An autonomous medicine delivery and safety monitoring method and robot

Abstract

The embodiment of the present application provides a kind of autonomous medicine delivery and safety monitoring method and robot, comprising: in response to received medicine delivery instruction, preloading the historical complexion health index database of user;Control robot moves to the target user location of user, and carries out identity verification in target user location;When identity verification passes, the personalized medication regimen and historical complexion health index database of user are called;Collect the blood pressure, heart rate, body temperature data of user, to obtain the complexion health index of user on the day;The complexion health index on the day is compared with historical complexion health index database, and the complexion health degree of user is obtained;The complexion health degree, physiological data and drug contraindication information are fused, and the medication risk result is generated;Based on medication risk result, the personalized medication regimen is adjusted, and target medication guidance information is dynamically generated and output.The method improves the precision, interactive experience and safety of medicine delivery service.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to an autonomous drug delivery and safety monitoring method and robot within the field of data processing technology. Background Technology

[0002] In related technologies, elderly people and patients with chronic diseases, among other specific groups, often need to take multiple medications regularly and in specific quantities in nursing homes, hospitals, and community homes. Currently, the main methods of medication delivery and monitoring still heavily rely on manual labor, i.e., medical staff or family members manually sort, deliver, and supervise the administration of medications. This method has the following significant drawbacks: First, it consumes a large amount of manpower and makes it difficult to guarantee the timeliness of medication delivery; second, manual operation inevitably carries the risk of medication errors, omissions, or dosage mistakes, making it difficult to ensure medication safety; third, monitors cannot monitor the patient's key physiological indicators such as body temperature and blood pressure in real time, making it difficult to promptly identify medication contraindications caused by changes in physical condition. For example, administering incompatible medications when a patient has a fever or abnormal blood pressure poses a serious safety hazard.

[0003] To alleviate the burden on manual labor, some automated medicine delivery robots or smart pillboxes have emerged on the market. However, these devices have relatively limited functions, mostly only capable of storing medicines, providing timed reminders, and simple delivery, falling into a "passive delivery" model. They generally lack the ability to deeply interact with users, and most importantly, their delivery process is completely disconnected from the user's real-time health status. These devices cannot proactively detect the user's physiological state before medication, nor do they have the ability to link health data with drug information for analysis to determine medication safety. Summary of the Invention

[0004] The purpose of this invention is to provide an autonomous drug delivery and safety monitoring method and robot, and the specific technical solution adopted is as follows:

[0005] In a first aspect, embodiments of the present invention provide a method for autonomous drug delivery and safety monitoring, the method comprising:

[0006] In response to the received medication delivery instruction, the user's historical facial health index database is preloaded;

[0007] The robot is controlled via CAN bus to move to the target user location and authenticate the user at the target user location;

[0008] If the user's identity is verified, the user's personalized medication plan and the historical facial health index database are retrieved.

[0009] Collect the user's blood pressure, heart rate, and body temperature data to obtain the user's facial health index for the day;

[0010] Among the deviation data values ​​of the user's complexion health index for the previous N days, select the valid complexion deviation value data that meets the preset standard processing conditions; where N is an integer greater than 1;

[0011] If the number of days corresponding to the effective facial color deviation value data is less than the preset number of days, the average health reference value matched by the facial color feature reference database of healthy people is used as the benchmark, and the degree of deviation of the facial color health index of the day from the average health reference value is used to calculate the user's facial color health level, until the number of days corresponding to the effective facial color deviation value data reaches the preset number of days, and the user's facial color health level is calculated based on the effective facial color deviation value data and the preset facial color health judgment threshold.

[0012] By combining the facial health level with the physiological data, the user's health status can be obtained;

[0013] Based on the user's health status and corresponding drug contraindication information, a medication risk result is generated;

[0014] Based on the medication risk results, the personalized medication plan is adjusted, and target medication guidance information is dynamically generated and output.

[0015] Secondly, a robot for autonomous drug delivery and safety monitoring is provided. The robot includes: a mobile chassis module, a drug storage and configuration module, a face recognition module with identity authentication and facial feature extraction functions, a voice interaction module, a water dispensing module, a health monitoring module, a drug lifting platform, and a storage compartment door, all electrically connected to a central control module via corresponding interfaces to form a data interaction and control link; wherein:

[0016] The central control module is fixed inside the robot's waterproof cavity, and integrates a control circuit board and a circuit board protective shell inside. The control circuit board is fixed inside the circuit board protective shell.

[0017] The central control module, in response to a received medication delivery command, triggers the health monitoring module to preload the user's historical facial health index database; controls the mobile chassis module via the CAN bus to move the robot to the target user location, and activates the facial recognition module to authenticate the user at the target user location; if the user's authentication is successful, it retrieves the user's personalized medication plan and the historical facial health index database; uses the health monitoring module to collect the user's blood pressure, heart rate, and body temperature data to obtain the user's daily facial health index; selects valid facial deviation values ​​that meet preset standard processing conditions from the deviation data values ​​of the user's facial health index over the previous N days; where N is an integer greater than 1; if the number of days corresponding to the valid facial deviation value data is less than the preset number of days, the health reference mean value matched with the healthy population facial feature reference database is used as the benchmark. The user's facial health level is calculated based on the degree to which the daily facial health index deviates from the health reference average, until the number of days corresponding to the effective facial deviation value data reaches the preset number of days. Based on the effective facial deviation value data and the preset facial health judgment threshold, the user's facial health level is calculated. The facial health level and the physiological data are integrated to obtain the user's health status. Based on the user's health status and corresponding drug contraindication information, a medication risk result is generated. Based on the medication risk result, the personalized medication plan is adjusted, and target medication guidance information is dynamically generated. Based on the target medication guidance information, the water dispensing module is controlled to add warm water, and the drug lifting platform and the compartment door are activated to retrieve the drug from the drug storage and configuration module through the drug lifting platform and deliver it to the user's target user location. The voice interaction module is controlled to output the user's facial health level and target medication guidance information.

[0018] Thirdly, a computer program product is provided, comprising: computer program code, which, when run on a computer, causes the computer to perform the method described in the first aspect or any possible implementation thereof.

[0019] Fourthly, a computer-readable storage medium is provided that stores computer program code, which, when executed on a computer, causes the computer to perform the methods described in the first aspect or any possible implementation thereof.

[0020] The present invention has the following beneficial effects: The robot's central control module, in response to a received medication delivery command, can promptly preload the user's historical facial health index database and control the robot to move to the target user's location via the CAN bus, where it verifies the user's identity. If the user's identity verification is successful, it retrieves the user's personalized medication plan and historical facial health index database; simultaneously, it collects the user's blood pressure, heart rate, and body temperature data to obtain the user's daily facial health index; then, from the deviation data values ​​of the user's facial health index over the previous N days, it selects valid facial deviation values ​​that meet preset standard processing conditions; where N is an integer greater than 1; if the number of days corresponding to the valid facial deviation values ​​is less than a preset number of days, it uses the health reference mean matched by the healthy population facial feature reference database as a benchmark, and calculates the user's facial health level based on the degree to which the daily facial health index deviates from the health reference mean, until the number of days corresponding to the valid facial deviation values ​​reaches the preset number of days. Based on the valid facial deviation values ​​and the preset facial health judgment threshold, the user's facial health level can be accurately calculated. Finally, the user's facial health status and physiological data are combined to obtain the user's health status. Based on the user's health status and corresponding drug contraindication information, a medication risk result is generated. The personalized medication plan is adjusted in a timely manner based on the medication risk result, and target medication guidance information is dynamically generated and output. In this way, by intelligently comparing and analyzing the user's pre-medication physiological data with drug contraindication information in the central control module, a fundamental shift from "passive delivery" to "proactive safety monitoring" is achieved. This allows for automatic alarms and suspension of medication delivery when a medication risk is detected, greatly improving the accuracy, interactive experience, and safety of the medication delivery service. Attached Figure Description

[0021] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the composition structure of a robot for autonomous drug delivery and safety monitoring provided in an embodiment of the present invention;

[0023] Figure 2 This is a schematic diagram of another component structure of a robot for autonomous drug delivery and safety monitoring provided in an embodiment of the present invention;

[0024] Figure 3This is a schematic diagram of another component structure of a robot for autonomous drug delivery and safety monitoring provided in an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram illustrating the implementation process of a self-delivery and safety monitoring method provided in an embodiment of the present invention;

[0026] Figure 5 This is another structural diagram of a robot for autonomous drug delivery and safety monitoring provided in an embodiment of the present invention;

[0027] Figure 6 This is a schematic diagram of another component structure of a robot for autonomous drug delivery and safety monitoring provided in an embodiment of the present invention;

[0028] Figure 7 This is a schematic diagram of another component structure of a robot for autonomous drug delivery and safety monitoring provided in an embodiment of the present invention;

[0029] Figure 8 This is another structural diagram of a robot for autonomous drug delivery and safety monitoring provided in an embodiment of the present invention;

[0030] Figure 9 This is a schematic diagram of another component structure of a robot for autonomous drug delivery and safety monitoring provided in an embodiment of the present invention;

[0031] Figure 10 This is a schematic diagram of the structure of a computer block device provided in an embodiment of the present invention. Detailed Implementation

[0032] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a self-administered medication delivery and safety monitoring method proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments may be combined from any suitable form.

[0033] In the description of the embodiments of the present invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present invention, "multiple" means two or more.

[0034] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0036] Current technologies fail to fundamentally address the critical safety issue of "taking the correct medication in the wrong physical condition." Therefore, overcoming the limitations of existing technologies that only offer "passive delivery" and embedding an effective proactive medication safety decision-making mechanism into medication delivery robots has become a core technical challenge urgently needing resolution in this field.

[0037] This invention provides a robot for autonomous drug delivery and safety monitoring. The robot includes: a mobile chassis module, a drug storage and configuration module, a face recognition module 1 with identity authentication and facial feature extraction functions, a voice interaction module, a water dispensing module, a health monitoring module 2, a drug lifting platform 10, and a door. These components are electrically connected to a central control module via corresponding interfaces, forming a data interaction and control link. In a specific example, such as... Figure 1 As shown, the robot includes: a face recognition module 1, a health monitoring module 2, a constant temperature water storage module 3, a disposable water cup dispenser 4, a water cup ejection mechanism 5, a central control module 6, omnidirectional Mecanum wheels 7, an omnidirectional drive motor 8, a mobile chassis 9, a medicine lifting platform 10, a medicine cup 11, a water cup 12, an intelligent medicine dispensing module 13, a door opening module 14, and an intelligent display module 15; wherein:

[0038] The central control module 6 is fixed inside the robot's waterproof cavity, and integrates a control circuit board 27 and a circuit board protective shell 28 inside. The control circuit board 27 is fixed inside the circuit board protective shell 28.

[0039] The mobile chassis module is located at the bottom of the robot body, the medicine storage and configuration module and the water dispensing module are located in the middle of the robot body, the face recognition module and the voice interaction module are arranged vertically in the first area of ​​the upper front of the robot body, and the health monitoring module is located in the second area of ​​the upper front of the robot body; wherein, the first area and the second area are adjacent to each other.

[0040] The central control module, in response to a received medication delivery command, triggers the health monitoring module to preload the user's historical facial health index database; controls the mobile chassis module via the CAN bus to move the robot to the target user location, and activates the facial recognition module to authenticate the user at the target user location; if the user's authentication is successful, it retrieves the user's personalized medication plan and the historical facial health index database; uses the health monitoring module to collect the user's blood pressure, heart rate, and body temperature data to obtain the user's daily facial health index; and selects valid facial deviation values ​​that meet preset standard processing conditions from the deviation data values ​​of the user's facial health index over the previous N days; where N is greater than 1. The number of days corresponding to the effective facial color deviation data is less than the preset number of days. Based on the average health reference value matched by the facial color feature reference database for healthy individuals, the degree to which the user's facial color health index deviates from the average health reference value is used to calculate the user's facial color health level. This process continues until the number of days corresponding to the effective facial color deviation data reaches the preset number of days. Based on the effective facial color deviation data and the preset facial color health judgment threshold, the user's facial color health level is calculated. The facial color health level and the physiological data are integrated to obtain the user's health status. Based on the user's health status and corresponding drug contraindication information, a medication risk result is generated. Based on the medication risk result, the personalized medication plan is adjusted, and target medication guidance information is dynamically generated and output.

[0041] The mobile chassis module includes a chassis body, an omnidirectional drive motor 8, and omnidirectional Mecanum wheels 7. The chassis body is made of lightweight, high-strength materials and integrates a drive control system. The drive motor 8 drives the omnidirectional Mecanum wheels 7 via gear transmission, enabling the robot to move omnidirectionally within a plane, allowing it to maneuver flexibly and precisely in narrow and complex environments such as wards and corridors.

[0042] Water intake module, including: Figure 2 The disposable water cup dispensing module shown Figure 8 The cup ejection module shown includes a disposable cup ejection module comprising: a rotor 16, a gear ring 17, a disposable cup 18, a limiting device 19, and a drive wheel 20; and a cup ejection plate 40, a pneumatic push rod 41, and a push rod motor 42.

[0043] Drug storage and configuration module, including Figure 3 The lifting module of the platform shown Figure 5 The shown compartment door opening module, Figure 6 The intelligent drug dispensing module shown and Figure 7The dispensing execution module shown includes: a platform lifting module, comprising: a platform 21 and a lifting push rod 22; a door opening module, comprising: a door motor 23, a motor mounting housing 24, a crank rocker arm 25, and a door 26; an intelligent dispensing module, comprising: an intelligent dispensing mechanism 29, a medicine conveyor belt motor 30, a dispensing mechanism support frame 31, a pill guide slide plate 32, and a medicine conveyor belt 33; and a dispensing execution module, comprising: a medicine storage device 34, a turntable top cover 35, pills 36, a dispensing turntable 37, a pill outlet pipe 38, and a turntable motor 39. The turntable has a diameter of 100 mm and four quantitative grooves (0.5 ml / groove), suitable for tablets and capsules with diameters of 3-8 mm. The drug dispensing module motor 23 uses a 39BYJ-40 stepper motor with a speed of 10 rpm. It drives a gear cam assembly mechanism via a drive gear and a driven gear (module 2, number of teeth 20). The cam profile curve is a sine curve with a stroke of 10 mm. A rocker arm mechanism controls the opening and closing of the dispensing tube. The single dispensing dosage error is ≤ ±1 tablet. Figure 3 The rotary table and dispensing tube of the dispensing machine work together to quantitatively dispense medicines, which are then transported to the loading platform via a conveyor belt and a sliding plate. The dispensing machine consists of: a rotary motor, a chassis body, a central control module, a door push rod mechanism, and a loading platform push rod mechanism.

[0044] Among them, the central control module, such as Figure 9 As shown, it includes: 27-control circuit board, 28-circuit board protective shell.

[0045] The facial recognition module includes a camera, such as the OV5640 high-definition camera with 1080P resolution, 30 frames per second (fps), a 3.6mm focal length, and a 60° field of view. It is fixed to the upper front of the robot via a bracket at a height of 1100mm, with the lens angled 15° downwards to avoid facial image distortion in backlit conditions. The image processing unit is integrated into the STM32H743VIT6 main control chip of the central control module. The facial information database is stored on a 16GB SD card, supporting the storage and retrieval of facial feature values, user IDs, and bound medication plans for ≥1000 authorized users. Data transmission between the database and the camera is achieved via a USB 3.0 interface. The camera captures user facial images in real time. The image processing unit extracts 128-dimensional facial feature vectors using a feature classifier and compares them with pre-stored feature vectors in the database using Euclidean distance, with a comparison threshold of 0.6. When the minimum Euclidean distance is ≤0.6, the identity authentication is considered successful, and the authentication response time is ≤2s. When three consecutive comparisons fail, the touch screen is triggered to display a 6-digit numeric password input interface, and an alarm message is pushed to the administrator terminal via the 4G module.

[0046] The voice interaction module includes a 7-inch capacitive touchscreen with a resolution of 1024×600 and a touch response time of ≤0.3 seconds. It connects to the central control module via an SPI interface and is mounted below the camera at a 10° angle for easy operation while standing or sitting. The speakers are WS1608 full-range speakers (8 ohms impedance, 3 watts power), symmetrically mounted on both sides of the touchscreen. The volume is adjustable from 30-80 dB and supports playback of ≥50 preset voice prompts, including medication guidance, risk warnings, and operation prompts. Through the collaboration of the touchscreen and speakers, two-way communication between the user and the robot is achieved.

[0047] The water dispensing module includes a food-grade PP storage container with a 2L capacity. It features a built-in PT100 temperature sensor (measuring range 0-100℃, error ±0.5℃) and a PTC heater (500W). The temperature control unit uses a PID algorithm to adjust the heating power, maintaining a water temperature of 40-50℃ (suitable for warm water needed for medication). A disposable cup storage box, with a capacity of 50 cups, is installed above the storage container. It accommodates 200ml disposable paper cups. The automatic cup ejection mechanism is linked to a gear cam combination mechanism, driven by a rotary motor. The cup ejection stroke is 100mm, ejecting one cup at a time with a success rate ≥99%. The water outlet of the storage container is equipped with a solenoid valve, providing a flow rate of 100ml / s and a single dispensing volume of 200ml with an error of ±10ml. Through the coordinated operation of all components of the water dispensing module, it provides users with the warm water needed for medication.

[0048] The health monitoring module includes a contact-type body temperature sensor and an arm-worn electronic blood pressure monitor. The body temperature sensor is a DS18B20 model, with a measurement range of -55℃ to 125℃ and an error of ±0.1℃. It is integrated into the inside of the wristband of the arm-worn electronic blood pressure monitor, with a skin contact area of ​​≥2cm². The arm-worn electronic blood pressure monitor uses the oscillometric measurement principle, measuring systolic blood pressure of 60-200 mmHg and diastolic blood pressure of 40-140 mmHg, with an error of ≤±5mmHg and a sampling frequency of 10 Hz. It communicates with the central control module via Bluetooth 5.0, with a data upload delay of ≤1s. The wristband is made of elastic nylon material, adaptable to wrist circumferences of 140-220mm, ensuring synchronous and accurate acquisition of body temperature and blood pressure data. After the user puts on the arm-worn electronic blood pressure monitor, they trigger the start button on the wristband, and the module automatically starts the data acquisition process: the body temperature sensor continuously collects data for 3 seconds and then takes the average value; the blood pressure monitor is inflated to systolic pressure +30 mmHg and then slowly deflated; the built-in pressure sensor collects arterial pulsation signals, and the systolic and diastolic pressure data are obtained through algorithm processing. The data acquisition process takes ≤30 seconds. After the acquisition is completed, the data is uploaded to the central control module via Bluetooth.

[0049] The central control module uses an STM32H743VIT6 main control chip with a 480MHz clock speed, 1MB Flash, and 512KB RAM. It is fixed in a waterproof cavity at the upper rear of the robot and integrates a power interface, CAN bus interface, Bluetooth module, 4G module, and SD card slot, supporting local storage and cloud data synchronization. The medication safety judgment submodule is implemented using C language programming and has a built-in drug knowledge base storing temperature contraindication thresholds, blood pressure contraindication thresholds, and contraindication descriptions for 1000 common drugs. Furthermore, this submodule integrates a health status judgment rule library based on the fusion of facial features and physiological parameters, specifically including but not limited to the following typical risk scenarios:

[0050] When making decisions, the medication safety judgment submodule integrates facial features extracted by the face recognition module with physiological parameters uploaded by the health monitoring module in real time, and performs matching analysis with the aforementioned rule base and drug contraindication knowledge base. This allows it to automatically trigger alarms and suspend medication dispensing when potential risks are detected.

[0051] In some examples, a door push rod mechanism is also provided, using an electric push rod of model 0041507 with a rated thrust of 150N and a stroke of 300mm. It is fixed to the upper inner side of the retrieval compartment door via a bracket. The extension end of the push rod is hinged to the compartment door and connected to the central control module via a GPIO interface. When it is determined that there is "no risk of medication use" and the medication has fallen into the storage plate, the central control module sends an "open door" command. The electric push rod shortens, driving the retrieval compartment door 11 to retract and open inward. After the user takes the medication and clicks "Confirm Medication Retrieval," the push rod extends, causing the compartment door to reset and close, simultaneously triggering the electromagnetic lock to prevent accidental activation or medication spillage.

[0052] In a specific example, a platform lifting device is also included, comprising a platform push rod mechanism and a platform. A miniature electric push rod, model 0041507, is used, with a rated thrust of 150 Newtons (N) and a stroke of 600 mm, and is electrically connected to the central control module. When it is determined that there is "no risk of medication use" and the medicine and water cup are already placed on the platform, the central control module sends a "rise" command. The electric push rod extends, driving the platform to rise to the target height, and simultaneously triggering an electromagnetic lock to prevent accidental activation or spillage of medicine.

[0053] This invention provides an autonomous medication delivery and safety monitoring method applied to a robot. The robot's automated medication delivery and health monitoring process is coordinated by a central control module according to a preset program, with each module executing sequentially. The specific solution of this autonomous medication delivery and safety monitoring method is described below with reference to the accompanying drawings. Please refer to... Figure 4 The diagram illustrates a flowchart of an embodiment of the present invention for a method of autonomous drug delivery and safety monitoring, which can be implemented through the following steps:

[0054] 401, in response to the received medication delivery instruction, preloads the user's historical facial health index database.

[0055] Here, the robot's central control module receives a drug delivery instruction (including the target user's identity, the drug to be delivered, and contraindications) from the cloud or local system, and simultaneously triggers the data integration module to connect with the medical / pharmacy management system to preload the user's historical health data.

[0056] In some possible implementations, firstly, the key facial color regions of the target frontal facial image of the user are subjected to illumination calibration and pixel standardization processing to obtain a processed facial image. For example, a facial image acquired through a high-definition camera of a facial recognition module is used, eliminating interference from facial makeup, hereditary color casts, and facial occlusion. The acquisition process meets the requirements of standard light source and fixed shooting parameters. The image processing unit can also eliminate interference from hair, moles, and beards in facial color feature extraction, thereby obtaining the target frontal facial image. Next, key facial color regions are cropped from the processed facial image, and various facial color feature parameters are extracted. These various facial color feature parameters and the key facial color regions are then quantified into the daily facial color health index. Finally, the historical facial color health index database is updated based on the daily facial color health index.

[0057] Here, the database of a user's complexion health index and deviation value for the previous N days is stored according to user identity. It is automatically updated and retained with the latest N days of valid complexion health index and deviation value data. The average complexion deviation value for the previous N days is automatically calculated daily based on the latest valid sample set.

[0058] The face recognition module includes: a high-definition camera, an image processing unit, an authorized user facial feature database, and a light source normalization processing unit. The high-definition camera is used to capture a frontal color face image of the user. The image processing unit incorporates the LBPH algorithm for user authentication. The light source normalization processing unit performs illumination calibration and pixel standardization processing on key facial color regions of the face image. The working process of the face recognition module is as follows: first, identity verification is completed through the LBPH algorithm; then, the same frame of color face image is reused to extract key facial color regions composed of the forehead, left cheek, right cheek, nose, and chin, which are then processed by light source normalization. After eliminating light source interference, the unit extracts one or more facial color feature parameters from hue (H), brightness (L), color saturation (S), lightness (V), red value (R), green value (G), blue value (B), red-green axis (a), yellow-blue axis (b), red chromaticity component (Cr), blue chromaticity component (Cb), and brightness component (Y) through 68 key point positioning, automatic facial region segmentation, and color space conversion algorithms. These parameters are then quantified into the daily facial color health index. The daily facial color health index is uploaded to the central control module and simultaneously stored in the user's personal facial color health index database for the previous N days (i.e., the historical facial color health index database) to update the historical facial color health index database.

[0059] 402. Control the robot to move to the target user location via the CAN bus, and authenticate the user at the target user location.

[0060] Here, the central control module plans the optimal movement path and controls the mobile chassis module via the CAN bus to drive the robot to move precisely to the target user's location.

[0061] 403. If the user's identity verification is successful, retrieve the user's personalized medication plan and the historical facial health index database.

[0062] Here, the central control module activates the LBPH algorithm facial recognition module to complete user authentication. After successful authentication, it retrieves the personalized medication plan and the user's facial health index database for the previous N days (i.e., the historical facial health index database).

[0063] 404. Collect the user's blood pressure, heart rate, and body temperature data to obtain the user's daily complexion health index.

[0064] Here, the central control module activates the health monitoring module to collect and upload user blood pressure, heart rate, and body temperature data. The central control module also receives the facial health index for the day, extracted and quantified from the same facial image by the facial recognition module.

[0065] Here, the health monitoring module includes a contact-type body temperature sensor, an arm-worn electronic blood pressure monitor, and a heart rate measurement unit (integrated into the arm-worn electronic blood pressure monitor); its health data collection process includes the following steps:

[0066] The first step is for the central control module to send a start command, and the health monitoring module to initialize.

[0067] The second step involves a contact-type body temperature sensor measuring the user's body temperature. The collected body temperature has a clinically normal reference range of 36.0–37.2°C.

[0068] The third step involves the automatic inflation of the cuff of the integrated blood pressure measurement unit, which simultaneously acquires blood pressure and heart rate signals using the oscillometric method. The normal clinical reference range for blood pressure is 90–139 mmHg for systolic pressure and 60–89 mmHg for diastolic pressure, and the normal clinical reference range for heart rate is 60–100 beats / min.

[0069] The fourth step involves filtering and noise reduction of the collected raw body temperature, blood pressure, and heart rate signals, calculating the body temperature, blood pressure (systolic pressure + diastolic pressure), and heart rate values, and uploading them to the central control module.

[0070] In some possible implementations, the daily complexion health index can be calculated through the following process:

[0071] First, based on the user's age and gender information, the health reference mean and standard deviation of each color facial feature parameter are matched from a preset healthy population facial feature reference database.

[0072] Here, the preset healthy population facial color feature reference database is constructed by collecting facial color images of a large sample of healthy people and statistically analyzing the mean and standard deviation of each facial color feature parameter according to different age groups and genders. Based on the user's gender and age information, the corresponding healthy reference mean and standard deviation of each facial color feature parameter are matched from the preset healthy population facial color feature reference database. This database is constructed by collecting facial color images of a large sample of healthy people and statistically analyzing the mean and standard deviation of each facial color feature parameter according to age groups of 18–30 years, 31–50 years, and 51–70 years, and genders.

[0073] Secondly, for the currently acquired facial images, facial makeup data, genetic color deviation data, facial occlusion data, and abnormal lighting data are excluded based on various facial color feature parameters to obtain the effective data in the facial images.

[0074] Here, the validity of the facial images and facial color feature parameters collected in this study is verified. Invalid data with facial makeup, hereditary color deviation, facial occlusion, and abnormal lighting are excluded. Only the valid data are used for subsequent quantitative calculations.

[0075] Next, based on the measured values ​​of the facial feature parameters and the corresponding health reference mean and standard deviation, a Gaussian decay function is used to calculate the deviation score of each facial feature parameter.

[0076] Here, the measured values ​​x of the color feature parameters of each color face and their corresponding health reference mean are used. with standard deviation The deviation score S for each facial feature parameter is calculated using a Gaussian decay function. The calculation formula is as follows: .

[0077] Furthermore, within each facial color region of the face image, a health score for each facial color region is calculated using a weighted average based on the deviation score of the facial color feature parameters contained in each facial color region and a preset index weight.

[0078] Here, regional weights are assigned to the facial color areas of the forehead, left cheek, right cheek, nose tip, and jaw. The region weight The forehead region weight is either a preset value or obtained through optimization using machine learning algorithms. The weights are 0.15–0.30, with the left and right cheek areas each having a weight of 0.15–0.25, and the nose tip area having a weight of 0.15–0.25. The weights are 0.15 to 0.25, the weights for the lower jaw region are 0.10 to 0.20, and the sum of the weights for all regions is 1.

[0079] Finally, based on the health score of each facial color area and the weight of each facial color area, the user's daily facial color health index is calculated.

[0080] Here, within each facial color region, a weighted health score Hr is calculated based on the deviation score S of multiple facial color feature parameters contained in that region and their preset index weights w. The calculation formula is as follows: Where n is the number of facial feature parameters selected in the region, and the sum of the weights of each indicator is 1.

[0081] Based on the health score (Hr) of each facial color region and its region weight The overall facial health index for the day is calculated using a weighted average, and the formula is as follows: Where M is the number of facial regions divided. Calculate the facial color deviation value Δ for the day. 当 The calculation formula is: Δ 当 =H 当 -H 标 .

[0082] 405. Select valid complexion deviation data that meets the preset standard processing conditions from the deviation data values ​​of the user's complexion health index for the previous N days.

[0083] 406. If the number of days corresponding to the effective facial color deviation value data is less than the preset number of days, the user's facial color health level is calculated based on the average health reference value matched by the facial color feature reference database of healthy people, and the degree to which the facial color health index of the day deviates from the average health reference value, until the number of days corresponding to the effective facial color deviation value data reaches the preset number of days. Based on the effective facial color deviation value data and the preset facial color health judgment threshold, the user's facial color health level is calculated.

[0084] Here, the difference between the daily facial health index and the known standard facial health index is compared with the average value of the historical facial health index database, and a threshold is used to determine the user's facial health level.

[0085] The preset standard processing conditions include: valid pre-collection: collection is completed under standard light source and fixed shooting parameters; after validity verification, interference from makeup, obstruction, and abnormal lighting is eliminated, and hereditary color deviation correction is completed; there are ≥3 valid facial color areas, and compliant facial color feature parameters are successfully extracted.

[0086] The calculation process is compliant: it accurately matches the baseline values ​​from a reference database of healthy individuals of the same age and gender; and it strictly calculates the daily facial health index and facial deviation value Δ. 当 The entire calculation process is completed without missing parameters or logical errors.

[0087] Storage update specifications: Data is stored according to user identity and precisely bound to user ID; it complies with the database's automatic rolling update rules, providing valid data within the latest N days, with no expired or mismatched data.

[0088] If a user's valid facial color deviation data for the previous N days is less than 5 days (i.e., the preset number of days), the average health reference value matched by the facial color feature reference database for healthy individuals will be used as the benchmark. The degree to which the facial color health index deviates from this benchmark value on that day will be used to assist in the judgment until the valid facial color deviation data reaches 5 days. Then, the deviation difference comparison judgment mode will be switched to the above-mentioned mode (i.e., the degree of facial color health will be calculated by comparing the valid facial color deviation data with the preset facial color health judgment threshold). If the valid facial color deviation data is less than N days but not less than 5 days, the average facial color deviation value Δ of the previous N days will be used as the benchmark. 均 In the calculation formula, the sample size is taken as the actual number of valid data. Thus, by analyzing whether the valid facial color deviation data is less than 5 days, the degree of facial health can be calculated in a targeted manner, improving the accuracy of facial health assessment.

[0089] In some possible implementations, the user's age, gender, and health status are obtained; these factors are combined to update a preset facial health assessment threshold in real time. During application, the preset facial health assessment threshold is adjusted personalizedally based on the user's age, gender, and health status in real time, with an adjustment range of 5 to 15 points. For example, the preset facial health assessment threshold is increased accordingly as age increases or health status declines.

[0090] In some possible implementations, if the number of days corresponding to the effective complexion deviation data reaches the preset number of days, the degree of complexion health can be calculated through the following process:

[0091] The first step is to calculate the mean of the effective facial color deviation data to obtain the average facial color deviation value of the previous N days;

[0092] Here, the average value of the facial color deviation for the previous N days is obtained by averaging the effective facial color deviation data and the corresponding number of days, where N is generally an integer greater than 5.

[0093] The second step is to obtain the daily complexion health index based on the difference between the daily complexion health index and the preset standard complexion health index;

[0094] Here, the preset standard facial health index can be a custom facial health index, obtained by calculating the mean, variance, or mode of facial health indices from multiple healthy individuals. Subtracting the current day's facial health index from the preset standard facial health index yields the daily facial health deviation value.

[0095] The third step is to determine the deviation difference between the daily complexion deviation value and the average complexion deviation value of the previous N days.

[0096] Here, the deviation value is obtained by subtracting the daily facial color deviation value from the average facial color deviation value of the previous N days.

[0097] The fourth step is to calculate the degree of facial health based on the deviation difference and the preset facial health judgment threshold.

[0098] Here, if the deviation difference is less than the preset facial health judgment threshold, the facial health level is determined to be that the facial health index of the day has no significant change relative to the average deviation level of the individual's previous N days, and the facial condition is stable; if the deviation difference is greater than or equal to the preset facial health judgment threshold, the facial health level is determined to be that the facial health index of the day has a significant change relative to the average deviation level of the individual's previous N days, and the facial condition is abnormal.

[0099] In some possible implementations, valid facial complexion deviation values ​​from the previous N days are retrieved from a database of the user's facial complexion health index and deviation values, denoted as the sample set {Δ1, Δ2, Δ3}. i ……Δ N} Calculate the average value Δ of the facial color deviation over the previous N days. 均 The calculation formula is: .

[0100] Calculate the difference Δ between the daily facial color deviation value and the average facial color deviation value of the previous N days. 差 The calculation formula is: Δ 差 =|Δ 当 -Δ 均 |;

[0101] The preset threshold for judging facial health is K, with a value ranging from 5 to 15 points. The deviation difference Δ 差 The facial complexion is compared with a threshold K to determine the degree of health. The specific criteria are as follows: Healthy: Δ 差 <K indicates that the facial health index on the current day has no significant change relative to the average deviation level of the individual's previous N days, and the facial condition is stable;

[0102] Unhealthy: Δ 差 ≥K indicates that the facial health index on the current day shows a significant deviation from the average level of the individual's previous N days, indicating an abnormality in facial condition.

[0103] In steps one through four above, the preset facial health assessment threshold can be a custom value. In a specific example, this threshold is activated when a user has less than 5 days of valid facial deviation data from the previous N days; it automatically switches to the official assessment mode after 5 days. The facial health index reference mean from a database of healthy individuals of the same age and gender is used as the unique standard value H. 标 (i.e., a preset standard facial health index), simultaneously matching the standard deviation of this group. After validity verification, firstly, the facial health index H for the day is calculated. 当 Then, calculate the absolute deviation (i.e., the difference in deviation) Δ. 偏 =|H 当 -H 标 | Quantify the magnitude of deviation from the standard value;

[0104] Judged according to the preset facial health assessment threshold K (e.g., 5~15 points): Δ 偏 <K indicates a healthy complexion, Δ 偏 ≥K indicates poor health;

[0105] The judgment results are linked with physiological data and matching results of 6 typical risk scenarios, and the corresponding drug dispensing and alarm strategies are executed according to the three-level rules of safe / cautious / unsafe.

[0106] 407. The facial health status and the physiological data are integrated to obtain the user's health status, and a medication risk result is generated based on the user's health status and the corresponding drug contraindication information.

[0107] Here, the central control module compares and analyzes the daily facial color deviation value with the average value of the user's previous N days, and determines the corresponding facial health level based on the judgment threshold. Then, it calls the drug contraindication / risk rule library, integrates the facial health level, physiological data collected by the health monitoring module and drug contraindication information to generate medication risk results. When the medication safety judgment submodule performs medication risk matching, it also includes preset typical risk scenario judgment rules.

[0108] The user's health status includes: healthy or unhealthy. A preset threshold for judging facial health is K, with a value ranging from 5 to 15 points. The deviation difference Δ... 差 The facial complexion is compared with a threshold K to determine the degree of health. The criteria for classification are as follows:

[0109] Health: Δ 差 <K indicates that the facial health index on the current day has no significant change relative to the average deviation level of the individual's previous N days, and the facial condition is stable;

[0110] Unhealthy: Δ 差 ≥K indicates that the facial health index on the current day shows a significant deviation from the average level of the individual's previous N days, indicating an abnormality in facial condition.

[0111] In some possible implementations, the central control module acquires preset risk scenario determination rules; and based on the user's health status and corresponding drug contraindication information, generates an initial medication risk result; then, based on the preset risk scenario determination rules, adjusts the initial medication risk result to obtain the final medication risk result. Specifically, the risk scenario determination rules include:

[0112] If facial flushing is detected, the user's complexion is deemed to be abnormally fluctuating or unhealthy, and the body temperature is ≥37.5℃, then the user is considered to have a fever and will be subject to the contraindications for nonsteroidal anti-inflammatory drugs and glucocorticoids.

[0113] If facial swelling is detected, the user's complexion is deemed to be abnormally fluctuating or unhealthy, and blood pressure is ≥140 / 90 mmHg, then the user is diagnosed with hypertension accompanied by fluid retention, and the user is matched with contraindications for nonsteroidal anti-inflammatory drugs, sodium-containing drugs, and mineralocorticoids.

[0114] If a user is found to have pale complexion, abnormal fluctuations in complexion or an unhealthy complexion, and systolic blood pressure <100 mmHg and heart rate >100 beats / min, then the user is considered to be in a state of hypovolemia or pre-shock, and the user is matched with the contraindications for the use of potent diuretics and vasodilators.

[0115] If asymmetrical swelling and flushing of the user's face are detected, and the heart rate is abnormally fast, it is determined to be an allergic or acute inflammatory state, and the contraindications for angiotensin-converting enzyme inhibitors and iodine contrast agents are matched.

[0116] If facial features such as drooping eyelids and unfocused gaze are detected in the user, even if the physiological signs are within the normal range, it is determined to be an abnormal state of consciousness, and the contraindications for sedative-hypnotic drugs, first-generation antihistamines, and central analgesics are matched.

[0117] If a user's facial expression of pain is detected, and their blood pressure and heart rate are abnormally elevated, and the cause of the pain is unclear, then the contraindications for using potent opioid analgesics should be matched.

[0118] 408. Based on the medication risk results, adjust the personalized medication plan and dynamically generate and output target medication guidance information.

[0119] In some possible implementations, if the medication risk outcome indicates a risk, an alarm message is generated and output, and the dispensing of the personalized medication plan is suspended or prohibited; if the medication risk outcome indicates no risk, medication guidance information is generated based on the personalized medication plan, and the user's facial health status and medication guidance information are output on the display interface.

[0120] In some possible implementations, if the target medication guidance information is within a safe level, the robot is controlled to deliver the medication and display the patient's facial health status and medication method.

[0121] If the target medication guidance information is classified as cautious, the robot will output an alarm message and suspend medication dispensing, and display the user's daily facial health index, deviation difference, and physiological index data so that the user can confirm the alarm message.

[0122] If the target medication guidance information is classified as unsafe, the robot will output a continuous alarm message and prohibit medication dispensing. It will also reacquire the user's blood pressure, heart rate, and body temperature data to adjust the target medication guidance information.

[0123] Here, the medication risk results in the target medication guidance information are divided into at least three levels: safe, cautious, and unsafe. This classification is based on the patient's complexion, combined with physiological data collected by the health monitoring module and the results of typical risk scenario assessments. The classification criteria are as follows:

[0124] Safety level: The complexion is healthy, and the body temperature, blood pressure and heart rate are all within the normal clinical reference range. No scenario was matched in the typical risk scenario judgment rules.

[0125] Caution level: The complexion is healthy, but one of the indicators of body temperature, blood pressure, or heart rate exceeds the normal clinical reference range, and it does not match any of the typical risk scenario judgment rules;

[0126] Unsafe level: The complexion is unhealthy, or two or more of the following indicators, such as body temperature, blood pressure, and heart rate, exceed the normal clinical reference range, or the condition matches any of the typical risk scenario judgment rules.

[0127] The execution strategies for each level are as follows:

[0128] The execution strategy corresponding to the safety level is as follows: the drug storage and configuration module dispenses drugs, the water dispensing module adds warm water, the drug lifting module raises and lowers the platform, and the door opening module opens the door. At the same time, the health status of the face and medication guidance are displayed through the voice interaction module or the display module.

[0129] The execution strategy corresponding to the caution level is to trigger a light alarm via voice / light, pause medication dispensing for 1 to 3 minutes, and push a risk warning and user health data to the medical staff / family members. The user health data includes the facial health index, deviation value, and physiological indicator data for the day. It is recommended to confirm manually before administering medication.

[0130] The execution strategy corresponding to the unsafe level is to trigger a continuous audible and visual alarm, prohibit medication dispensing, and push an emergency risk warning and user health data to the medical staff / family members. The health data includes the comparison results of the facial color deviation value of the current day and the previous N days, and physiological indicator data. It is recommended to check the physical condition and physiological abnormalities before re-evaluating the medication plan.

[0131] The central control module controls the corresponding module to execute the following processes based on the risk results: if there is a risk, an alarm is triggered, medication dispensing is paused / prohibited, and information is pushed; if there is no risk, medication dispensing, warm water addition, lifting and lowering of the loading platform, and opening of the compartment door are controlled, while simultaneously displaying the patient's complexion health status and medication instructions.

[0132] In some possible implementations, the central control module is further configured to: generate medication guidance information based on the personalized medication plan if the risk outcome indicates no risk; control the water dispensing module to add warm water based on the medication guidance information; and activate the drug lifting platform and the storage door to dispense medication from the storage door through the drug lifting platform and deliver it to the user's target user location; and control the voice interaction module to output the user's facial health status and medication guidance information.

[0133] In this embodiment of the invention, the robot's central control module receives a delivery instruction from a cloud server or local scheduling system. This instruction includes the target user's identity information and a list of medications to be delivered. The central control module then plans the optimal movement path and controls the mobile chassis module to start. Drive motors activate the omnidirectional Mecanum wheels, enabling the robot to autonomously move from the charging station or waiting point to the target user's location.

[0134] Once the robot arrives in front of the user, the camera is activated to capture an image of the user's face. The image processing unit extracts facial feature vectors and compares them with pre-stored authorized user information in the database. If authentication is successful, the central control module retrieves the user's personalized medication plan and service preferences; if authentication fails, a password verification message is displayed on the touchscreen, and the number of failures is recorded. Consecutive failures trigger an alarm to the administrator.

[0135] Once identity verification is successful, the central control module prompts the user to wear the arm-worn electronic blood pressure monitor via voice interaction. The health monitoring module automatically starts, simultaneously collecting the user's body temperature and blood pressure data. The data collection process takes approximately 30 seconds, and upon completion, the data is uploaded to the central control module in real time via Bluetooth.

[0136] The medication safety judgment submodule in the central control module then activates, retrieving contraindication information for the medication to be delivered from the local drug knowledge base. This information is then combined with facial features extracted by the facial recognition module (such as complexion, swelling, and expression) and real-time user health data for multi-dimensional fusion analysis, ultimately generating a decision result of "medication risk exists" or "no medication risk." A specific example is as follows:

[0137] If the system detects significant facial flushing and a body temperature ≥37.5℃, it classifies the user as having a fever. In this case, nonsteroidal anti-inflammatory drugs (NSAIDs) or corticosteroids should be avoided. While NSAIDs (such as ibuprofen) can reduce fever, corticosteroids may mask the symptoms and delay treatment if an undiagnosed bacterial or viral infection exists. The system will trigger a medication risk warning, recommending that related medications be used only after ruling out infectious fever or confirmation by a doctor to avoid masking signs of bacterial or viral infection, delaying anti-infective treatment, or even causing the infection to spread.

[0138] If a user experiences eyelid or facial edema and blood pressure ≥140 / 90 mmHg, they are diagnosed with hypertension accompanied by fluid retention. In this case, nonsteroidal anti-inflammatory drugs (NSAIDs), sodium-containing medications (such as certain antibiotic powders for injection), and mineralocorticoids are contraindicated. These medications may worsen water and sodium retention, raise blood pressure, increase cardiac workload, and induce acute heart failure or renal deterioration.

[0139] If pale complexion, cold and clammy skin (based on image reflection and texture analysis), systolic blood pressure <100 mmHg, and heart rate are detected, the patient is diagnosed with hypovolemia or a pre-shock state. In this case, potent diuretics (such as furosemide), vasodilators (such as nitroglycerin), and some antidepressants should be avoided to prevent further lowering of blood pressure, which could lead to shock or insufficient perfusion of vital organs.

[0140] If a user experiences asymmetrical facial swelling (especially of the lips and eyelids) accompanied by flushing, possibly along with an increased heart rate, it is considered an allergic reaction or acute inflammatory condition. In this case, angiotensin-converting enzyme inhibitors (such as captopril and enalapril) and iodine contrast agents should be avoided to prevent the induction of severe angioedema or anaphylactic shock, which could be life-threatening.

[0141] If a user exhibits drowsy ptosis, unfocused gaze, or an unusually calm expression, even if physiological parameters are normal, a change in consciousness or significant fatigue should be suspected. In such cases, sedative-hypnotic drugs (such as diazepam), first-generation antihistamines (such as diphenhydramine), and centrally acting analgesics should be avoided to prevent excessive sedation, respiratory depression, or loss of consciousness, which could increase the risk of falls, suffocation, and other accidents.

[0142] If a user displays clear signs of pain on their face (frowning, squinting, tugging at the cheek, etc.), possibly accompanied by elevated blood pressure and heart rate, and the cause of the pain is unclear, strong analgesics (especially opioids) should be avoided. Such drugs may mask the characteristics of serious conditions such as myocardial infarction or acute abdomen, delaying crucial diagnosis and treatment.

[0143] Based on the decision-making results, if the system determines there is a medication risk, the central control module controls the voice interaction module to issue a clear voice warning (e.g., "Warning: Your current body temperature is 38.2℃, and your face is flushed. Ibuprofen is not recommended. Please consult a doctor."). Simultaneously, the drug storage and preparation module is locked, preventing medication dispensing. Risk information is immediately pushed to medical staff or family members' terminals via the 4G module. If medication safety is confirmed, the central control module sends a dispensing command to the drug storage and preparation module. The drug preparation module accurately dispenses pills according to the user's prescription and transports them to the delivery platform via a drug conveyor belt. Simultaneously, the water dispensing module is activated, an automatic cup ejection mechanism dispenses a disposable cup, and the solenoid valve of the water storage container opens, injecting approximately 200ml of warm water at 40-50℃.

[0144] Once the medication and water cup are ready, the door push mechanism activates, opening the retrieval compartment door. Simultaneously, the platform push mechanism raises the platform to a convenient height for the user to access the medication. The robot provides voice and screen prompts to the user to retrieve and take the medication. After the user retrieves the medication and clicks "Confirm Take" on the touchscreen, the door and platform automatically reset. The entire medication delivery service is complete, and the robot can return to its standby point or perform the next task. By intelligently comparing and analyzing the user's pre-medication physiological data with medication contraindications in the central control module, a fundamental shift from "passive delivery" to "proactive safety monitoring" is achieved. This allows the robot to automatically issue alarms and pause medication dispensing when a risk is detected, significantly improving the accuracy, user experience, and safety of the medication delivery service.

[0145] Optionally, the transmission medium can be a wired link (e.g., but not limited to, coaxial cable, optical fiber, and Digital Subscriber Line (DSL)) or a wireless link (e.g., but not limited to, Wireless Fidelity (WIFI), Bluetooth, and mobile block device networks). It should be noted that the control block device provided in the above embodiments is only an example illustrating the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer block device can be divided into different functional modules to complete all or part of the functions described above. Furthermore, the method embodiments provided in the above embodiments belong to the same concept, and their specific implementation processes are detailed in the method embodiments, and will not be repeated here.

[0146] Figure 10 This is a schematic diagram of the structure of a computer block device provided in an embodiment of the present invention. For example, as shown... Figure 10 As shown, the computer block device 1000 includes: a memory 1001, a processor 1002, and a computer program 1003 stored in the memory 1001 and running on the processor 1002, wherein when the processor 1002 executes the computer program 1003, the computer block device can execute any of the aforementioned autonomous drug delivery and safety monitoring methods.

[0147] Furthermore, embodiments of the present invention also protect a control block device, which may include a memory and a processor. The memory stores executable program code, and the processor is used to call and execute the executable program code to perform a self-administered medication delivery and safety monitoring method provided by the embodiments of the present invention. Embodiments of the present invention can divide the control block device into functional modules based on the above method examples. For example, each module may correspond to a specific function, or two or more functions may be integrated into a single processing module. The integrated module can be implemented in hardware. It should be noted that the module division in the embodiments of the present invention is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. It should be noted that all relevant content of each step involved in the above method embodiments can be referenced to the functional description of the corresponding functional module, and will not be repeated here. It should be understood that the control block device provided by the embodiments of the present invention is used to execute the above-mentioned self-administered medication delivery and safety monitoring method, and therefore can achieve the same effect as the above-mentioned implementation method. When using integrated units, the control block device may include a processing module and a storage module. When the control block device is applied to a block device, the processing module can be used to control and manage the actions of the block device. The storage module can be used to support block devices in executing mutual program code, etc. The processing module can be a processor or controller, which can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. The processor can also be a combination of functions that implement computing capabilities, such as a combination of one or more microprocessors, a combination of Digital Signal Processing (DSP) and microprocessors, etc., and the storage module can be a memory.

[0148] Furthermore, the control block device provided in the embodiments of the present invention may specifically be a chip, component, or module. The chip may include a connected processor and a memory; wherein, the memory is used to store instructions, and when the processor calls and executes the instructions, the chip can execute the autonomous drug delivery and safety monitoring method provided in the above embodiments. The embodiments of the present invention also provide a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the aforementioned method steps to implement the autonomous drug delivery and safety monitoring method provided in the above embodiments.

[0149] This invention also provides a computer program product. When the computer program product is run on a computer, it causes the computer to execute the aforementioned related steps to achieve the autonomous drug delivery and safety monitoring method provided in the above embodiments. The control block device, computer-readable storage medium, computer program product, or chip provided in this invention are all used to execute the corresponding methods provided above. Therefore, the beneficial effects they achieve can be referred to in the beneficial effects of the corresponding methods provided above, and will not be repeated here. Through the description of the above embodiments, those skilled in the art can understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the control block device can be divided into different functional modules to complete all or part of the functions described above. In the embodiments provided by this invention, it should be understood that the disclosed control block device and method can be implemented in other ways. For example, the control block device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another control block device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, control block device or unit, and can be electrical, mechanical or other forms.

[0150] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some embodiments, multiple task processing and parallel processing are possible or may be advantageous. The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. The above content is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for autonomous medication delivery and safety monitoring, characterized in that, The method is applied to a robot, and the method includes: In response to the received medication delivery instruction, the user's historical facial health index database is preloaded; The robot is controlled via CAN bus to move to the target user location and authenticate the user at the target user location; If the user's identity is verified, the user's personalized medication plan and the historical facial health index database are retrieved. Collect the user's blood pressure, heart rate, and body temperature data to obtain the user's facial health index for the day; Among the deviation data values ​​of the user's complexion health index for the previous N days, valid complexion deviation value data that meet the preset standard processing conditions are selected; where N is an integer greater than 1; the deviation data value of the complexion health index for the previous N days is the difference between the complexion health index of each day in the complexion health index for the previous N days and the preset standard complexion health index; the valid complexion deviation value data is the deviation value data in which the image to which the deviation data value of the complexion health index for the previous N days meets the valid conditions before acquisition, the calculation process of each data meets the compliance conditions of the calculation process, and the data storage method meets the storage update specification conditions; wherein, the valid conditions before acquisition include: under standard light source The image acquisition was completed under fixed shooting parameters. The image was validated to exclude interference from makeup, occlusion, and abnormal lighting. Hereditary color deviation was corrected, and at least three valid facial color areas were identified, with compliant facial color feature parameters successfully extracted. The calculation process met compliance conditions, including: matching the baseline values ​​of a reference database of healthy individuals of the same age and gender, and completing the full calculation of the facial color health index and facial color deviation value for the day, with no missing parameters or logical errors. The storage and update met compliance conditions, including: storing data according to user identity, binding it to user identifiers, complying with the database's automatic rolling update rules, and ensuring that the stored data is valid within the latest N days, with no expired or mismatched data. If the number of days corresponding to the effective facial color deviation value data is less than the preset number of days, the average health reference value matched by the facial color feature reference database of healthy people is used as the benchmark, and the degree of deviation of the facial color health index of the day from the average health reference value is used to calculate the user's facial color health level, until the number of days corresponding to the effective facial color deviation value data reaches the preset number of days, and the user's facial color health level is calculated based on the effective facial color deviation value data and the preset facial color health judgment threshold. By combining the facial health level with the user's physiological data, the user's health status can be obtained; Based on the user's health status and corresponding drug contraindication information, a medication risk result is generated; Based on the medication risk results, the personalized medication plan is adjusted, and target medication guidance information is dynamically generated and output.

2. The method according to claim 1, characterized in that, The process of adjusting the personalized medication plan based on the medication risk results, and dynamically generating and outputting target medication guidance information, includes: If the medication risk results indicate a risk, generate and output alarm information, and suspend or prohibit the dispensing of the personalized medication plan; If the medication risk outcome indicates no risk, medication guidance information is generated based on the personalized medication plan, and the user's facial health status and medication guidance information are output on the display interface.

3. The method according to claim 1, characterized in that, The process of collecting the user's blood pressure, heart rate, and body temperature data to obtain the user's daily facial health index includes: Based on the user's age and gender information, the health reference mean and standard deviation of each facial feature parameter are matched from a preset healthy population facial color feature reference database; wherein, the healthy population facial color feature reference database is constructed by collecting facial color images of a large sample of healthy people and statistically analyzing the mean and standard deviation of each facial color feature parameter according to different age groups and genders. For the currently acquired facial images, based on various facial color feature parameters, data on facial makeup, genetic color cast, facial occlusion, and abnormal lighting are excluded to obtain the effective data in the facial images; Based on the measured values ​​of each facial color feature parameter and the corresponding health reference mean and standard deviation, the deviation score of each facial color feature parameter is calculated using a Gaussian decay function. Within each facial color region of the face image, a health score for each facial color region is calculated by weighting the deviation score of the facial color feature parameters contained in each facial color region and the preset index weight. The user's daily facial health index is calculated based on the health score and weight of each facial area.

4. The method according to claim 1, characterized in that, The step of calculating the user's facial health level based on the effective facial color deviation value data and the preset facial color health judgment threshold includes: The mean of the effective facial color deviation values ​​is calculated to obtain the average facial color deviation values ​​for the previous N days; The daily complexion health index is calculated based on the difference between the daily complexion health index and the preset standard complexion health index. Determine the deviation difference between the daily complexion deviation value and the average complexion deviation value of the previous N days; The degree of facial health is calculated based on the deviation difference and the preset facial health judgment threshold.

5. The method according to claim 4, characterized in that, The method further includes: If the deviation difference is less than the preset facial health judgment threshold, the facial health level is determined to be that the facial health index of the day has no significant change relative to the average deviation level of the individual's previous N days, and the facial condition is stable. If the deviation difference is greater than or equal to the preset facial health judgment threshold, the facial health level is determined to be that the facial health index of the day shows a significant change relative to the average deviation level of the individual's previous N days, and the facial condition is abnormal.

6. The method according to claim 1, characterized in that, The process of generating a medication risk result based on the user's health status and corresponding drug contraindication information includes: Obtain the preset risk scenario judgment rules; Based on the user's health status and corresponding drug contraindication information, an initial medication risk result is generated; The initial medication risk result is adjusted based on the preset risk scenario judgment rules to obtain the medication risk result.

7. The method according to claim 1, characterized in that, The method further includes: Lighting calibration and pixel normalization are performed on the key facial color regions of the target frontal face image of the user to obtain the processed face image; The key facial color regions are cropped from the processed face image and various facial color feature parameters are extracted; The various facial feature parameters and the key facial regions are quantified into the daily facial health index; The historical complexion health index database is updated based on the complexion health index of the day.

8. The method according to claim 1, characterized in that, The method further includes: Obtain the user's age, gender, and health status; The preset facial health assessment threshold is updated based on the user's age, gender, and health status.

9. The method according to claim 4, characterized in that, The method further includes: If the target medication guidance information is within the safe level, control the robot to deliver the medication and display the patient's complexion health level and medication method; If the target medication guidance information is classified as cautious, the robot will output an alarm message and suspend medication dispensing, and display the user's daily facial health index, deviation difference, and physiological index data so that the user can confirm the alarm message. If the target medication guidance information is classified as unsafe, the robot will output a continuous alarm message and prohibit medication dispensing. It will also reacquire the user's blood pressure, heart rate, and body temperature data to adjust the target medication guidance information.

10. A robot for autonomous drug delivery and safety monitoring, characterized in that, The robot includes: a mobile chassis module, a drug storage and configuration module, a face recognition module with identity authentication and facial feature extraction functions, a voice interaction module, a water dispensing module, a health monitoring module, a drug lifting platform, and a storage compartment door, all of which are electrically connected to the central control module via corresponding interfaces to form a data interaction and control link; wherein: The central control module is fixed inside the robot's waterproof cavity, and integrates a control circuit board and a circuit board protective shell inside. The control circuit board is fixed inside the circuit board protective shell. The central control module, in response to a received medication delivery command, triggers the health monitoring module to preload the user's historical facial health index database; controls the mobile chassis module via the CAN bus to move the robot to the target user location, and activates the facial recognition module to authenticate the user at the target user location; if the user's authentication is successful, it retrieves the user's personalized medication plan and the historical facial health index database; uses the health monitoring module to collect the user's blood pressure, heart rate, and body temperature data to obtain the user's daily facial health index; and calculates the deviation of the user's facial health index from the previous N days. From the data, valid facial color deviation values ​​that meet preset standard processing conditions are selected; where N is an integer greater than 1; the deviation data value of the facial color health index for the previous N days is the difference between the facial color health index of each day in the facial color health index for the previous N days and the preset standard facial color health index; the valid facial color deviation value data is: the deviation value data of the image to which the deviation data value of the facial color health index for the previous N days belongs meets the valid conditions before acquisition, the calculation process of each data meets the compliance conditions of the calculation process, and the data storage method meets the storage update specification conditions; wherein, the valid conditions before acquisition include: image acquisition is completed under standard light source and fixed shooting parameters, and the image is verified for validity. Excluding interference from makeup, concealing, and abnormal lighting, the genetic color deviation correction was completed, with at least three effective color areas and successful extraction of compliant color feature parameters. The calculation process met compliance conditions, including: matching the baseline values ​​of a reference database of healthy individuals of the same age and gender, completing the full calculation of the daily color health index and color deviation value, with no missing parameters or logical errors. Storage and update specifications included: storage categorized by user identity, bound to user identifiers, conforming to the database's automatic rolling update rules, and storing only valid data from the latest N days, with no expired or mismatched data. If the number of days corresponding to the valid color deviation value data is less than a preset number of days, the data is adjusted based on the healthy population's color deviation value. The user's facial health is calculated based on the average health reference value matched by the color feature reference database. The degree to which the daily facial health index deviates from the average health reference value is used to calculate the user's facial health level until the number of days corresponding to the effective facial deviation value data reaches the preset number of days. Based on the effective facial deviation value data and the preset facial health judgment threshold, the user's facial health level is calculated. The facial health level is then integrated with the user's physiological data to obtain the user's health status. Based on the user's health status and corresponding drug contraindication information, a medication risk result is generated. Based on the medication risk result, the personalized medication plan is adjusted, and target medication guidance information is dynamically generated.Based on the target medication guidance information, the water dispensing module is controlled to add warm water, and the drug lifting platform and compartment door are activated to retrieve medication from the drug storage and configuration module via the drug lifting platform and deliver it to the target user's location. The voice interaction module is also controlled to output the user's facial health status and target medication guidance information.

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