Cancer survivor post returning support system

Abstract

The invention relates to the technical field of post return support, in particular to a cancer surviver post return support system, which comprises a health state evaluation module, a post matching adjustment module, a working duration optimization module, a task load balancing module and a cooperative work scheduling module. According to the invention, through fusion analysis of health data and physiological parameters and evaluation standards of individual rehabilitation stages, post matching adjustment can accurately adapt to key ability indexes based on actual health states, so that adverse effects of a fixed post distribution mode on rehabilitation of survivors are avoided; daytime high-tolerance, moderate-load and low-tolerance time periods are divided according to the physical ability fluctuation trend, so that the task arrangement sequence can be matched with the real-time states of survivors, the situation that high-load tasks are concentrated in the low-tolerance time period is avoided, the work fitness is optimized, matching is conducted based on the post competency indexes of the members in the same group, the team task allocation strategy is optimized, and the task allocation efficiency is improved. And survivors can complete the adaptive task within the adaptive working duration.

Description

Technical Field

[0001] The present invention relates to the technical field of return-to-work support, and in particular to a return-to-work support system for cancer survivors. Background Art

[0002] The field of return-to-work support technology includes systems that provide work return solutions for people who are temporarily or permanently off work due to health conditions. This technical field involves aspects such as workflow management, medical rehabilitation support, personalized job adaptation, remote office technology, and enterprise policy adaptation. The core content includes job adjustment strategies based on individual health status assessments, task allocation mechanisms combined with vocational rehabilitation plans, remote collaboration tools that support flexible work modes, enterprise management platforms that comply with labor laws and regulations, and data sharing systems for cross-departmental collaboration. The overall technical field covers multiple aspects such as providing return-to-work process optimization, job recommendations, matching health monitoring with enterprise management mechanisms for people with specific health conditions, aiming to improve the feasibility and adaptability of personnel returning to work.

[0003] Among them, the cancer survivor return-to-work support system refers to an intelligent support system used to help cancer survivors return to work. The system covers technical matters such as health status assessment, job adaptation analysis, remote office and flexible scheduling management, recovery period task planning, and enterprise personnel management system docking. It includes health data analysis and dynamic update based on electronic health records, job matching methods combined with occupational adaptability assessment models, task allocation mechanisms based on workload prediction algorithms, flexible office solutions that integrate remote work interaction systems, and enterprise management platforms that adjust job requirements based on labor law constraints. The system enables cancer survivors to obtain appropriate job support and management during the recovery period through the comprehensive application of health data management, workflow optimization, and intelligent matching methods.

[0004] Traditional support systems mainly rely on electronic health records and occupational adaptability assessment models for job recommendations, but fail to monitor individual health status in real time. They only analyze health status based on static data, resulting in delayed job adjustments and inability to dynamically adapt to survivors' rehabilitation progress. In addition, task allocation does not take into account the fluctuation trend of survivors' physical fitness, and ignores changes in tolerance in different time periods, resulting in task loads that may be concentrated during survivors' physical fitness troughs, affecting work efficiency and health recovery, resulting in a mismatch between task scheduling and individual capabilities, and affecting survivors' adaptability. In addition, collaborative task allocation only adjusts job requirements based on the enterprise management platform, and does not refine the impact of individual competency index in task scheduling, resulting in a single task load balancing strategy, which is unable to fully utilize team resources to decompose high-intensity tasks, causing individual overload work and affecting the rehabilitation process and job adaptation effect. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a cancer survivor return-to-work support system.

[0006] In order to achieve the above-mentioned purpose, the present invention adopts the following technical solution: a cancer survivor return-to-work support system, the system comprising:

[0007] The health status assessment module obtains the medical data of cancer survivors, extracts the injury status parameters of the survivors, calculates the rehabilitation status baseline value, analyzes the rehabilitation stage of the survivors, and obtains the rehabilitation stage determination information;

[0008] The job matching and adjustment module calls the survivor's current work ability data based on the rehabilitation stage determination information, matches it with the key ability indicators required for the job, performs job scheduling, and obtains job adaptation and adjustment information;

[0009] The working hours optimization module calculates the recommended working hours range according to the job adaptation adjustment information and the rehabilitation stage determination information and the changes in the daily physiological parameters of the survivors to obtain the adaptive working hours range;

[0010] The task load balancing module analyzes the daily physical fitness fluctuation trend of the survivors based on the adaptive working time interval, calculates the tolerance level required for task execution according to the concentration and physical energy consumption of each work task, adjusts the task arrangement sequence, and obtains the task load distribution information for the time period;

[0011] The collaborative work scheduling module calls the task load distribution information of the time period, analyzes the part of the task load that exceeds the survivor's tolerance range, screens the adapted members to decompose the task load, and obtains the collaborative task scheduling assignment information.

[0012] The present invention has improvements in that the rehabilitation stage judgment information specifically includes early rehabilitation, mid-rehabilitation and stable rehabilitation, the job adaptation adjustment information includes job competency index, job ability requirement parameter matching and adjusted job information, the adaptive working time interval includes recommended working time and maximum working time, the time period task load allocation information includes high tolerance time period task allocation information, moderate load time period task allocation information and low tolerance time period task allocation information, and the collaborative task scheduling assignment information specifically refers to the task load decomposition ratio, collaborative task adjustment ratio and task matching degree of group members.

[0013] The present invention is improved in that the health status assessment module comprises:

[0014] The medical data acquisition submodule acquires the medical data of cancer survivors, extracts the injury status parameters of the survivors, including motor nerve injury index, sensory nerve injury index, muscle endurance decline index and cognitive decline index, and collects the health status data of the survivors, including blood oxygen saturation, heart rate stability and sleep duration, organizes and establishes a data set, and obtains the survivor status data set;

[0015] The injury status calculation submodule is based on the survivor status data set and uses the formula:

[0016]

[0017] Calculate baseline values ​​for rehabilitation status;

[0018] Among them, S r Represents the baseline value of recovery status, W i represents the weight of the i-th damage state parameter, P i represents the value of the i-th damage state parameter, T j represents the jth item of health status data, D represents the total deviation of the change of damage status parameters, n represents the total number of damage status parameters, and m represents the total number of health status data items.

[0019] The rehabilitation stage determination submodule calls the rehabilitation state reference value, sets the rehabilitation stage determination threshold, determines the rehabilitation stage of the survivor, which includes early rehabilitation, middle rehabilitation and stable rehabilitation, and obtains rehabilitation stage determination information.

[0020] The present invention is improved in that the position matching adjustment module comprises:

[0021] The ability requirement extraction submodule calls the survivor's work ability data, including hand control ability, cognitive stability and muscle strength, based on the rehabilitation stage determination information, and extracts the key ability indicators required for each position to obtain a position ability requirement data set;

[0022] The job competency assessment submodule is based on the job competency requirement data set and uses the formula:

[0023]

[0024] Calculate the job competency index of the survivors;

[0025] Among them, S p Represents job competency index, W p,k represents the weight of the kth key capability indicator, C k represents the demand value of the kth key capability indicator, B k represents the kth key capability indicator value of the survivor, T krepresents the balance threshold of the kth competency match, and N represents the total number of key competency indicators required for the position;

[0026] The job adaptation scheduling submodule sets a job matching threshold based on the job competency index, identifies matching job types, and performs job scheduling according to the job vacancy status to generate job adaptation adjustment information.

[0027] The present invention is improved in that the working time optimization module comprises:

[0028] The physiological parameter extraction submodule extracts the physiological parameters of the survivor, including heart rate fluctuation, blood oxygen level change, fatigue index and sleep quality fluctuation, based on the job adaptation adjustment information and rehabilitation stage determination information, and establishes a physiological parameter data set;

[0029] The physiological fluctuation calculation submodule calculates the fluctuation degree of daily physiological parameters based on the physiological parameter data set, and uses the mean square error and coefficient of variation to measure the fluctuation of each physiological parameter to obtain physiological parameter fluctuation information;

[0030] The working time calculation submodule is based on the physiological parameter fluctuation information, combined with the baseline working time recommended for the survivor's recovery stage, and based on the fluctuation tolerance threshold of the physiological parameters, using the formula:

[0031]

[0032] Calculate the recommended working hours and combine them with the maximum allowed working hours to obtain the adaptive working hours range;

[0033] Among them, T w Represents the recommended working hours, T b represents the baseline working hours recommended for the survivor’s recovery stage, P f represents the fluctuation degree of the fth physiological parameter, P th,f represents the fluctuation tolerance threshold of the fth physiological parameter, W b,f represents the importance weight of the fth physiological parameter, N b Represents the total number of items of physiological parameters.

[0034] The present invention is improved in that the task load balancing module comprises:

[0035] The physical energy fluctuation analysis submodule analyzes the daily physical energy fluctuation trend of the survivors based on the adaptive working time interval, calculates the physical energy consumption rate of each time period, and divides the high tolerance period, moderate load period and low tolerance period by setting the consumption threshold to obtain the physical energy tolerance period information;

[0036] The task tolerance calculation submodule is based on the physical tolerance period information, the concentration requirements and physical energy consumption of the task, and uses the formula:

[0037]

[0038] Calculate the tolerance level of the work task and obtain the task tolerance distribution information;

[0039] Among them, L t Represents the mission tolerance level, E t Represents the physical energy consumption of the task, C t Represents the concentration requirement level of the task, T t Represents the task execution time;

[0040] The task period scheduling submodule adjusts the task arrangement order based on the task tolerance distribution information, matches the high tolerance period, the moderate load period and the low tolerance period, optimizes the task execution time, and obtains the period task load distribution information.

[0041] The present invention is improved in that the collaborative work scheduling module includes:

[0042] The high-load task analysis submodule analyzes the part of the task load that exceeds the survivor's tolerance based on the task load distribution information of the time period, extracts the execution parameters of the overload task, including the task execution time, physical exertion index and concentration requirement, and obtains the high-load task parameters;

[0043] The task load decomposition submodule compares the job competency indexes of the same group members based on the high-load task parameters, selects suitable members to participate in the task load decomposition, and adopts the formula:

[0044]

[0045] Calculate the collaborative task adjustment ratio to obtain the member task allocation ratio;

[0046] Among them, R c represents the collaborative task adjustment ratio, S Z Represents the job competency index of member Z, S g represents the job competency index of member g, L Z represents the task tolerance level of member Z, L t Represents the tolerance level requirement of the original task, N d The total number of candidate members;

[0047] The collaborative task scheduling submodule adjusts task allocation based on the member task allocation ratio, optimizes task load distribution, and generates collaborative task scheduling assignment information.

[0048] Compared with the prior art, the advantages and positive effects of the present invention are:

[0049] In the present invention, through the fusion analysis of health data and physiological parameters, the individualized rehabilitation stage evaluation standard is used to enable job matching adjustment to accurately adapt key ability indicators based on actual health status, avoid the adverse effects of fixed job allocation mode on survivors' rehabilitation, and optimize the personalized working time range by calculating the daily change of physiological parameters, so as to achieve flexible adjustment of the rehabilitation period and avoid the limitation of fixed working hours on individual recovery ability. The daytime high tolerance, moderate load and low tolerance periods are divided according to the trend of physical fitness fluctuations, so that the task arrangement sequence can match the real-time status of the survivors, avoid the situation where high-load tasks are concentrated in the low-tolerance period, optimize work adaptability, match based on the job competency index of group members, decompose overloaded tasks, optimize team task allocation strategy, enable survivors to complete adaptation tasks within adaptive working hours, and at the same time improve the rationality of collaborative work, reduce individual burden, and avoid delays in rehabilitation process due to task overload. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 is a system flow chart of the present invention;

[0051] Figure 2 is a flow chart of the health status assessment module of the present invention;

[0052] Figure 3 This is a flow chart of the job matching adjustment module of the present invention;

[0053] Figure 4 This is a flow chart of the working time optimization module of the present invention;

[0054] Figure 5 It is a flow chart of the task load balancing module of the present invention;

[0055] Figure 6 This is a flow chart of the collaborative work scheduling module of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0058] See also Figure 1 The present invention provides a technical solution: a cancer survivor return-to-work support system, the system comprising:

[0059] The health status assessment module obtains the medical data of cancer survivors, extracts the injury status parameters of the survivors, which include motor nerve injury index, sensory nerve injury index, muscle endurance decline index and cognitive decline index, and combines the health status data of the survivors, which includes blood oxygen saturation, heart rate stability and sleep duration, to calculate the rehabilitation status baseline value, analyze the rehabilitation stage of the survivors, which includes early rehabilitation, mid-rehabilitation and stable rehabilitation, and obtain rehabilitation stage determination information;

[0060] The job matching and adjustment module calls the survivor's current work ability data based on the rehabilitation stage judgment information, matches it with the key ability indicators required for the job, including hand control ability, cognitive stability and muscle strength requirements, calculates the survivor's job competency index, identifies the matching job type, performs job scheduling, and obtains job adaptation and adjustment information;

[0061] The working hours optimization module extracts the physiological parameters of the survivors according to the job adaptation adjustment information and the rehabilitation stage determination information, calculates the fluctuation range of the physiological parameters of the survivors, and calculates the recommended working hours range according to the daily changes in physiological parameters to obtain the adaptive working hours range;

[0062] The task load balancing module analyzes the daily physical fitness fluctuation trend of the survivors based on the adaptive working time interval, divides the period into high tolerance period, moderate load period and low tolerance period, calculates the tolerance level required for task execution according to the concentration and physical exertion of each work task, adjusts the task arrangement order, and obtains the task load distribution information of the period;

[0063] The collaborative work scheduling module calls the task load distribution information of the time period, analyzes the part of the task load that exceeds the survivor's tolerance range, extracts the high-load task parameters, compares the job competency index of the members of the same group, selects the suitable members to decompose the task load, calculates the collaborative task adjustment ratio, adjusts the task distribution, and obtains the collaborative task scheduling assignment information;

[0064] The rehabilitation stage determination information is specifically the early rehabilitation period, the middle rehabilitation period and the stable rehabilitation period. The job adaptation adjustment information includes the job competency index, the matching degree of job ability requirement parameters and the adjusted job information. The adaptive working time range includes the recommended working time and the maximum working time. The time period task load allocation information includes the high tolerance time period task allocation information, the moderate load time period task allocation information and the low tolerance time period task allocation information. The collaborative task scheduling assignment information specifically refers to the task load decomposition ratio, the collaborative task adjustment ratio and the task matching degree of the same group members.

[0065] See also Figure 2 , the health status assessment module includes:

[0066] The medical data acquisition submodule acquires the medical data of cancer survivors, extracts the injury status parameters of the survivors, including motor nerve injury index, sensory nerve injury index, muscle endurance decline index and cognitive decline index, and collects the health status data of the survivors, including blood oxygen saturation, heart rate stability and sleep duration, organizes and establishes a data set, and obtains the survivor status data set;

[0067] The medical data acquisition submodule obtains the medical data of cancer survivors. Parameter extraction involves the acquisition of motor nerve damage indicators, sensory nerve damage indicators, muscle endurance decline indicators and cognitive decline index. Each indicator is measured using a specific sensor. For example, motor nerve damage indicators are measured by electromyography equipment, sensory nerve damage indicators are measured by nerve conduction velocity detection, muscle endurance is obtained through continuous strength testing, and cognitive decline is obtained through cognitive function tests such as MMSE scores. At the same time, health status data such as survivors' blood oxygen saturation, heart rate and sleep duration are collected. Blood oxygen saturation is measured by a pulse oximeter, heart rate is recorded by a heart rate monitor, and sleep duration is obtained by a sleep monitoring device. After integrating these data, a survivor status data set is constructed to provide an empirical basis for subsequent analysis.

[0068] The injury status calculation submodule is based on the survivor status data set and uses the formula:

[0069]

[0070] Calculate baseline values ​​for rehabilitation status;

[0071] Among them, S r Represents the baseline value of recovery status, Wi represents the weight of the i-th damage state parameter, P i represents the value of the i-th damage state parameter, T j represents the jth item of health status data, D represents the total deviation of the change of damage status parameters, n represents the total number of damage status parameters, and m represents the total number of health status data items.

[0072] The injury status calculation submodule calculates the baseline value of the rehabilitation status. The weight setting of the injury status parameters is explained. In medical practice, weights are usually set according to the clinical research results of the impact of each parameter on rehabilitation. For example, suppose that previous studies have shown that motor nerve damage has the greatest impact on rehabilitation, so the highest weight of 0.4 is given; sensory nerve damage is second, and the weight is set to 0.3; muscle endurance has a relatively small impact on rehabilitation, with a weight of 0.2; cognitive decline has the lightest impact, with a weight of 0.1. The weight distribution reflects the relative importance of different health indicators to the rehabilitation process. For example, the survivor's motor nerve damage index is set to 6, the sensory nerve damage index is set to 7, the muscle endurance decline index is set to 5, and the cognitive decline index is set to 8. Calculate the weighted sum:

[0073]

[0074] Process the health data, considering blood oxygen saturation of 98%, heart rate of 70 beats / minute, and sleep time of 7 hours, and calculate the corresponding The sum of the squares and its square root:

[0075]

[0076] The total deviation D of the change of the injury status parameter is set to 15. This deviation represents the total change from the baseline data to the current measurement, and its value is set based on the average change range obtained from clinical observations.

[0077] calculate:

[0078] The calculation shows that the rehabilitation status benchmark value is 21.53, which will be used for subsequent rehabilitation stage determination. Through specific calculation examples, it can be ensured that the calculation of the rehabilitation status benchmark value is not only based on theoretical and empirical weights, but also reflects the specific analysis of actual patient data, making the model more accurate and practical in actual applications.

[0079] The rehabilitation stage determination submodule calls the rehabilitation state benchmark value, sets the rehabilitation stage determination threshold, determines the rehabilitation stage of the survivor, which includes the early rehabilitation stage, the middle rehabilitation stage and the stable rehabilitation stage, and obtains the rehabilitation stage determination information;

[0080] The rehabilitation stage determination submodule calls the rehabilitation status baseline value, sets the rehabilitation stage determination threshold, and determines the survivor's rehabilitation stage by comparing the rehabilitation status baseline value with the threshold. The set threshold is set according to clinical rehabilitation standards and historical data. The rehabilitation stage includes early rehabilitation, mid-rehabilitation and stable rehabilitation. The determination threshold for each stage is different. For example, the early rehabilitation threshold is set at 30% of the baseline value, 50% in the mid-rehabilitation period, and 70% in the stable rehabilitation period. By comparing the rehabilitation status baseline value with these thresholds, the rehabilitation stage determination information is output to provide targeted treatment recommendations for the medical team.

[0081] See also Figure 3 , the job matching adjustment module includes:

[0082] The capability requirement extraction submodule uses the survivors’ work capability data, including hand control ability, cognitive stability, and muscle strength, based on the rehabilitation stage judgment information, and extracts the key capability indicators required for each position to obtain the position capability requirement data set;

[0083] The ability requirement extraction submodule calls the survivor's work ability data based on the rehabilitation stage judgment information, and specifically extracts key indicators including hand control ability, cognitive stability and muscle strength. Hand control ability is obtained through grip strength test, cognitive stability is evaluated through reaction time measurement, and muscle strength is obtained through maximum muscle contraction force evaluation. Each data is recorded by sensor equipment and organized into a survivor ability parameter set to extract key ability indicators required for different positions. For example, a certain position requires hand control ability to reach at least 45N of grip, cognitive reaction time no more than 0.8 seconds, and muscle strength to reach 80% of body weight. The system calculates and matches the survivor's ability data with the job requirement standards, thereby constructing a job ability requirement data set.

[0084] The job competency assessment submodule is based on the job competency requirement data set and uses the formula:

[0085]

[0086] Calculate the job competency index of the survivors;

[0087] Among them, S p Represents job competency index, W p,k represents the weight of the kth key capability indicator, C k represents the demand value of the kth key capability indicator, B k represents the kth key capability indicator value of the survivor, T k represents the balance threshold of the kth competency match, and N represents the total number of key competency indicators required for the position;

[0088] The job competency assessment submodule calculates the job competency index of the survivor based on the job competency requirement data set, sets the weights of key competency indicators, and sets the weight of hand control ability to 0.4, cognitive stability to 0.3, and muscle strength to 0.3. The weight values ​​are set based on the degree of job dependence on different abilities, and then sets the requirement value C of each ability indicator. k and the survivor's current ability level B k For example, the grip strength required for a certain job is C1 = 50N, and the actual grip strength of the survivor is B1 = 45N; the cognitive reaction time required by the job is C2 = 0.7s, and the cognitive reaction time of the survivor is B2 = 0.9s; the muscle strength required by the job is C3 = 80% of the body weight, and the actual muscle strength of the survivor is B3 = 75% of the body weight. At the same time, the balance threshold T of the ability matching is set k , for example, T1 = 5N, T2 = 0.2s, T3 = 5% of body weight, substitute these values ​​into the job competency index formula:

[0089]

[0090] Calculate the job competency index S p It is approximately 1.58, and this value will be used for job adaptation scheduling later.

[0091] The job adaptation scheduling submodule sets the job matching threshold based on the job competency index, identifies the matching job types, and performs job scheduling according to the job vacancy status to generate job adaptation adjustment information;

[0092] The job adaptation scheduling submodule sets the job matching threshold based on the job competency index to judge the job matching of the survivors. The threshold is set according to the historical matching data. For example, p Those with a job competency index greater than 1.5 are suitable positions, and those with a job competency index less than 1.5 are unsuitable positions. p =1.58, then its matching status meets the requirements, and then matching is performed according to the job vacancy status. For example, if a job requires 1 person and currently has 1 vacant person, the survivor is scheduled to enter the job and job adaptation adjustment information is generated.

[0093] See also Figure 4 , the working time optimization module includes:

[0094] The physiological parameter extraction submodule extracts the physiological parameters of the survivors based on the job adaptation adjustment information and the rehabilitation stage determination information, including heart rate fluctuations, blood oxygen level changes, fatigue index and sleep quality fluctuations, and establishes a physiological parameter data set;

[0095] The physiological parameter extraction submodule obtains the physiological parameters of the survivors based on the job adaptation adjustment information and the rehabilitation stage judgment information, and extracts heart rate fluctuations, blood oxygen level changes, fatigue index and sleep quality fluctuations. Each parameter is obtained through different measurement methods. Heart rate fluctuations are measured by a wearable heart rate monitoring device to continuously measure the difference between the highest heart rate and the lowest heart rate within one minute. The blood oxygen level change is calculated based on the difference between the maximum blood oxygen saturation and the minimum blood oxygen saturation of the fingertip oximeter in one day. The fatigue index is obtained by measuring the rate of strength loss of the survivors when their muscles are fatigued during repeated grip tests. Sleep quality fluctuations are calculated based on the changes in the proportion of deep sleep, light sleep and wakefulness time by the sleep monitoring device. After the various parameters are obtained, they are integrated to form a physiological parameter data set for subsequent physiological fluctuation analysis.

[0096] The physiological fluctuation calculation submodule calculates the fluctuation degree of daily physiological parameters based on the physiological parameter data set, uses the mean square error and coefficient of variation to measure the fluctuation of each physiological parameter, and obtains the physiological parameter fluctuation information;

[0097] The physiological fluctuation calculation submodule calculates the daily physiological parameter fluctuation degree based on the physiological parameter data set, and uses the mean square error and coefficient of variation to measure the fluctuation of each physiological parameter. The mean square error is calculated as follows:

[0098]

[0099] Among them, σ f is the mean square error of the fth physiological parameter, B f,t is the value of the fth physiological parameter measured at the tth time point, is the daily average value of the parameter, J is the number of measurements per day, and the coefficient of variation is calculated as follows:

[0100]

[0101] Among them, CV f is the coefficient of variation, which represents the relative fluctuation of the physiological parameter. For example, if the heart rate fluctuation data of a survivor is {65, 72, 68, 74, 70} times / minute, then the mean is The mean square error is calculated as follows:

[0102]

[0103] The coefficient of variation is calculated as follows:

[0104]

[0105] This value indicates the relative fluctuation amplitude of the heart rate and is used to evaluate the physiological stability of the survivor and form the information of physiological parameter fluctuation.

[0106] The working hours calculation submodule is based on the physiological parameter fluctuation information, combined with the baseline working hours recommended for the survivor's recovery stage, and the tolerance threshold of the physiological parameter fluctuation degree, using the formula:

[0107]

[0108] Calculate the recommended working hours and combine them with the maximum allowed working hours to obtain the adaptive working hours range;

[0109] Among them, T w Represents the recommended working hours, T b represents the baseline working hours recommended for the survivor’s recovery stage, P f represents the fluctuation degree of the fth physiological parameter, P th,f represents the fluctuation tolerance threshold of the fth physiological parameter, W b,f represents the importance weight of the fth physiological parameter, N b The total number of items representing physiological parameters;

[0110] The working time calculation submodule calculates the recommended working time based on the physiological parameter fluctuation information, combined with the baseline working time recommended for the survivor's rehabilitation stage, and the fluctuation tolerance threshold of the physiological parameters. For example, the baseline working time T recommended for the rehabilitation stage is set. b The importance weights of physiological parameters are set as follows: heart rate fluctuation W b,1 =0.4, blood oxygen level change W b,2 =0.3, fatigue index W b,3 =0.2, sleep quality fluctuation W b,4 =0.1, the fluctuation levels of various physiological parameters are P1 = 0.045, P2 = 0.03, P3 = 0.06, P4 = 0.02, and the corresponding fluctuation tolerance threshold is set as P th,1 =0.05, P th,2 =0.04, P th,3 =0.07, P th,4 =0.03, substitute the above parameters into the formula:

[0111]

[0112] Calculate the recommended working time T w ≈5.95 hours. This value is combined with the maximum allowed working time to determine the adaptive working time range.

[0113] See also Figure 5 , the task load balancing module includes:

[0114] The physical fitness fluctuation analysis submodule analyzes the daily physical fitness fluctuation trend of the survivors based on the adaptive working time interval, calculates the physical fitness consumption rate in each time period, and divides the high tolerance period, moderate load period and low tolerance period by setting the consumption threshold to obtain the physical fitness tolerance period information;

[0115] The physical energy fluctuation analysis submodule analyzes the daily physical energy fluctuation trend of the survivors based on the adaptive working time interval. It divides a day into multiple time periods, such as 6:00-10:00, 10:00-14:00, 14:00-18:00, and 18:00-22:00. In each time period, the survivor's heart rate, blood oxygen level, and fatigue index are recorded, and the physical energy consumption rate is calculated. The physical energy consumption rate is calculated by measuring the calorie consumption per unit time. For example, a survivor consumes 100 calories in the 6:00-10:00 period. The energy consumption during the period is 600kcal, and this period lasts for 4 hours, then the energy consumption rate is 600 / 4=150kcal / h. Then the consumption threshold is set. It is assumed that the high tolerance period threshold is greater than 180kcal / h, the moderate load period threshold is 120-180kcal / h, and the low tolerance period threshold is lower than 120kcal / h. In this case, the survivor is divided into the moderate load period during the 6:00-10:00 period. The energy consumption rates of all time periods are calculated and classified in turn to obtain the energy tolerance period information.

[0116] The task tolerance calculation submodule is based on the physical tolerance period information, the concentration requirements and physical energy consumption of the task, and uses the formula:

[0117]

[0118] Calculate the tolerance level of the work task and obtain the task tolerance distribution information;

[0119] Among them, L t Represents the mission tolerance level, E t Represents the physical energy consumption of the task, C t Represents the concentration requirement level of the task, T t Represents the task execution time;

[0120] The task tolerance calculation submodule calculates the tolerance level of the work task based on the physical tolerance period information, the concentration requirement and the physical energy consumption of the task. The physical energy consumption of the task is obtained by measuring the calorie consumption per unit time during the work process. For example, a task involves standing and carrying for a long time, the physical energy consumption is measured to be 200kcal, the task execution time is set to 2 hours, and the concentration requirement level is set to 2. Then substitute the data into the formula:

[0121]

[0122] The calculation shows that the tolerance level of the task is 104, and then the same calculation is performed on all tasks to form the task tolerance distribution information.

[0123] The task period scheduling submodule adjusts the task arrangement order based on the task tolerance distribution information, matches the high tolerance period, moderate load period and low tolerance period, optimizes the task execution time, and obtains the period task load distribution information.

[0124] The task period scheduling submodule adjusts the order of task arrangement based on the task tolerance distribution information, matches the task tolerance level with the physical tolerance period. If the tolerance level of a task is 104, it should be allocated to the high tolerance period, that is, the period when the daily physical energy consumption rate is greater than 180kcal / h. If the survivor's high tolerance period is 10:00-14:00, the task will be arranged to this period first. If there is a task scheduled in the high tolerance period, it will be adjusted according to the proximity of the tolerance level to avoid the high tolerance task being arranged in the low tolerance period, so as to ensure balanced workload distribution and finally obtain the time period task load distribution information.

[0125] See also Figure 6 ,The collaborative work scheduling module includes:

[0126] The high-load task analysis submodule analyzes the part of the task load that exceeds the survivor's tolerance based on the task load distribution information of the time period, extracts the execution parameters of the overload task, including task execution time, physical exertion index and concentration requirements, and obtains the high-load task parameters;

[0127] The high-load task analysis submodule identifies the part of the task load that exceeds the survivor's tolerance based on the task load distribution information of the time period, and determines the survivor's maximum tolerable task load, which is determined by his physical exertion capacity, concentration requirement and maximum continuous working time. For example, the survivor's maximum daily physical exertion is set to 2500kcal, the maximum load tolerance of a single task is 400kcal, the single concentration requirement does not exceed level 4, and the task execution time does not exceed 3 hours. For each task, its total physical exertion, concentration requirement and execution time are calculated. For example, a task takes 3.5 hours to execute, consumes 480kcal of physical exertion, and requires level 5 of concentration, which exceeds the survivor's tolerance. The task is marked as an overload task, and the execution parameters of the task are extracted, including execution time of 3.5 hours, physical exertion index of 480kcal, and concentration requirement of level 5, to obtain the high-load task parameters.

[0128] The task load decomposition submodule compares the job competency index of members in the same group based on the high-load task parameters, and selects suitable members to participate in the task load decomposition using the formula:

[0129]

[0130] Calculate the collaborative task adjustment ratio to obtain the member task allocation ratio;

[0131] Among them, R c represents the collaborative task adjustment ratio, S Z Represents the job competency index of member Z, S g represents the job competency index of member g, L Z represents the task tolerance level of member Z, L t Represents the tolerance level requirement of the original task, N d The total number of candidate members;

[0132] The task load decomposition submodule compares the job competency indexes of members in the same group based on the high-load task parameters, selects suitable members to participate in the task load decomposition, and obtains the job competency indexes of all members. For example, the job competency index S of a member Z is Z The position competency index of the team leader is 1.8, and the position competency indexes of other team members are 1.6, 1.5 and 1.9 respectively. The total competency index of the team leader is calculated as follows:

[0133]

[0134] At the same time, calculate the original task tolerance level L t and member Z's tolerance level L Z , assuming the tolerance level of the original task is L t is 120, member Z's mission tolerance level is L Z Assuming that 100, calculate the collaborative task adjustment ratio:

[0135]

[0136] The calculation shows that the collaborative task adjustment ratio of member Z is 0.3, that is, the member can share 30% of the task load. The task allocation ratio of all adapted members is calculated in turn to finally form the member task allocation ratio.

[0137] The collaborative task scheduling submodule adjusts task allocation based on the task allocation ratio of members, optimizes task load distribution, and generates collaborative task scheduling assignment information.

[0138] The collaborative task scheduling submodule adjusts the task allocation based on the task allocation ratio of members. First, the tasks are divided into different members according to the calculated adjustment ratio. For example, the total task time is 3.5 hours, and the task allocation ratio of member Z is 0.3. Then the member needs to execute: 3.5×0.3=1.05 hours; the remaining members are assigned tasks according to their calculated adjustment ratios. The adjusted task arrangement gives priority to matching the physical tolerance period of each member, and checks that the member's workload does not exceed its tolerance limit. If a member's load exceeds the tolerance limit, the allocation ratio is adjusted to finally generate the collaborative task scheduling assignment information.

[0139] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A cancer survivor return-to-work support system, characterized in that: The system comprises: The health status assessment module obtains the medical data of cancer survivors, extracts the injury status parameters of the survivors, calculates the rehabilitation status baseline value, analyzes the rehabilitation stage of the survivors, and obtains the rehabilitation stage determination information; The job matching and adjustment module calls the survivor's current work ability data based on the rehabilitation stage determination information, matches it with the key ability indicators required for the job, performs job scheduling, and obtains job adaptation and adjustment information; The working hours optimization module calculates the recommended working hours range according to the job adaptation adjustment information and the rehabilitation stage determination information and the changes in the daily physiological parameters of the survivors to obtain the adaptive working hours range; The task load balancing module analyzes the daily physical fitness fluctuation trend of the survivors based on the adaptive working time interval, calculates the tolerance level required for task execution according to the concentration and physical energy consumption of each work task, adjusts the task arrangement sequence, and obtains the task load distribution information for the time period; The collaborative work scheduling module calls the task load distribution information of the time period, analyzes the part of the task load that exceeds the survivor's tolerance range, screens the adapted members to decompose the task load, and obtains the collaborative task scheduling assignment information.

2. The cancer survivor return-to-work support system according to claim 1, characterized in that: The rehabilitation stage judgment information specifically includes the early rehabilitation period, the middle rehabilitation period and the stable rehabilitation period. The job adaptation adjustment information includes the job competency index, the job ability requirement parameter matching degree and the adjusted job information. The adaptive working time interval includes the recommended working time and the maximum working time. The time period task load allocation information includes the high tolerance time period task allocation information, the moderate load time period task allocation information and the low tolerance time period task allocation information. The collaborative task scheduling assignment information specifically refers to the task load decomposition ratio, the collaborative task adjustment ratio and the task matching degree of the same group members.

3. The cancer survivor return-to-work support system according to claim 1, characterized in that: The health status assessment module includes: The medical data acquisition submodule acquires the medical data of cancer survivors, extracts the injury status parameters of the survivors, including motor nerve injury index, sensory nerve injury index, muscle endurance decline index and cognitive decline index, and collects the health status data of the survivors, including blood oxygen saturation, heart rate stability and sleep duration, organizes and establishes a data set, and obtains the survivor status data set; The injury status calculation submodule is based on the survivor status data set and uses the formula: Calculate baseline values ​​for rehabilitation status; Among them, S r Represents the baseline value of recovery status, W i represents the weight of the i-th damage state parameter, P i represents the value of the i-th damage state parameter, T j represents the jth item of health status data, D represents the total deviation of the change of damage status parameters, n represents the total number of damage status parameters, and m represents the total number of health status data items. The rehabilitation stage determination submodule calls the rehabilitation state reference value, sets the rehabilitation stage determination threshold, determines the rehabilitation stage of the survivor, which includes early rehabilitation, middle rehabilitation and stable rehabilitation, and obtains rehabilitation stage determination information.

4. The cancer survivor return-to-work support system according to claim 1, characterized in that: The job matching and adjustment module includes: The ability requirement extraction submodule calls the survivor's work ability data, including hand control ability, cognitive stability and muscle strength, based on the rehabilitation stage determination information, and extracts the key ability indicators required for each position to obtain a position ability requirement data set; The job competency assessment submodule is based on the job competency requirement data set and uses the formula: Calculate the job competency index of the survivors; Among them, S p Represents job competency index, W p,k represents the weight of the kth key capability indicator, C k represents the demand value of the kth key capability indicator, B k represents the kth key capability indicator value of the survivor, T k represents the balance threshold of the kth competency match, and N represents the total number of key competency indicators required for the position; The job adaptation scheduling submodule sets a job matching threshold based on the job competency index, identifies matching job types, and performs job scheduling according to the job vacancy status to generate job adaptation adjustment information.

5. The cancer survivor return-to-work support system according to claim 1, characterized in that: The working time optimization module includes: The physiological parameter extraction submodule extracts the physiological parameters of the survivor, including heart rate fluctuation, blood oxygen level change, fatigue index and sleep quality fluctuation, based on the job adaptation adjustment information and rehabilitation stage determination information, and establishes a physiological parameter data set; The physiological fluctuation calculation submodule calculates the fluctuation degree of daily physiological parameters based on the physiological parameter data set, and uses the mean square error and the coefficient of variation to measure the fluctuation of each physiological parameter to obtain the physiological parameter fluctuation information; The working time calculation submodule is based on the physiological parameter fluctuation information, combined with the baseline working time recommended for the survivor's recovery stage, and based on the fluctuation tolerance threshold of the physiological parameters, using the formula: Calculate the recommended working hours and combine them with the maximum allowed working hours to obtain the adaptive working hours range; Among them, T w Represents the recommended working hours, T b represents the baseline working hours recommended for the survivor’s recovery stage, P f represents the fluctuation degree of the fth physiological parameter, P th,f represents the fluctuation tolerance threshold of the fth physiological parameter, W b,f represents the importance weight of the fth physiological parameter, N b Represents the total number of items of physiological parameters.

6. The cancer survivor return-to-work support system according to claim 1, characterized in that: The task load balancing module includes: The physical energy fluctuation analysis submodule analyzes the daily physical energy fluctuation trend of the survivors based on the adaptive working time interval, calculates the physical energy consumption rate of each time period, and divides the high tolerance period, moderate load period and low tolerance period by setting the consumption threshold to obtain the physical energy tolerance period information; The task tolerance calculation submodule is based on the physical tolerance period information, the concentration requirements and physical energy consumption of the task, and uses the formula: Calculate the tolerance level of the work task and obtain the task tolerance distribution information; Among them, L t Represents the mission tolerance level, E t Represents the physical energy consumption of the task, C t Represents the concentration requirement level of the task, T t Represents the task execution time; The task period scheduling submodule adjusts the task arrangement order based on the task tolerance distribution information, matches the high tolerance period, the moderate load period and the low tolerance period, optimizes the task execution time, and obtains the period task load distribution information.

7. The cancer survivor return-to-work support system according to claim 1, characterized in that: The collaborative work scheduling module includes: The high-load task analysis submodule analyzes the part of the task load that exceeds the survivor's tolerance based on the task load distribution information of the time period, extracts the execution parameters of the overload task, including the task execution time, physical exertion index and concentration requirement, and obtains the high-load task parameters; The task load decomposition submodule compares the job competency indexes of the same group members based on the high-load task parameters, selects suitable members to participate in the task load decomposition, and adopts the formula: Calculate the collaborative task adjustment ratio to obtain the member task allocation ratio; Among them, R c represents the collaborative task adjustment ratio, S Z Represents the job competency index of member Z, S g represents the job competency index of member g, L Z represents the task tolerance level of member Z, L t Represents the tolerance level requirement of the original task, N d The total number of candidate members; The collaborative task scheduling submodule adjusts task allocation based on the member task allocation ratio, optimizes task load distribution, and generates collaborative task scheduling assignment information.

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