A management method for high-density storage data in intelligent pharmacies

Abstract

The invention relates to a management method for high-density storage data of an intelligent pharmacy, and belongs to the technical field of industrial data management of storage equipment. The method comprises the following steps: step 1): clustering each original data set by adopting a three-dimensional space model method to obtain each clustered original data set; step 2): constructing a data structure model, and generating a hollow data structure model from the constructed data structure model by adopting a hollow penetration method; step 3): putting each clustered original data set into the hollow data structure model, and performing multi-dimensional compression on the hollow data structure model to obtain each high-density original data set; step 4): searching for each compressed original data set with high density; the invention has the beneficial effects of performing multi-dimensional compression on the hollow data structure model to obtain each high-density original data set, searching each original data set in the hollow data structure model, and enabling each high-density original data set to be centrally managed.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial data management of storage equipment, and in particular to a management method for high-density storage data of an intelligent pharmacy. Background Art

[0002] The warehouse management of modern enterprises requires a certain amount of human resources to carry out warehouse management. In actual warehouse management, manual management will inevitably be affected by human factors (labor intensity and long working hours), which is not only time-consuming and labor-intensive, but also has a high error rate. As artificial intelligence technology is applied in more and more fields, the problems handled by artificial intelligence technology are becoming more and more complex. At present, in artificial intelligence technology, general processors such as CPU (Central Processing Unit), GPU (Graphic Processing Unit), DSP (Digital Signal Processing) chips and other dedicated hardware devices are used to perform data handling and data processing operations. In big data processing, staff can act as coordinators of data resources and call the system data through operating resource management to realize the scheduling, management and allocation of resource data.

[0003] With the continuous development of intelligent warehouse management, the main difficulty in warehouse management of medical drugs, especially in intelligent warehouse system management (intelligent pharmacy), is that the quality of warehouse management strategy data directly affects the efficiency of intelligent warehouse system use. Once the warehouse management strategy data goes wrong, there will be chaos in warehouse management. Therefore, how to reasonably control the operation of intelligent warehouse system is a technical problem that needs to be solved urgently. Summary of the invention

[0004] The technical problem to be solved by the present invention is how to reasonably control the operation of an intelligent warehousing system. The present invention provides a management method for high-density storage data of an intelligent pharmacy, which performs multi-dimensional compression on a hollow data structure model to obtain high-density original data sets. After compression, the high-density original data sets will not be separated from the hollow data structure model. The original data are compactly concentrated, and the original data sets are searched in the hollow data structure model, so that the high-density original data sets can be centrally managed and the search of the original data sets is convenient.

[0005] In order to achieve the above object, the present invention is implemented by the following technical solutions:

[0006] A method for managing high-density storage data of an intelligent pharmacy comprises the following steps:

[0007] Step 1): collect each original data set, cluster each original data set using a three-dimensional space model method, and obtain each clustered original data set;

[0008] Step 2): construct a data structure model, and use the hollow penetration method to generate a hollow data structure model from the constructed data structure model;

[0009] Step 3): Put each clustered original data set into the hollow data structure model, perform multi-dimensional compression on the hollow data structure model, and obtain each high-density original data set;

[0010] Step 4): Search for each compressed original data set with high density.

[0011] Optionally, in step 1), the three-dimensional space model method is used to cluster each of the original data sets, using the following steps:

[0012] Step 11): setting each original data set in each of the three-dimensional space models accordingly; wherein the three-dimensional space model is a rectangular model;

[0013] Step 12): distilling the original data set in each of the rectangular models using a distillation method, so that the original data set after impurities are removed can be obtained in each rectangular model;

[0014] Step 13): Classify and place the rectangular models together according to the original data sets after impurities are removed.

[0015] Optionally, in step 1), the three-dimensional space model method is used to cluster each of the original data sets, using the following formula (1)-formula (2):

[0016] (1);

[0017] (2);

[0018] In formula (1), , , and For each rectangular model, , , and are the original data sets in each rectangular model, , , and These are the processes of distilling the original data sets in each rectangular model; or The process of distilling the original data sets in each rectangular model together;

[0019] In formula (2), after removing the original data sets corresponding to each rectangular model, , , and Impurities , , and back, , , and They are the original data sets after impurities are removed in each rectangular model; , , and They are the rectangular models of each original data set after impurity removal; or The rectangular models are classified and placed together, wherein the type of each original data set after impurity removal determines the type of each rectangular model.

[0020] Optionally, in step 2), the hollow data structure model is generated by the following steps:

[0021] Step 21): constructing the data structure model into a solid three-dimensional cube model;

[0022] Step 22): hollowing out the interior of the solid three-dimensional cube model using the hollow penetration method.

[0023] Furthermore, in step 22), the hollow penetration method adopts the following formula (3):

[0024] (3);

[0025] in, The solid three-dimensional model tool is a three-dimensional rectangular model tool, and the solid three-dimensional model tool can be moved from one direction to another direction in a straight line; is the bottom area of ​​the solid three-dimensional model tool, and the size of the bottom area of ​​the solid three-dimensional model tool can be changed; is the height of the solid three-dimensional model tool, the height of the solid three-dimensional model tool is adjustable, and the height of the solid three-dimensional model tool can be raised or lowered, To smoothly adjust the height of solid three-dimensional model tools;

[0026] The volume of the solid three-dimensional model tool can be changed. is a function of the fixed solid solid model tool volume size, The size of the solid solid model tool is fixed, and the solid solid model tool with the fixed size can be moved from one direction to another in a straight line;

[0027] The solid three-dimensional model tool with a fixed volume is moved in a straight line from one direction to another direction to hollow out the interior of the solid three-dimensional cube model.

[0028] Optionally, in step 3), each of the clustered original data sets is placed into the hollow data structure model according to the type of each of the original data sets.

[0029] Furthermore, the multi-dimensional compression is performed on the hollow data structure model using the following formula (4):

[0030] (4);

[0031] in, is the compression amount of the hollow data structure model in the length direction, and the compression direction in the length direction is from one direction to another direction or from another direction to one direction. is the compression amount of the hollow data structure model in the length direction per unit time;

[0032] is the compression amount of the hollow data structure model in the width direction, and the compression direction in the width direction is from one direction to another direction or from another direction to one direction, is the compression amount of the hollow data structure model in the width direction per unit time;

[0033] is the compression amount of the hollow data structure model in the height direction, and the compression direction in the height direction is from one direction to another direction or from another direction to one direction. is the compression amount of the hollow data structure model in the height direction per unit time;

[0034] is the compression amount of the hollow data structure model in the length, width and height directions per unit time; This is the compression result of the hollow data structure model.

[0035] Optionally, in step 4), when searching the compressed original data sets with high density, the search is performed according to the type of each original data set.

[0036] Beneficial effects of the present invention:

[0037] The present invention distributes each original data set to each corresponding rectangular three-dimensional space model, generates a hollow data structure model from the constructed data structure model, puts each original data set or each rectangular three-dimensional space model after clustering or classification into the hollow data structure model, and performs multi-dimensional compression on the hollow data structure model to obtain each high-density original data set. The compressed and high-density original data sets will not be separated from the hollow data structure model. Each original data is compactly concentrated, and each original data set is searched in the hollow data structure model, so that each high-density original data set can be centrally managed and the search of each original data set is convenient. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0039] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0040] Figure 2 It is a schematic diagram of the working principle of the present invention.

[0041] Icons: 1-first original data, 2-second original data, 3-third original data, 4-fourth original data, 5-hollow data structure model. DETAILED DESCRIPTION

[0042] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0043] Embodiment 1:

[0044] like Figure 1 As shown, this embodiment provides a method for managing high-density storage data of an intelligent pharmacy, comprising the following steps:

[0045] Step 1): collect each original data set, cluster each original data set using a three-dimensional space model method, and obtain each clustered original data set;

[0046] Step 2): construct a data structure model, and use the hollow penetration method to generate a hollow data structure model from the constructed data structure model;

[0047] Step 3): Put each clustered original data set into the hollow data structure model, perform multi-dimensional compression on the hollow data structure model, and obtain each high-density original data set;

[0048] Step 4): Search for each compressed original data set with high density.

[0049] Specifically, in step 1), the three-dimensional space model method is used to cluster each original data set, using the following steps:

[0050] Step 11): Set each original data set in each three-dimensional space model accordingly; wherein the three-dimensional space model is a rectangular model ( Figure 2 The first figure in the figure is a three-dimensional space model with four rectangular models, each rectangular model contains first original data 1, second original data 2, third original data 3 and fourth original data 4);

[0051] Step 12): distilling the original data set in each rectangular model using a distillation method, so that the original data set after impurities are removed can be obtained in each rectangular model;

[0052] Step 13): Classify and place the rectangular models together according to the original data sets after impurities are removed (as shown in the following example): Figure 2 The first figure shows the placement of the various rectangular models).

[0053] In addition, in step 1), the three-dimensional space model method is used to cluster each of the original data sets, using the following formula (1)-formula (2):

[0054] (1);

[0055] (2);

[0056] In formula (1), , , and For each rectangular model, , , and are the original data sets in each rectangular model, , , and These are the processes of distilling the original data sets in each rectangular model; or The process of distilling the original data sets in each rectangular model together;

[0057] In formula (2), after removing the original data sets corresponding to each rectangular model, , , and Impurities , , and back, , , and They are the original data sets after impurities are removed in each rectangular model; , , and They are the rectangular models of each original data set after impurity removal; or Each rectangular model is classified and placed together, wherein the type of each original data set after impurities are removed determines the type of each rectangular model, that is, each rectangular model is equipped with a different type of original data set, and each original data set is classified, and each rectangular model is divided into different types.

[0058] In step 2), the hollow data structure model is generated by the following steps:

[0059] Step 21): construct the data structure model into a solid three-dimensional cube model;

[0060] Step 22): The interior of the solid three-dimensional cube model is hollowed out by the hollow penetration method to obtain Figure 2 The second figure is the hollow data structure model 5. The thickness of the hollow data structure model 5 can be modified so that the thickness of the hollow data structure model 5 is the same as the thickness of each rectangular model.

[0061] Furthermore, in step 22), the hollow penetration method adopts the following formula (3):

[0062] (3);

[0063] in, It is a solid three-dimensional model tool. The solid three-dimensional model tool is a three-dimensional rectangular model tool. The solid three-dimensional model tool can be moved from one direction to another direction in a straight line; is the bottom area of ​​the solid three-dimensional model tool, and the size of the bottom area of ​​the solid three-dimensional model tool can be changed; is the height of the solid three-dimensional model tool, the height of the solid three-dimensional model tool is adjustable, and the height of the solid three-dimensional model tool can be raised or lowered, To smoothly adjust the height of solid three-dimensional model tools;

[0064] The volume of the solid three-dimensional model tool can be changed. is a function of the fixed solid solid model tool volume size, The size of the solid solid model tool is fixed, and the solid solid model tool with the fixed size can be moved from one direction to another in a straight line;

[0065] The solid three-dimensional model tool with a fixed volume is moved in a straight line from one direction to another direction to hollow out the interior of the solid three-dimensional cube model, thereby obtaining a hollow data structure model 5.

[0066] The hollow penetration method is to pass a solid three-dimensional model tool from one side outside the constructed data structure model through the inside of the constructed data structure model to the other side outside the constructed data structure model to generate a hollow data structure model 5 .

[0067] In step 3), each original data set after clustering is placed into the hollow data structure model according to the type of each original data set. Figure 2 The third picture of .

[0068] Furthermore, the hollow data structure model is compressed in multiple dimensions using the following formula (4):

[0069] (4);

[0070] in, is the compression amount of the hollow data structure model in the length direction, and the compression direction in the length direction is from one direction to another direction or from another direction to one direction. is the compression amount of the hollow data structure model in the length direction per unit time;

[0071] is the compression amount of the hollow data structure model in the width direction, and the compression direction in the width direction is from one direction to another direction or from another direction to one direction, is the compression amount of the hollow data structure model in the width direction per unit time;

[0072] is the compression amount of the hollow data structure model in the height or thickness direction. The compression direction in the height direction is from one direction to another direction or from another direction to one direction. is the compression amount of the hollow data structure model in the height direction per unit time;

[0073] is the compression amount of the hollow data structure model in the length, width and height directions per unit time; is the compression result of the hollow data structure model, namely Figure 2 The 4th picture of .

[0074] The hollow data structure model is compressed in length, width and height directions in a unit time to obtain compressed or high-density original data sets of the first original data 1 to the fourth original data 4 in the hollow data structure model.

[0075] In step 4), when searching for each compressed and high-density original data set, the search is performed according to the type of each original data set. Each compressed and high-density original data set will not be separated from the hollow data structure model within the hollow data structure model. Each original data is compactly concentrated, and each original data set is searched within the hollow data structure model.

[0076] Embodiment 2:

[0077] Based on Example 1, Figure 2 Combination Figure 1 ,like Figure 2 As shown, this embodiment is: step 1) is Figure 2 The first picture ( Figure 2 Counting from left to right), allocating each original data set, namely, the first original data 1 to the fourth original data 4, to each corresponding rectangular three-dimensional space model;

[0078] Step 2) is Figure 2 In the second figure, the hollow penetration method is used to generate a hollow data structure model 5 from the constructed data structure model;

[0079] Step 3) is Figure 2 In the third figure, each original data set after clustering or classification or each rectangular three-dimensional space model is placed into the hollow data structure model 5, and the hollow data structure model is multi-dimensionally compressed to obtain each high-density original data set, that is, Figure 2 The 4th figure of;

[0080] From step 4), it can be seen that the compressed and high-density original data sets will not be separated from the hollow data structure model, and the original data are compactly concentrated. In addition, searching for each original data set in the hollow data structure model enables the high-density original data sets to be centrally managed, and the search for each original data set is convenient.

[0081] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope of the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for managing high-density storage data in an intelligent pharmacy, characterized in that: The steps include: Step 1): collect each original data set, cluster each original data set using a three-dimensional space model method, and obtain each clustered original data set; In the step 1), the three-dimensional space model method is used to cluster each of the original data sets, using the following formula (1)-formula (2): (1)； (2)； In formula (1), , , and For each rectangular model, , , and are the original data sets in each rectangular model, , , and These are the processes of distilling the original data sets in each rectangular model; or The process of distilling the original data sets in each rectangular model together; In formula (2), after removing the original data sets corresponding to each rectangular model, , , and Impurities , , and back, , , and They are the original data sets after impurities are removed in each rectangular model; , , and They are the rectangular models of each original data set after impurity removal; or The rectangular models are classified and placed together, wherein the type of each original data set after impurity removal determines the type of each rectangular model; Step 2): construct a data structure model, and use the hollow penetration method to generate a hollow data structure model from the constructed data structure model; Step 3): Put each clustered original data set into the hollow data structure model, perform multi-dimensional compression on the hollow data structure model, and obtain each high-density original data set; Step 4): Search for each compressed original data set with high density.

2. The method for managing high-density storage data of an intelligent pharmacy according to claim 1, characterized in that: In step 1), the three-dimensional space model method is used to cluster each of the original data sets, using the following steps: Step 11): setting each original data set in each of the three-dimensional space models accordingly; wherein the three-dimensional space model is a rectangular model; Step 12): distilling the original data set in each of the rectangular models using a distillation method, so that the original data set after impurities are removed can be obtained in each rectangular model; Step 13): Classify and place the rectangular models together according to the original data sets after impurities are removed.

3. The method for managing high-density storage data of an intelligent pharmacy according to claim 1, characterized in that: In step 2), the hollow data structure model is generated by the following steps: Step 21): constructing the data structure model into a solid three-dimensional cube model; Step 22): hollowing out the interior of the solid three-dimensional cube model using the hollow penetration method.

4. The method for managing high-density storage data of an intelligent pharmacy according to claim 3, characterized in that: In the step 22), the hollow penetration method adopts the following formula (3): (3)； in, The solid three-dimensional model tool is a three-dimensional rectangular model tool, and the solid three-dimensional model tool can be moved from one direction to another direction in a straight line; is the bottom area of ​​the solid three-dimensional model tool, and the size of the bottom area of ​​the solid three-dimensional model tool can be changed; is the height of the solid three-dimensional model tool, the height of the solid three-dimensional model tool is adjustable, and the height of the solid three-dimensional model tool can be raised or lowered, To smoothly adjust the height of solid three-dimensional model tools; The volume of the solid three-dimensional model tool can be changed. is a function of the fixed solid solid model tool volume size, The size of the solid solid model tool is fixed, and the solid solid model tool with the fixed size can be moved from one direction to another in a straight line; The solid three-dimensional model tool with a fixed volume is moved in a straight line from one direction to another direction to hollow out the interior of the solid three-dimensional cube model.

5. The method for managing high-density storage data of an intelligent pharmacy according to claim 1, characterized in that: In the step 3), each of the clustered original data sets is placed into the hollow data structure model according to the type of each original data set.

6. The method for managing high-density storage data of an intelligent pharmacy according to claim 5, characterized in that: The multi-dimensional compression is performed on the hollow data structure model using the following formula (4): (4)； in, is the compression amount of the hollow data structure model in the length direction, and the compression direction in the length direction is from one direction to another direction or from another direction to one direction. is the compression amount of the hollow data structure model in the length direction per unit time; is the compression amount of the hollow data structure model in the width direction, and the compression direction in the width direction is from one direction to another direction or from another direction to one direction, is the compression amount of the hollow data structure model in the width direction per unit time; is the compression amount of the hollow data structure model in the height direction, and the compression direction in the height direction is from one direction to another direction or from another direction to one direction. is the compression amount of the hollow data structure model in the height direction per unit time; is the compression amount of the hollow data structure model in length, width and height directions per unit time; This is the compression result of the hollow data structure model.

7. The method for managing high-density storage data of an intelligent pharmacy according to claim 1, characterized in that: In the step 4), when searching the compressed and high-density original data sets, the search is performed according to the type of the original data sets.

Images