Stroke user-oriented physiological parameter intelligent detection processing method and device

Abstract

The invention discloses a stroke user-oriented physiological parameter intelligent detection processing method. The method comprises the steps of obtaining a physiological parameter information set and a nursing scheme information set of a stroke user; the physiological parameter information set comprises a blood pressure measurement data sequence, a blood glucose measurement data sequence, a body temperature measurement data sequence, a pulse measurement data sequence and an oxyhemoglobin saturation measurement data sequence corresponding to each type of nursing scheme information; the nursing scheme information set comprises nursing scheme information; classifying the physiological parameter information set and the nursing scheme information set to obtain a pre-nursing physiological parameter information set and a post-nursing physiological parameter information set corresponding to various nursing schemes; performing optimization analysis processing on the pre-nursing physiological parameter information set and the post-nursing physiological parameter information set corresponding to the various nursing schemes to obtain optimal nursing scheme information of the stroke user; the optimal nursing scheme information comprises time proportions of various nursing behaviors.

Description

Technical Field

[0001] The present invention relates to the field of medical data processing, and in particular to a method and device for intelligent detection and processing of physiological parameters for stroke users. Background Art

[0002] At present, the elderly are a high-risk group for stroke. Most stroke patients need to return home for rehabilitation treatment after treatment. The aging and decline of physiological functions of all systems in elderly patients seriously affect their functional recovery and quality of life, so their family caregivers need to spend more energy and time. How to reasonably formulate a rehabilitation plan in the postoperative home stage is of great significance for the rehabilitation of stroke in the elderly.

[0003] With the development of intelligent sensor technology, full-time intelligent monitoring and processing of the physiological indicators of stroke users can be realized. How to achieve comprehensive monitoring of stroke users through intelligent detection of their physiological indicators and form a home rehabilitation care plan with the best effect is an urgent problem to be solved. Summary of the invention

[0004] The present invention mainly solves the problem of how to achieve comprehensive monitoring of stroke users and form a home rehabilitation nursing plan with the best effect through intelligent detection of physiological indicators of stroke users. The present invention discloses a method and device for intelligent detection and processing of physiological parameters of stroke users.

[0005] In a first aspect of the embodiments of the present application, a method for intelligent detection and processing of physiological parameters for stroke users is disclosed, comprising:

[0006] S1, obtaining a physiological parameter information set and a nursing plan information set of a stroke user; the physiological parameter information set includes a blood pressure measurement data sequence, a blood sugar measurement data sequence, a body temperature measurement data sequence, a pulse measurement data sequence, and a blood oxygen saturation measurement data sequence corresponding to each type of nursing plan information; each type of measurement data sequence is a data sequence of each type of physiological parameter collected according to the same discrete sampling time; the nursing plan information set includes nursing plan information; the nursing plan information includes the occurrence time of the nursing plan and the time proportion value of each nursing behavior included; the nursing behavior includes health education behavior, self-management behavior, and social support behavior;

[0007] S2, classifying the physiological parameter information set and the nursing scheme information set to obtain a pre-nursing physiological parameter information set and a post-nursing physiological parameter information set corresponding to each nursing scheme;

[0008] S3, optimizing and analyzing the pre-nursing physiological parameter information set and the post-nursing physiological parameter information set corresponding to each type of nursing plan to obtain the optimal nursing plan information for the stroke user; the optimal nursing plan information includes the time proportion value of each type of nursing behavior.

[0009] The physiological parameter information set and the nursing scheme information set are classified and processed to obtain the pre-nursing physiological parameter information set and the post-nursing physiological parameter information set corresponding to each nursing scheme, including:

[0010] For each type of nursing plan information, according to the occurrence time of the nursing plan, the measurement data sequence corresponding to each nursing plan information is divided into a corresponding pre-nursing measurement data sequence and a post-nursing measurement data sequence, and all the pre-nursing measurement data sequences are used to construct a pre-nursing physiological parameter information set and a post-nursing physiological parameter information set corresponding to the nursing plan;

[0011] The pre-nursing physiological parameter information set includes a pre-nursing blood pressure measurement data sequence, a pre-nursing blood sugar measurement data sequence, a pre-nursing body temperature measurement data sequence, a pre-nursing pulse measurement data sequence, and a pre-nursing blood oxygen saturation measurement data sequence;

[0012] The post-nursing physiological parameter information set includes a post-nursing blood pressure measurement data sequence, a post-nursing blood sugar measurement data sequence, a post-nursing body temperature measurement data sequence, a post-nursing pulse measurement data sequence, and a post-nursing blood oxygen saturation measurement data sequence.

[0013] The optimizing and analyzing the pre-nursing physiological parameter information set and the post-nursing physiological parameter information set corresponding to the various nursing plans to obtain the optimal nursing plan information for the stroke user includes:

[0014] S31, obtaining a standard physiological parameter data set; the standard physiological parameter data set includes: blood pressure standard data, blood sugar standard data, body temperature standard data, pulse standard data, and blood oxygen saturation standard data;

[0015] S32, subtracting various measurement data sequences in the pre-nursing physiological parameter information set and the post-nursing physiological parameter information set corresponding to the various nursing schemes from the corresponding standard data in the standard physiological parameter data set and taking absolute values ​​to obtain a first physiological parameter information set and a second physiological parameter information set corresponding to the various nursing schemes;

[0016] S33, using a comprehensive evaluation model, performing physiological improvement evaluation processing on the first physiological parameter information set and the second physiological parameter information set corresponding to each nursing scheme, to obtain nursing effect evaluation values ​​of each nursing scheme;

[0017] S34, using the nursing plan quantitative analysis model, calculate and process the nursing effect evaluation values ​​and nursing plan information of the various nursing plans to obtain the optimal nursing plan information.

[0018] The comprehensive evaluation model is used to perform physiological improvement evaluation processing on the first physiological parameter information set and the second physiological parameter information set corresponding to each nursing scheme to obtain the nursing effect evaluation value of each nursing scheme, including:

[0019] S331, performing subtraction processing on each type of measurement data sequence of the first physiological parameter information set corresponding to each type of nursing scheme and each type of measurement data sequence corresponding to the second physiological parameter information set, to obtain a physiological parameter difference information set; the physiological parameter difference information set includes a blood pressure difference data sequence, a blood sugar difference data sequence, a body temperature difference data sequence, a pulse difference data sequence, and a blood oxygen saturation difference data sequence;

[0020] S332, representing the physiological parameter difference information set corresponding to each type of nursing scheme as a difference matrix corresponding to the nursing scheme; the row vector of the difference matrix is ​​each difference data sequence in the physiological parameter difference information set;

[0021] S333, performing time-frequency transformation on each row vector of the difference matrix to obtain a transformation matrix;

[0022] S334, performing information amount calculation processing on the transformation matrix to obtain an information vector;

[0023] S335, calculating and obtaining a correlation matrix of the difference matrix; the element of the i-th row and j-th column of the correlation matrix is ​​a correlation value between the i-th row vector and the j-th row vector of the difference matrix;

[0024] S336, decomposing the cross-correlation matrix to obtain a corresponding left decomposition matrix, a characteristic matrix, and a right decomposition matrix;

[0025] S337, extracting the diagonal elements of the feature matrix to obtain a feature vector;

[0026] S338, performing a vector dot multiplication operation using the feature vector and the information vector to obtain a nursing effect evaluation value of the nursing plan of the class;

[0027] S339, execute S331 to S338 for all types of nursing plans to obtain nursing effect evaluation values ​​of various types of nursing plans.

[0028] The decomposition process is calculated as follows:

[0029] Y=UAV,

[0030] Among them, U is the left decomposition matrix, Y is the cross-correlation matrix, A is the characteristic matrix, V is the right decomposition matrix, U and V are both orthogonal matrices, and A is a diagonal matrix.

[0031] The information volume calculation process is expressed as follows:

[0032]

[0033] Among them, H k is the kth element of the intermediate vector, p k is the kth element of the information vector, a k,i is the element in the kth row and ith column of the transformation matrix, and N is the column dimension of the transformation matrix.

[0034] The quantitative analysis model of the nursing plan is used to calculate and process the nursing effect evaluation values ​​and nursing plan information of the various nursing plans to obtain the optimal nursing plan information, including:

[0035] Determine the nursing effect evaluation value of each type of nursing plan as the dependent variable y, and determine the time proportion of health education behavior, self-management behavior, and social support behavior of each type of nursing plan as independent variables x1, x2, and x3 respectively;

[0036] Perform function fitting on all known independent variables x1, x2 and x3 and the corresponding dependent variable y to obtain the nursing effect function f(x1, x2, x3);

[0037] Under the condition that x1+x2+x3=1, find the combination of independent variables (x1, x2, x3) that makes f(x1, x2, x3) reach the maximum value. 10 ,x 20 ,x 30 );

[0038] Determine the (x 10 ,x 20 ,x 30 ), which is the optimal nursing plan information.

[0039] In a second aspect of the embodiments of the present invention, a physiological parameter intelligent detection and processing device for stroke users is disclosed, the device comprising:

[0040] A memory storing executable program code;

[0041] a processor coupled to the memory;

[0042] The processor calls the executable program code stored in the memory to execute the intelligent detection and processing method of physiological parameters for stroke users.

[0043] According to a third aspect of an embodiment of the present invention, a computer storable medium is disclosed, wherein the computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the intelligent detection and processing method of physiological parameters for stroke users.

[0044] According to a fourth aspect of the embodiments of the present invention, an information data processing terminal is disclosed. The information data processing terminal is used to implement the intelligent detection and processing method of physiological parameters for stroke users.

[0045] The beneficial effects of the present invention are:

[0046] The present invention realizes comprehensive monitoring of stroke users through full-time intelligent monitoring and processing of physiological indicators of stroke users under different nursing schemes, and comprehensively evaluates the physiological indicator data collected under different nursing schemes to obtain nursing effects; by establishing a quantitative relationship model between nursing effects and nursing schemes, the accuracy of nursing effect evaluation is improved, and a home rehabilitation nursing scheme with the best effect is formed.

[0047] In the process of comprehensive evaluation of the physiological indicator data collected under different nursing schemes, the present invention obtains the difference value by subtracting it from the normal standard indicator, and comprehensively compares the difference values ​​before and after nursing, establishes an information volume calculation and processing method and a matrix decomposition method, effectively realizes the extraction of difference information before and after nursing, and ensures the accuracy, comprehensiveness and rationality of the evaluation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart for implementing the method of the present invention. DETAILED DESCRIPTION

[0049] In order to better understand the content of the present invention, an embodiment is given here.

[0050] Figure 1 It is a flow chart for implementing the method of the present invention.

[0051] In order to solve the problem of how to realize comprehensive monitoring of stroke users and form a nursing plan with the best effect through intelligent detection of physiological indicators of stroke users, the present invention discloses a method and device for intelligent detection and processing of physiological parameters of stroke users.

[0052] In a first aspect of the embodiments of the present application, a method for intelligent detection and processing of physiological parameters for stroke users is disclosed, comprising:

[0053] S1, obtaining a physiological parameter information set and a nursing plan information set of a stroke user; the physiological parameter information set includes a blood pressure measurement data sequence, a blood sugar measurement data sequence, a body temperature measurement data sequence, a pulse measurement data sequence, and a blood oxygen saturation measurement data sequence corresponding to each type of nursing plan information; each type of measurement data sequence is a data sequence of each type of physiological parameter collected according to the same discrete sampling time; the nursing plan information set includes nursing plan information; the nursing plan information includes the occurrence time of the nursing plan and the time proportion value of each nursing behavior included; the nursing behavior includes health education behavior, self-management behavior, and social support behavior;

[0054] S2, classifying the physiological parameter information set and the nursing scheme information set to obtain a pre-nursing physiological parameter information set and a post-nursing physiological parameter information set corresponding to each nursing scheme;

[0055] S3, optimizing and analyzing the pre-nursing physiological parameter information set and the post-nursing physiological parameter information set corresponding to the various nursing plans to obtain the optimal nursing plan information for the stroke user; the optimal nursing plan information includes the time proportion value of various nursing behaviors;

[0056] The physiological parameter information set and the nursing scheme information set are classified and processed to obtain the pre-nursing physiological parameter information set and the post-nursing physiological parameter information set corresponding to each nursing scheme, including:

[0057] For each type of nursing plan information, according to the occurrence time of the nursing plan, the measurement data sequence corresponding to each nursing plan information is divided into a corresponding pre-nursing measurement data sequence and a post-nursing measurement data sequence, and all the pre-nursing measurement data sequences are used to construct a pre-nursing physiological parameter information set and a post-nursing physiological parameter information set corresponding to the nursing plan;

[0058] The pre-nursing physiological parameter information set includes a pre-nursing blood pressure measurement data sequence, a pre-nursing blood sugar measurement data sequence, a pre-nursing body temperature measurement data sequence, a pre-nursing pulse measurement data sequence, and a pre-nursing blood oxygen saturation measurement data sequence;

[0059] The post-nursing physiological parameter information set includes a post-nursing blood pressure measurement data sequence, a post-nursing blood sugar measurement data sequence, a post-nursing body temperature measurement data sequence, a post-nursing pulse measurement data sequence, and a post-nursing blood oxygen saturation measurement data sequence;

[0060] The optimizing and analyzing the pre-nursing physiological parameter information set and the post-nursing physiological parameter information set corresponding to the various nursing plans to obtain the optimal nursing plan information for the stroke user includes:

[0061] S31, obtaining a standard physiological parameter data set; the standard physiological parameter data set includes: blood pressure standard data, blood sugar standard data, body temperature standard data, pulse standard data, and blood oxygen saturation standard data;

[0062] S32, subtracting various measurement data sequences in the pre-nursing physiological parameter information set and the post-nursing physiological parameter information set corresponding to the various nursing schemes from the corresponding standard data in the standard physiological parameter data set and taking absolute values ​​to obtain a first physiological parameter information set and a second physiological parameter information set corresponding to the various nursing schemes;

[0063] S33, using a comprehensive evaluation model, performing physiological improvement evaluation processing on the first physiological parameter information set and the second physiological parameter information set corresponding to each nursing scheme, to obtain nursing effect evaluation values ​​of each nursing scheme;

[0064] S34, using a nursing plan quantitative analysis model, calculating and processing the nursing effect evaluation values ​​and nursing plan information of the various nursing plans to obtain optimal nursing plan information;

[0065] The comprehensive evaluation model is used to perform physiological improvement evaluation processing on the first physiological parameter information set and the second physiological parameter information set corresponding to each nursing scheme to obtain the nursing effect evaluation value of each nursing scheme, including:

[0066] S331, performing subtraction processing on each type of measurement data sequence of the first physiological parameter information set corresponding to each type of nursing scheme and each type of measurement data sequence corresponding to the second physiological parameter information set, to obtain a physiological parameter difference information set; the physiological parameter difference information set includes a blood pressure difference data sequence, a blood sugar difference data sequence, a body temperature difference data sequence, a pulse difference data sequence, and a blood oxygen saturation difference data sequence;

[0067] S332, representing the physiological parameter difference information set corresponding to each type of nursing scheme as a difference matrix corresponding to the nursing scheme; the row vector of the difference matrix is ​​each difference data sequence in the physiological parameter difference information set;

[0068] S333, performing time-frequency transformation on each row vector of the difference matrix to obtain a transformation matrix;

[0069] S334, performing information amount calculation processing on the transformation matrix to obtain an information vector;

[0070] S335, calculating and obtaining a correlation matrix of the difference matrix; the element of the i-th row and j-th column of the correlation matrix is ​​a correlation value between the i-th row vector and the j-th row vector of the difference matrix;

[0071] S336, decomposing the cross-correlation matrix to obtain a corresponding left decomposition matrix, a characteristic matrix, and a right decomposition matrix;

[0072] S337, extracting the diagonal elements of the feature matrix to obtain a feature vector;

[0073] S338, performing a vector dot multiplication operation using the feature vector and the information vector to obtain a nursing effect evaluation value of the nursing plan of the class;

[0074] S339, execute S331 to S338 for all types of nursing plans to obtain nursing effect evaluation values ​​of various types of nursing plans.

[0075] The decomposition process is calculated as follows:

[0076] Y=UAV,

[0077] Among them, U is the left decomposition matrix, Y is the cross-correlation matrix, A is the characteristic matrix, V is the right decomposition matrix, U and V are both orthogonal matrices, and A is a diagonal matrix;

[0078] The information volume calculation process is expressed as follows:

[0079]

[0080] Among them, H k is the kth element of the intermediate vector, p k is the kth element of the information vector, a k,i is the element in the kth row and ith column of the transformation matrix, and N is the column dimension of the transformation matrix;

[0081] The quantitative analysis model of the nursing plan is used to calculate and process the nursing effect evaluation values ​​and nursing plan information of the various nursing plans to obtain the optimal nursing plan information, including:

[0082] Determine the nursing effect evaluation value of each type of nursing plan as the dependent variable y, and determine the time proportion of health education behavior, self-management behavior, and social support behavior of each type of nursing plan as independent variables x1, x2, and x3 respectively;

[0083] Perform function fitting on all known independent variables x1, x2 and x3 and the corresponding dependent variable y to obtain the nursing effect function f(x1, x2, x3);

[0084] Under the condition that x1+x2+x3=1, find the combination of independent variables (x1, x2, x3) that makes f(x1, x2, x3) reach the maximum value. 10 ,x 20 ,x 30 );

[0085] Determine the (x 10 ,x 20 ,x 30 ), which is the optimal nursing plan information;

[0086] The function fitting process may adopt the best consistent approximation, the best equipartition approximation or the least squares curve fitting.

[0087] The time-frequency transformation can be implemented by using DEMON spectrum transformation to obtain a DEMON spectrum sequence, and a transformation matrix is ​​constructed using all DEMON spectrum sequences;

[0088] The blood pressure difference data sequence is obtained by subtracting the blood pressure measurement data sequence of the first physiological parameter information set corresponding to the same type of nursing plan from the blood pressure measurement data sequence corresponding to the second physiological parameter information set. The calculation process of other types of difference data sequences is the same as the above process.

[0089] The first physiological parameter information set is obtained by subtracting the corresponding standard data in the standard physiological parameter data set from the physiological parameter information set before nursing care; the second physiological parameter information set is obtained by subtracting the corresponding standard data in the standard physiological parameter data set from the physiological parameter information set after nursing care;

[0090] The pre-nursing measurement data sequence is a measurement data sequence measured before the nursing plan occurs; the post-nursing measurement data sequence is a measurement data sequence measured after the nursing plan occurs;

[0091] Under the condition that x1+x2+x3=1 is satisfied, the combination of independent variables (x1, x2, x3) that makes f(x1, x2, x3) reach the maximum value is found. 10 ,x 20 ,x 30 ), we can use the Monte Carlo simulation method to randomly generate several (x1,x2,x3) combinations that satisfy x1+x2+x3=1, calculate the f(x1,x2,x3) value of each combination, and determine the (x1,x2,x3) combination corresponding to the maximum value, which is the independent variable combination (x 10 ,x 20 ,x 30 ).

[0092] The health education behavior includes providing patients with information such as stroke-related knowledge, preventive measures, rehabilitation exercises, etc. through WeChat official accounts, mini-programs and other platforms to enhance patients' health literacy and behavioral intentions;

[0093] The self-management behaviors include guiding patients to develop and implement personalized self-management plans through WeChat group interactions, check-ins, etc., including diet, exercise, medication, emotions, etc., to enhance patients' self-efficacy and behavior control;

[0094] The social support behaviors include establishing a communication and mutual assistance network among patients, family members, medical staff and volunteers through WeChat groups, video conferences, etc., providing emotional, information and tool support, and enhancing patients' social norms and behavioral cues.

[0095] The blood pressure measurement data sequence is collected by using a blood pressure sensor;

[0096] The blood sugar measurement data sequence is collected by using a blood sugar sensor;

[0097] The body temperature measurement data sequence is collected by using a body temperature sensor;

[0098] The pulse measurement data sequence is collected by using a pulse sensor;

[0099] The blood oxygen saturation measurement data sequence is collected by using a blood oxygen saturation sensor.

[0100] In a second aspect of the embodiments of the present invention, a physiological parameter intelligent detection and processing device for stroke users is disclosed, the device comprising:

[0101] A memory storing executable program code;

[0102] a processor coupled to the memory;

[0103] The processor calls the executable program code stored in the memory to execute the intelligent detection and processing method of physiological parameters for stroke users.

[0104] According to a third aspect of an embodiment of the present invention, a computer storable medium is disclosed, wherein the computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the intelligent detection and processing method of physiological parameters for stroke users.

[0105] According to a fourth aspect of the embodiments of the present invention, an information data processing terminal is disclosed. The information data processing terminal is used to implement the intelligent detection and processing method of physiological parameters for stroke users.

[0106] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for intelligent detection and processing of physiological parameters for stroke users, characterized in that: include: S1, obtaining a set of physiological parameter information and a set of nursing plan information of a stroke user; The physiological parameter information set includes a blood pressure measurement data sequence, a blood sugar measurement data sequence, a body temperature measurement data sequence, a pulse measurement data sequence, and a blood oxygen saturation measurement data sequence corresponding to each type of nursing plan information; each type of measurement data sequence is a data sequence of each type of physiological parameter collected according to the same discrete sampling time; the nursing plan information set includes nursing plan information; the nursing plan information includes the occurrence time of the nursing plan and the time proportion value of each nursing behavior included; the nursing behavior includes health education behavior, self-management behavior, and social support behavior; S2, classifying the physiological parameter information set and the nursing scheme information set to obtain a pre-nursing physiological parameter information set and a post-nursing physiological parameter information set corresponding to each nursing scheme; S3, optimizing and analyzing the pre-nursing physiological parameter information set and the post-nursing physiological parameter information set corresponding to each type of nursing plan to obtain the optimal nursing plan information for the stroke user; the optimal nursing plan information includes the time proportion value of each type of nursing behavior.

2. The intelligent detection and processing method for physiological parameters of stroke users according to claim 1, characterized in that: The physiological parameter information set and the nursing scheme information set are classified and processed to obtain the pre-nursing physiological parameter information set and the post-nursing physiological parameter information set corresponding to each nursing scheme, including: For each type of nursing plan information, according to the occurrence time of the nursing plan, the measurement data sequence corresponding to each nursing plan information is divided into a corresponding pre-nursing measurement data sequence and a post-nursing measurement data sequence, and all the pre-nursing measurement data sequences are used to construct a pre-nursing physiological parameter information set and a post-nursing physiological parameter information set corresponding to the nursing plan; The pre-nursing physiological parameter information set includes a pre-nursing blood pressure measurement data sequence, a pre-nursing blood sugar measurement data sequence, a pre-nursing body temperature measurement data sequence, a pre-nursing pulse measurement data sequence, and a pre-nursing blood oxygen saturation measurement data sequence; The post-nursing physiological parameter information set includes a post-nursing blood pressure measurement data sequence, a post-nursing blood sugar measurement data sequence, a post-nursing body temperature measurement data sequence, a post-nursing pulse measurement data sequence, and a post-nursing blood oxygen saturation measurement data sequence.

3. The intelligent detection and processing method for physiological parameters of stroke users according to claim 1, characterized in that: The optimizing and analyzing the pre-nursing physiological parameter information set and the post-nursing physiological parameter information set corresponding to the various nursing plans to obtain the optimal nursing plan information for the stroke user includes: S31, obtaining a standard physiological parameter data set; the standard physiological parameter data set includes: blood pressure standard data, blood sugar standard data, body temperature standard data, pulse standard data, and blood oxygen saturation standard data; S32, subtracting various measurement data sequences in the pre-nursing physiological parameter information set and the post-nursing physiological parameter information set corresponding to the various nursing schemes from the corresponding standard data in the standard physiological parameter data set and taking absolute values ​​to obtain a first physiological parameter information set and a second physiological parameter information set corresponding to the various nursing schemes; S33, using a comprehensive evaluation model, performing physiological improvement evaluation processing on the first physiological parameter information set and the second physiological parameter information set corresponding to each nursing scheme, to obtain nursing effect evaluation values ​​of each nursing scheme; S34, using the nursing plan quantitative analysis model, calculate and process the nursing effect evaluation values ​​and nursing plan information of the various nursing plans to obtain the optimal nursing plan information.

4. The intelligent detection and processing method for physiological parameters of stroke users according to claim 3, characterized in that: The comprehensive evaluation model is used to perform physiological improvement evaluation processing on the first physiological parameter information set and the second physiological parameter information set corresponding to each nursing scheme to obtain the nursing effect evaluation value of each nursing scheme, including: S331, performing subtraction processing on each type of measurement data sequence of the first physiological parameter information set corresponding to each type of nursing scheme and each type of measurement data sequence corresponding to the second physiological parameter information set, to obtain a physiological parameter difference information set; the physiological parameter difference information set includes a blood pressure difference data sequence, a blood sugar difference data sequence, a body temperature difference data sequence, a pulse difference data sequence, and a blood oxygen saturation difference data sequence; S332, representing the physiological parameter difference information set corresponding to each type of nursing scheme as a difference matrix corresponding to the nursing scheme; the row vector of the difference matrix is ​​each difference data sequence in the physiological parameter difference information set; S333, performing time-frequency transformation on each row vector of the difference matrix to obtain a transformation matrix; S334, performing information amount calculation processing on the transformation matrix to obtain an information vector; S335, calculating and obtaining a correlation matrix of the difference matrix; the element of the i-th row and j-th column of the correlation matrix is ​​a correlation value between the i-th row vector and the j-th row vector of the difference matrix; S336, decomposing the cross-correlation matrix to obtain a corresponding left decomposition matrix, a characteristic matrix, and a right decomposition matrix; S337, extracting the diagonal elements of the feature matrix to obtain a feature vector; S338, performing a vector dot multiplication operation using the feature vector and the information vector to obtain a nursing effect evaluation value of the nursing plan of the class; S339, execute S331 to S338 for all types of nursing plans to obtain nursing effect evaluation values ​​of various types of nursing plans.

5. The intelligent detection and processing method for physiological parameters of stroke users according to claim 4, characterized in that: The decomposition process is calculated as follows: Y=UAV, Among them, U is the left decomposition matrix, Y is the cross-correlation matrix, A is the characteristic matrix, V is the right decomposition matrix, U and V are both orthogonal matrices, and A is a diagonal matrix.

6. The intelligent detection and processing method for physiological parameters of stroke users according to claim 4, characterized in that: The information volume calculation process is expressed as follows: Among them, H k is the kth element of the intermediate vector, p k is the kth element of the information vector, a k,i is the element in the kth row and ith column of the transformation matrix, and N is the column dimension of the transformation matrix.

7. The intelligent detection and processing method for physiological parameters of stroke users according to claim 4, characterized in that: The quantitative analysis model of the nursing plan is used to calculate and process the nursing effect evaluation values ​​and nursing plan information of the various nursing plans to obtain the optimal nursing plan information, including: Determine the nursing effect evaluation value of each type of nursing plan as the dependent variable y, and determine the time proportion of health education behavior, self-management behavior, and social support behavior of each type of nursing plan as independent variables x1, x2, and x3 respectively; Perform function fitting on all known independent variables x1, x2 and x3 and the corresponding dependent variable y to obtain the nursing effect function f(x1, x2, x3); Under the condition that x1+x2+x3=1, find the combination of independent variables (x1, x2, x3) that makes f(x1, x2, x3) reach the maximum value. 10 ,x 20 ,x 30 ); Determine the (x 10 ,x 20 ,x 30 ), which is the optimal nursing plan information.

8. An intelligent detection and processing device for physiological parameters of stroke patients, characterized in that: The device comprises: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the intelligent detection and processing method for physiological parameters of stroke users as described in any one of claims 1 to 7.

9. A computer storable medium, characterized in that: The computer storable medium stores computer instructions, and when the computer instructions are called by a computer, they are used to execute the intelligent detection and processing method for physiological parameters of stroke users according to any one of claims 1 to 7.

10. An information data processing terminal, characterized in that: The information data processing terminal is used to implement the intelligent detection and processing method of physiological parameters for stroke users as described in any one of claims 1 to 7.

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