Artificial intelligence-based static therapy pipeline design method and system

Abstract

The invention provides a static therapy pipeline design method and system based on artificial intelligence. The method comprises the steps that patient information of a patient to be subjected to static therapy is acquired, a trunk set to be subjected to static therapy is determined, and the trunk set to be subjected to static therapy comprises at least one static therapy trunk area; acquiring a historical static therapy record of the patient to be subjected to static therapy within a preset time before the current time point, and performing region selection based on the historical static therapy record and the static therapy trunk set to obtain a target trunk region of the patient to be subjected to static therapy; acquiring a corresponding region image based on the target trunk region, and creating a virtual trunk model having a mapping relationship with the target trunk region based on the region image; obtaining each virtual blood vessel in the virtual trunk model, and determining the virtual blood vessel meeting the medical ecological requirement as a to-be-injected blood vessel based on the ecological attribute of each virtual blood vessel; acquiring static therapy medicine information of the patient to be subjected to static therapy, and generating a medicine pipeline model connected with the blood vessel to be injected based on the static therapy medicine information. The auxiliary efficiency of the static therapy is at least improved.

Description

Technical Field

[0001] The present invention relates to data processing technology, and in particular to an artificial intelligence-based intravenous therapy pipeline design method and system. Background Art

[0002] Intravenous injection is a medical method that directly injects liquid substances such as blood, medicine, and nutrient solution into the vein. Intravenous injection can be divided into short-term and continuous. Short-term intravenous injection is usually injected directly into the vein with a syringe, which is the most common injection method; continuous intravenous injection is implemented by intravenous drip, commonly known as "drip".

[0003] The inventors found in their research that before performing an intravenous injection on a patient, medical staff are generally required to personally check the patient's medical records to determine the specific location of the intravenous injection, which is inefficient and difficult to determine an appropriate medical plan. Summary of the invention

[0004] Based on the above problems, the present invention is proposed to provide an artificial intelligence-based intravenous therapy pipeline design method and system that overcomes the above problems or at least partially solves the above problems, which has the advantages of high efficiency and suitable medical solutions.

[0005] According to one aspect of the present invention, there is provided a method for designing an intravenous therapy circuit based on artificial intelligence, comprising the following steps: Acquire patient information corresponding to the patient to be treated with static therapy, and determine a torso set to be treated with static therapy based on the patient information, wherein the torso set to be treated with static therapy includes at least one torso region to be treated with static therapy; Acquire historical static therapy records corresponding to the patient to be treated within a preset time before the current time point, and perform region selection based on the historical static therapy records and the static therapy torso set to obtain a target torso region corresponding to the patient to be treated; Acquiring a corresponding regional image based on a target torso region of the patient to be treated with static therapy, and creating a virtual torso model having a mapping relationship with the target torso region based on the regional image; Acquire each virtual blood vessel in the virtual torso model, and determine a virtual blood vessel that meets the medical ecological requirements as a blood vessel to be injected based on the ecological attributes of each virtual blood vessel; The intravenous drug information corresponding to the patient to be treated with intravenous therapy is acquired, and a drug pipeline model connected to the blood vessel to be injected is generated based on the intravenous drug information.

[0006] Optionally, in the method according to the present invention, determining a torso set to be treated statically based on the patient information includes: Determine the patient age and medical history of the patient to be treated with intravenous therapy based on the patient information, and retrieve a preset intravenous therapy trunk distribution table, wherein the preset intravenous therapy trunk distribution table includes multiple intravenous therapy trunk areas, and each intravenous therapy trunk area includes a corresponding age-appropriate interval and a disease-prohibited interval; Performing a primary screening based on the preset static therapy trunk distribution table, determining all static therapy trunk areas corresponding to the appropriate age ranges including the patient's age, and determining them as a primary screening group; A secondary screening is performed based on the primary screening group, and the to-be-treated trunk set is determined based on the secondary screening result.

[0007] Optionally, in the method according to the present invention, determining the trunk set to be treated statically based on the secondary screening result comprises: When the secondary screening result is that the primary screening group has at least one static treatment trunk area corresponding to a disease-free interval that does not include the patient's medical record, determining the at least one static treatment trunk area as a static treatment trunk set; When the secondary screening result is that the primary screening group does not have a static therapy trunk area corresponding to a forbidden disease interval that does not include the patient's medical record, the primary screening group is determined as a static therapy trunk set.

[0008] Optionally, in the method according to the present invention, performing region selection based on the historical static therapy records and the static therapy torso set to obtain a target torso region corresponding to the patient to be static therapy includes: determining a historical torso region based on the historical static therapy record, and comparing the historical torso region with all static therapy torso regions in the static therapy torso set; When the static therapy torso set includes a static therapy torso region and the static therapy torso region is the same region as the historical torso region, the static therapy torso region is determined as the target torso region corresponding to the patient to be treated with static therapy; When the static therapy trunk set includes a static therapy trunk area and the static therapy trunk area is not the same area as the historical trunk area, determining a trunk connection relationship between the static therapy trunk area and the historical trunk area, and determining the static therapy trunk area as a target trunk area corresponding to the patient to be treated with static therapy when the trunk connection relationship is an upper connection, and determining the historical trunk area as a target trunk area corresponding to the patient to be treated with static therapy when the trunk connection relationship is a lower connection; or When the static therapy trunk set includes multiple static therapy trunk areas, determining the trunk connection relationships between the multiple static therapy trunk areas and the historical trunk area respectively; When the trunk connection relationship between any static therapy trunk region and the historical trunk region is an upper connection, the static therapy trunk region is determined as the target trunk region corresponding to the patient to be static therapy; When the trunk connection relationship between all static therapy trunk regions and the historical trunk region is a lower connection, the historical trunk region is determined as the target trunk region corresponding to the patient to be static therapy.

[0009] Optionally, in the method according to the present invention, acquiring each virtual blood vessel in the virtual torso model, and determining a virtual blood vessel that meets the medical ecological requirements as a blood vessel to be injected based on the ecological attributes of each virtual blood vessel, includes: Determine each virtual blood vessel located in the virtual torso model based on a preset image recognition model, and establish a model coordinate system corresponding to the virtual torso model; Acquire each contour pixel point that respectively constitutes the blood vessel contour of each virtual blood vessel, and acquire each contour coordinate point corresponding to each contour pixel point based on the model coordinate point, and determine each contour coordinate point corresponding to the same blood vessel contour as a contour coordinate set; In each contour coordinate set, difference calculations are performed on contour coordinate points with the same horizontal coordinate to obtain horizontal differences, and the horizontal difference corresponding to the maximum value is determined as the first target difference; In each contour coordinate set, the contour coordinate points with the same longitudinal coordinate are respectively calculated to obtain longitudinal differences, and the longitudinal difference corresponding to the maximum value is determined as the second target difference; Determine the larger one of the first target difference and the second target difference as the contour size corresponding to the blood vessel contour, obtain contour sizes corresponding to the blood vessel contours, and determine the contour sizes as size attributes corresponding to the virtual blood vessels; Obtaining contour pixel values ​​corresponding to contour pixel points respectively located in each contour coordinate set; Calculating the mean of each contour pixel value corresponding to the same contour coordinate set to obtain the mean of each pixel corresponding to each blood vessel contour, and determining each pixel mean as the pixel attribute corresponding to each virtual blood vessel; Based on the preset determination strategy, the size attributes and pixel attributes corresponding to the same virtual blood vessel are comprehensively evaluated to obtain the attribute evaluation values ​​corresponding to each virtual blood vessel, and the virtual blood vessel corresponding to the largest attribute evaluation value is determined as the blood vessel to be injected.

[0010] Optionally, in the method according to the present invention, based on a preset determination strategy, a comprehensive attribute evaluation is performed on the size attribute and the pixel attribute corresponding to the same virtual blood vessel to obtain attribute evaluation values ​​corresponding to each virtual blood vessel, including: Retrieve the preset size evaluation weight and the preset pixel evaluation weight; The contour size and pixel mean value corresponding to the same virtual blood vessel are respectively multiplied by the preset size evaluation weight and the preset pixel evaluation weight to obtain a first product value and a second product value; The first product value and the second product value are normalized to obtain a first evaluation value and a second evaluation value, and the first evaluation value and the second evaluation value are summed to obtain each attribute evaluation value corresponding to each virtual blood vessel.

[0011] Optionally, in the method according to the present invention, obtaining intravenous drug information corresponding to the patient to be treated with intravenous therapy, and generating a drug pipeline model connected to the blood vessel to be injected based on the intravenous drug information, includes: Determine the patient's drug number based on the patient information corresponding to the patient to be treated with intravenous therapy, and retrieve a drug record table, wherein the drug record table includes multiple drug sequences, each of which includes different recorded drug numbers and recorded drug information corresponding to the different recorded drug numbers; Traverse the medication record table to find the same medication number as the patient's medication number. Deleting the drug sequence including the intravenous drug information from the drug record table, and determining the recorded drug information corresponding to the recorded drug number as the intravenous drug information corresponding to the patient drug number; Based on the intravenous drug information, each injectable drug corresponding to different drug types is determined, and the characteristics of each injectable drug are determined according to the injection sequence determination strategy and the mixed injection determination strategy, and the sequence characteristics and connection characteristics corresponding to each injectable drug are determined, and based on the sequence characteristics and connection characteristics, a drug pipeline model connected to the blood vessel to be injected is generated.

[0012] Optionally, in the method according to the present invention, characteristics of each injection drug are determined according to the injection sequence determination strategy, and the sequence characteristics corresponding to each injection drug are determined, including: Determine the components of each injectable medicine to obtain an initial chemical component group corresponding to each injectable medicine, wherein the initial chemical component group includes different component types and type ratios corresponding to the different component types; Retrieving a preset ratio threshold, and screening out component types whose corresponding type ratio is less than or equal to the preset ratio threshold from each of the initial chemical component groups to obtain each current chemical component group; Determine the content of each component corresponding to each component type in each current chemical component group, and determine the content coefficient based on the content of each component; Based on the preset chemical injection sorting table, the type coefficient of each component type in each current chemical component group is determined respectively, and the content coefficient and the type coefficient corresponding to the same component type are summed and calculated to obtain the type priority value corresponding to the component type; The priority values ​​of all types corresponding to the same current chemical component group are summed up to obtain the injection priority values ​​corresponding to each current chemical component group, and the current chemical component groups are sorted based on the injection priority values, and the injection sequences corresponding to the respective injection drugs are determined as the sequence characteristics.

[0013] Optionally, in the method according to the present invention, the characteristics of each injection drug are determined according to the mixed injection determination strategy, and the connection characteristics corresponding to each injection drug are determined, including: Retrieving a mixing record table, wherein the mixing record table includes a plurality of columns of mixing sequences, each column of mixing sequence includes at least two different injection drugs that can be mixed and injected; The mixing record table is traversed, and it is determined whether there are at least two injection drugs in the same mixing sequence in each injection drug, and all injection drugs in the same mixing sequence are determined as a mixing group with mixing characteristics, and all remaining injection drugs are determined as having independent characteristics.

[0014] Optionally, in the method according to the present invention, generating a drug pipeline model connected to the blood vessel to be injected based on the sequence characteristics and the connection characteristics includes: Determine the injection order corresponding to all the injectable drugs in the same mixed group, and determine the injection order with the largest value as corresponding to the mixed group; Generate a parallel pipeline model with a parallel structure based on all the injection drugs in each mixed group, and generate an independent pipeline model with an independent structure based on all the remaining injection drugs except the mixed group; Based on the injection sequence corresponding to the mixed group and the injection sequence corresponding to all remaining injection drugs except the mixed group, the parallel pipeline model and the independent pipeline model are connected in sequence to obtain a drug pipeline model that is connected to the blood vessel to be injected and presents a series structure.

[0015] Optionally, in the method according to the present invention, generating a drug pipeline model connected to the blood vessel to be injected based on the sequence characteristics and the connection characteristics includes: Extracting the sequence characteristics and the connection characteristics and comparing them with the trained training feature group respectively, if the features in the training feature group correspond to the sequence characteristics and the connection characteristics one by one, then retrieving the training drug pipeline model and drug injection cycle corresponding to the training feature group; Based on the location of each injected drug, the trained drug pipeline model is traversed and information is added to obtain a drug pipeline model; If the features in the training feature group do not completely correspond to the sequence characteristics and the connection characteristics, then based on the comparison between the features in the training feature group and the sequence characteristics and the connection characteristics, the feature similarity is obtained; If it is determined that there is a feature similarity greater than the selected threshold, the training feature group with the largest feature similarity is used as the feature group to be decomposed, and the feature group to be decomposed is decomposed based on the sequence characteristics and the connection characteristics and then reassembled to obtain the drug pipeline model; If it is determined that there is no feature similarity greater than the selected threshold, a parallel pipeline model and an independent pipeline model corresponding to all drugs are generated.

[0016] According to another aspect of the present invention, there is provided an artificial intelligence-based intravenous therapy circuit design system, comprising: an acquisition module, configured to acquire patient information corresponding to the patient to be treated with static therapy, and determine a torso set to be treated with static therapy based on the patient information, wherein the torso set to be treated with static therapy includes at least one torso region to be treated with static therapy; A region selection module is configured to obtain historical static therapy records corresponding to the patient to be treated within a preset time before a current time point, and perform region selection based on the historical static therapy records and the static therapy torso set to obtain a target torso region corresponding to the patient to be treated; A creation module is configured to acquire a corresponding regional image based on a target torso region of the patient to be treated with static therapy, and to create a virtual torso model having a mapping relationship with the target torso region based on the regional image; a determination module configured to obtain each virtual blood vessel in the virtual torso model, and determine a virtual blood vessel that meets the medical ecological requirements as a blood vessel to be injected based on the ecological attributes of each virtual blood vessel; The model generation module is configured to obtain intravenous drug information corresponding to the patient to be treated with intravenous therapy, and generate a drug pipeline model connected to the blood vessel to be injected based on the intravenous drug information.

[0017] According to the solution of the present invention, the server first obtains the patient information of the patient to be treated with static therapy, and determines the trunk set to be treated with static therapy based on the patient information, and the trunk set to be treated with static therapy includes at least one trunk area to be treated with static therapy; then, the server obtains the historical static therapy records of the patient to be treated with static therapy within a preset time before the current time point, and selects the area based on the historical static therapy records and the static therapy trunk set, so as to obtain the target trunk area corresponding to the patient to be treated with static therapy; then, the server obtains the corresponding regional image based on the target trunk area corresponding to the patient to be treated with static therapy, and creates a virtual trunk model with a mapping relationship with the target trunk area based on the regional image; then, the server obtains each virtual blood vessel located in the virtual trunk model, and determines the virtual blood vessel that meets the medical ecological requirements as the blood vessel to be injected based on the ecological attributes of each virtual blood vessel; finally, the server obtains the intravenous drug information corresponding to the patient to be treated with static therapy, and generates a drug pipeline model connected to the blood vessel to be injected based on the intravenous drug information. The present invention can provide medical staff with a reference for the injection area and the injection drug during intravenous injection to a large extent, and can reduce their working time to a certain extent, thereby improving the efficiency of intravenous injection. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flow chart of a method for designing a static therapy circuit based on artificial intelligence according to an embodiment of the present invention is shown; Figure 2 A schematic diagram of a drug pipeline model in this embodiment is shown; Figure 3 A schematic diagram showing the structure of terminal interconnection in this embodiment is shown; Figure 4 A structural block diagram of an artificial intelligence-based intravenous therapy pipeline design system according to another embodiment of the present invention is shown. DETAILED DESCRIPTION

[0019] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0020] Intravenous injection is a medical method that directly injects liquid substances such as blood, medicine, and nutrient solution into the vein. Intravenous injection can be divided into short-term and continuous. Short-term intravenous injection is usually injected directly into the vein with a syringe, which is the most common injection method; continuous intravenous injection is implemented by intravenous drip, commonly known as "drip".

[0021] The inventors found in their research that before performing an intravenous injection on a patient, medical staff are generally required to personally check the patient's medical records to determine the specific location of the intravenous injection, which wastes a certain amount of time.

[0022] In order to solve the problems existing in the above-mentioned prior art, the inventor proposes the solution of the present invention. One embodiment of the present invention provides a method for designing an intravenous therapy circuit based on artificial intelligence, which can be executed in a computing device.

[0023] Figure 1 A flowchart of an artificial intelligence-based intravenous therapy circuit design method according to an embodiment of the present invention is shown, and the method is suitable for being executed in a computing device (wherein the computing device can be understood as a terminal device with processing functions such as a computer and a mobile phone).

[0024] like Figure 1 As shown, the purpose of this embodiment is to implement an artificial intelligence-based intravenous therapy circuit design method, which starts with step S102. In step S102, the following contents are included: The medical end obtains the patient information corresponding to the patient to be treated with static therapy and sends it to the server. The server determines a torso set to be treated with static therapy based on the patient information, wherein the torso set to be treated with static therapy includes at least one torso area to be treated with static therapy.

[0025] For example, it can be explained that in actual scenarios, due to the different ages and symptoms of patients undergoing intravenous therapy, medical staff may use age and symptoms as a reference to select the corresponding appropriate area for intravenous injection. For example, the elderly may be more suitable for intravenous injection in the lower limbs, while young people may choose upper and lower limbs for intravenous injection; therefore, in order to be more in line with the actual scenario and ensure that the solution proposed in this embodiment has a certain medical reference, in this embodiment, when it is necessary to perform intravenous injection on the patient undergoing intravenous therapy, the server will first obtain the patient information of the patient undergoing intravenous therapy, wherein the patient information may include the patient's age, the patient's medical history, etc.; then the server can preliminarily determine all areas of the patient undergoing intravenous therapy that may be suitable for intravenous injection based on the patient information, that is, the intravenous therapy trunk area, and determine it as the corresponding trunk set to be treated with intravenous injection.

[0026] Furthermore, the above-mentioned “determining a torso set to be treated statically based on the patient information” further includes the following steps: Determine the patient age and medical history of the patient to be treated with intravenous therapy based on the patient information, and retrieve a preset intravenous therapy trunk distribution table, wherein the preset intravenous therapy trunk distribution table includes multiple intravenous therapy trunk areas, and each intravenous therapy trunk area includes a corresponding age-appropriate interval and a disease-prohibited interval; Performing a primary screening based on the preset static therapy trunk distribution table, determining all static therapy trunk areas corresponding to the appropriate age ranges including the patient's age, and determining them as a primary screening group; A secondary screening is performed based on the primary screening group, and the to-be-treated trunk set is determined based on the secondary screening result.

[0027] For example, in this embodiment, after obtaining the patient information of the patient to be treated with intravenous therapy, the server can determine the patient age and patient medical history of the patient to be treated with intravenous therapy, and the patient medical history includes the current and previous medical history content; at the same time, a preset intravenous therapy trunk distribution table is pre-set in the server, and there are multiple intravenous therapy trunk areas in the preset intravenous therapy trunk distribution table, such as intravenous therapy trunk areas such as scalp, upper limbs, and lower limbs, and each intravenous therapy trunk area includes a corresponding appropriate age range and a prohibited disease range. It should be noted that the appropriate age range can be understood as an age range suitable for injection of the area, and the prohibited disease range can be understood as a disease range that is not suitable for injection of the area; for example, the appropriate age range of the intravenous therapy trunk area of ​​the lower limbs is for people over 70 years old, and the prohibited disease range of the intravenous therapy trunk area of ​​the scalp is acute fever, etc.; Next, the server will retrieve the preset static therapy trunk distribution table, and perform a screening according to the preset static therapy trunk distribution table. The specific steps of the screening are that the server first determines all the static therapy trunk areas in the appropriate age range that includes the patient's age, and then determines these static therapy trunk areas as a primary screening group. Next, the server will perform a secondary screening of the static therapy trunk areas in the primary screening group according to the disease-free range, and determine the trunk set to be treated with static therapy based on the secondary screening results. This embodiment can screen and determine the trunk set to be treated with static therapy according to the appropriate age range and disease-free range of each static therapy trunk area. Not only is the process rapid, but the results are also relatively objective, and can provide a certain reference for medical staff.

[0028] Furthermore, the above-mentioned “determining the trunk set to be treated based on the secondary screening result” further includes the following steps: When the secondary screening result is that the primary screening group has at least one static treatment trunk area corresponding to a disease-free interval that does not include the patient's medical record, determining the at least one static treatment trunk area as a static treatment trunk set; When the secondary screening result is that the primary screening group does not have a static therapy trunk area corresponding to a forbidden disease interval that does not include the patient's medical record, the primary screening group is determined as a static therapy trunk set.

[0029] For example, in this embodiment, when the result of the secondary screening of the intravenous therapy trunk areas in the primary screening group by the server is that there is one or more intravenous therapy trunk areas in the primary screening group that are within the forbidden disease range that does not include the patient's medical records, it means that these intravenous therapy trunk areas are suitable for intravenous injection for the patient to be treated with intravenous therapy, and the server will determine the one or more intravenous therapy trunk areas as the intravenous therapy trunk set; when the result of the secondary screening of the intravenous therapy trunk areas in the primary screening group is that there is no intravenous therapy trunk area corresponding to the forbidden disease range that does not include the patient's medical records in the primary screening group, it means that all intravenous therapy trunk areas in the primary screening group are suitable for intravenous injection for the patient to be treated with intravenous therapy, and the server will determine the primary screening group as the intravenous therapy trunk set.

[0030] In step S104, the following contents are included: The server obtains historical static therapy records corresponding to the patient to be treated within a preset time before the current time point, and performs region selection based on the historical static therapy records and the static therapy torso set to obtain a target torso region corresponding to the patient to be treated.

[0031] For example, in this embodiment, when the patient to be treated with intravenous injection is about to start, that is, at the current time point, the server will obtain the historical intravenous injection records of the patient to be treated with intravenous injection within a preset time before the current time point. The historical intravenous injection records include specific areas where the patient to be treated with intravenous injection has been treated with intravenous injection. The preset time can be set by medical staff according to actual conditions, for example, six months. The server can select the intravenous injection torso area in the intravenous injection torso set according to the historical intravenous injection records, and determine the area of ​​the patient to be treated with intravenous injection suitable for intravenous injection during this intravenous treatment, that is, the target torso area.

[0032] Furthermore, the above-mentioned “selecting a region based on the historical static therapy record and the static therapy torso set to obtain a target torso region corresponding to the patient to be static therapy” further includes the following steps: determining a historical torso region based on the historical static therapy record, and comparing the historical torso region with all static therapy torso regions in the static therapy torso set; When the static therapy torso set includes a static therapy torso region and the static therapy torso region is the same region as the historical torso region, the static therapy torso region is determined as the target torso region corresponding to the patient to be treated with static therapy; When the static therapy trunk set includes a static therapy trunk area and the static therapy trunk area is not the same area as the historical trunk area, determining a trunk connection relationship between the static therapy trunk area and the historical trunk area, and determining the static therapy trunk area as a target trunk area corresponding to the patient to be treated with static therapy when the trunk connection relationship is an upper connection, and determining the historical trunk area as a target trunk area corresponding to the patient to be treated with static therapy when the trunk connection relationship is a lower connection; or When the static therapy trunk set includes multiple static therapy trunk areas, determining the trunk connection relationships between the multiple static therapy trunk areas and the historical trunk area respectively; When the trunk connection relationship between any static therapy trunk region and the historical trunk region is an upper connection, the static therapy trunk region is determined as the target trunk region corresponding to the patient to be static therapy; When the trunk connection relationship between all static therapy trunk regions and the historical trunk region is a lower connection, the historical trunk region is determined as the target trunk region corresponding to the patient to be static therapy.

[0033] For example, in this embodiment, after obtaining the historical intravenous therapy records of the patient to be treated with intravenous therapy within a preset time before the current time point, the server will determine the specific area where the patient previously received intravenous injections, that is, the historical torso area, based on the historical intravenous therapy records. Since during intravenous injections, the area for this intravenous injection needs to be above the historical torso area to prevent inflammation or leakage, for example, if the historical torso area is the back of the hand, the area for this intravenous injection can be the arm or other area located above the back of the hand, so the server will compare the historical torso area with all intravenous therapy torso areas in the intravenous therapy torso set; When the static therapy trunk set includes a static therapy trunk area and the static therapy trunk area is the same area as the historical trunk area, it means that the static therapy trunk area and the historical trunk area are overlapped. At this time, the server will determine the static therapy trunk area as the target trunk area of ​​the patient to be treated with static therapy; when the static therapy trunk set includes a static therapy trunk area and it is not the same area as the historical trunk area, the server will further determine the trunk connection relationship between the static therapy trunk area and the historical trunk area. When the static therapy trunk area is located above the historical trunk area, the trunk connection relationship is an upper connection; when the static therapy trunk area is located below the historical trunk area, the trunk connection relationship is a lower connection. Then, the server will determine the static therapy trunk area as the target trunk area of ​​the patient to be treated with static therapy when the trunk connection relationship is an upper connection, and determine the historical trunk area as the target trunk area of ​​the patient to be treated with static therapy when the trunk connection relationship is a lower connection. or When the static therapy trunk set includes multiple static therapy trunk areas, the server will determine the trunk connection relationships between the multiple static therapy trunk areas and the historical trunk areas. When the trunk connection relationship between any static therapy trunk area and the historical trunk area is an upper connection, the server will determine the static therapy trunk area as the target trunk area of ​​the patient to be treated with static therapy; when the trunk connection relationships between all static therapy trunk areas and the historical trunk areas are lower connections, the server will determine the historical trunk area as the target trunk area corresponding to the patient to be treated with static therapy.

[0034] It can be explained that the present embodiment can compare the historical trunk area with all the intravenous therapy trunk areas in the intravenous therapy trunk set, so as to determine the trunk connection relationship between the intravenous therapy trunk area and the historical trunk area. When the trunk connection relationships between all the intravenous therapy trunk areas in the intravenous therapy trunk set and the historical trunk areas are all lower connections, the server will determine the historical trunk area as the target trunk area corresponding to the patient to be treated with intravenous therapy, so as to avoid inflammation or leakage in the patient to be treated with intravenous injection, which can provide medical staff with a reference for the injection area during intravenous injection to a large extent.

[0035] In step S106, the following contents are included: The server acquires a corresponding regional image based on the target trunk region of the patient to be treated with intravenous therapy, and creates a virtual trunk model having a mapping relationship with the target trunk region based on the regional image.

[0036] For example, in this embodiment, after the server completes the determination of the target torso area for the patient undergoing static therapy, the server will obtain a regional image corresponding to the target torso area, and then create a virtual torso model having a mapping relationship with the target torso area based on the obtained regional image; here, the establishment of the virtual torso model can be achieved based on the digital twin technology, that is, corresponding sensor data is collected based on the target torso area, and a digital twin space corresponding to the target torso area, that is, a virtual torso model, is generated based on the collected sensor data.

[0037] In step S108, the following contents are included: The server obtains each virtual blood vessel in the virtual torso model, and determines a virtual blood vessel that meets the medical ecological requirements as a blood vessel to be injected based on the ecological attributes of each virtual blood vessel.

[0038] For example, in this embodiment, after creating a virtual torso model that has a mapping relationship with the target torso area, the server can obtain each virtual blood vessel in the virtual torso model, and determine the virtual blood vessels that meet the medical ecological requirements as the blood vessels to be injected based on the ecological properties of each virtual blood vessel.

[0039] Furthermore, the above-mentioned “obtaining each virtual blood vessel in the virtual torso model, and determining the virtual blood vessel that meets the medical ecological requirements as the blood vessel to be injected based on the ecological attributes of each virtual blood vessel” also includes the following steps: Determine each virtual blood vessel located in the virtual torso model based on a preset image recognition model, and establish a model coordinate system corresponding to the virtual torso model; Acquire each contour pixel point that respectively constitutes the blood vessel contour of each virtual blood vessel, and acquire each contour coordinate point corresponding to each contour pixel point based on the model coordinate point, and determine each contour coordinate point corresponding to the same blood vessel contour as a contour coordinate set; In each contour coordinate set, difference calculations are performed on contour coordinate points with the same horizontal coordinate to obtain horizontal differences, and the horizontal difference corresponding to the maximum value is determined as the first target difference; In each contour coordinate set, the contour coordinate points with the same longitudinal coordinate are respectively calculated to obtain longitudinal differences, and the longitudinal difference corresponding to the maximum value is determined as the second target difference; Determine the larger one of the first target difference and the second target difference as the contour size corresponding to the blood vessel contour, obtain contour sizes corresponding to the blood vessel contours, and determine the contour sizes as size attributes corresponding to the virtual blood vessels; Obtaining contour pixel values ​​corresponding to contour pixel points respectively located in each contour coordinate set; Calculating the mean of each contour pixel value corresponding to the same contour coordinate set to obtain the mean of each pixel corresponding to each blood vessel contour, and determining each pixel mean as the pixel attribute corresponding to each virtual blood vessel; Based on the preset determination strategy, the size attributes and pixel attributes corresponding to the same virtual blood vessel are comprehensively evaluated to obtain the attribute evaluation values ​​corresponding to each virtual blood vessel, and the virtual blood vessel corresponding to the largest attribute evaluation value is determined as the blood vessel to be injected.

[0040] For example, in this embodiment, after creating a virtual torso model with a mapping relationship with the target torso area, the server will determine each virtual blood vessel in the virtual torso model based on a preset image recognition model, which can be a neural network recognition method, and then the server will establish a coordinate system corresponding to the virtual torso model, namely, a model coordinate system. Next, the server will obtain all pixel points constituting the blood vessel contour of each virtual blood vessel, namely, each contour pixel point, and obtain the coordinate points corresponding to each contour pixel point based on the model coordinate point, namely, each contour coordinate point, and then determine each contour coordinate point corresponding to the same blood vessel contour as a contour coordinate set.

[0041] The server will perform difference calculations on the contour coordinate points with the same horizontal coordinates in each contour coordinate set to obtain each horizontal difference, and determine the horizontal difference corresponding to the maximum value as the first target difference. For example, if the contour coordinate points with the same horizontal coordinates are (1, 2), (1, 3), (1, 7), the calculation methods of each horizontal difference are 3-2=1, 7-2=5, 7-3=4, respectively, so that the horizontal difference of the maximum value 5 is determined as the first target difference; then, the server will perform difference calculations on the contour coordinate points with the same vertical coordinates in each contour coordinate set to obtain each longitudinal difference, and determine the longitudinal difference corresponding to the maximum value as the second target difference. At this time, the server will determine the larger one of the first target difference and the second target difference as the contour size of the blood vessel contour, thereby obtaining the contour size of each blood vessel contour, and determine each contour size as the size attribute of each virtual blood vessel.

[0042] Since each blood vessel is at a different distance from the epithelial area, the color depth of each blood vessel is different. Therefore, after determining the size attributes of each virtual blood vessel, the server will obtain the pixel values ​​corresponding to each contour pixel point in each contour coordinate set, that is, each contour pixel value, and then calculate the mean of each contour pixel value in the same contour coordinate set to obtain the mean value of each pixel of each blood vessel contour, and determine each pixel mean value as the pixel attribute of each virtual blood vessel.

[0043] Since thicker blood vessels that are closer to the epithelial area are more suitable for intravenous injection, the server will perform a comprehensive attribute evaluation on the size attributes and pixel attributes corresponding to the same virtual blood vessel based on a preset determination strategy, thereby obtaining attribute evaluation values ​​corresponding to each virtual blood vessel, and determining the virtual blood vessel with the largest attribute evaluation value as the blood vessel to be injected.

[0044] This embodiment can roughly obtain the outline size of each blood vessel outline based on the model coordinate system, which is a way to quickly obtain the size of each virtual blood vessel and can more objectively evaluate the comprehensive properties of the virtual blood vessels, saving a certain amount of working time.

[0045] Furthermore, the above-mentioned “based on the preset determination strategy, comprehensively evaluating the size attribute and the pixel attribute corresponding to the same virtual blood vessel to obtain the attribute evaluation values ​​corresponding to each virtual blood vessel” further includes the following steps: Retrieve the preset size evaluation weight and the preset pixel evaluation weight; The contour size and pixel mean value corresponding to the same virtual blood vessel are respectively multiplied by the preset size evaluation weight and the preset pixel evaluation weight to obtain a first product value and a second product value; The first product value and the second product value are normalized to obtain a first evaluation value and a second evaluation value, and the first evaluation value and the second evaluation value are summed to obtain each attribute evaluation value corresponding to each virtual blood vessel.

[0046] For example, in this embodiment, the server is pre-set with a preset size evaluation weight and a preset pixel evaluation weight. After determining the size attribute and pixel attribute of each virtual blood vessel, the server will retrieve the preset size evaluation weight and the preset pixel evaluation weight, and then multiply the contour size and pixel mean of the same virtual blood vessel by the preset size evaluation weight and the preset pixel evaluation weight to obtain a first product value and a second product value. Since the units of the contour size and the pixel mean are different, the server will divide the first product value and the second product value by the preset planning value, so as to normalize the first product value and the second product value to obtain the first evaluation value and the second evaluation value, and then sum the first evaluation value and the second evaluation value to obtain the attribute evaluation value of each virtual blood vessel.

[0047] In step S110, the following contents are included: The server obtains intravenous drug information corresponding to the patient to be treated with intravenous therapy, and based on the intravenous drug information, generates a drug pipeline model connected to the blood vessel to be injected and feeds it back to the medical end.

[0048] For example, in this embodiment, after determining the blood vessel to be injected, the server will obtain the intravenous drug information of the patient to be injected, and generate a drug pipeline model connected to the blood vessel to be injected based on the intravenous drug information, thereby completing the corresponding intravenous pipeline design for medical staff to refer to, thereby improving the corresponding user experience and medical assistance.

[0049] Furthermore, in this embodiment, the above-mentioned “obtaining intravenous drug information corresponding to the patient to be treated with intravenous therapy, and generating a drug pipeline model connected to the blood vessel to be injected based on the intravenous drug information” may also include the following steps: Determine the patient's drug number based on the patient information corresponding to the patient to be treated with intravenous therapy, and retrieve a drug record table, wherein the drug record table includes multiple drug sequences, each of which includes different recorded drug numbers and recorded drug information corresponding to the different recorded drug numbers; Traverse the medication record table to find the same medication number as the patient's medication number. Deleting the drug sequence including the intravenous drug information from the drug record table, and determining the recorded drug information corresponding to the recorded drug number as the intravenous drug information corresponding to the patient drug number; Based on the intravenous drug information, each injectable drug corresponding to different drug types is determined, and the characteristics of each injectable drug are determined according to the injection sequence determination strategy and the mixed injection determination strategy, and the sequence characteristics and connection characteristics corresponding to each injectable drug are determined, and based on the sequence characteristics and connection characteristics, a drug pipeline model connected to the blood vessel to be injected is generated.

[0050] For example, in this embodiment, since the intravenous drugs required for intravenous injection for each patient to be treated with intravenous therapy may be different, when creating the corresponding drug pipeline model, the corresponding patient drug number can be first determined based on the patient information of the patient to be treated with intravenous therapy. It can be explained that each patient to be treated with intravenous therapy has a corresponding patient drug number, and the patient drug number is unique and only; after obtaining the corresponding patient drug number, the drug record table pre-stored in the server can be retrieved, where the drug record table includes a plurality of columns of drug sequences arranged in a column direction, each column of drug sequence includes a recorded drug number and recorded drug information corresponding to the recorded drug number, and the recorded drug number having the same number content as the patient drug number is found by checking the drug record table, and the corresponding recorded drug information is further determined as the intravenous drug information corresponding to the patient to be treated with intravenous therapy; at the same time, since the corresponding drug information in the drug record table has been correspondingly retrieved, in order to reduce the corresponding data storage volume, the corresponding drug sequence can be deleted in the drug record table, thereby increasing the corresponding data processing volume; Finally, after obtaining the corresponding intravenous drug information, the various injectable drugs corresponding to different types of drugs can be determined based on the intravenous drug information. Since the ingredients of different drugs are different during intravenous injection, the corresponding injection order needs to be determined. For example, the injection order can be determined according to the following rules: 1. Salt first, then sugar. Replenish salt first and then sugar. This helps to replenish the missing components in the patient's body in time and adjust the patient's experience balance; 2. Crystalloid first, then colloid. Crystalloid refers to the liquid that the human body usually infuses, such as normal saline, and colloid refers to artificial or natural colloids, such as plasma albumin. Crystalloids can play a role in replenishing capacity. In addition, during the process of fluid replacement, proportional fluid replacement should be carried out according to the lost liquid components. After determining the sequence characteristics of each injectable drug based on the above rules, the connection characteristics of each injectable drug also need to be determined, that is, it is necessary to judge whether there is a possibility of mixed injection between the injectable drugs. If mixed injection is possible, the connection characteristics of the corresponding injectable drugs can be determined as mixed characteristics. If mixed injection is not possible, the connection characteristics of the corresponding injectable items can be determined as independent characteristics. After completing the determination of the sequence characteristics and connection characteristics of each injectable drug, the corresponding drug pipeline model is generated.

[0051] Furthermore, in this embodiment, the above-mentioned “determining the characteristics of each injection drug according to the injection sequence determination strategy, and determining the sequence characteristics corresponding to each injection drug” may also include the following steps: Determine the components of each injectable medicine to obtain an initial chemical component group corresponding to each injectable medicine, wherein the initial chemical component group includes different component types and type ratios corresponding to the different component types; Retrieving a preset ratio threshold, and screening out component types whose corresponding type ratio is less than or equal to the preset ratio threshold from each of the initial chemical component groups to obtain each current chemical component group; Determine the content of each component corresponding to each component type in each current chemical component group, and determine the content coefficient based on the content of each component; Based on the preset chemical injection sorting table, the type coefficient of each component type in each current chemical component group is determined respectively, and the content coefficient and the type coefficient corresponding to the same component type are summed and calculated to obtain the type priority value corresponding to the component type; The priority values ​​of all types corresponding to the same current chemical component group are summed up to obtain the injection priority values ​​corresponding to each current chemical component group, and the current chemical component groups are sorted based on the injection priority values, and the injection sequences corresponding to the respective injection drugs are determined as the sequence characteristics.

[0052] For example, in this embodiment, it should be noted that, as can be seen from the above content, the injection sequence of each injection drug is mainly affected by the different chemical components of each injection drug; Therefore, based on this, the corresponding components of each injection drug can be determined first, and the initial chemical component groups corresponding to each injection drug can be obtained, wherein the initial chemical component groups include different component types and the type ratios corresponding to the different component types; Next, since the chemical components in each injectable drug may be relatively complex, and the chemical components with a smaller proportion may have a smaller impact on the injection order, in order to reduce the corresponding data calculation amount, the chemical components with a smaller proportion may be removed. The removal method is to compare the component content of each chemical component with a preset ratio threshold, so as to screen out the component types with a ratio less than or equal to the preset threshold from the initial chemical component group, and obtain the corresponding current chemical component group; Next, the content of each component type in the current chemical component group can be determined to obtain the corresponding content of each component, and the corresponding content coefficient can be determined based on the content of each component. It should be noted that the determination of the type coefficient can be set based on the principle that the larger the component content, the higher the coefficient value. For example, the corresponding coefficient value is set according to different content intervals, and the corresponding content coefficient is determined by comparing the component content with the content interval. The higher the content coefficient, the higher the corresponding component content, thereby obtaining the content coefficients of all component types corresponding to the same current chemical component group; Next, after the content coefficient is determined, the corresponding preset chemical injection sorting table can be called to determine the type coefficient of each component type in each current chemical component group. It should be noted that the preset chemical injection sorting table lists the type coefficients corresponding to different component types, that is, by checking the preset chemical injection sorting table, the type coefficient of each component type in each current chemical component group can be found; Then, after obtaining the content coefficient and the type coefficient corresponding to the same component type, the type priority value corresponding to the component type can be obtained by summing up the values, and all the type priority values ​​corresponding to the same current chemical component group are summed up again to obtain the injection priority values ​​corresponding to each current chemical component group. Finally, the injection priority values ​​of each current chemical component group are sorted from large to small, so as to obtain the injection sequence corresponding to each injection drug, and the corresponding injection sequence can be determined as the sequence characteristic to be obtained.

[0053] Furthermore, in this embodiment, the above-mentioned “determining the characteristics of each injection drug according to the mixed injection determination strategy, and determining the connection characteristics corresponding to each injection drug” may also include the following steps: Retrieving a mixing record table, wherein the mixing record table includes a plurality of columns of mixing sequences, each column of mixing sequence includes at least two different injection drugs that can be mixed and injected; The mixing record table is traversed, and it is determined whether there are at least two injection drugs in the same mixing sequence in each injection drug, and all injection drugs in the same mixing sequence are determined as a mixing group with mixing characteristics, and all remaining injection drugs are determined as having independent characteristics.

[0054] For example, in this embodiment, in order to be able to determine the connection characteristics of each injectable drug, a corresponding mixing record table can be pre-set, wherein the mixing record table includes a plurality of mixing sequences arranged in a column direction, and each mixing sequence includes at least two different injectable drugs that can be mixed and injected; after completing the pre-setting of the mixing record table, by traversing the mixing record table, it is determined whether there are injectable drugs that can be mixed and injected among the injectable drugs, and the corresponding injectable drugs that can be mixed and injected are determined to have mixing characteristics, and the remaining injectable drugs can be determined as having independent characteristics.

[0055] Furthermore, in this embodiment, the above-mentioned “generating a drug pipeline model connected to the blood vessel to be injected based on the sequence characteristics and the connection characteristics” may also include the following steps: Determine the injection order corresponding to all the injectable drugs in the same mixed group, and determine the injection order with the largest value as corresponding to the mixed group; Generate a parallel pipeline model with a parallel structure based on all the injection drugs in each mixed group, and generate an independent pipeline model with an independent structure based on all the remaining injection drugs except the mixed group; Based on the injection sequence corresponding to the mixed group and the injection sequence corresponding to all remaining injection drugs except the mixed group, the parallel pipeline model and the independent pipeline model are connected in sequence to obtain a drug pipeline model that is connected to the blood vessel to be injected and presents a series structure.

[0056] For example, in this embodiment, after determining the sequence characteristics and connection characteristics of each injection drug through the above content, the corresponding drug pipeline model can be generated based on the corresponding characteristics; First, since the injection order of all the injected drugs in the mixed group is different, it needs to be unified accordingly, and the unified method is to determine the injection order with the largest value to correspond to the mixed group; Next, after the injection sequence is determined, a parallel pipeline model with a parallel structure can be generated based on all the injection drugs in each mixed group, and an independent pipeline model with an independent structure can be generated based on other injection drugs; Finally, the corresponding pipeline models can be connected according to the injection sequence to obtain a drug pipeline model with a series structure.

[0057] For example, Figure 2 As shown, Figure 2 A schematic diagram of the corresponding drug pipeline model is shown in Figure 2In the figure, we can see the corresponding parallel pipeline model presenting a parallel structure and the independent pipeline model presenting an independent structure, wherein the parallel pipeline model and the independent pipeline model together constitute a series pipeline model presenting a series structure, and the series pipeline model is connected to the corresponding virtual torso model for corresponding intravenous injection.

[0058] In addition, in this embodiment, the above-mentioned “generating a drug pipeline model connected to the blood vessel to be injected based on the sequence characteristics and the connection characteristics” can also be implemented based on the following method steps: The sequential characteristics and connection characteristics are extracted and compared with the trained training feature group respectively. If the features in the training feature group correspond one-to-one with the sequential characteristics and connection characteristics, the training drug pipeline model and drug injection cycle corresponding to the training feature group are retrieved.

[0059] For example, the present invention will compare the sequential characteristics and connection characteristics with the trained training feature group respectively, and the comparison also includes the comparison of drug types. When the sequential characteristics and connection characteristics are corresponding, the corresponding drug types are also corresponding. If the previous corresponding patients or other patients have the same needs as the currently injected drugs, the features in the training feature group at this time correspond one-to-one with the sequential characteristics and mixed characteristics, so the present invention can call up the training drug pipeline model and drug injection cycle corresponding to the training feature group, and quickly determine the training drug pipeline model that needs to be generated and a relatively accurate drug injection cycle. The drug injection cycle can be the average injection cycle of all previous users; Based on the position of each injected drug, the training drug pipeline model is traversed and information is added to obtain the drug pipeline model. The present invention adds corresponding drug information according to the position of each injected drug in the training drug pipeline model to obtain a drug pipeline model with the injected drug information; If the features in the training feature group do not completely correspond to the sequence characteristics and the connection characteristics, then the feature similarity is obtained based on the comparison between the features in the training feature group and the sequence characteristics and the connection characteristics. At this time, there is no historical injection scene corresponding to the current injection scene, so the features in the training feature group do not completely correspond to the sequence characteristics and the connection characteristics. At this time, there may be a word query for partial drug injection. At this time, the present invention can calculate the features in the training feature group and compare them with the sequence characteristics and the connection characteristics to obtain the feature similarity.

[0060] When calculating feature similarity, the present invention can be calculated by the following steps, including: The historical drugs corresponding to the historical sequence characteristics in the training feature group and the current drugs corresponding to the current sequence characteristics are obtained, and the first identical quantity value and the second different quantity value of the historical drugs and the current drugs are calculated.

[0061] Obtain the historical connection characteristics and the current connection characteristics in the training feature group, calculate the third identical number of values ​​and the fourth different number of values ​​of the historical connection characteristics and the current connection characteristics, and calculate the feature similarity by the following formula: in, is the feature similarity, is the first same quantity value, is the second different quantity value, is the third identical quantity value, is the fourth different quantity value, is the first weight value, is the second weight value, The larger the value is, the higher the similarity of features will be in the dimension corresponding to the sequential characteristic. The larger the value is, the higher the similarity of the features in the dimension corresponding to the connection feature will be. and is pre-set, where Preferably greater than .

[0062] If it is determined that there is a feature similarity greater than the selected threshold, the training feature group with the largest feature similarity is used as the feature group to be decomposed, and the feature group to be decomposed is decomposed based on the sequence characteristics and connection characteristics and then reassembled to obtain a drug pipeline model. At this time, it can be considered that the historically stored drug pipeline model that is similar to the current medication scenario is the most efficient to modify and reassemble the drug pipeline model based on the historically stored drug pipeline model. Therefore, at this time, the present invention will have a feature similarity greater than the selected threshold, then the training feature group with the largest feature similarity is used as the feature group to be decomposed, the feature group to be decomposed is decomposed, the sub-models corresponding to all different drugs are deleted, and sub-models of drugs that exist in the current drug but do not exist in the stored drug pipeline model are established. The sub-models of the corresponding drugs are automatically spliced ​​and set based on the sequence characteristics and connection characteristics, that is, the newly added sub-models are connected to the pipeline according to the corresponding sequence characteristics and connection characteristics, or the final drug pipeline model is obtained based on manual adjustment and splicing by medical staff. The present invention does not limit the splicing method. The final drug pipeline model is stored corresponding to the corresponding new sequence characteristics and connection characteristics. If it is determined that there is no feature similarity greater than the selected threshold, then the parallel pipeline model and independent pipeline model corresponding to all drugs are generated. At this time, it is proved that there is no relatively close stored drug pipeline model, so it is necessary to establish a parallel pipeline model and an independent pipeline model corresponding to each injected drug, and connect them in the corresponding order to obtain a training drug pipeline model, and calculate the injection cycle of all injections under the training drug pipeline model and calculate the average to obtain the drug injection cycle.

[0063] Furthermore, in this embodiment, after the creation of the corresponding drug pipeline model is completed, in order to enable the injected drug to complete the corresponding injection in the corresponding injection order, a corresponding clamping control model can also be generated, wherein the clamping control model is located on each pipeline model, and is used to clamp the corresponding pipeline model. By clamping the clamping control model, it can be ensured that the corresponding pipeline model is in a clamping state or an injection state, thereby realizing automatic control of each pipeline model and improving the corresponding medical auxiliary performance.

[0064] It should be noted that, in this embodiment, Figure 3 As shown, the corresponding method can be implemented by interconnecting terminals based on a server and a medical terminal.

[0065] In summary, according to the scheme of this embodiment, the server will first obtain the patient information of the patient to be treated with static therapy, and determine the torso set to be treated with static therapy based on the patient information, and the torso set to be treated with static therapy includes at least one torso area to be treated with static therapy; then, the server will obtain the historical static therapy record of the patient to be treated with static therapy within the preset time before the current time point, and select the area based on the historical static therapy record and the torso set to be treated with static therapy, so as to obtain the target torso area of ​​the patient to be treated with static therapy; then, the server will obtain the corresponding regional image based on the target torso area of ​​the patient to be treated with static therapy, and create a virtual torso model with a mapping relationship with the target torso area based on the regional image; then, the server will obtain each virtual blood vessel located in the virtual torso model, and determine the virtual blood vessel that meets the medical ecological requirements as the blood vessel to be injected based on the ecological attributes of each virtual blood vessel; finally, the server will obtain the intravenous drug information of the patient to be treated with static therapy, and generate a drug pipeline model connected to the blood vessel to be injected based on the intravenous drug information. The present invention can provide medical staff with a reference for the injection area and the injection drug during intravenous injection to a large extent, and can reduce their working time to a certain extent, thereby improving the efficiency of intravenous injection.

[0066] Another embodiment of the present invention provides an artificial intelligence-based intravenous therapy circuit design system. Figure 4 As the corresponding system block diagram, the system includes: an acquisition module, configured to acquire patient information corresponding to the patient to be treated with static therapy, and determine a torso set to be treated with static therapy based on the patient information, wherein the torso set to be treated with static therapy includes at least one torso region to be treated with static therapy; A region selection module is configured to obtain historical static therapy records corresponding to the patient to be treated within a preset time before a current time point, and perform region selection based on the historical static therapy records and the static therapy torso set to obtain a target torso region corresponding to the patient to be treated; A creation module is configured to acquire a corresponding regional image based on a target torso region of the patient to be treated with static therapy, and to create a virtual torso model having a mapping relationship with the target torso region based on the regional image; a determination module configured to obtain each virtual blood vessel in the virtual torso model, and determine a virtual blood vessel that meets the medical ecological requirements as a blood vessel to be injected based on the ecological attributes of each virtual blood vessel; The model generation module is configured to obtain intravenous drug information corresponding to the patient to be treated with intravenous therapy, and generate a drug pipeline model connected to the blood vessel to be injected based on the intravenous drug information.

[0067] In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the examples of the present invention. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to implement the content of the present invention described herein, and the above description of specific languages ​​is for the purpose of disclosing the preferred embodiment of the present invention.

[0068] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0069] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof.

[0070] Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in the devices described in the embodiment, or alternatively may be located in one or more devices different from the devices in the examples. The modules in the foregoing examples may be combined into one module or may be divided into multiple submodules.

[0071] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and furthermore may be divided into a plurality of submodules or subunits or subcomponents.

[0072] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is meant to be within the scope of the present invention and to form different embodiments.

[0073] In addition, some of the embodiments are described herein as methods or combinations of method elements that can be implemented by a processor of a computer system or by other devices that perform the functions. Therefore, a processor with necessary instructions for implementing the method or method elements forms a device for implementing the method or method elements. In addition, the elements described herein of the device embodiments are examples of devices for implementing the functions performed by the elements for the purpose of implementing the invention.

[0074] As used herein, unless otherwise specified, the use of ordinal numbers "first," "second," "third," etc. to describe common objects merely indicates that different instances of similar objects are involved, and is not intended to imply that the objects so described must have a given order in time, space, order, or in any other manner.

[0075] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments may be envisioned within the scope of the invention thus described. In addition, it should be noted that the language used in this specification is primarily selected for readability and instructional purposes, rather than for explaining or limiting the subject matter of the present invention.

Claims

1. A method for designing intravenous therapy circuits based on artificial intelligence, characterized in that: The following steps are involved: The medical end obtains the patient information corresponding to the patient to be treated with static therapy and sends it to the server, and the server determines a trunk set to be treated with static therapy based on the patient information, wherein the trunk set to be treated with static therapy includes at least one trunk area to be treated with static therapy; The server obtains historical static therapy records corresponding to the patient to be treated within a preset time before the current time point, and performs region selection based on the historical static therapy records and the static therapy torso set to obtain a target torso region corresponding to the patient to be treated; The server acquires a corresponding regional image based on the target torso region of the patient to be treated with static therapy, and creates a virtual torso model having a mapping relationship with the target torso region based on the regional image; The server obtains each virtual blood vessel in the virtual torso model, and determines a virtual blood vessel that meets the medical ecological requirements as a blood vessel to be injected based on the ecological attributes of each virtual blood vessel; The server obtains intravenous drug information corresponding to the patient to be treated with intravenous therapy, and based on the intravenous drug information, generates a drug pipeline model connected to the blood vessel to be injected and feeds it back to the medical end.

2. The artificial intelligence-based intravenous therapy circuit design method according to claim 1 is characterized in that: Determining a torso set to be treated statically based on the patient information includes: Determine the patient age and medical history of the patient to be treated with intravenous therapy based on the patient information, and retrieve a preset intravenous therapy trunk distribution table, wherein the preset intravenous therapy trunk distribution table includes multiple intravenous therapy trunk areas, and each intravenous therapy trunk area includes a corresponding age-appropriate interval and a disease-prohibited interval; Performing a primary screening based on the preset static therapy trunk distribution table, determining all static therapy trunk areas corresponding to the appropriate age ranges including the patient's age, and determining them as a primary screening group; A secondary screening is performed based on the primary screening group, and the to-be-treated trunk set is determined based on the secondary screening result.

3. The artificial intelligence-based intravenous therapy circuit design method according to claim 2 is characterized in that: Determining the to-be-treated trunk set based on the secondary screening result includes: When the secondary screening result is that the primary screening group has at least one static treatment trunk area corresponding to a disease-free interval that does not include the patient's medical record, determining the at least one static treatment trunk area as a static treatment trunk set; When the secondary screening result is that the primary screening group does not have a static therapy trunk area corresponding to a forbidden disease interval that does not include the patient's medical record, the primary screening group is determined as a static therapy trunk set.

4. The method for designing intravenous therapy circuit based on artificial intelligence according to claim 1 is characterized in that: Performing region selection based on the historical static therapy record and the static therapy torso set to obtain a target torso region corresponding to the patient to be static therapy includes: determining a historical torso region based on the historical static therapy record, and comparing the historical torso region with all static therapy torso regions in the static therapy torso set; When the static therapy torso set includes a static therapy torso region and the static therapy torso region is the same region as the historical torso region, the static therapy torso region is determined as the target torso region corresponding to the patient to be treated with static therapy; When the static therapy trunk set includes a static therapy trunk area and the static therapy trunk area is not the same area as the historical trunk area, determining a trunk connection relationship between the static therapy trunk area and the historical trunk area, and determining the static therapy trunk area as a target trunk area corresponding to the patient to be treated with static therapy when the trunk connection relationship is an upper connection, and determining the historical trunk area as a target trunk area corresponding to the patient to be treated with static therapy when the trunk connection relationship is a lower connection; or When the static therapy trunk set includes multiple static therapy trunk areas, determining the trunk connection relationships between the multiple static therapy trunk areas and the historical trunk area respectively; When the trunk connection relationship between any static therapy trunk area and the historical trunk area is an upper connection, the static therapy trunk area is determined as the target trunk area corresponding to the patient to be static therapy; When the trunk connection relationship between all static therapy trunk regions and the historical trunk region is a lower connection, the historical trunk region is determined as the target trunk region corresponding to the patient to be static therapy.

5. The artificial intelligence-based intravenous therapy circuit design method according to claim 1 is characterized in that: Acquiring each virtual blood vessel in the virtual torso model, and determining a virtual blood vessel that meets the medical ecological requirements as a blood vessel to be injected based on the ecological attributes of each virtual blood vessel, including: Determine each virtual blood vessel located in the virtual torso model based on a preset image recognition model, and establish a model coordinate system corresponding to the virtual torso model; Acquire each contour pixel point that respectively constitutes the blood vessel contour of each virtual blood vessel, and acquire each contour coordinate point corresponding to each contour pixel point based on the model coordinate point, and determine each contour coordinate point corresponding to the same blood vessel contour as a contour coordinate set; In each contour coordinate set, difference calculations are performed on contour coordinate points with the same horizontal coordinate to obtain horizontal differences, and the horizontal difference corresponding to the maximum value is determined as the first target difference; In each contour coordinate set, the contour coordinate points with the same longitudinal coordinate are respectively calculated to obtain longitudinal differences, and the longitudinal difference corresponding to the maximum value is determined as the second target difference; Determine the larger one of the first target difference and the second target difference as the contour size corresponding to the blood vessel contour, obtain contour sizes corresponding to the blood vessel contours, and determine the contour sizes as size attributes corresponding to the virtual blood vessels; Obtaining contour pixel values ​​corresponding to contour pixel points respectively located in each contour coordinate set; Calculating the mean of each contour pixel value corresponding to the same contour coordinate set to obtain the mean of each pixel corresponding to each blood vessel contour, and determining each pixel mean as the pixel attribute corresponding to each virtual blood vessel; Based on the preset determination strategy, the size attributes and pixel attributes corresponding to the same virtual blood vessel are comprehensively evaluated to obtain the attribute evaluation values ​​corresponding to each virtual blood vessel, and the virtual blood vessel corresponding to the largest attribute evaluation value is determined as the blood vessel to be injected.

6. The artificial intelligence-based intravenous therapy circuit design method according to claim 5 is characterized in that: Based on the preset determination strategy, the size attribute and pixel attribute corresponding to the same virtual blood vessel are comprehensively evaluated to obtain the attribute evaluation values ​​corresponding to each virtual blood vessel, including: Retrieve the preset size evaluation weight and the preset pixel evaluation weight; The contour size and pixel mean value corresponding to the same virtual blood vessel are respectively multiplied by the preset size evaluation weight and the preset pixel evaluation weight to obtain a first product value and a second product value; The first product value and the second product value are normalized to obtain a first evaluation value and a second evaluation value, and the first evaluation value and the second evaluation value are summed to obtain each attribute evaluation value corresponding to each virtual blood vessel.

7. The method for designing intravenous therapy circuit based on artificial intelligence according to claim 1 is characterized in that: Acquiring intravenous drug information corresponding to the patient to be treated with intravenous therapy, and generating a drug pipeline model connected to the blood vessel to be injected based on the intravenous drug information, including: Determine the patient's drug number based on the patient information corresponding to the patient to be treated with intravenous therapy, and retrieve a drug record table, wherein the drug record table includes multiple drug sequences, each of which includes different recorded drug numbers and recorded drug information corresponding to the different recorded drug numbers; Traverse the medication record table to find the same medication number as the patient's medication number. Deleting the drug sequence including the intravenous drug information from the drug record table, and determining the recorded drug information corresponding to the recorded drug number as the intravenous drug information corresponding to the patient drug number; Based on the intravenous drug information, each injectable drug corresponding to different drug types is determined, and the characteristics of each injectable drug are determined according to the injection sequence determination strategy and the mixed injection determination strategy, and the sequence characteristics and connection characteristics corresponding to each injectable drug are determined, and based on the sequence characteristics and connection characteristics, a drug pipeline model connected to the blood vessel to be injected is generated.

8. The artificial intelligence-based intravenous therapy circuit design method according to claim 7 is characterized in that: The characteristics of each injection drug are determined according to the injection sequence determination strategy, and the sequence characteristics corresponding to each injection drug are determined, including: Determine the components of each injectable medicine to obtain an initial chemical component group corresponding to each injectable medicine, wherein the initial chemical component group includes different component types and type ratios corresponding to the different component types; Retrieving a preset ratio threshold, and screening out component types whose corresponding type ratio is less than or equal to the preset ratio threshold from each of the initial chemical component groups to obtain each current chemical component group; Determine the content of each component corresponding to each component type in each current chemical component group, and determine the content coefficient based on the content of each component; Based on the preset chemical injection sorting table, the type coefficient of each component type in each current chemical component group is determined respectively, and the content coefficient and the type coefficient corresponding to the same component type are summed and calculated to obtain the type priority value corresponding to the component type; The priority values ​​of all types corresponding to the same current chemical component group are summed up to obtain the injection priority values ​​corresponding to each current chemical component group, and the current chemical component groups are sorted based on the injection priority values, and the injection sequences corresponding to the respective injection drugs are determined as the sequence characteristics.

9. The artificial intelligence-based intravenous therapy circuit design method according to claim 8 is characterized in that: The characteristics of each injection drug are determined according to the mixed injection determination strategy, and the connection characteristics corresponding to each injection drug are determined, including: Retrieving a mixing record table, wherein the mixing record table includes a plurality of columns of mixing sequences, each column of mixing sequence includes at least two different injection drugs that can be mixed and injected; The mixing record table is traversed, and it is determined whether there are at least two injection drugs in the same mixing sequence in each injection drug, and all injection drugs in the same mixing sequence are determined as a mixing group with mixing characteristics, and all remaining injection drugs are determined as having independent characteristics.

10. The artificial intelligence-based intravenous therapy circuit design method according to claim 9, characterized in that: Generating a drug pipeline model connected to the blood vessel to be injected based on the sequence characteristics and the connection characteristics includes: Determine the injection order corresponding to all the injectable drugs in the same mixed group, and determine the injection order with the largest value as corresponding to the mixed group; Generate a parallel pipeline model with a parallel structure based on all the injection drugs in each mixed group, and generate an independent pipeline model with an independent structure based on all the remaining injection drugs except the mixed group; Based on the injection sequence corresponding to the mixed group and the injection sequence corresponding to all remaining injection drugs except the mixed group, the parallel pipeline model and the independent pipeline model are connected in sequence to obtain a drug pipeline model that is connected to the blood vessel to be injected and presents a series structure.

11. The artificial intelligence-based intravenous therapy circuit design method according to claim 9, characterized in that: Generating a drug pipeline model connected to the blood vessel to be injected based on the sequence characteristics and the connection characteristics includes: Extracting the sequence characteristics and the connection characteristics and comparing them with the trained training feature group respectively, if the features in the training feature group correspond to the sequence characteristics and the connection characteristics one by one, then retrieving the training drug pipeline model and drug injection cycle corresponding to the training feature group; Based on the position of each injected drug, the trained drug pipeline model is traversed and information is added to obtain a drug pipeline model; If the features in the training feature group do not completely correspond to the sequence characteristics and the connection characteristics, then based on the comparison between the features in the training feature group and the sequence characteristics and the connection characteristics, the feature similarity is obtained; If it is determined that there is a feature similarity greater than the selected threshold, the training feature group with the largest feature similarity is used as the feature group to be decomposed, and the feature group to be decomposed is decomposed based on the sequence characteristics and the connection characteristics and then reassembled to obtain the drug pipeline model; If it is determined that there is no feature similarity greater than the selected threshold, a parallel pipeline model and an independent pipeline model corresponding to all drugs are generated.

12. An artificial intelligence based intravenous therapy circuit design system, characterized in that: include: an acquisition module, configured to acquire patient information corresponding to the patient to be treated with static therapy, and determine a torso set to be treated with static therapy based on the patient information, wherein the torso set to be treated with static therapy includes at least one torso region to be treated with static therapy; A region selection module is configured to obtain historical static therapy records corresponding to the patient to be treated within a preset time before a current time point, and perform region selection based on the historical static therapy records and the static therapy torso set to obtain a target torso region corresponding to the patient to be treated; A creation module is configured to acquire a corresponding regional image based on a target torso region of the patient to be treated with static therapy, and to create a virtual torso model having a mapping relationship with the target torso region based on the regional image; a determination module configured to obtain each virtual blood vessel in the virtual torso model, and determine a virtual blood vessel that meets the medical ecological requirements as a blood vessel to be injected based on the ecological attributes of each virtual blood vessel; The model generation module is configured to obtain intravenous drug information corresponding to the patient to be treated with intravenous therapy, and generate a drug pipeline model connected to the blood vessel to be injected based on the intravenous drug information.

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