CN112700168A - Method and device for quality inspection of capsule medicines - Google Patents
Method and device for quality inspection of capsule medicines Download PDFInfo
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Abstract
The invention discloses a method and a device for quality inspection of capsule medicines, which are characterized in that capsule weight information and standard weight information are obtained; judging whether the capsule weight information meets the requirement of the standard weight information; when the filling volume information is satisfied, obtaining the filling volume information of the capsule; inputting capsule weight information and capsule filling volume information into a first training model; obtaining a first output result of the first training model, wherein the first output result comprises a first medicine quantity standard-reaching rate which is used for evaluating the standard-reaching degree of the component proportion of the medicine powder in the capsule; judging whether the first medicine quantity standard-reaching rate meets a first preset condition or not; and when the first quality inspection result is met, obtaining a first quality inspection result, wherein the first quality inspection result indicates that the capsule medicine meets the quality inspection requirement. Solves the technical problems that the quality of the capsule medicine is not fully checked and the medicine content of the medicine is influenced in the prior art. The technical effects of synthesizing the weight and the volume of the capsule to analyze the content of the medicine, adding a neural network model to improve the accuracy of a quality inspection result and ensuring the stability of the medicine components in the capsule are achieved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a method and a device for quality inspection of capsule medicines.
Background
The encapsulated medicine is generally a powder or granules which is irritating to the esophagus and gastric mucosa, or a medicine which is bad in taste, easy to volatilize, easy to be decomposed by saliva in the oral cavity, and easy to be inhaled into the trachea. The medicines are filled into capsules, so that the medicine property of the medicines is protected from being damaged, and the digestive organs and the respiratory tract are protected. Removal of the capsule shell may result in loss of the drug, waste of the drug, and reduced efficacy. In addition, some drugs need to be dissolved and absorbed in the intestine, and the capsule protects the drug from gastric acid. The medicine is capsule prepared with special film forming material, such as gelatin, cellulose, polysaccharide, etc. and the capsule may be filled with medicine in powder or liquid form for easy swallowing.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
the technical problems that the quality of capsule medicines is not fully detected and the medicine content of the medicines is influenced exist in the prior art.
Disclosure of Invention
The embodiment of the application provides a method and a device for quality inspection of capsule medicines, and solves the technical problems that in the prior art, the quality inspection of the capsule medicines is insufficient, and the medicine content of the medicines is influenced. The method achieves the technical effects of synthesizing the weight and the volume of the capsule to analyze the content of the medicine, adding the neural network model to improve the accuracy of a quality inspection result, ensuring the stability of the medicine components in the capsule, avoiding the influence on the treatment effect caused by the non-uniform content of the powder medicine in the capsule and having high automation degree in the quality inspection process.
In view of the above problems, the embodiments of the present application provide a method and apparatus for quality inspection of capsule type drugs.
In a first aspect, the present embodiments provide a method for quality control of capsule type drugs, the method including: obtaining capsule weight information; obtaining standard weight information; judging whether the capsule weight information meets the requirement of standard weight information; when the requirement is met, obtaining the information of the filling volume of the capsule; inputting the capsule weight information and the capsule loading volume information into a first training model, wherein the first training model is obtained by training a plurality of groups of training data, and each group of the plurality of groups of training data comprises: the capsule weight information, the capsule loading volume information and identification information for identifying the standard reaching rate of the components of the predicted capsule dosage; obtaining a first output result of the first training model, wherein the first output result comprises a first medicine quantity standard-reaching rate which is used for evaluating the standard-reaching degree of the component proportion of the medicine powder in the capsule; judging whether the first medicine quantity standard-reaching rate meets a first preset condition or not; and when the first quality inspection result meets the quality inspection requirement, acquiring a first quality inspection result, wherein the first quality inspection result indicates that the capsule medicine meets the quality inspection requirement.
In another aspect, the present application also provides a device for quality control of capsule type medicine, the device comprising:
a first obtaining unit for obtaining capsule weight information;
a second obtaining unit for obtaining the standard weight information;
a first judging unit for judging whether the capsule weight information meets the requirement of standard weight information;
a third obtaining unit for obtaining capsule filling volume information when satisfied;
a first input unit, configured to input the capsule weight information and the capsule filling volume information into a first training model, where the first training model is obtained through training of multiple sets of training data, and each set of the multiple sets of training data includes: the capsule weight information, the capsule loading volume information and identification information for identifying the standard reaching rate of the components of the predicted capsule dosage;
a fourth obtaining unit, configured to obtain a first output result of the first training model, where the first output result includes a first drug quantity achievement rate, and the first drug quantity achievement rate is used to evaluate an achievement degree of a component proportion of drug powder in a capsule;
the second judging unit is used for judging whether the first medicine quantity standard-reaching rate meets a first preset condition or not;
and the fifth obtaining unit is used for obtaining a first quality inspection result when the first quality inspection result is met, wherein the first quality inspection result indicates that the capsule medicine meets the quality inspection requirement.
In a third aspect, the present invention provides an apparatus for quality testing of capsule type drugs, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
the embodiment of the application provides a method and a device for quality inspection of capsule medicines, which are used for acquiring capsule weight information; obtaining standard weight information; judging whether the capsule weight information meets the requirement of standard weight information; when the requirement is met, obtaining the information of the filling volume of the capsule; inputting the capsule weight information and the capsule loading volume information into a first training model, wherein the first training model is obtained by training a plurality of groups of training data, and each group of the plurality of groups of training data comprises: the capsule weight information, the capsule loading volume information and identification information for identifying the standard reaching rate of the components of the predicted capsule dosage; obtaining a first output result of the first training model, wherein the first output result comprises a first medicine quantity standard-reaching rate which is used for evaluating the standard-reaching degree of the component proportion of the medicine powder in the capsule; judging whether the first medicine quantity standard-reaching rate meets a first preset condition or not; and when the first quality inspection result meets the quality inspection requirement, acquiring a first quality inspection result, wherein the first quality inspection result indicates that the capsule medicine meets the quality inspection requirement. The method achieves the technical effects of synthesizing the weight and the volume of the capsule to analyze the content of the medicine, adding the neural network model to improve the accuracy of a quality inspection result, ensuring the stability of the medicine components in the capsule, avoiding the influence on the treatment effect caused by the non-uniform content of the powder medicine in the capsule and having high automation degree in the quality inspection process. Thereby solving the technical problems of insufficient quality inspection of the capsule medicine and influence on the medicine content in the prior art.
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 a method for quality control of capsule drugs according to an embodiment of the present application;
FIG. 2 is a schematic structural diagram of an apparatus for quality control of capsule type drugs according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: a first obtaining unit 11, a second obtaining unit 12, a first judging unit 13, a third obtaining unit 14, a first input unit 15, a fourth obtaining unit 16, a second judging unit 17, a fifth obtaining unit 18, a bus 300, a receiver 301, a processor 302, a transmitter 303, a memory 304, and a bus interface 306.
Detailed Description
The embodiment of the application provides a method and a device for quality inspection of capsule medicines, and solves the technical problems that in the prior art, the quality inspection of the capsule medicines is insufficient, and the medicine content of the medicines is influenced. The method achieves the technical effects of synthesizing the weight and the volume of the capsule to analyze the content of the medicine, adding the neural network model to improve the accuracy of a quality inspection result, ensuring the stability of the medicine components in the capsule, avoiding the influence on the treatment effect caused by the non-uniform content of the powder medicine in the capsule and having high automation degree in the quality inspection process. Hereinafter, example embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are merely some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited to the example embodiments described herein.
Summary of the application
The encapsulated medicine is generally a powder or granules which is irritating to the esophagus and gastric mucosa, or a medicine which is bad in taste, easy to volatilize, easy to be decomposed by saliva in the oral cavity, and easy to be inhaled into the trachea. The medicines are filled into capsules, so that the medicine property of the medicines is protected from being damaged, and the digestive organs and the respiratory tract are protected. Removal of the capsule shell may result in loss of the drug, waste of the drug, and reduced efficacy. The medicine is capsule prepared with special film forming material, such as gelatin, cellulose, polysaccharide, etc. and the capsule may be filled with medicine in powder or liquid form for easy swallowing. However, the prior art has the technical problems that the quality of the capsule medicine is not fully detected and the medicine content of the medicine is influenced.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
obtaining capsule weight information; obtaining standard weight information; judging whether the capsule weight information meets the requirement of standard weight information; when the requirement is met, obtaining the information of the filling volume of the capsule; inputting the capsule weight information and the capsule loading volume information into a first training model, wherein the first training model is obtained by training a plurality of groups of training data, and each group of the plurality of groups of training data comprises: the capsule weight information, the capsule loading volume information and identification information for identifying the standard reaching rate of the components of the predicted capsule dosage; obtaining a first output result of the first training model, wherein the first output result comprises a first medicine quantity standard-reaching rate which is used for evaluating the standard-reaching degree of the component proportion of the medicine powder in the capsule; judging whether the first medicine quantity standard-reaching rate meets a first preset condition or not; and when the first quality inspection result meets the quality inspection requirement, acquiring a first quality inspection result, wherein the first quality inspection result indicates that the capsule medicine meets the quality inspection requirement. The method achieves the technical effects of synthesizing the weight and the volume of the capsule to analyze the content of the medicine, adding the neural network model to improve the accuracy of a quality inspection result, ensuring the stability of the medicine components in the capsule, avoiding the influence on the treatment effect caused by the non-uniform content of the powder medicine in the capsule and having high automation degree in the quality inspection process.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, the present application provides a method for quality control of capsule type drugs, wherein the method includes:
step S100: obtaining capsule weight information;
step S200: obtaining standard weight information;
specifically, the weight of each capsule of the capsule medicine is different according to the form and the type of the encapsulated medicine, the weight of the same capsule medicine is the same according to the characteristics and the content of the encapsulated medicine, and the standard weight information is the single weight standard information of the corresponding capsule medicine.
Step S300: judging whether the capsule weight information meets the requirement of standard weight information;
further, the step S300: after judging whether the capsule weight information meets the requirement of standard weight information, the method comprises the following steps: step S910: and when the capsule weight information does not meet the requirement of the standard weight information, obtaining first reminding information.
Specifically, the weight information of each capsule is compared with the standard weight information, when the weight of the capsule does not meet the standard weight information, the capsule is indicated to be not qualified, and at the moment, first reminding information is sent to remind that the capsule is not qualified. And performing subsequent quality inspection on the capsules with the weight reaching the standard.
Step S400: when the requirement is met, obtaining the information of the filling volume of the capsule;
specifically, the information on the volume of the filled capsule, that is, how much space the filled capsule occupies, is calculated according to the capacity of the capsule and the position of the medicine in the capsule, and the information on the volume of the filled capsule is calculated according to the volume of the outer shell of the capsule and the proportion of the space occupied by the medicine in the capsule.
Step S500: inputting the capsule weight information and the capsule loading volume information into a first training model, wherein the first training model is obtained by training a plurality of groups of training data, and each group of the plurality of groups of training data comprises: the capsule weight information, the capsule loading volume information and identification information for identifying the standard reaching rate of the components of the predicted capsule dosage;
step S600: obtaining a first output result of the first training model, wherein the first output result comprises a first medicine quantity standard-reaching rate which is used for evaluating the standard-reaching degree of the component proportion of the medicine powder in the capsule;
specifically, whether the medicine in the capsule meets the expected standard requirements or not is estimated according to the components of the medicine, the corresponding density and the volume of the combined capsule, and in order to improve the accuracy of an analysis result, a Neural network model is added in the embodiment of the application, wherein the first training model is a Neural network model in machine learning, and a Neural Network (NN) is a complex Neural network system formed by widely interconnecting a large number of simple processing units (called neurons), reflects many basic characteristics of human brain functions, and is a highly complex nonlinear dynamical learning system. Neural network models are described based on mathematical models of neurons. Artificial Neural Networks (Artificial Neural Networks) are a description of the first-order properties of the human brain system. Briefly, it is a mathematical model. And through training of a large amount of training data, inputting the capsule weight information and the capsule loading volume information into a neural network model, and outputting a first medicine amount standard reaching rate.
Furthermore, the training process is essentially a supervised learning process, each group of supervised data comprises the capsule weight information, the capsule loading volume information and identification information for identifying the standard-reaching rate of the predicted capsule medicine amount component, the capsule weight information and the capsule loading volume information are input into a neural network model, the neural network model is continuously self-corrected and adjusted according to the identification information for identifying the standard-reaching rate of the predicted capsule medicine amount component, and the group of supervised learning is ended until the obtained output result is consistent with the identification information, and the next group of data supervised learning is carried out; and when the output information of the neural network model reaches the preset accuracy rate/reaches the convergence state, finishing the supervised learning process. Through the supervision and learning of the neural network model, the neural network model can process the input information more accurately, so that more accurate and suitable dose standard-reaching rate can be obtained, accurate quality inspection can be carried out on the capsule medicine, the standard-reaching degree of the medicine content in the capsule can be accurately analyzed, and the technical effect of guaranteeing the quality inspection of the capsule medicine is accurately achieved.
Step S700: judging whether the first medicine quantity standard-reaching rate meets a first preset condition or not;
step S800: and when the first quality inspection result meets the quality inspection requirement, acquiring a first quality inspection result, wherein the first quality inspection result indicates that the capsule medicine meets the quality inspection requirement.
Further, the step S700: after judging whether the first medicine quantity standard-reaching rate meets a first preset condition, the method comprises the following steps: step S1110: and when the first medicine quantity standard reaching rate does not meet a first preset condition, obtaining a second quality inspection result, wherein the second quality inspection result indicates that the capsule medicine does not meet the quality inspection requirement.
Specifically, whether the first dose standard-reaching rate meets the requirement of the content of the capsule medicine or not, namely a first preset condition is judged, the first preset condition is a standard condition determined according to the content of the components in the capsule medicine, when the first dose standard-reaching rate meets the first preset condition, the medicine components and the content of the medicine in the capsule meet the set requirement, if the first dose standard-reaching rate does not meet the condition, the medicine components in the medicine do not meet the requirement, if the powder is not uniform, the situation that more and less components exist exists, generally, the medicine in one capsule is a compound type and is not a single component, and the medicine components have different corresponding medicine densities, so that whether the compound powder in the capsule meets the requirement of the medicine components or not is determined according to the comprehensive analysis of the density, the volume and the weight of the medicine, if the compound powder meets the requirement of the medicine components, a first quality inspection result is obtained, and the quality, if the quality of the capsule medicine meets the quality requirement, a reminding message is sent to remind that the quality inspection fails, and the corresponding capsule is processed according to the quality inspection result, so that the quality of the capsule medicine meets the requirement. The method has the advantages that the purpose of analyzing the content of the medicine by integrating the weight and the volume of the capsule is achieved, the accuracy of a quality inspection result is improved by adding the neural network model, the stability of the components of the medicine in the capsule is ensured, the problem that the treatment effect is influenced due to the fact that the content of the powder medicine in the capsule is not uniform is avoided, the technical effect that the automation degree of the quality inspection process is high is solved, and the technical problems that the quality inspection of the capsule medicine is insufficient and the medicine content is influenced in the prior art are.
Further, the embodiment of the present application further includes:
step S1010: obtaining first environmental humidity information;
step S1020: obtaining capsule shell ingredient information;
step S1030: obtaining first prediction time according to the first environmental humidity information and the capsule shell component information, wherein the first prediction time is the longest time for predicting that the capsule is stored in the first environmental humidity information;
further, the step S1030: obtaining a first predicted time according to the first environmental humidity information and the capsule shell component information, wherein the first predicted time comprises the following steps:
step S1031: inputting the first environment humidity information and the capsule shell component information into a second training model, wherein the second training model is obtained by training a plurality of groups of training data, and each group of the plurality of groups of training data comprises: the first environmental humidity information, the capsule shell component information and the identification information identifying the longest time for which the predictive capsule is stored in the first environmental humidity information;
step S1032: obtaining a second output result of the second training model, the second output result including the first predicted time.
Step S1040: judging whether the first predicted time meets a second preset condition or not;
step S1050: when the first reminding information does not meet the requirement, second reminding information is obtained;
step S1060: and obtaining first drying information according to the second reminding information.
Further, the step S1060: according to the second reminding information, first drying information is obtained, and the method comprises the following steps:
step S1061: obtaining shell moisture resistance information according to the capsule shell component information;
step S1062: obtaining a first time difference value according to the shell moisture resistance information and the first predicted time;
step S1063: and obtaining the first drying information according to the first time difference value.
Specifically, the capsule medicine is kept dry, if the humidity is high, the quality of the capsule medicine is affected, meanwhile, the powder medicine in the capsule is not easy to store in a humid environment, the deterioration is easy to occur, the quality effect is affected, and the patient is likely to be hurt, so the quality of the capsule is further determined according to the humidity of the capsule, the quality inspection is carried out on the capsule, the environment for storing the capsule has certain influence on the humidity of the capsule, because the capsule medicine is not packaged, in order to avoid the deterioration of the capsule before packaging, the quality of the capsule medicine is ensured according to the first environment humidity information of the stored environment and the degree of influence of the humidity and the moisture of the components of the shell of the capsule, when the medicine is stored for no time exceeding the time limit of the humidity of the capsule, the quality inspection passing result is obtained for the packaged capsule, and if the time limit is exceeded, the non-passing result is sent, sending reminding information when the storage time of the stored unpackaged drugs exceeds the storage time, carrying out drying treatment in time according to the reminding, determining specific drying measures according to the humidity of the environment and the components of the capsule, drying the temperature and the time, further carrying out humidity and drug quality inspection on the drugs subjected to drying treatment to obtain a quality inspection result, realizing multi-directional quality inspection on the capsule drugs, and carrying out storage monitoring on the capsules before packaging and sealing to ensure the quality of the drugs. When storage time limit analysis is carried out according to first environment humidity information and capsule shell component information, in order to improve accuracy of an analysis result, a Neural network model is added, a second training model is the Neural network model in machine learning, and a Neural Network (NN) is a complex Neural network system formed by widely connecting a large number of simple processing units (called neurons), reflects many basic characteristics of human brain functions, and is a highly complex nonlinear dynamic learning system. Neural network models are described based on mathematical models of neurons. Artificial Neural Networks (Artificial Neural Networks) are a description of the first-order properties of the human brain system. Briefly, it is a mathematical model. And inputting the first environment humidity information and the capsule shell component information into a neural network model through training of a large amount of training data, and outputting the longest storage time as a storage time limit.
More specifically, the training process is essentially a supervised learning process, each group of supervised data includes the first environmental humidity information, the capsule shell component information and identification information identifying the longest time that the capsule is stored in the first environmental humidity information, the first environmental humidity information and the capsule shell component information are input into a neural network model, the neural network model performs continuous self-correction and adjustment according to the identification information identifying the longest time that the capsule is stored in the first environmental humidity information, and the neural network model finishes the group of supervised learning and performs the next group of data supervised learning until the obtained output result is consistent with the identification information; and when the output information of the neural network model reaches the preset accuracy rate/reaches the convergence state, finishing the supervised learning process. Through the supervision and learning of the neural network model, the neural network model can process the input information more accurately, the storage time limit which is the longest time for more accurate and suitable prediction storage is obtained, the humidity of the capsule medicine can be controlled, the dryness of the capsule can be accurately controlled, and the capsule can be subjected to multidirectional quality inspection by analyzing the humidity of the capsule, so that the quality of the capsule is ensured.
Further, the step S910: after the first reminding information is obtained, the embodiment of the application further includes:
step 1210: obtaining first weight information of a capsule medicine, wherein the first weight information is the weight of a first time;
step S1220: obtaining second weight information of the capsule medicine, wherein the second weight information is the weight at a second time, the first time is different from the second time, and the first time and the second time meet a third preset condition;
step S1230: obtaining a first weight difference value according to the first weight information and the second weight information;
step S1240: judging whether the first weight difference value exceeds a preset threshold value or not;
step S1250: and when the second quality inspection result exceeds the first quality inspection result, obtaining a second quality inspection result.
Specifically, the quality inspection of the capsule in the embodiment of the present application further includes a tightness inspection, the capsule whose weight does not satisfy the standard weight information requirement can be further analyzed for tightness, the weight before and after the capsule is shaken is respectively measured by shaking the capsule, whether powder leakage occurs or not is determined according to the weight difference between the two, the capsule can also be tested according to the volume difference, the third predetermined condition is to shake the capsule before the first time is no shaking, and the second time is after shaking, whether powder leakage occurs or not is determined according to the weight measurement of the capsule before and after the test, so as to determine the tightness of the capsule, the quality inspection result is determined according to the weight difference between the front and the rear, when the weight difference between the front and the rear is within the threshold range, the tightness is satisfactory, when the weight difference exceeds the threshold range, the tightness is unsatisfactory, and obtaining a quality inspection result. The quality inspection of the sealing performance of the capsule is achieved, and the technical effect that the medicine powder in the capsule is prevented from being exposed through the quality inspection of the sealing performance, so that the medicine content does not reach the standard is achieved.
Further, the step S500: after the capsule weight information and the capsule filling volume information are input into a first training model, the embodiment of the application comprises the following steps:
step S510: obtaining first training data and second training data in a plurality of groups of training data of the first training model until Nth training data, wherein N is a natural number greater than 1;
step S520: generating a first verification code according to the first training data, wherein the first verification code corresponds to the first training data one to one;
step S530: generating a second verification code according to the second training data and the first verification code, and generating an Nth verification code according to the Nth training data and the N-1 th verification code by analogy;
step S540: all training data and verification codes are copied and stored on M electronic devices, wherein M is a natural number larger than 1.
In particular, the blockchain technique, also referred to as a distributed ledger technique, is an emerging technique in which several computing devices participate in "accounting" together, and maintain a complete distributed database together. The blockchain technology has been widely used in many fields due to its characteristics of decentralization, transparency, participation of each computing device in database records, and rapid data synchronization between computing devices. Generating a first verification code according to the first training data, wherein the first verification code corresponds to the first training data one to one; generating a second verification code according to the second training data and the first verification code, wherein the second verification code corresponds to the second training data one to one; by analogy, generating an Nth verification code according to the Nth training data and the Nth-1 verification code, wherein N is a natural number larger than 1, respectively copying and storing all the training data and the verification code on M devices, wherein the first training data and the first verification code are stored on one device as a first storage unit, the second training data and the second verification code are stored on one device as a second storage unit, the Nth training data and the Nth verification code are stored on one device as an Nth storage unit, when the training data need to be called, after each subsequent node receives the data stored by the previous node, the data are checked and stored through a common identification mechanism, each storage unit is connected in series through a hash function, so that the screening condition is not easy to lose and destroy, and the training data are encrypted through the logic of a block chain, the safety of the training data is guaranteed, the accuracy of the first training model obtained through training of the training data is further guaranteed, and a foundation is laid for obtaining more accurate medicine quantity standard reaching rate in the follow-up process.
Example two
Based on the same inventive concept as the method for quality inspection of capsule type medicines in the previous embodiment, the invention also provides a device for quality inspection of capsule type medicines, as shown in fig. 2, the device comprises:
a first obtaining unit 11, said first obtaining unit 11 being configured to obtain capsule weight information;
a second obtaining unit 12, the second obtaining unit 12 being configured to obtain the standard weight information;
a first judging unit 13, wherein the first judging unit 13 is used for judging whether the capsule weight information meets the requirement of standard weight information;
a third obtaining unit 14, the third obtaining unit 14 being configured to obtain capsule filling volume information when satisfied;
a first input unit 15, where the first input unit 15 is configured to input the capsule weight information and the capsule filling volume information into a first training model, where the first training model is obtained through training of multiple sets of training data, and each of the multiple sets of training data includes: the capsule weight information, the capsule loading volume information and identification information for identifying the standard reaching rate of the components of the predicted capsule dosage;
a fourth obtaining unit 16, wherein the fourth obtaining unit 16 is configured to obtain a first output result of the first training model, and the first output result includes a first drug quantity achievement rate, and the first drug quantity achievement rate is used for evaluating an achievement degree of a component proportion of drug powder in a capsule;
a second judging unit 17, where the second judging unit 17 is configured to judge whether the first medicine quantity standard-reaching rate meets a first predetermined condition;
a fifth obtaining unit 18, where the fifth obtaining unit 18 is configured to obtain a first quality inspection result when the first quality inspection result is satisfied, where the first quality inspection result is that the capsule medicine meets the quality inspection requirement.
Further, the apparatus further comprises:
a sixth obtaining unit, configured to obtain first reminding information when the capsule weight information does not meet the requirement of the standard weight information.
Further, the apparatus further comprises:
a seventh obtaining unit configured to obtain first ambient humidity information;
an eighth obtaining unit for obtaining capsule shell ingredient information;
a ninth obtaining unit, configured to obtain a first predicted time according to the first ambient humidity information and the capsule shell component information, where the first predicted time is a longest time for which the capsule is predicted to be stored in the first ambient humidity information;
a third judging unit configured to judge whether the first predicted time satisfies a second predetermined condition;
a tenth obtaining unit, configured to obtain the second reminder information when the second reminder information does not meet the requirement;
an eleventh obtaining unit, configured to obtain first drying information according to the second reminding information.
Further, the apparatus further comprises:
a second input unit, configured to input the first ambient humidity information and the capsule shell composition information into a second training model, where the second training model is obtained through training of multiple sets of training data, and each set of the multiple sets of training data includes: the first environmental humidity information, the capsule shell component information and the identification information identifying the longest time for which the predictive capsule is stored in the first environmental humidity information;
a twelfth obtaining unit, configured to obtain a second output result of the second training model, where the second output result includes the first predicted time.
Further, the apparatus further comprises:
a thirteenth obtaining unit, configured to, when the first dose compliance rate does not satisfy the first predetermined condition, obtain a second quality inspection result, where the second quality inspection result is that the capsule medicine does not meet the quality inspection requirement.
Further, the apparatus further comprises:
a fourteenth obtaining unit for obtaining first weight information of the capsule medicine, the first weight information being a weight at a first time;
a fifteenth obtaining unit configured to obtain second weight information of the capsule medicine, the second weight information being a weight at a second time, the first time being different from the second time, and the first time and the second time satisfying a third predetermined condition;
a sixteenth obtaining unit, configured to obtain a first weight difference value according to the first weight information and the second weight information;
a fourth judging unit configured to judge whether the first weight difference exceeds a predetermined threshold;
a seventeenth obtaining unit configured to obtain a second quality inspection result when the second quality inspection result exceeds the first quality inspection result.
Further, the apparatus further comprises:
an eighteenth obtaining unit, configured to obtain shell moisture resistance information according to the capsule shell component information;
a nineteenth obtaining unit, configured to obtain a first time difference value according to the shell moisture resistance information and the first predicted time;
a twentieth obtaining unit configured to obtain the first drying information according to the first time difference value.
Further, the apparatus further comprises:
a twenty-first obtaining unit, configured to obtain first training data and second training data in multiple groups of training data of the first training model until nth training data, where N is a natural number greater than 1;
a first generating unit, configured to generate a first verification code according to the first training data, where the first verification code corresponds to the first training data one to one;
a second generating unit, configured to generate a second verification code according to the second training data and the first verification code, and generate an nth verification code according to the nth training data and the nth-1 verification code by analogy;
the first execution unit is used for copying and storing all training data and verification codes on M pieces of electronic equipment, wherein M is a natural number greater than 1.
Various changes and specific examples of the method for quality inspection of capsule type medicine in the first embodiment of fig. 1 are also applicable to the apparatus for quality inspection of capsule type medicine of the present embodiment, and the implementation method of the apparatus for quality inspection of capsule type medicine in the present embodiment is clear to those skilled in the art from the foregoing detailed description of the method for quality inspection of capsule type medicine, so for the brevity of the description, detailed description is omitted here.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to fig. 3.
Fig. 3 illustrates a schematic structural diagram of an electronic device according to an embodiment of the present application.
Based on the inventive concept of a method of quality inspection of capsule type medicaments as in the previous embodiments, the present invention also provides a device of quality inspection of capsule type medicaments, on which a computer program is stored, which program, when being executed by a processor, realizes the steps of any one of the methods of quality inspection of capsule type medicaments as described in the previous paragraphs.
Where in fig. 3 a bus architecture (represented by bus 300), bus 300 may include any number of interconnected buses and bridges, bus 300 linking together various circuits including one or more processors, represented by processor 302, and memory, represented by memory 304. The bus 300 may also link together various other circuits such as peripherals, voltage regulators, power management circuits, and the like, which are well known in the art, and therefore, will not be described any further herein. A bus interface 306 provides an interface between the bus 300 and the receiver 301 and transmitter 303. The receiver 301 and the transmitter 303 may be the same element, i.e., a transceiver, providing a means for communicating with various other systems over a transmission medium.
The processor 302 is responsible for managing the bus 300 and general processing, and the memory 304 may be used for storing data used by the processor 302 in performing operations.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
according to the method and the device for quality inspection of the capsule medicines, provided by the embodiment of the application, the weight information of the capsule is obtained; obtaining standard weight information; judging whether the capsule weight information meets the requirement of standard weight information; when the requirement is met, obtaining the information of the filling volume of the capsule; inputting the capsule weight information and the capsule loading volume information into a first training model, wherein the first training model is obtained by training a plurality of groups of training data, and each group of the plurality of groups of training data comprises: the capsule weight information, the capsule loading volume information and identification information for identifying the standard reaching rate of the components of the predicted capsule dosage; obtaining a first output result of the first training model, wherein the first output result comprises a first medicine quantity standard-reaching rate which is used for evaluating the standard-reaching degree of the component proportion of the medicine powder in the capsule; judging whether the first medicine quantity standard-reaching rate meets a first preset condition or not; and when the first quality inspection result meets the quality inspection requirement, acquiring a first quality inspection result, wherein the first quality inspection result indicates that the capsule medicine meets the quality inspection requirement. The method has the advantages that the purpose of analyzing the content of the medicine by integrating the weight and the volume of the capsule is achieved, the accuracy of a quality inspection result is improved by adding the neural network model, the stability of the components of the medicine in the capsule is ensured, the problem that the treatment effect is influenced due to the non-uniform content of the powder medicine in the capsule is avoided, and the technical effect that the automation degree of the quality inspection process is high is solved, so that the technical problems that the quality inspection of the capsule medicine is insufficient and the medicine content is influenced in the prior art are solved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, 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 a system for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method of quality testing a capsule-like pharmaceutical, wherein the method comprises:
obtaining capsule weight information;
obtaining standard weight information;
judging whether the capsule weight information meets the requirement of standard weight information;
when the requirement is met, obtaining the information of the filling volume of the capsule;
inputting the capsule weight information and the capsule loading volume information into a first training model, wherein the first training model is obtained by training a plurality of groups of training data, and each group of the plurality of groups of training data comprises: the capsule weight information, the capsule loading volume information and identification information for identifying the standard reaching rate of the components of the predicted capsule dosage;
obtaining a first output result of the first training model, wherein the first output result comprises a first medicine quantity standard-reaching rate which is used for evaluating the standard-reaching degree of the component proportion of the medicine powder in the capsule;
judging whether the first medicine quantity standard-reaching rate meets a first preset condition or not;
and when the first quality inspection result meets the quality inspection requirement, acquiring a first quality inspection result, wherein the first quality inspection result indicates that the capsule medicine meets the quality inspection requirement.
2. The method of claim 1, wherein said determining whether the capsule weight information meets the requirements for standard weight information comprises:
and when the capsule weight information does not meet the requirement of the standard weight information, obtaining first reminding information.
3. The method of claim 1, wherein the method comprises:
obtaining first environmental humidity information;
obtaining capsule shell ingredient information;
obtaining first prediction time according to the first environmental humidity information and the capsule shell component information, wherein the first prediction time is the longest time for predicting that the capsule is stored in the first environmental humidity information;
judging whether the first predicted time meets a second preset condition or not;
when the first reminding information does not meet the requirement, second reminding information is obtained;
and obtaining first drying information according to the second reminding information.
4. The method of claim 3, wherein said obtaining a first predicted time based on said first ambient humidity information, said capsule shell composition information, comprises:
inputting the first environment humidity information and the capsule shell component information into a second training model, wherein the second training model is obtained by training a plurality of groups of training data, and each group of the plurality of groups of training data comprises: the first environmental humidity information, the capsule shell component information and the identification information identifying the longest time for which the predictive capsule is stored in the first environmental humidity information;
obtaining a second output result of the second training model, the second output result including the first predicted time.
5. The method of claim 1, wherein said determining whether said first drug compliance rate meets a first predetermined condition comprises:
and when the first medicine quantity standard reaching rate does not meet a first preset condition, obtaining a second quality inspection result, wherein the second quality inspection result indicates that the capsule medicine does not meet the quality inspection requirement.
6. The method of claim 2, wherein after obtaining the first reminder information, the method comprises:
obtaining first weight information of a capsule medicine, wherein the first weight information is the weight of a first time;
obtaining second weight information of the capsule medicine, wherein the second weight information is the weight at a second time, the first time is different from the second time, and the first time and the second time meet a third preset condition;
obtaining a first weight difference value according to the first weight information and the second weight information;
judging whether the first weight difference value exceeds a preset threshold value or not;
and when the second quality inspection result exceeds the first quality inspection result, obtaining a second quality inspection result.
7. The method of claim 3, wherein the obtaining first drying information according to the second reminder information comprises:
obtaining shell moisture resistance information according to the capsule shell component information;
obtaining a first time difference value according to the shell moisture resistance information and the first predicted time;
and obtaining the first drying information according to the first time difference value.
8. The method of claim 1, wherein said inputting said capsule weight information and said capsule fill volume information into a first training model comprises:
obtaining first training data and second training data in a plurality of groups of training data of the first training model until Nth training data, wherein N is a natural number greater than 1;
generating a first verification code according to the first training data, wherein the first verification code corresponds to the first training data one to one;
generating a second verification code according to the second training data and the first verification code, and generating an Nth verification code according to the Nth training data and the N-1 th verification code by analogy;
all training data and verification codes are copied and stored on M electronic devices, wherein M is a natural number larger than 1.
9. An apparatus for quality testing of capsule type medicine, wherein the apparatus comprises:
a first obtaining unit for obtaining capsule weight information;
a second obtaining unit for obtaining the standard weight information;
a first judging unit for judging whether the capsule weight information meets the requirement of standard weight information;
a third obtaining unit for obtaining capsule filling volume information when satisfied;
a first input unit, configured to input the capsule weight information and the capsule filling volume information into a first training model, where the first training model is obtained through training of multiple sets of training data, and each set of the multiple sets of training data includes: the capsule weight information, the capsule loading volume information and identification information for identifying the standard reaching rate of the components of the predicted capsule dosage;
a fourth obtaining unit, configured to obtain a first output result of the first training model, where the first output result includes a first drug quantity achievement rate, and the first drug quantity achievement rate is used to evaluate an achievement degree of a component proportion of drug powder in a capsule;
the second judging unit is used for judging whether the first medicine quantity standard-reaching rate meets a first preset condition or not;
and the fifth obtaining unit is used for obtaining a first quality inspection result when the first quality inspection result is met, wherein the first quality inspection result indicates that the capsule medicine meets the quality inspection requirement.
10. An apparatus for quality testing of capsule type drugs, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of claims 1 to 8 when executing the program.
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Inventor after: Wang Hongjun Inventor after: Ma Shengnan Inventor after: Zhao Guangchao Inventor after: Pu Liping Inventor after: Luan Meili Inventor before: Wang Hongjun Inventor before: Ma Shengnan Inventor before: Super light super light Inventor before: Pu Liping Inventor before: Luan Meili |