Spatiotemporal consistency testing methods, devices, and readable storage media for cross-time zone medical collaboration

Abstract

This invention proposes a method, apparatus, and readable storage medium for testing the spatiotemporal consistency of cross-timezone medical collaboration, belonging to the field of medical information technology. The system aims to address data conflicts, decision delays, and clinical risks caused by spatiotemporal misalignment in cross-timezone medical collaboration. It includes a spatiotemporal scene configurator, a spatiotemporal label generator, a conflict resolution algorithm library, and a clinical impact assessor. Specifically, the spatiotemporal label generator adds four-dimensional coordinate labels to medical operations, resolving timezone conversion boundary issues; the conflict resolution algorithm library dynamically handles concurrent operation conflicts based on priority, combining automatic coverage and manual arbitration mechanisms; and the clinical impact assessor quantifies time lag risks. Through this system and method, the electronic medical record conflict rate is reduced by 91%, emergency operation response delay is shortened by 96%, and the clinical decision error rate is reduced by 92%, effectively verifying the robustness of the cross-timezone medical collaboration system and improving clinical safety.

Description

Technical Field

[0001] This invention relates to the field of medical information technology, and to an automated testing technology for spatiotemporal consistency of cross-time zone telemedicine collaboration platforms, which is particularly suitable for system robustness verification in scenarios such as cross-border electronic medical record operation, remote consultation, and emergency medical decision-making. Background Technology

[0002] In existing technologies, timezone-adaptive electronic medical record systems only support timezone conversion for a single user, failing to address data overlay caused by concurrent operations by multiple doctors (e.g., doctors in China and the US simultaneously modifying prescriptions). Medical collaboration conflict detection relies on manually marking operation sequences, making it difficult to dynamically simulate clinical decision delays caused by time differences. Furthermore, existing testing tools (such as Selenium) cannot simulate timezone-related clinical workflow dependencies (e.g., the impact of insulin injection time window deviations on treatment efficacy). These issues directly lead to data conflicts, decision delays, and potential clinical risks, severely restricting the reliability of cross-timezone medical collaboration.

[0003] Therefore, there is an urgent need for a testing system that can accurately simulate cross-time zone scenarios and automatically detect and resolve spatiotemporal conflicts. Summary of the Invention

[0004] This invention provides a method, apparatus, and readable storage medium for testing the spatiotemporal consistency of cross-time zone medical collaboration. It addresses the problems that existing technologies cannot solve the data conflicts of multi-user concurrent operations in cross-time zone medical collaboration, rely on manual marking of operation timing and have difficulty dynamically simulating decision delays caused by time differences, have high clinical risks caused by timestamp confusion, and that existing testing tools cannot meet the simulation requirements of time zone-related clinical workflows.

[0005] The core technology of this invention is to add four-dimensional coordinates (operator's time zone, patient's time zone, absolute UTC time, and medical event priority) to each medical operation through a spatiotemporal label generator, dynamically handle concurrent operation conflicts in combination with a conflict resolution algorithm library, quantify time delay risk using a clinical impact assessor, and build an automated testing framework to verify the spatiotemporal consistency of the cross-time zone medical collaboration system.

[0006] In a first aspect, the present invention provides a method for testing the spatiotemporal consistency of cross-time zone medical collaboration, the method comprising the following steps: Configure cross-time zone medical testing scenarios, including concurrent operation conflicts among operators in multiple time zones, treatment window misalignment caused by patient movement across time zones, and timing competition between emergency and routine operations. For each medical operation in the test scenario, generate a multi-dimensional spatiotemporal label containing the operator's time zone, the patient's time zone, absolute UTC time, and the medical event priority. Based on the priority of medical events in multidimensional spatiotemporal labels, conflict handling is performed on concurrent medical operations. The clinical risks caused by time delays during conflict resolution are quantified, and a clinical impact assessment report is generated to verify the spatiotemporal consistency of the cross-time zone medical collaboration system.

[0007] Furthermore, the step of generating multidimensional spatiotemporal labels also includes boundary processing of absolute UTC time, which includes daylight saving time conversion adaptation, International Date Line compensation, and leap second correction.

[0008] Furthermore, the steps for conflict resolution of concurrent medical operations based on medical event priority include: Calculate the priority of each concurrent operation, which is determined based on the maximum allowable delay, minimum required response time, and severity of the disease. If the priority difference of concurrent operations exceeds a preset threshold, then the high-priority operation will override the low-priority operation. If the priority difference does not exceed the preset threshold, a manual arbitration process will be triggered, and the arbitrator will confirm the final decision.

[0009] Furthermore, in the step of quantifying clinical risk, clinical risk is visualized through a time lag-efficacy decay curve, which is generated based on a pharmacokinetic model and reflects the relationship between time delay and the retention rate of treatment effect.

[0010] Furthermore, medical event priorities are mapped to a 0-1 range through standardized processing, which is based on the maximum and minimum priority values ​​in the current conflict scenario.

[0011] Secondly, the present invention provides a spatiotemporal consistency testing device for cross-time zone medical collaboration, comprising: The spatiotemporal scenario configurator is used to generate multi-dimensional cross-time zone medical test scenarios. Test scenarios include concurrent operation conflicts between operators in multiple time zones, misalignment of treatment windows caused by patients moving across time zones, and timing competition between emergency operations and routine operations. A spatiotemporal label generator is used to generate multidimensional spatiotemporal labels for each medical operation, including the operator's time zone, the patient's time zone, absolute UTC time, and the medical event priority. A conflict resolution algorithm library for handling conflicts in concurrent medical operations based on the priority of medical events in multidimensional spatiotemporal labels; The clinical impact assessor is used to quantify the clinical risks caused by time delays during concurrent operations and generate a clinical impact assessment report. Among them, the spatiotemporal label generator, conflict resolution algorithm library and clinical impact assessor work together through data interaction. The test scenario generated by the spatiotemporal scenario configurator drives the spatiotemporal label generator to generate labels. After the conflict resolution algorithm library processes the conflict based on the multidimensional spatiotemporal labels, the clinical impact assessor outputs the assessment results to verify the spatiotemporal consistency of the cross-time zone medical collaboration system.

[0012] Furthermore, the spatiotemporal tag generator is also used to handle time zone conversion boundary situations, including daylight saving time rule adaptation, International Date Line compensation, and leap second event correction; the time accuracy of the multidimensional spatiotemporal tag can be dynamically adjusted based on the priority of medical events, with the time accuracy of emergency operations at the millisecond level and the time accuracy of routine operations at the second level.

[0013] Furthermore, the conflict resolution algorithm library includes a priority decision module and a human arbitration module; The priority decision module is used to determine the priority of concurrent operations based on preset priority calculation rules and to handle conflicts according to priority differences. If the priority difference exceeds a preset threshold, the high-priority operation will override the low-priority operation. The manual arbitration module is used to trigger the manual arbitration process when the priority difference does not exceed a preset threshold. The manual arbitration process includes displaying the spatiotemporal tags of the conflicting operations, the priority difference, and the clinical impact assessment report, and the arbitrator confirms the final decision.

[0014] Furthermore, the preset priority calculation rules include: The initial priority is calculated based on the maximum allowable delay of the operation, the minimum required response time, and the severity of the illness. The original priority is standardized to the 0-1 range to obtain the standardized priority.

[0015] Furthermore, the clinical risk quantification method of the clinical impact assessor is: Risk value = Time delay × Treatment time window sensitivity coefficient, which is dynamically determined based on drug half-life, disease severity index and clinical guidelines.

[0016] Thirdly, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the above-described method for testing the spatiotemporal consistency of cross-time zone medical collaboration.

[0017] Fourthly, the present invention provides a readable storage medium storing a computer program, the computer program including program code for controlling a process to execute the process, the process including the spatiotemporal consistency test method for cross-time zone medical collaboration described above.

[0018] The main contributions and innovations of this invention are as follows: 1. Significantly reduced clinical risks: Electronic medical record conflict rate decreased from 23% to 2% (a 91% reduction), emergency operation response delay was shortened from 47 minutes to 2 minutes (a 96% reduction), and clinical decision error rate decreased from 8.5% to 0.7% (a 92% reduction), effectively solving medical errors caused by timestamp confusion.

[0019] 2. Addressing core technical pain points: For the first time, it achieves four-dimensional spatiotemporal label management for cross-time zone medical operations, overcomes problems such as concurrent conflicts in multiple time zones and decision delays caused by time differences, and supports accurate handling of boundary scenarios such as daylight saving time, leap seconds, and the International Date Line.

[0020] 3. Improve testing efficiency and automation: Replace manual marking of operation sequences, improve testing efficiency by 15 times, and reduce manual review costs; through dynamic priority algorithms and clinical impact quantification models, automate conflict resolution and visualize risks.

[0021] 4. Enhance system robustness: Integrate authoritative medical databases (such as FDA guidelines and WHO standards) to ensure that the test scenarios are consistent with clinical practice, provide reliable robustness verification tools for cross-time zone medical collaboration systems, and ensure the safety and compliance of cross-border medical collaboration.

[0022] Details of one or more embodiments of the present invention are set forth in the following drawings and description, so that other features, objects and advantages of the invention will be more readily understood. Attached Figure Description

[0023] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of a method for testing the spatiotemporal consistency of cross-time zone medical collaboration according to an embodiment of the present invention; Figure 2 This is an architectural diagram of a spatiotemporal consistency testing device for cross-time zone medical collaboration according to an embodiment of the present invention; Figure 3 This is a time lag-therapeutic effect decay curve according to an embodiment of the present invention; Figure 4 This is a conflict resolution path diagram according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0024] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with one or more embodiments of this specification. Rather, they are merely examples of apparatuses and methods consistent with some aspects of one or more embodiments of this specification as detailed in the appended claims.

[0025] It should be noted that the steps of the corresponding methods are not necessarily performed in the order shown and described in this specification in other embodiments. In some other embodiments, the methods may include more or fewer steps than described in this specification. Furthermore, a single step described in this specification may be broken down into multiple steps in other embodiments; and multiple steps described in this specification may be combined into a single step in other embodiments.

[0026] Existing technologies cannot resolve data conflicts caused by concurrent operations of multiple users in cross-time zone medical collaborations. They rely on manual marking of operation timings and are difficult to dynamically simulate decision delays caused by time differences. There are high clinical risks caused by timestamp confusion, and existing testing tools cannot meet the simulation needs of time zone-related clinical workflows.

[0027] Based on this, the present invention uses a spatiotemporal label generator to add four-dimensional coordinates to each medical operation to solve the problems existing in the prior art.

[0028] Example 1 This invention aims to propose a spatiotemporal consistency testing method for cross-timezone medical collaboration, which can be specifically referred to... Figure 1 The method includes the following steps: Step 1: Configure cross-time zone medical test scenarios. Test scenarios include concurrent operation conflicts between operators in multiple time zones, misalignment of treatment windows caused by patients moving across time zones, and timing competition between emergency operations and routine operations. In this embodiment, multi-dimensional test scenarios are generated, such as conflicts in concurrent operations by doctors in multiple time zones (e.g., doctors in New York, Berlin, and Tokyo simultaneously modifying the same electronic medical record); misalignment of treatment windows caused by patients moving across time zones (e.g., medication time adjustment when moving from UTC+8 to UTC-5); and timing competition between emergency events and routine operations (e.g., priority conflict between myocardial infarction rescue and routine laboratory tests).

[0029] In this embodiment, for example, a collaborative test of a multinational ICU insulin treatment protocol is conducted, and the test scenario is configured as follows: Simulated doctor distribution: New York (UTC-5), Berlin (UTC+1), Tokyo (UTC+9); Patient location: Singapore (UTC+8); Test event: Three doctors adjusted the insulin dose for the same patient within 5 minutes of each other.

[0030] Step 2: For each medical operation in the test scenario, generate a multi-dimensional spatiotemporal label containing the operator's time zone, the patient's time zone, the absolute UTC time, and the medical event priority; In this embodiment, a four-dimensional coordinate label (operator's time zone, patient's time zone, UTC (Coordinated Universal Time) absolute time, and medical event priority) is attached to each medical operation. By integrating daylight saving time rules, International Date Line compensation, and leap second event handling, the timestamp confusion caused by time zone differences is resolved. Dynamic updates to label precision (e.g., millisecond-level timestamps) are supported to meet the requirements of high-precision medical operations.

[0031] The time zone rule acquisition includes: calling the standard time zone public database (IANA TimeZoneDatabase) and dynamically loading global time zone rules, including daylight saving time conversion dates, time offsets, and historical change records.

[0032] The conversion algorithm logic is as follows: 1. Input: Operator's local time (including time zone identifier, such as "America / New_York"), target UTC time base.

[0033] 2. Processing steps: Parse the time zone identifier and retrieve the rule set from the database.

[0034] Apply Daylight Saving Time Conversion: If the local time is within the Daylight Saving Time effective period, automatically increase / decrease the time zone offset (e.g., EST→EDT:UTC-5→UTC-4).

[0035] International Date Line Compensation: When operations cross time zone boundaries, the date jump is calculated (e.g., from UTC+12 to UTC-11, the date increases / decreases by 1 day) and calibrated using a UTC timestamp.

[0036] 3. Boundary case handling: Daylight Saving Time Conversion Point: For operations around 2:00 AM, a time interval overlap detection algorithm is used (e.g., 02:00-03:00 may correspond to two UTC times), and the default is to use the actual timestamp of the clinical operation.

[0037] Date change line jump: Calculated by the absolute value of the time zone difference (|ΔTZ|≥20 hours is considered crossing the date line), the date is automatically adjusted while preserving the integrity of the operation sequence.

[0038] Leap second insertion: Extends the timeline when the UTC seconds count is 60 to avoid sorting conflicts caused by timestamp rollback.

[0039] Preferably, the present invention uses the IERS leap second announcement: the system integrates the official data source public interface of IERS to automatically obtain and update leap second information.

[0040] The steps for handling leap second events are as follows: Data Acquisition: The system periodically downloads leap second announcements from the IERS server daily and parses the effective date and adjustment value of the leap second in the announcement.

[0041] Event handling: When the system detects that the current time is close to a leap second event (such as UTC time 23:59:59), it automatically triggers the leap second compensation logic.

[0042] For example, during a leap second, when the timestamp transitions from 23:59:59 to 23:59:60 (or jumps directly to 00:00:00), the system needs to record an extra second.

[0043] Dynamic updates: Leap second information is stored in the configuration database and supports real-time synchronization.

[0044] Preferably, the timestamp correction process uses the standard time library (Python's pytz) to handle leap seconds, with the specific algorithm as follows: When converting from UTC to other time zones: first apply leap second compensation, then convert the time zone.

[0045] For example: 1. Input: UTC time “2025-12-31T23:59:60Z” (leap second time).

[0046] To convert to the New York time zone (UTC-5): First calculate it as "2025-12-31T18:59:60-05:00", but the standard time zone library may not support 60 seconds, so you need to customize it (such as treating 60 seconds as 00 seconds of the next minute).

[0047] 2. Leap Second Representation: Internally, the system uses an extended ISO8601 format (such as "2025-06-20T23:59:60Z") to store the timestamp, and converts it to a compatible format when outputting externally.

[0048] 3. Dynamic correction: The algorithm supports a configuration switch, allowing leap second processing to be enabled or disabled during medical procedures.

[0049] In this embodiment, the example code for this step is as follows: class TimeAwareMedicalTest: def __init__(self): self.clinical_rules = load_fda_guidelines("diabetes_care") # Load FDA diabetes treatment guidelines def simulate_operation_conflict(self): # Generate concurrent operation streams (with time-space labels) ops = [ MedicalOp(doctor_tz="America / New_York", priority=0.7, action="increase_dose"), MedicalOp(doctor_tz="Europe / Berlin", priority=0.9, action="decrease_dose"), MedicalOp(doctor_tz="Asia / Tokyo", priority=0.6, action="change_injection_time") ] # Conflict resolution algorithm library resolved_op = reduce(lambda x, y: self.resolve_conflict(x, y), ops) # Clinical Impact Assessment risk_score = self.calculate_risk(resolved_op.delay_hours) return RiskReport(resolved_op, risk_score) Preferably, this step supports the following implementation method for dynamically updating the timestamp with millisecond-level precision of the label: 1. Use a high-precision clock and synchronization protocol to ensure timestamps are accurate to milliseconds.

[0050] Clock source: Dependent on the system hardware clock (CPU's TSC register).

[0051] Synchronization mechanism: Integrates NTP (Network Time Protocol) to achieve network time synchronization and reduce drift.

[0052] 2. Use a high-precision time library in the code (Python's time.time() returns seconds).

[0053] Dynamic precision control: The system allows configuration of precision levels (milliseconds, microseconds) and automatic adjustment based on the priority of medical events (e.g., using milliseconds for severe events and seconds for routine events).

[0054] Storage and Representation: The timestamp format is extended to ISO8601 with milliseconds, such as "2025-06-20T08:00:00.123Z".

[0055] Example code is as follows: #Code: Generating timestamps with millisecond-level precision importtime fromdatetimeimportdatetime import ntplib #NTP client library classHighPrecisionTimeGenerator: def__init__(self): self.ntp_client=ntplib.NTPClient() self.sync_with_ntp() # Synchronize on startup defsync_with_ntp(self): # Obtain precise time (milliseconds) from an NTP server response=self.ntp_client.request('pool.ntp.org') self.offset = response.offset # Calculate the offset of the local clock from the NTP. #Scheduled sync (every 10 minutes) defget_current_utc_time(self): # Obtaining high-precision time: Combining system clock and NTP offset system_time = time.time() # Second-level precision, with decimal part (milliseconds) adjusted_time=system_time+self.offset # Convert to a datetime object (milliseconds) utc_time=datetime.utcfromtimestamp(adjusted_time) return utc_time.strftime("%Y-%m-%dT%H:%M:%S.%f")[:-3]+"Z" # Format to milliseconds, such as "2025-06-20T08:00:00.123Z" defdynamic_precision_update(self,priority): #Adjust precision dynamically based on medical event priority ifpriority=="CRITICAL": return self.get_current_utc_time() # Milliseconds else: returndatetime.utcnow().strftime("%Y-%m-%dT%H:%M:%SZ")#second level # Call in the time-space tag generator time_gen=HighPrecisionTimeGenerator() `time_label = time_gen.dynamic_precision_update(operation.priority)` # `operation.priority` is the value of the time label for the time label. Step 3: Based on the priority of medical events in the multidimensional spatiotemporal labels, perform conflict handling for concurrent medical operations; In this embodiment, conflict handling is based on dynamic merging of concurrent operation instructions according to clinical priority, specifically employing a three-level strategy: Level 1 Strategy: Automatically merge non-conflicting fields (such as patient basic information).

[0056] Secondary strategy: High-priority operations cover low-priority operations (e.g., emergency medication covers routine examinations).

[0057] Level 3 strategy: Trigger a manual arbitration agreement (e.g., when doctors have conflicting opinions).

[0058] The manual arbitration agreement is as follows: 1. The role of the arbitrator includes: System Administrator: Responsible for overseeing the arbitration process and verifying the legality of the doctor's identity.

[0059] Domain experts: Senior physicians designated by the hospital provide clinical interpretation and make final decisions on conflicting procedures.

[0060] 2. Provide a visual arbitration dashboard: display the spatiotemporal tags, priority differences, and quantitative reports of clinical impact of conflicting actions, allowing arbitrators to confirm the final decision.

[0061] 3. Decision feedback mechanism: If the arbitration result overturns the system's default overwrite operation, the version control mechanism needs to be triggered to restore the overwritten operation.

[0062] Preferably, the dynamic priority calculation formula involved in this step is: S Here, S represents the operation priority; MaxDelay is the maximum permissible delay, which is based on the treatment time window sensitivity coefficient and the failure threshold of clinical guidelines. MaxDelay is taken from the maximum safe delay time defined in authoritative medical databases (FDA guidelines), such as 1 hour for insulin injection and 4 hours for antibiotics; MinDelay is the minimum required response time, with a default value of 0, which can be adjusted to the actual execution time according to the operation type (such as electronic medical record updates); CriticismLevel is the severity level of the disease (such as Level 1: emergency resuscitation; Level 5: routine follow-up). The values ​​of MaxDelay and MinDelay are dynamically obtained through the risk model of the clinical impact quantification module, or queried from the pre-loaded guideline library.

[0063] In this embodiment, the insulin operation has MaxDelay=1 hour (based on a surge in clinical risk when the risk value is >1.6), MinDelay=0 hours, and CriticismLevel=1 (emergency rescue). Therefore, the priority S=1 / (1-0)×1=1. Furthermore, these values ​​of MaxDelay and MinDelay are not fixed and can be adjusted through a "self-optimizing feedback loop". The system updates MaxDelay and MinDelay based on real-time test data and obtains them from the latest WHO guidelines.

[0064] Therefore, when the priority S exceeds the 0-1 interval, priority standardization is required: the priority is mapped to the 0-1 interval and two decimal places are retained.

[0065] Failure to prioritize and standardize the process may pose the following risks to clinical decision-making: For example, an emergency priority value may be as high as 2.5 (such as in the case of a heart attack), while a routine procedure may be as low as 0.1 (such as in the case of a routine laboratory test).

[0066] Using raw values ​​directly can cause the following problems: The priority difference was excessively amplified (difference of 2.4), causing high-priority operations to forcibly override low-priority operations.

[0067] The human arbitration logic (Δpriority>0.3) in the Level 3 strategy is bypassed, which may hide potential clinical risks.

[0068] Therefore, the formula for standardization is as follows: ormalized

[0069] in, `ormalized` represents the normalized priority, with a value range of [0,1]; `S` represents the original priority. max S represents the maximum original priority value in the current conflict scenario. min This is the minimum original priority value in the current conflict scenario.

[0070] Step 4: Quantify the clinical risks caused by time delays during conflict resolution and generate a clinical impact assessment report to verify the spatiotemporal consistency of the cross-time zone medical collaboration system.

[0071] In this embodiment, by calculating the treatment risk value caused by time lag, and relating it to treatment time window offset (such as excessive antibiotic dosing interval), drug metabolic half-life (such as the decay of insulin activity in the body), and the severity index of the patient's underlying disease (such as the sensitivity of diabetes complicated with renal failure), the output report includes a time lag-efficacy decay curve. Figure 3 This curve quantifies the impact of time delay on treatment efficacy. The horizontal axis represents the operation delay time (hours), and the vertical axis represents the expected efficacy retention rate (%). (The remaining text appears to be unrelated and possibly machine-generated gibberish.) Figure 4 ).

[0072] The logic behind the time lag-efficacy decay curve is as follows: 1. Basic Model Construction: The curve is based on the exponential decay model:

[0073] Where E(t) is the efficacy retention rate after a delay of t hours. Initial efficacy (100%). t is the attenuation coefficient, and t is the delay time (in hours).

[0074] 2. Attenuation coefficient The calculation logic: Attenuation coefficient Considering drug metabolism characteristics and individual patient differences:

[0075] in, Drug half-life (e.g., insulin half-life is 0.8-0.2 hours), taken from the FDA drug database; Disease Severity Index, calculated using the following formula:

[0076] The underlying disease sensitivity coefficient is derived from the FDA drug response database, reflecting the inherent sensitivity of the disease to treatment delays; the complication correction value is based on the classification of the "2023 Diabetes Complications Management Consensus" (e.g., the correction value for renal failure is 0.8); the age weight is based on the "Pharmacokinetic Age Correction Parameter Table" (e.g., 1.2 for those over 65 years of age).

[0077] 3. Key inflection points and clinical decision thresholds: The curves indicate three key inflection points (based on authoritative clinical guidelines): Insulin: efficacy retention rate must be >95% within 1 hour (exceeding this indicates a surge in risk); Antibiotics: efficacy retention rate must be >90% within 4 hours (exceeding this indicates an increased risk of failure); Anticoagulants: efficacy retention rate must be >98% within 0.5 hours (exceeding this indicates a significant risk of crisis).

[0078] 4. Clinical application logic: The curve visually demonstrates the correlation between "delay time and efficacy retention rate". Combined with the risk value of the clinical impact assessor (risk value = delay time × treatment time window sensitivity coefficient), it provides a quantitative basis for the time lag risk of cross-time zone medical operations, helps to determine whether the delay is within a safe range (e.g., risk value <0.3 is acceptable), and supports conflict resolution decisions and clinical risk assessment.

[0079] In this embodiment, the key node logic of the path diagram (conflict resolution path diagram) revolves around the synergy between "conflict handling process" and "clinical risk quantification." The core logic uses a decision tree structure to clearly define the resolution path for operational conflicts and the corresponding risk levels. The specific logic is as follows: 1. Priority coverage of decision-making logic (core nodes) This node is used to determine the priority differences of concurrent operations and decide whether to handle them automatically or trigger manual arbitration. The logic is as follows: Calculate the priority difference between concurrent operations (Δ = priority of operation A - priority of operation B); If Δ > 0.3 (significant priority difference): directly execute the high-priority operation (such as emergency rescue over routine checks), which belongs to the secondary strategy; If Δ≤0.3 (priority is close): trigger the manual arbitration process (e.g., doctors disagree on cancer treatment plans), which is a level 3 strategy.

[0080] This logic balances automation efficiency with clinical safety by quantifying priority differences, avoiding inappropriate coverage due to minor priority differences.

[0081] 2. Clinical Impact Labeling Rules (Risk Association Nodes) Based on the type of conflict resolution path, the corresponding risk level is matched to achieve risk visualization. The rules are as follows: Level 1 strategy successful (not a conflicting field, such as modifying patient contact information): marked "Low risk (0-0.2)" and indicated in green; Secondary strategy coverage (high priority covers low priority, such as adjusting antibiotic dosage): marked "medium risk (0.2-0.5)" and indicated in yellow; When manual arbitration takes effect (medical intervention in decision-making, such as changing cancer treatment plans): marked "High Risk (0.5-0.8)" in orange; Emergency pause protocol (operational conflicts that may endanger life, such as conflicting CPR instructions): marked "critical risk (>0.8)" in red.

[0082] The risk level is calculated based on "risk value = time delay × treatment time window sensitivity coefficient", which is directly related to the safety threshold in clinical guidelines (e.g., risk value <0.3 is acceptable).

[0083] 3. Decision feedback and traceability logic (closed-loop point) Key nodes in the path graph must support decision traceability, including the following logic: All operational conflicts and their resolution results are logged, including the spatiotemporal tags of the conflicting operations, the priority calculation process, arbitration records, etc. If the result of manual arbitration overturns the system's default decision (such as negating a high-priority operation), the version control mechanism is triggered to restore the overwritten operation and ensure that the operation is traceable. The output report must be linked to the original operation log (e.g., clicking "Time Lag Risk Value" will allow you to view the corresponding timestamp, drug half-life parameters, etc.), in compliance with the requirements of HIPAA / GDPR and other regulations for the traceability of medical data.

[0084] In this embodiment, the test results are output as follows: Winning move: Reduce dosage instruction from the Berlin doctor (priority 0.9); Cause of the conflict: The New York and Berlin operation timestamps overlapped (UTC time 2025-06-20T12:00:00±5min). Clinical impact: Risk value for delay is 0.18 (acceptable range <0.3).

[0085] The test data was compared with existing technologies that do not incorporate the method of this invention. The test process was as follows: 50 repeated tests were run: each time simulating 3 doctors operating across time zones, the conflict resolution algorithm was invoked at a 100% rate, and the priority decision module processing time was less than 1 second. The results are shown in Table 1 below: Table 1

[0086] Based on the risk calculation of the clinical impact quantification module (risk value = delay × sensitivity coefficient), the delay of this invention was reduced from 47 minutes to 2 minutes, and the risk value was reduced from 3.76 (47 × 0.8) to 0.16 (2 × 0.8), which is within the acceptable range of <0.3.

[0087] Example 2 Based on the same concept, this embodiment tests the timing competition between emergency medical events and routine operations based on Embodiment 1. The specific steps are as follows: Step 1: Test Scenario Configuration Simulated doctor distribution: New York (UTC-5), London (UTC+0), Sydney (UTC+10); Patient location: Singapore (UTC+8); Test event: Doctor A (New York) initiates a myocardial infarction resuscitation operation (high priority), and Doctor B (Sydney) initiates a routine laboratory test operation (low priority). The operation timestamps overlap within 5 minutes of UTC time 2025-07-15T10:00:00Z.

[0088] Clinical parameter settings: Myocardial infarction resuscitation: Critical level = 1 (emergency resuscitation), minimum required response time = 0.1 hours, maximum allowable delay = 0.5 hours; Routine laboratory tests: Critical level = 5 (routine follow-up), minimum required response time = 24 hours, maximum allowable delay = 72 hours.

[0089] Heart attack resuscitation procedures (New York doctor): Criticality Level: CriticismLevel=1 (Emergency Rescue); Time window parameters: MaxDelay = 0.5 hours (maximum allowable delay), MinDelay = 0.1 hours (minimum required response time); Calculate: S = 1 / (0.5 - 0.1) × 1 = 2.5; By prioritizing the normalization: S_normalized=(2.5−0.104) / (2.5−0.104)≈0.95.

[0090] Routine laboratory procedures (Sydney doctor): Criticality level: CriticismLevel=5 (routine follow-up); Time window parameters: MaxDelay=72 hours, MinDelay=24 hours; Calculate: S = 1 / (72-24) × 5 ≈ 0.104; By prioritizing normalization: S_normalized = (0.104 − 0.104) / (2.5 − 0.104) = 0; If we directly set it to 0, it will be confused with other operations that are not involved in the conflict. In the clinical decision threshold, the risk value for low risk is between 0 and 0.2, so we set it to 0.2.

[0091] Step 2, Core Code Snippet (Python Code): class TimeAwareMedicalTest: definit(self): self.clinical_rules = load_clinical_guidelines(“cardiac_care”) # Load clinical guidelines for cardiac care def simulate_operation_conflict(self): # Generate concurrent operation streams (with time-space labels) ops = [ MedicalOp(doctor_tz="America / New_York", priority=0.95, action="start_heart_attack_protocol") # Heart attack resuscitation, priority 0.95 MedicalOp(doctor_tz="Australia / Sydney", priority=0.2, action="routine_blood_test") # Routine blood test, priority 0.2 ] # Conflict resolution algorithm library resolved_op = reduce(lambda x, y: self.resolve_conflict(x, y), ops) # Clinical Impact Assessment risk_score = self.calculate_risk(resolved_op.delay_hours) return RiskReport(resolved_op, risk_score) Step 2: Test Result Output: The conflict resolution report is as follows: Winning maneuver: The New York doctor's (priority 0.95) heart attack resuscitation order; Cause of the conflict: The New York and Sydney operation timestamps overlapped (UTC time 2025-07-15T10:00:00Z ±5min). Clinical impact: Delay risk value 0.15 (delay 0.1 hours × treatment time window sensitivity coefficient 1.5 = 0.15, low risk <0.3).

[0092] Example 3 like Figure 2 As shown, based on the same concept, this invention also proposes a spatiotemporal consistency testing device for cross-time zone medical collaboration, comprising: The spatiotemporal scenario configurator is used to generate multi-dimensional cross-time zone medical test scenarios. Test scenarios include concurrent operation conflicts between operators in multiple time zones, misalignment of treatment windows caused by patients moving across time zones, and timing competition between emergency operations and routine operations. A spatiotemporal label generator is used to generate multidimensional spatiotemporal labels for each medical operation, including the operator's time zone, the patient's time zone, absolute UTC time, and the medical event priority. A conflict resolution algorithm library for handling conflicts in concurrent medical operations based on the priority of medical events in multidimensional spatiotemporal labels; The clinical impact assessor is used to quantify the clinical risks caused by time delays during concurrent operations and generate a clinical impact assessment report. Among them, the spatiotemporal label generator, conflict resolution algorithm library and clinical impact assessor work together through data interaction. The test scenario generated by the spatiotemporal scenario configurator drives the spatiotemporal label generator to generate labels. After the conflict resolution algorithm library processes the conflict based on the multidimensional spatiotemporal labels, the clinical impact assessor outputs the assessment results to verify the spatiotemporal consistency of the cross-time zone medical collaboration system.

[0093] In this embodiment, the spatiotemporal tag generator is also used to handle time zone conversion boundary situations, including daylight saving time rule adaptation, International Date Line compensation, and leap second event correction; the time accuracy of the multidimensional spatiotemporal tag can be dynamically adjusted based on the priority of medical events, with the time accuracy of emergency operations at the millisecond level and the time accuracy of routine operations at the second level.

[0094] Preferably, the conflict resolution algorithm library includes a priority decision module and a manual arbitration module; The priority decision module is used to determine the priority of concurrent operations based on preset priority calculation rules and to handle conflicts according to priority differences. If the priority difference exceeds a preset threshold, the high-priority operation will override the low-priority operation. The manual arbitration module is used to trigger the manual arbitration process when the priority difference does not exceed a preset threshold. The manual arbitration process includes displaying the spatiotemporal tags of the conflicting operations, the priority difference, and the clinical impact assessment report, and the arbitrator confirms the final decision.

[0095] The preset priority calculation rules include: The initial priority is calculated based on the maximum allowable delay of the operation, the minimum required response time, and the severity of the illness. The original priority is standardized to the 0-1 range to obtain the standardized priority.

[0096] The clinical risk quantification method of the clinical impact assessor is as follows: Risk value = time delay × treatment time window sensitivity coefficient, which is dynamically determined based on drug half-life, disease severity index and clinical guidelines.

[0097] Example 4 This embodiment also provides an electronic device, see reference. Figure 5 It includes a memory 404 and a processor 402, wherein the memory 404 stores a computer program and the processor 402 is configured to run the computer program to perform the steps in any of the above method embodiments.

[0098] Specifically, the processor 402 may include a central processing unit (CPU), or an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement embodiments of the present invention.

[0099] Memory 404 may include a mass storage device for data or instructions. For example, and not limitingly, memory 404 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 404 may include removable or non-removable (or fixed) media. Where appropriate, memory 404 may be internal or external to a data processing device. In a particular embodiment, memory 404 is non-volatile memory. In a particular embodiment, memory 404 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable read-only memory (PROM), an erasable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), an electrically alterable read-only memory (EAROM), or flash memory, or a combination of two or more of these. Where appropriate, the RAM can be Static Random-Access Memory (SRAM) or Dynamic Random-Access Memory (DRAM). DRAM can be Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), Extended Data Out Dynamic Random-Access Memory (EDODRAM), Synchronous Dynamic Random-Access Memory (SDRAM), etc.

[0100] The memory 404 can be used to store or cache various data files that need to be processed and / or communicated, as well as possible computer program instructions executed by the processor 402.

[0101] The processor 402 reads and executes computer program instructions stored in the memory 404 to implement any of the spatiotemporal consistency testing methods for cross-time zone medical collaboration in the above embodiments.

[0102] Optionally, the electronic device may further include a transmission device 406 and an input / output device 408, wherein the transmission device 406 is connected to the processor 402, and the input / output device 408 is connected to the processor 402.

[0103] The transmission device 406 can be used to receive or send data via a network. Specific examples of the network described above may include wired or wireless networks provided by the communication provider of the electronic device. In one example, the transmission device includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, the transmission device 406 may be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0104] Input / output device 408 is used to input or output information.

[0105] Example 5 This embodiment also provides a readable storage medium storing a computer program, the computer program including program code for controlling a process to execute the process, the process including the spatiotemporal consistency test method for cross-time zone medical collaboration according to Embodiment 1.

[0106] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0107] Generally, various embodiments can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects of the invention can be implemented in hardware, while others can be implemented by firmware or software executed by a controller, microprocessor, or other computing device, but the invention is not limited thereto. Although various aspects of the invention may be shown and described as block diagrams, flowcharts, or using some other graphical representation, it should be understood that, by way of non-limiting example, these blocks, apparatuses, systems, techniques, or methods described herein can be implemented in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0108] Embodiments of the present invention can be implemented by computer software, which may be executable by a data processor of a mobile device, such as a processor entity, or by hardware, or by a combination of software and hardware. Computer software or programs (also referred to as program products) including software routines, applets, and / or macros can be stored in any device-readable data storage medium, and they include program instructions for performing specific tasks. The computer program product may include one or more computer-executable components configured to perform the embodiments when the program is run. The one or more computer-executable components may be at least one piece of software code or a portion thereof. Additionally, it should be noted in this respect that, as Figure 1 Any box in the logical flow can represent a program step, or interconnected logic circuits, boxes and functions, or a combination of program steps and logic circuits, boxes and functions. Software can be stored on physical media such as memory chips or blocks of storage implemented within a processor, magnetic media such as hard disks or floppy disks, and optical media such as DVDs and their data variants, CDs, etc. The physical medium is a non-transient medium.

[0109] Those skilled in the art should understand that the technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0110] The above embodiments are merely illustrative of several implementations of the present invention, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of the present invention should be determined by the appended claims.

Claims

1. A method for testing the spatiotemporal consistency of cross-timezone medical collaboration, characterized in that, Includes the following steps: Configure cross-time zone medical testing scenarios, including concurrent operation conflicts among operators in multiple time zones, misalignment of treatment windows caused by patient movement across time zones, and timing competition between emergency operations and routine operations. For each medical operation in the test scenario, generate a multi-dimensional spatiotemporal label containing the operator's time zone, the patient's time zone, absolute UTC time, and the medical event priority; Based on the priority of medical events in the multidimensional spatiotemporal labels, conflict resolution is performed on concurrent medical operations. The clinical risks caused by time delays during the conflict resolution process are quantified, and a clinical impact assessment report is generated to verify the spatiotemporal consistency of the cross-time zone medical collaboration system. The steps for handling conflicts in concurrent medical operations based on medical event priorities include: Calculate the priority of each concurrent operation, the priority being determined based on the maximum allowable delay, the minimum required response time, and the severity of the disease. If the priority difference of concurrent operations exceeds a preset threshold, then the high-priority operation will override the low-priority operation. If the priority difference does not exceed the preset threshold, a manual arbitration process is triggered, and the arbitrator confirms the final decision. In the step of quantifying clinical risk, the clinical risk is visualized through a time lag-efficacy decay curve, which is generated based on a pharmacokinetic model and reflects the correlation between time delay and the retention rate of treatment effect.

2. The spatiotemporal consistency testing method for cross-time zone medical collaboration as described in claim 1, characterized in that, The step of generating multidimensional spatiotemporal labels also includes boundary processing of the absolute UTC time, which includes daylight saving time conversion adaptation, International Date Line compensation, and leap second correction.

3. The spatiotemporal consistency testing method for cross-time zone medical collaboration as described in claim 1, characterized in that, The medical event priority is mapped to a 0-1 range through a standardization process, which is based on the maximum and minimum priority values ​​in the current conflict scenario.

4. An apparatus for implementing the spatiotemporal consistency testing method for cross-time zone medical collaboration as described in any one of claims 1-3, characterized in that, include: A spatiotemporal scenario configurator is used to generate multi-dimensional cross-time zone medical test scenarios, including concurrent operation conflicts of multiple time zone operators, misalignment of treatment windows caused by patient movement across time zones, and timing competition between emergency operations and routine operations. A spatiotemporal label generator is used to generate multidimensional spatiotemporal labels for each medical operation, including the operator's time zone, the patient's time zone, absolute UTC time, and the medical event priority. A conflict resolution algorithm library is used to handle conflicts in concurrent medical operations based on the priority of medical events in the multidimensional spatiotemporal labels. The clinical impact assessor is used to quantify the clinical risks caused by time delays during concurrent operations and generate a clinical impact assessment report. The spatiotemporal label generator, conflict resolution algorithm library, and clinical impact assessor work collaboratively through data interaction. The test scenario generated by the spatiotemporal scene configurator drives the spatiotemporal label generator to generate labels. After the conflict resolution algorithm library processes the conflict based on the multidimensional spatiotemporal labels, the clinical impact assessor outputs the assessment results to verify the spatiotemporal consistency of the cross-time zone medical collaboration system.

5. The spatiotemporal consistency testing device for cross-time zone medical collaboration as described in claim 4, characterized in that, The spatiotemporal tag generator is also used to handle time zone conversion boundary situations, including daylight saving time rule adaptation, International Date Line compensation, and leap second event correction; the time accuracy of the multidimensional spatiotemporal tag can be dynamically adjusted based on the priority of medical events, with the time accuracy of emergency operations at the millisecond level and the time accuracy of routine operations at the second level.

6. The spatiotemporal consistency testing device for cross-time zone medical collaboration as described in claim 4, characterized in that, The conflict resolution algorithm library includes a priority decision module and a manual arbitration module; The priority decision module is used to determine the priority of concurrent operations based on preset priority calculation rules and to handle conflicts according to priority differences. If the priority difference exceeds a preset threshold, the high-priority operation will override the low-priority operation. The manual arbitration module is used to trigger the manual arbitration process when the priority difference does not exceed the preset threshold. The manual arbitration process includes displaying the spatiotemporal tags of the conflicting operations, the priority difference, and the clinical impact assessment report, and the arbitrator confirms the final decision. The preset priority calculation rules include: The initial priority is calculated based on the maximum allowable delay of the operation, the minimum required response time, and the severity of the illness. The original priority is standardized to the 0-1 range to obtain the standardized priority; The clinical risk quantification method of the clinical impact assessor is: Risk value = Time delay × Treatment time window sensitivity coefficient, which is dynamically determined based on drug half-life, disease severity index and clinical guidelines.

7. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the spatiotemporal consistency testing method for cross-time zone medical collaboration as described in any one of claims 1 to 3.

8. A readable storage medium, characterized in that, The readable storage medium stores a computer program, the computer program including program code for controlling a process to execute the process, the process including the spatiotemporal consistency testing method for cross-time zone medical collaboration according to any one of claims 1 to 3.

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