running table, E j is the average of the data in the j-th column, L i is the cumulative abnormality degree of the data in the i-th row, M is the total number of columns of the running table, and a j is the weight of the j-th column of the running table.

[0138] The above merely provides the preferred embodiments of the present application, but is not intended to limit the present application. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall fall within the protection scope of the present application.

Claims

1. A method of evaluating a three-dimensional print model of historical print data, the method comprising: The method comprises: Obtaining an electric control element in a three-dimensional printer, and establishing a table with the number of columns being the total number of electric control elements; wherein the electric control element contains a level label for indicating the importance of the electric control element in the printing process; When receiving modeling data, obtaining the printing complexity of the modeling data, and selecting an electric control element according to the printing complexity; According to a preset frequency, obtaining the operation parameters of the selected electric control element, normalizing the operation parameters, inserting the table, and obtaining the operation table of the modeling data after printing is completed; Data compression is performed on the operation table, and the modeling data and the operation table after data compression are connected as reference data; For the modeling data being printed, the reference data is matched according to the modeling data, the operation table is verified based on the reference data, a stability is generated as an evaluation result.

2. The method of claim 1, wherein, The step of obtaining the printing complexity of the modeling data when receiving the modeling data, and selecting an electric control element according to the printing complexity comprises: When receiving the modeling data, locating the edge lines in the modeling data, and calculating the total length of the edge lines; Obtaining the fitting density of each component in the modeling data; the fitting density is the ratio of the actual volume of the component to the overall volume; the overall volume is the volume of the area surrounded by the component edge lines; According to the total length of the edge lines and the fitting density, the printing complexity of each component is determined; According to the printing complexity of all components, a reference level is determined, and electric control elements with a level greater than the reference level are selected in the table; The printing complexity is directly proportional to the total length of the edge lines and inversely proportional to the fitting density.

3. The method of claim 1, wherein, The step of obtaining the operation parameters of the selected electric control element according to a preset frequency, normalizing the operation parameters, inserting the table, and obtaining the operation table of the modeling data after printing is completed comprises: Receiving a frequency set by a user, and obtaining the operation parameters of the selected electric control element under the frequency; For each electric control element, the operation parameters at each time are converted into percentages according to historical end value parameters; the historical end value parameters represent boundary conditions, including maximum and minimum values; The percentages corresponding to all selected electric control elements at each time are obtained, the percentages are inserted into the corresponding columns, and the table is extended; When a printing completion signal is detected, the name of the modeling data is queried, the name is inserted into the table, and the table is output as the operation table of the modeling data.

4. The method of claim 1, wherein, The step of data compression on the operation table, connecting the modeling data and the operation table after data compression as reference data comprises: For a certain operation table, the abnormality degree of each data is calculated based on column data; For any row data, the level of the row corresponding electric control element is queried, the weight is determined according to the level, and the abnormality degrees of the data in the same row are accumulated based on the weight; According to the accumulated abnormality degrees, a preset number of rows in the operation table are extracted as reference data; Wherein, the abnormality degree is: The cumulative process is: In the formula, D ij is the abnormality degree of the data in the i-th row and j-th column of the running table, x ij is the data in the i-th row and j-th column of the running table, E j is the mean value of the data in the j-th column, L i is the cumulative abnormality degree of the data in the i-th row, M is the total column number of the running table, and α j is the weight of the j-th column of the running table.

5. The method of claim 1, wherein, The step of matching the reference data according to the modeling data, verifying the operation table based on the reference data, generating a stability as an evaluation result for the modeling data being printed comprises: For each modeling data, edge line points are selected on all edge lines according to a preset step length; Connecting a preset origin and the edge line points to obtain vectors; According to a preset sequence of the edge line points, the vectors are arranged to obtain a vector cluster; For the modeling data being printed, the target modeling data is matched based on a vector cluster, reference data of the target modeling data is read; An operation table of the modeling data being printed is obtained, tail data of the table is read in a time manner, the tail data is verified based on the reference data, and a stability degree is obtained as an evaluation result.

6. The method of claim 5, wherein, The comparison process of the vector cluster is as follows: For any two vector clusters, a preset default vector is used to fill in the vector cluster with a small number of vectors, and a matrix similarity of the two vector clusters after vector filling is calculated as a comparison result; The generation process of the stability is as follows: In the formula, W is the stability, A is the tail data, B is the reference data, N(A∩B) is the number of the intersection of A and B, and N(A) is the total number of elements of the tail data.

7. A three-dimensional printing model evaluation system of historical printing data, characterized by, The system comprises: A table creation module is configured to obtain electric control elements in a three-dimensional printer, and create a table with a column number equal to a total number of the electric control elements; the electric control elements have level labels for indicating importance of the electric control elements in a printing process; An electric control element selection module is configured to obtain a printing complexity of modeling data when the modeling data is received, and select electric control elements according to the printing complexity; A normalization module is configured to obtain operation parameters of the selected electric control elements according to a preset frequency, normalize the operation parameters, and insert the operation parameters into the table; after printing is completed, an operation table of the modeling data is obtained; A reference data generation module is configured to compress data of the operation table, and connect the modeling data and the operation table after data compression as reference data; A stability degree calculation module is configured to match the reference data according to the modeling data for the modeling data being printed, verify the operation table based on the reference data, and generate a stability degree as an evaluation result.

8. The three-dimensional printed model evaluation system of historical print data according to claim 7, wherein, The electric control element selection module comprises: An edge line positioning unit is configured to position edge lines in the modeling data when the modeling data is received, and calculate a total length of the edge lines; A density fitting unit is configured to obtain fitting densities of components in the modeling data; the fitting density is a ratio of an actual volume of the component to an overall volume; the overall volume is a volume of an area surrounded by the edge lines of the component; A complexity calculation unit is configured to determine printing complexities of the components according to the total length of the edge lines and the fitting densities; A selection execution unit is configured to determine a reference level according to the printing complexities of all the components, and select electric control elements with levels greater than the reference level in the table; The printing complexity is directly proportional to the total length of the edge lines and inversely proportional to the fitting densities.

9. The three-dimensional printed model evaluation system of historical print data according to claim 7, wherein, The normalization module comprises: A parameter acquisition unit is configured to receive a frequency set by a user, and obtain operation parameters of the selected electric control elements at the frequency; A parameter conversion unit is configured to convert the operation parameters at each time into percentages according to historical end value parameters for each electric control element; the historical end value parameters represent boundary conditions including maximum values and minimum values; A table extension unit is configured to obtain percentages corresponding to all the selected electric control elements at each time, insert the percentages into corresponding columns, and extend the table; A name insertion unit is configured to query a name of the modeling data when a printing completion signal is detected, insert the name into the table, and output the table as an operation table of the modeling data.

10. The three-dimensional print model evaluation system of historical print data according to claim 7, wherein, The reference data generation module comprises: An abnormality degree calculation unit is configured to calculate an abnormality degree of each data based on column data for a certain operation table; The abnormality degree accumulation unit is configured to, for any row of data, query a level of an electrically controlled element corresponding to the row, determine a weight according to the level, and accumulate abnormality degrees of each data in the same row based on the weight. The row extraction unit is configured to extract a preset number of rows in the running table as reference data according to the accumulated abnormality degrees. Wherein, the abnormality degree is: The cumulative process is: In the formula, D ij is the abnormality degree of the data in the i-th row and j-th column of the running table, x ij is the data in the i-th row and j-th column of the running table, E j is the mean value of the data in the j-th column, L i is the cumulative abnormality degree of the data in the i-th row, M is the total column number of the running table, and α j is the weight of the j-th column of the running table.

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