Server customization configuration information automatic recovery method based on self-developed MES

Abstract

The application discloses a server customization configuration information automatic recovery method based on self-developed MES, comprising the following steps: constructing a configuration recovery system deeply integrated with self-developed MES, the system containing at least one of a production order feature mapping module, a digital thread tracing module, an equipment health degree prediction module, a configuration unit repair module and a hierarchical image recovery module, each module executing configuration adaptation logic based on MES production data linkage, the application improves recovery efficiency and accuracy, with the help of a multi-module collaborative mechanism linked with self-developed MES, configuration data acquisition, matching and recovery operations can be completed without manual intervention, avoiding the low efficiency problem caused by traditional manual searching and checking. Through the production order feature mapping module, the digital thread tracing module and other modules, accurate adaptation of configuration and production scene is realized, the configuration error rate caused by manual judgment errors is significantly reduced, and it is ensured that the recovered configuration is highly matched with current production requirements.

Description

Technical Field

[0001] This invention belongs to the field of industrial automation technology, and in particular relates to an automated method for restoring customized server configuration information based on a self-developed MES. Background Technology

[0002] In industrial production, servers serve as the core carrier of MES (Manufacturing Execution System), and their customized configuration information (such as equipment communication parameters, production data acquisition rules, and process interface protocols) directly impacts the stability of the production process. Current technologies often employ a pattern of scheduled full backups and manual triggering for server configuration recovery, using periodic configuration snapshots and backup file libraries to preserve data. Simultaneously, some scenarios introduce basic monitoring systems to detect anomalies such as corrupted configuration files and failed parameter verifications, notifying maintenance personnel to intervene via alarms. This approach has been widely used in single production lines and fixed process scenarios, relying on human experience to complete configuration matching and recovery operations, thus supporting the basic continuity of industrial production.

[0003] With industrial development, production lines are becoming increasingly flexible, characterized by diverse product types and small batches. Customized server configurations require frequent adaptation to different work orders, equipment statuses, and production rhythms. Existing technologies, lacking deep integration with MES systems, struggle to achieve real-time correlation and dynamic adaptation of configuration data, resulting in low recovery efficiency and high manual intervention costs. Furthermore, traditional scheduled backups are prone to losing critical node configurations, full recovery modes consume significant resources, and cannot accurately repair localized anomalies, failing to meet the demands of high continuity and high adaptability in production. Therefore, an automated recovery solution deeply integrated with MES is urgently needed to address these issues. Summary of the Invention

[0004] To overcome the above-mentioned defects of the prior art, embodiments of the present invention provide an automated recovery method for server customized configuration information based on a self-developed MES, which solves the problems of low recovery efficiency, reliance on manual labor, poor adaptability, and lack of deep linkage with MES in the prior art.

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

[0006] The automated recovery method for customized server configuration information based on a self-developed MES includes the following steps:

[0007] (1) Construct a configuration recovery system that is deeply integrated with the self-developed MES. The system includes at least one of the following: production work order feature mapping module, digital thread tracing module, equipment health prediction module, configuration unit repair module and layered image recovery module. Each module executes configuration adaptation logic based on MES production data. The adaptation logic includes dynamically adjusting server resource allocation parameters according to the real-time production rhythm of MES and optimizing interface communication frequency based on equipment load threshold.

[0008] (2) When the server's customized configuration needs to be restored, the configuration monitoring module of MES will detect configuration anomalies in real time, including parameter verification failure and communication timeout, and trigger the corresponding module in the system to obtain configuration-related data. The related data includes at least one of production work order characteristics, product full life cycle configuration trajectory, equipment health data, configuration unit information and hierarchical image data, and all the related data come from the real-time database and historical record system of the self-developed MES.

[0009] (3) Based on the associated data, perform automated recovery operations to generate server-customized configurations adapted to the current production environment. The recovery operation calls the device control interface and data verification interface of the self-developed MES for linkage verification throughout the process. After the verification is passed, it is synchronized to the server operating environment.

[0010] Preferably, the operation steps of the production work order feature mapping module include: constructing a mapping model between production work order features and server configuration, wherein the mapping model uses historical work order successful configuration data recorded by the self-developed MES as samples, and learns the server configuration parameters corresponding to different work order features through data analysis; when the MES configuration monitoring module detects server configuration abnormalities, it obtains the features of the currently executing or about to be executed work orders through the MES work order management module; based on the mapping model and the obtained work order features, it matches and restores the most suitable customized server configuration scheme, and calls the MES process parameter library in real time for rule verification during the matching process.

[0011] Preferably, the work order features include process requirements, equipment combinations, and production capacity targets, and the server configuration parameters include data processing priority and equipment interface protocol, wherein the equipment interface protocol matches the MES equipment communication standard, and the data processing priority is dynamically adjusted according to the urgency of the work orders pushed by the MES in real time.

[0012] Preferably, the operation steps of the digital thread tracing module include: using the digital thread of the MES system to record the change trajectory of the server configuration at each stage of the product lifecycle, the product lifecycle including product design, process planning, production execution and quality inspection stages, configuration changes at each stage are identified by the version control module and synchronized to the configuration change log of the MES; when a configuration problem occurs and needs to be restored, the configuration status of the target stage is traced along the digital thread, and the production batch information of the MES is associated during the tracing process; the server configuration is restored to the configuration status of the target stage, and the change record system is called through the digital thread to view the reasons for the configuration changes and the operators in subsequent stages.

[0013] Preferably, after restoring the server configuration to the target stage configuration state, the MES production simulation interface is called to simulate the production process under this configuration state. The simulation data includes equipment response time and data acquisition accuracy to verify the effectiveness of the configuration restoration. If the verification fails, the system calculates the deviation value, and the target stage is traced and adjusted again through the digital thread based on the deviation value until the simulation verification passes.

[0014] Preferably, the operation steps of the equipment health prediction module include: real-time collection of production equipment operation data, including temperature, vibration frequency, and response delay, through the equipment monitoring module of the MES; constructing an equipment health prediction model based on the data; using the model to predict the time points when the equipment may fail or experience performance degradation at a preset cycle of 1-5 minutes / time; and performing pre-recovery of the customized configuration of the associated server 30-60 minutes before the equipment health drops to the warning threshold. The pre-recovery includes restoring the configuration related to the equipment in the server to the historical stable state recorded by the MES, and reserving buffer parameters that match the current performance of the equipment, including increasing the communication timeout threshold by 10%-20%.

[0015] Preferably, the device-related configuration includes configurations related to device communication, and the communication configuration conforms to the device interaction protocol specification of MES. After the pre-recovery operation is triggered, pre-recovery logs and device health trend data are pushed to the MES operation and maintenance terminal.

[0016] Preferably, the operation steps of the configuration unit repair module include: decomposing the customized configuration of the server into configuration units, wherein each configuration unit is a minimum set of configuration parameters for implementing a specific function, including communication port parameters of a single device and data encryption algorithm parameters; constructing a configuration unit library containing hardware driver parameters, software module interaction rules, and data transmission formats; synchronizing the data in the library with the configuration template library of the MES in real time; when a configuration is damaged or abnormal, analyzing the faulty configuration, extracting feature values ​​from the configuration operation log based on the MES log database, including error code frequency and parameter fluctuation range, and locating the abnormal configuration unit; replacing the abnormal configuration unit with a normal, same-source configuration unit retrieved from the configuration unit library; and fine-tuning the repaired configuration in conjunction with real-time production environment parameters collected by the MES, including workshop temperature and humidity and equipment load rate.

[0017] Preferably, when analyzing fault configurations, a multi-dimensional comparison method is used to compare the current configuration operation log with the historical normal logs recorded by the MES item by item. When the deviation value exceeds the preset threshold of 5%-15%, it is marked as an abnormal configuration unit, and an abnormal source tracing path is generated, including the associated configuration items and the scope of affected devices.

[0018] Preferably, the operation steps of the layered image recovery module include: using image splitting technology to divide the server configuration into different levels: basic configuration, customized business configuration, and temporary emergency configuration; creating independent image storage areas for different configuration levels; and controlling the read and write permissions of each area through the MES security management module; performing a full backup of the basic configuration image every day at midnight; performing real-time incremental backup of the customized business configuration image based on configuration changes; and performing on-demand backup of the temporary emergency configuration image when a work order is initiated. All backup operations are triggered by the MES backup scheduling module and a backup verification code is generated. When a configuration is corrupted, the MES configuration diagnosis module determines the level of configuration corruption and calls the corresponding level of image for recovery. During the recovery process, the integrity of the image is verified through the verification code. When only the temporary emergency configuration has a problem, only the temporary emergency configuration image is retrieved for recovery, and the recovered image is synchronized to the MES emergency configuration ledger.

[0019] The technical effects and advantages of this invention's automated recovery method for server-customized configuration information based on a self-developed MES are as follows:

[0020] 1. This invention improves recovery efficiency and accuracy. Leveraging a multi-module collaborative mechanism linked to a self-developed MES system, configuration data acquisition, matching, and recovery operations can be completed without manual intervention, avoiding the inefficiencies caused by traditional manual searching and verification. Through modules such as production work order feature mapping and digital thread tracing, it achieves precise adaptation between configuration and production scenarios, significantly reducing the configuration error rate caused by human judgment errors and ensuring that the recovered configuration is highly matched with current production needs.

[0021] 2. This invention ensures production continuity. Based on the pre-recovery mechanism of the equipment health prediction module, it can adapt the server configuration in advance before the equipment has a risk of failure, avoiding production interruptions caused by abnormal configuration. The configuration unit repair and layered image recovery modules support partial configuration repair, which can solve the problem without full recovery, reduce interference with normal production processes, and maximize the continuous operation of the production line.

[0022] 3. This invention enhances data integrity and traceability. The digital thread tracing module records the server configuration changes throughout the product lifecycle. Combined with the MES version control and change log system, it ensures that configuration data is traceable and uninterrupted, solving the problem of losing intermediate versions due to traditional scheduled backups. Simultaneously, the blockchain-style change log provides reliable data support for quality analysis and problem tracing, facilitating the rapid identification of production anomalies caused by configuration changes.

[0023] 4. This invention optimizes resource utilization and adaptability. The layered image recovery module adopts a differentiated backup strategy based on configuration level differences, reducing resource consumption caused by full backups. The configuration unit repair module only repairs abnormal parts, significantly reducing data transmission volume and saving bandwidth resources. Through the adaptation logic of MES real-time production data linkage, configuration recovery can dynamically respond to changes in production rhythm, equipment load, etc., improving the flexibility of configuration and production environment adaptation.

[0024] 5. This invention reduces operation and maintenance costs and technical barriers. The fully automated recovery process reduces reliance on professional operation and maintenance personnel, lowers manual operation costs and technical barriers. Mechanisms such as pre-recovery and automatic verification reduce the processing time for sudden failures, reduce production losses caused by configuration anomalies, and significantly improve the operation and maintenance economy and stability of industrial production. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the system flow of the automated recovery method for customized server configuration information based on self-developed MES proposed in this invention. Detailed Implementation

[0026] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0027] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0028] refer to Figure 1 The method described in this invention achieves automated and precise recovery of customized server configurations by constructing a configuration recovery system deeply integrated with a self-developed MES. It is applicable to industrial scenarios in multiple fields such as automobile manufacturing, electronic assembly, and steel production, and solves the problems of low efficiency, poor adaptability, and reliance on manual labor in traditional recovery methods.

[0029] Example 1

[0030] This embodiment provides an automated recovery method for customized server configuration information based on a self-developed MES, used for automated recovery based on the production work order feature mapping module. Specific implementation details include:

[0031] Application scenario: An engine block processing line in an automotive parts factory supports flexible production of multiple work orders (2.0T cylinder block, 3.0L cylinder block). The server needs to dynamically adjust configuration parameters such as data acquisition frequency and equipment communication protocol according to the characteristics of the work order.

[0032] Specific steps:

[0033] (1) Construct a configuration recovery system. Construct a configuration recovery system that includes a production work order feature mapping module. The module is deeply integrated with the work order management module and process parameter library of the self-developed MES. The core adaptation logic of the system is: dynamically adjust the server CPU resource allocation threshold (set to 70%) according to the real-time production rhythm of MES (e.g., 2 minutes / piece for 2.0T cylinder block work order), and optimize the interface communication frequency (50 times / second) based on the equipment load threshold (80% maximum load for grinding machine A) to ensure that the configuration matches the production rhythm.

[0034] (2) Obtaining related data: Due to a sudden power outage, the server configuration file was corrupted. The MES configuration monitoring module detected "Device communication protocol verification failed" (abnormal type) within 10 seconds and automatically triggered the production work order feature mapping module. The module obtains the current work order features (related data) through the MES work order management module: the process requirement is high-precision machining (tolerance ±0.02mm), the equipment combination is grinding machine A + boring machine B, and the production capacity target is 500 pieces / day. All data comes from the MES real-time database.

[0035] (3) Perform the recovery operation and verification. The module calls the pre-built mapping model (trained based on historical successful data from 500 batches of 2.0T work orders and 300 batches of 3.0L work orders) to match the appropriate configuration: data acquisition frequency of 100ms / time (adapting to high-precision processing requirements), and the device interface protocol adopts the MES custom "V2.1 Industrial Bus Protocol" (matching the communication standard of grinding machine A). The recovery operation writes the configuration to the server register through the MES device control interface, and performs 3 communication tests (100% success rate) by calling the data verification interface throughout the process. After successful verification, it is synchronized to the server running environment.

[0036] Implementation results:

[0037] Recovery time: 15 seconds (traditional manual search of historical configuration files takes an average of 20 minutes), improving efficiency by 80 times;

[0038] Configuration accuracy: 100% (the configuration error rate due to confusion of work order models in traditional manual configuration is about 8%);

[0039] Production impact: The production line did not stop during the recovery period, avoiding a production stoppage of more than 2 hours due to abnormal configuration (calculated based on single-shift capacity loss, the loss can be reduced by about 20,000 yuan).

[0040] Example 2

[0041] This embodiment provides an automated recovery method for customized server configuration information based on a self-developed MES, used for automated recovery based on a digital thread tracing module. Specific implementation details include:

[0042] Application scenario: A smartphone motherboard production line in an electronics assembly plant needs to trace the production configuration of batch P20230512 to analyze quality anomalies (motherboard soldering defect rate exceeds 0.5%). This batch had reached its optimal production state during the debugging phase.

[0043] Specific steps:

[0044] (1) Construct a configuration recovery system, which includes a digital thread tracing module. The module is linked with the MES version control module and the change log system. The entire lifecycle of server configuration is recorded through digital threads: design phase (V1.0, 2023.05.01) → process planning phase (V1.1, 2023.05.05) → debugging phase (V1.2, 2023.05.10) → mass production phase (V2.0, 2023.05.15). Changes at each stage are identified by the version control module and synchronized to the configuration change log. The adaptation logic is to dynamically adjust the tracing granularity according to the production stage (hourly change records are retained during the debugging phase).

[0045] (2) Obtain related data. The MES quality inspection module triggers the configuration traceability requirement, and the digital thread traceability module obtains related data: the configuration trajectory of the entire product life cycle (including parameters of each version), the production stage (debugging stage) corresponding to batch P20230512, and the data comes from the MES historical record system.

[0046] (3) Perform the recovery operation and verify. The module locates the configuration during the debugging stage along the digital thread (V1.2: SMT equipment interface parameters V1.1, AOI cache threshold 512MB). After recovery, the MES production simulation interface is called to simulate the production process, and the key indicators are output: equipment response time 200ms (historical debugging data 205ms), data acquisition accuracy 99.98% (historical 99.97%), deviation <3%, and verification passed. The change record system is called synchronously, and the reason for the configuration change is output: "V1.2 version has adjusted the cache threshold from 256MB to 512MB due to the large amount of AOI detection data".

[0047] Implementation results:

[0048] Traceability efficiency: Complete configuration restoration for a specific stage in 5 minutes (traditionally, manually reviewing paper change records takes an average of 4 hours), improving efficiency by 48 times;

[0049] Quality analysis support: By tracing the reasons for configuration changes, the welding defects were quickly located to be caused by data loss due to the reduction of the cache threshold (256MB) during the mass production stage. This guided the mass production configuration to be reverted to 512MB, reducing the defect rate to below 0.1%.

[0050] Data integrity: Digital thread logging ensures uninterrupted configuration traceability, improving data integrity by 100% compared to traditional backup methods (which are prone to losing intermediate versions).

[0051] Example 3

[0052] This embodiment provides an automated recovery method for customized server configuration information based on a self-developed MES, used for pre-recovery based on a device health prediction module. Specific implementation details include:

[0053] Application scenario: A hot rolling production line in a steel plant. The rolling mill is a key piece of equipment, and its operating status (temperature, vibration) directly affects the server communication configuration. Historical data shows that communication delays are likely to occur when the rolling mill health score is below 80.

[0054] Specific steps:

[0055] (1) Construct a configuration recovery system, which includes an equipment health prediction module. The module is linked with the MES equipment monitoring module in real time. Based on the historical operating data of the rolling mill (temperature 80-120℃, vibration frequency 15-25Hz, response delay 50-100ms), an equipment health model is constructed. The warning threshold is set at 80 points (out of 100). The adaptation logic is: 60 minutes for pre-recovery of critical equipment (rolling mill) and 30 minutes for non-critical equipment.

[0056] (2) Acquire related data: The module retrieves mill operation data from the MES real-time database every 3 minutes: current temperature 110℃, vibration frequency 25Hz (5Hz above normal range), health score 75 (below the warning threshold), and a communication failure is predicted to occur within 60 minutes. Related data includes the mill's historical stable configuration (communication timeout threshold 800ms) and current performance parameters.

[0057] (3) Perform recovery operation and verification. The module actively restores the configuration of the communication between the server and the rolling mill to the historical stable state (timeout threshold 800ms) and reserves buffer parameters (increased to 900ms, i.e., increased by 20% to adapt to vibration anomalies). After recovery, the module calls the MES device control interface to verify communication stability (no timeout for 10 consecutive data transmissions), synchronizes it to the server operating environment, and pushes the pre-recovery log and health trend chart to the operation and maintenance terminal.

[0058] Implementation results:

[0059] Fault prevention: The mill vibration frequency rose to 30Hz after 60 minutes, but the server did not experience communication interruption due to prior adaptation (in the traditional scenario, the downtime is 2 hours on average).

[0060] Production continuity: Avoiding hot rolling line shutdowns due to communication failures. Based on a profit of 200 yuan per ton of steel, a single pre-restoration can reduce losses by approximately 200,000 yuan.

[0061] Operational efficiency: No manual intervention required, pre-recovery response time <1 minute (traditional manual intervention averages 30 minutes).

[0062] Example 4

[0063] This embodiment provides an automated recovery method for customized server configuration information based on a self-developed MES, used for automated recovery based on the configuration unit repair module. Specific implementation details include:

[0064] Application scenario: The server of a food processing plant's packaging line was attacked by a virus, causing damage to the encryption algorithm parameters of the weighing equipment, resulting in "weighing data encryption failure". It is necessary to quickly repair the local configuration.

[0065] Specific steps:

[0066] (1) Construct a configuration recovery system. Construct a configuration recovery system that includes a configuration unit repair module. The module breaks down the server configuration into the smallest functional units (such as weighing equipment communication port parameters, AES encryption algorithm parameters, and inkjet printing equipment data format). Construct a configuration unit library and synchronize it with the MES configuration template library every 10 minutes. The adaptation logic is: the repair priority of core units (encryption parameters) is higher than that of non-core units (port parameters).

[0067] (2) Obtain related data. After the MES configuration monitoring module detects "weighing data encryption failure", it triggers the repair module to obtain related data from the MES log database: current running log (error code "E003" appears 100 times / minute, encryption parameter fluctuation ±30%), historical normal log (error code 0 times / minute, fluctuation ±5%), current production environment parameters (workshop temperature 25℃, equipment load rate 60%).

[0068] (3) Perform recovery operations and verification. The module uses a multi-dimensional comparison method and finds that the encryption parameter deviation is 30% > 15% (preset threshold), thus locating the abnormal unit. Retrieve normal AES-256 parameters from the configuration unit library to replace them, and fine-tune the response time (50ms → 60ms to adapt to 60% load) based on production environment parameters. Call the MES data verification interface to verify the encryption success rate (100%), and synchronize it to the server after passing the verification.

[0069] Implementation results:

[0070] Repair efficiency: Local repair is completed in 10 seconds (traditional full recovery takes an average of 30 minutes), improving efficiency by 180 times;

[0071] Resource consumption: Only one configuration unit (approximately 2KB of data) is repaired, saving 99.99% of bandwidth compared to a full recovery (500MB of data);

[0072] Production impact: The packaging line operated normally during the repair period, and no weighing data was lost (traditional methods may have resulted in 2,000 products being weighed abnormally).

[0073] Example 5

[0074] This embodiment provides an automated recovery method for customized server configuration information based on a self-developed MES, used for automated recovery based on a layered image recovery module. Specific implementation details include:

[0075] Application scenario: A cell testing line in a new energy battery factory has a server configuration consisting of a basic configuration (operating system parameters), a customized business configuration (detection voltage threshold), and a temporary emergency configuration (device offline caching strategy). An operator accidentally modified the temporary emergency configuration, causing the device to go offline.

[0076] Specific steps:

[0077] (1) Construct a configuration recovery system. Construct a configuration recovery system that includes a layered image recovery module. The module divides the configuration into three levels and creates independent storage areas. The permissions are controlled by the MES security management module: basic configuration (full backup at 3 am every day), customized business configuration (real-time incremental backup), and temporary emergency configuration (backup as needed when the work order is started). The adaptation logic is to dynamically adjust the backup strategy according to the change frequency.

[0078] (2) Obtain related data. The MES configuration diagnostic module determines that only the temporary emergency configuration is corrupted, triggering the layered image module. Obtain related data from the MES backup system: the latest temporary emergency configuration image (backed up when the work order is initiated) and the SHA256 checksum.

[0079] (3) Perform recovery operation and verification. The module calls the temporary emergency configuration image to restore, and verifies the integrity (consistent with the backup) through the verification code. After recovery, it is synchronized to the MES emergency configuration ledger and the data verification interface is called to verify the offline caching function (1000 detection data can be cached when the device is offline).

[0080] Implementation results:

[0081] Recovery speed: Recovery completed in 8 seconds (traditional full image recovery takes an average of 5 minutes), improving efficiency by 37 times;

[0082] Data security: Verification code checks ensure that the image has not been tampered with (traditional backups lack verification, posing a risk of data corruption);

[0083] Business continuity: Only temporary configuration is restored, basic and business configurations are running normally, and the testing line is not shut down (traditional full recovery requires a 10-minute shutdown).

[0084] Comparative Example 1

[0085] This comparison provides a traditional server configuration recovery method.

[0086] Method Description: A "daily scheduled full backup + manual triggered recovery" model is adopted, without an MES linkage mechanism. In case of configuration anomalies, operations personnel manually log into the backup system, select the most recent backup file (possibly from 24 hours ago), fully overwrite the current configuration, and then manually test its compatibility with the production environment.

[0087] Implementation defects:

[0088] Low efficiency: The average recovery time is 30 minutes (8-15 seconds for this invention), and each minute of delay results in a loss of approximately 1,000 yuan for the production line;

[0089] Data lag: The backup cycle is fixed, and configuration changes made on the same day are easily lost (such as key parameters in the debugging phase in Embodiment 2 of this invention), which leads to production anomalies after recovery;

[0090] Poor adaptability: Without MES data support, manual judgment is required to determine the matching between configuration and work orders and equipment, with an error rate of approximately 5% (the error rate of this invention is 0);

[0091] Resource waste: Full recovery consumes over 90% of bandwidth (this invention's layered / partial recovery saves 99% of resources);

[0092] Production interruption: Recovery process requires machine shutdown (this invention allows for full online recovery without machine shutdown).

[0093] Comparative conclusion: This invention, through a closed loop of "systematic construction - data linkage - automated recovery" deeply integrated with the self-developed MES, significantly outperforms traditional methods in terms of efficiency, accuracy, and production continuity, demonstrating outstanding substantive features and significant progress.

[0094] Compared with Examples 1-5 and Comparative Example 1, significant differences were observed in the core performance indicators, as detailed below:

[0095] Recovery efficiency: The average recovery time for Examples 1-5 is 8-15 seconds, with layered image recovery (Example 5) taking only 8 seconds, configuration unit repair (Example 4) taking 10 seconds, and production work order mapping (Example 1) taking 15 seconds; while the comparative examples take an average of 30 minutes, a difference in efficiency of 37-180 times. This difference stems from the fact that this invention achieves automated triggering and verification through real-time data linkage with MES, eliminating the need for manual searching, uploading, and verification.

[0096] Configuration accuracy: Examples 1-5 achieve 100% accuracy by matching the mapping model through the MES data verification interface; the comparison ratio relies on manual judgment, resulting in an 8% error rate due to work order confusion, incorrect parameter recording, etc., such as misadjusting the cache threshold in the electronic assembly scenario, which causes quality abnormalities.

[0097] Production continuity: All examples demonstrated online recovery with no downtime recorded. For instance, pre-recovery at a steel plant (Example 3) avoided a 2-hour downtime, reducing losses by 200,000 yuan; partial repair at a food plant (Example 4) ensured continuous operation of the packaging line. Comparative examples required downtime of over 30 minutes, resulting in a single-shift capacity loss of approximately 20,000 yuan per instance.

[0098] Resource consumption: The implementation examples use layered / partial recovery. For example, in Example 4, only 2KB of data is transmitted, and in Example 5, a single image is called. The comparative full recovery requires the transmission of 500MB of data, occupying 90% of the bandwidth and affecting production data transmission.

[0099] Adaptability: The implementation example dynamically adapts to data such as MES work order features and equipment health status. For example, the engine processing line (Example 1) adjusts the collection frequency according to the work order. The comparative example uses a fixed backup, which cannot match the real-time production needs and is prone to parameter incompatibility.

[0100] Data integrity: Example 2 retains the entire lifecycle configuration through digital threads, with no version loss; comparative regular backups are prone to missing intermediate changes, such as the loss of key parameters during the debugging phase, which makes quality traceability difficult.

[0101] In summary, this invention, through a systematic design deeply integrated with a self-developed MES, comprehensively surpasses traditional methods in terms of efficiency, accuracy, and continuity, achieving "dynamic adaptation, automatic recovery, and zero interruption," significantly reducing the losses caused by configuration anomalies in industrial scenarios.

[0102] The above embodiments can be implemented in whole or in part by software, hardware, firmware or other arbitrary combinations. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product.

[0103] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0104] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0105] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

[0106] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An automated recovery method for server customized configuration information based on a self-developed MES, characterized in that, Includes the following steps: (1) Construct a configuration recovery system deeply integrated with the self-developed MES. This system includes a production work order feature mapping module, a digital thread tracing module, an equipment health prediction module, a configuration unit repair module, and a layered image recovery module. Each module executes configuration adaptation logic based on MES production data. The adaptation logic includes dynamically adjusting server resource allocation parameters according to the real-time production rhythm of MES and optimizing interface communication frequency based on equipment load thresholds. The operation steps of the configuration unit repair module include: decomposing the customized configuration of the server into configuration units. The configuration unit is the minimum set of configuration parameters to achieve a specific function, including a single The device's communication port parameters and data encryption algorithm parameters are used to construct a configuration unit library containing hardware driver parameters, software module interaction rules, and data transmission formats. The data in this library is synchronized with the MES configuration template library in real time. When a configuration is damaged or abnormal, the faulty configuration is analyzed. Based on the MES log database, feature values ​​of the configuration operation logs are extracted, including the frequency of error codes and the range of parameter fluctuations, to locate the abnormal configuration unit. Normal configuration units of the same origin are retrieved from the configuration unit library to replace the abnormal configuration unit. The repaired configuration is then fine-tuned based on the production environment parameters collected in real time by the MES, including workshop temperature and humidity and equipment load rate. (2) When the server's customized configuration needs to be restored, the configuration monitoring module of MES will detect the configuration anomaly in real time, including parameter verification failure and communication timeout. Then, the corresponding module in the system will be triggered to obtain the configuration-related data. The related data includes at least one of the following: production work order characteristics, product life cycle configuration trajectory, equipment health data, configuration unit information and hierarchical image data. All the related data are from the real-time database and historical record system of the self-developed MES. (3) Based on the associated data, perform automated recovery operations to generate server customized configurations that are adapted to the current production environment. The recovery operation calls the device control interface and data verification interface of the self-developed MES for linkage verification throughout the process. After the verification is passed, it is synchronized to the server operating environment.

2. The automated recovery method for server customized configuration information based on self-developed MES as described in claim 1, characterized in that, The operation steps of the production work order feature mapping module include: constructing a mapping model between production work order features and server configurations, wherein the mapping model uses historical work order successful configuration data recorded by the self-developed MES as samples, and learns the server configuration parameters corresponding to different work order features through data analysis; when the MES configuration monitoring module detects server configuration anomalies, it obtains the features of the currently executing or about to be executed work orders through the MES work order management module; based on the mapping model and the obtained work order features, it matches and restores the most suitable customized server configuration scheme, and calls the MES process parameter library in real time for rule verification during the matching process.

3. The automated recovery method for server customized configuration information based on self-developed MES as described in claim 2, characterized in that, The work order features include process requirements, equipment combinations, and capacity targets. The server configuration parameters include data processing priority and equipment interface protocol, and the equipment interface protocol matches the MES equipment communication standard. The data processing priority is dynamically adjusted according to the urgency of the work orders pushed by the MES in real time.

4. The automated recovery method for server customized configuration information based on self-developed MES as described in claim 1, characterized in that, The operation steps of the digital thread tracing module include: using the digital thread of the MES system to record the change trajectory of the server configuration at each stage of the product lifecycle, which includes product design, process planning, production execution, and quality inspection stages; configuration changes at each stage are identified by the version control module and synchronized to the configuration change log of the MES; when a configuration problem occurs and needs to be restored, the configuration status of the target stage is traced along the digital thread, and the production batch information of the MES is associated during the tracing process; the server configuration is restored to the configuration status of the target stage, and the change record system is called through the digital thread to view the reasons for the configuration changes and the operators in subsequent stages.

5. The automated recovery method for server customized configuration information based on self-developed MES as described in claim 4, characterized in that, After restoring the server configuration to the target stage configuration state, the MES production simulation interface is called to simulate the production process under this configuration state. The simulation data includes equipment response time and data acquisition accuracy to verify the effectiveness of the configuration restoration. If the verification fails, the system calculates the deviation value, and based on the deviation value, it uses a digital thread to trace back and adjust the target stage until the simulation verification passes.

6. The automated recovery method for server customized configuration information based on self-developed MES as described in claim 1, characterized in that, The operation steps of the equipment health prediction module include: real-time collection of production equipment operation data, including temperature, vibration frequency, and response delay, through the equipment monitoring module of MES; construction of an equipment health prediction model based on the data; prediction of potential equipment failure or performance degradation time points at preset intervals of 1-5 minutes / time using the model; and pre-recovery of the customized configuration of the associated server 30-60 minutes before the equipment health drops to the warning threshold. The pre-recovery includes restoring the configuration related to the equipment in the server to the historical stable state recorded by MES, and reserving buffer parameters that match the current performance of the equipment, including increasing the communication timeout threshold by 10%-20%.

7. The automated recovery method for server customized configuration information based on self-developed MES as described in claim 6, characterized in that, The device-related configurations include configurations related to device communication, and the device communication-related configurations conform to the device interaction protocol specifications of MES. After the pre-recovery operation is triggered, pre-recovery logs and device health trend data are pushed to the MES operation and maintenance terminal.

8. The automated recovery method for server customized configuration information based on self-developed MES as described in claim 1, characterized in that, When analyzing fault configurations, a multi-dimensional comparison method is used. The current configuration operation log is compared with the historical normal logs recorded by MES, and the parameters are compared item by item. When the deviation value exceeds the preset threshold of 5%-15%, it is marked as an abnormal configuration unit, and an abnormal source tracing path is generated, including the associated configuration items and the scope of affected devices.

9. The automated recovery method for server customized configuration information based on self-developed MES as described in claim 1, characterized in that, The operation steps of the layered image recovery module include: using image splitting technology, the server configuration is divided into different levels: basic configuration, customized business configuration, and temporary emergency configuration. Independent image storage areas are created for different configuration levels, and the read and write permissions of each area are controlled by the MES security management module; the basic configuration image is backed up in full every morning, the customized business configuration image is backed up in real time based on configuration changes, and the temporary emergency configuration image is backed up as needed when a work order is initiated. All backup operations are triggered by the MES backup scheduling module and a backup verification code is generated; when the configuration is corrupted, the MES configuration diagnosis module determines the level of configuration corruption and calls the corresponding level of image for recovery. During the recovery process, the integrity of the image is verified through the verification code; when only the temporary emergency configuration has a problem, only the temporary emergency configuration image is retrieved for recovery, and the recovered image is synchronized to the MES emergency configuration ledger.

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