A rehabilitation training system and method based on virtual reality technology

Abstract

The present invention relates to the field of rehabilitation training technology, and in particular to a rehabilitation training system and method based on virtual reality technology, comprising a server and a client; the server comprises a data acquisition module, a rehabilitation training program formulation module, a rehabilitation optimal program identification module, and a virtual construction module; the virtual construction module is used to build a corresponding rehabilitation training virtual space based on the virtual reality technology according to the identified rehabilitation training optimal program, and formulate a current rehabilitation training model corresponding to the client in the rehabilitation training virtual space based on the rehabilitation training optimal program, and send feedback information to the client; the client is used to enter the rehabilitation training virtual space after receiving the corresponding feedback information from the server, and receive the corresponding current rehabilitation training model.

Description

Technical Field

[0001] The present invention relates to the technical field of rehabilitation training, and in particular to a rehabilitation training system and method based on virtual reality technology. Background Art

[0002] With the rapid development of virtual reality technology, its application in the field of rehabilitation training is becoming more and more extensive. Virtual reality technology provides a new method and means for rehabilitation training by simulating a real environment. However, the existing rehabilitation training systems based on virtual reality technology generally lack personalization in the form and content of rehabilitation training movements, and it is difficult to meet the actual needs of different patients.

[0003] At present, most rehabilitation training systems on the market use preset rehabilitation movements and training programs. Although the corresponding programs are highly universal, they are often not targeted and cannot be personalized according to the individual's physical condition, rehabilitation progress and interests. At the same time, the system's training time and scenes are often fixed and cannot be flexibly set according to the patient's actual situation. This limits the effect of rehabilitation training and user experience to a certain extent, and is inconvenient for patients to use. Summary of the invention

[0004] One of the purposes of the present invention is to provide a rehabilitation training system and method based on virtual reality technology, which can solve the problems of weak personalized settings and lack of pertinence in the existing virtual rehabilitation training process, which limits the user experience of customers in the rehabilitation training process and is inconvenient for customers to use.

[0005] In order to achieve the above object, a rehabilitation training system based on virtual reality technology is provided, including a server and a client;

[0006] The server includes:

[0007] The data acquisition module is used to receive the login information corresponding to the client, and obtain the basic information of the client corresponding to the client and the current condition status information of the client based on the login information;

[0008] A rehabilitation training program formulation module is used to randomly generate N initial populations based on the corresponding rehabilitation training action set and personalized action scene set in the database, based on the basic information of the customer and the current condition information of the customer, wherein the individuals of the initial population are the rehabilitation training actions corresponding to the customer, the personalized action scenes corresponding to the rehabilitation training actions, and the training time corresponding to each rehabilitation training;

[0009] The optimal rehabilitation program identification module is used to identify the optimal rehabilitation training program based on the N initial populations and the improved genetic algorithm;

[0010] The virtual construction module is used to build a corresponding rehabilitation training virtual space based on the identified optimal rehabilitation training plan and virtual reality technology, and formulate a rehabilitation training model corresponding to the customer in the rehabilitation training virtual space based on the optimal rehabilitation training plan, and send feedback information to the client;

[0011] The client is used to enter the rehabilitation training virtual space after receiving the corresponding feedback information from the server, and receive the corresponding rehabilitation training model for this time.

[0012] Technical principle and effect of this solution: In this solution, first, the client sends the login information, and then after receiving the login information sent by the client, the server obtains the basic information of the client and the current condition information of the client based on the login information, and then randomly formulates a rehabilitation training plan according to the rehabilitation training action set and personalized action scene set in the database, combined with the basic information of the client and the current motion state information of the client, so as to realize the random generation of the initial population, wherein the individuals of the initial population are the rehabilitation training actions corresponding to the client, the personalized action scenes corresponding to the rehabilitation training actions, and the training time corresponding to each rehabilitation training;

[0013] Then, the optimal rehabilitation training plan is identified from the N initial populations through an improved genetic algorithm, and the optimal rehabilitation training plan for the customer based on the current condition status information is formulated. Then, based on the optimal rehabilitation training plan, a corresponding rehabilitation training virtual space is built through virtual reality technology, and the rehabilitation training model is formulated in the rehabilitation training virtual space based on the optimal rehabilitation training plan, and then feedback information is sent to the client. After the client receives the feedback information sent by the server, it means that the client of the client can enter the rehabilitation training virtual space to receive the rehabilitation training model, so as to carry out the corresponding rehabilitation training. The problem that the existing virtual rehabilitation training process has weak personalized settings, lacks pertinence, limits the user experience of customers in the rehabilitation training process, and is inconvenient for customers to use is solved.

[0014] Furthermore, the optimal rehabilitation solution identification module includes:

[0015] The first screening module is used to perform preliminary screening on the randomly generated N initial populations based on preset constraints. If the constraints are met, the corresponding individual is a feasible solution, otherwise it is an infeasible solution and the individual is eliminated;

[0016] The restriction conditions are the number of rehabilitation training movements and the training time.

[0017] The rehabilitation training action quantity condition is:

[0018]

[0019] In the formula, s max is the maximum number of rehabilitation training movements, B d is the current condition of the client, B b The condition status under the same standard condition as the customer's corresponding condition, s b is the standard number of rehabilitation training actions corresponding to the standard condition, s s The number of rehabilitation training movements corresponding to the last rehabilitation training performed by the client;

[0020] The training time conditions are:

[0021]

[0022] In the formula, t k is the idle time corresponding to the customer, t i is the training time corresponding to rehabilitation training action i, t mix is the shortest training time corresponding to the customer, t b The standard training time corresponding to the standard condition;

[0023] A fitness calculation module, used to calculate the first fitness and the second fitness of the screened population;

[0024] The first fitness is calculated as follows:

[0025]

[0026] Where: F1 is the first fitness, D1 is the sum of the rationality of all rehabilitation training actions in the population, B y+1 is the patient's condition after completing the y+1th rehabilitation training action, g is the corresponding rehabilitation degree conversion parameter, P y+1 The fatigue level of the client after completing the y+1th rehabilitation training action;

[0027] The second fitness is calculated as follows:

[0028]

[0029] Where: F2 is the second fitness, D2 is the average fitness of the rehabilitation training actions and the corresponding personalized action scenes in the population for the customer; S x is the matching degree of the personalized action scene corresponding to the rehabilitation training action x, X xis the attractiveness of the personalized action scene corresponding to the rehabilitation training action x to the customer, ∝ is the corresponding weighting coefficient, a, b, c, v are constants, h, p, q are the corresponding dynamic change interval points;

[0030] A second screening module is used to select, within a first preset number of iterations, a population whose first fitness is less than or equal to a first fitness threshold according to the first fitness corresponding to the population, and select the first three populations with the smallest second fitness from the populations eliminated at this time, and save them into a spare library;

[0031] When the first preset number of iterations is exceeded, the current population is combined with the population in the standby library to form a new population, and a population whose second fitness corresponding to the current population is less than or equal to the preset second fitness is selected;

[0032] The hybridization and mutation module is used to perform hybridization and mutation operations on the population after secondary screening, and obtain the offspring population based on the preset selection strategy;

[0033] A loop module is used to continue executing the fitness calculation module after obtaining the offspring population until a preset number of iterations is met;

[0034] The output module is used to output the offspring population as the corresponding optimal solution set.

[0035] Beneficial effect: In this solution, the initial population is first screened so that the number of corresponding rehabilitation training movements and the training time meet the needs of customers, that is, the number of rehabilitation training movements is related to the customer's medical condition, avoiding the problem of excessive number leading to worsening of the customer's medical condition, and the training time is also compared with the standard training time, which greatly ensures the rationality of the number of movements and training time, and greatly improves the convergence speed of subsequent algorithms.

[0036] Then the first fitness and the second fitness are calculated for the screened population. Specifically, the rehabilitation degree corresponding to the current rehabilitation training action is calculated by comparing the previous and subsequent actions based on the patient's condition after completing the corresponding rehabilitation training actions. Then, the calculation is integrated with the corresponding customer fatigue level to calculate the rationality of the current rehabilitation training action, and then the sum of the rationalities of all rehabilitation training actions in the population is calculated, as well as the average fitness between the personalized work scene and the corresponding rehabilitation training action, so as to judge the fitness of the population. Subsequently, the population is screened for the second time according to the preset secondary screening strategy; the population after the second screening is subjected to hybridization and mutation operations to obtain the offspring population. After the offspring population is obtained, the fitness calculation module is continued to be executed until the preset number of iterations is met, and the offspring population is output as the corresponding optimal solution set.

[0037] Furthermore, the preset selection strategy is:

[0038] According to the first population and the second population, calculating the processing difference degree corresponding to the first population relative to the second population; the first population is the population corresponding to the hybridization and mutation operation, and the second population is the population corresponding to the hybridization and mutation operation before the operation;

[0039] Compare the processing difference with the preset difference threshold. If the processing difference is greater than or equal to the preset difference threshold, the first population is judged to be an invalid population, and the second population is hybridized and mutated again. If the processing difference is less than the preset difference threshold, a random number R between 0 and 1 is randomly generated. When the random number R is greater than the preset random threshold, the first population is judged to be a valid population and retained. Otherwise, the second population is hybridized and mutated again. The preset random threshold is positively correlated with the success of the hybridization and mutation operations corresponding to the previous iteration.

[0040] Beneficial effect: In this scheme, when judging whether the first population is a valid population, not only the processing difference before and after the hybridization and mutation operation is considered to avoid excessive differences before and after, which causes the population after the operation to be unable to retain the good genes of the population before the operation, but also, on the premise that the differences meet the requirements, the randomness of the effective judgment of the first population is increased, which can better realize the hybridization and mutation operations of the population.

[0041] Furthermore, the server also includes:

[0042] The training identification module is used to identify the rehabilitation type of the client based on the current condition status information of the client corresponding to the client after the corresponding client receives the rehabilitation training model, and based on the rehabilitation type, retrieve the rehabilitation training model corresponding to other current clients with the same rehabilitation type as the client;

[0043] A virtual sharing module is used to form a virtual training sharing space based on a sharing mechanism according to the current rehabilitation training model corresponding to other clients and the current rehabilitation training model corresponding to the current client;

[0044] The rehabilitation list module is used to obtain the rehabilitation training completion degree of the corresponding customer in real time based on the current customer and other current customers in the virtual training shared space, and generate the corresponding rehabilitation training list and send it to the client in real time.

[0045] Beneficial effects: In this solution, the rehabilitation type of the disease is first identified based on the disease status information corresponding to the current customer, so that the disease type of each customer can be identified, thereby facilitating rehabilitation training exchanges between customers with the same disease. At the same time, based on the disease rehabilitation type, other customers of the same type are bound to the training model corresponding to the current customer to form a virtual training shared space. In this way, customers enter the same virtual training shared space during rehabilitation training, encourage and cooperate with each other, and through the production of rehabilitation training lists, the rehabilitation training completion status of each customer in the virtual training shared space can be shared, thereby motivating customers in the virtual training shared space to undergo rehabilitation training and increasing customers' participation in rehabilitation training.

[0046] The present invention also provides a rehabilitation training method based on virtual reality technology, using the above-mentioned rehabilitation training system based on virtual reality technology, comprising the following steps:

[0047] S1. Receive login information corresponding to the client, and obtain basic customer information corresponding to the client and information about the customer's current medical condition based on the login information;

[0048] S2, according to the corresponding rehabilitation training action set and personalized action scene set in the database, based on the basic information of the customer and the current condition information of the customer, randomly generate N initial populations, the individuals of the initial population are the rehabilitation training actions corresponding to the customer, the personalized action scenes corresponding to the rehabilitation training actions, and the training time corresponding to each rehabilitation training;

[0049] S3, based on the N initial populations, identify the optimal rehabilitation training program based on the improved genetic algorithm;

[0050] S4. According to the identified optimal rehabilitation training plan, a corresponding rehabilitation training virtual space is built based on virtual reality technology, and a rehabilitation training model corresponding to the customer is formulated in the rehabilitation training virtual space based on the optimal rehabilitation training plan, and feedback information is sent to the client;

[0051] S5. After receiving the corresponding feedback information from the server, the client enters the rehabilitation training virtual space and receives the corresponding rehabilitation training model. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a logic block diagram of a rehabilitation training system based on virtual reality technology in Embodiment 1 of the present invention;

[0053] Figure 2 This is a flow chart of a rehabilitation training method based on virtual reality technology in Embodiment 1 of the present invention. DETAILED DESCRIPTION

[0054] The following is further described in detail through specific implementation methods:

[0055] Embodiment 1

[0056] A rehabilitation training system based on virtual reality technology, basically Figure 1 As shown, it includes a server and a client;

[0057] The server includes:

[0058] The data acquisition module is used to receive the login information corresponding to the client, and obtain the basic information of the client corresponding to the client and the current condition status information of the client based on the login information;

[0059] The rehabilitation training program formulation module is used to randomly generate N initial populations according to the corresponding rehabilitation training action set and personalized action scene set in the database, based on the basic information of the customer and the current condition information of the customer. The individuals of the initial population are the rehabilitation training actions corresponding to the customer, the personalized action scenes corresponding to the rehabilitation training actions, and the training time corresponding to each rehabilitation training; for example, the initial population is A-AA, B-BB, C-CC, D-DD, then A-AA is the corresponding rehabilitation training action A and personalized action scene AA; of course, another group can also be A-AA, B-AA, C-BB, D-DD, different rehabilitation training actions can correspond to different personalized action scenes, for example, the same is crossing obstacles, different personalized action scenes correspond to different effects, a single railing can be set in front, or the hurdle action can be combined with a basketball scene so that the customer can complete the rehabilitation training of crossing obstacles unconsciously by simulating a basketball scene.

[0060] The optimal rehabilitation program identification module is used to identify the optimal rehabilitation training program based on the N initial populations and the improved genetic algorithm;

[0061] The optimal rehabilitation solution identification module includes:

[0062] The first screening module is used to perform preliminary screening on the randomly generated N initial populations based on preset constraints. If the constraints are met, the corresponding individual is a feasible solution, otherwise it is an infeasible solution and the individual is eliminated;

[0063] The restriction conditions are the number of rehabilitation training movements and the training time.

[0064] The rehabilitation training action quantity condition is:

[0065]

[0066] In the formula, s max is the maximum number of rehabilitation training movements, B d is the current condition of the client, B b The condition status under the same standard condition as the customer's corresponding condition, s b is the standard number of rehabilitation training actions corresponding to the standard condition, s s The number of rehabilitation training movements corresponding to the last rehabilitation training performed by the client;

[0067] The training time conditions are:

[0068]

[0069] Where, t k is the idle time corresponding to the customer, t i is the training time corresponding to rehabilitation training action i, t mix is the shortest training time corresponding to the customer, t b The standard training time corresponding to the standard condition;

[0070] A fitness calculation module, used to calculate the first fitness and the second fitness of the screened population;

[0071] The first fitness is calculated as follows:

[0072]

[0073] Where: F1 is the first fitness, D1 is the sum of the rationality of all rehabilitation training actions in the population, B y+1 is the patient's condition after completing the y+1th rehabilitation training action, g is the corresponding rehabilitation degree conversion parameter, P y+1 The fatigue level of the client after completing the y+1th rehabilitation training action;

[0074] The second fitness is calculated as follows:

[0075]

[0076] Where: F2 is the second fitness, D2 is the average fitness of the rehabilitation training actions and the corresponding personalized action scenes in the population for the customer; S x is the matching degree of the personalized action scene corresponding to the rehabilitation training action x, X x is the attractiveness of the personalized action scene corresponding to the rehabilitation training action x to the customer, ∝ is the corresponding weighting coefficient, a, b, c, v are constants, h, p, q are the corresponding dynamic change interval points;

[0077] A second screening module is used to select, within a first preset number of iterations, a population whose first fitness is less than or equal to a first fitness threshold according to the first fitness corresponding to the population, and select the first three populations with the smallest second fitness from the populations eliminated at this time, and save them into a spare library;

[0078] When the first preset number of iterations is exceeded, the current population is combined with the population in the standby library to form a new population, and a population whose second fitness corresponding to the current population is less than or equal to the preset second fitness is selected;

[0079] The hybridization and mutation module is used to perform hybridization and mutation operations on the population after secondary screening, and obtain the offspring population based on the preset selection strategy;

[0080] The preset selection strategy is:

[0081] According to the first population and the second population, calculating the processing difference degree corresponding to the first population relative to the second population; the first population is the population corresponding to the hybridization and mutation operation, and the second population is the population corresponding to the hybridization and mutation operation before the operation;

[0082] Compare the processing difference with the preset difference threshold. If the processing difference is greater than or equal to the preset difference threshold, the first population is judged to be an invalid population, and the second population is hybridized and mutated again. If the processing difference is less than the preset difference threshold, a random number R between 0 and 1 is randomly generated. When the random number R is greater than the preset random threshold, the first population is judged to be a valid population and retained. Otherwise, the second population is hybridized and mutated again. The preset random threshold is positively correlated with the success of the hybridization and mutation operations corresponding to the previous iteration.

[0083] A loop module is used to continue executing the fitness calculation module after obtaining the offspring population until a preset number of iterations is met;

[0084] The output module is used to output the offspring population as the corresponding optimal solution set.

[0085] The virtual construction module is used to build a corresponding rehabilitation training virtual space based on the identified optimal rehabilitation training plan and virtual reality technology, and formulate a rehabilitation training model corresponding to the customer in the rehabilitation training virtual space based on the optimal rehabilitation training plan, and send feedback information to the client;

[0086] The client is used to enter the rehabilitation training virtual space after receiving the corresponding feedback information from the server, and receive the corresponding rehabilitation training model for this time.

[0087] The server also includes:

[0088] The training identification module is used to identify the rehabilitation type of the client based on the current condition status information of the client corresponding to the client after the corresponding client receives the rehabilitation training model, and based on the rehabilitation type, retrieve the rehabilitation training model corresponding to other current clients with the same rehabilitation type as the client;

[0089] A virtual sharing module is used to form a virtual training sharing space based on a sharing mechanism according to the current rehabilitation training model corresponding to other clients and the current rehabilitation training model corresponding to the current client;

[0090] The rehabilitation list module is used to obtain the rehabilitation training completion degree of the corresponding customer in real time based on the current customer and other current customers in the virtual training shared space, and generate the corresponding rehabilitation training list and send it to the client in real time.

[0091] like Figure 2 As shown, this embodiment also discloses a rehabilitation training method based on virtual reality technology, using the above-mentioned rehabilitation training system based on virtual reality technology, including the following steps:

[0092] S1. Receive login information corresponding to the client, and obtain basic customer information corresponding to the client and information about the customer's current medical condition based on the login information;

[0093] S2, according to the corresponding rehabilitation training action set and personalized action scene set in the database, based on the basic information of the customer and the current condition information of the customer, randomly generate N initial populations, the individuals of the initial population are the rehabilitation training actions corresponding to the customer, the personalized action scenes corresponding to the rehabilitation training actions, and the training time corresponding to each rehabilitation training;

[0094] S3, based on the N initial populations, identify the optimal rehabilitation training program based on the improved genetic algorithm;

[0095] S4. According to the identified optimal rehabilitation training plan, a corresponding rehabilitation training virtual space is built based on virtual reality technology, and a rehabilitation training model corresponding to the customer is formulated in the rehabilitation training virtual space based on the optimal rehabilitation training plan, and feedback information is sent to the client;

[0096] S5. After receiving the corresponding feedback information from the server, the client enters the rehabilitation training virtual space and receives the corresponding rehabilitation training model.

[0097] The above is only an embodiment of the present invention. The common sense such as the known specific structure and characteristics in the scheme is described too much here. The ordinary technicians in the relevant field know all the common technical knowledge in the technical field of the invention before the application date or priority date, can know all the existing technologies in the field, and have the ability to apply the conventional experimental means before that date. The ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the protection scope of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent. The protection scope required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A rehabilitation training system based on virtual reality technology, characterized in that: Including server and client; The server includes: The data acquisition module is used to receive the login information corresponding to the client, and obtain the basic information of the client corresponding to the client and the current condition status information of the client based on the login information; A rehabilitation training program formulation module is used to randomly generate N initial populations based on the corresponding rehabilitation training action set and personalized action scene set in the database, based on the basic information of the customer and the current condition information of the customer, wherein the individuals of the initial population are the rehabilitation training actions corresponding to the customer, the personalized action scenes corresponding to the rehabilitation training actions, and the training time corresponding to each rehabilitation training; The optimal rehabilitation program identification module is used to identify the optimal rehabilitation training program based on the N initial populations and the improved genetic algorithm; The virtual construction module is used to build a corresponding rehabilitation training virtual space based on the identified optimal rehabilitation training plan and virtual reality technology, and formulate a rehabilitation training model corresponding to the customer in the rehabilitation training virtual space based on the optimal rehabilitation training plan, and send feedback information to the client; The client is used to enter the rehabilitation training virtual space after receiving the corresponding feedback information from the server, and receive the corresponding rehabilitation training model; The optimal rehabilitation solution identification module includes: The first screening module is used to perform preliminary screening on the randomly generated N initial populations based on preset constraints. If the constraints are met, the corresponding individual is a feasible solution, otherwise it is an infeasible solution and the individual is eliminated; The restriction conditions are the number of rehabilitation training movements and the training time. The rehabilitation training action quantity condition is: In the formula, s max is the maximum number of rehabilitation training movements, B d is the current condition of the client, B b The condition status under the same standard condition as the customer's corresponding condition, s b is the standard number of rehabilitation training actions corresponding to the standard condition, s s The number of rehabilitation training movements corresponding to the last rehabilitation training performed by the client; The training time conditions are: Where, t k is the idle time corresponding to the customer, t i is the training time corresponding to rehabilitation training action i, t mix is the shortest training time corresponding to the customer, t b The standard training time corresponding to the standard condition; A fitness calculation module, used to calculate the first fitness and the second fitness of the screened population; The first fitness is calculated as follows: Where: F1 is the first fitness, D1 is the sum of the rationality of all rehabilitation training actions in the population, B y+1 is the patient's condition after completing the y+1th rehabilitation training action, g is the corresponding rehabilitation degree conversion parameter, P y+1 The fatigue level of the client after completing the y+1th rehabilitation training action; The second fitness is calculated as follows: Where: F2 is the second fitness, D2 is the average fitness of the rehabilitation training actions and the corresponding personalized action scenes in the population for the customer; S x is the matching degree of the personalized action scene corresponding to the rehabilitation training action x, x is the attractiveness of the personalized action scene corresponding to the rehabilitation training action x to the customer, ∝ is the corresponding weighting coefficient, a, b, c, v are constants, h, p, q are the corresponding dynamic change interval points; A second screening module is used to select, within a first preset number of iterations, a population whose first fitness is less than or equal to a first fitness threshold according to the first fitness corresponding to the population, and select the first three populations with the smallest second fitness from the populations eliminated at this time, and save them into a spare library; When the first preset number of iterations is exceeded, the current population is combined with the population in the standby library to form a new population, and a population whose second fitness corresponding to the current population is less than or equal to the preset second fitness is selected; The hybridization and mutation module is used to perform hybridization and mutation operations on the population after secondary screening, and obtain the offspring population based on the preset selection strategy; A loop module is used to continue executing the fitness calculation module after obtaining the offspring population until a preset number of iterations is met; An output module, used for outputting the offspring population as the corresponding optimal solution set; The preset selection strategy is: According to the first population and the second population, calculating the processing difference degree corresponding to the first population relative to the second population; the first population is the population corresponding to the hybridization and mutation operation, and the second population is the population corresponding to the hybridization and mutation operation before the operation; Compare the processing difference with the preset difference threshold. If the processing difference is greater than or equal to the preset difference threshold, the first population is judged to be an invalid population, and the second population is hybridized and mutated again. If the processing difference is less than the preset difference threshold, a random number R between 0 and 1 is randomly generated. When the random number R is greater than the preset random threshold, the first population is judged to be a valid population and retained. Otherwise, the second population is hybridized and mutated again. The preset random threshold is positively correlated with the success of the hybridization and mutation operations corresponding to the previous iteration.

2. A rehabilitation training system based on virtual reality technology according to claim 1, characterized in that: The server also includes: The training identification module is used to identify the rehabilitation type of the patient based on the patient's current condition status information after the corresponding client receives the rehabilitation training model, and retrieve the rehabilitation training model corresponding to other current clients with the same rehabilitation type as the patient based on the rehabilitation type; A virtual sharing module is used to form a virtual training sharing space based on a sharing mechanism according to the current rehabilitation training model corresponding to other clients and the current rehabilitation training model corresponding to the current client; The rehabilitation list module is used to obtain the rehabilitation training completion degree of the corresponding customer in real time based on the current customer and other current customers in the virtual training shared space, and generate the corresponding rehabilitation training list and send it to the client in real time.

3. A rehabilitation training method based on virtual reality technology, using a rehabilitation training system based on virtual reality technology according to any one of claims 1-2, characterized in that: The following steps are involved: S1. Receive login information corresponding to the client, and obtain basic customer information corresponding to the client and information about the customer's current medical condition based on the login information; S2, according to the corresponding rehabilitation training action set and personalized action scene set in the database, based on the basic information of the customer and the current condition information of the customer, randomly generate N initial populations, the individuals of the initial population are the rehabilitation training actions corresponding to the customer, the personalized action scenes corresponding to the rehabilitation training actions, and the training time corresponding to each rehabilitation training; S3, based on the N initial populations, identify the optimal rehabilitation training program based on the improved genetic algorithm; S4. According to the identified optimal rehabilitation training plan, a corresponding rehabilitation training virtual space is built based on virtual reality technology, and a rehabilitation training model corresponding to the customer is formulated in the rehabilitation training virtual space based on the optimal rehabilitation training plan, and feedback information is sent to the client; S5. After receiving the corresponding feedback information from the server, the client enters the rehabilitation training virtual space and receives the corresponding rehabilitation training model.

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