Control method and system for an infusion pump

Abstract

This invention discloses a control method and system for an infusion pump. First, it collects infusion pump operating parameters, patient and medical order related data, completes data binding, standardization, and encryption, and stably uploads the compliant dataset, outputting an encrypted standardized dataset. Then, based on the encrypted standardized dataset, it performs data analysis, trend prediction, and matching verification to generate optimization suggestions, outputting trend prediction results, warning levels, matching degree result sets, and drip rate optimization suggestions. Belonging to the field of infusion pump technology, this invention, by establishing a remote control architecture, has the advantage of enabling medical staff to complete all operations such as infusion pump drip rate adjustment, status verification, and parameter setting from remote locations such as nurse stations and mobile terminals, eliminating the need for repeated manual debugging and inspection work at each bedside, significantly freeing up medical staff's manpower and energy.

Description

Technical Field

[0001] This invention relates to the field of infusion pump technology, specifically to a control method and system for an infusion pump. Background Technology

[0002] Infusion pumps are core equipment for precise clinical infusion and are widely used in medical environments such as ICU, obstetrics, pediatrics, and pain management. The accuracy of their drip rate control directly affects the treatment effect and patient safety.

[0003] However, existing infusion pumps may suffer from low bedside operation efficiency in actual operation. Specifically, traditional infusion pumps only support local touch / button adjustments, requiring medical staff to check the drip rate and adjust parameters for each bed. For example, in the ICU of a top-tier hospital simultaneously treating 18 critically ill patients, each infusion pump requires manual checking every two hours, significantly increasing nurses' workload. Furthermore, if a patient experiences heart failure requiring an emergency reduction in drip rate, it may delay crucial intervention. Another example is treatments that require continuous monitoring and adjustment of medication drip rate and efficacy based on changes in the patient's condition. A central control and infusion pump monitoring system at a workstation (doctor's station or nurse's station) would facilitate monitoring changes in the patient's condition and targeted medication adjustments. Therefore, this infusion pump control method and system not only facilitates the work of medical staff and reduces their workload, but more importantly, it allows for real-time adjustment of medication drip rate based on patient condition monitoring. Summary of the Invention

[0004] To address the aforementioned technical problem of low efficiency in bedside procedures and to meet the need for adjusting medication at the workstation based on changes in the patient's condition, this invention provides the following technical solution:

[0005] A method for controlling an infusion pump, comprising the following specific steps:

[0006] S1, Multi-source data fusion acquisition and encrypted upload: Collect infusion pump operating parameters, patient and medical order related data, complete data binding, standardization and encryption, stably upload compliant datasets, and output encrypted standardized datasets;

[0007] S2, Multi-dimensional analysis and hierarchical early warning: Based on the encrypted standardized dataset output by S1, data parsing, trend prediction, and matching verification are carried out to generate optimization suggestions and output trend prediction results, early warning level, matching degree result set and drip rate optimization suggestions.

[0008] S3, tiered control and command issuance:

[0009] S31, Drip rate optimization calculation: Based on the matching result set and drip rate optimization suggestions output by S2, combined with the patient's weight and drug metabolism rate, the target drip rate value is calculated through an intelligent algorithm, and the control command is output;

[0010] S32, Control command security verification: Based on the control command output in S31, combined with the warning level and trend prediction results, verify whether the command is safe and output the security verification result;

[0011] S33, Encrypted command issuance: Based on the security verification result output by S32, if it passes, the control command will be encrypted using the AES-256 encryption algorithm, and the encrypted control command will be output.

[0012] S4, Pump-end command execution and closed-loop feedback verification: Based on the encrypted control command output by S3, complete identity verification, drive the pump to perform drip rate adjustment operation, and collect data of the entire execution process.

[0013] As a preferred embodiment of the control method for an infusion pump according to the present invention, the specific steps of step S1 are as follows:

[0014] S11, Real-time acquisition of all pump parameters: Real-time acquisition of the operating parameters of the infusion pump to form a set of pump status parameters; at the same time, acquisition of related information and output of the patient-medical order association set;

[0015] S12, Data Association and Standardization Processing: Based on the patient-medical order association set output in S11, the pump end status parameter set is bound with the patient identity and medical order requirements, invalid data is removed, data format is unified, and a standardized dataset is output.

[0016] S13, Encrypted Upload: The standardized dataset output in S12 is encrypted using the AES-256 encryption algorithm, and the encrypted standardized dataset is output through the 5G / medical dedicated wireless protocol.

[0017] As a preferred embodiment of the control method for an infusion pump according to the present invention, the specific steps of step S2 are as follows:

[0018] S21, Real-time data analysis and status modeling: Based on the encrypted standardized dataset output by S1, the operating status of each infusion pump is analyzed through edge computing technology, a single pump health status model is constructed, and a real-time status score of the pump end is output.

[0019] S22, Trend Prediction Analysis: Based on the real-time status score of the pump end output in S21, retrieve the big data of the patient corresponding to the infusion pump, combine it with the current real-time parameters to perform trend extrapolation, predict potential risks, and output the trend prediction results;

[0020] S23, Drip rate-medical order matching degree verification: Based on the real-time status score of the pump end output in S21 and the trend prediction result output in S22, compare the difference between the current drip rate and the drip rate required by the medical order, and at the same time predict the compliance of the drip rate with the medical order requirements within the predicted period, calculate the matching degree M, and output the matching degree result set.

[0021] S24, Abnormal Grading and Optimization Suggestion Generation: Based on the matching result set output in S23, abnormalities are divided into Level 1, Level 2 and Level 3, and warning levels are output; at the same time, based on the patient's condition and drug characteristics, drip rate optimization suggestions are output.

[0022] In a preferred embodiment of the control method for an infusion pump according to the present invention, the specific steps of step S4 are as follows:

[0023] S41, Command Reception and Authentication: After the target infusion pump receives the encrypted control command output by S3, it verifies the encryption algorithm and authorization identifier of the command through the security chip to ensure that the command has not been tampered with and outputs the verification result.

[0024] S42, Dynamic adjustment and execution of drip rate: Based on the verification result output by S41, if the verification is successful, the infusion pump is driven to adjust the drip rate from the current value to the calculated target drip rate value, and the parameter changes during the adjustment process are recorded synchronously, and the execution process data is output.

[0025] S43, Execution result feedback: Based on the execution process data output in S42, generate an execution result set and feedback it to update the single-pump health status model.

[0026] A control system for an infusion pump, comprising:

[0027] The multi-source data fusion acquisition and encrypted upload module collects infusion pump operating parameters, patient and medical order related data, completes data binding, standardization and encryption, stably uploads compliant datasets, and outputs encrypted standardized datasets.

[0028] The multi-dimensional analysis and hierarchical early warning module, based on the encrypted standardized dataset output by the multi-source data fusion acquisition and encrypted upload module, performs data parsing, trend prediction, and matching verification to generate optimization suggestions and output trend prediction results, early warning level, matching degree result set, and drip rate optimization suggestions.

[0029] The hierarchical control and command issuance module includes a drip rate optimization calculation unit, a control command security verification unit, and a command encryption issuance unit;

[0030] The drip rate optimization calculation unit, based on the matching result set and drip rate optimization suggestions output by the multi-dimensional analysis and graded early warning module, combined with the patient's weight and drug metabolism rate, calculates the target drip rate value through an intelligent algorithm and outputs control instructions;

[0031] The control command security verification unit, based on the control command output by the drip rate optimization calculation unit, and combined with the warning level and trend prediction results, verifies whether the command is safe and outputs the security verification result.

[0032] The instruction encryption issuing unit, based on the security verification result output by the control instruction security verification unit, if it passes, will use the AES-256 encryption algorithm to encrypt the control instruction and output the encrypted control instruction.

[0033] The pump-end command execution and closed-loop feedback verification module, based on the encrypted control command output by the hierarchical control and command issuance module, completes identity verification, drives the pump to perform drip rate adjustment operation, and collects data throughout the entire execution process.

[0034] As a preferred embodiment of the control system for an infusion pump according to the present invention, the multi-source data fusion acquisition and encrypted upload module includes:

[0035] The real-time full parameter acquisition unit at the pump end collects the operating parameters of the infusion pump in real time, forming a set of pump end status parameters; at the same time, it collects related information and outputs a patient-medical order association set.

[0036] The data association and standardization processing unit, based on the patient-medical order association set output by the real-time acquisition unit of all pump parameters, binds the pump status parameter set with the patient identity and medical order requirements, removes invalid data, completes data format unification, and outputs a standardized dataset;

[0037] The encrypted upload unit uses the AES-256 encryption algorithm to encrypt the standardized dataset output by the data association and standardization processing unit, and outputs the encrypted standardized dataset through the 5G / medical dedicated wireless protocol.

[0038] As a preferred embodiment of the control system for an infusion pump according to the present invention, the multi-dimensional analysis and hierarchical early warning module includes:

[0039] The real-time data parsing and status modeling unit, based on the encrypted standardized dataset output by the multi-source data fusion acquisition and encrypted upload module, analyzes the operating status of each infusion pump through edge computing technology, constructs a single pump health status model, and outputs a real-time status score for the pump end.

[0040] The trend prediction and analysis unit, based on the real-time status score of the pump end output by the real-time data parsing and status modeling unit, retrieves the big data of the patient corresponding to the infusion pump, combines the current real-time parameters to perform trend extrapolation, predict potential risks, and outputs the trend prediction results.

[0041] The drip rate-medical order matching degree verification unit, based on the real-time status score of the pump end output by the real-time data parsing and status modeling unit and the trend prediction result output by the trend prediction analysis unit, compares the difference between the current drip rate and the drip rate required by the medical order, and at the same time predicts the compliance of the drip rate with the medical order requirements within the time period, calculates the matching degree M, and outputs the matching degree result set.

[0042] The abnormality classification and optimization suggestion generation unit, based on the matching result set output by the drip rate-medical order matching degree verification unit, classifies abnormalities into level one, level two and level three, and outputs warning levels; at the same time, it outputs drip rate optimization suggestions according to the patient's condition and drug characteristics.

[0043] In a preferred embodiment of the control system for an infusion pump according to the present invention, the pump-end command execution and closed-loop feedback verification module includes:

[0044] The instruction receiving and authentication unit verifies the encryption algorithm and permission identifier of the instruction through a security chip after the target infusion pump receives the encrypted control instruction output by the graded control and instruction issuing module, ensuring that the instruction has not been tampered with, and outputs the verification result.

[0045] The drip rate dynamic adjustment and execution unit, based on the verification result output by the instruction receiving and authentication unit, if the verification is successful, drives the infusion pump to adjust the drip rate from the current value to the calculated target drip rate value, synchronously records the parameter changes during the adjustment process, and outputs the execution process data;

[0046] The execution result feedback unit generates an execution result set based on the dynamic adjustment of the drip rate and the execution process data output by the execution unit, and sends it back to update the single pump health status model.

[0047] Compared with existing technologies:

[0048] 1. By establishing a remote control architecture, it has the advantage of enabling medical staff to complete all operations such as adjusting the drip rate of infusion pumps, checking their status, and setting parameters from remote locations such as nurse stations and mobile terminals. This eliminates the need for repeated manual adjustments and inspections at each patient's bedside, significantly freeing up medical staff's time and energy. This allows them to devote more time to core diagnostic and treatment work such as patient condition monitoring and professional nursing care, completely solving the clinical pain points of cumbersome bedside operations and excessive consumption of medical resources, and optimizing the efficiency of ward diagnosis and treatment services.

[0049] 2. By integrating the operating data and status information of infusion pumps, it has the advantage of centralized and visual monitoring of infusion equipment throughout the hospital / ward. There is no need to check individual equipment separately. Problems such as drip rate deviation and pipeline abnormalities can be captured and warned in the first instance, completely eliminating data silos and realizing full-time visibility and control of infusion status. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the overall framework of the present invention;

[0051] Figure 2 This is a schematic diagram of the framework of the multi-source data fusion acquisition and encrypted upload module of the present invention;

[0052] Figure 3 This is a schematic diagram of the framework of the multi-dimensional analysis and hierarchical early warning module of the present invention;

[0053] Figure 4 This is a schematic diagram of the hierarchical control and command issuance module framework of the present invention;

[0054] Figure 5 This is a schematic diagram of the pump-end command execution and closed-loop feedback verification module framework of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0056] This invention provides a control method for an infusion pump, comprising the following specific steps:

[0057] S1, Multi-source data fusion acquisition and encrypted upload: Collect infusion pump operating parameters, patient and medical order related data, complete data binding, standardization and encryption, stably upload compliant datasets, and output encrypted standardized datasets;

[0058] The specific steps of S1 are as follows:

[0059] S11, Real-time Acquisition of All Pump Parameters: Through the infusion pump's built-in flow sensor, pressure sensor, and drip rate detection module, the pump's core operating parameters such as drip rate (unit: ml / h), tubing pressure, remaining medication volume, and battery power are collected in real time to form a set of pump status parameters; at the same time, the pump collects related information such as patient ID, doctor's order number, and medication name, and outputs a patient-doctor's order association set;

[0060] S12, Data Association and Standardization Processing: Based on the patient-medical order association set output in S11, the pump end status parameter set is bound to the patient identity and medical order requirements, and invalid data (such as temporary parameters generated by accidental button presses) is removed. The data format is unified (such as unified to JSON format), and a standardized dataset is output.

[0061] S13, Encrypted Upload: The standardized dataset output in S12 is encrypted using the medical-specific AES-256 encryption algorithm, and the encrypted standardized dataset is output through the 5G / medical-specific wireless protocol. At the same time, the upload timestamp is recorded to complete the data archiving.

[0062] S2, Multi-dimensional analysis and hierarchical early warning: Based on the encrypted standardized dataset output by S1, data parsing, trend prediction, and matching verification are carried out to generate optimization suggestions and output trend prediction results, early warning level, matching degree result set and drip rate optimization suggestions.

[0063] The specific steps of S2 are as follows:

[0064] S21, Real-time data analysis and status modeling: Based on the encrypted standardized dataset output by S1, the operating status of each infusion pump is analyzed through edge computing technology, a single pump health status model is constructed, and a real-time status score of the pump end is output (full score 100 points, 90 points or above is normal).

[0065] S22, Trend Prediction Analysis: Based on the real-time status score of the pump end output by S21, retrieve big data such as the historical infusion rate, abnormal alarm records, and drug tolerance of the corresponding patient for the infusion pump, combine it with the current real-time parameters to perform trend extrapolation, predict potential risks such as drip rate deviation and abnormal tubing pressure, and output the trend prediction results.

[0066] S23, Drip rate-medical order matching verification: Based on the real-time pump status score output in S21 and the trend prediction result output in S22, compare the difference between the current drip rate and the drip rate required by the medical order, and simultaneously predict the compliance of the drip rate with the medical order requirements within the predicted time period. Calculate the matching degree M (M=1-|current drip rate - medical order drip rate| / medical order drip rate) and output the matching degree result set; when M<90%, trigger a preliminary warning.

[0067] S24, Anomaly Classification and Optimization Suggestion Generation: Based on the matching result set output in S23, anomalies are classified into Level 1 (minor deviation, M=80%-90%), Level 2 (moderate anomaly, M=60%-80%), and Level 3 (severe anomaly, M<60%), and warning levels are output; at the same time, based on the patient's condition (such as heart failure, infection) and drug characteristics, drip rate optimization suggestions are output.

[0068] S3, Hierarchical Control and Command Issuance: Based on the matching result set and drip rate optimization suggestions output by S2, complete the drip rate calculation and security verification, and output the encrypted control command;

[0069] The specific steps of S3 are as follows:

[0070] S31, Drip rate optimization calculation: Based on the matching result set and drip rate optimization suggestions output by S2, combined with the patient's weight and drug metabolism rate, the target drip rate value is calculated through an intelligent algorithm, and the control command (including target drip rate, execution duration, and permission identifier) ​​is output; if the verification fails, the "insufficient permissions" prompt is displayed;

[0071] S32, Control command safety verification: Based on the control command output in S31, combined with the warning level and trend prediction results, verify whether the command is safe (e.g., whether it meets the medical order threshold, whether it conflicts with the current patient's condition, whether there is interference from multiple pumps controlling at the same time), and output the safety verification result;

[0072] S33, Encrypted Instruction Issuance: Based on the security verification result output in S32, if it passes, the control instruction will be encrypted using the medical-specific AES-256 encryption algorithm, and the encrypted control instruction will be output. At the same time, the instruction issuance time and operator information will be recorded to achieve traceability of the operation and provide instruction basis for subsequent closed-loop feedback.

[0073] S4, Pump-end command execution and closed-loop feedback verification: Based on the encrypted control command output by S3, complete identity verification, drive the pump to perform drip rate adjustment operation, and collect data of the entire execution process;

[0074] The specific steps of S4 are as follows:

[0075] S41, Command Reception and Authentication: After the target infusion pump receives the encrypted control command output by S3, it verifies the encryption algorithm and authorization identifier of the command through the built-in security chip to ensure that the command has not been tampered with, and outputs the verification result (pass / fail).

[0076] S42, Dynamic adjustment and execution of drip rate: Based on the verification result output by S41, if the verification is successful, the infusion pump is driven to adjust the drip rate from the current value to the calculated target drip rate value, and the parameter changes during the adjustment process (such as adjustment time and real-time drip rate fluctuations) are recorded simultaneously, and the execution process data is output.

[0077] S43, Execution result feedback: Based on the execution process data output in S42, generate an execution result set (including target drip rate, actual drip rate, adjustment time, and whether the execution was successful) and feed it back to update the single pump health status model; if the execution fails (such as motor failure), trigger a local audible and visual alarm and push it to the medical staff terminal simultaneously, forming a complete closed loop of "collection-analysis-control-feedback".

[0078] For a control system of an infusion pump, please refer to [link / reference]. Figure 1 ,include:

[0079] The multi-source data fusion acquisition and encrypted upload module collects infusion pump operating parameters, patient and medical order related data, completes data binding, standardization and encryption, stably uploads compliant datasets, and outputs encrypted standardized datasets.

[0080] The multi-dimensional analysis and hierarchical early warning module, based on the encrypted standardized dataset output by the multi-source data fusion acquisition and encrypted upload module, performs data parsing, trend prediction, and matching verification to generate optimization suggestions and output trend prediction results, early warning level, matching degree result set, and drip rate optimization suggestions.

[0081] The hierarchical control and command issuance module, based on the matching result set and drip rate optimization suggestions output by the multi-dimensional analysis and hierarchical early warning module, completes drip rate calculation and security verification, and outputs encrypted control commands;

[0082] The pump-end command execution and closed-loop feedback verification module, based on the encrypted control command output by the hierarchical control and command issuance module, completes identity verification, drives the pump to perform drip rate adjustment operation, and collects data throughout the entire execution process.

[0083] Please see Figure 2 The multi-source data fusion acquisition and encrypted upload module includes:

[0084] The real-time full-parameter acquisition unit at the pump end uses the infusion pump's built-in flow sensor, pressure sensor, and drip rate detection module to collect core operating parameters such as drip rate (unit: ml / h), tubing pressure, remaining medication volume, and battery power in real time, forming a set of pump end status parameters; at the same time, it collects associated information such as patient identity ID, doctor's order number, and drug name, and outputs a patient-doctor's order association set;

[0085] The data association and standardization processing unit, based on the patient-medical order association set output by the real-time acquisition unit of all pump parameters, binds the pump status parameter set with the patient identity and medical order requirements, removes invalid data (such as temporary parameters generated by accidental button presses), completes data format unification (such as unification to JSON format), and outputs a standardized dataset.

[0086] The encrypted upload unit uses the medical-specific AES-256 encryption algorithm to encrypt the standardized dataset output by the data association and standardization processing unit, and outputs the encrypted standardized dataset through the 5G / medical-specific wireless protocol, while recording the upload timestamp to complete data archiving.

[0087] Please see Figure 3 The multi-dimensional analysis and hierarchical early warning module includes:

[0088] The real-time data analysis and status modeling unit, based on the encrypted standardized dataset output by the multi-source data fusion acquisition and encrypted upload module, analyzes the operating status of each infusion pump through edge computing technology, constructs a single pump health status model, and outputs a real-time status score for the pump end (out of 100 points, with 90 points or above considered normal).

[0089] The trend prediction analysis unit, based on the real-time status score of the pump end output by the real-time data parsing and state modeling unit, retrieves big data such as the historical infusion rate, abnormal alarm records, and drug tolerance of the corresponding patient for the infusion pump, combines the current real-time parameters to perform trend extrapolation, predict potential risks such as drip rate deviation and abnormal tubing pressure, and outputs trend prediction results.

[0090] The drip rate-medical order matching verification unit, based on the real-time status score of the pump end output by the real-time data parsing and status modeling unit and the trend prediction result output by the trend prediction analysis unit, compares the difference between the current drip rate and the drip rate required by the medical order, and simultaneously predicts the compliance of the drip rate with the medical order requirement within the predicted time period, calculates the matching degree M (M=1-|current drip rate-medical order drip rate| / medical order drip rate), and outputs the matching degree result set; when M<90%, a preliminary warning is triggered;

[0091] The abnormality grading and optimization suggestion generation unit, based on the matching result set output by the drip rate-medical order matching degree verification unit, classifies abnormalities into Level 1 (minor deviation, M=80%-90%), Level 2 (moderate abnormality, M=60%-80%), and Level 3 (severe abnormality, M<60%), and outputs the warning level; at the same time, it outputs drip rate optimization suggestions according to the patient's condition (such as heart failure, infection) and drug characteristics.

[0092] Please see Figure 4 The hierarchical control and command issuance module includes:

[0093] The drip rate optimization calculation unit, based on the matching result set and drip rate optimization suggestions output by the multi-dimensional analysis and hierarchical early warning module, combined with the patient's weight and drug metabolism rate, calculates the target drip rate value through an intelligent algorithm and outputs the control command (including the target drip rate, execution duration, and permission identifier); if the verification fails, a "insufficient permissions" prompt is displayed.

[0094] The control command safety verification unit, based on the control command output by the drip rate optimization calculation unit, and combined with the warning level and trend prediction results, verifies whether the command is safe (e.g., whether it meets the medical order threshold, whether it conflicts with the current patient's condition, and whether there is interference from multiple pumps controlling at the same time), and outputs the safety verification result.

[0095] The instruction encryption issuing unit, based on the security verification result output by the control instruction security verification unit, if it passes, will use the medical-specific AES-256 encryption algorithm to encrypt the control instruction and output the encrypted control instruction. At the same time, it records the instruction issuance time and operator information to achieve operation traceability and provide instruction basis for subsequent closed-loop feedback.

[0096] Please see Figure 5 The pump-end command execution and closed-loop feedback verification module includes:

[0097] The instruction receiving and authentication unit verifies the encryption algorithm and permission identifier of the instruction through the built-in security chip after the target infusion pump receives the encrypted control instruction output by the graded control and instruction issuing module, ensuring that the instruction has not been tampered with, and outputs the verification result (pass / fail).

[0098] The drip rate dynamic adjustment and execution unit, based on the verification result output by the instruction receiving and authentication unit, if the verification is successful, drives the infusion pump to adjust the drip rate from the current value to the calculated target drip rate value, synchronously records the parameter changes during the adjustment process (such as adjustment time and real-time drip rate fluctuations), and outputs the execution process data.

[0099] The execution result feedback unit generates an execution result set (including target drip rate, actual drip rate, adjustment time, and whether the execution was successful) based on the dynamic adjustment of the drip rate and the execution process data output by the execution unit, and sends it back to update the single pump health status model. If the execution fails (such as motor failure), a local audible and visual alarm is triggered and pushed to the medical staff terminal simultaneously, forming a complete closed loop of "collection-analysis-control-feedback".

[0100] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A control method for an infusion pump, characterized in that, The specific steps are as follows: S1, Multi-source data fusion acquisition and encrypted upload: Collect infusion pump operating parameters, patient and medical order related data, complete data binding, standardization and encryption, stably upload compliant datasets, and output encrypted standardized datasets; S2, Multi-dimensional analysis and hierarchical early warning: Based on the encrypted standardized dataset output by S1, data parsing, trend prediction, and matching verification are carried out to generate optimization suggestions and output trend prediction results, early warning level, matching degree result set and drip rate optimization suggestions. S3, tiered control and command issuance: S31, Drip rate optimization calculation: Based on the matching result set and drip rate optimization suggestions output by S2, combined with the patient's weight and drug metabolism rate, the target drip rate value is calculated through an intelligent algorithm, and the control command is output; S32, Control command security verification: Based on the control command output in S31, combined with the warning level and trend prediction results, verify whether the command is safe and output the security verification result; S33, Encrypted command issuance: Based on the security verification result output by S32, if it passes, the control command will be encrypted using the AES-256 encryption algorithm, and the encrypted control command will be output. S4, Pump-end command execution and closed-loop feedback verification: Based on the encrypted control command output by S3, complete identity verification, drive the pump to perform drip rate adjustment operation, and collect data of the entire execution process.

2. The control method for an infusion pump according to claim 1, characterized in that, The specific steps of S1 are as follows: S11, Real-time acquisition of all pump parameters: Real-time acquisition of the operating parameters of the infusion pump to form a set of pump status parameters; at the same time, acquisition of related information and output of the patient-medical order association set; S12, Data Association and Standardization Processing: Based on the patient-medical order association set output in S11, the pump end status parameter set is bound with the patient identity and medical order requirements, invalid data is removed, data format is unified, and a standardized dataset is output. S13, Encrypted Upload: The standardized dataset output in S12 is encrypted using the AES-256 encryption algorithm, and the encrypted standardized dataset is output through the 5G / medical dedicated wireless protocol.

3. The control method for an infusion pump according to claim 1, characterized in that, The specific steps of S2 are as follows: S21, Real-time data analysis and status modeling: Based on the encrypted standardized dataset output by S1, the operating status of each infusion pump is analyzed through edge computing technology, a single pump health status model is constructed, and a real-time status score of the pump end is output. S22, Trend Prediction Analysis: Based on the real-time status score of the pump end output in S21, retrieve the big data of the patient corresponding to the infusion pump, combine it with the current real-time parameters to perform trend extrapolation, predict potential risks, and output the trend prediction results; S23, Drip rate-medical order matching degree verification: Based on the real-time status score of the pump end output in S21 and the trend prediction result output in S22, compare the difference between the current drip rate and the drip rate required by the medical order, and at the same time predict the compliance of the drip rate with the medical order requirements within the predicted period, calculate the matching degree M, and output the matching degree result set. S24, Abnormal Grading and Optimization Suggestion Generation: Based on the matching result set output in S23, abnormalities are divided into Level 1, Level 2 and Level 3, and warning levels are output; at the same time, based on the patient's condition and drug characteristics, drip rate optimization suggestions are output.

4. The control method for an infusion pump according to claim 1, characterized in that, The specific steps of S4 are as follows: S41, Command Reception and Authentication: After the target infusion pump receives the encrypted control command output by S3, it verifies the encryption algorithm and authorization identifier of the command through the security chip to ensure that the command has not been tampered with and outputs the verification result. S42, Dynamic adjustment and execution of drip rate: Based on the verification result output by S41, if the verification is successful, the infusion pump is driven to adjust the drip rate from the current value to the calculated target drip rate value, and the parameter changes during the adjustment process are recorded synchronously, and the execution process data is output. S43, Execution result feedback: Based on the execution process data output in S42, generate an execution result set and feedback it to update the single-pump health status model.

5. A control system for an infusion pump, characterized in that, include: The multi-source data fusion acquisition and encrypted upload module collects infusion pump operating parameters, patient and medical order related data, completes data binding, standardization and encryption, stably uploads compliant datasets, and outputs encrypted standardized datasets. The multi-dimensional analysis and hierarchical early warning module, based on the encrypted standardized dataset output by the multi-source data fusion acquisition and encrypted upload module, performs data parsing, trend prediction, and matching verification to generate optimization suggestions and output trend prediction results, early warning level, matching degree result set, and drip rate optimization suggestions. The hierarchical control and command issuance module includes a drip rate optimization calculation unit, a control command security verification unit, and a command encryption issuance unit; The drip rate optimization calculation unit, based on the matching result set and drip rate optimization suggestions output by the multi-dimensional analysis and graded early warning module, combined with the patient's weight and drug metabolism rate, calculates the target drip rate value through an intelligent algorithm and outputs control instructions; The control command security verification unit, based on the control command output by the drip rate optimization calculation unit, and combined with the warning level and trend prediction results, verifies whether the command is safe and outputs the security verification result. The instruction encryption issuing unit, based on the security verification result output by the control instruction security verification unit, if it passes, will use the AES-256 encryption algorithm to encrypt the control instruction and output the encrypted control instruction. The pump-end command execution and closed-loop feedback verification module, based on the encrypted control command output by the hierarchical control and command issuance module, completes identity verification, drives the pump to perform drip rate adjustment operation, and collects data throughout the entire execution process.

6. The control system for an infusion pump according to claim 5, characterized in that, The multi-source data fusion acquisition and encrypted upload module includes: The real-time full parameter acquisition unit at the pump end collects the operating parameters of the infusion pump in real time, forming a set of pump end status parameters; at the same time, it collects related information and outputs a patient-medical order association set. The data association and standardization processing unit, based on the patient-medical order association set output by the real-time acquisition unit of all pump parameters, binds the pump status parameter set with the patient identity and medical order requirements, removes invalid data, completes data format unification, and outputs a standardized dataset; The encrypted upload unit uses the AES-256 encryption algorithm to encrypt the standardized dataset output by the data association and standardization processing unit, and outputs the encrypted standardized dataset through the 5G / medical dedicated wireless protocol.

7. The control system for an infusion pump according to claim 5, characterized in that, The multi-dimensional analysis and hierarchical early warning module includes: The real-time data parsing and status modeling unit, based on the encrypted standardized dataset output by the multi-source data fusion acquisition and encrypted upload module, analyzes the operating status of each infusion pump through edge computing technology, constructs a single pump health status model, and outputs a real-time status score for the pump end. The trend prediction and analysis unit, based on the real-time status score of the pump end output by the real-time data parsing and status modeling unit, retrieves the big data of the patient corresponding to the infusion pump, combines the current real-time parameters to perform trend extrapolation, predict potential risks, and outputs the trend prediction results. The drip rate-medical order matching degree verification unit, based on the real-time status score of the pump end output by the real-time data parsing and status modeling unit and the trend prediction result output by the trend prediction analysis unit, compares the difference between the current drip rate and the drip rate required by the medical order, and at the same time predicts the compliance of the drip rate with the medical order requirements within the time period, calculates the matching degree M, and outputs the matching degree result set. The abnormality classification and optimization suggestion generation unit, based on the matching result set output by the drip rate-medical order matching degree verification unit, classifies abnormalities into level one, level two and level three, and outputs warning levels; at the same time, it outputs drip rate optimization suggestions according to the patient's condition and drug characteristics.

8. The control system for an infusion pump according to claim 5, characterized in that, The pump-end command execution and closed-loop feedback verification module includes: The instruction receiving and authentication unit verifies the encryption algorithm and permission identifier of the instruction through a security chip after the target infusion pump receives the encrypted control instruction output by the graded control and instruction issuing module, ensuring that the instruction has not been tampered with, and outputs the verification result. The drip rate dynamic adjustment and execution unit, based on the verification result output by the instruction receiving and authentication unit, if the verification is successful, drives the infusion pump to adjust the drip rate from the current value to the calculated target drip rate value, synchronously records the parameter changes during the adjustment process, and outputs the execution process data; The execution result feedback unit generates an execution result set based on the dynamic adjustment of the drip rate and the execution process data output by the execution unit, and sends it back to update the single pump health status model.

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