A medical data analysis and control system

Abstract

The present invention discloses a medical data analysis and control system, comprising a data acquisition module, a wheelchair usage data monitoring module and a wheelchair usage management module. The wheelchair usage data monitoring module is used to monitor the process of a patient using a shared wheelchair, analyze the process data of the patient using the wheelchair, and judge whether the shared wheelchair needs to reset the wheelchair usage data based on the analysis result. The present invention monitors the process of a patient using a shared wheelchair, analyzes the process data of the patient using the wheelchair, effectively distinguishes the riding processes of different patients, and promptly performs maintenance when a wheelchair transportation problem is found. At the same time, the connection relationship between wheelchair patients in different stages is obtained to obtain the influence weight between each stage, and the compatibility of the patient with the current wheelchair is obtained, thereby increasing the stability of the hospital's shared wheelchair data control system. The present invention has the characteristics of high accuracy in usage data monitoring and strong patient condition analysis capabilities.

Description

Technical Field

[0001] The present invention relates to the technical field of medical equipment data analysis and processing, and in particular to a medical data analysis and control system. Background Art

[0002] With the rapid development of the Internet, my country has entered the information age. In the process of information development, if proper management and control are not carried out, it will seriously affect the daily work of the medical industry. More and more medical equipment data in the medical system is stored in the database in the form of electronic data. Using information management methods to conduct data statistics on medical equipment information can greatly improve work quality and efficiency, improve the utilization of information, and avoid waste of information resources. However, there are still many problems in the information management of medical control data, which makes it difficult to improve the quality management of medical control data.

[0003] In the prior art, hospitals have fully considered the route flow of patients' medical treatment, and placed shared wheelchairs at the hospital's reception entrance, outpatient hall service desk and other crowded areas for patients' convenience. At the same time, the hospital's shared wheelchair usage data is regularly monitored and controlled, and the conditions of patients in the hospital are analyzed based on the wheelchair usage data. However, the amount of medical information in the hospital is huge every day, and patients use shared wheelchairs frequently. In particular, many patients have poor control over the process of riding a wheelchair. Shared wheelchairs are easily worn out after multiple uses. The degree of wear of shared wheelchairs gradually increases the impact on the monitoring of patients' conditions. Without resetting the usage data, it is impossible to effectively monitor the riding conditions of different patients, resulting in inaccurate monitoring of the patient's condition by the medical management and control system. Therefore, it is very necessary to design a medical data analysis and control system with high accuracy in usage data monitoring and strong ability to analyze the patient's condition. Summary of the invention

[0004] The purpose of the present invention is to provide a medical data analysis and control system to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a medical data analysis and control system, comprising a data acquisition module, a wheelchair usage data monitoring module and a wheelchair usage management module, the data acquisition module being used to collect information about patients who rent and use shared wheelchairs and their use of wheelchairs; the wheelchair usage data monitoring module being used to monitor the process of patients using shared wheelchairs, analyzing the process data of patients using wheelchairs, and judging whether the wheelchair usage data of the shared wheelchair needs to be reset based on the analysis results; the wheelchair usage management module being used to manage the reset of usage data of the shared wheelchair judged by the system, the data acquisition module, the wheelchair usage data monitoring module and the wheelchair usage management module being communicatively connected to each other.

[0006] According to the above technical solution, the data collection module includes a wheelchair data collection module and a patient information collection module. The wheelchair data collection module is used to collect the shared wheelchair equipment information configured in the hospital; the patient information collection module is used to obtain the personal information of patients who rent shared wheelchairs.

[0007] According to the above technical solution, the wheelchair usage data monitoring module includes a patient condition characteristic detection module and a characteristic weight analysis module. The patient condition characteristic detection module is used to detect the patient's condition characteristics based on the monitoring equipment set in the wheelchair; the characteristic weight analysis module is used to analyze the corresponding impact weights of different patient condition characteristics.

[0008] According to the above technical scheme, the patient's condition characteristic detection module includes a riding process characteristic analysis submodule, a getting on and off wheelchair process characteristic analysis submodule and a force sensor detection subunit. The riding process characteristic analysis submodule is used to analyze the patient's condition characteristic values ​​during the patient's riding of a wheelchair; the getting on and off wheelchair process characteristic analysis submodule is used to analyze the characteristic values ​​corresponding to the patient's condition respectively during the patient's getting on and off the wheelchair; the force sensor detection subunit is used to detect the force value of the target area.

[0009] According to the above technical solution, the operation method of the medical data analysis and control system includes the following steps:

[0010] The rental terminal for renting shared wheelchairs in the hospital is connected to the data management and control system to monitor the scenarios of patients renting shared wheelchairs in the hospital;

[0011] After the system detects that the patient has obtained the shared wheelchair for rent by scanning the QR code, it authenticates the identity information of the renter, retrieves the patient's weight information from the hospital database based on the identity information, and uses the camera set up in the rental area to capture the image of the patient riding the shared wheelchair, and divides the image information into three different time nodes when the patient rides the wheelchair;

[0012] Obtain the corresponding impact weights of the characteristic time nodes during the patient's use of the wheelchair, monitor the patient's use of the shared wheelchair, analyze the process data of the patient's use of the wheelchair, and determine whether the shared wheelchair needs to have its usage data reset based on the analysis results;

[0013] When the system determines that the target shared wheelchair needs to be managed by resetting the usage data, the system will transmit the usage data reset information to the prompt device carried by the management personnel in the hospital.

[0014] According to the above technical solution, the method of dividing the patient's wheelchair riding into three different time nodes specifically includes:

[0015] After the patient enters the monitoring area of ​​the data control system in a wheelchair, the communication equipment installed in the wheelchair establishes a real-time communication connection with the monitoring device in the hospital, and the communication equipment of the wheelchair receives the monitoring information from the monitoring device;

[0016] Retrieve the device features of the shared wheelchair in the database, and retrieve the action feature models of the three short-term behaviors of the patient sitting on the wheelchair, the patient sitting on the wheelchair, and the patient leaving the wheelchair by setting the medical data sub-database in the database;

[0017] A camera arranged in the monitoring device captures images of the patient moving in a wheelchair, and transmits the image information to a medical analysis terminal, which extracts features from the image information, retrieves from the intercepted image information and divides the three time nodes of the patient riding the wheelchair according to an action feature model of the patient's corresponding behavior, and divides the stages of the patient riding the wheelchair into patient condition characteristic 1, patient condition characteristic 2 and patient condition characteristic 3 according to the time nodes, corresponding to the three time nodes of the patient sitting on the wheelchair, the patient riding in the wheelchair and the patient leaving the wheelchair, and obtains the corresponding feature values ​​of the patient's condition characteristic 1, the patient's condition characteristic 2 and the patient's condition characteristic 3 in turn.

[0018] According to the above technical solution, the method for obtaining the characteristic value corresponding to the patient's condition characteristic 1 is specifically as follows:

[0019] At the time when the patient sits on the wheelchair, the streamline of the patient's trunk during the bending process of the legs and cervical spine is observed. According to the force sensor set in the back cushion of the wheelchair, the detection area of ​​the force sensor covers the entire area of ​​the patient's trunk from top to bottom, from the cervical spine to the thigh. In the detection area of ​​the force sensor, a total of n force detection result output positions are set correspondingly, and the distances between adjacent force detection result output positions are the same. The detection results f1, f2...fn obtained from n detection positions are obtained, and the corresponding leaning ratio value of the trunk leaning on the wheelchair at the time when the patient sits on the wheelchair is , where f 01 、f 02 ……f 0n The standard force values ​​of the positions of the force detection results of the shared wheelchair extracted from the database are output respectively, the leaning ratio value is multiplied by the weight of the patient included in the database, and the multiplication result is recorded as the corresponding characteristic value A of the patient's condition characteristic 1.

[0020] According to the above technical solution, the method for obtaining the characteristic value corresponding to the patient's condition characteristic 2 is specifically as follows:

[0021] A distance sensor is set in the center point of the connecting plane between the wheelchair back area and the seat cushion area. When the force sensor detects that the force on the back is lower than the minimum force value, the distance sensor starts the internal infrared transmitter to emit infrared rays at an angle of 45 degrees toward the wheelchair, and detects the length value L of the infrared ray extending to the entity. The length value extended to the entity is measured with the built-in timer. After the leaning force of the self-force sensor is reduced to 0, the corresponding growth rate of the elapsed time is obtained as the characteristic value B corresponding to the patient's condition characteristic 2.

[0022] According to the above technical solution, the method for obtaining the characteristic value corresponding to the patient's condition characteristic 3 is specifically as follows:

[0023] Force sensors are installed on the outer armrests of the hand support rods on both sides of the shared wheelchair. When it is detected that a patient is using a shared wheelchair, the pressing force of the patient's hands on the hand support rods is monitored. The force sensor obtains the average pressing force of the two hands as F, and the average pressing pressure is recorded as the characteristic value C corresponding to the patient's condition characteristics.

[0024] According to the above technical solution, the detection method for obtaining the corresponding influence weights of the three characteristic time nodes during the patient's wheelchair riding process is specifically as follows:

[0025] Retrieving historical information of the shared wheelchair from the database, and obtaining information on the total number of times the wheelchair was rented and used and the total time of use from the retrieved historical information;

[0026] Retrieve the first influence function corresponding to the total time used in the process of wheelchair rental and the influence coefficient of the detection results of feature 1 and feature 2 from the database, determine the influence coefficient of the detection results of feature 1 and feature 2 based on the total time information used in the first influence function, compare with the influence coefficient of the detection results of feature 1 and feature 2 obtained in the previous shared wheelchair use stage, and reduce the influence weights of feature 1 and feature 2 accordingly based on the proportion result, wherein the influence weights of feature 1 and feature 2 are equal, the sum of the influence weights of feature 1, feature 2 and feature 3 is 100%, and subtract the influence weights of feature 1 and feature 2 from 100% to obtain the influence weight of feature 3;

[0027] Retrieve the second influence function corresponding to the total number of times the wheelchair is used during rental and use and the influence coefficient of the detection result of feature 3 from the database, determine the influence coefficient of the detection result of feature 3 based on the total number of times the wheelchair is used in the second influence function, compare it with the detection result of feature 3 obtained in the previous shared wheelchair use stage, reduce the influence weight of feature 3 based on the comparison result, subtract the influence weight of feature 3 from 100% and divide it by 2 to obtain the influence weights of feature 1 and feature 2, where the corresponding time of the previous shared wheelchair use stage is the fixed wheelchair use data reset time node obtained from the database;

[0028] The final patient wheelchair fit is , where ψ1, ψ2, and ψ3 are the unit conversion parameters corresponding to the characteristic values ​​of the disease characteristics of feature 1, feature 2, and feature 3, respectively, and δ1, δ2, and δ3 are the count-level conversion parameters corresponding to the characteristic values ​​of the disease characteristics of feature 1, feature 2, and feature 3 obtained from the database, respectively, where δ1=1;

[0029] The obtained patient wheelchair fitness μ is compared with the wheelchair fitness limit value μ0 set in the hospital's shared wheelchair data management and control system. If μ<0.8μ0, the system determines that the target shared wheelchair needs to reset its usage data.

[0030] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention monitors the process of patients using shared wheelchairs, analyzes the process data of patients using wheelchairs, improves the monitoring continuity and uniqueness of the hospital shared wheelchair data management and control system, effectively distinguishes the riding processes of different patients, and promptly performs maintenance when the wheelchair has a transportation problem. By detecting the corresponding body function positions of patients at different stages of wheelchair riding, and obtaining the influence weights between each stage based on the connection relationship between wheelchair patients at different stages, the compatibility of the patient with the current wheelchair is obtained, thereby increasing the stability of the hospital shared wheelchair data management and control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0032] Figure 1 It is a schematic diagram of the system module composition of an embodiment of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] See also Figure 1 , an embodiment of the present invention provides a technical solution: a medical data analysis and control system, comprising:

[0035] A data acquisition module, a wheelchair usage data monitoring module and a wheelchair usage management module. The data acquisition module is used to collect information about patients who rent and use shared wheelchairs and their use of wheelchairs. The wheelchair usage data monitoring module is used to monitor the process of patients using shared wheelchairs, analyze the process data of patients using wheelchairs, and determine whether the wheelchair usage data of the shared wheelchair needs to be reset based on the analysis results. The wheelchair usage management module is used to reset the usage data of the shared wheelchair determined by the system. The data acquisition module, the wheelchair usage data monitoring module and the wheelchair usage management module are communicated with each other.

[0036] The embodiments of the present invention monitor the process of patients using shared wheelchairs and analyze the process data of patients using wheelchairs, thereby improving the monitoring continuity and uniqueness of the hospital shared wheelchair data management and control system, effectively distinguishing the riding processes of different patients, and timely performing maintenance when the wheelchair has a transportation problem. By detecting the corresponding body function positions of patients at different stages of wheelchair riding, and obtaining the influence weights of each stage based on the connection relationship between wheelchair patients at different stages, the compatibility of the patient with the current wheelchair is obtained, thereby increasing the stability of the hospital shared wheelchair data management and control system.

[0037] The data collection module includes a wheelchair data collection module and a patient information collection module. The wheelchair data collection module is used to collect the shared wheelchair equipment information configured in the hospital; the patient information collection module is used to obtain the personal information of patients who rent shared wheelchairs.

[0038] The wheelchair usage data monitoring module includes a patient condition characteristic detection module and a characteristic weight analysis module. The patient condition characteristic detection module is used to detect the patient's condition characteristics based on the monitoring equipment set in the wheelchair; the characteristic weight analysis module is used to analyze the corresponding impact weights of different patient condition characteristics.

[0039] The patient condition characteristic detection module includes a riding process characteristic analysis submodule, a getting on and off wheelchair process characteristic analysis submodule and a force sensor detection subunit. The riding process characteristic analysis submodule is used to analyze the patient's condition characteristic values ​​during the patient's riding of the wheelchair; the getting on and off wheelchair process characteristic analysis submodule is used to analyze the characteristic values ​​corresponding to the patient's condition during the patient's getting on and off the wheelchair; the force sensor detection subunit is used to detect the force value of the target area.

[0040] In a preferred embodiment, the operation method of the medical data analysis and control system includes the following steps:

[0041] The rental terminal for renting shared wheelchairs in the hospital is connected to the data management and control system to monitor the scenarios of patients renting shared wheelchairs in the hospital;

[0042] After the system detects that the patient has obtained the shared wheelchair for rent by scanning the QR code, it authenticates the identity information of the renter, retrieves the patient's weight information from the hospital database based on the identity information, and uses the camera set up in the rental area to capture the image of the patient riding the shared wheelchair, and divides the image information into three different time nodes when the patient rides the wheelchair;

[0043] Obtain the corresponding impact weights of the characteristic time nodes during the patient's use of the wheelchair, monitor the patient's use of the shared wheelchair, analyze the process data of the patient's use of the wheelchair, and determine whether the shared wheelchair needs to have its usage data reset based on the analysis results;

[0044] When the system determines that the target shared wheelchair needs to be managed by resetting the usage data, it will transmit the usage data reset information to the prompt device carried by the management personnel in the hospital, and perform maintenance and repair on the detection device of the shared wheelchair.

[0045] In this embodiment, the method of dividing the patient's wheelchair riding into three different time nodes specifically includes:

[0046] After the patient enters the monitoring area of ​​the data control system in a wheelchair, the communication equipment installed in the wheelchair establishes a real-time communication connection with the monitoring device in the hospital, and the communication equipment of the wheelchair receives the monitoring information from the monitoring device;

[0047] Retrieve the device features of the shared wheelchair in the database, and retrieve the action feature models of the three short-term behaviors of the patient sitting on the wheelchair, the patient sitting on the wheelchair, and the patient leaving the wheelchair by setting the medical data sub-database in the database;

[0048] The camera arranged in the monitoring device captures the picture of the patient moving in the wheelchair, and transmits the picture information to the medical analysis terminal, which extracts features from the picture information, retrieves and divides the three time nodes of the patient riding the wheelchair based on the action feature model of the patient's corresponding behavior from the intercepted picture information, and divides the stages of the patient riding the wheelchair into patient's condition characteristic 1, patient's condition characteristic 2 and patient's condition characteristic 3 according to the time nodes, corresponding to the three time nodes of the patient sitting on the wheelchair, the patient riding in the wheelchair and the patient leaving the wheelchair, and obtains the corresponding feature values ​​of the patient's condition characteristic 1, the patient's condition characteristic 2 and the patient's condition characteristic 3 in turn.

[0049] In this embodiment, the method for obtaining the characteristic value corresponding to the patient's condition characteristic 1 is specifically as follows:

[0050] At the time when the patient sits on the wheelchair, the trunk streamline of the patient's legs and cervical spine bending process is observed. According to the force sensor set in the back cushion of the wheelchair, the detection area of ​​the force sensor covers the entire area of ​​the patient's trunk from top to bottom, from the cervical spine to the thigh. In the detection area of ​​the force sensor, a total of n force detection result output positions are set correspondingly. The distances between adjacent force detection result output positions are the same. The detection results f1, f2...fn obtained from n detection positions are obtained. The corresponding leaning ratio value of the trunk leaning on the wheelchair at the time when the patient sits on the wheelchair is , where f 01 、f 02 ……f 0n The standard force values ​​of the positions of the force detection results of the shared wheelchair extracted from the database are output respectively, the leaning ratio value is multiplied by the weight of the patient included in the database, and the multiplication result is recorded as the corresponding characteristic value A of the patient's condition characteristic 1.

[0051] In this embodiment, the method for obtaining the characteristic value corresponding to the patient's condition characteristic 2 is specifically as follows:

[0052] A distance sensor is set at the center point of the connecting plane between the wheelchair back area and the seat cushion area. When the force sensor detects that the force on the back is lower than the minimum force value, the distance sensor activates the internal infrared transmitter to emit infrared rays at an angle of 45 degrees toward the wheelchair. The length value L of the infrared ray extending to the entity is detected. The length value extended to the entity is measured with the built-in timer. After the leaning force of the self-force sensor is reduced to 0, the corresponding growth rate of the elapsed time is obtained as the characteristic value B corresponding to the patient's condition characteristic 2.

[0053] When the patient leaves the wheelchair, the severity of the patient's current condition is directly proportional to the time required for the patient to stand up from sitting in the wheelchair. The more serious the patient's condition is, the longer the time required for the patient to stand up from sitting in the wheelchair; conversely, the milder the patient's condition is, the shorter the time required for the patient to stand up from sitting in the wheelchair. At the same time, since the patient's body moves from a sitting state to a standing state, the overall trend is from bottom to top, about 45° upward, the detection direction of the infrared sensor is consistent with the overall movement trend of the patient's body, and can be used to detect the real-time distance between the patient and the wheelchair and make a comparison.

[0054] In this embodiment, the method for obtaining the characteristic value corresponding to the patient's condition characteristic 3 is specifically as follows:

[0055] Force sensors are installed on the outer armrests of the hand support rods on both sides of the shared wheelchair. When it is detected that a patient is using a shared wheelchair, the pressing force of the patient's hands on the hand support rods is monitored. The force sensor obtains the average pressing force of the two hands as F, and the average pressing pressure is recorded as the characteristic value C corresponding to the patient's condition characteristics.

[0056] The pressure that the patient presses on the hand support bar is inversely proportional to the safety status of the environment in which the patient is exposed to. The greater the pressure the patient presses on the hand support bar, the lower the safety status value of the environment in which the patient is riding in the wheelchair; conversely, the smaller the pressure the patient presses on the hand support bar, the higher the safety status value of the environment in which the patient is riding in the wheelchair; and the force sensor detects the pressure that the patient presses on the hand support bar during riding, which can reflect whether the patient is nervous during riding. The safety status value, as a measure of whether the patient is nervous during riding in a wheelchair, can also reflect the relationship between the patient's condition and the travel process of the shared wheelchair. By monitoring the pressure that the patient presses on the hand support bar, the monitoring continuity and uniqueness of the hospital's shared wheelchair data management and control system can be improved, and the riding processes of different patients can be effectively distinguished, and maintenance can be performed in time when transportation problems are found in the wheelchair.

[0057] In this embodiment, the detection method for obtaining the influence weights corresponding to the three characteristic time nodes during the patient's wheelchair riding process is specifically as follows:

[0058] Retrieving historical information of the shared wheelchair from the database, and obtaining information on the total number of times the wheelchair was rented and used and the total time of use from the retrieved historical information;

[0059] Retrieve the first influence function corresponding to the total usage time during the wheelchair rental process and the influence coefficient of the detection results of feature 1 and feature 2 from the database, determine the influence coefficient of the detection results of feature 1 and feature 2 based on the total usage time information in the first influence function, compare with the influence coefficient of the detection results of feature 1 and feature 2 obtained in the previous shared wheelchair usage stage, and reduce the influence weights of feature 1 and feature 2 accordingly based on the proportion result, where the influence weights of feature 1 and feature 2 are equal, the sum of the influence weights of feature 1, feature 2 and feature 3 is 100%, and subtract the influence weights of feature 1 and feature 2 from 100% to obtain the influence weight of feature 3;

[0060] Retrieve the second influence function corresponding to the total number of times the wheelchair is used during rental and use and the influence coefficient of the detection result of feature 3 from the database, determine the influence coefficient of the detection result of feature 3 based on the total number of times the wheelchair is used in the second influence function, compare it with the detection result of feature 3 obtained in the previous shared wheelchair use stage, reduce the influence weight of feature 3 based on the comparison result, subtract the influence weight of feature 3 from 100% and divide it by 2 to obtain the influence weights of feature 1 and feature 2, where the corresponding time of the previous shared wheelchair use stage is the fixed wheelchair use data reset time node obtained from the database;

[0061] The final patient wheelchair fit is , where ψ1, ψ2, and ψ3 are the unit conversion parameters corresponding to the characteristic values ​​of the disease characteristics of feature 1, feature 2, and feature 3, respectively; δ1, δ2, and δ3 are the count-level conversion parameters corresponding to the characteristic values ​​of the disease characteristics of feature 1, feature 2, and feature 3 obtained from the database, respectively; where δ1=1, the count-level conversion parameter can make the ratio of the product of the characteristic values ​​of disease characteristics 2 and 3 and the corresponding count-level conversion parameter to the characteristic value of disease characteristic 1 be between (0, 10);

[0062] The obtained patient wheelchair fitness μ is compared with the wheelchair fitness limit value μ0 set in the hospital's shared wheelchair data management and control system. If μ<0.8μ0, the system determines that the target shared wheelchair needs to reset its usage data.

[0063] Establish a connection between the patient being in a wheelchair and the patient being close to being in a wheelchair, and judge the influence weight of the patient's condition characteristics when the patient is in a wheelchair or not in a wheelchair by the intelligence of the wheelchair in the transition state. Wheelchair information, including the total historical use time of the wheelchair (after a long period of use, the influence weight of the feature 2 detection result needs to be reduced, and the influence weight of the corresponding feature 1 and feature 3 detection results will increase, and the detection accuracy of the force sensor set in the wheelchair. After the wheelchair is tested with the force sensor many times, the detection accuracy of the force sensor decreases, resulting in a larger error range of the feature 1 and feature 3 detection results. The influence weight of the feature 1 and feature 3 detection results needs to be reduced, and the influence weight of the corresponding feature 2 detection result will increase.

[0064] The influence weights of the detection results of feature 1 and feature 2 obtained by the total historical time of the wheelchair and the influence weight of the detection result of feature 3 obtained by the total historical number of times 1 passes through the wheelchair are added. The average value of the influence weights of the two features and the corresponding influence weight values ​​of the remaining affected features are calculated. Since the influence weights of feature 1 and feature 2 are equal, the average values ​​of the influence weight values ​​of feature 1 and feature 2 are calculated. , the average value of the influence weight of feature 3 is calculated as .

[0065] By detecting the corresponding body function positions of patients in different stages of wheelchair riding, and obtaining the influence weights of each stage based on the connection relationship between wheelchair patients in different stages, the compatibility of the patient with the current wheelchair is obtained, thereby increasing the stability of the hospital's shared wheelchair data management and control system.

[0066] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0067] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A medical data analysis and control system, characterized by: It includes a data acquisition module, a wheelchair usage data monitoring module and a wheelchair usage management module. The data acquisition module is used to collect information about patients who rent and use shared wheelchairs and their use of wheelchairs; the wheelchair usage data monitoring module is used to monitor the process of patients using shared wheelchairs, analyze the process data of patients using wheelchairs, and judge whether the wheelchair usage data of the shared wheelchair needs to be reset based on the analysis results; the wheelchair usage management module is used to manage the reset of usage data of the shared wheelchair judged by the system, and the data acquisition module, the wheelchair usage data monitoring module and the wheelchair usage management module are communicated with each other; The data collection module includes a wheelchair data collection module and a patient information collection module. The wheelchair data collection module is used to collect the shared wheelchair equipment information configured in the hospital; the patient information collection module is used to obtain the personal information of the patients who rent the shared wheelchair; The wheelchair usage data monitoring module includes a patient condition characteristic detection module and a characteristic weight analysis module, wherein the patient condition characteristic detection module is used to detect the patient condition characteristic based on the monitoring device set in the wheelchair; The feature weight analysis module is used to analyze the corresponding impact weights of different patient condition features; The patient condition characteristic detection module includes a riding process characteristic analysis submodule, a wheelchair getting on and off process characteristic analysis submodule and a force sensor detection subunit. The riding process characteristic analysis submodule is used to analyze the patient's condition characteristic value during the patient's riding process; the wheelchair getting on and off process characteristic analysis submodule is used to analyze the characteristic values ​​corresponding to the patient's condition during the patient's getting on and off the wheelchair; the force sensor detection subunit is used to detect the force value of the target area; The operation method of the medical data analysis and control system comprises the following steps: The rental terminal for renting shared wheelchairs in the hospital is connected to the data management and control system to monitor the scenarios of patients renting shared wheelchairs in the hospital; After the system detects that the patient has obtained the shared wheelchair for rent by scanning the QR code, it authenticates the identity information of the renter, retrieves the patient's weight information from the hospital database based on the identity information, and uses the camera set up in the rental area to capture the image of the patient riding the shared wheelchair, and divides the image information into three different time nodes when the patient rides the wheelchair; Obtain the corresponding impact weights of the characteristic time nodes during the patient's use of the wheelchair, monitor the patient's use of the shared wheelchair, analyze the process data of the patient's use of the wheelchair, and determine whether the shared wheelchair needs to have its usage data reset based on the analysis results; When the system determines that the target shared wheelchair needs to be reset, the data reset information will be transmitted to the prompt device carried by the hospital manager; The method for dividing the three different time nodes of the patient riding a wheelchair specifically includes: After the patient enters the monitoring area of ​​the data control system in a wheelchair, the communication device installed in the wheelchair establishes a real-time communication connection with the monitoring device in the hospital, and the communication device of the wheelchair receives the monitoring information from the monitoring device; Retrieve the device features of the shared wheelchair in the database, and retrieve the action feature models of the three short-term behaviors of the patient sitting on the wheelchair, the patient sitting on the wheelchair, and the patient leaving the wheelchair by setting the medical data sub-database in the database; A camera arranged in the monitoring device captures images of the patient moving in a wheelchair, and transmits the image information to a medical analysis terminal, which extracts features from the image information, retrieves from the intercepted image information and divides the three time nodes of the patient riding the wheelchair according to an action feature model of the patient's corresponding behavior, and divides the stages of the patient riding the wheelchair into patient condition characteristic 1, patient condition characteristic 2 and patient condition characteristic 3 according to the time nodes, corresponding to the three time nodes of the patient sitting on the wheelchair, the patient riding in the wheelchair and the patient leaving the wheelchair, and obtains the corresponding feature values ​​of the patient's condition characteristic 1, the patient's condition characteristic 2 and the patient's condition characteristic 3 in turn.

2. A medical data analysis and control system according to claim 1, characterized in that: The method for obtaining the characteristic value corresponding to the patient's condition characteristic 1 is specifically as follows: At the time when the patient sits on the wheelchair, the streamline of the patient's trunk during the bending process of the legs and cervical spine is observed. According to the force sensor set in the back cushion of the wheelchair, the detection area of ​​the force sensor covers the entire area of ​​the patient's trunk from top to bottom, from the cervical spine to the thigh. In the detection area of ​​the force sensor, a total of n force detection result output positions are set correspondingly, and the distances between adjacent force detection result output positions are the same. The detection results f1, f2...fn obtained from n detection positions are obtained, and the corresponding leaning ratio value of the trunk leaning on the wheelchair at the time when the patient sits on the wheelchair is , where f 01 、f 02 ……f 0n The standard force values ​​of the positions of the force detection results of the shared wheelchair extracted from the database are output respectively, the leaning ratio value is multiplied by the weight of the patient included in the database, and the multiplication result is recorded as the corresponding characteristic value A of the patient's condition characteristic 1.

3. A medical data analysis and control system according to claim 2, characterized in that: The method for obtaining the characteristic value corresponding to the patient's condition characteristic 2 is specifically as follows: A distance sensor is set in the center point of the connecting plane between the wheelchair back area and the seat cushion area. When the force sensor detects that the force on the back is lower than the minimum force value, the distance sensor starts the internal infrared transmitter to emit infrared rays at an angle of 45 degrees toward the wheelchair, and detects the length value L of the infrared ray extending to the entity. The length value extended to the entity is measured with the built-in timer. After the leaning force of the self-force sensor is reduced to 0, the corresponding growth rate of the elapsed time is obtained as the characteristic value B corresponding to the patient's condition characteristic 2.

4. A medical data analysis and control system according to claim 3, characterized in that: The method for obtaining the characteristic value corresponding to the patient's condition characteristic 3 is specifically as follows: Force sensors are installed on the outer armrests of the hand support rods on both sides of the shared wheelchair. When it is detected that a patient is using a shared wheelchair, the pressing force of the patient's hands on the hand support rods is monitored. The force sensor obtains the average pressing force of the two hands as F, and the average pressing pressure is recorded as the characteristic value C corresponding to the patient's condition characteristics.

5. A medical data analysis and control system according to claim 4, characterized in that: The detection method for obtaining the influence weights corresponding to the three characteristic time nodes during the patient's wheelchair riding process is specifically as follows: Retrieving historical information of the shared wheelchair from the database, and obtaining information on the total number of times the wheelchair was rented and used and the total time of use from the retrieved historical information; Retrieve the first influence function corresponding to the total time used in the process of wheelchair rental and the influence coefficient of the detection results of feature 1 and feature 2 from the database, determine the influence coefficient of the detection results of feature 1 and feature 2 based on the total time information used in the first influence function, compare with the influence coefficient of the detection results of feature 1 and feature 2 obtained in the previous shared wheelchair use stage, and reduce the influence weights of feature 1 and feature 2 accordingly based on the proportion result, wherein the influence weights of feature 1 and feature 2 are equal, the sum of the influence weights of feature 1, feature 2 and feature 3 is 100%, and subtract the influence weights of feature 1 and feature 2 from 100% to obtain the influence weight of feature 3; Retrieve the second influence function corresponding to the total number of times the wheelchair is used during rental and use and the influence coefficient of the detection result of feature 3 from the database, determine the influence coefficient of the detection result of feature 3 based on the total number of times the wheelchair is used in the second influence function, compare it with the detection result of feature 3 obtained in the previous shared wheelchair use stage, reduce the influence weight of feature 3 based on the comparison result, subtract the influence weight of feature 3 from 100% and divide it by 2 to obtain the influence weights of feature 1 and feature 2, wherein the corresponding time of the previous shared wheelchair use stage is the fixed wheelchair use data reset time node obtained from the database; The final patient wheelchair fit is , where ψ1, ψ2, and ψ3 are the unit conversion parameters corresponding to the characteristic values ​​of the disease characteristics of feature 1, feature 2, and feature 3, respectively, and δ1, δ2, and δ3 are the count-level conversion parameters corresponding to the characteristic values ​​of the disease characteristics of feature 1, feature 2, and feature 3 obtained from the database, respectively, where δ1=1; The obtained patient wheelchair fitness μ is compared with the wheelchair fitness limit value μ0 set in the hospital's shared wheelchair data management and control system. If μ<0.8μ0, the system determines that the target shared wheelchair needs to reset its usage data.

Images