CN115276665B

CN115276665B - Intelligent management method and system for bulk drugs

Info

Publication number: CN115276665B
Application number: CN202211186216.2A
Authority: CN
Inventors: 金海峰
Original assignee: Jiangsu Senxinda Biotechnology Co ltd
Current assignee: Jiangsu Senxinda Biotechnology Co ltd
Priority date: 2022-09-28
Filing date: 2022-09-28
Publication date: 2022-12-20
Anticipated expiration: 2042-09-28
Also published as: CN115276665A

Abstract

The invention relates to the technical field of digital data processing, in particular to an intelligent management method and system for bulk drugs, wherein the method comprises the steps of obtaining labels on a bulk drug bag to obtain label data; constructing a dynamic dictionary, wherein the abscissa of the dynamic dictionary represents the data type, and the ordinate represents the data of the same type; acquiring the length of intercepted data according to the data type of the historical data, constructing a histogram of the length of the intercepted data, and obtaining a screening threshold value according to the number of data types in the histogram and the occurrence frequency of the data under each type; obtaining a target data type according to a screening threshold, calculating similarity according to various arrangement modes of intercepted data under the target data type, obtaining a basic dictionary based on the similarity, and establishing a dictionary table according to the basic dictionary; and compressing and storing the tag data according to the dictionary table. According to the scheme, the data of the raw material medicine package is compressed and stored through an improved traditional LZW algorithm, so that the intelligent management of the raw material medicines is better performed.

Description

Intelligent management method and system for bulk drugs

Technical Field

The invention relates to the technical field of digital data processing, in particular to an intelligent management method and system for bulk drugs.

Background

The raw material medicaments are raw materials of medicaments, and the medicaments can be used for clinical application after being processed and synthesized. The storage management of the bulk drugs generally comprises a series of operations such as warehousing acceptance of the bulk drugs, ex-warehouse picking, in-warehouse storage, sorting and printing, and the like, and in the process, a large amount of data exists when an informatization system is adopted for storage, so that the storage condition management of the bulk drugs is difficult.

Data is usually required to be compressed when a large amount of data is stored, the storage management of the bulk drug usually records the encoding and the time stamp of the bulk drug, a large amount of continuously repeated bytes and strings exist in a corresponding data stream, and the compression effect of the LZW compression algorithm is usually better when the large amount of continuously repeated bytes and strings exist in the data stream are compressed. The traditional LZW compression algorithm is to construct a dictionary in which a current character string can be found

Then, the encoder always inputs the character and connects the input character to the encoder

After that, until a certain character is input

Searching fails in the dictionary, at which time stopping, replacing corresponding characters or character strings in the original data by dictionary indexes, and then replacing

And storing the data into a dictionary. Although only one effective character is added into the traditional LZW compression algorithm

However, too many characters are stored, which wastes memory space and increases processing time.

Disclosure of Invention

In order to solve the technical problems, the invention aims to provide an intelligent management method and system for bulk drugs, and the adopted technical scheme is as follows:

in a first aspect, an embodiment of the present invention provides an intelligent management method for a pharmaceutical raw material, including the following steps:

obtaining a label on a raw material medicine package to obtain label data;

constructing a dynamic dictionary, wherein the abscissa of the dynamic dictionary represents the data type, and the ordinate of the dynamic dictionary represents the data of the same type; acquiring the length of intercepted data according to the data type of the historical data, constructing a histogram of the length of the intercepted data, and obtaining a screening threshold value according to the number of data types in the histogram and the occurrence frequency of the data under each type; obtaining a target data type according to a screening threshold, calculating similarity according to various arrangement modes of intercepted data under the target data type, obtaining a basic dictionary based on the similarity, and establishing a dictionary table according to the basic dictionary;

and compressing and storing the label data according to the dictionary table.

Further, the calculation formula of the screening threshold is as follows:

wherein, the first and the second end of the pipe are connected with each other,

representing the screening threshold, S representing the number of data types in the intercepted data segment,

indicating the number of occurrences of the ith type of data,

indicating the most frequent type of occurrences.

Further, the calculation formula of the similarity is as follows:

wherein the content of the first and second substances,

representing similarity, m represents the number of data types greater than the screening threshold,

representing the same probability of the corresponding location of data type j.

Further, the method for obtaining the intercepted data length includes:

and taking the ratio of the data type in the current historical data segment to the total data type as the length of the intercepted data.

Further, the method for obtaining the target data type according to the screening threshold includes:

and when the occurrence frequency of the data under each type is greater than the screening threshold value, the corresponding type is the target data type.

Further, the method for obtaining a base dictionary based on similarity includes:

and when the similarity is smaller than the similarity threshold value, taking the arrangement mode corresponding to the intercepted data as a basic dictionary.

In a second aspect, an embodiment of the present invention further provides an intelligent management system for a bulk drug, including a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements any one of the steps of the method when executing the computer program.

The embodiment of the invention at least has the following beneficial effects: and constructing a positioning dictionary, adaptively acquiring the optimal dictionary length according to the distribution type of the historical data and the statistical characteristics to obtain a dictionary table, and compressing and storing the data of the raw material medicine package by using the dictionary table so as to better perform intelligent management on the raw material medicines.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.

Fig. 1 is a flowchart illustrating steps of a method for intelligently managing a pharmaceutical substance according to an embodiment of the present invention;

FIG. 2 is a diagram of a dynamic dictionary provided in the present invention

FIG. 3 is a diagram illustrating dictionary updates provided by the present invention;

FIG. 4 is a schematic diagram of a statistical histogram provided by the present invention;

fig. 5 is a schematic diagram of a basic dictionary according to the present invention.

Detailed Description

In order to further explain the technical means and effects of the present invention adopted to achieve the predetermined invention purpose, the following detailed description of the specific implementation, structure, features and effects of the method and system for intelligently managing bulk drugs according to the present invention with reference to the accompanying drawings and preferred embodiments is provided below. In the following description, the different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

The following describes a specific scheme of the intelligent management method and system for bulk drugs provided by the invention in detail with reference to the accompanying drawings.

Referring to fig. 1, a flowchart of steps of a method for intelligently managing a drug substance according to an embodiment of the present invention is shown, where the method includes the following steps:

and step S001, obtaining the label on the raw material medicine package to obtain the label data.

When the bulk drugs are put in and taken out of a warehouse, the labels on the bulk drug packages are scanned by a scanning instrument to obtain corresponding label data, wherein the label data mainly comprise batch numbers of the drugs and corresponding date information, and also comprise information such as the types, the warehousing-in and warehousing-out time and the quantity of the collected bulk drugs. The acquired data are transmitted to the data processing system, the data processing system compresses the acquired data, and the processed data are transmitted to the storage system for data storage, so that the data management is facilitated.

Step S002, constructing a dynamic dictionary, wherein the abscissa of the dynamic dictionary represents the data type, and the ordinate represents the data of the same type; acquiring the length of intercepted data according to the data type of the historical data, constructing a histogram of the length of the intercepted data, and obtaining a screening threshold value according to the number of data types in the histogram and the occurrence frequency of the data under each type; and obtaining a target data type according to the screening threshold, intercepting the similarity between data under the target data type, obtaining a basic dictionary based on the similarity, and establishing a dictionary table according to the basic dictionary.

Specifically, the traditional LZW algorithm achieves the purpose of compressing data by constructing a dictionary, and can find the current character string in the dictionary

After that, until a certain character is input

The search in the dictionary fails, at which time it stops, replacing the corresponding character or character string in the original data by the dictionary index, and then replacing the corresponding character or character string in the original data with the dictionary index

Stored in a dictionary, although only one effective character is added in the traditional LZW compression algorithm

However, too many characters are stored, which wastes memory space and increases processing time. Therefore, it is desirable to dynamically update the dictionary when the dictionary is built, so as to achieve multiplexing of the dictionary, reduce the storage space of the dictionary, and reduce the data processing time.

The dictionary constructed by the traditional LZW algorithm has a large amount of redundant information, and the occupied space is large when the dictionary is stored, so that the traditional LZW algorithm is improved aiming at the situation, and the improvement method comprises the following steps:

the invention provides a new dynamic dictionary: and a coordinate system is constructed by taking the upper left corner as an origin, the x axis represents the data type in the dictionary, and the y axis represents the data of the same type. As shown in fig. 2: the x axis represents the appearance sequence of data, for example, x =1 is text information such as: the first data in "TO BE OR NOT TO BE OR TO BE OR NOT", the y-axis represents the location data for the same type of data. The dictionary is 0 at the initial time, and when the data storage is started, the dictionary reads in the first data of the text information, as shown in fig. 2

When the stored information of T is (1, 1), the reading of the second data in the text information is continued, as in the example

When the dictionary is updated, the x-axis and the y-axis are updated simultaneously, and the x-axis records second data, as shown in FIG. 2

At this time

The storage information of (2, 1), because the dictionary does not contain the updated combination data, the combination data is updated, i.e. the y-axis information is recorded, i.e. the combination data

The storage information of (1, 2), when the combined data appears again later

Then, the combined data is directly recorded

The storage information of (1, 2) is only needed; similarly, the dictionary is continuously recorded and updated, and the dictionary update is schematically illustrated in fig. 3.

According to the dynamic dictionary, each time we record coordinate information of data, as shown in FIG. 2, when the combined data appears

When we store the information as (1, 4).

When the data is compressed by the method, the dictionary needs to be built from 0, but when the dictionary is built, the data compression rate is small and the dictionary updating is complicated due to the fact that the dictionary is built from 0, so that the initial dictionary generation is carried out according to the data type. And (3) intercepting data, wherein for the time sequence information of the bulk drugs, high-frequency information in different time periods can be different, so that a plurality of data segments are intercepted according to the data types, and an initial dictionary is generated in a self-adaptive manner according to the distribution condition of the high-frequency information in the plurality of data segments. According to the historical data, the data type can be known, for example, the data type in the historical data is usually numbers and English letters, and the data type is the number of the type of the numbers plus the number of the type of the letters, so the length of the intercepted data is as follows:

in the formula, A represents the proportion of data types, when the value of A is larger than or equal to the empirical value, the empirical value is A =0.7, the data section with the corresponding length is intercepted, S represents the data type in the current data section, and N represents the total type of data. Starting from the first data, adding 1 to the S value when different types of data appear, wherein the S initial value is 0, the data types in the data section are continuously increased along with the reading of the data, when the value of S is increased to a certain moment, the value of A is equal to 0.7, the logging of the data is stopped, and the current data length is intercepted as reference data. And establishing a statistical histogram to perform statistics on the data in the intercepted data segment, wherein the statistical histogram is shown in fig. 4, S in the statistical histogram represents the number of types of data, and M represents the occurrence frequency of each type of data.

For different types of data, the more likely the data is to be continuous character strings, so the initial dictionary length is obtained according to the statistical condition of the histogram, namely:

in the formula

indicating the number of occurrences of the ith type of data,

indicating the most frequent type of occurrences. According to the screening threshold

Screening S types of data when

Has a value of greater than or equal to

When the data is marked, the data of the type is marked, and the data types larger than the threshold value are marked as

。

E.g. data type S is

A total of 26, wherein

Meet the screening threshold requirement, i.e. intercept data for the segment

The initial dictionary preferably contains 3 bits for the intercepted data, but the statistical features can only obtain the statistical features of the data, and the distribution of the data, i.e. the distribution situation of the data, is unknown

Is not determined, so the obtained length is

The sliding window slides in the intercepted data to obtain

In an arrangement of

In a certain arrangement, e.g. in

Arranged in such a way that will

As an initial dictionary; if it is

The arrangement mode is arranged according to a plurality of arrangement modes, the frequency of each arrangement mode is counted, if the frequency is more than or equal to two, the similarity between the arrangement mode and other arrangement modes is calculated, if the arrangement mode is not similar to the other arrangement modes, the corresponding arrangement mode is used as a basic dictionary, otherwise, the similarity is discarded, and the similarity is as follows: the corresponding positions are the same and are marked as 1, otherwise, the corresponding positions are 0, for example:

and

the similarity sequence of (a) is 111100, then:

in the formula

Representing similarity, m represents the number of data types greater than a threshold,

representing the same probability of the corresponding position of the data type j, if the probability is the same, the probability is 1, otherwise, the probability is 0

Most of the description sequences are the same, so that it is not necessary to have this sequence as a base dictionary.

By dividing the data into multiple ends, repeating the above operations to obtain multiple base dictionaries, as shown in fig. 5, a corresponding dictionary table is established according to the base dictionaries to obtain an improved conventional LZW algorithm.

And S003, compressing and storing the label data by using the improved traditional LZW algorithm.

Specifically, a corresponding dictionary table is established by utilizing a basic dictionary to compress and store the label data, the stored data is analyzed to manage the raw material medicines, and the management of the raw material medicines is carried out through the storage time, the number of the input and output storehouses and the access frequency, so that the abnormity of the raw material medicines such as weathering and dampness caused by improper storage is prevented, meanwhile, the use condition of the raw material medicines is better tracked, and the raw material medicines are better purchased.

In summary, the invention provides an intelligent management method for bulk drugs, which obtains a label on a bulk drug package to obtain label data; constructing a dynamic dictionary, wherein the abscissa of the dynamic dictionary represents the data type, and the ordinate represents the data of the same type; acquiring the length of intercepted data according to the data type of the historical data, constructing a histogram of the length of the intercepted data, and obtaining a screening threshold value according to the number of data types in the histogram and the occurrence frequency of the data under each type; obtaining a target data type according to a screening threshold, calculating similarity according to various arrangement modes of intercepted data under the target data type, obtaining a basic dictionary based on the similarity, and establishing a dictionary table according to the basic dictionary; and compressing and storing the label data according to the dictionary table. According to the scheme, the data of the raw material medicine package is compressed and stored through an improved traditional LZW algorithm, so that the intelligent management of the raw material medicines is better performed.

Based on the same inventive concept as the method, the embodiment of the present invention further provides an intelligent management system for a drug substance, which includes a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor implements the steps of any one of the above methods when executing the computer program.

It should be noted that: the sequence of the above embodiments of the present invention is only for description, and does not represent the advantages or disadvantages of the embodiments. And specific embodiments thereof have been described above. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit of the present invention.

Claims

1. An intelligent management method for bulk drugs is characterized by comprising the following steps:

acquiring a label on a raw material medicine bag to obtain label data;

constructing a dynamic dictionary, wherein the abscissa of the dynamic dictionary represents the data type, the ordinate represents the data of the same type, the abscissa and the ordinate are updated simultaneously when the dynamic dictionary is updated, and the ordinate information is recorded to update the combined data when the updated dynamic dictionary does not contain the updated combined data; acquiring the length of intercepted data according to the data type of the historical data, constructing a histogram of the length of the intercepted data, and obtaining a screening threshold value according to the number of data types in the histogram and the occurrence frequency of the data under each type; obtaining a target data type according to a screening threshold, calculating similarity according to various arrangement modes of intercepted data under the target data type, obtaining a basic dictionary based on the similarity, and establishing a dictionary table according to the basic dictionary;

compressing and storing the label data according to the dictionary table;

the calculation formula of the screening threshold is as follows:

indicating the number of occurrences of the ith type of data,

indicating the occurrence number of the type with the largest occurrence number;

the method for acquiring the intercepted data length comprises the following steps:

and acquiring a ratio between the data type and the total data type in the current historical data segment, and taking the current historical data segment as the intercepted data length when the ratio is larger than an empirical value.

2. The intelligent management method for bulk drugs according to claim 1, characterized in that the similarity calculation formula is:

wherein the content of the first and second substances,

representing the same probability of the corresponding location of data type j.

3. The method for intelligently managing bulk drugs according to claim 1, wherein the method for obtaining the target data type according to the screening threshold comprises:

4. The intelligent management method for bulk drugs according to claim 1, wherein the method for obtaining the base dictionary based on similarity comprises:

5. An intelligent drug substance management system comprising a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the intelligent drug substance management method according to any one of claims 1-4 when executing the computer program.