A method, equipment and medium for park operation and maintenance data management

Abstract

This application discloses a method, equipment, and medium for managing park operation and maintenance data. The method includes: acquiring operation and maintenance data through multi-source collection nodes; preprocessing and differentially verifying the authenticity of the data; creating a consortium blockchain framework based on multi-node consensus; constructing multiple specialized evidence storage chains on this framework and configuring their data writing rules to be mapped to the main chain to form a complete consortium blockchain; classifying and storing the verified data into the corresponding evidence storage chains according to data type; and retrieving and identifying anomalies in the chain data based on a five-dimensional distributed index to achieve full lifecycle traceability. This invention addresses four major pain points of existing systems: poor data authenticity, centralized evidence storage that is easily tampered with, single traceability dimension, and unreliable energy and carbon assessment. It achieves a 0% data tampering rate, traceability response time ≤3 seconds, a 73% reduction in audit cycle, and a false data rate ≤0.5%. It is adaptable to multiple park scenarios, can deeply collaborate with existing operation and maintenance systems, reduces operation and maintenance costs, and improves management efficiency.

Description

Technical Field

[0001] This application relates to the field of trusted data storage and traceability technology, and in particular to a method, equipment and medium for park operation and maintenance data management. Background Technology

[0002] With the rapid construction of smart parks, industrial parks, and commercial complexes, operation and maintenance data is experiencing exponential growth. However, existing operation and maintenance data management systems are limited by their technical architecture and struggle to meet the core requirements of "trustworthy evidence storage and accurate traceability," highlighting a significant pain point in the industry.

[0003] Existing park operation and maintenance data management systems passively receive data without filtering out sensor interference. Inspection check-ins rely heavily on location tracking, leading to issues like fake check-ins, work order modifications only retaining the final version, and missing key change tracing, resulting in low data reliability. Furthermore, the traceability system is limited to a single data type, supporting only simple queries and failing to correlate multi-dimensional information. This poses risks such as unauthorized administrator tampering and system vulnerability attacks, requiring manual cross-system verification, with traceability for a single record taking over 30 minutes. In addition, some data, such as energy carbon data, is not stored correctly, with inconsistent formats and missing correlations, resulting in a 25% deviation in energy-saving renovation effect assessments and audit cycles exceeding 15 days.

[0004] In summary, existing technologies have not formed a complete closed loop of source verification, multi-dimensional evidence storage, accurate traceability, and intelligent auditing, making it difficult to meet the core requirements of reliable, traceable, and auditable park operation and maintenance data. Summary of the Invention

[0005] To address the technical problems mentioned above, this application provides a method, device, and medium for managing park operation and maintenance data. The method includes: acquiring operation and maintenance data through multi-source acquisition nodes deployed within the park; the operation and maintenance data includes at least equipment fault data, work order flow data, and energy consumption data; preprocessing the operation and maintenance data and verifying its authenticity according to preset differentiation rules to obtain authentic operation and maintenance data; creating a consortium blockchain framework according to preset node deployment rules and preset node configuration rules; creating specialized evidence storage chains on the consortium blockchain framework, configuring preset data writing and indexing rules for each specialized evidence storage chain, and establishing an association mapping relationship between the main chain of the consortium blockchain framework and the data of each specialized evidence storage chain to generate a complete consortium blockchain framework; classifying and storing the authentic operation and maintenance data into each specialized evidence storage chain according to the type of the authentic operation and maintenance data through the complete consortium blockchain framework; and retrieving and identifying anomalies in the evidence-stored data in each specialized evidence storage chain according to preset five-dimensional distributed indexing rules to complete the full lifecycle traceability of operation and maintenance data.

[0006] In one example, maintenance data is acquired through multi-source acquisition nodes deployed within the park. Specifically, this includes: acquiring fault signals and energy consumption data of park equipment via device sensing terminals deployed on park equipment, using the LoRa protocol and a preset sampling frequency; synchronizing the entire process of work order creation, allocation, modification, and handling data through a standardized interface connected to the park's work order management system, using RESTful API and WebSocket protocols; acquiring inspection check-in and fault reporting data via mobile inspection terminals configured for maintenance personnel, using the 5G mobile communication protocol; the inspection check-in data includes location information and on-site images; and acquiring heterogeneous maintenance data from security and fire protection systems via a deployed third-party system standardized gateway, using JSON data format.

[0007] In one example, the operation and maintenance data is preprocessed, and the authenticity of the preprocessed operation and maintenance data is verified according to preset differentiation rules to obtain authentic operation and maintenance data. Specifically, this includes: identifying outliers in the operation and maintenance data using edge computing units deployed on the multi-source acquisition nodes according to the 3σ criterion, and replacing the outliers with historical averages; encoding the number and device ID fields in the outlier-replaced data using preset encoding rules to complete the preprocessing of the operation and maintenance data; verifying the GPS positioning accuracy of the preprocessed mobile inspection terminal's check-in data and calculating the matching degree between the check-in location and the actual location of the target device; when the matching degree is not lower than a preset threshold and image recognition technology confirms that the acquired image contains the target device number, ... If the data is deemed genuine, a manual review process is initiated. For pre-processed work order modification data in the work order management system, the operator's identity, modification time, operation IP address, and comparison information of the modified content are recorded. When the modification involves fault level and responsible person fields, the corresponding electronic approval process record is verified. This record includes the approver's identity and approval opinion. The pre-processed equipment operation and energy consumption data from the equipment sensing terminal are compared with historical data from the same period on a preset historical time day. If the deviation exceeds a preset deviation value, the self-check program of the corresponding sensor is triggered, and the data authenticity is determined based on the self-check results. The verified work order modification data, equipment operation and energy consumption data, and attendance data are integrated to obtain genuine operation and maintenance data.

[0008] In one example, a consortium blockchain framework is created according to preset node deployment and configuration rules. Specifically, this includes: deploying five distributed nodes to form an initial consortium blockchain framework; these five distributed nodes include two core nodes, deployed at the park management center and the operations and maintenance room respectively; and three participating nodes, deployed and connected by a third-party auditing institution, an equipment supplier, and a park industry regulatory department respectively; configuring server resources for the distributed nodes; configuring a practical Byzantine fault-tolerant consensus mechanism in the initial consortium blockchain framework and setting a minimum node count threshold required for consensus verification; and performing network interconnection and identity authentication between the five distributed nodes to complete the creation of the consortium blockchain framework.

[0009] In one example, on the consortium blockchain framework, a dedicated evidence storage chain is created, and preset data writing and indexing rules are configured for each dedicated evidence storage chain. An association mapping relationship is established between the main chain of the consortium blockchain framework and the data of each dedicated evidence storage chain to generate a complete consortium blockchain framework. Specifically, this includes: on the consortium blockchain framework, creating in parallel an operation and maintenance evidence storage chain, an equipment parameter modification traceability chain, and an energy and carbon data evidence storage chain; configuring data writing rules for the operation and maintenance evidence storage chain; the data writing rules include receiving and storing verified inspection check-in records, work order flow records, and fault handling records. Each record is forcibly associated with the operator's identity identifier, timestamp, and operation terminal MAC address information when written, and an index association is established with the corresponding equipment identity identifier; and configuring data writing rules for the equipment parameter modification traceability chain. The data writing rules for the traceability chain are configured; the data writing rules include receiving and storing modification records of equipment operating parameters; the data writing rules for the energy and carbon data storage chain are configured; the data writing rules include receiving and storing energy consumption data, energy-saving renovation scheme metadata, post-renovation energy consumption data, and effect evaluation results data at fixed time intervals; a dedicated hash calculation method is configured for each of the special storage chains to generate the SHA-256 hash value of the data before data writing, and the SHA-256 hash value is submitted to the chain along with the data; in the main chain of the consortium chain framework, a master index entry is created for each data record written to each special storage chain to establish the association mapping relationship between the main chain and the data of each special storage chain, forming the complete consortium chain framework.

[0010] In one example, based on the type of the actual operation and maintenance data, the complete consortium blockchain framework categorizes and stores the actual operation and maintenance data into each dedicated evidence storage chain. Specifically, this includes: generating a unique SHA-256 hash value for the actual operation and maintenance data according to the hash calculation method configured in the dedicated evidence storage chain; packaging the actual operation and maintenance data with the SHA-256 hash value; and initiating an evidence storage transaction request to the complete consortium blockchain framework. The evidence storage transaction request is verified by the consensus mechanism of the complete consortium blockchain framework. When a preset threshold number of nodes in the framework pass the verification, the transaction request is confirmed by consensus, and the actual operation and maintenance data and the SHA-256 hash value are written into the corresponding dedicated evidence storage chain, and the associated index entries of the main chain are updated synchronously.

[0011] In one example, based on a preset five-dimensional distributed indexing rule, the data stored in each special-purpose evidence storage chain is retrieved and anomaly identified to complete the full lifecycle traceability of operation and maintenance data. Specifically, this includes: constructing a unified distributed index for the special-purpose evidence storage chain based on five dimensions: data type, time range, operating subject, equipment number, and associated project; performing multi-scenario data traceability queries based on the unified distributed index; query scenarios include tracing the full lifecycle operation and maintenance records based on equipment number, tracing the entire process operation and associated data based on work order number, tracing the energy and carbon transformation effect based on project identifier, and tracing operational behavior based on personnel identifier; during the retrieval process, anomaly operation identification is performed on the data in the special-purpose evidence storage chain based on preset rules; the anomaly operation includes at least work order anomalies, equipment parameter anomalies, and inspection check-in anomalies; when an anomaly operation is identified, the relevant evidence storage record is automatically locked and an alarm is triggered.

[0012] In one example, after completing the full lifecycle tracing of operation and maintenance data, application service interfaces matching the role permissions of the service requester are provided. Specifically, these include: providing operation and maintenance personnel with operation interfaces for work order processing, parameter modification requests, and fault reporting; providing management decision-makers with analysis interfaces for energy and carbon report generation, operation and maintenance audit analysis, and alarm management; and providing audit institutions with verification interfaces for data hash verification, special audit working paper generation, and annotation. The operations performed by the operation interfaces, analysis interfaces, and verification interfaces are all synchronously stored in the corresponding special evidence storage chain of the consortium blockchain framework.

[0013] On the other hand, embodiments of this application provide a park operation and maintenance data management device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform any of the above-mentioned park operation and maintenance data management methods.

[0014] On the other hand, embodiments of this application provide a non-volatile computer storage medium for park operation and maintenance data management, which stores computer-executable instructions that can execute any of the above-mentioned park operation and maintenance data management methods.

[0015] The above-described technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: By employing edge preprocessing and differentiated verification mechanisms, the authenticity of data such as inspection reports, work orders, and energy consumption is verified, eliminating false data from the data collection stage and reducing the false data rate to below 0.5%. A nuclear consortium blockchain framework and multi-chain parallel storage mechanism, combined with consensus verification and hash verification, ensure tamper-proof data storage, with a data tampering rate approaching 0%. A unified retrieval system is built based on a five-dimensional distributed index, supporting cross-chain and cross-business full lifecycle traceability, with an average traceability response time of no more than 3 seconds, significantly improving auditing and investigation efficiency. Auditing and maintenance costs are reduced, and seamless integration with existing work order and energy / carbon systems is achieved through standardized interfaces, supporting flexible node deployment in small and medium-sized parks while controlling implementation costs while ensuring full functionality. Attached Figure Description

[0016] To more clearly illustrate the technical solution of this application, some embodiments of this application will be described in detail below with reference to the accompanying drawings, in which: Figure 1 A flowchart illustrating a park operation and maintenance data management method provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a park operation and maintenance data management device provided in an embodiment of this application. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0018] Some embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0019] Figure 1 This is a flowchart illustrating a park operation and maintenance data management method provided in an embodiment of this application. This method can be applied to different business domains. Certain input parameters or intermediate results in this process allow for manual intervention and adjustment to help improve accuracy.

[0020] The analysis method involved in the embodiments of this application can be implemented by a terminal device or a server, and this application does not impose any special limitations on it. For ease of understanding and description, the following embodiments are all described in detail using a server as an example.

[0021] Based on this Figure 1 The process may include the following steps: S101: Obtain operation and maintenance data through multi-source acquisition nodes deployed in the park; the operation and maintenance data includes at least equipment fault data, work order flow data and energy consumption data.

[0022] In some embodiments of this application, this step achieves comprehensive and real-time collection of operation and maintenance data by deploying multi-source acquisition nodes in various facilities and systems within the park.

[0023] Taking a smart park (covering an area of ​​500,000 square meters, including 10 office buildings, 5 power distribution rooms, and 200 air conditioning units) as an example, the specific data collection method is as follows: First, deploy equipment sensing terminals on the equipment (such as 200 air conditioning units and power distribution cabinets in the 5 power distribution rooms). The terminal integrates sensors such as current, voltage, and power, and uses the LoRa low-power wide area network protocol for data transmission. The sampling frequency is set to 1 time / second to continuously collect equipment fault signals and real-time energy consumption data.

[0024] Secondly, by connecting to the park's existing ServiceNow work order management system through a standardized RESTful API and WebSocket protocol, work order data can be synchronized in seconds, ensuring real-time linkage between operation and maintenance instructions and execution records.

[0025] Meanwhile, 50 maintenance personnel were equipped with mobile inspection terminals that integrate GPS positioning modules and 13-megapixel cameras, which upload inspection check-in and fault reporting data containing precise location information and photos of on-site equipment via 5G mobile network.

[0026] Furthermore, by deploying a standardized data gateway, the system connects to the park's security and fire protection systems in JSON format, enabling unified access to heterogeneous security and fire protection maintenance data. All data collection nodes employ differentiated transmission protocols and frequencies based on their data types and real-time requirements, collectively forming a comprehensive, multi-modal maintenance data collection network covering equipment, work orders, personnel, and the environment.

[0027] S102: The operation and maintenance data is preprocessed, and the authenticity of the preprocessed operation and maintenance data is verified according to the preset differentiation rules to obtain the real operation and maintenance data.

[0028] In some embodiments of this application, after data collection, real-time preprocessing and preliminary verification are first performed on the edge side near the data source to improve data quality and credibility.

[0029] Specifically, on the Huawei LiteOS edge computing nodes deployed in five zones within the park, preprocessing is performed on the received raw data: for device sensor data, based on 3D... The criteria are used to identify and remove outliers. For example, if the instantaneous energy consumption of an air conditioner at noon in summer exceeds three times its historical average for the same period in the past 7 days, it is determined to be abnormal interference data. The system automatically replaces it with the average for the same period in the past 7 days and marks the data as "abnormal replacement".

[0030] Meanwhile, key identifier fields such as work order number and equipment ID are formatted and standardized, using a unified coding rule of "region, type, number" (e.g., "B-Air-035" represents air conditioning equipment number 35 in area B) to ensure consistency and relevance of data across systems. The entire preprocessing process has a latency controlled within 100 milliseconds, and the data validity rate is no less than 99.5%.

[0031] After preprocessing, differentiated in-depth authenticity verification is performed based on data type: For check-in data uploaded by mobile inspection terminals, the system verifies its GPS positioning accuracy (≤3 meters indoors, ≤5 meters outdoors) and calculates the geographical matching degree between the check-in location and the actual location of the target device; when the matching degree is ≥90% and the target device number is confirmed to be included in the photo through image recognition technology, it is determined to be a genuine and valid check-in; otherwise, a second manual review process is automatically triggered, and the operation and maintenance administrator reviews the photo and location information.

[0032] For work order modification records from the work order management system, the system mandates the recording of four pieces of information for each modification: "Operator ID - Modification Timestamp - Operation IP Address - Comparison of Modified Content". If the modified content involves key fields such as fault level or responsible person, it must be associated with a completed electronic approval process record to be considered valid.

[0033] For the operation and energy consumption data uploaded by the device sensing terminal, the system compares it with the historical data of the same device in the same period of the past 7 days. If the deviation exceeds 20%, the system will automatically trigger the self-test program of the corresponding sensor of the device and determine the authenticity of the data based on the self-test results returned by the sensor.

[0034] All verified work order data, equipment data, and attendance data are integrated into "real operation and maintenance data," and the verification results and timestamps are marked before proceeding to the subsequent evidence storage process.

[0035] S103: Create a consortium blockchain framework based on the preset node deployment rules and preset node configuration rules.

[0036] In some embodiments of this application, in order to build a trusted and tamper-proof evidence storage environment, this step creates a consortium blockchain underlying framework based on multi-party consensus.

[0037] In the implementation scenario of this smart park, five distributed nodes are first deployed according to the node deployment rules to form the initial consortium blockchain framework: two core nodes are deployed on the core server of the park management center and the high-availability server of the operation and maintenance room, respectively, and undertake core responsibilities such as main chain data synchronization, transaction sorting and computing power support; the three participating nodes are deployed and connected by a third-party auditing institution, suppliers of important equipment such as air conditioners and regulatory departments of the park's industry, respectively, and participate in the consensus as supervisors and witnesses.

[0038] Node hardware resources are allocated according to configuration rules. Core nodes use high-performance server configurations (no less than 8 CPU cores, 32GB memory, and 1TB SSD storage) to support high-frequency data processing; participating nodes can use medium configurations (no less than 4 CPU cores and 16GB memory).

[0039] At the network level, two-way authentication and secure network interconnection are achieved between all nodes, and the data synchronization latency between nodes is configured to not exceed 500 milliseconds. At the software level, a consensus mechanism based on practical Byzantine fault tolerance is enabled in this consortium blockchain framework, and the minimum number of nodes required for consensus verification is set at 3 nodes. This allows the consortium blockchain network to tolerate Byzantine failures in no more than 33% of its nodes, thereby ensuring consensus security while maintaining high efficiency.

[0040] After completing the above node deployment, resource configuration, consensus configuration and network interconnection, an initial consortium blockchain framework with multi-party supervision, high fault tolerance and real-time synchronization characteristics is created, providing underlying support for the subsequent on-chain storage of business data.

[0041] S104: On the consortium blockchain framework, create a special evidence storage chain, configure preset data writing and indexing rules for each special evidence storage chain, and establish an association mapping relationship between the main chain of the consortium blockchain framework and the data of each special evidence storage chain to generate a complete consortium blockchain framework.

[0042] In some embodiments of this application, on top of the established consortium blockchain underlying framework, in order to meet the needs of classification management, efficient retrieval and independent auditing of different business data, three logically independent but shared underlying consensus special evidence storage chains are initialized in parallel, and refined data governance rules are configured for them.

[0043] The first chain is the operation and maintenance evidence storage chain, which is specifically used to store process data related to personnel operations. Its data writing rules stipulate that all verified inspection check-in records, work order full-process status records, and fault handling operation records must be forcibly associated with the triplet information of operator identity - timestamp accurate to milliseconds - MAC address of the operation terminal when writing, and indexed and associated with the device ID operated (such as "B-Air-035") or the work order number processed.

[0044] The second item is the equipment parameter modification traceability chain, which focuses on recording the history of changes to equipment operating parameters. Its writing rules require that any equipment parameter modification record must include seven core elements in a structured format: equipment number, parameter value before modification, parameter value after modification, identity of the person making the modification, identity of the approver, reason for modification, and timestamp. It also adopts a chain storage structure, that is, the storage content of the current record contains a reference to the hash value of the previous version record, thus forming an unbreakable modification trajectory chain.

[0045] The third item is the energy and carbon data storage chain, which serves the park's energy and carbon management needs. Its writing rules are set to receive and store raw energy consumption data in a fixed time interval sequence. It also supports the associated storage of metadata of energy-saving renovation projects, energy consumption data of the same period after renovation, and effect evaluation results in third-party evaluation reports. The data from these different stages are linked through a unique renovation project ID.

[0046] Each dedicated evidence storage chain is configured with its own hash calculation method, generating a unique hash value for its content before data is written. The data body and hash value are then packaged and submitted to the chain. Simultaneously, in the main chain of the consortium blockchain, a corresponding master index entry is created for each data record successfully written to a dedicated sub-chain. This entry includes at least the dedicated chain identifier to which the record belongs, its transaction hash value in the sub-chain, and key business fields for fast retrieval.

[0047] Through this association mapping relationship of main chain index plus sub-chain storage, a complete consortium blockchain framework is built that can both classify and isolate business data and support global unified retrieval and verification.

[0048] S105: Based on the type of the actual operation and maintenance data, the actual operation and maintenance data is classified and stored in each special evidence storage chain through the complete consortium blockchain framework.

[0049] In some embodiments of this application, after data verification and consortium blockchain framework construction are completed, this step performs a trusted evidence storage operation to securely and immutably write the real operation and maintenance data onto the corresponding blockchain.

[0050] The system first automatically routes the data to the corresponding special evidence storage chain in the complete consortium blockchain framework based on the data's business attributes and metadata identifiers. For example, it routes the attendance record of maintenance personnel inspecting air conditioner No. 35 in Zone B to the maintenance operation evidence storage chain; it routes the application and approval record of changing the air conditioner temperature from 24℃ to 25℃ to the equipment parameter modification traceability chain; and it routes the current and energy consumption readings of the air conditioner every 15 minutes to the energy and carbon data evidence storage chain.

[0051] For each piece of data to be stored, the system executes the pre-defined writing process of its target special chain. Before writing, the system calls the SHA-256 hash algorithm to generate a unique hash value for the data content. Then, the data content and the hash value are packaged together and encapsulated into a storage transaction request, which is then broadcast to the consortium blockchain framework.

[0052] Furthermore, the transaction request then enters the network's consensus process. According to the configured Practical Byzantine Fault Tolerance consensus rules, at least three nodes are required to verify the transaction. Once the minimum threshold number of nodes has verified the transaction and reached a consensus, the transaction is confirmed as valid, and the data content and its hash value are permanently written as a new block or transaction into the corresponding dedicated notarization chain. At the same time, the main chain's global index is updated synchronously, adding an index entry pointing to this new data.

[0053] After successful notarization, the system generates and returns notarization credentials for the data, including its transaction hash on the blockchain, the timestamp of successful notarization, and a list of nodes participating in consensus verification, thus completing the classified and trusted storage of this real operational data. For example, the current data and its hash value e5f6g7h8... of air conditioner unit 35 in Zone B at 14:30 on May 20, 2024, were successfully written to the Energy Carbon Data Notarization Chain and can be queried on the main chain using the device ID and timestamp index.

[0054] S106: Based on the preset five-dimensional distributed indexing rules, retrieve and identify anomalies in the data stored in each special evidence storage chain to complete the full lifecycle traceability of operation and maintenance data.

[0055] In some embodiments of this application, this step provides powerful traceability and insight capabilities after the data has been stored on the blockchain.

[0056] First, based on five core dimensions—data type, time range, operating entity, device number, and associated project—the system constructs a unified, distributed inverted index cluster for all the stored data in the special evidence storage chain. The index update frequency reaches the second level, ensuring that the retrieval response latency for a single piece of data does not exceed 500 milliseconds.

[0057] Based on this five-dimensional index, the system supports various high-value traceability query scenarios. For example, in the single-device full lifecycle traceability scenario, by inputting the device number B-Air-035, the system can automatically retrieve all historical inspection check-in records, related fault work orders and processing records of the device from the operation and maintenance evidence chain; extract all parameter change trajectories such as temperature and mode from the device parameter modification traceability chain; summarize its historical energy consumption curves from the energy and carbon data evidence chain; and finally integrate them to generate a visualized holographic operation and maintenance file of the device with a timeline.

[0058] In the scenario of full-process traceability of work orders, by entering a work order number, the system can display the complete process of the work order from creation, dispatch, multiple modifications, on-site handling to closure, and also display screenshots of abnormal equipment current when the fault occurred and photos of on-site troubleshooting by maintenance personnel, forming a closed-loop evidence chain of event-work order-person-image.

[0059] In the scenario of tracking the effects of energy and carbon retrofits, by entering the ID of a lighting energy-saving retrofit project, the system can automatically extract the electricity consumption data for the three months before and after the retrofit from the energy and carbon data storage chain. Based on the accounting model built into standards such as GB / T 32151, it can automatically calculate the accurate energy saving rate and carbon emission reduction. The generated assessment report includes the on-chain hash value of key data, which can be verified by third parties with one click.

[0060] In the scenario of tracing operational behavior, by entering a maintenance personnel ID and a month, the system can calculate the completion rate of all inspection tasks for that month, the average processing time of work orders, the parameter modification requests initiated, and their approval status.

[0061] While performing retrieval and tracing, the system's built-in rule engine performs real-time scanning and identification based on a preset abnormal feature library. For example, it can identify abnormal work orders (such as the same work order being modified more than 3 times within 1 hour, or the fault level being modified without approval); abnormal equipment parameters (such as setting the air conditioner temperature to below the safe range of 16℃); and abnormal inspection check-in (such as the check-in location matching the actual equipment location less than 90%, or the check-in record not containing photos of the on-site equipment).

[0062] Once the above-mentioned abnormal operation is detected, the system will immediately and automatically lock the relevant evidence storage records and push a tiered alarm message to the park management decision-making platform. The alarm message includes the type of abnormality, a detailed description, and a link to the on-chain data evidence storage that can be directly viewed.

[0063] The alarm event itself will also be recorded as a new operation log, ensuring the traceability of the control process. Through the above-mentioned multi-dimensional, efficient, and intelligent retrieval and identification mechanisms, the goal is to achieve accurate traceability and transparent supervision of the entire lifecycle of any maintenance object, process, or operation within the park.

[0064] It should be noted that, although the embodiments in this application are based on... Figure 1 Steps S101 to S106 will be described sequentially, but this does not mean that steps S101 and S106 must be performed in a strict order. The reason this embodiment follows this order is... Figure 1 The order in which steps S101 to S106 are described is provided to facilitate understanding of the technical solutions of the embodiments of this application by those skilled in the art. In other words, in the embodiments of this application, the order of steps S101 to S106 can be appropriately adjusted according to actual needs.

[0065] pass Figure 1 This method utilizes edge preprocessing and differentiated verification mechanisms to verify the authenticity of data such as inspection reports, work orders, and energy consumption, eliminating false data from the data collection stage and reducing the false data rate to below 0.5%. Employing a nuclear consortium blockchain framework and a multi-chain parallel storage mechanism, combined with consensus verification and hash verification, it achieves tamper-proof data storage, with a data tampering rate approaching 0%. A unified retrieval system is built based on a five-dimensional distributed index, supporting cross-chain and cross-business full lifecycle traceability, with an average traceability response time of no more than 3 seconds, significantly improving auditing and investigation efficiency. Auditing and maintenance costs are reduced, and seamless integration with existing work order and energy / carbon systems via standardized interfaces supports flexible node deployment in small and medium-sized parks, controlling implementation costs while ensuring full functionality.

[0066] Figure 2 A schematic diagram of a park operation and maintenance data management device provided in this application embodiment includes: At least one processor; and, A memory that is communicatively connected to at least one processor; wherein, A campus operation and maintenance data management method in which the memory stores instructions that can be executed by at least one processor, such that at least one processor can perform any of the above-mentioned tasks.

[0067] Some embodiments of this application provide a non-volatile computer storage medium for park operation and maintenance data management, which stores computer-executable instructions that can execute any of the above-mentioned park operation and maintenance data management methods.

[0068] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.

[0069] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0070] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0071] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0072] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0073] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0074] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0075] Memory may include non-persistent storage in computer-readable media, random access memory (RAM), and non-volatile memory such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0076] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0077] It should also be noted that 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 process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0078] The above are merely embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the technical principles of this application should fall within the protection scope of this application.

Claims

1. A method for managing park operation and maintenance data, characterized in that, The method includes: Operation and maintenance data is acquired through multi-source acquisition nodes deployed within the park; the operation and maintenance data includes at least equipment fault data, work order flow data, and energy consumption data. The operation and maintenance data is preprocessed, and the authenticity of the preprocessed operation and maintenance data is verified according to preset differentiation rules to obtain the real operation and maintenance data. Create a consortium blockchain framework based on preset node deployment rules and preset node configuration rules; On the consortium blockchain framework, a special evidence storage chain is created, and preset data writing and indexing rules are configured for each special evidence storage chain. The association mapping relationship between the main chain of the consortium blockchain framework and the data of each special evidence storage chain is established to generate a complete consortium blockchain framework. Based on the type of the actual operation and maintenance data, the actual operation and maintenance data is classified and stored in each dedicated evidence storage chain through the complete consortium blockchain framework; Based on the preset five-dimensional distributed indexing rules, the data stored in each special evidence storage chain is retrieved and anomalies are identified to complete the full lifecycle traceability of operation and maintenance data.

2. The method according to claim 1, characterized in that, The acquisition of operation and maintenance data through multi-source acquisition nodes deployed within the park specifically includes: By using device sensing terminals deployed on equipment in the park and employing the LoRa protocol at a preset sampling frequency, fault signals and energy consumption data of the park equipment are collected. By connecting to the standardized interface of the park's work order management system, and using RESTful API and WebSocket protocol, the entire process of work order creation, allocation, modification and processing data is synchronized. The mobile inspection terminal configured for maintenance personnel uses the 5G mobile communication protocol to collect inspection check-in and fault reporting data; the inspection check-in data includes location information and on-site images. By deploying a standardized gateway from a third-party system, using JSON data format, heterogeneous operation and maintenance data from security and fire protection systems can be collected.

3. The method according to claim 1, characterized in that, The process of preprocessing the operation and maintenance data and verifying its authenticity according to preset differentiation rules to obtain authentic operation and maintenance data specifically includes: According to the 3σ criterion, outliers in the operation and maintenance data are identified by edge computing units deployed on the side of multi-source acquisition nodes, and the outliers are replaced with the historical average values ​​for the same period. By using preset encoding rules, the number and device ID fields in the data after the outlier replacement are encoded to complete the preprocessing of operation and maintenance data; For the pre-processed check-in data of the mobile inspection terminal, verify the GPS positioning accuracy and calculate the matching degree between the check-in location and the actual location of the target device. When the matching degree is not lower than the preset threshold and the image recognition technology confirms that the collected image contains the target device number, it is determined to be real attendance data; otherwise, the manual review process is triggered. For the pre-processed work order modification data in the work order management system, record the operator's identity, modification time, operation IP address, and comparison information of the modified content; When the modifications involve the fault level and the person responsible for handling the issue, verify the corresponding electronic approval process record; the record includes the approver's identity and approval opinion. The preprocessed equipment operation and energy consumption data of the equipment sensing terminal are compared with the historical data of the same period of the same preset historical time day; If the deviation exceeds the preset deviation value, the self-test program of the corresponding sensor is triggered, and the authenticity of the data is determined based on the self-test results. The verified work order modification data, equipment operation and energy consumption data, and attendance data are integrated to obtain the actual operation and maintenance data.

4. The method according to claim 1, characterized in that, The step of creating a consortium blockchain framework based on preset node deployment rules and preset node configuration rules specifically includes: Five distributed nodes are deployed to form the initial consortium blockchain framework; the five distributed nodes include two core nodes, which are deployed in the park management center and the operation and maintenance room, respectively; and three participating nodes, which are deployed and connected by a third-party auditing institution, an equipment supplier, and the park's industry regulatory department, respectively. Configure server resources for distributed nodes; Configure a practical Byzantine fault-tolerant consensus mechanism in the initial consortium blockchain framework and set a minimum node number threshold required for consensus verification; Five distributed nodes are interconnected and authenticated to complete the creation of the consortium blockchain framework.

5. The method according to claim 1, characterized in that, On the consortium blockchain framework, a dedicated evidence storage chain is created, and preset data writing and indexing rules are configured for each dedicated evidence storage chain. Furthermore, a mapping relationship is established between the main chain of the consortium blockchain framework and the data of each dedicated evidence storage chain to generate a complete consortium blockchain framework. Specifically, this includes: On the aforementioned consortium blockchain framework, operation and maintenance evidence storage chains, equipment parameter modification traceability chains, and energy and carbon data evidence storage chains are created in parallel. Configure data writing rules for the operation and maintenance evidence storage chain; the data writing rules include receiving and storing verified inspection check-in records, work order flow records and fault handling records. When each record is written, it is forcibly associated with the operator's identity, timestamp, and operation terminal MAC address information, and an index is established with the corresponding device identity. Modify the traceability chain configuration data writing rules for the device parameters; the data writing rules include receiving and storing modification records of device operating parameters; Configure data writing rules for the energy and carbon data storage chain; the data writing rules include receiving and storing energy consumption data, energy-saving renovation scheme metadata, post-renovation energy consumption data and effect evaluation results data at fixed time intervals. Configure a dedicated hash calculation method for each of the aforementioned special evidence storage chains, generate the SHA-256 hash value of the data before writing the data, and submit the SHA-256 hash value and the data together to the chain; In the main chain of the consortium blockchain framework, a master index entry is created for each data record written to each special evidence storage chain, so as to establish the association mapping relationship between the main chain and the data of each special evidence storage chain, thus forming the complete consortium blockchain framework.

6. The method according to claim 1, characterized in that, The step of classifying and storing the real operation and maintenance data into each dedicated evidence storage chain according to the type of the real operation and maintenance data, through the complete consortium blockchain framework, specifically includes: Based on the hash calculation method configured in the dedicated evidence storage chain, a unique SHA-256 hash value for the actual operation and maintenance data is generated, and the actual operation and maintenance data and the SHA-256 hash value are packaged together to initiate an evidence storage transaction request to the complete consortium chain framework. The notarization transaction request is verified by the consensus mechanism of the complete consortium blockchain framework. When a preset threshold number of nodes in the framework pass the verification, the transaction request is confirmed by consensus, and the real operation and maintenance data and SHA-256 hash value are written into the corresponding special notarization chain, and the associated index entries of the main chain are updated synchronously.

7. The method according to claim 1, characterized in that, The process involves retrieving and identifying anomalies in the data stored in each specialized evidence storage chain according to a preset five-dimensional distributed indexing rule, in order to complete the full lifecycle traceability of operation and maintenance data. Specifically, this includes: A unified distributed index for the special evidence storage chain is constructed based on five dimensions: data type, time range, operating entity, device number, and associated projects. The unified distributed index is used to perform multi-scenario data traceability queries. The query scenarios include tracing the entire lifecycle operation and maintenance records based on the device number, tracing the entire process operation and related data based on the work order number, tracing the energy and carbon transformation effect based on the project identifier, and tracing the operation behavior based on the personnel identifier. During the retrieval process, abnormal operations are identified in the data of the special evidence storage chain based on preset rules; the abnormal operations include at least work order abnormalities, equipment parameter abnormalities, and inspection check-in abnormalities. When an abnormal operation is detected, the relevant evidence records will be automatically locked and an alarm will be triggered.

8. The method according to claim 1, characterized in that, After completing the full lifecycle traceability of operation and maintenance data, application service interfaces matching the role permissions of the service requester are provided, specifically including: Provide operation and maintenance personnel with operation interfaces for work order processing, parameter modification requests, and fault reporting; Provides management decision-makers with analytics interfaces for generating energy and carbon reports, performing operations and maintenance audits, and managing alarms. The system provides verification interfaces for data hash verification, special audit working paper generation, and annotation to the auditing role; the operations performed by the operation interface, analysis interface, and verification interface are all synchronously stored in the corresponding special evidence storage chain of the consortium blockchain framework.

9. A park operation and maintenance data management device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform a campus operation and maintenance data management method as described in any one of claims 1-8.

10. A storage medium for park operation and maintenance data management, storing computer-executable instructions, characterized in that, The computer-executable instructions are capable of executing the park operation and maintenance data management method described in any one of claims 1-8.

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