CN116629690A - Pharmacy informatization full-flow management system based on big data analysis - Google Patents
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Abstract
The invention relates to the technical field of pharmaceutical information, and particularly discloses a pharmaceutical informationized full-flow management system based on big data analysis, which comprises the following steps: the invention analyzes the qualification coefficient of the raw material crushing process, thereby relieving the harm of dust to the body of staff, and analyzes the running rotating speed and crushing time of the raw material crusher, so as to ensure the accuracy of the quality analysis of the medicine.
Description
Technical Field
The invention relates to the technical field of pharmaceutical information, in particular to a pharmaceutical informationized full-flow management system based on big data analysis.
Background
Along with the development of science and technology, the development of the medical industry is more and more rapid, in the development of the medical industry, the support of the medicine industry is not separated, in recent years, the medicine industry is gradually industrialized and automated, in the development process of the medicine industry, granular medicines are widely applied clinically, the scattering property, the adhesiveness, the aggregation property and the hygroscopicity of the granular medicines are very small, the dispersing dosage is further facilitated, the taking is more convenient, the market share of the granular medicines is gradually increased, and in the production process of the granular medicines, if the production quality of the granular medicines is disqualified, the medicine effect of the granular medicines is influenced on one hand, the required treatment effect is not achieved, and on the other hand, the income and the reputation of medicine companies are influenced, and the long-term development of the medicine companies is not facilitated.
The production quality of the existing granular medicines has the following defects: (1) The production quality analysis of current granular medicine is not high to the attention degree of raw materials crushing process, and on the one hand the raw materials can appear some dust in crushing process, flows along with the flow of air, not only pollutes the air quality, and then threatens staff's healthy to a certain extent, and on the other hand some operating parameter of raw materials rubbing crusher plays vital effect to the kibbling quality of raw materials, and prior art neglects this level, leads to medicine quality analysis's accuracy not high, and then can't ensure the normal output efficiency of medicine.
(2) In the production quality of the existing granular medicine, in the mixing process of other substances such as raw materials, auxiliary materials and the like, the analysis force on the flow performance of the mixed object is not deep enough, the mixture object can have certain flow in the transverse and longitudinal layers, the flow performance corresponding to the mixture object reflects whether the mixing is sufficient to a certain extent, the analysis force in the aspect of the prior art is deficient, the phenomenon of insufficient mixing of the mixture object exists, and the production quality of the granular medicine is difficult to ensure.
Disclosure of Invention
In order to overcome the defects in the background technology, the embodiment of the invention provides a pharmaceutical informationized full-flow management system based on big data analysis, which can effectively solve the problems related to the background technology.
The aim of the invention can be achieved by the following technical scheme: pharmaceutical informatization full-flow management system based on big data analysis includes: the raw material crusher is suitable for an operation parameter analysis module, a raw material crushing process parameter analysis module, a raw material crushing quality analysis module, a mixing process detection module, a mixing fluidity analysis module, a granulation quality analysis module, a granular medicine comprehensive quality analysis module, an early warning terminal and a cloud database.
The raw material crusher proper operation parameter analysis module is used for obtaining the expected output corresponding to the batch of granular medicines, further analyzing the raw material consumption corresponding to the batch of granular medicines, and analyzing proper operation parameters corresponding to the raw material crusher according to the raw material consumption, wherein the proper operation parameters comprise proper operation rotating speed and proper crushing time.
The raw material crushing process analysis module is used for acquiring a monitoring video in the raw material crushing process through a monitoring system, so as to analyze the qualification coefficient of the raw material crushing process.
The raw material crushing quality analysis module is used for acquiring the running rotating speed of the raw material crusher corresponding to each unit detection time length and the actual crushing time length corresponding to the raw material crusher, and comprehensively analyzing the crushing quality coefficient corresponding to the raw material by combining the qualified coefficient of the raw material crushing process.
The mixing process detection module is used for carrying out video detection on the mixing process of the raw materials to be mixed.
The mixing fluidity analysis module is used for analyzing the flow performance coefficient corresponding to the mixture based on the video of the mixing process of the raw materials to be mixed.
The granulation quality analysis module is used for randomly selecting a set number of finished product particles, collecting images of the finished product particles, further analyzing reasonable size coefficients corresponding to the finished product particles, weighing the screened qualified particles, further obtaining the weight of the qualified particles, and similarly obtaining the weight of the eliminated particles, and further comprehensively analyzing quality evaluation coefficients corresponding to the finished product particles.
The granular medicine comprehensive quality analysis module is used for analyzing the comprehensive quality coefficient corresponding to the granular medicine.
And the early warning terminal is used for carrying out corresponding early warning according to the comprehensive quality coefficient corresponding to the granular medicine.
The cloud database is used for storing the raw material consumption corresponding to the unit predicted yield, storing the allowable attachment area of the residual raw materials and the allowable attachment area of the raw materials to be mixed, and storing the reference area corresponding to the finished product particles.
Further, the method for analyzing the raw material consumption corresponding to the batch of granular medicines and the proper operation parameters corresponding to the raw material crusher according to the raw material consumption comprises the following specific steps: s1, multiplying the expected output corresponding to the granular medicines of the batch by the raw material consumption corresponding to the unit expected output stored in the cloud database, and further obtaining the raw material consumption corresponding to the granular medicines of the batch.
S2, screening the proper operation rotating speed corresponding to the batch of granular medicines from the raw material consumption interval corresponding to the preset proper operation rotating speeds and taking the proper operation rotating speed as the proper operation rotating speed corresponding to the raw material crusher.
S3, screening the proper crushing time corresponding to the batch of granular medicines, and taking the proper crushing time corresponding to the raw material crusher.
Further, the raw material crushing process qualification coefficient comprises the following specific analysis method: A1. dividing monitoring videos in the raw material crushing process according to a preset video frame number, further obtaining each monitoring picture in the raw material crushing process, identifying whether dust exists in each monitoring picture belonging to the raw material crushing process, marking a dust pollution index corresponding to a monitoring picture belonging to the raw material crushing process as alpha if dust does not exist in a certain monitoring picture belonging to the raw material crushing process, otherwise, obtaining a dust contour area corresponding to the monitoring picture belonging to the raw material crushing process, and multiplying the dust pollution index corresponding to a preset unit dust contour area by the dust pollution index corresponding to the monitoring picture belonging to the raw material crushing process to obtain a dust pollution index alpha' corresponding to the monitoring picture belonging to the raw material crushing process.
A2. During the process of crushing the obtained raw materialsThe dust pollution index corresponding to each monitoring picture is marked as epsilon i Wherein ε is i The number of each monitoring screen is denoted by =α or α', i, i=1, 2
A3. And after the working of the raw material crusher is finished, carrying out image acquisition on the inner wall of the raw material crusher, further identifying the residual raw material corresponding to the inner wall of the raw material crusher, acquiring the corresponding attachment area of the residual raw material, and marking the area corresponding to the residual raw material inside the raw material crusher as SS.
A4. And marking the crushed raw materials as raw materials to be mixed, and similarly, obtaining the attachment area of the inner wall of the raw material crusher corresponding to the raw materials to be mixed, and marking the attachment area as SF.
A5. Extracting the allowable attachment area of the residual raw materials and the allowable attachment area of the raw materials to be mixed from a cloud database, respectively marking the allowable attachment area as SS 'and SF', and comprehensively analyzing the qualified coefficient of the raw material crushing process, wherein the calculation formula is as follows:wherein eta is represented as a pass coefficient of the raw material crushing process, n is represented as the number of monitoring pictures, lambda 1 、λ 2 、λ 3 Respectively expressed as weight coefficients of the preset residual raw materials, the raw materials to be mixed and the dust pollution index, and e expressed as natural constants.
Further, the crushing quality coefficient corresponding to the raw materials comprises the following specific analysis method:
B1. comparing the running rotating speed of the raw material pulverizer corresponding to each unit detection time length with the proper running rotating speed of the raw material pulverizer one by one, analyzing the proper running rotating speed coefficient of the raw material pulverizer, wherein the calculation formula is as follows:wherein mu is expressed as a proper coefficient of the running rotation speed corresponding to the raw material grinder, beta m The running rotation speed corresponding to the m unit detection time length of the raw material crusher is set, beta' is the proper running rotation speed corresponding to the raw material crusher, m is the number of each unit detection time length, and l is the tableShown as the number of unit detection durations.
B2. Screening the maximum operation rotation speed and the minimum operation rotation speed from the operation rotation speeds corresponding to the set unit detection time periods of the raw material crusher, and marking the maximum operation rotation speed and the minimum operation rotation speed as beta respectively max 、β min Further, the running rotation speed fluctuation coefficient corresponding to the raw material crusher is analyzed, and the calculation formula is as follows:wherein->Expressed as a fluctuation coefficient of the running rotational speed corresponding to the raw material pulverizer.
B3. Comparing the actual crushing time length corresponding to the raw material crusher with the proper crushing time length corresponding to the raw material crusher, and analyzing the proper coefficient of the crushing time length corresponding to the raw material crusher, wherein the calculation formula is as follows:wherein sigma is expressed as a suitable coefficient of the crushing duration corresponding to the raw material crusher, T is expressed as an actual crushing duration corresponding to the raw material crusher, and T' is expressed as a suitable crushing duration corresponding to the raw material crusher.
B4. The specific calculation formula of the crushing quality coefficient corresponding to the analysis raw materials is as follows:wherein->Expressed as the corresponding crushing quality coefficient of the raw materials, gamma 1 、γ 2 、γ 3 Respectively expressed as weight coefficients of qualified preset raw material crushing process, proper running speed of the material conveying crusher, fluctuation of running speed of the raw material crusher and belonging crushing duration of the raw material crusher.
Further, the flow coefficient of performance of the analysis mixture is as follows:
C1. dividing the video of the mixing process of the raw materials to be mixed into all pictures to be evaluated according to the preset video frame number, marking the mixed raw materials to be mixed as a mixture, and further obtaining all pictures to be evaluated corresponding to the mixture.
C2: acquiring the corresponding transverse length, longitudinal length and distribution area of each picture to be evaluated based on each picture to be evaluated corresponding to the mixture, and marking the corresponding transverse length, longitudinal length and distribution area of each picture to be evaluated to which the mixture belongs as HL respectively j 、ZL j 、MJ j Where j is denoted as the number of each monitor screen, j=1, 2,..k.
C3. Analyzing a lateral flow difference value theta between a j+1th picture to be evaluated to which the mixture belongs and a j-th picture to be evaluated based on a lateral length corresponding to each picture to be evaluated to which the mixture belongs j+1,j ＝HL j+1 -HL j Wherein HL is j+1 Expressed as the corresponding transverse length of the j+1th picture to be evaluated to which the mixture belongs.
C4. The analytical mixture has a corresponding lateral flow uniformity coefficient, and the calculation formula is as follows:wherein->Expressed as the corresponding lateral flow uniformity coefficient, HL, of the mixture j+1 The transverse length corresponding to the j+1st picture to be evaluated to which the mixture belongs is expressed, and k is expressed as the number of pictures to be evaluated.
C5. Comparing the j+1th picture to be evaluated and the transverse flow difference corresponding to the j picture to be evaluated, screening the maximum flow difference and the minimum flow difference corresponding to the mixture, and marking the maximum flow difference and the minimum flow difference as theta max 、θ min And analyzing the corresponding transverse flow fluctuation coefficient of the mixture according to the calculation formulaWhere ζ is expressed as the corresponding lateral flow fluctuation coefficient of the mixture.
C6. Analysis of mixture pairsThe corresponding longitudinal flow uniformity coefficient is calculated by the following formula:wherein θ is expressed as the longitudinal flow uniformity coefficient corresponding to the mixture, ZL j+1 Expressed as the longitudinal length corresponding to the j+1th picture to be evaluated to which the mixture belongs.
C7. Based on the distribution area corresponding to each picture to be evaluated, the reasonable area growth coefficient corresponding to the mixture is analyzed, and the calculation formula is as follows:wherein ρ is expressed as a reasonable coefficient of area growth corresponding to the mixture, MJ j+1 The distribution area corresponding to the j+1st picture to be evaluated to which the mixture belongs is shown.
C8. The flow performance coefficient corresponding to the comprehensive analysis mixture is calculated as the following formulaWherein phi is expressed as the flow coefficient, χ, of the mixture 1 、χ 2 、χ 3 、χ 4 Respectively expressed as the corresponding occupation factor of uniform transverse flow, fluctuation transverse flow, uniform longitudinal flow and reasonable area increase of the mixture.
Further, the size reasonable coefficient corresponding to the finished product particles comprises the following specific analysis method: D1. based on the collected images of the finished particles, the contours of the finished particles are identified, the corresponding areas of the finished particles are obtained, and the areas are marked as CM h Where h is expressed as the number of each finished particle, h=1, 2,..g.
D2. Comparing the corresponding area of each finished product particle with the corresponding reference area of the finished product particle stored in the cloud database, and further analyzing the reasonable size coefficient corresponding to the finished product particle, wherein the calculation formula is as follows:wherein HP is expressed as a reasonable size coefficient corresponding to the finished product particles, CM' is expressed as a reference area corresponding to the finished particles, and g is expressed as the number of finished particles.
Further, the quality evaluation coefficient corresponding to the finished product particles comprises the following specific analysis methods: E1. based on the weight of qualified finished product particles and the weight of eliminated finished product particles, the qualification coefficient corresponding to the finished product particles is analyzed, and the calculation formula is as follows:wherein HG is expressed as a qualified coefficient corresponding to the finished product particles, ZT and ZF are respectively expressed as the weight of the qualified finished product particles and the weight of the eliminated finished product particles.
E2. Obtaining reasonable size coefficients corresponding to the finished product particles, and comprehensively analyzing quality evaluation coefficients corresponding to the finished product particles by combining the qualified coefficients corresponding to the finished product particles, wherein the calculation formula is as follows: pj=e (hp×δ) 1 +HG*δ 2 ) Wherein PJ is expressed as a quality evaluation coefficient, delta, corresponding to the finished product particles 1 、δ 2 Respectively expressed as the proportionality coefficient of reasonable size and qualified finished product particles.
Further, the calculation formula of the comprehensive quality coefficient corresponding to the granular medicine is as follows:wherein psi is expressed as the comprehensive quality coefficient corresponding to the granular medicine, ay 1 、Ay 2 、Ay 3 Respectively expressed as the influence factors of the preset raw material crushing quality, the mixture flowability and the finished product particle quality evaluation.
Further, the corresponding early warning is carried out according to the comprehensive quality coefficient corresponding to the granular medicine, and the specific method comprises the following steps: and comparing the comprehensive quality coefficient corresponding to the granular medicine with a preset comprehensive quality threshold of the granular medicine, and if the comprehensive quality coefficient corresponding to the granular medicine is smaller than or equal to the comprehensive quality threshold of the granular medicine, carrying out abnormal early warning on the quality of the granular medicine.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects: (1) According to the invention, the raw material crushing process qualification coefficient is analyzed in the raw material crushing process analysis module, on one hand, some dust possibly occurring in the raw material crushing process is analyzed, and the influence degree of the dust on the air quality is further reduced, so that the harm of the dust to the body of staff is relieved, and on the other hand, the operation rotating speed and the crushing time of the raw material crusher are analyzed, the accuracy of medicine quality analysis is ensured, and the normal output efficiency of medicines is ensured.
(2) In the mixing process of other substances such as raw materials, auxiliary materials and the like, the flow performance of the mixed objects is analyzed through the transverse flow and the longitudinal flow of the mixed objects, the phenomenon of insufficient mixing of the mixed objects is avoided, the standardization of the mixing process is further ensured, and the production quality of the granular medicines is ensured to a certain extent.
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The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
Fig. 1 is a schematic diagram of the module connection of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the present invention provides a pharmaceutical informatization full-flow management system based on big data analysis, comprising: the raw material crusher is suitable for an operation parameter analysis module, a raw material crushing process parameter analysis module, a raw material crushing quality analysis module, a mixing process detection module, a mixing fluidity analysis module, a granulation quality analysis module, a granular medicine comprehensive quality analysis module, an early warning terminal and a cloud database.
The device comprises a raw material crusher, a raw material crushing process parameter analysis module, a mixing process detection module, a mixing flow analysis module, a particle medicine comprehensive quality analysis module, a pre-warning terminal, a cloud database, a raw material crusher proper operation parameter analysis module, a raw material crushing process parameter analysis module and a granulation quality analysis module, wherein the raw material crusher proper operation parameter analysis module and the raw material crushing process parameter analysis module are connected with the raw material crushing quality analysis module, the mixing flow detection module is connected with the mixing flow analysis module, the raw material crushing quality analysis module, the mixing flow analysis module and the granulation quality analysis module are connected with the particle medicine comprehensive quality analysis module, the particle medicine comprehensive quality analysis module is connected with the pre-warning terminal, and the cloud database is respectively connected with the raw material crusher proper operation parameter analysis module, the raw material crushing process parameter analysis module and the granulation quality analysis module.
The raw material crusher proper operation parameter analysis module is used for obtaining the expected output corresponding to the batch of granular medicines, further analyzing the raw material consumption corresponding to the batch of granular medicines, and analyzing proper operation parameters corresponding to the raw material crusher according to the raw material consumption, wherein the proper operation parameters comprise proper operation rotating speed and proper crushing time.
In a specific embodiment of the present invention, the method for analyzing the raw material usage amount corresponding to the batch of granular medicines and the appropriate operation parameters corresponding to the raw material crusher according to the raw material usage amount comprises: s1, multiplying the expected output corresponding to the granular medicines of the batch by the raw material consumption corresponding to the unit expected output stored in the cloud database, and further obtaining the raw material consumption corresponding to the granular medicines of the batch.
S2, screening the proper operation rotating speed corresponding to the batch of granular medicines from the raw material consumption interval corresponding to the preset proper operation rotating speeds and taking the proper operation rotating speed as the proper operation rotating speed corresponding to the raw material crusher.
S3, screening the proper crushing time corresponding to the batch of granular medicines, and taking the proper crushing time corresponding to the raw material crusher.
The raw material crushing process analysis module is used for acquiring a monitoring video in the raw material crushing process through a monitoring system, so as to analyze the qualification coefficient of the raw material crushing process.
In a specific embodiment of the invention, the raw material crushing process qualification coefficient comprises the following specific analysis method: A1. dividing monitoring videos in the raw material crushing process according to a preset video frame number, further obtaining each monitoring picture in the raw material crushing process, identifying whether dust exists in each monitoring picture belonging to the raw material crushing process, marking a dust pollution index corresponding to a monitoring picture belonging to the raw material crushing process as alpha if dust does not exist in a certain monitoring picture belonging to the raw material crushing process, otherwise, obtaining a dust contour area corresponding to the monitoring picture belonging to the raw material crushing process, and multiplying the dust pollution index corresponding to a preset unit dust contour area by the dust pollution index corresponding to the monitoring picture belonging to the raw material crushing process to obtain a dust pollution index alpha' corresponding to the monitoring picture belonging to the raw material crushing process.
It should be noted that, the specific method for identifying whether dust exists in each monitoring picture belonging to the raw material crushing process is as follows: and matching each monitoring picture belonging to the raw material crushing process with a preset dust characteristic image, if the matching of a certain monitoring picture belonging to the raw material crushing process with the dust characteristic image is successful, judging that dust exists in the monitoring picture belonging to the raw material crushing process, otherwise, judging that no dust exists in the monitoring picture belonging to the raw material crushing process.
A2. Acquiring dust pollution indexes corresponding to all monitoring pictures in the raw material crushing process, and marking the dust pollution indexes as epsilon i Wherein ε is i The number of each monitoring screen is denoted by =α or α', i, i=1, 2.
A4. And marking the crushed raw materials as raw materials to be mixed, and similarly, obtaining the attachment area of the inner wall of the raw material crusher corresponding to the raw materials to be mixed, and marking the attachment area as SF.
A5. Extracting the allowable attachment area of the residual raw materials and the allowable attachment area of the raw materials to be mixed from a cloud database, respectively marking the allowable attachment area as SS 'and SF', and comprehensively analyzing the qualified coefficient of the raw material crushing process, wherein the calculation formula is as follows:wherein eta is represented as a pass coefficient of the raw material crushing process, n is represented as the number of monitoring pictures, lambda 1 、λ 2 、λ 3 Respectively expressed as weight coefficients of the preset residual raw materials, the raw materials to be mixed and the dust pollution index, and e expressed as natural constants.
According to the invention, the raw material crushing process qualification coefficient is analyzed in the raw material crushing process analysis module, on one hand, some dust possibly occurring in the raw material crushing process is analyzed, and the influence degree of the dust on the air quality is further reduced, so that the harm of the dust to the body of staff is relieved, and on the other hand, the operation rotating speed and the crushing time of the raw material crusher are analyzed, the accuracy of medicine quality analysis is ensured, and the normal output efficiency of medicines is ensured.
The raw material crushing quality analysis module is used for acquiring the running rotating speed of the raw material crusher corresponding to each unit detection time length and the actual crushing time length corresponding to the raw material crusher, and comprehensively analyzing the crushing quality coefficient corresponding to the raw material by combining the qualified coefficient of the raw material crushing process.
In a specific embodiment of the present invention, the crushing quality coefficient corresponding to the raw material is specifically analyzed by: B1. comparing the running rotating speed of the raw material pulverizer corresponding to each unit detection time length with the proper running rotating speed of the raw material pulverizer one by one, analyzing the proper running rotating speed coefficient of the raw material pulverizer, wherein the calculation formula is as follows:wherein mu is expressed as a proper coefficient of the running rotation speed corresponding to the raw material grinder, beta m The running speed corresponding to the m-th unit detection time length is set for the raw material crusher, beta' is the proper running speed corresponding to the raw material crusher, m is the number of each unit detection time length, and l is the number of the unit detection time lengths.
B2. Screening the maximum operation rotation speed from the operation rotation speeds corresponding to the set unit detection time lengths of the raw material pulverizerAnd a minimum operating speed, labeled as beta, respectively max 、β min Further, the running rotation speed fluctuation coefficient corresponding to the raw material crusher is analyzed, and the calculation formula is as follows:wherein->Expressed as a fluctuation coefficient of the running rotational speed corresponding to the raw material pulverizer.
B3. Comparing the actual crushing time length corresponding to the raw material crusher with the proper crushing time length corresponding to the raw material crusher, and analyzing the proper coefficient of the crushing time length corresponding to the raw material crusher, wherein the calculation formula is as follows:wherein sigma is expressed as a suitable coefficient of the crushing duration corresponding to the raw material crusher, T is expressed as an actual crushing duration corresponding to the raw material crusher, and T' is expressed as a suitable crushing duration corresponding to the raw material crusher.
B4. The specific calculation formula of the crushing quality coefficient corresponding to the analysis raw materials is as follows:wherein->Expressed as the corresponding crushing quality coefficient of the raw materials, gamma 1 、γ 2 、γ 3 Respectively expressed as weight coefficients of qualified preset raw material crushing process, proper running speed of the material conveying crusher, fluctuation of running speed of the raw material crusher and belonging crushing duration of the raw material crusher.
The mixing process detection module is used for carrying out video detection on the mixing process of the raw materials to be mixed.
The mixing fluidity analysis module is used for analyzing the flow performance coefficient corresponding to the mixture based on the video of the mixing process of the raw materials to be mixed.
In a specific embodiment of the present invention, the flow coefficient of performance of the analytical mixture is as follows: C1. dividing the video of the mixing process of the raw materials to be mixed into all pictures to be evaluated according to the preset video frame number, marking the mixed raw materials to be mixed as a mixture, and further obtaining all pictures to be evaluated corresponding to the mixture.
C2: acquiring the corresponding transverse length, longitudinal length and distribution area of each picture to be evaluated based on each picture to be evaluated corresponding to the mixture, and marking the corresponding transverse length, longitudinal length and distribution area of each picture to be evaluated to which the mixture belongs as HL respectively j 、ZL j 、MJ j Where j is denoted as the number of each monitor screen, j=1, 2,..k.
C3. Analyzing a lateral flow difference value theta between a j+1th picture to be evaluated to which the mixture belongs and a j-th picture to be evaluated based on a lateral length corresponding to each picture to be evaluated to which the mixture belongs j+1,j ＝HL j+1 -HL j Wherein HL is j+1 Expressed as the corresponding transverse length of the j+1th picture to be evaluated to which the mixture belongs.
C4. The analytical mixture has a corresponding lateral flow uniformity coefficient, and the calculation formula is as follows:wherein->Expressed as the corresponding lateral flow uniformity coefficient, HL, of the mixture j+1 The transverse length corresponding to the j+1st picture to be evaluated to which the mixture belongs is expressed, and k is expressed as the number of pictures to be evaluated.
C5. Comparing the j+1th picture to be evaluated and the transverse flow difference corresponding to the j picture to be evaluated, screening the maximum flow difference and the minimum flow difference corresponding to the mixture, and marking the maximum flow difference and the minimum flow difference as theta max 、θ min And analyzing the corresponding transverse flow fluctuation coefficient of the mixture according to the calculation formulaWherein xi representsIs the corresponding transverse flow fluctuation coefficient of the mixture.
C6. The analytical mixture has a corresponding longitudinal flow uniformity coefficient, and the calculation formula is as follows:wherein θ is expressed as the longitudinal flow uniformity coefficient corresponding to the mixture, ZL j+1 Expressed as the longitudinal length corresponding to the j+1th picture to be evaluated to which the mixture belongs.
C7. Based on the distribution area corresponding to each picture to be evaluated, the reasonable area growth coefficient corresponding to the mixture is analyzed, and the calculation formula is as follows:wherein ρ is expressed as a reasonable coefficient of area growth corresponding to the mixture, MJ j+1 The distribution area corresponding to the j+1st picture to be evaluated to which the mixture belongs is shown.
C8. The flow performance coefficient corresponding to the comprehensive analysis mixture is calculated as the following formulaWherein phi is expressed as the flow coefficient, χ, of the mixture 1 、χ 2 、χ 3 、χ 4 Respectively expressed as the corresponding occupation factor of uniform transverse flow, fluctuation transverse flow, uniform longitudinal flow and reasonable area increase of the mixture.
In the mixing process of other substances such as raw materials, auxiliary materials and the like, the flow performance of the mixed objects is analyzed through the transverse flow and the longitudinal flow of the mixed objects, the phenomenon of insufficient mixing of the mixed objects is avoided, the standardization of the mixing process is further ensured, and the production quality of the granular medicines is ensured to a certain extent.
The granulation quality analysis module is used for randomly selecting a set number of finished product particles, collecting images of the finished product particles, further analyzing reasonable size coefficients corresponding to the finished product particles, weighing the screened qualified particles, further obtaining the weight of the qualified particles, and similarly obtaining the weight of the eliminated particles, and further comprehensively analyzing quality evaluation coefficients corresponding to the finished product particles.
In a specific embodiment of the present invention, the size reasonable coefficient corresponding to the finished product particle is specifically analyzed by: D1. based on the collected images of the finished particles, the contours of the finished particles are identified, the corresponding areas of the finished particles are obtained, and the areas are marked as CM h Where h is expressed as the number of each finished particle, h=1, 2,..g.
D2. Comparing the corresponding area of each finished product particle with the corresponding reference area of the finished product particle stored in the cloud database, and further analyzing the reasonable size coefficient corresponding to the finished product particle, wherein the calculation formula is as follows:where HP is expressed as a size reasonable coefficient corresponding to the finished particles, CM' is expressed as a reference area corresponding to the finished particles, and g is expressed as the number of finished particles.
In a specific embodiment of the present invention, the quality evaluation coefficient corresponding to the finished product particle is specifically analyzed by: E1. based on the weight of qualified finished product particles and the weight of eliminated finished product particles, the qualification coefficient corresponding to the finished product particles is analyzed, and the calculation formula is as follows:wherein HG is expressed as a qualified coefficient corresponding to the finished product particles, ZT and ZF are respectively expressed as the weight of the qualified finished product particles and the weight of the eliminated finished product particles.
E2. Obtaining reasonable size coefficients corresponding to the finished product particles, and comprehensively analyzing quality evaluation coefficients corresponding to the finished product particles by combining the qualified coefficients corresponding to the finished product particles, wherein the calculation formula is as follows:wherein PJ is expressed as a quality evaluation coefficient, delta, corresponding to the finished product particles 1 、δ 2 Respectively expressed as the proportionality coefficient of reasonable size and qualified finished product particles.
The granular medicine comprehensive quality analysis module is used for analyzing the comprehensive quality coefficient corresponding to the granular medicine.
In a specific embodiment of the present invention, the calculation formula of the comprehensive quality coefficient corresponding to the granular medicine is:wherein psi is expressed as the comprehensive quality coefficient corresponding to the granular medicine, ay 1 、Ay 2 、Ay 3 Respectively expressed as the influence factors of the preset raw material crushing quality, the mixture flowability and the finished product particle quality evaluation.
And the early warning terminal is used for carrying out corresponding early warning according to the comprehensive quality coefficient corresponding to the granular medicine.
In a specific embodiment of the present invention, the corresponding early warning is performed according to the comprehensive quality coefficient corresponding to the granular medicine, and the specific method thereof is as follows: and comparing the comprehensive quality coefficient corresponding to the granular medicine with a preset comprehensive quality threshold of the granular medicine, and if the comprehensive quality coefficient corresponding to the granular medicine is smaller than or equal to the comprehensive quality threshold of the granular medicine, carrying out abnormal early warning on the quality of the granular medicine.
The cloud database is used for storing the raw material consumption corresponding to the unit predicted yield, storing the allowable attachment area of the residual raw materials and the allowable attachment area of the raw materials to be mixed, and storing the reference area corresponding to the finished product particles.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art can be made to the described embodiments without departing from the scope of the invention or from the scope of the invention as defined in the accompanying claims.
Claims (9)
1. The pharmaceutical informatization full-flow management system based on big data analysis is characterized by comprising: the raw material crusher is suitable for an operation parameter analysis module, a raw material crushing process parameter analysis module, a raw material crushing quality analysis module, a mixing process detection module, a mixing fluidity analysis module, a granulation quality analysis module, a granular medicine comprehensive quality analysis module, an early warning terminal and a cloud database;
the raw material crusher proper operation parameter analysis module is used for obtaining the expected output corresponding to the batch of granular medicines, further analyzing the raw material consumption corresponding to the batch of granular medicines, and analyzing proper operation parameters corresponding to the raw material crusher according to the raw material consumption, wherein the proper operation parameters comprise proper operation rotating speed and proper crushing time;
the raw material crushing process analysis module is used for acquiring a monitoring video in the raw material crushing process through a monitoring system so as to analyze the qualification coefficient of the raw material crushing process;
the raw material crushing quality analysis module is used for acquiring the running rotating speed of the raw material crusher corresponding to each unit detection time length and the actual crushing time length corresponding to the raw material crusher, and comprehensively analyzing the crushing quality coefficient corresponding to the raw material by combining the qualified coefficient of the raw material crushing process;
the mixing process detection module is used for carrying out video detection on the mixing process of the raw materials to be mixed;
the mixing fluidity analysis module is used for analyzing the flow performance coefficient corresponding to the mixture based on the video of the mixing process of the raw materials to be mixed;
the granulation quality analysis module is used for randomly selecting a set number of finished product particles, collecting images of the finished product particles, further analyzing reasonable size coefficients corresponding to the finished product particles, weighing the screened qualified particles, further obtaining the weight of the qualified particles, and similarly obtaining the weight of the eliminated particles, further comprehensively analyzing quality evaluation coefficients corresponding to the finished product particles;
the granular medicine comprehensive quality analysis module is used for analyzing the comprehensive quality coefficient corresponding to the granular medicine;
the early warning terminal is used for carrying out corresponding early warning according to the comprehensive quality coefficient corresponding to the granular medicine;
the cloud database is used for storing the raw material consumption corresponding to the unit predicted yield, storing the allowable attachment area of the residual raw materials and the allowable attachment area of the raw materials to be mixed, and storing the reference area corresponding to the finished product particles.
2. The big data analysis-based pharmaceutical informatization full-flow management system according to claim 1, wherein: the method for analyzing the raw material consumption corresponding to the batch of granular medicines and the proper operation parameters corresponding to the raw material crusher comprises the following specific steps:
s1, multiplying the expected output corresponding to the batch of granular medicines by the raw material consumption corresponding to the unit expected output stored in a cloud database, so as to obtain the raw material consumption corresponding to the batch of granular medicines;
s2, screening the proper operation rotating speed corresponding to the batch of granular medicines from the raw material consumption interval corresponding to the preset proper operation rotating speeds and taking the proper operation rotating speed as the proper operation rotating speed corresponding to the raw material crusher;
s3, screening the proper crushing time corresponding to the batch of granular medicines, and taking the proper crushing time corresponding to the raw material crusher.
3. The big data analysis-based pharmaceutical informatization full-flow management system according to claim 1, wherein: the method for specifically analyzing the qualification coefficient of the raw material crushing process comprises the following steps:
A1. dividing a monitoring video in the raw material crushing process according to a preset video frame number, further obtaining each monitoring picture in the raw material crushing process, identifying whether dust exists in each monitoring picture belonging to the raw material crushing process, if no dust exists in a certain monitoring picture belonging to the raw material crushing process, marking a dust pollution index corresponding to the monitoring picture belonging to the raw material crushing process as alpha, otherwise, obtaining a dust contour area corresponding to the monitoring picture belonging to the raw material crushing process, and multiplying the dust pollution index corresponding to a preset unit dust contour area by the dust pollution index corresponding to the monitoring picture belonging to the raw material crushing process to obtain a dust pollution index alpha' corresponding to the monitoring picture belonging to the raw material crushing process;
A2. acquiring dust pollution indexes corresponding to all monitoring pictures in the raw material crushing process, and marking the dust pollution indexes as epsilon i Wherein ε is i The number of each monitoring screen is denoted by =α or α', i, i=1, 2
A3. After the working of the raw material crusher is finished, carrying out image acquisition on the inner wall of the raw material crusher, further identifying the residual raw materials corresponding to the inner wall of the raw material crusher, acquiring the corresponding attachment areas of the residual raw materials, and marking the areas corresponding to the residual raw materials in the raw material crusher as SS;
A4. marking the crushed raw materials as raw materials to be mixed, and similarly, obtaining the attachment area of the inner wall of a raw material crusher corresponding to the raw materials to be mixed, and marking the attachment area as SF;
A5. extracting the allowable attachment area of the residual raw materials and the allowable attachment area of the raw materials to be mixed from a cloud database, respectively marking the allowable attachment area as SS 'and SF', and comprehensively analyzing the qualified coefficient of the raw material crushing process, wherein the calculation formula is as follows:wherein eta is represented as a pass coefficient of the raw material crushing process, n is represented as the number of monitoring pictures, lambda 1 、λ 2 、λ 3 Respectively expressed as weight coefficients of the preset residual raw materials, the raw materials to be mixed and the dust pollution index, and e expressed as natural constants.
4. The big data analysis-based pharmaceutical informatization full-flow management system according to claim 3, wherein: the crushing quality coefficient corresponding to the raw materials comprises the following specific analysis method:
B1. comparing the running rotating speed of the raw material pulverizer corresponding to each unit detection time length with the proper running rotating speed of the raw material pulverizer one by one, analyzing the proper running rotating speed coefficient of the raw material pulverizer, wherein the calculation formula is as follows:wherein mu is expressed as a proper coefficient of the running rotation speed corresponding to the raw material grinder, beta m Denoted as the running rotation speed of the raw material grinder corresponding to the set mth unit detection time length, and beta' denoted as the raw material grinder correspondingM is the number of each unit detection duration, and l is the number of unit detection durations;
B2. screening the maximum operation rotation speed and the minimum operation rotation speed from the operation rotation speeds corresponding to the set unit detection time periods of the raw material crusher, and marking the maximum operation rotation speed and the minimum operation rotation speed as beta respectively max 、β min Further, the running rotation speed fluctuation coefficient corresponding to the raw material crusher is analyzed, and the calculation formula is as follows:wherein->The running speed fluctuation coefficient is expressed as the running speed fluctuation coefficient corresponding to the raw material crusher;
B3. comparing the actual crushing time length corresponding to the raw material crusher with the proper crushing time length corresponding to the raw material crusher, and analyzing the proper coefficient of the crushing time length corresponding to the raw material crusher, wherein the calculation formula is as follows:wherein sigma is represented as a suitable coefficient of the crushing duration corresponding to the raw material crusher, T is represented as an actual crushing duration corresponding to the raw material crusher, and T' is represented as a suitable crushing duration corresponding to the raw material crusher;
B4. the specific calculation formula of the crushing quality coefficient corresponding to the analysis raw materials is as follows:wherein->Expressed as the corresponding crushing quality coefficient of the raw materials, gamma 1 、γ 2 、γ 3 Respectively expressed as weight coefficients of qualified preset raw material crushing process, proper running speed of the material conveying crusher, fluctuation of running speed of the raw material crusher and belonging crushing duration of the raw material crusher.
5. The big data analysis-based pharmaceutical informatization full-flow management system according to claim 4, wherein: the flow performance coefficient corresponding to the analysis mixture is specifically as follows:
C1. dividing a video of a mixing process of raw materials to be mixed into various pictures to be evaluated according to a preset video frame number, marking the mixed raw materials to be mixed as a mixture, and further obtaining various pictures to be evaluated corresponding to the mixture;
c2: acquiring the corresponding transverse length, longitudinal length and distribution area of each picture to be evaluated based on each picture to be evaluated corresponding to the mixture, and marking the corresponding transverse length, longitudinal length and distribution area of each picture to be evaluated to which the mixture belongs as HL respectively j 、ZL j 、MJ j Where j is denoted as the number of each monitoring screen, j=1, 2, k;
C3. analyzing a lateral flow difference value theta between a j+1th picture to be evaluated to which the mixture belongs and a j-th picture to be evaluated based on a lateral length corresponding to each picture to be evaluated to which the mixture belongs j+1,j ＝HL j+1 -HL j Wherein HL is j+1 The corresponding transverse length of the j+1st picture to be evaluated to which the mixture belongs is expressed;
C4. the analytical mixture has a corresponding lateral flow uniformity coefficient, and the calculation formula is as follows:wherein->Expressed as the corresponding lateral flow uniformity coefficient, HL, of the mixture j+1 The transverse length corresponding to the j+1st picture to be evaluated to which the mixture belongs is expressed, and k is expressed as the number of pictures to be evaluated;
C5. comparing the j+1th picture to be evaluated and the transverse flow difference corresponding to the j picture to be evaluated, screening the maximum flow difference and the minimum flow difference corresponding to the mixture, and marking the maximum flow difference and the minimum flow difference as theta max 、θ min And analyzing the corresponding lateral direction of the mixture accordinglyThe flow fluctuation coefficient is calculated by the formulaWherein ζ is expressed as the corresponding lateral flow coefficient of fluctuation of the mixture;
C6. the analytical mixture has a corresponding longitudinal flow uniformity coefficient, and the calculation formula is as follows:wherein->Expressed as the corresponding longitudinal flow uniformity coefficient of the mixture, ZL j+1 The longitudinal length corresponding to the j+1st picture to be evaluated to which the mixture belongs is expressed;
C7. based on the distribution area corresponding to each picture to be evaluated, the reasonable area growth coefficient corresponding to the mixture is analyzed, and the calculation formula is as follows:wherein ρ is expressed as a reasonable coefficient of area growth corresponding to the mixture, MJ j+1 Representing the distribution area corresponding to the j+1st picture to be evaluated to which the mixture belongs;
C8. the flow performance coefficient corresponding to the comprehensive analysis mixture is calculated as the following formulaWherein phi is expressed as the flow coefficient, χ, of the mixture 1 、χ 2 、χ 3 、χ 4 Respectively expressed as the corresponding occupation factor of uniform transverse flow, fluctuation transverse flow, uniform longitudinal flow and reasonable area increase of the mixture.
6. The big data analysis-based pharmaceutical informatization full-flow management system according to claim 5, wherein: the size reasonable coefficient corresponding to the finished product particles comprises the following specific analysis method:
D1. based on the collected images of the finished particles, the contours of the finished particles are identified, the corresponding areas of the finished particles are obtained, and the areas are marked as CM h Where h is expressed as the number of each finished particle, h=1, 2,..g;
D2. comparing the corresponding area of each finished product particle with the corresponding reference area of the finished product particle stored in the cloud database, and further analyzing the reasonable size coefficient corresponding to the finished product particle, wherein the calculation formula is as follows:where HP is expressed as a size reasonable coefficient corresponding to the finished particles, CM' is expressed as a reference area corresponding to the finished particles, and g is expressed as the number of finished particles.
7. The big data analysis-based pharmaceutical informatization full-flow management system according to claim 6, wherein: the quality evaluation coefficient corresponding to the finished product particles comprises the following specific analysis methods:
E1. based on the weight of qualified finished product particles and the weight of eliminated finished product particles, the qualification coefficient corresponding to the finished product particles is analyzed, and the calculation formula is as follows:wherein HG is expressed as a qualified coefficient corresponding to the finished product particles, ZT and ZF are respectively expressed as the weight of the qualified finished product particles and the weight of the eliminated finished product particles;
E2. obtaining reasonable size coefficients corresponding to the finished product particles, and comprehensively analyzing quality evaluation coefficients corresponding to the finished product particles by combining the qualified coefficients corresponding to the finished product particles, wherein the calculation formula is as follows:wherein PJ is expressed as a quality evaluation coefficient, delta, corresponding to the finished product particles 1 、δ 2 Respectively expressed as the proportionality coefficient of reasonable size and qualified finished product particles.
8. The big data analysis-based pharmaceutical informatization full-flow management system according to claim 7, wherein: the comprehensive quality coefficient corresponding to the granular medicine is calculated according to the following formula:wherein psi is expressed as the comprehensive quality coefficient corresponding to the granular medicine, ay 1 、Ay 2 、Ay 3 Respectively expressed as the influence factors of the preset raw material crushing quality, the mixture flowability and the finished product particle quality evaluation.
9. The big data analysis-based pharmaceutical informatization full-flow management system according to claim 1, wherein: the corresponding early warning is carried out according to the comprehensive quality coefficient corresponding to the granular medicine, and the specific method comprises the following steps: and comparing the comprehensive quality coefficient corresponding to the granular medicine with a preset comprehensive quality threshold of the granular medicine, and if the comprehensive quality coefficient corresponding to the granular medicine is smaller than or equal to the comprehensive quality threshold of the granular medicine, carrying out abnormal early warning on the quality of the granular medicine.
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