Software remote upgrading method and system for elevator controller

Abstract

The invention relates to the technical field of software remote upgrading, in particular to a software remote upgrading method and system for an elevator controller. The method comprises the following steps: carrying out software upgrading port parameter extraction on the elevator controller, carrying out port communication protocol stack simulation, and then carrying out pre-upgrading environment sandbox construction to obtain pre-upgrading environment sandbox data; performing multi-thread fragmentation transmission interruption simulation on the software upgrading file according to the pre-upgrading environment sandbox data, and designing a breakpoint interruption transmission mechanism to obtain interruption retransmission efficiency balance mechanism data; performing normalization design on the interrupt retransmission efficiency balance mechanism data, and then performing network change perception optimization to obtain interrupt retransmission network perception optimization mechanism data; and performing automatic sensing firmware design on the interrupt retransmission network sensing optimization mechanism data to obtain interrupt retransmission sensing firmware parameters. According to the method, the software remote upgrading technology is optimized, so that the software remote upgrading technology is more perfect.

Description

Technical Field

[0001] The present invention relates to the technical field of software remote upgrade, and particularly to a software remote upgrade method and system for an elevator controller. Background Art

[0002] Through an Internet connection, elevator operators or manufacturers can remotely monitor the operating status of elevators in the cloud, promptly discover and solve potential problems. When software upgrade is required, the operator only needs to select the corresponding upgrade package through a dedicated management platform and send it to the elevator controller. After receiving the upgrade instruction, the controller automatically downloads and installs it. This method not only greatly reduces the frequency of on-site manual operations, but also reduces labor costs and safety risks, and improves the service efficiency of elevators. In addition, another important advantage of the remote upgrade method is the ability to centrally manage and upgrade multiple elevators. For large buildings or complexes, there are often multiple elevators. Upgrading them one by one in the traditional way would be extremely cumbersome. Through remote upgrade, managers can uniformly schedule and batch upgrade all elevators, thereby improving management efficiency and ensuring that each elevator always operates in the best state. This centralized management ability is particularly suitable for the regular upgrade and maintenance provided by elevator manufacturers in after-sales service, and can provide a better experience for users. However, there are problems in a traditional software remote upgrade method for an elevator controller, such as the lack of a clear breakpoint resumption mechanism for situations like interruption and restart, power failure, etc. during the upgrade process, and unclear network perception for interrupted retransmission. Summary of the Invention

[0003] Based on this, it is necessary to provide a software remote upgrade method and system for an elevator controller to solve at least one of the above technical problems.

[0004] To achieve the above object, a software remote upgrade method for an elevator controller, the method includes the following steps:

[0005] Step S1: Extract software upgrade port parameters of the elevator controller to obtain elevator controller software upgrade port parameters; simulate a port communication protocol stack according to the elevator controller software upgrade port parameters to obtain a port communication protocol simulation stack; construct a pre-upgrade environment sandbox through the port communication protocol simulation stack to obtain pre-upgrade environment sandbox data;

[0006] Step S2: Obtain a software upgrade file; simulate multi-threaded shard transfer interruption for the software upgrade file according to the pre-upgrade environment sandbox data to obtain multi-threaded shard transfer interruption simulation data; design a breakpoint and resume mechanism according to the multi-threaded shard transfer interruption simulation data to obtain interruption retransmission efficiency balance mechanism data;

[0007] Step S3: Normalize the data of the interruption retransmission efficiency balancing mechanism to obtain the normalized interruption retransmission mechanism data; perform network change awareness optimization on the normalized interruption retransmission mechanism data to obtain the network awareness optimization mechanism data for interruption retransmission;

[0008] Step S4: Design an automated awareness firmware for the network awareness optimization mechanism data of interruption retransmission to obtain the firmware parameters for interruption retransmission awareness; embed the firmware parameters for interruption retransmission awareness into the cloud platform to execute the software remote upgrade method.

[0009] The present invention first extracts the software upgrade port of the elevator controller, which can effectively identify the interface for communication. This process is crucial as it lays the foundation for subsequent communication protocol simulation. Subsequently, using the extracted port, the construction of the communication protocol stack is simulated to ensure correct data transmission when communicating with the elevator controller. By setting up a pre-upgrade environment sandbox, a secure and isolated test environment is created to simulate and verify the software upgrade process. This stage provides a reliable technical basis for subsequent actual upgrades, ensuring the stability and security of the system. The software upgrade file obtained first is the core component of the entire upgrade process. By simulating the interruption of multi-threaded shard transmission of this file through the pre-upgrade environment sandbox, the interruption situation during transmission can be evaluated, providing a basis for subsequent design. Through the simulation of the interruption situation, an effective breakpoint resumption mechanism is designed to ensure that after the transmission is interrupted, the system can quickly resume and re-transmit the incomplete data. The design of this mechanism not only improves the efficiency of data transmission but also reduces the risk of upgrade failure caused by unstable networks. In order to standardize the data of the interruption resumption efficiency balancing mechanism to make it more general and adaptable. Through normalization, it can be ensured that the resumption mechanisms under different environments and conditions can maintain consistent performance. In addition, combined with the perception optimization of network changes, the system can monitor the network status in real time and automatically adjust the resumption strategy according to the current network conditions. This intelligent optimization method not only improves the stability of the system but also enhances the adaptability of the software upgrade process under different network conditions, further reducing the uncertainty of the user experience. The design of the automated perception firmware is a key step. By embedding the interruption resumption perception firmware parameters into the cloud platform, it realizes the intelligent management of the software remote upgrade process. This firmware can monitor the running status of the system in real time and react quickly when an interruption occurs to ensure the integrity and accuracy of the data. This embedded solution greatly improves the automation degree of remote upgrades, reduces the need for manual intervention, and thus enhances the operation efficiency and user satisfaction of the entire system. Finally, the implementation of this step provides strong technical support for the software maintenance of the elevator controller, ensuring the smooth progress of the upgrade process. Therefore, the present invention is an optimization of a traditional software remote upgrade method for elevator controllers, solving the problems existing in the traditional software remote upgrade method for elevator controllers, such as the lack of a clear breakpoint resumption mechanism for interruptions, restarts, power outages, etc. during the upgrade process, and the unclear perception of the interruption resumption network. A clear breakpoint resumption mechanism is established, improving the clarity of the perception of the interruption resumption network.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Extract the software upgrade port parameters of the elevator controller to obtain the software upgrade port parameters of the elevator controller;

[0012] Step S12: Determine the transmission protocol based on the software upgrade port parameters of the elevator controller to obtain the port transmission protocol data;

[0013] Step S13: Simulate the port communication protocol stack according to the port transmission protocol data and the software upgrade port parameters of the elevator controller to obtain the port communication protocol simulation stack;

[0014] Step S14: Build a pre-upgrade environment sandbox through the port communication protocol simulation stack to obtain the pre-upgrade environment sandbox data.

[0015] The extraction of the software upgrade port parameters of the elevator controller in the present invention is the crucial first step. By identifying and extracting the specific ports used for software upgrade in the controller, the communication interfaces required for the upgrade can be clarified. This process not only ensures the accuracy of subsequent operations but also lays the foundation for system stability. Through clear port definitions, communication problems caused by incorrect port selection can be reduced, thus ensuring the smooth transmission of information during the subsequent upgrade process and enhancing the reliability of the system. Based on the extracted software upgrade port parameters of the elevator controller, it is crucial to determine the corresponding transmission protocol. By analyzing the characteristics and functions of the ports, the most suitable transmission protocol can be selected to ensure the efficient and secure transmission of data. This process takes into account various factors such as bandwidth, latency, and error detection capabilities, etc., so that the finally selected transmission protocol can maximize the data transmission efficiency and reduce the error rate. An effective transmission protocol provides a reliable guarantee for the entire software upgrade process and is a prerequisite for a successful upgrade. The simulation of the port communication protocol stack according to the determined port transmission protocol data and the software upgrade port parameters of the elevator controller is an in-depth application of the results of the previous two steps. At this stage, a complete communication protocol stack can be established through simulation, thus providing a standardized framework for subsequent data exchange. The simulated protocol stack can truly reflect various situations that occur during the actual upgrade process, providing the necessary data support for the testing and optimization of the upgrade plan. Through this simulation process, potential problems can be discovered early, the communication strategy can be optimized, and thus the risks encountered during the actual upgrade can be reduced. Building a pre-upgrade environment sandbox through the port communication protocol simulation stack forms a safe test environment. This sandbox can not only simulate the real upgrade environment but also allow the testing and verification of the upgrade process under different conditions, thus ensuring the security and reliability of the upgrade. By conducting multiple iterative tests in the sandbox, potential vulnerabilities and defects can be effectively discovered and repaired, ensuring that unforeseen errors will not occur during the actual upgrade. Finally, the construction of this sandbox provides strong support for the entire software upgrade process, making the final implementation smoother and reducing the impact on the normal operation of the elevator.

[0016] Preferably, step S2 includes the following steps:

[0017] Step S21: Obtain the software upgrade file;

[0018] Step S22: Perform multi-threaded shard transfer interruption simulation on the software upgrade file according to the pre-upgrade environment sandbox data to obtain multi-threaded shard transfer interruption simulation data;

[0019] Step S23: Design a breakpoint resume mechanism for the software upgrade file according to the multi-threaded shard transfer interruption simulation data to obtain transfer interruption breakpoint resume mechanism data;

[0020] Step S24: Perform network fluctuation efficiency balance compensation on the transfer interruption breakpoint resume mechanism data to obtain interruption retransmission efficiency balance mechanism data.

[0021] Obtaining the software upgrade file in the present invention is an important link in the entire upgrade process. This step involves downloading or verifying the required upgrade file to ensure its integrity and applicability. Software upgrade files usually contain fixes, feature enhancements, and security patches. Therefore, correctly obtaining and verifying these files is crucial for a successful upgrade. By ensuring that the obtained files are up-to-date and error-free, a solid foundation can be laid for subsequent transfer and installation, minimizing the risk of upgrade failure caused by file problems. Simulate the interruption of multi-threaded sharded transfer of the software upgrade file based on the pre-upgrade environment sandbox. This simulation process can simulate the interruption situation encountered during data transfer, thereby obtaining relevant data on the interruption of multi-threaded sharded transfer. Through this step, the performance of multi-threaded transfer when encountering network fluctuations or other interruptions can be effectively tested and evaluated, helping to identify potential bottlenecks and problems. This pre-simulation provides an important basis for subsequent design and optimization of the transfer mechanism, ensuring that various unstable factors can be efficiently handled during the actual transfer process. Design a breakpoint resume mechanism for transfer interruption based on the obtained data on the interruption of multi-threaded sharded transfer. This mechanism can quickly locate the transferred data segments after the transfer interruption and resume the transfer from the breakpoint, greatly improving the efficiency and reliability of data transfer. By implementing breakpoint resume, the system can not only avoid re-transmitting the completed part but also reduce the waiting time of users during the upgrade process. The introduction of this mechanism is of great significance for enhancing the user experience and reducing network resource waste, making the entire software upgrade process more efficient and convenient. Design a network fluctuation efficiency balance compensation for the breakpoint resume mechanism of transfer interruption to form data on the interruption retransmission efficiency balance mechanism. This mechanism can real-time monitor the network status and automatically adjust the transfer strategy according to the current network conditions to ensure effective data transfer under network fluctuations. By balancing the transfer efficiency and the retransmission mechanism, the system can maximize the transfer rate while maintaining data integrity. This dynamic adaptability not only enhances the stability of the system but also improves the success rate of software upgrade, ensuring that users can obtain a smooth upgrade experience under various network conditions.

[0022] Preferably, step S23 includes the following steps:

[0023] Step S231: Mark the interruption position in the memory of different sharded areas for the simulation data of multi-threaded sharded transfer interruption to obtain the sharded memory write interruption position data;

[0024] Step S232: Construct an interrupted file snapshot of the software upgrade file according to the sharded memory write interruption position data to obtain the write interruption file snapshot;

[0025] Step S233: Match the hash value of the transfer interruption point for the sharded memory write interruption position data according to the write interruption file snapshot to obtain the file transfer interruption point hash value;

[0026] Step S234: Based on the file transfer breakpoint hash value and the snapshot of the written interrupted file, perform cumulative ACK confirmation on the shard memory to write the interrupted location data, and obtain the file write cumulative ACK confirmation mode;

[0027] Step S235: According to the file transfer breakpoint hash value and the file write cumulative ACK confirmation mode, design a breakpoint resume mechanism for the software upgrade file, and obtain the transmission interruption breakpoint resume mechanism data.

[0028] The present invention marks the memory write interruption positions of different shard regions for the multi-threaded shard transmission interruption simulation data, obtaining the shard memory write interruption position data. This process allows the system to accurately record the write status of each shard in memory, especially when a transmission interruption occurs. Through this marking mechanism, the system can clearly understand which data segments have been successfully written and which have not been completed, thus providing an accurate basis for subsequent data recovery. This accuracy is crucial for designing an effective breakpoint retransmission mechanism and can significantly improve the success rate and efficiency of the upgrade. Construct an interruption file snapshot of the software upgrade file according to the shard memory write interruption position data, obtaining the write interruption file snapshot. Through this snapshot, the system can save the current write status during a transmission interruption, ensuring that it can continue from the most recent write status during subsequent recovery. The implementation of this step can effectively prevent data loss and avoid retransmitting the completed parts. The write interruption file snapshot provides important reference data for subsequent file retransmission, making the entire upgrade process more efficient and reliable. According to the write interruption file snapshot, match the transmission interruption point hash values for the shard memory write interruption position data. This process can quickly verify the integrity and consistency of the data by generating hash values. During the file transmission process, the hash matching mechanism can ensure that the system can accurately identify which data segments have been successfully transmitted and written during an interruption, thus providing strong support for resuming the transmission. By this method, not only the efficiency of data verification is improved, but also the system's resistance to potential errors is enhanced, ensuring the security of data transmission. Based on the file transmission interruption point hash values and the write interruption file snapshot, perform cumulative ACK confirmation on the shard memory write interruption position data, obtaining the file write cumulative ACK confirmation mode. The establishment of this mode enables the system to real-time confirm which data segments have been successfully received and written, thus providing accurate information feedback for subsequent data retransmission. By implementing the cumulative ACK confirmation mechanism, the system can not only reduce the waste of network resources caused by data retransmission, but also significantly improve the overall transmission efficiency, ensuring a smoother experience for users during the software upgrade process. According to the file transmission interruption point hash values and the file write cumulative ACK confirmation mode, design a breakpoint and resume transmission mechanism for the software upgrade file. This mechanism can quickly locate and resume to the last successfully written state after a transmission interruption, enabling transmission to continue from the breakpoint. By combining the hash values and ACK confirmation, the system can ensure the integrity and consistency of each data segment, significantly improving the efficiency and reliability of data transmission. This design not only improves the success rate of software upgrade, but also greatly reduces the unnecessary waiting time for users, optimizing the overall upgrade experience.

[0029] Preferably, step S24 includes the following steps:

[0030] Step S241: Conduct a discontinuous transmission network fluctuation simulation on the discontinuous transmission mechanism data at the breakpoint of the transmission interruption to obtain discontinuous transmission network fluctuation data;

[0031] Step S242: Conduct a sudden increasing trend analysis on the discontinuous transmission network fluctuation data to obtain fluctuation sudden increasing trend data;

[0032] Step S243: Conduct a linear regression analysis on the fluctuation sudden increasing trend data to obtain increasing trend linear regression data;

[0033] Step S244: Conduct a transmission efficiency mapping based on the increasing trend linear regression data to obtain network increasing fluctuation transmission efficiency mapping data;

[0034] Step S245: Conduct a network fluctuation - transmission efficiency structure stability analysis on the network increasing fluctuation transmission efficiency mapping data to obtain network fluctuation - transmission efficiency structure stability data;

[0035] Step S246: Conduct a network fluctuation efficiency balance compensation on the discontinuous transmission mechanism data at the breakpoint of the transmission interruption according to the network fluctuation - transmission efficiency structure stability data to obtain interruption re - transmission efficiency balance mechanism data, where the network fluctuation efficiency balance compensation specifically includes network fluctuation efficiency balance judgment and network fluctuation efficiency balance compensation. The network fluctuation efficiency balance judgment specifically includes the evaluation of the transmission efficiency fluctuation amplitude, interruption frequency and average recovery time, and the deviation degree of the fluctuation - efficiency mapping. The network fluctuation efficiency balance compensation specifically includes balancing the transmission efficiency and the number of re - transmissions.

[0036] The present invention conducts a simulation of the data transmission interruption breakpoint retransmission mechanism under network fluctuations to obtain data on network fluctuations during retransmission. This simulation process aims to mimic the fluctuations encountered in the actual network environment, including issues such as bandwidth changes, latency, and packet loss. By systematically simulating these network fluctuations, it is possible to better understand the interruption scenarios that occur during the transmission process and their impact on the overall transmission efficiency. This provides the basic data for subsequent compensation and optimization mechanisms, ensuring that the designed mechanisms can effectively handle complex network conditions in reality. An analysis of the sudden increasing trend of the data on network fluctuations during retransmission is carried out to obtain data on the sudden increasing trend of fluctuations. By analyzing the sudden trends in the network fluctuation data, specific patterns of deteriorating network conditions and their temporal characteristics can be identified. This analysis helps to predict peak network loads and potential times of fluctuations, thus laying the foundation for designing more intelligent transmission mechanisms. Understanding the increasing trend of fluctuations can effectively guide how the system dynamically adjusts transmission strategies in the future, improving the stability and reliability of transmission. A linear regression analysis is performed on the data of the sudden increasing trend of fluctuations to obtain data on the linear regression of the increasing trend. Linear regression analysis can reveal the basic laws and correlations of the fluctuation trends, providing quantitative data support. This process not only helps the system understand the specific impact of different fluctuation factors on transmission efficiency but also provides a theoretical basis for designing new optimization algorithms. Through the mathematical modeling of the trends, it is possible to effectively predict the transmission efficiency under specific network conditions, providing a reference for subsequent adjustments and optimizations. The characteristics of network fluctuations are correlated with the actual transmission efficiency. Through this mapping, the system can evaluate the transmission performance in different network fluctuation situations in real time, helping to determine the optimal transmission strategy. Establishing this mapping can not only improve the efficiency of data transmission but also provide timely adjustment suggestions for the system when encountering network fluctuations to optimize the user experience. An analysis of the structural stability of the network fluctuation - transmission efficiency mapping data for network increasing fluctuations is carried out to obtain data on the structural stability of network fluctuations - transmission efficiency. This analysis process focuses on evaluating the stability and consistency of transmission efficiency under different network fluctuation conditions. Through systematic stability analysis, it is possible to identify which network environments have a greater impact on transmission efficiency, providing a clear direction for subsequent adjustments. This analysis not only improves the system's adaptability to network fluctuations but also effectively reduces the uncertainty caused by fluctuations, enhancing the overall reliability of transmission. Based on the data of the structural stability of network fluctuations - transmission efficiency, a compensation for the balance of network fluctuation efficiency is carried out on the data of the transmission interruption breakpoint retransmission mechanism to obtain data on the retransmission efficiency balance mechanism for interruptions. By applying the results of the stability analysis to the breakpoint retransmission mechanism, a set of dynamically adjustable compensation strategies can be designed to ensure a high transmission efficiency even under network fluctuations. This balance mechanism can adjust the retransmission strategy in real time, intelligently select the optimal transmission method according to the network state, greatly enhancing the user experience during software upgrades and reducing the upgrade failure rate caused by network instability.

[0037] Preferably, step S245 includes the following steps:

[0038] Perform partial autocorrelation calculation on the network increasing fluctuation transmission efficiency mapping data to obtain transmission efficiency partial autocorrelation data;

[0039] Perform structural feature sampling on the network increasing fluctuation transmission efficiency mapping data according to the transmission efficiency partial autocorrelation data to obtain fluctuation - efficiency correlation sampling data;

[0040] Perform extreme outlier analysis on the fluctuation - efficiency correlation sampling data to obtain fluctuation - efficiency extreme outlier data;

[0041] Perform network fluctuation - transmission efficiency structural stability analysis on the network increasing fluctuation transmission efficiency mapping data according to the fluctuation - efficiency extreme outlier data to obtain network fluctuation - transmission efficiency structural stability data, where the network fluctuation - transmission efficiency structural stability analysis is specifically to evaluate the overall structural characteristics and stability of the network transmission efficiency with respect to network fluctuation changes.

[0042] The present invention performs partial autocorrelation calculation on the network increasing fluctuation transmission efficiency mapping data to obtain the partial autocorrelation data of the transmission efficiency. Partial autocorrelation analysis is a powerful time series analysis tool that can reveal the correlation of the transmission efficiency at different time delays. Through this calculation, the system can identify the fluctuation patterns of the transmission efficiency within a specific time interval, helping to understand the impact of historical fluctuations on the current transmission performance. The acquisition of this information is of great significance for optimizing the transmission mechanism and predicting future network performance changes in advance. According to the partial autocorrelation data of the transmission efficiency, structural feature sampling is performed on the network increasing fluctuation transmission efficiency mapping data to obtain the fluctuation-efficiency correlation sampling data. This step aims to extract representative samples from the partial autocorrelation analysis for in-depth study of the relationship between fluctuations and efficiency. Through feature sampling, the system can more clearly analyze the efficiency performance under different fluctuation conditions, thereby identifying potential optimization points. This process not only improves the accuracy of data analysis but also provides a reliable basis for subsequent decision-making, contributing to the formulation of more effective network transmission strategies. Extreme outlier analysis is performed on the fluctuation-efficiency correlation sampling data to obtain the fluctuation-efficiency extreme outlier data. Extreme outlier analysis can identify samples with significant outliers between fluctuations and transmission efficiency. These outliers usually reflect extreme network conditions or abnormal transmission performance and are the key to understanding network behavior in depth. By analyzing these extreme values, the system can discover potential problems and risks, thus providing an improvement direction for network management. The implementation of this step helps to improve the stability and reliability of the overall network. According to the fluctuation-efficiency extreme outlier data, network fluctuation-transmission efficiency structural stability analysis is performed on the network increasing fluctuation transmission efficiency mapping data to obtain the network fluctuation-transmission efficiency structural stability data. By combining extreme outliers for structural stability analysis, the system can comprehensively evaluate the stability of the transmission efficiency under different network conditions. The result of this analysis reveals the potential impact of network fluctuations on the transmission efficiency, helping to identify conditions that may lead to a decline in transmission performance. This step provides key data support for optimizing the network architecture to ensure efficient and stable transmission in a dynamic network environment.

[0043] Preferably, step S3 includes the following steps:

[0044] Step S31: Perform normalization design on the data of the interruption retransmission efficiency balance mechanism to obtain the normalized mechanism data of the interruption retransmission;

[0045] Step S32: Perform retransmission dynamic bandwidth testing according to the normalized mechanism data of the interruption retransmission to obtain the dynamic bandwidth data of the interruption retransmission;

[0046] Step S33: Perform cross-layer collaborative optimization on the dynamic bandwidth data of the interruption retransmission to obtain the cross-layer collaborative optimization data of the interruption retransmission;

[0047] Step S34: Optimize the data of the interruption retransmission normalization mechanism for network change awareness based on the cross-layer collaborative optimization data of the interruption retransmission, so as to obtain the data of the interruption retransmission network awareness optimization mechanism.

[0048] The present invention performs normalization design on the data of the interruption retransmission efficiency balance mechanism to obtain the data of the interruption retransmission normalization mechanism. This processing process eliminates the influence caused by the difference in data scale by converting different retransmission efficiency indicators into a unified standard form, making subsequent analysis and comparison more accurate. After normalization, the system can more effectively evaluate the performance of the retransmission mechanism under various network conditions, providing a solid foundation for subsequent dynamic testing and optimization. The beneficial effect of this process is to improve the consistency and accuracy of data processing, thus helping to formulate more effective optimization strategies. Perform dynamic bandwidth testing for interruption retransmission based on the data of the interruption retransmission normalization mechanism to obtain the dynamic bandwidth data of the interruption retransmission. By performing bandwidth testing in a variety of network environments, the system can evaluate the actual performance of the retransmission mechanism under different conditions. The acquisition of dynamic bandwidth data not only provides an in-depth understanding of the network resource usage situation but also helps to identify the bottlenecks and unstable factors that occur during the retransmission process. The results of this testing can provide a practical basis for subsequent optimization schemes to ensure efficient data transmission in a changing network environment. Perform cross-layer collaborative optimization on the dynamic bandwidth data of the interruption retransmission to obtain the cross-layer collaborative optimization data of the interruption retransmission. The purpose of cross-layer collaborative optimization is to integrate network protocols and mechanisms at different layers to achieve more efficient resource management and scheduling. By analyzing the dynamic bandwidth data, the system can establish closer cooperation between the application layer, the transport layer, and the network layer, thereby improving the overall transmission performance. The beneficial effect of this optimization process is that through information sharing and collaborative decision-making, the system can more flexibly respond to network changes and enhance the reliability and efficiency of transmission. Optimize the data of the interruption retransmission normalization mechanism for network change awareness based on the cross-layer collaborative optimization data of the interruption retransmission to obtain the data of the interruption retransmission network awareness optimization mechanism. This optimization process focuses on enhancing the sensitivity and adaptability of the system to network state changes. By analyzing the data obtained from cross-layer collaborative optimization, the system can dynamically adjust the retransmission strategy to better adapt to the changes in real-time network conditions. This network awareness optimization mechanism not only improves the transmission efficiency but also reduces transmission interruptions caused by network fluctuations, ultimately enhancing the user experience and satisfaction. The implementation of this step ensures that the system can still maintain high performance in a changing network environment.

[0049] Preferably, step S33 includes the following steps:

[0050] Step S331: Analyze the bandwidth utilization efficiency of the dynamic bandwidth data of the interruption retransmission to obtain the retransmission bandwidth utilization efficiency data, where the bandwidth utilization rate = (actual used bandwidth / total bandwidth) × 100%;

[0051] Step S332: Optimize the network layer routing selection based on the maximum bandwidth utilization rate according to the retransmission bandwidth utilization efficiency data to obtain network layer routing selection optimization data;

[0052] Step S333: Perform QoS support processing on the network layer routing selection optimization data to obtain routing optimization QoS support data;

[0053] Step S334: Perform cross-layer collaborative optimization according to the routing optimization QoS support data to obtain cross-layer collaborative optimization data for interrupted retransmission.

[0054] The present invention analyzes the bandwidth utilization efficiency of the interrupted retransmission dynamic bandwidth data to obtain the retransmission bandwidth utilization efficiency data. The purpose of this analysis is to evaluate the actual utilization of bandwidth resources during the retransmission process under specific network conditions. By calculating the bandwidth utilization efficiency, the system can identify potential waste or insufficiency in bandwidth usage, thereby understanding which factors affect the effective use of bandwidth during the implementation of the retransmission mechanism. This step provides the basic data for subsequent optimizations, helps improve the overall utilization efficiency of network resources, and reduces the delay caused by retransmission. Optimize the network layer routing selection based on the retransmission bandwidth utilization efficiency data to obtain network layer routing selection optimization data. Based on the analysis results of the bandwidth utilization efficiency, the system can adjust the routing strategy of the network layer to optimize the transmission path of data packets in the network. By selecting a more efficient route, the system can reduce the delay and improve the success rate of data transmission. This optimization process not only enhances the performance of the network but also ensures that bandwidth waste can be minimized during retransmission, thus providing a smoother network experience for users. Perform QoS support processing on the network layer routing selection optimization data to obtain routing optimization QoS support data. The QoS (Quality of Service) support processing aims to ensure that different types of data flows can obtain appropriate services according to their priorities and bandwidth requirements. By combining the results of routing selection optimization, the system can allocate corresponding network resources according to different application requirements, thereby ensuring that critical applications are given priority. The implementation of this step can significantly improve the user experience, especially in application scenarios such as video streaming and real-time communication that are sensitive to delay and bandwidth. Perform cross-layer collaborative optimization according to the routing optimization QoS support data to obtain cross-layer collaborative optimization data for interrupted retransmission. This process integrates the information of the application layer, transport layer, and network layer to ensure that different layers can coordinate with each other to jointly respond to changes in network conditions. Cross-layer collaborative optimization enables each layer to make dynamic adjustments based on shared data and policies, thereby enhancing the overall transmission efficiency and stability of the network. Ultimately, this optimization not only improves the success rate of retransmission but also effectively maintains the quality of service during network fluctuations, providing a more reliable connection experience for users.

[0055] Preferably, step S4 includes the following steps:

[0056] Step S41: Perform platform compatibility adjustment on the data of the interrupt retransmission network awareness optimization mechanism to obtain the interrupt retransmission network awareness compatible mechanism;

[0057] Step S42: Conduct automated awareness firmware design on the interrupt retransmission network awareness compatible mechanism to obtain the interrupt retransmission awareness firmware parameters;

[0058] Step S43: Embed the interrupt retransmission awareness firmware parameters into the cloud platform to execute the software remote upgrade method.

[0059] The present invention performs platform compatibility adjustment on the data of the interrupt retransmission network awareness optimization mechanism to obtain the interrupt retransmission network awareness compatible mechanism. The core purpose of this adjustment is to ensure that the interrupt retransmission mechanism can operate seamlessly on different hardware and software platforms. Through compatibility optimization, the system can adapt to various network environments and device types, thereby enhancing its possibility of wide application. The beneficial effect of this process is to enhance the flexibility and portability of the system, enabling it to maintain excellent performance under various network conditions, and ultimately providing users with a more stable connection experience. Conduct automated awareness firmware design on the interrupt retransmission network awareness compatible mechanism to obtain the interrupt retransmission awareness firmware parameters. This design aims to improve the real-time awareness ability of the firmware for network conditions through automated means. By embedding intelligent algorithms, the firmware can autonomously monitor the network status and make corresponding adjustments, thereby optimizing the retransmission process. The beneficial effect of this firmware is that the system can respond to network changes in real time, improve the efficiency and success rate of retransmission, reduce the dependence on manual intervention, and enhance the overall intelligent level of the system. Embed the interrupt retransmission awareness firmware parameters into the cloud platform to execute the software remote upgrade method. The implementation of this step enables the update and maintenance of the firmware to be carried out through the cloud platform, and users can enjoy the latest functions and optimizations without manual intervention. This remote upgrade method significantly improves the convenience and efficiency of firmware management, enabling the system to quickly respond to newly emerging network challenges and user needs. Through continuous updates, the system not only maintains good performance but also provides higher service quality and reliability in a rapidly changing network environment, thereby enhancing the user experience.

[0060] Preferably, the present invention also provides a software remote upgrade system for an elevator controller, which is used to execute the software remote upgrade method for an elevator controller as described above. The software remote upgrade system for an elevator controller includes:

[0061] Pre-upgrade environment sandbox construction module, which is used to extract software upgrade port parameters of the elevator controller to obtain the software upgrade port parameters of the elevator controller; simulate the port communication protocol stack according to the software upgrade port parameters of the elevator controller to obtain the port communication protocol simulation stack; construct a pre-upgrade environment sandbox through the port communication protocol simulation stack to obtain pre-upgrade environment sandbox data;

[0062] Interrupt retransmission mechanism design module, which is used to obtain the software upgrade file; simulate the interruption of multi-threaded shard transmission of the software upgrade file according to the pre-upgrade environment sandbox data to obtain multi-threaded shard transmission interruption simulation data; design a breakpoint resume mechanism according to the multi-threaded shard transmission interruption simulation data to obtain data on the interruption retransmission efficiency balance mechanism;

[0063] Network awareness optimization module, which is used to perform normalization design on the data of the interruption retransmission efficiency balance mechanism to obtain data on the interruption retransmission normalization mechanism; perform network change awareness optimization on the data of the interruption retransmission normalization mechanism to obtain data on the interruption retransmission network awareness optimization mechanism;

[0064] Automated execution module, which is used to perform automated awareness firmware design on the data of the interruption retransmission network awareness optimization mechanism to obtain interruption retransmission awareness firmware parameters; embed the interruption retransmission awareness firmware parameters into the cloud platform to execute the software remote upgrade method.

[0065] The beneficial effects of the present invention are as follows. First, by extracting the software upgrade port of the elevator controller, the interface for communication can be effectively identified. This process is crucial as it lays the foundation for subsequent communication protocol simulation. Subsequently, using the extracted port, the construction of the communication protocol stack is simulated to ensure correct data transmission when communicating with the elevator controller. By setting up a pre-upgrade environment sandbox, a secure and isolated test environment is created, enabling the simulation and verification of the software upgrade process. This stage provides a reliable technical basis for subsequent actual upgrades, ensuring the stability and security of the system. The software upgrade file obtained first is a core component of the entire upgrade process. By simulating the interruption of multi-threaded shard transmission of this file through the pre-upgrade environment sandbox, the interruption situation during transmission can be evaluated, providing a basis for subsequent design. Through the simulation of the interruption situation, an effective breakpoint resumption mechanism is designed, which can ensure that after the transmission is interrupted, the system can quickly resume and re-transmit the incomplete data. The design of this mechanism not only improves the efficiency of data transmission but also reduces the risk of upgrade failure caused by unstable networks. To standardize the data of the interruption resumption efficiency balance mechanism and make it more general and adaptable. Through normalization, it can be ensured that the resumption mechanisms under different environments and conditions can maintain consistent performance. In addition, combined with the perception optimization of network changes, the system can monitor the network status in real time and automatically adjust the resumption strategy according to the current network conditions. This intelligent optimization method not only improves the stability of the system but also enhances the adaptability of the software upgrade process under different network conditions, further reducing the uncertainty of the user experience. The design of the automatic perception firmware is a key step. By embedding the interruption resumption perception firmware parameters into the cloud platform, intelligent management of the software remote upgrade process is achieved. This firmware can monitor the running status of the system in real time and react quickly when an interruption occurs, ensuring the integrity and accuracy of the data. This embedded solution greatly improves the automation level of remote upgrades, reduces the need for manual intervention, and thus enhances the operation efficiency of the entire system and user satisfaction. Finally, the implementation of this step provides strong technical support for the software maintenance of the elevator controller, ensuring the smooth progress of the upgrade process. Therefore, the present invention is an optimization of the traditional software remote upgrade method for elevator controllers, solving the problems in the traditional software remote upgrade method for elevator controllers, such as the lack of a clear breakpoint resumption mechanism for situations like interruption restart and power failure during the upgrade process, and unclear perception of the interruption resumption network. A clear breakpoint resumption mechanism is established, improving the clarity of the perception of the interruption resumption network. Brief Description of the Drawings

[0066] Figure 1 It is a schematic diagram of the step flow of a software remote upgrade method for an elevator controller;

[0067] Figure 2 For Figure 1 it is a schematic diagram of the detailed implementation steps of step S2 in

[0068] The realization, functional features and advantages of the present invention will be further described with reference to the embodiments and the accompanying drawings. Specific embodiments

[0069] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative work belong to the scope of protection of the present invention.

[0070] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus the repeated description thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0071] It should be understood that although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly, the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the associated listed items.

[0072] To achieve the above object, please refer to Figures 1 to 2 , a software remote upgrade method for an elevator controller, the method comprising the following steps:

[0073] Step S1: Extract software upgrade port parameters of the elevator controller to obtain elevator controller software upgrade port parameters; simulate a port communication protocol stack according to the elevator controller software upgrade port parameters to obtain a port communication protocol simulation stack; construct a pre-upgrade environment sandbox through the port communication protocol simulation stack to obtain pre-upgrade environment sandbox data;

[0074] Step S2: Obtain the software upgrade file; perform multi-threaded shard transfer interruption simulation on the software upgrade file according to the pre-upgrade environment sandbox data to obtain multi-threaded shard transfer interruption simulation data; design a breakpoint resume mechanism based on the multi-threaded shard transfer interruption simulation data to obtain interruption retransmission efficiency balance mechanism data;

[0075] Step S3: Perform normalization design on the interruption retransmission efficiency balance mechanism data to obtain interruption retransmission normalization mechanism data; perform network change awareness optimization on the interruption retransmission normalization mechanism data to obtain interruption retransmission network awareness optimization mechanism data;

[0076] Step S4: Perform automated awareness firmware design on the interruption retransmission network awareness optimization mechanism data to obtain interruption retransmission awareness firmware parameters; embed the interruption retransmission awareness firmware parameters into the cloud platform to execute the software remote upgrade method.

[0077] In the embodiment of the present invention, refer to Figure 1 As described, it is a schematic diagram of the step flow of a software remote upgrade method for an elevator controller in the present invention. In this example, the software remote upgrade method for an elevator controller includes the following steps:

[0078] Step S1: Extract software upgrade port parameters of the elevator controller to obtain elevator controller software upgrade port parameters; perform port communication protocol stack simulation according to the elevator controller software upgrade port parameters to obtain a port communication protocol simulation stack; construct a pre-upgrade environment sandbox through the port communication protocol simulation stack to obtain pre-upgrade environment sandbox data;

[0079] In the embodiment of the present invention, to extract the software upgrade port parameters of the elevator controller, first use the hardware interface of the elevator controller to directly connect to the development device. Through a serial port communication tool, send specific instructions to obtain the communication port information of the controller. This process involves using a specified instruction set to ensure accurate identification of the port number and protocol type. After successful extraction, record the information of the elevator controller software upgrade port parameters to form the "elevator controller software upgrade port parameters" data. Then, based on the extracted port information, perform port communication protocol stack simulation. Use computer programming languages such as C++ or Python to develop the corresponding protocol stack implementation. By constructing protocol layers, simulate the behaviors of common protocols such as TCP / IP and UDP to ensure data sending and receiving, and form the "port communication protocol simulation stack" data. Finally, rely on this protocol stack to create a pre-upgrade environment sandbox in a virtual machine environment. Use containerization technologies such as Docker, and through defining network rules and resource configurations, ensure that the environment is similar to the behavior of the actual elevator controller, thereby obtaining the "pre-upgrade environment sandbox" data.

[0080] Step S2: Obtain the software upgrade file; perform multi-threaded shard transfer interruption simulation on the software upgrade file according to the pre-upgrade environment sandbox data to obtain multi-threaded shard transfer interruption simulation data; design a breakpoint resume mechanism based on the multi-threaded shard transfer interruption simulation data to obtain interruption resume efficiency balance mechanism data;

[0081] In the embodiment of the present invention, after the construction of the pre-upgrade environment sandbox is completed, the next step is to obtain the software upgrade file. This operation first sets an FTP or HTTP server and uploads the firmware file to be upgraded to a specified directory. To ensure the integrity and correctness of the file, it can be verified through hash verification (such as MD5 or SHA256) to form the "software upgrade file" data. According to the constructed pre-upgrade environment sandbox, perform multi-threaded shard transfer interruption simulation on the software upgrade file. Using the thread pool technology, divide the file into multiple small pieces and use network programming methods (such as Socket programming) to achieve multi-threaded concurrent transmission. Set the transmission interruption conditions, such as simulating network packet loss or delay, and perform interruption simulation by capturing exception handling. Record the status information of each transmission interruption to generate the "multi-threaded shard transfer interruption simulation data". According to the above simulation data, design a breakpoint resume mechanism. This mechanism uses the collected data to analyze the reasons for retransmission failure and formulate a retransmission strategy. Set the retransmission threshold and the maximum number of retransmissions, and optimize the retransmission process through algorithms to form the "interruption resume efficiency balance mechanism data" to achieve the stability and reliability of the transmission.

[0082] Step S3: Perform normalization design on the interruption resume efficiency balance mechanism data to obtain interruption resume normalization mechanism data; perform network change perception optimization on the interruption resume normalization mechanism data to obtain interruption resume network perception optimization mechanism data;

[0083] In the embodiment of the present invention, to perform normalization design on the interruption resume efficiency balance mechanism data, it is first necessary to define a normalization mathematical model. For the retransmitted data, perform normalization design on the retransmission efficiency in different transmission states through linear transformation or Z-score standardization. Adopt a suitable normalization method, such as min-max normalization, to ensure that the retransmission efficiency is within the range of [0,1] to obtain the "interruption resume normalization mechanism data". On this basis, perform network change perception optimization. By real-time monitoring the network status, collect parameters such as delay, bandwidth, and packet loss rate, and establish a dynamic monitoring mechanism. Use a state machine model to classify and identify network changes, and adopt a feedback control method to dynamically adjust the retransmission strategy. Combine the normalization mechanism and dynamically adjust the retransmission parameters through feedback to obtain the "interruption resume network perception optimization mechanism data" to ensure the high efficiency of the retransmission process under different network states.

[0084] Step S4: Automatically design the firmware for the interruption retransmission network awareness optimization mechanism data to obtain the interruption retransmission awareness firmware parameters; embed the interruption retransmission awareness firmware parameters into the cloud platform to execute the software remote upgrade method.

[0085] In the embodiment of the present invention, the firmware for automatic awareness is designed according to the interruption retransmission network awareness optimization mechanism data. First, formulate the basic architecture of the firmware, including a sensing module, a data processing module, and a communication module. Use embedded programming techniques (such as the C language) to write the corresponding firmware code. The sensing module is responsible for real-time monitoring of the network status and transmitting the data to the data processing module, which is responsible for analysis and judgment. When implemented, data is collected through a polling mechanism or an interrupt mechanism to form the "interruption retransmission awareness firmware parameter" data. To embed the designed interruption retransmission awareness firmware parameters into the cloud platform, the cloud platform environment needs to be built first. Use a cloud service provider (such as AWS or Azure) to create a virtual server and deploy the relevant dependencies. Connect the firmware to the cloud platform through the API interface to achieve real-time upload and processing of data. Finally, the firmware can automatically execute the software remote upgrade method to ensure efficient software upgrade of the elevator controller during actual operation.

[0086] Preferably, step S1 includes the following steps:

[0087] Step S11: Extract the software upgrade port parameters of the elevator controller to obtain the software upgrade port parameters of the elevator controller;

[0088] Step S12: Determine the transmission protocol based on the software upgrade port parameters of the elevator controller to obtain the port transmission protocol data;

[0089] Step S13: Simulate the port communication protocol stack according to the port transmission protocol data and the software upgrade port parameters of the elevator controller to obtain the port communication protocol simulation stack;

[0090] Step S14: Build a pre-upgrade environment sandbox through the port communication protocol simulation stack to obtain the pre-upgrade environment sandbox data.

[0091] In the embodiments of the present invention, for extracting the port parameters for software upgrading of the elevator controller, it is first necessary to connect to the physical interface of the elevator controller, usually the RS-232 or RS-485 serial port. Using a serial communication tool, set the corresponding baud rate, data bits, stop bits, and parity bits to ensure that the communication parameters are consistent with the elevator controller. Send a preset instruction sequence, such as "AT+GET_PORT", which is designed to request the controller to return the information of its available communication ports. After receiving the response from the controller, by parsing the returned data format, extract the actual software upgrading port of the elevator controller and record it as the data of "elevator controller software upgrading port parameters". This process ensures that the subsequent steps can accurately dock with the communication capabilities of the controller. Based on the extracted elevator controller software upgrading port parameters, determine the transmission protocol. First, it is necessary to refer to relevant technical documents or standards and analyze various transmission protocols supported by the elevator controller, such as TCP / IP, UDP, Modbus, etc. By comparing the technical parameters of the elevator controller, select a protocol that is compatible with its hardware and has efficient data transmission characteristics. For example, if the elevator controller supports TCP / IP, set the identifier of the transmission protocol as "TCP". After determination, generate the "port transmission protocol data", recording key parameters such as protocol type, version number, maximum data transfer unit (MTU), etc. This data will be used as the basis for subsequent simulation of the communication protocol stack to ensure that the selected protocol meets the actual requirements of the controller. According to the "port transmission protocol data" and the "elevator controller software upgrading port parameters", simulate the port communication protocol stack. First, it is necessary to define the structure of the simulated protocol stack, including the application layer, transport layer, and network layer. In the application layer, design specific command formats to support software upgrade requests and status feedback. In the transport layer, utilize the characteristics of the TCP or UDP protocol to establish a connection management mechanism and a data packet transmission mechanism to ensure the reliability of data transmission. In the network layer, set the IP address and routing strategy. Use a programming language to implement this simulation process. For example, use the socket library of Python for network communication simulation, create the corresponding socket object and implement data sending and receiving. After testing, form the data of the "port communication protocol simulation stack" to ensure that the communication at each level can proceed normally. Build a pre-upgrade environment sandbox through the "port communication protocol simulation stack". First, create a new virtual network in the virtual machine environment, configure the IP address and subnet mask required for communication with the elevator controller to simulate the real network environment. Then, use Docker container technology to deploy the simulated protocol stack into the sandbox. Each container runs an independent service to simulate different functional modules of the elevator controller, including data reception, processing, and response mechanisms. Inside the sandbox, run test cases for the port communication protocol, record information such as latency and packet loss rate during the data transmission process by simulating actual upgrade requests and responses, and ensure that the sandbox environment is highly consistent with the actual operating environment.Finally, "pre-upgrade environment sandbox" data is generated to provide a reliable test platform for the subsequent software upgrade process.

[0092] Preferably, step S2 includes the following steps:

[0093] Step S21: Obtain the software upgrade file;

[0094] Step S22: Perform multi-threaded shard transfer interruption simulation on the software upgrade file according to the pre-upgrade environment sandbox data to obtain multi-threaded shard transfer interruption simulation data;

[0095] Step S23: Design a breakpoint resume mechanism for the software upgrade file according to the multi-threaded shard transfer interruption simulation data to obtain transfer interruption breakpoint resume mechanism data;

[0096] Step S24: Perform network fluctuation efficiency balance compensation on the transfer interruption breakpoint resume mechanism data to obtain interruption retransmission efficiency balance mechanism data.

[0097] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0098] Step S21: Obtain the software upgrade file;

[0099] In the embodiment of the present invention, the software upgrade file is obtained by the software developer. The process of obtaining the software upgrade file needs to first determine the source and type of the file. The specified upgrade file is usually a firmware file with the format of.bin or.hex. For this purpose, an FTP server or an HTTP server is configured, and the target file is uploaded to the specified directory. During the upload process, a file integrity verification method is adopted, and the SHA-256 algorithm is used to calculate the hash value of the file to ensure that the file has not been tampered with before transmission. After the upload is completed, the system needs to send a request to the server to obtain the metadata of the file, including the file name, size, and hash value, and perform verification to confirm the successful upload. Record the relevant information as "software upgrade file" data for use in subsequent steps to ensure that the exact file path and attributes can be found in the subsequent transmission process.

[0100] Step S22: Perform multi-threaded shard transfer interruption simulation on the software upgrade file according to the pre-upgrade environment sandbox data to obtain multi-threaded shard transfer interruption simulation data;

[0101] In the embodiment of the present invention, according to the constructed "pre-upgrade environment sandbox", multi-threaded fragmented transmission interruption simulation is performed on the "software upgrade file". First, the software upgrade file is split into several small pieces, and the size of each small piece is determined by the maximum transmission unit (MTU) to ensure that the network limit is not exceeded during the fragmentation process. Using the thread pool technology, multiple threads are created to execute the transmission of file fragments in parallel, and each thread is responsible for sending a fragment. Through Socket programming, the network transmission conditions are set to simulate network interruption situations, such as packet loss or latency. During the transmission process, by deliberately introducing network latency and disconnecting the connection, the responses of each thread are observed, and the transmission status and failure situations of each fragment are recorded. Finally, "multi-threaded fragmented transmission interruption simulation data" is generated for subsequent analysis of the performance during interruption.

[0102] Step S23: Design a breakpoint resumption mechanism for the software upgrade file according to the multi-threaded fragmented transmission interruption simulation data to obtain transmission interruption breakpoint resumption mechanism data;

[0103] In the embodiment of the present invention, a breakpoint resumption mechanism is designed for the "software upgrade file" according to the "multi-threaded fragmented transmission interruption simulation data". The core of this mechanism is to be able to continue the transmission from the exact position where the interruption occurs after the transmission is interrupted. First, define the sequence number of each fragment to ensure that each fragment has a unique identifier. Next, design the retransmission logic. When a transmission interruption is detected, record the sequence number of the fragments that have been successfully transmitted and the total number of fragments. Adopt a state machine model and set the retransmission strategy, including the maximum number of retransmissions and the retransmission interval time. When the transmission resumes, start the retransmission directly from the sequence number of the interrupted fragment by querying the record. This process will generate "transmission interruption breakpoint resumption mechanism data" to ensure effective resumption of transmission when the network is unstable.

[0104] Step S24: Perform network fluctuation efficiency balance compensation on the transmission interruption breakpoint resumption mechanism data to obtain interruption retransmission efficiency balance mechanism data.

[0105] In the embodiment of the present invention, network fluctuation efficiency balance compensation is performed on the "transmission interruption breakpoint resumption mechanism data". First, a monitoring mechanism for network fluctuations needs to be established to collect parameters such as latency, bandwidth, and packet loss rate in real time. By analyzing the network state data, identify the impact of network fluctuations on the transmission efficiency. Then, adopt a weighted average algorithm to dynamically adjust the transmission rate under different network states, and set appropriate retransmission strategies and data sending rates. During this process, use the PID control algorithm in control theory to automatically adjust the retransmission interval and sending rate to achieve load balancing. Through this compensation mechanism, the transmission delay caused by network fluctuations can be effectively addressed, and finally, "interruption retransmission efficiency balance mechanism data" is generated to ensure efficient transmission under various network conditions.

[0106] Preferably, step S23 includes the following steps:

[0107] Step S231: Mark the memory write interruption positions of different shard regions for the multi-threaded shard transfer interruption simulation data to obtain the shard memory write interruption position data;

[0108] Step S232: Construct an interrupted file snapshot for the software upgrade file according to the shard memory write interruption position data to obtain the write interruption file snapshot;

[0109] Step S233: Match the transmission interruption point hash values for the shard memory write interruption position data according to the write interruption file snapshot to obtain the file transmission interruption point hash values;

[0110] Step S234: Perform cumulative ACK confirmation on the shard memory write interruption position data based on the file transmission interruption point hash values and the write interruption file snapshot to obtain the file write cumulative ACK confirmation mode;

[0111] Step S235: Design a breakpoint resume mechanism for the software upgrade file according to the file transmission interruption point hash values and the file write cumulative ACK confirmation mode to obtain the transmission interruption breakpoint resume mechanism data.

[0112] In the embodiments of the present invention, memory write interruption position marks are made for different shard regions of "multi-threaded shard transmission interruption simulation data". First, the size of the shards and the memory mapping region are defined, and each data block after sharding the "software upgrade file" is mapped in memory. Memory is allocated for each shard, and a flag bit is set for each shard to indicate the write status of the current shard. When performing multi-threaded transmission, the write status of each shard is updated in real time through the shared memory mechanism of multi-threads. When a simulated interruption event occurs, record the shard region where the interruption occurs and its corresponding memory address to form "shard memory write interruption position data". These marked information provides a basis for subsequent snapshot construction and interruption handling, ensuring that the write position can be accurately located when an interruption occurs. An interruption file snapshot of the software upgrade file is constructed according to the "shard memory write interruption position data". The specific steps include: First, create a file snapshot structure, which is used to save the information of the shards that have been successfully written and the shards marked as interrupted. During the file writing process, whenever a shard is successfully written, immediately update the snapshot structure, and record the serial number of the successfully written shard and the corresponding memory address in the snapshot. When an interruption occurs, use the previously recorded "shard memory write interruption position data" to determine the last successfully written shard at the time of interruption, and save the snapshot data together with the information of this shard to form a "write interruption file snapshot". This snapshot provides an accurate reference point for subsequent interruption recovery, ensuring that the data that has been successfully transmitted is not repeated during retransmission. Perform transmission interruption point hash value matching on the "shard memory write interruption position data" according to the "write interruption file snapshot". First, for each successfully written shard, calculate its hash value using the SHA-256 algorithm. When each shard is written to the snapshot, its hash value should be recorded at the same time. In the case of simulating a transmission interruption, by comparing the hash values in the "write interruption file snapshot" with the hash values in the "shard memory write interruption position data", determine which shards have been successfully transmitted and which shards are not yet completed. After the matching is completed, generate "file transmission interruption point hash value", which provides the necessary information for subsequent breakpoint retransmission, ensuring that only the shards that have not been successfully transmitted are retransmitted. Based on the "file transmission interruption point hash value" and the "write interruption file snapshot", perform cumulative ACK confirmation on the "shard memory write interruption position data". Design an ACK mechanism and define the sending rules of the ACK confirmation signal. When a shard is successfully received, the receiving end should immediately send an ACK signal to the sending end to inform the successfully received data. By maintaining a confirmation list, record the received ACK information, and match the successfully written shards in the "write interruption file snapshot" with the ACK confirmation signal. Finally, form a "file write cumulative ACK confirmation mode" to ensure that after a transmission interruption, the successfully transmitted shards can be accurately identified through the confirmation signal, and the shard data that needs to be retransmitted can be determined.Design a breakpoint resuming mechanism for software upgrade files according to the "file transfer breakpoint hash value" and the "file writing cumulative ACK confirmation mode". First, combine the previously recorded hash value with the ACK confirmation result to establish a status table to record the transfer status (success, pending retransmission) of each shard. In the transfer recovery stage, determine which shard to start retransmitting by querying the status table. The retransmission mechanism adopts a state-based retransmission strategy, setting the delay and number of retries during retransmission. If the retransmission still fails, log it and perform timeout processing to ensure that the system can promptly feedback the status. Finally, form the "transfer interruption breakpoint resuming mechanism data" to ensure effective recovery in case of transfer interruption and avoid retransmitting the successfully transferred shard data.

[0113] Preferably, step S24 includes the following steps:

[0114] Step S241: Simulate network fluctuations during breakpoint resuming for the transfer interruption breakpoint resuming mechanism data to obtain breakpoint resuming network fluctuation data;

[0115] Step S242: Analyze the sudden increasing trend of the breakpoint resuming network fluctuation data to obtain fluctuation sudden increasing trend data;

[0116] Step S243: Perform linear regression analysis on the fluctuation sudden increasing trend data to obtain increasing trend linear regression data;

[0117] Step S244: Map the transfer efficiency according to the increasing trend linear regression data to obtain network increasing fluctuation transfer efficiency mapping data;

[0118] Step S245: Analyze the structural stability of network fluctuations - transfer efficiency for the network increasing fluctuation transfer efficiency mapping data to obtain network fluctuations - transfer efficiency structural stability data;

[0119] Step S246: Perform network fluctuation efficiency balance compensation on the transfer interruption breakpoint resuming mechanism data according to the network fluctuations - transfer efficiency structural stability data to obtain interruption retransmission efficiency balance mechanism data, where the network fluctuation efficiency balance compensation is specifically network fluctuation efficiency balance judgment and network fluctuation efficiency balance compensation. The network fluctuation efficiency balance judgment is specifically the evaluation of the transfer efficiency fluctuation amplitude, interruption frequency, average recovery time, and fluctuation - efficiency mapping deviation degree. The network fluctuation efficiency balance compensation is specifically to balance the transfer efficiency and the number of retransmissions.

[0120] In the embodiments of the present invention, a discontinuous transmission network fluctuation simulation is performed on the "data of the discontinuous transmission breakpoint discontinuous transmission mechanism". This step first establishes a network environment simulation model to simulate the transmission situations under various network states. By setting parameters such as bandwidth, delay, and packet loss rate, multiple network scenarios are created. The random number generation algorithm is used to simulate the network fluctuation situations that occur during the transmission process, such as sudden increases in delay or random loss of data packets. Multiple experiments are recorded for each simulation situation, and the transmission data under different conditions are collected, including the number of successfully transmitted fragments, transmission delay, and packet loss rate. Finally, the "discontinuous transmission network fluctuation data" is generated, providing a basic data set for subsequent data analysis to ensure that the impact of network fluctuations on transmission efficiency can be truly reflected. An analysis of the sudden increase trend is performed on the "discontinuous transmission network fluctuation data". First, the collected network fluctuation data is sorted in chronological order, and the network state information at each time point is marked. By observing the change trend of the data fluctuation, the time series analysis method is adopted to identify the sudden fluctuation situations in the data. The threshold detection algorithm is used to set a fluctuation threshold to determine whether the data fluctuation above this threshold belongs to a sudden situation, and the occurrence frequency and duration of the sudden events are recorded. Finally, the "fluctuation sudden increase trend data" is formed to describe the increasing trend of network fluctuations within a specific time period, providing support for further analysis. A linear regression analysis is performed on the "fluctuation sudden increase trend data". First, the time series data of the sudden fluctuations is selected, and a linear regression model is established, with time as the independent variable and the fluctuation amplitude as the dependent variable. The least squares method is used to calculate the parameters of the linear regression equation to obtain the best fit line. By calculating the regression coefficient, the trend intensity of the sudden fluctuations is evaluated, and the regression equation is obtained to describe the change law of the fluctuations. During this process, the determination coefficient R 2, the fitting degree of the data by the quantization model. Finally, "increasing trend linear regression data" is generated, providing a theoretical basis for subsequent transmission efficiency mapping. Transmission efficiency mapping is performed based on the "increasing trend linear regression data". First, a transmission efficiency model is set, which is based on the linear regression result and correlates the change of network fluctuation with the transmission efficiency. By inputting the fluctuation amplitude in the regression equation into the transmission efficiency model, the transmission efficiency value under specific network fluctuation conditions is obtained. Using the interpolation algorithm and combining historical transmission data, a relationship graph between fluctuation and transmission efficiency is plotted to form "network increasing fluctuation transmission efficiency mapping data". This mapping provides the expected transmission efficiency under different network fluctuation states, providing empirical support for subsequent analysis. Network fluctuation - transmission efficiency structural stability analysis is performed on the "network increasing fluctuation transmission efficiency mapping data". First, stability indicators between network fluctuation and transmission efficiency are defined, including the sensitivity of the impact of fluctuation on transmission efficiency. Through statistical analysis methods, the change of transmission efficiency under different fluctuation conditions is calculated to construct a stability model. The analysis of variance method is used to evaluate the fluctuation degree of transmission efficiency under different network states and identify the key factors affecting transmission efficiency. Finally, "network fluctuation - transmission efficiency structural stability data" is formed, which is used to reflect the change characteristics of transmission efficiency under variable network conditions and lay a foundation for the design of subsequent compensation mechanisms. Network fluctuation efficiency balance compensation is performed on the "transmission interruption breakpoint resume mechanism data" according to the "network fluctuation - transmission efficiency structural stability data". First, combining the stability analysis results, fluctuation compensation rules are set to ensure that when the network fluctuation is large, the retransmission strategy and data sending rate are adjusted. The feedback control algorithm is used to monitor the network state in real time. When it is detected that the fluctuation exceeds the set threshold, the transmission rate is dynamically adjusted to maintain the transmission efficiency. Specifically, when implementing, by setting control parameters and optimizing the retransmission interval, it is ensured that under network fluctuation conditions, the transmission efficiency and the number of retransmissions can be balanced. Finally, "interruption retransmission efficiency balance mechanism data" is generated to ensure stable data transmission under various network conditions and reduce the adverse effects brought by network fluctuation

[0121] Preferably, step S245 includes the following steps:

[0122] Perform partial autocorrelation calculation on the network increasing fluctuation transmission efficiency mapping data to obtain transmission efficiency partial autocorrelation data;

[0123] Perform structural feature sampling on the network increasing fluctuation transmission efficiency mapping data according to the transmission efficiency partial autocorrelation data to obtain fluctuation - efficiency correlation sampling data;

[0124] Perform extreme outlier analysis on the fluctuation - efficiency correlation sampling data to obtain fluctuation - efficiency extreme outlier data;

[0125] Based on the outlier data of fluctuation - efficiency extreme values, perform network fluctuation - transmission efficiency structure stability analysis on the network increasing - fluctuation transmission efficiency mapping data to obtain network fluctuation - transmission efficiency structure stability data, where the network fluctuation - transmission efficiency structure stability analysis is specifically to evaluate the overall structural characteristics and stability of the network transmission efficiency with the change of network fluctuation.

[0126] In the embodiments of the present invention, partial autocorrelation calculation is performed on the "network increasing - fluctuation transmission efficiency mapping data". First, convert the transmission efficiency mapping data into time - series data for time - series analysis. By setting the lag order, analyze the autocorrelation of the transmission efficiency data and calculate the partial autocorrelation coefficients at different lag times. The specific method is to use the Yule - Walker equation and solve the partial autocorrelation coefficients by the least - squares method. The results will reflect the correlation between the current transmission efficiency and the past transmission efficiency at different time lags. This process generates "transmission efficiency partial autocorrelation data", which lays the foundation for subsequent structural feature sampling. Perform structural feature sampling on the "network increasing - fluctuation transmission efficiency mapping data" according to the "transmission efficiency partial autocorrelation data". First, set a sampling interval based on the partial autocorrelation results to ensure the effectiveness and representativeness of sampling. When sampling, select the transmission efficiency data at specific time points to form a sample set. By the uniform sampling method, ensure that the selected samples cover different fluctuation intervals and reflect the overall change characteristics of the transmission efficiency. Finally, obtain the "fluctuation - efficiency correlation sampling data", which provides a reliable basis for subsequent outlier analysis. Perform extreme - value outlier analysis on the "fluctuation - efficiency correlation sampling data". This step first uses the Z - score method to calculate the standard score of each sample to identify outliers exceeding the set threshold. Set the threshold to 3, screen out all samples with a standard score greater than 3, and identify these samples as extreme - value outliers. Record the specific values of the outliers and their positions in the time series to generate "fluctuation - efficiency extreme - value outlier data". The results of this step provide key information for further structural stability analysis. Perform network fluctuation - transmission efficiency structure stability analysis on the "network increasing - fluctuation transmission efficiency mapping data" according to the "fluctuation - efficiency extreme - value outlier data". First, combine the outlier data with the transmission efficiency mapping data and use the robustness test method to evaluate the response of the system under extreme fluctuations. Specifically, when implementing, use the regression analysis method, take the outlier as the independent variable and the transmission efficiency as the dependent variable to establish a regression model to judge the influence degree of extreme fluctuations on the transmission efficiency. At the same time, by analyzing stability indicators such as the goodness of fit in the structural equation model, evaluate the stable relationship between network fluctuation and transmission efficiency. Finally, generate "network fluctuation - transmission efficiency structure stability data" to quantify the influence of network fluctuation on transmission efficiency and provide data support for the subsequent design of the compensation mechanism.

[0127] Preferably, step S3 includes the following steps:

[0128] Step S31: Normalize the data of the interruption retransmission efficiency balance mechanism to obtain the normalized mechanism data of interruption retransmission;

[0129] Step S32: Conduct a retransmission dynamic bandwidth test based on the normalized mechanism data of interruption retransmission to obtain the dynamic bandwidth data of interruption retransmission;

[0130] Step S33: Perform cross-layer collaborative optimization on the dynamic bandwidth data of interruption retransmission to obtain the cross-layer collaborative optimization data of interruption retransmission;

[0131] Step S34: Perform network change perception optimization on the normalized mechanism data of interruption retransmission according to the cross-layer collaborative optimization data of interruption retransmission to obtain the network perception optimization mechanism data of interruption retransmission.

[0132] In the embodiments of the present invention, normalization design is performed on the "interrupt retransmission efficiency balance mechanism data" so as to convert data of different magnitudes into values within a unified range. In the specific implementation process, first, various index data of the interrupt retransmission efficiency are collected, and these indexes include the retransmission success rate, retransmission delay, etc. In this process, by comparing the retransmission efficiencies in different situations, it can be ensured that the efficiency values under various conditions are evaluated under the same standard. Finally, the "interrupt retransmission normalization mechanism data" is generated, providing standardized basic data for subsequent dynamic bandwidth tests. Based on the "interrupt retransmission normalization mechanism data", a retransmission dynamic bandwidth test is carried out to evaluate the impact of network state changes on the retransmission efficiency. First, a set of dynamic bandwidth test schemes is designed, setting the starting bandwidth, bandwidth fluctuation range and test duration of the test. Using a timer and a bandwidth monitoring tool, data transmission is periodically carried out under different network states, and the amount of data retransmitted and the number of successful retransmissions each time are recorded. By analyzing the retransmission efficiency under different bandwidth conditions, the "interrupt retransmission dynamic bandwidth data" is obtained, and these data will reflect how network fluctuations affect the retransmission performance in actual operations and provide a basis for further optimization. Cross-layer cooperative optimization is carried out on the "interrupt retransmission dynamic bandwidth data" to improve the overall performance during the retransmission process. This step first analyzes the interaction relationships between different network layers (such as the link layer, network layer and transport layer) to determine the key factors affecting the retransmission efficiency. Then, combined with the dynamic bandwidth data, a cooperative optimization algorithm (such as a genetic algorithm or a particle swarm optimization algorithm) is used to optimize each layer. During specific implementation, an objective function is set, taking the retransmission success rate and delay as the optimization objectives, and the configuration parameters of each layer are adjusted through iterative calculation to find the best cooperative working state. Finally, the "interrupt retransmission cross-layer cooperative optimization data" is generated for subsequent network change perception optimization. According to the "interrupt retransmission cross-layer cooperative optimization data", network change perception optimization is performed on the "interrupt retransmission normalization mechanism data". First, a network change perception model is established, using the data of the historical network state and the current state, and through statistical analysis and pattern recognition techniques, the characteristics and trends of network changes are determined. Then, combined with the cross-layer cooperative optimization results, the retransmission strategy is adjusted. For example, when the network bandwidth decreases, small data packets are preferentially selected for retransmission, or large data packets are transmitted when the network is stable. During the implementation process, by dynamically monitoring the network state, the retransmission parameters are adjusted in real time to achieve a rapid response to network changes. Finally, the "interrupt retransmission network perception optimization mechanism data" is obtained, providing a more flexible and efficient retransmission strategy for the remote upgrade of the elevator controller.

[0133] Preferably, step S33 includes the following steps:

[0134] Step S331: Analyze the bandwidth utilization efficiency of the interrupted retransmission dynamic bandwidth data to obtain the retransmission bandwidth utilization efficiency data, where the bandwidth utilization rate = (actual used bandwidth / total bandwidth) × 100%;

[0135] Step S332: Optimize the network layer routing selection based on the maximum bandwidth utilization rate according to the retransmission bandwidth utilization efficiency data to obtain the network layer routing selection optimization data;

[0136] Step S333: Perform QoS support processing on the network layer routing selection optimization data to obtain the routing optimization QoS support data;

[0137] Step S334: Perform cross-layer collaborative optimization according to the routing optimization QoS support data to obtain the interrupted retransmission cross-layer collaborative optimization data.

[0138] In the embodiments of the present invention, the bandwidth utilization efficiency of "interrupt retransmission dynamic bandwidth data" is analyzed to evaluate the bandwidth usage effect during the retransmission process under different network conditions. First, collect the dynamic bandwidth data under various network states, and combine it with the actual bandwidth value used during the transmission process to calculate the bandwidth utilization rate. By statistically analyzing the bandwidth utilization rates at different time periods and under different conditions, "retransmission bandwidth utilization efficiency data" is generated. This data will provide basic information for subsequent network optimization and help identify the links with low bandwidth utilization efficiency. Optimize the network layer routing selection according to the "retransmission bandwidth utilization efficiency data" to improve the network transmission efficiency during the data retransmission process. During the implementation process, first analyze the existing network topology structure and routing protocol, and record the transmission delay and bandwidth conditions of different paths. Utilize the bandwidth utilization efficiency data, adopt a routing selection algorithm based on cost minimization, calculate the cost of each path for different network conditions, and select the path with the lowest cost as the preferred choice for retransmission. This process dynamically updates the routing table, enabling the retransmitted data to be transmitted through the optimal path, and finally generates "network layer routing selection optimization data". Perform QoS support processing on the "network layer routing selection optimization data" to ensure that the data transmission during the retransmission process meets specific quality of service requirements. First, define the QoS parameters of different service levels according to the type and real-time requirements of the retransmitted data, including bandwidth guarantee, delay limit, and packet loss rate control. Then, analyze the optimized routing selection data, and use queuing theory and network traffic models to match and adjust the QoS parameters for each route. By dynamically configuring the QoS policies of routers and switches, ensure that the transmission of critical data is not affected when the network is busy, and finally generate "routing optimization QoS support data". Perform cross-layer collaborative optimization according to the "routing optimization QoS support data" to improve the performance of the overall retransmission mechanism. During the implementation, first identify the dependencies between layers, such as the interactions between the application layer, transport layer, and network layer, to ensure that the optimization strategies of each layer complement each other. Then, adopt a cross-layer feedback mechanism to summarize and analyze the data collected from different layers, and dynamically adjust the resource allocation and configuration of each layer through a control algorithm. For example, when the network layer routing changes, timely adjust the retransmission strategy of the transport layer and the data sending frequency of the application layer. This process aims to eliminate the contradictions and conflicts between different layers, achieve the best configuration of resources, and finally form "interrupt retransmission cross-layer collaborative optimization data".

[0139] Preferably, step S4 includes the following steps:

[0140] Step S41: Adjust the platform compatibility of the interrupt retransmission network awareness optimization mechanism data to obtain an interrupt retransmission network awareness compatible mechanism;

[0141] Step S42: Design an automated awareness firmware for the interrupt retransmission network awareness compatible mechanism to obtain interrupt retransmission awareness firmware parameters;

[0142] Step S43: Embed the interrupt retransmission awareness firmware parameters into the cloud platform to execute the software remote upgrade method.

[0143] In the embodiment of the present invention, platform compatibility adjustment is performed on the "interrupt retransmission network awareness optimization mechanism data" to ensure that the mechanism can operate smoothly in different hardware and operating system environments. First, identify the existing elevator controller platforms and their related operating system versions, and collect the technical specifications and resource configurations of each platform. Then, analyze the interfaces and protocols relied on by the interrupt retransmission mechanism to ensure compatibility with different platforms. On this basis, adopt the abstract layer design method to construct a compatibility layer, enabling the interrupt retransmission mechanism to adapt to the hardware resources and operating system calls of different platforms. This process includes writing adapter codes for different platforms to unify the command and response formats during the data interaction process, and finally forming the "interrupt retransmission network awareness compatibility mechanism". Perform automated awareness firmware design on the "interrupt retransmission network awareness compatibility mechanism" to improve the efficiency and response speed during the retransmission process. During the design process, first define the functional modules of the firmware, including data monitoring, fault detection, and status feedback. Utilize embedded development technology to convert each functional module into an independent code segment, and adopt an event-driven mechanism to achieve real-time awareness of the network status. For each network condition, the firmware should design corresponding response strategies to quickly initiate the retransmission mechanism in case of network fluctuations or interruptions. Through strict testing and verification of the firmware code, ensure its stability and reliability under different network conditions, and finally generate the "interrupt retransmission awareness firmware parameters". Embed the "interrupt retransmission awareness firmware parameters" into the cloud platform to execute the software remote upgrade method. During the implementation process, first prepare the development environment of the cloud platform to ensure compatibility with the firmware interface. Then, use containerization technology to package the awareness firmware into a container image to ensure its rapid deployment on the cloud platform. Subsequently, conduct integration testing of the firmware and the cloud platform to verify that when performing software upgrades, the firmware can collect the status information of the elevator controller in real time and upload it to the cloud for analysis and storage. To ensure the security and integrity of the data, design encryption and authentication mechanisms to ensure the reliability of the firmware during data transmission. After completion, confirm the successful deployment of the firmware and ensure that the cloud platform can effectively support remote upgrade operations, and finally achieve the effective embedding and use of the "interrupt retransmission awareness firmware parameters".

[0144] Preferably, the present invention also provides a software remote upgrade system for an elevator controller, which is used to execute the software remote upgrade method for an elevator controller as described above. The software remote upgrade system for an elevator controller includes:

[0145] Pre-upgrade environment sandbox construction module, which is used to extract software upgrade port parameters of the elevator controller to obtain the software upgrade port parameters of the elevator controller; simulate the port communication protocol stack according to the software upgrade port parameters of the elevator controller to obtain the port communication protocol simulation stack; construct the pre-upgrade environment sandbox through the port communication protocol simulation stack to obtain the pre-upgrade environment sandbox data;

[0146] Interrupt retransmission mechanism design module, which is used to obtain the software upgrade file; simulate the interruption of multi-threaded shard transmission of the software upgrade file according to the pre-upgrade environment sandbox data to obtain the multi-threaded shard transmission interruption simulation data; design the breakpoint resume mechanism according to the multi-threaded shard transmission interruption simulation data to obtain the data of the interrupt retransmission efficiency balance mechanism;

[0147] Network awareness optimization module, which is used to perform normalization design on the data of the interrupt retransmission efficiency balance mechanism to obtain the data of the interrupt retransmission normalization mechanism; perform network change awareness optimization on the data of the interrupt retransmission normalization mechanism to obtain the data of the interrupt retransmission network awareness optimization mechanism;

[0148] Automated execution module, which is used to perform automated awareness firmware design on the data of the interrupt retransmission network awareness optimization mechanism to obtain the interrupt retransmission awareness firmware parameters; embed the interrupt retransmission awareness firmware parameters into the cloud platform to execute the software remote upgrade method.

[0149] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be included in the present invention.

[0150] The above are only the specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A software remote upgrade method for an elevator controller, characterized in that: The following steps are involved: Step S1: extracting the software upgrade port parameters of the elevator controller to obtain the software upgrade port parameters of the elevator controller; simulating the port communication protocol stack according to the software upgrade port parameters of the elevator controller to obtain the port communication protocol simulation stack; constructing a pre-upgrade environment sandbox through the port communication protocol simulation stack to obtain pre-upgrade environment sandbox data; Step S2: Obtain software upgrade files; Perform multi-threaded fragment transmission interruption simulation on the software upgrade file according to the pre-upgrade environment sandbox data to obtain multi-threaded fragment transmission interruption simulation data; design the breakpoint interruption transmission mechanism according to the multi-threaded fragment transmission interruption simulation data to obtain the interruption retransmission efficiency balance mechanism data; Step S3: normalizing and designing the interruption-retransmission efficiency balancing mechanism data to obtain interruption-retransmission normalized mechanism data; Perform network change perception optimization on the interruption retransmission normalization mechanism data to obtain the interruption retransmission network perception optimization mechanism data; Step S4: Designing automatic perception firmware for the interruption retransmission network perception optimization mechanism data to obtain interruption retransmission perception firmware parameters; The interrupt retransmission awareness firmware parameters are embedded into the cloud platform to execute the software remote upgrade method.

2. The software remote upgrade method for an elevator controller according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: extracting the software upgrade port parameters of the elevator controller to obtain the software upgrade port parameters of the elevator controller; Step S12: determining the transmission protocol based on the elevator controller software upgrade port parameters to obtain port transmission protocol data; Step S13: simulating the port communication protocol stack according to the port transmission protocol data and the elevator controller software upgrade port parameters to obtain a port communication protocol simulation stack; Step S14: construct a pre-upgrade environment sandbox through a port communication protocol simulation stack to obtain pre-upgrade environment sandbox data.

3. The software remote upgrade method for an elevator controller according to claim 2, characterized in that: Step S2 includes the following steps: Step S21: Obtain software upgrade files; Step S22: performing multi-threaded slice transmission interruption simulation on the software upgrade file according to the pre-upgrade environment sandbox data to obtain multi-threaded slice transmission interruption simulation data; Step S23: Designing a breakpoint and interruption transmission mechanism for the software upgrade file according to the multi-threaded slicing transmission interruption simulation data, and obtaining transmission interruption breakpoint and interruption transmission mechanism data; Step S24: Perform network fluctuation efficiency balance compensation on the transmission interruption breakpoint interruption mechanism data to obtain interruption retransmission efficiency balance mechanism data.

4. The software remote upgrade method for an elevator controller according to claim 3, characterized in that: Step S23 includes the following steps: Step S231: marking memory write interruption positions of different sharding regions on the multi-threaded sharding transmission interruption simulation data to obtain sharding memory write interruption position data; Step S232: constructing an interruption file snapshot for the software upgrade file according to the interruption position data written into the shard memory, and obtaining a write interruption file snapshot; Step S233: matching the transmission interruption point hash value of the shard memory write interruption position data according to the write interruption file snapshot to obtain the file transfer interruption point hash value; Step S234: performing cumulative ACK confirmation on the shard memory write interruption position data based on the file transfer interruption point hash value and the write interruption file snapshot to obtain a file write cumulative ACK confirmation mode; Step S235: Design a breakpoint and transmission mechanism for the software upgrade file according to the file transfer breakpoint hash value and the file write cumulative ACK confirmation mode to obtain transmission interruption breakpoint and transmission mechanism data.

5. The software remote upgrade method for an elevator controller according to claim 3, characterized in that: Step S24 includes the following steps: Step S241: performing a transmission interruption network fluctuation simulation on the transmission interruption breakpoint interruption mechanism data to obtain the transmission interruption network fluctuation data; Step S242: performing a sudden increasing trend analysis on the interrupted network fluctuation data to obtain fluctuation sudden increasing trend data; Step S243: performing linear regression analysis on the fluctuation sudden increasing trend data to obtain increasing trend linear regression data; Step S244: performing transmission efficiency mapping according to the increasing trend linear regression data to obtain network increasing fluctuation transmission efficiency mapping data; Step S245: performing a network fluctuation-transmission efficiency structure stability analysis on the network incremental fluctuation transmission efficiency mapping data to obtain network fluctuation-transmission efficiency structure stability data; Step S246: Perform network fluctuation efficiency balance compensation on the transmission interruption breakpoint and transmission mechanism data according to the network fluctuation-transmission efficiency structure stability data to obtain the interruption and retransmission efficiency balance mechanism data, wherein the network fluctuation efficiency balance compensation is specifically the network fluctuation efficiency balance judgment and the network fluctuation efficiency balance compensation, the network fluctuation efficiency balance judgment is specifically the evaluation of the transmission efficiency fluctuation amplitude, the interruption frequency and the average recovery time, and the fluctuation-efficiency mapping deviation, and the network fluctuation efficiency balance compensation is specifically the balance of transmission efficiency and the number of retransmissions.

6. The software remote upgrade method for an elevator controller according to claim 5, characterized in that: Step S245 includes the following steps: Perform partial autocorrelation calculation on the network incremental fluctuation transmission efficiency mapping data to obtain transmission efficiency partial autocorrelation data; According to the partial autocorrelation data of transmission efficiency, the structural feature sampling of the network incremental fluctuation transmission efficiency mapping data is carried out to obtain the fluctuation-efficiency correlation sampling data; Perform extreme value outlier analysis on the fluctuation-efficiency correlation sampling data to obtain fluctuation-efficiency extreme value outlier data; According to the fluctuation-efficiency extreme value outlier data, the network fluctuation-transmission efficiency mapping data is subjected to a network fluctuation-transmission efficiency structural stability analysis to obtain the network fluctuation-transmission efficiency structural stability data. The network fluctuation-transmission efficiency structural stability analysis specifically evaluates the overall structural characteristics and stability of the network transmission efficiency as it changes with network fluctuations.

7. The software remote upgrade method for an elevator controller according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: normalizing the interruption-retransmission efficiency balancing mechanism data to obtain interruption-retransmission normalized mechanism data; Step S32: Perform a retransmission dynamic bandwidth test according to the interruption retransmission normalization mechanism data to obtain the interruption retransmission dynamic bandwidth data; Step S33: performing cross-layer collaborative optimization on the interruption and retransmission dynamic bandwidth data to obtain the interruption and retransmission cross-layer collaborative optimization data; Step S34: Perform network change-aware optimization on the interruption-retransmission normalization mechanism data according to the interruption-retransmission cross-layer collaborative optimization data to obtain the interruption-retransmission network-aware optimization mechanism data.

8. The software remote upgrade method for an elevator controller according to claim 7, characterized in that: Step S33 includes the following steps: Step S331: performing bandwidth utilization efficiency analysis on the interrupted retransmission dynamic bandwidth data to obtain retransmission bandwidth utilization efficiency data, wherein bandwidth utilization rate = (actually used bandwidth / total bandwidth) × 100%; Step S332: performing network layer routing optimization based on maximum bandwidth utilization according to the retransmission bandwidth utilization efficiency data to obtain network layer routing optimization data; Step S333: Perform QoS support processing on the network layer routing optimization data to obtain routing optimization QoS support data; Step S334: Perform cross-layer collaborative optimization according to the route optimization QoS support data to obtain interruption retransmission cross-layer collaborative optimization data.

9. The software remote upgrade method for an elevator controller according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: adjusting the platform compatibility of the interruption-retransmission network-aware optimization mechanism data to obtain an interruption-retransmission network-aware compatible mechanism; Step S42: Designing an automatic perception firmware for the interruption and retransmission network perception compatibility mechanism to obtain interruption and retransmission perception firmware parameters; Step S43: embed the interrupt retransmission perception firmware parameters into the cloud platform to execute the software remote upgrade method.

10. A software remote upgrade system for an elevator controller, characterized in that: For executing the software remote upgrade method for an elevator controller according to claim 1, the software remote upgrade system for an elevator controller comprises: The pre-upgrade environment sandbox construction module is used to extract the software upgrade port parameters of the elevator controller to obtain the elevator controller software upgrade port parameters; simulate the port communication protocol stack according to the elevator controller software upgrade port parameters to obtain the port communication protocol simulation stack; construct the pre-upgrade environment sandbox through the port communication protocol simulation stack to obtain the pre-upgrade environment sandbox data; The interruption and retransmission mechanism design module is used to obtain software upgrade files; perform multi-threaded fragmentation transmission interruption simulation on the software upgrade files according to the pre-upgrade environment sandbox data to obtain multi-threaded fragmentation transmission interruption simulation data; perform breakpoint and interruption transmission mechanism design according to the multi-threaded fragmentation transmission interruption simulation data to obtain interruption and retransmission efficiency balance mechanism data; The network perception optimization module is used to perform normalization design on the interruption retransmission efficiency balance mechanism data to obtain the interruption retransmission normalization mechanism data; perform network change perception optimization on the interruption retransmission normalization mechanism data to obtain the interruption retransmission network perception optimization mechanism data; The automated execution module is used to perform automated perception firmware design on the interruption and retransmission network perception optimization mechanism data to obtain interruption and retransmission perception firmware parameters; the interruption and retransmission perception firmware parameters are embedded into the cloud platform to execute the software remote upgrade method.

Images