An adaptive post-operative analgesic management method, system, device, and medium

Abstract

The application provides a kind of adaptive postoperative analgesia management method, system, equipment and medium, it is related to intelligent medical technology field, the method is by combining the age of patient, weight and liver and kidney function index before operation and operation related information, individualized estimation is carried out to postoperative pain intensity and initial analgesia scheme containing basic infusion rate, single additional dose upper limit and additional infusion lock time is generated, real-time acquisition is carried out to the pressure in pump, drug liquid flow and patient additional infusion pressing frequency and other multidimensional working state data of electronic analgesia pump after operation, equipment operation abnormality is dynamically identified, and individualized adjustment scheme is generated by fusing physiological parameters and equipment state, under the premise of ensuring that equipment is not malfunction and the parameter after adjustment does not exceed the safety dose upper limit based on patient physiological condition setting, automatically issue optimized control parameter to analgesia pump execution, to realize safe, accurate, closed-loop adaptive analgesia management, effectively improve analgesic effect and reduce artificial intervention burden.

Description

Technical Field

[0001] This application relates to the field of smart healthcare technology, and in particular to an adaptive postoperative analgesia management method, system, device, and medium. Background Technology

[0002] With the widespread use of electronic analgesia pumps in postoperative pain management, the surge in daily surgeries in hospitals has led to the need to manage hundreds of analgesia devices simultaneously. Each patient's analgesia cycle typically exceeds 48 hours, making the traditional management model, which relies on manual inspection and experience-based settings, insufficient to meet the refined requirements for analgesic effectiveness and safety. Existing technologies generally suffer from problems such as inadequate preoperative assessment, lack of individualized analgesia protocols, and inability to identify operational anomalies like tubing blockages in real time. Furthermore, the patient's additional pressure application (patient-controlled analgesia) is not effectively translated into a basis for dosage adjustment, resulting in both insufficient analgesia and the risk of overdose. Summary of the Invention

[0003] This application provides an adaptive postoperative analgesia management method, system, device, and medium to solve one or more technical problems existing in the prior art, and at least provides a beneficial option or creates conditions that can dynamically optimize infusion parameters based on individual patient characteristics and real-time analgesia behavior, thereby improving analgesia effects and reducing the burden on medical staff while ensuring medication safety.

[0004] On the one hand, this application provides an adaptive postoperative analgesia management method, including the following steps: Before the operation, the patient's physiological parameters and surgery-related information are obtained to make an individualized prediction of the patient's postoperative pain intensity in order to generate an initial analgesia plan; wherein, the physiological parameters include at least age, weight and liver and kidney function indicators; the initial analgesia plan includes a basic infusion rate, a maximum single dose, and a lockout time for additional infusion; Postoperatively, the system receives real-time operational status data of the electronic analgesia pump associated with the patient and identifies any operational abnormalities of the electronic analgesia pump. The operational status data includes pump internal pressure data, drug flow rate data, and the frequency of additional infusion presses by the patient on the electronic analgesia pump. Based on the physiological parameter information and the operating status data of the electronic analgesia pump, a personalized adjustment plan for the initial analgesia plan is generated; If it is confirmed that the electronic analgesia pump is not malfunctioning and the personalized adjustment plan does not exceed the safe dose limit set based on the physiological parameter information, the control parameters corresponding to the personalized adjustment plan are automatically sent to the electronic analgesia pump to perform automatic adjustment of the infusion parameters.

[0005] Furthermore, the surgery-related information includes at least the type of surgery and the size of the incision, and the surgery duration is automatically entered after the surgery. In the process of predicting postoperative pain intensity, the operation duration and the incision size are weighted and fused to generate a surgical trauma index. Combined with the age, weight and liver and kidney function indicators, an individualized pain intensity prediction value on a scale of 0-10 is output.

[0006] Furthermore, the malfunction of the electronic analgesia pump includes at least one of tubing blockage or drug depletion; The process of identifying whether the electronic analgesia pump is malfunctioning includes the following steps: The pump pressure data and drug flow data of the electronic analgesia pump are collected synchronously at a frequency of not less than 1 Hz, and the pressure change rate and flow change rate within the current sampling window are calculated. The pressure change rate refers to the change in pump pressure per unit time within the sampling window, and the flow rate change rate refers to the change in drug flow rate within the pump per unit time within the sampling window. If the following situation occurs in multiple consecutive sampling cycles: the pressure change rate is greater than the preset pressure rise rate threshold and the flow rate change rate is less than the preset flow rate fall rate threshold, then it is determined to be an abnormal pipeline blockage; the pressure rise rate threshold and flow rate fall rate threshold are set according to the model of the electronic analgesia pump and are initialized through an unloaded self-test process before each new patient uses it.

[0007] If the flow rate of the medicine solution is detected to be zero but the pressure inside the pump is within the normal baseline range, it is determined that the medicine solution is exhausted.

[0008] Furthermore, the personalized adjustment scheme includes adjustments to the base infusion rate, the maximum single additional dose, and the additional infusion lockout time; The single adjustment of the basic infusion rate does not exceed the preset maximum step size, and its adjustment rate does not exceed the preset maximum rate of change. At the same time, the adjusted basic infusion rate does not exceed the safe rate limit determined based on the liver and kidney function indicators and the type of analgesic drug used. The adjustment of the upper limit of the single additional dose shall not exceed the maximum safe dose for a single dose determined based on the liver and kidney function indicators; The adjustment of the additional infusion lock time shall not be less than the preset minimum lock time; in: Basic infusion rate adjustment rule: If the frequency of additional infusion presses exceeds a preset first threshold, the basic infusion rate will be automatically increased according to the degree of excess. The additional infusion lockout time adjustment rules include two trigger scenarios: Scenario 1: If the frequency of pressing the additional infusion button exceeds a preset second threshold within the preset observation window after the additional infusion lock time is released, the additional infusion lock time will be automatically shortened. Scenario 2: If, within the preset observation window after the additional infusion lock time is released, there are two or more consecutive presses with an adjacent interval less than the preset third threshold, the additional infusion lock time will be automatically shortened.

[0009] Furthermore, regarding the basic infusion rate adjustment rule, if the additional infusion press frequency... Exceeding the preset first threshold Then, based on the relative proportion exceeding the first threshold, the base infusion rate is automatically increased. Updated base infusion rate The following calculation formula must be satisfied: ; in, This indicates the configurable first adjustable sensitivity coefficient; , This indicates the upper limit of the safe rate determined by the liver and kidney function indicators and the type of analgesic drug used; , This indicates the preset maximum rate of change.

[0010] Furthermore, regarding the additional infusion lock time adjustment rule, in scenario one, if the additional infusion press frequency is within the preset observation window after the additional infusion lock time is released... Exceeding the preset second threshold The additional infusion lockout time will be shortened according to the degree of excess. Updated additional bet lock time The following calculation formula must be satisfied: ; in, This indicates a configurable second adjustable sensitivity coefficient. This indicates the minimum lock time set.

[0011] Furthermore, regarding the additional infusion lock time adjustment rule, in scenario two, if continuous events occur within the preset observation window... The next press operation. And the interval between any two adjacent presses is less than the preset third threshold. Then, based on the number of consecutive presses. Shorten the lock-in time for additional infusions Updated additional bet lock time The following calculation formula must be satisfied: ; in, This indicates a configurable third adjustment sensitivity coefficient. This indicates the minimum lock time set.

[0012] On the other hand, this application provides an adaptive postoperative analgesia management system, including the following modules: The preoperative assessment module is configured to: acquire the patient's physiological parameters and surgery-related information before surgery, and perform an individualized prediction of the patient's postoperative pain intensity to generate an initial analgesia plan; wherein, the physiological parameters include at least age, weight, and liver and kidney function indicators; the initial analgesia plan includes a baseline infusion rate, a maximum single dose, and a lockout time for additional infusion; The device monitoring module is configured to: receive real-time operating status data of the electronic analgesia pump associated with the patient after surgery, and identify whether the electronic analgesia pump is malfunctioning; wherein, the operating status data includes pump pressure data, drug flow rate data, and the frequency of additional infusion pressure applied by the patient to the electronic analgesia pump; The dynamic optimization module is configured to generate a personalized adjustment plan for the initial analgesia plan based on the physiological parameter information and the working status data of the electronic analgesia pump. The automatic execution module is configured to: upon confirming that the electronic analgesia pump is operating normally and that the personalized adjustment plan does not exceed the safe dose limit set based on the physiological parameter information, automatically send the control parameters corresponding to the personalized adjustment plan to the electronic analgesia pump to perform automatic adjustment of the infusion parameters.

[0013] On the other hand, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the aforementioned adaptive postoperative analgesia management method.

[0014] On the other hand, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned adaptive postoperative analgesia management method.

[0015] The beneficial effects of this application are as follows: This application provides an adaptive postoperative analgesia management method. This method, by combining the patient's age, weight, liver and kidney function indicators, and surgical information preoperatively, individually predicts postoperative pain intensity and generates an initial analgesia plan including a baseline infusion rate, a single bolus dose limit, and a bolus dose lockout time. Postoperatively, it collects multi-dimensional operational status data in real time, such as pump pressure, drug flow rate, and the frequency of bolus dose presses by the patient, dynamically identifies equipment malfunctions, and integrates physiological parameters with equipment status to generate a personalized adjustment plan. Under the premise of ensuring the equipment is fault-free and the adjusted parameters do not exceed the safe dose limit set based on the patient's physiological conditions, the optimized control parameters are automatically sent to the analgesia pump for execution, thereby achieving safe, precise, and closed-loop adaptive analgesia management, effectively improving analgesia efficacy and reducing the burden of manual intervention. This application also provides corresponding systems, media, and devices. The beneficial effects of the systems, media, and devices are similar to those of the method and will not be elaborated here.

[0016] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0017] The accompanying drawings are provided to further understand the technical solutions of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0018] Figure 1 This is a flowchart of the adaptive postoperative analgesia management method provided in this application; Figure 2 This is a structural diagram of the adaptive postoperative analgesia management system provided in this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0020] The present application will be further described below with reference to the accompanying drawings and specific embodiments. The described embodiments should not be considered as limitations on the present application, and all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present application.

[0021] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0022] 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 application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0023] Postoperative pain is a common clinical problem after surgery. Inadequate analgesia can not only affect patient comfort but also delay recovery, increase the risk of complications, and even develop into chronic pain. Therefore, scientific, safe, and individualized postoperative analgesia management is crucial. Electronic analgesia pumps, especially patient-controlled analgesia (PCA) pumps, have become the mainstream technology for postoperative analgesia because they allow patients to administer analgesics as needed within a safe range set by the physician. The operating mode of traditional PCA pumps is usually set by the anesthesiologist before surgery based on experience or simple assessments (such as age and weight), with a fixed set of parameters including the baseline infusion rate, single booster dose, and lockout time, which remain unchanged throughout the analgesia period.

[0024] However, existing technologies have significant drawbacks. First, preoperative analgesia planning is too rudimentary, lacking in-depth consideration of individual patient differences. Current methods often ignore the crucial impact of liver and kidney function on drug metabolism and fail to systematically integrate objective indicators such as the degree of surgical trauma (e.g., type of surgery, incision size, actual operation time), leading to a significant discrepancy between the initial plan and the patient's actual needs, easily resulting in insufficient analgesia or drug accumulation. Second, existing PCA pumps generally lack intelligent sensing and diagnostic capabilities for device operation. When abnormalities such as tubing blockage or drug depletion occur, the device typically only issues general alarms, unable to accurately identify the type of fault, let alone feed such abnormal information back into the analgesia strategy adjustment logic, posing a safety hazard.

[0025] Furthermore, and most critically, the existing system fails to effectively utilize the patient's most direct postoperative feedback signal—the act of applying additional infusion pressure. Frequent pressure application by the patient essentially reflects insufficient analgesia, but traditional PCA pumps mechanically execute a "push once, administer once" logic. They neither proactively increase the basal infusion to prevent persistent pain nor dynamically optimize the lockout time based on pressure frequency or intensity. This leads to a vicious cycle of pain-pressure-brief relief, resulting in a poor analgesic experience and increasing the burden on healthcare staff in handling ineffective alarms and manual interventions. Finally, the entire analgesia process is open-loop, lacking a dynamic optimization mechanism based on multi-source data fusion. Physiological parameters, surgical information, equipment status, and patient behavior are disconnected, failing to form a closed-loop feedback loop. This makes the analgesia protocol rigid and unable to adapt to the dynamic changes in pain intensity at different postoperative stages.

[0026] In summary, existing postoperative analgesia technologies have significant shortcomings in areas such as individualized prediction, equipment anomaly identification, patient behavior response, and closed-loop dynamic adjustment. There is an urgent need for an intelligent analgesia management solution that can deeply integrate preoperative assessment, real-time equipment monitoring, and patient behavior analysis, and on this basis, achieve safe, automated, and precise parameter optimization.

[0027] To address the aforementioned issues, this application provides an adaptive postoperative analgesia management method, system, device, and medium. It constructs an adaptive analgesia management mechanism that integrates preoperative individualized assessment with postoperative multi-source real-time feedback. Specifically, an initial analgesia plan is generated preoperatively by comprehensively considering the patient's age, weight, liver and kidney function indicators, surgical type, incision size, and surgical duration. Postoperatively, data such as the pump pressure, drug flow rate, and frequency of additional infusion presses from the electronic analgesia pump are continuously collected. This accurately identifies operational anomalies such as tubing blockage or drug depletion to ensure infusion safety. Furthermore, based on physiological parameters and patient behavior, a personalized adjustment strategy is dynamically generated. Under the premise of strictly adhering to the safe dosage limits determined by liver and kidney function and drug type, the basic infusion rate, single additional dose limit, and additional infusion lockout time are automatically optimized. When the analgesic effect remains unsatisfactory, an alarm is proactively triggered to notify medical staff. This achieves a shift from static drug administration to closed-loop intelligent control, significantly improving the effectiveness, safety, and automation level of analgesia.

[0028] First, the adaptive postoperative analgesia management method provided by the embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0029] Reference Figure 1 The implementation process of the adaptive postoperative analgesia management method provided in this application embodiment includes, but is not limited to, the following steps.

[0030] Step S110: Before the operation, obtain the patient's physiological parameters and surgery-related information, and make an individualized prediction of the patient's postoperative pain intensity to generate an initial analgesia plan.

[0031] The physiological parameters include at least age, weight, and liver and kidney function indicators. The initial analgesia protocol includes the basal infusion rate, the maximum single bolus dose, and the lockout time for bolus infusions.

[0032] In step S110, a scientific and individualized analgesia starting point is established before the surgery. This step involves systematically collecting key physiological parameters such as the patient's age, weight, and liver and kidney function indicators, combined with surgery-related information, to quantitatively predict the intensity of postoperative pain. This prediction departs from the traditional, extensive setting relying on physician experience, instead adopting a data-driven approach to generate an initial analgesia plan containing three core elements: the baseline infusion rate, the upper limit of a single bolus dose, and the lockout time for bolus infusion. This plan fully considers the matching relationship between the patient's own metabolic capacity and the degree of surgical trauma, laying a safe and precise foundation for the subsequent analgesia process and effectively avoiding the risks of insufficient analgesia or drug overdose due to improper initial parameter settings.

[0033] Step S120: After the operation, receive real-time operating status data of the electronic analgesia pump associated with the patient and identify whether there is any abnormality in the operation of the electronic analgesia pump.

[0034] The operational status data includes pump internal pressure data, drug flow rate data, and the frequency of additional infusions pressed by the patient on the electronic analgesia pump.

[0035] In step S120, the operational status of the analgesic device is continuously monitored and anomaly diagnosed during the postoperative phase. This step involves receiving multi-dimensional operational status data in real time from the electronic analgesia pump linked to the specific patient. This data includes pump pressure data reflecting tubing patency, drug flow rate data representing the actual dosage, and the frequency of additional infusion presses reflecting the patient's subjective pain perception. Based on this real-time data stream, the system can proactively identify any operational anomalies, such as infusion failures due to tubing obstruction or drug depletion. This monitoring mechanism not only ensures the reliability of drug infusion but also provides a crucial safety prerequisite for subsequent automatic parameter adjustments, ensuring that any optimization operations are based on the premise of normal device operation.

[0036] Step S130: Based on physiological parameter information and the working status data of the electronic analgesia pump, a personalized adjustment plan for the initial analgesia plan is generated.

[0037] In step S130, static individual characteristics and dynamic behavioral feedback are integrated to generate a targeted analgesia strategy optimization plan. This step is based on the patient's inherent attributes represented by the physiological parameters obtained preoperatively, while also incorporating the current analgesia response reflected by the real-time postoperative data on the electronic analgesia pump's operating status, and comprehensively analyzing both. Through this multi-source information fusion, the system can determine whether the current initial analgesia plan meets the patient's actual needs, and accordingly generate a personalized adjustment plan targeting one or more of the following: baseline infusion rate, maximum single bolus dose, or bolus infusion lockout time. The core significance of this plan lies in transforming analgesia management from a fixed model to a dynamic adaptive model, enabling the drug infusion strategy to intelligently evolve according to changes in the patient's postoperative pain.

[0038] Step S140: After confirming that the electronic analgesia pump is operating normally and that the personalized adjustment plan does not exceed the safe dose limit set based on physiological parameter information, the control parameters corresponding to the personalized adjustment plan are automatically sent to the electronic analgesia pump to perform automatic adjustment of the infusion parameters.

[0039] In step S140, the automated closed-loop execution of analgesic parameters is achieved under strict safety constraints. This step sets dual verification conditions: first, it confirms that the electronic analgesia pump itself has no operational abnormalities, ensuring that the device is in a state where it can execute commands normally; second, it verifies that the generated personalized adjustment plan does not exceed the safe dosage limit preset based on the patient's physiological parameter information, preventing medication overdose due to algorithm adjustments. Only when both of these preconditions are met simultaneously will the system automatically send the adjusted control parameters to the electronic analgesia pump, allowing the device to immediately execute the change in infusion parameters. This mechanism, while giving the system autonomous optimization capabilities, firmly safeguards the bottom line of medical safety, achieving an organic unity of intelligence and safety.

[0040] In some embodiments of this application, the surgery-related information includes at least the surgery type and incision size, and the surgery duration is automatically entered after the surgery. Specifically, when predicting postoperative pain intensity, the surgery duration and incision size are weighted and fused to generate a surgical trauma index, which is then combined with age, weight, and liver and kidney function indicators to output an individualized pain intensity prediction value on a scale of 0-10.

[0041] Specifically, the type of surgery provides a qualitative assessment of the complexity of the procedure and the extent of tissue damage; the incision size reflects the physical scale of the surface trauma; and the duration of the operation indirectly reflects the intensity of the intraoperative procedure and the length of potential tissue traction or exposure. These three pieces of information together form the quantitative basis for assessing the surgical trauma suffered by the patient, avoiding the limitations of rough estimations based solely on subjective experience or a single indicator.

[0042] When predicting postoperative pain intensity, the system weights and fuses surgical duration and incision size using preset weighting coefficients to generate a comprehensive surgical trauma index. This index effectively integrates trauma information from both temporal and spatial dimensions, reflecting the actual degree of tissue damage more accurately than using either parameter alone. Subsequently, this surgical trauma index is combined with the patient's individual physiological characteristics, such as age, weight, and liver and kidney function indicators, and input into a preset prediction model or rule engine. Age affects pain sensitivity and drug metabolism capacity, weight is associated with drug distribution volume, and liver and kidney function directly determines the clearance efficiency of analgesics. These factors work together to ultimately output an individualized pain intensity prediction value on a scale of 0 to 10. This prediction value not only reflects the expected severity of pain but also incorporates the patient's tolerance potential to the drug, providing a scientific and structured basis for generating a safe and effective initial analgesia plan.

[0043] In some embodiments of this application, the aforementioned pre-defined prediction model employs a rule engine architecture centered on a decision tree. This decision tree consists of multi-level decision nodes and leaf nodes. Each internal node represents a judgment condition for a clinical feature, each branch corresponds to a different value range, and the final leaf node outputs a pain intensity score after comprehensive calculation. This decision tree design fully integrates clinical experience and data-driven methods, ensuring that the model is both highly interpretable and adaptable to individual differences.

[0044] The decision tree first groups patients into broad categories based on the type of surgery. At the root node or first-level judgment, procedures with extensive trauma and deep tissue damage, such as open surgery, thoracic surgery, and joint replacement, are classified as high-risk, while laparoscopic surgery, superficial tumor resection, and endoscopic procedures are classified as medium- or low-risk. Each category corresponds to a baseline pain score offset; for example, the initial offset is five points for high-risk, three points for medium-risk, and one point for low-risk, thus establishing a baseline for pain prediction. Based on this, the decision tree moves to the next judgment level, introducing two continuous variables: incision size and surgical duration. The system multiplies the incision length (in centimeters) and surgical duration (in minutes) by weighting coefficients calibrated through regression analysis of numerous historical cases, and then sums them to generate a continuous surgical trauma index. This index is then mapped through a pre-defined nonlinear gain function that simulates clinically observed physiological patterns: when the trauma index is below a certain threshold, the pain score increases slowly; once it exceeds this threshold, the pain score rises sharply with the trauma index, thus more realistically reflecting the pain escalation effect of severe trauma.

[0045] Subsequently, the decision tree further refines the initial score by incorporating the patient's individual physiological parameters. At the age assessment node, if the patient is over seventy years old, the system will moderately increase the pain score to reflect the higher risk of pain perception, based on the changes in pain sensitivity and slower drug metabolism in elderly patients, while automatically suppressing the intensity of the drug response in the subsequent dose mapping stage. At the weight assessment node, the system uses the distribution volume parameter in the pharmacokinetic model based on whether the body mass index is abnormal, indirectly affecting the conversion relationship between the score and the dose. At the liver and kidney function assessment node, the system classifies creatinine clearance into four levels: normal, mildly impaired, moderately impaired, and severely impaired. Each level not only triggers the corresponding drug dose upper limit rule but also inversely constrains the effective upper limit of the final pain score. For example, even if the trauma index is high in patients with severe renal insufficiency, their pain score is limited to a safe range to prevent excessive induction of medication. The entire reasoning process is implemented using a path traversal of the decision tree. All judgment thresholds, weight coefficients, and gain function parameters are derived from authoritative clinical guidelines, multi-center research data, or the hospital's long-term accumulated postoperative analgesia database, and have been verified by the anesthesiology expert team.

[0046] Ultimately, the decision tree model outputs a personalized pain intensity prediction score between 0 and 10. This score not only quantifies the severity of the patient's expected postoperative pain but also incorporates their potential response capability and safety margin to analgesic intervention. The entire model is compact and computationally efficient, requiring no cloud training or complex neural networks. It can run in real-time on local hospital servers, anesthesia workstations, or edge computing devices, fully meeting the stringent requirements of medical scenarios for data privacy protection, response latency control, and system reliability. More importantly, because the decision path is clearly visible, medical staff can trace the scoring generation logic layer by layer to understand why a particular patient was given a specific analgesia plan, thereby enhancing their trust and acceptance of the system and truly enabling technology to empower clinical practice rather than replace clinical judgment.

[0047] In some embodiments of this application, the initial analgesia regimen is generated based on individualized pain intensity predictions, combined with the pharmacological properties of the selected analgesic drug and the patient's physiological safety boundaries, through a structured parameter mapping mechanism. This mechanism does not simply linearly convert the predicted values ​​into dosing parameters, but rather employs a segmented, drug-specific rule base to ensure the regimen is both effective and safe.

[0048] Specifically, the system first calls the corresponding parameter configuration template based on the pre-selected analgesic drug type (such as sufentanil, morphine, hydromorphone, or ropivacaine). Each drug template contains a preset pain score-parameter mapping table, which divides the predicted pain intensity values ​​from 0 to 10 into several intervals. Each interval corresponds to a set of baseline infusion rates, maximum single bolus doses, and default bolus infusion lockout times. For example, for sufentanil, a pain score of 0 to 3 might correspond to a baseline infusion rate of 0.5 ml / hour, a single bolus dose of 0.5 ml, and a lockout time of 30 minutes; while a score of 7 to 10 might correspond to a baseline infusion rate of 2.0 ml / hour, a single bolus dose of 1.5 ml, and a lockout time of 20 minutes. This mapping relationship is derived from the drug's clinical usage guidelines, therapeutic window width, and duration of action characteristics, ensuring that high-scoring patients receive stronger analgesic coverage while avoiding unnecessary drug exposure for low-scoring patients.

[0049] Building upon this foundation, the system further incorporates patient liver and kidney function indicators to dynamically adjust the aforementioned parameters. If a patient has abnormal liver function, the system automatically reduces the basal infusion rate and upper limit of single bolus dose for opioids primarily metabolized by the liver, even if the patient's pain score is high. If the patient has impaired kidney function, even stricter dose restrictions are imposed on drugs cleared by the kidneys. For example, when creatinine clearance is below 30 ml / min, the upper limit of single bolus dose is forcibly limited to below a certain safety threshold, regardless of the pain score. Furthermore, body weight is also used to correct the dosage unit; for some drugs administered in kilograms of body weight, the system converts the absolute dose into an individualized rate based on the patient's actual weight.

[0050] The final generated initial analgesia protocol includes three core control parameters: a basal infusion rate to maintain stable blood drug concentrations to prevent background pain, a single bolus dose limit to restrict the maximum dose requested by the patient each time to prevent overdose, and a bolus infusion lockout time to set the minimum interval between two effective compressions to avoid repeated administration within a short period. These three parameters together constitute a safe, personalized, and executable analgesia starting point that responds to predicted pain intensity while strictly adhering to pharmacological constraints and physiological safety boundaries, providing a reliable foundation for dynamic optimization in the subsequent postoperative phase. The entire generation process is fully automated and can be completed within seconds after the anesthesiologist confirms the surgical information and drug selection, significantly improving preoperative preparation efficiency and the scientific rigor of the protocol.

[0051] In some embodiments of this application, the malfunction of the electronic analgesia pump includes at least one of tubing blockage or drug depletion. Step S120, identifying whether the electronic analgesia pump is malfunctioning, includes the following steps.

[0052] Step S210: Synchronously collect the pump pressure data and drug flow data of the electronic analgesia pump at a frequency of not less than 1 Hz, and calculate the pressure change rate and flow change rate within the current sampling window.

[0053] Among them, the pressure change rate refers to the change in pump pressure per unit time within the sampling window, and the flow rate change rate refers to the change in pump flow rate per unit time within the sampling window.

[0054] In step S210, the basic data stream required for anomaly identification is established. The system synchronously collects the pump pressure data and drug flow rate data of the electronic analgesic pump at a sampling frequency of no less than once per second, ensuring strict alignment of the two in the time dimension to avoid misjudgment due to asynchronous sampling. Based on this, the system calculates the pressure change rate and flow rate change rate for each sliding or fixed sampling window. The pressure change rate is defined as the change in pump pressure per unit time within the sampling window, reflecting the dynamic trend of pipeline resistance; the flow rate change rate is defined as the change in drug flow rate per unit time within the sampling window, characterizing the stability of actual infusion capacity. These two derived parameters are more sensitive to capturing subtle anomalies in the equipment's operating status than the raw data and are key inputs for subsequent fault diagnosis.

[0055] Step S220: If the following situation occurs in multiple consecutive sampling cycles: the pressure change rate is greater than the preset pressure rise rate threshold, and the flow rate change rate is less than the preset flow rate fall rate threshold, then it is determined to be an abnormal pipeline blockage. The pressure rise rate threshold and flow rate fall rate threshold are set according to the model of the electronic analgesia pump and are initialized through an empty-load self-test process before each new patient uses it.

[0056] In step S220, the system accurately identifies pipeline blockage as a high-risk anomaly. When two conditions are met simultaneously across multiple consecutive sampling cycles—the pressure change rate exceeds a preset pressure rise rate threshold, and the flow rate change rate is lower than a preset flow rate fall rate threshold—the system determines that pipeline blockage has occurred. This logic is based on fluid mechanics principles: when the pipeline is blocked, the pump must overcome greater resistance, leading to a rapid pressure rise, but the actual output of the medication significantly decreases or even stops. To improve accuracy, the pressure rise rate threshold and flow rate fall rate threshold are not fixed values, but are pre-configured according to the specific model of the electronic analgesic pump and initialized through an no-load self-test process before each new patient's use. The no-load self-test involves running the pump without a patient connection and with the pipeline unobstructed, recording the baseline pressure and flow fluctuations during normal operation, thereby dynamically calibrating the thresholds, effectively eliminating individual device differences and environmental interference, and significantly improving the specificity and robustness of blockage identification.

[0057] In step S230, if the flow rate of the medicine is detected to be zero but the pressure inside the pump is within the normal baseline range, it is determined that the medicine is exhausted.

[0058] In step S230, the system detects the depletion of medication to prevent ineffective analgesia or equipment damage caused by running the pump without a pump. This step is determined by monitoring whether the medication flow rate drops to zero and verifying whether the pump pressure remains within the normal baseline range. The normal baseline range refers to the typical operating pressure range corresponding to the target flow rate set by the electronic analgesia pump in the current infusion mode. When the medication is completely depleted, although the pump continues to run, effective pressure cannot be established in the tubing due to the lack of liquid to push it. Therefore, the pressure value will drop to near the no-load level, rather than the high pressure state experienced during blockage. By jointly determining the zero flow rate and the pressure within the normal baseline range, the system can effectively distinguish between medication depletion and complete blockage or other sensor malfunctions, ensuring the accuracy of alarm information and promptly prompting medical staff to replace the medication bag or address the tubing, thus ensuring continuous analgesia and patient safety.

[0059] In some embodiments of this application, the core of the personalized adjustment scheme lies in the dynamic optimization of three key parameters: the baseline infusion rate, the maximum single bolus dose, and the bolus infusion lock-in time. Simultaneously, multiple safety constraint mechanisms are strictly embedded to ensure that any adjustments are performed within clinically acceptable safety boundaries. This design not only gives the system the flexibility to respond to individual patient needs but also fundamentally eliminates the medication risks that may arise from the algorithm's autonomous decision-making.

[0060] In some embodiments of this application, the personalized adjustment scheme specifically includes the following.

[0061] (1) The single adjustment of the basic infusion rate shall not exceed the preset maximum step size, and its adjustment rate shall not exceed the preset maximum rate of change. At the same time, the adjusted basic infusion rate shall not exceed the safe rate limit determined based on liver and kidney function indicators and the type of analgesic drug used.

[0062] The first aspect sets three limits for adjusting the baseline infusion rate to balance analgesic efficacy and drug safety. First, a single adjustment must not exceed the preset maximum step size to prevent a sudden spike in blood drug concentration that could lead to adverse reactions such as respiratory depression. Second, the adjustment rate, i.e., the allowable cumulative change per unit time, must not exceed the preset maximum rate of change to avoid cumulative overdose within a short period. Finally, regardless of the level of need determined by the patient's pressure behavior or pain assessment, the adjusted baseline infusion rate must never exceed the upper limit of the safe rate, determined by the patient's liver and kidney function indicators and the type of analgesic used. For example, for opioids excreted by the kidneys, if the patient's creatinine clearance is significantly reduced, the upper limit of the safe rate will be automatically lowered by the system. Even if the algorithm suggests a higher infusion rate, it will be forcibly cut off within this safe threshold, thereby achieving pharmacokinetic-guided individualized dosage control.

[0063] (2) The adjustment of the upper limit of a single additional dose shall not exceed the maximum safe dose for a single dose determined based on liver and kidney function indicators.

[0064] The second aspect imposes clear physiological constraints on the adjustment of the upper limit of a single dose. This adjustment limit is not determined arbitrarily by the algorithm, but is strictly tied to the patient's liver and kidney function. The system pre-calculates the maximum safe single dose that the patient can tolerate under the current medication based on data such as liver function enzyme levels or glomerular filtration rate. Any upward adjustment suggestions generated based on behavioral feedback (such as frequent pressure) will be automatically trimmed to the safe boundary if this dose limit is exceeded. This mechanism effectively prevents the system from misinterpreting a need for a larger single dose when the patient is pressing frequently due to severe pain, thus avoiding the risk of acute drug poisoning, and is particularly suitable for elderly individuals or those with organ dysfunction.

[0065] (3) The adjustment of the additional infusion lock time shall not be lower than the preset minimum lock time.

[0066] The third point sets an inviolable minimum for shortening the lockout time for additional infusions. While the system can dynamically shorten the lockout time based on the patient's compression intensity to improve responsiveness, the adjusted lockout time must never fall below the system's preset minimum lockout time. This minimum lockout time is a rigid safety valve based on the analgesic's onset time, peak effect time, and safety interval. For example, opioids typically require a lockout interval of at least five minutes to ensure the previous dose takes full effect and avoids additive effects. By forcibly maintaining this minimum time window, the system improves the timeliness of analgesia while always respecting pharmacokinetic principles, preventing potential overdose risks due to excessively shortened intervals.

[0067] In some embodiments of this application, the basic infusion rate adjustment rule specifically includes the following: if the frequency of additional infusion presses exceeds a preset first threshold, the basic infusion rate is automatically increased according to the degree of excess.

[0068] Specifically, the adjustment rule for the basal infusion rate uses the patient's active compression behavior as the core feedback signal, transforming passive, responsive analgesia into proactive, preventative management. When the system detects that the frequency of the patient's additional compressions to the electronic analgesia pump exceeds a preset first threshold, it is considered that the current basal analgesia level is insufficient to cover the patient's background pain, posing a risk of persistent analgesia inadequacy. At this point, the system no longer relies solely on a single additional dose to relieve momentary pain, but automatically and progressively increases the basal infusion rate based on the degree to which the compression frequency exceeds the first threshold. This adjustment mechanism can enhance the steady-state level of blood drug concentration in advance, reducing the number of times the patient repeatedly triggers compressions due to pain, thereby breaking the vicious cycle of pain-compression-temporary relief, improving overall analgesic comfort, and reducing the equipment burden and nursing interference caused by frequent operations.

[0069] In some embodiments of this application, the additional infusion lock time adjustment rule includes two triggering scenarios: (1) Scenario 1: If the frequency of pressing the additional infusion button exceeds the preset second threshold within the preset observation window after the additional infusion lock time is released, the additional infusion lock time will be automatically shortened.

[0070] The first scenario focuses on the overall level of compression frequency: If, within a preset observation window after the additional infusion lockout time is released, the patient's additional infusion compression frequency exceeds a preset second threshold, it indicates that the patient has experienced pain multiple times in a short period, and the existing lockout time is too long to meet their analgesic rhythm. Based on this, the system automatically shortens the additional infusion lockout time, allowing the patient to obtain the next additional infusion opportunity earlier during subsequent pain attacks, improving the timeliness and flexibility of analgesia.

[0071] (2) Scenario 2: If, within the preset observation window after the additional infusion lock time is released, there are two or more consecutive presses and the interval between them is less than the preset third threshold, the additional infusion lock time will be automatically shortened.

[0072] The second scenario focuses on the temporal distribution characteristics of compression behavior: if a patient performs more than two consecutive compressions within the same preset observation window, and the interval between any two adjacent compressions is less than a preset third threshold, it indicates that the patient has experienced intense, rapid pain stimulation, and may even feel severe pain again before the previous medication has fully taken effect. This compression pattern reflects a more significant lag in the analgesia regimen than simply exceeding the frequency limit. Upon identifying such high-density compression sequences, the system will also trigger a mechanism to shorten the lockout time to accommodate the patient's current highly sensitive pain state. By simultaneously considering both the total frequency and the compression interval, this rule can more accurately distinguish between occasional compressions and actual insufficient analgesia, avoiding misadjustments and ensuring that parameter optimization truly serves clinical needs.

[0073] In some embodiments of this application, for the basic infusion rate adjustment rule, if an additional infusion press frequency is added... Exceeding the preset first threshold Then, based on the relative proportion exceeding the first threshold, the base infusion rate is automatically increased. Updated base infusion rate The following calculation formula (1) must be satisfied: (1); In formula (1), This indicates the configurable first adjustable sensitivity coefficient. , This indicates the upper limit of the safe rate determined by liver and kidney function indicators and the type of analgesic drug used. , This indicates the preset maximum rate of change.

[0074] Specifically, formula (1) serves to provide a quantitative, controllable, and safe mathematical model for dynamically adjusting the baseline infusion rate, enabling the system to achieve precise and progressive optimization of analgesia intensity based on real-time changes in the patient's compression frequency during additional infusions. When the patient's compression frequency is detected... Exceeding the preset first threshold At that time, the system, based on the relative proportion of the excess portion to the threshold, combined with a configurable first adjustment sensitivity coefficient, and current baseline infusion rate Calculate the new target rate This ensures that the adjustment is proportional to the increase in pain demand, avoiding the risk of overdose due to a large, one-time increase. Simultaneously, the formula uses two hard constraints—the new rate... The safe rate limit, determined by liver and kidney function and the type of drug, must not be exceeded. And the adjusted rate of change does not exceed the preset maximum rate of change. — By firmly limiting the algorithm output within a clinically safe range, we can ensure both the sensitivity of the response and the safety of the automated decision-making loop, thereby improving the analgesic effect while effectively avoiding serious adverse reactions such as respiratory depression.

[0075] In some embodiments of this application, regarding the rules for adjusting the additional infusion lockout time, in scenario one, if the additional infusion press frequency is within a preset observation window after the additional infusion lockout time is released... Exceeding the preset second threshold The additional infusion lockout time will be shortened according to the degree of excess. Updated additional bet lock time The following calculation formula (2) must be satisfied: (2); In formula (2), This indicates a configurable second adjustable sensitivity coefficient. This indicates the minimum lock time set.

[0076] Specifically, formula (2) provides a continuous, adjustable, and safe calculation mechanism for the dynamic shortening of the additional infusion lockout time based on the degree of compression frequency exceeding the limit, ensuring that the system can intelligently adjust its response speed according to the urgency of the patient's analgesia needs within the observation window. When the patient's additional infusion compression frequency is detected... Exceeding the preset second threshold At that time, the system, based on the relative proportion of the excess portion to the threshold, combined with a configurable second adjustment sensitivity coefficient, Compress the current lock time proportionally. This allows patients to receive the next dose earlier when pain recurs, thus improving the timeliness and comfort of analgesia. Simultaneously, the formula updates the lock-in time using a maximum value function. Forced constraints at the minimum locking time set by the system This approach prevents safety risks such as drug superposition or respiratory depression caused by excessively shortened intervals, thus achieving dual protection of analgesic response flexibility and medication safety.

[0077] In some embodiments of this application, for the additional infusion lock time adjustment rule, in scenario two, if continuous events occur within a preset observation window... The next press operation. And the interval between any two adjacent presses is less than the preset third threshold. Then, based on the number of consecutive presses. Shorten the lock-in time for additional infusions Updated additional bet lock time The following calculation formula (3) must be satisfied: (3); In formula (3), This indicates a configurable third adjustment sensitivity coefficient. This indicates the minimum lock time set.

[0078] Specifically, formula (3) provides a quantitative, graded time-shortening mechanism based on the number of presses to precisely respond to the patient's urgent analgesia needs when the patient performs multiple consecutive presses with very short intervals within a preset observation window. When the system detects consecutive presses... Second-rate( The operation involves pressing the button, and the time interval between any two consecutive presses is less than a preset third threshold. This indicates that the patient is experiencing a continuous, high-intensity pain stimulus, and the existing lockout time is significantly lagging behind the pain rhythm. At this point, the system adjusts the lockout based on the number of consecutive presses. The sensitivity coefficient can be adjusted via a configurable third adjustment. The additional infusion lock time is dynamically shortened by decreasing by a fixed step size with each additional press. Updated lockout time The value is calculated using a formula, and its maximum value is used to ensure that it is not less than the minimum locking time set by the system. This improves the analgesic response speed while strictly avoiding the risk of drug accumulation due to excessively short intervals, thus achieving intelligent and safe response to high-density pain events.

[0079] In some embodiments of this application, if the frequency of additional infusion presses does not decrease to below a preset effective threshold or the decrease is less than the preset minimum improvement ratio within two or more consecutive preset observation windows after automatic adjustment, an analgesic effect assessment failure alarm is triggered, and a notification message is sent to the medical staff terminal through the hospital communication system.

[0080] Specifically, this application establishes a closed-loop assessment and proactive early warning mechanism based on analgesic effect feedback to identify abnormal situations where pain relief is not effectively achieved despite automatic parameter optimization. After completing a personalized adjustment, the system does not immediately declare analgesia successful but continuously monitors the trend of the patient's additional infusion compression frequency over two or more consecutive preset observation windows. If the compression frequency fails to decrease below the preset effective threshold during this period, it indicates that the patient's pain level has not been substantially relieved; or even if it decreases, but the reduction is less than the preset minimum improvement percentage, it indicates that the current adjustment strategy is ineffective or that other complex factors not covered by the model exist, such as non-nociceptive pain, psychogenic pain, drug tolerance, or device-related delivery pathway issues. Once any of the above conditions are met, the system determines that the analgesic effect assessment has failed and immediately triggers an alarm process, pushing a notification message containing patient information, current analgesic parameters, and behavioral data to the mobile terminal or workstation of the anesthesiologist or responsible nurse through the hospital's existing communication system. This mechanism effectively compensates for the limitations of fully automated systems, introducing human intervention in a timely manner when the algorithm cannot solve the problem. This not only ensures patient safety but also improves clinical response efficiency, truly realizing a postoperative pain management model that combines intelligent assistance with medical care leadership.

[0081] Secondly, this application provides an adaptive postoperative analgesia management system, including a preoperative assessment module, a device monitoring module, a dynamic optimization module, and an automatic execution module.

[0082] The preoperative assessment module is configured to: acquire the patient's physiological parameters and surgery-related information before surgery; and perform individualized predictions of postoperative pain intensity to generate an initial analgesia plan. The physiological parameters include at least age, weight, and liver and kidney function indicators. The initial analgesia plan includes the baseline infusion rate, the maximum single bolus dose, and the bolus infusion lockout time.

[0083] The device monitoring module is configured to receive real-time operational status data from the patient-associated electronic analgesia pump after surgery and identify any operational abnormalities. This operational status data includes pump internal pressure data, drug flow rate data, and the frequency of additional infusions administered by the patient to the electronic analgesia pump.

[0084] The dynamic optimization module is configured to generate a personalized adjustment plan for the initial analgesia regimen based on physiological parameter information and the working status data of the electronic analgesia pump.

[0085] The automatic execution module is configured to automatically send the control parameters corresponding to the personalized adjustment plan to the electronic analgesia pump to perform automatic adjustment of the infusion parameters, provided that the electronic analgesia pump is confirmed to be operating normally and the personalized adjustment plan does not exceed the safe dose limit set based on physiological parameter information.

[0086] In one embodiment of this application, the hardware components of the electronic analgesia pump include a drive unit, a human-machine interface, a sensor module, a communication module, and a safety alarm system. The drive unit uses a stepper motor with a push rod structure to drive the syringe for drug delivery, or a peristaltic pump to propel the drug solution by squeezing the infusion tubing, ensuring accuracy and stability during infusion. The human-machine interface includes physical buttons for patients to press and a display screen for showing device status, remaining drug volume, and other information, allowing patients to request additional medication and monitor their current analgesia status in real time. The sensor module, a core component of high-end models, integrates a pressure sensor to monitor tubing resistance to identify blockages or air bubbles, and a flow or drip rate sensor to verify whether the actual infusion volume matches the command, thereby improving operational safety. The communication module supports Wi-Fi or Bluetooth connectivity, uploading device operating data to the hospital's central monitoring system in real time for remote monitoring and data management. The safety alarm system automatically identifies and alerts to abnormal situations such as tubing blockage, drug depletion, low battery power, and unauthorized operation, ensuring the entire infusion process operates safely and reliably under multiple protection mechanisms.

[0087] Furthermore, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the aforementioned adaptive postoperative analgesia management method.

[0088] Furthermore, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned adaptive postoperative analgesia management method.

[0089] In summary, the adaptive postoperative analgesia management method, system, device, and medium provided in this application have the following technical effects.

[0090] This approach achieves a fundamental shift from static, experience-based drug administration to dynamic, closed-loop intelligent control by integrating individualized preoperative assessment with multi-source real-time postoperative feedback. In the preoperative phase, the system comprehensively considers the patient's age, weight, liver and kidney function indicators, surgical type, incision size, and actual surgical duration to generate a scientifically quantified predicted pain intensity value. Based on this, an initial analgesia plan is formulated, including the baseline infusion rate, maximum single bolus dose, and bolus infusion lockout time, significantly improving the accuracy and safety of initial parameters. In the postoperative phase, the system synchronously collects pump pressure, drug flow rate, and patient compression behavior data at a frequency of at least 1 Hz. This not only accurately identifies operational anomalies such as tubing blockage or drug depletion, ensuring safe equipment operation, but also dynamically optimizes the analgesia strategy based on the patient's actual pain feedback: automatically increasing the baseline infusion rate when the compression frequency is too high, and intelligently shortening the lockout time when compressions are too frequent or the intervals are too short. All adjustments are strictly limited to safety boundaries determined by liver and kidney function and drug type. Furthermore, if the analgesic effect does not significantly improve after automatic adjustment, the system will proactively trigger an alarm and notify medical staff, forming a closed-loop management system that integrates human and machine collaboration. Overall, this solution effectively improves the timeliness, individualization, and automation of analgesia while ensuring medication safety, reduces the workload of medical staff, improves the postoperative experience for patients, and provides a practical technical path for intelligent anesthesia and refined perioperative management.

[0091] It should be noted that in all specific embodiments of this application, all data processing activities related to user identity or personal characteristics, such as user information, user behavior data, historical data, and location information, will be conducted in accordance with the principles of legality, legitimacy, and necessity. All data collection, use, storage, and processing will be subject to compliance with applicable national and regional laws, regulations, and industry standards, and informed consent from users will be obtained in a clear and explicit manner before processing. For the processing of sensitive personal information, separate consent from users will be obtained through prominent means such as pop-up prompts and independent confirmation pages. If any processing conflicts with laws and regulations, the laws and regulations will prevail, and necessary data processing will only be carried out within the scope permitted by laws and regulations, ensuring that all data-based applications, analyses, and technical implementations are conducted within the scope permitted by laws and regulations.

[0092] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this application are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0093] Furthermore, although this application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding this application. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of ordinary skill of an engineer. Therefore, those skilled in the art can implement the application set forth in the claims using ordinary skill. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of this application, which is determined by the full scope of the appended claims and their equivalents.

[0094] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several programs to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0095] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable programs for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, a program execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can retrieve and execute a program from or in conjunction with such a program execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit a program for use by or in conjunction with a program execution system, apparatus, or device.

[0096] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Additionally, computer-readable media can even be paper or other suitable media on which programs can be printed, for example, by optically scanning the paper or other media, then editing, interpreting, or, if necessary, processing it in a suitable manner to obtain the program electronically, and then storing it in computer memory.

[0097] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable program execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0098] In the foregoing description of this specification, the reference to terms such as "one embodiment / implementation," "another embodiment / implementation," or "certain embodiments / implementations," etc., indicates that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in an embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0099] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0100] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. An adaptive postoperative analgesia management method, characterized in that, Includes the following steps: Before the operation, the patient's physiological parameters and surgery-related information are obtained to make an individualized prediction of the patient's postoperative pain intensity in order to generate an initial analgesia plan; wherein, the physiological parameters include at least age, weight and liver and kidney function indicators; the initial analgesia plan includes a basic infusion rate, a maximum single dose, and a lockout time for additional infusion; Postoperatively, the system receives real-time operational status data of the electronic analgesia pump associated with the patient and identifies any operational abnormalities of the electronic analgesia pump. The operational status data includes pump internal pressure data, drug flow rate data, and the frequency of additional infusion presses by the patient on the electronic analgesia pump. Based on the physiological parameter information and the operating status data of the electronic analgesia pump, a personalized adjustment plan for the initial analgesia plan is generated; If it is confirmed that the electronic analgesia pump is not malfunctioning and the personalized adjustment plan does not exceed the safe dose limit set based on the physiological parameter information, the control parameters corresponding to the personalized adjustment plan are automatically sent to the electronic analgesia pump to perform automatic adjustment of the infusion parameters.

2. The adaptive postoperative analgesia management method according to claim 1, characterized in that, The surgical information includes at least the surgical type and incision size, and the surgical duration is automatically filled in after the operation. In the process of predicting postoperative pain intensity, the operation duration and the incision size are weighted and fused to generate a surgical trauma index. Combined with the age, weight and liver and kidney function indicators, an individualized pain intensity prediction value on a scale of 0-10 is output.

3. The adaptive postoperative analgesia management method according to claim 1, characterized in that, The abnormal operation of the electronic analgesia pump includes at least one of the following: tubing blockage or depletion of medication. The process of identifying whether the electronic analgesia pump is malfunctioning includes the following steps: The pump pressure data and drug flow data of the electronic analgesia pump are collected synchronously at a frequency of not less than 1 Hz, and the pressure change rate and flow change rate within the current sampling window are calculated. The pressure change rate refers to the change in pump pressure per unit time within the sampling window, and the flow rate change rate refers to the change in drug flow rate within the pump per unit time within the sampling window. If the following situation occurs in multiple consecutive sampling cycles: the pressure change rate is greater than the preset pressure rise rate threshold and the flow rate change rate is less than the preset flow rate fall rate threshold, then it is determined to be an abnormal pipeline blockage; the pressure rise rate threshold and flow rate fall rate threshold are set according to the model of the electronic analgesia pump and are initialized through an unloaded self-test process before each new patient uses it. If the flow rate of the medicine solution is detected to be zero but the pressure inside the pump is within the normal baseline range, it is determined that the medicine solution is exhausted.

4. The adaptive postoperative analgesia management method according to claim 1, characterized in that, The personalized adjustment scheme includes adjustments to the base infusion rate, the maximum single bolus dose, and the bolus infusion lockout time; The single adjustment of the basic infusion rate does not exceed the preset maximum step size, and its adjustment rate does not exceed the preset maximum rate of change. At the same time, the adjusted basic infusion rate does not exceed the safe rate limit determined based on the liver and kidney function indicators and the type of analgesic drug used. The adjustment of the upper limit of the single additional dose shall not exceed the maximum safe dose for a single dose determined based on the liver and kidney function indicators; The adjustment of the additional infusion lock time shall not be less than the preset minimum lock time; in: Basic infusion rate adjustment rule: If the frequency of additional infusion presses exceeds a preset first threshold, the basic infusion rate will be automatically increased according to the degree of excess. The additional infusion lockout time adjustment rules include two trigger scenarios: Scenario 1: If the frequency of pressing the additional infusion button exceeds a preset second threshold within the preset observation window after the additional infusion lock time is released, the additional infusion lock time will be automatically shortened. Scenario 2: If, within the preset observation window after the additional infusion lock time is released, there are two or more consecutive presses with an adjacent interval less than the preset third threshold, the additional infusion lock time will be automatically shortened.

5. The adaptive postoperative analgesia management method according to claim 4, characterized in that, Regarding the basic infusion rate adjustment rule, if the additional infusion press frequency... Exceeding the preset first threshold Then, based on the relative proportion exceeding the first threshold, the base infusion rate is automatically increased. Updated base infusion rate The following calculation formula must be satisfied: ； in, This indicates the configurable first adjustable sensitivity coefficient; , This indicates the upper limit of the safe rate determined by the liver and kidney function indicators and the type of analgesic drug used; , This indicates the preset maximum rate of change.

6. The adaptive postoperative analgesia management method according to claim 4, characterized in that, Regarding the additional infusion lock time adjustment rule, in scenario one, if the additional infusion press frequency is within the preset observation window after the additional infusion lock time is released... Exceeding the preset second threshold The additional infusion lockout time will be shortened according to the degree of excess. Updated additional bet lock time The following calculation formula must be satisfied: ； in, This indicates a configurable second adjustable sensitivity coefficient. This indicates the minimum lock time set.

7. The adaptive postoperative analgesia management method according to claim 4, characterized in that, Regarding the additional infusion lock time adjustment rule, in scenario two, if continuous events occur within the preset observation window... The next press operation. And the interval between any two adjacent presses is less than the preset third threshold. Then, based on the number of consecutive presses. Shorten the lock-in time for additional infusions Updated additional bet lock time The following calculation formula must be satisfied: ； in, This indicates a configurable third adjustment sensitivity coefficient. This indicates the minimum lock time set.

8. An adaptive postoperative analgesia management system, characterized in that, Includes the following modules: The preoperative assessment module is configured to: acquire the patient's physiological parameters and surgery-related information before surgery, and perform an individualized prediction of the patient's postoperative pain intensity to generate an initial analgesia plan; wherein, the physiological parameters include at least age, weight, and liver and kidney function indicators; the initial analgesia plan includes a baseline infusion rate, a maximum single dose, and a lockout time for additional infusion; The device monitoring module is configured to: receive real-time operating status data of the electronic analgesia pump associated with the patient after surgery, and identify whether the electronic analgesia pump is malfunctioning; wherein, the operating status data includes pump pressure data, drug flow rate data, and the frequency of additional infusion pressure applied by the patient to the electronic analgesia pump; The dynamic optimization module is configured to generate a personalized adjustment plan for the initial analgesia plan based on the physiological parameter information and the working status data of the electronic analgesia pump. The automatic execution module is configured to: upon confirming that the electronic analgesia pump is operating normally and that the personalized adjustment plan does not exceed the safe dose limit set based on the physiological parameter information, automatically send the control parameters corresponding to the personalized adjustment plan to the electronic analgesia pump to perform automatic adjustment of the infusion parameters.

9. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the adaptive postoperative analgesia management method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the adaptive postoperative analgesia management method as described in any one of claims 1 to 7.

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