Intelligent closed-loop management method and system for whole diagnosis and treatment cycle

Abstract

The invention provides a diagnosis and treatment full-cycle intelligent closed-loop management method and system, and relates to a data processing technology and analysis of early-stage data uploaded by an electronic information system, the electronic information system can be a hospital information system, an electronic medical record system, a laboratory information system and the like, permission information is responded, and a state comparison structure is constructed; constructing a standard comparison structure based on the reference data, performing branch comparison on the state comparison structure and the standard comparison structure, and generating comparison information for early warning of high-risk medium and high risks; executing corresponding stage operation based on the progress stage of the comparison information, wherein the progress stage comprises a preoperative stage, a middle stage and a later stage of anesthesia induction; the method comprises the steps of performing visualization processing on perioperative period management and stage operation corresponding to the perioperative period management, generating a visual time sequence chain, receiving change information of an electronic information system user side on the visual time sequence chain, generating a visual feedback chain and performing medium-high risk early warning.

Description

Intelligent closed-loop management method and system for the entire diagnosis and treatment cycle Technical Field

[0001] This invention relates to data processing technology, and more particularly to a method and system for intelligent closed-loop management of the entire diagnosis and treatment cycle. Background Technology

[0002] Intelligent closed-loop management throughout the entire treatment cycle encompasses the entire process of assessment, monitoring, and resuscitation from the pre-operative, mid-operative, and post-operative stages. Currently, although hospitals possess various platforms such as hospital information management systems, electronic medical record systems, and anesthesia systems to record patient data, these systems typically operate independently and rely heavily on manual or rule-based management. In the existing management process, anesthesiologists often need to manually integrate fragmented information to assess risk, leading to delays and a high degree of subjectivity in risk identification.

[0003] Currently, monitoring of anesthesia throughout the entire process mainly relies on simple detection of limb movements. This monitoring method has a large margin of error, ignoring the fact that neural control usually recovers from proximal to distal during anesthesia recovery. It cannot effectively distinguish whether the patient is in a state of spontaneous awakening, or experiencing involuntary neural reflexes or passive displacement caused by medical staff moving the patient. Therefore, it is prone to false alarms or missed alarms.

[0004] Therefore, how to automatically assess the state of awakening based on actual circumstances, thereby improving the accuracy of the assessment, has become an urgent problem to be solved. Summary of the Invention

[0005] This invention provides an intelligent closed-loop management method and system for the entire diagnosis and treatment cycle, which can automatically judge the awakening based on the actual situation, thereby improving the accuracy of the judgment.

[0006] In a first aspect, the present invention provides an intelligent closed-loop management method for the entire diagnosis and treatment cycle, comprising: performing preliminary analysis on preliminary data uploaded by the user terminal, responding to permission information, collecting detection data of the corresponding detection sites of the target personnel based on an image acquisition device, and constructing a status comparison structure of the target personnel; retrieving reference data based on the preliminary data, constructing a standard comparison structure based on the reference data, performing branch comparison between the status comparison structure and the standard comparison structure, and generating comparison information; performing corresponding stage operations based on the progress stage of the comparison information, wherein the progress stage includes a mid-stage and a late-stage stage; visualizing the comparison information and the stage operations corresponding to the comparison information to generate a visual time-series chain, receiving change information of the visual time-series chain from the user terminal, and generating a visual feedback chain and risk warning.

[0007] Optionally, in one possible implementation of the first aspect, the step of performing preliminary analysis on the preliminary data uploaded by the user and responding to permission information includes: performing preliminary analysis on the preliminary data uploaded by the user to generate feedback information, the feedback information including permission information and preoperative restriction information; when it is determined that the feedback information is permission information, responding to the permission information; when it is determined that the feedback information is preoperative restriction information, responding to the preoperative restriction information, retrieving standard data, comparing the standard data with the preliminary data, and sending the difference data to the user.

[0008] Optionally, in one possible implementation of the first aspect, the step of constructing a state comparison structure for the target person based on the detection data of the corresponding detection parts of the target person acquired by the image acquisition device includes: acquiring the surgical site of the target person and using the remaining body parts as detection sites; controlling the image acquisition device to acquire site data of the detection sites; determining that the site data contains exposed parts of other persons, and when the exposed parts are in contact with and move synchronously with the detection sites, the corresponding site data is used as non-detection data, and the remaining site data is used as detection data; and processing the basic structure tree based on the detection data to obtain the state comparison structure of the target person.

[0009] Optionally, in one possible implementation of the first aspect, the process of processing the basic structure tree based on the detection data to obtain the state comparison structure of the target personnel includes: constructing a basic structure tree based on the main body and subordinate body parts of the personnel; taking the main body as the first active body part and determining the first node of the first active body part in the basic structure tree; taking the detection body part of the detection data within a preset monitoring period as the second active body part and determining the second node of the second active body part in the basic structure tree, and binding the detection data with the corresponding second node; deleting the remaining nodes in the basic structure tree other than the first node and the second node, sequentially connecting the second nodes according to the activity order of the second active body parts within the preset monitoring period, and connecting the topmost second node with the first node to generate the state comparison structure of the target personnel.

[0010] Optionally, in one possible implementation of the first aspect, the construction of the basic structure tree based on the main body and subordinate parts of a person includes: constructing a parent node based on the main body; constructing child nodes connected to the parent node according to the subordinate parts of the main body; taking the subordinate parts as the current main body and the child nodes as the current parent nodes, repeating the above steps of constructing child nodes connected to the parent node until the current main body has no subordinate parts, generating the basic structure tree, and sequentially numbering the nodes in the basic structure tree.

[0011] Optionally, in one possible implementation of the first aspect, the step of constructing a standard comparison structure based on the reference data includes: taking the main body part and the body part of the reference data as reference parts, determining the reference node of the reference part in the basic structure tree, and binding the reference data to the corresponding reference node; deleting the remaining nodes other than the reference nodes in the basic structure tree, and generating a standard comparison structure.

[0012] Optionally, in one possible implementation of the first aspect, the step of performing branch comparison between the state comparison structure and the standard comparison structure to generate comparison information includes: based on the hierarchical connection relationship, sequentially counting the node numbers in each branch of the standard comparison structure to obtain a standard number sequence for each branch in the standard comparison structure; based on the hierarchical connection relationship, sequentially counting the node numbers in each branch of the state comparison structure to obtain a real-time number sequence for each branch in the state comparison structure; and generating comparison information when a real-time number sequence that is consistent with the standard number sequence is determined, and the detection data of nodes with the same number are all located within the reference data.

[0013] Optionally, in one possible implementation of the first aspect, it further includes: when it is determined that there is no real-time numbering sequence consistent with the standard numbering sequence, controlling the acquisition device to continuously monitor the target personnel. Optionally, in one possible implementation of the first aspect, performing corresponding stage operations based on the progress stage of the comparison information includes: when it is determined that the progress stage of the comparison information is in the intermediate stage, generating anesthesia information and sending it to the user terminal; when it is determined that the progress stage of the comparison information is in the late stage, generating viewing information and sending it to the user terminal.

[0014] A second aspect of the present invention provides an intelligent closed-loop management system for the entire diagnosis and treatment cycle, comprising: a response module, used to perform preliminary analysis on preliminary data uploaded by the user terminal, respond to permission information, collect detection data of the corresponding detection sites of the target personnel based on an image acquisition device, and construct a status comparison structure of the target personnel; a comparison module, used to retrieve reference data based on the preliminary data, construct a standard comparison structure based on the reference data, perform branch comparison between the status comparison structure and the standard comparison structure, and generate comparison information; an execution module, used to execute corresponding stage operations based on the progress stage of the comparison information, the progress stage including a mid-stage and a late-stage; and a feedback module, used to visualize the comparison information and the stage operations corresponding to the comparison information, generate a visual time-series chain, receive change information of the visual time-series chain from the user terminal, and generate a visual feedback chain and risk warnings.

[0015] A third aspect of the present invention provides a storage medium storing a computer program, which, when executed by a processor, is used to implement the first aspect of the present invention and various methods possibly involved in the first aspect.

[0016] The beneficial effects of this invention are as follows: 1. This invention collects data from the target person's detection site and constructs a state comparison structure, while simultaneously retrieving reference data to construct a standard comparison structure. The two are then compared branch by branch to generate comparison information, whereas existing technologies often rely on the doctor's subjective judgment. By matching the patient's movement path with the standard resuscitation path, it effectively distinguishes between spontaneous awakening and unconscious reflexes and passive displacement. This improves the accuracy of judgment and allows for automatic execution of corresponding operations based on the stage of progression.

[0017] 2. This invention employs a two-stage screening process when constructing the state comparison structure. First, surgical sites are excluded to avoid interference from surgical procedures. Second, non-detection data that comes into contact with and moves synchronously with medical personnel is identified and removed, retaining only the patient's motion data. This eliminates false alarms caused by movement by medical personnel or surgical procedures, significantly improving the reliability of the monitoring. Attached Figure Description

[0018] Figure 1 is a flowchart of the intelligent closed-loop management method for the entire diagnosis and treatment cycle provided by the present invention; Figure 2 is a schematic diagram of the basic structure tree provided by the present invention; Figure 3 is a schematic diagram of the state comparison structure provided by the present invention; Figure 4 is a schematic diagram of the structure of the intelligent closed-loop management system for the entire diagnosis and treatment cycle provided by the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein.

[0021] It should be understood that in the various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0022] It should be understood that in this invention, "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to such process, method, product, or device.

[0023] It should be understood that in this invention, "multiple" refers to two or more. "And / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "Contains A, B, and C", "Contains A, B, and C" means that all three A, B, and C are contained; "Contains A, B, or C" means that one of A, B, and C is contained; "Contains A, B, and / or C" means that any one, two, or three of A, B, and C are contained.

[0024] It should be understood that in this invention, "B corresponding to A", "B corresponding to A", "A and B correspond", or "B and A correspond" means that B is associated with A, and B can be determined based on A. Determining B based on A does not mean determining B solely based on A; B can also be determined based on A and / or other information. Matching A and B is defined as a similarity between A and B that is greater than or equal to a preset threshold.

[0025] Depending on the context, "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection."

[0026] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0027] This invention provides an intelligent closed-loop management method for the entire diagnosis and treatment cycle, as shown in Figure 1, including S1-S4: S1, performing preliminary analysis on the preliminary data uploaded by the user terminal, responding to permission information, collecting detection data of the corresponding detection parts of the target personnel based on the image acquisition device, and constructing a status comparison structure of the target personnel.

[0028] It's important to note that traditional anesthesia monitoring is often rather crude, judging awakening as soon as any significant movement of the patient's limbs is detected. However, such movements could be unconscious reflexes or even caused by medical staff moving the patient. A tree structure has hierarchical relationships, and anesthesia recovery follows a physiological pattern: proximal muscles, such as the thigh and upper arm, may regain muscle tone first, while distal fine muscles, such as the fingers and eyebrows, regain conscious control later. Utilizing the parent-child node logic of a tree structure filters out invalid, coarse movements and captures subtle, effective movements, thereby improving accuracy. Current technology, however, judges awakening as a mere 10cm movement of the patient's upper arm, which could be due to movement by medical staff or unconscious twitching, leading to a significant possibility of false alarms.

[0029] The initial data refers to the patient's medical record data entered before the surgery begins. This can include basic information such as the patient's vital signs and the type of surgery. The authorization information is the start command issued after analysis confirming that the patient's current state meets the requirements. The detection area includes body parts other than the surgical area, such as the arms and legs. The user terminal can be a doctor's mobile device such as a phone or tablet, or a hospital's electronic information system, such as an electronic medical record system or hospital information system. Understandably, the server receives and checks the patient's file from the operating room. If it confirms that the requirements are met, it responds with the authorization information. The camera then begins to capture the patient's limb movements in non-surgical areas in real time. The patient's moving parts and the data of those movements, such as the degree of finger flexion, are generated into a dynamically changing tree-like data structure. This structure clearly marks which body parts have moved at any given moment, facilitating subsequent determination of whether the patient has regained consciousness.

[0030] In some embodiments, step S1 (performing preliminary analysis of the preliminary data uploaded by the user and responding with permission information) includes S11-S13: S11, performing preliminary analysis of the preliminary data uploaded by the user and generating feedback information, the feedback information including permission information and preoperative restriction information.

[0031] The preliminary data can be electronic data including the patient's age, weight, type of surgery, method of anesthesia, and past medical history.

[0032] It is not difficult to understand that the server will automatically analyze the preliminary data corresponding to the target person uploaded by the user. This can be done by comparing the preliminary data with standard ranges. For example, if the data such as age and weight are within the range, permission information is generated; otherwise, preoperative restriction information is generated. Alternatively, the data can be input into a pre-trained neural network for automatic judgment. This is existing technology and will not be elaborated here.

[0033] S12, if the feedback information is determined to be permission information, respond with permission information.

[0034] Understandably, when the feedback information is confirmed as consent, it means that the patient meets the requirements for anesthesia, and the monitoring device will be controlled to monitor the patient's condition during the subsequent surgery and postoperative process.

[0035] S13, when the feedback information is determined to be preoperative restriction information, respond to the preoperative restriction information, retrieve standard data, compare the standard data with the previous data, and send the difference data to the user terminal.

[0036] The standard data refers to parameter ranges that conform to specifications, which can be pre-stored on the server by the user based on actual circumstances. Understandably, when the feedback information is pre-operative restriction information, the system will respond to this information and then compare the standard data with the previous data to see which data falls outside the standard range. This data, such as age or weight, will be considered as discrepancy data. The server will then send this discrepancy data, along with the degree of difference, to the user's device.

[0037] In some embodiments, step S1 (collecting detection data of the corresponding detection area of ​​the target person based on the image acquisition device and constructing the state comparison structure of the target person) includes S14-S17: S14, obtaining the surgical site of the target person and using the remaining body parts as detection areas.

[0038] It should be noted that the area being operated on by the surgeon during the operation cannot be used for recovery monitoring. This is because the operation will cause the area to move, and different surgeons use different monitoring areas, resulting in different real-time readings.

[0039] The surgical site refers to the body part that needs to be operated on, such as the left arm. The rest of the body parts other than the left arm will be used as the testing sites.

[0040] S15, control the image acquisition device to acquire location data of the detected area.

[0041] Understandably, during and after surgery, instructions will be sent to the acquisition device, which may be a camera. The acquisition device will capture images of the detected area with movement, and then identify the part of the body that is moving in the image, as well as the corresponding range of motion, i.e., part data. For example, if the server detects that a finger has bent to a certain degree, it will automatically monitor the image and identify the corresponding range of motion. This is existing technology and will not be elaborated on here.

[0042] S16, when it is determined that there are exposed parts of other personnel in the part data, and the exposed parts are in contact with the detection part and move synchronously, the corresponding part data is used as non-detection data, and the remaining part data is used as detection data.

[0043] It's important to note that nurses or doctors frequently need to adjust patients' surgical sites. This limb movement doesn't necessarily mean the patient is awake. If it's unclear whether the movement is spontaneous or caused by someone else, the false alarm rate will be very high. Therefore, the device will identify whether other people's limbs are visible in the image, such as a doctor's hand—that is, any exposed part of another person's body. If the device sees a doctor's hand grasping a patient's hand and moving together, that data is considered invalid. Only movements that occur spontaneously without anyone touching the patient are considered valid data and retained.

[0044] The exposed areas refer to the body parts of healthcare workers exposed in the area data, such as a doctor's hands. Understandably, the server will identify whether any body parts other than the patient's are present in the image. If it detects that a doctor's hand and a patient's leg are close together and both are moving in the same direction, the device will determine that this is doctor intervention and treat the movement data for that period as non-detection data. Conversely, if the patient's arm moves on its own without anyone touching it, the corresponding area data will be treated as detection data.

[0045] Through the above implementation method, our solution involves a two-step screening process. First, it removes images where the patient's body parts are not moving. Second, it removes images where the movement is caused by medical staff from the images where there is movement, and only processes images where the patient's body parts move voluntarily.

[0046] S17, Based on the detection data, the basic structure tree is processed to obtain the state comparison structure of the target personnel.

[0047] In some embodiments, step S17 (processing the basic structure tree based on the detection data to obtain the state comparison structure of the target person) includes S171-S174: S171, constructing the basic structure tree based on the main body and subordinate body parts of the person.

[0048] It should be noted that the human body's neural control is hierarchical, like a tree, with commands sent from the brain through the spinal cord and trunk to the extremities. Therefore, we establish a tree-like structure that conforms to the awake state. The body is divided into the trunk and subordinate branches. In some embodiments, step S171 (constructing a basic tree structure based on the main body parts and subordinate parts of the person) includes: S1711, constructing a parent node based on the main body parts.

[0049] It should be noted that the human nervous system operates hierarchically, like a tree, with commands originating from the brain, passing through the spinal cord and trunk, and finally reaching the extremities. Therefore, we consider the body as the most crucial starting point.

[0050] The main part can be the entire body, such as the entire torso. A parent node is constructed based on the main part.

[0051] S1712, construct child nodes connected to the parent node based on the subordinate parts of the main part.

[0052] It should be noted that the limbs of the human body extend in a hierarchical manner and are subordinate to each other. For example, there is a hand on the arm, and fingers on the hand.

[0053] In this context, a subordinate part is a body part that is subordinate to the main part, such as an arm or thigh connected to the torso. S1713, the subordinate part is taken as the current main part, and the child node is taken as the current parent node. The steps of constructing child nodes connected to the parent node are repeated until the current main part has no subordinate parts. Then, a basic structure tree is generated, and the nodes in the basic structure tree are numbered sequentially.

[0054] It's easy to understand that, for example, an arm has a hand, and a hand has fingers. We need to take the arm as the current main body part and its child node as the current parent node. Then, we obtain the subordinate part of the arm, namely the hand, and connect the hand's node to the arm's node. Next, we use the hand as the parent node to connect the finger's child nodes. For example, see Figure 2: the parent node is the entire body part A of the target person, connected to the arm a; the arm is connected to the hand 1; and the hand connects to the five fingers, numbered 1.1, 1.2, 1.3, 1.4, and 1.5 from the thumb to the little finger. It's worth noting that there are many body parts; this example only uses the arm.

[0055] S172, take the main part as the first active part, and determine the first node of the first active part in the basic structure tree.

[0056] It is not difficult to understand that both finger movements and arm movements are dependent on the whole body. Therefore, as long as there are subsequent movements of a part, the person is the main part, that is, the body is also in an active state. Subsequently, the structure is compared with the constructed state to determine whether it is a true awakening.

[0057] It is understandable that the entire body part is taken as the first active part, and the first active part is determined as the first node in the basic structure tree, that is, the parent node in the basic structure tree is the first node.

[0058] S173, the detection location of the detection data within the preset monitoring time period is taken as the second active location, the second active location is determined as the second node in the basic structure tree, and the detection data is bound to the corresponding second node.

[0059] It should be noted that, in order to analyze the awakening state, we selected those parts that were detected to be moving within an observation time window, preserved them on the tree diagram, and bound the specific amount and manner of the movement to the nodes.

[0060] The preset monitoring duration is an observation period set manually based on actual conditions, such as 10 minutes. The second active body part refers to the body part that actually moved during this period, such as the fingers. Understandably, the server detects movement in the patient's arm, palm, and fingers within the preset monitoring duration. Therefore, it finds the nodes representing the arm, palm, and fingers in the basic tree structure and marks them as second nodes. Subsequently, the device binds the data such as the amplitude of movement recorded by the sensors to these two nodes.

[0061] S174, delete all nodes except the first and second nodes in the basic tree structure, connect the second node level by level according to the activity order of the second active part within the preset monitoring time, and connect the top second node with the first node to generate the status comparison structure of the target personnel.

[0062] It's important to note that the basic tree structure contains many inactive parts, so corresponding nodes were deleted based on the actual situation. Furthermore, the sequence of actions is crucial for determining awakening. For example, is the arm moved first, then the hand, and then the fingers, or vice versa? If it's the other way around, it indicates that the initial finger movement was caused by twitching and not awakening. We deleted all inactive parts, leaving only the moved parts. Then, based on the chronological order of the actions, we connected these remaining points into a line. This was done to generate a state comparison structure containing only valid action paths, thus facilitating the determination of whether it conforms to the laws of neural recovery.

[0063] Among them, the active sequence refers to the order in which actions occur at different parts within a preset monitoring period, and the state comparison structure refers to the dynamic model that displays the current action transmission path.

[0064] Understandably, we removed all the unmoved nodes, such as the left leg, leaving only the body and the previously marked arm and hand. Subsequently, based on the chronological order of the actions, the arms, hands, and thumbs (see Figure 3) were connected sequentially from the body's parent node to the nodes of the arms, hands, and thumbs, forming an action chain, i.e., the state comparison structure.

[0065] S2, retrieve reference data based on previous data, construct a standard comparison structure based on the reference data, perform branch comparison between the state comparison structure and the standard comparison structure, and generate comparison information.

[0066] It should be noted that we will use the target person's previous data as a basis to retrieve video data of patients who have awakened from similar patients from the historical database, such as filtering based on the target person's gender, age, weight, etc.

[0067] It's easy to understand that the server will use previous data as a benchmark to select body part data from people with similar parameters who are in an conscious state, i.e., reference data. For example, it can be filtered by a combination of factors such as weight, age, and surgical site, selecting those with differences less than a preset value. This is existing technology and will not be elaborated here. The server will obtain the corresponding person's limb movements before waking up, as well as the amplitude of the corresponding movements, and use this reference data to construct a standard comparison structure. The state comparison structure is then compared with the standard comparison structure to generate comparison information, i.e., the person is waking up. Here, the reference data refers to historical data of similar patients with similar conditions to the target person retrieved from the historical database.

[0068] In some embodiments, step S2 (constructing a standard comparison structure based on the reference data) includes S21-S22: S21, taking the main body part and the body part of the reference data as reference parts, determining the reference node of the reference part in the basic structure tree, and binding the reference data with the corresponding reference node.

[0069] It should be noted that under standard awakening conditions, the corresponding awakened limbs differ among individuals. That is, even among individuals with similar parameters such as age, weight, and surgical site, the awakened limbs may vary from person to person. Therefore, we need to integrate all awakening data of similar individuals from the historical database to form a standard comparison structure for easy comparison in the future.

[0070] Understandably, the server uses the main body parts and the moving body parts in the reference data as reference parts. It then identifies the nodes corresponding to these reference parts and marks them as reference nodes. Next, the server binds values ​​such as the amplitude of movement in the reference data to these nodes.

[0071] S22, delete all nodes except the reference node in the basic tree structure to generate the standard alignment structure.

[0072] Understandably, nodes in the reference data that did not take action were deleted to obtain a standard comparison structure that integrates the awakening processes of all patients of the same type in historical data.

[0073] In some embodiments, step S2 (compare the state comparison structure with the standard comparison structure to generate comparison information) includes S23-S25: ​​S23, based on the hierarchical connection relationship, the node numbers in each branch of the standard comparison structure are counted sequentially to obtain the standard number sequence of each branch in the standard comparison structure.

[0074] It should be noted that, in order to improve the efficiency and accuracy of the comparison, we have converted the three-dimensional tree-like path into a numbered sequence, so that subsequent numerical comparisons can be performed directly.

[0075] Among them, the hierarchical connection relationship is the connection relationship between nodes at different levels in the tree diagram. For example, the connection relationship from top to bottom is from the parent node to the bottommost node.

[0076] Understandably, the server scans the pre-established standard comparison structure, starting from the root and moving to the end of each branch, recording the number of each node, such as Aa-1-1.1, Aa-1-1.2.

[0077] S24. Based on the hierarchical connection relationship, the node numbers in each branch of the state comparison structure are counted sequentially to obtain the real-time number sequence of each branch in the state comparison structure.

[0078] Similarly, the node numbers in each branch of the state comparison structure are counted sequentially to obtain the real-time number sequence of each branch in the state comparison structure.

[0079] S25, when a real-time number sequence that is consistent with the standard number sequence is determined, and the detection data of the nodes corresponding to the same number are all located within the reference data, comparison information is generated.

[0080] It's important to note that the first step is to determine if the awakening path is correct, followed by the degree of correction, such as the range of finger flexion. Consistent sequence indicates that the order of nerve recovery follows physiological laws and is not random movement. Data within the acceptable range indicates that the amplitude and speed of the movement are within the normal awakening range, not violent convulsions or weak tremors. Only when the sequence and amplitude of the movement all meet the standards can it be concluded that the person is in a state of awakening. It can be understood that when a real-time numbering sequence is consistent with the standard numbering sequence, and the detection data of nodes corresponding to the same number are all within the reference data, it indicates that the movement path is correct and within the range specified by the standard, and comparison information is generated.

[0081] Based on the above embodiments, the method further includes: when it is determined that there is no real-time number sequence consistent with the standard number sequence, controlling the acquisition device to continuously monitor the target person. It is understood that when it is determined that there is no real-time number sequence consistent with the standard number sequence, no awakening signal will be issued; instead, a command to continue working will be sent to the camera or sensor. The acquisition device will ignore the previous invalid action, continue recording, and wait to capture the patient's next limb response until an action conforming to the standard pattern is captured.

[0082] S3, perform corresponding stage operations based on the progress stage of the comparison information, the progress stage includes the intermediate stage and the late stage.

[0083] In some embodiments, step S3 (performing corresponding stage operations based on the progress stage of the comparison information) includes: when it is determined that the progress stage of the comparison information is the intermediate stage, generating anesthesia information and sending it to the user terminal.

[0084] It is easy to understand that when the information comparison is in the intermediate stage, it is necessary to continue anesthesia and send the generated anesthesia information to the user.

[0085] When it is determined that the comparison information is in a later stage, viewing information is generated and sent to the user's terminal.

[0086] It is easy to understand that when the comparison information is in the later stage, medical staff need to be dispatched to review it and send the generated review information to the user's terminal.

[0087] S4. Visualize the comparison information and the corresponding stage operations to generate a visual time-series chain, receive the user's modification information on the visual time-series chain, and generate a visual feedback chain and risk warning.

[0088] Understandably, the server will convert the time when the comparison information is generated, as well as the time period corresponding to the stage operation of the comparison information, into a visual timeline, i.e., a visual time sequence chain, using existing technologies such as time axis or Gantt chart. For example, if the comparison information is generated at 10:00, it can be connected to the timeline of medical staff viewing and inquiring from 10:10 to 10:20. That is, the timeline records each time and the event that occurred at that time.

[0089] Subsequently, users can retrieve this timeline and modify the information on it. For example, they can change 10:10 to 10:08, which will prompt personnel to check earlier. They can also modify events within the time frame, such as anesthesia dosage, and provide reminders to relevant personnel regarding the viewing time or dosage based on the updated information. The risk warning is a reminder message provided after a user actively modifies the information.

[0090] This allows for the subsequent integration of patient physiological data, medical history, surgical type, and other information into existing neural networks or artificial intelligence algorithms for training. This enables the analysis and prediction of patients' anesthesia risks, the identification and warning of potential medium- to high-risk events, and the display of this information on a screen through an intuitive and concise interface for medical staff to operate.

[0091] Referring to Figure 4, which is a schematic diagram of the intelligent closed-loop management system for the entire diagnosis and treatment cycle provided in this embodiment of the invention, the intelligent closed-loop management system for the entire diagnosis and treatment cycle includes: a response module, used to perform preliminary analysis on the preliminary data uploaded by the user terminal, respond to permission information, collect detection data of the corresponding detection parts of the target personnel based on the image acquisition device, and construct a status comparison structure of the target personnel; a comparison module, used to retrieve reference data based on the preliminary data, construct a standard comparison structure based on the reference data, perform branch comparison between the status comparison structure and the standard comparison structure, and generate comparison information; an execution module, used to execute corresponding stage operations based on the progress stage of the comparison information, the progress stage including the intermediate stage and the late stage; and a feedback module, used to visualize the comparison information and the stage operations corresponding to the comparison information, generate a visual time sequence chain, receive change information of the visual time sequence chain from the user terminal, and generate a visual feedback chain and risk warning.

[0092] The present invention also provides a readable storage medium storing a computer program, which, when executed by a processor, is used to implement the methods provided in the various embodiments described above.

[0093] The readable storage medium can be a computer storage medium or a communication medium. A communication medium includes any medium that facilitates the transfer of computer programs from one location to another. A computer storage medium can be any available medium accessible to a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application-Specific Integrated Circuit (ASIC). Alternatively, the ASIC can be located in a user equipment. Of course, the processor and the readable storage medium can also exist as discrete components in a communication device. The readable storage medium can be a read-only memory (ROM), random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0094] The present invention also provides a program product including executable instructions stored in a readable storage medium. At least one processor of the device can read the executable instructions from the readable storage medium, and the at least one processor executes the executable instructions to cause the device to implement the methods provided in the various embodiments described above.

[0095] In the embodiments of the above-described device, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent closed-loop management of the entire diagnosis and treatment cycle, characterized in that: include: The system performs preliminary analysis on the data uploaded by the user, responds to permission information, collects detection data of the corresponding detection parts of the target personnel based on the image acquisition device, and constructs a status comparison structure for the target personnel. It retrieves reference data based on the preliminary data, constructs a standard comparison structure based on the reference data, and performs branch comparisons between the status comparison structure and the standard comparison structure to generate comparison information. Based on the progress stage of the comparison information, it performs corresponding stage operations, including mid-stage and late-stage stages. It visualizes the comparison information and the corresponding stage operations to generate a visual time-series chain, receives changes to the visual time-series chain from the user, and generates a visual feedback chain and risk warnings.

2. The method according to claim 1, characterized in that, The step of performing preliminary analysis on the preliminary data uploaded by the user and responding to permission information includes: performing preliminary analysis on the preliminary data uploaded by the user to generate feedback information, the feedback information including permission information and preoperative restriction information; when the feedback information is determined to be permission information, responding to the permission information; when the feedback information is determined to be preoperative restriction information, responding to the preoperative restriction information, retrieving standard data, comparing the standard data with the preliminary data, obtaining difference data and sending it to the user.

3. The method according to claim 1, characterized in that, The method of acquiring detection data of the target personnel corresponding to the detection sites using an image acquisition device and constructing a state comparison structure for the target personnel includes: acquiring the surgical site of the target personnel and using the remaining body parts as detection sites; controlling the image acquisition device to acquire site data of the detection sites; determining that the site data contains exposed sites of other personnel, and when the exposed sites are in contact with and move synchronously with the detection sites, the corresponding site data is used as non-detection data, and the remaining site data is used as detection data; and processing the basic structure tree based on the detection data to obtain the state comparison structure of the target personnel.

4. The method according to claim 3, characterized in that, The process of processing the basic structure tree based on the detection data to obtain the state comparison structure of the target personnel includes: constructing a basic structure tree based on the main body and subordinate body parts of the personnel; determining the first node of the first active body part in the basic structure tree by taking the main body part as the first active body part; determining the second node of the second active body part in the basic structure tree by taking the detection body part of the detection data within a preset monitoring period as the second active body part, and binding the detection data with the corresponding second node; deleting the remaining nodes in the basic structure tree except for the first and second nodes, sequentially connecting the second nodes according to the activity order of the second active body parts within the preset monitoring period, and connecting the topmost second node with the first node to generate the state comparison structure of the target personnel.

5. The method according to claim 4, characterized in that, The construction of the basic structure tree based on the main body and subordinate parts of the personnel includes: constructing a parent node based on the main body; constructing child nodes connected to the parent node according to the subordinate parts of the main body; taking the subordinate parts as the current main body and the child nodes as the current parent nodes, repeating the above steps of constructing child nodes connected to the parent node until the current main body has no subordinate parts, generating the basic structure tree, and numbering the nodes in the basic structure tree sequentially.

6. The method according to claim 4, characterized in that, The step of constructing a standard comparison structure based on the reference data includes: taking the main body part and the body part of the reference data as reference parts, determining the reference node of the reference part in the basic structure tree, and binding the reference data with the corresponding reference node; deleting the remaining nodes in the basic structure tree other than the reference node, and generating a standard comparison structure.

7. The method according to claim 5, characterized in that, The step of performing branch comparison between the state comparison structure and the standard comparison structure to generate comparison information includes: based on the hierarchical connection relationship, sequentially counting the node numbers in each branch of the standard comparison structure to obtain the standard number sequence of each branch in the standard comparison structure; based on the hierarchical connection relationship, sequentially counting the node numbers in each branch of the state comparison structure to obtain the real-time number sequence of each branch in the state comparison structure; and generating comparison information when a real-time number sequence that is consistent with the standard number sequence is determined, and the detection data of the nodes corresponding to the same number are all located within the reference data.

8. The method according to claim 6, characterized in that, Also includes: When it is determined that there is no real-time number sequence that matches the standard number sequence, the control acquisition device continuously monitors the target personnel.

9. The method according to claim 8, characterized in that, The step of performing corresponding stage operations based on the progress stage of the comparison information includes: when the progress stage of the comparison information is determined to be the intermediate stage, generating anesthesia information and sending it to the user terminal; when the progress stage of the comparison information is determined to be the late stage, generating viewing information and sending it to the user terminal.

10. A smart closed-loop management system for the entire diagnosis and treatment cycle, characterized in that: include: The response module is used to perform preliminary analysis on the preliminary data uploaded by the user, respond to permission information, collect detection data of the corresponding detection parts of the target personnel based on the image acquisition device, and construct a status comparison structure of the target personnel. The comparison module is used to retrieve reference data based on the preliminary data, construct a standard comparison structure based on the reference data, and perform branch comparison between the status comparison structure and the standard comparison structure to generate comparison information. The execution module is used to execute corresponding stage operations based on the progress stage of the comparison information, the progress stage includes the intermediate stage and the late stage. The feedback module is used to visualize the comparison information and the stage operations corresponding to the comparison information, generate a visual time sequence chain, receive the user's change information on the visual time sequence chain, and generate a visual feedback chain and risk warning.

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