Visual processing method and system for medical consumable data and computer equipment

Abstract

The invention relates to the technical field of intelligent medical treatment, and particularly discloses a visual processing method and system for medical consumable data and computer equipment, and the method comprises the steps: obtaining variable configuration information; the variable configuration information comprises an image drawing type, an X-axis statistical variable, a Y-axis statistical variable and image division information; wherein the statistical dimension of the X-axis statistical variable is different from the statistical dimension of the Y-axis statistical variable; judging whether to display the sub-graph according to the sub-graph information; when the image division information is not, a first image layer is generated according to the image drawing type, the medical consumable data corresponding to the X-axis statistical variable and the medical consumable data corresponding to the Y-axis statistical variable, and the first image layer comprises a first combined image. And generating a first image layer according to the image drawing type in the variable configuration information, the medical consumable data corresponding to the X-axis statistical variable and the medical consumable data corresponding to the Y-axis statistical variable. According to the invention, the information of multiple dimensions is displayed in the first layer at one time, which is helpful for a user to carry out rapid visual analysis from multiple data dimensions.

Description

Technical Field

[0001] The present invention relates to the field of intelligent medical technology, and particularly to a method, a system, and a computer device for visual processing of medical consumable data. Background Art

[0002] In the existing high-value medical consumable management system, the usage situation of high-value medical consumables is often statistically analyzed in the form of list queries. However, this tabular-based representation form is difficult to intuitively display information in multiple dimensions and the relationships between various dimensions, etc., and it is impossible to understand the usage situation of high-value consumables in multiple dimensions such as each department, each time period, and various diseases in the hospital as a whole, so it is difficult to achieve full-process supervision and control of the consumable usage situation. At the same time, when analyzing the usage situation of medical consumables in different dimensions, it is necessary to reselect field conditions, and the operation steps are relatively cumbersome, with a certain learning cost for clinical applications. Summary of the Invention

[0003] Based on this, in view of the problems existing in the existing high-value consumable management system, it is necessary to provide a method, a system, a computer device, and a storage device for visual processing of medical consumable data.

[0004] A method for visual processing of medical consumable data includes obtaining variable configuration information; the variable configuration information includes an image drawing type, an X-axis statistical variable, a Y-axis statistical variable, and sub-graph information; wherein, the statistical dimension of the X-axis statistical variable is different from the statistical dimension of the Y-axis statistical variable; judging whether to display sub-graphs according to the sub-graph information; when the sub-graph information is no, generating a first layer according to the image drawing type, the medical consumable data corresponding to the X-axis statistical variable, and the medical consumable data corresponding to the Y-axis statistical variable, and the first layer includes a first combined graph.

[0005] In one embodiment, the variable configuration information further includes a sub-graph dimension, and the sub-graph dimension is any one of the X-axis statistical variables. After judging whether to display sub-graphs according to the sub-graph information, the method further includes when the sub-graph information is yes, judging whether the variable configuration information includes at least two X-axis statistical variables with different statistical dimensions; when the variable configuration information includes at least two X-axis statistical variables with different statistical dimensions, generating a first layer according to the image drawing type, the medical consumable data corresponding to the sub-graph dimension, and the medical consumable data corresponding to the Y-axis statistical variable, and the first layer includes a first sub-graph, and the number of the first sub-graphs is determined according to the size of the sub-graph dimension.

[0006] In one embodiment, after generating the first layer, the method further includes, when the image type of the first layer is a composite image, selecting the X-axis statistical variable corresponding to the X-axis in the first layer as a new sub-image dimension based on a sub-image instruction, performing sub-image splitting on the first layer, and generating a second layer according to the image drawing type, the medical consumable data corresponding to the sub-image dimension, and the medical consumable data corresponding to the Y-axis statistical variable. The second layer includes second sub-images, and the number of the second sub-images is determined according to the size of the sub-image dimension.

[0007] In one embodiment, the variable configuration information further includes a sub-image mode. After generating the first layer, the method further includes, when the image type of the first layer is a sub-image and the sub-image mode is overall sub-image splitting, selecting the X-axis statistical variable corresponding to the X-axis in the first layer as a new sub-image dimension based on a sub-image instruction, performing sub-image splitting on the first layer, and generating a second layer according to the image drawing type, the medical consumable data corresponding to all the sub-image dimensions in the first layer, and the medical consumable data corresponding to the Y-axis statistical variable. The second layer includes second sub-images, and the number of the second sub-images is determined according to the size of the sub-image dimension.

[0008] In one embodiment, the variable configuration information further includes a sub-image mode. After generating the first layer, the method further includes, when the image type of the first layer is a sub-image and the sub-image mode is partial sub-image splitting, selecting the X-axis statistical variable corresponding to the X-axis in the first layer as a new sub-image dimension based on a sub-image instruction, performing sub-image splitting on the first layer, and generating a second layer according to the image drawing type, the medical consumable data corresponding to the sub-image dimension in the selected first sub-image of the first layer, and the medical consumable data corresponding to the Y-axis statistical variable. The second layer includes second sub-images, and the number of the second sub-images is determined according to the size and type of the sub-image dimension.

[0009] In one embodiment, when the type of the sub-image dimension includes M pictures and the size of the sub-image dimension is N, the number of the second sub-images is M * N, where both M and N are non-zero natural numbers.

[0010] A visualization processing system for medical consumable data, comprising a variable configuration module for obtaining variable configuration information; the variable configuration information includes an image drawing type, an X-axis statistical variable, a Y-axis statistical variable, and sub-graph information; wherein, the statistical dimension of the X-axis statistical variable is different from the statistical dimension of the Y-axis statistical variable; an image processing module, connected to the variable configuration module, for determining whether to display sub-graphs according to the sub-graph information, and also for generating a first layer according to the image drawing type, the medical consumable data corresponding to the X-axis statistical variable, and the medical consumable data corresponding to the Y-axis statistical variable when the sub-graph information is no, the first layer includes a first combined graph.

[0011] A computer device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the visualization processing method for medical consumable data described in any one of the above embodiments.

[0012] A computer-readable storage medium stores a computer program thereon, and when the computer program is executed by a processor, it implements the steps of the visualization processing method for medical consumable data described in any one of the above embodiments.

[0013] A computer program product includes a computer program, and when the computer program is executed by a processor, it implements the steps of the visualization processing method for medical consumable data described in any one of the above embodiments.

[0014] The above visualization processing method for medical consumable data determines whether the image is displayed in the form of sub-graphs based on the sub-graph information in the variable configuration information by obtaining the variable configuration information. When the sub-graph information is no, it is determined that the image is displayed in the form of a combined graph, and a first layer is generated according to the image drawing type, the medical consumable data corresponding to the X-axis statistical variable, and the medical consumable data corresponding to the Y-axis statistical variable in the variable configuration information. Multiple dimensions of information are displayed at one time in the form of the first combined graph in the first layer, which helps users grasp the global characteristics of the statistical variables and enables rapid visualization analysis of the usage of medical consumables from multiple dimensions, facilitating the hospital to achieve efficient supervision and pre-planning of the usage of medical consumables. Description of the Drawings

[0015] In order to more clearly illustrate the embodiments of the present specification or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments recorded in the present specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0016] Figure 1Schematic diagram of the application environment of the visualization processing method for medical consumable data in one embodiment of the present application;

[0017] Figure 2 Schematic diagram of the method flow of the visualization processing method for medical consumable data in one embodiment of the present application;

[0018] Figure 3a Schematic diagram of the variable configuration information in the first embodiment of the present application;

[0019] Figure 3b Schematic diagram of the first layer in the first embodiment of the present application;

[0020] Figure 4 Schematic diagram of the method flow of the visualization processing method for medical consumable data in another embodiment of the present application;

[0021] Figure 5a Schematic diagram of the variable configuration information in the second embodiment of the present application;

[0022] Figure 5b Partial schematic diagram of the first layer in the second embodiment of the present application;

[0023] Figure 6 Schematic diagram of the method flow of the sub - figure operation on the first layer in one embodiment of the present application;

[0024] Figure 7a Schematic diagram of the variable configuration information in the third embodiment of the present application;

[0025] Figure 7b Schematic diagram of the second layer in the third embodiment of the present application;

[0026] Figure 8a Schematic diagram of the variable configuration information in the fourth embodiment of the present application;

[0027] Figure 8b Partial schematic diagram of the second layer in the fourth embodiment of the present application;

[0028] Figure 9a Schematic diagram of the variable configuration information in the fifth embodiment of the present application;

[0029] Figure 9b Partial schematic diagram of the second layer in the fifth embodiment of the present application;

[0030] Figure 10 Schematic diagram of the structure of the visualization processing system for medical consumable data in one embodiment of the present application;

[0031] Figure 11 Schematic diagram of the device structure for implementing the visualization processing method for medical consumable data in one embodiment of the present application;

[0032] Figure 12 This is the internal structure diagram of a computer device in one embodiment of the present application. Detailed implementation manners

[0033] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Preferred embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the description of the present invention in this specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0035] Figure 1 This is a schematic diagram of the application environment of the visualization processing method for medical consumable data in one embodiment of the present application. The visualization processing method for medical consumable data provided by the embodiments of the present application can be applied to an application environment as Figure 1 shown. Among them, the terminal 102 communicates with the server 104 through a network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or placed in the cloud or other network servers. Among them, the terminal 102 can be, but is not limited to, various personal computers, laptop computers, smart phones, tablet computers, Internet of Things devices, and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, smart in-vehicle devices, etc. The portable wearable devices can be smart watches, smart bracelets, head-mounted devices, etc. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0036] In some embodiments, the visualization processing method of medical consumables data can be executed by a visualization processing system of medical consumables data. For example, the visualization processing method of medical consumables data can be stored in a storage device (such as the built-in storage unit of the visualization processing system of medical consumables data or an external storage device) in the form of a program or instruction. When the program or instruction is executed, the visualization processing method of medical consumables data can be implemented. The visualization processing system of medical consumables data disclosed in this application for implementing the above visualization processing method of medical consumables data can be either a device with a large amount of computing resources (for example, a computer, a server, cloud computing, etc.) or a device with limited computing resources (for example, a hardware circuit such as an FPGA chip board or an ASIC chip board).

[0037] It should also be noted that the relevant information (including but not limited to user input information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this disclosure are all information and data that have been authorized by the user or fully authorized by all parties.

[0038] To solve one or more problems as described in the background art, an embodiment of this application provides a simulation exercise method, system, and computer device based on a large model. Figure 2 FIG. is a schematic flowchart of the visualization processing method of medical consumables data in one embodiment of this application. In one embodiment, as Figure 2 shown, a visualization processing method of medical consumables data is provided. Taking the application environment in Figure 1 as an example for illustration, it may include the following steps S100 to step S300.

[0039] Step S100: Obtain variable configuration information; the variable configuration information includes an image drawing type, an X-axis statistical variable, a Y-axis statistical variable, and sub-graph information; wherein, the statistical dimensions of the X-axis statistical variable and the Y-axis statistical variable are different.

[0040] The user can configure the variable configuration information according to the analysis requirements. In this embodiment, the variable configuration information may include an image drawing type, an X-axis statistical variable, a Y-axis statistical variable, and sub-graph information. Among them, the image drawing type may refer to the display type of the statistical image, for example, it may be a bar chart, a line chart, a bar graph, a bubble chart, a radar chart, a pie chart, etc. The X-axis statistical variable may refer to the statistical variable corresponding to the X-axis coordinate in the statistical image, and the Y-axis statistical variable may refer to the statistical variable corresponding to the Y-axis coordinate in the statistical image. Further, the display form of the X-axis statistical variable can also be configured. When the X-axis statistical variable is a continuous variable, the minimum value and / or maximum value, interval interval, etc. of the X-axis statistical variable can be set.

[0041] The statistical dimensions of the statistical variables on the X-axis are different from those of the statistical variables on the Y-axis to prevent duplication of the selected statistical dimensions and affect the validity of the drawn statistical image. Additionally, the statistical variable on the Y-axis can be an operation variable for statistical analysis of the statistical variable on the X-axis. Based on the statistical variable on the X-axis, the analysis target can be clarified, and based on the statistical variable on the Y-axis, the statistical, analytical, or calculation method for the analysis target can be clarified. The statistical variables on the X-axis can include, but are not limited to, department names, operator names, discharge times, prices, etc., and the statistical variables on the Y-axis can include, but are not limited to, counting, summing, averaging, maximum value, minimum value. For example, when the statistical variable on the X-axis is configured as the department name and the statistical variable on the Y-axis is configured as counting, the counting situation corresponding to each department can be determined based on the statistical variables on the X-axis and the Y-axis.

[0042] In one embodiment, the variable configuration information includes X-axis statistical variables of at least one statistical dimension and Y-axis statistical variables of at least one statistical dimension, and the variable configuration information includes X-axis statistical variables of no more than three statistical dimensions and Y-axis statistical variables of no more than one statistical dimension. At the same time, when the variable configuration information includes multiple X-axis statistical variables, the statistical dimensions of each X-axis statistical variable are different. That is, when configuring a statistical image, the user must select at least one X-axis statistical variable and one Y-axis statistical variable. At the same time, the user can also configure X-axis statistical variables of no more than three different statistical dimensions according to the analysis requirements to achieve the technical effect of intuitively displaying information in multiple dimensions.

[0043] Step S200: Determine whether to display subgraphs according to the subgraph information.

[0044] In this embodiment, based on the subgraph information, it can be determined whether the user hopes to perform visual analysis on the selected data in the form of subgraphs or a combined graph. Among them, a combined graph can refer to gathering all the data selected by the user in one image for display; a subgraph can refer to displaying the data selected by the user as multiple different images according to the data of the specified X dimension (subgraph dimension).

[0045] Step S300: When the subgraph information is no, generate a first layer according to the image drawing type, the medical consumable data corresponding to the X-axis statistical variable, and the medical consumable data corresponding to the Y-axis statistical variable. The first layer includes a first combined graph.

[0046] When the sub - figure information is no, it can be determined that the user hopes to display the statistical image in the form of a combined figure. Thus, a first layer can be generated according to the image drawing type, the medical consumable data corresponding to the statistical variable on the X - axis, and the medical consumable data corresponding to the statistical variable on the Y - axis. The first layer includes a first combined figure. That is, based on the first combined figure, the user can intuitively understand the multi - dimensional statistical information of the medical consumable data. Specifically, the medical consumable data corresponding to the statistical variable on the X - axis is used as the variable on the X - axis of the statistical image, and the medical consumable data corresponding to the statistical variable on the Y - axis is used as the variable on the Y - axis of the statistical image, and the first combined figure is drawn in the form of the image drawing type.

[0047] Figure 3a It is a schematic diagram of the variable configuration information in the first embodiment of the present application. Figure 3b It is a schematic diagram of the first layer in the first embodiment of the present application. According to Figure 3a the variable configuration situation, the first layer as shown in Figure 3b can be generated. In this embodiment, the specific operation steps of the visualization method of medical consumable data are described according to the configuration situation shown in Figure 3a , but it should not be construed as a limitation of the scope of the invention patent. The user can configure the image drawing type as a bar chart. At the same time, according to Figure 3a , it can be known that three X - axis statistical variables X1, X2, X3 with different statistical dimensions and one Y - axis statistical variable Y are configured. Among them, X1 is configured as gender, X2 is configured as the discharge time, and at the same time, the time granularity of the discharge time is limited. The time granularity of the discharge time is limited to months, that is, the discharge time is statistically counted and displayed with months as the smallest statistical unit. X3 is configured as the name of the discharge department, and Y is configured as the count. At the same time, the sub - figure information is no. Based on the above variable configuration information, the first layer as shown in Figure 3b can be obtained.

[0048] In the medical consumable data, the discharge time mainly includes four kinds of data: 2024 - 01, 2024 - 02, 2024 - 03, and 2024 - 04. The gender includes two kinds of data: male and female. The name of the discharge department includes the Pulmonary Oncology Department. Therefore, on the X - axis of the first combined figure in the first layer, there is mainly a large display area for the Pulmonary Oncology Department. Among them, the display area where the Pulmonary Oncology Department is located is divided into 4 different bar chart display areas, which respectively represent different discharge times. In the 4 different bar chart display areas, there are two different - colored columns to display the statistical results of gender data. Among them, one - colored column represents that the gender is female, and the other - colored column represents that the gender is male. In addition, the Y - axis represents the count value for different - dimensional data on the X - axis. Based on Figure 3b , the user can intuitively understand the usage quantity of medical consumables by patients of different genders in different discharge times in different discharge departments. For example,Figure 3b The leftmost column represents that the number of medical consumables used by female patients among those discharged from the Pulmonary Oncology Department in January 2024 is 2,000.

[0049] It can be seen that by using the visualization processing method for medical consumable data provided in this application, by converting medical consumable data into visual statistical images, statistical information can be presented more intuitively, which helps users quickly understand the selected statistical variables. When users select multiple analysis dimensions, presenting the information of multiple dimensions at once in the form of images helps users grasp the global characteristics of statistical variables. By quickly performing visual analysis on the usage of medical consumables from multiple dimensions, efficient statistical analysis of the usage of medical consumables can be achieved, which is more conducive to hospitals evaluating the rationality and accuracy of the use of medical consumables, thereby realizing the whole-process supervision and control of high-value medical consumables.

[0050] Figure 4 FIG. is a schematic flowchart of the visualization processing method for medical consumable data in another embodiment of this application. In one embodiment, after determining whether to display sub-graphs according to sub-graph information, the method may further include the following steps S400 to step S500.

[0051] Step S400: When the sub-graph information is yes, determine whether the variable configuration information includes X-axis statistical variables of at least two different statistical dimensions.

[0052] When the sub-graph information is yes, it can be determined that the user hopes to display statistical images in the form of a combined graph. Among them, a sub-graph can refer to dividing and displaying multiple statistical images based on the information of a certain dimension according to a certain graph or a certain set of data. Through the sub-graph operation, multiple sub-graph results can be displayed at once based on the set sub-graph rules, and the information of each dimension of the selected statistical variables can be understood more intuitively and in detail.

[0053] In this embodiment, when the user configures variable configuration information and hopes to display the selected data in the form of sub-graphs, the user can also configure the sub-graph dimension to determine the division dimension of the sub-graph. Among them, the sub-graph dimension can be any one of the X-axis statistical variables. For example, when the variable configuration information includes three X-axis statistical variables X1, X2, and X3 of different statistical dimensions, the sub-graph dimension is any one selected from the X-axis statistical variables X1, X2, and X3.

[0054] Considering that when dividing sub-graphs, multiple statistical images are divided and displayed based on the information of the sub-graph dimension, therefore, when the user performs the sub-graph operation, at least two X-axis statistical variables of different statistical dimensions should be configured.

[0055] Step S500: When the variable configuration information includes X-axis statistical variables of at least two statistical dimensions, a first layer is generated according to the image drawing type, the medical consumable data corresponding to the sub-graph dimension, and the medical consumable data corresponding to the Y-axis statistical variable. The first layer includes first sub-graphs, and the number of first sub-graphs is determined according to the size of the sub-graph dimension.

[0056] When the variable configuration information includes X-axis statistical variables of at least two statistical dimensions, it can be determined that the user can display the statistical image in the form of sub-graphs. Thus, a first layer can be generated according to the image drawing type, the medical consumable data corresponding to the sub-graph dimension, and the medical consumable data corresponding to the Y-axis statistical variable. The first layer may include first sub-graphs. The number of first sub-graphs can be determined according to the size of the sub-graph dimension. The size of the sub-graph dimension may refer to the number of categories of variables selected for the sub-graph dimension. For example, when the sub-graph dimension is gender, since the number of optional items for gender is 2 (male, female), the size of the sub-graph dimension is 2. When forming the first layer according to the variable configuration, since there is no reference relationship of the X-axis statistical variable of the first layer to the previous layer, at this time, whether the sub-graph mode selects local sub-graphs or overall sub-graphs, the formed first layer is the same.

[0057] Based on each first sub-graph shown in the first layer, the user can intuitively understand the refined statistical information of multiple dimensions for the medical consumable data. Specifically, the number of first sub-graphs is determined according to the size of the sub-graph dimension. The medical consumable data corresponding to the X-axis statistical variables that are not selected as the sub-graph dimension are used as the variables on the X-axis in each first sub-graph, and the medical consumable data corresponding to the Y-axis statistical variable are used as the variables on the Y-axis in the first sub-graph, and each first sub-graph is drawn in the form of the image drawing type.

[0058] Figure 5a It is a schematic diagram of variable configuration information in the second embodiment of the present application. Figure 5b It is a partial schematic diagram of the first layer in the second embodiment of the present application. According to Figure 5a the variable configuration, a first layer as shown in Figure 5b can be generated. In this embodiment, taking Figure 5aThe following describes the specific operation steps of the visualization method for medical consumables data in the shown configuration situation, but it should not be construed as a limitation on the scope of the invention patent. The user can configure the image drawing type as a bar chart, with two different statistical dimension X-axis statistical variables X1 and X2 and one Y-axis statistical variable Y configured. Among them, X1 is configured as price, and at the same time, the statistical method of the price variable is also limited. The minimum value is configured as 0, and the interval is configured as 2000. That is, when statistically analyzing the price data of medical consumables, for medical consumables with a price greater than 0, they are statistically analyzed and displayed at intervals of 2000. X2 is configured as gender, and Y is configured as count. When statistically analyzing the price data based on a minimum value of 0 and an interval of 2000 in the medical consumables data, the price data of medical consumables can be divided into six price intervals: [0.0 - 2000.0], [2000.0 - 4000.0], [4000.0 - 6000.0], [6000.0 - 8000.0], [8000.0 - 10000.0], and [10000.0 - 12000.0]. Gender includes two types of data: male and female. At the same time, the sub-graph information is configured by the user as yes, and the user configures X1 as the sub-graph dimension. Therefore, based on the above variable configuration information, the first layer as shown in Figure 5b can be obtained. The first layer includes six different first sub-graphs.

[0059] Among them, the first first sub-graph shows the statistical situation of the quantity of medical consumables used by female and male patients respectively in the price interval of [0.0 - 2000.0]. The second first sub-graph shows the statistical situation of the quantity of medical consumables used by female and male patients respectively in the price interval of [2000.0 - 4000.0]. The third first sub-graph shows the quantity of medical consumables used by female and male patients respectively in the price interval of [4000.0 - 6000.0], and so on. The sixth first sub-graph shows the quantity of medical consumables used by female and male patients respectively in the price interval of [10000.0 - 12000.0].

[0060] Based on Figure 5b , the user can not only respectively determine the statistical results of the quantity of medical consumables used by patients of different genders in different price intervals through the statistical images of the six first sub-graphs for targeted analysis, but also comprehensively understand the number of patients of different genders in different discharge departments at different discharge times by combining the six first sub-graphs. For example, Figure 5b the first first sub-graph shows the statistical situation of the quantity of medical consumables used by female and male patients respectively in the price interval of [0.0 - 2000.0]. According to Figure 5bUsers can intuitively understand the difference comparison of the quantities of medical consumables used by female patients and male patients in the price range of [0.0 - 2000.0].

[0061] Figure 6 It is a schematic flowchart of the method for splitting the first layer in one of its own embodiments. In one embodiment, after generating the first layer, the method may further include the following step S610.

[0062] Step S610: When the image type of the first layer is a combined image, based on the splitting instruction, select the X - axis statistical variable corresponding to the X - axis in the first layer as the new splitting dimension, split the first layer, and generate a second layer according to the image drawing type, the medical consumable data corresponding to the splitting dimension, and the medical consumable data corresponding to the Y - axis statistical variable. The second layer includes second sub - graphs, and the number of second sub - graphs is determined according to the size of the splitting dimension.

[0063] After generating the first layer according to the variable configuration information, the user can also, according to the analysis requirements, continue to perform the splitting operation on the first layer based on the first layer. That is, when a new and more refined analysis dimension appears in the case of the already generated statistical image, the user can continue to complete the splitting operation according to different dimensions based on the current image. The user can generate a new statistical image without re - checking the drawing information, thereby reducing the operation steps of the user and saving operation time. The user can, on the basis of the first layer, achieve the splitting operation for the first layer by configuring the splitting rules.

[0064] The user can give a splitting instruction by configuring the splitting dimension, thereby splitting the first layer based on the splitting instruction. Since the image type of the first layer is a combined image and there is only one first combined image in the first layer, a second layer can be generated according to the image drawing type, the medical consumable data corresponding to the splitting dimension, and the medical consumable data corresponding to the Y - axis statistical variable. The second layer may include second sub - graphs. The number of second sub - graphs can be determined according to the size of the splitting dimension. The size of the splitting dimension can refer to the number of categories of the selected variable of the splitting dimension. For example, when the splitting dimension is the name of the discharge department, if the number of item names of the discharge department name is n, then the size of the splitting dimension is n. That is, the first combined image is divided into n second sub - graphs based on the name of the discharge department, and each second sub - graph represents the statistical image corresponding to one of the discharge department names.

[0065] Based on each second sub - figure shown in the second layer, users can intuitively understand the refined statistical information of medical consumables data in multiple dimensions. Specifically, the number of second sub - figures is determined according to the size of the sub - figure dimension. The medical consumables data corresponding to the X - axis statistical variables that are not selected as the sub - figure dimension are used as the variables on the X - axis in each second sub - figure, and the medical consumables data corresponding to the Y - axis statistical variable are used as the variables on the Y - axis in the second sub - figure. Each second sub - figure in the second layer is drawn in the form of an image drawing type.

[0066] Figure 7a It is a schematic diagram of variable configuration information in the third embodiment of this application. Figure 7b It is a schematic diagram of the second layer in the third embodiment of this application. According to Figure 7a the variable configuration situation, sub - dividing the Figure 3b shown first layer can generate the second layer as shown in Figure 7b . In this embodiment, the specific operation steps of the visualization method of medical consumables data are described according to the Figure 7a shown configuration situation, but it should not be construed as a limitation on the scope of the invention patent. When sub - dividing the Figure 3b shown first layer, the image type in the first layer is a combined figure. When the user needs to sub - divide the first combined figure from the gender dimension on the basis of the first layer, X1 can be configured as the sub - figure dimension.

[0067] Based on Figure 7a the shown variable configuration information, the second layer as shown in Figure 7b can be obtained. The second layer includes two different second sub - figures. Among them, the first second sub - figure shows the statistical situation of female patients in the Department of Pulmonary Oncology at different discharge times, and the second second sub - figure shows the statistical situation of male patients in the Department of Pulmonary Oncology at different discharge times. The X - axis in the two second sub - figures is mainly divided into 4 different bar - chart display areas, which respectively represent different discharge times. The 4 different bar - chart display areas also include the columns corresponding to the Department of Pulmonary Oncology. In addition, the Y - axis represents the count value for the data in different dimensions of the X - axis.

[0068] Based on Figure 7b , users can not only determine the quantity difference of patients of a specific gender in the Department of Pulmonary Oncology at different discharge times through the statistical images of the two second sub - figures. For example, the number of medical consumables used by female patients discharged from the Department of Pulmonary Oncology in February 2024 is the least, and the number of medical consumables used by female patients discharged from the Department of Pulmonary Oncology in March 2024 is the most, etc. Based on Figure 7bIt is also possible to combine two second sub - figures to comprehensively understand the differences in the quantities of medical consumables used by patients of different genders in the Department of Pulmonary Oncology at different discharge times. For example, the quantity of medical consumables used by male patients discharged from the Department of Pulmonary Oncology in February 2024 is also the least, and the quantity of medical consumables used by male patients discharged from the Department of Pulmonary Oncology in March 2024 is also the most. The changing trends of the quantities of medical consumables used by patients of different genders discharged from the Department of Pulmonary Oncology in different months are the same. The quantity of medical consumables used by female patients is generally more than that used by male patients, etc.

[0069] In one embodiment, please refer to Figure 6 , after generating the first layer, the method may further include step S620.

[0070] Step S620: When the image type of the first layer is a sub - figure and the sub - figure mode is an overall sub - figure, based on the sub - figure instruction, select the X - axis statistical variable corresponding to the X - axis in the first layer as the new sub - figure dimension, perform sub - figure on the first layer, and generate a second layer according to the image drawing type, the medical consumable data corresponding to all sub - figure dimensions in the first layer, and the medical consumable data corresponding to the Y - axis statistical variable. The second layer includes second sub - figures, and the number of second sub - figures is determined according to the size of the sub - figure dimension.

[0071] Similarly, the user can give a sub - figure instruction by configuring the sub - figure dimension, sub - figure mode, etc., so as to perform sub - figure on the first layer based on the sub - figure instruction. In this embodiment, the sub - figure mode may also include an overall sub - figure and a partial sub - figure. When the sub - figure mode configured by the user is an overall sub - figure, it indicates that the user hopes that the data in each second sub - figure in the second layer is an analysis of all the data in the first layer. Therefore, a second layer can be generated according to the image drawing type, the medical consumable data corresponding to all sub - figure dimensions in the first layer, and the medical consumable data corresponding to the Y - axis statistical variable, where the second layer may include second sub - figures. Similarly, the number of second sub - figures can be determined according to the size of the sub - figure dimension.

[0072] Based on each second sub - figure shown in the second layer, the user can also intuitively understand the refined statistical information for multiple dimensions of the medical consumable data. Specifically, determine the number of second sub - figures according to the size of the sub - figure dimension, use the medical consumable data corresponding to the X - axis statistical variables that are not selected as the sub - figure dimension as the variables of the X - axis in each second sub - figure, and use the medical consumable data corresponding to the Y - axis statistical variable as the variable of the Y - axis in the second sub - figure, and draw each second sub - figure in the second layer in the form of the image drawing type.

[0073] Figure 8a This is a schematic diagram of the variable configuration information in the fourth embodiment of the present application. Figure 8b This is a partial schematic diagram of the second layer in the fourth embodiment of the present application. According toFigure 8a The variable configuration of Figure 5b For the overall sub - mapping of the first layer shown in Figure 8b The second layer shown in Figure 8b For Figure 5b The second layer obtained after the overall sub - mapping operation on the first layer shown in Figure 8a The specific operation steps of the visualization processing method of medical consumables data are described according to the configuration status shown in Figure 8a As shown in Figure 5a The variable configuration information of the first layer shown in Figure 8a In

[0074] Among them, Figure 5b Is the data selected from all the medical consumables data in the system according to the minimum price of 0, the price range of 2000, and gender as the statistical variable, that is, Figure 5b The data included in Figure 8a Based on the variable configuration information of Figure 5b When performing the overall sub - mapping on Figure 8b The statistical data in Figure 5b Is re - counted and displayed according to the data included in

[0075] Based on Figure 8a The variable configuration information shown in Figure 5b For the overall sub - mapping of Figure 8b The second layer shown in

[0076] Similarly, the user is based on Figure 8bIt is possible to determine the difference in the quantity of medical consumables used by patients of a specific gender in the department of pulmonary oncology at different discharge times through the statistical images of two second sub - figures. It is also possible to comprehensively understand the difference in medical consumables used between patients of different genders in the department of pulmonary oncology at different discharge times by combining the two second sub - figures.

[0077] In one embodiment, please refer to Figure 6 , after generating the first layer, the method may further include step S630.

[0078] Step S630: When the image type of the first layer is a sub - figure and the sub - figure mode is a local sub - figure, based on the sub - figure instruction, select the X - axis statistical variable corresponding to the X - axis in the first layer as the new sub - figure dimension, perform sub - figure division on the first layer, and generate a second layer according to the image drawing type, the medical consumable data corresponding to the sub - figure dimension in the selected first sub - figure in the first layer, and the medical consumable data corresponding to the Y - axis statistical variable. The second layer includes second sub - figures, and the number of second sub - figures is determined according to the size and type of the sub - figure dimension.

[0079] Similarly, the user can give a sub - figure instruction by configuring the sub - figure dimension, sub - figure mode, etc., so as to perform sub - figure division on the first layer based on the sub - figure instruction. In this embodiment, the sub - figure mode may also include an overall sub - figure and a local sub - figure. When the sub - figure mode configured by the user is a local sub - figure, it indicates that the user hopes that the data in each second sub - figure in the second layer is an analysis of the statistical data in one or more first sub - figures in the first layer. Therefore, a second layer can be generated according to the image drawing type, the medical consumable data corresponding to the sub - figure dimension in the selected first sub - figure in the first layer, and the medical consumable data corresponding to the Y - axis statistical variable, where the second layer may include second sub - figures. Similarly, the number of second sub - figures can be determined according to the size of the sub - figure dimension.

[0080] Based on each second sub - figure shown in the second layer, the user can also intuitively understand the refined statistical information of the medical consumable data in multiple dimensions. Specifically, determine the number of second sub - figures according to the size and type of the sub - figure dimension, use the medical consumable data corresponding to the remaining unselected X - axis statistical variables in the selected first sub - figure as the variables on the X - axis in each second sub - figure, and use the medical consumable data corresponding to the Y - axis statistical variable as the variable on the Y - axis in the statistical image, and draw each second sub - figure in the second layer in the form of the image drawing type.

[0081] Figure 9a is a schematic diagram of variable configuration information in the fifth embodiment of this application, Figure 9b is a partial schematic diagram of the second layer in the fifth embodiment of this application. According to Figure 9a the variable configuration situation of Figure 5bLocal sub - mapping of the first layer shown can generate, as Figure 9b shown in the second layer, Figure 9b which is the second layer obtained after performing local sub - mapping operations on the first layer shown in Figure 5b . The specific operation steps of the visualization processing method for medical consumables data will be described in the configuration shown in Figure 9a , but it should not be construed as a limitation on the scope of the invention patent. When performing local sub - mapping on the first layer shown in Figure 5b , the image type in the first layer is local sub - mapping. When the user needs to perform local sub - mapping on the first composite map from the gender dimension based on the first layer, X1 can be configured as the sub - mapping dimension of the local sub - mapping.

[0082] Based on the above variable configuration information, a second layer as shown in Figure 9b can be obtained. The second layer includes multiple different second sub - maps. Among them, the first second sub - map shows the statistical situation of the number of medical consumables used by male patients in the Department of Pulmonary Oncology in different discharge months, the second second sub - map shows the statistical situation of the number of medical consumables used by female patients in the Department of Pulmonary Oncology in different discharge months, and so on. The X - axis in multiple second sub - maps is mainly divided into different bar chart display areas according to different discharge months, and each of these different bar chart display areas includes columns corresponding to the Department of Pulmonary Oncology. In addition, the Y - axis represents the count value for different - dimensional data on the X - axis.

[0083] Based on Figure 9b , the user can not only determine the difference in the number of medical consumables used by patients of a specific gender in the Department of Pulmonary Oncology in different discharge months through the statistical images of multiple second sub - maps, but also comprehensively understand the difference in the number of medical consumables used by patients of different genders in the Department of Pulmonary Oncology in different discharge months by combining multiple second sub - maps.

[0084] In one embodiment, when the types of sub - mapping dimensions include M pictures, the size of the sub - mapping dimension is N, and the number of second sub - maps is M * N, where both M and N are non - zero natural numbers. When performing local sub - mapping on the existing statistical images, according to the selected group of images, based on the selected sub - mapping dimension and all the information included in this dimension, individual sub - mapping is performed on each image in this group of images. The selected group of images can be one or more images.

[0085] The number of pictures shown in the sub - figure operation depends on the size of the selected sub - figure dimension and the type of the selected sub - figure dimension. When the type of the selected sub - figure dimension contains M pictures and the size of the selected sub - figure dimension is N, the number of sub - figures shown in this round is M * N. For example, when the X - axis statistical variables of the selected sub - figure all contain information of three dimensions, the three X - axis statistical variables are X1, X2, and X3 respectively, and the sizes of X1, X2, and X3 are 3, 2, and 2 respectively. When the number of selected sub - figures is 2 and the sub - figure dimension is X2, the sub - figure result is 2×2 = 4, that is, local sub - figure division of the selected sub - figure will obtain 4 sub - figure images.

[0086] In one embodiment, the second layer can also be further sub - figure processed. The specific operation of sub - figure division for the second layer is similar to that for the first layer, which will not be elaborated here.

[0087] In one embodiment, the layer with the sub - figure image type can also be combined. Taking the second layer as an example, the processing steps of the combination process are described. When the image type of the second layer is a sub - figure, based on the combination instruction, the target second sub - figure selected for combination in the second layer is determined. According to the image drawing type, the medical consumable data corresponding to all X - axis statistical variables and the medical consumable data corresponding to the Y - axis statistical variables in the target second sub - figure selected for combination, a third layer is generated. The third layer can include a third combined figure. For example, for Figure 7b the second layer shown in the figure is combined, and the third combined figure in the obtained third layer is the same as Figure 3b the first combined figure in the first layer shown in the figure.

[0088] In one embodiment, similarly, the specific operation of combining the sub - figures in other layers is similar to that of combining the sub - figures in the second layer, which will not be elaborated here.

[0089] It should be understood that although the steps in the flowchart of the accompanying drawings of the specification are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings of the specification may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0090] Based on the description of the embodiments of the visualization processing method for medical consumable data above, the present disclosure also provides a visualization processing system for medical consumable data. The system may include a system (including a distributed system), software (application), module, component, server, client, etc. that uses the method described in the embodiments of this specification and combines the necessary implementation hardware. Based on the same innovative concept, the systems in one or more embodiments provided by the embodiments of the present disclosure are as described in the following embodiments. Since the implementation solutions for the system to solve problems are similar to those of the method, the implementation of the specific system in the embodiments of this specification can refer to the implementation of the foregoing method, and the repeated parts will not be elaborated. As used hereinafter, the term "unit" or "module" may be a combination of software and / or hardware that can implement a predetermined function. Although the systems described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0091] Figure 10 FIG. 4 is a schematic structural diagram of a visualization processing system for medical consumable data in one embodiment of the present application. In one embodiment, the visualization processing system for medical consumable data may include a variable configuration module 100 and an image processing module 200.

[0092] The user can configure the variable configuration information according to the analysis requirements and input the variable configuration information into the variable configuration module 100. The variable configuration module 100 may be used to obtain the variable configuration information. In this embodiment, the variable configuration information may include an image drawing type, an X-axis statistical variable, a Y-axis statistical variable, and sub-graph information. Among them, the image drawing type may refer to the display type of the statistical image, such as a bar chart, a line chart, a bar graph, a bubble chart, a radar chart, a pie chart, etc. The X-axis statistical variable may refer to the statistical variable corresponding to the X-axis coordinate in the statistical image, and the Y-axis statistical variable may refer to the statistical variable corresponding to the Y-axis coordinate in the statistical image. Further, the display form of the X-axis statistical variable can also be configured. When the X-axis statistical variable is a variable with more numerical types, the minimum value and / or maximum value, interval interval, etc. of the X-axis statistical variable can be set. The statistical dimension of the X-axis statistical variable is different from that of the Y-axis statistical variable to prevent the selected statistical dimensions from being repeated and affecting the validity of the drawn statistical image.

[0093] The image processing module 200 may be connected to the variable configuration module 100. The image processing module 200 may be used to determine whether to display sub-graphs according to the sub-graph information, and is also used to generate a first layer including a first combined graph according to the image drawing type, the medical consumable data corresponding to the X-axis statistical variable, and the medical consumable data corresponding to the Y-axis statistical variable when the sub-graph information is no.

[0094] Based on the sub - image information, the image processing module 200 can determine whether the user hopes to perform visual analysis on the selected data in the form of sub - images or a combined image. Among them, a combined image can refer to gathering all the data selected by the user in one image for display; a sub - image can refer to displaying the data selected by the user as multiple different images. When the sub - image information is no, the image processing module 200 can determine that the user hopes to display the statistical image in the form of a combined image, and thus can generate a first layer based on the image drawing type, the medical consumable data corresponding to the statistical variable on the X - axis, and the medical consumable data corresponding to the statistical variable on the Y - axis. The first layer includes a first combined image. That is, based on the first combined image, the user can intuitively understand the multi - dimensional statistical information of the medical consumable data. Specifically, the image processing module 200 uses the medical consumable data corresponding to the statistical variable on the X - axis as the variable on the X - axis of the statistical image, and the medical consumable data corresponding to the statistical variable on the Y - axis as the variable on the Y - axis of the statistical image, and draws the first combined image in the form of the image drawing type.

[0095] The visualization processing system for medical consumable data provided in this application realizes the visualization of medical consumable data based on the visualization processing method of medical consumable data. By converting medical consumable data into visual statistical images, it can more intuitively display statistical information, helping users quickly understand the selected statistical variable situation. When the user selects multiple analysis dimensions, displaying the information of multiple dimensions at one time in the form of images helps users grasp the global characteristics of the statistical variables. By quickly performing visual analysis on the usage of medical consumables from multiple dimensions, it can achieve efficient statistical analysis of the usage of medical consumables, which is more conducive to hospitals evaluating the rationality and accuracy of the use of medical consumables, thereby realizing the full - process supervision and control of high - value medical consumables.

[0096] It can be understood that each of the above - mentioned method, device, etc. embodiments in this specification is described in a progressive manner. The same / similar parts between each embodiment can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments. For the relevant parts, refer to the description of other method embodiments.

[0097] Figure 11 This is a schematic structural diagram of a device for implementing the visualization processing method of medical consumable data in one embodiment of this application. Refer to Figure 11, the visualization processing device S00 for medical consumables data may include a processing component S20, which further includes one or more processors, and memory resources represented by a memory S22 for storing instructions executable by the processing component S20, such as application programs. The application programs stored in the memory S22 may include one or more modules each corresponding to a set of instructions. In addition, the processing component S20 is configured to execute instructions to perform the above-mentioned visualization processing method for medical consumables data.

[0098] The visualization processing device S00 for medical consumables data may further include: a power supply component S24 configured to perform power management of the visualization processing device S00 for medical consumables data, a wired or wireless network interface S26 configured to connect the visualization processing device S00 for medical consumables data to a network, and an input / output (I / O) interface S28. The visualization processing device S00 for medical consumables data may operate based on an operating system stored in the memory S22, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD or the like.

[0099] In an exemplary embodiment, there is also provided a computer-readable storage medium including instructions, such as the memory S22 including instructions, and the above instructions can be executed by the processor of the visualization processing device S00 for medical consumables data to complete the above method. The storage medium may be a computer-readable storage medium. For example, the computer-readable storage medium may be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0100] In an exemplary embodiment, there is also provided a computer program product, and the computer program product includes instructions, and the above instructions can be executed by the processor of the visualization processing device S00 for medical consumables data to complete the above method.

[0101] In one embodiment, there is provided a computer device, and the computer device may be a server, and its internal structural diagram may be as Figure 12 shown Figure 12The internal structure diagram of the computer device in one embodiment of the present application. The computer device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data related to users and tasks used in the above-mentioned visualization processing method of medical consumables data. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a visualization processing method of medical consumables data.

[0102] Those skilled in the art can understand that Figure 12 the structure shown in [the figure] is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0103] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memories. Non-volatile memories can include read-only memory (ROM), magnetic tapes, floppy disks, flash memories, optical memories, high-density embedded non-volatile memories, resistive random access memories (ReRAM), magnetoresistive random access memories (MRAM), ferroelectric random access memories (FRAM), phase change memories (PCM), graphene memories, etc. Volatile memories can include random access memory (RAM) or external cache memories, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, etc., without limitation.

[0104] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other. The key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the hardware + program type embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, reference can be made to the partial description of the method embodiments.

[0105] It should be noted that the above-mentioned devices, electronic devices, servers, etc. may also include other implementation manners according to the description of the method embodiments. The specific implementation manners may refer to the description of the relevant method embodiments. At the same time, the new embodiments formed by the mutual combination of the features among the various methods, as well as the device, equipment, and server embodiments, still fall within the scope of the embodiments covered by the present disclosure, and will not be elaborated one by one here.

[0106] In the description of this specification, the descriptions referring to terms such as "some embodiments", "other embodiments", "ideal embodiments", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic description of the above terms does not necessarily refer to the same embodiment or example.

[0107] The technical features of the above-mentioned embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the various technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered that it is within the scope described in this specification.

[0108] The above-mentioned embodiments only represent several implementation manners of the present invention, and the description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention patent shall be subject to the appended claims.

Claims

1. A method for visualizing medical consumables data, characterized in that: include: Get variable configuration information; The variable configuration information includes image drawing type, X-axis statistical variables, Y-axis statistical variables, and sub-graph information; wherein the statistical dimension of the X-axis statistical variables is different from the statistical dimension of the Y-axis statistical variables; Determining whether to display a sub-picture according to the sub-picture information; When the sub-image information is no, a first layer is generated according to the image drawing type, the medical consumables data corresponding to the X-axis statistical variable, and the medical consumables data corresponding to the Y-axis statistical variable, and the first layer includes a first combined image.

2. The method for visualizing medical consumables data according to claim 1, characterized in that: The variable configuration information further includes a sub-graph dimension, where the sub-graph dimension is any one of the X-axis statistical variables. After determining whether to display a sub-graph according to the sub-graph information, the method further includes: When the sub-graph information is yes, determining whether the variable configuration information includes at least two X-axis statistical variables of different statistical dimensions; When the variable configuration information includes the X-axis statistical variables of at least two statistical dimensions, a first layer is generated according to the image drawing type, the medical consumables data corresponding to the sub-image dimension, and the medical consumables data corresponding to the Y-axis statistical variable, the first layer including a first sub-image, and the number of the first sub-images is determined according to the size of the sub-image dimension.

3. The method for visualizing medical consumables data according to claim 2, characterized in that: After generating the first layer, the method further includes: When the image type of the first layer is a combined image, the X-axis statistical variable corresponding to the X-axis in the first layer is selected as a new sub-image dimension based on the sub-image instruction, the first layer is sub-imaged, and a second layer is generated according to the image drawing type, the medical consumables data corresponding to the sub-image dimension, and the medical consumables data corresponding to the Y-axis statistical variable. The second layer includes second sub-images, and the number of the second sub-images is determined according to the size of the sub-image dimension.

4. The method for visualizing medical consumables data according to claim 2, characterized in that: The variable configuration information also includes a sub-image mode. After generating the first layer, the method further includes: When the image type of the first layer is a sub-image and the sub-image mode is an overall sub-image, the X-axis statistical variable corresponding to the X-axis in the first layer is selected as a new sub-image dimension based on the sub-image instruction, the first layer is sub-imaged, and a second layer is generated according to the image drawing type, the medical consumables data corresponding to all the sub-image dimensions in the first layer, and the medical consumables data corresponding to the Y-axis statistical variable, the second layer includes second sub-images, and the number of the second sub-images is determined according to the size of the sub-image dimensions.

5. The method for visualizing medical consumables data according to claim 2, characterized in that: The variable configuration information also includes a sub-image mode. After generating the first layer, the method further includes: When the image type of the first layer is a sub-image and the sub-image mode is a local sub-image, the X-axis statistical variable corresponding to the X-axis in the first layer is selected as a new sub-image dimension based on the sub-image instruction, the first layer is sub-imaged, and a second layer is generated according to the image drawing type, the medical consumables data corresponding to the sub-image dimension in the first sub-image selected in the first layer, and the medical consumables data corresponding to the Y-axis statistical variable, the second layer includes a second sub-image, and the number of the second sub-images is determined according to the size of the sub-image dimension and the type of the sub-image dimension.

6. The method for visualizing medical consumables data according to claim 5, characterized in that: When the type of the sub-image dimension includes M pictures, the size of the sub-image dimension is N, and the number of the second sub-images is M*N, where M and N are both non-zero natural numbers.

7. A visualization processing system for medical consumables data, characterized in that: include: Variable configuration module, used to obtain variable configuration information; The variable configuration information includes image drawing type, X-axis statistical variables, Y-axis statistical variables, and sub-graph information; wherein the statistical dimension of the X-axis statistical variables is different from the statistical dimension of the Y-axis statistical variables; An image processing module is connected to the variable configuration module, and is used to determine whether to display a sub-image based on the sub-image information. When the sub-image information is negative, a first layer is generated based on the image drawing type, the medical consumables data corresponding to the X-axis statistical variable, and the medical consumables data corresponding to the Y-axis statistical variable, wherein the first layer includes a first combined image.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for visualizing medical consumables data described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for visualizing medical consumables data described in any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for visualizing medical consumables data described in any one of claims 1 to 6 are implemented.

Images