Nursing system for tumor patient in radiotherapy and chemotherapy period

Abstract

The invention relates to the technical field of medical information systems, in particular to a nursing system for tumor patients in a chemoradiotherapy period, which comprises a physiological monitoring module, a threshold evaluation module, a path switching module, a task decomposition module and a nursing scheduling module. According to the method, by continuously collecting multi-dimensional physiological data such as the heart rate, the blood oxygen saturation degree, the blood pressure and the serum creatinine of the patient and conducting noise suppression processing, stable bearing state information can be obtained, and dynamic quantitative evaluation of the tolerance degree is achieved in combination with deviation analysis of the nursing base line; the nursing task is subjected to priority and rhythm adjustment when the bearing index exceeds the limit, the high-load task is split by utilizing fluctuation prediction, and the interval duration is set, so that the risk caused by physiological burden concentration is reduced, the nursing execution rhythm matched with the real-time state of a patient is formed, and the flexibility of load distribution and the instantaneity of intervention response are realized. And the accuracy and safety of whole-course nursing are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of medical information systems, in particular to a nursing system for tumor patients in radiotherapy and chemotherapy period. BACKGROUND

[0002] The technical field of medical information systems relates to the use of computer software and hardware and network communication means to collect, store, manage and transmit various types of information generated during medical treatment. It covers core matters such as electronic medical record management system, medical image information system, hospital management information system, clinical decision support system, remote medical platform and patient health record system, aiming to realize information exchange and data sharing within and across medical institutions, support information-based operation in multiple links such as diagnosis, treatment, nursing and management, and improve the digital level and traceability of medical services.

[0003] Among them, the traditional nursing system for tumor patients in radiotherapy and chemotherapy period refers to the process of recording the treatment plan, drug dosage, treatment progress and adverse reaction of patients by nursing staff based on paper or electronic documents such as medical record form, chemotherapy drug registration book, radiotherapy schedule and laboratory test sheet, and collecting feedback information from patients through telephone follow-up, ward patrol and on-site inquiry, so as to complete the continuous tracking and management of the changes of patients in radiotherapy and chemotherapy period and their nursing needs.

[0004] In the prior art, patient status monitoring relies on periodic examination and non-continuous feedback, data collection is limited by time interval, and state changes are not captured in time. The nursing task arrangement is executed according to the fixed plan, and there is a lack of response mechanism to short-term tolerance fluctuation of patients. When physiological carrying capacity decreases during treatment, task load is difficult to adjust in time, which may cause patients to bear high nursing intensity in a short period of time, increase the probability of adverse reactions and affect the continuity and effectiveness of subsequent intervention. SUMMARY

[0005] In order to solve the technical problems in the prior art that patient status monitoring relies on periodic examination and non-continuous feedback, data collection is limited by time interval, and state changes are not captured in time. The nursing task arrangement is executed according to the fixed plan, and there is a lack of response mechanism to short-term tolerance fluctuation of patients. When physiological carrying capacity decreases during treatment, task load is difficult to adjust in time, which may cause patients to bear high nursing intensity in a short period of time, increase the probability of adverse reactions and affect the continuity and effectiveness of subsequent intervention, the embodiments of the present application provide a nursing system for tumor patients in radiotherapy and chemotherapy period. The technical solution is as follows:

[0006] On the one hand, a nursing system for tumor patients in radiotherapy and chemotherapy period is provided, which comprises: The physiological monitoring module obtains the heart rate, blood oxygen saturation, systolic pressure, diastolic pressure and serum creatinine concentration of the tumor patient in the chemotherapy period through a monitoring device, carries out noise elimination processing on the data by using a sliding average filtering algorithm, generates patient physiological bearing state data, and transmits the data to the threshold evaluation module; The threshold evaluation module calls the patient physiological bearing state data, carries out deviation operation on the values in the data and the nursing bearing baseline value, calculates a comprehensive bearing index, generates a path switching trigger signal when the bearing index exceeds the chemotherapy nursing bearing threshold, and transmits the signal to the path switching module; The path switching module calls the path switching trigger signal, obtains the remaining task set of the current chemotherapy nursing task queue, detects the load attribute identifier, adjusts the load nursing task based on the signal descending order sorting, generates a nursing intervention path adjustment scheme, and transmits the scheme to the task decomposition module. The task decomposition module receives the nursing intervention path adjustment scheme, calculates the tolerance change amplitude, predicts the fluctuation amplitude by using a multiple regression analysis model, decomposes the high-load task and configures the interval length when the fluctuation threshold is exceeded, generates a nursing task execution configuration scheme, and transmits the scheme to the nursing scheduling module.

[0007] As a further scheme of the present application, the patient physiological bearing state data includes heart rate, blood oxygen saturation, systolic pressure, diastolic pressure, serum creatinine concentration, comprehensive bearing index and bearing change trend, the path switching trigger signal includes nursing path adjustment priority, task load sorting identifier and path switching trigger reason, the nursing intervention path adjustment scheme includes task adjustment order, nursing intervention type and adjustment adaptation condition, and the nursing task execution configuration scheme includes task execution frequency, interval length setting and task load level.

[0008] As a further scheme of the present application, the physiological monitoring module includes: The data acquisition sub-module obtains the heart rate, blood oxygen saturation, systolic pressure, diastolic pressure and serum creatinine concentration of the tumor patient in the chemotherapy period through a monitoring device, unifies the time sequence format, synchronously processes the multi-dimensional data of physiological parameters, sorts the time sequence of physiological parameters according to the time stamp, and generates physiological original sequence values; The data filtering sub-module, based on the physiological original sequence values, carries out denoising processing on the data according to the fluctuation amplitude of the time sequence by using a sliding average filtering algorithm, and obtains denoised physiological sequence values in combination with the dynamic changes of the time sequence of heart rate, blood oxygen saturation, systolic pressure, diastolic pressure and serum creatinine concentration; The window length of the sliding average filtering algorithm is adaptively adjusted based on the original fluctuation amplitude; The carrying state generation submodule generates physiological carrying state data by comprehensively scoring the physiological parameters using a weighted calculation method according to the denoised physiological sequence value, analyzing the change trend in combination with the weighted interval value of the systolic pressure, diastolic pressure and heart rate.

[0009] As a further scheme of the present application, the threshold evaluation module comprises: The data deviation operation submodule obtains a total value of the index value deviation by performing difference operation and summation on the values of the same monitoring index and the baseline value in combination with the nursing carrying baseline value based on the monitoring values in the patient physiological carrying state data; The comprehensive carrying index calculation submodule obtains a comprehensive index value of the carrying state by performing weighted calculation on the deviation in combination with the weight coefficient of the differential monitoring index in the carrying evaluation system after normalization processing; The path switching signal generation submodule obtains a path switching trigger signal by performing single comparison between the index value and the radiotherapy and chemotherapy nursing carrying threshold value in combination with the comprehensive index value of the carrying state.

[0010] As a further scheme of the present application, the total value of the index value deviation is obtained by performing difference calculation and summation on the values of the same monitoring index in the patient physiological carrying state data and the corresponding nursing carrying baseline value; The comprehensive index value of the carrying state is obtained by performing weighted calculation and normalization processing in combination with the weight coefficient of the monitoring index based on the total value of the index value deviation; The radiotherapy and chemotherapy nursing carrying threshold value is a preset numerical limit used for comparison with the comprehensive index value of the carrying state; The normalization processing adopts a linear normalization method.

[0011] As a further scheme of the present application, the path switching module comprises: The trigger signal acquisition submodule acquires the path switching trigger signal, parses the timestamp field and task identification field in the signal, matches the task number in the current task queue, arranges the task numbers in descending order of the timestamp, and generates a task ordering sequence; The task load detection submodule calls the task ordering sequence, extracts the load attribute identification field corresponding to the task number, calculates the load deviation of the task according to the load attribute identification field and the set load reference value, arranges the load deviation in descending order of the numerical value, and generates a load ordering list; The nursing path adjustment submodule calls the load ordering list, adjusts the execution order of the nursing task in the intervention path according to the list order, compares the adjusted task order with the original task path table in the field position, calculates the order change rate, and generates a nursing intervention path adjustment scheme.

[0012] As a further aspect of the present invention, the task decomposition module includes: The tolerance fluctuation calculation submodule calculates the tolerance difference before and after the intervention based on the intervention measures data in the nursing intervention path adjustment plan, combined with the physiological monitoring data before and after the intervention and the execution time, and generates the tolerance change amplitude value through ratio calculation. The fluctuation amplitude prediction submodule calls the tolerance change amplitude value, inputs the intervention frequency data, intervention intensity data and recovery time data as independent variables into the multiple regression analysis model, establishes the fluctuation amplitude regression equation, and performs weighted calculations based on the regression coefficients to obtain the fluctuation amplitude prediction value. The task interval configuration submodule calculates the difference between the predicted fluctuation value and the fluctuation threshold, filters tasks whose predicted values ​​exceed the threshold, splits them into sub-tasks and sets the interval duration, and generates a nursing task execution configuration scheme.

[0013] As a further aspect of the present invention, the tolerance change range value refers to a value calculated based on the ratio of the tolerance difference before and after the intervention to the tolerance before the intervention; The predicted value of the fluctuation range refers to the value obtained by multiple regression calculation based on the tolerance change range value, intervention frequency data, intervention intensity data, and recovery time data. The fluctuation threshold refers to a preset numerical limit used for comparison with the predicted fluctuation amplitude.

[0014] As a further solution of the present invention, the nursing scheduling module calls the task order and interval duration configuration in the nursing task execution configuration scheme to construct a scheduling execution sequence, sets the execution time node based on the current timestamp and monitors the matching status to trigger the nursing task execution instruction, summarizes the execution time node and the corresponding nursing task, and generates a nursing task execution instruction set; The nursing task execution instruction set includes execution time nodes, task identification information, and execution triggering conditions.

[0015] As a further aspect of the present invention, the nursing scheduling module includes: The task sequence construction submodule, based on the task order and interval configuration in the nursing task execution configuration scheme, lists the tasks in sequence and assigns them corresponding sequence numbers, calculates and records the interval between adjacent tasks, combines the task number and the interval into a continuous task sequence, and generates a task sequence number sequence. The execution time setting submodule calls the task sequence number sequence, determines the start time of the first task based on the current timestamp, calculates the task execution time node by accumulating the interval duration, associates the original task information with the task number and matches it with the corresponding node to obtain a list of task execution time nodes; The instruction set generation submodule checks the matching status between the time nodes and the current timestamp based on the task execution time node list, organizes the successfully matched tasks into instruction entries, arranges the instructions in chronological order, and obtains the nursing task execution instruction set.

[0016] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following: By continuously collecting multidimensional physiological data such as heart rate, blood oxygen saturation, blood pressure, and serum creatinine from patients and performing noise suppression processing, stable load status information can be obtained. Combined with deviation analysis from the nursing baseline, dynamic quantitative assessment of tolerance can be achieved. When the load index exceeds the limit, the priority and rhythm of nursing tasks can be adjusted. High-load tasks can be broken down and intervals can be set using fluctuation prediction to reduce the risk of concentrated physiological burden. A nursing execution rhythm that matches the patient's real-time status can be formed, achieving flexibility in load allocation and immediacy in intervention response, thereby improving the accuracy and safety of nursing care throughout the process. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of a nursing system for cancer patients undergoing radiotherapy and chemotherapy provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the system framework of the present invention; Figure 3 This is a flowchart of the physiological monitoring module in this invention; Figure 4 This is a flowchart of the threshold evaluation module in this invention; Figure 5 This is a flowchart of the path switching module in this invention; Figure 6 This is a flowchart of the task decomposition module in this invention; Figure 7 This is a flowchart of the nursing scheduling module in this invention. Detailed Implementation

[0019] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0020] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0021] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0022] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0023] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0024] This invention provides a nursing system for cancer patients undergoing radiotherapy and chemotherapy, such as... Figures 1-2 The diagram shown illustrates a nursing system for cancer patients undergoing radiotherapy and chemotherapy. The system includes: The physiological monitoring module acquires heart rate, blood oxygen saturation, systolic blood pressure, diastolic blood pressure and serum creatinine concentration of cancer patients undergoing radiotherapy and chemotherapy through monitoring equipment. It uses a moving average filtering algorithm to remove noise from the data and generate patient physiological bearing status data, which is then transmitted to the threshold assessment module. The threshold assessment module calls up the patient's physiological carrying status data, calculates the deviation between the data values ​​and the nursing carrying baseline value, calculates the comprehensive carrying index, and generates a path switching trigger signal when the carrying index exceeds the radiotherapy and chemotherapy nursing carrying threshold, which is then transmitted to the path switching module. The path switching module calls the path switching trigger signal, obtains the set of remaining tasks in the current radiotherapy and chemotherapy nursing task queue, detects the load attribute identifier, adjusts the load nursing tasks based on the signal in descending order, generates a nursing intervention path adjustment plan, and passes it to the task decomposition module. The task decomposition module receives nursing intervention path adjustment plans, calculates the tolerance change range, uses a multivariate regression analysis model to predict the fluctuation range, decomposes high-load tasks and configures the interval duration when the fluctuation threshold is exceeded, generates a nursing task execution configuration plan, and transmits it to the nursing scheduling module. The nursing scheduling module calls the task order and interval duration configuration in the nursing task execution configuration scheme to build the scheduling execution sequence, sets the execution time node based on the current timestamp and monitors the matching status to trigger the nursing task execution instruction, summarizes the execution time node and the corresponding nursing task, and generates a nursing task execution instruction set; Patient physiological load data include heart rate, blood oxygen saturation, systolic blood pressure, diastolic blood pressure, serum creatinine concentration, comprehensive load index, and load change trend. Pathway switching trigger signals include nursing pathway adjustment priority, task load sorting identifier, and pathway switching trigger reason. Nursing intervention pathway adjustment plan includes task adjustment order, nursing intervention type, and adjustment adaptation conditions. Nursing task execution configuration plan includes task execution frequency, interval duration setting, and task load level. Nursing task execution instruction set includes execution time node, task identification information, and execution trigger conditions.

[0025] Specifically, such as Figure 2 , 3 As shown, the physiological monitoring module includes: The data acquisition submodule acquires heart rate, blood oxygen saturation, systolic blood pressure, diastolic blood pressure and serum creatinine concentration of cancer patients undergoing radiotherapy and chemotherapy through monitoring equipment, unifies the time series format, synchronously processes the multidimensional data of physiological parameters, sorts the time series of physiological parameters by timestamp, and generates raw physiological sequence values. The monitoring equipment acquires heart rate, blood oxygen saturation, systolic blood pressure, diastolic blood pressure, and serum creatinine concentration in cancer patients undergoing radiotherapy and chemotherapy. The monitoring equipment collects heart rate data from a bedside electrocardiogram monitor, with values ​​ranging from [value range missing]. Second-rate Take a patient's heart rate Second-rate Blood oxygen saturation is obtained using a pulse oximeter and is within the normal range. Take a patient's blood oxygen saturation as Systolic blood pressure is measured using an electronic blood pressure monitor and is within the normal range. Take a patient's systolic blood pressure as Diastolic blood pressure measurement range Take a patient's diastolic blood pressure as Serum creatinine concentration is obtained through laboratory blood tests and is within the normal range. The serum creatinine concentration of a certain patient was taken as... The unified time series format standardizes the five collected physiological parameters according to a unified timestamp format, using the format "year-month-day-hour:minute:second", such as " ".

[0026] Heart rate The times / minute record is [2024−03−15 −08:30:00, HR, 78 blood oxygen saturation Record as [2024−03−15 − 08:30:00, Sp O 2 ，97] systolic blood pressure Record as [2024−03−15 − 08:30:00, SBP, 125 diastolic blood pressure Record as [2024−03−15 − 08:30:00, DBP, 82 serum creatinine Record as [2024−03−15 − 08:30:00, SCr, 89 When processing multidimensional data of physiological parameters synchronously, the five parameters at the same time are combined into a data vector, such as time... The data vector is ,time The data vector is ,time The data vector is The time series of physiological parameters, sorted by timestamp, arranges data vectors from multiple time points in chronological order to form a time series matrix. The first row... Time data The second line Time data The third line Time data This process is repeated to form a complete time series data matrix, generating the original physiological sequence values.

[0027] The data filtering submodule, based on the original physiological sequence values, uses a moving average filtering algorithm to denoise the data according to the fluctuation amplitude of the time series. It combines the dynamic changes of time series such as heart rate, blood oxygen saturation, systolic blood pressure, diastolic blood pressure, and serum creatinine concentration to obtain denoised physiological sequence values. When using the moving average filtering algorithm based on the original physiological sequence values, the sliding window size is first set to... At each time point, for heart rate time series data: ; Perform filtering and calculate the first... The moving average at each time point is Second-rate , No. The moving average at each time point is Second-rate , No. The moving average at each time point is Second-rate When denoising data based on the fluctuation amplitude of a time series, the absolute value of the difference between adjacent time points is calculated. In a heart rate sequence... and the The difference in time points is times / minute, the and the The difference in time points is times / minute, when the difference value exceeds the preset threshold When noise is detected at times per minute, data points exceeding a threshold are replaced with the average of the two preceding and following time points. For example, the first... Time point The frequency / minute was replaced with BHz / minute, combined with the dynamic changes of time series such as heart rate, blood oxygen saturation, systolic blood pressure, diastolic blood pressure, and serum creatinine concentration, to analyze the blood oxygen saturation series. Using the same moving average algorithm, the window size is , No. The moving average at each time point is For systolic blood pressure sequence Processing, the first The moving average at each time point is Diastolic blood pressure sequence , No. The moving average at each time point is Serum creatinine sequence: ; No. The moving average at each time point is The denoised physiological sequence values ​​were obtained.

[0028] Table 1: Comparison of Patient Physiological Parameters Before and After Noise Reduction Time point Heart rate before denoising (times / minute) Heart rate after denoising (times / minute) Oxygen saturation before denoising (%) Oxygen saturation after denoising (%) Systolic pressure before denoising (mmHg) Systolic pressure after denoising (mmHg) T1 78 78.0 97 97.0 125 125.0 T2 80 79.2 96 96.8 128 126.8 T3 76 79.0 98 97.0 122 126.2 T4 82 79.6 95 97.2 130 126.4 T5 79 79.0 99 97.0 126 126.6 As shown in Table 1, the fluctuations in physiological parameter data are significantly reduced after denoising, and the data sequence is smoother.

[0029] The physiological load status generation submodule uses a weighted calculation method to comprehensively score physiological parameters based on the denoised physiological sequence values. It combines the weighted interval values ​​of systolic blood pressure, diastolic blood pressure, and heart rate to analyze their changing trends and generate physiological load status data. When comprehensively scoring physiological parameters based on denoised physiological sequence values ​​using a weighted calculation method, the weighting coefficients of the physiological parameters are first determined. The weight of heart rate is set as follows: Statistical analysis revealed the percentage of impact of heart rate on patients' physiological state. The weighting of blood oxygen saturation is set to Its importance in respiratory function The systolic blood pressure weight is set to The percentage of cardiac systolic function status The diastolic blood pressure weight is set to The proportion reflecting the elasticity of blood vessels The serum creatinine weight was set as The percentage reflecting the state of kidney function Regarding heart rate values Second-rate Standardization is performed using the formula S = (actual value - minimum normal value) / (maximum normal value - minimum normal value). The standardized heart rate score is: ; blood oxygen saturation The standardized score is: ; systolic blood pressure The standardized score is: ; diastolic blood pressure The standardized score is: ; serum creatinine The standardized score is: ; The overall score is calculated as follows: ; When combining the weighted interval values ​​of systolic blood pressure, diastolic blood pressure, and heart rate, systolic blood pressure diastolic blood pressure Heart rate Second-rate Multiply by their respective weights, the weighted average of systolic blood pressure is: Diastolic blood pressure weighted value Heart rate weighted value The sum of the three weighted interval values ​​is When analyzing its changing trend, the weighted interval values ​​of three consecutive time points are compared. The weighted interval value at time is , The weighted interval value at time is , The weighted interval value at time is The trend was initially upward followed by a downward movement, with the upward movement being [missing information]. The decrease was The stability index of the trend is: ; When the stability index is less than The time was determined to be a time of significant trend fluctuation, greater than When the trend is determined to be relatively stable, physiological bearing status data is generated.

[0030] Specifically, such as Figure 2 , 4 As shown, the threshold evaluation module includes: The data deviation calculation submodule, based on the monitoring values ​​in the patient's physiological carrying status data and combined with the nursing carrying baseline value, performs difference calculation on the values ​​of the same monitoring indicators and the baseline value and sums them to obtain the total deviation of the indicator values; Based on the monitoring values ​​in the patient's physiological load data, heart rate is extracted from the aforementioned physiological load data. pulses / minute, blood oxygen saturation systolic blood pressure diastolic blood pressure Serum creatinine The five key monitoring indicators, combined with the nursing baseline value, are used to call the preset standard baseline reference value. The heart rate baseline value is set as follows: times per minute, this baseline value is analyzed For example, the mean heart rate of patients undergoing radiotherapy and chemotherapy was obtained, and the baseline value of blood oxygen saturation was set as follows: The baseline value for systolic blood pressure is set to The baseline value for diastolic blood pressure is set to The baseline value of serum creatinine was set as follows: When calculating the difference between the values ​​of the same monitoring indicators and the baseline values, a subtraction operation is performed, and the heart rate deviation is calculated as follows: Second-rate Blood oxygen saturation deviation is calculated as follows: The systolic pressure deviation is calculated as follows: Diastolic blood pressure deviation is calculated as follows: The serum creatinine deviation was calculated as follows: When summing multiple deviation values, a summation operation is performed. First, the indicator deviation values ​​are standardized. The heart rate deviation is standardized to... Blood oxygen saturation deviation standardized to systolic blood pressure deviation standardized as Diastolic blood pressure deviation standardized to Serum creatinine deviation standardized to The sum of the index value offsets is calculated as follows: This yields the sum of the index value offsets.

[0031] The comprehensive carrying capacity index calculation submodule calls the sum of the index value offsets, calculates the offsets by weighting according to the weight coefficients of the differentiated monitoring indicators in the carrying capacity assessment system, performs normalization processing, and obtains the comprehensive carrying capacity index value. Call the sum of indicator value offsets When calculating the weighting coefficients of differentiated monitoring indicators within the load assessment system, preset weighting parameters are invoked, with the heart rate weighting coefficient set to [value missing]. This weight is determined through an expert scoring method. Several clinical experts discussed the importance of heart rate. Score, average score Score, weighted as follows The weighting coefficient for blood oxygen saturation is set to Average expert rating Score, weighted as The systolic blood pressure weighting coefficient is set to Average expert rating Score, weighted as The diastolic blood pressure weighting coefficient is set to Average expert rating Score, weighted as The serum creatinine weighting coefficient was set to Average expert rating Score, weighted as When performing a weighted calculation of the offset, a multiplication operation is performed. The heart rate-weighted offset is... The weighted offset of blood oxygen saturation is The systolic pressure weighted offset is The diastolic pressure weighted offset is The serum creatinine weighted offset is The total weighted offset is When performing normalization, a linear normalization method is used, and the maximum offset is set to... The minimum offset is The normalization formula is: ; The comprehensive index value of load-bearing status is obtained.

[0032] Table 2: Weighting Coefficients for Load-Bearing Capacity Assessment Indicators Monitoring index Mean expert score (points) Weighting coefficient Standardized deviation value Weighted offset Heart rate 7.5 0.25 0.1 0.025 Oxygen saturation 9.0 0.30 0.2 0.06 Systolic pressure 6.0 0.20 0.12 0.024 Diastolic pressure 4.5 0.15 0.1 0.015 Serum creatinine 3.0 0.10 0.056 0.0056 As shown in Table 2, the weight coefficients of the monitoring indicators were determined based on expert scores, with blood oxygen saturation receiving the highest weight of 0.30 and serum creatinine receiving the lowest weight of 0.10.

[0033] The path switching signal generation submodule calls the comprehensive index value of the carrying status and compares the index value with the radiotherapy and chemotherapy nursing carrying threshold in a single comparison. If the index value exceeds the radiotherapy and chemotherapy nursing carrying threshold, a path switching trigger signal is obtained. Call the comprehensive index value of the bearer status When comparing the index value with the nursing tolerance threshold for radiotherapy and chemotherapy in a single instance, the standard for setting the tolerance threshold is first determined. The nursing tolerance threshold for radiotherapy and chemotherapy is determined through retrospective analysis. For example, nursing records for patients undergoing radiotherapy and chemotherapy are used to determine when the composite index value exceeds [a certain threshold]. The probability of patients experiencing significant adverse reactions is [percentage missing]. When the index value exceeds The probability of adverse reactions increases to When the index value exceeds When the probability of adverse reactions reaches Based on the principle of risk control, the threshold for the nursing care of radiotherapy and chemotherapy is set as follows: When performing comparison operations, the current composite index value will be used. With threshold Perform numerical comparisons, and the judgment condition is: Substituting the values ​​into the equations is worthwhile. If the comparison result is false, it means that the current index value has not exceeded the carrying capacity threshold. If the index value exceeds the radiotherapy and chemotherapy nursing carrying capacity threshold, the path switching logic will be executed. When the conditions are met, the system automatically generates a path switching trigger signal, and the signal code is set to... When the condition is not met, the signal is encoded as Under the current circumstances ,therefore This indicates that a path switch is not required if the overall index value reaches a certain level at other times. Exceeding the threshold At that time, the system will generate The path switching trigger signal is obtained.

[0034] Specifically, such as Figure 2 , 5 As shown, the path switching module includes: The trigger signal acquisition submodule acquires the path switching trigger signal, parses the timestamp field and task identifier field in the signal, matches the task number in the current task queue, sorts them in descending order of timestamp, and generates a task sorting sequence. Get path switching trigger signal When parsing the timestamp field in a signal, time information is extracted from the signal data structure. The timestamp field format is " The current signal timestamp is " The year is analyzed as The month is The date is , hours minutes The number of seconds is During task identifier field parsing, task encoding information is read from the signal structure. The task identifier field contains task type encoding and task sequence number. The current task identifier is "". ",in Indicates the task category, Indicates the task sequence number. This indicates a heart rate monitoring task. When matching a task number in the current task queue, the task queue data stored in the system is retrieved. The task queue contains task numbers. Five tasks, compare the task identifier field one by one. "Compared to the task number in the queue, the first task" Match successful, second task Matching failed, third task Matching failed, fourth task Matching failed, fifth task Matching failed; the matching task is now confirmed. When sorted in descending order by timestamp, retrieve the timestamp information corresponding to the task. The task timestamp is " " The task timestamp is " " The task timestamp is " " The task timestamp is " " The task timestamp is " The timestamps are converted to numerical form for comparison. Time is Second, Time is Second, Time is Second, Time is Second, Time is Seconds, sorted in descending order to obtain the sequence Generate a task sorting sequence.

[0035] The task load detection submodule calls the task sorting sequence, extracts the load attribute identifier field corresponding to the task number, calculates the load offset of the task based on the load attribute identifier field and the set load benchmark value, sorts the load offsets in descending order of value, and generates a load sorting list. When calling the task sorting sequence and extracting the load attribute identifier field corresponding to the task number, the task's load attribute is read from the task database. The task load attribute is identified as " The corresponding load value is , The task load attribute is identified as " The corresponding load value is , The task load attribute is identified as " The corresponding load value is , The task load attribute is identified as " The corresponding load value is , The task load attribute is identified as " The corresponding load value is When calculating the load offset of a task based on the load attribute identifier field and the set load baseline value, a preset baseline value is called. The load baseline value is analyzed... For example, to determine the average workload of similar tasks, the baseline value for the heart rate monitoring task is set as follows: The baseline value for blood oxygen monitoring is set as follows: The baseline value for blood pressure monitoring tasks is set as follows: The baseline value for serum creatinine testing was set as follows: The baseline value for the electrocardiogram monitoring task is set as follows: The task load offset is calculated using the following formula: ; in, The representative task number is Overall task load offset value Representative at the The task number in this test is The value of the load attribute identifier field. The representative task number is The load baseline value, Representative at the The task number in this test is The load attribute weighting coefficient, Represents the equipment condition fluctuation factor, with values ​​ranging from 1 to 10. , This represents the total number of checks performed during the detection process for this task, and its value is [value missing]. , This represents the detection sequence number index, with a value range of 1 to... , The subscript represents the task number.

[0036] right The task was calculated, and the three detection load values ​​were as follows: , , The weighting coefficients are respectively , , benchmark value Calculate the first term: ; The first test item is: ; The second test item is: ; The third test item is: ; Summation , , .

[0037] Calculate the second term: ; The molecule is: ; The denominator is: ; have to , ; Similar to calculating the offsets of other tasks , , , The load offsets are sorted in descending order of their numerical values. Generate a load sorting list.

[0038] The nursing pathway adjustment submodule calls the load sorting list, adjusts the execution order of nursing tasks in the intervention pathway according to the list order, compares the adjusted task order with the original task pathway table by field position, calculates the order change rate, and generates a nursing intervention pathway adjustment plan. When calling the load sorting list and adjusting the execution order of nursing tasks in the intervention path according to the list order, the task execution order in the original task path table is read. The original path order is as follows: The location numbers are respectively Reorder the tasks according to the load sort list. The corresponding new position number is When comparing the adjusted task order with the original task path table by field position, the differences between the original and new positions of each task are compared one by one. Original position is The new location is The position changes to , Original position is The new location is The position changes to , Original position is The new location is The position changes to , Original position is The new location is The position changes to , Original position is The new location is The position changes to When calculating the rate of change of order, the positional changes are summed to obtain... The total number of tasks is The maximum change value is The rate of change in sequence is calculated as follows: Right now When the rate of change exceeds A significant adjustment is defined as a change in the rate of change. When the change rate is below a certain threshold, it is considered a moderate adjustment. The current rate of change is considered a minor adjustment. This falls under the scope of a major adjustment, and the adjustment plan includes a task reordering sequence. rate of change index "Major adjustment to grade identifier" and "Adjustment to timestamp" "Generate nursing intervention pathway adjustment plans; Table 3: Comparison Table of Adjustments to the Order of Nursing Tasks Task number Original execution location New execution location Location change value Load offset T−005−ECG 5 1 4 0.612 T−004−SCr 4 2 2 0.423 T−001−HR 1 3 2 0.397 T−003−SpO2 3 4 1 0.285 T−002−BP 2 5 3 0.156 As shown in Table 3, the T−005−ECG task was adjusted to be executed first due to its highest load offset, with an overall order change rate of 60%.

[0039] Specifically, such as Figure 2 , 6 As shown, the task decomposition module includes: The tolerance fluctuation calculation submodule calculates the tolerance difference before and after the intervention based on the intervention data in the nursing intervention pathway adjustment plan, combined with the physiological monitoring data and execution time before and after the intervention, and generates the tolerance change amplitude value through ratio calculation. Based on the intervention data in the nursing intervention pathway adjustment plan, specific parameters of multiple intervention measures were extracted from the adjustment plan. Task interventions include ECG monitoring frequency per hour. Each time, the monitoring duration is [time]. minute, Task intervention measures include blood collection frequency per Hour Next, the amount of data collected , Task interventions include heart rate monitoring frequency per minute Times, monitoring duration minute, Task interventions include blood oxygen monitoring frequency per minute Each time, monitoring duration minute, Task interventions include the frequency of blood pressure measurements per minute Measurement duration Minutes were taken, and the patient's baseline physiological parameters before the intervention were read in conjunction with physiological monitoring data before and after the intervention, including pre-intervention heart rate. Blood oxygen saturation (times / minute) systolic blood pressure diastolic blood pressure serum creatinine Post-intervention physiological parameters were obtained through continuous monitoring, including post-intervention heart rate. Blood oxygen saturation (times / minute) systolic blood pressure diastolic blood pressure serum creatinine Execution time data is obtained from task execution records. Task execution time minute, Task execution time minute, Task execution time minute, Task execution time minute, Task execution time Minutes were used to calculate the difference in tolerance before and after the intervention, using standardized differences. The heart rate tolerance difference was: ; The difference in blood oxygen tolerance is: ; The systolic blood pressure tolerance difference is: ; The diastolic blood pressure tolerance difference is: ; The serum creatinine tolerance difference was: ; The ratio calculation involves comparing multiple tolerance differences with their corresponding execution durations. The heart rate tolerance variation value is: ; The range of change in blood oxygen tolerance is: ; The systolic blood pressure tolerance variation range is: ; The diastolic blood pressure tolerance variation range is: ; The range of change in serum creatinine tolerance was: ; The overall tolerance variation value was calculated by weighted average. This generates the tolerance change range value.

[0040] The fluctuation amplitude prediction submodule calls the tolerance change amplitude value, inputs the intervention frequency data, intervention intensity data, and recovery time data as independent variables into the multiple regression analysis model, establishes the fluctuation amplitude regression equation, and performs weighted calculations based on the regression coefficients to obtain the fluctuation amplitude prediction value. Call the tolerance change magnitude value When using intervention frequency data, intervention intensity data, and recovery time data as independent variables in a multiple regression analysis model, the values ​​of the variables are first determined. Intervention frequency data is extracted from nursing records, including ECG monitoring frequency. Blood collection frequency per hour Heart rate monitoring frequency (times / hour) Blood oxygen monitoring frequency (times / hour) Blood pressure measurement frequency (times / hour) Intervention intensity data was obtained through standardized scale assessment, including ECG monitoring intensity. Points, blood collection intensity Points, heart rate monitoring intensity Points, blood oxygen monitoring intensity Points, blood pressure measurement intensity The strength rating standard is: point, The sub-segment represents mild intervention. The sub-groups represented severe interventions. Recovery time data was obtained by observing the time it took for patients' physiological parameters to return to baseline, and recovery time was also measured after electrocardiogram monitoring. Minutes, recovery time after blood collection Minutes, recovery time after heart rate monitoring Minutes, recovery time after blood oxygen monitoring Minutes, recovery time after blood pressure measurement When establishing the regression equation for the fluctuation amplitude, a multiple linear regression model is used. ,in This represents the predicted value of the fluctuation range. This represents a comprehensive index indicating the frequency of interventions. This represents the comprehensive index of intervention intensity. This indicates the comprehensive index of recovery time. Represents the intercept term. Represents the regression coefficient. To represent the error term, calculate the comprehensive index of intervention frequency: ; Comprehensive Intervention Intensity Index: ; Recovery Time Overall Index: ; The regression coefficients were determined by fitting the original data. , , , The coefficients are analyzed For example, nursing data for similar patients were calculated using the least squares method. When performing weighted calculations based on regression coefficients, the independent variable was multiplied by its corresponding coefficient and then summed. This yields the predicted value of the fluctuation range.

[0041] The task interval configuration submodule calculates the difference between the predicted fluctuation value and the fluctuation threshold, filters tasks whose predicted values ​​exceed the threshold, splits them into sub-tasks and sets the interval duration, and generates a nursing task execution configuration scheme. Based on the predicted value of fluctuation range When calculating the difference with the fluctuation threshold, a preset fluctuation threshold parameter is used. The fluctuation threshold is determined by clinical safety standards. When the fluctuation amplitude exceeds... The risk of adverse reactions in patients increases. When the fluctuation range exceeds Increased risk of adverse reactions When the fluctuation range exceeds Increased risk of adverse reactions A fluctuation threshold is set based on safety considerations. Perform difference calculation A positive difference indicates that the predicted value exceeds the threshold. When filtering tasks whose predicted values ​​exceed the threshold, the local fluctuations in the predicted values ​​of each task are checked one by one. Local predictions for the task , Local predictions for the task , Local predictions for the task , Local predictions for the task , Local predictions for the task Compare the task predictions with the threshold. , Task Exceeding the threshold Task Not exceeding the threshold Task Exceeding the threshold Task Not exceeding the threshold Task Tasks that do not exceed the threshold are filtered out. When breaking it down into subtasks The task was broken down; the original task included electrocardiogram monitoring. Minutes are broken down into Each subtask Minutes, subtask number is ,right The task was broken down; the original task included heart rate monitoring. Minutes are broken down into Each subtask Minutes, subtask number is The interval duration should be determined based on the task intensity and the patient's recovery ability. The subtask interval duration is set to The interval is defined as minutes, determined by analyzing the time required for the patient's physiological parameters to return to a stable state after electrocardiogram monitoring. The subtask interval duration is set to Minutes, configuration scheme includes subtask sequences Corresponding interval duration The total execution time is calculated as the sum of the subtask duration and the interval duration, in minutes. Total time is minute, Total time is Within minutes, a nursing task execution configuration plan is generated.

[0042] Table 4: Details of Task Splitting Configuration Scheme Original task number Subtask number Execution duration (minutes) Interval duration (minutes) Fluctuation prediction value Whether to exceed the threshold T−005−ECG T−005−ECG−1 30 20 0.062 Yes T−005−ECG T−005−ECG−2 30 20 0.062 Yes T−005−ECG T−005−ECG−3 30 20 0.062 Yes T−005−ECG T−005−ECG−4 30 0 0.062 Yes T−001−HR T−001−HR−1 30 10 0.055 Yes T−001−HR T−001−HR−2 30 10 0.055 Yes As shown in Table 4, tasks exceeding the fluctuation threshold of 0.05 were broken down into multiple sub-tasks, and corresponding interval durations were set to control the patient's physiological load.

[0043] Specifically, such as Figure 2 , 7 As shown, the nursing scheduling module includes: The task sequence construction submodule, based on the task order and interval configuration in the nursing task execution configuration scheme, lists the tasks in sequence and assigns them corresponding sequence numbers, calculates and records the interval between adjacent tasks, combines the task number and interval into a continuous task sequence, and generates a task sequence number sequence. Based on the task order and interval configuration in the nursing task execution configuration scheme, task execution order data is extracted from the configuration scheme. The configuration scheme includes sub-task sequences and corresponding interval configurations. When listing tasks sequentially and assigning them corresponding sequence numbers, the numbers are allocated according to the task's position in the sequence. The first task... Corresponding sequential number The second task Corresponding sequence number The third task Corresponding sequence number The fourth task Corresponding sequence number The fifth task Corresponding sequential number The sixth task Corresponding sequential number The seventh task Corresponding sequence number The eighth task Corresponding sequential number The ninth task Corresponding sequential number The tenth task Corresponding sequential number Calculate the time interval between adjacent tasks and record the time interval between each pair of adjacent tasks. and Interval time Minutes, Task and Interval time Minutes, Task and Interval time Minutes, Task and Interval time Minutes, Task and Interval time Minutes, Task and Interval time Minutes, Task and Interval time Minutes, Task and Interval time Minutes, Task and Interval time Minutes are used to construct a data structure containing task identifiers and interval information when combining task numbers and interval durations into a continuous task sequence. The first combination is... The second combination is The third combination is The fourth combination is The fifth combination is The sixth combination is The seventh group is The eighth combination is The ninth combination is The tenth group is The array of consecutive task sequences is constructed as follows: Generate a sequence of task order numbers.

[0044] The execution time setting submodule calls the task sequence number sequence, determines the start time of the first task based on the current timestamp, calculates the task execution time node by accumulating the interval duration, associates the original task information with the task number and matches it with the corresponding node to obtain a list of task execution time nodes; When calling the task sequence number sequence, the system reads the current timestamp when determining the start time of the first task based on the current timestamp. The current timestamp is "". ", parsing the timestamp to get the year" ,month ,date ,Hour ,minute , seconds Convert timestamps to minutes for calculation, starting time base value. Minutes, first task The start time is set to the current time. When calculating the task execution time node using minutes and cumulative intervals, the start time of each task is calculated individually, starting with the first task. Start time minutes, execution time minutes, end time minutes, interval Minutes, second task Start time minutes, end time minutes, interval Minutes, third task Start time minutes, end time minutes, interval Minutes, fourth task Start time minutes, end time minutes, interval Minutes, fifth task Start time minutes, end time minutes, interval Minutes, sixth task Start time minutes, end time Every minute, when associating the original task information based on the task number, the original task attributes corresponding to the subtask are extracted from the task database. Related to the original task The task type is electrocardiogram monitoring, and the monitoring equipment is... A lead-based electrocardiograph monitors the electrocardiogram waveform as its parameter. Related to the original task The task type is heart rate monitoring, the monitoring device is a heart rate monitor, and the monitoring parameter is the heart rate value. When matching with the corresponding node, the task information is combined with the time node. The first matching item is (T-005-ECG-1, ECG monitoring, 12-lead ECG machine, T1=900 minutes, T...). 1end =930 minutes), the second matching item is (T-005-ECG-2, ECG monitoring, 12-lead ECG machine, T2=950 minutes, T 2end =980 minutes), the third matching item is (T-005-ECG-3, ECG monitoring, 12-lead ECG machine, T3=1000 minutes, T 3end =1030 minutes), the fourth matching item is (T-005-ECG-4, ECG monitoring, 12-lead ECG machine, T4=1050 minutes, T 4end =1080 minutes), the fifth matching item is (T-001-HR-1, heart rate monitoring, heart rate monitor, T5=1080 minutes, T 5end =1110 minutes), the sixth matching item is (T-001-HR-2, heart rate monitoring, heart rate monitor, T6=1120 minutes, T 6end =1150 minutes), obtain the list of task execution time nodes.

[0045] The instruction set generation submodule checks the matching status between the time nodes and the current timestamp based on the task execution time node list, organizes the successfully matched tasks into instruction entries, arranges the instructions in chronological order, and obtains the nursing task execution instruction set. Based on the task execution time node list, when checking the matching status between the time node and the current timestamp, the system's real-time timestamp is read. The current detection time is "". ", convert to minutes Minute by minute, compare the start time of each task with the current time, starting with the first task. Start time minutes, comparison The conditions are met, the task has reached its execution time, the second task... Start time minutes, comparison The conditions are met, and the task's execution time has arrived. (Third task) Start time minutes, comparison The conditions are not met, the task has not reached its execution time, fourth task. Start time minutes, comparison The conditions are not met, the task has not reached its execution time, fifth task. Start time minutes, comparison The conditions are not met, the task has not reached its execution time, sixth task. Start time minutes, comparison The conditions are not met, the task has not reached its execution time, and the successfully matched task is... When organizing successfully matched tasks into instruction entries, an instruction data structure is constructed. The first instruction entry contains the task number. Command type: "Start ECG monitoring", Device identifier: " "Lead ECG machine", execution parameter "monitoring duration" Minutes, sampling frequency Lead configuration standard "Lead", "Instruction Priority", "High", "Instruction Status", "Pending Execution", "Instruction Timestamp" The second instruction entry contains the task number. Command type: "Start ECG monitoring", Device identifier: " "Lead ECG machine", execution parameter "monitoring duration" Minutes, sampling frequency Lead configuration standard "Lead", "Instruction Priority" "High", "Instruction Status" "Currently Executing", "Instruction Timestamp" When arranging instructions in chronological order, they are sorted in ascending order based on their timestamps, with the first instruction's timestamp being "". "Corresponding minutes" The second instruction timestamp " "Corresponding minutes" ,Compare The first instruction comes first, and the instruction order is as follows: The instruction set data structure is constructed as InstructionSet={Cmd1:(T-005-ECG-1, Start ECG monitoring, 12-lead ECG machine, 30-minute monitoring, high priority, pending execution), Cmd2:(T-005-ECG-2, Start ECG monitoring, 12-lead ECG machine, 30-minute monitoring, high priority, currently executing)}, thus obtaining the nursing task execution instruction set.

[0046] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A nursing system for cancer patients undergoing radiotherapy and chemotherapy, characterized in that, The system includes: The physiological monitoring module acquires heart rate, blood oxygen saturation, systolic blood pressure, diastolic blood pressure and serum creatinine concentration of cancer patients undergoing radiotherapy and chemotherapy through monitoring equipment. It uses a moving average filtering algorithm to remove noise from the data and generate patient physiological bearing status data, which is then transmitted to the threshold assessment module. The threshold assessment module calls the patient's physiological carrying status data, performs deviation calculation on the values ​​in the data and the nursing carrying baseline value, calculates the comprehensive carrying index, and when the carrying index exceeds the radiotherapy and chemotherapy nursing carrying threshold, generates a path switching trigger signal and transmits it to the path switching module. The path switching module calls the path switching trigger signal, obtains the set of remaining tasks in the current radiotherapy and chemotherapy nursing task queue, detects the load attribute identifier, adjusts the load nursing tasks based on the signal descending order, generates a nursing intervention path adjustment plan, and passes it to the task decomposition module. The task decomposition module receives the nursing intervention path adjustment plan, calculates the tolerance change range, uses a multivariate regression analysis model to predict the fluctuation range, decomposes high-load tasks and configures the interval duration when the fluctuation threshold is exceeded, generates a nursing task execution configuration plan, and transmits it to the nursing scheduling module.

2. The nursing system for cancer patients undergoing radiotherapy and chemotherapy according to claim 1, characterized in that, The patient's physiological load status data includes heart rate, blood oxygen saturation, systolic blood pressure, diastolic blood pressure, serum creatinine concentration, comprehensive load index, and load change trend. The path switching trigger signal includes nursing path adjustment priority, task load sorting identifier, and path switching trigger reason. The nursing intervention path adjustment scheme includes task adjustment order, nursing intervention type, and adjustment adaptation conditions. The nursing task execution configuration scheme includes task execution frequency, interval duration setting, and task load level.

3. The nursing system for cancer patients undergoing radiotherapy and chemotherapy according to claim 1, characterized in that, The physiological monitoring module includes: The data acquisition submodule acquires heart rate, blood oxygen saturation, systolic blood pressure, diastolic blood pressure and serum creatinine concentration of cancer patients undergoing radiotherapy and chemotherapy through monitoring equipment, unifies the time series format, synchronously processes the multidimensional data of physiological parameters, sorts the time series of physiological parameters by timestamp, and generates raw physiological sequence values. The data filtering submodule, based on the original physiological sequence values, uses a moving average filtering algorithm to denoise the data according to the fluctuation amplitude of the time series. Combined with the dynamic changes of time series such as heart rate, blood oxygen saturation, systolic blood pressure, diastolic blood pressure, and serum creatinine concentration, the denoised physiological sequence values ​​are obtained. The physiological bearing status generation submodule uses a weighted calculation method to comprehensively score physiological parameters based on the denoised physiological sequence values. It combines the weighted interval values ​​of systolic blood pressure, diastolic blood pressure, and heart rate to analyze their changing trends and generate patient physiological bearing status data.

4. The nursing system for cancer patients undergoing radiotherapy and chemotherapy according to claim 3, characterized in that, The threshold evaluation module includes: The data deviation calculation submodule, based on the monitoring values ​​in the patient's physiological carrying status data and combined with the nursing carrying baseline value, performs difference calculation on the values ​​of the same monitoring indicators and the baseline value and sums them to obtain the total deviation of the indicator values; The comprehensive carrying capacity index calculation submodule calls the sum of the index values ​​offset, calculates the offsets by weighting according to the weight coefficients of the differentiated monitoring indicators in the carrying capacity assessment system, performs normalization processing, and obtains the comprehensive carrying capacity index value. The path switching signal generation submodule calls the comprehensive index value of the carrying status and compares the index value with the radiotherapy and chemotherapy nursing carrying threshold in a single comparison. If the index value exceeds the radiotherapy and chemotherapy nursing carrying threshold, a path switching trigger signal is obtained.

5. The nursing system for cancer patients undergoing radiotherapy and chemotherapy according to claim 4, characterized in that, The sum of the index values ​​refers to the value obtained by calculating the difference between the same monitoring index values ​​in the patient's physiological carrying status data and the corresponding nursing carrying baseline values ​​and then summing them up. The comprehensive index value of bearing status refers to the value obtained by weighting and normalizing the sum of the numerical offsets of the indicators and the weighting coefficients of the monitoring indicators. The radiotherapy and chemotherapy nursing load threshold refers to a preset numerical limit used for comparison with the comprehensive index value of the load status; The normalization process employs a linear normalization method.

6. The nursing system for cancer patients undergoing radiotherapy and chemotherapy according to claim 4, characterized in that, The path switching module includes: The trigger signal acquisition submodule acquires the path switching trigger signal, parses the timestamp field and task identifier field in the signal, matches the task number in the current task queue, sorts them in descending order of timestamp, and generates a task sorting sequence. The task load detection submodule calls the task sorting sequence, extracts the load attribute identifier field corresponding to the task number, calculates the load offset of the task based on the load attribute identifier field and the set load benchmark value, sorts the load offsets in descending order of numerical value, and generates a load sorting list. The nursing pathway adjustment submodule calls the load sorting list, adjusts the execution order of nursing tasks in the intervention pathway according to the list order, compares the adjusted task order with the original task pathway table by field position, calculates the order change rate, and generates a nursing intervention pathway adjustment plan.

7. The nursing system for cancer patients undergoing radiotherapy and chemotherapy according to claim 6, characterized in that, The task decomposition module includes: The tolerance fluctuation calculation submodule calculates the tolerance difference before and after the intervention based on the intervention measures data in the nursing intervention path adjustment plan, combined with the physiological monitoring data before and after the intervention and the execution time, and generates the tolerance change amplitude value through ratio calculation. The fluctuation amplitude prediction submodule calls the tolerance change amplitude value, inputs the intervention frequency data, intervention intensity data and recovery time data as independent variables into the multiple regression analysis model, establishes the fluctuation amplitude regression equation, and performs weighted calculations based on the regression coefficients to obtain the fluctuation amplitude prediction value. The task interval configuration submodule calculates the difference between the predicted fluctuation value and the fluctuation threshold, filters tasks whose predicted values ​​exceed the threshold, splits them into sub-tasks and sets the interval duration, and generates a nursing task execution configuration scheme.

8. The nursing system for cancer patients undergoing radiotherapy and chemotherapy according to claim 7, characterized in that, The tolerance change value refers to the value calculated based on the ratio of the tolerance difference before and after the intervention to the tolerance before the intervention; The predicted value of the fluctuation range refers to the value obtained by multiple regression calculation based on the tolerance change range value, intervention frequency data, intervention intensity data, and recovery time data. The fluctuation threshold refers to a preset numerical limit used for comparison with the predicted fluctuation amplitude.

9. The nursing system for cancer patients undergoing radiotherapy and chemotherapy according to claim 1, characterized in that, The nursing scheduling module calls the task order and interval duration configuration in the nursing task execution configuration scheme to construct a scheduling execution sequence, sets the execution time node based on the current timestamp and monitors the matching status to trigger the nursing task execution instruction, summarizes the execution time node and the corresponding nursing task, and generates a nursing task execution instruction set. The nursing task execution instruction set includes execution time nodes, task identification information, and execution triggering conditions.

10. The nursing system for cancer patients undergoing radiotherapy and chemotherapy according to claim 9, characterized in that, The nursing scheduling module includes: The task sequence construction submodule, based on the task order and interval configuration in the nursing task execution configuration scheme, lists the tasks in sequence and assigns them corresponding sequence numbers, calculates and records the interval between adjacent tasks, combines the task number and the interval into a continuous task sequence, and generates a task sequence number sequence. The execution time setting submodule calls the task sequence number sequence, determines the start time of the first task based on the current timestamp, calculates the task execution time node by accumulating the interval duration, associates the original task information with the task number and matches it with the corresponding node to obtain a list of task execution time nodes; The instruction set generation submodule checks the matching status between the time nodes and the current timestamp based on the task execution time node list, organizes the successfully matched tasks into instruction entries, arranges the instructions in chronological order, and obtains the nursing task execution instruction set.

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