Cooperative control method and system for integrated rehabilitation equipment

Abstract

The invention provides a cooperative control method and system for integrated rehabilitation equipment, and the method comprises the steps: obtaining to-be-trained data and communication data, carrying out the preprocessing, and taking the sub-equipment in each rehabilitation equipment as a node to construct a training sub-graph; according to the training sequence, sequentially filling the preprocessed data to be trained into the corresponding training sub-graphs to obtain initial training sub-graphs; then the target rehabilitation device with the highest intelligent degree is determined, the convenience of user operation is improved, the initial training sub-graph of the target rehabilitation device serves as a main graph, and the remaining initial training sub-graphs serve as auxiliary graphs; mapping all the auxiliary graphs into the main graph according to a principle of combining nodes with the same training effect to form a complete training graph; and converting the complete training graph by using a corresponding communication conversion strategy to enable the rehabilitation equipment to complete rehabilitation training according to the complete training graph. According to the invention, a plurality of existing rehabilitation devices can be synchronously trained, the user operation is convenient, and the economic and human input can be reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of rehabilitation medical equipment, and in particular relates to a collaborative control method and system for integrated rehabilitation equipment. Background Art

[0002] The development of medical devices has provided great convenience for doctors and patients, especially medical devices for rehabilitation training, which have greatly improved the patient's recovery process and effectively reduced the difficulty of care for medical workers.

[0003] At present, various corresponding rehabilitation equipment has been developed on the market for different diseased parts, which are generally divided into upper limb training rehabilitation equipment and lower limb training rehabilitation equipment according to the branches of the body. Upper limb rehabilitation training equipment includes wrist joint trainers, elbow joint trainers, forearm and wrist joint combined trainers, etc. Lower limb training rehabilitation training equipment includes ankle joint trainers, standing trainers and other training equipment. For some patients who need combined training, multifunctional training equipment has also appeared on the market, such as forearm rotation trainers, wrist flexion and extension trainers, shoulder rotation trainers, compound wall pullers, shoulder ladders, rib bars, pulley ring trainers, pulley ring trainers, etc. Combined into a composite rehabilitation training equipment.

[0004] For a small number of people who suffer from partial diseases, such as elbow diseases, an elbow trainer can complete rehabilitation training. However, for people who have diseases in both the elbow and lower limbs, it is too expensive to purchase multi-functional training equipment. Although purchasing a single rehabilitation trainer can reduce costs, patients can only train in different time periods. For example, patients with ankle and elbow diseases often have limited funds and have to buy a single rehabilitation device first and then another. If multiple diseased parts are trained simultaneously, human assistance is required, and the cost of human investment is too high. Summary of the invention

[0005] In order to solve the above problems existing in the prior art, the present invention provides a coordinated control method and system for integrated rehabilitation equipment. The technical problem to be solved by the present invention is achieved through the following technical solutions:

[0006] Beneficial effects:

[0007] In a first aspect, the present invention provides a collaborative control method for an integrated rehabilitation device, comprising:

[0008] S100, acquiring the to-be-trained data and communication data of all rehabilitation devices, and performing preprocessing to obtain the preprocessed to-be-trained data and communication data; the rehabilitation devices include upper limb rehabilitation devices and lower limb rehabilitation devices;

[0009] S200, for each rehabilitation device, taking any sensing sub-device on the rehabilitation device as a sensing node, taking any controlling sub-device as a controlling node, and taking any motion sub-device controlled by the controlling sub-device as a motion node, so as to construct a training subgraph;

[0010] S300, according to the training order during training, sequentially filling the pre-processed data to be trained into the corresponding training sub-graph to obtain an initial training sub-graph;

[0011] S400, determining a target rehabilitation device with the highest intelligence level among the upper limb rehabilitation devices, and using the initial training subgraph of the target rehabilitation device as a main graph, and using the initial training subgraphs of the remaining rehabilitation devices as auxiliary graphs;

[0012] S500, mapping all auxiliary graphs into the main graph according to the principle of merging nodes with the same training function to form a complete training graph;

[0013] S600, using the preprocessed communication data to determine the corresponding communication conversion strategy, and using the communication conversion strategy to convert the complete training graph into a communication type suitable for the corresponding rehabilitation device and send it to the rehabilitation device, so that the rehabilitation device completes the rehabilitation training according to the complete training graph.

[0014] In a second aspect, the present invention provides a collaborative control system for integrated rehabilitation equipment, wherein the collaborative control system connects all rehabilitation equipment, and the collaborative control system includes:

[0015] An acquisition module is configured to acquire the to-be-trained data and communication data of all rehabilitation devices, and perform preprocessing to obtain the preprocessed to-be-trained data and communication data; the rehabilitation devices include upper limb rehabilitation devices and lower limb rehabilitation devices;

[0016] A construction module is configured to construct a training subgraph by taking, for each rehabilitation device, any sensing sub-device on the rehabilitation device as a sensing node, any controlling sub-device as a controlling node, and any motion sub-device controlled by the controlling sub-device as a motion node;

[0017] A filling module is configured to fill the pre-processed training data into the corresponding training sub-graph in sequence according to the training order during training to obtain an initial training sub-graph;

[0018] A determination module is configured to determine a target rehabilitation device with the highest intelligence among the upper limb rehabilitation devices, and use the initial training subgraph of the target rehabilitation device as the main graph, and use the initial training subgraphs of the remaining rehabilitation devices as auxiliary graphs;

[0019] A composition module is configured to map all auxiliary graphs into the main graph according to the principle of merging nodes with the same training function to form a complete training graph;

[0020] The rehabilitation module is configured to use the preprocessed communication data to determine the corresponding communication conversion strategy, and use the communication conversion strategy to convert the complete training graph into a communication type suitable for the corresponding rehabilitation device and send it to the rehabilitation device, so that the rehabilitation device completes the rehabilitation training according to the complete training graph.

[0021] The present invention provides a collaborative control method and system for integrated rehabilitation equipment, which obtains and preprocesses data to be trained and communication data, and uses sub-devices in each rehabilitation equipment as nodes to construct a training sub-graph; according to the training order during training, the pre-processed data to be trained is sequentially filled into the corresponding training sub-graph to obtain an initial training sub-graph; the target rehabilitation equipment with the highest intelligence level is determined in the upper limb rehabilitation equipment to improve the convenience of user operation, and the initial training sub-graph of the target rehabilitation equipment is used as the main graph, and the initial training sub-graphs of the remaining rehabilitation equipment are used as auxiliary graphs; according to the principle of merging nodes with the same training function, all auxiliary graphs are mapped to the main graph to form a complete training graph; the corresponding communication conversion strategy is used to convert the complete training graph so that the rehabilitation equipment completes rehabilitation training according to the complete training graph. The present invention can enable multiple existing rehabilitation equipment to be trained synchronously, and the user operation is convenient, which can reduce economic and manpower investment.

[0022] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a collaborative control method of an integrated rehabilitation device provided by the present invention;

[0024] Figure 2 is a schematic diagram of the elbow joint trainer provided by the present invention;

[0025] Figure 3 is a schematic diagram of the process of S100 provided by the present invention;

[0026] Figure 4 is a schematic diagram of the process of S200 provided by the present invention;

[0027] Figure 5 It is a schematic diagram of the process of S300 provided by the present invention;

[0028] Figure 6 is a flow chart of S500 provided by the present invention;

[0029] Figure 7 is a schematic diagram of the process of S600 provided by the present invention;

[0030] Figure 8 It is a structural schematic diagram of a cooperative control system of an integrated rehabilitation device provided by the present invention. DETAILED DESCRIPTION

[0031] The present invention is further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0032] like Figure 1 As shown, the present invention provides a collaborative control method for an integrated rehabilitation device, comprising:

[0033] S100, acquiring the to-be-trained data and communication data of all rehabilitation devices, and performing preprocessing to obtain the preprocessed to-be-trained data and communication data; the rehabilitation devices include upper limb rehabilitation devices and lower limb rehabilitation devices;

[0034] The upper limb rehabilitation device in this application can be Figure 2 The elbow joint trainer shown, the lower limb rehabilitation equipment can be an ankle joint trainer. Both the upper limb rehabilitation equipment and the lower limb rehabilitation equipment are motor-driven driving devices to complete the rehabilitation training of the corresponding parts. The rehabilitation equipment needs to achieve the control effect, and the corresponding devices need to communicate. The sensors on the upper limb and lower limb rehabilitation equipment are sensing sub-devices, the controllers are control sub-devices, and the equipment that directly contacts the patient's part for training is the controlled equipment, such as the swing arm and wrist guard in the elbow joint trainer. The controlled equipment is generally a mechanical structure. The training data of the rehabilitation equipment generally includes the training duration, training part, various parameters during training, such as stretching degree, strength, movable angle, speed, curvature, etc. Of course, the intelligent rehabilitation equipment can include the user's login information and the training data generated after the training is completed. These training data become the historical training data in the rehabilitation equipment log. In some schemes, historical training data can be used to correct the patient's current training data (data to be trained).

[0035] S200, for each rehabilitation device, taking any sensing sub-device on the rehabilitation device as a sensing node, taking any controlling sub-device as a controlling node, and taking any motion sub-device controlled by the controlling sub-device as a motion node, so as to construct a training subgraph;

[0036] Due to the various types of rehabilitation equipment, inconsistent intelligence levels and other reasons. The various sensing sub-devices, control sub-devices and motion sub-devices in the rehabilitation equipment are not synchronized between different rehabilitation equipment, and it is even more impossible to achieve synchronous perception or coordinated training during training. For example, the training process of the elbow rehabilitation equipment and the ankle trainer often relies on the patient's own autonomy or the assistance of his family to complete the coordinated training, and the purpose of unified and synchronous training of the machine cannot be achieved. This step requires a node in the current training subgraph of each sub-device to pave the way for the subsequent unified formation of a complete training graph. In this process, for rehabilitation equipment with a higher degree of intelligence, the information can be collected directly from the rehabilitation equipment, and for rehabilitation equipment with a lower degree of intelligence, these node information can be collected manually.

[0037] S300, according to the training order during training, sequentially filling the pre-processed data to be trained into the corresponding training sub-graph to obtain an initial training sub-graph;

[0038] The training order of the rehabilitation device of the present application can be determined by starting a specific controlled sub-device, or by the order of the patient's training parts. The former can be obtained by using the training start time of the controlled sub-device, and the latter can be determined from the generation time of the historical training data in the log.

[0039] S400, determining a target rehabilitation device with the highest intelligence level among the upper limb rehabilitation devices, and using the initial training subgraph of the target rehabilitation device as a main graph, and using the initial training subgraphs of the remaining rehabilitation devices as auxiliary graphs;

[0040] It is worth noting that some of the multiple rehabilitation devices may be purchased by the user first, and some may be purchased later. According to the rules of technological development, rehabilitation devices purchased later are generally more intelligent. Of course, some rehabilitation devices are linked to cost or performance. Generally, rehabilitation devices with higher costs or higher performance are more intelligent. In this step, one or more rehabilitation devices can be selected as target rehabilitation devices based on cost or performance.

[0041] S500, mapping all auxiliary graphs into the main graph according to the principle of merging nodes with the same training function to form a complete training graph;

[0042] It is worth mentioning that there may be sub-modules with repeated functions in multiple rehabilitation devices. For example, the control sub-module controls the rotation amplitude of the motor during training and thus controls the extension of the training. Therefore, the nodes with the same training function can be merged. The nodes in the auxiliary graph can be merged according to the same function and finally converged to form a complete training graph.

[0043] S600, using the preprocessed communication data to determine the corresponding communication conversion strategy, and using the communication conversion strategy to convert the complete training graph into a communication type suitable for the corresponding rehabilitation device and send it to the rehabilitation device, so that the rehabilitation device completes the rehabilitation training according to the complete training graph.

[0044] It is worth noting that different manufacturers of rehabilitation equipment purchase different communication devices or communication interfaces, which results in different communication protocols and data formats for transmission. Therefore, in order to achieve the purpose of synchronous training, the complete training diagram can be sent to each rehabilitation device synchronously. If the data format or communication format of these rehabilitation devices is different from that of the main device, that is, the target rehabilitation device, it is necessary to determine the corresponding communication conversion strategy according to the communication data, so as to convert the complete training diagram into the communication type of the corresponding rehabilitation device and send it to each rehabilitation device.

[0045] As an optional embodiment of the present invention, refer to Figure 3 , S100 includes:

[0046] S110, acquiring training data from the control terminal of each rehabilitation device and acquiring communication data from the communication terminal; the communication type includes the interface type used for communication and the data transmission format corresponding to the interface type;

[0047] It is worth noting that the interface type of this step can be an existing interface type, such as RS485, USB, RS232, Ethernet interface, etc. Since the models of rehabilitation equipment are different, the interface type will also be different.

[0048] S120, for each rehabilitation device, determining a communication type according to the communication data of the rehabilitation device; the training data at least includes stretching degree, speed, pressure and duration during training;

[0049] In addition to stretching degree, speed, pressure, and duration, the training data in this step may also include twist angle, average speed of joint twisting, maximum twist angle, etc. Depending on the rehabilitation equipment, these training data will change adaptively.

[0050] S130, marking the communication data with a communication type label according to labels corresponding to different communication types;

[0051] In this step, different communication data may be labeled with communication type labels according to different communication types, such as USB communication type, CAN communication type, or RS485 communication type, to provide a foundation for the subsequent steps.

[0052] S140, grouping the aggregated communication data having the same communication type tag into one group, and adding a group number tag to the communication data in the same group, to obtain the communication data after preprocessing;

[0053] S150, for each rehabilitation device, aggregate the to-be-trained data of sub-devices with the same function on the rehabilitation device into a group of training data, and label the group of to-be-trained data with a device type label to obtain preprocessed to-be-trained data.

[0054] In this step, for the same rehabilitation device, the data to be trained with the same function are aggregated into a group and labeled with the device type, which can speed up the efficiency of the subsequent merging process.

[0055] As an optional embodiment of the present invention, refer to Figure 4 , S200 includes:

[0056] S210, for each rehabilitation device, taking any sensing sub-device on the rehabilitation device as a sensing node, taking any controlling sub-device as a controlling node, and taking any moving sub-device controlled by the controlling sub-device as a moving node;

[0057] S220, determining the most initial sensing node among all sensing nodes, and taking the sensing node as the current node, and searching for a target node having a connection relationship with the current node; the target node is a control node or a motion node;

[0058] It is worth noting that the sensor or sensing device at the end is the initial sensing node, which is controlled by the control sub-device or connected to the motion sub-device. Each sensing sub-device is the current node, and all target nodes can be obtained by searching in sequence.

[0059] S230, taking the line segments connecting the target node and the current node, and the target node and the target node as the target edge;

[0060] S240, assigning a weight coefficient to each target edge according to the in-degree of each target node;

[0061] In this step, a weight coefficient is assigned to each target edge according to the in-degree of each target node. A high in-degree indicates that the data transmitted between the sensor and the control sub-device or the motion sub-device is more important, or the control signal is more important, so a higher weight coefficient is assigned to it. Otherwise, a lower weight coefficient is assigned to it.

[0062] S250, taking each target node as the current node, and repeating the process of S220 until all nodes are traversed to obtain a training subgraph of the rehabilitation equipment.

[0063] This step requires traversing all the sensing nodes for each rehabilitation device, so that connections are established between all the sensing nodes and the control nodes, the motion nodes, between the control nodes, and between the control nodes and the motion nodes, and finally a training subgraph of the rehabilitation device is obtained.

[0064] As an optional embodiment of the present invention, refer to Figure 5 , S300 includes:

[0065] S310, obtaining historical training data from the log of each rehabilitation device and determining a historical complete training graph corresponding to the historical training data;

[0066] It is worth noting that the historical training data and historical complete training graphs can be obtained from the log of the rehabilitation equipment or from a database specifically storing historical training data. In the present application, obtaining them from the log is more convenient.

[0067] S320, inputting the historical complete training graph corresponding to the historical training data into the graph neural network, obtaining each layer of network nodes of the graph neural network for transmitting the characteristics of the historical training data, collecting the network nodes involved in the transmission of the characteristics of the historical complete training graph in the graph neural network, and determining the transmission order of each historical training data by using the transmission relationship of the network nodes;

[0068] The current layer network nodes receive the features transmitted from the previous layer network nodes, and directly receive the source network nodes of the historical complete training graph to extract the features of the historical training data;

[0069] It is worth noting that the graph neural network can transfer the features in the complete historical training graph at each layer. The graph neural network can cluster according to edges or nodes during the transfer process, so that similar features can gradually converge together and different features gradually disperse. According to the clustering principle, similar features converge together, and vice versa. During the transfer process, due to the different historical training data, the transfer order of network nodes is different, and the transfer order of each historical training data can be known.

[0070] S330, for each rehabilitation device, classify the historical training data of the rehabilitation device according to the sub-device type to obtain classification data;

[0071] S340, determining a node corresponding to the classification data in the corresponding training subgraph, and using the transmission order of the classification data as attribute information of the node;

[0072] S350, for each rehabilitation device, determining the execution order of each sub-device according to the order in which the control sub-devices in the rehabilitation device execute the pre-processed training data and the response time of the motion sub-device;

[0073] It is worth noting that the control sub-device in each rehabilitation device outputs a control signal to control the sensing sub-device or the motion sub-device. Due to different communication response times, that is, different response durations, the time it takes for the training data to be transmitted to the sub-device is different. It is necessary to determine the execution order based on the order of execution and the response duration. The execution order of the motion sub-device with a longer response time is later, and the control sub-device executes the pre-processed training data later. The longer the response time, the later the execution order of the corresponding sub-device. The execution time of the control sub-device plus the response time of the motion sub-device plus the communication time is the execution time of each sub-device. According to the execution time, the execution order of each sub-device can be determined.

[0074] S360, using the execution order of each sub-device, adding node connection relationships in the training sub-graph, and determining the training order of each node in the training sub-graph;

[0075] It is worth noting that the execution order of each sub-device is controlled by other sub-devices, or the data comes from other sub-devices, so it is necessary to add connection relationships to the corresponding nodes.

[0076] S370, filling the node attributes, training order and data to be trained into the corresponding nodes of the corresponding training subgraph to obtain the initial training subgraph.

[0077] As an optional implementation of the present invention, determining the target rehabilitation device with the highest intelligence level among the upper limb rehabilitation devices in S400 includes:

[0078] The communication efficiencies of all upper limb rehabilitation devices are compared to determine the upper limb rehabilitation devices whose communication efficiencies exceed a threshold;

[0079] The threshold is a value set based on industry experience or actual conditions and can also be changed.

[0080] A Turing test is performed on upper limb rehabilitation devices of all communication efficiency thresholds, and the upper limb rehabilitation device with the highest accuracy in the test results is selected as the target rehabilitation device with the highest intelligence.

[0081] In this embodiment, when determining the highest intelligence level, the communication efficiency is first used for screening, and then the Turing test is performed. Of course, if the actual situation does not allow, the rehabilitation device with the highest cost can also be selected as the target rehabilitation device with the highest intelligence level.

[0082] As an optional embodiment of the present invention, refer to Figure 6 , S500 includes:

[0083] S510, for any first node of each auxiliary graph, determine whether there is a second node in other auxiliary graphs that has the same training order as the first node, and if so, determine that the first node is a sibling node of the second node;

[0084] It is worth noting that the purpose of this step is to confirm whether there are nodes with the same training order between the auxiliary graphs. If so, it means that the two nodes are brother nodes in the graph and are trained synchronously.

[0085] S520, for any first node in the auxiliary graph, determine whether the main graph has a third node with the same node attribute as the first node, and if so, determine whether the training order of the third node is the same, and if so, confirm that the training function of the third node is the same as that of the first node;

[0086] This step determines the nodes with exactly the same node attributes in the auxiliary. Since the attribute information is the transmission order of the classified data, it can be confirmed that the device types and data features of the two are exactly the same in the transmission order of the graph neural network. If the training order is also the same, it can be completely determined that the training effects of the two nodes are the same. The device type carries a label in the preprocessing stage, so it can be determined directly by looking at the label.

[0087] S530, if there is no target node in the main graph having the same node attribute as the first node, determining whether the to-be-trained data of the target node is the same as that of the first node; if the to-be-trained data is the same as that of the first node, determining that the first node is a child node of the target node;

[0088] It is worth noting that if the attributes are different, it means that the device type and the transmission order may be different. Then we can check whether the data to be trained are the same. If they are the same, the data of the node may be transmitted to it by other nodes of the same generation as the first node, and it can be determined that the first node is a child node of the target node.

[0089] S540, all auxiliary graphs are connected according to brother nodes and parent-child nodes to form a complete auxiliary graph, and according to the principle of merging nodes with the same training function, the complete auxiliary graph is mapped to the main graph to obtain a complete training graph.

[0090] As an optional embodiment of the present invention, refer to Figure 7 , S600 includes:

[0091] S610, receiving a rehabilitation training request sent by any rehabilitation device, and determining pre-processed communication data corresponding to the rehabilitation device;

[0092] S620, using the communication data preprocessed in S610, determining the communication data format of the rehabilitation device;

[0093] S630, determining a corresponding communication conversion strategy according to the communication data format;

[0094] It is worth noting that different communication data formats have predetermined bit data, and their data frames specify what data a certain bit is. Therefore, it is necessary to refill the data bits according to the data format, and the process of converting the bits is the communication conversion strategy.

[0095] S640: Using the communication conversion strategy, the complete training graph is converted into a communication type suitable for a corresponding rehabilitation device and sent to the rehabilitation device, so that the rehabilitation device completes the rehabilitation training according to the complete training graph.

[0096] It is worth noting that all rehabilitation devices receive a complete training graph with node attributes, training order, and data to be trained. Since the training is for the same patient, there is no need to keep it confidential. The rehabilitation devices can know the training data of other rehabilitation devices, so that the training order is the same during the training process and the training can be synchronized and completed. Users can operate conveniently and reduce human assistance to a certain extent.

[0097] As an optional implementation manner of the present invention, S640 includes:

[0098] S641, using the communication protocol agreed upon in the communication conversion strategy and the bit data format agreed upon in the communication protocol, convert the complete training graph into a communication type suitable for the corresponding rehabilitation device to obtain a data packet to be transmitted;

[0099] S642, transmit the data packet to be transmitted to the corresponding rehabilitation device, so that the rehabilitation device parses the transmission data packet to obtain the complete training graph, and restores its own control nodes and motion nodes from the complete training graph, and enables the control sub-device corresponding to the control node to control the motion sub-device corresponding to the motion node in sequence according to the training order in the complete training graph to complete the rehabilitation training.

[0100] Among them, the collaborative control method of the integrated rehabilitation equipment is implemented on a processor integrated in the collaborative control system. The collaborative control system also integrates multiple types of communication modules. The communication module is internally provided with a communication protocol and a communication conversion protocol. The communication module is externally provided with a communication interface adapted to the rehabilitation equipment. The communication interface is connected to the communication interface of the rehabilitation equipment by plugging and unplugging. The communication protocol stipulates the data transmission format between the collaborative control system and the communication interface, and the communication conversion protocol stipulates the data conversion method between different rehabilitation equipment.

[0101] like Figure 8As shown, the present invention provides a collaborative control system for integrated rehabilitation equipment, wherein the collaborative control system connects all rehabilitation equipment, and the collaborative control system includes:

[0102] The acquisition module 81 is configured to acquire the training data and communication data of all rehabilitation devices, and perform preprocessing to obtain the preprocessed training data and communication data; the rehabilitation devices include upper limb rehabilitation devices and lower limb rehabilitation devices;

[0103] A construction module 82 is configured to construct a training subgraph for each rehabilitation device by taking any sensing sub-device on the rehabilitation device as a sensing node, taking any controlling sub-device as a controlling node, and taking any motion sub-device controlled by the controlling sub-device as a motion node;

[0104] A filling module 83 is configured to fill the pre-processed training data into the corresponding training sub-graph in accordance with the training order during training to obtain an initial training sub-graph;

[0105] A determination module 84 is configured to determine a target rehabilitation device with the highest intelligence among the upper limb rehabilitation devices, and use the initial training subgraph of the target rehabilitation device as the main graph, and use the initial training subgraphs of the remaining rehabilitation devices as auxiliary graphs;

[0106] The composition module 85 is configured to map all auxiliary graphs into the main graph according to the principle of merging nodes with the same training function to form a complete training graph;

[0107] The rehabilitation module 86 is configured to use the preprocessed communication data to determine the corresponding communication conversion strategy, and use the communication conversion strategy to convert the complete training image into a communication type suitable for the corresponding rehabilitation device and send it to the rehabilitation device, so that the rehabilitation device completes the rehabilitation training according to the complete training image.

[0108] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0109] Although the present application is described herein in conjunction with various embodiments, in the process of implementing the claimed application, those skilled in the art may understand and implement other variations of the disclosed embodiments by reviewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude a plurality of components or steps.

[0110] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions or substitutions can be made without departing from the concept of the present invention, which should be regarded as falling within the protection scope of the present invention.

Claims

1. A collaborative control method for integrated rehabilitation equipment, characterized in that: include: S100, acquiring the to-be-trained data and communication data of all rehabilitation devices, and performing preprocessing to obtain the preprocessed to-be-trained data and communication data; The rehabilitation equipment includes upper limb rehabilitation equipment and lower limb rehabilitation equipment; S200, for each rehabilitation device, taking any sensing sub-device on the rehabilitation device as a sensing node, taking any controlling sub-device as a controlling node, and taking any motion sub-device controlled by the controlling sub-device as a motion node, so as to construct a training subgraph; S300, according to the training order during training, sequentially filling the pre-processed data to be trained into the corresponding training sub-graph to obtain an initial training sub-graph; S400, determining a target rehabilitation device with the highest intelligence level among the upper limb rehabilitation devices, and using the initial training subgraph of the target rehabilitation device as a main graph, and using the initial training subgraphs of the remaining rehabilitation devices as auxiliary graphs; S500, mapping all auxiliary graphs into the main graph according to the principle of merging nodes with the same training function to form a complete training graph; S600, using the preprocessed communication data to determine the corresponding communication conversion strategy, and using the communication conversion strategy to convert the complete training graph into a communication type suitable for the corresponding rehabilitation device and send it to the rehabilitation device, so that the rehabilitation device completes the rehabilitation training according to the complete training graph.

2. The collaborative control method of integrated rehabilitation equipment according to claim 1, characterized in that: S100 includes: S110, acquiring training data from the control terminal of each rehabilitation device and acquiring communication data from the communication terminal; the communication type includes the interface type used for communication and the data transmission format corresponding to the interface type; S120, for each rehabilitation device, determining a communication type according to the communication data of the rehabilitation device; the training data at least includes stretching degree, speed, pressure and duration during training; S130, marking the communication data with a communication type label according to labels corresponding to different communication types; S140, grouping the aggregated communication data having the same communication type tag into one group, and adding a group number tag to the communication data in the same group, to obtain the communication data after preprocessing; S150, for each rehabilitation device, aggregate the to-be-trained data of sub-devices with the same function on the rehabilitation device into a group of training data, and label the group of to-be-trained data with a device type label to obtain preprocessed to-be-trained data.

3. The collaborative control method of integrated rehabilitation equipment according to claim 1, characterized in that: S200 includes: S210, for each rehabilitation device, taking any sensing sub-device on the rehabilitation device as a sensing node, taking any controlling sub-device as a controlling node, and taking any moving sub-device controlled by the controlling sub-device as a moving node; S220, determining the most initial sensing node among all sensing nodes, and taking the sensing node as the current node, and searching for a target node having a connection relationship with the current node; the target node is a control node or a motion node; S230, taking the line segments connecting the target node and the current node, and the target node and the target node as the target edge; S240, assigning a weight coefficient to each target edge according to the in-degree of each target node; S250, taking each target node as the current node, and repeating the process of S220 until all nodes are traversed to obtain a training subgraph of the rehabilitation equipment.

4. The collaborative control method of integrated rehabilitation equipment according to claim 1, characterized in that: S300 includes: S310, acquiring historical training data from the log of each rehabilitation device and determining a historical complete training graph corresponding to the historical training data; S320, inputting the historical complete training graph corresponding to the historical training data into the graph neural network, obtaining each layer of network nodes of the graph neural network for transmitting the characteristics of the historical training data, collecting the network nodes involved in the transmission of the characteristics of the historical complete training graph in the graph neural network, and determining the transmission order of each historical training data by using the transmission relationship of the network nodes; The current layer network nodes receive the features transmitted from the previous layer network nodes, and directly receive the source network nodes of the historical complete training graph to extract the features of the historical training data; S330, for each rehabilitation device, classify the historical training data of the rehabilitation device according to the sub-device type to obtain classification data; S340, determining a node corresponding to the classification data in the corresponding training subgraph, and using the transmission order of the classification data as attribute information of the node; S350, for each rehabilitation device, determining the execution order of each sub-device according to the order in which the control sub-devices in the rehabilitation device execute the pre-processed training data and the response time of the motion sub-device; S360, using the execution order of each sub-device, adding node connection relationships in the training sub-graph, and determining the training order of each node in the training sub-graph; S370, filling the node attributes, training order and data to be trained into the corresponding nodes of the corresponding training subgraph to obtain the initial training subgraph.

5. The collaborative control method of integrated rehabilitation equipment according to claim 1, characterized in that: The target rehabilitation devices with the highest intelligence among the upper limb rehabilitation devices in S400 include: The communication efficiencies of all upper limb rehabilitation devices are compared to determine the upper limb rehabilitation devices whose communication efficiencies exceed a threshold; A Turing test is performed on upper limb rehabilitation devices of all communication efficiency thresholds, and the upper limb rehabilitation device with the highest accuracy in the test results is selected as the target rehabilitation device with the highest intelligence.

6. The collaborative control method of integrated rehabilitation equipment according to claim 1, characterized in that: S500 includes: S510, for any first node of each auxiliary graph, determine whether there is a second node in other auxiliary graphs that has the same training order as the first node, and if so, determine that the first node is a sibling node of the second node; S520, for any first node in the auxiliary graph, determine whether the main graph has a third node with the same node attribute as the first node, and if so, determine whether the training order of the third node is the same, and if so, confirm that the training function of the third node is the same as that of the first node; S530, if there is no target node in the main graph having the same node attribute as the first node, determining whether the to-be-trained data of the target node is the same as that of the first node; if the to-be-trained data is the same as that of the first node, determining that the first node is a child node of the target node; S540, all auxiliary graphs are connected according to brother nodes and parent-child nodes to form a complete auxiliary graph, and according to the principle of merging nodes with the same training function, the complete auxiliary graph is mapped to the main graph to obtain a complete training graph.

7. The collaborative control method of integrated rehabilitation equipment according to claim 1, characterized in that: S600 includes: S610, receiving a rehabilitation training request sent by any rehabilitation device, and determining pre-processed communication data corresponding to the rehabilitation device; S620, using the communication data preprocessed in S610, determining the communication data format of the rehabilitation device; S630, determining a corresponding communication conversion strategy according to the communication data format; S640: Using the communication conversion strategy, the complete training graph is converted into a communication type suitable for a corresponding rehabilitation device and sent to the rehabilitation device, so that the rehabilitation device completes the rehabilitation training according to the complete training graph.

8. The collaborative control method of integrated rehabilitation equipment according to claim 7, characterized in that: S640 includes: S641, using the communication protocol agreed upon in the communication conversion strategy and the bit data format agreed upon in the communication protocol, convert the complete training graph into a communication type suitable for the corresponding rehabilitation device to obtain a data packet to be transmitted; S642, transmit the data packet to be transmitted to the corresponding rehabilitation device, so that the rehabilitation device parses the transmission data packet to obtain the complete training graph, and restores its own control nodes and motion nodes from the complete training graph, and enables the control sub-device corresponding to the control node to control the motion sub-device corresponding to the motion node in sequence according to the training order in the complete training graph to complete the rehabilitation training.

9. The collaborative control method of integrated rehabilitation equipment according to claim 1, characterized in that: The collaborative control method of the integrated rehabilitation equipment is implemented on a collaborative control system integrated with a processor. The collaborative control system also integrates multiple types of communication modules. The communication module is internally provided with a communication protocol and a communication conversion protocol. The communication module is externally provided with a communication interface adapted to the rehabilitation equipment. The communication interface is connected to the communication interface of the rehabilitation equipment by plugging and unplugging. The communication protocol stipulates the data transmission format between the collaborative control system and the communication interface. The communication conversion protocol stipulates the data conversion method between different rehabilitation equipment.

10. A collaborative control system for integrated rehabilitation equipment, characterized in that: The collaborative control system connects all rehabilitation equipment, and the collaborative control system includes: An acquisition module is configured to acquire the to-be-trained data and communication data of all rehabilitation devices, and perform preprocessing to obtain the preprocessed to-be-trained data and communication data; the rehabilitation devices include upper limb rehabilitation devices and lower limb rehabilitation devices; A construction module is configured to construct a training subgraph by taking, for each rehabilitation device, any sensing sub-device on the rehabilitation device as a sensing node, any controlling sub-device as a controlling node, and any motion sub-device controlled by the controlling sub-device as a motion node; A filling module is configured to fill the pre-processed training data into the corresponding training sub-graph in sequence according to the training order during training to obtain an initial training sub-graph; A determination module is configured to determine a target rehabilitation device with the highest intelligence among the upper limb rehabilitation devices, and use the initial training subgraph of the target rehabilitation device as the main graph, and use the initial training subgraphs of the remaining rehabilitation devices as auxiliary graphs; A composition module is configured to map all auxiliary graphs into the main graph according to the principle of merging nodes with the same training function to form a complete training graph; The rehabilitation module is configured to use the preprocessed communication data to determine the corresponding communication conversion strategy, and use the communication conversion strategy to convert the complete training graph into a communication type suitable for the corresponding rehabilitation device and send it to the rehabilitation device, so that the rehabilitation device completes the rehabilitation training according to the complete training graph.

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