Automatic following type medical infusion system and method

Abstract

The invention relates to an automatic following type medical infusion system and method, and relates to the technical field of medical infusion, and the system comprises a binding strength module which is used for obtaining the dynamically updated binding strength of a patient and infusion equipment; the tracking precision module is used for obtaining the tracking precision of the patient subjected to binding strength calibration; the path planning module is used for obtaining an optimal moving path of the infusion equipment; the speed control module is used for obtaining the current speed of the self-adaptively controlled infusion equipment according to the optimal path and the feedback value of the tracking precision; the obstacle avoidance decision module is used for obtaining the threat degree of the obstacle according to the current speed and the obstacle scanning data of the three-dimensional laser radar, and determining an obstacle avoidance decision value of the infusion equipment based on the threat; and the automatic following module is used for obtaining the motion control quantity of the infusion equipment according to the optimal path of the infusion equipment, the current speed and the cooperative feedback of the obstacle avoidance decision value. By means of the system, infusion equipment can accurately and automatically follow a patient to conduct infusion.

Description

Technical Field

[0001] The present invention relates to an intelligent sensor, and more specifically to the field of medical infusion technology, and more specifically to an automatic follow-up medical infusion system, method, electronic device and non-transitory computer-readable storage medium. Background Art

[0002] Today, during hospital infusions, patients often need to carry an IV stand or cart to move around. Whether going to an exam, using the restroom, or taking a short break, they must manually push and pull the cart. Existing medical infusion devices primarily consist of IV stands with casters, requiring manual movement by the patient or a caregiver.

[0003] However, patients need to control the infusion cart with one hand, which limits their freedom of movement and can easily lead to the risk of falls or medical accidents. On the other hand, in the crowded and space-constrained environment of hospitals, manual carts are not only inconvenient but may also hinder the passage of others. Summary of the Invention

[0004] The present invention aims to solve the technical problems existing in the prior art and provides an automatic following medical infusion system and method, which can enable an infusion device to accurately and automatically follow a patient for infusion.

[0005] The technical solution of the present invention to solve the above technical problems is as follows: The present invention provides an automatic follow-up medical infusion system, the system comprising: A binding strength module, configured to obtain a dynamically updated binding strength between the patient and the infusion device based on a matching result between the RFID tag signal worn by the patient and the identity database; A tracking accuracy module, configured to obtain a tracking accuracy of the patient calibrated by binding strength based on the binding strength and distance data and speed data collected in real time by multiple sensors; a path planning module, configured to obtain an optimal path for the infusion device to move based on the tracking accuracy and the spatial topology data acquired by the environment perception module; a speed control module, configured to obtain a current speed of the infusion device under adaptive control according to the optimal path and the feedback value of the tracking accuracy; an obstacle avoidance decision module, configured to obtain a threat level of the obstacle based on the current speed and obstacle scanning data of the three-dimensional laser radar, and determine an obstacle avoidance decision value for the infusion device based on the threat level; The automatic following module is used to obtain the motion control amount of the infusion device based on the coordinated feedback of the optimal path, current speed and obstacle avoidance decision value of the infusion device.

[0006] Furthermore, the binding strength module is further configured to: Obtaining an initial binding coefficient for representing a calibration value of the initial connection strength between the patient and the infusion device; Construct a time decay term based on the time decay factor and the current moment; Get the periodic fluctuation factor used to simulate the periodic fluctuation of the binding signal; The initial binding coefficient is processed according to the integral value of the signal strength of the binding signal at a historical moment, the time decay term, and the periodic fluctuation factor to obtain the binding strength.

[0007] Furthermore, the tracking accuracy module is also used to: Configuring dynamic weights for different sensors to obtain sensor weights for each of the sensors; fusing the relative distances and relative velocities measured by the sensors according to the sensor weights of the sensors; The credibility of the fused data is adjusted according to the reliability index of each sensor, and the final accuracy calibration is performed in combination with the real-time binding strength to obtain the tracking accuracy.

[0008] Furthermore, the path planning module is also used to: According to the path curvature function and tracking accuracy of each path, the control overhead items of positioning and tracking accuracy are constructed; Calculating the square of the rate of change of the angle of the infusion device traveling along each of the paths; Calculating the unit energy consumption of the infusion device under the control of each of the paths; The control overhead item, the square of the angle change rate and the unit energy consumption are integrated and then differentiated to obtain the optimal path.

[0009] Furthermore, the speed control module is further configured to: Adjusting the tracking accuracy according to the speed response coefficient to obtain an adjusted tracking accuracy; calculating a time rate of change of the binding strength; Calculate the cumulative path cost based on the path impact factor and the optimal path at each historical moment; The current speed is determined according to the adjusted tracking accuracy, the time rate of change of the binding strength, and the accumulated path cost.

[0010] Furthermore, the obstacle avoidance decision module is also used to: Obtaining the threat level of each obstacle; Calculating the threat level of each obstacle based on the distance and azimuth between the infusion device and each obstacle; Determining the obstacle avoidance sensitivity of each obstacle according to the threat degree and threat level of each obstacle, and integrating the obstacle avoidance sensitivities of all obstacles in the path; The fused obstacle avoidance sensitivity is dynamically adjusted in combination with the current speed of the infusion device to obtain the obstacle avoidance decision value.

[0011] Furthermore, the automatic following module is also used for: Calculating the rate of change of the optimal path over time; The motion control amount is determined according to the rate of change of the optimal path over time, the current speed, and the obstacle avoidance decision value, combined with the control gain coefficients corresponding to the above parameters.

[0012] In addition, to achieve the above-mentioned purpose, the present invention also provides an automatic follow-up medical infusion method, which comprises: According to the matching result of the RFID tag signal worn by the patient and the identity database, a dynamically updated binding strength between the patient and the infusion device is obtained; Obtaining a tracking accuracy of the patient calibrated by the binding strength according to the binding strength and the distance data and speed data collected in real time by multiple sensors; Obtaining an optimal movement path for the infusion device based on the tracking accuracy and the spatial topology data acquired by the environment perception module; Obtaining a current speed of the infusion device under adaptive control according to the optimal path and the feedback value of the tracking accuracy; Obtaining a threat level of the obstacle based on the current speed and obstacle scanning data from a three-dimensional laser radar, and determining an obstacle avoidance decision value for the infusion device based on the threat level; The motion control amount of the infusion device is obtained according to the coordinated feedback of the optimal path, current speed and obstacle avoidance decision value of the infusion device.

[0013] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby realizing an automatic follow-up medical infusion method as described above.

[0014] In addition, to achieve the above-mentioned purpose, the present invention also proposes a non-transitory computer-readable storage medium, in which a computer software program is stored. When the computer software program is executed by a processor, it implements an automatic follow-up medical infusion method as described above.

[0015] The beneficial effects of the present invention are: (1) Traditional infusion methods often require patients to be restricted to a static infusion stand. However, this solution uses an automatic following system to achieve dynamic movement of the infusion equipment, allowing patients to walk freely, check up and down, go to the toilet, and other daily activities during the infusion process, greatly improving the hospitalization experience.

[0016] (2) By integrating multiple sensors such as ultrasound, infrared, and vision, the system has high-precision target recognition and tracking capabilities. The binding strength B(t) is introduced into the mathematical model to dynamically evaluate the stability of the patient-device association and ensure that the system continues to follow without deviation.

[0017] (3) The path curvature, smoothness, and energy consumption are jointly optimized through the optimal path L(θ), so that the system can achieve stable operation in complex environments such as hospital corridors, elevators, corners, and wards, and the path adjustment is smooth and does not disturb others. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A scene diagram of an automatic follow-up medical infusion method provided by the present invention; Figure 2 A schematic structural diagram of an automatic follow-up medical infusion system provided by the present invention; Figure 3 A flow chart of an automatic follow-up medical infusion method provided by the present invention; Figure 4 A schematic diagram of the hardware structure of a possible electronic device provided by the present invention; Figure 5 A schematic diagram of the hardware structure of a possible computer-readable storage medium provided by the present invention. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0020] See also Figure 1 , Figure 1 The intelligent sensor of the present invention provides a scene diagram of an automatic follow-up medical infusion method. Figure 1 As shown, the terminal and server are connected via a network, such as a wired or wireless network. Terminals include, but are not limited to, portable devices such as mobile phones and tablets installed with various network platform applications, as well as fixed devices such as computers, kiosks, and advertising machines. The server provides various business services to users, including service push servers and user recommendation servers.

[0021] It should be noted that Figure 1The scenario diagram of an automatic follow-up medical infusion method shown is only an example. The terminal, server, and application scenario described in the embodiment of the present invention are intended to more clearly illustrate the technical solution of the embodiment of the present invention, and do not generate any limitation on the technical solution provided by the embodiment of the present invention. Ordinary technicians in this field can know that with the evolution of the system and the emergence of new business scenarios, the technical solution provided by the embodiment of the present invention is also applicable to similar technical problems.

[0022] Among them, the terminal can be used to: According to the matching result of the RFID tag signal worn by the patient and the identity database, a dynamically updated binding strength between the patient and the infusion device is obtained; Obtaining a tracking accuracy of the patient calibrated by the binding strength according to the binding strength and the distance data and speed data collected in real time by multiple sensors; Obtaining an optimal movement path for the infusion device based on the tracking accuracy and the spatial topology data acquired by the environment perception module; Obtaining a current speed of the infusion device under adaptive control according to the optimal path and the feedback value of the tracking accuracy; Obtaining a threat level of the obstacle based on the current speed and obstacle scanning data from a three-dimensional laser radar, and determining an obstacle avoidance decision value for the infusion device based on the threat level; The motion control amount of the infusion device is obtained according to the coordinated feedback of the optimal path, current speed and obstacle avoidance decision value of the infusion device.

[0023] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby realizing an automatic follow-up medical infusion method as described above.

[0024] See also Figure 2 , Figure 2 This is a structural schematic diagram of an automatic follow-up medical infusion system provided by the present invention.

[0025] like Figure 2 As shown, an automatic follow-up medical infusion system proposed in an embodiment of the present invention includes: The binding strength module 201 is used to obtain a dynamically updated binding strength between the patient and the infusion device based on a matching result between the RFID tag signal worn by the patient and the identity database; A tracking accuracy module 202 is configured to obtain a tracking accuracy of the patient calibrated by binding strength based on the binding strength and the distance data and speed data collected in real time by multiple sensors; A path planning module 203 is configured to obtain an optimal path for the infusion device to move based on the tracking accuracy and the spatial topology data acquired by the environment perception module; A speed control module 204 is configured to obtain a current speed of the infusion device under adaptive control according to the optimal path and the feedback value of the tracking accuracy; an obstacle avoidance decision module 205 for obtaining a threat level of the obstacle based on the current speed and obstacle scanning data of the three-dimensional laser radar, and determining an obstacle avoidance decision value for the infusion device based on the threat level; The automatic following module 206 is configured to obtain a motion control value of the infusion device based on the coordinated feedback of the optimal path, current speed, and obstacle avoidance decision value of the infusion device.

[0026] In some embodiments, the binding strength module 201 may also be used to: Obtaining an initial binding coefficient for representing a calibration value of the initial connection strength between the patient and the infusion device; Construct a time decay term based on the time decay factor and the current moment; Get the periodic fluctuation factor used to simulate the periodic fluctuation of the binding signal; The initial binding coefficient is processed according to the integral value of the signal strength of the binding signal at a historical moment, the time decay term, and the periodic fluctuation factor to obtain the binding strength.

[0027] The binding strength can be expressed as: , in, is the binding strength, is the initial binding coefficient, is the time decay factor, is the periodic fluctuation coefficient, is the binding frequency, is the signal strength function, It is the present moment.

[0028] In the specific implementation, is the initial binding coefficient, a calibration value for the initial connection strength between the system and the patient. The larger the value, the stronger the initial state. is the time decay term, as the binding decays over time (e.g., the patient leaves or the signal is lost). The larger the value, the faster the decay. is the periodic fluctuation factor, which simulates the periodic fluctuation of the binding signal (such as antenna interference caused by patient movement). is the cumulative signal strength, which indicates the total signal energy / intensity accumulated from time 0 to the current time.

[0029] when When the signal is constant (such as a stable signal), the integral term becomes , which is convenient for numerical calculation.

[0030] The following numerical examples are given, assuming that: 2, dimensionless; =0.1, the unit is ; =0.3, dimensionless; =π / 5, the unit is rad / s; 1.5, constant; t=10, unit: s.

[0031] The calculation process is as follows: ≈0.3679; 1 0.3 1=0.7; 1.5 10=15; 2.0 0.3679 0.7 15=2.0 0.25753 15≈2.0 3.8629≈7.7258.

[0032] In the present invention, if If the signal is a time-varying function (such as a fluctuating signal), the integral term will become more complicated and needs to be processed by numerical integration or symbolic integration. =0 will directly result in B(t)=0 (indicating binding failure).

[0033] In some embodiments, the tracking accuracy module 202 may also be used to: Configuring dynamic weights for different sensors to obtain sensor weights for each of the sensors; fusing the relative distances and relative velocities measured by the sensors according to the sensor weights of the sensors; The credibility of the fused data is adjusted according to the reliability index of each sensor, and the final accuracy calibration is performed in combination with the real-time binding strength to obtain the tracking accuracy.

[0034] Among them, the tracking accuracy can be expressed as: , in, is the tracking accuracy, is the sensor weight of the ith sensor, is the distance influence factor of the i-th sensor, is the normalized velocity influence factor of the ith sensor, is the reliability index of the i-th sensor, is the normalized relative distance, is the normalized relative velocity, is the binding strength.

[0035] In the specific implementation, It is the tracking accuracy. The larger it is, the higher the "effort" of the system is, which means that the current positioning difficulty or deviation is large. is the weight of the i-th sensor, which indicates the influence of the sensor on the tracking accuracy. It is the normalized distance influence factor. The larger it is, the more sensitive the system is to distance error. It is the speed influencing factor. The larger it is, the more sensitive the system is to speed error. It is the sensor reliability index, which indicates the confidence level of the sensor output result and exponentially amplifies or suppresses the error term. is the normalized relative distance, defined as: ,in The maximum distance measurement range is as follows: for example, the actual measured relative distance between the target (patient) and the system (infusion device) is 0.5m. is the normalized relative velocity, defined as: ,in The maximum measurable relative speed, for example, the target (patient) moves 0.2 m / s relative to the system (infusion device) per second. is the binding strength, which comes from the previous step and represents the credibility of the tracking basis. The larger the value, the more stable the system.

[0036] Now assume: Maximum distance , maximum speed , Currently measured: , It turns out that: , Assume that the parameter values ​​of the first sensor are as follows: 、 、 、 , Binding strength ; The first sensor pair The contributions are: , It can be understood that if there are multiple sensors, the data of other sensor items can be added together to obtain the data under multiple sensors. The value is sufficient and will not be repeated here.

[0037] In the example of the present invention, due to the binding strength is relatively high, and the denominator is large, so the system considers the current target tracking to be “credible” and does not require much correction intervention. decrease), the same deviation will result in a higher , that is, it is considered that "tracking error is amplified", triggering more aggressive control behaviors (such as accelerating approach, path correction, etc.).

[0038] In some embodiments, the path planning module is further configured to: According to the path curvature function and tracking accuracy of each path, the control overhead items of positioning and tracking accuracy are constructed; Calculating the square of the rate of change of the angle of the infusion device traveling along each of the paths; Calculating the unit energy consumption of the infusion device under the control of each of the paths; The control overhead item, the square of the angle change rate and the unit energy consumption are integrated and then differentiated to obtain the optimal path.

[0039] Among them, the optimal path can be expressed as: , in, is the optimal path, is the normalized path curvature, is the smoothness weight coefficient, is the energy consumption weight coefficient, is the normalized energy consumption, It is the planning cycle, is the normalized tracking accuracy.

[0040] In the specific implementation, It is the optimal path and the goal of path planning optimization. The smaller the value, the better the path. It is the normalized tracking accuracy, which indicates the current accuracy of target tracking. The greater the uncertainty, the larger this item is. The normalized path curvature is calculated through the path curvature function. The more tortuous the path, the greater the curvature, which may affect feasibility and stability. It is the square of the rate of change of the angle of the infusion device on each path, and is weighted for smoothness to avoid sharp turns in the path, enhancing patient experience and control stability. It is the normalized unit energy consumption under path control, including wheel control energy consumption, acceleration loss, etc. is the planning period, which is generally the local path planning update period (e.g., 2–5 seconds).

[0041] It is the control cost item of positioning and tracking accuracy. When the target motion is uncertain ( The path needs to avoid high curvature areas to reduce the loss probability. It is the path smoothness, reducing sharp turns and improving the stability of infusion equipment. Used to minimize energy consumption, reduce operating costs within the hospital, and improve battery life.

[0042] It can be understood that the path planning of the present invention is essentially to find a function , making This type of problem can be solved using variational methods or shortest path optimization algorithms (such as dynamic programming, reinforcement learning, etc.).

[0043] Assuming that the system is planned with an approximately constant curvature path (such as a constant speed turn), the integral function can be simplified: ; ; ; ; ; Then the integrand in the integral is: ; Assume T = 10s, then: It means that under the current tracking accuracy, path curvature, velocity smoothness and energy consumption weight conditions, the total cost of the path is 8.55.

[0044] In summary, in the present invention, if a larger curvature (sharp turn) is used, that is, If the value increases, the cost of item 1 will increase, which is not conducive to accurate tracking. If you want to reduce energy consumption (slow down and go straight), you can control Reduce and optimize the overall path. Parameters μ and γ can be configured according to the hospital's actual preferences, such as emphasizing stability and energy saving.

[0045] In some embodiments, the speed control module 204 may also be configured to: Adjusting the tracking accuracy according to the speed response coefficient to obtain an adjusted tracking accuracy; calculating a time rate of change of the binding strength; Calculate the cumulative path cost based on the path impact factor and the optimal path at each historical moment; The current speed is determined according to the adjusted tracking accuracy, the time rate of change of the binding strength, and the accumulated path cost.

[0046] Among them, the current speed of the infusion device can be expressed as: , in, is the current speed, is the velocity response coefficient, is the path impact factor, is the normalized tracking accuracy, is the normalized mean value of path goodness, , or can be written as , is the normalized cumulative path cost, is the normalized binding strength change rate, i.e., the time rate of change of the binding strength, ,in is the maximum rate of change.

[0047] In the specific implementation, this formula is the core function in the control system for real-time adjustment of following speed, which combines the dynamic information of binding strength change, target tracking accuracy and path planning.

[0048] This value reflects the system's immediate speed response based on the current tracking accuracy and the patient's binding status. When binding is enhanced and tracking accuracy is high, the system tends to increase speed to closely follow the patient. Conversely, if binding deteriorates or tracking becomes unreliable, this value approaches 0 or a negative value, reducing speed to ensure safety and accuracy.

[0049] Represents the system's consideration of the overall feasibility and quality of the path: when the path score is high (i.e., the path is smooth, safe, and has low energy consumption), the system can choose to increase the speed; if the path is complex, has large curvature, or has high energy consumption, the speed will automatically slow down to ensure smooth travel and energy efficiency and safety.

[0050] Now assume that the values ​​of the parameters are as follows: ; ; ; ; but: .

[0051] The current speed is 1.44m / s, which is a typical indoor walking speed (suitable for hospital environment). When the path cost is too high (close to 0), the speed naturally decreases → improving the robustness of the system. All parameters can be adjusted to adapt to the needs of different departments, patient populations or hospital environments.

[0052] In summary, the speed control of the present invention is not simply based on proportional adjustment of target distance or current position. Instead, it is controlled through two main lines: dynamic binding change and path cost accumulation. This achieves rapid response to binding status (such as device start-up, re-capture after loss, etc.), a smooth running path (no unnecessary rapid movement due to twists and turns or complex environments), and combined with multi-sensor precision to ensure tracking reliability.

[0053] In some embodiments, the obstacle avoidance decision module 205 may also be used to: Get the distance normalized scale and the azimuth normalized scale; constructing a distance impact factor term based on the distance normalization scale, the distance between the infusion device and each obstacle, and a reference distance; constructing an angle influence factor term according to the normalized azimuth scale, the azimuth of each obstacle, and the azimuth of the infusion device; The obstacle avoidance decision value is determined according to the distance influence factor item and the angle influence factor item.

[0054] Among them, the obstacle avoidance decision value can be expressed as: , , in, is the obstacle avoidance decision value, is the distance between the infusion device and the jth obstacle, r is the reference distance between the infusion device and the reference point (such as the origin), is the current azimuth, is the azimuth of the j-th obstacle, is the distance normalization scale, is the azimuth normalized scale, is the current speed, is the normalized threat level of the j-th obstacle, and m is the total number of obstacles.

[0055] In the specific implementation, is the distance impact factor item, which represents the relative radial distance error between the current device and the obstacle. This is the angle impact factor, which indicates the relative angle deviation between the device and the obstacle. A larger value indicates that the obstacle is not directly in front of the device or in a non-primary direction.

[0056] is a two-dimensional Gaussian function centered at the obstacle ( , ), construct an attenuation field: The closer the distance or angle is to the center of the obstacle, the larger the value, indicating "more need to avoid"; The distance or angle deviates from the center of the obstacle, and the value decays rapidly, indicating "no need to avoid excessively".

[0057] Now assume that: , two obstacles; ; Obstacle 1: , , , , ; Obstacle 2: , , , also normalized; Assumptions ,so ; Current speed , calculate: For obstacle 1: ; ; For obstacle 2: ; ; merge: .

[0058] The current obstacle avoidance decision value is 1.506. A larger value indicates stronger interference from the obstacle ahead. The system should immediately: reduce speed or make an emergency stop, trigger path replanning, or issue a safety warning (such as a voice or light prompt). This allows for precise control of the impact of each obstacle and reflects the actual risk differences of different obstacle types (people, walls, and moving objects).

[0059] In some embodiments, the automatic following module 206 may also be used to: Calculating the rate of change of the optimal path over time; The motion control amount is determined according to the rate of change of the optimal path over time, the current speed, and the obstacle avoidance decision value, combined with the control gain coefficients corresponding to the above parameters.

[0060] Among them, the motion control quantity can be expressed as: , in, is the motion control quantity, is the normalized current speed, is the normalized obstacle avoidance decision value, is the normalized rate of change of the optimal path over time, They are the first control gain coefficient, the second control gain coefficient and the third control gain coefficient respectively.

[0061] In the specific implementation, It is the motion control quantity, which is ultimately output as a comprehensive control instruction to the drive motor (wheel set) to control the motion behavior of the infusion system. is the normalized current velocity, the current desired velocity (reflecting the following intention) obtained based on the binding strength and path integral. It is the normalized obstacle avoidance decision value, which represents the risk level of the obstacle ahead. The larger the value, the more dangerous it is. It is the normalized rate of change of the optimal path over time, which represents the rate of change of path complexity. The higher the dynamics, the faster the response is required. To determine the degree of influence of each subsystem on the final motion control, experimental parameter adjustment and optimization are required.

[0062] Assume the following values: m / s, ; , ; , ; Normalized results: ; ; ; final: , Assumptions If they are 0.5, 0.3 and 0.2 respectively, then:

[0063] In summary, this invention uses this formula to weightedly integrate three key factors: speed, obstacle avoidance, and path change. This creates a unified, adjustable, and dimensionally consistent control output, effectively improving the system's dynamic responsiveness and stability in complex environments. This design achieves an optimal balance between tracking accuracy, safe obstacle avoidance, and smooth travel for the intelligent infusion system, ensuring safety, comfort, and system adaptability during patient movement. It serves as the core control foundation for implementing the automatic following function.

[0064] See also Figure 3 , provides a flow chart of an automatic follow-up medical infusion method of the present invention, comprising the following steps: Step 301: Obtain a dynamically updated binding strength between the patient and the infusion device based on a matching result between the RFID tag signal worn by the patient and the identity database; Step 302: Obtaining a tracking accuracy of the patient calibrated by the binding strength based on the binding strength and the distance data and speed data collected in real time by multiple sensors; Step 303: Obtaining an optimal movement path of the infusion device based on the tracking accuracy and the spatial topology data acquired by the environment perception module; Step 304: Obtaining a current speed of the infusion device under adaptive control according to the optimal path and the feedback value of the tracking accuracy; Step 305: Obtain a threat level of the obstacle based on the current speed and obstacle scanning data from the three-dimensional laser radar, and determine an obstacle avoidance decision value for the infusion device based on the threat level. Step 306: Obtain the motion control amount of the infusion device based on the coordinated feedback of the optimal path, current speed, and obstacle avoidance decision value of the infusion device.

[0065] It should be noted that, for the specific embodiments and beneficial effects of the above steps 301-305, please refer to the above description of modules 201-205, which will not be repeated here.

[0066] See also Figure 4 , Figure 4 Schematic diagram of an embodiment of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, an embodiment of the present invention provides an electronic device 400, including a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420. When the processor 420 executes the computer program 411, the following steps are implemented: According to the matching result of the RFID tag signal worn by the patient and the identity database, a dynamically updated binding strength between the patient and the infusion device is obtained; Obtaining a tracking accuracy of the patient calibrated by the binding strength according to the binding strength and the distance data and speed data collected in real time by multiple sensors; Obtaining an optimal movement path for the infusion device based on the tracking accuracy and the spatial topology data acquired by the environment perception module; Obtaining a current speed of the infusion device under adaptive control according to the optimal path and the feedback value of the tracking accuracy; Obtaining a threat level of the obstacle based on the current speed and obstacle scanning data from a three-dimensional laser radar, and determining an obstacle avoidance decision value for the infusion device based on the threat level; The motion control amount of the infusion device is obtained according to the coordinated feedback of the optimal path, current speed and obstacle avoidance decision value of the infusion device.

[0067] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby realizing an automatic follow-up medical infusion method as described above.

[0068] See also Figure 5 , Figure 5 Schematic diagram of an embodiment of a computer-readable storage medium provided by an embodiment of the present invention. Figure 5 As shown, this embodiment provides a computer-readable storage medium 500 on which a computer program 411 is stored. When the computer program 411 is executed by a processor, the following steps are implemented: According to the matching result of the RFID tag signal worn by the patient and the identity database, a dynamically updated binding strength between the patient and the infusion device is obtained; Obtaining a tracking accuracy of the patient calibrated by the binding strength according to the binding strength and the distance data and speed data collected in real time by multiple sensors; Obtaining an optimal movement path for the infusion device based on the tracking accuracy and the spatial topology data acquired by the environment perception module; Obtaining a current speed of the infusion device under adaptive control according to the optimal path and the feedback value of the tracking accuracy; Obtaining a threat level of the obstacle based on the current speed and obstacle scanning data from a three-dimensional laser radar, and determining an obstacle avoidance decision value for the infusion device based on the threat level; The motion control amount of the infusion device is obtained according to the coordinated feedback of the optimal path, current speed and obstacle avoidance decision value of the infusion device.

[0069] In addition, to achieve the above-mentioned purpose, the present invention also proposes an electronic device, comprising: a memory for storing a computer software program; a processor for reading and executing the computer software program, thereby realizing an automatic follow-up medical infusion method as described above.

[0070] It should be noted that, in the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0071] Those skilled in the art will appreciate that embodiments of the present invention may be provided as systems, methods, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0072] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes 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 a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0073] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system that is implemented in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0074] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0075] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0076] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An automatic follow-up medical infusion system, characterized in that: The system comprises: A binding strength module, configured to obtain a dynamically updated binding strength between the patient and the infusion device based on a matching result between the RFID tag signal worn by the patient and the identity database; A tracking accuracy module, configured to obtain a tracking accuracy of the patient calibrated by binding strength based on the binding strength and distance data and speed data collected in real time by multiple sensors; a path planning module, configured to obtain an optimal path for the infusion device to move based on the tracking accuracy and the spatial topology data acquired by the environment perception module; a speed control module, configured to obtain a current speed of the infusion device under adaptive control according to the optimal path and the feedback value of the tracking accuracy; an obstacle avoidance decision module, configured to obtain a threat level of the obstacle based on the current speed and obstacle scanning data of the three-dimensional laser radar, and determine an obstacle avoidance decision value for the infusion device based on the threat level; The automatic following module is used to obtain the motion control amount of the infusion device based on the coordinated feedback of the optimal path, current speed and obstacle avoidance decision value of the infusion device.

2. The automatic follow-up medical infusion system according to claim 1, characterized in that: The binding strength module is further configured to: Obtaining an initial binding coefficient for representing a calibration value of the initial connection strength between the patient and the infusion device; Construct a time decay term based on the time decay factor and the current moment; Get the periodic fluctuation factor used to simulate the periodic fluctuation of the binding signal; The initial binding coefficient is processed according to the integral value of the signal strength of the binding signal at a historical moment, the time decay term, and the periodic fluctuation factor to obtain the binding strength.

3. The automatic follow-up medical infusion system according to claim 2, characterized in that: The tracking accuracy module is also used to: Configuring dynamic weights for different sensors to obtain sensor weights for each of the sensors; fusing the relative distances and relative velocities measured by the sensors according to the sensor weights of the sensors; The credibility of the fused data is adjusted according to the reliability index of each sensor, and the final accuracy calibration is performed in combination with the real-time binding strength to obtain the tracking accuracy.

4. The automatic follow-up medical infusion system according to claim 3, characterized in that: The path planning module is also used to: According to the path curvature function and tracking accuracy of each path, the control overhead items of positioning and tracking accuracy are constructed; Calculating the square of the rate of change of the angle of the infusion device traveling along each of the paths; Calculating the unit energy consumption of the infusion device under the control of each of the paths; The control overhead item, the square of the angle change rate and the unit energy consumption are integrated and then differentiated to obtain the optimal path.

5. The automatic follow-up medical infusion system according to claim 4, characterized in that: The speed control module is further configured to: Adjusting the tracking accuracy according to the speed response coefficient to obtain an adjusted tracking accuracy; calculating a time rate of change of the binding strength; Calculate the cumulative path cost based on the path impact factor and the optimal path at each historical moment; The current speed is determined according to the adjusted tracking accuracy, the time rate of change of the binding strength, and the accumulated path cost.

6. The automatic follow-up medical infusion system according to claim 5, characterized in that: The obstacle avoidance decision module is also used to: Get the distance normalized scale and the azimuth normalized scale; constructing a distance impact factor term based on the distance normalization scale, the distance between the infusion device and each obstacle, and a reference distance; constructing an angle influence factor term according to the normalized azimuth scale, the azimuth of each obstacle, and the azimuth of the infusion device; The obstacle avoidance decision value is determined according to the distance influence factor item and the angle influence factor item.

7. The automatic follow-up medical infusion system according to claim 6, characterized in that: The automatic following module is also used for: Calculating the rate of change of the optimal path over time; The motion control amount is determined according to the rate of change of the optimal path over time, the current speed, and the obstacle avoidance decision value, combined with the control gain coefficients corresponding to the above parameters.

8. An automatic follow-up medical infusion method, which implements the system according to claim 1, characterized in that: The method comprises: Obtaining a dynamically updated binding strength between the patient and the infusion device based on a match result between the RFID tag signal worn by the patient and the identity database; Obtaining a tracking accuracy of the patient calibrated by the binding strength according to the binding strength and the distance data and speed data collected in real time by multiple sensors; Obtaining an optimal movement path for the infusion device based on the tracking accuracy and the spatial topology data acquired by the environment perception module; Obtaining a current speed of the infusion device under adaptive control according to the optimal path and the feedback value of the tracking accuracy; Obtaining a threat level of the obstacle based on the current speed and obstacle scanning data from a three-dimensional laser radar, and determining an obstacle avoidance decision value for the infusion device based on the threat level; The motion control amount of the infusion device is obtained according to the coordinated feedback of the optimal path, current speed and obstacle avoidance decision value of the infusion device.

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