Transfusion dripping speed alarm

Abstract

The invention provides an infusion dripping speed alarm. The infusion dripping speed alarm comprises a connector, a sensor, an alarm body and a controller. The connector is connected with the outer wall of the dropper, the sensor is arranged on the connector and used for detecting the dripping speed of liquid medicine in the dropper, and the alarm is connected with the sensor; the signal input end of the controller is connected with the sensor, and the signal output end is connected with the alarm; the controller is used for receiving the dripping speed, sent by the sensor, of the liquid medicine in the dropper and comparing the dripping speed of the liquid medicine with a preset dripping speed threshold value, and if the dripping speed of the liquid medicine is higher than or lower than the preset dripping speed threshold value, the controller controls the alarm to send out an alarm signal. By arranging the connector, when a patient is transfused, the connector only needs to be arranged on the dropper in a sleeving mode, operation is easy, the sensor is arranged to detect the dripping speed of liquid medicine in the dropper, an alarm signal can be sent out when the dripping speed is higher than or lower than a preset dripping speed threshold value, medical staff can know the dripping speed, and the transfusion safety is improved.

Description

Technical Field

[0001] The invention relates to the technical field of medical devices, in particular to an infusion dripping rate alarm. Background Art

[0002] During intravenous infusion, patients or caregivers often need to pay attention to the speed of infusion, the amount of remaining medicine, and whether the infusion is complete. This will cause patients or caregivers to be highly nervous and extremely tired. In addition, in the current infusion process, most medical staff rely on experience to judge the speed of the drip rate, which not only wastes the energy and time of patients or caregivers, but also artificial non-objective judgments. For some patients or drugs that require precise control of the drip rate, especially vasoactive drugs, chemotherapy drugs, critically ill patients or places such as nursing homes, there are also safety risks.

[0003] At present, the drip alarm products on the market are mostly complex in structure, complicated in appearance design, large in size, high in power consumption, complicated in detection, high in cost, not very convenient to use, not very practical, and the drip rate alarm cannot be known by medical staff. In addition, some patients' family members may adjust the drip rate of the infusion set at will, which poses a safety hazard. Summary of the invention

[0004] The object of the present invention is to provide an infusion drip rate alarm to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] An infusion dripping rate alarm is used to monitor the dripping rate of a liquid medicine in a dropper of an infusion device, comprising: a connector, a sensor, an alarm, and a controller;

[0007] The connector is connected to the outer wall of the dropper, the sensor is arranged on the connector, the sensor is used to detect the dripping speed of the liquid medicine in the dropper, and the alarm is connected to the sensor;

[0008] The signal input end of the controller is connected to the sensor, and the signal output end is connected to the alarm; the controller is used to receive the dripping speed of the medicine liquid in the dropper sent by the sensor, and compare the dripping speed of the medicine liquid with a preset dripping speed threshold. If the dripping speed of the medicine liquid is higher or lower than the preset dripping speed threshold, the controller controls the alarm to send an alarm signal.

[0009] Optionally, it further includes a client, wherein the client is connected to the controller;

[0010] The controller is also used for transmitting the alarm signal to the client when the alarm device sends out the alarm signal;

[0011] The client is distributed based on the Internet of Things network system.

[0012] Optionally, the connector is an inverted cone-shaped mounting piece, a through groove is provided on the side wall of the mounting piece, the width of the through groove is larger than the diameter of the infusion tube of the infusion pump, and the mounting piece is sleeved on the top end of the dropper; the sensor is arranged at the bottom end of the mounting piece.

[0013] Optionally, the sensor includes: a photoelectric sensor, the photoelectric sensor is used to count the number of drops of the liquid medicine dripping within a predetermined time period and transmit it to the controller;

[0014] The controller is used to calculate the dripping speed of the liquid medicine in the dropper according to the predetermined time length and the number of drops.

[0015] Optionally, the sensor further includes: an environmental sensor, which is used to detect the temperature and humidity of the environment in which the infusion pump is located.

[0016] Optionally, the method for the controller to process the pulse signal of the photoelectric sensor includes:

[0017] Acquiring the pulse signal;

[0018] Filtering the pulse signal using a Kalman filter algorithm;

[0019] The dripping speed of the liquid medicine in the dropper is calculated based on the filtered pulse signal.

[0020] Optionally, the Kalman filter algorithm includes:

[0021] predicting a state of a pulse signal detected by the photoelectric sensor;

[0022] The state of the current pulse signal is updated based on the predicted state of the pulse signal, so as to calculate the dripping speed of the liquid medicine in the dropper based on the updated pulse signal.

[0023] Optionally, the algorithm for predicting the state of the pulse signal detected by the photoelectric sensor includes a prediction algorithm and an update algorithm;

[0024] The prediction algorithm includes: state prediction and covariance calculation;

[0025] The formula for state prediction is:

[0026] hat{x}k=A\hat{x}{k-1}+Buk; where hat{x}k is the predicted state

[0027] The formula for the covariance operation is:

[0028] Pk = AP{k-1}AT+Q, where Pk is the predicted covariance, P{k-1} is the state estimation error covariance at the previous moment, and Q is the process noise covariance matrix;

[0029] xk=Ax{k-1}+Buk+wk; xk: the state of the system at time k; A is the state transfer matrix, describing the change of the state from time k-1 to time k; uk: control input; B is the influence matrix of control input; wk is process noise, usually assumed to be zero-mean Gaussian noise with covariance matrix Q.

[0030] Optionally, the update algorithm includes: a Kalman gain algorithm, a state update algorithm, and a covariance update algorithm;

[0031] The formula of the Kalman gain algorithm is:

[0032] Kk=Pk-HTHPk-HT+R{-1}

[0033] Among them, Kk is the Kalman gain, which determines the weighted proportion of the predicted value and the observed value;

[0034] The formula of the state update algorithm is:

[0035] hat{x}k=hat{x}k+Kkzk-H\hat{x}k;

[0036] Among them, hat{x}k is the updated state estimate, zk-H\hat{x}k is the residual between the observed value and the predicted value;

[0037] The formula of the covariance update algorithm is:

[0038] Pk=I-KkHPk;

[0039] Among them, Pk is the updated state estimation error covariance, I is the unit matrix; zk=Hxk+vk; zk is the observation value, the measurement result obtained at time k; H is the observation matrix, which converts the state of the system into the observation space; vk is the observation noise, zero-mean Gaussian noise, with a covariance matrix R.

[0040] Optionally, the alarm is an audible and visual alarm.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] The present application provides an infusion drip rate alarm, which is used to monitor the drip rate of the liquid medicine in the dropper of the infusion device, including: a connector, a sensor, an alarm, and a controller; the connector is connected to the outer wall of the dropper, the sensor is arranged on the connector, the sensor is used to detect the drip rate of the liquid medicine in the dropper, and the alarm is connected to the sensor; the signal input end of the controller is connected to the sensor, and the signal output end is connected to the alarm; the controller is used to receive the drip rate of the liquid medicine in the dropper sent by the sensor, and compare the drip rate of the liquid medicine with a preset drip rate threshold, and when the drip rate of the liquid medicine is higher or lower than the preset drip rate threshold, the controller controls the alarm to send an alarm signal. By setting a connector, when the patient is infusing, it is only necessary to put the connector on the dropper, which is simple to operate. By setting a sensor to detect the drip rate of the liquid medicine in the dropper, an alarm signal can be sent when the drip rate is higher or lower than the preset drip rate threshold, so that the medical staff can know it, thereby improving the safety of the infusion. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A schematic diagram of the connector structure.

[0044] Figure 2 The present invention is a connection diagram of an infusion drip rate alarm.

[0045] In the figure: 1. Connecting machine; 2. Sensor; 3. Alarm. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] In addition, when an element in the present invention is referred to as being "fixed to" or "disposed on" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only and are not intended to be the only embodiment.

[0048] See also Figure 1-2In an embodiment of the present invention, an infusion drip rate alarm is used to monitor the drip rate of the medicine liquid in the dropper of the infusion device, including: a connector, a sensor, an alarm, and a controller; the connector is connected to the outer wall of the dropper, the sensor is arranged on the connector, the sensor is used to detect the drip rate of the medicine liquid in the dropper, and the alarm is connected to the sensor; the signal input end of the controller is connected to the sensor, and the signal output end is connected to the alarm; the controller is used to receive the drip rate of the medicine liquid in the dropper sent by the sensor, and compare the drip rate of the medicine liquid with a preset drip rate threshold. If the drip rate of the medicine liquid is higher or lower than the preset drip rate threshold, the controller controls the alarm to send an alarm signal.

[0049] In this embodiment, the connector can be a sleeve or a clip. In this application, it is only necessary to use the connector to connect the sensor to the dropper of the infusion set, or it is also feasible to set up the sensor on one side of the dropper. In this embodiment, the sensor is preferably a photoelectric sensor, which generally consists of a light source (such as an LED) and a photodetector (such as a photodiode or a photodiode). The two are usually arranged in pairs, that is, the light beam emitted by the light source will pass through the path of the droplet, and the photodetector will receive the light signal passing through the droplet.

[0050] In this embodiment, the connection between the above-mentioned sensor and the controller can be wired or wireless. Similarly, the connection between the above-mentioned controller and the alarm can be wired or wireless, and the connection between the above-mentioned controller and the client is preferably a wireless connection.

[0051] In this embodiment, the hardware of the controller can be directly purchased, and the user can write a predetermined operation program into the controller according to actual needs. As a preferred embodiment, the controller can be equipped with an input device and a display screen. The function of the input device is that the user inputs a preset drip rate threshold to the controller through the input device, and the display is used to display the drip rate of the liquid medicine in real time. Specifically: the nurse can set a normal infusion range, i.e., the aforementioned preset drip rate threshold, according to the doctor's order. When it is higher than the preset drip rate threshold, the alarm alarms, and then when it is lower than the preset drip rate threshold, the alarm alarms.

[0052] Of course, in order to further clarify whether it is higher than the preset drip rate threshold or lower than the preset drip rate threshold, the alarm can be set with different alarm signals, for example, when it is higher than the preset drip rate threshold, the alarm sounds and lights an alarm, when it is lower than the preset drip rate threshold, the alarm voice alarm, or when it is higher than the preset drip rate threshold, the alarm voice alarm, when it is lower than the preset drip rate threshold, the alarm sounds and lights an alarm. Of course, the above examples do not limit the alarm used in this application to only realize two alarm functions, and other types of alarms can also be used, which is not limited in this application. In this application, it is only necessary to provide a device that sends an alarm signal when the drip rate of the liquid medicine is higher or lower than the preset drip rate threshold.

[0053] Furthermore, the infusion drip rate alarm provided by the present application, by providing a connector, only needs to be put on the dropper when the patient is receiving an infusion, and the operation is simple. By providing a sensor to detect the drip rate of the medicine in the dropper, an alarm signal can be issued when the drip rate is higher or lower than a preset drip rate threshold, so that medical staff can be informed, thereby improving the safety of infusion.

[0054] In a specific implementation, it also includes a client, which is connected to the controller; the controller is also used to transmit the alarm signal to the client when the alarm sends out an alarm signal; the client is distributed based on the Internet of Things network system.

[0055] In this embodiment, a client is introduced. In a specific application, the client can be a medical staff's work computer, notebook, smart phone, etc., or a patient's family member's work computer, notebook, smart phone, etc. This application does not limit it. In order to better monitor the drip rate of the medicine, this embodiment sets the aforementioned client. In the specific layout process, the client and the controller can be arranged based on the existing Internet of Things network to achieve information synchronization.

[0056] Specifically: Internet of Things (IoT) refers to connecting various objects in the physical world to the Internet through various information sensing devices (such as sensors, RFID, smart devices, etc.) to achieve information exchange and intelligent control between objects and objects, objects and people. In short, the core of the Internet of Things is to make "everything connected", interconnecting devices, sensors and systems through the Internet to achieve functions such as automation, intelligence, remote monitoring and data analysis. By applying it in this application, the client can realize the function of synchronously displaying the dripping speed of the medicine in the ward so that the patient's family or medical staff can monitor in real time.

[0057] In a specific embodiment, the connector is an inverted cone-shaped mounting piece, a through groove is provided on the side wall of the mounting piece, the width of the through groove is greater than the diameter of the infusion tube of the infusion pump, and the mounting piece is sleeved on the top end of the dropper; the sensor is arranged at the bottom end of the mounting piece.

[0058] like Figure 1 As shown, in order to facilitate the installation of the connector, the present application sets the connector as an inverted cone-shaped mounting piece. During the assembly process, after the infusion tube passes through the through slot, the inverted cone-shaped mounting piece is buckled on the top of the dropper. In order to provide observation, the material of the connector can be a transparent material, which is convenient for medical staff to observe the dripping condition of the liquid medicine in the dropper. The transparent material can be plastic, etc.

[0059] In a specific embodiment, the sensor includes: a photoelectric sensor, which is used to count the number of drops of the medicine liquid dripping within a predetermined time period and transmit it to the controller; the controller is used to calculate the dripping rate of the medicine liquid in the dropper based on the predetermined time period and the number of drops.

[0060] The number of drops is detected by monitoring the changes in light generated when the droplets fall. Every time a drop passes through the sensor, it changes the light intensity or reflection, and the sensor records these changes and counts; every time a drop blocks the light beam, the change in light intensity of the detector is recorded as a pulse signal. The signal output by the sensor is usually filtered and amplified to ensure that noise does not interfere with the accurate counting of the number of drops. For example: the counting module in the controller calculates the number of pulses generated per unit time, that is, the number of droplets. For example, if the sensor generates 50 pulses in 1 second, it can be concluded that the drip rate is 50 drops / second.

[0061] In a specific embodiment, the sensor further includes: an environmental sensor, which is used to detect the temperature and humidity of the environment in which the infusion device is located. Environmental factors such as temperature, humidity, and air pressure may also affect the size of the droplets and the viscosity of the liquid, thereby changing the generation pattern of the droplets and affecting the accuracy of the drip rate measurement. Install an environmental sensor to monitor parameters such as temperature and humidity in real time, and adjust the algorithm for drip rate measurement based on these environmental data. For example, add a temperature compensation factor to adjust the change in droplet volume. Calibrate the sensor regularly to ensure measurement accuracy under different environmental conditions.

[0062] In a specific embodiment, the method for the controller to process the pulse signal of the photoelectric sensor includes: acquiring the pulse signal; filtering the pulse signal using a Kalman filter algorithm; and calculating the dripping rate of the liquid medicine in the dropper based on the filtered pulse signal.

[0063] In a specific embodiment, the Kalman filter algorithm includes: predicting the state of the pulse signal detected by the photoelectric sensor; updating the state of the current pulse signal based on the predicted state of the pulse signal; and calculating the dripping rate of the medicine in the dropper based on the updated pulse signal.

[0064] In this embodiment, Kalman filter is a recursive algorithm for estimating the state of a linear dynamic system, which uses the observed data and prior knowledge of the system to estimate the system state, while optimizing noise and uncertainty.

[0065] In a specific embodiment, the algorithm for predicting the state of the pulse signal detected by the photoelectric sensor includes a prediction algorithm and an update algorithm;

[0066] The prediction algorithm includes: state prediction and covariance calculation;

[0067] The formula for state prediction is:

[0068] hat{x}k=A\hat{x}{k-1}+Buk; where hat{x}k is the predicted state

[0069] The formula for the covariance operation is:

[0070] Pk = AP{k-1}AT+Q, where Pk is the predicted covariance, P{k-1} is the state estimation error covariance at the previous moment, and Q is the process noise covariance matrix;

[0071] xk=Ax{k-1}+Buk+wk; xk: the state of the system at time k; A is the state transfer matrix, describing the change of the state from time k-1 to time k; uk: control input; B is the influence matrix of control input; wk is process noise, usually assumed to be zero-mean Gaussian noise with covariance matrix Q.

[0072] In a specific implementation, the update algorithm includes: a Kalman gain algorithm, a state update algorithm, and a covariance update algorithm;

[0073] The formula of the Kalman gain algorithm is:

[0074] Kk=Pk-HTHPk-HT+R{-1}

[0075] Among them, Kk is the Kalman gain, which determines the weighted proportion of the predicted value and the observed value;

[0076] The formula of the state update algorithm is:

[0077] hat{x}k=hat{x}k+Kkzk-H\hat{x}k;

[0078] Among them, hat{x}k is the updated state estimate, zk-H\hat{x}k is the residual between the observed value and the predicted value;

[0079] The formula of the covariance update algorithm is:

[0080] Pk=I-KkHPk;

[0081] Among them, Pk is the updated state estimation error covariance, I is the unit matrix; zk=Hxk+vk; zk is the observation value, the measurement result obtained at time k; H is the observation matrix, which converts the state of the system into the observation space; vk is the observation noise, zero-mean Gaussian noise, with a covariance matrix R.

[0082] Advantages of Kalman filtering:

[0083] Optimality: For linear systems, Kalman filtering is the optimal estimator, which can provide the minimum mean square error estimation under noise and uncertainty. Real-time: Kalman filtering is a recursive algorithm that only needs the current observation value and the previous state estimate, so it can update the state estimate in real time and is suitable for dynamic systems. Adaptability: It can make adaptive adjustments according to different changes in process noise and observation noise in practical applications.

[0084] Kalman filtering is a very powerful and efficient algorithm, which is widely used in real-time estimation and signal processing, especially for noise and uncertainty in dynamic systems. Its main advantage is that it can use historical information for recursive estimation and provide optimal state estimation through two steps of prediction and update. In many modern technology applications, Kalman filtering is one of the core technologies. Applied in the technical solution of this application, it can realize the function of more accurate monitoring and calculation of the dripping speed of the liquid medicine.

[0085] In a specific implementation, the alarm is an audible and visual alarm.

[0086] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

[0087] In addition, it should be understood that although the present specification is described according to implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment may also be appropriately combined to form other implementation modes that can be understood by those skilled in the art.

Claims

1. An infusion drip rate alarm, used to monitor the drip rate of the liquid medicine in the dropper of the infusion device, characterized in that: include: Connectors, sensors, alarms, controllers; The connector is connected to the outer wall of the dropper, the sensor is arranged on the connector, the sensor is used to detect the dripping speed of the liquid medicine in the dropper, and the alarm is connected to the sensor; The signal input end of the controller is connected to the sensor, and the signal output end is connected to the alarm; the controller is used to receive the dripping speed of the medicine liquid in the dropper sent by the sensor, and compare the dripping speed of the medicine liquid with a preset dripping speed threshold. If the dripping speed of the medicine liquid is higher or lower than the preset dripping speed threshold, the controller controls the alarm to send an alarm signal.

2. The infusion drip rate alarm according to claim 1, characterized in that: Also included is a client, the client being connected to the controller; The controller is also used for transmitting the alarm signal to the client when the alarm device sends out the alarm signal; The client is distributed based on the Internet of Things network system.

3. The infusion drip rate alarm according to claim 1, characterized in that: The connector is an inverted cone-shaped mounting piece, a through slot is provided on the side wall of the mounting piece, the width of the through slot is greater than the diameter of the infusion tube of the infusion set, the mounting piece is sleeved on the top end of the dropper; the sensor is arranged at the bottom end of the mounting piece.

4. The infusion drip rate alarm according to claim 1, characterized in that: The sensor includes: a photoelectric sensor, the photoelectric sensor is used to count the number of drops of the liquid medicine dripping within a predetermined time period and transmit it to the controller; The controller is used to calculate the dripping speed of the liquid medicine in the dropper according to the predetermined time length and the number of drops.

5. The infusion drip rate alarm according to claim 4, characterized in that: The sensor further includes: an environmental sensor, which is used to detect the temperature and humidity of the environment in which the infusion set is located.

6. The infusion drip rate alarm according to claim 5, characterized in that: The method for the controller to process the pulse signal of the photoelectric sensor includes: Acquiring the pulse signal; Filtering the pulse signal using a Kalman filter algorithm; The dripping speed of the liquid medicine in the dropper is calculated based on the filtered pulse signal.

7. The infusion drip rate alarm according to claim 6, characterized in that: The Kalman filter algorithm includes: predicting a state of a pulse signal detected by the photoelectric sensor; The state of the current pulse signal is updated based on the predicted state of the pulse signal, so as to calculate the dripping speed of the liquid medicine in the dropper based on the updated pulse signal.

8. The infusion drip rate alarm according to claim 7, characterized in that: The algorithm for predicting the state of the pulse signal detected by the photoelectric sensor includes a prediction algorithm and an update algorithm; The prediction algorithm includes: state prediction and covariance calculation; The formula for state prediction is: hat{x}k=A\hat{x}{k-1}+Buk; where hat{x}k is the predicted state The formula for the covariance operation is: Pk = AP{k-1}AT+Q, where Pk is the predicted covariance, P{k-1} is the state estimation error covariance at the previous moment, and Q is the process noise covariance matrix; xk=Ax{k-1}+Buk+wk; xk: the state of the system at time k; A is the state transfer matrix, describing the change of the state from time k-1 to time k; uk: control input; B is the influence matrix of control input; wk is process noise, usually assumed to be zero-mean Gaussian noise with covariance matrix Q.

9. The infusion drip rate alarm according to claim 8, characterized in that: The update algorithms include: Kalman gain algorithm, state update algorithm, and covariance update algorithm; The formula of the Kalman gain algorithm is: Kk=Pk-HTHPk-HT+R{-1}; Among them, Kk is the Kalman gain, which determines the weighted proportion of the predicted value and the observed value; The formula of the state update algorithm is: hat{x}k=hat{x}k+Kkzk-H\hat{x}k; Among them, hat{x}k is the updated state estimate, zk-H\hat{x}k is the residual between the observed value and the predicted value; The formula of the covariance update algorithm is: Pk=I-KkHPk; Among them, Pk is the updated state estimation error covariance, I is the unit matrix; zk=Hxk+vk; zk is the observation value, the measurement result obtained at time k; H is the observation matrix, which converts the state of the system into the observation space; vk is the observation noise, zero-mean Gaussian noise, with a covariance matrix R.

10. The infusion drip rate alarm according to claim 1, characterized in that: The alarm is an audible and visual alarm.

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