Hospital infection treatment all-element digitalization tracing method and system based on internet of things

Abstract

This invention discloses a method and system for full-element digital traceability of hospital infection control based on the Internet of Things (IoT), relating to the field of medical informatics. The method includes: structurally decomposing and parametrically annotating the infection control standard operating procedure (SOP) text to construct a structured SOP digital knowledge base; collecting infection control monitoring data in real time and generating alarm information when a preset threshold is triggered; associating the knowledge base with a standardized set of treatment instructions; dispatching the execution task to a designated execution terminal and simultaneously activating IoT sensing layer devices to collect all elements of the execution process in real time; binding and hash-verifying the full-element execution data with alarm information and retest results to generate a traceability chain. This application addresses the technical problems of insufficient digitalization and lack of closed-loop management in existing hospital infection control and traceability systems, achieving the technical effect of improving the digitalization level of hospital infection control and traceability and realizing closed-loop management of the entire infection control process.

Description

Technical Field

[0001] This application relates to the field of medical informatics, and in particular to a method and system for full-element digital traceability of hospital infection control based on the Internet of Things. Background Technology

[0002] Hospital infection control is directly related to medical quality and patient safety, and is a core aspect that must be strictly controlled in the daily operation of medical institutions. Currently, medical institutions mainly rely on manual execution of standard operating procedures for infection control, manual recording of treatment processes, and manual verification of rectification effects, with traceability information retained through paper or simple electronic forms. This model, which heavily relies on manual execution and recording, is prone to problems such as non-standard process execution, incomplete data collection, and low information correlation, resulting in the inability to fully trace the treatment process, difficulties in tracing the source of problems, and challenges in effectively verifying the effectiveness of rectification.

[0003] Currently, hospital infection control and tracing suffer from insufficient digitalization and a lack of closed-loop management throughout the entire process. Summary of the Invention

[0004] This application provides a digital traceability method and system for all elements of hospital infection control based on the Internet of Things (IoT). It employs structured decomposition and parameterized annotation of infection control standard operating procedure (SOP) texts to construct a structured SOP digital knowledge base. Through an IoT platform, it collects infection control monitoring data in real time, generates alarm information when data triggers preset thresholds, and generates standardized treatment instruction sets based on the knowledge base linked to the alarm information. These instructions are then dispatched to designated execution terminals, and IoT sensing devices are simultaneously activated to collect all elements of the execution process data. The application also binds and hash-verifies the all-element execution data, alarm information, and retest results to generate a digital infection control audit traceability chain. These technical means address the technical problems of insufficient digitalization and lack of closed-loop management in existing hospital infection control and traceability systems, achieving the technical effect of improving the digitalization level of hospital infection control and traceability and realizing closed-loop management of the entire infection control process.

[0005] This application provides a method for full-element digital traceability of hospital infection control based on the Internet of Things (IoT), including: structurally decomposing and parametrically annotating the text of infection control standard operating procedures (SOPs) to construct a structured SOP digital knowledge base; collecting infection control monitoring data in real time based on an IoT platform, and generating alarm information when the infection control monitoring data triggers a preset threshold; associating the alarm information with the structured SOP digital knowledge base to match and generate a standardized treatment instruction set; dispatching the execution task to a designated execution terminal via the IoT according to the standardized treatment instruction set, and simultaneously activating IoT sensing layer devices to collect all elements of the execution process in real time to obtain full-element execution data; binding and hash-verifying the full-element execution data with the alarm information and the retest results after execution to generate a digital infection control audit traceability chain.

[0006] In a possible implementation, the infection control standard operating procedure (SOP) text is structurally decomposed and parameterized to construct a structured SOP digital knowledge base. The following processing is performed: the first text in the infection control SOP text is semantically segmented into three parts according to the time logic of infection control treatment: preparation stage text segment, execution stage text segment, and verification stage text segment, to obtain a first stage text set; entity relations are extracted from the first stage text set, and the extracted entities are mapped to a parameterizable first set of digital fields; a first SOP-ID is assigned to the first standard operating procedure corresponding to the first text, and a first applicable scenario tag is associated with the first SOP-ID; the first SOP-ID, the first set of digital fields, and the first applicable scenario tag are encapsulated into a first SOP digital knowledge unit; multiple first SOP digital knowledge units are stored in the structured SOP digital knowledge base using the SOP-ID as the primary key and the applicable scenario tag as the index.

[0007] In one possible implementation, based on an IoT platform, real-time sensor monitoring data is collected. When the sensor monitoring data triggers a preset threshold, an alarm message is generated, and the following processing is performed: At least one sensor monitoring data source is accessed in real-time through the IoT platform to collect the sensor monitoring data reported by the sensor monitoring data source; when the sensor monitoring data meets preset triggering conditions, a preset threshold is determined to be triggered; and the alarm message is generated based on the monitoring data source type corresponding to the preset threshold and the spatial attribution information carried by the data source.

[0008] In a possible implementation, the structured SOP digital knowledge base is associated with the alarm information, and a standardized handling instruction set is matched and generated. The following processing is performed: the handling location and risk type identifier are extracted from the alarm information to generate a first search condition; the applicable scenario tags of the structured SOP digital knowledge base are matched with the first search condition to retrieve the target SOP-ID; the target digital field set of the target SOP-ID is called, and the target digital field set is instantiated, replaced, and parameter concatenated to generate the standardized handling instruction set.

[0009] In a possible implementation, the following processing is performed: the standardized disposal instruction set includes spatial coordinate instructions, role task instructions, material requisition instructions, and quality control parameter instructions.

[0010] In a possible implementation, based on the standardized handling instruction set, the execution task is dispatched to the designated execution terminal via the Internet of Things (IoT), and the IoT sensing layer devices are simultaneously activated to collect all elements of the execution process in real time, obtaining full-element execution data. The following processing is then performed: a service area is defined based on the spatial coordinate instructions; the target execution terminal is identified based on the role task instructions; the real-time online status of the target execution terminal is queried, and available terminals that are idle and within the service area are selected; the execution task is pushed to the available terminal based on the role task instructions; and the sensing device group associated with the available terminal in the IoT sensing layer is activated based on the material requisition instructions and the quality control parameter instructions to collect personnel element data, equipment element data, material element data, environmental element data, and compliance element data of the execution process, obtaining the full-element execution data.

[0011] In a possible implementation, the full-element execution data is bound and hash-verified with the alarm information and the retest results after execution to generate a digital infection control audit traceability chain. The following processing is performed: extracting the alarm event ID from the alarm information, extracting the instruction batch ID from the standardized handling instruction set, extracting the execution task ID from the full-element execution data, and extracting the retest sample number from the retest results; concatenating the alarm event ID, instruction batch ID, execution task ID, and retest sample number according to the timestamp and performing a hash operation to generate a traceability root hash value; writing the traceability root hash value to a blockchain notarization node to obtain a notarization certificate; logically associating and encapsulating the traceability root hash value, the notarization certificate, the alarm information, the standardized handling instruction set, the full-element execution data, and the retest results to generate the digital infection control audit traceability chain.

[0012] This application also provides an IoT-based digital traceability system for all elements of hospital infection control, including: a structured SOP digital knowledge base construction module, used to perform structured decomposition and parameterized annotation of infection control standard operating procedure text to construct a structured SOP digital knowledge base; an alarm information generation module, used to collect infection control monitoring data in real time based on an IoT platform, and generate alarm information when the infection control monitoring data triggers a preset threshold; a standardized treatment instruction set generation module, used to associate the alarm information with the structured SOP digital knowledge base, match and generate a standardized treatment instruction set; a full-element acquisition module, used to dispatch the execution task to the designated execution terminal through the IoT according to the standardized treatment instruction set, and simultaneously start the IoT sensing layer device to collect all elements of the execution process in real time to obtain full-element execution data; and a digital infection control audit traceability chain generation module, used to bind and hash-verify the full-element execution data with the alarm information and the retest results after execution to generate a digital infection control audit traceability chain.

[0013] The proposed method and system for full-element digital traceability of hospital infection control based on the Internet of Things (IoT) firstly involves structurally decomposing and parametrically annotating the infection control standard operating procedure (SOP) text to construct a structured SOP digital knowledge base. Next, based on an IoT platform, infection control monitoring data is collected in real time. When the monitoring data triggers a preset threshold, an alarm is generated. Then, the alarm information is associated with the structured SOP digital knowledge base to match and generate a standardized treatment instruction set. Following this standardized instruction set, the execution task is dispatched to a designated execution terminal via the IoT, and simultaneously, IoT sensing layer devices are activated to collect all elements of the execution process in real time, obtaining full-element execution data. Finally, the full-element execution data is bound and hash-verified with the alarm information and the post-execution retest results to generate a digital infection control audit traceability chain. Through this process, the proposed method and system achieve the technical effect of improving the digitalization of hospital infection control and traceability, and realizing closed-loop management of the entire infection control process. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0015] Figure 1This is a flowchart illustrating the IoT-based digital traceability method for all elements of hospital infection management provided in this application embodiment.

[0016] Figure 2 A schematic diagram of the structure of the Internet of Things-based digital traceability system for all elements of hospital infection management provided in this application embodiment.

[0017] Figure labeling: Structured SOP digital knowledge base construction module 10, alarm information generation module 20, standardized handling instruction set generation module 30, full-element data collection module 40, digital infection control audit traceability chain generation module 50. Detailed Implementation

[0018] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0019] This application provides an embodiment of a method for full-element digital traceability of hospital infection control based on the Internet of Things, such as... Figure 1 As shown, the method includes: Step S100: The infection control standard operating procedure text is decomposed in a structured manner and annotated with parameters to build a structured SOP digital knowledge base.

[0020] Specifically, natural language processing tools are used to structure unstructured standard operating procedure (SOP) texts, rule matching and entity extraction tools are used to convert the text into fields, and an index is built using unique identifiers and scenario tags. For example, Python regular expressions and rule engines are used to segment the text, MySQL or MongoDB are used to store structured data, and an index is built using SOP-ID as the primary key, forming a searchable and callable structured SOP digital knowledge base.

[0021] In one possible implementation, the infection control standard operating procedure (SOP) text is structurally decomposed and parametrically annotated to construct a structured SOP digital knowledge base. Step S100 further includes step S110, which involves semantically segmenting the first text in the infection control SOP text into preparation stage text segments, execution stage text segments, and verification stage text segments according to the time logic of infection control procedures, thereby obtaining a first-stage text set. Specifically, text segmentation is performed using preset keyword rule matching, setting stage keywords such as preparation, donning, configuration, isolation, disinfection, sterilization, termination, inspection, verification, and retesting. Sentence boundary recognition and string matching algorithms are used to divide the text into corresponding stages. For example, for the SOP text on contact isolation of multidrug-resistant bacteria, the preparation of protective equipment is identified as the preparation stage, the execution of isolation operations is identified as the execution stage, and the verification of isolation effects is identified as the verification stage.

[0022] Step S120 involves extracting entity relationships from the text set of the first stage and mapping the extracted entities to a parameterizable first set of numeric fields. Specifically, an entity extraction method based on infection control domain dictionaries and rules is used to construct an entity dictionary containing personnel roles, equipment names, material names, environmental areas, and quality control parameters. The extracted entities are then mapped to field names and value ranges. For example, from "disposable isolation gowns, medical surgical masks, quick-drying hand sanitizers, and yellow medical waste bags are used in the ward for patients with multidrug-resistant bacteria," fields such as protective equipment type, hand sanitization method, and medical waste classification are extracted and mapped.

[0023] Step S130: Assign a first SOP-ID to the first standard operating procedure corresponding to the first text, and associate the first SOP-ID with a first applicable scenario tag. Specifically, assign a unique SOP-ID to each standard operating procedure, generate a unique SOP-ID using an auto-incrementing sequence, and associate it with a corresponding applicable scenario tag. The scenario tag adopts a multi-level classification system, including department type, risk level, infection type, and treatment type, and is stored in the database as a tag field. For example, if the SOP-ID is SOP-MDR-001, the scenario tag is multidrug-resistant bacteria, contact isolation, general ward, high risk; if the SOP-ID is SOP-OT-002, the scenario tag is operating room, terminal disinfection, high risk.

[0024] Step S140: Encapsulate the first SOP-ID, the first set of numeric fields, and the first applicable scenario tag into a first SOP digital knowledge unit. Specifically, it is encapsulated using a JSON structured format. Each knowledge unit includes an SOP-ID, a set of numeric fields, an applicable scenario tag, a version number, an effective date, and the department that prepared the document, forming a knowledge object with a unified structure. For example, all fields and tags corresponding to SOP-MDR-001 are encapsulated into a single callable unit.

[0025] Step S150: Using SOP-ID as the primary key and applicable scenario tags as the index, multiple first SOP digital knowledge units are stored in the structured SOP digital knowledge base. Specifically, a data table is created in a relational database using SOP-ID as the primary key, and a composite index is created for the applicable scenario tag field to support fast retrieval based on multiple conditions. For example, in MySQL, a composite index is created for the fields of department, infection type, and risk level to achieve second-level retrieval of the corresponding SOP by scenario tag.

[0026] Step S200: Based on the Internet of Things platform, real-time sensor monitoring data is collected. When the sensor monitoring data triggers a preset threshold, an alarm message is generated.

[0027] Specifically, the IoT platform uses the MQTT protocol to access IoT devices. It receives data in real time and compares it with preset thresholds. If the triggering conditions are met, an alarm is generated. The alarm carries information such as device number, location, value, time, and risk type. For example, EMQX is used as the MQTT message server to receive data reported by sensors in real time.

[0028] In one possible implementation, based on an IoT platform, infection control monitoring data is collected in real time. When the infection control monitoring data triggers a preset threshold, an alarm message is generated. Step S200 further includes step S210, which involves accessing at least one infection control monitoring data source in real time through the IoT platform to collect the infection control monitoring data reported by the infection control monitoring data source. Specifically, environmental microbial sensors, surface disinfection sensors, medical waste monitoring sensors, personnel wearable recognition cameras, access control positioning devices, etc., are accessed through an IoT gateway and uniformly converted to the MQTT protocol for uploading to the platform. For example, terminal disinfection sensors in operating rooms report disinfection concentration, disinfection time, and environmental microbial content data in real time.

[0029] Step S220: When the sensor monitoring data meets the preset trigger conditions, a preset threshold is determined to be triggered. Specifically, a rule engine is set at the edge of the IoT platform, using numerical comparison, continuous timeout, and interval determination logic. If the conditions are met, a trigger is determined. For example, the threshold for the microbial content on the surface of a multidrug-resistant bacteria ward is less than or equal to 10 CFU / cm³. 2 When the data is greater than 10 cfu / cm 2 If the duration exceeds 10 seconds, an alarm will be triggered.

[0030] Step S230: Generate the alarm information based on the monitoring data source type corresponding to the preset threshold and the spatial attribution information carried by the data source. Specifically, read the spatial location, department, and risk type corresponding to the device number from the device configuration table and combine them into structured alarm information. For example, the alarm information is: "Multidrug-resistant bacteria in bed 3 of general ward have excessive microbial levels on the contact isolation surface, current value 25 cfu / cm³". 2 Threshold 10 cfu / cm 2 .

[0031] Step S300: Based on the alarm information, associate it with the structured SOP digital knowledge base, match and generate a standardized set of handling instructions.

[0032] Specifically, the processing location and risk type in the alarm information are used as search conditions. Scene tag matching is performed in the structured SOP digital knowledge base. After recalling the target SOP-ID, the field is instantiated to generate an instruction set containing spatial coordinates, role tasks, material requisition, and quality control parameters.

[0033] In one possible implementation, based on the alarm information and the structured SOP digital knowledge base, a standardized treatment instruction set is matched and generated. Step S300 further includes step S310, extracting the processing location and risk type identifier from the alarm information to generate a first search condition. Specifically, the department, ward number, and risk type are extracted from the alarm text through string matching and keyword extraction, and structured into search conditions. For example, the location is extracted from the alarm as bed 3 in a general ward, and the risk type is "multidrug-resistant bacteria contact isolation failure".

[0034] Step S320: Match the applicable scenario tags of the structured SOP digital knowledge base with the first search criteria to retrieve the target SOP-ID. Specifically, perform a multi-tag matching query in the structured SOP digital knowledge base, sort by matching degree and risk level, and retrieve the highest priority SOP-ID. For example, if the search criteria are general ward + multidrug-resistant bacteria + contact isolation, retrieve SOP-MDR-001.

[0035] Step S330: The target numerical field set of the target SOP-ID is invoked, and the target numerical field set is instantiated, replaced, and parameterized to generate the standardized disposal instruction set. The standardized disposal instruction set includes spatial coordinate instructions, role / task instructions, material requisition instructions, and quality control parameter instructions. Specifically, the numerical fields in the target SOP are replaced with the actual location, personnel, and material information in the alarm to generate executable instructions. For example, the generated instructions are: Spatial coordinates: Bed 3, general ward; Role / task: Responsible nurse performs contact isolation; Material requisition: Disposable isolation gown, yellow medical waste bag, quick-drying hand sanitizer; Quality control parameters: Hand disinfection for at least 15 seconds each time, medical waste double-sealed.

[0036] Step S400: According to the standardized processing instruction set, the execution task is dispatched to the designated execution terminal via the Internet of Things, and the Internet of Things sensing layer device is activated simultaneously to collect all elements of the execution process in real time and obtain full-element execution data.

[0037] Specifically, based on the spatial coordinates and roles in the standardized disposal instruction set, tasks are pushed to online idle terminals, and corresponding IoT sensing devices are activated to collect five types of data: personnel, equipment, materials, environment, and compliance.

[0038] In one possible implementation, according to the standardized handling instruction set, the execution task is dispatched to the designated execution terminal via the Internet of Things (IoT), and the IoT sensing layer device is simultaneously activated to collect all elements of the execution process in real time, obtaining full-element execution data. Step S400 further includes step S410, delineating the service area according to the spatial coordinate instruction; and identifying the target execution terminal according to the role task instruction. Specifically, the service area is determined by dividing the area into electronic fences and departmental areas, and the corresponding handheld data terminal, nurse station computer, and mobile nursing terminal are matched from the personnel role table. For example, the handheld data terminal of the responsible nurse in the area of ​​bed 3 in a general ward is the target execution terminal.

[0039] Step S420: Query the real-time online status of the target execution terminal and filter available terminals that are idle and within the service range. Specifically, the IoT platform maintains a terminal status table in real time, including online, offline, idle, and busy states. It uses UWB indoor positioning to determine whether a terminal is within the service range and filters out online and idle terminals.

[0040] Step S430: Based on the role task instruction, the task is pushed to the available terminal. Specifically, the instruction is pushed to the terminal App via the WebSocket push interface. The terminal displays a task prompt and supports confirmation of receipt. For example, a nurse's handheld data terminal receives a pop-up task and displays the content of the multidrug-resistant bacteria contact isolation treatment and the completion deadline.

[0041] Step S440: Based on the material requisition instruction and the quality control parameter instruction, activate the sensing device group associated with the available terminal in the IoT sensing layer to collect personnel element data, equipment element data, material element data, environmental element data, and compliance element data during the execution process, thereby obtaining the full-element execution data. Specifically, personnel data is collected through UWB positioning or RFID work badges, equipment data is collected through the operating status of disinfection equipment, material data is collected through material RFID tags, environmental data is collected through microbial sensors, and compliance data is collected through process node timestamp comparison. For example, during terminal disinfection in the operating room, the nurse's location is collected, the disinfection equipment records operating parameters, the disinfectant label records the usage amount, and the time sensor records the start and end times of disinfection.

[0042] Step S500: Bind and hash-verify the full-element execution data with the alarm information and the retest results after execution to generate a digital infection control audit traceability chain.

[0043] Specifically, the unique identifier of each link is extracted, and after being concatenated by timestamp, a hash operation is performed. The hash value is written into the blockchain for evidence storage, and then all data, hash, and evidence storage certificate are encapsulated into a traceability chain.

[0044] In one possible implementation, the full-element execution data is bound and hash-verified with the alarm information and the post-execution retest results to generate a digital infection control audit traceability chain. Step S500 further includes step S510, which extracts the alarm event ID from the alarm information, the instruction batch ID from the standardized handling instruction set, the execution task ID from the full-element execution data, and the retest sample number from the retest results. Specifically, all identifiers use a unique string format, for example, the alarm event ID is ALARM-MDR-2026001, the instruction batch ID is ORDER-MDR-0001, the execution task ID is TASK-MDR-0001, and the retest sample number is SAMPLE-MDR-0001.

[0045] Step S520: The alarm event ID, the instruction batch ID, the execution task ID, and the retest sample number are concatenated according to their timestamps and then hashed to generate a traceability root hash value. Specifically, strings are concatenated in chronological order according to their timestamps, and a fixed-length hash value is calculated using the SHA-256 hash algorithm. This hash value uniquely corresponds to the entire process of this hospital infection control. For example, the SHA-256 hash of the IDs corresponding to the contact isolation of multidrug-resistant bacteria is calculated after concatenating them according to their timestamps.

[0046] Step S530: Write the traceability root hash value into the blockchain evidence storage node to obtain the evidence storage certificate. Specifically, a medical industry consortium blockchain node is used, and the traceability root hash, timestamp, and hospital institution identifier are used as on-chain data. The evidence storage smart contract is called to complete the on-chain process, and the block height and transaction hash are returned as evidence storage certificates to ensure that the hash value cannot be tampered with.

[0047] Step S540 involves logically associating and encapsulating the traceability root hash value, the evidence storage certificate, the alarm information, the standardized handling instruction set, the full-element execution data, and the retest results to generate the digital infection control audit traceability chain. Specifically, structured data encapsulation is used to associate all data with hashes and certificates, forming a complete traceability chain that is queryable, verifiable, and auditable. This supports one-click backtracking of the entire infection control process data, such as contact isolation of multidrug-resistant bacteria or terminal disinfection of operating rooms, by alarm ID.

[0048] This application employs a structured decomposition and parameterized annotation of infection control standard operating procedure (SOP) text to construct a structured SOP digital knowledge base. It collects infection control monitoring data in real time through an IoT platform, generates alarm information when data triggers preset thresholds, and generates a standardized set of handling instructions based on the knowledge base linked to the alarm information. These instructions are then dispatched to designated execution terminals, and IoT sensing devices are simultaneously activated to collect all elements of the execution process data. The execution data, alarm information, and retest results are bound together and hash-verified to generate a digital infection control audit traceability chain. These technical means address the existing technical problems of insufficient digitalization and lack of closed-loop management in hospital infection control and traceability, achieving the technical effect of improving the digitalization of hospital infection control and traceability and realizing closed-loop management of the entire infection control process.

[0049] In the above text, refer to Figure 1 This paper describes in detail an IoT-based digital traceability method for hospital infection control according to embodiments of the present invention. Next, we will refer to... Figure 2 This invention describes a digital traceability system for all elements of hospital infection management based on the Internet of Things (IoT).

[0050] The IoT-based digital traceability system for hospital infection control, as described in this embodiment, addresses the technical problems of insufficient digitalization and lack of closed-loop management in existing hospital infection control and traceability systems. It aims to improve the digitalization level of hospital infection control and traceability and achieve closed-loop management of the entire infection control process. The IoT-based digital traceability system for hospital infection control includes: a structured SOP digital knowledge base construction module 10, an alarm information generation module 20, a standardized treatment instruction set generation module 30, a full-element data collection module 40, and a digital infection control audit traceability chain generation module 50.

[0051] The structured SOP digital knowledge base construction module 10 is used to perform structured decomposition and parameterized annotation of infection control standard operating procedure text to construct a structured SOP digital knowledge base; the alarm information generation module 20 is used to collect infection control monitoring data in real time based on the Internet of Things platform, and generate alarm information when the infection control monitoring data triggers a preset threshold; the standardized handling instruction set generation module 30 is used to associate the alarm information with the structured SOP digital knowledge base, match and generate a standardized handling instruction set; the full-element acquisition module 40 is used to dispatch the execution task to the designated execution terminal through the Internet of Things according to the standardized handling instruction set, and simultaneously start the Internet of Things sensing layer device to collect all elements of the execution process in real time to obtain full-element execution data; the digital infection control audit traceability chain generation module 50 is used to bind and hash-verify the full-element execution data with the alarm information and the retest results after execution to generate a digital infection control audit traceability chain.

[0052] The detailed description of the specific configuration of the structured SOP digital knowledge base construction module 10 is as follows: As mentioned above, the infection control standard operating procedure text is structurally decomposed and parameterized to construct a structured SOP digital knowledge base. The structured SOP digital knowledge base construction module 10 may further include: a semantic segmentation unit for semantically segmenting the first text in the infection control standard operating procedure text, segmenting it into preparation stage text segments, execution stage text segments, and verification stage text segments according to the time logic of infection control treatment, to obtain a first stage text set; and an entity relation extraction unit for extracting entity relations from the first stage text set. The system extracts entities and maps them to a parameterizable first set of digital fields. A first SOP-ID allocation unit is used to allocate a first SOP-ID to the first standard operating procedure corresponding to the first text and associate the first SOP-ID with a first applicable scenario tag. A first SOP digital knowledge encapsulation unit is used to encapsulate the first SOP-ID, the first set of digital fields, and the first applicable scenario tag into a first SOP digital knowledge unit. A structured SOP digital knowledge base generation unit is used to store multiple first SOP digital knowledge units into the structured SOP digital knowledge base, with the SOP-ID as the primary key and the applicable scenario tag as the index.

[0053] The alarm information generation module 20 is described in detail below: As mentioned above, based on the Internet of Things (IoT) platform, it collects sensor monitoring data in real time. When the sensor monitoring data triggers a preset threshold, it generates alarm information. The alarm information generation module 20 may further include: a sensor monitoring data acquisition unit for accessing at least one sensor monitoring data source in real time through the IoT platform and collecting the sensor monitoring data reported by the sensor monitoring data source; a preset threshold determination unit for determining whether to trigger a preset threshold when the sensor monitoring data meets preset trigger conditions; and an alarm information generation unit for generating the alarm information according to the monitoring data source type corresponding to the preset threshold and the spatial attribution information carried by the data source.

[0054] The standardized handling instruction set generation module 30 is described in detail below: As mentioned above, based on the alarm information associated with the structured SOP digital knowledge base, a standardized handling instruction set is matched and generated. The standardized handling instruction set generation module 30 may further include: a first retrieval condition generation unit for extracting the processing location and risk type identifier from the alarm information and generating a first retrieval condition; a target SOP-ID recall unit for matching the applicable scenario tags of the structured SOP digital knowledge base with the first retrieval condition and recalling the target SOP-ID; and an instantiation replacement unit for calling the target digital field set of the target SOP-ID, performing instantiation replacement and parameter concatenation on the target digital field set, and generating the standardized handling instruction set.

[0055] The instantiation replacement unit may further include: the standardized disposal instruction set includes spatial coordinate instructions, role task instructions, material requisition instructions, and quality control parameter instructions.

[0056] The detailed description of the specific configuration of the all-element acquisition module 40 is explained as follows: As mentioned above, according to the standardized handling instruction set, the execution task is dispatched to the designated execution terminal through the Internet of Things (IoT), and the IoT sensing layer device is simultaneously activated to collect all elements of the execution process in real time to obtain all-element execution data. The all-element acquisition module 40 may further include: a range and terminal determination unit for defining the service range according to the spatial coordinate instruction; identifying the target execution terminal according to the role task instruction; an available terminal filtering unit for querying the real-time online status of the target execution terminal and filtering available terminals that are idle and within the service range; an execution task push unit for pushing the execution task to the available terminal according to the role task instruction; and an all-element data acquisition unit for activating the sensing device group associated with the available terminal in the IoT sensing layer according to the material call instruction and the quality control parameter instruction, collecting personnel element data, equipment element data, material element data, environmental element data, and compliance element data of the execution process to obtain the all-element execution data.

[0057] The detailed configuration of the digital infection control audit traceability chain generation module 50 is explained below: As mentioned above, the digital infection control audit traceability chain is generated by binding and hashing the full-element execution data with the alarm information and the retest results after execution. The digital infection control audit traceability chain generation module 50 may further include: an information extraction unit for extracting alarm event ID from the alarm information, instruction batch ID from the standardized handling instruction set, execution task ID from the full-element execution data, and retest sample number from the retest results; a hash operation unit for concatenating the alarm event ID, instruction batch ID, execution task ID, and retest sample number according to timestamps and then performing a hash operation to generate a traceability root hash value; a certificate acquisition unit for writing the traceability root hash value into a blockchain certificate node to obtain a certificate; and an encapsulation unit for logically associating and encapsulating the traceability root hash value, the certificate, the alarm information, the standardized handling instruction set, the full-element execution data, and the retest results to generate the digital infection control audit traceability chain.

[0058] The IoT-based digital traceability system for hospital infection control provided in this invention can execute the IoT-based digital traceability method for hospital infection control provided in any embodiment of this invention, and has the corresponding functional modules and beneficial effects of the method.

[0059] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0060] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for full-element digital traceability of hospital infection control based on the Internet of Things, characterized in that: The method includes: The infection control standard operating procedure (SOP) text is decomposed in a structured manner and annotated with parameters to construct a structured SOP digital knowledge base; Based on the Internet of Things platform, real-time sensing and monitoring data is collected, and alarm information is generated when the sensing and monitoring data triggers a preset threshold. Based on the alarm information, associate it with the structured SOP digital knowledge base, match and generate a standardized set of handling instructions; According to the standardized processing instruction set, the execution task is dispatched to the designated execution terminal through the Internet of Things, and the Internet of Things sensing layer device is activated simultaneously to collect all elements of the execution process in real time and obtain full-element execution data. The execution data of all elements is bound and hash-verified with the alarm information and the retest results after execution to generate a digital infection control audit traceability chain.

2. The IoT-based digital traceability method for all elements of hospital infection control as described in claim 1, characterized in that, The infection control standard operating procedure (SOP) text is decomposed into a structured form and annotated with parameters to construct a structured SOP digital knowledge base, including: The first text in the infection control standard operating procedure text is semantically segmented and divided into preparation stage text segment, execution stage text segment, and verification stage text segment according to the time logic of infection control treatment to obtain the first stage text set. Entity relations are extracted from the first-stage text set, and the extracted entities are mapped to a parameterizable first set of numeric fields. Assign a first SOP-ID to the first standard operating procedure corresponding to the first text, and associate the first SOP-ID with a first applicable scenario tag; The first SOP-ID, the first set of digital fields, and the first applicable scenario label are encapsulated into a first SOP digital knowledge unit; Using SOP-ID as the primary key and applicable scenario tags as the index, multiple first SOP digital knowledge units are stored in the structured SOP digital knowledge base.

3. The IoT-based digital traceability method for all elements of hospital infection control as described in claim 1, characterized in that, Based on an IoT platform, real-time sensor monitoring data is collected. When the sensor monitoring data triggers a preset threshold, an alarm message is generated, including: The system accesses at least one sensor monitoring data source in real time through an IoT platform and collects the sensor monitoring data reported by the sensor monitoring data source. When the sensor monitoring data meets the preset triggering conditions, a preset threshold is determined to be triggered; The alarm information is generated based on the monitoring data source type corresponding to the preset threshold and the spatial affiliation information carried by the data source.

4. The IoT-based digital traceability method for all elements of hospital infection control as described in claim 1, characterized in that, Based on the alarm information, the structured SOP digital knowledge base is associated with the data to match and generate a standardized set of handling instructions, including: Extract the processing location and risk type identifier from the alarm information to generate the first search criteria; Match the applicable scenario tags of the structured SOP digital knowledge base with the first search criteria to retrieve the target SOP-ID; The target numeric field set of the target SOP-ID is invoked, and the target numeric field set is instantiated, replaced, and concatenated with parameters to generate the standardized processing instruction set.

5. The IoT-based digital traceability method for all elements of hospital infection control as described in claim 4, characterized in that, The standardized handling instruction set includes spatial coordinate instructions, role and task instructions, material requisition instructions, and quality control parameter instructions.

6. The IoT-based digital traceability method for all elements of hospital infection control as described in claim 5, characterized in that, According to the standardized processing instruction set, the execution task is dispatched to the designated execution terminal via the Internet of Things (IoT), and the IoT sensing layer device is simultaneously activated to collect all elements of the execution process in real time, obtaining full-element execution data, including: The service area is defined according to the spatial coordinate instructions; the target execution terminal is identified according to the role and task instructions. Query the real-time online status of the target execution terminal and filter available terminals that are idle and within the service range; According to the role's task instructions, the task will be pushed to the available terminal; Based on the material requisition instruction and the quality control parameter instruction, the sensing device group associated with the available terminal in the IoT sensing layer is activated to collect personnel element data, equipment element data, material element data, environmental element data and compliance element data during the execution process, thereby obtaining the full-element execution data.

7. The IoT-based digital traceability method for all elements of hospital infection control as described in claim 1, characterized in that, The full-element execution data is bound and hash-verified with the alarm information and the post-execution retest results to generate a digital infection control audit traceability chain, including: Extract the alarm event ID from the alarm information, extract the instruction batch ID from the standardized handling instruction set, extract the execution task ID from the full-element execution data, and extract the retest sample number from the retest results; The alarm event ID, the instruction batch ID, the execution task ID, and the retest sample number are concatenated according to their timestamps and then hashed to generate a trace root hash value. Write the traceability root hash value into the blockchain evidence storage node to obtain the evidence storage certificate; The traceability root hash value, the evidence storage certificate, the alarm information, the standardized handling instruction set, the full-element execution data, and the retest results are logically associated and encapsulated to generate the digital infection control audit traceability chain.

8. A digital traceability system for all elements of hospital infection control based on the Internet of Things, characterized in that: The system is used to implement the Internet of Things-based digital traceability method for all elements of hospital infection control as described in any one of claims 1-7, and the system includes: The structured SOP digital knowledge base construction module is used to perform structured decomposition and parameterized annotation of infection control standard operating procedure texts to build a structured SOP digital knowledge base. The alarm information generation module is used to collect sensor monitoring data in real time based on the Internet of Things platform, and generate alarm information when the sensor monitoring data triggers a preset threshold. The standardized handling instruction set generation module is used to match and generate a standardized handling instruction set based on the alarm information and the structured SOP digital knowledge base. The full-element acquisition module is used to dispatch the execution task to the designated execution terminal through the Internet of Things according to the standardized processing instruction set, and simultaneously start the Internet of Things sensing layer device to collect all elements of the execution process in real time to obtain full-element execution data. The digital infection control audit traceability chain generation module is used to bind and hash-verify the full-element execution data with the alarm information and the retest results after execution to generate a digital infection control audit traceability chain.

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