CN114418218A - Intelligent early warning method and system for production of medical injection molding - Google Patents
Intelligent early warning method and system for production of medical injection molding Download PDFInfo
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Abstract
The invention discloses an intelligent early warning method and system for production of medical injection molding parts, wherein the method comprises the following steps: matching the first application demand information according to a preset medical quality standard to obtain a production quality requirement; obtaining first production image information through an image acquisition device; performing convolution kernel characteristic acquisition on the first production image information to obtain first production characteristic information; inputting the production quality requirement and the first production characteristic information into an injection molding production quality evaluation model to obtain a first production quality result; obtaining the quality difference degree of the injection molding part according to the first production quality result; and if the quality difference degree of the injection molding piece is larger than the preset quality difference degree, sending a first early warning instruction, and adjusting and controlling a first production parameter based on the quality difference degree of the injection molding piece. The injection molding machine solves the technical problems that in the prior art, real-time quality analysis is not carried out on injection moldings, so that production early warning cannot be timely carried out, and the production quality of the injection moldings is influenced.
Description
Technical Field
The invention relates to the field of production management, in particular to an intelligent early warning method and system for production of medical injection molding parts.
Background
Plastics have extremely important applications in the medical field, and the injection-molded products are the most used, and because of the particularity of the production of medical injection-molded products, stricter precision and biological safety are provided for production and processing, so that the method has important significance for strict quality control of medical injection-molded parts in the production process.
However, the prior art has the technical problem that the injection molding part is not subjected to real-time quality analysis, so that the production early warning cannot be timely carried out, and the production quality of the injection molding part is further influenced.
Disclosure of Invention
The application solves the technical problems that the injection molding cannot be subjected to real-time quality analysis in the prior art, so that the production early warning cannot be performed in time, and the production quality of the injection molding is influenced, and achieves the technical effects of improving the accuracy and the efficiency of quality analysis results by performing real-time monitoring and analysis on the production flow of the injection molding, thereby realizing the timely early warning on the production quality and ensuring the production quality and the production efficiency of the injection molding.
In view of the above problems, the invention provides an intelligent early warning method and system for production of medical injection molding parts.
In a first aspect, the present application provides a method for intelligent early warning of production of medical injection molded parts, the method comprising: obtaining first application requirement information, wherein the first application requirement information comprises application requirement information of a first injection molding; matching the first application requirement information according to a preset medical quality standard to obtain the production quality requirement of the first injection molding piece; obtaining first production image information through an image acquisition device, wherein the first production image information is production image information of the first injection molding piece; performing convolution kernel characteristic collection on the first production image information to obtain first production characteristic information; inputting the production quality requirement and the first production characteristic information into an injection molding production quality evaluation model to obtain a first production quality result; obtaining the quality difference degree of the injection molding part according to the first production quality result; and if the quality difference degree of the injection molding piece is larger than the preset quality difference degree, sending a first early warning instruction, and adjusting and controlling a first production parameter based on the quality difference degree of the injection molding piece.
On the other hand, this application still provides an intelligent early warning system of medical injection molding production, the system includes: the first obtaining unit is used for obtaining first application requirement information, and the first application requirement information comprises application requirement information of a first injection molding piece; the second obtaining unit is used for matching the first application requirement information according to a preset medical quality standard to obtain the production quality requirement of the first injection molding piece; the third obtaining unit is used for obtaining first production image information through an image acquisition device, and the first production image information is the production image information of the first injection molding piece; the fourth obtaining unit is used for performing convolution kernel characteristic acquisition on the first production image information to obtain first production characteristic information; a fifth obtaining unit, configured to input the production quality requirement and the first production characteristic information into an injection molding production quality evaluation model, and obtain a first production quality result; a sixth obtaining unit, configured to obtain a quality difference of the injection molded part according to the first production quality result; and the first processing unit is used for sending a first early warning instruction if the quality difference degree of the injection molding is greater than the preset quality difference degree, and adjusting and controlling a first production parameter based on the quality difference degree of the injection molding.
In a third aspect, the present application provides an electronic device comprising a bus, a transceiver, a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program implements the steps of any of the methods when executed by the processor.
In a fourth aspect, the present application also provides a computer-readable storage medium having a computer program stored thereon, which when executed by a processor, performs the steps of any of the methods described above.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the technical scheme includes that application demand information of the medical injection molding is matched according to a preset medical quality standard to obtain production quality requirements of the injection molding, convolution kernel characteristic acquisition is carried out on real-time production image information of the injection molding to obtain corresponding production characteristic information, the production quality requirements and the production characteristic information are input into an injection molding production quality evaluation model to obtain a first production quality result output by the model, injection molding quality difference is obtained based on the production quality result, when the injection molding quality difference is larger than a preset quality difference, the system gives an early warning, and production parameters are adjusted and controlled based on the injection molding quality difference. And then reach through carrying out real time monitoring analysis to injection molding production flow, improve the accuracy and the efficiency of quality analysis result to realize carrying out timely early warning to production quality, guarantee injection molding production quality and production efficiency's technological effect.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
FIG. 1 is a schematic flow chart of an intelligent early warning method for production of a medical injection molded part according to the present application;
FIG. 2 is a schematic flow chart illustrating the process of obtaining first production characteristic information in the intelligent early warning method for medical injection molding production according to the present application;
FIG. 3 is a schematic flow chart illustrating the process of determining a first production parameter in the intelligent warning method for the production of a medical injection molded part according to the present application;
FIG. 4 is a schematic flow chart illustrating the first production quality result supplementing process in the intelligent early warning method for medical injection molding production according to the present application;
FIG. 5 is a schematic structural diagram of an intelligent early warning system for production of medical injection molded parts according to the present application;
fig. 6 is a schematic structural diagram of an exemplary electronic device of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a fifth obtaining unit 15, a sixth obtaining unit 16, a first processing unit 17, a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, an operating system 1151, an application 1152 and a user interface 1160.
Detailed Description
In the description of the present application, it will be appreciated by those skilled in the art that the present application may be embodied as methods, apparatuses, electronic devices, and computer-readable storage media. Thus, the present application may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), a combination of hardware and software. Furthermore, in some embodiments, the present application may also be embodied in the form of a computer program product in one or more computer-readable storage media having computer program code embodied therein.
The computer-readable storage media described above may take any combination of one or more computer-readable storage media. The computer-readable storage medium includes: an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of the computer-readable storage medium include: a portable computer diskette, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, a flash memory, an optical fiber, a compact disc read-only memory, an optical storage device, a magnetic storage device, or any combination thereof. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, device, or system.
According to the technical scheme, the data acquisition, storage, use, processing and the like meet relevant regulations of national laws.
The method, the device and the electronic equipment are described by the flow chart and/or the block diagram.
It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions. These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner. Thus, the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
The present application is described below with reference to the drawings attached hereto.
Example one
As shown in fig. 1, the present application provides an intelligent warning method for production of medical injection-molded parts, which is applied to an intelligent warning system for production of medical injection-molded parts, the system including an image acquisition device, the method including:
step S100: obtaining first application requirement information, wherein the first application requirement information comprises application requirement information of a first injection molding;
in particular, plastics are used in the medical field, and the injection-molded products are the most used, and because of the special properties of the medical injection-molded products, the production process is provided with more strict precision and biological safety, so that the method has important significance for strict quality control of the medical injection-molded parts in the production process.
The first injection molding is a medical injection molding to be produced, such as a respirator, an oxygen generator, an injector, a pipette, a transfusion device, an oxygen mask, an atomization mask, an endotracheal tube, a blood transfusion device, a heart support, a syringe and the like. The first application requirement information is application requirement information of the first injection molding part, and the application requirement information meets the application safety requirements of medical plastic products, and meets the requirements of product design, raw material selection, application performance, strength and the like of the injection molding part, so that the using effect of the medical injection molding part is ensured.
Step S200: matching the first application requirement information according to a preset medical quality standard to obtain the production quality requirement of the first injection molding piece;
specifically, the preset medical quality standard is a quality standard which is required to be met by medical plastic products specified by the state, the first application requirement information is matched according to the preset medical quality standard, the production quality requirement of the matched first injection molding piece is obtained, the production quality requirement comprises the requirements of the injection molding piece on mechanical strength, cleanliness, thermal stability, hydrophobicity, size, toxicity, hydrophilicity, biological safety and the like, and the production quality of the medical injection molding piece is ensured.
Step S300: obtaining first production image information through the image acquisition device, wherein the first production image information is production image information of the first injection molding piece;
specifically, the production image information of the first injection molding piece is monitored and collected in real time through the image collecting device, and the image collecting device comprises a camera, a video camera or monitoring equipment such as a camera on production detection equipment and is used for collecting the image information of the production condition of the injection molding piece in real time and providing an image basis for subsequent production quality analysis.
Step S400: performing convolution kernel characteristic collection on the first production image information to obtain first production characteristic information;
as shown in fig. 2, further to perform convolution kernel feature acquisition on the first production image information to obtain first production feature information, step S400 of the present application further includes:
step S410: performing traversal convolution calculation on the first production image information according to a first preset convolution core to obtain a first convolution calculation result;
step S420: obtaining structural characteristics of the injection molding part according with a preset convolution value range according to the first convolution calculation result;
step S430: obtaining a second preset convolution kernel, and obtaining injection molding burr value characteristics based on the second preset convolution kernel;
step S440: and obtaining surface color difference characteristics of the injection molding according to a third preset convolution kernel, and performing characteristic fusion on the structural characteristics of the injection molding, the burr value characteristics of the injection molding and the surface color difference characteristics of the injection molding to obtain first production characteristic information.
Specifically, convolution kernel feature acquisition is performed on the first production image information, when the convolution kernel is image processing, given input images, pixels in a small region of the input images become each corresponding pixel in the output images after weighted averaging, wherein a weight is defined by a function, and the function is called a convolution kernel. The convolution kernel focuses on local features, namely set standard features, and the matching degree of the features is acquired and evaluated according to the numerical value of the convolution kernel at the local feature part. Firstly, extracting characteristic values of structural characteristics of the medical injection molding part, wherein the first preset convolution kernel is the design structural characteristics of the first injection molding part and comprises standard design structural characteristics such as structural composition, size and the like of the injection molding part.
And performing traversal convolution calculation on the first production image information through the first preset convolution core, so as to obtain a first convolution calculation result, namely a matching degree evaluation result. The preset convolution value range is a preset value range which accords with the characteristic, the structural characteristic of the injection molding part which accords with the preset convolution value range is obtained according to the first convolution calculation result, namely, the first convolution calculation result is obtained through the matching of the first preset convolution kernel and the characteristic of the first production image information, and the structural characteristic of the medical injection molding part can be obtained according to the first convolution calculation result. The second preset convolution kernel is the characteristic of the burr value of the injection molding part in the same calculation process, the burr value is a raised area or a protrusion on the surface of the injection molding part, the surface smoothness of the injection molding part is indicated, and the smaller the burr value is, the better the production quality of the product is indicated.
The third preset convolution kernel is the surface color difference characteristic of the injection molding part, and has an application standard for the color of the injection molding part in order to ensure the use effect of medical supplies, and the smaller the color difference is, the more standard the production quality of the injection molding part is. And carrying out characteristic fusion on the structural characteristics of the injection molding part, the burr value characteristics of the injection molding part and the surface color difference characteristics of the injection molding part to obtain the first production characteristic information in the production flow of the medical injection molding part. By monitoring and analyzing the production process of the injection molding piece in real time, the production characteristic information of the injection molding piece is accurately and completely acquired, and a production data basis is provided for the subsequent production quality evaluation of the fish medical injection molding piece.
Step S500: inputting the production quality requirement and the first production characteristic information into an injection molding production quality evaluation model to obtain a first production quality result;
further, the step S500 of inputting the production quality requirement and the first production characteristic information into a production quality evaluation model of the injection molded part to obtain a first production quality result further includes:
step S510: selecting an encryption algorithm based on the first application requirement information;
step S520: encrypting the production quality requirement and the first production characteristic information based on the encryption algorithm;
step S530: and inputting the encrypted production quality requirement and the encrypted first production characteristic information into an injection molding production quality evaluation model to obtain a first production quality result.
Specifically, the production quality requirement and the first production characteristic information are input into an injection molding production quality evaluation model, the injection molding production quality evaluation model is a deep convolution neural network model, is a feedforward neural network which comprises convolution calculation and has a deep structure, can perform supervised learning and unsupervised learning, and is used for performing production quality machining on the medical injection molding which is produced and machined. Based on the first application requirement information, namely, an encryption algorithm is selected according to the production level requirement of the injection molding part, the higher the production level of the injection molding part is, the higher the precision requirement is, the higher the encryption level is, and the selected encryption algorithm is correspondingly different.
The basic process of encryption is to process the original plain text file or data according to a certain algorithm to make it become an unreadable segment of code as "ciphertext", so that the original content can be displayed only after inputting the corresponding key, and the purpose of protecting the data from being stolen and read by an illegal person is achieved through the way. And encrypting the production quality requirement and the first production characteristic information based on the encryption algorithm, wherein the encryption algorithm comprises DES, 3DES, IDEA international data encryption algorithm and the like, and inputting the encrypted production quality requirement and the first production characteristic information into an injection molding production quality evaluation model to obtain a first production quality result, and the first production quality result comprises whether the production quality of the injection molding is qualified or not and a corresponding production quality grade. By encrypting the training data, the safety of the production data is guaranteed, and the output quality assessment result is more accurate and reasonable, so that the detection efficiency and accuracy of the quality detection result are improved.
Step S600: obtaining the quality difference degree of the injection molding part according to the first production quality result;
step S700: and if the quality difference degree of the injection molding piece is larger than the preset quality difference degree, sending a first early warning instruction, and adjusting and controlling a first production parameter based on the quality difference degree of the injection molding piece.
Specifically, the quality difference degree of the injection molding is calculated according to the difference between the first production quality result and the standard production quality requirement, and the quality difference degree between products and requirements is analyzed. And judging whether the quality difference of the injection molding is greater than a preset quality difference, wherein the preset quality difference is a preset quality difference within the allowance of meeting production requirements, if the quality difference of the injection molding is greater than the preset quality difference, namely the production processing quality of the injection molding does not meet the production requirements, and sending a first early warning instruction for producing waste products, wherein the first early warning instruction is used for carrying out quality early warning on unqualified products. And adjusting and controlling first production parameters such as injection molding temperature, injection molding time, injection amount and the like based on the quality difference of the injection molded parts. The injection molding production process is monitored and analyzed in real time, so that the accuracy and the efficiency of quality analysis results are improved, the production quality is early warned in time, and the injection molding production quality and the production efficiency are guaranteed.
As shown in fig. 3, further, the steps of the present application further include:
step S810: obtaining a production parameter value threshold of the first injection molding piece;
step S820: randomly obtaining N production parameters from the production parameter value threshold, wherein N is a positive integer;
step S830: calculating the N production parameters according to a genetic algorithm to obtain N predicted production quality curves, wherein the N predicted production quality curves correspond to the N production parameters one by one;
step S840: obtaining an ideal production quality curve, and comparing the N predicted production curves with the ideal production quality curve to obtain a first production parameter, wherein the predicted production quality curve corresponding to the first production parameter has the maximum similarity with the ideal production quality curve;
step S850: and if the similarity between the predicted production quality curve corresponding to the first production parameter and the ideal production quality curve meets the similarity requirement, determining the first production parameter.
Specifically, in order to produce the first injection molding part, the intelligent production system which takes the production parameter value threshold of the first injection molding part as the injection molding part firstly randomly and uniformly extracts a certain number of production parameter values from the production parameter value threshold according to the production parameter range set by the production requirement, and further calculates the N production parameters based on the genetic algorithm to calculate the predicted production quality curve corresponding to each production parameter value. Each production parameter value uniquely corresponds to a predicted production quality curve. The essence of the genetic algorithm is that random search is continuously carried out in a solution space, new solutions are continuously generated in the search process, and a more optimal solution algorithm is reserved, so that the realization difficulty is low, and a satisfactory result can be obtained in a short time.
The genetic algorithm directly operates the structural object when in use, has no limitation of derivation and function continuity, has inherent implicit parallelism and better global optimization capability, adopts a probabilistic optimization method, can automatically acquire and guide an optimized search space without determining rules, and adaptively adjusts the search direction, so the genetic algorithm is widely applied to various fields. The ideal production quality curve is a production quality curve in an ideal parameter state and is an optimal state of production quality, and all production curves predicted by the system are compared with the ideal production quality curve, so that the first production parameter is obtained. And the similarity between the predicted production quality curve corresponding to the first production parameter and the ideal production quality curve is the largest.
And further judging whether the predicted production quality curve corresponding to the first production parameter and the ideal production quality curve meet a preset similarity requirement of the system, and determining the first production parameter when the similarity of the predicted production quality curve corresponding to the first production parameter and the ideal production quality curve meets the similarity requirement, wherein the preset similarity requirement of the system refers to a lowest similarity value comprehensively determined by the system based on the production requirements of injection molding parts and the like. By randomly and uniformly selecting the production parameter values of the injection molding part and predicting the production quality conditions under different production parameter values, the production parameters of the product closest to the ideal production quality are obtained, the system prediction accuracy and effectiveness are improved, and the production quality of the medical injection molding part is ensured.
As shown in fig. 4, further, the steps of the present application further include:
step S910: constructing a physical and chemical detection index set of the injection molding, wherein the physical and chemical detection index set of the injection molding comprises physical strength detection, chemical stability detection and biological safety detection;
step S920: performing quality detection on the first injection molding according to the injection molding physical and chemical detection index set to obtain a first physical and chemical detection result;
step S930: and supplementing the first production quality result according to the first physical and chemical detection result to obtain a second production quality result.
Specifically, in order to ensure the quality detection comprehensiveness of the injection molding, a physical and chemical detection index set of the injection molding is constructed, and the physical and chemical detection index set of the injection molding comprises physical strength detection, chemical stability detection and biological safety detection. The physical strength detection is to detect tensile, bending, compression, hydrophilicity, ductility, shock resistance and the like of the injection molding part, and ensure the application strength of the medical product; the chemical stability detection is used for detecting the chemical properties of the injection molding part, including thermal stability, acid and alkali resistance, corrosion resistance and the like, and ensures the application stability of the medical product; the biological safety detection is used for detecting the biological properties of the injection molding, including toxicity, surface bacteria and virus detection and the like, and ensures the application safety of the medical product.
And performing quality detection on the first injection molding according to the injection molding physical and chemical detection index set to obtain a corresponding physical and chemical detection result, and supplementing the first production quality result according to the first physical and chemical detection result to obtain a second production quality result after supplementing the physical and chemical detection result. The injection molding piece is subjected to supplementary detection on the physical and chemical properties, so that the quality detection result is more comprehensive and accurate, the chemical stability and the biological safety of the injection molding piece are ensured, and the production quality of the medical injection molding piece is improved.
Further, step S930 of the present application further includes:
step S931: obtaining a first weight distribution result based on an entropy weight method, wherein the first weight distribution result is weight information of each index in the injection molding physical and chemical detection index set;
step S932: performing weighted calculation on the first physical and chemical detection result according to the first weight distribution result to obtain a first weighted calculation result;
step S933: and obtaining a second physical and chemical detection result based on the first weighting calculation result.
Specifically, the weight information of each index in the injection molding physical and chemical detection index set is distributed based on an entropy weight method, the entropy weight method is an objective weighting method, the degree of dispersion of a certain index can be judged by using an entropy value for the physical and chemical detection index, and the larger the entropy value is, the larger the information content is, the smaller the entropy value is, the smaller the information content is. The smaller the information entropy value is, the larger the dispersion degree of the index is, the larger the weight which is the influence of the index on the comprehensive evaluation is, so that the weight of each index can be calculated by using the information entropy, and a basis is provided for the multi-index comprehensive evaluation.
The larger the weight information of the index in the injection molding physical and chemical detection index set is, the larger the influence of the index on the production quality evaluation result of the first injection molding is. And performing weighted calculation on the first physical and chemical detection result according to the first weight distribution result to obtain a calculated first weighted calculation result, and correcting the first physical and chemical detection result based on the first weighted calculation result to obtain a corrected second physical and chemical detection result. And weight distribution is carried out on each index in the injection molding physical and chemical detection index set by an entropy weight method, so that the weight distribution result is more objective and reasonable, and the accuracy of the quality detection result of the medical injection molding is improved.
Further, the method further comprises the following steps:
step S1010: carrying out environmental sampling on an injection molding production workshop to obtain a production environment sample data set;
step S1020: performing sample analysis on the production environment sample data set to obtain a first qualified sample data set;
step S1030: obtaining a first production environment qualified rate according to the proportion of the first qualified sample data set in the production environment sample data set;
step S1040: and if the first production environment qualification rate does not reach the preset production environment qualification rate, sending a second early warning instruction, wherein the second early warning instruction is used for early warning that the production environment of the injection molding piece is unqualified.
In particular, for medical plastic articles, the production workshop environment must be a clean workshop production of one hundred thousand grades or more, which is a mandatory requirement of the state, and the production environment must be dust-free and sterile. Therefore, environment sampling is carried out on the injection molding production workshop, including workshop air, equipment surfaces, production workshop personnel and the like, and a production environment sample data set is obtained. And performing sample analysis on the production environment sample data set, such as sample bacteria number, sample bacteria distribution, sample bacteria type, virus type and the like, and comparing with the medical production standard workshop environment to obtain a first qualified sample data set meeting the standard.
And obtaining a first production environment qualification rate of qualified samples according to the proportion of the first qualified sample data set in the environment sample data set. And judging whether the first production environment qualification rate reaches a preset production environment qualification rate, wherein the preset production environment qualification rate is a preset production environment qualification rate standard, if the first production environment qualification rate does not reach the preset production environment qualification rate, the judgment result shows that the production environment quality of the medical injection molding does not reach the standard, and a second early warning instruction is sent, and the second early warning instruction is used for early warning that the production environment of the injection molding is unqualified. The injection molding piece is subjected to production quality assessment and supplement in combination with the sanitation of a production workshop, the environmental quality of the production workshop is guaranteed, and the technical effect of improving the production qualification rate of the medical injection molding piece is further achieved.
In summary, the intelligent early warning method and system for medical injection molding production provided by the application have the following technical effects:
the technical scheme includes that application demand information of the medical injection molding is matched according to a preset medical quality standard to obtain production quality requirements of the injection molding, convolution kernel characteristic acquisition is carried out on real-time production image information of the injection molding to obtain corresponding production characteristic information, the production quality requirements and the production characteristic information are input into an injection molding production quality evaluation model to obtain a first production quality result output by the model, injection molding quality difference is obtained based on the production quality result, when the injection molding quality difference is larger than a preset quality difference, the system gives an early warning, and production parameters are adjusted and controlled based on the injection molding quality difference. And then reach through carrying out real time monitoring analysis to injection molding production flow, improve the accuracy and the efficiency of quality analysis result to realize carrying out timely early warning to production quality, guarantee injection molding production quality and production efficiency's technological effect.
Example two
Based on the same inventive concept as the intelligent early warning method for producing the medical injection molding part in the previous embodiment, the invention also provides an intelligent early warning system for producing the medical injection molding part, as shown in fig. 5, the system comprises:
a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain first application requirement information, where the first application requirement information includes application requirement information of a first injection molding;
the second obtaining unit 12 is configured to match the first application requirement information according to a preset medical quality standard, and obtain a production quality requirement of the first injection molded part;
a third obtaining unit 13, where the third obtaining unit 13 is configured to obtain first production image information through an image acquisition device, where the first production image information is production image information of the first injection-molded part;
a fourth obtaining unit 14, where the fourth obtaining unit 14 is configured to perform convolution kernel feature acquisition on the first production image information to obtain first production feature information;
a fifth obtaining unit 15, where the fifth obtaining unit 15 is configured to input the production quality requirement and the first production characteristic information into an injection molding production quality evaluation model, and obtain a first production quality result;
a sixth obtaining unit 16, wherein the sixth obtaining unit 16 is configured to obtain a quality difference degree of the injection molded part according to the first production quality result;
and the first processing unit 17 is used for sending a first early warning instruction if the quality difference degree of the injection molding is greater than the preset quality difference degree, and adjusting and controlling a first production parameter based on the quality difference degree of the injection molding.
Further, the system further comprises:
a seventh obtaining unit, configured to perform traversal convolution calculation on the first production image information according to a first predetermined convolution kernel to obtain a first convolution calculation result;
the eighth obtaining unit is used for obtaining structural characteristics of the injection molding part according with a preset convolution numerical range according to the first convolution calculation result;
a ninth obtaining unit, configured to obtain a second predetermined convolution kernel, and obtain a burr value feature of the injection molding based on the second predetermined convolution kernel;
and the tenth obtaining unit is used for obtaining the surface color difference characteristics of the injection molding part according to a third preset convolution kernel, and performing characteristic fusion on the structural characteristics of the injection molding part, the burr value characteristics of the injection molding part and the surface color difference characteristics of the injection molding part to obtain the first production characteristic information.
Further, the system further comprises:
an eleventh obtaining unit, configured to obtain a production parameter value threshold of the first injection molded part;
a twelfth obtaining unit, configured to randomly obtain N production parameters from the production parameter value threshold, where N is a positive integer;
a thirteenth obtaining unit, configured to calculate the N production parameters according to a genetic algorithm, and obtain N predicted production quality curves, where the N predicted production quality curves correspond to the N production parameters one to one;
a fourteenth obtaining unit, configured to obtain an ideal production quality curve, compare the N predicted production curves with the ideal production quality curve, and obtain a first production parameter, where a similarity between a predicted production quality curve corresponding to the first production parameter and the ideal production quality curve is the largest;
a first determining unit, configured to determine the first production parameter if a similarity between a predicted production quality curve corresponding to the first production parameter and the ideal production quality curve meets a similarity requirement.
Further, the system further comprises:
the injection molding physical and chemical detection index set comprises physical strength detection, chemical stability detection and biological safety detection;
a fifteenth obtaining unit, configured to perform quality detection on the first injection molded part according to the injection molded part physical and chemical detection index set, so as to obtain a first physical and chemical detection result;
a sixteenth obtaining unit, configured to supplement the first production quality result according to the first physicochemical detection result, and obtain a second production quality result.
Further, the system further comprises:
a seventeenth obtaining unit, configured to obtain a first weight distribution result based on an entropy weight method, where the first weight distribution result is weight information of each index in the injection molding physicochemical detection index set;
an eighteenth obtaining unit, configured to perform weighted calculation on the first physicochemical detection result according to the first weight distribution result, so as to obtain a first weighted calculation result;
a nineteenth obtaining unit configured to obtain a second physicochemical detection result based on the first weighting calculation result.
Further, the system further comprises:
a twentieth obtaining unit, configured to perform environmental sampling on the injection molding production workshop to obtain a production environment sample data set;
a twenty-first obtaining unit, configured to perform sample analysis on the production environment sample data set to obtain a first qualified sample data set;
a twenty-second obtaining unit, configured to obtain a first production environment yield according to a proportion of the first qualified sample data set in the production environment sample data set;
the first early warning unit is used for sending a second early warning instruction if the first production environment qualified rate does not reach the preset production environment qualified rate, and the second early warning instruction is used for early warning that the production environment of the injection molding piece is unqualified.
Further, the system further comprises:
a first selection unit configured to select an encryption algorithm based on the first application requirement information;
a first encryption unit configured to encrypt the production quality requirement and the first production characteristic information based on the encryption algorithm;
a twenty-third obtaining unit, configured to input the encrypted production quality requirement and the first production characteristic information into an injection molding production quality evaluation model, and obtain the first production quality result.
Various changes and specific examples of the intelligent early warning method for the production of the medical injection molding in the first embodiment of fig. 1 are also applicable to the intelligent early warning system for the production of the medical injection molding in the present embodiment, and through the foregoing detailed description of the intelligent early warning method for the production of the medical injection molding, a person skilled in the art can clearly know the implementation method of the intelligent early warning system for the production of the medical injection molding in the present embodiment, so for the brevity of the description, detailed description is not repeated here.
In addition, the present application further provides an electronic device, which includes a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the transceiver, the memory, and the processor are connected via the bus, respectively, and when the computer program is executed by the processor, the processes of the above-mentioned method for controlling output data are implemented, and the same technical effects can be achieved, and are not described herein again to avoid repetition.
Exemplary electronic device
In particular, referring to fig. 6, the present application further provides an electronic device comprising a bus 1110, a processor 1120, a transceiver 1130, a bus interface 1140, a memory 1150, and a user interface 1160.
In this application, the electronic device further includes: a computer program stored on the memory 1150 and executable on the processor 1120, the computer program, when executed by the processor 1120, implementing the various processes of the method embodiments of controlling output data described above.
A transceiver 1130 for receiving and transmitting data under the control of the processor 1120.
In this application, a bus architecture (represented by bus 1110), bus 1110 may include any number of interconnected buses and bridges, bus 1110 connecting various circuits including one or more processors, represented by processor 1120, and memory, represented by memory 1150.
Processor 1120 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits in hardware or instructions in software in a processor. The processor described above includes: general purpose processors, central processing units, network processors, digital signal processors, application specific integrated circuits, field programmable gate arrays, complex programmable logic devices, programmable logic arrays, micro-control units or other programmable logic devices, discrete gates, transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in this application may be implemented or performed. For example, the processor may be a single core processor or a multi-core processor, which may be integrated on a single chip or located on multiple different chips.
Processor 1120 may be a microprocessor or any conventional processor. The method steps disclosed in connection with the present application may be performed directly by a hardware decoding processor or by a combination of hardware and software modules within the decoding processor. The software modules may reside in random access memory, flash memory, read only memory, programmable read only memory, erasable programmable read only memory, registers, and the like, as is known in the art. The readable storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
The bus 1110 may also connect various other circuits such as peripherals, voltage regulators, or power management circuits to provide an interface between the bus 1110 and the transceiver 1130, as is well known in the art. Therefore, it will not be further described in this application.
The transceiver 1130 may be one element or may be multiple elements, such as multiple receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. For example: the transceiver 1130 receives external data from other devices, and the transceiver 1130 transmits data processed by the processor 1120 to other devices. Depending on the nature of the computer device, a user interface 1160 may also be provided, such as: touch screen, physical keyboard, display, mouse, speaker, microphone, trackball, joystick, stylus.
It is to be appreciated that in the subject application, the memory 1150 can further include memory remotely located from the processor 1120, which can be coupled to a server via a network. One or more portions of the above-described network may be an ad hoc network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, the internet, a public switched telephone network, a plain old telephone service network, a cellular telephone network, a wireless fidelity network, and a combination of two or more of the above. For example, the cellular telephone network and the wireless network may be global mobile communications devices, code division multiple access devices, global microwave interconnect access devices, general packet radio service devices, wideband code division multiple access devices, long term evolution devices, LTE frequency division duplex devices, LTE time division duplex devices, long term evolution advanced devices, universal mobile communications devices, enhanced mobile broadband devices, mass machine type communications devices, ultra-reliable low-latency communications devices, and the like.
It will be appreciated that the memory 1150 in the subject application can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. Wherein the nonvolatile memory includes: read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, or flash memory.
The volatile memory includes: random access memory, which acts as an external cache. By way of example, and not limitation, many forms of RAM are available, such as: static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronous link dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described herein includes, but is not limited to, the above-described and any other suitable types of memory.
In the present application, memory 1150 stores the following elements of operating system 1151 and application programs 1152: an executable module, a data structure, or a subset thereof, or an expanded set thereof.
Specifically, the operating system 1151 includes various device programs, such as: a framework layer, a core library layer, a driver layer, etc. for implementing various basic services and processing hardware-based tasks. Applications 1152 include various applications such as: media player, browser, used to realize various application services. A program implementing the method of the present application may be included in the application 1152. The application programs 1152 include: applets, objects, components, logic, data structures, and other computer device-executable instructions that perform particular tasks or implement particular abstract data types.
In addition, the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements each process of the above method for controlling output data, and can achieve the same technical effect, and is not described herein again to avoid repetition.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An intelligent early warning method for medical injection molding production, which is applied to an intelligent early warning system for medical injection molding production, wherein the system comprises an image acquisition device, and the method comprises the following steps:
obtaining first application requirement information, wherein the first application requirement information comprises application requirement information of a first injection molding;
matching the first application requirement information according to a preset medical quality standard to obtain the production quality requirement of the first injection molding piece;
obtaining first production image information through the image acquisition device, wherein the first production image information is production image information of the first injection molding piece;
performing convolution kernel characteristic collection on the first production image information to obtain first production characteristic information;
inputting the production quality requirement and the first production characteristic information into an injection molding production quality evaluation model to obtain a first production quality result;
obtaining the quality difference degree of the injection molding part according to the first production quality result;
and if the quality difference degree of the injection molding piece is larger than the preset quality difference degree, sending a first early warning instruction, and adjusting and controlling a first production parameter based on the quality difference degree of the injection molding piece.
2. The method of claim 1, wherein said performing a convolution kernel feature acquisition on said first production image information to obtain first production feature information comprises:
performing traversal convolution calculation on the first production image information according to a first preset convolution core to obtain a first convolution calculation result;
obtaining structural characteristics of the injection molding part according with a preset convolution value range according to the first convolution calculation result;
obtaining a second preset convolution kernel, and obtaining injection molding burr value characteristics based on the second preset convolution kernel;
and obtaining surface color difference characteristics of the injection molding according to a third preset convolution kernel, and performing characteristic fusion on the structural characteristics of the injection molding, the burr value characteristics of the injection molding and the surface color difference characteristics of the injection molding to obtain first production characteristic information.
3. The method of claim 1, wherein the method comprises:
obtaining a production parameter value threshold of the first injection molding piece;
randomly obtaining N production parameters from the production parameter value threshold, wherein N is a positive integer;
calculating the N production parameters according to a genetic algorithm to obtain N predicted production quality curves, wherein the N predicted production quality curves correspond to the N production parameters one by one;
obtaining an ideal production quality curve, and comparing the N predicted production curves with the ideal production quality curve to obtain a first production parameter, wherein the predicted production quality curve corresponding to the first production parameter has the maximum similarity with the ideal production quality curve;
and if the similarity between the predicted production quality curve corresponding to the first production parameter and the ideal production quality curve meets the similarity requirement, determining the first production parameter.
4. The method of claim 1, wherein the method comprises:
constructing a physical and chemical detection index set of the injection molding, wherein the physical and chemical detection index set of the injection molding comprises physical strength detection, chemical stability detection and biological safety detection;
performing quality detection on the first injection molding according to the injection molding physical and chemical detection index set to obtain a first physical and chemical detection result;
and supplementing the first production quality result according to the first physical and chemical detection result to obtain a second production quality result.
5. The method of claim 4, wherein the method comprises:
obtaining a first weight distribution result based on an entropy weight method, wherein the first weight distribution result is weight information of each index in the injection molding physical and chemical detection index set;
performing weighted calculation on the first physical and chemical detection result according to the first weight distribution result to obtain a first weighted calculation result;
and obtaining a second physical and chemical detection result based on the first weighting calculation result.
6. The method of claim 1, wherein the method comprises:
carrying out environmental sampling on an injection molding production workshop to obtain a production environment sample data set;
performing sample analysis on the production environment sample data set to obtain a first qualified sample data set;
obtaining a first production environment qualified rate according to the proportion of the first qualified sample data set in the production environment sample data set;
and if the first production environment qualification rate does not reach the preset production environment qualification rate, sending a second early warning instruction, wherein the second early warning instruction is used for early warning that the production environment of the injection molding piece is unqualified.
7. The method of claim 1, wherein said inputting said production quality requirement and said first production characteristic information into an injection molded part production quality assessment model to obtain a first production quality result comprises:
selecting an encryption algorithm based on the first application requirement information;
encrypting the production quality requirement and the first production characteristic information based on the encryption algorithm;
and inputting the encrypted production quality requirement and the encrypted first production characteristic information into an injection molding production quality evaluation model to obtain a first production quality result.
8. An intelligent early warning system for the production of medical injection molded parts, the system comprising:
the first obtaining unit is used for obtaining first application requirement information, and the first application requirement information comprises application requirement information of a first injection molding piece;
the second obtaining unit is used for matching the first application requirement information according to a preset medical quality standard to obtain the production quality requirement of the first injection molding piece;
the third obtaining unit is used for obtaining first production image information through an image acquisition device, and the first production image information is the production image information of the first injection molding piece;
the fourth obtaining unit is used for performing convolution kernel characteristic acquisition on the first production image information to obtain first production characteristic information;
a fifth obtaining unit, configured to input the production quality requirement and the first production characteristic information into an injection molding production quality evaluation model, and obtain a first production quality result;
a sixth obtaining unit, configured to obtain a quality difference of the injection molded part according to the first production quality result;
and the first processing unit is used for sending a first early warning instruction if the quality difference degree of the injection molding is greater than the preset quality difference degree, and adjusting and controlling a first production parameter based on the quality difference degree of the injection molding.
9. An intelligent pre-warning electronic device for the production of medical injection molded parts, comprising a bus, a transceiver, a memory, a processor and a computer program stored on the memory and executable on the processor, the transceiver, the memory and the processor being connected via the bus, characterized in that the computer program realizes the steps of the method according to any one of claims 1-7 when executed by the processor.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of claims 1-7.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114660076A (en) * | 2022-05-19 | 2022-06-24 | 张家港市欧凯医疗器械有限公司 | Medical tube coating quality detection method and system |
| CN114880361A (en) * | 2022-07-07 | 2022-08-09 | 张家港市欧凯医疗器械有限公司 | Production, processing, supervision and management method and system for bladder fistulation tube |
| CN115564337A (en) * | 2022-10-24 | 2023-01-03 | 南珠建材(清远)有限公司 | Quality evaluation method and system for concrete pipe pile |
| CN116563293A (en) * | 2023-07-11 | 2023-08-08 | 南通玖方新材料科技有限公司 | Photovoltaic carrier production quality detection method and system based on machine vision |
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- 2022-01-20 CN CN202210064249.3A patent/CN114418218A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114660076A (en) * | 2022-05-19 | 2022-06-24 | 张家港市欧凯医疗器械有限公司 | Medical tube coating quality detection method and system |
| CN114880361A (en) * | 2022-07-07 | 2022-08-09 | 张家港市欧凯医疗器械有限公司 | Production, processing, supervision and management method and system for bladder fistulation tube |
| CN115564337A (en) * | 2022-10-24 | 2023-01-03 | 南珠建材(清远)有限公司 | Quality evaluation method and system for concrete pipe pile |
| CN116563293A (en) * | 2023-07-11 | 2023-08-08 | 南通玖方新材料科技有限公司 | Photovoltaic carrier production quality detection method and system based on machine vision |
| CN116563293B (en) * | 2023-07-11 | 2023-11-21 | 南通玖方新材料股份有限公司 | Photovoltaic carrier production quality detection method and system based on machine vision |
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