Multi-printing equipment cooperative wireless control algorithm and integration method

Abstract

The invention provides a multi-printing equipment cooperative wireless control algorithm and integration method, and the method comprises the steps: transmitting an adjustment instruction sequence to target printing equipment through a wireless communication channel, obtaining a response confirmation signal of the equipment, and determining that the correction execution of a printing position is completed; according to the printing position, correcting an execution completion signal, updating a shared synchronous control model of all printing devices, and judging the stability of the model in multi-device cooperation; parameters of the wireless control system are adjusted through the future printing deviation predicted value, updated parameter feedback information is obtained, and the response optimization effect of the whole printing system is determined; key performance indexes are extracted from the response optimization effect of the whole printing system, whether the preset printing synchronization precision requirement is met or not is judged, and a final verification result is obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of information technology, and in particular to a multi-printing device cooperative wireless control algorithm and integration method. BACKGROUND

[0002] In the modern textile and printing industry, the importance of multi-device cooperative control technology is self-evident, which is directly related to the improvement of production efficiency and product quality, especially in the scenario where multiple devices are needed to complete complex pattern printing. Through wireless control, the cooperative operation of multiple devices can not only optimize the production process, but also significantly reduce the errors caused by human intervention. However, research and application in this field still face many challenges and need to be broken through. At present, although many solutions have tried to realize the cooperation between devices through wired or simple wireless methods, these methods often lack adaptability in dynamic environments, especially when the number of devices increases or the complexity of tasks increases, the system is prone to slow response or imbalance in coordination. More importantly, existing methods lack the ability to quickly perceive and adjust to sudden deviations when facing complex real-time interaction between devices, which makes it difficult to guarantee the overall production precision, especially in businesses that require high-precision pattern splicing, the problem is particularly prominent. Focusing on technical difficulties, the core problem is how to ensure the position synchronization of multiple devices under wireless control. When a device deviates from the printing position due to mechanical wear or external interference, the coordination of the entire system will be affected. This position deviation not only destroys the output quality of a single device, but also causes obvious misalignment in the splicing of the overall pattern due to the interdependence between devices. Further, this deviation will be amplified in multi-device cooperation due to the lack of real-time feedback, especially in high-speed production environments, the accumulation of deviation may cause the entire batch of products to be scrapped. Therefore, how to real-time perceive and correct the printing position deviation of a single device under wireless control, while ensuring that other devices can dynamically adjust to maintain the integrity of the overall pattern, becomes a key problem in improving the precision of multi-device cooperative printing. This problem is particularly specific in actual business, for example, when producing large-format decorative fabrics, if a device deviates slightly when printing the edge of the pattern, it will not be adjusted in time, which will cause subsequent devices to fail to align the pattern, ultimately affecting the appearance and value of the entire fabric. SUMMARY

[0003] The present application provides a multi-printing device cooperative wireless control algorithm and integration method, mainly including: Obtain real-time position data and printing running state information from the printing device through a multi-device sensor network, and use a standard wireless transmission method to collect the data to a central processing unit to obtain the current printing position deviation value; According to the comparison between the current printing position deviation value and the preset alignment threshold value, if the deviation value exceeds the threshold value, a inter-device feedback loop mechanism is activated, the synchronization operation parameters of the adjacent printing device are obtained, and the alignment consistency state of the overall printing pattern is judged; A position offset vector is extracted from the judgment result of the overall printing pattern alignment consistency state, a general position correction method is used to process the offset vector, and an adjustment instruction sequence of the printing device is obtained; The adjustment instruction sequence is sent to the target printing device through a wireless communication channel, the response confirmation signal of the device is obtained, and it is determined that the printing position correction execution is completed; According to the printing position correction execution completion signal, the shared synchronization control model of all printing devices is updated, and the stability of the model in multi-device cooperation is judged; If the stability of the shared synchronization control model is insufficient, the position deviation trend is obtained from the historical printing data, and the deviation trend is processed by using a general trend prediction method to obtain a future printing deviation prediction value; The parameters of the wireless control system are adjusted through the future printing deviation prediction value, the updated parameter feedback information is obtained, and the response optimization effect of the entire printing system is determined; Key performance indicators are extracted from the response optimization effect of the entire printing system, whether the preset printing synchronization precision requirement is reached is judged, and a final verification result is obtained.

[0004] The technical scheme provided by the embodiment of the application can include the following beneficial effects: The application discloses a multi-device printing position correction method, aiming at the fusion service problems of real-time position deviation leading to overall pattern misalignment, system instability and difficulty in predicting future deviation trend in multi-device cooperative printing, real-time position data and running state information are collected through a multi-device sensor network, and are collected to a central processing unit to calculate a current deviation value, which is compared with a preset threshold value, if the threshold value is exceeded, a feedback loop is activated to obtain adjacent device parameters, the pattern alignment state is judged, the offset vector is extracted and a general correction method is used to generate an adjustment instruction sequence, which is wirelessly sent to the target device for confirmation execution, and then the shared synchronization control model is updated, if the stability is insufficient, the future deviation is predicted from the historical data to adjust the wireless control parameters, and the key performance indicators are extracted to verify whether the synchronization precision requirement is met. The method highlights the core invention points of the sensor network and the feedback loop, ensures the logical association between deviation real-time correction and model dynamic optimization, finally realizes high-precision synchronization, response optimization and stability improvement of the printing system, and significantly improves the multi-device cooperative printing efficiency and quality. BRIEF DESCRIPTION OF DRAWINGS

[0005] Fig. 1 It is a flowchart of a multi-printing device cooperative wireless control algorithm and integrated method of the application.

[0006] Fig. 2 This is a schematic diagram of a multi-printing equipment collaborative wireless control algorithm and integration method according to the present invention.

[0007] Fig. 3 This is another schematic diagram of a multi-printing equipment collaborative wireless control algorithm and integration method according to the present invention. Detailed Implementation

[0008] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0009] like Figs. 1-3 This embodiment of a multi-printing equipment collaborative wireless control algorithm and integration method may specifically include: S101. Real-time position data and printing operation status information are obtained from the printing equipment through a multi-device sensor network, and the data is collected to the central processing unit using a standard wireless transmission method to obtain the current printing position deviation value.

[0010] Real-time position and operational status information is acquired from the printing equipment via a sensor network. This information is transmitted wirelessly to the central processing unit to obtain a preliminary aggregated dataset. Based on this dataset, the real-time position information is standardized using a preset coordinate calibration method to correct the position data, resulting in a corrected position dataset. If the corrected position dataset deviates from the preset standard position range, the specific deviation value is calculated through comparative analysis to determine the deviation result of the current printing position. For operational status information, key status parameters are extracted from the aggregated dataset and compared against preset threshold ranges to determine if the equipment is operating normally. If the operational status parameters exceed the preset threshold range, correlation analysis is performed based on the position deviation results. A support vector machine algorithm is used to classify the relationship between abnormal states and position deviations to determine the impact range of abnormal states. Based on the classification results, position deviation data and abnormal state parameters within the impact range are acquired. Multi-dimensional data integration generates comprehensive operational status information for the equipment, yielding the final analysis result. This comprehensive status information is used to continuously monitor the operating status and position deviation of the printing equipment. A real-time data update mechanism dynamically adjusts the analysis results to determine the latest deviation and status information.

[0011] For example, in the scenario of monitoring printing equipment, acquiring real-time location and operational status information through sensor networks is a core component. Sensors can be installed at key points on the print head and conveyor belt of the equipment to collect position coordinates and operating parameters, such as speed and temperature, in real time. This data is transmitted to a central processing unit via standard wireless transmission methods, such as Wi-Fi or ZigBee, to form a preliminary aggregated dataset. Assuming the position data of a printing press is X = 12.5 meters, Y = 8.3 meters, and the operating speed is 2.5 meters per second, this data will be aggregated for subsequent analysis.

[0012] For example, for the standardization and calibration of position information, a preset coordinate calibration method can be used. Assuming the standard coordinate system has the device center as its origin, if the position data collected by the sensor has systematic errors, such as a deviation of 0.2 meters, the data can be corrected using a calibration algorithm to X=12.3 meters and Y=8.1 meters, forming a corrected position dataset. If the corrected data deviates from the preset standard range of X=12.0 to 12.5 meters and Y=8.0 to 8.5 meters, the specific deviation value is calculated, such as an X-axis deviation of 0.3 meters and a Y-axis deviation of 0.1 meters, to obtain the deviation result of the current printing position. This helps to accurately locate equipment problems and improve printing accuracy.

[0013] For example, regarding operational status information, key parameters such as speed and temperature are extracted from the aggregated dataset and compared with preset threshold ranges. Assuming a speed threshold of 2.0 to 3.0 m / s and a temperature threshold of 20 to 30 degrees Celsius, if the actual speed is 3.2 m / s, exceeding the range, it is judged as an abnormal state. Combining the position deviation results, a support vector machine algorithm is used to analyze the relationship between the anomaly and the deviation, classifying the range of impact of the anomaly, such as areas with significant X-axis deviation. This classification clarifies the specific impact of the anomaly on position accuracy, optimizing subsequent adjustment strategies.

[0014] For example, when generating comprehensive status information, position deviation data and abnormal parameters within the affected area are integrated to form a multi-dimensional report. Suppose the report shows that speed anomalies mainly affect the X-axis deviation area of ​​0.3 meters, it can be determined that the equipment needs speed adjustment or position sensor calibration. This integrated analysis can comprehensively reflect the equipment status and provide a basis for maintenance.

[0015] For example, by using a comprehensive status information continuous monitoring device, a real-time data update mechanism can be employed to dynamically adjust the analysis results. If new data indicates that the speed has recovered to 2.8 m / s and the position deviation has decreased to 0.1 m, the status information is updated to ensure the timeliness of the monitoring results. This mechanism can promptly identify problems, reduce printing errors, and improve production efficiency.

[0016] S102. The current printing position deviation value is compared with the preset alignment threshold. If the deviation value exceeds the threshold, the inter-device feedback loop mechanism is activated to obtain the synchronous operation parameters of adjacent printing devices and determine the alignment consistency of the overall printed pattern.

[0017] Based on the comparison results of the positional deviation value and the alignment threshold, if the deviation value exceeds the preset alignment threshold, synchronous operating parameters are obtained from adjacent devices through the device interaction mechanism to determine the operating parameter dataset of adjacent devices. According to the obtained operating parameter dataset, the parameters are uniformly formatted using a preset standardization method to obtain a standardized parameter set. For the standardized parameter set, the differences in operating parameters between devices are analyzed. By comparing the fluctuation range between parameters, it is determined whether there are significant inconsistencies, thus obtaining the parameter consistency results between devices. If the parameter consistency results between devices show inconsistencies, key difference data is extracted from the parameter set and correlated with the alignment status of the printed pattern to determine the impact range of the difference data on the overall alignment status. Based on the impact range of the difference data, specific operating parameters and alignment status information within the impact range are obtained. Through multi-dimensional data integration, a coordination and adjustment scheme between devices is generated, resulting in coordinated parameter adjustment suggestions. For the coordinated parameter adjustment suggestions, a feedback loop mechanism is used to transmit the adjustment suggestions to relevant devices, update the device operating parameters, and determine whether the overall alignment consistency of the printed pattern has been optimized, thus obtaining the final alignment status analysis result. If deviations still exist based on the final alignment analysis results, the latest operating parameters are obtained again through the equipment synchronization mechanism, and the parameter comparison and adjustment process is executed cyclically to determine the continuous stability of the alignment consistency of the printed pattern.

[0018] For example, in a printing equipment monitoring scenario, when comparing the positional deviation value with the alignment threshold, if the deviation exceeds a preset range, the operating parameters of adjacent devices can be obtained through a device interaction mechanism. The core of this interaction mechanism lies in achieving data sharing between devices, aiming to identify potential influencing factors. Suppose the positional deviation value of a printing device is 0.5 meters, while the preset threshold is 0.2 meters, which is significantly outside the range. At this time, the system will automatically send a request to adjacent devices to collect their operating parameters, such as conveyor belt speed and print head position, forming a preliminary dataset.

[0019] For example, standardization is a crucial step in processing the acquired operational parameter dataset. The purpose of standardization is to unify the data format across different devices, facilitating subsequent analysis. Assuming adjacent device A has a speed of 2.8 m / s and device B has a speed of 3.0 m / s, with position data of X = 10.2 m and X = 10.5 m respectively, standardization converts the data into a unified unit and format, forming a standardized parameter set. This processing method helps eliminate format differences between data, providing a reliable basis for subsequent comparisons.

[0020] For example, when analyzing a standardized set of parameters, the focus is on comparing the differences in operating parameters between devices. By observing the range of parameter fluctuations, it is possible to determine whether there are significant inconsistencies. Suppose the speed fluctuation ranges for devices A and B are 0.1 m / s and 0.3 m / s, respectively, and their position fluctuation ranges are 0.2 m and 0.5 m, respectively. If the fluctuation ranges exceed preset standards, it is considered an inconsistency. This analysis helps identify potential problems between devices, providing a basis for subsequent adjustments.

[0021] For example, if parameter consistency results show inconsistencies, extracting key difference data and performing correlation analysis in conjunction with the alignment status of the printed pattern is a crucial step. Suppose device B experiences significant speed fluctuations and its printed pattern alignment shows a 0.4-meter offset; correlation analysis can determine that the impact of this speed difference on the overall alignment is concentrated in the X-axis direction. This analytical approach helps to accurately pinpoint the root cause of the problem.

[0022] For example, the core of solving the problem lies in generating a coordinated adjustment plan between devices to address the impact range of discrepancies in data. Suppose the speed parameter within the impact range is 3.2 m / s, exceeding the standard range. Through multi-dimensional data integration, the system might suggest adjusting the speed to 2.9 m / s while simultaneously fine-tuning the position parameters. This approach effectively balances the operating status of the devices.

[0023] For example, after the coordinated parameter adjustment suggestions are transmitted to the relevant equipment and the parameters are updated via a feedback loop mechanism, it is necessary to determine whether the alignment of the printed pattern has been optimized. Assuming the deviation value decreases from 0.5 meters to 0.1 meters after adjustment, it indicates a significant improvement in alignment. This feedback mechanism ensures the effectiveness of the adjustment.

[0024] For example, if the final alignment analysis still shows a deviation, the latest parameters are retrieved again through the equipment synchronization mechanism, and the process is cyclically adjusted. Assuming the latest data indicates a deviation of 0.3 meters, the system will trigger parameter comparison and adjustment again until a stable state is reached. This continuous mechanism ensures the alignment consistency of the printed pattern.

[0025] S103. Extract the position offset vector from the judgment result of the overall printing pattern alignment consistency, process the offset vector using a general position correction method, and obtain the adjustment instruction sequence of the printing equipment.

[0026] Step 1: Obtain the specific data of positional offset from the judgment result of the printed pattern, and decompose the positional offset using a preset vector decomposition method to obtain a set of decomposed offset vectors. Step 2: For the decomposed offset vector set, analyze each vector one by one using a standardized correction process, and perform parameter matching through preset correction rules to determine the corrected vector adjustment scheme. Step 3: Based on the corrected vector adjustment scheme, obtain the corresponding equipment calibration requirements, generate a preliminary equipment calibration parameter set, and determine whether the equipment calibration parameter set conforms to the preset operating parameter range. If not, generate a new calibration parameter set through parameter smoothing. Step 4: For the generated calibration parameter set, obtain the constraints related to the operating parameters, analyze the feasibility of the parameter set through condition comparison, and if there are parameters that do not conform to the constraints, perform local corrections to obtain an optimized parameter combination. Step 5: Based on the optimized parameter combination, generate the equipment calibration instruction sequence, and determine the final instruction distribution format through instruction sequence format conversion. Step 6: For the final instruction distribution format, obtain the target device's receiving protocol, and encode the instruction sequence using a protocol adaptation method to obtain an adapted instruction data packet. Step 7: Based on the adapted instruction data packet, transmit data through the device communication interface, determine whether there is data loss or abnormality during the transmission process, and if so, use the retransmission mechanism to complete the data and ensure the integrity of the instruction data packet.

[0027] For example, in the scenario of monitoring printing equipment, the acquisition and processing of position offset data can be analyzed in detail from multiple perspectives. Firstly, regarding the acquisition of position offset data, assuming that a pattern position offset of 0.5 meters is detected during the operation of a printing machine, it can be decomposed into offset vectors of 0.3 meters horizontally and 0.2 meters vertically using vector decomposition. This decomposition method helps to more accurately locate the specific direction of the offset, providing a clear basis for subsequent correction.

[0028] For example, when performing standardization correction on the decomposed set of offset vectors, each vector can be analyzed individually. Suppose a horizontal offset of 0.3 meters exceeds the preset range of 0.1 meters, the system will match an adjustment parameter according to the correction rules, suggesting that the horizontal offset be corrected to within 0.05 meters. This method of analyzing each vector individually ensures that the processing of each offset vector conforms to the standard.

[0029] For example, when generating a set of calibration parameters for an equipment, suppose the initial conveyor belt speed adjustment value in the parameter set is 2.5 m / s, but the preset range is 2.0 to 2.3 m / s, which clearly does not meet the requirements. In this case, through parameter smoothing, the speed value is adjusted to 2.2 m / s, forming a new parameter set. This process avoids abrupt parameter changes and ensures the stability of equipment operation.

[0030] For example, in the feasibility analysis of the calibration parameter set, suppose the constraint requires the linkage ratio of the velocity parameter to the position parameter to be 1:2, while the current parameter set ratio is 1:1.5, which does not meet the condition. The system will perform a local correction on the velocity parameter, adjusting the ratio to 1:2 to form an optimized parameter combination. This correction method can ensure the coordination between parameters.

[0031] For example, when generating a sequence of equipment calibration instructions, assuming the optimized parameter combination includes a speed of 2.2 m / s and a position offset of 0.05 m, the system will convert it into a standard instruction format, such as adjustment steps arranged in chronological order. This format conversion ensures the clarity and executability of the instructions.

[0032] For example, regarding the adaptation of command distribution formats, assuming the target device's receiving protocol requires data packets to be transmitted in a specific encoding method, the system will encode the command sequence into a format that conforms to the protocol, forming an adapted data packet. This adaptation process ensures the compatibility of commands across different devices.

[0033] For example, during the transmission of instruction data packets, if some data loss is detected, the system will use a retransmission mechanism to complete the lost part and ensure the integrity of the data packets.

[0034] For example, if the lost data segment is the speed parameter part, the retransmitted complete data packet contains all the necessary information. This mechanism can effectively avoid execution deviations caused by transmission errors.

[0035] S104. Send the adjustment instruction sequence to the target printing equipment through the wireless communication channel, obtain the response confirmation signal of the equipment, and determine that the printing position correction has been completed.

[0036] Step 1: Send an adjustment command sequence to the target printing equipment via a wireless communication channel. Encapsulate the command sequence using a preset communication protocol to obtain encapsulated data units. Step 2: Distribute the encapsulated data units via a transmission channel. Obtain signal stability indicators during transmission to determine if an interruption has occurred. If an interruption occurs, redistribute the data units via a backup channel to ensure the data units arrive completely at the target equipment. Step 3: Obtain a response signal from the target equipment and decode it using a signal analysis tool to obtain decoded feedback information. Step 4: Analyze the confirmation feedback content contained in the decoded feedback information. If the confirmation feedback indicates that the correction execution is incomplete, match the exception type using a preset error code table to determine the exception handling strategy. Step 5: Based on the determined exception handling strategy, send a supplementary command sequence to the target equipment via a wireless communication channel to obtain a new response signal and determine if the correction execution has returned to normal. Step 6: Based on the multiple obtained response signals, use status judgment logic to comprehensively evaluate the printing position correction execution status to obtain the final execution status result. Step 7: Based on the final execution status result, generate a status log file through the device interaction interface to determine the complete data archive of the correction execution.

[0037] For example, in the business scenario of instruction transmission and correction execution in printing equipment, the process of sending adjustment instruction sequences via wireless communication channels can be analyzed from the perspective of communication protocol adaptability. Suppose a printing device needs to receive a set of adjustment instructions. The system will encapsulate the instruction sequence into specific data units according to the communication protocol supported by the device. This encapsulation method ensures that the instructions have format compatibility before transmission. Furthermore, assuming the protocol requires data units to be grouped in fixed lengths, the system will split the instructions into multiple small data blocks, each 128 bytes long, ensuring that parsing failures due to format issues do not occur during transmission.

[0038] For example, the process of distributing data units through transmission channels can be discussed from the perspective of signal stability monitoring.

[0039] In one possible implementation, the system collects signal strength data in real time during transmission. If the signal strength is detected to be below a preset threshold of 50%, it determines that there may be a risk of interruption. At this point, the system automatically switches to a backup channel to redistribute data units, ensuring that the data is delivered completely to the target device. This dual-channel mechanism can effectively cope with the uncertainties of the transmission environment.

[0040] For example, in the process of acquiring and decoding the response signal from the target device, the explanation can be based on the precision of the feedback information parsing. Suppose the response signal returned by the device contains multiple segments of encoded information; the system will use a specialized parsing tool to decode each segment and extract the key confirmation fields. If the confirmation field indicates that the instruction has been received but not executed, the system will record this status and proceed to the next step of analysis. This meticulous decoding method helps to quickly pinpoint the root cause of the problem.

[0041] For example, the handling of incomplete feedback can be refined from the perspective of exception type matching. Suppose the feedback indicates that the correction is incomplete, the system will query a preset error code table, find the corresponding exception type "execution timeout," and formulate a retry strategy accordingly. This error code-based strategy matching can quickly respond to abnormal situations.

[0042] For example, the process of sending supplementary command sequences and determining when correction is restored can be analyzed from the perspective of command iteration. Suppose that after the initial supplementary command is sent, the device returns a new response signal indicating that correction is still incomplete. The system will then adjust the command parameters, for example, reducing the adjustment step size from 0.1 meters to 0.05 meters, and send the command again. This gradual adjustment method can progressively approach the optimal correction state.

[0043] For example, the comprehensive evaluation of multiple response signals can be explained by the thoroughness of the status judgment logic. The system integrates multiple feedback data; assuming that two out of three feedbacks show normal correction and one shows an abnormality, the overall judgment is that further confirmation is needed. This multi-round evaluation logic can avoid misjudgments based on a single feedback.

[0044] For example, the process of generating and archiving status log files can be explored from a data traceability perspective. Assuming the final execution status is "correction complete," the system will generate a log file containing timestamps and execution details through the device interface and store it in the database. This archiving method facilitates subsequent review and analysis, improving management efficiency.

[0045] S105. Based on the printing position correction completion signal, update the shared synchronous control model of all printing equipment and determine the stability of the model in multi-device collaboration.

[0046] Step 1: Extract key identifier data from the printed position correction completion signal, and segment the signal content using a preset parsing tool to obtain structured signal units. Step 2: For the structured signal units, distribute update instructions to multiple devices through a shared synchronization mechanism to ensure that all devices receive consistent signal unit data. Step 3: Based on the received signal unit data, trigger a refresh operation of the synchronization control model, and batch adjust the model parameters using preset refresh rules to obtain the updated model version. Step 4: For the updated model version, run a simulation task in a multi-device collaborative scenario. If the stability performance is detected to be lower than a preset threshold, redistribute the model version through a backup synchronization channel to determine whether the stability has recovered to the expected range. Step 5: Obtain the operating status logs of multiple devices using a real-time data acquisition tool in the collaborative scenario, analyze key fields related to stability performance in the logs, and determine the model's adaptability in actual collaboration. Step 6: Based on the analysis results of the operating status logs, use a logistic regression algorithm to predict and evaluate the stability performance of the control model, obtaining predicted stability trend data. Step 7: Based on the predicted stability trend data, send optimization instructions to the synchronization control module through a preset feedback channel to determine the model's continued applicability in subsequent collaborative scenarios.

[0047] For example, in the processing of the printed position correction completion signal, the extraction of key identification data is a crucial step. Assuming the signal contains multiple segments of coded information, the system uses pre-defined parsing tools to segment the signal content according to timestamps and data types, forming structured signal units. This segmentation method facilitates subsequent data distribution and analysis, ensuring that each part of the signal can be accurately identified and utilized.

[0048] For example, when distributing structured signal units, the system sends update commands to multiple devices through a shared synchronization mechanism. Suppose five printing machines need to receive data synchronously; the system uses a unified synchronization protocol to ensure that the signal unit data received by each device is completely consistent. This mechanism effectively avoids deviations in data distribution and improves the coordination of multi-device collaboration.

[0049] For example, when a synchronous control model refresh operation is triggered, the system will adjust the model parameters in batches according to preset rules. Assuming the refresh rules specify that each parameter adjustment ranges from 0.2 to 0.5 units, the system will automatically select an appropriate adjustment range based on feedback values ​​from the signal unit data, generating an updated model version. This method allows the model to quickly adapt to the new operating environment.

[0050] For example, running simulation tasks in multi-device collaborative scenarios is particularly important for stability testing of updated model versions. If the stability performance is detected to be below a preset threshold of 60% during the simulation task, the system will immediately redistribute the model version through a backup synchronization channel and continuously monitor whether the stability has recovered to the expected range. This dual-channel switching mechanism can effectively handle unexpected problems.

[0051] For example, by acquiring operational status logs from multiple devices through real-time data acquisition tools, the adaptability of the model can be analyzed in depth. Assuming the logs record the runtime and error frequency of each device, the system will extract key fields, such as records with an error rate exceeding 5%, to assess the model's performance in actual collaboration. This analytical approach helps identify potential problems.

[0052] For example, when using logistic regression to predict and evaluate stability performance, the system generates trend data based on historical log data. If the prediction indicates that stability may drop to 70% within the next 24 hours, the system will record this trend in advance, providing a basis for subsequent optimization. This predictive mechanism allows the system to be more forward-looking.

[0053] For example, based on predicted stability trend data, the system sends optimization instructions to the synchronization control module through a preset feedback channel. Assuming the optimization instructions include adjusting the model parameter step size to 0.1 units, the system will continuously monitor its performance in subsequent collaborative scenarios to ensure the model's continued applicability. This feedback mechanism can continuously improve the model's operating efficiency.

[0054] S106. If the stability of the shared synchronization control model is insufficient, the position deviation trend is obtained from historical printing data, and the deviation trend is processed by a general trend prediction method to obtain the future printing deviation prediction value.

[0055] Step 1: Extract raw data related to positional deviations from historical records, and classify and organize the data using a preset filtering tool to obtain a structured deviation dataset. Step 2: Analyze the change trajectories contained in the structured deviation dataset, and model the trajectories using time series forecasting methods to obtain a prediction model for deviation changes. Step 3: Based on the prediction model for deviation changes, generate printing deviation prediction results for a future period, and save the prediction results through a preset storage module to determine the availability of the prediction data. Step 4: For the saved prediction data, use a preset mapping tool to associate it with the parameters of the synchronization control to obtain deviation correction parameters suitable for model adjustment. Step 5: Based on the obtained deviation correction parameters, send an update command to the synchronization control module. If the stability fluctuations after parameter application exceed a preset threshold, reload the prediction data through a backup data channel to determine whether stability has recovered. Step 6: Based on the stability assessment results, obtain real-time deviation feedback in multi-device collaborative scenarios, and analyze the differences between the feedback data and the prediction results using a preset comparison tool to determine the matching degree of the deviation prediction. Step 7: Based on the matching degree analysis results of the deviation prediction, if the matching degree is lower than the preset standard, the updated deviation dataset is extracted by supplementing the historical records, and the analysis process of the change trajectory is re-triggered to obtain the updated prediction model.

[0056] For example, when processing raw data related to positional deviations, historical records can be organized using specific filtering tools. Suppose a printing system records deviation data for the past 30 days, including the offset and time point of each printing task. The filtering tool would divide the data into multiple subsets according to chronological order and deviation type, such as lateral and vertical deviations. This classification method facilitates subsequent analysis of the changing patterns of different types of deviations, laying the foundation for further modeling.

[0057] For example, time series forecasting methods can be used to analyze the trajectory of changes in structured deviation datasets. Suppose the dataset shows that the lateral deviation of a device has gradually increased from 0.1 mm to 0.3 mm over the past 7 days. Based on this trend, the system can build a predictive model, predicting that the deviation may reach 0.4 mm in the next 3 days. This modeling approach can identify potential problems in advance, providing a basis for subsequent corrections.

[0058] For example, when generating and storing printing deviation prediction results, the system compares the predicted data with actual needs to determine availability. If the prediction shows a deviation that may exceed 0.5 mm within the next 24 hours, the storage module marks it as high-priority data, ensuring fast access during subsequent calls. This storage method helps improve data processing efficiency.

[0059] For example, in the correlation processing between predicted data and synchronization control parameters, a mapping tool can be used to convert the deviation value into a specific adjustment parameter. Assuming a predicted deviation of 0.4 mm, the mapping tool will generate a corresponding correction parameter, such as adjusting the printhead position by 0.3 mm to offset the deviation. This correlation method ensures that parameter adjustments are targeted.

[0060] For example, after sending an update command to the synchronization control module, if stability fluctuations are detected to exceed a preset threshold, such as volatility exceeding 5%, the system will reload the predicted data through a backup data channel. If the volatility drops to 2% after reloading, stability is considered restored. This backup mechanism can respond promptly to abnormal situations.

[0061] For example, when obtaining real-time deviation feedback in multi-device collaborative scenarios, comparison tools can be used to analyze the difference between the feedback and the prediction. Suppose a device's actual deviation is 0.2 mm, while the predicted value is 0.4 mm, the system will record this difference and analyze the cause, which may be due to incomplete device calibration synchronization. This comparison method helps improve the accuracy of predictions.

[0062] For example, if the deviation prediction matching degree is lower than a preset standard, such as below 80%, the system will supplement the dataset by extracting data from historical records. Assuming the new data includes deviation records from the past 15 days, the system will reanalyze the change trajectory and generate a more accurate prediction model. This update mechanism continuously optimizes the prediction results, ensuring model adaptability.

[0063] S107. Adjust the parameters of the wireless control system based on the predicted future printing deviation value, obtain updated parameter feedback information, and determine the response optimization effect of the entire printing system.

[0064] Step 1: Extract key deviation data from the predicted future printing deviation values, and perform stratified processing of the deviation data using a preset classification tool to obtain stratified deviation data groups. Step 2: Analyze the correspondence between the stratified deviation data groups and the parameters of the wireless control system. Generate a preliminary parameter adjustment plan through a preset comparison module to determine the initial adjustment direction. Step 3: Based on the preliminary adjustment direction, send a parameter update command to the control module. If the updated feedback data exceeds a preset threshold, obtain supplementary deviation data through a backup data channel to determine the applicability of the parameter adjustment. Step 4: Based on the applicability of the parameter adjustment, obtain real-time monitoring data of the system status, and process the monitoring data using time series analysis to obtain the dynamic trend of the system response. Step 5: Based on the dynamic trend of the system response, optimize the deviation correction plan using a preset correction tool to generate a final parameter configuration plan suitable for the control module. Step 6: For the final parameter configuration plan, send an execution command to the wireless control system. Obtain the feedback data after execution through a real-time data acquisition module to determine the final optimized state of the system. Step 7: Based on the final state of system optimization, use data analysis tools to perform in-depth analysis of the feedback data, generate subsequent reference data for deviation correction, and determine the stability of system operation.

[0065] For example, when processing predicted future printing deviations, key deviation data can be stratified using a classification tool. Assuming the predicted values ​​include multiple types of deviation data, such as lateral and longitudinal offsets, the classification tool will categorize these data into high-risk and low-risk groups based on their impact range and priority. The specific stratification method can be based on the magnitude of the deviation value; for example, deviations exceeding 0.3 mm could be classified as high-risk, while those below this value would be classified as low-risk. This stratification facilitates subsequent targeted analysis of the characteristics of different groups of data.

[0066] For example, when analyzing the correlation between the stratified deviation data sets and the parameters of the wireless control system, the comparison module can identify potential connections between the deviations and the parameters. Assuming that the lateral deviation data in the high-risk group is related to the printhead's movement speed parameter, the comparison module will initially suggest reducing the movement speed to decrease the deviation. This initial solution is generated based on the summarization of patterns in historical data, ensuring that the adjustment direction is reasonable.

[0067] For example, after sending a parameter update command to the control module, if the feedback data exceeds a preset threshold, such as a deviation still exceeding 0.5 mm, supplementary data is obtained through a backup data channel. This supplementary data may include deviation records from the past week to determine whether the current adjustment is appropriate. This method can promptly identify deficiencies in parameter adjustments, providing data support for subsequent optimization.

[0068] For example, when acquiring real-time monitoring data of the system status to assess the applicability of parameter adjustments, time series analysis can be used to process this data. Assuming the monitoring data reflects changes in the system's response time and deviation after adjustment, the analysis results may show a decreasing trend in deviation over a short period. Capturing this dynamic trend helps in understanding how well the system adapts to parameter adjustments.

[0069] For example, when optimizing a deviation correction scheme based on dynamic trends, a preset correction tool can refine the scheme. If the trend indicates a slow rate of deviation reduction, the tool might suggest further fine-tuning of the printhead position, such as adjusting it by 0.1 mm at a time, until the ideal state is achieved. The final parameter configuration will then better suit the actual needs.

[0070] For example, after sending an execution command to the wireless control system, feedback data can be obtained through a real-time data acquisition module to determine the system's optimization status. If the feedback shows the deviation has stabilized within 0.2 millimeters, the optimization effect is considered good. This feedback mechanism can intuitively reflect the execution results, providing a basis for subsequent improvements.

[0071] For example, when performing in-depth analysis of feedback data, data analysis tools can generate subsequent reference data. Suppose the analysis reveals significant fluctuations in deviations over certain time periods, possibly due to changes in the equipment's operating environment; the reference data will record these patterns. This analysis helps determine the system's stability and provides support for long-term maintenance.

[0072] S108. Extract key performance indicators from the response optimization effect of the entire printing system, determine whether the preset printing synchronization accuracy requirements are met, and obtain the final verification result.

[0073] Step 1: Obtain core performance data from the response optimization results of the printing system. Use a pre-set data filtering tool to classify and organize the data, obtaining a categorized performance dataset. Step 2: Analyze the correlation between the categorized performance dataset and synchronization accuracy. Use a pre-set comparison module to match the data and determine if the synchronization accuracy meets the standard. Step 3: If the synchronization accuracy meets the standard, correlate the matching results with system status data. If it does not meet the standard, obtain supplementary performance data through a backup data channel to arrive at an adjusted matching conclusion. Step 4: Based on the adjusted matching conclusion, obtain real-time monitoring records of the system status. Use a time series processing tool to segment and analyze the monitoring records to determine the stability trend of the system status. Step 5: Based on the stability trend of the system status, perform in-depth analysis of the performance evaluation data using a pre-set analysis framework to obtain a comprehensive performance evaluation index value. Step 6: Based on the comprehensive performance evaluation index value, combine the results analysis module to perform a final verification of the validation conclusion, generating optimization reference data suitable for the printing system.

[0074] For example, when extracting core performance data from the response optimization results of a printing system, the data can be categorized and organized using a pre-set data filtering tool. Assuming the system generates a large amount of performance data after operation, the filtering tool will divide it into two categories based on its source and importance: critical performance data and secondary performance data. Critical performance data might include printing speed and accuracy errors, while secondary data might include ambient temperature or equipment operating time. After categorization, critical performance data will be prioritized and organized into datasets for subsequent analysis. This categorization method helps focus on core issues and improves the relevance of data processing.

[0075] For example, when analyzing the correlation between the categorized performance dataset and synchronization accuracy, a pre-defined comparison module can be used for data matching. Assuming the synchronization accuracy standard requires an error control within 0.2 mm, the comparison module will compare the error data in the dataset with the standard value one by one. If an error value of 0.1 mm is found in a set of data, it is determined to meet the standard; if the error value is 0.3 mm, it is marked as not meeting the standard. This matching process can quickly filter out abnormal data that requires attention, providing a basis for subsequent adjustments.

[0076] For example, when synchronization accuracy does not meet standards, obtaining supplementary performance data through a backup data channel is an effective approach. Assuming the initial data has a high error rate, the backup channel might retrieve historical performance records from the past 24 hours to analyze for similar error patterns. If the supplementary data indicates that the error is related to equipment load during a specific time period, an adjusted matching conclusion can be drawn. This supplementary mechanism avoids misjudgments caused by insufficient data.

[0077] For example, after obtaining real-time monitoring records of the system status, segmented analysis using time series processing tools is an important means of assessing stability. Suppose the monitoring records show that the system's error fluctuation range over the past hour is between 0.05 mm and 0.15 mm, the tool will divide the data into 10-minute segments and analyze the fluctuation trends of each segment. If a sudden increase in fluctuation is detected in a certain segment, it may indicate a potential problem with the equipment. This segmented analysis helps to promptly capture changes in the system status.

[0078] For example, to analyze the stability trend of a system's state, a comprehensive index value can be obtained by deeply analyzing performance evaluation data using a pre-defined analytical framework. Assuming the analytical framework uses stability, error range, and response speed as the three main evaluation dimensions, if the stability score reaches 85 points, the error range is controlled within 0.1 mm, and the response speed is 2 adjustments per second, the comprehensive index value might be 90 points, indicating excellent system performance. This analytical method can comprehensively reflect the system's operating status.

[0079] For example, when performing final verification of the validation conclusions based on comprehensive index values ​​and the results analysis module, optimization reference data can be generated. Suppose the index values ​​show a slight performance decrease under high load, the analysis module will suggest reducing the load during specific periods and generate corresponding parameter adjustment suggestions, such as reducing the printing speed from 100 sheets per minute to 80 sheets per minute. This reference data provides a clear direction for subsequent system optimization.

[0080] The above embodiments are merely one of the preferred embodiments of the present invention and should not be used to limit the scope of protection of the present invention. Any modifications or refinements made to the main design concept and spirit of the present invention that are not of substantial significance, but solve the same technical problem as the present invention, should be included within the scope of protection of the present invention.

Claims

1. A collaborative wireless control algorithm and integration method for multiple printing equipment, characterized in that, The method includes: Real-time position data and printing operation status information are obtained from the printing equipment through a multi-device sensor network. The data is then collected to the central processing unit using a standard wireless transmission method to obtain the current printing position deviation value. The current printing position deviation value is compared with a preset alignment threshold. If the deviation value exceeds the threshold, the inter-device feedback loop mechanism is activated to obtain the synchronous operation parameters of adjacent printing devices and determine the alignment consistency of the overall printed pattern. The position offset vector is extracted from the judgment result of the overall printing pattern alignment consistency, and the offset vector is processed by a general position correction method to obtain the adjustment instruction sequence of the printing equipment. The adjustment command sequence is sent to the target printing equipment via a wireless communication channel, and the response confirmation signal of the equipment is obtained to determine that the printing position correction has been completed. Based on the completion signal of the printing position correction, update the shared synchronous control model of all printing equipment and determine the stability of the model in multi-device collaboration; If the stability of the shared synchronization control model is insufficient, the position deviation trend is obtained from historical printing data, and the deviation trend is processed by a general trend prediction method to obtain the future printing deviation prediction value. The parameters of the wireless control system are adjusted by the predicted future printing deviation values, the updated parameter feedback information is obtained, and the response optimization effect of the entire printing system is determined. Key performance indicators are extracted from the response optimization effect of the entire printing system to determine whether the preset printing synchronization accuracy requirements are met, and the final verification results are obtained.

2. The multi-printing equipment collaborative wireless control algorithm and integration method according to claim 1, characterized in that, The process involves acquiring real-time position data and printing operation status information from the printing equipment via a multi-device sensor network, and then using a standard wireless transmission method to aggregate the data to a central processing unit to obtain the current printing position deviation value, including: The sensor network acquires real-time location and operating status information from the printing equipment, and the collected information is transmitted to the central processing unit using standard wireless transmission methods to obtain a preliminary aggregated dataset. Based on the aggregated dataset, the real-time location information is standardized, and the location data is corrected using a preset coordinate calibration method to determine the corrected location dataset. If the corrected position dataset deviates from the preset standard position range, the specific deviation value is calculated through comparative analysis to obtain the deviation result of the current printing position; Based on the operational status information, key status parameters are extracted from the aggregated dataset and compared using a preset threshold range to determine whether the equipment is operating normally. If the operating status parameters exceed the preset threshold range, a correlation analysis is performed based on the position deviation results. The support vector machine algorithm is used to classify the relationship between the abnormal state and the position deviation to determine the influence range of the abnormal state. Based on the classification results, obtain the location deviation data and abnormal state parameters within the affected area, and generate comprehensive status information of equipment operation through multi-dimensional data integration to obtain the final analysis results; The integrated status information is used to continuously monitor the operating status and positional deviation of the printing equipment. A real-time data update mechanism is used to dynamically adjust the analysis results and determine the latest deviation and status information.

3. The multi-printing equipment collaborative wireless control algorithm and integration method according to claim 1, characterized in that, The step of comparing the current printing position deviation value with a preset alignment threshold, and if the deviation value exceeds the threshold, activating the inter-device feedback loop mechanism to obtain the synchronous operating parameters of adjacent printing devices and determine the overall alignment consistency of the printed pattern, includes: Based on the comparison results between the position deviation value and the alignment threshold, if the deviation value exceeds the preset alignment threshold, the synchronous operation parameters are obtained from the adjacent devices through the device interaction mechanism to determine the operation parameter dataset of the adjacent devices. Based on the obtained runtime parameter dataset, the parameters are uniformly formatted using a preset standardization method to obtain a standardized parameter set. For a standardized set of parameters, the differences in operating parameters between various devices are analyzed. By comparing the fluctuation ranges between parameters, it is determined whether there are significant inconsistencies, and the parameter consistency results between devices are obtained. If the parameter consistency results between devices show inconsistencies, key difference data are extracted from the parameter set and correlated with the alignment status of the printed pattern to determine the scope of the impact of the difference data on the overall alignment status. Based on the impact range of the differential data, specific operating parameters and alignment status information within the impact range are obtained. Through multi-dimensional data integration, a coordination and adjustment plan between devices is generated, and parameter adjustment suggestions after coordination are obtained. Based on the coordinated parameter adjustment suggestions, the adjustment suggestions are transmitted to the relevant equipment through a feedback loop mechanism to update the equipment's operating parameters, determine whether the overall alignment consistency of the printed pattern has been optimized, and obtain the final alignment status analysis results. If deviations still exist based on the final alignment analysis results, the latest operating parameters are obtained again through the equipment synchronization mechanism, and the parameter comparison and adjustment process is executed cyclically to determine the continuous stability of the alignment consistency of the printed pattern.

4. The multi-printing equipment collaborative wireless control algorithm and integration method according to claim 1, characterized in that, The step involves extracting a position offset vector from the judgment result of the overall printed pattern alignment consistency, processing the offset vector using a general position correction method, and obtaining an adjustment command sequence for the printing equipment, including: Step 1: Obtain the specific data of position offset from the judgment result of the printed pattern, and use the preset vector decomposition method to decompose the position offset to obtain the set of decomposed offset vectors. Step 2: For the decomposed set of offset vectors, each vector is analyzed one by one using a standardized correction process. Parameter matching is performed using preset correction rules to determine the adjustment scheme for the corrected vectors. Step 3: Based on the corrected vector adjustment scheme, obtain the corresponding equipment calibration requirements, generate a preliminary equipment calibration parameter set, and determine whether the equipment calibration parameter set meets the preset operating parameter range. If it does not meet the preset range, generate a new calibration parameter set through parameter smoothing. Step 4: For the generated calibration parameter set, obtain the constraints related to the operating parameters, analyze the feasibility of the parameter set by condition comparison, and if there are parameters that do not meet the constraints, make local corrections to obtain the optimized parameter combination. Step 5: Based on the optimized parameter combination, generate the instruction sequence for equipment calibration, and determine the final instruction distribution format through instruction sequence format conversion processing; Step 6: For the final instruction distribution format, obtain the target device's receiving protocol, encode the instruction sequence using a protocol adaptation method, and obtain the adapted instruction data packet; Step 7: Based on the adapted instruction data packet, transmit data through the device communication interface, determine whether there is data loss or abnormality during the transmission process, and if so, use the retransmission mechanism to complete the data and ensure the integrity of the instruction data packet.

5. The multi-printing equipment collaborative wireless control algorithm and integration method according to claim 1, characterized in that, The step of sending the adjustment command sequence to the target printing equipment via a wireless communication channel, obtaining the response confirmation signal from the equipment, and determining that the printing position correction has been completed includes: Step 1: Send an adjustment instruction sequence to the target printing equipment through a wireless communication channel, and encapsulate the instruction sequence using a preset communication protocol to obtain encapsulated data units; Step 2: For the packaged data unit, data is distributed through the transmission channel, the signal stability index during the transmission process is obtained, and it is determined whether the transmission is interrupted. If the transmission is interrupted, the data unit is redistributed through the backup channel to ensure that the data unit arrives at the target device intact. Step 3: Obtain the response signal from the target device, and use a signal analysis tool to decode the response signal to obtain the decoded feedback information; Step 4: Based on the decoded feedback information, analyze the confirmation feedback content contained therein. If the confirmation feedback content indicates that the correction execution has not been completed, then match the exception type through the preset error code table to determine the exception handling strategy. Step 5: Based on the determined anomaly handling strategy, send a supplementary instruction sequence to the target device through the wireless communication channel, obtain the new response signal, and determine whether the correction execution has returned to normal. Step 6: Based on the multiple acquired response signals, use status judgment logic to comprehensively evaluate the execution status of printing position correction and obtain the final execution status result; Step 7: Based on the final execution status result, generate a status log file through the device interaction interface to determine the complete data archive of the correction execution.

6. The multi-printing equipment collaborative wireless control algorithm and integration method according to claim 1, characterized in that, The step of updating the shared synchronous control model of all printing equipment based on the printing position correction completion signal and determining the stability of the model in multi-equipment collaboration includes: Step 1: Extract key identification data from the printed position correction completion signal, and use a preset parsing tool to segment the signal content to obtain structured signal units; Step 2: For structured signal units, update instructions are distributed to multiple devices through a shared synchronization mechanism to ensure that all devices receive consistent signal unit data; Step 3: Based on the received signal unit data, trigger the refresh operation of the synchronization control model, adjust the model parameters in batches according to the preset refresh rules, and obtain the updated model version; Step 4: For the updated model version, run the simulation task in a multi-device collaboration scenario. If the stability performance is found to be lower than the preset threshold, redistribute the model version through the backup synchronization channel to determine whether the stability has recovered to the expected range. Step 5: Use real-time data acquisition tools in collaborative scenarios to obtain the operating status logs of multiple devices, analyze the key fields related to stability performance in the logs, and determine the adaptability of the model in actual collaboration. Step 6: Based on the analysis results of the running status log, use the logistic regression algorithm to predict and evaluate the stability performance of the control model, and obtain the predicted stability trend data; Step 7: Based on the predicted stability trend data, send optimization instructions to the synchronization control module through a preset feedback channel to determine the model's continued applicability in subsequent collaborative scenarios.

7. The multi-printing equipment collaborative wireless control algorithm and integration method according to claim 1, characterized in that, If the stability of the shared synchronization control model is insufficient, the position deviation trend is obtained from historical printing data, and a general trend prediction method is used to process the deviation trend to obtain a predicted value for future printing deviations, including: Step 1: Extract raw data related to location deviation from historical records, and use preset filtering tools to classify and organize the data to obtain a structured deviation dataset; Step 2: For the structured deviation dataset, analyze the change trajectory contained therein, and use time series forecasting methods to model the trajectory to obtain a prediction model for deviation changes; Step 3: Based on the prediction model of deviation changes, generate the printing deviation prediction results for a future period of time, save the prediction results through a preset storage module, and determine the availability of the prediction data; Step 4: For the saved prediction data, use a preset mapping tool to associate it with the parameters of the synchronization control to obtain the deviation correction parameters suitable for model adjustment; Step 5: Based on the obtained deviation correction parameters, send an update command to the synchronization control module. If the stability fluctuation after the parameter application is detected to exceed the preset threshold, reload the predicted data through the backup data channel to determine whether the stability has been restored. Step 6: Based on the stability assessment results, obtain real-time deviation feedback in multi-device collaboration scenarios, analyze the differences between the feedback data and the prediction results using a preset comparison tool, and determine the matching degree of the deviation prediction. Step 7: Based on the matching degree analysis results of the deviation prediction, if the matching degree is lower than the preset standard, the updated deviation dataset is extracted by supplementing the historical records, and the analysis process of the change trajectory is re-triggered to obtain the updated prediction model.

8. The multi-printing equipment collaborative wireless control algorithm and integration method according to claim 1, characterized in that, The process of adjusting the parameters of the wireless control system based on the predicted future printing deviation value, obtaining updated parameter feedback information, and determining the response optimization effect of the entire printing system includes: Step 1: Extract key deviation data from the predicted future printing deviation values, and use a preset classification tool to perform stratification processing on the deviation data to obtain stratified deviation data groups; Step 2: For the stratified deviation data set, analyze its correspondence with the parameters of the wireless control system, generate a preliminary parameter adjustment plan through the preset comparison module, and determine the preliminary adjustment direction; Step 3: Based on the initial adjustment direction, send a parameter update command to the control module. If the updated feedback data is detected to exceed the preset threshold, obtain supplementary deviation data through the backup data channel to determine the applicability of the parameter adjustment. Step 4: Based on the applicability results of parameter adjustments, obtain real-time monitoring data of the system status, and use time series analysis methods to process the monitoring data to obtain the dynamic trend of system response. Step 5: Based on the dynamic trend of the system response, optimize the deviation correction scheme using a preset correction tool to generate a final parameter configuration scheme suitable for the control module; Step 6: For the final parameter configuration scheme, send the execution command to the wireless control system, obtain the feedback data after execution through the real-time data acquisition module, and determine the final state of system optimization; Step 7: Based on the final state of system optimization, use data analysis tools to perform in-depth analysis of the feedback data, generate subsequent reference data for deviation correction, and determine the stability of system operation.

9. The multi-printing equipment collaborative wireless control algorithm and integration method according to claim 1, characterized in that, The process of extracting key performance indicators from the response optimization effect of the entire printing system, determining whether the preset printing synchronization accuracy requirements have been met, and obtaining the final verification result includes: Step 1: Obtain core performance data from the response optimization results of the printing system, and use a preset data filtering tool to classify and organize the data to obtain the classified performance dataset; Step 2: For the classified performance dataset, analyze its correlation with synchronization accuracy, and use a preset comparison module to match the data to determine whether the synchronization accuracy meets the standard. Step 3: If the synchronization accuracy meets the standard, then associate the matching result with the system status data; If it does not meet the standard, supplementary performance data will be obtained through the backup data channel to arrive at the adjusted matching conclusion; Step 4: Based on the adjusted matching results, obtain real-time monitoring records of the system status, and use time series processing tools to perform segmented analysis on the monitoring records to determine the stability trend of the system status; Step 5: Based on the stability trend of the system state, perform in-depth analysis of the performance evaluation data through a preset analysis framework to obtain the comprehensive performance evaluation index value; Step 6: Based on the comprehensive index values ​​of the performance evaluation, and in conjunction with the results analysis module, perform a final verification of the validation conclusions to generate optimized reference data suitable for the printing system.

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