Intelligent infusion drop number monitoring method and device

Abstract

The invention discloses an intelligent infusion drop number monitoring method and device, and relates to the technical field of medical instruments.The intelligent infusion drop number monitoring method comprises the steps that a liquid drop passing signal in an infusion dropping funnel is detected in real time, and the current drop number is determined according to the liquid drop passing signal; collecting motion state data of the infusion equipment in real time, filtering the motion state data to eliminate noise interference, and determining the shaking amplitude; when the shaking amplitude exceeds a preset threshold value or the liquid drop passing signal is abnormal, a control instruction is generated to adjust the infusion monitoring state; the current drop number data is uploaded to the cloud server integrated with a hospital information system, so that remote monitoring is achieved, accurate and real-time monitoring of the infusion drop number is achieved, detection errors caused by factors such as equipment shaking or drip chamber fogging are effectively reduced, the accuracy and reliability of infusion monitoring are improved, and the safety of infusion monitoring is improved. Medical staff can conveniently and remotely monitor and manage the infusion condition of a patient, and the efficiency and quality of medical services are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical devices, and in particular to an intelligent infusion drop count monitoring method and device. Background Art

[0002] During traditional infusion processes, medical staff usually rely on visual observation of the dripping of liquid in the infusion bucket to monitor the infusion. This method is not only inefficient, but also prone to inaccurate monitoring due to subjective factors and environmental interference.

[0003] With the development of medical technology, infusion drop count monitoring systems that use infrared transmitters and receivers to detect droplet passing signals have emerged. However, most existing systems of this type do not fully consider the complex factors in the actual infusion environment. For example, when the patient moves the infusion device or the ambient temperature changes, causing the drip bucket to fog up, the infrared signal is easily interfered with, causing the detected droplet passing signal to be abnormal, which in turn affects the accuracy of the drop count. In addition, the shaking of the infusion device may also cause unstable situations such as droplet splashing, further exacerbating monitoring errors. The existence of these problems makes it difficult for traditional infusion drop count monitoring systems to operate stably and accurately in complex and changeable clinical infusion scenarios, posing potential risks to patients' infusion safety and hindering medical staff's refined management and remote monitoring of the infusion process.

[0004] The above content is only used to assist in understanding the technical solution of the present invention and does not constitute an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of the present invention is to provide an intelligent infusion drop count monitoring method and device, equipment and storage medium, aiming to solve the technical problem of inaccurate droplet passing signal detection caused by interference from factors such as shaking of the infusion equipment and fog in the drip bucket.

[0006] To achieve the above objectives, the present invention provides an intelligent infusion drop count monitoring method, which comprises the following steps: Real-time detection of a droplet passing signal in the infusion drop bucket, and determination of the current number of drops based on the droplet passing signal; Collecting motion state data of the infusion device in real time, filtering the motion state data to eliminate noise interference, and determining the shaking amplitude; When the shaking amplitude exceeds a preset threshold or the droplet passing signal is abnormal, generating a control instruction to adjust the infusion monitoring state; The current drop count data is uploaded to a cloud server integrated with the hospital information system to achieve remote monitoring.

[0007] In one embodiment, the step of detecting a droplet passing signal in the infusion droplet bucket in real time and determining the current number of drops based on the droplet passing signal includes: The infrared transmitter and receiver detect the droplet passing signal in the infusion dropper at a preset frequency; The current number of drops is calculated according to the time interval of the droplet passing signal.

[0008] In one embodiment, the steps of collecting the motion state data of the infusion device in real time, filtering the motion state data to eliminate noise interference, and determining the shaking amplitude include: collecting motion state data of the infusion device in real time, wherein the motion state data includes acceleration and angular velocity data; Using a Kalman filter algorithm to filter the acceleration and angular velocity data to obtain filtered motion state data to eliminate noise interference; The real-time shaking amplitude of the infusion device is determined according to the filtered motion state data.

[0009] In one embodiment, the step of generating a control instruction to adjust the infusion monitoring state when the shaking amplitude exceeds a preset threshold or the droplet passing signal is abnormal includes: When the shaking amplitude exceeds a preset threshold, the data filtering mechanism is triggered, the statistics of the current number of drops are suspended, and an infusion status prompt is generated based on the historical stable flow rate value.

[0010] In one embodiment, the step of generating a control instruction to adjust the infusion monitoring state when the shaking amplitude exceeds a preset threshold or the droplet passing signal is abnormal includes: When the droplet passing signal is abnormal, the motor-driven clamping device is triggered to periodically squeeze the outside of the drip bucket to remove mist or attachments in the infusion drip bucket; After the extrusion is completed, the infrared signal effect of the infusion dropper is detected by a dual-channel infrared sensor group; The cleaning effect is evaluated based on the infrared signal effect, and the frequency and strength of subsequent squeezing operations are adjusted according to the cleaning effect.

[0011] In one embodiment, the step of generating a control instruction to adjust the infusion monitoring state when the shaking amplitude exceeds a preset threshold or the droplet passing signal is abnormal includes: When the shaking amplitude recovers to below the preset threshold or the squeezing action is effective in cleaning, the current drop count statistics based on the droplet passing signal are restored, and the historical stable flow rate value is updated.

[0012] In one embodiment, the method further comprises: Obtain the remaining infusion volume; determining an estimated infusion completion time based on the updated historical stable flow rate value and the remaining infusion volume; Sending the estimated infusion completion time to a cloud server for display on a monitoring interface of a hospital information system; When the deviation between the estimated infusion completion time and the actual infusion progress exceeds a preset time range, an early warning mechanism is automatically triggered to ensure normal infusion.

[0013] In addition, to achieve the above-mentioned purpose, the present invention also provides an intelligent infusion drop count monitoring device, which includes: A data acquisition module is used to detect the droplet passing signal in the infusion droplet bucket in real time and determine the current number of drops based on the droplet passing signal; a processing module for collecting motion state data of the infusion device in real time, filtering the motion state data to eliminate noise interference, and determining the shaking amplitude; A control module, configured to generate a control instruction to adjust an infusion monitoring state when the shaking amplitude exceeds a preset threshold or the droplet passing signal is abnormal; The data display module is used to upload the current drop count data to a cloud server integrated with the hospital information system to achieve remote monitoring.

[0014] In addition, to achieve the above-mentioned purpose, the present invention also proposes an intelligent infusion drop count monitoring device, which includes: a memory, a processor, and an intelligent infusion drop count monitoring program stored in the memory and executable on the processor, wherein the intelligent infusion drop count monitoring program is configured to implement the steps of the intelligent infusion drop count monitoring method described above.

[0015] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which an intelligent infusion drop number monitoring program is stored. When the intelligent infusion drop number monitoring program is executed by a processor, the steps of the intelligent infusion drop number monitoring method described above are implemented.

[0016] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the intelligent infusion drop count monitoring method as described above.

[0017] One or more technical solutions proposed in this application have at least the following technical effects: Real-time detection of droplet passing signals in the infusion drip bucket, and determination of the current number of drops based on the droplet passing signals; real-time acquisition of motion status data of the infusion device, filtering of the motion status data to eliminate noise interference, and determination of the shaking amplitude; when the shaking amplitude exceeds the preset threshold or the droplet passing signal is abnormal, generation of control instructions to adjust the infusion monitoring status; uploading of the current drop count data to a cloud server integrated with the hospital information system to achieve remote monitoring, thus achieving accurate and real-time monitoring of the infusion drop count, effectively reducing detection errors caused by factors such as equipment shaking or fogging of the drip bucket, improving the accuracy and reliability of infusion monitoring, facilitating medical staff to remotely monitor and manage patients' infusion conditions, and improving the efficiency and quality of medical services. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 A flow chart illustrating the first embodiment of the intelligent infusion drop count monitoring method of this application; Figure 2 A flow chart illustrating the second embodiment of the intelligent infusion drop count monitoring method of this application; Figure 3 This is a schematic diagram of the module structure of the intelligent infusion drop count monitoring device according to an embodiment of the present application; Figure 4 Schematic diagram of the device structure of the hardware operating environment involved in the intelligent infusion drop count monitoring method in the embodiment of the present application.

[0021] The purpose, features and advantages of this application will be further explained with reference to the accompanying drawings in conjunction with the embodiments. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0023] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0024] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication, and program execution capabilities, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device capable of implementing the aforementioned functions, a smart infusion drop count monitoring device, an Internet of Vehicles platform, etc. This embodiment and the following embodiments will be described below using a smart infusion drop count monitoring device as an example.

[0025] Based on this, the embodiment of the present application provides an intelligent infusion drop number monitoring method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the intelligent infusion drop count monitoring method of the present application.

[0026] In this embodiment, the intelligent infusion drop count monitoring method includes steps S10 to S40: Step S10, detecting the droplet passing signal in the infusion drop bucket in real time, and determining the current drop number according to the droplet passing signal; It should be noted that the infusion drip chamber is an important component of the infusion device. It is usually a transparent bucket-shaped container. The liquid in the infusion bottle or infusion bag flows into the drip chamber through the infusion tube. Under the action of gravity, the liquid forms droplets in the drip chamber and falls one by one. By observing the falling speed and number of droplets, the progress and speed of the infusion can be understood. When a droplet falls through a specific detection area of ​​the drip bucket, it blocks or reflects the infrared light emitted by the infrared transmitter, causing the signal received by the infrared receiver to change. This signal change is the droplet passing signal, marking a droplet passing event. The system counts the number of drops by identifying and analyzing these signals. The current drop count is based on the collected droplet passing signal. Through specific algorithms and logical operations, the number of infusion drops at a specific moment or time period is calculated, reflecting the real-time progress of infusion and providing medical staff with accurate infusion status information.

[0027] In a feasible implementation, step S10 includes steps A11 to A12: A11: Detects the droplet passing signal in the infusion dropper through the infrared transmitter and receiver at a preset frequency; It should be noted that the droplet passing signal in the infusion drip bucket is detected in real time, and the current number of drops is determined based on the droplet passing signal; the motion state data of the infusion device is collected in real time, the motion state data is filtered to eliminate noise interference, and the shaking amplitude is determined; when the shaking amplitude exceeds the preset threshold or the droplet passing signal is abnormal, a control instruction is generated to adjust the infusion monitoring state; the current drop count data is uploaded to a cloud server integrated with the hospital information system to achieve remote monitoring, thereby achieving accurate and real-time monitoring of the infusion drop count, effectively reducing detection errors caused by factors such as equipment shaking or drip bucket fogging, improving the accuracy and reliability of infusion monitoring, facilitating medical staff to remotely monitor and manage patients' infusion conditions, and improving the efficiency and quality of medical services.

[0028] A12: Calculate the current number of drops based on the time interval between droplet passing signals.

[0029] It should be noted that the current number of drops is calculated based on the time interval between droplet passing signals. The system accurately records the time point of each droplet passing signal, calculates the adjacent time intervals, and converts it into the current number of drops according to a preset algorithm, thereby determining the real-time drip rate of the infusion.

[0030] It should be understood that this solution is applied to an intelligent infusion drop count monitoring system, including: Drop count detection module: includes an infrared transmitter and receiver, used to detect the droplet passing signal in the infusion droplet bucket in real time; Dynamic compensation module: integrated three-axis gyroscope sensor, used to monitor the motion state of the infusion device. When the shaking amplitude exceeds the preset threshold, Trigger the data filtering mechanism, discard the current drop count data and maintain the historical stable flow rate value; Self-cleaning module: It includes a motor-driven clamping device, which is configured on the outside of the drip bucket and detects signal interference in the drip bucket (such as liquid droplets or mist interference) through a pressure sensor. Trigger the clamping device to periodically squeeze the drip bucket, simulating manual squeezing action to remove mist or attachments; Control unit: connected to the above modules, used to receive sensor signals, execute dynamic compensation algorithms and control the action of the clamping device.

[0031] Step S20: collecting motion state data of the infusion device in real time, filtering the motion state data to eliminate noise interference, and determining the shaking amplitude; It should be understood that the gyroscope sensor used to collect the motion status data of the infusion device is a gyroscope model LIS2DH12TR, with a sensitivity ranging from 1000LSB / g (±2g) to 83LSB / g (+16g) and a bandwidth range of 0.5Hz to 672Hz.

[0032] The data filtering mechanism includes the following steps: Real-time collection of gyroscope angular velocity and acceleration data; Eliminate noise interference through Kalman filtering algorithm; When the shaking amplitude exceeds the threshold for ≥0.5s, it is determined to be effective shaking, the drop count is suspended and an alarm is issued.

[0033] It should be noted that infusion equipment refers to various hardware devices related to the infusion process, including infusion stands, infusion pumps, infusion tubes, and monitoring systems installed on them. In this solution, the focus is on the overall movement of the infusion equipment, as its shaking may affect the drop count monitoring; Motion state data reflects the movement of the infusion device in space, and mainly includes acceleration data and angular velocity data. Acceleration data reflects the linear acceleration changes of the device in all directions, including the acceleration of gravity when stationary and the dynamic acceleration when moving; Angular velocity data describes the angular velocity of the device around different axes, that is, the speed of rotation; Since the collected motion data often contains noise, which may come from electronic noise in the sensor itself, environmental interference (such as electromagnetic interference), or other unrelated vibration sources, filtering processing uses specific algorithms (such as Kalman filtering) to analyze and process the data, removing or reducing noise components, retaining the true motion information, improving the purity and reliability of the data, and making subsequent determination of the shaking amplitude more accurate. Through filtering processing, the influence of noise on the motion state data is reduced or removed, misjudgment or data distortion caused by noise is avoided, and it is ensured that the determination of the shaking amplitude can truly reflect the actual movement of the infusion device.

[0034] Based on the filtered motion state data, data analysis and calculations are used to determine the current level of infusion device vibration, or vibration amplitude. This vibration amplitude is a key indicator of device stability. Excessive vibration amplitude can negatively impact the accuracy of drop count monitoring, necessitating real-time monitoring and assessment.

[0035] In a feasible implementation, step S20 includes steps A21 to A23: A21: Real-time acquisition of motion data of the infusion device, including acceleration and angular velocity data; It should be noted that the real-time acquisition of the infusion device's motion data includes acceleration and angular velocity data. Using high-precision sensors, the system dynamically captures the device's acceleration changes in space (reflecting the device's linear acceleration or deceleration in all directions) and angular velocity data (reflecting the speed of the device's rotation around different axes).

[0036] A22: Use the Kalman filter algorithm to filter the acceleration and angular velocity data to obtain filtered motion state data to eliminate noise interference; It should be noted that the Kalman filter algorithm is used to filter the acceleration and angular velocity data to obtain filtered motion state data to eliminate noise interference. The Kalman filter algorithm is a highly efficient autoregressive filtering algorithm that can process the collected noisy motion state data. Through an iterative process of prediction and update, it can isolate the true and useful signal and remove random interference components, making the data more accurate and smooth.

[0037] A23: Determine the real-time shaking amplitude of the infusion device based on the filtered motion state data.

[0038] It should be noted that the real-time vibration amplitude of the infusion device is determined based on the filtered motion state data. By comprehensively analyzing the processed acceleration and angular velocity data, the vibration amplitude of the infusion device at the current moment is calculated, providing a basis for subsequent determination of whether the threshold has been exceeded.

[0039] Step S30, when the shaking amplitude exceeds a preset threshold or the droplet passing signal is abnormal, generating a control instruction to adjust the infusion monitoring state; It should be noted that the preset threshold is a standard value set in advance based on the normal equipment shaking range during the infusion process and the drop count monitoring accuracy requirements. When the monitored shaking amplitude exceeds this threshold, it indicates that the shaking of the infusion equipment may have a significant impact on the accuracy of the drop count monitoring, and appropriate measures need to be taken to adjust it. Abnormal droplet passing signals refer to droplet passing signals that exhibit characteristics inconsistent with normal infusion patterns, such as abnormal signal intensity fluctuations, irregular time intervals, and discontinuities. These abnormalities may be caused by a variety of factors, such as fogging in the drip chamber causing infrared signal scattering or attenuation, droplet splashing, and infusion tubing blockage or distortion, which can affect the system's normal drop count statistics. When excessive shaking amplitude or abnormal droplet passing signals are detected, the system generates corresponding control instructions based on preset control logic and algorithms. These instructions are used to adjust the operating status of the infusion monitoring system to adapt to the current abnormal situation and ensure the normal operation of the system and the reliability of the data; Adjusting the infusion monitoring status involves changing the infusion monitoring process and method based on control instructions. For example, if the shaking amplitude is excessive, real-time drop count statistics are suspended, and the infusion status is estimated using historical stable flow rate values. If the droplet passing signal is abnormal, the self-cleaning module is activated to remove interfering factors such as mist or attachments in the drip bucket, allowing the monitoring system to provide the most accurate infusion status information possible under abnormal conditions.

[0040] In a feasible implementation, step S30 includes step A31: A31: When the shaking amplitude exceeds the preset threshold, the data filtering mechanism is triggered, the current drop count is paused, and an infusion status prompt is generated based on the historical stable flow rate value.

[0041] It should be noted that when the vibration amplitude exceeds a preset threshold, the data filtering mechanism is triggered, the current drop count is suspended, and an infusion status prompt is generated based on the historical stable flow rate value. When the detected vibration amplitude reaches or exceeds the system's pre-set threshold, the system determines that the vibration at this time may cause a large error in the drop count detection. Therefore, the current drop count is suspended and the infusion status prompt is generated based on the flow rate value recorded during the previous stable infusion phase for medical staff to refer to and remind them to pay attention to the infusion situation.

[0042] Step S40 , uploading the current drop count data to a cloud server integrated with the hospital information system to achieve remote monitoring.

[0043] It should be noted that the system utilizes a wireless transmission module (e.g., one based on Wi-Fi, 4G / 5G, or other wireless communication technologies) to package and organize the current drip count data determined in step S10 and transmit it to a cloud server according to a pre-set data transmission protocol. This cloud server is deeply integrated with the hospital information system (HIS). Medical staff can log in to the HIS through an internal work terminal (e.g., a computer, tablet, or other device) and view the current drip count data for each patient's infusion in real time on the corresponding monitoring interface. This enables remote, real-time monitoring of the infusion process, improving the convenience and timeliness of medical services.

[0044] In practice, in actual hospital wards, this intelligent infusion drop count monitoring system is installed on each patient's IV stand. During the infusion process, the system closely coordinates the aforementioned steps, accurately monitoring the drop count while also keeping an eye on the IV stand's swaying state. If any anomalies are detected, the system promptly adjusts the monitoring strategy and alerts medical staff. Medical staff no longer need to be constantly at the patient's side. By viewing the infusion monitoring section in the hospital information system on a computer at the nurse's station or a handheld mobile nursing terminal, they can obtain real-time information on key information, such as the number of drops infused for all patients. This allows for efficient nursing arrangements and improves the overall quality and efficiency of medical services.

[0045] This embodiment provides an intelligent infusion drop count monitoring method. This method uses a multi-axis gyroscope to monitor the device's motion state in real time. Combined with a filtering algorithm, it distinguishes normal vibration from disturbing vibration, avoiding the misjudgment problem of traditional single sensors. Furthermore, it utilizes a motor-driven clamping device for drip bucket cleaning for the first time. The cleaning effect is verified through both pressure feedback and infrared signals, addressing the industry challenge of fog interference. The method detects droplet passage signals within the drip bucket in real time and determines the current drop count based on these signals. The method also collects motion state data from the infusion device in real time, filters the data to eliminate noise interference, and determines the amplitude of the vibration. When the amplitude exceeds a preset threshold or the droplet passage signal is abnormal, a control instruction is generated to adjust the infusion monitoring state. The method also uploads the current drop count data to a cloud server integrated with the hospital information system for remote monitoring. This method enables accurate, real-time monitoring of the infusion drop count, effectively reducing detection errors caused by factors such as device vibration or drip bucket fogging. This method improves the accuracy and reliability of infusion monitoring, facilitates remote monitoring and management of patients' infusions by medical staff, and enhances the efficiency and quality of medical services.

[0046] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 2 Step S30 includes steps S301 to S303: Step S301: When the droplet passing signal is abnormal, the motor-driven clamping device is triggered to periodically squeeze the outside of the drip bucket to remove mist or attachments in the infusion drip bucket; It should be understood that the clamping device includes a stepper motor, a transmission gear set and a flexible silicone clamp, the clamping pressure range is 0.1-0.5N, and the extrusion frequency is 1-3 times / second. And after the squeezing action, the cleaning effect is judged by the recovery of the infrared receiver signal 912.

[0047] The surface of the flexible silicone chuck is coated with a hydrophobic coating with a contact angle of ≥150°, which is used to reduce droplet adhesion and enhance cleaning efficiency14.

[0048] It should be noted that the presence of characteristics of the droplet passing signal that do not conform to the normal pattern, such as abnormal fluctuations in signal intensity, disordered time intervals, or discontinuity, may be caused by factors such as mist in the drip chamber, droplet splashing, or blockage or distortion of the infusion tube; The system activates the motor-driven clamping device according to the preset control logic. The device is installed on the outside of the drip bucket. The rotation of the motor drives the clamping device to squeeze the drip bucket. The clamping device regularly and periodically squeezes the outside of the drip bucket at a preset frequency and force. This squeezing action simulates manual squeezing of the drip bucket, changing its shape to disperse or discharge the mist inside, while loosening and removing foreign matter adhering to the inner wall of the drip bucket. During the squeezing process, the liquid flow and pressure changes inside the drip bucket help to dissipate the mist and remove the attachments, thereby reducing interference with the droplet passing signal and improving the accuracy of monitoring.

[0049] Step S302: After the squeezing is completed, the infrared signal effect of the infusion dropper is detected by the dual-channel infrared sensor group; It should be noted that the dual-channel infrared sensor group is a sensor array composed of two independent infrared detection units, which use different wavelengths (such as 850nm and 1550nm) or detection modes (transmission / reflection).

[0050] The infrared signal effect can be divided into the transmitted light intensity: the baseline value in the clean state is 100% (no attachments). When there is crystal or fog on the inner wall, the transmittance decreases exponentially (formula: , where α is the material absorption coefficient and d is the thickness of the attachment).

[0051] Scattering pulse stability: During normal infusion, the droplet interval time is normally distributed (standard deviation σ≤0.2s). If there is wall hanging or bubble interference, the σ value will increase.

[0052] Step S303 , evaluating the cleaning effect based on the infrared signal effect, and adjusting the frequency and strength of subsequent squeezing operations according to the cleaning effect.

[0053] It should be understood that the squeezing frequency and the force frequency can be adjusted, for example, from the default 10 minutes / time to 2-30 minutes / time dynamically, by changing the duty cycle of the motor drive PWM signal.

[0054] Furthermore, when the shaking amplitude recovers to below a preset threshold or the squeezing action is effective in cleaning, the current drop count statistics based on the droplet passing signal are restored and the historical stable flow rate value is updated.

[0055] It should be noted that the system can also analyze and determine the effectiveness of the squeezing action in removing mist and debris based on the pressure change data detected by the pressure sensor. For example, if the pressure change trend indicates that the mist has basically dissipated or the debris has loosened and fallen off, the cleaning effect is considered to be good.

[0056] During the squeezing process, the pressure sensor continuously records the changes in pressure values. These changes reflect the impact of the squeezing action on the drip bucket and the removal of mist and attachments inside the drip bucket.

[0057] Based on the evaluation results, the system automatically adjusts the frequency and force of subsequent squeezes in the clamping device. If the cleaning effect is good, the squeeze frequency and force can be appropriately reduced to reduce interference with the infusion process; if the cleaning effect is poor, the squeeze frequency and force can be appropriately increased to enhance the cleaning effect.

[0058] The method also includes: Obtain the remaining infusion volume; Determine the estimated infusion completion time based on the updated historical stable flow rate value and the remaining infusion volume; Send the estimated infusion completion time to the cloud server for display on the monitoring interface of the hospital information system; When the deviation between the estimated infusion completion time and the actual infusion progress exceeds the preset time range, the early warning mechanism is automatically triggered to ensure normal infusion.

[0059] It should be noted that when the shaking amplitude returns to below the preset threshold or the squeezing action is effective, the system considers that the droplet passing signal has returned to normal, restarts the current drop count based on the droplet passing signal, and updates the historical stable flow rate value based on the new drop count data, providing a more accurate reference for subsequent infusion monitoring; Obtain the remaining amount of infusion in the current infusion bag or infusion bottle through sensors or other measuring means on the infusion device; Calculate the estimated infusion completion time based on the updated historical stable flow rate value and the remaining infusion volume. Expected infusion completion time = Remaining infusion volume / (Historical stable flow rate value × Conversion factor between drop count and flow rate); Sending the estimated infusion completion time to the cloud server: The calculated estimated infusion completion time is sent to the cloud server via the wireless transmission module so as to be displayed on the monitoring interface of the hospital information system; When the deviation between the estimated infusion completion time and the actual infusion progress exceeds the preset time range, the system automatically triggers the early warning mechanism to remind medical staff to pay attention to the infusion status in time and take corresponding measures to ensure the normal progress of the infusion.

[0060] This embodiment provides an intelligent infusion drop count monitoring method. By real-time detection of droplet passing signals and motion status data and taking corresponding control measures in abnormal situations, it can effectively reduce monitoring errors caused by factors such as equipment shaking and drip bucket fogging, and improve the accuracy and reliability of infusion drop count monitoring.

[0061] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the intelligent infusion drop count monitoring method of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0062] This application also provides an intelligent infusion drop count monitoring device, please refer to Figure 3 , the intelligent infusion drop count monitoring device comprises: The data acquisition module 10 is used to detect the droplet passing signal in the infusion drop bucket in real time and determine the current number of drops based on the droplet passing signal; The processing module 20 is used to collect the motion state data of the infusion device in real time, filter the motion state data to eliminate noise interference, and determine the shaking amplitude; A control module 30, configured to generate a control instruction to adjust the infusion monitoring state when the shaking amplitude exceeds a preset threshold or the droplet passing signal is abnormal; The data display module 40 is used to upload the current drop count data to a cloud server integrated with the hospital information system to achieve remote monitoring.

[0063] The intelligent infusion drop count monitoring device provided in this application utilizes the intelligent infusion drop count monitoring method described in the aforementioned embodiments, resolving the technical issue of inaccurate droplet passage signal detection caused by interference from factors such as infusion device shaking and mist within the drip bucket. Compared to the prior art, the beneficial effects of the intelligent infusion drop count monitoring device provided in this application are the same as those of the intelligent infusion drop count monitoring method described in the aforementioned embodiments. Other technical features of the intelligent infusion drop count monitoring device are the same as those disclosed in the aforementioned embodiments and are not further elaborated upon here.

[0064] In one embodiment, the data acquisition module 10 is further configured to detect a droplet passing signal in the infusion drop bucket at a preset frequency through an infrared transmitter and a receiver; The current number of drops is calculated based on the time interval of the droplet passing signal.

[0065] In one embodiment, the processing module 20 is further configured to collect motion state data of the infusion device in real time, the motion state data including acceleration and angular velocity data; The Kalman filter algorithm is used to filter the acceleration and angular velocity data to obtain filtered motion state data to eliminate noise interference; The real-time shaking amplitude of the infusion device is determined based on the filtered motion state data.

[0066] In one embodiment, the control module 30 is further configured to trigger a data filtering mechanism when the shaking amplitude exceeds a preset threshold, suspend the current drop count statistics, and generate an infusion status prompt based on a historical stable flow rate value.

[0067] In one embodiment, the control module 30 is further configured to trigger a motor-driven clamping device to periodically squeeze the outside of the drip bucket when a droplet passing signal is abnormal, so as to clear mist or attachments in the infusion drip bucket; After the extrusion is completed, the infrared signal effect of the infusion dropper is detected by the dual-channel infrared sensor group; The cleaning effect is evaluated based on the infrared signal effect, and the frequency and strength of subsequent squeezing operations are adjusted according to the cleaning effect.

[0068] In one embodiment, the control module 30 is further configured to restore the current drop count statistics based on the droplet passing signal and update the historical stable flow rate value when the shaking amplitude recovers to below a preset threshold or the squeezing action is effective in cleaning.

[0069] In one embodiment, the control module 30 is further configured to obtain the remaining infusion volume; Determine the estimated infusion completion time based on the updated historical stable flow rate value and the remaining infusion volume; Send the estimated infusion completion time to the cloud server for display on the monitoring interface of the hospital information system; When the deviation between the estimated infusion completion time and the actual infusion progress exceeds the preset time range, the early warning mechanism is automatically triggered to ensure normal infusion.

[0070] The present application provides an intelligent infusion drop count monitoring device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the intelligent infusion drop count monitoring method in the above-mentioned embodiment 1.

[0071] Reference below Figure 4, which shows a schematic diagram of the structure of an intelligent infusion drop count monitoring device suitable for implementing the embodiments of the present application. The intelligent infusion drop count monitoring device in the embodiments of the present application can include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 4 The intelligent infusion drop count monitoring device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0072] like Figure 4 As shown, the intelligent infusion drop count monitoring device may include a processing device 1001 (e.g., a central processing unit, graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in ROM (Read Only Memory) 1002 or programs loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the intelligent infusion drop count monitoring device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, hard disk, etc.; and communication devices 1009. Communication devices 1009 can allow the intelligent infusion drop count monitoring device to communicate with other devices wirelessly or wired to exchange data. While the figure shows an intelligent infusion drop count monitoring device with various systems, it should be understood that implementation or presence of all the illustrated systems is not required. More or fewer systems may alternatively be implemented or present.

[0073] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0074] The intelligent infusion drop count monitoring device provided in this application utilizes the intelligent infusion drop count monitoring method described in the aforementioned embodiment, resolving the technical issue of inaccurate droplet passage signal detection caused by interference from factors such as infusion device shaking and mist within the drip bucket. Compared to the prior art, the beneficial effects of the intelligent infusion drop count monitoring device provided in this application are the same as those of the intelligent infusion drop count monitoring method described in the aforementioned embodiment. Other technical features of this intelligent infusion drop count monitoring device are the same as those disclosed in the aforementioned embodiment and are not further elaborated here.

[0075] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0076] The above are only specific embodiments of the present application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0077] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, and the computer-readable program instructions are used to execute the intelligent infusion drop count monitoring method in the above-mentioned embodiment.

[0078] The computer-readable storage medium provided herein may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more wires, a portable computer disk, a hard disk, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash memory), optical fiber, CD-ROM (CD-Read Only Memory), optical storage device, magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including, but not limited to, wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0079] The computer-readable storage medium may be included in the intelligent infusion drop count monitoring device; or may exist independently without being assembled into the intelligent infusion drop count monitoring device.

[0080] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the intelligent infusion drop count monitoring device, the intelligent infusion drop count monitoring device is enabled to: detect the droplet passing signal in the infusion drop bucket in real time, and determine the current drop count based on the droplet passing signal; collect the motion state data of the infusion device in real time, filter the motion state data to eliminate noise interference, and determine the shaking amplitude; when the shaking amplitude exceeds a preset threshold or the droplet passing signal is abnormal, generate a control instruction to adjust the infusion monitoring state; upload the current drop count data to a cloud server integrated with the hospital information system to realize remote monitoring.

[0081] The computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a LAN (Local Area Network) or a WAN (Wide Area Network), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0082] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0083] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0084] The computer-readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned intelligent infusion drop count monitoring method. This computer-readable storage medium can address the technical issue of inaccurate droplet passage signal detection caused by interference from factors such as infusion device sway and mist within the drip bucket. Compared to the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the intelligent infusion drop count monitoring method provided in the aforementioned embodiments, and are not further elaborated here.

[0085] The present application also provides a computer program product, comprising a computer program, which implements the steps of the above-mentioned intelligent infusion drop count monitoring method when executed by a processor.

[0086] The computer program product provided in this application can address the technical issue of inaccurate droplet passage signal detection caused by interference from factors such as infusion device shaking and mist within the drip chamber. Compared to the prior art, the beneficial effects of the computer program product provided in this application are similar to those of the intelligent infusion drop count monitoring method provided in the aforementioned embodiment, and are not further elaborated here.

[0087] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. An intelligent infusion drop count monitoring method, characterized in that: The method comprises: Real-time detection of a droplet passing signal in the infusion drop bucket, and determination of the current number of drops based on the droplet passing signal; Collecting motion state data of the infusion device in real time, filtering the motion state data to eliminate noise interference, and determining the shaking amplitude; When the shaking amplitude exceeds a preset threshold or the droplet passing signal is abnormal, generating a control instruction to adjust the infusion monitoring state; The current drop count data is uploaded to a cloud server integrated with the hospital information system to achieve remote monitoring.

2. The method according to claim 1, wherein The step of detecting the droplet passing signal in the infusion drop bucket in real time and determining the current number of drops according to the droplet passing signal includes: The infrared transmitter and receiver detect the droplet passing signal in the infusion dropper at a preset frequency; The current number of drops is calculated according to the time interval of the droplet passing signal.

3. The method according to claim 1, wherein The steps of collecting the motion state data of the infusion device in real time, filtering the motion state data to eliminate noise interference, and determining the shaking amplitude include: collecting motion state data of the infusion device in real time, wherein the motion state data includes acceleration and angular velocity data; Using a Kalman filter algorithm to filter the acceleration and angular velocity data to obtain filtered motion state data to eliminate noise interference; The real-time shaking amplitude of the infusion device is determined according to the filtered motion state data.

4. The method according to claim 1, wherein The step of generating a control instruction to adjust the infusion monitoring state when the shaking amplitude exceeds a preset threshold or the droplet passing signal is abnormal includes: When the shaking amplitude exceeds a preset threshold, the data filtering mechanism is triggered, the statistics of the current number of drops are suspended, and an infusion status prompt is generated based on the historical stable flow rate value.

5. The method according to claim 1, wherein The step of generating a control instruction to adjust the infusion monitoring state when the shaking amplitude exceeds a preset threshold or the droplet passing signal is abnormal includes: When the droplet passing signal is abnormal, the motor-driven clamping device is triggered to periodically squeeze the outside of the drip bucket to remove mist or attachments in the infusion drip bucket; After the extrusion is completed, the infrared signal effect of the infusion dropper is detected by a dual-channel infrared sensor group; The cleaning effect is evaluated based on the infrared signal effect, and the frequency and strength of subsequent squeezing operations are adjusted according to the cleaning effect.

6. The method according to claim 1, wherein The step of generating a control instruction to adjust the infusion monitoring state when the shaking amplitude exceeds a preset threshold or the droplet passing signal is abnormal includes: When the shaking amplitude recovers to below the preset threshold or the squeezing action is effective in cleaning, the current drop count statistics based on the droplet passing signal are restored, and the historical stable flow rate value is updated.

7. The method according to claim 6, wherein The method further comprises: Obtain the remaining infusion volume; determining an estimated infusion completion time based on the updated historical stable flow rate value and the remaining infusion volume; Sending the estimated infusion completion time to a cloud server for display on a monitoring interface of a hospital information system; When the deviation between the estimated infusion completion time and the actual infusion progress exceeds a preset time range, an early warning mechanism is automatically triggered to ensure normal infusion.

8. An intelligent infusion drop count monitoring device, characterized in that: The device comprises: A data acquisition module is used to detect the droplet passing signal in the infusion droplet bucket in real time and determine the current number of drops based on the droplet passing signal; a processing module for collecting motion state data of the infusion device in real time, filtering the motion state data to eliminate noise interference, and determining the shaking amplitude; A control module, configured to generate a control instruction to adjust an infusion monitoring state when the shaking amplitude exceeds a preset threshold or the droplet passing signal is abnormal; The data display module is used to upload the current drop count data to a cloud server integrated with the hospital information system to achieve remote monitoring.

9. An intelligent infusion drop count monitoring device, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the intelligent infusion drop count monitoring method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the intelligent infusion drop count monitoring method according to any one of claims 1 to 7 are implemented.

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