CN116128272B - Production process supervision platform for medicines - Google Patents
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Abstract
The invention provides a production process supervision platform of medicines, which comprises the following components: the flow acquisition module acquires a production flow for producing the target medicine, determines a sub-production image corresponding to each sub-production flow in the production flow, and determines a medicine form image corresponding to the target medicine for each sub-production flow; the learning module is used for carrying out first learning on the sub-production image and second learning on the medicine form image, and establishing a sub-monitoring model for monitoring the sub-production flow; the model construction module is used for acquiring the production sequence of the production flow, correlating each sub-monitoring model based on the production sequence of the production flow and constructing a comprehensive monitoring model based on the correlation result; and the supervision module is used for monitoring the production flow according to the comprehensive monitoring model, generating a supervision report based on the monitoring result, and simultaneously, carrying out alarm operation when production errors exist in the production flow. The supervision effect on the production flow of the target medicine is improved.
Description
Technical Field
The invention relates to the technical field of monitoring and data processing, in particular to a production flow supervision platform for medicines.
Background
Along with the continuous improvement of medical level, more and more diseases have corresponding therapeutic drugs, and qualified therapeutic drugs are basic conditions for treating corresponding diseases, so that the supervision of the production flow of the drugs is extremely important;
at present, along with the intellectualization of drug production, the therapeutic drugs are mostly produced by adopting a production line, but at present, the supervision of the production process of the drugs is mostly adopting manual sampling detection, so that the accuracy of detection cannot be guaranteed, a great amount of manpower and physics are wasted, meanwhile, the qualification of the production process of the drugs cannot be ensured at any time due to the manual sampling detection, thereby causing poor supervision effect on the production process of the drugs, and once things appear, immeasurable serious consequences are caused;
therefore, the invention provides a drug production process supervision platform.
Disclosure of Invention
The invention provides a production flow supervision platform of a drug, which is used for accurately and effectively constructing a comprehensive monitoring model corresponding to a production flow of the target drug by analyzing sub-production images and drug form images of the production flow of the target drug, and finally, monitoring the production flow of the target drug through the comprehensive monitoring model, so that timely alarm operation when production errors occur is realized, the production reliability and the production accuracy of the target drug are ensured, the production safety of the target drug is also ensured, and the supervision effect on the production flow of the target drug is improved.
The invention provides a production process supervision platform of medicines, which comprises the following steps:
the flow acquisition module is used for acquiring a production flow for producing the target medicine, determining a sub-production image corresponding to each sub-production flow in the production flow, and simultaneously determining a medicine form image corresponding to the target medicine for each sub-production flow;
the learning module is used for carrying out first learning on the sub-production image and carrying out second learning on the medicine form image, and meanwhile, a sub-monitoring model for monitoring the corresponding sub-production flow is established based on the first learning result and the second learning result;
the model construction module is used for acquiring the production sequence of the production flow, correlating each sub-monitoring model based on the production sequence of the production flow, and constructing a comprehensive monitoring model based on the correlation result;
and the monitoring module is used for monitoring the production flow of the target medicine according to the comprehensive monitoring model, generating a monitoring report based on the monitoring result, generating an alarm control instruction when production errors exist in the production flow, and controlling the alarm device to perform alarm operation according to the alarm control instruction.
Preferably, a production process supervision platform of a drug, a process acquisition module, includes:
The characteristic acquisition unit is used for acquiring the drug attribute of the target drug and determining the drug characteristic identification of the target drug according to the drug attribute of the target drug;
the first matching unit is used for carrying out first matching in a preset drug production management library according to the drug characteristic identification of the target drug, and obtaining a production file of the target drug based on a first matching result;
the second matching unit is used for acquiring the production requirement of the user, performing second matching in the production file of the target medicine based on the production requirement of the user, and acquiring the production flow for producing the target medicine based on the second matching result.
Preferably, a production process supervision platform of a drug, a process acquisition module, includes:
the flow reading unit is used for reading the production flow of the production of the target medicine and determining the operation process corresponding to each sub-production flow;
an image generation unit configured to:
performing process simulation on the operation process corresponding to each sub-production flow in a computer, and determining a sub-production image corresponding to each sub-production flow based on a simulation result;
and determining a medicine morphology image of the corresponding target medicine in each sub-production flow based on the simulation result.
Preferably, a drug production process supervision platform, a learning module, includes:
a first learning unit configured to:
carrying out graying treatment on the sub-production image to obtain a first target gray level image, positioning a target medicine in the first target gray level image, and obtaining contour pixel points of the target medicine in the first target gray level image based on pixel point color distribution characteristics of the first target gray level image;
performing first marking on the target medicine in the first target gray level image based on the outline pixel points in the first target gray level image, and generating a first tracking factor based on a first marking result;
reading the sub-production flow, determining the operation gesture of the sub-production flow, simultaneously determining the region acting on the target medicine in the first target gray level image based on the operation gesture of the sub-production flow, and taking the region acting on the target medicine in the first target gray level image as the region of interest;
reading the region of interest, determining the distribution characteristics of edge pixels of the region of interest and pixels inside the region of interest, performing second marking on the edge pixels of the region of interest and performing third marking on the pixels inside the region of interest according to the distribution characteristics of the pixels inside the region of interest, and generating a second tracking factor based on a second marking result and a third marking result;
Acquiring a target association relation between an operation gesture and a drug form of a target drug in a sub-production flow, mapping a second tracking factor in a first tracking factor based on the target association relation, and generating a third tracking factor based on a mapping result;
the second learning unit is used for carrying out graying treatment on the medicine form image of the target medicine corresponding to the sub-production flow, obtaining a second target gray level image, reading the second target gray level image, determining the pixel point distribution of the target medicine in the second target gray level image, carrying out fourth marking on the pixel points of the target medicine in the second target gray level image based on the pixel point distribution, and generating a fourth tracking factor based on a fourth marking result;
the sub-monitoring model construction unit is used for constructing a sub-monitoring model for monitoring the corresponding sub-production flow based on the first tracking factor, the second tracking factor, the third tracking factor and the fourth tracking factor.
Preferably, a production process supervision platform of a drug, a sub-monitoring model construction unit, includes:
a network node determining subunit, configured to construct a first network node based on a third tracking factor, construct a second network node based on the first tracking factor, construct a third network node based on the second tracking factor, and construct a fourth network node based on a fourth tracking factor;
A connection relation establishing subunit, configured to establish a first connection relation between the first network node and the second network node, and establish a second connection relation between the first network node and the third network node; simultaneously, a third connection relation is established between the fourth network node and the first network node;
the model construction subunit is configured to establish a sub-monitoring model based on the first network node, the second network node, the third network node, the fourth network node, the first connection relationship, the second connection relationship, and the third connection relationship.
Preferably, a drug production process supervision platform, a model construction module, includes:
the production sequence determining unit is used for reading the production flow, determining the logic keywords in the production flow, and determining the production sequence of the production flow according to the logic keywords in the production flow;
the sequencing unit is used for sequencing each sub-production flow in the generation flow based on the production sequence of the production flow, and numbering the sub-monitoring model corresponding to each sub-production flow based on the sequencing result;
and the comprehensive monitoring model generating unit is used for sequencing and correlating the sub monitoring models from the sequence of the numbering results from the small to the large, and generating the comprehensive monitoring model according to the correlation results.
Preferably, a production process supervision platform of a drug, a supervision module, includes:
the monitoring unit is used for acquiring a production task of the target medicine, determining a production link of the target medicine based on the production task, and extracting a link identifier of the production link;
the image acquisition unit is used for matching the link identification with the preset monitoring terminal identification, starting a target preset monitoring terminal corresponding to the production link based on the matching result to acquire a real-time production image corresponding to the production link in real time, and inputting the real-time production image into the comprehensive monitoring model based on the preset event input interface;
the model touch unit is used for configuring an active detection needle in the comprehensive monitoring model, carrying out association deployment on the active detection needle and the comprehensive monitoring model, and carrying out active detection according to a preset event input interface of the active detection on the comprehensive monitoring model based on an association deployment result;
an analysis unit for:
when the input of the real-time production image of the preset event input interface is actively detected, a trigger signal is generated, a monitoring analysis flow in the comprehensive monitoring model is started based on the trigger signal, pixel point scanning is carried out on the input real-time production image based on the monitoring analysis flow, and the target image characteristics of each real-time production image are extracted;
Determining the production process and production standard of the target medicine in the current production link based on the target image characteristics, correlating the obtained production process and production standard with the corresponding real-time production image, adding a target timestamp to the real-time production image based on the correlation result, and recording and storing the real-time production image and the corresponding production process and production standard according to the development sequence of the target timestamp based on the addition result.
Preferably, a drug production process supervision platform, the analysis unit comprises:
the result acquisition subunit is used for acquiring the obtained analysis result and extracting a target timestamp corresponding to the real-time production image in the analysis result;
the comparison subunit is used for comparing the production process corresponding to each real-time production image and the production standard with the preset production requirement based on the target time stamp, and determining a standard real-time production image and an abnormal production image based on the comparison result;
the report generation subunit is used for calling a target template from a preset template library, recording the obtained real-time production image, the corresponding production process and production standard in the target template based on the time stamp development sequence, and marking the standard real-time production image and the abnormal production image in the real-time production image based on the first marking mode and the second marking mode to obtain the supervision report.
Preferably, a production process supervision platform of a drug, a supervision module, includes:
the report acquisition unit is used for acquiring the obtained supervision report, extracting production parameters corresponding to the abnormal production flow recorded in the supervision report, extracting configuration parameters of the abnormal production flow, and determining standard parameters corresponding to the abnormal production flow based on the configuration parameters;
the instruction generation unit is used for performing difference operation on the production parameters and the standard parameters to obtain production errors, calling preset instruction elements based on production types corresponding to abnormal production flows when the production errors are larger than a preset threshold, and combining the instruction elements based on preset logic combination rules to generate an alarm control instruction;
and the alarm unit is used for controlling the alarm device to perform corresponding alarm operation according to the production type corresponding to the abnormal production flow based on the alarm control instruction.
Compared with the prior art, the invention has the beneficial effects that:
the sub-production image and the medicine morphology image of the production flow of the target medicine are analyzed, so that an accurate and effective construction of a comprehensive monitoring model corresponding to the production flow of the target medicine is realized, and finally, the production flow of the target medicine is monitored through the comprehensive monitoring model, so that timely alarm operation when production errors occur is realized, the production reliability and the production accuracy of the target medicine are ensured, the production safety of the target medicine is also ensured, and the supervision effect on the production flow of the target medicine is improved.
By determining the first tracking factor, the second tracking factor, the third tracking factor and the fourth tracking factor, the overall tracking of the operation gesture and the medicine form of the target medicine in the corresponding sub-production flow is effectively realized, and the constructed sub-monitoring model is further enabled to realize overall monitoring, so that the accuracy of monitoring the production flow of the target medicine is improved.
The production task of the target medicine is determined, the production link of the target medicine is accurately and effectively confirmed according to the production task, the corresponding preset monitoring terminal is started according to the confirmation result to monitor the production link of the target medicine in real time, and the obtained real-time production image is input into the comprehensive monitoring model for analysis processing, so that the accurate and effective determination of the production process and the production standard of the current production link is realized, finally, the analysis result is recorded, data support is provided for accurately judging and supervising the production flow of the target medicine, production abnormality is conveniently found in time, and the production reliability of the target medicine is ensured.
By analyzing the obtained supervision report, the production errors of the production parameters corresponding to the abnormal production flow and the standard parameters are accurately and effectively determined, corresponding alarm control quality is timely generated when the production errors are larger than a preset threshold value, the alarm device is controlled to perform corresponding alarm operation according to the alarm control instruction, management staff can find abnormal conditions in time conveniently, corresponding emergency measures are conveniently taken in time, the supervision degree of the drug production flow is guaranteed, and the production reliability of the drug is guaranteed.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of a drug manufacturing process supervision platform in accordance with an embodiment of the present invention;
FIG. 2 is a block diagram of a process acquisition module in a drug manufacturing process monitoring platform according to an embodiment of the present invention;
fig. 3 is a block diagram of a learning module in a drug production process supervision platform according to an embodiment of the present invention.
Fig. 4 is a full flow chart of production in a drug production process monitoring platform according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Example 1
The present embodiment provides a drug production process supervision platform, as shown in fig. 1, including:
the flow acquisition module is used for acquiring a production flow for producing the target medicine, determining a sub-production image corresponding to each sub-production flow in the production flow, and simultaneously determining a medicine form image corresponding to the target medicine for each sub-production flow;
the learning module is used for carrying out first learning on the sub-production image and carrying out second learning on the medicine form image, and meanwhile, a sub-monitoring model for monitoring the corresponding sub-production flow is established based on the first learning result and the second learning result;
the model construction module is used for acquiring the production sequence of the production flow, correlating each sub-monitoring model based on the production sequence of the production flow, and constructing a comprehensive monitoring model based on the correlation result;
and the monitoring module is used for monitoring the production flow of the target medicine according to the comprehensive monitoring model, generating a monitoring report based on the monitoring result, generating an alarm control instruction when production errors exist in the production flow, and controlling the alarm device to perform alarm operation according to the alarm control instruction.
In this embodiment, the target drug may be a drug that needs to be produced, for example, a drug that is required for treating diabetes.
In this embodiment, the production flow may be a step of characterizing the production of the target drug, a specific production content of each step, or the like.
In this embodiment, the sub-production process may be a small production step included in the production process.
In this embodiment, the sub-production image may be a production image corresponding to each sub-production flow, that is, an image obtained after image acquisition of the production process of each step, and is not unique.
In this embodiment, the drug morphology image may be an image representing the shape and appearance characteristics of the target drug corresponding to each sub-production process.
In this embodiment, the first learning may be training and processing the sub-production image corresponding to each sub-production process, so as to facilitate building a sub-monitoring model for monitoring different sub-production processes.
In this embodiment, the second learning may be training and processing the drug morphology images, so as to facilitate building a sub-monitoring model for monitoring different sub-production flows.
In this embodiment, the sub-monitoring models may be monitoring models for monitoring different sub-production processes, each sub-monitoring model corresponding to one sub-production process.
In this embodiment, the integrated monitoring model may be an overall model obtained by associating the obtained sub-monitoring models according to a production sequence of a production flow.
In this embodiment, the supervisory report may be a report generated according to the monitoring result, for recording the production condition of the target drug by the production process.
In this embodiment, the alarm control instruction may be to control the alarm device to perform an alarm operation.
In this embodiment, the production process (production full process) of the target drug (diabetes drug) includes: establishing a GMP-iPSC seed bank, establishing an iPSC bank, carrying out resuscitating and expanding culture on the iPSC working bank, and carrying out freezing transportation on iPSC differentiated PP differentiated pancreatic islet beta, wherein qPCR, flow cytometry, OCT4, SOX2, nanog and the like are required for establishing the GMP-iPSC seed bank and carrying out resuscitating and expanding culture on the iPSC working bank; the three-level library is required for quality detection (the required technology is qPCR, flow cytometry, OCT4, SOX2, nanog and iPSC three-germ layer identification, STEMdiff differentiation identification, qPCR, flow cytometry, AFP, tuj1, SMA and the like; the detection of iPSC differentiation PP differentiation islet beta freezing transportation comprises (post-implantation detection: cell activity detection, histological detection: dithizone staining, analytical detection: human gene expression and human islet cell specific factor detection, safety detection: immune resistance and immune response detection, blood convention and the like; the three-level library detection method also comprises market modules: product delivery-product tracking-product sales-after-product sales: production complete flow and market modules, and the detection is particularly shown in figure 4).
The beneficial effects of the technical scheme are as follows: the sub-production image and the medicine morphology image of the production flow of the target medicine are analyzed, so that an accurate and effective construction of a comprehensive monitoring model corresponding to the production flow of the target medicine is realized, and finally, the production flow of the target medicine is monitored through the comprehensive monitoring model, so that timely alarm operation when production errors occur is realized, the production reliability and the production accuracy of the target medicine are ensured, the production safety of the target medicine is also ensured, and the supervision effect on the production flow of the target medicine is improved.
Example 2
On the basis of embodiment 1, this embodiment provides a production process supervision platform for a drug, as shown in fig. 2, a process acquisition module includes:
the characteristic acquisition unit is used for acquiring the drug attribute of the target drug and determining the drug characteristic identification of the target drug according to the drug attribute of the target drug;
the first matching unit is used for carrying out first matching in a preset drug production management library according to the drug characteristic identification of the target drug, and obtaining a production file of the target drug based on a first matching result;
the second matching unit is used for acquiring the production requirement of the user, performing second matching in the production file of the target medicine based on the production requirement of the user, and acquiring the production flow for producing the target medicine based on the second matching result.
In this embodiment, the drug property may be a drug type or the like that characterizes the drug of interest.
In this embodiment, the drug characteristic identifier may be a label tag that marks different target drugs, by which the target drugs can be quickly distinguished.
In this embodiment, the preset drug production management library is set in advance, and is used for storing production files corresponding to different drugs.
In this embodiment, the first matching may be matching the drug characteristic identification with a preset drug production management library.
In this embodiment, the production file may be a production process of recording the target drug, and each production file corresponds to a target drug.
In this embodiment, the production demand may be a demand characterizing the production speed, throughput of the target drug by the user.
In this embodiment, the second matching may be to match the production requirements with the production file, thereby enabling a determination of the production flow of the target drug.
The beneficial effects of the technical scheme are as follows: the production files corresponding to the target medicaments are locked by determining and analyzing the medicament attributes of the target medicaments, and then the production requirements of the target medicaments are matched with the corresponding production files, so that the production flow of the target medicaments is locked, and the production process of the medicaments is accurately monitored according to the production flow.
Example 3
On the basis of embodiment 1, this embodiment provides a production process supervision platform of a drug, and a process acquisition module includes:
the flow reading unit is used for reading the production flow of the production of the target medicine and determining the operation process corresponding to each sub-production flow;
an image generation unit configured to:
performing process simulation on the operation process corresponding to each sub-production flow in a computer, and determining a sub-production image corresponding to each sub-production flow based on a simulation result;
and determining a medicine morphology image of the corresponding target medicine in each sub-production flow based on the simulation result.
In this embodiment, the operation procedure may be a specific operation step corresponding to each sub-production flow, that is, a specific content to be executed.
In this embodiment, the process simulation may be a virtual simulation of a specific operation corresponding to the sub-production flow in the calculation, so as to facilitate determination of the production condition of the sub-production flow.
The beneficial effects of the technical scheme are as follows: the production process of the target medicine is read, so that the accurate and reliable determination of the operation process of the target medicine is realized, the operation process is simulated in a computer, and the accurate and reliable acquisition of the sub-production image corresponding to each sub-production process and the medicine form image of the target medicine is realized through simulation, so that the comprehensive monitoring model is convenient to construct, the accurate monitoring of the production process of the target medicine is realized, and the production reliability and the production accuracy of the target medicine are ensured.
Example 4
On the basis of embodiment 1, this embodiment provides a production process supervision platform for a drug, as shown in fig. 3, a learning module, including:
a first learning unit configured to:
carrying out graying treatment on the sub-production image to obtain a first target gray level image, positioning a target medicine in the first target gray level image, and obtaining contour pixel points of the target medicine in the first target gray level image based on pixel point color distribution characteristics of the first target gray level image;
performing first marking on the target medicine in the first target gray level image based on the outline pixel points in the first target gray level image, and generating a first tracking factor based on a first marking result;
reading the sub-production flow, determining the operation gesture of the sub-production flow, simultaneously determining the region acting on the target medicine in the first target gray level image based on the operation gesture of the sub-production flow, and taking the region acting on the target medicine in the first target gray level image as the region of interest;
reading the region of interest, determining the distribution characteristics of edge pixels of the region of interest and pixels inside the region of interest, performing second marking on the edge pixels of the region of interest and performing third marking on the pixels inside the region of interest according to the distribution characteristics of the pixels inside the region of interest, and generating a second tracking factor based on a second marking result and a third marking result;
Acquiring a target association relation between an operation gesture and a drug form of a target drug in a sub-production flow, mapping a second tracking factor in a first tracking factor based on the target association relation, and generating a third tracking factor based on a mapping result;
the second learning unit is used for carrying out graying treatment on the medicine form image of the target medicine corresponding to the sub-production flow, obtaining a second target gray level image, reading the second target gray level image, determining the pixel point distribution of the target medicine in the second target gray level image, carrying out fourth marking on the pixel points of the target medicine in the second target gray level image based on the pixel point distribution, and generating a fourth tracking factor based on a fourth marking result;
the sub-monitoring model construction unit is used for constructing a sub-monitoring model for monitoring the corresponding sub-production flow based on the first tracking factor, the second tracking factor, the third tracking factor and the fourth tracking factor.
In this embodiment, the first target gradation image may be an image obtained by subjecting the sub-production image to gradation processing.
In this embodiment, the positioning of the target drug in the first gray scale image may be positioning the position of the target drug in the first gray scale image, and may be according to the shape of the target drug or the like.
In this embodiment, the color distribution characteristic of the pixels of the first target gray scale image may be a color (such as white) of the pixels of the target drug, so as to determine the distribution in the first target gray scale image, that is, determine the area in the first target gray scale image by the color characteristic of the pixels of the target drug, so as to determine the contour pixels of the target drug in the first target gray scale image.
In this embodiment, the first tracking factor may be obtained after marking (first marking) the outline pixel point of the target drug in the first target gray level image, so as to track the target drug in the first target gray level image (because each sub-production process of the target drug is a dynamic operation environment during the production process, the morphology of the target drug is dynamically changed, and thus, the tracking and identification of the morphology of the target drug is effectively realized through the first tracking factor).
In this embodiment, the operational gesture may be an operational action of the sub-production flow.
In this embodiment, the region that acts on the target drug in the first target grayscale image determined based on the operation posture of the sub-production flow may be, for example, a region that acts on the target drug (affects the target drug) in performing the ipsc workflow resuscitator expansion culture in the first target grayscale image.
In this embodiment, the region of interest is a region acting on the target drug in the first target grayscale image, and the region of interest includes a region where the target drug is located.
In this embodiment, the edge pixel point of the region of interest may be an outline pixel point of the region of interest in the first target gray scale image.
In this embodiment, the distribution characteristics of the pixels inside the region of interest may be the color distribution condition of the pixels inside, the distribution density of the pixels under each color, and the like.
In this embodiment, the second tracking factor may be determined based on marking the edge pixels of the region of interest (second marking) and based on marking the pixels inside the region of interest (third marking), and is used to track the region of interest on the first target gray scale image.
In this embodiment, the target association relationship may be a relationship in which each operation posture in the sub-production flow corresponds to a drug form change of the target drug.
In this embodiment, the third tracking factor may be determined by mapping the second tracking factor in the first tracking factor, so as to achieve tracking of the drug form of the target drug corresponding to each operation posture.
In this embodiment, the second target gray scale image may be a result of graying the drug form image of the target drug.
In this embodiment, the fourth tracking factor may be determined based on marking (fourth marking) the pixel point of the target drug in the second target gray scale image, so as to track the form of the target drug in the second target gray scale image, and the form of the target drug in the second target gray scale image is a state, that is, the final form of the target drug after each sub-production process is performed.
The beneficial effects of the technical scheme are as follows: by determining the first tracking factor, the second tracking factor, the third tracking factor and the fourth tracking factor, the overall tracking of the operation gesture and the medicine form of the target medicine in the corresponding sub-production flow is effectively realized, and the constructed sub-monitoring model is further enabled to realize overall monitoring, so that the accuracy of monitoring the production flow of the target medicine is improved.
Example 5
On the basis of embodiment 4, this embodiment provides a production process supervision platform of a drug, and a sub-monitoring model construction unit includes:
A network node determining subunit, configured to construct a first network node based on a third tracking factor, construct a second network node based on the first tracking factor, construct a third network node based on the second tracking factor, and construct a fourth network node based on a fourth tracking factor;
a connection relation establishing subunit, configured to establish a first connection relation between the first network node and the second network node, and establish a second connection relation between the first network node and the third network node; simultaneously, a third connection relation is established between the fourth network node and the first network node;
the model construction subunit is configured to establish a sub-monitoring model based on the first network node, the second network node, the third network node, the fourth network node, the first connection relationship, the second connection relationship, and the third connection relationship.
In this embodiment, the connection relationships of the first network node, the second network node, the third network node, and the fourth network node are as follows: the fourth network node is connected to the first network node, and the first network node is connected to the second network node and the third network node, respectively.
The beneficial effects of the technical scheme are as follows: by constructing the connection relation among the first network node, the second network node, the third network node and the fourth network node, the linkage among the network nodes is realized, and the monitoring efficiency of the sub-monitoring model is further improved.
Example 6
On the basis of embodiment 1, this embodiment provides a production process supervision platform for a drug, and a model building module includes:
the production sequence determining unit is used for reading the production flow, determining the logic keywords in the production flow, and determining the production sequence of the production flow according to the logic keywords in the production flow;
the sequencing unit is used for sequencing each sub-production flow in the generation flow based on the production sequence of the production flow, and numbering the sub-monitoring model corresponding to each sub-production flow based on the sequencing result;
and the comprehensive monitoring model generating unit is used for sequencing and correlating the sub monitoring models from the sequence of the numbering results from the small to the large, and generating the comprehensive monitoring model according to the correlation results.
In this embodiment, the logic keywords may be pieces of key data that characterize specific execution steps of the production flow and associated words that link the execution steps in the production flow.
In this embodiment, numbering the sub-monitoring models corresponding to each sub-production process based on the sorting result may be to set a sequence number for each sub-monitoring model, so as to implement distinguishing and associating the sub-monitoring models.
The beneficial effects of the technical scheme are as follows: the logic keywords in the production flow are determined by reading the production flow, the production sequence of the production flow is determined according to the logic keywords, and finally the sub-monitoring models corresponding to each sub-production flow are associated according to the production sequence, so that the comprehensive monitoring model is accurately and effectively constructed, and convenience and guarantee are provided for effectively supervising the production flow of the medicine.
Example 7
On the basis of embodiment 1, this embodiment provides a production process supervision platform of a drug, and a supervision module includes:
the monitoring unit is used for acquiring a production task of the target medicine, determining a production link of the target medicine based on the production task, and extracting a link identifier of the production link;
the image acquisition unit is used for matching the link identification with the preset monitoring terminal identification, starting a target preset monitoring terminal corresponding to the production link based on the matching result to acquire a real-time production image corresponding to the production link in real time, and inputting the real-time production image into the comprehensive monitoring model based on the preset event input interface;
the model touch unit is used for configuring an active detection needle in the comprehensive monitoring model, carrying out association deployment on the active detection needle and the comprehensive monitoring model, and carrying out active detection according to a preset event input interface of the active detection on the comprehensive monitoring model based on an association deployment result;
An analysis unit for:
when the input of the real-time production image of the preset event input interface is actively detected, a trigger signal is generated, a monitoring analysis flow in the comprehensive monitoring model is started based on the trigger signal, pixel point scanning is carried out on the input real-time production image based on the monitoring analysis flow, and the target image characteristics of each real-time production image are extracted;
determining the production process and production standard of the target medicine in the current production link based on the target image characteristics, correlating the obtained production process and production standard with the corresponding real-time production image, adding a target timestamp to the real-time production image based on the correlation result, and recording and storing the real-time production image and the corresponding production process and production standard according to the development sequence of the target timestamp based on the addition result.
In this embodiment, the production task may be to characterize the production volume required for the target drug, the production flow that the current target drug needs to undergo, etc.
In this embodiment, the production link may be a characterization of the production steps currently required to be performed when producing the target drug, so as to facilitate monitoring of the production process of the target drug.
In this embodiment, the link identifier may be a marking tag for marking different production links, and the production links may be rapidly distinguished by the identifier.
In this embodiment, the preset monitor terminal identifiers are set in advance, and are used for marking a type of marking tag of different preset monitor terminals, and one identifier corresponds to one preset monitor terminal.
In this embodiment, the target preset monitoring terminal may be a monitoring terminal adapted to monitor production links of the target drug, where the preset monitoring terminals corresponding to each production link are different.
In this embodiment, the real-time production image may be a monitoring image obtained after the corresponding production link is monitored by the target preset monitoring terminal, and is used for recording the production condition of the current production link.
In this embodiment, the preset event input interface is set in advance, and is used for inputting the acquired real-time production image into the constructed integrated monitoring model, that is, for switching on the target preset monitoring terminal and the integrated monitoring model.
In this embodiment, the active probe is disposed in the integrated monitoring model, and is configured to monitor the working state of the input interface of the preset event in real time, that is, when there is input of the real-time production image, generate a trigger signal in time, and trigger the integrated monitoring model to analyze the obtained real-time production image.
In this embodiment, the associated deployment may be to deploy the active probe in the integrated monitoring model, so as to facilitate the cooperation with the integrated monitoring model.
In this embodiment, the trigger signal is used to trigger the integrated monitoring model to analyze and process the received real-time production image.
In this embodiment, the monitoring analysis flow is a flow for identifying, analyzing and processing real-time production images corresponding to different production links in the integrated monitoring model.
In this embodiment, the target image feature may be a subject pose recorded in a key image area related to a production process or production standard in a real-time production image, a current production behavior, or the like.
In this embodiment, the target time stamp is used to characterize the acquisition time of each real-time production image, so as to facilitate recording of real-time production at different times.
In this embodiment, the target timestamp development order may be a chronological order.
The beneficial effects of the technical scheme are as follows: the production task of the target medicine is determined, the production link of the target medicine is accurately and effectively confirmed according to the production task, the corresponding preset monitoring terminal is started according to the confirmation result to monitor the production link of the target medicine in real time, and the obtained real-time production image is input into the comprehensive monitoring model for analysis processing, so that the accurate and effective determination of the production process and the production standard of the current production link is realized, finally, the analysis result is recorded, data support is provided for accurately judging and supervising the production flow of the target medicine, production abnormality is conveniently found in time, and the production reliability of the target medicine is ensured.
Example 8
On the basis of embodiment 7, this embodiment provides a production process supervision platform for a drug, the analysis unit includes:
the result acquisition subunit is used for acquiring the obtained analysis result and extracting a target timestamp corresponding to the real-time production image in the analysis result;
the comparison subunit is used for comparing the production process corresponding to each real-time production image and the production standard with the preset production requirement based on the target time stamp, and determining a standard real-time production image and an abnormal production image based on the comparison result;
the report generation subunit is used for calling a target template from a preset template library, recording the obtained real-time production image, the corresponding production process and production standard in the target template based on the time stamp development sequence, and marking the standard real-time production image and the abnormal production image in the real-time production image based on the first marking mode and the second marking mode to obtain the supervision report.
In this embodiment, the preset production requirement is a standard production standard corresponding to the current production link, and is set in advance.
In this embodiment, determining the standard real-time production image and the abnormal production image based on the comparison result may be performed according to the production process and the degree of coincidence between the production standard and the preset production requirement, when the production process and the production standard coincide with the preset production requirement, the current real-time production image is determined to be the standard real-time production image, and when the production process and the production standard have errors with the preset production requirement, the current real-time production image is determined to be the abnormal production image.
In this embodiment, the preset template library is set in advance, and is used for storing different record templates.
In this embodiment, the target recording template may be a template suitable for recording the current real-time production image and the corresponding production process and production standard, and is one of the preset template libraries.
In the embodiment, the first labeling mode and the second labeling mode are set in advance, and are different in two labeling modes, and the standard real-time production image and the abnormal production image are marked respectively, so that management staff can conveniently and intuitively and effectively check the recording condition.
The beneficial effects of the technical scheme are as follows: the production process and the production standard of the obtained real-time production image are compared with the corresponding preset production requirements, so that abnormal conditions existing in the production environment are timely and effectively confirmed, corresponding supervision reports are generated according to the confirmation results, management staff can conveniently and timely conduct corresponding management operations according to the supervision reports, production reliability of target medicines is guaranteed, and supervision effectiveness of the production flow of the target medicines is improved.
Example 9
On the basis of embodiment 1, this embodiment provides a production process supervision platform of a drug, and a supervision module includes:
The report acquisition unit is used for acquiring the obtained supervision report, extracting production parameters corresponding to the abnormal production flow recorded in the supervision report, extracting configuration parameters of the abnormal production flow, and determining standard parameters corresponding to the abnormal production flow based on the configuration parameters;
the instruction generation unit is used for performing difference operation on the production parameters and the standard parameters to obtain production errors, calling preset instruction elements based on production types corresponding to abnormal production flows when the production errors are larger than a preset threshold, and combining the instruction elements based on preset logic combination rules to generate an alarm control instruction;
and the alarm unit is used for controlling the alarm device to perform corresponding alarm operation according to the production type corresponding to the abnormal production flow based on the alarm control instruction.
In this embodiment, the abnormal production process may be a production process that does not meet the production requirements in the production process of the target drug.
In this embodiment, the production parameter may be a specification or a content of a pharmaceutical ingredient, which characterizes the current production of the target drug by the abnormal production process.
In this embodiment, the configuration parameter may be a production criterion that characterizes the abnormal production flow theory for the target drug.
In this embodiment, the standard parameters may be standard specifications and component contents of the target drug corresponding to the abnormal production flow.
In this embodiment, the preset threshold is set in advance, and is used to characterize the maximum value of the allowable production error, and can be adjusted.
In this embodiment, the command elements are components for constituting the alarm control command, and are set in advance.
In this embodiment, the preset logic combination rule is set in advance, and is used to define the combination mode between preset command elements.
The beneficial effects of the technical scheme are as follows: by analyzing the obtained supervision report, the production errors of the production parameters corresponding to the abnormal production flow and the standard parameters are accurately and effectively determined, corresponding alarm control quality is timely generated when the production errors are larger than a preset threshold value, the alarm device is controlled to perform corresponding alarm operation according to the alarm control instruction, management staff can find abnormal conditions in time conveniently, corresponding emergency measures are conveniently taken in time, the supervision degree of the drug production flow is guaranteed, and the production reliability of the drug is guaranteed.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (9)
1. A pharmaceutical manufacturing process supervision platform comprising:
the flow acquisition module is used for acquiring a production flow for producing the target medicine, determining a sub-production image corresponding to each sub-production flow in the production flow, and simultaneously determining a medicine form image corresponding to the target medicine for each sub-production flow;
the learning module is used for carrying out first learning on the sub-production image and carrying out second learning on the medicine form image, and meanwhile, a sub-monitoring model for monitoring the corresponding sub-production flow is established based on the first learning result and the second learning result;
the model construction module is used for acquiring the production sequence of the production flow, correlating each sub-monitoring model based on the production sequence of the production flow, and constructing a comprehensive monitoring model based on the correlation result;
the monitoring module is used for monitoring the production flow of the target medicine according to the comprehensive monitoring model, generating a monitoring report based on the monitoring result, generating an alarm control instruction when production errors exist in the production flow, and controlling the alarm device to perform alarm operation according to the alarm control instruction;
wherein, the learning module includes:
A first learning unit configured to:
carrying out graying treatment on the sub-production image to obtain a first target gray level image, positioning a target medicine in the first target gray level image, and obtaining contour pixel points of the target medicine in the first target gray level image based on pixel point color distribution characteristics of the first target gray level image;
performing first marking on the target medicine in the first target gray level image based on the outline pixel points in the first target gray level image, and generating a first tracking factor based on a first marking result;
reading the sub-production flow, determining the operation gesture of the sub-production flow, simultaneously determining the region acting on the target medicine in the first target gray level image based on the operation gesture of the sub-production flow, and taking the region acting on the target medicine in the first target gray level image as the region of interest;
reading the region of interest, determining the distribution characteristics of edge pixels of the region of interest and pixels inside the region of interest, performing second marking on the edge pixels of the region of interest and performing third marking on the pixels inside the region of interest according to the distribution characteristics of the pixels inside the region of interest, and generating a second tracking factor based on a second marking result and a third marking result;
Acquiring a target association relation between an operation gesture and a drug form of a target drug in a sub-production flow, mapping a second tracking factor in a first tracking factor based on the target association relation, and generating a third tracking factor based on a mapping result;
the second learning unit is used for carrying out graying treatment on the medicine form image of the target medicine corresponding to the sub-production flow, obtaining a second target gray level image, reading the second target gray level image, determining the pixel point distribution of the target medicine in the second target gray level image, carrying out fourth marking on the pixel points of the target medicine in the second target gray level image based on the pixel point distribution, and generating a fourth tracking factor based on a fourth marking result;
the sub-monitoring model construction unit is used for constructing a sub-monitoring model for monitoring the corresponding sub-production flow based on the first tracking factor, the second tracking factor, the third tracking factor and the fourth tracking factor.
2. The pharmaceutical manufacturing process monitoring platform of claim 1, wherein the process acquisition module comprises:
the characteristic acquisition unit is used for acquiring the drug attribute of the target drug and determining the drug characteristic identification of the target drug according to the drug attribute of the target drug;
The first matching unit is used for carrying out first matching in a preset drug production management library according to the drug characteristic identification of the target drug, and obtaining a production file of the target drug based on a first matching result;
the second matching unit is used for acquiring the production requirement of the user, performing second matching in the production file of the target medicine based on the production requirement of the user, and acquiring the production flow for producing the target medicine based on the second matching result.
3. The pharmaceutical manufacturing process monitoring platform of claim 1, wherein the process acquisition module comprises:
the flow reading unit is used for reading the production flow of the production of the target medicine and determining the operation process corresponding to each sub-production flow;
an image generation unit configured to:
performing process simulation on the operation process corresponding to each sub-production flow in a computer, and determining a sub-production image corresponding to each sub-production flow based on a simulation result;
and determining a medicine morphology image of the corresponding target medicine in each sub-production flow based on the simulation result.
4. The drug manufacturing process supervision platform of claim 1, wherein the sub-monitoring model building unit comprises:
A network node determining subunit, configured to construct a first network node based on a third tracking factor, construct a second network node based on the first tracking factor, construct a third network node based on the second tracking factor, and construct a fourth network node based on a fourth tracking factor;
a connection relation establishing subunit, configured to establish a first connection relation between the first network node and the second network node, and establish a second connection relation between the first network node and the third network node; simultaneously, a third connection relation is established between the fourth network node and the first network node;
the model construction subunit is configured to establish a sub-monitoring model based on the first network node, the second network node, the third network node, the fourth network node, the first connection relationship, the second connection relationship, and the third connection relationship.
5. The drug manufacturing process monitoring platform of claim 1, wherein the model building module comprises:
the production sequence determining unit is used for reading the production flow, determining the logic keywords in the production flow, and determining the production sequence of the production flow according to the logic keywords in the production flow;
the sequencing unit is used for sequencing each sub-production flow in the generation flow based on the production sequence of the production flow, and numbering the sub-monitoring model corresponding to each sub-production flow based on the sequencing result;
And the comprehensive monitoring model generating unit is used for sequencing and correlating the sub monitoring models from the sequence of the numbering results from the small to the large, and generating the comprehensive monitoring model according to the correlation results.
6. The pharmaceutical manufacturing process monitoring platform of claim 1, wherein the monitoring module comprises:
the monitoring unit is used for acquiring a production task of the target medicine, determining a production link of the target medicine based on the production task, and extracting a link identifier of the production link;
the image acquisition unit is used for matching the link identification with the preset monitoring terminal identification, starting a target preset monitoring terminal corresponding to the production link based on the matching result to acquire a real-time production image corresponding to the production link in real time, and inputting the real-time production image into the comprehensive monitoring model based on the preset event input interface;
the model touch unit is used for configuring an active detection needle in the comprehensive monitoring model, carrying out association deployment on the active detection needle and the comprehensive monitoring model, and carrying out active detection according to a preset event input interface of the active detection on the comprehensive monitoring model based on an association deployment result;
an analysis unit for:
When the input of the real-time production image of the preset event input interface is actively detected, a trigger signal is generated, a monitoring analysis flow in the comprehensive monitoring model is started based on the trigger signal, pixel point scanning is carried out on the input real-time production image based on the monitoring analysis flow, and the target image characteristics of each real-time production image are extracted;
determining the production process and production standard of the target medicine in the current production link based on the target image characteristics, correlating the obtained production process and production standard with the corresponding real-time production image, adding a target timestamp to the real-time production image based on the correlation result, and recording and storing the real-time production image and the corresponding production process and production standard according to the development sequence of the target timestamp based on the addition result.
7. The pharmaceutical manufacturing process monitoring platform of claim 6, wherein the analysis unit comprises:
the result acquisition subunit is used for acquiring the obtained analysis result and extracting a target timestamp corresponding to the real-time production image in the analysis result;
the comparison subunit is used for comparing the production process corresponding to each real-time production image and the production standard with the preset production requirement based on the target time stamp, and determining a standard real-time production image and an abnormal production image based on the comparison result;
The report generation subunit is used for calling a target template from a preset template library, recording the obtained real-time production image, the corresponding production process and production standard in the target template based on the time stamp development sequence, and marking the standard real-time production image and the abnormal production image in the real-time production image based on the first marking mode and the second marking mode to obtain the supervision report.
8. The pharmaceutical manufacturing process monitoring platform of claim 1, wherein the monitoring module comprises:
the report acquisition unit is used for acquiring the obtained supervision report, extracting production parameters corresponding to the abnormal production flow recorded in the supervision report, extracting configuration parameters of the abnormal production flow, and determining standard parameters corresponding to the abnormal production flow based on the configuration parameters;
the instruction generation unit is used for performing difference operation on the production parameters and the standard parameters to obtain production errors, calling preset instruction elements based on production types corresponding to abnormal production flows when the production errors are larger than a preset threshold, and combining the instruction elements based on preset logic combination rules to generate an alarm control instruction;
And the alarm unit is used for controlling the alarm device to perform corresponding alarm operation according to the production type corresponding to the abnormal production flow based on the alarm control instruction.
9. The drug manufacturing process monitoring platform of claim 1, wherein the model building module comprises:
the dimension determining unit is used for obtaining an evaluation dimension for evaluating the comprehensive monitoring model, wherein the evaluation dimension comprises the following components: the working speed of the comprehensive monitoring model and the working accuracy of the comprehensive monitoring model;
the calculation unit is used for evaluating the comprehensive monitoring model based on the working speed of the comprehensive monitoring model and the working accuracy of the comprehensive monitoring model and calculating the target score of the comprehensive monitoring model;
a qualification verification unit, configured to:
acquiring a preset scoring threshold value, comparing the target score with the preset scoring threshold value, and judging whether the comprehensive monitoring model is qualified or not;
when the target score is equal to or greater than a preset score threshold, judging that the comprehensive monitoring model is qualified;
otherwise, judging that the comprehensive monitoring model is unqualified, and transmitting unqualified information to the user terminal.
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