Intravenous therapy information management system, method and equipment

Abstract

The invention discloses an intravenous therapy information management system, method and device, and relates to the technical field of medical information management. The vein treatment information management system comprises an initial imaging definition analysis prompt module, a vein imaging definition operation optimization prompt module and a vein difference optimization prompt module. According to the method, the first definition judgment result is obtained by performing the first definition judgment on the vein depth blood vessel image, whether to perform probe intelligent assistance and information recording is judged according to the first definition judgment result, and then whether to perform vein difference analysis is judged according to the second definition judgment result obtained according to the obtained second vein depth blood vessel image; if vein difference analysis is carried out, a third definition judgment result is obtained according to the optimized vein image to judge whether vein difference optimization control is carried out or not, and the effect of improving the acquisition management accuracy of the vein depth blood vessel image is achieved; the problem that in the prior art, vein depth blood vessel images recorded by vein channel information management are inaccurate is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical information management, and particularly to an intravenous therapy information management system, method and device. Background Art

[0002] With the development of medical technology, intravenous therapy, as an important treatment method, is widely used in clinical practice. The intravenous therapy information management system aims to improve the safety, efficiency and traceability of intravenous therapy. This system can record, monitor and analyze various data during the intravenous therapy process in real time, ensuring that the drug use, dose control and patient response during the treatment process are tracked in a timely manner. By optimizing the management process, reducing errors in manual operations, and strengthening the evaluation and feedback of treatment effects, the quality of medical services and the treatment experience of patients have been greatly improved. With the development of informatization and intelligence, the intravenous therapy information management system will become an indispensable part of modern medicine.

[0003] Existing intravenous therapy information management technologies mostly rely on manual records and traditional electronic medical record systems, suffering from problems such as untimely information entry, unstable data transmission, and difficulty in real-time monitoring of the patient treatment process. Traditional systems are difficult to accurately track each treatment link, and are prone to drug formulation errors or failure to detect allergic reactions in a timely manner. In addition, most existing technologies lack comprehensive analysis and intelligent warning functions for the intravenous therapy process, and are unable to effectively integrate multi-faceted data for precise management. Although some hospitals have adopted some automated devices, the overall intelligent level and data analysis ability of the system are still limited, making it difficult to comprehensively improve treatment safety and management efficiency.

[0004] For example, the intelligent PICC intravenous therapy information management system and method announced in the invention patent announcement with the publication number of: CN118762838B includes: preprocessing the PICC catheter image to generate a corrected image, and extracting the feature vectors in the corrected image; performing linear combination based on the feature vectors, and converting the result of the linear combination into the first probability of infection risk; performing convolution and pooling processing on the corrected image based on the first probability, and outputting the second probability corresponding to each risk category of the corrected image; generating an analysis and prediction result based on the first probability and the second probability and displaying it.

[0005] For example, the expert information management optimization method and system based on the intravenous therapy data platform announced in the invention patent announcement with the announcement number of: CN117524434B, including: collecting patient treatment data through the intravenous therapy data platform to obtain a patient treatment data set, where the patient treatment data set includes patient basic information data, adverse event reaction data, follow-up effect evaluation data, and catheterization parameter data; collecting intravenous catheterization expert information from the hospital management information system to obtain an expert personal qualification data set; performing data mining and fusion on the expert personal qualification data set to obtain expert ability map data; extracting corresponding intravenous catheterization expert data from the patient basic information data to obtain an expert treatment behavior data set.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, it is found that the above technology has at least the following technical problems: In the prior art, in the intravenous therapy information management system, although many steps have been automated and intelligent, the positioning and tracking records of intravenous cannulation are still not accurate enough. Currently, ultrasound positioning technology can be used for the positioning of intravenous cannulation. Although ultrasound positioning technology has been able to help accurately locate blood vessels, there are still some technical limitations. Especially in obese patients, the ultrasound images may not be clear enough, resulting in increased difficulty in cannulation and inaccurate venous depth blood vessel images in the management records of venous access information. Summary of the Invention

[0007] The embodiments of the present application provide an intravenous therapy information management system, method and device, which solve the problem of inaccurate venous depth blood vessel images in the management records of venous access information in the prior art and improve the accuracy of obtaining and managing venous depth blood vessel images.

[0008] The embodiments of the present application provide an intravenous therapy information management system, including: an initial imaging clarity analysis and prompt module, an intravenous imaging clarity operation optimization prompt module, and an intravenous difference optimization prompt module: among them, the initial imaging clarity analysis and prompt module is used to perform a first clarity judgment on the venous depth blood vessel image to obtain a first clarity judgment result, and judge whether to perform probe intelligent assistance and information recording based on the first clarity judgment result; the intravenous imaging clarity operation optimization prompt module is used to perform a second clarity judgment on the second venous depth blood vessel image obtained after probe operation based on the information recording to obtain a second clarity judgment result, and judge whether to perform venous difference analysis based on the second clarity judgment result; the intravenous difference optimization prompt module is used to, if venous difference analysis is performed, perform a third clarity judgment on the obtained optimized venous image to obtain a third clarity judgment result, and judge whether to perform venous difference optimization control based on the third clarity judgment result.

[0009] Further, the first clarity determination of the deep vein vascular image to obtain the first clarity determination result is carried out as follows: Step 1, obtain the image clarity parameters of the deep vein vascular image and the corresponding reference image clarity parameters. The image clarity parameters include image signal-to-noise ratio and image spatial resolution, and the reference image clarity parameters include reference image signal-to-noise ratio and reference image spatial resolution; Step 2, compare the image clarity parameters with the corresponding reference image clarity parameters. If the image clarity parameters are not less than the corresponding reference image clarity parameters, record the first clarity determination result as performing a positioning mark; otherwise, record the first clarity determination result as re-acquiring the image.

[0010] Further, the determination of whether to perform probe intelligent assistance and information recording based on the first clarity determination result is carried out as follows: If the first clarity determination result is to re-acquire the image, then perform probe intelligent assistance to obtain a second deep vein vascular image. The specific process is as follows: Obtain the probe imaging parameters to obtain a probe application evaluation index, and record information on the deep vein vascular image based on the probe application evaluation index. Preset that medical staff re-acquire the image according to the information record to obtain a second deep vein vascular image. The information record includes data feedback and operation marks; The probe imaging parameters include probe-skin distance, probe angle, probe applied pressure, and probe position compliance; The probe application evaluation index represents the quantitative data of the combined influence degree of the probe-skin distance, probe angle, probe applied pressure, and probe position compliance on the probe application evaluation; The specific process of obtaining the probe application evaluation index is as follows: Obtain the probe application weights from the preset database. The probe application weights include probe distance weight, probe angle weight, probe pressure weight, and probe position weight; Obtain the reference probe imaging data. The reference probe imaging data includes the reference probe-skin distance range, reference probe angle range, and reference probe applied pressure range; Obtain the distance deviation degree value by performing range deviation processing on the probe-skin distance and the corresponding reference probe-skin distance range; Obtain the angle deviation degree value by performing range deviation processing on the probe angle and the corresponding reference probe angle range; Obtain the pressure deviation degree value by performing range deviation processing on the probe applied pressure and the corresponding reference probe time pressure range; Obtain the position compensation value by performing position compliance difference processing on the probe position compliance; Perform influence degree distribution processing on the distance deviation degree value, angle deviation degree value, pressure deviation degree value, and position compensation value with the corresponding probe application weights to obtain the probe application evaluation index; The range deviation processing is used to quantify the deviation degree of the probe imaging parameters from the corresponding data range of the reference probe imaging data; The position compliance difference processing is used to quantify the deviation degree of the probe position; The influence degree distribution processing is used to comprehensively quantify the influence degree of the distance deviation degree value, angle deviation degree value, pressure deviation degree value, and position compensation value on the probe operation standardization.

[0011] Further, it is determined whether to perform venous difference analysis based on the second clarity determination result, and the specific process is as follows: perform a second clarity judgment on the second venous depth blood vessel image to obtain the second clarity determination result, and the second clarity determination result includes performing positioning marking and performing analysis of the second venous depth blood vessel image; if the second clarity determination result is to perform analysis of the second venous depth blood vessel image, then determine whether to introduce an individual difference factor based on the obtained optimized probe application evaluation index: if the optimized probe application evaluation index is not less than the optimized evaluation threshold obtained from the preset database, then introduce the individual difference factor, otherwise continue with probe intelligent assistance until the second clarity determination result is to perform positioning marking or the determination result based on the optimized probe application evaluation index is to introduce the individual difference factor.

[0012] Further, the specific acquisition method of the optimized probe application evaluation index is as follows: obtain the probe application evaluation weights from the preset database, and the probe application evaluation weights include a probe pressure weight factor, a probe angle weight factor, and a probe position weight factor; obtain the optimized probe imaging parameters by obtaining the probe imaging parameters after performing probe operations based on the feedback of the information record, and obtain the probe operation adjustment values in the information record, and the probe operation adjustment values include a probe pressure adjustment value, a probe angle adjustment value, and a probe position deviation degree value; perform a difference change process on the optimized probe imaging parameters and the probe imaging parameters to obtain the corresponding probe difference change value, and the difference change process is used to quantify the correctness of the probe operation change after performing probe intelligent assistance; perform a comparative analysis process on the probe difference change value and the corresponding probe operation adjustment value to obtain the corresponding probe operation adjustment compliance value, and the comparative analysis process is used to quantify the compliance degree between the operation change of the probe imaging parameters and the probe operation adjustment value; perform a probe optimization comprehensive evaluation process on the probe operation adjustment compliance value, the corresponding probe application evaluation weights, and the probe application evaluation index to obtain the optimized probe application evaluation index, and the probe optimization comprehensive evaluation process is used to comprehensively quantify the compliance degree between the change situation of the probe application evaluation index after performing probe intelligent assistance and the probe standard operation.

[0013] Further, the specific method for obtaining the individual difference factor is as follows: Obtain individual difference data and perform normalization processing. The individual difference data includes subcutaneous fat thickness, muscle thickness, body weight, skin elasticity, and muscle density; obtain individual difference weights from a preset database. The individual difference weights include subcutaneous fat thickness weight, muscle thickness weight, body weight weight, skin elasticity weight, and muscle density weight; perform elastic correlation processing on skin elasticity to obtain a skin elasticity effect value. The elastic correlation processing is used to describe the correlation relationship between skin elasticity and individual differences; perform difference influence degree allocation processing on subcutaneous fat thickness, muscle thickness, body weight, skin elasticity effect value, and muscle density with the corresponding individual difference weights to obtain an individual difference factor. The difference influence degree allocation processing is used to comprehensively quantify the influence degree of individual differences generated by subcutaneous fat thickness, muscle thickness, body weight, skin elasticity, and muscle density on ultrasonic imaging; the individual difference factor represents the quantitative data of the influence degree of individual differences generated by subcutaneous fat thickness, muscle thickness, body weight, skin elasticity, and muscle density on ultrasonic imaging.

[0014] Further, perform a third clarity judgment on the obtained optimized vein image to obtain a third clarity determination result, and judge whether to perform vein difference optimization control based on the third clarity determination result. The specific process is as follows: S1, obtain an adjustment ratio and image optimization data of the second vein depth blood vessel image from a preset database. The image optimization data includes image contrast and image brightness; S2, perform optimization operation on the individual difference factor through the adjustment ratio to obtain an individual difference factor to be optimized. The optimization operation represents a way to construct a numerical relationship between the adjustment ratio and the individual difference factor; S3, construct a mapping set between the individual difference factor to be optimized and the adjustment multiple, input the individual difference factor to be optimized into the mapping set to obtain the corresponding adjustment multiple, and perform image processing operation on the image optimization data based on the adjustment multiple to obtain adjusted image optimization data. The image processing operation represents a way to construct a numerical relationship between the individual difference factor to be optimized and the image optimization data; S4, obtain an optimized vein depth image according to the adjusted image optimization data, and perform a third clarity judgment on the optimized vein depth image to obtain a third clarity determination result. The third clarity determination result includes performing positioning marking and performing vein difference optimization control; S5, if vein difference optimization control is to be performed, obtain an optimization strength evaluation value, perform ratio optimization processing on the adjustment ratio to obtain a new adjustment ratio, and execute S2 until the third clarity determination result is to perform positioning marking or the individual difference factor to be optimized is lower than the individual difference factor optimization limit value; S6, if the individual difference factor to be optimized is lower than the individual difference factor optimization limit value, perform a marking positioning risk prompt.

[0015] Further, the specific method for obtaining the optimization intensity evaluation value is as follows: obtain the optimization intensity weight from a preset database, where the optimization intensity weight includes an image signal-to-noise ratio weight and an image spatial resolution weight; obtain the current image clarity parameter and obtain the individual difference factor optimization limit value and the reference image clarity parameter from the preset database; perform signal-to-noise ratio deviation processing on the image signal-to-noise ratio and the corresponding reference image signal-to-noise ratio to obtain a signal-to-noise ratio deviation value, where the signal-to-noise ratio deviation processing is used to quantify the gap degree between the image signal-to-noise ratio and the reference image signal-to-noise ratio; perform spatial resolution deviation processing on the image spatial resolution and the corresponding reference image spatial resolution to obtain a spatial resolution deviation value, where the spatial resolution deviation processing is used to quantify the gap degree between the image spatial resolution and the reference image spatial resolution; obtain an optimization difference degree value through optimization difference processing on the signal-to-noise ratio deviation value, the spatial resolution deviation value, and the corresponding optimization intensity weight; perform optimization progress evaluation processing on the current image clarity parameter and the image clarity parameter respectively to obtain corresponding signal-to-noise ratio optimization progress values and spatial resolution optimization progress values, where the optimization progress evaluation processing is used to quantify the change degree of the image clarity parameter during the optimization process; perform optimization progress change processing based on the signal-to-noise ratio optimization progress value and the spatial resolution optimization progress value combined with the corresponding optimization intensity weight to obtain an optimization progress change value, where the optimization progress change processing is used to comprehensively quantify the change degree of the image clarity parameter during the optimization process; perform difference processing on the individual difference factor and the corresponding individual difference factor optimization limit value to obtain a difference optimization intensity compensation value, where the difference processing is used to quantify the change situation of the optimized degree of the individual difference factor; perform comparison and analysis processing on the optimization progress change value and the difference optimization intensity compensation value to obtain a value to be optimized degree, where the comparison and analysis processing is used to quantify the optimization degree of the current individual difference factor; perform comprehensive processing on the optimization difference degree value and the value to be optimized degree to obtain an optimization intensity evaluation value, where the comprehensive processing is used to comprehensively quantify the optimization intensity degree of the current individual difference factor for optimizing the venous depth image.

[0016] The embodiment of the present application provides a method for managing venous treatment information, and the specific steps are as follows: perform a first clarity judgment on the venous depth blood vessel image to obtain a first clarity determination result, and judge whether to perform probe intelligent assistance and information recording based on the first clarity determination result; perform a second clarity judgment on the second venous depth blood vessel image obtained after the probe operation based on the information recording to obtain a second clarity determination result, and judge whether to perform venous difference analysis based on the second clarity determination result; if venous difference analysis is performed, then perform a third clarity judgment on the obtained optimized venous image to obtain a third clarity determination result, and judge whether to perform venous difference optimization control based on the third clarity determination result.

[0017] An embodiment of the present application provides an electronic device, which includes a memory for storing computer program instructions and a processor for executing the program instructions. When the computer program instructions are executed by the processor, the electronic device is triggered to execute the operations of the venous treatment information management system.

[0018] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. By performing a first clarity judgment on the venous depth blood vessel image to obtain a first clarity determination result, and based on this, determining whether to perform probe intelligent assistance and information recording. Then, perform a second clarity judgment on the obtained second venous depth blood vessel image to obtain a second clarity determination result to determine whether to perform venous difference analysis. If venous difference analysis is performed, perform a third clarity judgment on the obtained optimized venous image to obtain a third clarity determination result to determine whether to perform venous difference optimization control, so as to obtain a clearer venous depth blood vessel image, thereby improving the acquisition management accuracy of the venous depth blood vessel image and effectively solving the problem that the venous depth blood vessel image recorded in the management of venous channel information in the prior art is inaccurate.

[0019] 2. By obtaining the probe application evaluation weight from the preset database, then obtaining the probe imaging parameters for probe operation after obtaining the feedback based on information recording to obtain optimized probe imaging parameters, and at the same time obtaining the probe operation adjustment value in the information recording. Finally, perform an optimized probe application evaluation operation on the obtained data, probe imaging parameters, and probe application evaluation index to obtain an optimized probe application evaluation index, so as to more intuitively evaluate whether the probe operation is optimized, and thus realize the improvement of the accuracy of the probe operation.

[0020] 3. By obtaining individual difference data and the individual difference weight from the preset database, then performing normalization processing on the individual difference data, and finally performing individual difference operation in combination with the individual difference weight to obtain an individual difference factor, so as to obtain more accurate individual difference quantification data, and thus realize more accurate individual difference optimization control. Description of the Drawings

[0021] Figure 1 It is a schematic structural diagram of a venous treatment information management system provided by an embodiment of the present application. Detailed Embodiments

[0022] The embodiments of the present application solve the problem of inaccurate venous depth vascular images recorded in venous channel information management in the prior art by providing a venous treatment information management system, method and device. The image clarity parameters of the venous depth vascular images and the corresponding reference image clarity parameters are obtained, and then the image clarity parameters are compared with the corresponding reference image clarity parameters. If the image clarity parameters are not less than the corresponding reference image clarity parameters, the first clarity judgment result is recorded as positioning marking, otherwise the first clarity judgment result is recorded as re-acquiring the image, and then it is determined whether to perform probe intelligent assistance and information recording based on the first clarity judgment result. Finally, a second clarity judgment is performed on the second venous depth vascular image obtained to obtain a second clarity judgment result to determine whether to perform venous difference analysis. If venous difference analysis is performed, a third clarity judgment is performed on the optimized venous image to obtain a third clarity judgment result to determine whether to perform venous difference optimization control, thereby improving the accuracy of acquisition and management of venous depth vascular images.

[0023] The technical solution in the embodiment of the present application is to solve the problem of inaccurate venous depth and blood vessel images recorded in the above-mentioned venous channel information management. The overall idea is as follows: A first clarity judgment is performed on the vein depth blood vessel image to obtain a first clarity judgment result, and based on this, it is determined whether to perform probe intelligent assistance and information recording. Then, a second clarity judgment result is obtained based on the obtained second vein depth blood vessel image to determine whether to perform vein difference analysis. If vein difference analysis is performed, a third clarity judgment result is obtained based on the optimized vein image to determine whether to perform vein difference optimization control, thereby achieving the effect of improving the accuracy of acquisition and management of vein depth blood vessel images.

[0024] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0025] like Figure 1As shown in the figure, it is a schematic structural diagram of a venous treatment information management system provided by an embodiment of the present application. The venous treatment information management system provided by an embodiment of the present application includes: an initial imaging clarity analysis and prompt module, a venous imaging clarity operation optimization and prompt module, and a venous difference optimization and prompt module. Among them, the initial imaging clarity analysis and prompt module is used to perform a first clarity judgment on the venous depth blood vessel image to obtain a first clarity judgment result, and judge whether to perform probe intelligent assistance and information recording based on the first clarity judgment result; the venous imaging clarity operation optimization and prompt module is used to perform a second clarity judgment on the second venous depth blood vessel image obtained by performing probe operation based on the information recording to obtain a second clarity judgment result, and judge whether to perform venous difference analysis based on the second clarity judgment result; the venous difference optimization and prompt module is used to, if venous difference analysis is performed, perform a third clarity judgment on the obtained optimized venous image to obtain a third clarity judgment result, and judge whether to perform venous difference optimization control based on the third clarity judgment result.

[0026] In this embodiment, venous treatment information management includes venous access management. The venous treatment information management system records and tracks the patient's intravenous catheterization process, including the catheterization location, the equipment used, nursing measures, etc., to prevent complications. Among them, the management of the venous depth blood vessel image of the ultrasound imaging scanned when positioning the catheterization location is particularly important. The venous depth blood vessel image is obtained through ultrasound imaging technology. Ultrasonic waves are emitted by the probe and interact with tissues, and the returned reflected waves are received and converted into images. The blood flow in the blood vessels will affect the reflection of ultrasonic waves, making the blood vessels appear in the image. By using a high-frequency ultrasound probe (usually between 7-15 MHz), high-resolution imaging of superficial blood vessels can be performed. However, for deep veins, lower-frequency ultrasonic waves (usually between 3-5 MHz) can be used to provide better penetration and depth imaging. The venous depth blood vessel image is recorded and managed in the venous treatment information management system, and the image can also be analyzed through the venous treatment information management system to obtain a clearer and more accurate venous depth blood vessel image, thereby helping the preset medical staff to position more accurately when catheterizing patients (especially obese patients).

[0027] Further, perform a first clarity judgment on the venous deep vascular image to obtain a first clarity determination result. The specific steps are as follows: Step 1, obtain the image clarity parameters of the venous deep vascular image and the corresponding reference image clarity parameters. The image clarity parameters include the image signal-to-noise ratio and the image spatial resolution, and the reference image clarity parameters include the reference image signal-to-noise ratio and the reference image spatial resolution; Step 2, compare the image clarity parameters with the corresponding reference image clarity parameters. If the image clarity parameters are not less than the corresponding reference image clarity parameters, record the first clarity determination result as making a positioning mark; otherwise, record the first clarity determination result as re-acquiring the image. Making a positioning mark means that the clarity of this image supports the determination of the venous cannulation position. The first clarity determination result includes making a positioning mark and re-acquiring the image.

[0028] In this embodiment, through the judgment of the first clarity, it can be quickly determined whether the clarity of the venous deep vascular image supports accurate venous cannulation positioning; making a positioning mark is obtained by marking the position where venous cannulation can be performed in the venous deep vascular image; re-acquiring the image means that it is necessary to prompt the preset medical staff to re-scan with the probe to obtain a new venous deep vascular image; through the first clarity judgment, each venous deep vascular image obtained by probe scanning can be quickly screened to obtain an image with clarity meeting the requirements of venous cannulation, improving the efficiency and accuracy of obtaining venous deep vascular images.

[0029] Specifically, by calculating the average value of the pixel values of the venous deep vascular image, the signal intensity is obtained, and then the standard deviation of its pixel values is calculated to obtain the image noise. Substitute the signal intensity and the image noise into the signal-to-noise ratio formula to obtain the image signal-to-noise ratio. The signal-to-noise ratio formula is as follows: ; Among them, represents the signal intensity, represents the image noise, represents the image signal-to-noise ratio, and the unit of the image signal-to-noise ratio is ; The image spatial resolution is obtained by using a resolution test target for testing. The unit of the image spatial resolution is ; The common standard method for the resolution test target is to evaluate the resolution of the venous deep vascular image by using a test target with a known spatial resolution. The test target (Phantom) usually contains a series of structures such as lines, dots or other shapes with different spacings, and the intervals of these structures gradually decrease until the image can no longer distinguish these structures.

[0030] Specifically, the reference image clarity parameter is obtained from a preset database. In a specific embodiment, the signal-to-noise ratio and spatial resolution of the reference image can be obtained by consulting materials. According to the results of consulting materials, the signal-to-noise ratio of the reference image is at least 20 , and the spatial resolution of the reference image is at least 2 .

[0031] Furthermore, based on the first clarity determination result, it is judged whether to perform probe intelligent assistance and information recording. The specific process is as follows: If the first clarity determination result is to re-acquire the image, then probe intelligent assistance is performed to obtain the second venous depth blood vessel image. The specific process is as follows: Obtain the probe imaging parameters to get the probe application evaluation index, and record information on the venous depth blood vessel image based on the probe application evaluation index. The preset medical staff re-acquire the image according to the information record to obtain the second venous depth blood vessel image. The information record includes data feedback and operation marks. Data feedback means marking the probe imaging parameters and the probe application evaluation index on the corresponding parts of the image to be optimized; operation marks mean marking the data that needs to be adjusted for the probe operation at the corresponding parts; the probe imaging parameters include the probe-skin distance, probe angle, probe applied pressure, and probe position compliance; the probe application evaluation index represents the quantitative data of the combined influence degree of the probe-skin distance, probe angle, probe applied pressure, and probe position compliance on the probe application evaluation. The specific process of obtaining the probe application evaluation index is as follows: Obtain the distance deviation degree value by performing range deviation processing on the probe-skin distance and the corresponding reference probe-skin distance range; obtain the angle deviation degree value by performing range deviation processing on the probe angle and the corresponding reference probe angle range; obtain the pressure deviation degree value by performing range deviation processing on the probe applied pressure and the corresponding reference probe time pressure range; obtain the position compensation value by performing position compliance difference processing on the probe position compliance; perform influence degree distribution processing on the distance deviation degree value, angle deviation degree value, pressure deviation degree value, and position compensation value with the corresponding probe application weights to obtain the probe application evaluation index; range deviation processing is used to quantify the deviation degree of the probe imaging parameters from the corresponding data range of the reference probe imaging data; position compliance difference processing is used to quantify the deviation degree of the probe position; influence degree distribution processing is used to comprehensively quantify the influence degree of the distance deviation degree value, angle deviation degree value, pressure deviation degree value, and position compensation value on the probe operation standardization.

[0032] The specific limit expression of the probe application evaluation index is as follows: ; In the formula, n represents the number of times the probe scans, , represents the total number of times the probe scans, represents the probe-skin distance corresponding to the nth probe scan, represents the probe angle corresponding to the nth probe scan, represents the probe applied pressure corresponding to the nth probe scan, represents the probe position compliance corresponding to the nth probe scan, represents the maximum probe-skin distance, represents the maximum probe angle, represents the maximum probe applied pressure, represents the minimum probe-skin distance, represents the minimum probe angle, represents the minimum probe applied pressure, represents the probe distance weight, represents the probe angle weight, represents the probe pressure weight, represents the probe position weight, represents the probe application evaluation index corresponding to the nth probe scan.

[0033] In this embodiment, the specific method of probe intelligent assistance is to record information in the venous depth blood vessel image, prompting the preset medical staff to adjust the operation of the probe according to the relevant data recorded in the information to approach the standard operation, so as to obtain a more reliable venous depth blood vessel image; among them, the information recording is mainly reflected by marking in the venous depth blood vessel image. It should be added that the operation marks are analyzed by inputting the venous depth blood vessel image into a deep learning model (such as InceptionNet and VGGNet, Visual Geometry Group Network) to output the deviations of the probe angle, probe pressure and probe position, that is, the corresponding probe operation adjustment values; by analyzing the probe application evaluation index to judge whether the reason why the venous depth blood vessel image does not have positioning marks is caused by non-standard probe operation, so as to record information in time for feedback interaction, helping the preset medical staff to obtain a clearer venous depth blood vessel image, thereby improving the accuracy of venous intubation through the venous depth blood vessel image.

[0034] In this embodiment, the algorithm comprehensively analyzes the probe imaging parameters, reference probe imaging data, and probe application weights to obtain the probe application evaluation index. In the formula, as the probe-skin distance deviates more from the reference probe-skin distance range, the corresponding probe application evaluation index is larger. Similarly, as the probe angle and the pressure applied by the probe deviate more from the corresponding reference probe angle range and reference probe pressure range, the corresponding probe application evaluation index is also larger, indicating that the probe operation is less standardized; when the probe position compliance is larger, the corresponding probe application evaluation index is smaller, indicating that the probe operation is more standardized; through the analysis of the probe application evaluation index, it is helpful to give the preset medical staff more intuitive feedback results on the probe operation, thereby realizing more standardized probe operation by the preset medical staff.

[0035] It should be explained that the probe-skin distance is obtained through the automatic depth measurement function built into the ultrasound device, the probe angle is monitored and recorded through the gyroscope equipped with the ultrasound device (especially high-end models), the pressure applied by the probe is measured in real time through the force sensor integrated in the ultrasound device, the probe position compliance represents the ratio of the area of the body part corresponding to the vein intubation required in the vein depth blood vessel image to the preset body part area, the body part area is obtained through medical image analysis software (such as: OsiriX, 3D Slicer, ImageJ, etc.), the preset body part area is automatically measured by the ultrasound device for the corresponding body part, and the application of the probe position compliance reflects the correctness of the patient's body part scanned by the preset medical staff when operating the probe, and indirectly reflects the accuracy of the preset medical staff's selection of the probe operation position.

[0036] It should be added that the reference probe imaging data is obtained from the preset database. In a specific embodiment, the reference probe-skin distance range, reference probe angle range, and reference probe pressure range are set according to the probe operation manual.

[0037] Specifically, the probe application weights are obtained from the preset database, and the probe application weights represent the influence degree of the probe imaging parameters on the probe application evaluation index. There is a unique mapping relationship between each probe imaging parameter and the probe application weight, and the value range is between 0 and 1; for example, a mapping set of the probe imaging parameters and the preset probe application weights is constructed, and the real-time probe-skin distance, probe angle, pressure applied by the probe, and probe position compliance are input into the mapping set to obtain the corresponding probe distance weight, probe angle weight, probe pressure weight, and probe position weight respectively, which represent the influence degrees of the probe-skin distance, probe angle, pressure applied by the probe, and probe position compliance on the probe application evaluation index, and the sum of the four is 1.

[0038] Further, based on the second clarity determination result, it is determined whether to perform venous difference analysis. The specific process is as follows: The second venous depth blood vessel image is subjected to a second clarity determination to obtain a second clarity determination result, and the second clarity determination result includes performing positioning marking and analyzing the second venous depth blood vessel image; if the second clarity determination result is to analyze the second venous depth blood vessel image, it is determined whether to introduce an individual difference factor based on the obtained optimized probe application evaluation index: if the optimized probe application evaluation index is less than the optimized evaluation threshold obtained from the preset database, the individual difference factor is introduced, otherwise, the probe intelligent assistance continues until the second clarity determination result is to perform positioning marking or the result of the optimized probe application evaluation index determination is to introduce the individual difference factor.

[0039] In this embodiment, after the preset medical staff performs probe intelligent assistance, the probe is used to scan again to re-obtain an image; the second clarity determination result further determines whether the image obtained by the preset medical staff after standard probe operation supports positioning marking, and also indirectly confirms the necessity of introducing the individual difference factor, laying a foundation for subsequent image optimization measures, so as to obtain a more accurate venous depth blood vessel image.

[0040] It should be added that the specific steps for performing a second clarity determination on the second venous depth blood vessel image to obtain a second clarity determination result are as follows: Obtain the image clarity parameter of the second venous depth blood vessel image and the corresponding reference image clarity parameter; compare the image clarity parameter with the corresponding reference image clarity parameter. If the image clarity parameters are not less than the corresponding reference image clarity parameters, record the second clarity determination result as performing positioning marking, otherwise record the second clarity determination result as analyzing the second venous depth blood vessel image.

[0041] Specifically, the optimized evaluation threshold is obtained from the preset database. In a specific embodiment, the probe imaging parameters corresponding to the venous depth blood vessel image in the historical data and the corresponding optimized probe imaging parameters are substituted into the specific limit expression of the optimized probe application evaluation index to obtain a corresponding data set of the optimized probe application evaluation index, and the average value operation is performed on this data set to obtain the optimized evaluation threshold.

[0042] Furthermore, the specific method for obtaining the optimized probe application evaluation index is as follows: Obtain the probe application evaluation weights from a preset database, where the probe application evaluation weights include a probe pressure weight factor, a probe angle weight factor, and a probe position weight factor; obtain the optimized probe imaging parameters by obtaining the probe imaging parameters after performing probe operations based on the recorded feedback, and obtain the probe operation adjustment values in the recorded feedback, where the probe operation adjustment values include a probe pressure adjustment value, a probe angle adjustment value, and a probe position deviation degree value; perform a difference change process on the optimized probe imaging parameters and the probe imaging parameters to obtain the corresponding probe difference change value, where the difference change process is used to quantify the correctness of the probe operation change after performing probe intelligent assistance; perform a comparative analysis process on the probe difference change value and the corresponding probe operation adjustment value to obtain the corresponding probe operation adjustment compliance value, where the comparative analysis process is used to quantify the degree of compliance between the operation change of the probe imaging parameters and the probe operation adjustment value; perform a probe optimization comprehensive evaluation process on the probe operation adjustment compliance value, the corresponding probe application evaluation weights, and the probe application evaluation index to obtain the optimized probe application evaluation index, where the probe optimization comprehensive evaluation process is used to comprehensively quantify the degree of compliance between the change of the probe application evaluation index after performing probe intelligent assistance and the probe standard operation.

[0043] The specific constraint expression for the optimized probe application evaluation index is as follows: ; In the formula, n represents the number of probe scans, , represents the total number of probe scans, represents the probe applied pressure corresponding to the nth probe scan, represents the probe angle corresponding to the nth probe scan, represents the probe position compliance corresponding to the nth probe scan, represents the probe applied pressure corresponding to the (n + 1)th probe scan, represents the probe angle corresponding to the (n + 1)th probe scan, represents the probe position compliance corresponding to the (n + 1)th probe scan, represents the probe pressure adjustment value corresponding to the nth probe scan, represents the probe angle adjustment value corresponding to the nth probe scan, represents the probe position deviation degree value corresponding to the nth probe scan, represents the probe pressure weight factor, represents the probe angle weight factor, represents the probe position weight factor, represents the probe application evaluation index corresponding to the nth probe scan, Represents the optimized probe application evaluation index corresponding to the (n + 1)-th probe scan.

[0044] In this embodiment, the algorithm comprehensively analyzes the probe imaging parameters, probe application evaluation weights, probe operation adjustment values, probe application evaluation indices, and optimized probe imaging parameters to obtain the optimized probe application evaluation index. In the formula, when the absolute value of the difference between the ratio of the probe difference change value to the corresponding probe operation adjustment value and the value 1 (i.e., , and ) is closer to 0, it indicates that the optimization of the probe operation by the preset medical staff is more standardized. Then, the corresponding optimized probe application evaluation index is smaller, indicating that the error of the probe operation by the preset medical staff is smaller. Through the analysis of the optimized probe application evaluation index, it helps to timely feedback the effect of the probe operation to the preset medical staff, so as to timely assist the preset medical staff to intervene in the probe operation, and then obtain a more accurate, true, and reliable venous depth blood vessel image.

[0045] Specifically, the probe application evaluation weight is obtained from the preset database. The probe application evaluation weight represents the influence degree of the optimized probe imaging parameters on the optimized probe application evaluation index. There is a unique mapping relationship between each optimized probe imaging parameter and the probe application evaluation weight, and the value range is between 0 and 1. For example, a mapping set of the optimized probe imaging parameters and the preset probe application evaluation weights is constructed, and the real-time optimized probe imaging parameters are input into the mapping set to respectively obtain the corresponding probe application evaluation weights, which respectively represent the influence degrees of the probe angle, probe applied pressure, and probe position compliance corresponding to the (n + 1)-th probe scan on the optimized probe application evaluation index, and the sum of the three is 1.

[0046] Furthermore, the specific acquisition method of the individual difference factor is as follows: Obtain the individual difference data and perform normalization processing. The individual difference data includes subcutaneous fat thickness, muscle thickness, body weight, skin elasticity, and muscle density. Obtain the individual difference weights from the preset database. The individual difference weights include subcutaneous fat thickness weight, muscle thickness weight, body weight weight, skin elasticity weight, and muscle density weight. Perform elastic correlation processing on the skin elasticity to obtain the skin elasticity effect value. The elastic correlation processing is used to describe the correlation relationship between the skin elasticity and the individual differences. Perform difference influence degree allocation processing on the subcutaneous fat thickness, muscle thickness, body weight, skin elasticity effect value, and muscle density and the corresponding individual difference weights to obtain the individual difference factor. The difference influence degree allocation processing is used to comprehensively quantify the influence degree of the individual differences generated by the subcutaneous fat thickness, muscle thickness, body weight, skin elasticity, and muscle density on ultrasonic imaging. The individual difference factor represents the quantified data of the influence degree of the individual differences generated by the subcutaneous fat thickness, muscle thickness, body weight, skin elasticity, and muscle density on ultrasonic imaging.

[0047] The specific limit expressions of the individual difference factors are as follows: ; In the formula, represents the subcutaneous fat thickness of the patient, represents the muscle thickness of the patient, represents the weight of the patient, represents the skin elasticity of the patient, represents the muscle density of the patient, represents the subcutaneous fat thickness weight, represents the muscle thickness weight, represents the weight weight, represents the skin elasticity weight, represents the muscle density weight, represents the individual difference factor of the patient.

[0048] In this embodiment, the algorithm combines the individual difference data and the individual difference weights for comprehensive analysis to obtain the individual difference factor. In the formula, the subcutaneous fat thickness, muscle thickness, weight, and muscle density are all positively correlated with the individual difference factor, indicating that the greater the subcutaneous fat thickness, muscle thickness, weight, and muscle density, the greater the corresponding individual difference factor. Then, it means that the greater the possibility of obesity of the current patient, the greater the impact on ultrasonic imaging, and the greater the corresponding individual difference factor; while the skin elasticity is negatively correlated with the individual difference factor, indicating that as the skin elasticity increases, the impact on ultrasonic imaging becomes smaller, and the corresponding individual difference factor becomes smaller; by quantifying the individual difference data, the individual difference factor is obtained, which is beneficial to more accurately optimizing the venous depth blood vessel images to different degrees according to the individual differences of obese patients, ensuring the reliability of the venous depth blood vessel images.

[0049] It should be explained that the ultrasonic device can measure the thickness of subcutaneous fat through high-frequency sound waves. The ultrasonic waves can clearly show the boundary lines of the skin, fat, and muscle layers, so as to accurately measure the thickness of the fat layer to obtain the subcutaneous fat thickness; the muscle thickness can be accurately measured through ultrasonic imaging technology to obtain the muscle thickness, the weight can be obtained through a digital weighing scale, the skin elasticity can be measured by measuring the skin with an elasticity measuring instrument (such as Cutometer), and the muscle density can be obtained from the muscle density map obtained by CT scanning.

[0050] Specifically, the individual difference weight is obtained from a preset database, and the individual difference weight represents the influence degree of individual difference data on the individual difference factor. There is a unique mapping relationship between each individual difference data and the individual difference weight, and the value range is between 0 and 1. For example, a mapping set of individual difference data and preset individual difference weights is constructed, and the real-time subcutaneous fat thickness, muscle thickness, body weight, skin elasticity, and muscle density are input into the mapping set to obtain the corresponding subcutaneous fat thickness weight, muscle thickness weight, body weight weight, skin elasticity weight, and muscle density weight respectively, which represent the influence degrees of subcutaneous fat thickness, muscle thickness, body weight, skin elasticity, and muscle density on the individual difference factor, and the sum of the five is 1.

[0051] Further, the obtained optimized vein image is subjected to a third clarity judgment to obtain a third clarity determination result, and it is judged whether to perform vein difference optimization control based on the third clarity determination result. The specific process is as follows: S1, obtain the adjustment ratio and the image optimization data of the second vein depth blood vessel image from the preset database, and the image optimization data includes image contrast and image brightness; S2, perform an optimization operation on the individual difference factor through the adjustment ratio to obtain the individual difference factor to be optimized, and the optimization operation represents a way of constructing a numerical relationship between the adjustment ratio and the individual difference factor; S3, construct a mapping set of the individual difference factor to be optimized and the adjustment multiple, input the individual difference factor to be optimized into the mapping set to obtain the corresponding adjustment multiple, and perform an image processing operation on the image optimization data based on the adjustment multiple to obtain the adjusted image optimization data, and the image processing operation represents a way of constructing a numerical relationship between the individual difference factor to be optimized and the image optimization data; S4, obtain the optimized vein depth image according to the adjusted image optimization data, and perform a third clarity judgment on the optimized vein depth image to obtain a third clarity determination result, and the third clarity determination result includes performing a positioning mark and performing vein difference optimization control; S5, if vein difference optimization control is to be performed, then obtain an optimization strength evaluation value, perform a ratio optimization process on the adjustment ratio to obtain a new adjustment ratio, and execute S2 until the third clarity determination result is to perform a positioning mark or the individual difference factor to be optimized is lower than the individual difference factor optimization limit value; S6, if the individual difference factor to be optimized is lower than the individual difference factor optimization limit value, then perform a marked positioning risk prompt.

[0052] In this embodiment, the optimization operation specifically represents performing a multiplication operation on the adjustment ratio and the individual difference factor; the image processing operation specifically represents performing a multiplication operation on the adjustment multiple and the image optimization data; the ratio optimization process specifically represents performing a multiplication operation on the optimization strength evaluation value and the adjustment ratio; the adjustment ratio is the initial default ratio for optimizing the venous depth image by the individual difference factor set by the preset medical staff in the system; the image contrast and the image brightness can be directly obtained through OpenCV in the Python image processing library. OpenCV is a powerful computer vision library that can directly process the brightness and contrast of images; through the method of this embodiment, the control of the strength of optimizing the venous depth image by the individual difference factor is more rigorous, further ensuring the authenticity of the venous depth blood vessel image, and corresponding risk warnings are also given, improving the rigor of the preset medical staff in determining the venous intubation position.

[0053] It should be added that the specific steps for obtaining the third clarity determination result by performing the third clarity judgment on the optimized venous depth image are as follows: obtaining the image clarity parameter of the optimized venous depth image and the corresponding reference image clarity parameter; comparing the image clarity parameter with the corresponding reference image clarity parameter. If the image clarity parameters are all not less than the corresponding reference image clarity parameters, then record the third clarity determination result as performing a positioning mark, otherwise record the third clarity determination result as performing venous difference optimization control.

[0054] Specifically, the optimization limit value of the individual difference factor is obtained from the preset database. In a specific embodiment, the optimization limit value of the individual difference factor is preset by the preset medical staff according to the specific individual difference situation of the patient.

[0055] Further, the specific method for obtaining the optimization intensity evaluation value is as follows: Obtain the optimization intensity weights from a preset database, where the optimization intensity weights include the image signal-to-noise ratio weight and the image spatial resolution weight; obtain the individual difference factor optimization limit value and the reference image clarity parameter from the preset database; obtain the current image clarity parameter and the individual difference factor optimization limit value and the reference image clarity parameter from the preset database; perform signal-to-noise ratio deviation processing on the image signal-to-noise ratio and the corresponding reference image signal-to-noise ratio to obtain a signal-to-noise ratio deviation value, and the signal-to-noise ratio deviation processing is used to quantify the gap degree between the image signal-to-noise ratio and the reference image signal-to-noise ratio; perform spatial resolution deviation processing on the image spatial resolution and the corresponding reference image spatial resolution to obtain a spatial resolution deviation value, and the spatial resolution deviation processing is used to quantify the gap degree between the image spatial resolution and the reference image spatial resolution; obtain an optimization difference degree value through optimization difference processing of the signal-to-noise ratio deviation value, the spatial resolution deviation value, and the corresponding optimization intensity weights; perform optimization progress evaluation processing on the current image clarity parameter and the image clarity parameter respectively to obtain the corresponding signal-to-noise ratio optimization progress value and spatial resolution optimization progress value, and the optimization progress evaluation processing is used to quantify the change degree of the image clarity parameter during the optimization process; perform optimization progress change processing based on the signal-to-noise ratio optimization progress value and the spatial resolution optimization progress value combined with the corresponding optimization intensity weights to obtain an optimization progress change value, and the optimization progress change processing is used to comprehensively quantify the change degree of the image clarity parameter during the optimization process; perform difference processing on the individual difference factor and the corresponding individual difference factor optimization limit value to obtain a difference optimization intensity compensation value, and the difference processing is used to quantify the change situation of the optimized degree of the individual difference factor; perform comparison and analysis processing on the optimization progress change value and the difference optimization intensity compensation value to obtain a to-be-optimized degree value, and the comparison and analysis processing is used to quantify the optimization degree of the current individual difference factor; perform comprehensive processing on the optimization difference degree value and the to-be-optimized degree value to obtain an optimization intensity evaluation value, and the comprehensive processing is used to comprehensively quantify the optimization intensity degree of the current individual difference factor on optimizing the venous depth image.

[0056] The specific limit expression of the optimization intensity evaluation value is as follows: ; In the formula, n represents the number of probe scans, , represents the total number of probe scans, represents the individual difference factor of the patient, represents the individual difference factor optimization limit value, represents the image signal-to-noise ratio corresponding to the nth probe scan, represents the image spatial resolution corresponding to the nth probe scan, represents the image signal-to-noise ratio corresponding to the (n + 1)th probe scan, Represents the image spatial resolution corresponding to the (n + 1)-th probe scan, Represents the signal-to-noise ratio of the reference image, Represents the spatial resolution of the reference image, Represents the signal-to-noise ratio weight of the image, Represents the spatial resolution weight of the image, Represents the optimization intensity evaluation value of the (n + 1)-th probe scan.

[0057] In this embodiment, the algorithm combines the image sharpness parameter, the current image sharpness parameter, the individual difference factor optimization limit, the reference image sharpness parameter, and the optimization intensity weight for comprehensive analysis to obtain the optimization intensity evaluation value. In the formula, when the relative deviation between the current image sharpness parameter and the corresponding reference image sharpness parameter (i.e., and ) is larger, then the corresponding optimization intensity evaluation value is larger, indicating that the current venous depth blood vessel image is less optimized; as the sum of the relative deviations between the current image sharpness parameter and the image sharpness parameter (i.e., and ) and the difference between the individual difference factor and the individual difference factor optimization limit (i.e., , not zero) increases, it indicates a higher degree of optimization, then the corresponding optimization intensity evaluation value is smaller, indicating a lower degree of continued optimization; through the optimization intensity evaluation value, the optimization of the venous depth blood vessel image can be dynamically controlled, and at the same time, it is ensured that the optimized venous depth blood vessel image is closer to the actual situation, ensuring the usability of the venous depth blood vessel image.

[0058] Specifically, the optimization intensity weight is obtained from a preset database. The optimization intensity weight represents the influence degree of the image sharpness parameter on the optimization intensity evaluation value. Each image sharpness parameter and the optimization intensity weight have a unique mapping relationship, and the value range is between 0 and 1; for example, a mapping set of the image sharpness parameter and the preset optimization intensity weight is constructed, and the real-time signal-to-noise ratio and spatial resolution of the image are input into the mapping set to respectively obtain the corresponding signal-to-noise ratio weight and spatial resolution weight of the image, which respectively represent the influence degrees of the signal-to-noise ratio and spatial resolution of the image on the optimization intensity evaluation value, and the sum of the two is 1.

[0059] An embodiment of the present application provides a method for managing intravenous treatment information. The specific steps are as follows: Perform a first clarity judgment on the intravenous depth blood vessel image to obtain a first clarity determination result, and based on the first clarity determination result, determine whether to perform probe intelligent assistance and information recording; Perform a second clarity judgment on the second intravenous depth blood vessel image obtained after probe operation based on information recording to obtain a second clarity determination result, and based on the second clarity determination result, determine whether to perform intravenous difference analysis; If intravenous difference analysis is performed, perform a third clarity judgment on the obtained optimized intravenous image to obtain a third clarity determination result, and based on the third clarity determination result, determine whether to perform intravenous difference optimization control.

[0060] In this embodiment, intravenous difference analysis means introducing an individual difference factor to perform intravenous difference optimization control on the optimized intravenous depth image; The method provided by the embodiment of the present application obtains a clear and accurate intravenous depth blood vessel image through multiple clarity judgments on the intravenous depth blood vessel image and corresponding optimization measures in each step of operation, which not only helps to more accurately locate veins and blood vessels, but also is beneficial for the intravenous treatment information management system to optimize the corresponding treatment plan.

[0061] An embodiment of the present application provides an electronic device. The electronic device includes a memory for storing computer program instructions and a processor for executing the program instructions. Among them, when the computer program instructions are executed by the processor, the electronic device is triggered to execute the operations of an intravenous treatment information management system.

[0062] In summary, the embodiment of the present application performs a first clarity judgment on the intravenous depth blood vessel image to obtain a first clarity determination result, and based on this, determines whether to perform probe intelligent assistance and information recording. Then, perform a second clarity judgment on the obtained second intravenous depth blood vessel image to obtain a second clarity determination result to determine whether to perform intravenous difference analysis. If intravenous difference analysis is performed, perform a third clarity judgment on the obtained optimized intravenous image to obtain a third clarity determination result to determine whether to perform intravenous difference optimization control, so as to obtain a clearer intravenous depth blood vessel image, thereby improving the acquisition management accuracy of the intravenous depth blood vessel image and effectively solving the problem that the intravenous depth blood vessel image recorded in the management of intravenous channel information in the prior art is inaccurate.

[0063] Those skilled in the art should understand that the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0064] The present invention is described with reference to the flowcharts and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general purpose computers, special purpose computers, embedded processors, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 one or more of the blocks.

[0065] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 one or more of the blocks.

[0066] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are performed on the computer or other programmable device to produce a computer-implemented process, whereby the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 one or more of the blocks.

[0067] Although the preferred embodiments of the present invention have been described, additional changes and modifications can be made by those skilled in the art once they learn of the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present invention.

[0068] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. An intravenous treatment information management system, characterized in that: It includes the initial imaging clarity analysis prompt module, the vein imaging clarity operation optimization prompt module and the vein difference optimization prompt module: The initial imaging clarity analysis prompt module is used to perform a first clarity judgment on the venous depth blood vessel image to obtain a first clarity judgment result, and determine whether to perform probe intelligent assistance and information recording based on the first clarity judgment result; The vein imaging clarity operation optimization prompt module is used to perform a second clarity judgment on the second vein depth blood vessel image obtained by performing probe operation based on information recording, obtain a second clarity judgment result, and determine whether to perform vein difference analysis based on the second clarity judgment result; The vein difference optimization prompt module is used to perform a third clarity judgment on the obtained optimized vein image to obtain a third clarity judgment result if vein difference analysis is performed, and to determine whether to perform vein difference optimization control based on the third clarity judgment result.

2. The intravenous therapy information management system according to claim 1, characterized in that: The first clarity determination is performed on the vein depth blood vessel image to obtain a first clarity determination result, and the specific steps are as follows: Step 1, obtaining image clarity parameters of the venous depth blood vessel image and corresponding reference image clarity parameters, wherein the image clarity parameters include image signal-to-noise ratio and image spatial resolution, and the reference image clarity parameters include reference image signal-to-noise ratio and reference image spatial resolution; Step 2: compare the image clarity parameters with the corresponding reference image clarity parameters. If the image clarity parameters are not less than the corresponding reference image clarity parameters, the first clarity determination result is recorded as positioning mark, otherwise the first clarity determination result is recorded as re-acquiring the image.

3. The intravenous therapy information management system according to claim 2, characterized in that: The specific process of judging whether to perform probe intelligent assistance and information recording based on the first clarity determination result is as follows: If the first definition determination result is to reacquire the image, the probe intelligent assistance is performed to obtain the second venous depth blood vessel image. The specific process is as follows: Acquire probe imaging parameters to obtain a probe application evaluation index, record information of the venous deep blood vessel image based on the probe application evaluation index, and pre-set medical personnel to reacquire the image according to the information record to obtain a second venous deep blood vessel image, wherein the information record includes data feedback and operation marks; The probe imaging parameters include probe-skin distance, probe angle, probe pressure and probe position compliance; The probe application evaluation index represents quantitative data of the degree of influence of the probe-skin distance, probe angle, probe pressure and probe position compliance on the probe application evaluation; The specific process of obtaining the probe application evaluation index is as follows: Acquire probe application weights from a preset database, wherein the probe application weights include probe distance weights, probe angle weights, probe pressure weights, and probe position weights; Acquiring reference probe imaging data, wherein the reference probe imaging data includes a reference probe-skin distance range, a reference probe angle range, and a reference probe applied pressure range; The distance between the probe and the skin is deviated from the corresponding reference distance between the probe and the skin to obtain a distance deviation value. The angle deviation degree value is obtained by performing range deviation processing on the probe angle and the corresponding reference probe angle range; The pressure deviation degree value is obtained by performing range deviation processing on the pressure applied to the probe and the corresponding reference probe time pressure range; The position compensation value is obtained by performing position compliance difference processing on the probe position compliance; The spacing deviation value, angle deviation value, pressure deviation value and position compensation value are processed with the corresponding probe application weights for influence degree distribution to obtain a probe application evaluation index; The range deviation processing is used to quantify the degree of deviation between the probe imaging parameters and the corresponding data range of the reference probe imaging data; The position coincidence difference processing is used to quantify the degree of deviation of the probe position; The influence degree distribution process is used to comprehensively quantify the influence degree of the spacing deviation degree value, the angle deviation degree value, the pressure deviation degree value and the position compensation value on the probe operation standardization.

4. The intravenous therapy information management system according to claim 1, characterized in that: The specific process of determining whether to perform vein difference analysis based on the second clarity determination result is as follows: Performing a second clarity judgment on the second vein depth blood vessel image to obtain a second clarity judgment result, wherein the second clarity judgment result includes performing positioning marking and performing second vein depth blood vessel image analysis; If the second clarity determination result is to perform a second venous depth blood vessel image analysis, then it is determined whether to introduce an individual difference factor based on the obtained optimized probe application evaluation index: If the optimized probe application evaluation index is not less than the optimized evaluation threshold obtained from the preset database, the individual difference factor is introduced; otherwise, the probe intelligent assistance is continued until the second clarity judgment result is to perform positioning marking or the judgment result based on the optimized probe application evaluation index is to introduce the individual difference factor.

5. The intravenous therapy information management system according to claim 4, characterized in that: The specific method of obtaining the optimization probe application evaluation index is as follows: Acquire a probe application evaluation weight from a preset database, wherein the probe application evaluation weight includes a probe pressure weight factor, a probe angle weight factor, and a probe position weight factor; Obtaining probe imaging parameters for performing probe operation based on feedback from the information record to obtain optimized probe imaging parameters, and obtaining probe operation adjustment values ​​in the information record, wherein the probe operation adjustment values ​​include a probe pressure adjustment value, a probe angle adjustment value, and a probe position deviation degree value; Performing difference change processing on the optimized probe imaging parameters and the probe imaging parameters to obtain corresponding probe difference change values, wherein the difference change processing is used to quantify the correctness of the probe operation change after the probe intelligent assistance is performed; Comparing and analyzing the probe difference change value and the corresponding probe operation adjustment value to obtain the corresponding probe operation adjustment compliance value, wherein the comparative analysis is used to quantify the degree of compliance between the operation change of the probe imaging parameter and the probe operation adjustment value; The probe operation adjustment compliance value, the corresponding probe application evaluation weight and the probe application evaluation index are subjected to probe optimization comprehensive evaluation processing to obtain an optimized probe application evaluation index. The probe optimization comprehensive evaluation processing is used to comprehensively quantify the changes in the probe application evaluation index after probe intelligent assistance and the degree of compliance with the probe standard operation.

6. The intravenous therapy information management system according to claim 4, characterized in that: The specific method of obtaining the individual difference factor is: Acquiring individual difference data and performing normalization processing, wherein the individual difference data includes subcutaneous fat thickness, muscle thickness, body weight, skin elasticity and muscle density; Acquire individual difference weights from a preset database, wherein the individual difference weights include subcutaneous fat thickness weight, muscle thickness weight, body weight weight, skin elasticity weight, and muscle density weight; Performing elasticity correlation processing on skin elasticity to obtain a skin elasticity effect value, wherein the elasticity correlation processing is used to describe the correlation relationship between skin elasticity and individual differences; The subcutaneous fat thickness, muscle thickness, body weight, skin elasticity effect value and muscle density are processed with the corresponding individual difference weights for differential influence to obtain individual difference factors; The difference influence degree distribution process is used to comprehensively quantify the influence degree of individual differences caused by subcutaneous fat thickness, muscle thickness, body weight, skin elasticity and muscle density on ultrasound imaging; The individual difference factor represents quantitative data of the degree of influence of individual differences caused by subcutaneous fat thickness, muscle thickness, body weight, skin elasticity and muscle density on ultrasound imaging.

7. The intravenous therapy information management system according to claim 1, characterized in that: The third definition judgment is performed on the obtained optimized vein image to obtain a third definition judgment result, and whether to perform vein difference optimization control is determined based on the third definition judgment result. The specific process is as follows: S1, acquiring an adjustment ratio and image optimization data of a second venous depth blood vessel image from a preset database, wherein the image optimization data includes image contrast and image brightness; S2, performing an optimization operation on the individual difference factor by adjusting the ratio to obtain the individual difference factor to be optimized, wherein the optimization operation represents a method of constructing a numerical relationship between the adjustment ratio and the individual difference factor; S3, constructing a mapping set of individual difference factors to be optimized and adjustment multiples, inputting the individual difference factors to be optimized into the mapping set to obtain corresponding adjustment multiples, and performing image processing operations on the image optimization data based on the adjustment multiples to obtain adjusted image optimization data, wherein the image processing operations represent the manner in which the numerical relationship between the constructed individual difference factors to be optimized and the image optimization data is obtained; S4, obtaining an optimized vein depth image according to the adjusted image optimization data, performing a third clarity judgment on the optimized vein depth image to obtain a third clarity judgment result, wherein the third clarity judgment result includes performing positioning marking and performing vein difference optimization control; S5, if the vein difference optimization control is performed, the optimization strength evaluation value is obtained, and the adjustment ratio is optimized to obtain a new adjustment ratio and S2 is executed until the third clarity determination result is to perform positioning marking or the individual difference factor to be optimized is lower than the individual difference factor optimization limit; S6: If the individual difference factor to be optimized is lower than the individual difference factor optimization limit, a risk warning will be issued.

8. The intravenous therapy information management system according to claim 7, characterized in that: The specific method for obtaining the optimization strength evaluation value is as follows: Acquire an optimization strength weight from a preset database, wherein the optimization strength weight includes an image signal-to-noise ratio weight and an image spatial resolution weight; Obtain current image clarity parameters and obtain individual difference factor optimization limits and reference image clarity parameters from a preset database; Performing signal-noise ratio deviation processing on the image signal-noise ratio and the corresponding reference image signal-noise ratio to obtain a signal-noise ratio deviation value, wherein the signal-noise ratio deviation processing is used to quantify the difference between the image signal-noise ratio and the reference image signal-noise ratio; Performing spatial resolution deviation processing on the image spatial resolution and the corresponding reference image spatial resolution to obtain a spatial resolution deviation value, wherein the spatial resolution deviation processing is used to quantify the degree of difference between the image spatial resolution and the reference image spatial resolution; The optimized difference degree value is obtained by performing optimized difference processing on the signal-to-noise ratio deviation value, the spatial resolution deviation value and the corresponding optimization strength weight; Performing optimization progress evaluation processing on the current image clarity parameter and the image clarity parameter to obtain corresponding signal-to-noise ratio optimization progress value and spatial resolution optimization progress value, respectively, wherein the optimization progress evaluation processing is used to quantify the degree of change of the image clarity parameter during the optimization process; Based on the signal-to-noise ratio optimization progress value and the spatial resolution optimization progress value combined with the corresponding optimization strength weight, an optimization progress change process is performed to obtain an optimization progress change value, wherein the optimization progress change process is used to comprehensively quantify the degree of change of the image clarity parameter during the optimization process; Performing difference processing on the individual difference factor and the corresponding individual difference factor optimization limit to obtain a difference optimization intensity compensation value, wherein the difference processing is used to quantify the change in the degree of optimization of the individual difference factor; Compare and analyze the optimization progress change value with the difference optimization strength compensation value to obtain the degree value to be optimized, and the comparison and analysis process is used to quantify the optimization degree of the current individual difference factor; The optimization difference degree value and the to-be-optimized degree value are comprehensively processed to obtain an optimization strength evaluation value, and the comprehensive processing is used to comprehensively quantify the optimization strength degree of the current individual difference factor on the optimization of the vein depth image.

9. A method for managing intravenous treatment information, characterized in that: The specific steps are as follows: Performing a first clarity judgment on the vein depth blood vessel image to obtain a first clarity judgment result, and judging whether to perform probe intelligent assistance and information recording based on the first clarity judgment result; Performing a second clarity judgment on the second vein depth blood vessel image obtained by performing probe operation based on information recording to obtain a second clarity judgment result, and judging whether to perform vein difference analysis based on the second clarity judgment result; If the vein difference analysis is performed, a third clarity judgment is performed on the obtained optimized vein image to obtain a third clarity judgment result, and whether to perform vein difference optimization control is determined based on the third clarity judgment result.

10. An electronic device, characterized in that: The electronic device includes a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the electronic device is triggered to perform the operation of an intravenous treatment information management system as described in any one of claims 1-8.

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