An in-hospital self-driving navigation wheelchair

Abstract

The application discloses an automatic driving navigation wheelchair in a hospital, relates to the technical field of navigation wheelchairs, and has beneficial effects such as personalized navigation service, improved efficiency, improved patient waiting experience, enhanced wheelchair use convenience, optimized resource allocation, and improved hospital service level, and helps improve the overall technical level and modern image of the hospital, is very beneficial to improving the competitiveness and attracting patients of the hospital, provides more convenient and comfortable medical treatment experience for patients, improves the hospital operation efficiency and service quality, the personalized service can consider the needs and preferences of different patients, select the most suitable path, and thus improve the satisfaction and comfort of the patients, the types of each medical treatment path are displayed on the wheelchair display screen, the patients can independently select the path suitable for themselves, increase the participation and satisfaction of the patients, and improve the medical treatment experience.

Description

Technical Field

[0001] This invention relates to the field of navigation wheelchair technology, and more specifically to an autonomous driving navigation wheelchair for use in hospitals. Background Technology

[0002] With the increasing aging of the population, patient care and management within hospitals are becoming increasingly important. Many patients in hospitals are unable to walk due to age, illness, or surgery and require wheelchairs for mobility. However, manually pushing wheelchairs requires the intervention of medical staff, which puts pressure on hospital efficiency and human resources. Therefore, there is a need for an autonomous, navigation-enabled wheelchair for use in hospitals.

[0003] Due to the lack of automated navigation wheelchairs in current technologies, hospital staff need to spend a significant amount of time and energy guiding patients, leading to low work efficiency and impacting the overall operational efficiency of the hospital. Clearly, image processing methods have at least the following problems: 1. Existing automated navigation wheelchairs may fail to accurately plan routes and navigate in complex hospital environments, resulting in the wheelchair not reaching its target location precisely, affecting patient access efficiency. Furthermore, patients' choice of route may be influenced by personal conditions, physical condition, and comfort levels. If current technology does not consider these individual needs, patients may be navigated to unsuitable routes, leading to a poor experience. If patients do not have the right to choose their route, they may be navigated to busy or complex routes, increasing inconvenience and affecting the comfort of their visit.

[0004] 2. Existing technology may not consider the comfort needs of patients in waiting areas, failing to provide the most suitable resting spots. Patients may not enjoy a comfortable resting environment, affecting their experience during the waiting period. They may need to make multiple adjustments and moves within the waiting area to find the optimal resting spot. This can increase inconvenience and distress for patients, especially those with mobility impairments, potentially impacting their comfort and safety during movement. Furthermore, without optimal resting spot analysis and route planning, hospital resource utilization may be inefficient. Patients may concentrate at certain resting spots, while others may be wasted or underutilized, leading to an uneven distribution of resources. Summary of the Invention

[0005] To address the aforementioned technical shortcomings, the purpose of this invention is to provide an autonomous driving navigation wheelchair for use in hospitals.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides an in-hospital autonomous driving navigation wheelchair, including an in-hospital autonomous driving navigation wheelchair body and an in-hospital autonomous driving navigation wheelchair control system for the operation of the in-hospital autonomous driving navigation wheelchair body. The in-hospital autonomous driving navigation wheelchair control system includes: a path parameter acquisition module: used to acquire the various treatment paths corresponding to the department to which the target patient needs to reach at the current moment, thereby acquiring the path feature parameters and path complexity parameters corresponding to each treatment path. The path feature parameters include path distance, path duration, and personnel density. The path complexity parameters include the number of turns, the number of obstacles, and the width corresponding to each obstacle. The path feature evaluation coefficient and path complexity evaluation coefficient corresponding to each treatment path are analyzed and obtained.

[0007] The path type analysis module is used to analyze the comprehensive path evaluation coefficient corresponding to each medical treatment path based on the path feature evaluation coefficient and path complexity evaluation coefficient. Then, it analyzes the path type corresponding to each medical treatment path, displays the path type corresponding to each medical treatment path on the target patient's wheelchair display screen, and performs autonomous driving navigation based on the medical treatment path selected by the target patient.

[0008] The waiting environment impact factor acquisition module is used to obtain the waiting environment parameters corresponding to each wheelchair rest area in each waiting area of ​​the department where the target patient arrives and needs to wait. The waiting environment parameters include light intensity and distance from each service facility, and then the waiting environment impact factors corresponding to each wheelchair rest area in each waiting area are analyzed.

[0009] Wheelchair accessibility parameter acquisition module: This module is used to acquire wheelchair accessibility parameters for each wheelchair rest area in each waiting area. The wheelchair accessibility parameters include travel distance, rest area area, and passage area. The module then analyzes and obtains the wheelchair accessibility evaluation coefficient for each wheelchair rest area in each waiting area.

[0010] The optimal wheelchair rest area analysis module is used to analyze the optimal wheelchair rest area for the target patient at the current moment based on the wheelchair accessibility evaluation coefficient of each wheelchair rest area in each waiting area, and ask the target patient whether to use autonomous driving navigation to the optimal wheelchair rest area.

[0011] Preferably, the specific process for obtaining the medical routes corresponding to the departments the target patient needs to reach at the current moment is as follows: A1. The target patient scans the QR code on the display screen of a rental wheelchair in the hospital to authorize the rental wheelchair to bind to the target patient's corresponding medical card. Then the rental wheelchair can obtain the target patient's registered departments for the day. The rental wheelchair display screen displays the names of the target patient's registered departments for the day. At the same time, the rental wheelchair display screen can also manually input the names of each department. If the rental wheelchair is not authorized to bind to the target patient's corresponding medical card, the rental wheelchair display screen can only manually input the names of each department.

[0012] A2. When a target patient selects a specific registration department, that department is recorded as the treatment department. The hospital's internal positioning system is used to obtain the current location information of the target patient's rented wheelchair. Then, through the hospital's internal map and navigation system, the various treatment routes that the target patient needs to take to reach the treatment department at the current moment are obtained.

[0013] Preferably, the analysis obtains the path feature evaluation coefficients corresponding to each medical treatment path. The specific analysis process is as follows: the path distance, path duration, and personnel density corresponding to each medical treatment path are denoted as S. i D i and F i Where i represents the number corresponding to each medical treatment path, i = 1, 2, ..., u, and u is any integer greater than 2. Substitute these values ​​into the calculation formula. In the process, the path feature evaluation coefficient φ corresponding to the i-th medical treatment path is obtained. i Where S′, D′, and F′ represent the standard path distance, standard path duration, and standard personnel density corresponding to the set medical treatment path, respectively. These are the weighting factors corresponding to the path distance, path duration, and population density of the set medical treatment path, respectively, where e represents the natural constant.

[0014] Preferably, the analysis yields the path complexity evaluation coefficient for each treatment path. The specific analysis process is as follows: the number of turns, the number of obstacles, and the width of each obstacle for each treatment path are denoted as G. i H i and Where y represents the number corresponding to each obstacle, y = 1, 2, ..., m, where m is any integer greater than 2. Substitute these values ​​into the calculation formula. In the process, the path complexity evaluation coefficient corresponding to the i-th medical treatment path is obtained. Where G′, H′, and K′ represent the standard number of turns, standard number of obstacles, and standard width of obstacles corresponding to the set medical treatment path, respectively; ζ1, ζ2, and ζ3 represent the weighting factors corresponding to the number of turns, the number of obstacles, and the width of obstacles corresponding to the set medical treatment path, respectively; and e represents the natural constant.

[0015] Preferably, the analysis yields the comprehensive path evaluation coefficients corresponding to each medical treatment path. The specific analysis process is as follows: Substitute the path characteristic evaluation coefficients and path complexity evaluation coefficients corresponding to each medical treatment path into the calculation formula. In the process, the comprehensive path evaluation coefficient χi corresponding to the i-th medical treatment path is obtained, where ρ1 and ρ2 are the weight factors corresponding to the path feature evaluation coefficient and the path complexity evaluation coefficient, respectively.

[0016] Preferably, the analysis of the path type corresponding to each medical treatment path is carried out in the following specific process:

[0017] The comprehensive path evaluation coefficient corresponding to each medical treatment path is compared with the comprehensive path evaluation coefficient range corresponding to each path type in the database. If the comprehensive path evaluation coefficient corresponding to a certain medical treatment path is within the comprehensive path evaluation coefficient range corresponding to a certain path type in the database, then the path type in the database is recorded as the path type corresponding to that medical treatment path. In this way, the path types corresponding to each medical treatment path are analyzed.

[0018] Preferably, the analysis obtains the waiting environment influencing factors corresponding to each wheelchair rest area in each waiting area. The specific analysis process is as follows: the light intensity and distance from each wheelchair rest area in each waiting area to each service facility are respectively denoted as... and Where v represents the number corresponding to each waiting area, v = 1, 2, ..., z, u is any integer greater than 2, g represents the number corresponding to each wheelchair rest area, g = 1, 2, ..., n, n is any integer greater than 2, and p represents the number corresponding to each wheelchair rest area, p = 1, 2, ..., c, c is any integer greater than 2. Substituting these values ​​into the calculation formula... In the process, the waiting environment impact factor corresponding to the g-th wheelchair rest area in the v-th waiting area is obtained. Where Q′ and W′ represent the standard light intensity and standard distance from the service facility for the set wheelchair rest area, respectively; ψ1 and ψ2 represent the weighting factors for the light intensity and distance from the service facility for the set wheelchair rest area, respectively; and e represents the natural constant.

[0019] Preferably, the analysis obtains the wheelchair accessibility evaluation coefficients corresponding to each wheelchair rest area in each waiting area. The specific analysis process is as follows: the driving distance, rest area area, and passage area corresponding to each wheelchair rest area in each waiting area are respectively denoted as... and Substitute into the calculation formula In the process, the wheelchair accessibility evaluation coefficient corresponding to the g-th wheelchair rest area in the v-th waiting area is obtained. Where E′, R′, and T′ represent the standard driving distance, standard rest area, and standard passage area corresponding to the set wheelchair rest area, respectively; υ1, υ2, and υ3 represent the weighting factors corresponding to the driving distance, rest area, and passage area corresponding to the set wheelchair rest area, respectively; and e represents the natural constant.

[0020] Preferably, the analysis of the optimal wheelchair rest area corresponding to the target patient at the current moment is carried out as follows: the wheelchair accessibility assessment coefficients of each wheelchair rest area in each waiting area are arranged in descending order, and the wheelchair rest area in the waiting area with the largest wheelchair accessibility assessment coefficient is recorded as the optimal wheelchair rest area corresponding to the target patient at the current moment.

[0021] The beneficial effects of this invention are as follows: 1. This invention provides an autonomous driving navigation wheelchair for use in hospitals. Through personalized navigation services, improved efficiency, enhanced patient waiting experience, improved wheelchair usability, optimized resource allocation, and improved hospital service levels, this invention helps to improve the overall technical level and modern image of hospitals. It is also very beneficial to enhance the competitiveness of hospitals and attract patients, providing patients with a more convenient and comfortable medical experience, while improving hospital operational efficiency and service quality.

[0022] 2. In this embodiment of the invention, by acquiring and analyzing the characteristic and complexity parameters of each medical treatment path, the wheelchair can provide patients with personalized navigation solutions. This personalized service can take into account the needs and preferences of different patients, select the most suitable path, thereby improving patient satisfaction and comfort. The wheelchair display screen shows the types of medical treatment paths, and patients can choose the path that suits them best, increasing patient participation and satisfaction, and enhancing the medical treatment experience.

[0023] 3. In this embodiment of the invention, by acquiring and analyzing the convenience parameters of wheelchair rest areas, such as travel distance, rest area area, and passage area, the system can ensure that wheelchair use in hospitals is more convenient and safer. After a patient arrives at their designated department, the system can analyze and recommend the optimal wheelchair rest area, taking into account waiting environment parameters such as light intensity and distance to service facilities. This ensures that patients can be more comfortable and relaxed while waiting. Through the implementation of the intelligent wheelchair system, hospital space and facilities can be utilized more efficiently, while reducing the need for medical staff in navigation and patient assistance, which helps improve the overall operational efficiency of the hospital. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the system module connections of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Examples of embodiments of the present invention Figure 1 As shown, an autonomous driving navigation wheelchair for use in hospitals includes an autonomous driving navigation wheelchair body and an autonomous driving navigation wheelchair control system for the operation of the autonomous driving navigation wheelchair body. The autonomous driving navigation wheelchair control system includes: a path parameter acquisition module: used to acquire the various treatment paths that the target patient needs to reach the department at the current moment, thereby acquiring the path feature parameters and path complexity parameters corresponding to each treatment path. The path feature parameters include path distance, path duration, and personnel density. The path complexity parameters include the number of turns, the number of obstacles, and the width of each obstacle. The system analyzes and obtains the path feature evaluation coefficient and path complexity evaluation coefficient corresponding to each treatment path.

[0028] It should be noted that the specific route patients need to take is obtained through the hospital's internal map and navigation system, and the route distance and duration are calculated by combining the actual distance and time. Personnel density is obtained through the hospital's internal surveillance cameras, counting the number of people passing through that route at the current moment.

[0029] It should also be noted that the number of turns and obstacles along the patient's path is determined using a 3D map model of the hospital. The width of the obstacles is obtained through on-site measurements or map markings.

[0030] In a specific embodiment, the process of obtaining the medical routes corresponding to the departments the target patient needs to reach at the current moment is as follows: A1. The target patient scans the QR code on the display screen of a rental wheelchair in the hospital to authorize the rental wheelchair to be bound to the target patient's medical card. Then the rental wheelchair can obtain the target patient's registered departments for the day. The rental wheelchair display screen displays the names of the target patient's registered departments for the day. At the same time, the rental wheelchair display screen can also manually input the names of the departments. If the rental wheelchair is not authorized to be bound to the target patient's medical card, the rental wheelchair display screen can only manually input the names of the departments.

[0031] A2. When a target patient selects a specific registration department, that department is recorded as the treatment department. The hospital's internal positioning system is used to obtain the current location information of the target patient's rented wheelchair. Then, through the hospital's internal map and navigation system, the various treatment routes that the target patient needs to take to reach the treatment department at the current moment are obtained.

[0032] In another specific embodiment, the analysis yields the path feature evaluation coefficients corresponding to each medical treatment path. The specific analysis process is as follows: the path distance, path duration, and personnel density corresponding to each medical treatment path are denoted as S. i D i and F i Where i represents the number corresponding to each medical treatment path, i = 1, 2, ..., u, and u is any integer greater than 2. Substitute these values ​​into the calculation formula. In the process, the path feature evaluation coefficient φ corresponding to the i-th medical treatment path is obtained. i Where S′, D′, and F′ represent the standard path distance, standard path duration, and standard personnel density corresponding to the set medical treatment path, respectively. These are the weighting factors corresponding to the path distance, path duration, and population density of the set medical treatment path, respectively, where e represents the natural constant.

[0033] It should be noted that, All are greater than 0 and less than 1.

[0034] It should also be noted that data on path distance, path duration, and personnel density for each treatment path should be collected. This data can be obtained through field surveys, statistical data collection, etc. Path distance, path duration, and personnel density should be standardized using the max-min standardization method or Z-score standardization method. Weighting factors should be set for path distance, path duration, and personnel density based on actual needs and professional knowledge. These weighting factors represent the importance of each indicator and can be allocated according to the actual situation.

[0035] In another specific embodiment, the analysis yields the path complexity evaluation coefficient for each treatment path. The specific analysis process is as follows: the number of turns, the number of obstacles, and the width of each obstacle for each treatment path are denoted as G. i H i and Where y represents the number corresponding to each obstacle, y = 1, 2, ..., m, where m is any integer greater than 2. Substitute these values ​​into the calculation formula. In the process, the path complexity evaluation coefficient corresponding to the i-th medical treatment path is obtained. Where G′, H′, and K′ represent the standard number of turns, standard number of obstacles, and standard width of obstacles corresponding to the set medical treatment path, respectively; ζ1, ζ2, and ζ3 represent the weighting factors corresponding to the number of turns, the number of obstacles, and the width of obstacles corresponding to the set medical treatment path, respectively; and e represents the natural constant.

[0036] It should be noted that ζ1, ζ2, and ζ3 are all greater than 0 and less than 1.

[0037] It should also be noted that data on the number of turns, the number of obstacles, and the width of each obstacle are collected for each treatment path. The number of turns, the number of obstacles, and the width of obstacles are standardized. Based on actual needs and professional knowledge, weighting factors are set for the number of turns, the number of obstacles, and the width of obstacles.

[0038] The path type analysis module is used to analyze the comprehensive path evaluation coefficient corresponding to each medical treatment path based on the path feature evaluation coefficient and path complexity evaluation coefficient. Then, it analyzes the path type corresponding to each medical treatment path, displays the path type corresponding to each medical treatment path on the target patient's wheelchair display screen, and performs autonomous driving navigation based on the medical treatment path selected by the target patient.

[0039] In a specific embodiment, the analysis yields the comprehensive path evaluation coefficients corresponding to each medical treatment path. The specific analysis process is as follows: Substituting the path feature evaluation coefficients and path complexity evaluation coefficients corresponding to each medical treatment path into the calculation formula... In the process, the comprehensive path evaluation coefficient χ corresponding to the i-th medical treatment path is obtained. i , where ρ1 and ρ2 are the weight factors corresponding to the path feature evaluation coefficient and the path complexity evaluation coefficient, respectively.

[0040] It should be noted that the weighting factors for the path characteristic evaluation coefficient and path complexity evaluation coefficient in setting up the medical treatment path are determined based on actual needs and expert opinions.

[0041] In another specific embodiment, the analysis of the path type corresponding to each medical treatment path is carried out as follows: the comprehensive path evaluation coefficient corresponding to each medical treatment path is compared with the comprehensive path evaluation coefficient range corresponding to each path type in the database. If the comprehensive path evaluation coefficient corresponding to a certain medical treatment path is within the comprehensive path evaluation coefficient range corresponding to a certain path type in the database, then the path type in the database is recorded as the path type corresponding to the medical treatment path. In this way, the path type corresponding to each medical treatment path is analyzed.

[0042] It should be noted that the path types include straight paths, winding paths, and complex paths. For example, if the patient has strong mobility, a straight path can be chosen to shorten the travel time; if the patient needs to avoid densely populated areas or areas with many obstacles, a complex path can be chosen.

[0043] In this embodiment of the invention, by acquiring and analyzing the characteristic and complexity parameters of each medical treatment path, wheelchairs can provide patients with personalized navigation solutions. This personalized service takes into account the needs and preferences of different patients, selecting the most suitable path, thereby improving patient satisfaction and comfort. The wheelchair display screen shows the types of medical treatment paths, allowing patients to choose the path that suits them best, increasing patient participation and satisfaction, and enhancing the medical treatment experience.

[0044] The waiting environment impact factor acquisition module is used to obtain the waiting environment parameters corresponding to each wheelchair rest area in each waiting area of ​​the department where the target patient arrives and needs to wait. The waiting environment parameters include light intensity and distance from each service facility, and then the waiting environment impact factors corresponding to each wheelchair rest area in each waiting area are analyzed.

[0045] It should be noted that the wheelchair is equipped with a light intensity meter to measure light intensity, and a positioning system installed on the wheelchair to determine the current location and distance to various service facilities. The measured distance data is then transmitted to the device.

[0046] In a specific embodiment, the analysis yields the waiting environment influencing factors corresponding to each wheelchair rest area in each waiting area. The specific analysis process is as follows:

[0047] The light intensity and distance from each wheelchair rest area in each waiting area to each service facility are respectively denoted as: and Where v represents the number corresponding to each waiting area, v = 1, 2, ..., z, u is any integer greater than 2, g represents the number corresponding to each wheelchair rest area, g = 1, 2, ..., n, n is any integer greater than 2, and p represents the number corresponding to each wheelchair rest area, p = 1, 2, ..., c, c is any integer greater than 2. Substituting these values ​​into the calculation formula... In the process, the waiting environment impact factor corresponding to the g-th wheelchair rest area in the v-th waiting area is obtained. Where Q′ and W′ represent the standard light intensity and standard distance from the service facility for the set wheelchair rest area, respectively; ψ1 and ψ2 represent the weighting factors for the light intensity and distance from the service facility for the set wheelchair rest area, respectively; and e represents the natural constant.

[0048] It should be noted that ψ1 and ψ2 are both greater than 0 and less than 1.

[0049] It should also be noted that data on the light intensity and distance from service facilities in each wheelchair rest area of ​​each waiting area are collected, and the light intensity and distance from service facilities are standardized. Based on actual needs and professional knowledge, weighting factors are set for the light intensity and distance from service facilities in the wheelchair rest areas.

[0050] Wheelchair accessibility parameter acquisition module: This module is used to acquire wheelchair accessibility parameters for each wheelchair rest area in each waiting area. The wheelchair accessibility parameters include travel distance, rest area area, and passage area. The module then analyzes and obtains the wheelchair accessibility evaluation coefficient for each wheelchair rest area in each waiting area.

[0051] In a specific embodiment, the analysis yields wheelchair accessibility evaluation coefficients for each wheelchair rest area in each waiting area. The specific analysis process is as follows: the driving distance, rest area area, and passage area corresponding to each wheelchair rest area in each waiting area are respectively denoted as... and Substitute into the calculation formula In the process, the wheelchair accessibility evaluation coefficient corresponding to the g-th wheelchair rest area in the v-th waiting area is obtained. Where E′, R′, and T′ represent the standard driving distance, standard rest area, and standard passage area corresponding to the set wheelchair rest area, respectively; υ1, υ2, and υ3 represent the weighting factors corresponding to the driving distance, rest area, and passage area corresponding to the set wheelchair rest area, respectively; and e represents the natural constant.

[0052] It should be noted that υ1, υ2, and υ3 are all greater than 0 and less than 1.

[0053] It should also be noted that data on the driving distance, rest area, and passage area of ​​each wheelchair rest area in each waiting area are collected, and the driving distance, rest area, and passage area are standardized. Based on actual needs and professional knowledge, weighting factors are set for the driving distance, rest area, and passage area.

[0054] The optimal wheelchair rest area analysis module is used to analyze the optimal wheelchair rest area for the target patient at the current moment based on the wheelchair accessibility evaluation coefficient of each wheelchair rest area in each waiting area, and ask the target patient whether to use autonomous driving navigation to the optimal wheelchair rest area.

[0055] In a specific embodiment, the analysis of the optimal wheelchair rest area corresponding to the target patient at the current moment is carried out as follows: the wheelchair accessibility assessment coefficients corresponding to each wheelchair rest area in each waiting area are arranged in descending order, and the wheelchair rest area in the waiting area with the largest wheelchair accessibility assessment coefficient is recorded as the optimal wheelchair rest area corresponding to the target patient at the current moment.

[0056] In this embodiment of the invention, by acquiring and analyzing convenience parameters of wheelchair rest areas, such as travel distance, rest area size, and passageway area, the system can ensure more convenient and safer use of wheelchairs in hospitals. After a patient arrives at their designated department, the system can analyze and recommend the optimal wheelchair rest area, taking into account waiting environment parameters such as light intensity and distance to service facilities. This ensures that patients can be more comfortable and relaxed while waiting. The implementation of the intelligent wheelchair system allows for more efficient use of hospital space and facilities, while reducing the need for medical staff in navigation and patient assistance, thus contributing to improved overall hospital operational efficiency.

[0057] This invention provides an autonomous driving navigation wheelchair for use in hospitals. Through personalized navigation services, improved efficiency, enhanced patient waiting experience, improved wheelchair usability, optimized resource allocation, and improved hospital service levels, it helps to improve the overall technical level and modern image of hospitals. It is also very beneficial to enhance the hospital's competitiveness and attract patients, providing patients with a more convenient and comfortable medical experience, while improving hospital operational efficiency and service quality.

[0058] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A hospital-based autonomous driving navigation wheelchair, comprising an autonomous driving navigation wheelchair body and a control system for controlling the autonomous driving navigation wheelchair, characterized in that, The control system for the autonomous navigation wheelchair includes: Path parameter acquisition module: used to acquire the various medical paths that the target patient needs to reach the medical department at the current moment, thereby acquiring the path feature parameters and path complexity parameters corresponding to each medical path. The path feature parameters include path distance, path duration and personnel density, and the path complexity parameters include the number of turns, the number of obstacles and the width of each obstacle. The analysis yields the path feature evaluation coefficient and path complexity evaluation coefficient corresponding to each medical path. The path type analysis module is used to analyze the comprehensive path evaluation coefficient corresponding to each medical treatment path based on the path feature evaluation coefficient and path complexity evaluation coefficient, and then analyze the path type corresponding to each medical treatment path. The path type corresponding to each medical treatment path is then displayed on the target patient's wheelchair display screen, and autonomous driving navigation is performed according to the medical treatment path selected by the target patient. Waiting environment impact factor acquisition module: When a target patient arrives at the department and needs to wait, it acquires the waiting environment parameters corresponding to each wheelchair rest area in each waiting area of ​​the department at the current time. The waiting environment parameters include light intensity and distance to each service facility, and then analyzes and obtains the waiting environment impact factors corresponding to each wheelchair rest area in each waiting area. Wheelchair accessibility parameter acquisition module: used to acquire wheelchair accessibility parameters corresponding to each wheelchair rest area in each waiting area. Wheelchair accessibility parameters include travel distance, rest area area and passage area, and then analyze to obtain the wheelchair accessibility evaluation coefficient corresponding to each wheelchair rest area in each waiting area. The optimal wheelchair rest area analysis module is used to analyze the optimal wheelchair rest area for the target patient at the current moment based on the wheelchair accessibility evaluation coefficient of each wheelchair rest area in each waiting area, and ask the target patient whether to use autonomous driving navigation to the optimal wheelchair rest area.

2. The hospital-based autonomous driving navigation wheelchair as described in claim 1, characterized in that, The specific process for obtaining the various medical routes that the target patient needs to reach the department at the current moment is as follows: A1. When a target patient scans the QR code on a rental wheelchair display screen in the hospital and authorizes the rental wheelchair to be linked to the target patient's corresponding medical card, the rental wheelchair can obtain the target patient's registered departments for that day, and the rental wheelchair display screen will display the names of the target patient's registered departments for that day. At the same time, the rental wheelchair display screen can also manually input the names of each department. If the target patient does not authorize the rental wheelchair to be linked to the target patient's corresponding medical card, the rental wheelchair display screen can only manually input the names of each department. A2. When a target patient selects a specific registration department, that department is recorded as the treatment department. The hospital's internal positioning system is used to obtain the current location information of the target patient's rented wheelchair. Then, through the hospital's internal map and navigation system, the various treatment routes that the target patient needs to take to reach the treatment department at the current moment are obtained.

3. The hospital-based autonomous driving navigation wheelchair as described in claim 2, characterized in that, The analysis yielded path feature evaluation coefficients for each medical visit path. The specific analysis process is as follows: Let S be the path distance, path duration, and population density corresponding to each medical treatment path. i D i and F i Where i represents the number corresponding to each medical treatment path, i = 1, 2, ..., u, and u is any integer greater than 2. Substitute these values ​​into the calculation formula. In the process, the path feature evaluation coefficient φ corresponding to the i-th medical treatment path is obtained. i Where S′, D′, and F′ represent the standard path distance, standard path duration, and standard personnel density corresponding to the set medical treatment path, respectively. These are the weighting factors corresponding to the path distance, path duration, and population density of the set medical treatment path, respectively, where e represents the natural constant.

4. The hospital-based autonomous driving navigation wheelchair as described in claim 3, characterized in that, The analysis yielded the path complexity evaluation coefficient for each medical treatment path. The specific analysis process is as follows: Let G be the number of turns, the number of obstacles, and the width of each obstacle along each treatment path. i H i and Where y represents the number corresponding to each obstacle, y = 1, 2, ..., m, where m is any integer greater than 2. Substitute these values ​​into the calculation formula. In the process, the path complexity evaluation coefficient corresponding to the i-th medical treatment path is obtained. Where G′, H′, and K′ represent the standard number of turns, standard number of obstacles, and standard width of obstacles corresponding to the set medical treatment path, respectively; ζ1, ζ2, and ζ3 represent the weighting factors corresponding to the number of turns, the number of obstacles, and the width of obstacles corresponding to the set medical treatment path, respectively; and e represents the natural constant.

5. The hospital-based autonomous driving navigation wheelchair as described in claim 4, characterized in that, The analysis yielded the comprehensive path evaluation coefficient for each medical treatment path. The specific analysis process is as follows: Substitute the path characteristic evaluation coefficient and path complexity evaluation coefficient corresponding to each medical treatment path into the calculation formula. In the process, the comprehensive path evaluation coefficient χi corresponding to the i-th medical treatment path is obtained, where ρ1 and ρ2 are the weight factors corresponding to the path feature evaluation coefficient and the path complexity evaluation coefficient, respectively.

6. The hospital-based autonomous driving navigation wheelchair as described in claim 5, characterized in that, The analysis of the path types corresponding to each medical treatment path is as follows: The comprehensive path evaluation coefficient corresponding to each medical treatment path is compared with the comprehensive path evaluation coefficient range corresponding to each path type in the database. If the comprehensive path evaluation coefficient corresponding to a certain medical treatment path is within the comprehensive path evaluation coefficient range corresponding to a certain path type in the database, then the path type in the database is recorded as the path type corresponding to that medical treatment path. In this way, the path types corresponding to each medical treatment path are analyzed.

7. The hospital-based autonomous driving navigation wheelchair as described in claim 1, characterized in that, The analysis yielded the waiting environment influencing factors for each wheelchair rest area in each waiting zone. The specific analysis process is as follows: The light intensity and distance from each wheelchair rest area in each waiting area to each service facility are respectively denoted as: and Where v represents the number corresponding to each waiting area, v = 1, 2, ..., z, u is any integer greater than 2, g represents the number corresponding to each wheelchair rest area, g = 1, 2, ..., n, n is any integer greater than 2, and p represents the number corresponding to each wheelchair rest area, p = 1, 2, ..., c, c is any integer greater than 2. Substituting these values ​​into the calculation formula... In the process, the waiting environment impact factor corresponding to the g-th wheelchair rest area in the v-th waiting area is obtained. Where Q′ and W′ represent the standard light intensity and standard distance from the service facility for the set wheelchair rest area, respectively; ψ1 and ψ2 represent the weighting factors for the light intensity and distance from the service facility for the set wheelchair rest area, respectively; and e represents the natural constant.

8. The hospital-based autonomous driving navigation wheelchair as described in claim 7, characterized in that, The analysis yielded wheelchair accessibility evaluation coefficients for each wheelchair rest area in each waiting zone. The specific analysis process is as follows: The driving distance, rest area, and passage area corresponding to each wheelchair rest area in each waiting area are respectively denoted as: and Substitute into the calculation formula In the process, the wheelchair accessibility evaluation coefficient corresponding to the g-th wheelchair rest area in the v-th waiting area is obtained. Where E′, R′, and T′ represent the standard driving distance, standard rest area, and standard passage area corresponding to the set wheelchair rest area, respectively; υ1, υ2, and υ3 represent the weighting factors corresponding to the driving distance, rest area, and passage area corresponding to the set wheelchair rest area, respectively; and e represents the natural constant.

9. The hospital-based autonomous driving navigation wheelchair as described in claim 8, characterized in that, The analysis of the optimal wheelchair rest area for the target patient at the current moment is performed as follows: The wheelchair accessibility assessment coefficients for each wheelchair rest area in each waiting area are arranged in descending order, and the wheelchair rest area in the waiting area with the highest wheelchair accessibility assessment coefficient is recorded as the best wheelchair rest area for the target patient at the current moment.

Images