Anesthesia nursing information monitoring method and system based on cloud computing

Abstract

The invention discloses an anesthesia nursing information monitoring method and system based on cloud computing, and belongs to the technical field of intelligent medical assistance. The method comprises the steps of receiving operation anesthesia basic information and sending the information to a basic query database to obtain multiple pieces of candidate operation information; calculating the difference degree to determine target operation information; sending the operation anesthesia basic information and the target operation information to a verification supplement database to obtain information of a plurality of operation anesthesia process nodes, and distributing the information to different execution nodes for execution; the real-time execution state of the node information of the operation anesthesia process is visually displayed in a map mode; when it is predicted that the state of the node information of a certain operation anesthesia process is about to be abnormal, alarm information is sent out. According to the method, anesthesia information full-process monitoring of basic query and supplementary verification is executed based on cloud computing local and cloud double databases, abnormity can be monitored in real time through a visual means, and the accuracy of an operation scheme and the safety degree of the operation process can be improved.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent medical assistance technology, and in particular to an anesthesia care information monitoring method and system based on cloud computing, a computer-readable storage medium for implementing the method, a computer program product, and an electronic device. Background Art

[0002] Anesthesia is a method of using drugs to make patients or other injected living things lose their sense of pain so that surgery or other treatments can be performed. Ensuring that patients can smoothly undergo surgical treatment under painless and safe conditions is the basic task of anesthesia clinical monitoring.

[0003] There are many ways to anesthetize an organism in medicine, including solid drug anesthesia, liquid drug anesthesia, gas anesthesia, general anesthesia or local anesthesia. Clinicians usually choose them based on the actual situation of the patient and their experience, and record the data to determine the surgical goals and anesthesia process.

[0004] With the development of Internet of Things technology, the use of existing integrated platforms and software technologies to implement automated and intelligent management of surgical anesthesia process information monitoring has become a necessity for modern surgical anesthesia scientific management. For example, the Chinese invention patent publication CN111991664A proposes an operating method for an intelligent anesthesia auxiliary control system. The steps that require a lot of manual experience to participate in recording, memorizing and operating are analyzed and implemented by a computer system, and the sedation depth can meet the surgical requirements with a small dose of sedatives. In addition, the Chinese invention patent CN116324373A also points out that the gas delivery system can provide medical gas to the patient, such as providing anesthesia (for example, when the gas delivery system is constructed as an anesthesia machine) and / or assisted breathing (for example, when the gas delivery system is constructed as a ventilator).

[0005] The inventors found that the current management and monitoring of anesthesia information during surgery still relies on local data processing, and all of them are simple threshold controls; at the same time, the selection of surgical anesthesia plans is based on the decomposition of the plans determined by the doctor, and there is no secondary verification process. All of these may lead to the failure to timely detect the possible anesthesia risks during surgery, and the selection of surgical anesthesia plans also lacks safety. Summary of the invention

[0006] In response to the above technical problems, the technical solution of the present invention is to perform full-process monitoring of anesthesia information with basic queries and supplementary verification based on a cloud computing dual database, and can monitor abnormalities in real time through visualization means, thereby improving the accuracy of surgical plans and the safety of the surgical process.

[0007] In a first aspect of the present invention, a cloud computing-based anesthesia care information monitoring method is proposed. The method is implemented based on a cloud database, and the cloud database includes a basic query database and a verification supplementary database.

[0008] The method comprises the following steps S100-S700: S100: receiving basic information on surgical anesthesia, wherein the basic information on surgical anesthesia includes operator health information and anesthesia nursing information; S200: sending the operator's health information to the basic query database to obtain a plurality of candidate surgery information, each of which includes a surgery type name and surgery contraindication information; S300: Calculating the difference between the anesthesia care information and each candidate surgery information to determine the target surgery information; S400: Sending the basic surgical anesthesia information and the target surgical information to the verification supplement database, wherein the verification supplement database feeds back a plurality of surgical anesthesia process node information; each of the surgical anesthesia process node information has at least one supplementary verification mark; S500: Allocating the plurality of surgical anesthesia process node information to different execution nodes for execution; S600: Visually displaying the real-time execution status of the plurality of surgical anesthesia process node information in a graph manner; S700: When it is predicted that the status of a certain surgical anesthesia process node information is about to become abnormal, an alarm message is issued.

[0009] In the technical solution of the present invention, the operator's health information includes the operator's basic information and the operator's latest diagnosis and treatment information; The anesthesia care information includes the type of surgery requirement, the time of surgery requirement, and surgery contraindications determined based on the operator's health information.

[0010] The basic query database stores a plurality of historical successful surgery information, each of which includes the name of the surgery type, surgical contraindication information, and the corresponding historical surgeon basic information and historical surgeon diagnosis and treatment information; The verification supplementary database is used to record the surgical anesthesia process node information corresponding to each historical successful surgical information.

[0011] The supplementary verification mark includes a priority mark, a preceding dependency mark or a following dependency mark; The priority mark is used to represent the priority of a certain surgical anesthesia process node information, and the higher the priority, the higher the monitoring level of the surgical anesthesia process node information; The preceding dependency mark is used to indicate that the execution of a certain surgical anesthesia process node information depends on the execution result of at least another preceding surgical anesthesia process node information; The post-dependency flag is used to indicate that the execution result of a certain surgical anesthesia process node information requires verification of at least another post-surgical anesthesia process node information.

[0012] The step S700 predicts that the state of a certain surgical anesthesia process node information is about to be abnormal, specifically including that a certain surgical anesthesia process node information has one of the following situations or any combination thereof: (1) The priority of a certain surgical anesthesia process node information is higher than that of another surgical anesthesia process node information, but the visualization information of the other surgical anesthesia process node information exists on the visualization map, while the visualization information of the certain surgical anesthesia process node information does not exist; (2) The anesthesia process node information of the previous surgery of the anesthesia process node information of a certain surgery has not been visualized, while the anesthesia process node information of the certain surgery has been visualized; (3) When a certain surgical anesthesia process node information has a post-dependency flag, its corresponding post-surgical anesthesia process node information does not feedback verification information within a preset period of time.

[0013] The step S600 visualizes the real-time execution status of the multiple surgical anesthesia process node information in a graphical manner, specifically including: Aggregating the plurality of surgical anesthesia process node information into a target graph, wherein each node of the target graph corresponds to the surgical anesthesia process node; When the execution node executes the corresponding surgical anesthesia process node, the surgical anesthesia process node is highlighted, and the highlighting includes a supplementary verification mark that visually displays the surgical anesthesia process node.

[0014] In a second aspect of the present invention, in order to implement the method described in the first aspect, a cloud computing-based anesthesia care information monitoring system is proposed, wherein the system includes a cloud database, and the cloud database includes a basic query database and a verification supplementary database.

[0015] The system includes the following functional module components: A surgical anesthesia basic information receiving module is used to receive surgical anesthesia basic information; the surgical anesthesia basic information includes operator health information and anesthesia nursing information; A basic query module, used for sending the operator's health information to the basic query database to obtain a plurality of candidate surgery information, each of which includes a surgery type name and surgery contraindication information; A target surgery information determination module, used to calculate the difference between the anesthesia care information and each candidate surgery information, and determine the target surgery information; A verification and supplementation module, used for sending the basic surgical anesthesia information and the target surgical information to the verification and supplementation database, wherein the verification and supplementation database feeds back a plurality of surgical anesthesia process node information; each of the surgical anesthesia process node information has at least one supplementary verification mark; The supplementary verification mark includes a priority mark, a preceding dependency mark or a following dependency mark; An execution module, used to distribute the plurality of surgical anesthesia process node information to different execution nodes for execution; A visualization module, used to visualize the real-time execution status of the multiple surgical anesthesia process node information in a graphical manner; The prediction warning module is used to issue an alarm message when it is predicted that the status of a certain surgical anesthesia process node information is about to become abnormal.

[0016] The operator's health information includes the operator's basic information and the operator's latest diagnosis and treatment information; The anesthesia care information includes the type of surgery requirement, the time of surgery requirement, and surgery contraindications determined based on the operator's health information; The basic query database stores a plurality of historical successful surgery information, each of which includes the name of the surgery type, surgical contraindication information, and the corresponding historical surgeon basic information and historical surgeon diagnosis and treatment information; The verification supplementary database is used to record the surgical anesthesia process node information corresponding to each historical successful surgical information.

[0017] The visualization module aggregates the information of the plurality of surgical anesthesia process nodes into a binary tree, each node of the binary tree corresponds to the surgical anesthesia process node; when the execution node executes the corresponding surgical anesthesia process node, the surgical anesthesia process node is highlighted, and the highlighting includes visually displaying a supplementary verification mark of the surgical anesthesia process node; The prediction warning module traverses the binary tree in depth-first order according to a preset period, and issues an alarm message when it is predicted that the state of a certain surgical anesthesia process node information is about to become abnormal.

[0018] Part or all of the steps of the cloud computing-based anesthesia care information monitoring method described in the first aspect can be automatically implemented through various forms of electronic devices through computer program instructions; the computer program instructions can be stored in different forms of storage media and loaded into computer electronic devices for execution.

[0019] Therefore, in the third aspect of the present invention, a computer-readable storage medium is also provided for storing computer instructions. When the computer instructions are executed on an electronic device, the electronic device executes the cloud computing-based anesthesia care information monitoring method described in the first aspect.

[0020] In the fourth aspect of the present invention, an electronic device is also proposed, which includes a processor and a memory, the memory is used to store instructions, and the processor is used to call the instructions in the memory, so that the electronic device executes the cloud computing-based anesthesia care information monitoring method described in the first aspect above.

[0021] In a fifth aspect of the present invention, a computer program product is also proposed, the product comprising a computer program, and when the computer program is executed, the cloud computing-based anesthesia care information monitoring method described in the first aspect is implemented.

[0022] The technical solution of the present invention first receives basic information on surgical anesthesia and sends it to a basic query database to obtain multiple candidate surgical information; calculates the difference to determine the target surgical information; sends the basic information on surgical anesthesia and the target surgical information to a verification and supplementary database to obtain multiple surgical anesthesia process node information and then distributes them to different execution nodes for execution; visualizes the real-time execution status of the surgical anesthesia process node information in the form of a graph; and issues an alarm message when it is predicted that the status of a certain surgical anesthesia process node information is about to become abnormal. The technical solution of the present invention is based on a dual database of a local resource library and cloud computing data resources to perform basic query and supplementary verification of anesthesia information throughout the process, and can monitor abnormalities in real time through visualization means, which can improve the accuracy of surgical plans and the safety of surgical procedures.

[0023] Further advantages of the present invention will be further reflected in detail in the specific embodiments section in conjunction with the drawings of the specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 It is a main flow diagram of a method for monitoring anesthesia nursing information based on cloud computing according to an embodiment of the present invention; Figure 2 It is a schematic diagram of data interaction control when the technical solution of the present invention is specifically implemented; Figure 3This is a schematic diagram of working nodes of a specific anesthesia scheme using the technical solution of the present invention; Figure 4 It is a schematic diagram of the functional modules of an anesthesia nursing information monitoring system based on cloud computing according to an embodiment of the present invention; Figure 5 It is a schematic diagram of a computer-readable storage medium and an electronic device for implementing the anesthesia nursing information monitoring method based on cloud computing described in the present invention. DETAILED DESCRIPTION

[0026] Here, exemplary embodiments will be described in detail, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments that are the same as the present application. Instead, they are only examples of devices and methods that are the same as some aspects of the present application as detailed in the attached claims.

[0027] In the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories) or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0028] At the same time, in the specific implementation of this application, if user-related data is involved, when the embodiment of this application is applied to a specific product or technology, the user's permission or consent must be obtained, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0029] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0030] See also Figure 1 , Figure 1 It is a main flow chart of a method for monitoring anesthesia nursing information based on cloud computing according to an embodiment of the present invention.

[0031] Figure 1 The method includes steps S100-S700, and each step is specifically implemented as follows: S100: receiving basic information on surgical anesthesia, wherein the basic information on surgical anesthesia includes operator health information and anesthesia nursing information; S200: sending the operator's health information to the basic query database to obtain a plurality of candidate surgery information, each of which includes a surgery type name and surgery contraindication information; S300: Calculating the difference between the anesthesia care information and each candidate surgery information to determine the target surgery information; S400: Sending the basic surgical anesthesia information and the target surgical information to the verification supplement database, wherein the verification supplement database feeds back a plurality of surgical anesthesia process node information; each of the surgical anesthesia process node information has at least one supplementary verification mark; S500: Allocating the plurality of surgical anesthesia process node information to different execution nodes for execution; S600: Visually displaying the real-time execution status of the plurality of surgical anesthesia process node information in a graph manner; S700: When it is predicted that the status of a certain surgical anesthesia process node information is about to become abnormal, an alarm message is issued.

[0032] Next, combine Figure 2 The data interaction control schematic diagram of the technical solution of the present invention is shown in FIG. Figure 1 Some steps included in the embodiment are specifically introduced.

[0033] S100: receiving basic information on surgical anesthesia; As a specific example, a medical clinical terminal may be configured in the clinic, and the medical clinical terminal may be an electronic device configured with a human-computer interactive visual interface, and basic information on surgical anesthesia may be received through the electronic device.

[0034] Preferably, the basic information on surgical anesthesia includes operator health information and anesthesia care information.

[0035] In a specific embodiment, the operator's health information includes the operator's basic information and the operator's latest diagnosis and treatment information.

[0036] The basic information of the surgeon includes the surgeon's gender, age, blood type, blood pressure and other basic physical examination data. The latest diagnosis and treatment information of the surgeon is the latest diagnosis and treatment conclusion given by the clinician, such as what disease the surgeon suffers from, what surgery may be required, and the anesthesia care information corresponding to each surgery. Anesthesia care information can usually be a specific anesthesia plan, such as solid drug anesthesia, liquid drug anesthesia, gas anesthesia, general anesthesia or local anesthesia.

[0037] For the convenience of description, assume that the latest diagnosis and treatment information is described as follows: Patient A is diagnosed with disease a1, and the possible surgeries that may be arranged include S1 and S2 (surgical requirement types). The preliminary anesthesia care information corresponding to surgery S1 is T1, the contraindication information is Q1, and the surgery time is D1. The preliminary anesthesia care information corresponding to surgery S2 is T2, the contraindication information is Q2, and the surgery time is D2. These are the existing preliminary data given by doctors based on their experience.

[0038] However, clinically, due to the large number of patients, the above basic information may contain errors or loopholes and cannot be discovered in time. If the subsequent surgical process is arranged directly according to such preliminary data, there is a certain risk.

[0039] To this end, the embodiment of the present application has been further improved: anesthesia care information is further supplemented on the operator's health information, that is, the anesthesia care information also includes the type of surgery requirement, the surgery requirement time and surgery contraindications determined based on the operator's health information.

[0040] When implementing it, refer to Figure 2 , the step S100 further includes: S101: Input the basic information of the surgeon and the latest diagnosis and treatment information of the surgeon through the clinical terminal device; S102: The local host cluster of the cloud computing searches historical cases based on the basic information of the surgeon and the latest diagnosis and treatment information of the surgeon, and obtains a plurality of candidate diagnosis and treatment plans corresponding to the basic information of the surgeon and the latest diagnosis and treatment information of the surgeon; S103: Determine the type of surgery requirement, the time of surgery requirement, and the contraindications of surgery as the anesthesia care information based on the comparison between the multiple candidate diagnosis and treatment plans and the latest diagnosis and treatment information of the surgeon; S104: Send the anesthesia care information and the operator's health information as basic surgical anesthesia information to an intraoperative monitoring device for receiving.

[0041] Wherein, the step S103 includes: When the surgeon's latest diagnosis and treatment information does not conflict with the multiple candidate diagnosis and treatment plans, the type of surgery requirement, the time of surgery requirement, and the contraindications to surgery included in the surgeon's latest diagnosis and treatment information are used as the anesthesia care information; Otherwise, the latest diagnosis and treatment information of the operator is fed back to the clinician for modification, and then the process returns to step S101; or, the latest diagnosis and treatment information of the operator is fed back to the clinician for confirmation, and then the process directly enters step S200.

[0042] Preferably, the operator's latest diagnosis and treatment information does not conflict with the multiple candidate diagnosis and treatment plans, which means that the operator's latest diagnosis and treatment information is included in at least one candidate diagnosis and treatment plan among the multiple candidate diagnosis and treatment plans.

[0043] Figure 2 It is shown that the method is implemented based on a cloud database, and the cloud database includes a basic query database and a verification supplementary database.

[0044] On the basis of step S100, proceed to step S200: send the operator's health information to the basic query database to obtain a plurality of candidate surgery information, each of which includes a surgery type name and surgery contraindication information.

[0045] Step S100 is to perform a preliminary verification of the accuracy of the latest diagnosis and treatment information of the surgeon in the local host cluster of the cloud computing (usually a formal verification based on the local data database already available in the hospital); however, local resources usually have limitations and a certain lag, therefore, the embodiment of the present application is further improved as follows: The basic query database in the cloud stores multiple historical successful surgery information, each of which includes the surgery type name, surgery contraindication information, and the corresponding historical surgeon basic information and historical surgeon diagnosis and treatment information; The operator's health information is sent to the basic query database to obtain a plurality of candidate surgery information, each of which includes a surgery type name and surgery contraindication information.

[0046] At this point, based on the broader resources supported by the cloud, multiple candidate surgery information can be further obtained; Since the input information at this time (the surgeon's health information = the surgeon's basic information and the surgeon's latest diagnosis and treatment information) is verified and relatively accurate information, better candidate results can be further obtained based on the support of the cloud database.

[0047] At this time, step S300 is executed: calculating the difference between the anesthesia care information and each candidate operation information to determine the target operation information.

[0048] Difference comparison can also be called similarity comparison: For example, the anesthesia care information being compared = {S1, S2 (surgical requirement type), the preliminary anesthesia care information corresponding to surgery S1 is T1, the contraindication information is Q1, and the operation time is D1; ​​the preliminary anesthesia care information corresponding to surgery S2 is T2, the contraindication information is Q2, and the operation time is D2}; a candidate surgical information = {SS1, SS2 (surgical requirement type), the preliminary anesthesia care information corresponding to surgery SS1 is ST1, the contraindication information is SQ1, the preliminary anesthesia care information corresponding to surgery SS2 is ST2, and the contraindication information is SQ2}.

[0049] At this time, the candidate surgery information with the smallest difference can be determined.

[0050] In one selection, after confirmation by the doctor, the candidate surgery information with the smallest difference can be used as the target surgery information; In another option, after confirmation by a doctor, the compared anesthesia care information may be modified based on the candidate surgery information with the smallest difference, thereby obtaining the target surgery information.

[0051] Next, the process proceeds to step S400 , where the database being interacted with is a verification supplementary database.

[0052] The verification supplementary database is used to record the surgical anesthesia process node information corresponding to each historical successful surgical information.

[0053] After determining the target surgical information, you need to proceed to the anesthesia plan selection and anesthesia information management for the corresponding type of surgery.

[0054] At this time, step S400 is: sending the basic surgical anesthesia information and the target surgical information to the verification supplement database, and the verification supplement database feeds back multiple surgical anesthesia process node information; each of the surgical anesthesia process node information has at least one supplementary verification mark.

[0055] The supplementary verification mark includes a priority mark, a preceding dependency mark or a following dependency mark; The priority mark is used to represent the priority of a certain surgical anesthesia process node information, and the higher the priority, the higher the monitoring level of the surgical anesthesia process node information; The preceding dependency mark is used to indicate that the execution of a certain surgical anesthesia process node information depends on the execution result of at least another preceding surgical anesthesia process node information; The post-dependency flag is used to indicate that the execution result of a certain surgical anesthesia process node information requires verification of at least another post-surgical anesthesia process node information.

[0056] Further integration Figure 3 , the above process is described as follows.

[0057] Assume that after determining the target surgery information in step S300, it is known that a first type of surgery needs to be performed, and the anesthesia scheme to be adopted for the first type of surgery is gas anesthesia.

[0058] At this time, the gas anesthesia corresponding to the first type of surgery fed back in step S400 includes Figure 3 Multiple surgical anesthesia process node information M1, M2...M5 and N1, N2,...N8 are shown; In a specific surgical scenario, the gas anesthesia plan specifically includes: in order to achieve the purpose of anesthesia, an appropriate amount of anesthetic gas needs to be introduced into the human body; however, anesthetic gas cannot be directly delivered to the human body, and gases such as oxygen, carbon dioxide and nitrous oxide need to be used as carriers to form a mixed gas with the anesthetic gas and then delivered to the breathing circuit.

[0059] Oxygen N1 and carbon dioxide obtained from the hospital gas supply system are both high-pressure gases. First, these high-pressure gases need to be reduced in pressure by pressure reducing valves M2 and N2 to a range suitable for use by respiratory anesthesia machine N4, and then introduced into flow meters M3 and N3; When the oxygen N1 pressure reaches the specified threshold, the nitrous oxide valve is opened and the nitrous oxide M1 is also introduced into the flow meter M3; the flow control valve M4 / N7 in the flow meter will control the gas flow according to the gas concentration to ensure that the oxygen content of the mixed gas is above 25%. The provided anesthetic N5 is converted into anesthetic steam in the evaporator N6, and other gases such as oxygen and nitrous oxide are adjusted and controlled by the flow meter and introduced into the anesthetic evaporator and mixed with anesthetic steam M5 / N8. After the gas is passed to the gas outlet, it is sent into the breathing circuit according to the set gas transmission method and reaches the patient's body during surgery to achieve the anesthetic effect.

[0060] Taking node N1 as an example, node N1 has the highest priority of the branch, has no predecessor node (predecessor dependency mark), and only has a successor node (postdependent mark); the execution status of node N1 is normal oxygen output and is verified by the operation of the subsequent node N2 (postoperative anesthesia process node information, i.e., pressure reducing valve N2); Obviously, the condition for the normal operation of node N1 is that node N2 also operates normally and feeds back the results; As a further prediction judgment, if there is no visualization information of the surgical anesthesia process node N1 information but there is visualization information of the surgical anesthesia process node N2 information on the visualization map, it may mean that the oxygen N1 pressure is abnormal; That is, the exception caused by "the priority of a certain surgical anesthesia process node information is higher than that of another surgical anesthesia process node information, but the visualization information of the other surgical anesthesia process node information exists on the visualization map, while the visualization information of the certain surgical anesthesia process node information does not exist"; Taking node M1 as an example, node M1 has the highest priority of this branch (but lower than the highest priority of node N1, because the nitrous oxide valve is opened only after the oxygen N1 pressure reaches the specified threshold), has no predecessor node (predecessor dependency mark), and only has a successor node (postdependent mark); the execution status of node M1 is normal output of nitrous oxide and is verified by the operation of the subsequent node M2 ​​(post-operative anesthesia process node information, i.e., pressure reducing valve M2).

[0061] Obviously, the condition for the normal operation of node M1 is that node M2 ​​also operates normally and feeds back results; Similarly, the functions and operations of other nodes can refer to the above-mentioned principle description. Those skilled in the art can obtain at least one supplementary verification mark for each surgical anesthesia process node information based on the before and after execution requirements described above, namely, a priority mark, a preceding dependency mark or a following dependency mark.

[0062] Continue to execute steps S500-S600 as follows: S500: Allocating the plurality of surgical anesthesia process node information to different execution nodes for execution; S600: Visually displaying the real-time execution status of the multiple surgical anesthesia process node information in the form of a graph.

[0063] Preferably, the step S600 visualizes the real-time execution status of the plurality of surgical anesthesia process node information in a graphical manner, specifically including: Aggregating the plurality of surgical anesthesia process node information into a target graph, wherein each node of the target graph corresponds to the surgical anesthesia process node; When the execution node executes the corresponding surgical anesthesia process node, the surgical anesthesia process node is highlighted, and the highlighting includes a supplementary verification mark that visually displays the surgical anesthesia process node.

[0064] In the above example, it can actually be divided into two independent parallel branches (M1-M3 and N1-N3), and other branches executed before and after (N6-M4-M5; N4 / N5 - N6, N4-N8), etc. The target graph can be a directed acyclic graph or a binary tree.

[0065] It can be understood that according to the above principle description, it is suitable Figure 3 The target graph is preferably a binary tree because there are two main branches.

[0066] On this basis, the step S700 predicts that the state of a certain surgical anesthesia process node information is about to become abnormal, specifically including that a certain surgical anesthesia process node information has one of the following situations or any combination thereof: (1) The priority of a certain surgical anesthesia process node information is higher than that of another surgical anesthesia process node information, but the visualization information of the other surgical anesthesia process node information exists on the visualization map, while the visualization information of the certain surgical anesthesia process node information does not exist; (2) The anesthesia process node information of the previous surgery of the anesthesia process node information of a certain surgery has not been visualized, while the anesthesia process node information of the certain surgery has been visualized; (3) When a certain surgical anesthesia process node information has a post-dependency flag, its corresponding post-surgical anesthesia process node information does not feedback verification information within a preset period of time.

[0067] certainly, Figure 3 The example is a specific introduction to the gas anesthesia scheme. When the anesthesia scheme determined by the basic information of surgical anesthesia and the target surgical information is of other types, the number and order of the corresponding multiple surgical anesthesia process nodes are different. However, as long as multiple surgical anesthesia process node information is fed back based on the verification supplementary database, the multiple surgical anesthesia process node information can be visualized in the form of a graph, and the real-time execution status can be displayed after the multiple surgical anesthesia process node information is assigned to different execution nodes for execution.

[0068] Based on the above method embodiment, Figure 4 A schematic diagram showing the functional module composition of an anesthesia nursing information monitoring system based on cloud computing according to an embodiment of the present invention is shown. Figure 4 The system includes a cloud database, which includes a basic query database and a verification supplementary database.

[0069] The system includes the following functional module components: A surgical anesthesia basic information receiving module is used to receive surgical anesthesia basic information; the surgical anesthesia basic information includes operator health information and anesthesia nursing information; A basic query module, used for sending the operator's health information to the basic query database to obtain a plurality of candidate surgery information, each of which includes a surgery type name and surgery contraindication information; A target surgery information determination module, used to calculate the difference between the anesthesia care information and each candidate surgery information, and determine the target surgery information; A verification and supplementation module, used for sending the basic surgical anesthesia information and the target surgical information to the verification and supplementation database, wherein the verification and supplementation database feeds back a plurality of surgical anesthesia process node information; each of the surgical anesthesia process node information has at least one supplementary verification mark; The supplementary verification mark includes a priority mark, a preceding dependency mark or a following dependency mark; An execution module, used to distribute the plurality of surgical anesthesia process node information to different execution nodes for execution; A visualization module, used to visualize the real-time execution status of the multiple surgical anesthesia process node information in a graphical manner; The prediction warning module is used to issue an alarm message when it is predicted that the status of a certain surgical anesthesia process node information is about to become abnormal.

[0070] The operator's health information includes the operator's basic information and the operator's latest diagnosis and treatment information; The anesthesia care information includes the type of surgery requirement, the time of surgery requirement, and surgery contraindications determined based on the operator's health information; The basic query database stores a plurality of historical successful surgery information, each of which includes the name of the surgery type, surgical contraindication information, and the corresponding historical surgeon basic information and historical surgeon diagnosis and treatment information; The verification supplementary database is used to record the surgical anesthesia process node information corresponding to each historical successful surgical information.

[0071] The visualization module aggregates the information of the plurality of surgical anesthesia process nodes into a binary tree, each node of the binary tree corresponds to the surgical anesthesia process node; when the execution node executes the corresponding surgical anesthesia process node, the surgical anesthesia process node is highlighted, and the highlighting includes visually displaying a supplementary verification mark of the surgical anesthesia process node; The prediction warning module traverses the binary tree in depth-first order according to a preset period, and issues an alarm message when it is predicted that the state of a certain surgical anesthesia process node information is about to become abnormal.

[0072] The reason why depth-first traversal is adopted instead of other traversal methods is obviously more in line with the actual situation of the binary tree flowchart involved in the above example, because the depth of each branch in the graph needs to be judged from the beginning to the bottom.

[0073] See also Figure 5 An embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by an electronic device including a processor, the steps of the cloud computing-based anesthesia care information monitoring method as described in the above embodiment are implemented.

[0074] The units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0075] The steps of the methods or algorithms described in connection with the embodiments disclosed herein may be implemented directly in hardware, in software modules executed by a processor, or in a combination thereof. The software modules may be placed in a random access memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0076] The present invention provides multiple embodiments, each of which can constitute an independent technical solution and may contribute to the prior art and solve corresponding technical problems. However, it should be noted that different embodiments can be combined with each other without violating logic; at the same time, each embodiment can solve at least one technical problem, but it is not required that each individual embodiment solve multiple or all technical problems.

[0077] For other technologies, principles, algorithms, or models not detailed in this application, reference may be made to the prior art.

[0078] The method embodiments and systems of the present invention have been shown and described above. However, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A cloud computing-based anesthesia nursing information monitoring method, the method is implemented based on a cloud database, the cloud database includes a basic query database and a verification supplementary database; It is characterized in that The method comprises: S100: receiving basic information on surgical anesthesia, wherein the basic information on surgical anesthesia includes operator health information and anesthesia nursing information; S200: sending the operator's health information to the basic query database to obtain a plurality of candidate surgery information, each of which includes a surgery type name and surgery contraindication information; S300: Calculating the difference between the anesthesia care information and each candidate surgery information to determine the target surgery information; S400: Sending the basic surgical anesthesia information and the target surgical information to the verification supplement database, wherein the verification supplement database feeds back a plurality of surgical anesthesia process node information; each of the surgical anesthesia process node information has at least one supplementary verification mark; S500: Allocating the plurality of surgical anesthesia process node information to different execution nodes for execution; S600: Visually displaying the real-time execution status of the plurality of surgical anesthesia process node information in a graph manner; S700: When it is predicted that the status of a certain surgical anesthesia process node information is about to become abnormal, an alarm message is issued.

2. The method for monitoring anesthesia nursing information based on cloud computing according to claim 1, characterized in that: The operator's health information includes the operator's basic information and the operator's latest diagnosis and treatment information; The anesthesia care information includes the type of surgery requirement, the time of surgery requirement, and surgery contraindications determined based on the operator's health information.

3. The method for monitoring anesthesia nursing information based on cloud computing according to claim 1, characterized in that: The basic query database stores a plurality of historical successful surgery information, each of which includes the name of the surgery type, surgical contraindication information, and the corresponding historical surgeon basic information and historical surgeon diagnosis and treatment information; The verification supplementary database is used to record the surgical anesthesia process node information corresponding to each historical successful surgical information.

4. The method for monitoring anesthesia nursing information based on cloud computing according to claim 1, characterized in that: The supplementary verification mark includes a priority mark, a preceding dependency mark or a following dependency mark; The priority mark is used to represent the priority of a certain surgical anesthesia process node information, and the higher the priority, the higher the monitoring level of the surgical anesthesia process node information; The preceding dependency mark is used to indicate that the execution of a certain surgical anesthesia process node information depends on the execution result of at least another preceding surgical anesthesia process node information; The post-dependency flag is used to indicate that the execution result of a certain surgical anesthesia process node information requires verification of at least another post-surgical anesthesia process node information.

5. The method for monitoring anesthesia nursing information based on cloud computing according to claim 4, characterized in that: The step S700 predicts that the state of a certain surgical anesthesia process node information is about to be abnormal, specifically including that a certain surgical anesthesia process node information has one of the following situations or any combination thereof: (1) The priority of a certain surgical anesthesia process node information is higher than that of another surgical anesthesia process node information, but the visualization information of the other surgical anesthesia process node information exists on the visualization map, while the visualization information of the certain surgical anesthesia process node information does not exist; (2) The anesthesia process node information of the previous surgery of the anesthesia process node information of a certain surgery has not been visualized, while the anesthesia process node information of the certain surgery has been visualized; (3) When a certain surgical anesthesia process node information has a post-dependency flag, its corresponding post-surgical anesthesia process node information does not feedback verification information within a preset period of time.

6. The method for monitoring anesthesia nursing information based on cloud computing according to claim 4, characterized in that: The step S600 visualizes the real-time execution status of the multiple surgical anesthesia process node information in a graphical manner, specifically including: Aggregating the plurality of surgical anesthesia process node information into a target graph, wherein each node of the target graph corresponds to the surgical anesthesia process node; When the execution node executes the corresponding surgical anesthesia process node, the surgical anesthesia process node is highlighted, and the highlighting includes a supplementary verification mark that visually displays the surgical anesthesia process node.

7. A cloud computing-based anesthesia care information monitoring system, the system comprising a cloud database, the cloud database comprising a basic query database and a verification supplementary database; It is characterized in that The system comprises: A surgical anesthesia basic information receiving module is used to receive surgical anesthesia basic information; the surgical anesthesia basic information includes operator health information and anesthesia nursing information; A basic query module, used for sending the operator's health information to the basic query database to obtain a plurality of candidate surgery information, each of which includes a surgery type name and surgery contraindication information; A target surgery information determination module, used to calculate the difference between the anesthesia care information and each candidate surgery information, and determine the target surgery information; A verification and supplementation module, used for sending the basic surgical anesthesia information and the target surgical information to the verification and supplementation database, wherein the verification and supplementation database feeds back a plurality of surgical anesthesia process node information; each of the surgical anesthesia process node information has at least one supplementary verification mark; The supplementary verification mark includes a priority mark, a preceding dependency mark or a following dependency mark; An execution module, used to distribute the plurality of surgical anesthesia process node information to different execution nodes for execution; A visualization module, used to visualize the real-time execution status of the multiple surgical anesthesia process node information in a graphical manner; The prediction warning module is used to issue an alarm message when it is predicted that the status of a certain surgical anesthesia process node information is about to become abnormal.

8. The anesthesia nursing information monitoring system based on cloud computing according to claim 7, characterized in that: The operator's health information includes the operator's basic information and the operator's latest diagnosis and treatment information; The anesthesia care information includes the type of surgery requirement, the time of surgery requirement, and surgery contraindications determined based on the operator's health information; The basic query database stores a plurality of historical successful surgery information, each of which includes the name of the surgery type, surgical contraindication information, and the corresponding historical surgeon basic information and historical surgeon diagnosis and treatment information; The verification supplementary database is used to record the surgical anesthesia process node information corresponding to each historical successful surgical information.

9. The anesthesia nursing information monitoring system based on cloud computing according to claim 7, characterized in that: The visualization module aggregates the information of the plurality of surgical anesthesia process nodes into a binary tree, each node of the binary tree corresponds to the surgical anesthesia process node; when the execution node executes the corresponding surgical anesthesia process node, the surgical anesthesia process node is highlighted, and the highlighting includes visually displaying a supplementary verification mark of the surgical anesthesia process node; The prediction warning module traverses the binary tree in depth-first order according to a preset period, and issues an alarm message when it is predicted that the state of a certain surgical anesthesia process node information is about to become abnormal.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that: When the computer program instructions are executed by the processor, a cloud computing-based anesthesia care information monitoring method according to any one of claims 1 to 5 is implemented.

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