A digitalization-driven engineering construction output whole-chain disposal method

Abstract

This application provides a digitally driven, end-to-end disposal method for engineering construction outputs, comprising: S1, deploying a distributed message queue to generate standardized data packets for real-time push; S2, applying a two-phase commit protocol across multiple systems to determine if conflicts occur and obtain consistency verification results; S3, employing a retry mechanism for conflicting parts, extracting key fields from synchronized data versions, calculating unique identifiers using a hash algorithm, and obtaining index keys for cross-system queries; S4, querying related systems using the index key, and adjusting relevant entries if discrepancies exist in the corresponding inventory records to obtain an integrated dataset; S5, distributing the dataset to the decision-making module on the end-to-end management platform, repeating the above process for subsequent new data sources entered on-site, and obtaining continuously updated full lifecycle management records. This application effectively improves the efficiency and accuracy of output disposal in engineering construction.

Description

Technical Field

[0001] This invention relates to the field of intelligent construction waste treatment technology, and in particular to a digitally driven method for the whole-chain disposal of engineering construction outputs. Background Technology

[0002] In the field of engineering construction, digitally driven end-to-end management has become a crucial path to improve efficiency, reduce costs, and ensure project quality. This field directly relates to the overall progress and resource allocation of engineering projects, and has undeniable value in promoting industry modernization. With continuous technological advancements, how to achieve full lifecycle management of engineering outputs through digital means has become one of the core issues for industry development. However, current management methods often reveal shortcomings in cross-system collaboration when facing complex projects. Many solutions lack effective integration mechanisms when handling information flow between different systems, leading to delays or errors in data transmission. This problem not only affects the collaborative efficiency of various project stages but may also lead to resource waste or decision-making errors due to information asymmetry, especially in engineering projects involving multiple stakeholders. Focusing on the technical level, the biggest challenge in engineering construction output management lies in overcoming data barriers and achieving real-time exchange between heterogeneous systems. Due to differences in formats and protocols, seamless integration between different systems is difficult, often requiring manual intervention or multiple conversions during information transmission, increasing the risk of errors. A deeper problem is the lack of a unified and coordinated mechanism to ensure data consistency across multiple systems. When data is updated in one stage, other systems cannot detect and adjust accordingly in a timely manner. For example, in a specific business scenario, when new output information is entered on-site via mobile devices, if the relevant data cannot be immediately transmitted to the materials management or financial accounting stages, it will lead to inaccurate inventory data or delayed cost statistics, thus affecting subsequent decision-making and resource allocation. Therefore, how to achieve real-time data exchange and consistency assurance in a multi-system environment has become a key issue in the entire chain of engineering construction output disposal. Solving this problem is not only about the efficient flow of information, but also directly affects the overall efficiency of project management and the collaborative effect of each stage. Summary of the Invention

[0003] To address the technical problems mentioned in the background section, this invention provides a digitally-driven, end-to-end method for handling engineering construction outputs, mainly comprising:

[0004] S1. Deploy a distributed message queue, using output information collected from on-site equipment as the initial data source. Employ the Kafka algorithm to process high-throughput event streams and generate standardized data packets for real-time push. S2. Apply a two-phase commit protocol across multiple systems. If the data source format matches the target system's protocol, directly transmit synchronized data, checking for conflicts to obtain consistency verification results. S3. For conflicting parts, employ a retry mechanism. Roll back mismatched items through log records, determine the resolved synchronized data version, extract key fields from the synchronized data version, and calculate a unique identifier using a hash algorithm to obtain an index key for cross-system queries. S4. Query related systems using the index key. If there are discrepancies in the inventory records corresponding to the index key, adjust the relevant entries and check the consistency after the update to obtain the integrated dataset. S5. Distribute to the decision module on the full-chain management platform. Merge multi-source inputs through aggregation queries to determine an optimized resource allocation scheme. Derive a feedback loop from the optimized allocation scheme, repeating the above process for subsequent new data sources entered on-site, resulting in continuously updated full lifecycle management records.

[0005] Furthermore, step S1 includes: separating the temperature data from the vibration data and parsing them into structured information units containing timestamps, device IDs, and physical quantity values.

[0006] Furthermore, this includes converting the structured information units into a unified JSON or binary format.

[0007] Furthermore, step S2 includes:

[0008] Step S21: The data packets to be synchronized are obtained from multiple systems through the data acquisition module. Each data packet is initially parsed to obtain the content structure of the standardized data.

[0009] Step S22: Using a preset format comparison tool, determine whether the standardized data format is consistent with the protocol of the target system. If they are consistent, mark it as a state that can be directly transmitted and determine the transmission path.

[0010] Step S23: For data packets marked as ready for direct transmission, perform a direct transmission operation to send the synchronization data to the target system and obtain the status log during the transmission process;

[0011] Step S24: Extract key fields from the status log during transmission and apply a conflict detection mechanism. If duplicate or missing data packets are detected during data packet processing, trigger a rollback operation to determine whether retransmission is necessary.

[0012] Step S25: For data packets that have not triggered a rollback operation, execute the first phase of the two-phase commit protocol, pre-commit synchronization data to the target system, and obtain the pre-commit response result;

[0013] Step S26: Based on the response result of the pre-commit, execute the second phase of the two-phase commit protocol. If the response result shows no abnormalities, then formally commit the synchronization data, complete the consistency verification, and obtain the final synchronization state.

[0014] Furthermore, the first stage includes: the source system sending a pre-commit request to the target database, and the target database temporarily locking the relevant resources and sending a ready signal after receiving the request.

[0015] Furthermore, the second stage includes: if the feedback received by the source system shows no abnormalities, then a formal submission instruction is issued.

[0016] Furthermore, step S3 includes:

[0017] Key fields that do not meet the preset criteria are sorted according to their timestamps. At the same time, the data record corresponding to the earliest timestamp is retrieved and locked, and it is established as the priority processing object. Key business data is extracted from the priority processing object, and a hash algorithm is applied to calculate and generate a temporary unique identifier.

[0018] Furthermore, a temporary unique identifier is used to query the storage location of synchronized data across systems and establish a correlation.

[0019] The technical solution provided by this invention has the following beneficial effects:

[0020] This invention discloses a digitally-driven method for the entire chain of engineering construction output disposal. It proposes an integrated solution for complex business scenarios involving high-throughput processing of data collected from on-site equipment, ensuring data consistency across multiple systems, and optimizing resource allocation. This invention deploys a distributed message queue, combines efficient algorithms to process event streams, generates standardized data packets, and employs a two-phase commit protocol to ensure data synchronization consistency across multiple systems. For conflicting data, a retry mechanism and log rollback are introduced to ensure data version accuracy. Simultaneously, this invention utilizes unique identifiers to generate index keys, enabling cross-system queries and data integration, resolving inventory record discrepancies, and ultimately forming an optimized allocation scheme through aggregated queries, generating a feedback loop to continuously update the entire lifecycle management records. The core innovation of this invention lies in achieving closed-loop management of the entire chain from data collection to resource allocation through digital means, effectively improving the efficiency and accuracy of output disposal in engineering construction and contributing to intelligent decision-making. Attached Figure Description

[0021] Figure 1 This is a flowchart of a digitally driven, full-chain disposal method for engineering construction outputs according to the present invention.

[0022] Figure 2This is a schematic diagram of a digitally driven, end-to-end disposal method for engineering construction outputs according to the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] like Figures 1-2 As shown, this invention provides a digitally driven, end-to-end disposal method for engineering construction outputs, which specifically includes:

[0025] S1 deploys a distributed message queue, using output information collected from on-site equipment as the initial data source, and employs the Kafka algorithm to process high-throughput event streams, generating standardized data packets for real-time push.

[0026] Specifically, output information is acquired from the engineering site through a distributed queue, establishing a stable data acquisition channel to obtain preliminary data stream content. Based on the preliminary data stream content, the output information is categorized and organized using a device acquisition module to determine structured information units. If the structured information units meet preset integrity conditions, they are formatted using a message processing mechanism to obtain standardized data records. For the standardized data records, Kafka technology is used for high-throughput event stream processing to generate a real-time updated data stream sequence. If the real-time updated data stream sequence meets preset transmission conditions, it is distributed to downstream systems through a real-time push mechanism to determine usable standardized packets. After obtaining the standardized packets, they are verified and compared against the data source to determine whether they are consistent with the initial data, resulting in the final output result. Based on the final output result, combined with streaming computing technology, the data stream is continuously monitored to determine if there are any abnormal fluctuations and to generate dynamically adjusted feedback signals.

[0027] For example, in the context of an automated production line in a precision manufacturing engineering site, building a stable data acquisition channel through distributed queuing technology can effectively solve the network congestion problem caused by high-frequency, bursty data streams generated by on-site sensors.

[0028] For example, when CNC machine tools on the production line output output information such as spindle speed, cutting temperature and vibration frequency at millisecond-level frequency, the distributed queue acts as a buffer to ensure that even under network fluctuations, this raw data can be completely and orderly ingested, forming the initial data stream content and avoiding production blind spots caused by data packet loss.

[0029] Specifically, based on the initial data stream content, the device acquisition module classifies and organizes these mixed raw messages.

[0030] For example, temperature data is separated from vibration data and parsed into structured information units containing timestamps, device IDs, and physical quantity values. During this process, the system sets preset integrity conditions, such as requiring each data entry to include a timestamp accurate to milliseconds and a valid checksum. If the structured information unit meets these conditions, the message processing mechanism converts it into a unified JSON or binary format—a standardized data record—thus eliminating data silos caused by differences in protocols between different device manufacturers.

[0031] In one possible implementation, Kafka technology is used for high-throughput event stream processing of standardized data records. Due to the massive amounts of data generated in precision manufacturing—for example, tens of thousands of status records per second—Kafka can act as a high-performance message middleware, organizing these records into a real-time updated data stream sequence, ensuring low latency and high concurrency in data processing. When the data stream sequence reaches preset transmission conditions, such as a buffer accumulation of 10MB or a time interval of 500ms, a real-time push mechanism distributes this data to downstream production execution systems, forming usable standardized packets, ensuring that upper-layer applications can obtain production status in a timely manner.

[0032] Understandably, to ensure the absolute reliability of the data, the system will perform verification and comparison against the data source after obtaining the standardized package.

[0033] For example, by using sampling hash comparison, the system confirms that the temperature values ​​received downstream are consistent with the initial data emitted by the field sensors, thus obtaining the final output and preventing data tampering or damage during transmission. Finally, by combining streaming computing technology to continuously monitor the data stream, if the spindle temperature shows an abnormal upward trend within 10 consecutive sampling points, indicating abnormal fluctuations, the system will immediately generate a dynamic adjustment feedback signal, automatically triggering the cooling system or reducing the rotation speed, achieving closed-loop management from data acquisition to intelligent control. This end-to-end processing method not only improves data quality and availability but also significantly reduces equipment failure rates through a real-time feedback mechanism.

[0034] S2 applies a two-phase commit protocol between multiple systems. If the data source format matches the target system protocol, it directly transmits synchronized data and determines whether a conflict has occurred to obtain a consistency verification result.

[0035] Optionally, this step also includes:

[0036] Step S21: The data packets to be synchronized are obtained from multiple systems through the data acquisition module. Each data packet is initially parsed to obtain the content structure of the standardized data.

[0037] Step S22: Using a preset format comparison tool, determine whether the standardized data format is consistent with the protocol of the target system. If they are consistent, mark it as a state that can be directly transmitted and determine the transmission path.

[0038] Step S23: For data packets marked as ready for direct transmission, perform a direct transmission operation to send the synchronization data to the target system and obtain the status log during the transmission process.

[0039] Step S24: Extract key fields from the status log during transmission, apply a conflict detection mechanism, and if duplicates or missing data packets are detected during data packet processing, trigger a rollback operation to determine whether retransmission is necessary.

[0040] Step S25: For data packets that have not triggered a rollback operation, execute the first phase of the two-phase commit protocol, pre-commit synchronization data to the target system, and obtain the pre-commit response result.

[0041] Step S26: Based on the response result of the pre-commit, execute the second phase of the two-phase commit protocol. If the response result shows no abnormalities, then formally commit the synchronization data, complete the consistency verification, and obtain the final synchronization state.

[0042] In one possible implementation, the system extracts key fields such as device number and operating parameters from the raw data packets obtained from multiple sensor systems at the engineering site.

[0043] For example, when a raw message containing 1024 bytes is acquired, the parsing module converts it into structured units containing information such as a device temperature of 45.5 degrees Celsius and a pressure of 1.2 MPa. This parsing process transforms the chaotic underlying signals into logically clear information blocks, laying the foundation for subsequent synchronization work.

[0044] In one possible implementation, the parsed data structure needs to be compared with the protocol of the target storage system.

[0045] For example, if the pressure field defined in the source data is a floating-point type, and the target system's receiving protocol also supports this type, then the protocol is considered compatible. In this case, the system will automatically plan the optimal transmission path and mark the data packet as a task flow that can be directly sent. This verification mechanism can identify potential compatibility issues in advance, ensuring a smooth transition of data between different systems.

[0046] In one possible implementation, for tagged data packets, the system performs immediate transmission and records a status log.

[0047] For example, the log module monitors the unique sequence number of each data packet in real time. If the conflict detection phase detects a duplicate of data packet number 105, or if data packet number 104 fails to arrive within a preset time, the conflict detection mechanism will intervene immediately. At this point, the system will withdraw the processing instructions for the current batch and restore the system to the stable state of data packet number 103 through a rollback operation. This approach effectively avoids logical errors caused by data redundancy or loss.

[0048] In one possible implementation, a two-phase commit protocol is introduced to ensure the rigor of cross-system interactions.

[0049] For example, in the first phase, the source system sends a pre-commit request to the target database. Upon receiving the request, the target database temporarily locks the relevant resources and sends a ready signal. In the second phase, if the feedback received by the source system indicates no anomalies, it issues a formal commit command. This mechanism ensures a high degree of data consistency in a multi-node environment, ultimately resulting in accurate synchronization.

[0050] S3 employs a retry mechanism for conflicting parts, rolls back mismatches through log records, determines the resolved synchronized data version, extracts key fields such as output quantity and timestamp from the synchronized data version, calculates a unique identifier using a hash algorithm, and obtains an index key for cross-system queries.

[0051] Specifically, for conflicting data items, a retry process is initiated. Detailed rollback records are retrieved from log traces, and the specific field differences of the conflicting data items are analyzed to identify unresolved conflict points. Based on these unresolved conflict points, a pre-defined comparison tool is used to verify the key fields in the synchronized data one by one, determining whether the field values ​​meet pre-defined standards. If not, they are marked as pending items. For the key fields marked as pending items, they are sorted by timestamps to obtain the data record corresponding to the earliest timestamp, determining the priority conflicting data items to be processed. From the priority conflicting data items, the output quantity and related fields are extracted, and a temporary unique identifier is generated using a hash algorithm to obtain a reference value for subsequent comparisons. Based on the temporary unique identifier, the storage location of the relevant synchronized data is queried across systems. If the query results show that multiple versions of the record exist in the storage location, the version with the latest timestamp is selected to determine the final synchronization basis. Based on the final synchronization basis, the index value is updated, and the updated index value is associated with records in the cross-system to obtain a complete synchronization mapping relationship.

[0052] In one possible implementation, for conflicting data items that occur during the synchronization process, the system first initiates a retry process and then analyzes the rollback records in the log trace.

[0053] Specifically, the system compares the field differences of conflicting data items in the source data and the target system to identify unresolved conflict points.

[0054] For example, when processing a generator set operation record, if the log shows that the "output power" field failed to write, the system will call a preset comparison tool to check the key fields in the record one by one. Suppose the standard protocol requires power fluctuations to be within 5%, while the current record's fluctuation value is 7%, the system will determine that this field does not meet the preset standard and mark it as an item to be processed. This precise difference analysis mechanism can quickly locate the root cause of the problem and avoid the waste of resources caused by blind retries.

[0055] For example, for key fields marked as pending processing, the system sorts them according to timestamps to ensure the sequential nature of data processing. The system retrieves and locks the data record corresponding to the earliest timestamp, establishing it as the priority processing object. Subsequently, the system extracts key business data from this priority record, such as the "output quantity" of 1000 pieces and the related "batch number" field, and applies a hash algorithm to calculate a temporary unique identifier for these combined information. This process transforms complex business data into reference values ​​that are easy for computers to recognize, providing a reliable basis for subsequent efficient comparison.

[0056] In one embodiment, the system uses a generated temporary unique identifier to query the storage location of synchronized data across systems. If the query results show multiple versions of records, such as finding snapshots of the same batch of products at different times in the database, the system selects the latest version as the final synchronization basis based on the timestamp. Subsequently, the system updates the index value based on this basis and establishes an association between the updated index value and the records in the cross-system, thereby forming a complete synchronization mapping relationship. This association mechanism based on the latest version and unique identifier effectively solves the data consistency problem in a distributed environment, ensuring that the final index points to the latest and most accurate business status.

[0057] S4. Query related systems, such as the materials management and finance modules, through the index key. If there are differences in the inventory records corresponding to the index key, adjust the relevant entries and determine the consistency after the update to obtain the integrated dataset.

[0058] Specifically, inventory record data is retrieved from the materials management and finance modules using index key values. A pre-established mapping table is used for join queries to determine the corresponding record content between the two modules. For the records obtained from the join query, the pandas library in Python is used to perform record comparison operations. If discrepancies are found in the inventory records, the pandas library is used to filter the discrepancies, resulting in a list of items requiring adjustment. Based on the list of items requiring adjustment, SQL update statements are used to implement the adjustments and update the relevant records. Using the updated records, the pandas library in Python is used to perform update verification, obtaining the consistency check results between the two modules to confirm that the data meets the consistency requirements. Based on the consistency check results, the pandas library in Python is used to merge the records that meet the consistency requirements into a unified dataset, resulting in a complete integrated dataset.

[0059] In one possible implementation, the system uses pre-generated index key values ​​to establish a logical connection between the materials management module and the finance module.

[0060] Specifically, the unique codes of spare parts in the materials module are mapped one-to-one with the asset numbers in the financial ledger through a mapping table.

[0061] For example, regarding the inventory of bearings for a certain model of generator set, the materials module records the inbound batch number as 20240510. By searching the mapping table, the system can accurately locate the corresponding asset original value and depreciation record in the finance module. This kind of relational query ensures the logical alignment of cross-module data, laying the foundation for subsequent in-depth comparison.

[0062] Specifically, the system extracts the associated records and uses data analysis tools to verify key dimensions such as inventory quantity, unit price, and total amount item by item.

[0063] For example, if the materials module shows an inventory quantity of 1500 meters for a certain type of cable, while the finance module records an asset quantity of 1480 meters, the system will automatically identify a 20-meter discrepancy. Through a pre-defined filtering mechanism, these discrepancies are extracted into a discrepancy list. This filtering not only focuses on the numerical value but also covers the logical consistency of timestamps, ensuring that each discrepancy can be traced back to the specific point in time when the business transaction occurred.

[0064] In one embodiment, the system performs a precise item adjustment process for each item in the discrepancy list.

[0065] Specifically, the system generates corresponding database update instructions based on the verified physical inventory results.

[0066] For example, if the financial module is found to have missed recording a certain VAT invoice for goods entering the warehouse, the system will automatically complete the corresponding financial entry, ensuring that the book value of the goods is synchronized with the physical value of the goods. This adjustment method avoids secondary errors that may be introduced by manual intervention and ensures the authenticity of the underlying data source.

[0067] In one embodiment, after the entry adjustment is completed, the system will restart the consistency check procedure.

[0068] Specifically, the system rereads the updated material inventory table and financial balance sheet, and performs a second comparison using data analysis tools.

[0069] For example, when verifying the latest status of generator spare parts, the system confirms that 1,000 records in the materials module perfectly match the 1,000 updated records in the finance module. This verification mechanism provides immediate feedback on adjustments, ensuring that data meets preset consistency standards during the data transfer process.

[0070] Understandably, the system logically merges all compliant records based on the verification results.

[0071] Specifically, the system integrates the physical attribute fields of the materials module and the value attribute fields of the finance module into a unified dataset.

[0072] For example, the specifications, storage location, purchase price, and depreciation period of bearings can be combined into a single complete business record. This integrated dataset provides a single source of fact for subsequent cross-system decision-making, eliminates information silos, and significantly improves the efficiency of cross-departmental collaboration and the utilization value of data assets.

[0073] S5. After obtaining the integrated dataset, it is distributed to the decision-making module on the full-chain management platform. By aggregating and querying multiple sources of input, an optimized allocation scheme for resource allocation is determined. A feedback loop is derived from the optimized allocation scheme. The above process is repeated for new data sources entered on-site, resulting in continuously updated full lifecycle management records.

[0074] Specifically, the entire platform acquires and integrates datasets from multiple data sources, categorizes and organizes the data using structured processing methods, resulting in a pre-processed dataset. Based on this dataset, an aggregation query is executed in the decision-making module to integrate the correlation information between multiple data sources and determine the priority ranking of resource allocation. An initial optimization plan is generated based on the priority ranking of resource allocation, and a preliminary verification of the plan is performed using preset logical rules to determine if it meets the allocation balance conditions. If the verification result shows that the allocation balance conditions are not met, the optimization plan is regenerated by adjusting the resource allocation ratio, and the adjusted plan content is obtained. Based on the adjusted plan content, combined with a feedback loop mechanism, on-site data and newly added data are collected in real time to obtain dynamically updated data input. Through dynamically updated data input, the aggregation query and optimization plan generation process is repeatedly executed to continuously update the full lifecycle management records. The random forest algorithm from machine learning is used to perform trend analysis on the continuously updated management records to determine the potential demand direction for future resource allocation.

[0075] Specifically, data acquisition and structured processing across the entire supply chain platform are fundamental to resource allocation. The system not only reads static inventory from the internal inventory database but also accesses real-time streaming data from the logistics and transportation system. Assuming the raw data includes unstructured logistics logs and structured financial statements, structured processing maps these heterogeneous data into unified fields such as "Material ID," "Current Location," and "Availability Status." This standardization eliminates data silos, providing cleaned input for subsequent efficient calculations.

[0076] For example, when executing an aggregate query in the decision-making module, the system correlates inventory turnover with urgent order demand. If only 50 units of a certain type of critical component remain in stock, while the associated urgent order demand is for 200 units, the aggregate query will identify this supply-demand gap and, based on the urgency of order delivery, set the allocation priority of that component to the highest level. This process ensures that limited logistics resources are prioritized for high-value or high-risk business nodes.

[0077] In one possible implementation, the initial optimization plan generated for the aforementioned high-priority needs might suggest transferring goods from the three nearest warehouses. At this point, pre-defined logical rules will verify the feasibility of the plan, such as checking whether the remaining inventory in each warehouse meets the minimum safety level and whether transportation costs exceed the budget threshold. If the verification finds that the inventory in one of the warehouses will fall below the safety line of 10 units after the transfer, i.e., the allocation balance condition is not met, the system will automatically trigger an adjustment mechanism to reduce the transfer volume from that warehouse and instead increase the allocation ratio from the remote central warehouse until the plan passes verification. This automated trial-and-error and correction mechanism significantly reduces the error rate of manual planning and ensures the operability of the plan during implementation.

[0078] It's worth noting that the random forest algorithm played a crucial role in trend analysis of the entire lifecycle management records. The system used three years of historical allocation records as a training set, with input features including seasonal factors, promotional activities, and supplier delivery delay rates. The algorithm constructed multiple decision trees for voting; if the analysis showed that the probability of a particular raw material being out of stock at the end of each quarter exceeded 85%, the system would issue an early warning, suggesting increased stockpiling in the middle of the quarter. This machine learning-based predictive capability effectively mitigated supply chain fluctuations, improved overall operational robustness, and enabled a shift from reactive response to proactive forecasting.

[0079] If the technical solution of this application involves the processing of personal information, the relevant products have established a sound user authorization mechanism: before collecting, using, or sharing personal information, the obligation to inform is fulfilled in accordance with the law, and the individual's voluntary and explicit consent is obtained; if sensitive personal information is involved, the user's separate and explicit consent is further obtained. Specific measures include, but are not limited to: setting up prominent prompts in the information collection area, or clearly displaying the processing rules (including the processor, purpose, method, information type, etc.) through electronic interfaces such as pop-ups, checkboxes, and active submissions, to ensure that users voluntarily authorize based on their knowledge. All personal information processing activities strictly comply with national laws and regulations, especially the relevant provisions of the "Personal Information Protection Law of the People's Republic of China," to effectively safeguard the legitimate rights and interests of personal information subjects.

[0080] The above description of the embodiments is only for the purpose of helping to understand the technical solutions and core ideas of this application; those skilled in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or make equivalent substitutions for some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A digitally-driven, end-to-end method for handling engineering construction outputs, characterized in that, The method includes: S1. Deploy a distributed message queue, using output information collected from on-site equipment as the initial data source. Employ the Kafka algorithm to process high-throughput event streams and generate standardized data packets for real-time push. S2. Apply a two-phase commit protocol across multiple systems. If the data source format matches the target system's protocol, directly transmit synchronized data, checking for conflicts to obtain consistency verification results. S3. Employ a retry mechanism for conflicting parts, rolling back mismatches through log records to determine the resolved synchronized data version. Extract key fields from the synchronized data version, calculate a unique identifier using a hash algorithm, and obtain an index key for cross-system queries. S4. Query related systems using the index key. If there are discrepancies in the inventory records corresponding to the index key, adjust the relevant entries and check the consistency after the update to obtain the integrated dataset. S5. Distribute the dataset to the decision-making module on the full-chain management platform. Merge multi-source inputs through aggregation queries to determine an optimized resource allocation scheme. Derive a feedback loop from the optimized allocation scheme, repeating the above process for subsequent new data sources entered on-site, resulting in continuously updated full lifecycle management records.

2. The method according to claim 1, characterized in that, Step S1 includes: separating temperature data from vibration data and parsing them into structured information units containing timestamps, device IDs, and physical quantity values.

3. The method according to claim 2, characterized in that, include: The structured information units are converted into a unified JSON or binary format.

4. The method according to claim 1, characterized in that, Step S2 includes: Step S21: The data packets to be synchronized are obtained from multiple systems through the data acquisition module. Each data packet is initially parsed to obtain the content structure of the standardized data. Step S22: Using a preset format comparison tool, determine whether the standardized data format is consistent with the protocol of the target system. If they are consistent, mark it as a state that can be directly transmitted and determine the transmission path. Step S23: For data packets marked as ready for direct transmission, perform a direct transmission operation to send the synchronization data to the target system and obtain the status log during the transmission process; Step S24: Extract key fields from the status log during transmission and apply a conflict detection mechanism. If duplicate or missing data packets are detected during data packet processing, trigger a rollback operation to determine whether retransmission is necessary. Step S25: For data packets that have not triggered a rollback operation, execute the first phase of the two-phase commit protocol, pre-commit synchronization data to the target system, and obtain the pre-commit response result; Step S26: Based on the response result of the pre-commit, execute the second phase of the two-phase commit protocol. If the response result shows no abnormalities, then formally commit the synchronization data, complete the consistency verification, and obtain the final synchronization state.

5. The method according to claim 4, characterized in that, The first stage includes: the source system sending a pre-commit request to the target database, and the target database temporarily locking the relevant resources and sending a ready signal after receiving the request.

6. The method according to claim 5, characterized in that, The second stage includes: if the feedback received by the source system shows no abnormalities, then a formal submission instruction is issued.

7. The method according to claim 1, characterized in that, Step S3 includes: Key fields that do not meet the preset criteria are sorted according to their timestamps. At the same time, the data record corresponding to the earliest timestamp is retrieved and locked, and it is established as the priority processing object. Key business data is extracted from the priority processing object, and a hash algorithm is applied to calculate and generate a temporary unique identifier.

8. The method according to claim 7, characterized in that, Use temporary unique identifiers to query the storage location of synchronized data across systems and establish associations.

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