CN110969374A - Method and device for automatically adjusting parameters and computer readable storage medium - Google Patents
Method and device for automatically adjusting parameters and computer readable storage medium Download PDFInfo
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Abstract
The invention relates to a data processing technology, and discloses a parameter automatic adjustment method, a device and a computer readable storage medium, wherein the method comprises the following steps: generating a first autonomy table according to quality evaluation indexes and price indexes of medical products purchased by each medical institution in each preset period; generating a second autovariate table according to the financial data of each administrative region in each preset period; generating a factor table according to the purchase amount of the medical instrument products purchased by each medical institution in each preset period; calculating the association strength of each independent variable in the first independent variable table and the second independent variable table and each dependent variable in the dependent variable table; generating a basic value of each scoring item weight in the medical instrument centralized purchase evaluation index based on the correlation strength; and substituting each medicine data into an evaluation index algorithm based on an artificial intelligence technology, and automatically adjusting each scoring item weight value in the centralized purchase evaluation index of the medical instruments according to the basic value of each scoring item weight value. The invention solves the problem of mass data calculation and improves the data processing efficiency.
Description
Technical Field
The present invention relates to the field of data processing, and in particular, to a method and an apparatus for automatically adjusting parameters, and a computer-readable storage medium.
Background
As the medical instrument is a special commodity, the quality safety is extremely high, and all countries carry out strict supervision to ensure the effectiveness and the safety of the medical instrument. At present, the quality of medical instruments is basically controlled by adopting general evaluation indexes in all medical instrument types or medical instrument centralized purchasing systems in regions, and the evaluation index parameters in each medical instrument centralized purchasing system are manually adjusted, so that the existing management method for medical instrument data is single, and the data processing efficiency is low.
Disclosure of Invention
The invention provides a method and a device for automatically adjusting parameters of a centralized purchasing evaluation index of a medical instrument and a computer-readable storage medium, and mainly aims to provide a specific and effective scheme for automatically adjusting the weight of a scoring item in the centralized purchasing evaluation index of the medical instrument and solve the problem of mass data calculation required by parameter adjustment of an evaluation index algorithm.
In order to achieve the above object, the present invention provides an automatic parameter adjusting method applied to an electronic device, the automatic parameter adjusting method comprising:
generating a first autonomy table according to quality evaluation indexes and price indexes of medical products purchased by each medical institution in each preset period;
generating a second autovariate table according to the financial data of each administrative region in each preset period;
generating a factor table according to the purchase amount of the purchased medical instrument products of each medical institution in each preset period;
calculating the association strength of each independent variable in the first independent variable table and the second independent variable table and each dependent variable in the dependent variable table;
generating a basic value of each scoring item weight in the medical instrument centralized purchase evaluation index based on the correlation strength;
substituting each medicine data into an evaluation index algorithm based on an artificial intelligence technology, and automatically adjusting each scoring item weight value in the medicine centralized purchase evaluation index according to the basic value of each scoring item weight value.
Optionally, the step of calculating the strength of association between each independent variable in the first independent variable table and the second independent variable table and each dependent variable in the dependent variable table comprises:
and measuring the correlation strength of each variable and the dependent variable by using a Phi (Phi) coefficient by utilizing a Pearson's chi-square test.
Optionally, the step of substituting each medical data into an evaluation index algorithm based on an artificial intelligence technology and automatically adjusting each weighted value of each scoring item in the medical instrument centralized purchase evaluation index includes:
and completing automatic adjustment of the weight values of all scoring items in the medical instrument centralized purchase evaluation index through fitting calculation.
Optionally, the step of generating a first autovariate table according to the quality evaluation index and the price index of the medical product purchased by each medical institution per preset period includes:
collecting quality evaluation indexes and price indexes of medical products purchased by each medical institution in each preset period;
preprocessing the collected quality evaluation indexes and price indexes of the medical products purchased by each medical institution in each preset period to generate a quality evaluation index and price index set;
selecting a first random function, taking each element in the quality evaluation index and price index set as an independent variable, and calculating a function value of each element under the first random function;
and automatically generating a first self-variable table according to the calculated function value.
Optionally, the step of generating a second autovariate table according to the financial data of each administrative area per preset period includes:
collecting financial data of each administrative region in each preset period;
preprocessing the collected financial data of each administrative area in each preset period to generate a financial data set;
selecting a second random function, taking each element in the financial data set as an independent variable, and calculating a function value of each element under the second random function;
and automatically generating a second auto-variable table according to the calculated function value.
The invention also provides an electronic device, which comprises a memory and a processor, wherein the memory is stored with a parameter automatic adjusting program of the medical instrument centralized procurement evaluation index, which can run on the processor, and the parameter automatic adjusting program of the medical instrument centralized procurement evaluation index realizes the following steps when being executed by the processor:
generating a first autonomy table according to quality evaluation indexes and price indexes of medical products purchased by each medical institution in each preset period;
generating a second autovariate table according to the financial data of each administrative region in each preset period;
generating a factor table according to the purchase amount of the purchased medical instrument products of each medical institution in each preset period;
calculating the association strength of each independent variable in the first independent variable table and the second independent variable table and each dependent variable in the dependent variable table;
generating a basic value of each scoring item weight in the medical instrument centralized purchase evaluation index based on the correlation strength;
substituting each medicine data into an evaluation index algorithm based on an artificial intelligence technology, and automatically adjusting the weight value of each scoring item in the centralized purchase evaluation index of the medicine.
Optionally, the step of calculating the strength of association between each independent variable in the first independent variable table and the second independent variable table and each dependent variable in the dependent variable table comprises:
and measuring the correlation strength of each variable and the dependent variable by using a Phi (Phi) coefficient by utilizing a Pearson's chi-square test.
Optionally, the step of substituting each medical data into an evaluation index algorithm based on an artificial intelligence technology and automatically adjusting each weighted value of each scoring item in the medical instrument centralized purchase evaluation index includes:
and completing automatic adjustment of the weight values of all scoring items in the medical instrument centralized purchase evaluation index through fitting calculation.
Optionally, the step of generating a first autovariate table according to the quality evaluation index and the price index of the medical product purchased by each medical institution per preset period includes:
collecting quality evaluation indexes and price indexes of medical products purchased by each medical institution in each preset period;
preprocessing the collected quality evaluation indexes and price indexes of the medical products purchased by each medical institution in each preset period to generate a quality evaluation index and price index set;
selecting a first random function, taking each element in the quality evaluation index and price index set as an independent variable, and calculating a function value of each element under the first random function;
and automatically generating a first self-variable table according to the calculated function value.
In addition, to achieve the above object, the present invention further provides a computer-readable storage medium, on which a parameter automatic adjustment program of a centralized procurement evaluation index of pharmaceutical instruments is stored, where the parameter automatic adjustment program is executable by one or more processors to implement the steps of the parameter automatic adjustment method.
The method, the device and the computer readable storage medium for automatically adjusting the parameters of the medical instrument centralized purchase evaluation index provide a specific and effective scheme for automatically adjusting the weight of the scoring item in the medical instrument centralized purchase evaluation index; by means of the artificial intelligence technology, the problem of massive data calculation required by parameter adjustment of the evaluation index algorithm is solved, and data processing efficiency is improved.
Drawings
Fig. 1 is a schematic flow chart of a method for automatically adjusting parameters according to an embodiment of the present invention;
fig. 2 is a schematic diagram illustrating an internal structure of an electronic device according to an embodiment of the invention;
fig. 3 is a block diagram illustrating an automatic adjustment procedure based on parameters in an electronic device according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a parameter automatic adjusting method. Referring to fig. 1, a schematic flow chart of a method for automatically adjusting parameters of a centralized procurement evaluation index of pharmaceutical instruments according to an embodiment of the present invention is shown. The method may be performed by a device, which may be implemented by software and/or hardware, and in this embodiment, the device is an intelligent terminal.
In this embodiment, the method for automatically adjusting parameters includes:
s101, generating a first autonomy table according to quality evaluation indexes and price indexes of medical products purchased by each medical institution in each preset period;
s102, generating a second autovariate table according to the financial data of each administrative region in each preset period;
s103, generating a factor table according to the purchase amount of the purchased medical products of each medical institution in each preset period;
s104, calculating the association strength of each independent variable in the first independent variable table and the second independent variable table and each dependent variable in the dependent variable table;
s105, generating a basic value of each scoring item weight in the medical instrument centralized purchase evaluation index based on the correlation strength;
and S106, substituting the medicine data into an evaluation index algorithm based on an artificial intelligence technology, and automatically adjusting the weight value of each scoring item in the centralized purchase evaluation index of the medicine.
The step of calculating the strength of association between each independent variable in the first independent variable table and the second independent variable table and each dependent variable in the dependent variable table comprises the following steps:
and measuring the correlation strength of each variable and the dependent variable by using a Phi (Phi) coefficient by utilizing a Pearson's chi-square test.
Among them, the Pearson's chi-squared test (English) is one of the best known chi-squared tests (other commonly used chi-squared tests include Foley continuity correction, likelihood ratio test, unary mixture test, etc., and the probability distribution of their statistical values is similar to chi-squared distribution, so called chi-squared test). The "Pearson Caller's Chi-test" was first published by Call Pearson in 1900 and was used for testing of categorical variables. In the scientific literature, when the chi-square test is mentioned without a particular indication of type, the pearson chi-square test is generally referred to.
Phi in Phi (Phi) -series numbers is the magnitude of the correlation coefficient, indicating the degree of correlation between the two factors. Generally, when the value of Φ is less than 0.3, the correlation is weak; when the value of phi is greater than 0.6, the correlation is strong.
The basic value of the weight of each scoring item in the medical instrument centralized purchase evaluation index is generated based on the correlation strength, and the basic value refers to the ratio score specified for each scoring item according to the workload of each scoring item and the importance degree of the influence on the overall capacity in order to show the degree that the related evaluation index meets the specified requirement by data in the index evaluation process.
The steps of substituting each medicine data into an evaluation index algorithm based on an artificial intelligence technology and automatically adjusting the weight value of each scoring item in the centralized purchase evaluation index of the medicine comprise:
and completing automatic adjustment of the weight values of all scoring items in the medical instrument centralized purchase evaluation index through fitting calculation.
Wherein, fitting refers to knowing several discrete function values { f1, f2, …, fn } of a certain function, and adjusting several coefficients f (λ 1, λ 2, …, λ n) to be determined in the function to make the difference (least squares meaning) between the function and the known point set minimum.
The step of generating a first autovariate table according to the quality evaluation index and the price index of medical instrument products purchased by each medical institution in each preset period comprises the following steps:
collecting quality evaluation indexes and price indexes of medical products purchased by each medical institution in each preset period;
preprocessing the collected quality evaluation indexes and price indexes of the medical products purchased by each medical institution in each preset period to generate a quality evaluation index and price index set;
selecting a first random function, taking each element in the quality evaluation index and price index set as an independent variable, and calculating a function value of each element under the first random function;
and automatically generating a first self-variable table according to the calculated function value.
The step of generating a second autovariate table according to the financial data of each administrative region in each preset period comprises the following steps:
collecting financial data of each administrative region in each preset period;
preprocessing the collected financial data of each administrative area in each preset period to generate a financial data set;
selecting a second random function, taking each element in the financial data set as an independent variable, and calculating a function value of each element under the second random function;
and automatically generating a second auto-variable table according to the calculated function value.
The quality evaluation indexes of the medical instrument product comprise at least one of a medicine sampling inspection condition, a medicine GMP certification condition, a medicine GMP implementation condition, an administrative punishment condition related to the medicine quality, a research and development innovation condition, an enterprise operation condition and a medicine use evaluation condition.
Wherein, GMP, called as GOOD MANUFACTURING PRACTICES, means "production quality management practice" or "GOOD operation practice" and "GOOD MANUFACTURING standard" in Chinese. GMP is a mandatory standard suitable for pharmaceutical, food and other industries, and requires enterprises to meet the requirements of sanitary quality according to relevant national regulations from the aspects of raw materials, personnel, facilities and equipment, production process, packaging and transportation, quality control and the like, so that an operable operation specification is formed to help the enterprises to improve the sanitary environment of the enterprises, discover problems existing in the production process in time and improve the problems. In brief, GMP requires that manufacturing enterprises for pharmaceutical and food products, etc. should have good production facilities, reasonable production processes, perfect quality management and strict detection systems, to ensure that the quality of the final product (including food safety and hygiene, etc.) meets the regulatory requirements.
The medicine random inspection condition comprises a batch of unqualified medicine random inspection, the medicine GMP certification condition comprises a version that an enterprise or a product passes the medicine GMP certification, the medicine GMP implementation condition comprises the number of serious violation of medicine GMP, the research and development innovation condition comprises whether the medicine is original, whether the medicine is or was a traditional Chinese medicine variety protection product, and whether the medicine is or was a high-quality premium product determined by the country, the enterprise business condition comprises at least one of the share of the same-name medicine market, the business income of the enterprise medical owner and the total profit of the enterprise, and the medicine use evaluation condition comprises the evaluation related to the medicine quality published by a medicine user or a professional through the Internet.
In this embodiment, the drug quality index data may be acquired in various forms, so as to obtain a drug quality credit evaluation result.
The output or display mode of the drug quality credit evaluation result comprises the following steps: displaying the quality credit level of the medicine through a color bar, wherein different colors can represent different quality credit levels, and different quality credit levels can also be represented through different shades of the same color; the quality credit rating of the drug is expressed by a drug quality risk index (numerical value), which may be represented by a higher numerical value representing a higher quality credit rating, a lower numerical value representing a lower quality credit rating, or a higher numerical value representing a lower quality credit rating, and a lower numerical value representing a lower quality credit rating. The quality credit level of the pharmaceutical product is displayed graphically or symbolically, either by different graphical or symbolic representations of quality credit levels or by different numbers of graphical or symbolic representations of different quality credit levels.
The output or display of the quality credit rating of the drug may also take other forms, which may vary according to the choice, and may be designed by those skilled in the art according to the needs.
The step of generating the basic value of each scoring item weight in the medical instrument centralized purchase evaluation index based on the correlation strength is to automatically generate the basic value of each scoring item weight in the evaluation index based on the correlation strength.
The parameter automatic adjustment method for the medical instrument centralized purchase evaluation index can provide a specific and effective scheme for automatically adjusting the weight of the scoring item in the medical instrument centralized purchase evaluation index; by means of the artificial intelligence technology, the problem of massive data calculation required by parameter adjustment of the evaluation index algorithm is solved, and data processing efficiency is improved.
The invention also provides an electronic device 1. Fig. 2 is a schematic view of an internal structure of an electronic device according to an embodiment of the invention.
In this embodiment, the electronic device 1 may be a computer, an intelligent terminal or a server. The electronic device 1 comprises at least a memory 11, a processor 13, a communication bus 15, and a network interface 17. In this embodiment, the electronic device 1 is an intelligent terminal.
The memory 11 includes at least one type of readable storage medium, which includes a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, and the like. The memory 11 may in some embodiments be an internal storage unit of the electronic device, for example a hard disk of the electronic device. The memory 11 may be an external storage device of the electronic apparatus in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a flash Card (FlashCard), and the like, which are provided on the electronic apparatus. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic apparatus. The memory 11 may be used not only to store application software installed in the electronic apparatus 1 and various types of data, such as codes of the parameter auto-adjustment program 111, but also to temporarily store data that has been output or is to be output.
The processor 13 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor or other data Processing chip in some embodiments, and is used for executing program codes stored in the memory 11 or Processing data.
The communication bus 15 is used to realize connection communication between these components.
The network interface 17 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), and is typically used to establish a communication link between the electronic apparatus 1 and other electronic devices.
Optionally, the electronic device 1 may further comprise a user interface, which may comprise a Display (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface may also comprise a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device and for displaying a visualized user interface.
While FIG. 2 shows only the electronic device 1 with the components 11-17, those skilled in the art will appreciate that the configuration shown in FIG. 2 does not constitute a limitation of the electronic device, and may include fewer or more components than shown, or some components in combination, or a different arrangement of components.
In the embodiment of the electronic device 1 shown in fig. 2, the memory 11 stores therein an automatic parameter adjustment program 111; the processor 13, when executing the parameter auto-adjustment program 111 stored in the memory 11, implements the following steps:
generating a first autonomy table according to quality evaluation indexes and price indexes of medical products purchased by each medical institution in each preset period;
generating a second autovariate table according to the financial data of each administrative region in each preset period;
generating a factor table according to the purchase amount of the purchased medical instrument products of each medical institution in each preset period;
calculating the association strength of each independent variable in the first independent variable table and the second independent variable table and each dependent variable in the dependent variable table;
generating a basic value of each scoring item weight in the medical instrument centralized purchase evaluation index based on the correlation strength;
substituting each medicine data into an evaluation index algorithm based on an artificial intelligence technology, and automatically adjusting each scoring item weight value in the medicine centralized purchase evaluation index according to the basic value of each scoring item weight value.
The step of calculating the strength of association between each independent variable in the first independent variable table and the second independent variable table and each dependent variable in the dependent variable table comprises the following steps:
and measuring the correlation strength of each variable and the dependent variable by using a Phi (Phi) coefficient by utilizing a Pearson's chi-square test.
Among them, the Pearson's chi-squared test (English) is one of the best known chi-squared tests (other commonly used chi-squared tests include Foley continuity correction, likelihood ratio test, unary mixture test, etc., and the probability distribution of their statistical values is similar to chi-squared distribution, so called chi-squared test). The "Pearson Caller's Chi-test" was first published by Call Pearson in 1900 and was used for testing of categorical variables. In the scientific literature, when the chi-square test is mentioned without a particular indication of type, the pearson chi-square test is generally referred to.
Phi in Phi (Phi) -series numbers is the magnitude of the correlation coefficient, indicating the degree of correlation between the two factors. Generally, when the value of Φ is less than 0.3, the correlation is weak; when the value of phi is greater than 0.6, the correlation is strong.
The steps of substituting each medicine data into an evaluation index algorithm based on an artificial intelligence technology and automatically adjusting the weight value of each scoring item in the centralized purchase evaluation index of the medicine comprise:
and completing automatic adjustment of the weight values of all scoring items in the medical instrument centralized purchase evaluation index through fitting calculation.
Wherein, fitting refers to knowing several discrete function values { f1, f2, …, fn } of a certain function, and adjusting several coefficients f (λ 1, λ 2, …, λ n) to be determined in the function to make the difference (least squares meaning) between the function and the known point set minimum.
The step of generating a first autovariate table according to the quality evaluation index and the price index of medical instrument products purchased by each medical institution in each preset period comprises the following steps:
collecting quality evaluation indexes and price indexes of medical products purchased by each medical institution in each preset period;
preprocessing the collected quality evaluation indexes and price indexes of the medical products purchased by each medical institution in each preset period to generate a quality evaluation index and price index set;
selecting a first random function, taking each element in the quality evaluation index and price index set as an independent variable, and calculating a function value of each element under the first random function;
and automatically generating a first self-variable table according to the calculated function value.
The step of generating a second autovariate table according to the financial data of each administrative region in each preset period comprises the following steps:
collecting financial data of each administrative region in each preset period;
preprocessing the collected financial data of each administrative area in each preset period to generate a financial data set;
selecting a second random function, taking each element in the financial data set as an independent variable, and calculating a function value of each element under the second random function;
and automatically generating a second auto-variable table according to the calculated function value.
The quality evaluation indexes of the medical instrument product comprise at least one of a medicine sampling inspection condition, a medicine GMP certification condition, a medicine GMP implementation condition, an administrative punishment condition related to the medicine quality, a research and development innovation condition, an enterprise operation condition and a medicine use evaluation condition.
Wherein, GMP, called as GOOD MANUFACTURING PRACTICES, means "production quality management practice" or "GOOD operation practice" and "GOOD MANUFACTURING standard" in Chinese. GMP is a mandatory standard suitable for pharmaceutical, food and other industries, and requires enterprises to meet the requirements of sanitary quality according to relevant national regulations from the aspects of raw materials, personnel, facilities and equipment, production process, packaging and transportation, quality control and the like, so that an operable operation specification is formed to help the enterprises to improve the sanitary environment of the enterprises, discover problems existing in the production process in time and improve the problems. In brief, GMP requires that manufacturing enterprises for pharmaceutical and food products, etc. should have good production facilities, reasonable production processes, perfect quality management and strict detection systems, to ensure that the quality of the final product (including food safety and hygiene, etc.) meets the regulatory requirements.
The medicine random inspection condition comprises a batch of unqualified medicine random inspection, the medicine GMP certification condition comprises a version that an enterprise or a product passes the medicine GMP certification, the medicine GMP implementation condition comprises the number of serious violation of medicine GMP, the research and development innovation condition comprises whether the medicine is original, whether the medicine is or was a traditional Chinese medicine variety protection product, and whether the medicine is or was a high-quality premium product determined by the country, the enterprise business condition comprises at least one of the share of the same-name medicine market, the business income of the enterprise medical owner and the total profit of the enterprise, and the medicine use evaluation condition comprises the evaluation related to the medicine quality published by a medicine user or a professional through the Internet.
In this embodiment, the drug quality index data may be acquired in various forms, so as to obtain a drug quality credit evaluation result.
The output or display mode of the drug quality credit evaluation result comprises the following steps: displaying the quality credit level of the medicine through a color bar, wherein different colors can represent different quality credit levels, and different quality credit levels can also be represented through different shades of the same color; the quality credit rating of the drug is expressed by a drug quality risk index (numerical value), which may be represented by a higher numerical value representing a higher quality credit rating, a lower numerical value representing a lower quality credit rating, or a higher numerical value representing a lower quality credit rating, and a lower numerical value representing a lower quality credit rating. The quality credit level of the pharmaceutical product is displayed graphically or symbolically, either by different graphical or symbolic representations of quality credit levels or by different numbers of graphical or symbolic representations of different quality credit levels.
The output or display of the quality credit rating of the drug may also take other forms, which may vary according to the choice, and may be designed by those skilled in the art according to the needs.
The step of generating the basic value of each scoring item weight in the medical instrument centralized purchase evaluation index based on the correlation strength is to automatically generate the basic value of each scoring item weight in the evaluation index based on the correlation strength.
The electronic device provided by the embodiment can provide a specific and effective scheme for automatically adjusting the weights of the scoring items in the evaluation indexes of centralized medicine purchase; the problem of mass data calculation required by parameter adjustment of the evaluation index algorithm is solved through an artificial intelligence technology.
Furthermore, an embodiment of the present invention further provides a computer-readable storage medium, where a parameter automatic adjustment program 111 for a centralized procurement evaluation index of medical instruments is stored on the computer-readable storage medium, and the parameter automatic adjustment program 111 for the centralized procurement evaluation index of medical instruments is executable by one or more processors to implement the following operations:
generating a first autonomy table according to quality evaluation indexes and price indexes of medical products purchased by each medical institution in each preset period;
generating a second autovariate table according to the financial data of each administrative region in each preset period;
generating a factor table according to the purchase amount of the purchased medical instrument products of each medical institution in each preset period;
calculating the association strength of each independent variable in the first independent variable table and the second independent variable table and each dependent variable in the dependent variable table;
generating a basic value of each scoring item weight in the medical instrument centralized purchase evaluation index based on the correlation strength;
substituting each medicine data into an evaluation index algorithm based on an artificial intelligence technology, and automatically adjusting each scoring item weight value in the medicine centralized purchase evaluation index according to the basic value of each scoring item weight value.
The embodiment of the computer readable storage medium of the present invention is substantially the same as the embodiments of the electronic device and the method, and will not be described herein in a repeated manner.
Alternatively, in other embodiments, the automatic parameter adjustment program 111 for the medication instrument centralized procurement evaluation index may be further divided into one or more modules, and the one or more modules are stored in the memory 11 and executed by one or more processors (in this embodiment, the processor 13) to implement the present invention.
For example, referring to fig. 3, a schematic diagram of program modules of the automatic parameter adjustment program 111 for index of centralized procurement of medical instruments in an embodiment of the electronic device according to the present invention is shown, in this embodiment, the automatic parameter adjustment program 111 for index of centralized procurement of medical instruments may be divided into a first generation module 10, a second generation module 20, a third generation module 30, a calculation module 40, a fourth generation module 50, and an adjustment module 60, exemplarily:
the first generation module 10 is configured to generate a first autovariate table according to quality evaluation indexes and price indexes of medical products purchased by each medical institution in each preset period;
the second generating module 20 is configured to generate a second autovariate table according to the financial data of each administrative area in each preset period;
the third generating module 30 is configured to generate a factor table according to the purchase amount of the purchased medical instrument product of each medical institution in each preset period;
the calculating module 40 is configured to calculate the strength of association between each independent variable in the first independent variable table and each dependent variable in the dependent variable table;
the generating module 50 is configured to generate a basic value of each scoring item weight in the medical instrument centralized procurement evaluation index based on the association strength;
the adjusting module 60 is configured to substitute the medicine data into an evaluation index algorithm based on an artificial intelligence technology, and automatically adjust a weight system of each scoring item in the centralized procurement evaluation index of the medical instruments according to a basic value of each scoring item weight.
The functions or operation steps of the first generating module 10, the second generating module 20, the third generating module 30, the calculating module 40, the generating module 50, and the adjusting module 60 when executed are substantially the same as those of the above embodiments, and are not described herein again.
It should be noted that the above-mentioned numbers of the embodiments of the present invention are merely for description, and do not represent the merits of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, apparatus, article, or method that includes the element.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. An automatic parameter adjusting method is applied to an electronic device, and comprises the following steps:
generating a first independent variable table according to quality evaluation indexes and price indexes of medical products purchased by each medical institution in each preset period, wherein the first independent variables comprise independent variables corresponding to the quality evaluation indexes and the price indexes;
generating a second independent variable table according to the financial data of each administrative region in each preset period, wherein the second independent variables comprise independent variables corresponding to the financial data;
generating a dependent variable table according to the purchase amount of the purchased medical instrument products of each medical institution in each preset period, wherein the dependent variable table comprises dependent variables corresponding to the purchase amount;
calculating the association strength of each independent variable in the first independent variable table and the second independent variable table and each dependent variable in the dependent variable table;
generating a basic value of each scoring item weight in the medical instrument centralized purchase evaluation index based on the correlation strength;
and automatically adjusting the weight value of each scoring item in the centralized purchasing evaluation index of the medical instruments according to the basic value of each scoring item weight.
2. The method of claim 1, wherein the step of calculating the strength of association of each independent variable in the first independent variable table and the second independent variable table with each dependent variable in the dependent variable table comprises:
and calculating the association strength of each independent variable in the first independent variable table and the second independent variable table and each dependent variable in the dependent variable table by using a Phi (Phi) coefficient by using a Pearson's chi-square test.
3. The method of claim 2, wherein the step of automatically adjusting the weight value of each scoring item in the index of centralized procurement and evaluation of pharmaceutical products according to the basic value of each scoring item comprises:
and (4) obtaining a new evaluation algorithm by fitting and calculating the basic value of each evaluation item weight, and finishing automatic adjustment of each evaluation item weight in the medical instrument centralized purchase evaluation index.
4. The method according to claim 1 or 2, wherein the step of generating the first autovariate table according to the quality evaluation index and the price index of the medical product purchased by each medical institution per preset period comprises:
collecting quality evaluation indexes and price indexes of medical products purchased by each medical institution in each preset period;
preprocessing the collected quality evaluation indexes and price indexes of the medical products purchased by each medical institution in each preset period to generate a quality evaluation index and price index set;
selecting a first random function, taking each element in the quality evaluation index and price index set as an independent variable, and calculating a function value of each element under the first random function;
and automatically generating a first self-variable table according to the calculated function value.
5. The method according to claim 4, wherein the step of generating a second autovariate table based on the financial data of each administrative area per preset period comprises:
collecting financial data of each administrative region in each preset period;
preprocessing the collected financial data of each administrative area in each preset period to generate a financial data set;
selecting a second random function, taking each element in the financial data set as an independent variable, and calculating a function value of each element under the second random function;
and automatically generating a second auto-variable table according to the calculated function value.
6. An electronic device, comprising a memory and a processor, wherein the memory has stored thereon a parameter auto-adjustment program operable on the processor, and wherein the parameter auto-adjustment program, when executed by the processor, performs the steps of:
generating a first independent variable table according to quality evaluation indexes and price indexes of medical products purchased by each medical institution in each preset period, wherein the first independent variables comprise independent variables corresponding to the quality evaluation indexes and the price indexes;
generating a second independent variable table according to the financial data of each administrative region in each preset period, wherein the second independent variables comprise independent variables corresponding to the financial data;
generating a dependent variable table according to the purchase amount of the purchased medical instrument products of each medical institution in each preset period, wherein the dependent variable table comprises dependent variables corresponding to the purchase amount;
calculating the association strength of each independent variable in the first independent variable table and the second independent variable table and each dependent variable in the dependent variable table;
generating a basic value of each scoring item weight in the medical instrument centralized purchase evaluation index based on the correlation strength;
and automatically adjusting the weight value of each scoring item in the centralized purchasing evaluation index of the medical instruments according to the basic value of each scoring item weight.
7. The electronic device of claim 6, wherein the step of calculating the strength of association of each independent variable in the first independent variable table and the second independent variable table with each dependent variable in the dependent variable table comprises:
and calculating the association strength of each independent variable in the first independent variable table and the second independent variable table and each dependent variable in the dependent variable table by using a Phi (Phi) coefficient by using a Pearson's chi-square test.
8. The electronic device of claim 7, wherein the step of automatically adjusting the weight value of each scoring item in the centralized procurement evaluation index of the medication according to the base value of each scoring item comprises:
and (4) obtaining a new evaluation algorithm by fitting and calculating the basic value of each evaluation item weight, and finishing automatic adjustment of each evaluation item weight in the medical instrument centralized purchase evaluation index.
9. The electronic device according to claim 6 or 7, wherein the step of generating the first autovariate table according to the quality evaluation index and the price index of the medical products purchased by each medical institution per preset period comprises:
collecting quality evaluation indexes and price indexes of medical products purchased by each medical institution in each preset period;
preprocessing the collected quality evaluation indexes and price indexes of the medical products purchased by each medical institution in each preset period to generate a quality evaluation index and price index set;
selecting a first random function, taking each element in the quality evaluation index and price index set as an independent variable, and calculating a function value of each element under the first random function;
and automatically generating a first self-variable table according to the calculated function value.
10. A computer-readable storage medium having stored thereon a parameter auto-adjustment program executable by one or more processors to implement the steps of the parameter auto-adjustment method of any one of claims 1 to 5.
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