Multi-device collaborative online control system and method

Abstract

The application discloses a multi-device cooperative online control system and method, belongs to the technical field of multi-device cooperative control, and aims to solve the problems of low synchronization accuracy and response lag when multiple devices cooperatively act. By accessing and identifying trigger and response execution devices, calibrating time reference deviation, receiving trigger state and auxiliary state data to construct multi-dimensional state data set, analyzing and judging trigger condition, generating point-by-point trigger initial signal, comparing with sequence end signal timestamp to update sequence and incrementally transmitting to the central control platform, the communication delay is measured by test signal, online control instructions are generated and iterated in real time to drive device cooperative action, the instructions are optimized according to type identification and updated dynamically through the updated interface, and the correction data set is constructed based on the feedback of the returned execution state, the execution deviation is calculated to correct the online control instruction. Finally, high-precision synchronization and efficient response of multi-device cooperative action are realized, and the stability and adaptability of the online control system are improved.

Description

Technical Field

[0001] This invention relates to the field of multi-device collaborative control technology, and more specifically to an online control system and method for multi-device collaboration. Background Technology

[0002] In the field of multi-device collaborative control, multiple devices need to work together to complete complex tasks. For example, the coordinated operation of imaging and treatment equipment in the medical field, and the coordinated operation of multiple robotic arms in an industrial production line, all require multiple devices to maintain a high degree of synchronization in time and motion to ensure task accuracy and safety.

[0003] However, current multi-device collaborative control technologies still face numerous practical challenges. At the time reference level, most systems rely solely on a one-time calibration during initial access, which is insufficient to address clock drift caused by hardware differences and environmental interference during device operation. As operating time accumulates, the time axis deviations of each device gradually widen, directly causing timing misalignments in action execution. In the status judgment stage, existing solutions often focus on the core status data of a single device, lacking systematic fusion analysis of the auxiliary status of responding devices. This makes them susceptible to being misled by transient disturbance signals, leading to misjudgments or delays in trigger logic, and making it difficult to adapt to dynamic changes in complex scenarios. Regarding signal transmission and command generation, traditional full-data transmission modes have a high proportion of redundant information when updating the trigger signal sequence, not only increasing the communication burden but also extending the command response cycle. Simultaneously, communication delay measurements often use fixed periods, failing to dynamically capture trend changes in delay. Control commands generated based on static delay data are highly susceptible to timing deviations in device actions when there are systematic fluctuations in delay. Furthermore, during execution, the deviation between the actual operating state of the device and the command requirements often lacks real-time perception and correction mechanisms. Long-term accumulation of deviations not only reduces collaborative accuracy but may also lead to safety hazards in high-risk scenarios. These problems make it difficult for existing multi-device collaborative control systems to meet the requirements of high-precision collaborative scenarios in terms of synchronization accuracy, response speed, anti-interference capability, and reliability, thus limiting the further application of multi-device collaborative technology in complex fields. Therefore, in order to overcome these limitations, this invention proposes an online control system and method for multi-device collaboration. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide an online control system and method for multi-device collaboration. It primarily solves problems such as low synchronization accuracy due to inconsistent time bases, lack of dynamic adaptation of triggering logic to multi-dimensional states, response lag caused by signal transmission redundancy, impact of communication delay variations on the timing of collaborative actions, and inability to correct deviations in real time during execution. Through device calibration, multi-dimensional trigger judgment, dynamic instruction updates, communication delay measurement, and execution deviation correction, high-precision synchronization and reliable response for multi-device collaboration are achieved.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] Multi-device collaborative online control systems include:

[0007] It connects to and identifies triggering and response devices in the online control system, and calibrates the time reference deviation;

[0008] It receives trigger status data from the triggering execution device and auxiliary status data from the response execution device, constructs a multi-dimensional status dataset, and uses multi-dimensional judgment rules to perform real-time analysis to determine whether the triggering conditions are met. In response, it generates a point-to-point initial trigger signal, updates the trigger signal sequence by comparing it with the start timestamp of the end trigger signal in the trigger signal sequence, and incrementally transmits it to the central control platform.

[0009] The central control platform receives dynamically updated trigger signal sequences, measures the communication delay between the trigger execution device and the response execution device by sending test signals, generates and iterates online control commands in real time to drive the trigger execution device and the response execution device to coordinate their actions, optimizes the online control commands according to the type identifier, and dynamically updates the online control commands according to the configured update interface.

[0010] Based on the execution status feedback returned by the triggering execution device and the response execution device, a correction dataset is constructed to calculate the execution deviation, identify abnormal execution deviations, and correct online control commands.

[0011] Specifically, the steps for generating and iterating online control commands include:

[0012] Receive the trigger signal sequence dynamically updated by the central control platform, extract the type identifier, execution parameters and reference timestamp of the terminal trigger signal, and retrieve the one-way communication delay between the trigger execution device and the response execution device measured by the test signal sent by the central control platform;

[0013] Generate initial online control commands, including execution parameters of the triggering and responding execution devices, and start timestamps, and optimize the initial online control commands according to the type identifier;

[0014] The execution parameters of the triggering device are obtained by parsing the end trigger signal, and the execution parameters of the response device are obtained through a preset mapping rule between trigger strength and response parameters; the start timestamp is the sum of the base timestamp and the corresponding device one-way communication delay.

[0015] The optimized initial online control command is synchronously sent to the trigger execution device and the response execution device through a preset communication link. After receiving the command, the trigger execution device and the response execution device convert the timestamp in the online control command into the time corresponding to the local standardized timestamp and perform timestamp alignment.

[0016] After the timestamps of the triggering execution device and the responding execution device are aligned, the action is executed according to the instruction and the execution status feedback is transmitted back in real time, including the actual start time, the actual value of the execution parameters, and the progress of the action.

[0017] Specifically, the steps for optimizing the initial online control command based on the type identifier include:

[0018] If the type is identified as point-to-point, the initial online control command is set to single-execution mode, the start timestamp and the end timestamp are the same, the execution parameters are fixed, and the parameter adjustment channel is closed.

[0019] If the type is identified as continuous, the initial online control command is set to continuous execution mode, split into start command, dynamic execution command and temporary termination command, and the online control command is dynamically updated according to the configured update interface;

[0020] The update interfaces include the execution parameter update interface, the duration update interface, and the termination timestamp update interface;

[0021] The start command includes execution parameters and a start timestamp, used to trigger device startup;

[0022] The dynamically executed instruction includes execution parameters and a duration stamp that are adjusted according to the update frequency of the trigger signal sequence. The duration stamp is used to identify the effective runtime segment of the dynamically executed instruction and is calculated by the update frequency of the trigger signal sequence and the duration of the trigger signal type. An execution parameter update interface and a duration update interface are configured to update the execution parameters and duration stamp of the dynamically executed instruction.

[0023] The temporary termination instruction includes execution parameters and a temporary termination timestamp, and configures a termination timestamp update interface to optimize the actual termination time based on the trigger signal type identifier.

[0024] Specifically, the steps for dynamically updating online control commands based on the configured update interface include:

[0025] During the execution of online control commands, if the type of the trigger signal at the end of the trigger signal sequence dynamically updated by the central control platform is continuous and has not changed, the execution parameter update interface of the dynamic execution command is called to merge the execution parameters of the trigger signal at the end of the trigger signal sequence with the current execution parameters to generate updated execution parameters.

[0026] Based on the reference timestamp of the end trigger signal, the update frequency of the trigger signal sequence, and the duration of the trigger state, the duration timestamp of the dynamically executed instruction is recalculated, and the effective runtime is updated through the duration update interface.

[0027] Call the termination timestamp update interface of the temporary termination instruction, obtain the temporary termination timestamp based on the preset theoretical duration of the end trigger signal, and update the temporary termination timestamp.

[0028] The triggering and response execution devices execute actions according to the updated online control instructions and send back execution status feedback in real time until the trigger signal type identifier at the end of the trigger signal sequence in the central control platform changes and the update interface is closed.

[0029] Specifically, the steps for generating the point-to-point initial trigger signal include:

[0030] Receive trigger status data and response status data output by the triggering execution device, and store the trigger status data and response status data as a multi-dimensional status dataset according to a preset format template;

[0031] The multi-dimensional state dataset is analyzed in real time using multi-dimensional judgment rules to determine whether it meets the triggering conditions.

[0032] The multi-dimensional judgment rules include standard threshold judgment rules and trend development judgment rules. Among them, the standard threshold judgment rules perform interval verification on trigger state data and auxiliary state data based on a preset state threshold range.

[0033] After the standard threshold judgment rule is passed, the trend development judgment rule determines whether the state that meets the standard threshold judgment rule is stable by tracking the slope, duration and fluctuation range of the changes in the trigger state data and auxiliary state data within the state threshold range.

[0034] If the triggering conditions are met, a point-to-point initial trigger signal is generated. The point-to-point initial trigger signal includes a start timestamp based on the initial time when the triggering conditions are met, execution parameters of the response execution device configured based on the triggering state data strength, and a point-to-point identifier.

[0035] Specifically, the steps for updating the trigger signal sequence include:

[0036] The initial trigger signal is updated in the trigger signal sequence and compared with the start timestamp of the final trigger signal in the sequence:

[0037] If the time interval between the two signals is less than the preset merging threshold, the signal merging optimization process is initiated.

[0038] The start timestamp of the end trigger signal is retained as the starting point of the merged trigger signal. The execution parameters of the point-to-point initial trigger signal and the end trigger signal are merged to form a new trigger signal and updated to the end of the trigger signal sequence. The type identifier of the new trigger signal is changed to continuous.

[0039] Otherwise, add the point-to-point initial trigger signal as a new independent trigger signal to the end of the trigger signal sequence;

[0040] The updated trigger signal sequence is added with a unique identifier and configuration check code of the triggering device. Based on the preset communication link, it is transmitted to the central control platform by incrementally transmitting only the last trigger signal in the trigger signal sequence.

[0041] Specifically, the steps for measuring the communication delay between the triggering execution device and the responding execution device include:

[0042] Configure a basic calibration period and establish a sliding window mechanism. The basic calibration period is dynamically adjusted by analyzing the fluctuation range of the communication delay difference within the sliding window. If the fluctuation range is less than the preset stability threshold, the communication status is determined to be stable, the basic calibration period is maintained, and the basic calibration period is used as the real-time calibration period to send test signals. Otherwise, the communication status is determined to be abnormal, and the basic calibration period is shortened according to the ratio of the fluctuation range to the stability threshold, and used as the real-time calibration period.

[0043] Test signals are generated according to the real-time calibration cycle. The test signals include a standardized timestamp, a test signal identifier, and a verification field. Based on the preset communication link, the central control platform sends the test signals to the trigger execution device and the response execution device respectively, and the sending time is recorded as the test start timestamp.

[0044] After receiving the test signal, the trigger execution device and the response execution device extract the standardized timestamp in the test signal, record the test signal reception time in combination with the local clock, and generate a reception feedback signal containing the reception time and the device's own identifier, which is then transmitted back to the central control platform through the original communication link.

[0045] The central control platform receives the feedback signals from the triggering and responding execution devices, verifies the unique identifier and verification field of the test signal, and calculates the one-way communication delay between the triggering and responding execution devices based on the receiving time of the triggering and responding execution devices in the feedback signal and the standardized timestamp in the test signal, and calculates the communication delay difference between the triggering and responding execution devices.

[0046] Specifically, the step of measuring the communication delay between the triggering execution device and the responding execution device also includes:

[0047] Based on the communication delay difference within the sliding window, a linear fitting algorithm is used to calculate the slope of the communication delay difference trend. If the absolute value of the trend slope is less than the preset trend threshold, the communication delay is determined to have no trend; otherwise, the communication delay is determined to have a trend. The trend threshold is set based on the maximum allowable drift rate of the communication delay, which is determined based on the delay change curve of the device operation.

[0048] If the communication delay is determined to be non-trending, the current one-way communication delay will be used for real-time calibration cycle updates and online control command generation.

[0049] If the communication delay is determined to have a trend, a communication anomaly warning is triggered. At the same time, the real-time calibration cycle is shortened according to the ratio of the absolute value of the trend slope to the preset trend threshold, and the one-way communication delay data of the previous calibration cycle is called to generate online control commands.

[0050] If the triggering device or the response device fails to send back the received feedback signal within the preset feedback time, the measurement is marked as invalid, the test process is restarted, and a communication link abnormality alarm is issued.

[0051] Specifically, the steps for calibrating online control commands include:

[0052] It continuously receives execution status feedback from triggering and responding execution devices, parses out the actual start time, actual values ​​of execution parameters, and action progress, retrieves unified timeline data and communication latency differences, and establishes a calibration dataset.

[0053] Execution deviations, including startup deviations, execution parameter deviations, and schedule deviations, are calculated based on the calibration dataset.

[0054] Configure an execution deviation threshold, and mark execution deviations that exceed the threshold as abnormal execution deviations and correct them.

[0055] The adjusted online control commands are sent to the triggering and response execution devices, and the online control commands are updated and executed according to a unified timeline.

[0056] The system dynamically tracks and corrects the execution status feedback of the online control command, recalculates the execution deviation, and determines that the correction is effective if the abnormal execution deviation is less than the corresponding execution deviation threshold within the preset evaluation period; otherwise, the correction steps are repeated until the abnormal execution deviation is less than the corresponding execution deviation threshold.

[0057] If the number of calibrations within the statistical evaluation period exceeds the preset calibration threshold, the equipment is deemed to be malfunctioning, an alarm is generated, and the execution of online control commands is suspended.

[0058] Online control methods for multi-device collaboration include:

[0059] Step S1: Connect to and identify the triggering and response execution devices in the online control system, and calibrate the time reference deviation;

[0060] Step S2: Receive the trigger status data of the triggering execution device and the auxiliary status data of the response execution device, construct a multi-dimensional status dataset, which is used to perform real-time analysis through multi-dimensional judgment rules to determine whether the triggering conditions are met, generate a point-to-point initial trigger signal, update the trigger signal sequence by comparing it with the start timestamp of the end trigger signal in the trigger signal sequence, and incrementally transmit it to the central control platform;

[0061] Step S3: The central control platform receives the dynamically updated trigger signal sequence, measures the communication delay time between the trigger execution device and the response execution device by sending test signals, generates and iterates online control commands in real time to drive the trigger execution device and the response execution device to coordinate their actions, optimizes the online control commands according to the type identifier, and dynamically updates the online control commands according to the configured update interface.

[0062] Step S4: Based on the execution status feedback returned by the triggering execution device and the response execution device, construct a correction dataset to calculate the execution deviation, identify abnormal execution deviations, and correct the online control commands.

[0063] The beneficial effects of this invention are:

[0064] This application effectively improves the time synchronization accuracy of multi-device collaboration by calibrating the time base when multiple devices are connected, fusing and analyzing multi-dimensional state data and dynamically judging trigger logic, incrementally transmitting trigger signal sequences, dynamically measuring and adapting communication delays, dynamically updating online control commands in real time, and correcting execution deviations in a closed loop. This significantly improves the timing consistency, real-time response, anti-interference capability, and operational reliability of multi-device collaborative actions, meeting the stringent requirements of multi-device collaboration in high-precision and complex scenarios. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of the online control system for multi-device collaboration of the present invention;

[0066] Figure 2 A flowchart illustrating the specific steps involved in generating a dynamically updated trigger signal sequence for this invention;

[0067] Figure 3 This is a flowchart illustrating the communication delay time of the measurement device in this invention.

[0068] Figure 4 A flowchart illustrating the specific steps involved in generating and iterating online control commands according to this invention;

[0069] Figure 5 This is a flowchart of the online control method for multi-device collaboration according to the present invention. Detailed Implementation

[0070] Please see Figure 1 This embodiment describes a multi-device collaborative online control system, including a reference synchronization module, a signal decision module, a signal execution module, and a dynamic calibration module:

[0071] The reference synchronization module establishes the time and communication reference for the online control system. It uses a device authentication protocol to connect and identify triggering and responding devices. Triggering devices monitor target states and generate trigger signals to detect changes in target state in real time and output trigger signals to initiate or terminate the collaborative process. Responding devices receive control commands and execute corresponding actions to perform preset actions in response to trigger signals. The module loads inherent device attribute parameters, such as communication interface type and default response latency characteristics, based on the hardware specifications of the triggering and responding devices to ensure parameter matching with device physical performance. A unified time axis is generated based on a standard time source, and clock offset is calculated. Synchronization calibration commands are output, communication link parameters are configured, and connectivity is tested to calibrate the time reference deviation between the triggering and responding devices, thus establishing a foundation for time consistency in multi-device collaboration.

[0072] In this embodiment, during the system startup phase, device access is completed through a preset device authentication protocol, and inherent attribute parameters are automatically loaded, including communication interface type, default response latency characteristics, and maximum allowable execution error range. A unified time axis is generated based on a configured standard time source, and the clock offset of each device is calculated using the round-trip time measurement method. Microsecond-level synchronization calibration commands are output at a set period to control the time reference deviation within a preset range. The preset range of time reference deviation is determined based on the minimum allowable time difference for multi-device collaborative actions, such as the synchronization accuracy requirements of imaging equipment and injection equipment in medical scenarios, through industry standards or device collaborative test data. Communication link parameters are pre-configured, including transmission baud rate, signal encoding format, and verification algorithm. Link connectivity is tested, and the initial link signal-to-noise ratio and bit error rate are recorded to provide basic data for subsequent processes.

[0073] When the triggering device is an imaging device such as DSA, the responding device is a high-pressure contrast agent injector. The system completes the connection and identification of the DSA and the high-pressure contrast agent injector through a preset device authentication protocol, automatically loading their inherent attribute parameters. The DSA parameters include its communication interface type (e.g., dedicated medical bus interface), exposure response delay characteristics (e.g., X-ray tube start-up delay), and maximum allowable exposure dose range. The high-pressure contrast agent injector parameters include its communication interface type (e.g., high-pressure fluid control interface), injection start-up delay characteristics (e.g., motor drive delay), and maximum injection rate and pressure limits. This completes device connection and basic parameter configuration. Next, a unified timeline is generated based on the configured standard time source. The clock offset of the DSA and the high-pressure contrast agent injector is calculated in real time using round-trip time measurement. Microsecond-level synchronization calibration commands are output at set intervals, continuously adjusting the local clocks of both until the deviation between the DSA's exposure timeline and the high-pressure contrast agent injector's injection timeline is controlled within a preset range, ensuring consistency in the time reference for exposure and injection actions. Finally, the communication link parameters between the DSA and the high-pressure contrast agent injector are pre-configured, such as the transmission baud rate and verification algorithm. The link connectivity is detected by sending test signals, and the initial link signal-to-noise ratio and bit error rate are recorded to provide basic data for subsequent dynamic calibration and anomaly tracing, thus completing the establishment of a communication benchmark for the two to work together.

[0074] The signal decision module processes the triggering logic, generates dynamically updated trigger signal sequences, and transmits them to the central control platform. It continuously receives trigger status data from the triggering execution devices and integrates auxiliary status data from the response execution devices. Based on multi-dimensional judgment rules, it dynamically analyzes status changes, distinguishes between continuous and point-to-point triggering types, and adds a pre-trigger buffer mechanism for point-to-point triggering. It optimizes and generates structured trigger signal sequences through time interval comparison and signal merging, and dynamically updates them according to the triggering status data. Incremental transmission is used to ensure that the triggering logic received by downstream modules always matches the current state, achieving real-time adaptability and reliability of triggering decisions.

[0075] In this embodiment, the signal decision module performs dynamic trigger logic processing based on the status monitoring data of the trigger execution device. It continuously receives trigger status data collected by the trigger execution device and integrates it with the auxiliary status data of the response execution device. Based on built-in multi-dimensional judgment rules, it dynamically analyzes the real-time status, continuously verifying whether the status remains within a valid range using standard threshold judgment rules and eliminating sudden disturbances through trend development judgment rules to verify the authenticity of the status. Then, it generates a point-to-point initial trigger signal and updates it to the trigger signal sequence. By comparing the start timestamp of the final trigger signal in the trigger signal sequence with the start timestamp of the final trigger signal, if the time interval is less than a preset merging threshold, the start signal merging is optimized: the start timestamp of the final signal is retained, execution parameters are integrated, and the identifier is changed to a continuous type. If the time interval is greater than or equal to the preset merging threshold, the initial trigger signal is added as an independent element to the end of the sequence. The final generated structured trigger signal sequence is refreshed synchronously with the trigger status data update. After adding the device's unique identifier and configuration check code, it is output incrementally to ensure that downstream modules can accurately parse the latest trigger logic, achieving dynamic matching between trigger decisions and real-time status.

[0076] When the triggering device is an imaging device such as DSA, the signal decision module continuously receives DSA trigger status data, including radiation dose, exposure frame rate, image clarity parameters, and X-ray tube operating status. It also integrates auxiliary status data from the syringe, including contrast agent balance, injection pressure, plunger movement status, and ready signal. Based on multi-dimensional judgment rule analysis, it uses standard thresholds to determine whether the DSA exposure dose is within the effective imaging range and whether the syringe pressure is safe. Trend-based judgment rules eliminate transient electromagnetic interference from the DSA and brief pressure fluctuations in the syringe. When generating a point-injection initial trigger signal, the initial moment when the DSA exposure meets the standard is used as the start timestamp. The syringe injection parameters are configured based on the exposure intensity, and a point-injection identifier is added. After updating this to the trigger signal sequence, it is compared with the start timestamp of the final signal: if the interval is less than a preset merging threshold, the signals are merged; if the interval meets the threshold, it is added as an independent signal. The updated sequence is equipped with a unique DSA identifier and a medical-grade checksum. It is sent to the syringe via incremental transmission, transmitting only the newly added or modified signals to reduce transmission redundancy and ensure that the syringe adapts to the trigger logic in real time, thereby achieving precise coordination between DSA exposure and contrast agent injection.

[0077] Please see Figure 2 Preferably, the specific steps for generating a dynamically updated trigger signal sequence include:

[0078] The system receives trigger status data output from the triggering execution device, including core data reflecting the target status such as sensor monitoring values ​​and external event pulse signals. Simultaneously, it acquires auxiliary status data from the response execution device, including readiness status, operating parameters, and safety status indicators. The trigger status data and auxiliary status data are stored as a multi-dimensional status dataset according to a preset format template, providing comprehensive status information for subsequent analysis. The preset format template is a standardized data structure framework containing device identifier fields, timestamp fields, data type identifiers, parameter value fields, and data validity check bits. This framework unifies the storage format of trigger status data from the triggering execution device and auxiliary status data from the response execution device, ensuring that status data of different types and sources are organized according to consistent rules, thus achieving structured management of the multi-dimensional status dataset.

[0079] The multi-dimensional state dataset is analyzed in real time using multi-dimensional judgment rules to determine whether triggering conditions are met. These multi-dimensional judgment rules include standard threshold judgment rules and trend development judgment rules. Standard threshold judgment rules are rules that perform interval verification on the triggering state data of the triggering execution device and the auxiliary state data of the response execution device based on a preset state threshold range. The state threshold range is set based on the effective working range of the triggering execution device and the safe operating parameters of the response execution device, derived from the device's factory calibration data and industry safety specifications. Standard threshold judgment rules are used to determine whether the device state meets basic coordination conditions, ensuring that the triggering execution device state is within its effective working range and the response execution device state meets safe operating requirements, providing a basic validity basis for trigger determination. Trend development judgment rules are deep verification rules performed after the standard threshold judgment rules are passed, to verify whether the state is a stable and valid true state, rather than a false conformity caused by sudden disturbances. It tracks the slope, duration, and fluctuation amplitude of changes in trigger state data and auxiliary state data within the state threshold range to determine whether the state that meets the standard threshold judgment rules is stable or a transient phenomenon affected by disturbances. This is used to eliminate false trigger conditions, ensure that the trigger judgment is based on a real and stable state, provide a reliable basis for the accurate differentiation of subsequent trigger types, and improve the anti-interference ability and judgment accuracy of the trigger logic.

[0080] When the triggering conditions are met, a point-to-point initial trigger signal is generated. Using the initial time of meeting the triggering conditions as the base timestamp, the execution parameters are configured based on the strength of the current triggering state data. A point-to-point identifier is added to the trigger signal to indicate a possible transition to a continuous triggering state. The strength configuration of the triggering state data execution parameters refers to adjusting the execution parameters of the response execution device according to the quantified indicators of the triggering state data output by the triggering execution device, ensuring that the intensity of the response execution device's action matches the intensity of the triggering state; this is achieved through a preset intensity parameter mapping table.

[0081] The initial trigger signal is updated to the trigger signal sequence and compared with the start timestamp of the end trigger signal in the trigger signal sequence. If the time interval between the two is less than the preset merging threshold, it indicates that it is a segmented triggering of a continuous event. The signal merging optimization process is as follows: the start timestamp of the end trigger signal is retained as the starting point of the merged trigger signal. The execution parameters of the current initial trigger signal are merged with the end trigger signal to form a new trigger signal, which is updated to the end of the trigger signal sequence. The type identifier of the trigger signal is changed to continuous. The merging threshold is set based on the minimum time resolution of the trigger signal sequence and the event continuity judgment standard. For example, in a medical scenario, if the interval between the continuous actions of DSA exposure and syringe injection is less than the response cycle of human physiological signals, it is judged as the same event. The merging threshold is determined by such physiological response data or device action response delay.

[0082] If the time interval is greater than or equal to the preset merging threshold, the initial trigger signal will be added as a new independent trigger signal to the end of the trigger signal sequence.

[0083] The updated trigger signal sequence is added with a unique identifier and configuration check code of the triggering execution device. Based on the preset communication link, it is transmitted to the central control platform in an incremental manner, transmitting only the trigger signal at the end of the sequence. This method can reduce data transmission redundancy, improve the signal parsing efficiency of the response execution device, and ensure that it can obtain the latest trigger logic changes in real time. This allows it to quickly adapt to the dynamic updates of the trigger signal sequence and achieve precise coordination between the actions of the triggering execution device and the response execution device.

[0084] The signal execution module is used by the central control platform to generate and iterate online control commands in real time based on dynamically updated trigger signal sequences. It synchronously drives the trigger execution device and the response execution device to work together. By tracking the type identifier of the trigger signal and the changes in execution parameters, it dynamically updates the command logic. By sending test signals to measure the communication delay time of the devices, it combines a communication delay calibration mechanism to ensure the spatiotemporal synchronization of the actions of the trigger execution device and the response execution device, thus achieving precise closed-loop control from the trigger signal sequence to the coordinated execution.

[0085] In this embodiment, after receiving the trigger signal sequence, the signal execution module extracts the type identifier, execution parameters, and timestamp of the trigger signal, and compares the identifier difference between the current instruction and the historical instructions. If the identifier changes from point-to-point to continuous, the instruction iteration process is immediately started, the original single execution instruction is terminated, and a continuous instruction containing continuous type and dynamic parameter refresh mechanism is generated. The control logic is reconfigured according to the new execution parameters. Simultaneously, a communication delay test is started: a test signal with a timestamp is sent to the trigger execution device and the response execution device, the round-trip time of the signal is recorded, the communication delay difference between the two is calculated, and based on this, the execution time of the control instructions of the trigger execution device and the response execution device is set to be consistent, so that the actual execution time of the two is consistent.

[0086] When the triggering device is an imaging device such as DSA, the signal execution module analyzes the changes in the identifiers of point-to-point and continuous types in the trigger signal sequence, iterates the single injection command of the syringe into a continuous injection command, and simultaneously adjusts the injection rate according to the new parameters of DSA exposure intensity. The communication delay difference between the DSA and the syringe is measured by test signals. If the syringe receives the signal 80 microseconds slower than the DSA, the execution time of the DSA is set to T, and the execution time of the syringe is set to T-80 microseconds in the collaborative control command. The commands are then sent to both simultaneously. At their respective execution times, the DSA begins exposure, and the syringe begins injection, ensuring synchronized actions. If the DSA exposure signal degrades from continuous to point-to-point due to state fluctuations, the signal execution module immediately terminates the continuous injection command of the syringe, generates a single supplementary injection command, adjusts the execution times of both according to the communication delay difference, and sends them simultaneously. This method maintains high-precision synchronization between the triggering and responding devices, ensuring high-quality contrast imaging.

[0087] Please see Figure 3 Preferably, the specific steps for measuring the communication delay time of the measuring device include:

[0088] The basic calibration cycle is configured based on the update frequency of the trigger signal sequence and the stability of the communication link, and a sliding window mechanism is established. The basic calibration cycle is dynamically adjusted by analyzing the fluctuation amplitude of the communication delay difference within the sliding window. If the fluctuation amplitude is less than the preset stability threshold, the communication state is determined to be stable, the basic calibration cycle is maintained, and the basic calibration cycle is used as the real-time calibration cycle to send test signals. Otherwise, the communication state is determined to be abnormal, and the basic calibration cycle is shortened according to the ratio of the fluctuation amplitude to the stability threshold, which is then used as the real-time calibration cycle to enhance the monitoring density under abnormal conditions.

[0089] Test signals are generated according to the real-time calibration cycle. The test signals include a standardized timestamp provided by the reference synchronization module, a unique identifier for the test signal, and a verification field.

[0090] The central control platform sends test signals to the trigger execution device and the response execution device respectively through a preset communication link, and records the sending time as the test start timestamp.

[0091] After the trigger execution device and the response execution device receive the test signal, they immediately extract the standardized timestamp, combine it with the local clock to record the receiving time, generate a receiving feedback signal containing the receiving time and the device's own identifier, and transmit it back to the central control platform through the original link.

[0092] The central control platform receives the feedback signals from the triggering and responding execution devices. It verifies the validity and correspondence of the data by checking the unique identifier and verification field of the test signal. If the verification fails, the measurement is marked as invalid, a communication link abnormality alarm is triggered, and the test is re-initiated.

[0093] The one-way communication delay between the trigger execution device and the response execution device is obtained by subtracting the standardized timestamp in the test signal from the reception time of the trigger execution device and the response execution device, respectively. The difference between the one-way communication delay between the response execution device and the trigger execution device is calculated as the communication delay difference.

[0094] Based on the communication delay difference within the sliding window, a linear fitting algorithm is used to calculate the slope of the communication delay difference trend: if the absolute value of the trend slope is less than the preset trend threshold, the communication delay is determined to have no trend; if the absolute value of the trend slope is not less than the preset trend threshold, the communication delay is determined to have a trend; the trend threshold is set based on the maximum allowable drift rate of the communication delay, which is determined based on the delay change curve of the device during long-term operation.

[0095] If the communication delay is determined to be non-trending, the current one-way communication delay will be used for real-time calibration cycle updates and online control command generation.

[0096] If the communication delay is determined to have a trend, the current measurement data is marked as unreliable, a communication anomaly warning is triggered, and the real-time calibration cycle is shortened according to the ratio of the absolute value of the trend slope to the preset trend threshold. The use of the one-way communication delay for generating online control commands is prohibited, and the one-way communication delay data that has been verified to be valid in the previous calibration cycle is called to generate online control commands.

[0097] If the triggering device or the response device fails to send back the received feedback signal within the preset feedback time, the measurement is marked as invalid, the test process is restarted according to the real-time calibration cycle, and a communication link abnormality alarm is issued. The preset feedback time is set based on the maximum round-trip time of the communication link plus the buffer time.

[0098] Please see Figure 4 Preferably, the specific steps for generating and iterating online control commands include:

[0099] It receives the trigger signal sequence dynamically updated by the central control platform, verifies the unique device identifier and configuration check code in the trigger signal sequence, extracts the type identifier, execution parameters and reference timestamp of the end trigger signal, and retrieves the one-way communication delay between the trigger execution device and the response execution device and the communication delay difference between the two as measured by the test signal sent by the central control platform.

[0100] Based on the above data, an initial online control command is generated. The command includes the execution parameters of the triggering and responding execution devices and the start timestamp. The execution parameters of the triggering execution device are obtained by parsing the end trigger signal. The execution parameters of the responding execution device are obtained through a preset mapping rule between trigger strength and response parameters. That is, based on the trigger state data strength of the triggering execution device, it is converted into the specific action parameters of the responding execution device, so that the action strength of the responding execution device matches the trigger state strength of the triggering execution device. The start timestamp is determined based on the trigger signal reference timestamp and combined with the one-way communication delay between the triggering and responding execution devices. The start timestamp of the triggering execution device is the sum of the reference timestamp and its own one-way communication delay, and the start timestamp of the responding execution device is the sum of the reference timestamp and its own one-way communication delay, ensuring the synchronization accuracy of the actual start times of the triggering and responding execution devices.

[0101] Optimize initial online control commands based on type identifier:

[0102] If the type is identified as point-to-point, set the initial online control command to single-execution mode, with the start and end timestamps being the same, the execution parameters being fixed and unadjustable and matching the strength of the trigger state data, and the parameter adjustment channel being closed to ensure that the command terminates immediately upon execution, thus avoiding unnecessary actions.

[0103] If the type is identified as persistent, the initial online control command is set to continuous execution mode, split into a start command, a dynamic execution command, and a temporary termination command, and the persistent online control command is dynamically updated according to the configured update interface:

[0104] The update interface includes an execution parameter update interface, a duration update interface, and a termination timestamp update interface; the start instruction includes execution parameters and a start timestamp, used to trigger device startup; the dynamic execution instruction includes execution parameters and a duration timestamp that can be adjusted according to the update frequency of the trigger signal sequence, configures the execution parameter update interface to update the execution parameters according to preset fusion rules, and configures the duration update interface to adjust the effective running period; the duration timestamp is used to identify the effective running period of the dynamic execution instruction, and is calculated by the update frequency of the trigger signal sequence and the duration of the trigger signal type identifier; the temporary termination instruction includes initial execution parameters and a temporary termination timestamp, configures the termination timestamp update interface, and is used to subsequently optimize the actual termination time according to the trigger signal type identifier.

[0105] The optimized online control command is synchronously sent to the trigger execution device and the response execution device through a preset communication link. After receiving the command, the trigger execution device and the response execution device convert the timestamp in the online control command into the corresponding time of the local standardized timestamp according to the unified timeline of the reference synchronization module, and perform timestamp alignment. The timestamps include: start timestamp, end timestamp, duration timestamp, and temporary termination timestamp; ensuring that the start time, running segment, and temporary termination node of the trigger execution device and the response execution device are logically consistent.

[0106] After the timestamps of the triggering execution device and the responding execution device are aligned, the action is executed according to the instruction and the execution status feedback is transmitted back in real time, including the actual start time, the actual value of the execution parameters, and the progress of the action.

[0107] Preferably, the specific steps for dynamically updating online control commands based on the configured update interface include:

[0108] During the execution of continuous online control commands, the signal execution module receives the trigger signal sequence dynamically updated by the central control platform. First, it verifies the unique device identifier and configuration check code in the trigger signal to ensure that the trigger signal source is legitimate and the data is complete. Then, it extracts the type identifier, execution parameters, reference timestamp, and trigger status duration of the new trigger signal as the basis for dynamic updates.

[0109] If the type of the final trigger signal in the dynamically updated trigger signal sequence of the central control platform is continuous and has not changed, the signal execution module calls the execution parameter update interface of the dynamic execution instruction. According to the preset fusion rules, such as allocating weights according to the time ratio of the new trigger signal and the current continuous instruction or taking the average of the parameters, the execution parameters of the final trigger signal are fused with the current execution parameters of the dynamic execution instruction to generate updated execution parameters. Simultaneously, the updated execution parameters are pushed to the trigger execution device and the response execution device to ensure that the action intensity of the trigger execution device and the response execution device matches the latest trigger state.

[0110] Based on the reference timestamp of the end trigger signal, the update frequency of the trigger signal sequence, and the duration of the trigger state, the duration stamp of the dynamic execution instruction is recalculated. The effective runtime of the dynamic execution instruction is synchronously updated through the duration update interface to ensure that the runtime of the trigger execution device and the response execution device is consistent with the continuous logic of the trigger signal.

[0111] The signal execution module calls the termination timestamp update interface of the temporary termination instruction. Starting from the reference timestamp of the end trigger signal, it obtains the temporary termination timestamp according to the preset theoretical duration of the end trigger signal, updates the temporary termination timestamp, and ensures that the temporary termination node is synchronized with the duration trend of the trigger signal.

[0112] The triggering execution device and the response execution device execute actions according to the updated instructions and send back execution status feedback in real time. When the interval between the base timestamp of the end trigger signal and the start timestamp of the current continuous instruction is greater than or equal to the merging threshold, or when the type identifier of the end trigger signal changes, the signal execution module closes the update interface, stops dynamic updates, and enters the formal termination instruction generation process.

[0113] The dynamic calibration module is used to track the collaborative execution deviation between the triggering and responding execution devices in real time. Based on the execution status feedback returned by both, including the actual start time, actual values ​​of execution parameters, action progress, unified time axis of the reference synchronization module, and communication delay data of the signal execution module, key data is extracted, and the time calibration parameters and execution parameter correction values ​​are dynamically adjusted to correct the online control commands. This continuously optimizes the action synchronization accuracy of the triggering and responding execution devices, ensuring that the execution logic of the trigger signal sequence is consistent with the actual action effect, and improving the system's adaptability and reliability under complex working conditions.

[0114] In this embodiment, the dynamic calibration module receives the execution status feedback from the triggering execution device and the response execution device in real time through a preset interface, and extracts key data including the deviation between the actual start time and the theoretical start timestamp, the difference between the actual value of the execution parameter and the fusion parameter, and the matching degree between the action running progress and the duration timestamp. At the same time, it retrieves the clock offset compensation data of the reference synchronization module and the latest communication delay difference measured by the signal execution module. Based on the above data, the dynamic calibration module performs calibration as follows: It calculates the average timestamp deviation and average execution parameter deviation between the triggering and responding execution devices using a sliding window. If the average deviation is within a preset threshold, it is considered normal fluctuation. If it exceeds the threshold, a corresponding level of calibration strategy is triggered based on the deviation magnitude. For timestamp deviation, the timestamp fine-tuning coefficient of the signal execution module is adjusted using the latest communication delay difference data to ensure real-time synchronization of the action sequence of the triggering and responding execution devices. For execution parameter deviation, a correction value is generated based on the intensity of the trigger status data and the direction of the deviation, and pushed to the device through the execution parameter update interface of the signal execution module. If the deviation is an instantaneous fluctuation, the correction is ignored. If moderate or higher deviations are detected for multiple consecutive cycles, or a severe deviation occurs in a single instance, it is determined to be a systemic anomaly, and a device status warning is issued. Simultaneously, the calibration cycle of the communication delay test is forcibly shortened to increase monitoring density. After the calibration parameters are adjusted, the next round of execution status feedback is continuously tracked. If the deviation still exceeds the standard, the reference synchronization module is linked to re-execute the device clock calibration, forming a closed-loop verification.

[0115] When the triggering device is an imaging device such as DSA, the specific function of the dynamic calibration module is as follows: It receives the actual exposure time of the DSA, the actual radiation dose, the actual injection time of the syringe, and the actual injection rate in real time, and calculates the time and parameter deviations between them. If the time deviation exceeds the threshold for multiple consecutive cycles, the dynamic calibration module retrieves the latest communication delay difference, calculates the compensation value, and pushes it to the signal execution module to adjust the syringe's start timestamp and duration timestamp to ensure synchronization between the actual start times of exposure and injection. If the injection rate deviation exceeds the threshold, and the DSA exposure intensity shows a stable trend, the dynamic calibration module generates an injection rate correction value according to the deviation ratio, and adjusts the syringe rate through the execution parameter update interface to match the contrast agent concentration with the radiation dose. If severe deviations occur for multiple consecutive cycles due to syringe mechanical wear or other reasons, the dynamic calibration module issues an equipment accuracy anomaly warning, while simultaneously shortening the communication delay test cycle and updating the calibration parameters each cycle. If the deviation still exceeds the standard after adjustment, the linkage reference synchronization module recalibrates the clock offset between the DSA and the syringe until the deviation returns to normal. Through the above dynamic calibration mechanism, the dynamic matching of contrast imaging quality and injection safety is always ensured.

[0116] Preferably, the specific steps for modifying online control commands include:

[0117] It continuously receives execution status feedback from triggering and responding execution devices, analyzes the actual start time, actual values ​​of execution parameters, and action progress, and simultaneously retrieves the unified time axis data from the reference synchronization module and the latest measured communication delay difference from the signal execution module to establish a calibration dataset.

[0118] Based on the calibration dataset, the execution deviation is calculated, including startup deviation, execution parameter deviation, and schedule deviation. The startup deviation is calculated by the deviation between the actual startup time and the theoretical startup timestamp of the online control command; the execution parameter deviation is calculated by the difference between the actual values ​​of the execution parameters and the execution parameters of the online control command; and the schedule deviation is calculated by the deviation between the action progress and the duration timestamp.

[0119] Based on the minimum acceptable error of equipment operation, such as the start-up deviation threshold referencing the time synchronization accuracy requirements of the triggering and response execution devices, execution deviation thresholds are configured, including start-up deviation thresholds, execution parameter deviation thresholds, and progress deviation thresholds. These are used to define the acceptable range of execution deviations, distinguish between normal fluctuations and abnormal deviations requiring intervention, and provide a judgment standard for deviation correction. If an execution deviation exceeds the corresponding execution deviation threshold, it is marked as an abnormal execution deviation, and deviation correction is performed on the abnormal execution deviation.

[0120] For the marked abnormal start deviation, the start time reference of the trigger execution device and the response execution device is recalculated based on the latest communication delay difference data of the signal execution module, and a time correction value is generated: based on a unified time axis, the compensation amount of their respective one-way communication delay and abnormal start deviation is superimposed, and written into the online control command through the timestamp update interface of the signal execution module to ensure the synchronization accuracy of the start time of the trigger execution device and the response execution device; the compensation amount of the abnormal start deviation is the superposition value of the latest communication delay difference and the historical deviation trend. For example, if the response execution device is continuously lagging, the compensation amount needs to cover the delay difference and the cumulative lag to ensure the synchronization accuracy of the corrected timestamp.

[0121] For abnormal execution parameter deviations, a parameter correction value is generated according to a preset ratio based on the deviation direction and trigger state data intensity. This correction is calculated by combining the deviation direction, trigger state data intensity, and the specific value of the abnormal execution parameter deviation, based on a preset deviation correction ratio mapping rule. This ensures that the corrected execution parameters match the trigger state intensity while eliminating the deviation. The corrected parameters are then synchronized to the online control commands of the triggering and responding execution devices via the execution parameter update interface. The preset deviation correction ratio mapping rule is set based on the sensitivity of the execution parameters to the trigger state. For example, in a high-sensitivity scenario, a 15% correction value is generated for every 10% deviation exceeding the execution parameter deviation threshold. The ratio is determined through multiple calibration experiments.

[0122] For abnormal progress deviations, the system determines whether the progress is lagging or ahead based on the direction of the deviation. If the progress is lagging, the temporary termination timestamp is extended by a preset time extension coefficient, and the update cycle of the dynamic execution command is simultaneously adjusted from the default value to a preset high-frequency update cycle. The adjustment range is fixed through configuration parameters. If the progress is ahead, the duration timestamp compression ratio is calculated based on the deviation percentage. A temporary termination check trigger threshold is set, and the action execution intensity is reduced by a preset attenuation ratio through the execution parameter update interface. All adjustment parameters are read from the system's preset configuration file to ensure consistent quantification standards. The time extension coefficient is the ratio of the progress lag to the remaining execution time, ensuring that the extension value covers the lag without exceeding the safe execution duration. The high-frequency update cycle is set based on the response speed of equipment parameter adjustments and must be less than the minimum adjustment cycle of the equipment action.

[0123] After completing the abnormal deviation correction, the adjusted online control command is sent synchronously to the trigger execution device and the response execution device. The two devices update the online control command according to the unified time axis and execute the action according to the corrected timestamp and execution parameters.

[0124] The system dynamically tracks and corrects the execution status feedback, recalculates various execution deviations, and determines that the correction is effective if all abnormal execution deviations are reduced to within the corresponding execution deviation threshold within the preset evaluation period; otherwise, the correction steps are repeated until the execution deviation is less than the corresponding execution deviation threshold. The evaluation period is set based on a complete execution cycle of the equipment operation, such as the entire duration of contrast agent injection, to ensure that the correction effect can be observed completely.

[0125] The system counts the number of corrections performed on abnormal execution deviations within a preset evaluation period. If the number exceeds a preset correction threshold, the system is deemed to be malfunctioning, an alarm is immediately generated, and the linkage signal execution module suspends the execution of current online control commands. The calibration process will restart after the equipment fault has been identified and the issue has been resolved. The correction threshold is determined based on the statistical probability and confidence level of the equipment fault, using historical fault data and maintenance records.

[0126] Please see Figure 5 This embodiment also introduces a multi-device collaborative online control method, including:

[0127] Step S1: Connect to and identify the triggering and response execution devices in the online control system, and calibrate the time reference deviation;

[0128] Step S2: Receive the trigger status data of the triggering execution device and the auxiliary status data of the response execution device, construct a multi-dimensional status dataset, which is used to perform real-time analysis through multi-dimensional judgment rules to determine whether the triggering conditions are met, generate a point-to-point initial trigger signal, update the trigger signal sequence by comparing it with the start timestamp of the end trigger signal in the trigger signal sequence, and incrementally transmit it to the central control platform;

[0129] Step S3: The central control platform receives the dynamically updated trigger signal sequence, measures the communication delay time between the trigger execution device and the response execution device by sending test signals, generates and iterates online control commands in real time to drive the trigger execution device and the response execution device to coordinate their actions, optimizes the online control commands according to the type identifier, and dynamically updates the online control commands according to the configured update interface.

[0130] Step S4: Based on the execution status feedback returned by the triggering execution device and the response execution device, construct a correction dataset to calculate the execution deviation, identify abnormal execution deviations, and correct the online control commands.

[0131] Preferably, the steps of generating and iterating online control commands include:

[0132] Receive the trigger signal sequence dynamically updated by the central control platform, extract the type identifier, execution parameters and reference timestamp of the terminal trigger signal, and retrieve the one-way communication delay between the trigger execution device and the response execution device measured by the test signal sent by the central control platform;

[0133] Generate initial online control commands, including execution parameters of the triggering and responding execution devices, and start timestamps, and optimize the initial online control commands according to the type identifier;

[0134] The execution parameters of the triggering device are obtained by parsing the end trigger signal, and the execution parameters of the response device are obtained through a preset mapping rule between trigger strength and response parameters; the start timestamp is the sum of the base timestamp and the corresponding device one-way communication delay.

[0135] The optimized initial online control command is synchronously sent to the trigger execution device and the response execution device through a preset communication link. After receiving the command, the trigger execution device and the response execution device convert the timestamp in the online control command into the time corresponding to the local standardized timestamp and perform timestamp alignment.

[0136] After the timestamps of the triggering execution device and the responding execution device are aligned, the action is executed according to the instruction and the execution status feedback is transmitted back in real time, including the actual start time, the actual value of the execution parameters, and the progress of the action.

[0137] Preferably, the specific steps for optimizing the initial online control command based on the type identifier include:

[0138] If the type is identified as point-to-point, the initial online control command is set to single-execution mode, the start timestamp and the end timestamp are the same, the execution parameters are fixed and cannot be adjusted, and the parameter adjustment channel is closed.

[0139] If the type is identified as continuous, the initial online control command is set to continuous execution mode, split into start command, dynamic execution command and temporary termination command, and the online control command is dynamically updated according to the configured update interface;

[0140] The update interfaces include the execution parameter update interface, the duration update interface, and the termination timestamp update interface;

[0141] The start command includes execution parameters and a start timestamp, used to trigger device startup;

[0142] The dynamically executed instruction includes execution parameters and a duration stamp that are adjusted according to the update frequency of the trigger signal sequence. The duration stamp is used to identify the effective runtime segment of the dynamically executed instruction and is calculated by the update frequency of the trigger signal sequence and the duration of the trigger signal type. An execution parameter update interface and a duration update interface are configured to update the execution parameters and duration stamp of the dynamically executed instruction.

[0143] The temporary termination instruction includes execution parameters and a temporary termination timestamp, and configures a termination timestamp update interface to optimize the actual termination time based on the trigger signal type identifier.

[0144] Preferably, the steps for dynamically updating the online control commands according to the configured update interface include:

[0145] During the execution of online control commands, if the type of the trigger signal at the end of the trigger signal sequence dynamically updated by the central control platform is continuous and has not changed, the execution parameter update interface of the dynamic execution command is called to merge the execution parameters of the trigger signal at the end of the trigger signal sequence with the current execution parameters to generate updated execution parameters.

[0146] Based on the reference timestamp of the end trigger signal, the update frequency of the trigger signal sequence, and the duration of the trigger state, the duration timestamp of the dynamically executed instruction is recalculated, and the effective runtime is updated through the duration update interface.

[0147] Call the termination timestamp update interface of the temporary termination instruction, obtain the temporary termination timestamp based on the preset theoretical duration of the end trigger signal, and update the temporary termination timestamp.

[0148] The triggering and response execution devices execute actions according to the updated online control instructions and send back execution status feedback in real time until the trigger signal type identifier at the end of the trigger signal sequence in the central control platform changes and the update interface is closed.

[0149] Working principle and its effects:

[0150] This application achieves high-precision online control of multiple devices by constructing a closed-loop collaborative mechanism encompassing equipment calibration, signal processing, command execution, and dynamic correction. Its core is to address issues such as low synchronization accuracy and response lag in multi-device collaboration through unified time reference, multi-dimensional state fusion, dynamic command adaptation, and real-time deviation correction, ultimately improving the overall collaborative efficiency of the system.

[0151] Specifically, the system first connects to and identifies the triggering and responding execution devices and calibrates the time base, laying the foundation for time consistency in coordinated actions. Next, it receives status data from both types of devices to construct a multi-dimensional status dataset. Triggering conditions are analyzed using standard thresholds and trend development judgment rules, generating a point-to-point initial trigger signal. This is then combined with the end signal timestamp to update the sequence and transmit incrementally, reducing redundant data and improving response speed. After receiving the sequence, the central control platform dynamically measures communication latency, generates online control commands containing execution parameters and timestamps, optimizes the command mode according to point-to-point and continuous identifiers, and iterates in real time through an update interface to ensure dynamic matching between commands and device status. Finally, a correction dataset is constructed based on the execution feedback from the devices, deviations are calculated, and closed-loop correction is performed to ensure action accuracy. This series of mechanisms gradually eliminates timing deviations, signal misjudgments, transmission delays, and execution errors in the coordinated process from four levels: time synchronization, signal reliability, command adaptability, and execution accuracy.

[0152] In summary, this application achieves comprehensive improvements in timeline alignment, action logic matching, and real-time status response of multiple devices through precise coordination and dynamic optimization across multiple stages. It significantly enhances the synchronization accuracy, anti-interference capability, and operational reliability of collaborative actions, effectively meeting the demand for high-precision multi-device coordination in complex scenarios such as industrial production and medical diagnosis and treatment.

[0153] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A multi-device collaborative online control system, characterized in that, The online control system includes a central control platform, as well as a reference synchronization module, a signal decision module, a signal execution module, and a dynamic calibration module. The reference synchronization module is used to access and identify the trigger execution device and the response execution device in the online control system, and to calibrate the time reference deviation; The signal decision module is used to receive the trigger status data of the trigger execution device and the auxiliary status data of the response execution device, construct a multi-dimensional status dataset, and perform real-time analysis through multi-dimensional judgment rules to determine whether the trigger conditions are met. In response, it generates a point-to-point initial trigger signal, updates the trigger signal sequence by comparing it with the start timestamp of the end trigger signal in the trigger signal sequence, and incrementally transmits it to the central control platform. The signal execution module is used to receive dynamically updated trigger signal sequences, measure the communication delay time between the trigger execution device and the response execution device by sending test signals, generate and iterate online control commands in real time, drive the trigger execution device and the response execution device to coordinate actions, optimize the online control commands according to the type identifier, and dynamically update the online control commands according to the configured update interface. The dynamic calibration module is used to construct a calibration dataset based on the execution status feedback sent back by the triggering execution device and the response execution device, and to calculate the execution deviation, identify abnormal execution deviations, and correct the online control commands.

2. The multi-device collaborative online control system as described in claim 1, characterized in that, The step of generating and iterating online control commands includes: Receive the trigger signal sequence dynamically updated by the central control platform, extract the type identifier, execution parameters and reference timestamp of the terminal trigger signal, and retrieve the one-way communication delay between the trigger execution device and the response execution device measured by the test signal sent by the central control platform; Generate initial online control commands, including execution parameters of the triggering and responding execution devices, and start timestamps, and optimize the initial online control commands according to the type identifier; The execution parameters of the triggering device are obtained by parsing the end trigger signal, and the execution parameters of the response device are obtained through a preset mapping rule between trigger strength and response parameters; the start timestamp is the sum of the base timestamp and the corresponding device one-way communication delay. The optimized initial online control command is synchronously sent to the trigger execution device and the response execution device through a preset communication link. After receiving the command, the trigger execution device and the response execution device convert the timestamp in the online control command into the time corresponding to the local standardized timestamp and perform timestamp alignment. After the timestamps of the triggering execution device and the responding execution device are aligned, the action is executed according to the instruction and the execution status feedback is transmitted back in real time, including the actual start time, the actual value of the execution parameters and the progress of the action.

3. The multi-device collaborative online control system as described in claim 2, characterized in that, The specific steps for optimizing the initial online control command based on the type identifier include: If the type is identified as point-to-point, the initial online control command is set to single-execution mode, the start timestamp and the end timestamp are the same, the execution parameters are fixed, and the parameter adjustment channel is closed. If the type is identified as continuous, the initial online control command is set to continuous execution mode, split into start command, dynamic execution command and temporary termination command, and the online control command is dynamically updated according to the configured update interface; The update interfaces include an execution parameter update interface, a duration update interface, and a termination timestamp update interface; The start command includes execution parameters and a start timestamp, used to trigger device startup; The dynamic execution instruction includes execution parameters and a duration stamp that are adjusted according to the update frequency of the trigger signal sequence. The duration stamp is used to identify the effective runtime segment of the dynamic execution instruction and is calculated by the update frequency of the trigger signal sequence and the duration of the trigger signal type identifier. An execution parameter update interface and a duration update interface are configured to update the execution parameters and duration stamp of the dynamic execution instruction. The temporary termination instruction includes execution parameters and a temporary termination timestamp, and configures a termination timestamp update interface to optimize the actual termination time based on the trigger signal type identifier.

4. The multi-device collaborative online control system as described in claim 3, characterized in that, The step of dynamically updating the online control commands according to the configured update interface includes: During the execution of online control commands, if the type of the trigger signal at the end of the trigger signal sequence dynamically updated by the central control platform is continuous and has not changed, the execution parameter update interface of the dynamic execution command is called to merge the execution parameters of the trigger signal at the end of the trigger signal sequence with the current execution parameters to generate updated execution parameters. Based on the reference timestamp of the end trigger signal, the update frequency of the trigger signal sequence, and the duration of the trigger state, the duration timestamp of the dynamically executed instruction is recalculated, and the effective runtime is updated through the duration update interface. Call the termination timestamp update interface of the temporary termination instruction, obtain the temporary termination timestamp based on the preset theoretical duration of the end trigger signal, and update the temporary termination timestamp. The triggering and response execution devices execute actions according to the updated online control instructions and send back execution status feedback in real time until the trigger signal type identifier at the end of the trigger signal sequence in the central control platform changes and the update interface is closed.

5. The multi-device collaborative online control system as described in claim 1, characterized in that, The specific steps for generating the point-to-point initial trigger signal include: Receive trigger status data and response status data output by the triggering execution device and store the trigger status data and response status data as a multi-dimensional status dataset according to a preset format template; The multi-dimensional state dataset is analyzed in real time using multi-dimensional judgment rules to determine whether it meets the triggering conditions. The multi-dimensional judgment rules include standard threshold judgment rules and trend development judgment rules. The standard threshold judgment rules perform interval verification on the trigger state data and auxiliary state data based on a preset state threshold range. After the standard threshold judgment rule is passed, the trend development judgment rule determines whether the state that meets the standard threshold judgment rule is stable by tracking the slope, duration and fluctuation range of the changes of the trigger state data and auxiliary state data within the state threshold range. If the triggering conditions are met, a point-to-point initial trigger signal is generated. The point-to-point initial trigger signal includes a start timestamp based on the initial time when the triggering conditions are met, execution parameters of the response execution device configured based on the triggering state data strength, and a point-to-point identifier.

6. The multi-device collaborative online control system as described in claim 5, characterized in that, The specific steps for updating the trigger signal sequence include: The point-to-point initial trigger signal is updated in the trigger signal sequence and compared with the start timestamp of the final trigger signal in the trigger signal sequence: If the time interval between the two signals is less than the preset merging threshold, the signal merging optimization process is initiated. The start timestamp of the end trigger signal is retained as the starting point of the merged trigger signal. The execution parameters of the point-to-point initial trigger signal and the end trigger signal are merged to form a new trigger signal and updated to the end of the trigger signal sequence. The type identifier of the new trigger signal is changed to continuous. Otherwise, the point-to-point initial trigger signal is added as a new independent trigger signal to the end of the trigger signal sequence; The updated trigger signal sequence is added with a unique identifier and configuration check code of the triggering device, and transmitted to the central control platform through an incremental method that only transmits the last trigger signal in the trigger signal sequence based on a preset communication link.

7. The multi-device collaborative online control system as described in claim 1, characterized in that, The step of measuring the communication delay between the triggering execution device and the response execution device includes: Configure a basic calibration period and establish a sliding window mechanism. The basic calibration period is dynamically adjusted by analyzing the fluctuation range of the communication delay difference within the sliding window. If the fluctuation range is less than the preset stability threshold, the communication status is determined to be stable, the basic calibration period is maintained, and the basic calibration period is used as the real-time calibration period to send test signals. Otherwise, the communication status is determined to be abnormal, and the basic calibration period is shortened according to the ratio of the fluctuation range to the stability threshold, and used as the real-time calibration period. Test signals are generated according to a real-time calibration cycle. The test signals include a standardized timestamp, a test signal identifier, and a verification field. Based on a preset communication link, the central control platform sends the test signals to the trigger execution device and the response execution device respectively, and the sending time is recorded as the test start timestamp. After the triggering execution device and the response execution device receive the test signal, they extract the standardized timestamp in the test signal, record the test signal reception time in combination with the local clock, and generate a reception feedback signal containing the reception time and the device's own identifier, which is then transmitted back to the central control platform through the original communication link. The signal execution module receives the received feedback signals from the trigger execution device and the response execution device, verifies the unique identifier and verification field of the test signal; based on the receiving time of the trigger execution device and the response execution device in the received feedback signal and the standardized timestamp in the test signal, it calculates the one-way communication delay between the trigger execution device and the response execution device, and calculates the communication delay difference between the trigger execution device and the response execution device.

8. The multi-device collaborative online control system as described in claim 7, characterized in that, The step of measuring the communication delay between the triggering execution device and the response execution device further includes: Based on the communication delay difference within the sliding window, a linear fitting algorithm is used to calculate the slope of the communication delay difference trend. If the absolute value of the trend slope is less than the preset trend threshold, the communication delay is determined to have no trend; otherwise, the communication delay is determined to have a trend. The trend threshold is set based on the maximum allowable drift rate of the communication delay, which is determined based on the delay change curve of the device operation. If the communication delay is determined to be non-trending, the current one-way communication delay will be used for real-time calibration cycle updates and online control command generation. If the communication delay is determined to have a trend, a communication anomaly warning is triggered. At the same time, the real-time calibration cycle is shortened according to the ratio of the absolute value of the trend slope to the preset trend threshold, and the one-way communication delay data of the previous calibration cycle is called to generate online control commands. If the triggering device or the response device fails to send back the received feedback signal within the preset feedback time, the measurement is marked as invalid, the test process is restarted, and a communication link abnormality alarm is issued.

9. The multi-device collaborative online control system as described in claim 1, characterized in that, The steps for correcting the online control command include: It continuously receives execution status feedback from triggering and responding execution devices, analyzes the actual start time, actual values ​​of execution parameters and action progress, retrieves unified time axis data and communication latency difference, and establishes a calibration dataset. Execution deviations, including startup deviations, execution parameter deviations, and schedule deviations, are calculated based on the calibration dataset. Configure an execution deviation threshold, and mark execution deviations that exceed the threshold as abnormal execution deviations and correct them. The adjusted online control commands are sent to the triggering and response execution devices, and the online control commands are updated and executed according to a unified timeline. The system dynamically tracks and corrects the execution status feedback of the online control command, recalculates the execution deviation, and determines that the correction is effective if the abnormal execution deviation is less than the corresponding execution deviation threshold within the preset evaluation period; otherwise, the correction steps are repeated until the abnormal execution deviation is less than the corresponding execution deviation threshold. If the number of calibrations within the statistical evaluation period exceeds the preset calibration threshold, the equipment is deemed to be malfunctioning, an alarm is generated, and the execution of online control commands is suspended.

10. A multi-device collaborative online control method, implemented based on the multi-device collaborative online control system according to any one of claims 1-9, characterized in that, The online control system includes a central control platform, as well as a reference synchronization module, a signal decision module, a signal execution module, and a dynamic calibration module; the method includes the following steps: Step S1: Access and identify the trigger execution device and response execution device in the online control system through the reference synchronization module, and calibrate the time reference deviation; Step S2: The signal decision module receives the trigger status data of the trigger execution device and the auxiliary status data of the response execution device, constructs a multi-dimensional status dataset, and performs real-time analysis through multi-dimensional judgment rules to determine whether the trigger conditions are met. In response, a point-to-point initial trigger signal is generated. By comparing the trigger signal sequence with the start timestamp of the end trigger signal in the trigger signal sequence, the trigger signal sequence is updated and incrementally transmitted to the central control platform. Step S3 receives the dynamically updated trigger signal sequence through the signal execution module, measures the communication delay time between the trigger execution device and the response execution device by sending test signals, generates and iterates online control commands in real time to drive the trigger execution device and the response execution device to coordinate actions, optimizes the online control commands according to the type identifier, and dynamically updates the online control commands according to the configured update interface. Step S4: The dynamic calibration module constructs a calibration dataset based on the execution status feedback sent back by the trigger execution device and the response execution device, which is used to calculate the execution deviation, identify abnormal execution deviations, and correct the online control commands.

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