Software remote upgrade method and system for elevator controller

Abstract

The present invention relates to the field of network communication technology, and in particular to a method and system for remote software upgrade of an elevator controller. The method comprises the following steps: processing a software upgrade request for an elevator controller to obtain upgrade request feedback data; constructing a controller state matrix according to the upgrade request feedback data to obtain controller state matrix data; intelligently identifying the operation mode according to the controller state matrix data, and performing upgradeable time window analysis to generate optimal upgrade time window data; formulating a software transmission scheduling task for the elevator controller through the optimal upgrade time window data to generate software transmission scheduling task data; performing a software upgrade operation based on the software transmission scheduling task data to generate software upgrade result data. The present invention realizes efficient upgrade of the elevator controller software through intelligent upgrade window calculation and precise software file scheduling.

Description

Technical Field

[0001] The present invention relates to the field of network communication technology, and in particular to a software remote upgrading method and system for an elevator controller. Background Art

[0002] With the acceleration of urbanization and the popularity of high-rise buildings, elevators have become an indispensable vertical transportation tool in modern buildings. As the core component of elevator operation, the performance and stability of the elevator control system are directly related to the safety of the elevator and the comfort experience of passengers. The functions of elevator controllers are becoming more and more powerful, integrating more intelligent functions such as automatic diagnosis, remote monitoring, energy optimization, etc., which greatly improve the operation efficiency and service quality of elevators. At the same time, the software system has become more complex and requires frequent software updates and upgrades to fix security vulnerabilities, optimize system performance, add new functions, etc. However, the traditional method of upgrading the software of elevator controllers is usually that the staff disassembles each elevator controller on site and downloads the upgrade software package to the controller through a wired connection (such as a serial port or a debug port). This process is cumbersome and requires each controller to be upgraded one by one, which is extremely inefficient, especially for a large number of elevators in large buildings; the selection of upgrade time is often not scientifically based, and it is carried out during peak hours or when the elevator is frequently used. These not only affect the efficiency and safety of the upgrade, but also increase the operation and maintenance costs. Summary of the invention

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

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

[0005] Step S1: using the monitoring center server to perform network topology analysis on the elevator controller to obtain the network topology data of the controller; performing endpoint authentication processing according to the network topology data of the controller to obtain security verification token data; performing software upgrade request processing according to the security verification token data to obtain upgrade request feedback data;

[0006] Step S2: construct a controller state matrix according to the upgrade request feedback data to obtain controller state matrix data; perform state differentiation processing according to the controller state matrix data, and bind the elevator operation state data to generate red device operation state data;

[0007] Step S3: intelligently identify the operation mode according to the red device operation status data to obtain elevator operation mode characteristic data; perform upgradeable time window analysis according to the elevator operation mode characteristic data to obtain upgradeable time window data; perform upgrade window optimization processing on the upgradeable time window data to generate optimal upgrade time window data;

[0008] Step S4: Formulate a software transmission scheduling task for the elevator controller based on the optimal upgrade time window data and the controller network topology data to generate software transmission scheduling task data; perform a software upgrade operation based on the software transmission scheduling task data to generate software upgrade result data.

[0009] The present invention can accurately grasp the location and connection relationship of each controller through a comprehensive understanding of the elevator controller network. Endpoint authentication processing is performed according to the controller network topology data to ensure the authenticity of the identity of each controller, improve the security of the system, prevent unauthorized devices from accessing the network, and protect the system from potential network attacks. By verifying and processing the upgrade request, it is possible to effectively screen and confirm which controllers need to be upgraded, ensure the legitimacy and necessity of the upgrade request, avoid unnecessary upgrade operations, and improve the utilization efficiency of system resources. By constructing a controller state matrix, the current operating status of each controller can be fully understood, which is convenient for system status monitoring. According to the controller state matrix data, state differentiation processing can be performed to accurately identify the controllers that are abnormal or need attention (elevator controllers marked in red), and timely carry out targeted maintenance and processing to improve the overall operation reliability of the elevator system. By intelligently identifying the operation mode of the elevator, the working characteristics and usage of the elevator can be accurately understood. According to the characteristic data of the elevator operation mode, the upgradeable time window analysis can be performed to find the relatively idle period of the elevator operation, and try to avoid upgrading operations during peak or busy periods, thereby reducing the impact on the normal operation of the elevator. By optimizing the upgrade time window, the upgrade schedule can be further refined and optimized to ensure that the upgrade operation is carried out in the most appropriate time period and minimize the interference to the user. According to the optimal time window and network topology data, the transmission scheduling task can be formulated to ensure that the software upgrade process is efficient and smooth, and the delay and error in the transmission process can be reduced. By accurately executing the scheduling task for software upgrade, the success rate and accuracy of the upgrade process can be ensured, the stability and security of the system can be improved, and the efficient and reliable operation of the elevator system after the upgrade can be ensured. Therefore, a software remote upgrade method for an elevator controller of the present invention automatically performs network topology analysis and endpoint authentication through a monitoring center server to ensure the security of equipment and data, and updates the network topology structure data in real time; constructs a controller state matrix according to the upgrade request feedback data, comprehensively monitors the operation status data of the elevator controller, processes and analyzes the status data in real time, and accurately binds the operation status of the equipment; at the same time, intelligent identification and analysis of the operation mode are performed, the upgrade time window is scientifically analyzed and optimized, and a flexible upgrade plan is formulated to reduce the impact on the normal operation of the elevator; through the optimal upgrade time window data and the controller network topology structure data, efficient software transmission scheduling task formulation and upgrade operation are performed to ensure the reliability and upgrade efficiency of the upgrade process.

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

[0011] Step S11: Use the monitoring center server to scan the device port of the elevator controller to generate device port list data; perform IP address allocation query according to the device port list data to obtain an IP address allocation table;

[0012] Step S12: Perform network protocol identification according to the IP address allocation table and the device port list data, and perform network topology analysis to obtain controller network topology data;

[0013] Step S13: Analyze the communication link status of the elevator controller through the controller network topology data, and calculate the path weight to obtain communication path weight data; evaluate the comprehensive communication index according to the communication path weight data to obtain the optimal communication path data;

[0014] Step S14: performing endpoint authentication processing on the elevator controller through the optimal communication path data to obtain security verification token data;

[0015] Step S15: encrypt the security verification token data and the preset software upgrade request instruction using the SHA-256 algorithm to obtain remote upgrade request package data, wherein the remote upgrade request package data includes the software upgrade request instruction, the error request verification code, the request timestamp and the security verification token data;

[0016] Step S16: The monitoring center server distributes the remote upgrade request package data to the elevator controller for upgrading request parsing, and performs analysis result feedback processing on the monitoring center server to obtain upgrading request feedback data.

[0017] The present invention uses the monitoring center server to scan the device port of the elevator controller, which can fully understand the open port situation of the elevator controller, perform IP address allocation query according to the device port list data, and obtain the IP address allocation table. By querying and sorting the IP address allocation table, the position and address allocation of each device in the network can be clarified, IP address conflicts can be avoided, and network management efficiency can be improved. By identifying the network protocol and analyzing the topological structure, the connection relationship and communication protocol between the elevator controllers can be understood in detail, and a complete network topology diagram can be constructed. By analyzing the communication link state and calculating the path weight, the reliability and communication quality of each path can be evaluated, and the optimal communication path can be found, which can significantly improve the efficiency and stability of data transmission and ensure the timely and reliable transmission of key data. Using the optimal communication path for endpoint authentication can ensure the efficiency and accuracy of the authentication process, prevent unauthorized devices from accessing the network, and improve the overall security of the system. Using the SHA-256 algorithm for encryption processing can ensure the integrity and security of the request packet data and prevent the data from being tampered with or illegally intercepted during transmission. By distributing the encrypted upgrade request packet data to the elevator controller and feeding back the parsing results, the accurate transmission and processing of the upgrade request can be ensured, and feedback information can be obtained in time.

[0018] Preferably, step S16 comprises the following steps:

[0019] Step S161: the elevator controller performs error request verification code analysis on the remote upgrade request packet data to obtain the analyzed original verification code data;

[0020] Step S162: Performing a data packet integrity check based on the parsed original verification code data to obtain integrity verification data;

[0021] Step S163: verifying the request validity on the request timestamp based on the integrity check data to obtain request validity verification data;

[0022] Step S164: parsing the software upgrade request instruction through the request validity verification data to generate parsed request instruction data, wherein the parsed request instruction data includes version upgrade comparison instruction data and real-time status collection instruction data;

[0023] Step S165: using the security verification token data to perform authority verification processing on the parsing request instruction data to obtain request authority verification data;

[0024] Step S166: performing version number difference analysis based on the requested authority verification data and version upgrade comparison instruction data, and performing upgrade necessity evaluation to obtain upgrade necessity evaluation data;

[0025] Step S167: judging the necessity of upgrading according to the upgrade necessity evaluation data; when the current software version is equal to the upgrade software version, marking the elevator controller as a version synchronization elevator controller, generating device version synchronization status data; when the current software version is lower than the upgrade software version, marking the elevator controller as an elevator controller to be synchronized, using the real-time status acquisition instruction data to collect the elevator operation status of the elevator controller, and obtaining periodic elevator operation status data;

[0026] Step S168: Integrate the operation status data of the periodic elevator operation status data and the elevator controller to be synchronized, and generate the device version synchronization status data;

[0027] Step S169: The device version synchronization status data and the device version synchronization status data are transmitted to the monitoring center server for request feedback processing to obtain upgrade request feedback data.

[0028] The present invention can ensure that the request packet is not tampered with or damaged during the transmission process by parsing the error request verification code, thereby ensuring the integrity and reliability of the data. Through the integrity check, it can be further confirmed that no data loss or damage occurs in the data packet during the transmission process. By verifying the validity of the timestamp, it can be confirmed that the request packet is generated and transmitted within a preset time window, preventing replay attacks and expired requests, and improving the security of the system. By parsing the request instruction, the specific content of the upgrade request can be accurately identified and decomposed. By performing permission verification on the request instruction data, it is ensured that only authorized upgrade requests can be executed, preventing unauthorized operations, and improving the security and management efficiency of the system. Through version number difference analysis, the difference between the current software version and the target upgrade version can be accurately judged, and it is evaluated whether it is necessary to upgrade, avoid unnecessary upgrade operations, and save system resources. By transmitting the synchronization status data to the monitoring center server, centralized management and monitoring of the upgrade process can be achieved, and feedback information can be obtained in a timely manner.

[0029] Preferably, step S2 comprises the following steps:

[0030] Step S21: extracting the ID identifier of the elevator controller to obtain the elevator controller ID data;

[0031] Step S22: Performing upgrade status evaluation and quantitative processing according to the upgrade request feedback data, and constructing a controller state matrix using the elevator controller ID data to obtain controller state matrix data;

[0032] Step S23: Mark the state color according to the controller state matrix data to obtain controller color marking data, wherein the version synchronized elevator controller is marked in green, the elevator controller to be synchronized is marked in red, and the elevator controller being upgraded is marked in yellow;

[0033] Step S24: Monitor the upgrade progress of the yellow-marked controller. When the software upgrade is successful, mark the yellow-marked controller as green; when the software upgrade fails, mark the yellow-marked controller as red; do not upgrade the green-marked controller, and monitor the controller status regularly;

[0034] Step S25: Use the device version synchronization status data in the upgrade request feedback data to bind the elevator operation status data to the red marked controller to generate red device operation status data.

[0035] The present invention can uniquely identify each elevator controller by extracting the ID identifier of the elevator controller, ensuring that each controller can be accurately identified and managed in subsequent operations. By evaluating and quantifying the upgrade status, the current status of each elevator controller can be fully understood, and the status information can be integrated into the controller status matrix. Through color marking, the status of each elevator controller can be intuitively displayed to distinguish controllers with different upgrade states. Different processing strategies are applied to controllers of different colors to ensure that all elevator controllers can achieve software upgrades. By binding the operating status data of the controllers to be synchronized, the real-time operating conditions of these controllers can be fully grasped, so that the appropriate time can be selected for the upgrade operation while ensuring the normal operation of the elevator, reducing the impact on user use and improving the success rate and efficiency of the upgrade operation.

[0036] Preferably, step S3 comprises the following steps:

[0037] Step S31: normalize the red equipment operating status data to obtain standard elevator operating status data;

[0038] Step S32: intelligently identify the operation mode according to the standard elevator operation status data to obtain elevator operation mode characteristic data;

[0039] Step S33: extracting elevator status evaluation indicators according to standard elevator operation status data to generate elevator status evaluation indicator data, wherein the elevator status evaluation indicator data includes elevator operation frequency, elevator failure rate, elevator load rate and elevator downtime;

[0040] Step S34: performing an upgradeable time window analysis based on the elevator state evaluation index data and the elevator operation mode characteristic data to obtain upgradeable time window data;

[0041] Step S35: Optimizing the executable time length according to the upgradeable time window data to generate optimized upgrade time window data;

[0042] Step S36: summing the upgrade window risk coefficients of the optimized upgrade time window data using a preset upgrade risk impact coefficient matrix to obtain upgrade window risk data; and marking the upgradeable time window data with the minimum upgrade window risk as the optimal upgrade time window data according to the upgrade window risk data.

[0043] The present invention normalizes the red equipment operation status data, and can convert the operation status data of different sources and formats into a unified standard data format, eliminating the differences between the data. By intelligently identifying the operation mode of the elevator, the working characteristics and usage of the elevator can be accurately understood, and the operation rules of different elevators in different time periods can be identified. The elevator state evaluation index is extracted according to the standard elevator operation status data, and the operation status and health status of the elevator can be comprehensively evaluated. Based on the elevator state evaluation index data and the elevator operation mode characteristic data, the upgradeable time window analysis can be performed to find out the time period when the elevator is relatively idle or has the least impact during operation, determine the time window suitable for software upgrade, reduce the impact of the upgrade operation on the normal operation of the elevator, and improve user satisfaction and the continuity of system operation. By optimizing the duration of the upgradeable time window, the upgrade time window can be further refined and adjusted to ensure that the upgrade operation is completed within the optimal time period, maximize the efficiency and success rate of the upgrade operation, and reduce the interruption time of the elevator service. According to the upgrade window risk data, the upgradeable time window data with the minimum upgrade window risk is marked as the optimal upgrade time window data. By calculating and analyzing the risk coefficient of the upgrade window, the potential risk of the upgrade operation in each time window can be evaluated, and the time window with the minimum risk can be selected for upgrade to ensure the security and reliability of the upgrade operation and reduce the system risk caused by the upgrade operation.

[0044] Preferably, step S4 comprises the following steps:

[0045] Step S41: Performing version upgrade logic grouping processing on the elevator controller to generate controller group configuration data;

[0046] Step S42: formulating software transmission scheduling tasks for controller group configuration data using the optimal upgrade time window data and the controller network topology data to generate software transmission scheduling task data;

[0047] Step S43: Based on the software transmission scheduling task data, the monitoring center server transmits the new version software file to the elevator controller. When the elevator controller receives the new version software file, the elevator controller enters an upgrade preparation state;

[0048] Step S44: When the elevator controller reaches the start time of the optimal upgrade window time, the monitoring center server sends an encrypted upgrade authorization instruction and performs software integrity verification processing to obtain file integrity verification data;

[0049] Step S45: Perform software upgrade operation and restart the software based on the file integrity verification data. The elevator controller sends an upgrade success signal to the monitoring center server and generates software upgrade result data.

[0050] The present invention classifies elevator controllers according to their specific upgrade requirements or functional characteristics through logical grouping processing, which helps to allocate similar controllers to the same group and improve the execution efficiency of upgrade tasks. By combining the upgrade time window data and the network topology structure, the upgrade tasks of the controller are effectively planned and arranged to ensure that the upgrade operation is completed within the optimal time window, minimize the interference with the elevator operation, and improve the success rate of the upgrade operation. By transmitting the new version of the software file, the elevator controller can obtain the latest software update, enter the upgrade preparation state, prepare for subsequent upgrade operations, and ensure the smooth progress of the upgrade operation. By sending encrypted upgrade authorization instructions and performing software integrity verification processing, the security and reliability of the upgrade operation are ensured. By performing software upgrade operations and restarting the software, it is ensured that the new version of the software file is correctly applied to the elevator controller so that it can operate normally.

[0051] The present invention also provides a software remote upgrade system for an elevator controller, which executes the software remote upgrade method for an elevator controller as described above. The software remote upgrade system for an elevator controller comprises:

[0052] The topology authentication module is used to use the monitoring center server to perform network topology analysis on the elevator controller to obtain the network topology data of the controller; perform endpoint authentication processing according to the network topology data of the controller to obtain security verification token data; perform software upgrade request processing according to the security verification token data to obtain upgrade request feedback data;

[0053] The upgrade status analysis module is used to construct the controller status matrix according to the upgrade request feedback data to obtain the controller status matrix data; perform state differentiation processing according to the controller status matrix data, and bind the elevator operation status data to generate the red device operation status data;

[0054] The intelligent upgrade window calculation module is used to perform intelligent identification of the operation mode according to the operation status data of the red equipment to obtain the characteristic data of the elevator operation mode; perform upgradeable time window analysis according to the characteristic data of the elevator operation mode to obtain upgradeable time window data; perform upgrade window optimization processing on the upgradeable time window data to generate the best upgrade time window data;

[0055] The software transmission upgrade module is used to formulate software transmission scheduling tasks for the elevator controller based on the optimal upgrade time window data and the controller network topology data, and generate software transmission scheduling task data; perform software upgrade operations based on the software transmission scheduling task data, and generate software upgrade result data. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1It is a schematic flow chart of the steps of the software remote upgrade method and system for an elevator controller of the present invention;

[0057] Figure 2 for Figure 1 Detailed implementation steps of step S2 in the flowchart;

[0058] Figure 3 for Figure 1 Detailed implementation steps of step S4 in FIG.

[0059] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0060] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0061] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0062] 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 used only to distinguish one unit from another unit. 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 listed associated items.

[0063] To achieve this, please refer to Figures 1 to 3 The present invention provides a software remote upgrade method for an elevator controller, comprising the following steps:

[0064] Step S1: using the monitoring center server to perform network topology analysis on the elevator controller to obtain the network topology data of the controller; performing endpoint authentication processing according to the network topology data of the controller to obtain security verification token data; performing software upgrade request processing according to the security verification token data to obtain upgrade request feedback data;

[0065] Step S2: construct a controller state matrix according to the upgrade request feedback data to obtain controller state matrix data; perform state differentiation processing according to the controller state matrix data, and bind the elevator operation state data to generate red device operation state data;

[0066] Step S3: intelligently identify the operation mode according to the red device operation status data to obtain elevator operation mode characteristic data; perform upgradeable time window analysis according to the elevator operation mode characteristic data to obtain upgradeable time window data; perform upgrade window optimization processing on the upgradeable time window data to generate optimal upgrade time window data;

[0067] Step S4: Formulate a software transmission scheduling task for the elevator controller based on the optimal upgrade time window data and the controller network topology data to generate software transmission scheduling task data; perform a software upgrade operation based on the software transmission scheduling task data to generate software upgrade result data.

[0068] The present invention can accurately grasp the location and connection relationship of each controller through a comprehensive understanding of the elevator controller network. Endpoint authentication processing is performed according to the controller network topology data to ensure the authenticity of the identity of each controller, improve the security of the system, prevent unauthorized devices from accessing the network, and protect the system from potential network attacks. By verifying and processing the upgrade request, it is possible to effectively screen and confirm which controllers need to be upgraded, ensure the legitimacy and necessity of the upgrade request, avoid unnecessary upgrade operations, and improve the utilization efficiency of system resources. By constructing a controller state matrix, the current operating status of each controller can be fully understood, which is convenient for system status monitoring. According to the controller state matrix data, state differentiation processing can be performed to accurately identify the controllers that are abnormal or need attention (elevator controllers marked in red), and timely carry out targeted maintenance and processing to improve the overall operation reliability of the elevator system. By intelligently identifying the operation mode of the elevator, the working characteristics and usage of the elevator can be accurately understood. According to the characteristic data of the elevator operation mode, the upgradeable time window analysis can be performed to find the relatively idle period of the elevator operation, and try to avoid upgrading operations during peak or busy periods, thereby reducing the impact on the normal operation of the elevator. By optimizing the upgrade time window, the upgrade schedule can be further refined and optimized to ensure that the upgrade operation is carried out in the most appropriate time period and minimize the interference to the user. According to the optimal time window and network topology data, the transmission scheduling task can be formulated to ensure that the software upgrade process is efficient and smooth, and the delay and error in the transmission process can be reduced. By accurately executing the scheduling task for software upgrade, the success rate and accuracy of the upgrade process can be ensured, the stability and security of the system can be improved, and the efficient and reliable operation of the elevator system after the upgrade can be ensured. Therefore, a software remote upgrade method for an elevator controller of the present invention automatically performs network topology analysis and endpoint authentication through a monitoring center server to ensure the security of equipment and data, and updates the network topology structure data in real time; constructs a controller state matrix according to the upgrade request feedback data, comprehensively monitors the operation status data of the elevator controller, processes and analyzes the status data in real time, and accurately binds the operation status of the equipment; at the same time, intelligent identification and analysis of the operation mode are performed, the upgrade time window is scientifically analyzed and optimized, and a flexible upgrade plan is formulated to reduce the impact on the normal operation of the elevator; through the optimal upgrade time window data and the controller network topology structure data, efficient software transmission scheduling task formulation and upgrade operation are performed to ensure the reliability and upgrade efficiency of the upgrade process.

[0069] In the embodiment of the present invention, reference Figure 1 The above is a schematic flow chart of the steps of a software remote upgrade method for an elevator controller of the present invention. In this embodiment, the software remote upgrade method for an elevator controller includes the following steps:

[0070] Step S1: using the monitoring center server to perform network topology analysis on the elevator controller to obtain the network topology data of the controller; performing endpoint authentication processing according to the network topology data of the controller to obtain security verification token data; performing software upgrade request processing according to the security verification token data to obtain upgrade request feedback data;

[0071] In an embodiment of the present invention, a network scanning tool is used to scan all ports of the target elevator controller to generate device port list data. In combination with a network management tool, the port list data is used to query the IP address allocation to determine the IP address of each device and the type of protocol used. A network topology analysis tool is used to draw a network topology diagram based on the device connection relationship to identify the device communication path. A network monitoring tool is used to evaluate the link stability between controllers, and the optimal communication path is determined by a path weight calculation algorithm. A secure communication protocol is established, and the controller and the monitoring server mutually verify the digital certificates to generate security verification token data. The software upgrade request instruction is combined with the security verification token data, and encrypted data is generated by hash calculation, and other data is combined to generate a remote upgrade request packet.

[0072] Step S2: construct a controller state matrix according to the upgrade request feedback data to obtain controller state matrix data; perform state differentiation processing according to the controller state matrix data, and bind the elevator operation state data to generate red device operation state data;

[0073] In an embodiment of the present invention, a unique ID identifier is generated by reading controller hardware information such as a MAC address or a serial number. The controller network interface information is obtained using a command line tool, and the MAC address is extracted as an ID. Status analysis is performed based on the upgrade request feedback data, and the controller is divided into three states: upgrade required (0), upgrade not required (1), and upgrading (3). An empty matrix is ​​created using the NumPy library, with the number of rows corresponding to the number of controllers and the number of columns 1 representing the state. The corresponding state values ​​are filled into the matrix based on the feedback data, and merged with the controller ID data to generate controller state matrix data. The state information of each controller is extracted from the state matrix. If the state is 0, it is marked as red and needs to be synchronized; if the state is 3, it is marked as yellow and is being upgraded; if the state is 1, it is marked as green and does not need to be upgraded. A remote monitoring tool is used to monitor the controller upgrade progress in real time, and the yellow mark is automatically changed to green or red based on the success or failure notification. The green-marked controller is regularly connected to obtain the operating status and performance data to determine whether there is an abnormality. The device version synchronization status data and the controller ID are extracted from the feedback data and matched with the red-marked controller. The operating status information of each red controller is bound to the corresponding upgrade status data to generate the red device operating status data.

[0074] Step S3: intelligently identify the operation mode according to the red device operation status data to obtain elevator operation mode characteristic data; perform upgradeable time window analysis according to the elevator operation mode characteristic data to obtain upgradeable time window data; perform upgrade window optimization processing on the upgradeable time window data to generate optimal upgrade time window data;

[0075] In the embodiment of the present invention, the red equipment operation status data is normalized by using a data processing tool, and each index value is scaled to the same scale range. The normalized data is processed by using machine learning algorithms and data mining techniques such as cluster analysis and classification algorithms, and different elevator operation modes are identified to obtain elevator operation mode characteristic data. The evaluation indicators such as elevator operation frequency, failure rate, load rate, and downtime are extracted from the standard elevator operation status data by using data analysis and statistical methods to obtain elevator state evaluation index data. The state evaluation index data is integrated with the operation mode characteristic data to identify the time period when the elevator operation state is stable and suitable for software upgrade, that is, the upgradeable time window, and generate upgradeable time window data. According to the software upgrade duration, the start and end time of the upgradeable time window is adjusted to ensure that the upgrade operation can be completed within the predetermined range. A 7×24 hour matrix is ​​established, each cell represents a one-hour time period, and the corresponding risk coefficient is set. The optimized time window data is matched with the matrix, and the comprehensive risk assessment of each time window is calculated according to the preset impact coefficient. The time window with the smallest risk coefficient is marked as the best upgrade time window data.

[0076] Step S4: Formulate a software transmission scheduling task for the elevator controller based on the optimal upgrade time window data and the controller network topology data to generate software transmission scheduling task data; perform a software upgrade operation based on the software transmission scheduling task data to generate software upgrade result data.

[0077] In an embodiment of the present invention, controllers are grouped according to their model, location, frequency of use, and fault history. Using the optimal upgrade time window data and the controller network topology data, controllers in the same group are assigned to the same time period for upgrading, and the software transmission order is determined using a graph theory algorithm. The shortest time required to complete the upgrade is calculated for each group, and the process is repeated to generate software transmission scheduling task data, including the upgrade time period, order, and estimated completion time for each group. The monitoring center obtains the first batch of controllers to be upgraded and the transmission order, establishes a secure connection with the relevant controllers through an encrypted channel, and transmits the new version of the software file. After receiving the file, the controller uses a hash algorithm to perform an integrity check, and replies with a confirmation message after passing. Based on the optimal upgrade time window, the monitoring center generates an encrypted upgrade authorization instruction and sends it to the controller. The controller decrypts and obtains the authorization content, verifies the integrity of the local software, and returns the verification data to the monitoring center. After the verification is passed, the controller automatically executes the software upgrade operation, replacing or overwriting the original version. After the upgrade is completed, the new software is restarted and loaded, and an upgrade success status signal is sent to the monitoring center.

[0078] Preferably, step S1 comprises the following steps:

[0079] Step S11: Use the monitoring center server to scan the device port of the elevator controller to generate device port list data; perform IP address allocation query according to the device port list data to obtain an IP address allocation table;

[0080] Step S12: Perform network protocol identification according to the IP address allocation table and the device port list data, and perform network topology analysis to obtain controller network topology data;

[0081] Step S13: Analyze the communication link status of the elevator controller through the controller network topology data, and calculate the path weight to obtain communication path weight data; evaluate the comprehensive communication index according to the communication path weight data to obtain the optimal communication path data;

[0082] Step S14: performing endpoint authentication processing on the elevator controller through the optimal communication path data to obtain security verification token data;

[0083] Step S15: encrypt the security verification token data and the preset software upgrade request instruction using the SHA-256 algorithm to obtain remote upgrade request package data, wherein the remote upgrade request package data includes the software upgrade request instruction, the error request verification code, the request timestamp and the security verification token data;

[0084] Step S16: The monitoring center server distributes the remote upgrade request package data to the elevator controller for upgrading request parsing, and performs analysis result feedback processing on the monitoring center server to obtain upgrading request feedback data.

[0085] In the embodiment of the present invention, for example, Nmap is used to execute the command nmap -p -<controller_ip> To scan all ports of the target elevator controller. After the scan is completed, export the scan results to a text file to generate device port list data. Then, use the generated device port list data in combination with network management tools such as Wireshark or Snort to query the IP address allocation. These tools can parse network data packets, extract IP address information from them, and then determine the IP address of each device. For example, use P0f to monitor the network traffic of the elevator controller, and determine the protocol type used by each port by observing the TCP and UDP packet header information. At the same time, use network topology analysis tools such as NetBrain or GNS3 to perform topological analysis on the network. These tools can draw a network topology diagram based on the connection relationship between devices and identify the communication path between each device. For example, use Zabbix to monitor the Ping response time and packet loss rate between elevator controllers to evaluate the stability of the communication link. At the same time, use path weight calculation algorithms such as Dijkstra or Bellman-Ford algorithms to calculate the path weight of the network topology structure. These algorithms can calculate the optimal communication path based on the communication delay or bandwidth between devices. For example, the Dijkstra algorithm is used to calculate the shortest path between elevator controllers, and the path with the smallest communication delay is considered as the optimal communication path. Finally, based on the communication path weight data, the comprehensive communication index evaluation method, such as the quality factor method or the weighted average method, is used to comprehensively evaluate each communication path. These methods can comprehensively consider factors such as the stability, delay and bandwidth of the communication link to determine the optimal communication path. For example, the TLS / SSL protocol is used for communication, in which the elevator controller and the monitoring center server each have their own digital certificates, and each other verifies the validity of the certificate when communicating. Once the two parties have established a secure communication channel, the security verification token data can be generated. For example, according to the symmetric key generated during the TLS / SSL handshake process, a secure hash algorithm, such as HMAC-SHA256, is used to encrypt the communication content and generate the security verification token data. The security verification token data and the software upgrade request instruction are converted into a byte stream. Then, the byte stream is hashed using the SHA-256 algorithm to generate a fixed-length hash value. This hash value will be regarded as encrypted data. Next, the software upgrade request instruction, the error request verification code, the request timestamp, and the security verification token data are combined into remote upgrade request packet data. For example, according to a predetermined communication protocol, the software upgrade request instruction, the error request verification code, the request timestamp, and the security verification token data are parsed. Next, the elevator controller verifies and processes the parsing result to ensure the legitimacy and security of the request.If the analysis is successful, the monitoring center server will be fed back with the analysis result, for example, a feedback data packet is sent to indicate the processing status of the analysis result.

[0086] Preferably, step S16 comprises the following steps:

[0087] Step S161: the elevator controller performs error request verification code analysis on the remote upgrade request packet data to obtain the analyzed original verification code data;

[0088] Step S162: Performing a data packet integrity check based on the parsed original verification code data to obtain integrity verification data;

[0089] Step S163: verifying the request validity on the request timestamp based on the integrity check data to obtain request validity verification data;

[0090] Step S164: parsing the software upgrade request instruction through the request validity verification data to generate parsed request instruction data, wherein the parsed request instruction data includes version upgrade comparison instruction data and real-time status collection instruction data;

[0091] Step S165: using the security verification token data to perform authority verification processing on the parsing request instruction data to obtain request authority verification data;

[0092] Step S166: performing version number difference analysis based on the requested authority verification data and version upgrade comparison instruction data, and performing upgrade necessity evaluation to obtain upgrade necessity evaluation data;

[0093] Step S167: judging the necessity of upgrading according to the upgrade necessity evaluation data; when the current software version is equal to the upgrade software version, marking the elevator controller as a version synchronization elevator controller, generating device version synchronization status data; when the current software version is lower than the upgrade software version, marking the elevator controller as an elevator controller to be synchronized, using the real-time status acquisition instruction data to collect the elevator operation status of the elevator controller, and obtaining periodic elevator operation status data;

[0094] Step S168: Integrate the operation status data of the periodic elevator operation status data and the elevator controller to be synchronized, and generate the device version synchronization status data;

[0095] Step S169: The device version synchronization status data and the device version synchronization status data are transmitted to the monitoring center server for request feedback processing to obtain upgrade request feedback data.

[0096] In an embodiment of the present invention, a corresponding checksum algorithm, such as CRC or MD5, is used to check the data in the request packet to ensure that no error occurs during the data transmission process. For example, the checksum of the data block in the request packet is calculated by the CRC algorithm, and then compared with the error request verification code in the request packet. If the two match, it means that no error occurs during the data transmission process, and the elevator controller can continue to process the request packet data. If they do not match, it means that the data has been damaged or tampered with, and the elevator controller needs to refuse to process the request packet. The data in the request packet is checked for overall integrity to ensure that the integrity of the data is not damaged. For example, by comparing the data block in the request packet with the expected checksum, it is determined whether the data has been tampered with or damaged. If the data integrity check passes, it means that the data in the request packet has not been tampered with, and the elevator controller can continue to process the request. If the data integrity check fails, corresponding processing is required, such as refusing to process the request or requesting retransmission. The elevator controller compares the current time with the timestamp in the request packet to determine whether the time difference is within an acceptable range. If the time difference is within a reasonable range, the request is considered valid and subsequent processing continues. If the time difference exceeds a preset range, the request is refused to be processed. The elevator controller parses the request instruction and determines the operation to be performed, such as version upgrade comparison and real-time status acquisition. The elevator controller uses the security verification token data to verify the source and authority of the request. For example, by matching the security verification token carried in the request with the preset security token, the legitimacy of the request authority is confirmed. If the verification is successful, the subsequent steps are allowed to be executed; if the verification fails, the subsequent operations are refused. The elevator controller checks the relationship between the current software version and the upgrade software version. If the current software version is the same as the upgrade software version, it indicates that the elevator controller is already the latest version and does not need to be upgraded. In this case, the elevator controller will be marked as a version synchronization elevator controller and generate device version synchronization status data. If the current software version is lower than the upgrade software version, it indicates that the elevator controller needs to be upgraded. In this case, the elevator controller will be marked as a synchronization-required elevator controller. In order to further confirm the necessity of the upgrade, the elevator controller will use the real-time status acquisition instruction data to collect the elevator operation status and obtain periodic elevator operation status data. The elevator controller sends the collected status data feedback to the monitoring center server and generates corresponding feedback data.

[0097] Preferably, step S2 comprises the following steps:

[0098] Step S21: extracting the ID identifier of the elevator controller to obtain the elevator controller ID data;

[0099] Step S22: Performing upgrade status evaluation and quantitative processing according to the upgrade request feedback data, and constructing a controller state matrix using the elevator controller ID data to obtain controller state matrix data;

[0100] Step S23: Mark the state color according to the controller state matrix data to obtain controller color marking data, wherein the version synchronized elevator controller is marked in green, the elevator controller to be synchronized is marked in red, and the elevator controller being upgraded is marked in yellow;

[0101] Step S24: Monitor the upgrade progress of the yellow-marked controller. When the software upgrade is successful, mark the yellow-marked controller as green; when the software upgrade fails, mark the yellow-marked controller as red; do not upgrade the green-marked controller, and monitor the controller status regularly;

[0102] Step S25: Use the device version synchronization status data in the upgrade request feedback data to bind the elevator operation status data to the red marked controller to generate red device operation status data.

[0103] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S2 in the embodiment are shown in the flowchart. In this embodiment, step S2 includes:

[0104] Step S21: extracting the ID identifier of the elevator controller to obtain the elevator controller ID data;

[0105] In the embodiment of the present invention, a unique ID identifier is generated by reading the hardware information of the controller, such as a MAC address or a serial number. For example, a command line tool such as ifconfig or ipconfig can be used to view the network interface information of the controller, from which the MAC address is extracted as the ID identifier.

[0106] Step S22: Performing upgrade status evaluation and quantitative processing according to the upgrade request feedback data, and constructing a controller state matrix using the elevator controller ID data to obtain controller state matrix data;

[0107] In an embodiment of the present invention, the upgrade status of the elevator controller is analyzed based on the upgrade request feedback data, and the elevator controller is divided in combination with the upgrade status index. For example, there are three elevator controllers, marked as A, B, and C, respectively, where controller A needs to be upgraded, controller B does not need to be upgraded, and controller C is being upgraded. These states are represented by the values ​​0, 1, and 3, respectively, where 0 represents the need for upgrade, 1 represents the need for upgrade, and 3 represents the upgrading. Python's NumPy library will be used for matrix operations. For example, an empty NumPy array is created, in which the number of rows corresponds to the number of controllers, and the number of columns is 1, representing the state. Then, according to the state information in the upgrade request feedback data, the corresponding values ​​are filled into the array. Finally, the controller ID data and the state data are merged into a matrix.

[0108] Step S23: Mark the state color according to the controller state matrix data to obtain controller color marking data, wherein the version synchronized elevator controller is marked in green, the elevator controller to be synchronized is marked in red, and the elevator controller being upgraded is marked in yellow;

[0109] In the embodiment of the present invention, the status information of each controller is extracted from the controller status matrix data. Then, conditional judgment is performed according to the status of each controller to determine the corresponding color mark. For example, if the status of a controller indicates 0, it means synchronization is required and it is marked as red; if the status indicates 3, it is marked as yellow; if the status indicates 1, it is marked as green. The controller ID is matched with the corresponding color mark data to clearly understand the status of each controller.

[0110] Step S24: Monitor the upgrade progress of the yellow-marked controller. When the software upgrade is successful, mark the yellow-marked controller as green; when the software upgrade fails, mark the yellow-marked controller as red; do not upgrade the green-marked controller, and monitor the controller status regularly;

[0111] In an embodiment of the present invention, a remote monitoring tool is used to monitor the upgrade progress of the controller to obtain the upgrade status of the controller in real time. For example, if the upgrade of a certain controller is successful, the monitoring software will receive a corresponding success notification and then automatically change the mark of the controller from yellow to green. On the contrary, if the upgrade of a certain controller fails, the monitoring software will receive a corresponding failure notification and then change the mark of the controller from yellow to red. By setting a regular monitoring task, a regular connection is made to each green-marked controller, and its current operating status and performance data are obtained to determine whether there are any abnormal conditions.

[0112] Step S25: Use the device version synchronization status data in the upgrade request feedback data to bind the elevator operation status data to the red marked controller to generate red device operation status data.

[0113] In the embodiment of the present invention, the device version synchronization status data and the controller ID are extracted from the upgrade request feedback data, and the upgrade status data are matched with the controller marked in red. This can be achieved through the unique identifier of the controller, such as the serial number or ID of the controller. Then, the operating status information of each red-marked controller is bound to the corresponding upgrade status data to generate red device operating status data. Then, the program will generate a data file containing the latest operating status of each red-marked elevator controller.

[0114] Preferably, step S3 comprises the following steps:

[0115] Step S31: normalize the red equipment operating status data to obtain standard elevator operating status data;

[0116] Step S32: intelligently identify the operation mode according to the standard elevator operation status data to obtain elevator operation mode characteristic data;

[0117] Step S33: extracting elevator status evaluation indicators according to standard elevator operation status data to generate elevator status evaluation indicator data, wherein the elevator status evaluation indicator data includes elevator operation frequency, elevator failure rate, elevator load rate and elevator downtime;

[0118] Step S34: performing an upgradeable time window analysis based on the elevator state evaluation index data and the elevator operation mode characteristic data to obtain upgradeable time window data;

[0119] Step S35: Optimizing the executable time length according to the upgradeable time window data to generate optimized upgrade time window data;

[0120] Step S36: summing the upgrade window risk coefficients of the optimized upgrade time window data using a preset upgrade risk impact coefficient matrix to obtain upgrade window risk data; and marking the upgradeable time window data with the minimum upgrade window risk as the optimal upgrade time window data according to the upgrade window risk data.

[0121] In an embodiment of the present invention, data processing tools such as Pandas library or MATLAB in Python are used to normalize the red device operation status data. Here, methods such as minimum-maximum normalization or Z-score standardization can be used to scale the values ​​of each indicator to the same scale range. Machine learning algorithms and data mining techniques are used to process these data using cluster analysis or classification algorithms to identify different operation modes of the elevator. For example, the K-means clustering algorithm can be used to divide the operation state of the elevator into different clusters, each cluster representing an operation mode, such as peak hours, non-peak hours or fault states, to obtain elevator operation mode feature data, describing the characteristic performance of the elevator under different operation modes; or, a support vector machine (SVM) can be used to train and classify according to the characteristics of the standard elevator operation state data. Through the trained model, different operation modes of the elevator can be identified. When extracting elevator state evaluation indicators based on standard elevator operation state data, data analysis and statistical methods are used to extract multiple important indicators from the standard elevator operation state data, including elevator operation frequency, elevator failure rate, elevator load rate and elevator downtime. For example, the elevator operation frequency can be calculated by counting the number of elevator starts per day or per week; the elevator failure rate can be estimated based on the ratio of the number of failures to the total operation time; the elevator load rate can be calculated by counting the ratio of the passenger capacity of the elevator during each operation to the rated passenger capacity; the elevator downtime refers to the length of time the elevator stops running due to failure or maintenance. By extracting and calculating these indicators, the elevator status evaluation index data can be obtained to evaluate the operation status and performance of the elevator. The elevator status evaluation index data and the elevator operation mode characteristic data are integrated to obtain comprehensive elevator operation information and identify the time periods when the elevator operation status is relatively stable and suitable for software upgrades. These time periods are upgradeable time windows. For example, if within a certain time period, the elevator operation frequency is low, the failure rate is relatively stable and low, the load rate is small, and the elevator operation mode characteristic data indicates that the elevator is in a non-peak period or non-special working mode, then this time period is an upgradeable time window, and a series of upgradeable time window data can be obtained. According to the duration of the software upgrade, adjust the start and end time of the upgradeable time window to ensure that the upgrade operation can be completed within the predetermined time range. For example, a certain elevator has a suitable upgrade time window between 1am and 4am every day, during which software upgrades can be performed. However, it is estimated that this upgrade operation will take 2 hours to complete. Therefore, when performing window sliding adjustment processing, the upgrade time window needs to be delayed by 1 hour to ensure that the upgrade operation does not exceed the scheduled time range. Create a 7×24 hour matrix, with each cell representing a one-hour time period.Each cell contains a risk factor, and the smaller the value, the higher the risk tolerance. The risk factor can be determined based on factors such as system load, user activity, and the probability of security incidents in different time periods. For example, nighttime and holidays can be set to a lower risk factor, while weekday daytime can be set to a higher risk factor. Match the optimized upgrade time window data with this matrix, and calculate the risk factor of each time window based on the preset impact coefficient. For example, in the preset matrix, the upgrade risk factor for the elevator peak period is higher, while the upgrade risk factor for the off-peak period is lower. Then, when matching the time window of the peak period, the corresponding risk factor will be higher. Next, sum the risk factors of each time window to obtain a comprehensive risk assessment for each time window. Mark the time window with the smallest risk factor as the best upgrade time window data.

[0122] Preferably, step S32 includes the following steps:

[0123] Step S321: performing piecewise linear discretization processing according to the standard elevator operation status data to obtain discrete elevator operation status data;

[0124] Step S322: Acquire historical elevator operation status data; perform spectrum transformation processing on the historical elevator operation status data using a fast Fourier transform algorithm to generate elevator status spectrum matrix data;

[0125] Step S323: Perform frequency component analysis according to the elevator status spectrum matrix data, and screen the main periodic components to generate operation periodic component matrix data;

[0126] Step S324: Calculate component index similarity on the periodic component matrix data to generate periodic component similarity matrix data; perform Laplace eigenvalue decomposition on the periodic component similarity matrix data, and perform periodic feature clustering identification to generate operation cycle mode clustering data;

[0127] Step S325: Calculate the mean of pattern feature parameters according to the operation cycle pattern clustering data to generate pattern feature vector data;

[0128] Step S326: constructing a pattern feature space for the operation cycle pattern clustering data through the pattern feature vector data, and performing space dimensionality reduction processing to obtain a reduced-dimensional pattern feature space;

[0129] Step S327: training a preset support vector machine model for pattern recognition through the dimension reduction pattern feature space to obtain an operation pattern recognition model;

[0130] Step S328: Perform pattern feature matching processing on the discrete elevator operation status data based on the operation mode recognition model to generate elevator operation mode feature data.

[0131] In an embodiment of the present invention, continuous elevator operation status information is obtained from standard elevator operation status data. For example, it includes the hourly passenger flow data, passenger flow load, and data of each elevator stop floor. Then, these continuous status data are divided into multiple discrete intervals. For example, for the hourly passenger flow data, the passenger flow can be divided into three intervals of low, medium, and high according to a certain range; for the passenger flow load, it can be divided into three intervals of light load, medium load, and heavy load according to the carrying capacity of the elevator; for the data of each elevator stop floor, low, medium, and high floors can be discretized according to the floor number. The status data in each interval is discretized and converted into discrete state values. Historical elevator operation status data is collected, such as hourly passenger flow changes, load changes, and the frequency of stop floors. Then, these time domain data are converted into state spectrum data in the frequency domain by a fast Fourier transform algorithm. In this way, the spectrum characteristics of the elevator operation status can be obtained, including the intensity information of different frequency components. The frequency components in the spectrum matrix are analyzed to screen out the main periodic components. For example, analyze the hourly passenger flow data of the elevator to find out the main passenger flow peak cycle and trough cycle; analyze the load change to determine the periodic law of high load and low load; analyze the frequency of the stop floor to find out the common stop floor cycle. By screening out the main periodic components, the main change law of the elevator operation state can be determined. Calculate the similarity between each periodic component, such as calculating the similarity of passenger flow peak and trough, the similarity of load change, the similarity of the stop floor, etc. Then, use the Laplace eigenvalue decomposition algorithm to process these similarity matrix data to identify different periodic modes of elevator operation. For example, through similarity calculation, different operation modes of the elevator on weekdays and weekends, as well as the operation modes during peak hours and non-peak hours can be identified. Extract feature parameters for each operation cycle mode, such as the hourly passenger flow mean, load mean, stop floor mean, etc. Then, calculate the mean of these feature parameters to generate mode feature vector data. For example, for the operation mode during peak hours, the average passenger flow, average load and average number of stop floors can be calculated; for the operation mode during trough hours, the corresponding mean feature vector can also be calculated. The high-dimensional pattern feature vector data is constructed into a pattern feature space. For example, the feature vectors of passenger flow, load, and stop floors are constructed into a high-dimensional space. Then, the high-dimensional space is reduced in dimension using a dimensionality reduction algorithm (such as principal component analysis), and the pattern feature vector is projected into a low-dimensional space to simplify the feature dimension. The support vector machine model is trained using the reduced-dimensional pattern feature vector data. Through training, an elevator operation mode recognition model is constructed to automatically identify the operating status of the elevator in practical applications. For example, the support vector machine model is used to identify the characteristics of the elevator in different operating modes such as peak hours, off-peak hours, weekdays, and weekends.The discretized elevator operation status data is input into the trained support vector machine model for pattern feature matching. For example, the discrete passenger flow data, load data and stop floor data of the current hour are input into the model to match the operation mode characteristics of the current elevator and generate elevator operation mode feature data.

[0132] Preferably, step S34 includes the following steps:

[0133] Step S341: constructing a time series state feature vector according to the elevator operation mode feature data to generate time series mode feature vector data;

[0134] Step S342: performing false neighbor point calculation on the time series pattern feature vector data to generate zero point determination delay time data;

[0135] Step S343: performing vector time delay processing on the time series pattern feature vector data by determining the delay time data at the zero point to generate a state delay vector;

[0136] Step S344: performing feature vector combination on the timing pattern feature vector data and the state delay vector to generate delay embedding matrix data;

[0137] Step S345: performing maximum Lyapunov exponent calculation according to the delayed embedding matrix data to generate Lyapunov exponent data; performing chaos degree quantification processing according to the Lyapunov exponent data to obtain chaos degree data;

[0138] Step S346: performing chaos time series upgrade window prediction on the delay embedding matrix data based on the elevator state evaluation index data and the chaos degree data to obtain prediction time window data;

[0139] Step S347: Perform effective upgrade window analysis on the prediction time window data to obtain upgradeable time window data.

[0140] In an embodiment of the present invention, the operation mode of the elevator in different time periods is extracted from the elevator operation mode feature data, including passenger flow, load rate, and stop frequency. Using these time series data, a time series state feature vector is constructed. For example, the daily data is divided into time periods at the hour level, and the operation mode features of each time period are vectorized. For example, each time period may contain average passenger flow, average load rate, and stop frequency, and these features are combined into a vector to generate time series pattern feature vector data. Using the delayed coordinate embedding method, the time series pattern feature vector data is embedded into a high-dimensional space according to a certain time delay. Then, the distance between each embedded vector and its nearest neighbor vector is calculated to determine which neighboring points are false (i.e., due to noise or other factors). Through this calculation, the zero-point delay time data can be determined, that is, the real delay of the feature vector in time is found. Using the delay time data determined by the zero point, the time series pattern feature vector data is subjected to vector time delay processing. For example, according to the previously determined time delay, the time series pattern feature vector data is displaced. For example, when the determined delay time is 10 minutes, all feature vectors are moved back 10 minutes. In this way, a state delay vector can be generated to make the data more consistent with the actual time series characteristics. The original feature vector of each time period is combined with its corresponding state delay vector into a new feature vector to form a new matrix. Each row of this matrix represents the state of a time period, which contains information about the original features and delay features. The Lyapunov index calculation method is applied to the delay embedding matrix data to analyze the dispersion and divergence of the data in the time series, and the Lyapunov index data of each time period is obtained. Then, according to these index data, the degree of chaos is quantified to determine the degree of chaos of the elevator operation state, for example, to determine whether the elevator operation is stable or chaotic in a certain time period. For indicators such as elevator operation frequency, failure rate, load rate and downtime, appropriate features are selected for modeling. For example, the operation frequency can be expressed as the number of operations per hour, and the failure rate can be expressed as the number of failures per hundred hours. The chaos degree data is fused with the above evaluation indicators to form a complete data set containing time series features and chaos features. The model is constructed using a chaotic time series analysis method, such as a chaotic time series prediction model. The input of the model is the integrated feature data, and the output is the predicted time window data. The model is trained. The training data set may include historical elevator operation data and chaos degree data. The training goal is to predict whether the elevator operation status is stable and whether it is suitable for software upgrade in a certain period of time in the future. The trained model is used to predict future elevator operation data. The elevator status evaluation index data and chaos degree data in the future period of time are input, and the model outputs the predicted time window data.For example, the chaotic time series prediction model is used to predict the operation frequency of an elevator. The result shows that the operation frequency is highest between 8 and 10 a.m. every day, with 20 operations per hour, and lowest between 2 and 4 p.m., with only 5 operations per hour. Combined with the chaos degree data, it is found that the chaos degree index is higher in the morning, indicating that the operation is more chaotic and not suitable for upgrading; while the chaos degree index is lower in the afternoon, indicating that the operation is more stable. Through model prediction, the time window between 2 and 4 p.m. is identified as a time period suitable for software upgrade. Based on the predicted time window data, the feasibility of each time window is further analyzed. For example, check the scheduled use of the elevator in the predicted time window, emergency maintenance arrangements and other factors to ensure that upgrading during these time periods will not affect the normal use of the elevator.

[0141] Preferably, step S4 comprises the following steps:

[0142] Step S41: Performing version upgrade logic grouping processing on the elevator controller to generate controller group configuration data;

[0143] Step S42: formulating software transmission scheduling tasks for controller group configuration data using the optimal upgrade time window data and the controller network topology data to generate software transmission scheduling task data;

[0144] Step S43: Based on the software transmission scheduling task data, the monitoring center server transmits the new version software file to the elevator controller. When the elevator controller receives the new version software file, the elevator controller enters an upgrade preparation state;

[0145] Step S44: When the elevator controller reaches the start time of the optimal upgrade window time, the monitoring center server sends an encrypted upgrade authorization instruction and performs software integrity verification processing to obtain file integrity verification data;

[0146] Step S45: Perform software upgrade operation and restart the software based on the file integrity verification data. The elevator controller sends an upgrade success signal to the monitoring center server and generates software upgrade result data.

[0147] As an example of the present invention, refer to Figure 2 As shown, Figure 1 Detailed implementation steps of step S4 in the embodiment are shown in the flowchart. In this embodiment, step S4 includes:

[0148] Step S41: Performing version upgrade logic grouping processing on the elevator controller to generate controller group configuration data;

[0149] In the embodiment of the present invention, for example, there are three types of elevator controllers A, B, and C in a certain building. Model A is installed on the upper floors, model B is installed on the middle floors, and model C is installed on the lower floors. The collected data information includes 10 controllers of model A, 15 controllers of model B, and 5 controllers of model C. Controllers of the same model and installed in the same position are preliminarily grouped. For example, model A controllers on the upper floors are grouped into one group, model B controllers on the middle floors are grouped into one group, and model C controllers on the lower floors are grouped into one group. Among the 10 model A controllers on the upper floors, 5 are used more frequently (more than 100 times a day), and the remaining 5 are used less frequently (less than 50 times a day). The 5 controllers with higher frequency of use are grouped into one group, and the 5 controllers with lower frequency of use are grouped into another group. Among the 15 model B controllers on the middle floors, 3 have more fault history records (more than 5 faults per month), and the remaining 12 have fewer fault records (less than 2 faults per month). The 3 controllers with more fault history records are grouped into one group, and the 12 controllers with fewer fault records are grouped into another group.

[0150] Step S42: formulating software transmission scheduling tasks for controller group configuration data using the optimal upgrade time window data and the controller network topology data to generate software transmission scheduling task data;

[0151] In an embodiment of the present invention, the time periods available for upgrading of each controller are obtained through the optimal upgrade time window database, and the controllers are grouped using a graph theory algorithm based on these time periods combined with the controller network topology data, so that the controllers in the same group can complete the software upgrade in the same time period, and the upgrade time periods of controllers in different groups are staggered. For each group, the shortest time required to complete the upgrade of the group is calculated based on factors such as the network bandwidth and controller performance of the controllers, and the software transmission order of the group is determined. This process is repeated for all groups, and finally the software transmission scheduling task data of the entire system is generated, including information such as the upgrade time period, upgrade order, and expected completion time of each group.

[0152] Step S43: Based on the software transmission scheduling task data, the monitoring center server transmits the new version software file to the elevator controller. When the elevator controller receives the new version software file, the elevator controller enters an upgrade preparation state;

[0153] In an embodiment of the present invention, the monitoring center server obtains the first batch of elevator controllers that need to be upgraded and the corresponding software transmission order. Then, the monitoring center server uses an encrypted channel such as HTTPS or VPN to establish a secure connection with the relevant elevator controllers in accordance with the order of determining the software transmission scheduling task, and uses a reliable file transfer protocol such as breakpoint resume to transmit the new version of the software file. While receiving the new version of the software file, the elevator controller uses a hash algorithm (such as SHA-256) to perform an integrity check on the software file, and replies with a confirmation message after the check passes. Subsequently, the elevator controller enters an upgrade preparation state, in which the elevator can still operate normally, but suspends accepting new passenger calls, waits for the current operation to complete, and prohibits updating the control logic. After receiving the confirmation messages from all elevator controllers, the monitoring center server continues to transmit the software files of the next batch of elevator controllers according to the scheduled data until the software files of all controllers are transmitted. At the same time, the monitoring center monitors the status of each controller through a heartbeat packet mechanism to ensure that it enters and maintains an upgrade ready state.

[0154] Step S44: When the elevator controller reaches the start time of the optimal upgrade window time, the monitoring center server sends an encrypted upgrade authorization instruction and performs software integrity verification processing to obtain file integrity verification data;

[0155] In the embodiment of the present invention, based on the previously set optimal upgrade time window, when the preset upgrade start time is reached, the monitoring center server generates an encrypted upgrade authorization instruction using an encrypted communication protocol and a pre-shared key and sends it to the elevator controller. After receiving the encrypted upgrade authorization instruction, the elevator controller uses the same encryption algorithm and key to decrypt the authorization instruction to obtain the plain text authorization instruction content. After decryption, the elevator controller verifies the integrity of the locally stored software to be upgraded according to the integrity check value contained in the authorization instruction. The integrity verification can use an encrypted hash algorithm such as SHA-256, MD5, etc. to calculate the hash value of the software file and compare it with the check value in the authorization instruction. If the verification passes, it means that the local software file has not been tampered with, and the integrity of the software file can be preliminarily trusted. The elevator controller returns the verification result as file integrity verification data to the monitoring center server and waits for subsequent upgrade instructions.

[0156] Step S45: Perform software upgrade operation and restart the software based on the file integrity verification data. The elevator controller sends an upgrade success signal to the monitoring center server and generates software upgrade result data.

[0157] In the embodiment of the present invention, once the file integrity verification data is verified, the software upgrade module will automatically perform the software upgrade operation to replace or overwrite the original software version. After the software upgrade is completed, the operating system of the elevator controller will automatically restart to load and run the newly upgraded software version. During the restart process, the hardware and software resources of the elevator controller will be correctly initialized and configured. When the new version of the software is successfully started and runs, the elevator controller will send a status signal of successful upgrade to the monitoring center server through the network communication module.

[0158] Preferably, step S42 includes the following steps:

[0159] Step S421: performing an upgrade transmission topology structure analysis on the controller group configuration data through the controller network topology structure data to obtain upgrade transmission topology structure data;

[0160] Step S422: performing controller node routing analysis according to the upgraded transmission topology data to generate controller node routing data, wherein the controller node routing data includes neighboring node information, controller node ID, network IP address, and network MAC address;

[0161] Step S423: constructing a binary tree overlay network for the controller node routing data based on the upgraded transmission topology structure data to generate binary tree overlay network topology data;

[0162] Step S424: performing a step-by-step transmission query of the target node based on the binary tree-like overlapping network topology data, and generating a peer transmission line to obtain upgraded transmission line data;

[0163] Step S425: Calculate the point-to-point transmission duration of the upgrade transmission line data using the preset software transmission time data to obtain the transmission line duration data;

[0164] Step S426: Use the optimal upgrade window time to perform timeout judgment on the transmission line duration data. When the transmission line duration data exceeds the optimal upgrade time, the upgraded transmission line data is corrected to obtain the corrected transmission line data; when the transmission line duration data does not exceed the optimal upgrade time, the upgraded transmission line data is marked as the target transmission line data.

[0165] Step S427: Formulate node scheduling tasks according to the modified transmission line data and the target transmission line data to generate software transmission scheduling task data;

[0166] In an embodiment of the present invention, the controller group configuration data is upgraded and the transmission topology structure is analyzed through the controller network topology structure data, and the elevator controller node topology structure that needs to be upgraded is extracted. The controller node is routed and analyzed by means of a routing control protocol (such as OSPF, BGP, etc.) to generate controller node routing data. The controller node routing data includes neighboring node information, node ID, IP address, MAC address, etc. of each node. A binary tree overlapping network is constructed for the controller node routing data based on the upgraded transmission topology structure data of step S421 using a tree topology generation algorithm (such as Prim algorithm, etc.). When constructing, the geographical location of the controller node, network load, and other factors are considered to make the network topology reasonable, redundant, and efficient. With the help of a routing search algorithm (such as Dijkstra algorithm, etc.), the best transmission path from the software server to each controller node is gradually found. At the same time, a peer-to-peer network transmission protocol (such as BitTorrent, etc.) is used to generate a peer-to-peer transmission connection for the transmission line between each two nodes, and finally complete upgrade transmission line data is obtained. Combined with the software size, network protocol characteristics, etc., the theoretical transmission time of each transmission line is calculated to generate transmission line time data. Read the optimal upgrade window time, that is, the longest upgrade transmission time allowed. Use this time to compare the calculated transmission line duration data. For transmission lines whose duration exceeds the optimal window, re-call the routing search algorithm, correct the transmission path of the line, and obtain the corrected transmission line data; for lines whose duration does not exceed the window, directly mark them as target transmission line data. Based on the workflow definition engine of the scheduling system, combine node hardware specifications, software version and other information to formulate software transmission scheduling tasks, including sending nodes, receiving nodes, transmission time windows, etc., and finally generate software transmission scheduling task data.

[0167] The present invention also provides a software remote upgrade system for an elevator controller, which executes the software remote upgrade method for an elevator controller as described above. The software remote upgrade system for an elevator controller comprises:

[0168] The topology authentication module is used to use the monitoring center server to perform network topology analysis on the elevator controller to obtain the network topology data of the controller; perform endpoint authentication processing according to the network topology data of the controller to obtain security verification token data; perform software upgrade request processing according to the security verification token data to obtain upgrade request feedback data;

[0169] The upgrade status analysis module is used to construct the controller status matrix according to the upgrade request feedback data to obtain the controller status matrix data; perform state differentiation processing according to the controller status matrix data, and bind the elevator operation status data to generate the red device operation status data;

[0170] The intelligent upgrade window calculation module is used to perform intelligent identification of the operation mode according to the operation status data of the red equipment to obtain the characteristic data of the elevator operation mode; perform upgradeable time window analysis according to the characteristic data of the elevator operation mode to obtain upgradeable time window data; perform upgrade window optimization processing on the upgradeable time window data to generate the best upgrade time window data;

[0171] The software transmission upgrade module is used to formulate software transmission scheduling tasks for the elevator controller based on the optimal upgrade time window data and the controller network topology data, and generate software transmission scheduling task data; perform software upgrade operations based on the software transmission scheduling task data, and generate software upgrade result data.

[0172] The beneficial effect of this application is that, through a comprehensive understanding of the elevator controller network and endpoint authentication processing, the location, connection relationship and identity of the controller can be accurately grasped, the system security can be improved, unauthorized devices can be prevented from accessing the network, and potential network attacks can be avoided. Verification and processing of upgrade requests can screen and confirm the controllers that need to be upgraded, avoid unnecessary upgrade operations, and improve the efficiency of system resource utilization. Constructing a controller state matrix can comprehensively monitor the operating status of each controller. Differentiated processing based on the state matrix data can timely identify controllers with abnormal operation or needing attention, and carry out targeted maintenance and processing to improve the overall operation reliability of the elevator system. By intelligently identifying the operation mode and usage of the elevator, combined with the analysis of the upgradeable time window, it is possible to find a relatively idle period for the elevator to upgrade, avoid upgrading during peak or busy periods, and reduce the impact on normal operation. Optimizing the upgrade time window can further refine and optimize the upgrade schedule to ensure that it is upgraded in the most appropriate period. Formulating transmission scheduling tasks based on the optimal time window and network topology data can ensure that the software upgrade process is efficient and smooth, reducing delays and errors. Accurately executing scheduling tasks for upgrading can improve the success rate and accuracy of the upgrade and ensure the efficient and reliable operation of the elevator system after the upgrade.

[0173] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0174] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may 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 the embodiments shown herein, but should conform 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: using the monitoring center server to perform network topology analysis on the elevator controller to obtain the network topology data of the controller; performing endpoint authentication processing according to the network topology data of the controller to obtain security verification token data; performing software upgrade request processing according to the security verification token data to obtain upgrade request feedback data; Step S2: constructing a controller state matrix according to the upgrade request feedback data to obtain controller state matrix data; Perform state differentiation processing according to the controller state matrix data, and bind the elevator operation state data to generate red device operation state data; wherein step S2 includes: Step S21: extracting the ID identifier of the elevator controller to obtain the elevator controller ID data; Step S22: Performing upgrade status evaluation and quantitative processing according to the upgrade request feedback data, and constructing a controller state matrix using the elevator controller ID data to obtain controller state matrix data; Step S23: Mark the state color according to the controller state matrix data to obtain controller color marking data, wherein the version synchronized elevator controller is marked in green, the elevator controller to be synchronized is marked in red, and the elevator controller being upgraded is marked in yellow; Step S24: Monitor the upgrade progress of the yellow-marked controller. When the software upgrade is successful, mark the yellow-marked controller as green; when the software upgrade fails, mark the yellow-marked controller as red; do not upgrade the green-marked controller, and monitor the controller status regularly; Step S25: using the device version synchronization status data in the upgrade request feedback data to bind the elevator operation status data to the red marked controller, and generate red device operation status data; Step S3: intelligently identify the operation mode according to the red device operation status data to obtain elevator operation mode characteristic data; perform upgradeable time window analysis according to the elevator operation mode characteristic data to obtain upgradeable time window data; perform upgrade window optimization processing on the upgradeable time window data to generate optimal upgrade time window data; wherein, step S3 includes: Step S31: normalize the red equipment operation status data to obtain standard elevator operation status data; Step S32: intelligently identify the operation mode according to the standard elevator operation status data to obtain elevator operation mode characteristic data; Step S33: extracting elevator status evaluation indicators according to standard elevator operation status data to generate elevator status evaluation indicator data, wherein the elevator status evaluation indicator data includes elevator operation frequency, elevator failure rate, elevator load rate and elevator downtime; Step S34: performing an upgradeable time window analysis based on the elevator state evaluation index data and the elevator operation mode characteristic data to obtain upgradeable time window data; Step S35: Optimizing the executable time length according to the upgradeable time window data to generate optimized upgrade time window data; Step S36: summing the upgrade window risk coefficients of the optimized upgrade time window data using a preset upgrade risk impact coefficient matrix to obtain upgrade window risk data; marking the upgradeable time window data with the minimum upgrade window risk as the optimal upgrade time window data according to the upgrade window risk data; Step S4: Formulate a software transmission scheduling task for the elevator controller based on the optimal upgrade time window data and the controller network topology data to generate software transmission scheduling task data; perform a software upgrade operation based on the software transmission scheduling task data to generate software upgrade result data.

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: Use the monitoring center server to scan the device port of the elevator controller to generate device port list data; perform IP address allocation query according to the device port list data to obtain an IP address allocation table; Step S12: Perform network protocol identification according to the IP address allocation table and the device port list data, and perform network topology analysis to obtain controller network topology data; Step S13: Analyze the communication link status of the elevator controller through the controller network topology data, and calculate the path weight to obtain communication path weight data; evaluate the comprehensive communication index according to the communication path weight data to obtain the optimal communication path data; Step S14: performing endpoint authentication processing on the elevator controller through the optimal communication path data to obtain security verification token data; Step S15: encrypt the security verification token data and the preset software upgrade request instruction using the SHA-256 algorithm to obtain remote upgrade request package data, wherein the remote upgrade request package data includes the software upgrade request instruction, the error request verification code, the request timestamp and the security verification token data; Step S16: The monitoring center server distributes the remote upgrade request package data to the elevator controller for upgrading request parsing, and performs analysis result feedback processing on the monitoring center server to obtain upgrading request feedback data.

3. The software remote upgrade method for an elevator controller according to claim 2, characterized in that: Upgrade request feedback data, step S16 includes the following steps: Step S161: the elevator controller performs error request verification code analysis on the remote upgrade request packet data to obtain the analyzed original verification code data; Step S162: Performing a data packet integrity check based on the parsed original verification code data to obtain integrity verification data; Step S163: verifying the request validity on the request timestamp based on the integrity check data to obtain request validity verification data; Step S164: parsing the software upgrade request instruction through the request validity verification data to generate parsed request instruction data, wherein the parsed request instruction data includes version upgrade comparison instruction data and real-time status collection instruction data; Step S165: using the security verification token data to perform authority verification processing on the parsing request instruction data to obtain request authority verification data; Step S166: performing version number difference analysis based on the requested authority verification data and version upgrade comparison instruction data, and performing upgrade necessity evaluation to obtain upgrade necessity evaluation data; Step S167: judging the necessity of upgrading according to the upgrade necessity evaluation data; when the current software version is equal to the upgrade software version, marking the elevator controller as a version synchronization elevator controller, generating device version synchronization status data; when the current software version is lower than the upgrade software version, marking the elevator controller as an elevator controller to be synchronized, using the real-time status acquisition instruction data to collect the elevator operation status of the elevator controller, and obtaining periodic elevator operation status data; Step S168: Integrate the operation status data of the periodic elevator operation status data and the elevator controller to be synchronized, and generate the device version synchronization status data; Step S169: The device version synchronization status data and the device version synchronization status data are transmitted to the monitoring center server for request feedback processing to obtain upgrade request feedback data.

4. The software remote upgrade method for an elevator controller according to claim 1, characterized in that: Step S32 includes the following steps: Step S321: performing piecewise linear discretization processing according to the standard elevator operation status data to obtain discrete elevator operation status data; Step S322: Acquire historical elevator operation status data; perform spectrum transformation processing on the historical elevator operation status data using a fast Fourier transform algorithm to generate elevator status spectrum matrix data; Step S323: Perform frequency component analysis according to the elevator status spectrum matrix data, and screen the main periodic components to generate operation periodic component matrix data; Step S324: Calculate component index similarity on the periodic component matrix data to generate periodic component similarity matrix data; perform Laplace eigenvalue decomposition on the periodic component similarity matrix data, and perform periodic feature clustering identification to generate operation cycle mode clustering data; Step S325: Calculate the mean of pattern feature parameters according to the operation cycle pattern clustering data to generate pattern feature vector data; Step S326: constructing a pattern feature space for the operation cycle pattern clustering data through the pattern feature vector data, and performing space dimensionality reduction processing to obtain a reduced-dimensional pattern feature space; Step S327: training a preset support vector machine model for pattern recognition through the dimension reduction pattern feature space to obtain an operation pattern recognition model; Step S328: Perform pattern feature matching processing on the discrete elevator operation status data based on the operation mode recognition model to generate elevator operation mode feature data.

5. The software remote upgrade method for an elevator controller according to claim 1, characterized in that: Step S34 includes the following steps: Step S341: constructing a time series state feature vector according to the elevator operation mode feature data to generate time series mode feature vector data; Step S342: performing false neighbor point calculation on the time series pattern feature vector data to generate zero point determination delay time data; Step S343: performing vector time delay processing on the time series pattern feature vector data by determining the delay time data at the zero point to generate a state delay vector; Step S344: performing feature vector combination on the timing pattern feature vector data and the state delay vector to generate delay embedding matrix data; Step S345: performing maximum Lyapunov exponent calculation according to the delayed embedding matrix data to generate Lyapunov exponent data; performing chaos degree quantification processing according to the Lyapunov exponent data to obtain chaos degree data; Step S346: performing chaos time series upgrade window prediction on the delay embedding matrix data based on the elevator state evaluation index data and the chaos degree data to obtain prediction time window data; Step S347: Perform effective upgrade window analysis on the prediction time window data to obtain upgradeable time window data.

6. The software remote upgrade method for an elevator controller according to claim 1, characterized in that: Step S4 includes the following steps: Step S41: Performing version upgrade logic grouping processing on the elevator controller to generate controller group configuration data; Step S42: formulating software transmission scheduling tasks for controller group configuration data using the optimal upgrade time window data and the controller network topology data to generate software transmission scheduling task data; Step S43: Based on the software transmission scheduling task data, the monitoring center server transmits the new version software file to the elevator controller. When the elevator controller receives the new version software file, the elevator controller enters an upgrade preparation state; Step S44: When the elevator controller reaches the start time of the optimal upgrade window time, the monitoring center server sends an encrypted upgrade authorization instruction and performs software integrity verification processing to obtain file integrity verification data; Step S45: Perform software upgrade operation and restart the software based on the file integrity verification data. The elevator controller sends an upgrade success signal to the monitoring center server and generates software upgrade result data.

7. The software remote upgrade method for an elevator controller according to claim 6, characterized in that: Step S42 includes the following steps: Step S421: performing an upgrade transmission topology structure analysis on the controller group configuration data through the controller network topology structure data to obtain upgrade transmission topology structure data; Step S422: performing controller node routing analysis according to the upgraded transmission topology data to generate controller node routing data, wherein the controller node routing data includes neighboring node information, controller node ID, network IP address, and network MAC address; Step S423: constructing a binary tree overlay network for the controller node routing data based on the upgraded transmission topology structure data to generate binary tree overlay network topology data; Step S424: performing a step-by-step transmission query of the target node based on the binary tree-like overlapping network topology data, and generating a peer transmission line to obtain upgraded transmission line data; Step S425: Calculate the point-to-point transmission duration of the upgrade transmission line data using the preset software transmission time data to obtain the transmission line duration data; Step S426: using the optimal upgrade window time to perform timeout judgment on the transmission line duration data, when the transmission line duration data exceeds the optimal upgrade time, the upgraded transmission line data is corrected to obtain the corrected transmission line data; when the transmission line duration data does not exceed the optimal upgrade time, the upgraded transmission line data is marked as the target transmission line data; Step S427: Formulate node scheduling tasks according to the modified transmission line data and the target transmission line data, and generate software transmission scheduling task data.

8. 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 topology authentication module is used to use the monitoring center server to perform network topology analysis on the elevator controller to obtain the network topology data of the controller; perform endpoint authentication processing according to the network topology data of the controller to obtain security verification token data; perform software upgrade request processing according to the security verification token data to obtain upgrade request feedback data; The upgrade status analysis module is used to construct the controller state matrix according to the upgrade request feedback data to obtain the controller state matrix data; perform state differentiation processing according to the controller state matrix data, and bind the elevator operation state data to generate the red device operation state data; the upgrade status analysis module is used to: Step S21: extracting the ID identifier of the elevator controller to obtain the elevator controller ID data; Step S22: Performing upgrade status evaluation and quantitative processing according to the upgrade request feedback data, and constructing a controller state matrix using the elevator controller ID data to obtain controller state matrix data; Step S23: Mark the state color according to the controller state matrix data to obtain controller color marking data, wherein the version synchronized elevator controller is marked in green, the elevator controller to be synchronized is marked in red, and the elevator controller being upgraded is marked in yellow; Step S24: Monitor the upgrade progress of the yellow-marked controller. When the software upgrade is successful, mark the yellow-marked controller as green; when the software upgrade fails, mark the yellow-marked controller as red; do not upgrade the green-marked controller, and monitor the controller status regularly; Step S25: using the device version synchronization status data in the upgrade request feedback data to bind the elevator operation status data to the red marked controller, and generate red device operation status data; The intelligent upgrade window calculation module is used to perform intelligent identification of the operation mode according to the red equipment operation status data to obtain the elevator operation mode characteristic data; perform upgradeable time window analysis according to the elevator operation mode characteristic data to obtain upgradeable time window data; perform upgrade window optimization processing on the upgradeable time window data to generate the best upgrade time window data; the intelligent upgrade window calculation module is used to: Step S31: normalize the red equipment operation status data to obtain standard elevator operation status data; Step S32: intelligently identify the operation mode according to the standard elevator operation status data to obtain elevator operation mode characteristic data; Step S33: extracting elevator status evaluation indicators according to standard elevator operation status data to generate elevator status evaluation indicator data, wherein the elevator status evaluation indicator data includes elevator operation frequency, elevator failure rate, elevator load rate and elevator downtime; Step S34: performing an upgradeable time window analysis based on the elevator state evaluation index data and the elevator operation mode characteristic data to obtain upgradeable time window data; Step S35: Optimizing the executable time length according to the upgradeable time window data to generate optimized upgrade time window data; Step S36: summing the upgrade window risk coefficients of the optimized upgrade time window data using a preset upgrade risk impact coefficient matrix to obtain upgrade window risk data; marking the upgradeable time window data with the minimum upgrade window risk as the optimal upgrade time window data according to the upgrade window risk data; The software transmission upgrade module is used to formulate software transmission scheduling tasks for the elevator controller based on the optimal upgrade time window data and the controller network topology data, and generate software transmission scheduling task data; perform software upgrade operations based on the software transmission scheduling task data, and generate software upgrade result data.

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