Nervous system data monitoring method and system based on multi-terminal interconnection

Abstract

The invention provides a nervous system data monitoring method and system based on multi-terminal interconnection. The method comprises the following steps: a server distributes a nerve acquisition request uploaded by a distribution end, and obtains each patient end which responds to the nerve acquisition request and uploads each piece of application information; the server performs circulation sorting on the patient ends based on the registration information uploaded by the patient ends, and controls the distribution end to perform equipment circulation on the nerve acquisition equipment based on the obtained circulation sequence; the server responds to any one patient end to receive the nerve acquisition equipment, and adds a preset equipment state detection strategy to the patient end, so that the patient end performs equipment state detection on the nerve acquisition equipment based on the preset equipment state detection strategy; and the server responds to the completion of equipment state detection by the patient end, obtains neural data output by the patient end by using the neural acquisition equipment, and forwards the neural data to a medical care end corresponding to the patient end. According to the invention, the efficiency of nerve collection is at least improved.

Description

Technical Field

[0001] The present invention relates to data processing technology, and in particular to a method and system for monitoring nervous system data based on multi-terminal interconnection. Background Art

[0002] In daily life, patients with brain damage often find it inconvenient to go to the hospital for relevant nerve collection and other examinations, and need to borrow nerve collection equipment to complete the corresponding examinations at home.

[0003] The inventors found in their research that when multiple patients need to borrow nerve acquisition equipment at the same time, there may be conflicts in the borrowing time or it may be difficult to coordinate when a patient wants to change the borrowing time, which may cause the patient's nerve acquisition time to be delayed, resulting in the doctor's inability to make a timely diagnosis of the patient's condition. Summary of the invention

[0004] Based on the above problems, the present invention is proposed to provide a method and system for monitoring nervous system data based on multi-terminal interconnection, which overcomes the above problems or at least partially solves the above problems.

[0005] According to one aspect of the present invention, a method for monitoring nervous system data based on multi-terminal interconnection is provided, comprising the following steps: The server distributes the nerve collection request uploaded by the distribution terminal, and obtains each patient terminal that uploads each registration information in response to the nerve collection request; The server performs a flow order on each patient terminal based on each registration information uploaded by each patient terminal, and controls the distribution terminal to perform device flow on the neural acquisition device based on the obtained flow sequence; In response to any patient end receiving the neural acquisition device, the server adds a preset device state detection strategy to the patient end, so that the patient end performs device state detection on the neural acquisition device based on the preset device state detection strategy; In response to the patient end completing the device status detection, the server obtains the neural data output by the patient end using the neural acquisition device, and transfers the neural data to the medical care end corresponding to the patient end.

[0006] Optionally, in the method according to the present invention, the server performs a flow sorting on each patient terminal based on each registration information uploaded by each patient terminal, and controls the distribution terminal to perform device flow on the neural acquisition device based on the obtained flow sequence, including: The server obtains the upload time of each piece of information corresponding to each piece of registration information, and sorts the flow of each patient terminal based on the time sequence of the upload time of each piece of information to obtain a first flow sequence; The server obtains the number of terminals of each patient terminal in the first circulation sequence, and divides the terminal terminal into time periods having the same number as the number of terminals based on the retrieved monitoring date to obtain each monitoring time period; The server sorts each monitoring period based on the chronological order to obtain a period sequence corresponding to the monitoring date; The server configures each patient terminal in the first flow sequence and each monitoring period in the time period sequence one by one, and controls the distribution terminal to perform device flow on the neural acquisition device within the corresponding monitoring period based on the first flow sequence.

[0007] Optionally, in the method according to the present invention, the server configures each patient terminal in the first flow sequence with each monitoring time period in the time period sequence one by one, and then further includes: The server obtains the terminal numbers corresponding to the patient terminals, and establishes a time period adjustment layer based on the terminal numbers, wherein the time period adjustment layer includes vertically arranged activity slots corresponding to the terminal numbers; The server fills each monitoring period into each corresponding active slot, and sends the period adjustment layer to each patient terminal in the first flow sequence in sequence; In response to a first selection interaction performed by any patient end on an activity slot corresponding to itself in the time period adjustment layer and a second selection interaction performed with other activity slots, the server swaps the positions of the two to complete the update of the activity slot corresponding to the patient end, and locks the updated activity slot corresponding to the patient end.

[0008] Optionally, in the method according to the present invention, the server performs a flow sorting on each patient terminal based on each registration information uploaded by each patient terminal, and controls the distribution terminal to perform device flow on the neural acquisition device based on the obtained flow sequence, including: The server obtains the first GPS information corresponding to the distribution terminal and each second GPS information corresponding to each patient terminal, and determines the position relationship of each terminal between each second GPS information and the first GPS information; The server establishes a plane coordinate system with the distribution terminal as the origin based on the first GPS information, and determines each patient coordinate point corresponding to each patient terminal in the plane coordinate system based on the position relationship of each terminal; The server performs quadrant grouping of each patient coordinate point based on the plane coordinate system to obtain a first area group including all patient coordinate points located in a first quadrant, a second area group including all patient coordinate points located in a second quadrant, a third area group including all patient coordinate points located in a third quadrant, and a fourth area group including all patient coordinate points located in a fourth quadrant; The server determines the distances between each patient terminal and the distribution terminal based on the positional relationships between each patient terminal and the distribution terminal in the same regional group, and sorts the patient terminals according to the order of distance, to obtain second flow sequences corresponding to each regional group. The server performs distribution configuration of different distribution terminals for each regional group, and controls each distribution terminal to perform device flow on the neural acquisition device based on the respectively corresponding second flow sequence.

[0009] Optionally, in the method according to the present invention, The server determines the distances between each patient terminal and the distribution terminal based on the positional relationships between each patient terminal and the distribution terminal in the same regional group, and sorts the patient terminals according to the order of distance, to obtain second flow sequences corresponding to each regional group, including: Acquire two patient terminals located in the same area group and close to the distribution terminal, and use them as the starting node and the ending node respectively; Determine a patient end that is closest to the starting node, except for the end node, as the next flow node of the starting node, and re-determine the flow node as the starting node; Determine a patient terminal that is closest to the end node, except for the start node, as the previous flow node of the end node, and re-determine the flow node as the end node; Repeat the above steps of determining the starting node and the ending node until all the nodes of the patient terminals are determined; All the patient ends that are sequentially determined as starting nodes are arranged forwardly based on the node determination order to obtain a first subsequence; Reversely arrange all the patient ends that are sequentially determined as end nodes based on the node determination order to obtain a second subsequence; Sequence combination is performed based on the first subsequence and the second subsequence to obtain second flow sequences corresponding to each area group.

[0010] Optionally, in the method according to the present invention, in response to any patient end receiving the neural acquisition device, the server adds a preset device state detection strategy to the patient end, so that the patient end performs device state detection on the neural acquisition device based on the preset device state detection strategy, including: Retrieving a historical status voucher layer and sending it to the patient end, wherein the historical status voucher layer includes vertically arranged device status record areas, each device status record area includes a horizontally arranged image voucher sub-area and a text description sub-area, and the image voucher sub-area and the text description sub-area are filled with a historical status voucher image and a historical status description text respectively; In response to the patient terminal selecting and interacting with any device status recording area for a preset time, the server triggers a lighting unit pre-installed on the patient terminal to illuminate the neural acquisition device, and triggers a first acquisition unit pre-installed on the patient terminal to acquire an image of the neural acquisition device, thereby obtaining a current status credential image; The server replaces the current status credential image with the historical status credential image located in the image credential sub-area, and obtains the text update made by the patient end based on the historical status description text located in the text description sub-area to obtain the current status credential layer corresponding to the patient end.

[0011] Optionally, in the method according to the present invention, the method further comprises: The server, in response to the credential addition request sent by the patient terminal based on the historical status credential layer, establishes a newly added status record area downward along the device status record area at the end position in the historical status credential layer; The server triggers the lighting unit pre-installed on the patient end to illuminate the nerve acquisition device, and triggers the first acquisition unit pre-installed on the patient end to acquire images of the nerve acquisition device to obtain a newly added status credential image; The server fills the newly added status voucher image into the image voucher sub-area located in the newly added status record area, and obtains the text added by the patient end based on the text voucher sub-area to obtain the current status voucher layer corresponding to the patient end; The server obtains each first area frame line corresponding to all device status record areas including the current status credential image, each second area frame line corresponding to all newly added status record areas, and performs pixel configuration of the first pixel value on each first area frame line, and performs pixel configuration of the second pixel value on each second area frame line.

[0012] Optionally, in the method according to the present invention, in response to the completion of the device status detection by the patient end, the server obtains the neural data output by the patient end using the neural acquisition device, and transfers the neural data to the medical care end corresponding to the patient end, including: In response to the patient end completing the device status detection, the server triggers a second acquisition unit pre-set on the nerve acquisition device to acquire an image of the nerve acquisition device, and obtains a device front view corresponding to the nerve acquisition device; Performing image recognition on the front view of the device, and when the recognition result shows that the front view of the device includes the patient's face, determining the eyebrow center point corresponding to the eyebrow center of the face based on the patient's face; Determine, in the front view of the device, a vertical bisector that vertically divides the front view of the device, and obtain a leftward region and a rightward region located on both sides of the vertical bisector; Determine a horizontal distance value between the brow center point and a vertical bisector located in the front view of the device; When the eyebrow point is located in the left area and the horizontal distance value is greater than the preset distance value, the voice unit pre-set on the neural acquisition device is triggered to play the first voice content corresponding to the rightward direction; When the eyebrow center point is located in the rightward area and the horizontal distance value is greater than the preset distance value, the voice unit pre-set on the neural acquisition device is triggered to play the second voice content corresponding to the leftward direction; When the horizontal distance value is less than or equal to the preset distance value, the nerve acquisition device is triggered to collect data from the patient end to obtain nerve data; The neural data is encrypted based on a preset encryption strategy, and the decrypted neural data is transferred to a medical terminal corresponding to the patient terminal for viewing.

[0013] Optionally, in the method according to the present invention, encrypting the neural data based on a preset encryption strategy includes: The server obtains the terminal number corresponding to the patient terminal, and determines the flow sequence number corresponding to the patient terminal based on the flow sequence; Arrange the terminal number and the flow sequence number in a forward direction to obtain a data encryption sequence corresponding to the patient terminal; A neural data key corresponding to the data encryption sequence is generated based on a hash encryption algorithm.

[0014] Optionally, in the method according to the present invention, the method further comprises: Determine a medical terminal corresponding to the patient terminal as an access terminal for accessing the neural data; Sending a neural data key corresponding to the neural data to the access terminal, and generating a corresponding access verification link and a verification identity set; In response to the access terminal accessing data based on the corresponding access verification link, access verification is performed based on the corresponding neural data key and the verification identity set, and neural data corresponding to the neural data key is fed back to the access terminal if the verification passes.

[0015] According to another aspect of the present invention, a neural system data monitoring system based on multi-terminal interconnection is provided, comprising: A distribution module is configured to distribute the neural acquisition request uploaded by the distribution terminal, and obtain each patient terminal that uploads each registration information in response to the neural acquisition request; The transfer module is configured to perform a transfer sequence on each patient terminal based on each registration information uploaded by each patient terminal, and control the distribution terminal to perform device transfer on the neural acquisition device based on the obtained transfer sequence; A detection module is configured to, in response to any patient end receiving the neural acquisition device, add a preset device state detection strategy to the patient end, so that the patient end performs device state detection on the neural acquisition device based on the preset device state detection strategy; The transfer module is configured to obtain the neural data output by the patient end using the neural acquisition device in response to the patient end completing the device status detection, and transfer the neural data to the medical care end corresponding to the patient end.

[0016] According to the solution of the present invention, the server can distribute the neural acquisition request uploaded by the distribution end, and then obtain each patient end that uploads each registration information, and sort the flow of each patient end according to the time sequence of the patient end information upload time, so that the patient end with an earlier registration time can enjoy the priority of using the neural acquisition equipment, and the server can also allow the patient end to adjust the corresponding monitoring period according to its own needs according to the time sequence of each information upload time, which has a certain flexibility. The present invention can also group each patient end according to its terminal position, and obtain a second flow sequence according to the order of the distance of each terminal, which can reduce a certain equipment flow path, thereby saving the corresponding flow time. Then the server can make any patient end detect the equipment status after receiving the neural acquisition equipment, and record and update the damage when the neural acquisition equipment is damaged, so that the server can subsequently ask the patient who used the neural acquisition equipment for relevant compensation based on the relevant records provided by the patient end. When the patient completes the detection of the device status, the server will start to obtain the neural data output by the patient using the neural acquisition device, and transfer the neural data to the medical care end corresponding to the patient, so that the medical care end can perform a one-to-one diagnosis on the patient. The present invention can facilitate patients to perform neural acquisition at home, protect the privacy of patients' neural data, and improve the corresponding diagnostic efficiency of doctors. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A flowchart of a method for monitoring nervous system data based on multi-terminal interconnection according to an embodiment of the present invention is shown; Figure 2 A schematic diagram of a historical status credential layer according to an embodiment of the present invention is shown; Figure 3 A schematic diagram showing a newly added status recording area according to an embodiment of the present invention is shown; Figure 4 A structural block diagram of a nervous system data monitoring system based on multi-terminal interconnection according to another embodiment of the present invention is shown. DETAILED DESCRIPTION

[0018] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0019] In daily life, patients with brain damage often find it inconvenient to go to the hospital for relevant nerve collection and other examinations, and need to borrow nerve collection equipment to complete the corresponding examinations at home.

[0020] The inventors found in their research that when multiple patients need to borrow nerve acquisition equipment at the same time, there may be conflicts in the borrowing time or it may be difficult to coordinate when a patient wants to change the borrowing time, which may cause the patient's nerve acquisition time to be delayed, resulting in the doctor's inability to make a timely diagnosis of the patient's condition.

[0021] To solve the problems existing in the above-mentioned prior art, the inventor proposes the solution of the present invention. One embodiment of the present invention provides a method for monitoring nervous system data based on multi-terminal interconnection, which can be executed in a computing device, wherein the computing device can be understood as a terminal with data processing function, such as a mobile phone or a computer.

[0022] Figure 1 FIG. 4 is a flowchart of a method for monitoring nervous system data based on multi-terminal interconnection according to an embodiment of the present invention. Figure 1 As shown, the method starts at step S102, and in step S102, includes the following contents: The server distributes the neural acquisition request uploaded by the distribution end, and obtains each patient end that uploads each registration information in response to the neural acquisition request.

[0023] For example, in this embodiment, the distribution end can be understood as a terminal for medical staff to distribute nerve acquisition equipment. The distribution end can also collect relevant information of patients who need nerve acquisition online, that is, upload nerve acquisition requests. When some patients with brain damage are inconvenient to go to the hospital, but need to perform nerve acquisition on the head so that doctors can make corresponding diagnoses, they can fill in the registration information according to the nerve acquisition request uploaded by the distribution end online. At this time, the server will obtain each patient end that uploaded the registration information, so as to facilitate the subsequent distribution of nerve acquisition equipment to each patient end.

[0024] In step S104, the following contents are included: The server performs a flow sorting on each patient terminal based on each registration information uploaded by each patient terminal, and controls the distribution terminal to perform device flow on the neural acquisition device based on the obtained flow sequence.

[0025] For example, in this embodiment, since there may be multiple patients who need to perform neural acquisition, the server will sort each patient according to the registration information uploaded by each patient, that is, perform flow sorting, thereby obtaining a flow sequence, and control the distribution end to transfer the neural acquisition equipment to each patient in turn according to the flow sequence, so that each patient can perform neural acquisition in an orderly manner.

[0026] Furthermore, the above-mentioned “the server performs a flow sorting on each patient terminal based on each registration information uploaded by each patient terminal, and controls the distribution terminal to perform device flow on the neural acquisition device based on the obtained flow sequence” also includes the following steps: The server obtains the upload time of each piece of information corresponding to each piece of registration information, and sorts the flow of each patient terminal based on the time sequence of the upload time of each piece of information to obtain a first flow sequence; The server obtains the number of terminals of each patient terminal in the first circulation sequence, and divides the terminal terminal into time periods having the same number as the number of terminals based on the retrieved monitoring date to obtain each monitoring time period; The server sorts each monitoring period based on the chronological order to obtain a period sequence corresponding to the monitoring date; The server configures each patient terminal in the first flow sequence and each monitoring period in the time period sequence one by one, and controls the distribution terminal to perform device flow on the neural acquisition device within the corresponding monitoring period based on the first flow sequence.

[0027] For example, in this embodiment, the monitoring date can be understood as the date on which the neural acquisition equipment is circulated to the patient end. Since the neural acquisition equipment may be circulated to multiple patient ends on the same monitoring date, the server will obtain the specific time when each patient end uploads the registration information, that is, the time of uploading each information, and sort the flow of each patient end according to the chronological order of the time of uploading each information, thereby obtaining the first flow sequence.

[0028] Then, the server will determine the number of terminals of each patient in the first flow sequence, and divide the monitoring date into the same number of time periods according to the number of terminals, so as to obtain each monitoring period. For example, if the number of terminals is 4 and the monitoring date is 8 hours, the server will divide the monitoring date into 4 segments, that is, 4 monitoring periods are obtained, and the duration of each monitoring period is 2 hours. And the server will sort the monitoring periods according to the time sequence of the monitoring periods, so as to obtain the time period sequence of the corresponding monitoring date.

[0029] Next, the server will configure each patient terminal in the first flow sequence with each monitoring time period in the time period sequence one by one, that is, the monitoring time period corresponding to the patient with an earlier registration time can also be earlier. Then, the server will control the distribution terminal to circulate the nerve acquisition equipment in the corresponding monitoring time period according to the first flow sequence. For example, if the first monitoring time period is from 8:00 to 10:00, the distribution terminal will distribute the nerve acquisition equipment to the patient terminal located at the first position in the first flow sequence at 8:00, and will transfer the nerve acquisition equipment to the patient terminal located at the second position in the first flow sequence at 10:00, and then continue to circulate the nerve acquisition equipment in the corresponding monitoring time period according to the first flow sequence, and after the nerve acquisition is completed at the patient terminal located at the last position in the first flow sequence, the distribution terminal will take the nerve acquisition equipment back to the hospital.

[0030] This embodiment can sort the flow of each patient terminal according to the chronological order of the time when each information is uploaded, so that the patient terminal with an earlier registration time can enjoy a higher priority in using the neural acquisition device.

[0031] Furthermore, the above-mentioned "the server configures each patient terminal in the first flow sequence with each monitoring time period in the time period sequence one by one" also includes the following steps: The server obtains the terminal numbers corresponding to the patient terminals, and establishes a time period adjustment layer based on the terminal numbers, wherein the time period adjustment layer includes vertically arranged activity slots corresponding to the terminal numbers; The server fills each monitoring period into each corresponding active slot, and sends the period adjustment layer to each patient terminal in the first flow sequence in sequence; In response to a first selection interaction performed by any patient end on an activity slot corresponding to itself in the time period adjustment layer and a second selection interaction performed with other activity slots, the server swaps the positions of the two to complete the update of the activity slot corresponding to the patient end, and locks the updated activity slot corresponding to the patient end.

[0032] For example, in this embodiment, since the time when the patient end can perform nerve acquisition may conflict with the monitoring time period configured by the first flow sequence obtained by the server according to the time sequence of each information upload time, the server will first obtain the terminal number of each patient end, and establish a time period adjustment layer according to each terminal number. The time period adjustment layer includes each activity slot corresponding to each terminal number arranged vertically. The server will fill each monitoring time period into the corresponding activity slot, such as Figure 2 shown.

[0033] The server will then send the time period adjustment layer in sequence to each patient terminal in the first flow sequence. After receiving the time period adjustment layer, each patient terminal can select the monitoring time period according to its needs. The patient terminal needs to first click on the active slot corresponding to it in the time period adjustment layer, that is, perform the first selection interaction, and then click on the active slot corresponding to the monitoring time period in which neural acquisition is desired, that is, perform the second selection interaction. At this time, the server will swap the positions of the two active slots to complete the update of the active slot corresponding to the patient terminal, and will lock the updated active slot corresponding to the patient terminal to prevent the active slot from being selected by other patient terminals again.

[0034] This embodiment can enable the patient to adjust the corresponding monitoring period according to their own needs according to the chronological order of the uploading time of each information, and has a certain degree of flexibility.

[0035] Furthermore, the above-mentioned "the server performs a flow sorting on each patient terminal based on each registration information uploaded by each patient terminal, and controls the distribution terminal to perform device flow on the neural acquisition device based on the obtained flow sequence" also includes the following steps: The server obtains the first GPS information corresponding to the distribution terminal and each second GPS information corresponding to each patient terminal, and determines the position relationship of each terminal between each second GPS information and the first GPS information; The server establishes a plane coordinate system with the distribution terminal as the origin based on the first GPS information, and determines each patient coordinate point corresponding to each patient terminal in the plane coordinate system based on the position relationship of each terminal; The server performs quadrant grouping of each patient coordinate point based on the plane coordinate system to obtain a first area group including all patient coordinate points located in a first quadrant, a second area group including all patient coordinate points located in a second quadrant, a third area group including all patient coordinate points located in a third quadrant, and a fourth area group including all patient coordinate points located in a fourth quadrant; The server determines the distances between each patient terminal and the distribution terminal based on the positional relationships between each patient terminal and the distribution terminal in the same regional group, and sorts the patient terminals according to the order of distance, to obtain second flow sequences corresponding to each regional group. The server performs distribution configuration of different distribution terminals for each regional group, and controls each distribution terminal to perform device flow on the neural acquisition device based on the respectively corresponding second flow sequence.

[0036] For example, in this embodiment, the server will obtain the GPS information of the distribution end, that is, the first GPS information, and obtain the GPS information of each patient end, that is, each second GPS information, and determine the terminal location relationship between each second GPS information and the first GPS information.

[0037] The server will establish a plane coordinate system with the distribution terminal as the origin according to the first GPS information, and determine the patient coordinate points corresponding to the patient terminals in the plane coordinate system according to the position relationship of each terminal. Then, the server will group the patient coordinate points by quadrant according to the quadrant position of each patient coordinate point in the plane coordinate system, thereby obtaining a first area group including all patient coordinate points in the first quadrant, a second area group including all patient coordinate points in the second quadrant, a third area group including all patient coordinate points in the third quadrant, and a fourth area group including all patient coordinate points in the fourth quadrant.

[0038] Then, the server will determine the distances between each patient terminal and the distribution terminal according to the positional relationships between each terminal in the same regional group, and sort the patient terminals based on the order of distance, thereby obtaining the second flow sequences corresponding to each regional group, that is, obtaining four second flow sequences. Next, the server will configure the distribution of different distribution terminals for the four regional groups, and control each distribution terminal to perform device flow on the neural acquisition device according to the corresponding second flow sequences.

[0039] This embodiment can group the terminals of each patient according to their positions, and obtain a second flow sequence according to the order of the distances of the terminals, which can reduce a certain equipment flow path and thus save corresponding flow time.

[0040] For example, in this embodiment, in the process of performing corresponding device circulation according to the above-mentioned second circulation sequence, since the device circulation is based on near to far, the corresponding neural acquisition device will gradually move away from the distribution end during the device circulation process. This situation makes it inconvenient to return the neural acquisition device after completing the neural acquisition of all patients. Therefore, the best solution is to ensure that the neural acquisition device can be located at a position closer to the distribution end after completing the device circulation of all patients. Based on this, this embodiment can also include the following steps: The server determines the distances between each patient terminal and the distribution terminal based on the positional relationships between each patient terminal and the distribution terminal in the same regional group, and sorts the patient terminals according to the order of distance, to obtain second flow sequences corresponding to each regional group, including: Acquire two patient terminals located in the same area group and close to the distribution terminal, and use them as the starting node and the ending node respectively; Determine a patient end that is closest to the starting node, except for the end node, as the next flow node of the starting node, and re-determine the flow node as the starting node; Determine a patient terminal that is closest to the end node, except for the start node, as the previous flow node of the end node, and re-determine the flow node as the end node; Repeat the above steps of determining the starting node and the ending node until all the nodes of the patient terminals are determined; All the patient ends that are sequentially determined as starting nodes are arranged forwardly based on the node determination order to obtain a first subsequence; Reversely arrange all the patient ends that are sequentially determined as end nodes based on the node determination order to obtain a second subsequence; Sequence combination is performed based on the first subsequence and the second subsequence to obtain second flow sequences corresponding to each area group.

[0041] For example, in this embodiment, in order to ensure that the corresponding neural acquisition device can be located at a position closer to the distribution end after the device transfer is completed, two patient ends located in the same area group and closer to the segment end can be obtained and determined as the corresponding starting node and ending node respectively. Here, the starting node can be understood as the patient end corresponding to the first device transfer, and the ending node can be understood as the patient end corresponding to the last device transfer; after completing the determination of the first starting node and the ending node, it can be repeated again, that is, the patient end that is closest to the starting node and other than the end node is determined as the next transfer node, and the transfer node is determined as the new starting node. At the same time, it is also necessary to determine the patient end that is closest to the end node and other than the end node. The patient end other than the starting node is determined as the next flow node, and the flow node is determined as the new end node. After completing the node determination for all patient ends, the corresponding first subsequence can be determined based on all patient ends determined as the starting node, and at the same time, the corresponding second subsequence can be determined based on all patient ends determined as the end node. By combining the first subsequence with the second subsequence, the corresponding second flow sequence can be obtained. Here, since the patient end that performs the equipment transfer for the first time and the patient end that performs the transfer for the last time in the second flow sequence are both located at a relatively close distance to the distribution end, after completing the process of all corresponding equipment transfers, it can be ensured that the neural acquisition device is located near the distribution end, thereby facilitating the return of the neural acquisition device.

[0042] In step S106, the following contents are included: In response to any patient end receiving the neural acquisition device, the server adds a preset device status detection strategy to the patient end, so that the patient end performs device status detection on the neural acquisition device based on the preset device status detection strategy.

[0043] For example, in this embodiment, since the neural acquisition device may be scratched or otherwise damaged during use, in order to clarify which patient end damaged the neural acquisition device during use after the neural acquisition device is transferred, the server will add a preset device status detection strategy to any patient end after receiving the neural acquisition device, so that the current patient end can perform device status detection on the neural acquisition device based on the preset device status detection strategy.

[0044] In this embodiment, after receiving the neural acquisition device, each patient will first check the status of the device, and record and update the damage if the neural acquisition device is damaged, so that the server can subsequently request relevant compensation from the last patient who used the neural acquisition device based on the relevant records provided by the patient.

[0045] Furthermore, the above-mentioned “the server responds to any patient end receiving the neural acquisition device, adding a preset device state detection strategy to the patient end, so that the patient end performs device state detection on the neural acquisition device based on the preset device state detection strategy” also includes the following steps: Retrieving a historical status voucher layer and sending it to the patient end, wherein the historical status voucher layer includes vertically arranged device status record areas, each device status record area includes a horizontally arranged image voucher sub-area and a text description sub-area, and the image voucher sub-area and the text description sub-area are filled with a historical status voucher image and a historical status description text respectively; In response to the patient terminal selecting and interacting with any device status recording area for a preset time, the server triggers a lighting unit pre-installed on the patient terminal to illuminate the neural acquisition device, and triggers a first acquisition unit pre-installed on the patient terminal to acquire an image of the neural acquisition device, thereby obtaining a current status credential image; The server replaces the current status credential image with the historical status credential image located in the image credential sub-area, and obtains the text update made by the patient end based on the historical status description text located in the text description sub-area to obtain the current status credential layer corresponding to the patient end.

[0046] For example, in this embodiment, since the neural acquisition device may have scratches or other damage before the device is circulated, the distribution end will record the current status of the neural acquisition device before the device is circulated, that is, generate a historical status voucher layer. Then, when the neural acquisition device begins to circulate, each patient end needs to detect the device status after receiving the neural acquisition device. When it is found that the device status does not match the device status displayed in the historical status voucher layer, it needs to be updated accordingly.

[0047] When any patient receives a neural acquisition device, the server will first retrieve the historical status voucher layer and send it to the patient. The historical status voucher layer includes vertically arranged device status record areas, and each device status record area includes horizontally arranged image voucher sub-areas and text description sub-areas. The image voucher sub-areas and text description sub-areas are used to fill in the historical status voucher image and historical status description text, respectively. Figure 3 shown.

[0048] After receiving the historical status voucher layer, the patient can compare the device status of the current neural acquisition device. When it is found that the device status is more damaged than the damage shown in the historical status voucher layer, the patient can click on the corresponding device status record area and select the preset time for interaction. At this time, the server will trigger the lighting unit pre-installed on the patient to illuminate the neural acquisition device, so that the image can be clearer during subsequent image acquisition. Then trigger the first acquisition unit pre-installed on the patient to acquire images of the neural acquisition device, thereby obtaining the current status voucher image. And the patient can describe the current device status in the text description sub-area.

[0049] Then, the server will replace the current status voucher image with the historical status voucher image in the image voucher sub-area, and update the text based on the historical status description text in the text description sub-area of ​​the patient end, so as to obtain the current status voucher layer corresponding to the patient end. Then, the server will send the current status voucher layer to the next patient end along with the device flow, that is, for the next patient end, the current status voucher layer is the historical status voucher layer.

[0050] Furthermore, the above method further comprises the following steps: The server, in response to the credential addition request sent by the patient terminal based on the historical status credential layer, establishes a newly added status record area downward along the device status record area at the end position in the historical status credential layer; The server triggers the lighting unit pre-installed on the patient end to illuminate the nerve acquisition device, and triggers the first acquisition unit pre-installed on the patient end to acquire images of the nerve acquisition device to obtain a newly added status credential image; The server fills the newly added status voucher image into the image voucher sub-area located in the newly added status record area, and obtains the text added by the patient end based on the text voucher sub-area to obtain the current status voucher layer corresponding to the patient end; The server obtains each first area frame line corresponding to all device status record areas including the current status credential image, each second area frame line corresponding to all newly added status record areas, and performs pixel configuration of the first pixel value on each first area frame line, and performs pixel configuration of the second pixel value on each second area frame line.

[0051] For example, in this embodiment, when the patient side detects the device status and finds that there is new damage compared to the historical status credential layer, a credential addition request can be sent based on the historical status credential layer. After receiving the credential addition request, the server will establish a new status record area along the device status record area at the end position in the historical status credential layer, so that the patient side can make corresponding device status records in the new status record area.

[0052] The server will trigger the lighting unit pre-installed on the patient end to illuminate the neural acquisition device, and trigger the first acquisition unit pre-installed on the patient end to acquire images of the neural acquisition device, thereby obtaining a newly added state credential image. Then, the server will fill the newly added state credential image into the image credential sub-area of ​​the newly added state record area, and obtain the text added by the patient end in the text credential sub-area, thereby obtaining the current state credential layer corresponding to the patient end.

[0053] Next, the server will obtain the regional frame lines of the device status recording area of ​​all current status credential images, that is, the first regional frame lines, and the regional frame lines of all newly added status recording areas, that is, the second regional frame lines, and perform pixel configuration of the first pixel value on each first regional frame line, for example, the first pixel value can be configured as blue, and perform pixel configuration of the second pixel value on each second regional frame line, for example, the second pixel value can be configured as red.

[0054] This embodiment can configure its regional frame lines to different pixel values ​​according to different status update situations, which can improve a certain degree of identification, so as to quickly determine the specific situation of the status update of each patient end, and facilitate the subsequent request for corresponding compensation from the patient end according to the damage of the neural acquisition equipment.

[0055] In step S108, the following contents are included: In response to the patient end completing the device status detection, the server obtains the neural data output by the patient end using the neural acquisition device, and transfers the neural data to the medical care end corresponding to the patient end.

[0056] For example, in this embodiment, when the patient side completes the detection of the device status, the server will begin to obtain the neural data output by the patient side using the neural acquisition device, and transfer the neural data to the medical side corresponding to the patient side, so that the medical side can perform a one-to-one diagnosis on the patient side.

[0057] Furthermore, the above-mentioned "the server, in response to the patient end completing the device status detection, obtains the neural data output by the patient end using the neural acquisition device, and transfers the neural data to the medical care end corresponding to the patient end", also includes the following steps: In response to the patient end completing the device status detection, the server triggers a second acquisition unit pre-set on the nerve acquisition device to acquire an image of the nerve acquisition device, and obtains a device front view corresponding to the nerve acquisition device; Performing image recognition on the front view of the device, and when the recognition result shows that the front view of the device includes the patient's face, determining the eyebrow center point corresponding to the eyebrow center of the face based on the patient's face; Determine, in the front view of the device, a vertical bisector that vertically divides the front view of the device, and obtain a leftward region and a rightward region located on both sides of the vertical bisector; Determine a horizontal distance value between the brow center point and a vertical bisector located in the front view of the device; When the eyebrow point is located in the left area and the horizontal distance value is greater than the preset distance value, the voice unit pre-set on the neural acquisition device is triggered to play the first voice content corresponding to the rightward direction; When the eyebrow center point is located in the rightward area and the horizontal distance value is greater than the preset distance value, the voice unit pre-set on the neural acquisition device is triggered to play the second voice content corresponding to the leftward direction; When the horizontal distance value is less than or equal to the preset distance value, the nerve acquisition device is triggered to collect data from the patient end to obtain nerve data; The neural data is encrypted based on a preset encryption strategy, and the decrypted neural data is transferred to a medical terminal corresponding to the patient terminal for viewing.

[0058] For example, in this embodiment, after the patient completes the detection of the device status, the server will trigger the second acquisition unit pre-set on the neural acquisition device to perform image acquisition on the neural acquisition device, thereby obtaining a device front view corresponding to the neural acquisition device, and performing image recognition on the device front view. When the recognition result is that the device front view includes the patient's face, it means that the current patient has worn the neural acquisition device. At this time, in order to ensure the accuracy of neural acquisition, the server will determine whether the current patient has worn the neural acquisition device correctly.

[0059] First, the server determines the position of the center of the eyebrows on the patient's face, i.e., the center of the eyebrows point, and then determines a vertical bisector in the front view of the device that can vertically divide the front view of the device, and obtains the left area and the right area on both sides of the vertical bisector. Then, the server will determine the horizontal distance value between the eyebrow point and the vertical bisector in the front view of the device. When the eyebrow point is located in the left area and the horizontal distance value is greater than the preset distance value, it means that the patient needs to turn the worn neural acquisition device to the right so that the neural acquisition device is worn in the center. Therefore, the server will trigger the voice unit pre-set on the neural acquisition device to play the first voice content corresponding to the right turn; when the eyebrow point is located in the right area and the horizontal distance value is greater than the preset distance value, it means that the patient needs to turn the worn neural acquisition device to the left so that the neural acquisition device is worn in the center. Therefore, the server will trigger the voice unit pre-set on the neural acquisition device to play the second voice content corresponding to the left turn; when the horizontal distance value is less than or equal to the preset distance value, it means that the patient has worn the neural acquisition device in the center, so the server will trigger the neural acquisition device to collect data from the patient and obtain neural data.

[0060] The server will then encrypt the neural data based on a preset encryption strategy, and transfer the decrypted neural data to the medical staff corresponding to the patient for review and corresponding one-to-one diagnosis.

[0061] From the above content, it can be known that when the patient wears the neural acquisition device, it will be judged based on the corresponding device front view whether the patient wears the corresponding neural acquisition device in the center, so as to guide the patient to make corresponding adjustments when it is not worn in the center, and then guide the patient to complete the corresponding data collection process, that is, to make the corresponding horizontal translation adjustment; in another case, if the patient has completed the corresponding centering, but the corresponding device is still offset in the vertical direction, it may also affect the subsequent data collection process. Therefore, after completing the horizontal translation adjustment, the vertical translation adjustment is also required: It should be noted that in order to complete the translation adjustment based on the vertical direction, the corresponding positioning mark can be set on the neural acquisition device in advance, wherein the positioning mark is located on the vertical center line of the neural acquisition device, and the positioning label can be a color block with a regular shape, such as a yellow square, a red circle or a blue rectangle.

[0062] For example, in this embodiment, the corresponding translation adjustment based on the vertical direction can be performed by the following method steps: In response to the horizontal distance value being less than or equal to a preset distance value, a neural acquisition device located in the front view of the device is identified, and a vertical center line corresponding to the neural acquisition device is acquired; Determine the upper endpoint located on the vertical center line, and obtain the mark center point corresponding to the positioning mark; Acquire a marking line segment consisting of the upper endpoint and the marking center point on the vertical center line, and determine a marking distance corresponding to the marking line segment; When the marked distance is greater than the retrieved preset vertical distance and the distance difference between the two is within a first preset threshold interval, triggering a voice unit pre-set on the neural acquisition device to play a corresponding downwardly adjusted third voice content; When the marked distance is less than the retrieved preset vertical distance and the distance difference between the two is within the second preset threshold interval, triggering the voice unit pre-set on the neural acquisition device to play the corresponding fourth voice content adjusted upward; When the distance difference between the marked distance and the retrieved preset vertical distance is outside the first preset threshold interval and the second preset threshold interval, the neural acquisition device is triggered to collect data from the patient end to obtain neural data.

[0063] For example, in this embodiment, when performing the corresponding translation adjustment based on the vertical direction, it is necessary to make adjustments based on the relative position of the positioning mark located on the neural acquisition device in the front view of the device. Therefore, the mark distance between the positioning mark and the top edge of the neural acquisition device can be obtained based on the front view of the device. When the mark distance is greater than the preset vertical distance and the distance difference between the two is within the first preset threshold interval, it indicates that the corresponding neural acquisition device is worn in a downward position. At this time, it is necessary to play the corresponding third voice content to instruct the patient to make adjustments; similarly, when the mark distance is less than the preset vertical distance and the distance difference between the two is within the second preset threshold interval, it indicates that the corresponding neural acquisition device is worn in an upward position. At this time, it is necessary to play the corresponding fourth voice content to instruct the patient to make adjustments; thereby completing the translation adjustment of the neural acquisition device based on the vertical direction, and improving the corresponding data acquisition accuracy and user experience.

[0064] Furthermore, the above-mentioned “encrypting the neural data based on a preset encryption strategy” further includes the following steps: The server obtains the terminal number corresponding to the patient terminal, and determines the flow sequence number corresponding to the patient terminal based on the flow sequence; Arrange the terminal number and the flow sequence number in a forward direction to obtain a data encryption sequence corresponding to the patient terminal; A neural data key corresponding to the data encryption sequence is generated based on a hash encryption algorithm.

[0065] For example, in this embodiment, after obtaining the neural data of the patient, the server will obtain the terminal number of the patient, and determine the flow sequence number corresponding to the patient according to the flow sequence. Then, the server will arrange the terminal number and the flow sequence number in a forward direction to obtain the data encryption sequence corresponding to the patient. For example, when the terminal number is 1-B and the corresponding flow sequence number is 5, the data encryption sequence obtained based on the forward arrangement is 1-B5; after obtaining the corresponding data encryption sequence, the neural data key corresponding to the data encryption sequence can be generated based on the hash encryption algorithm.

[0066] It should be noted that the hash encryption algorithm is a relatively common encryption algorithm in the prior art, so this embodiment does not make a specific introduction to it; in this embodiment, since the terminal number is generated corresponding to each patient end, each terminal number is unique, and the corresponding flow serial number is also generated corresponding to each patient end, therefore, each flow needs to be unique, which further improves the corresponding encryption security and information security, prevents criminals from intercepting and viewing neural data, and prevents information leakage.

[0067] Furthermore, the above method further comprises the following steps: Determine a medical terminal corresponding to the patient terminal as an access terminal for accessing the neural data; Sending a neural data key corresponding to the neural data to the access terminal, and generating a corresponding access verification link and a verification identity set; In response to the access terminal accessing data based on the corresponding access verification link, access verification is performed based on the corresponding neural data key and the verification identity set, and neural data corresponding to the neural data key is fed back to the access terminal if the verification passes.

[0068] For example, in this embodiment, the server will determine the medical end corresponding to the patient end as the access terminal for accessing the neural data, and send the neural data key of the corresponding neural data to the access terminal, thereby generating a corresponding access verification link and verification identity set.

[0069] When the access terminal accesses data based on the corresponding access verification link, the server will perform access verification based on the corresponding neural data key and verification identity set, and if the verification passes, it will feedback the neural data corresponding to the neural data key to the access terminal.

[0070] According to the solution of the present invention, the server can distribute the neural acquisition request uploaded by the distribution end, and then obtain each patient end that uploads each registration information, and sort the flow of each patient end according to the time sequence of the patient end information upload time, so that the patient end with an earlier registration time can enjoy the priority of using the neural acquisition equipment, and the server can also allow the patient end to adjust the corresponding monitoring period according to its own needs according to the time sequence of each information upload time, which has a certain flexibility. The present invention can also group each patient end according to its terminal position, and obtain a second flow sequence according to the order of the distance of each terminal, which can reduce a certain equipment flow path, thereby saving the corresponding flow time. Then the server can make any patient end detect the equipment status after receiving the neural acquisition equipment, and record and update the damage when the neural acquisition equipment is damaged, so that the server can subsequently ask the patient who used the neural acquisition equipment for relevant compensation based on the relevant records provided by the patient end. When the patient completes the detection of the device status, the server will start to obtain the neural data output by the patient using the neural acquisition device, and transfer the neural data to the medical care end corresponding to the patient, so that the medical care end can perform a one-to-one diagnosis on the patient. The present invention can facilitate patients to perform neural acquisition at home, protect the privacy of patients' neural data, and improve the corresponding diagnostic efficiency of doctors.

[0071] Another embodiment of the present invention provides a neural system data monitoring system based on multi-terminal interconnection, Figure 4 As the corresponding system block diagram, the system includes: A distribution module is configured to distribute the neural acquisition request uploaded by the distribution terminal, and obtain each patient terminal that uploads each registration information in response to the neural acquisition request; The transfer module is configured to perform a transfer sequence on each patient terminal based on each registration information uploaded by each patient terminal, and control the distribution terminal to perform device transfer on the neural acquisition device based on the obtained transfer sequence; A detection module is configured to, in response to any patient end receiving the neural acquisition device, add a preset device state detection strategy to the patient end, so that the patient end performs device state detection on the neural acquisition device based on the preset device state detection strategy; The transfer module is configured to obtain the neural data output by the patient end using the neural acquisition device in response to the patient end completing the device status detection, and transfer the neural data to the medical care end corresponding to the patient end.

[0072] In the description provided herein, algorithms and displays are not inherently related to any particular computer, virtual system or other device. Various general purpose systems can also be used together with the examples of the present invention. According to the above description, it is obvious that the structure required for constructing such systems. In addition, the present invention is not directed to any specific programming language either. It should be understood that various programming languages ​​can be utilized to implement the content of the present invention described herein, and the above description of specific languages ​​is for the purpose of disclosing the preferred embodiment of the present invention.

[0073] In the description provided herein, a large number of specific details are described. However, it is understood that embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures and techniques are not shown in detail so as not to obscure the understanding of this description.

[0074] Similarly, it should be understood that in order to streamline the present disclosure and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the present invention, various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof.

[0075] Those skilled in the art will appreciate that the modules or units or components of the devices in the examples disclosed herein may be arranged in the devices described in the embodiment, or alternatively may be located in one or more devices different from the devices in the examples. The modules in the foregoing examples may be combined into one module or may be divided into multiple submodules.

[0076] Those skilled in the art will appreciate that the modules in the devices in the embodiments may be adaptively changed and arranged in one or more devices different from the embodiments. The modules or units or components in the embodiments may be combined into one module or unit or component, and furthermore may be divided into a plurality of submodules or subunits or subcomponents.

[0077] Furthermore, those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is meant to be within the scope of the present invention and to form different embodiments.

[0078] In addition, some of the embodiments are described herein as methods or combinations of method elements that can be implemented by a processor of a computer system or by other devices that perform the functions. Therefore, a processor with necessary instructions for implementing the method or method elements forms a device for implementing the method or method elements. In addition, the elements described herein of the device embodiments are examples of devices for implementing the functions performed by the elements for the purpose of implementing the invention.

[0079] As used herein, unless otherwise specified, the use of ordinal numbers "first," "second," "third," etc. to describe common objects merely indicates that different instances of similar objects are involved, and is not intended to imply that the objects so described must have a given order in time, space, order, or in any other manner.

[0080] Although the present invention has been described according to a limited number of embodiments, it will be apparent to those skilled in the art, with the benefit of the above description, that other embodiments may be envisioned within the scope of the invention thus described. In addition, it should be noted that the language used in this specification is primarily selected for readability and instructional purposes, rather than for explaining or limiting the subject matter of the present invention.

Claims

1. A method for monitoring nervous system data based on multi-terminal interconnection, characterized in that: The following steps are involved: The server distributes the nerve collection request uploaded by the distribution terminal, and obtains each patient terminal that uploads each registration information in response to the nerve collection request; The server performs a flow order on each patient terminal based on each registration information uploaded by each patient terminal, and controls the distribution terminal to perform device flow on the neural acquisition device based on the obtained flow sequence; In response to any patient end receiving the neural acquisition device, the server adds a preset device state detection strategy to the patient end, so that the patient end performs device state detection on the neural acquisition device based on the preset device state detection strategy; In response to the patient end completing the device status detection, the server obtains the neural data output by the patient end using the neural acquisition device, and transfers the neural data to the medical care end corresponding to the patient end.

2. The multi-terminal interconnected nervous system data monitoring method according to claim 1, characterized in that: The server performs a flow sorting on each patient terminal based on each registration information uploaded by each patient terminal, and controls the distribution terminal to perform device flow on the neural acquisition device based on the obtained flow sequence, including: The server obtains the upload time of each piece of information corresponding to each piece of registration information, and sorts the flow of each patient terminal based on the time sequence of the upload time of each piece of information to obtain a first flow sequence; The server obtains the number of terminals of each patient terminal in the first circulation sequence, and divides the terminal terminal into time periods having the same number as the number of terminals based on the retrieved monitoring date to obtain each monitoring time period; The server sorts each monitoring period based on the chronological order to obtain a period sequence corresponding to the monitoring date; The server configures each patient terminal in the first flow sequence and each monitoring period in the time period sequence one by one, and controls the distribution terminal to perform device flow on the neural acquisition device within the corresponding monitoring period based on the first flow sequence.

3. The multi-terminal interconnected nervous system data monitoring method according to claim 2 is characterized in that: The server configures each patient terminal in the first flow sequence with each monitoring time period in the time period sequence one by one, and then further includes: The server obtains the terminal numbers corresponding to the patient terminals, and establishes a time period adjustment layer based on the terminal numbers, wherein the time period adjustment layer includes vertically arranged activity slots corresponding to the terminal numbers; The server fills each monitoring period into each corresponding active slot, and sends the period adjustment layer to each patient terminal in the first flow sequence in sequence; In response to a first selection interaction performed by any patient end on an activity slot corresponding to itself in the time period adjustment layer and a second selection interaction performed with other activity slots, the server swaps the positions of the two to complete the update of the activity slot corresponding to the patient end, and locks the updated activity slot corresponding to the patient end.

4. The multi-terminal interconnected nervous system data monitoring method according to claim 1, characterized in that: The server performs a flow sorting on each patient terminal based on each registration information uploaded by each patient terminal, and controls the distribution terminal to perform device flow on the neural acquisition device based on the obtained flow sequence, including: The server obtains the first GPS information corresponding to the distribution terminal and each second GPS information corresponding to each patient terminal, and determines the position relationship of each terminal between each second GPS information and the first GPS information; The server establishes a plane coordinate system with the distribution terminal as the origin based on the first GPS information, and determines each patient coordinate point corresponding to each patient terminal in the plane coordinate system based on the position relationship of each terminal; The server performs quadrant grouping of each patient coordinate point based on the plane coordinate system to obtain a first area group including all patient coordinate points located in a first quadrant, a second area group including all patient coordinate points located in a second quadrant, a third area group including all patient coordinate points located in a third quadrant, and a fourth area group including all patient coordinate points located in a fourth quadrant; The server determines the distances between each patient terminal and the distribution terminal based on the positional relationships between each patient terminal and the distribution terminal in the same regional group, and sorts the patient terminals according to the order of distance, to obtain second flow sequences corresponding to each regional group. The server performs distribution configuration of different distribution terminals for each regional group, and controls each distribution terminal to perform device flow on the neural acquisition device based on the respectively corresponding second flow sequence.

5. The multi-terminal interconnected nervous system data monitoring method according to claim 4, characterized in that: The server determines the distances between each patient terminal and the distribution terminal based on the positional relationships between each patient terminal and the distribution terminal in the same regional group, and sorts the patient terminals according to the order of distance, to obtain second flow sequences corresponding to each regional group, including: Acquire two patient terminals located in the same area group and close to the distribution terminal, and use them as the starting node and the ending node respectively; Determine a patient end that is closest to the starting node, except for the end node, as the next flow node of the starting node, and re-determine the flow node as the starting node; Determine a patient terminal that is closest to the end node, except for the start node, as the previous flow node of the end node, and re-determine the flow node as the end node; Repeat the above steps of determining the starting node and the ending node until all the nodes of the patient terminals are determined; All the patient ends that are sequentially determined as starting nodes are arranged forwardly based on the node determination order to obtain a first subsequence; Reversely arrange all the patient ends that are sequentially determined as end nodes based on the node determination order to obtain a second subsequence; Sequence combination is performed based on the first subsequence and the second subsequence to obtain second flow sequences corresponding to each area group.

6. The multi-terminal interconnected nervous system data monitoring method according to claim 1, characterized in that: In response to any patient end receiving the neural acquisition device, the server adds a preset device state detection strategy to the patient end, so that the patient end performs device state detection on the neural acquisition device based on the preset device state detection strategy, including: Retrieving a historical status voucher layer and sending it to the patient end, wherein the historical status voucher layer includes vertically arranged device status record areas, each device status record area includes a horizontally arranged image voucher sub-area and a text description sub-area, and the image voucher sub-area and the text description sub-area are filled with a historical status voucher image and a historical status description text respectively; In response to the patient terminal selecting and interacting with any device status recording area for a preset time, the server triggers a lighting unit pre-installed on the patient terminal to illuminate the neural acquisition device, and triggers a first acquisition unit pre-installed on the patient terminal to acquire an image of the neural acquisition device, thereby obtaining a current status credential image; The server replaces the current status credential image with the historical status credential image located in the image credential sub-area, and obtains the text update made by the patient end based on the historical status description text located in the text description sub-area to obtain the current status credential layer corresponding to the patient end.

7. The multi-terminal interconnected nervous system data monitoring method according to claim 6, characterized in that: The method further comprises: The server, in response to the credential addition request sent by the patient terminal based on the historical status credential layer, establishes a newly added status record area downward along the device status record area at the end position in the historical status credential layer; The server triggers the lighting unit pre-installed on the patient end to illuminate the nerve acquisition device, and triggers the first acquisition unit pre-installed on the patient end to acquire images of the nerve acquisition device to obtain a newly added status credential image; The server fills the newly added status voucher image into the image voucher sub-area located in the newly added status record area, and obtains the text added by the patient end based on the text voucher sub-area to obtain the current status voucher layer corresponding to the patient end; The server obtains each first area frame line corresponding to all device status record areas including the current status credential image, each second area frame line corresponding to all newly added status record areas, and performs pixel configuration of the first pixel value on each first area frame line, and performs pixel configuration of the second pixel value on each second area frame line.

8. The multi-terminal interconnected nervous system data monitoring method according to claim 1, characterized in that: The server, in response to the patient end completing the device status detection, obtains the neural data output by the patient end using the neural acquisition device, and transmits the neural data to the medical care end corresponding to the patient end, including: In response to the patient end completing the device status detection, the server triggers a second acquisition unit pre-set on the nerve acquisition device to acquire an image of the nerve acquisition device, and obtains a device front view corresponding to the nerve acquisition device; Performing image recognition on the front view of the device, and when the recognition result shows that the front view of the device includes the patient's face, determining the eyebrow center point corresponding to the eyebrow center of the face based on the patient's face; Determine, in the front view of the device, a vertical bisector that vertically divides the front view of the device, and obtain a leftward region and a rightward region located on both sides of the vertical bisector; Determine a horizontal distance value between the brow center point and a vertical bisector located in the front view of the device; When the eyebrow point is located in the left area and the horizontal distance value is greater than the preset distance value, the voice unit pre-set on the neural acquisition device is triggered to play the first voice content corresponding to the rightward direction; When the eyebrow center point is located in the rightward area and the horizontal distance value is greater than the preset distance value, the voice unit pre-set on the neural acquisition device is triggered to play the second voice content corresponding to the leftward direction; When the horizontal distance value is less than or equal to the preset distance value, the nerve acquisition device is triggered to collect data from the patient end to obtain nerve data; The neural data is encrypted based on a preset encryption strategy, and the decrypted neural data is transferred to a medical terminal corresponding to the patient terminal for viewing.

9. The multi-terminal interconnected nervous system data monitoring method according to claim 8, characterized in that: Encrypting the neural data based on a preset encryption strategy includes: The server obtains the terminal number corresponding to the patient terminal, and determines the flow sequence number corresponding to the patient terminal based on the flow sequence; Arrange the terminal number and the flow sequence number in a forward direction to obtain a data encryption sequence corresponding to the patient terminal; A neural data key corresponding to the data encryption sequence is generated based on a hash encryption algorithm.

10. The multi-terminal interconnected nervous system data monitoring method according to claim 9, characterized in that: The method further comprises: Determine a medical terminal corresponding to the patient terminal as an access terminal for accessing the neural data; Sending a neural data key corresponding to the neural data to the access terminal, and generating a corresponding access verification link and a verification identity set; In response to the access terminal accessing data based on the corresponding access verification link, access verification is performed based on the corresponding neural data key and the verification identity set, and neural data corresponding to the neural data key is fed back to the access terminal if the verification passes.

11. A neural system data monitoring system based on multi-terminal interconnection, characterized in that: include: A distribution module is configured to distribute the neural acquisition request uploaded by the distribution terminal, and obtain each patient terminal that uploads each registration information in response to the neural acquisition request; The transfer module is configured to perform a transfer sequence on each patient terminal based on each registration information uploaded by each patient terminal, and control the distribution terminal to perform device transfer on the neural acquisition device based on the obtained transfer sequence; A detection module is configured to, in response to any patient end receiving the neural acquisition device, add a preset device state detection strategy to the patient end, so that the patient end performs device state detection on the neural acquisition device based on the preset device state detection strategy; The transfer module is configured to obtain the neural data output by the patient end using the neural acquisition device in response to the patient end completing the device status detection, and transfer the neural data to the medical care end corresponding to the patient end.

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