Property service trace data processing method and system based on AI and block chain evidence storage
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-25
- Publication Date
- 2026-04-10
Smart Images
Figure CN121833840A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The application relates to the technical field, in particular to a property service trace data processing method, system and device based on AI and blockchain storage and a medium. BACKGROUND
[0002] With the continuous development of residential communities, office buildings, industrial parks and other types of scenes, the demand for management and supervision of property services is increasing. Effective processing of property service trace data is of great significance in improving service quality, ensuring service transparency and handling disputes. Effective property service trace management can provide more reliable service protection for property owners, and also helps property management to standardize service processes and improve management efficiency. With the increasing demand for service quality and transparency, how to accurately and efficiently process property service trace data has become an important problem to be solved in this field.
[0003] The prior art solves the problem of property service trace management by using manual paper records. Property personnel fill out inspection forms, work orders and other forms to record the service process and results. However, the manual recording method can preserve basic service information to some extent and is suitable for some scenarios with low informationization requirements. Traditional electronic work order systems use APP or PC to let property personnel submit text or picture reports, which improves the speed and convenience of information transmission and has been widely used in a certain range. Some systems introduce cameras to record the service scene through the installation of cameras, which can record the service scene and provide a basis for service supervision.
[0004] However, the conventional means of the prior art generally have the problem of insufficient authenticity, and cannot automatically verify whether the service behavior actually occurred and whether it conforms to the standard. Moreover, even if the storage is performed, the original data is tamperable, resulting in weak evidence effectiveness, which is difficult to use as effective evidence in legal disputes. At the same time, relying on manual review of a large number of pictures or videos is inefficient, costly and slow to respond. The service process, results and storage do not form an automated closed loop, and the management granularity is coarse. SUMMARY
[0005] The application aims to provide a property service trace data processing method based on AI and blockchain storage, which can automatically identify and compare whether the actual service and the contract content are consistent, improve the tamper-proof nature of the evidence package storage, and support integrated traceability verification processing.
[0006] In a first aspect, the application provides a property service trace data processing method based on AI and blockchain storage, which adopts the following technical solution: A property service trace data processing method based on AI and blockchain storage, comprising: inputting the property service contract into a plurality of pre-trained deep learning models, outputting a structured contract service content recognition result as a service standard; packaging the service record of the enterprise into a service standard evidence package with the service standard, generating a unique digital fingerprint through encryption, the service record including a service record recorded according to the service standard; entering the unique digital fingerprint of the service standard evidence package and the evidence package attribute into a blockchain, and returning a blockchain transaction ID as a notarization certificate; responding to a query request, obtaining the corresponding notarization record from the blockchain network based on the task ID or the blockchain transaction ID in the evidence package attribute.
[0007] By adopting the above technical solutions, the pre-trained deep learning model is used to process the property service contract to obtain a structured recognition result as a service standard, and the notarization record is packaged into a service standard evidence package with the service standard to generate a unique digital fingerprint through encryption, so that the data can be safely encrypted and uniquely identified. The unique digital fingerprint of the service evidence package and the evidence package attribute are entered into the blockchain, and the transaction ID is used as the notarization certificate, so that the data cannot be tampered with. The notarization record can be queried by the task ID or the blockchain transaction ID, so that the data can be traced and verified. The authenticity and automation of the property service trace data processing are improved, the legal evidence effectiveness is enhanced, the data tampering is reliably prevented, and the owner's trust is improved.
[0008] In a preferred example, the application can be further configured to: the step of packaging the notarization record of the enterprise into a service standard evidence package with the service standard, and generating a unique digital fingerprint through encryption, the notarization record including a service record recorded according to the service standard, comprising: packaging the notarization record and the service standard into a JSON format service standard evidence package; calculating the SHA-256 hash value of the service standard evidence package as a unique digital fingerprint.
[0009] By adopting the above technical solutions, the notarization record and the service standard are packaged into a JSON format service standard evidence package, ensuring that the data structure is standardized and compatible, facilitating transmission and analysis of different systems, and that JSON is lightweight and easy to read, reducing data processing redundancy. The SHA-256 hash value is calculated to generate a unique digital fingerprint, and small changes in the service standard evidence package data will significantly change the hash value, ensuring the sensitivity and uniqueness of the digital fingerprint to the original data, laying a foundation for the accuracy and tamper resistance of the blockchain notarization, and strengthening the reliability of the service standard evidence package as legal evidence.
[0010] The application can be further configured in a preferred example as follows: the step of entering the unique digital fingerprint of the service standard evidence package and the evidence package attribute into the blockchain, and taking the returned blockchain transaction ID as the evidence storage certificate, comprises: entering the hash value, timestamp and task ID of the service standard evidence package into the blockchain; taking the returned blockchain transaction ID as the evidence storage certificate.
[0011] By adopting the above technical solution, the hash value, timestamp and task ID of the service standard evidence package are entered into the blockchain, and the returned blockchain transaction ID is taken as the evidence storage certificate, which can maximize the protection of data authenticity. Any modification of the original data will cause the hash to be mismatched, thereby ensuring the data integrity.
[0012] The application can be further configured in a preferred example as follows: after the step of responding to the query request, obtaining the corresponding evidence storage record from the blockchain network based on the task ID or the blockchain transaction ID in the evidence package attribute, further comprising: comparing the evidence storage record in the service standard evidence package with the service standard to verify the data integrity.
[0013] By adopting the above technical solution, the differences between the actual service content and the service standard can be found in time when querying the blockchain evidence storage record, thereby improving the transparency and traceability of property services.
[0014] The application can be further configured in a preferred example as follows: before the step of packaging the evidence storage record of the enterprise and the service standard into a service standard evidence package, generating a unique digital fingerprint through encryption, and the evidence storage record comprising the service record recorded according to the service standard, further comprising: collecting images or videos in the service process as evidence storage records.
[0015] By adopting the above technical solution, the on-site situation of service execution can be recorded intuitively, and the image or video metadata has strong objectivity and persuasiveness, providing a concrete basis for evidence storage records, making the subsequent comparison and verification with the service standard evidence package more operable and accurate, and further enhancing the real traceability of the service process.
[0016] The application can be further configured in a preferred example as follows: before the step of packaging the evidence storage record of the enterprise and the service standard into a service standard evidence package, generating a unique digital fingerprint through encryption, and the evidence storage record comprising the service record recorded according to the service standard, further comprising: collecting GPS positions, timestamps and device IDs in the service process as evidence storage records.
[0017] By adopting the above technical solution, the evidence record has multi-dimensional attributes of spatiotemporal coordinates and device association, which effectively avoids false records or spatiotemporal misalignment during the service process. It provides hard data support for subsequent comparison with the service location, time range and equipment requirements preset in the service standard evidence package, and further improves the rigor of the evidence package and the reliability of the verification results.
[0018] In a preferred embodiment, this application can be further configured as follows: the step of verifying data integrity by comparing the evidence records and service standards in the service standard evidence package includes: Play back the video from the service standard evidence package.
[0019] By adopting the above technical solutions, the dynamic demonstration process of the standard service process can be presented intuitively, providing verification personnel with a visual reference benchmark, enabling them to clearly grasp the standardized steps, operational details and key nodes of service execution.
[0020] Secondly, this application provides a property service trace data processing system based on AI and blockchain evidence storage, which adopts the following technical solution: A property service record data processing system based on AI and blockchain evidence storage includes: Contract generation service standard module: This module is used to input property service contracts into several pre-trained deep learning models and output the structured contract service content recognition results as service standards. Service Standard Evidence Package Encryption Module: This module is used to package the enterprise's evidence records with the service standards into a service standard evidence package, and generate a unique digital fingerprint through encryption. The evidence records include service records recorded according to the service standards. Service Standard Evidence Package Storage Module: This module is used to record the unique digital fingerprint and attributes of the service standard evidence package into the blockchain and use the returned blockchain transaction ID as a storage certificate. Evidence storage record query module: Used to respond to query requests and retrieve the corresponding evidence storage record from the blockchain network based on the task ID or blockchain transaction ID in the evidence package attributes.
[0021] Thirdly, this application provides an electronic device that adopts the following technical solution: An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described method for processing property service trace data based on AI and blockchain evidence storage.
[0022] Fourthly, this application provides a computer storage medium, as follows: A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the aforementioned method for processing property service trace data based on AI and blockchain evidence storage.
[0023] Fifthly, this application provides a computer program product, which adopts the following technical solution: A computer program product, characterized in that the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer executes the aforementioned method for processing property service trace data based on AI and blockchain evidence storage.
[0024] In summary, this application has the following beneficial technical effects: This application can use a pre-trained deep learning model to identify contract service content as a service standard, and combine it with the enterprise's evidence storage records. Through blockchain evidence storage, it can achieve secure encryption and unique identification of service data for online contracts and offline evidence storage, so as to ensure the authenticity of service data. At the same time, it realizes the full automation of the process from contract data collection to evidence storage, reduces manual review, and solves the problems of existing technologies relying on manual labor and low efficiency. Attached Figure Description
[0025] Figure 1 This is a flowchart of a property service trace data processing method based on AI and blockchain evidence storage in one embodiment of this application.
[0026] Figure 2 This is a flowchart of a sub-step of step S2 in one embodiment of this application.
[0027] Figure 3 This is a flowchart of a sub-step of step S3 in one embodiment of this application.
[0028] Figure 4 This is a flowchart of the steps added after step S4 in one embodiment of this application.
[0029] Figure 5 This is an additional step added before step S2 in one embodiment of this application. Figure 1 .
[0030] Figure 6 This is an additional step added before step S2 in one embodiment of this application. Figure 2 .
[0031] Figure 7 This is a flowchart of a sub-step of step S40 in one embodiment of this application.
[0032] Figure 8 This is a schematic diagram of the structure of a property service trace data processing system based on AI and blockchain evidence storage, which is one embodiment of this application.
[0033] Figure 9 This is a schematic block diagram of an electronic device in one embodiment of this application.
[0034] Attached reference numerals: 1. Contract generation service standard module; 2. Service standard evidence package encryption module; 3. Service standard evidence package storage module; 4. Storage record query module. Detailed Implementation
[0035] The following is in conjunction with the appendix Figures 1-9 This application will be described in further detail.
[0036] It should be noted that, in the embodiments of this invention, the data related to object information, when applied to specific products or technologies, requires the permission or consent of the object. Furthermore, the collection, use, and processing of this data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if the embodiments of this invention involve data related to an object, it must be obtained with the object's authorization and consent, the authorization and consent of relevant departments, and in accordance with the relevant laws, regulations, and standards of the country and region. If the embodiments involve personal information, the acquisition of all personal information requires the individual's consent. If sensitive information is involved, the separate consent of the information subject is required. The embodiments also need to be implemented with the object's authorization and consent.
[0037] refer to Figure 1 A method for processing property service trace data based on AI and blockchain evidence storage, specifically including: S1. Input the property service contract into several pre-trained deep learning models, and output the structured contract service content recognition results as the service standard.
[0038] Specifically, for the identified property service obligations clauses, the system will break them down into multiple dimensions such as service provider, service action, service recipient, service standard, service space, and service frequency. For example, "clean the lobby floor twice a day" will be parsed as {Action: Cleaning, Recipient: Floor, Standard: Cleanliness, Space: Lobby, Frequency: 2 times / day}.
[0039] At the same time, parameterized obligations are associated with specific grid sets in the spatial grid system, and owner obligations (such as fees) and key supporting clauses (such as liability for breach of contract) are quantified into rules that can be triggered for calculation. This transforms the originally unstructured contract text, which relies on human understanding, into a structured data model that can be directly read, processed, and executed by machines.
[0040] Specifically, based on the contract parameter framework established by identifying key contract parameters, corresponding actual performance parameters are extracted from the structured identification results generated by several deep learning models. For example, the actual cleaning area (grid list) and actual cleaning completion score are extracted from the identification results of cleaning behavior; the actual inspection points (grid list), actual arrival time, and actual inspection item status are extracted from the identification results of inspection behavior.
[0041] Furthermore, the property service parameters are not extracted in isolation, but are closely bound to the specific service tasks, time windows, and spatial location grids that triggered this identification, forming a data package of actual performance with spatiotemporal context. This enables the AI's ability to identify complex service scenarios to output standardized performance data aligned with the contract terms, allowing the system to accurately compare the actual services with those stipulated in the contract at the same data level, such as comparing "the contract stipulates cleaning grid G101" with "grids G101 and G102 were actually cleaned".
[0042] S2. Package the company's evidence records and service standards into a service standard evidence package, and generate a unique digital fingerprint through encryption. The evidence records include service records recorded according to the service standards.
[0043] Specifically, the company's evidence records and service standards are integrated according to a preset packaging format to generate a complete data packet. A preset cryptographic algorithm is used to perform a one-way hash calculation on the entire content of this data packet, generating a fixed-length and unique digital fingerprint. This creates a unique and sensitive digital identity for the entire service evidence packet; any minor alteration to the data within the packet will result in a significant change to the fingerprint, thus providing a layer of encryption protection against tampering during the data packaging stage.
[0044] The enterprise's evidence records will record the unique code of one or more spatial grids involved in each service behavior, and can automatically generate quantitative data that occurred in these grids during this service based on the identification results, such as the duration of the service operation and the proportion of the grid area covered. This makes the packaged service standard evidence package a self-descriptive, high information density data unit, realizing the transformation from raw data stream to structured evidence objects with clear business semantics.
[0045] S3. Record the unique digital fingerprint and attributes of the service standard evidence package into the blockchain, and use the returned blockchain transaction ID as a certificate of evidence storage.
[0046] Specifically, the unique data fingerprint of the service standard evidence package provides a legally recognized timestamp and proof of existence, and by utilizing the distributed and immutable characteristics of blockchain, it achieves high credibility and resistance to single-point repudiation of the evidence results.
[0047] S4. Respond to the query request and retrieve the corresponding evidence record from the blockchain network based on the task ID or blockchain transaction ID in the evidence package attributes.
[0048] Specifically, the system provides a verification interface that allows users to initiate queries by submitting service task identifiers or blockchain transaction credentials. Based on the task ID or blockchain transaction ID, the system can automatically perform collaborative retrieval and comparison verification of on-chain evidence records and off-chain original evidence packages, thereby constructing a transparent verification closed loop that connects the front and back ends. This enables any stakeholder to conveniently and independently verify the completeness and authenticity of service evidence, greatly improving the efficiency of traceability and verification and the degree of judicial acceptance.
[0049] refer to Figure 2 Furthermore, in one embodiment, step S2 is refined into the following sub-steps: S20. Package the evidence records and service standards into a JSON format service standard evidence package.
[0050] Specifically, the generated JSON format service evidence package is structured to carry and associate multi-dimensional information. It includes not only encrypted or indexed original image / video stream data, metadata such as time and location, and structured recognition results output by several deep learning models, but also spatial grid identifier fields and quantitative statistical summary fields.
[0051] The service standard evidence package records the unique code of one or more spatial grids involved in each service action, and can automatically generate quantitative data that occurred in these grids during the service based on the identification results, such as the duration of the service operation and the proportion of grid area covered. This makes the evidence package a self-describing, high-information-density data unit, realizing the transformation from raw data stream to structured evidence objects with clear business semantics.
[0052] Furthermore, the unified JSON format not only facilitates system transmission, storage, and parsing, but more importantly, it strongly binds the "behavior-location-quantification result" generated in a service event, forming a complete data snapshot. This provides a standardized, machine-processable data foundation for subsequent location-based cost allocation, service performance analysis, and cross-evidence package aggregation statistics.
[0053] S21. Calculate and obtain the SHA-256 hash value of the service standard evidence package as a unique digital fingerprint.
[0054] Specifically, the collision-resistant SHA-256 algorithm is used to generate a fixed-length unique digital fingerprint, or hash value. This unique digital fingerprint, combined with the internal structured characteristics of the evidence package, forms a hierarchical integrity protection mechanism. The system calculates a hash for the entire evidence package, and this hash value is extremely sensitive to any changes to any byte within the package. This hash value serves as the unique, immutable identity credential for the evidence package in the digital world. Furthermore, the overall hash value of the service evidence package can form an internal verification relationship with certain key fields within the evidence package, such as the hash of the identification result and the hash of the original data fragment, thereby providing the service evidence package with cryptographically strong integrity and authenticity guarantees.
[0055] Furthermore, any tampering with the data within the service standard evidence package—whether it's modifying an identification conclusion, replacing an image, or altering a grid ID—will cause the final calculated hash value to be mismatched with the fingerprint initially stored on the blockchain, making it easily detectable. Therefore, transforming the service evidence package, which contains complex business logic, into a trusted digital entity with strong anti-counterfeiting features provides a crucial, immutable original data anchor for subsequent blockchain-based evidence storage and judicial acceptance, further ensuring the security and traceability of service data.
[0056] In addition, refer to Figure 3 Furthermore, in one embodiment, step S3 is refined into the following sub-steps: S30. Record the hash value, timestamp, and task ID of the service standard evidence package into the blockchain.
[0057] Specifically, during transaction construction, the system does not simply list data. Instead, it binds hash values, timestamps, and task IDs to a time proof from a trusted time source or the blockchain itself, forming a logically self-consistent evidence unit. This generated evidence unit is sent to a distributed ledger network maintained by multiple consensus nodes. The consensus algorithm of the distributed ledger network ensures that once data is confirmed, it cannot be unilaterally revoked or tampered with, thus providing legally valid proof of existence and a timestamp service for the digital fingerprint of the service standard evidence package.
[0058] S31. Use the returned blockchain transaction ID as proof of authenticity.
[0059] Specifically, the blockchain transaction ID, or TxID, is strongly correlated with the previously generated service evidence package, its internal gridded identification results, and the original task information. Any subsequent verification party, such as the owner, property manager, or arbitration institution, only needs to know this TxID to independently query and verify the authenticity and status of the retrieved evidence records from the blockchain network, without relying on the original evidence storage system's backend database. This creates a decentralized trust credential that does not depend on a single authority.
[0060] Because TxID is lightweight and transferable, and it corresponds to immutable evidence stored on the blockchain, it can greatly simplify the process of presenting and verifying evidence, and supports efficient and transparent integrated traceability and verification processing.
[0061] In addition, refer to Figure 4 Furthermore, in one embodiment, after step S4, step S40 is added: S40. Compare the service content evidence package with the service standard evidence package to verify the integrity of the data.
[0062] Specifically, the evidence storage records not only include the original images or video streams and their metadata during the acquisition process, but more importantly, they introduce spatial grid coding as the core annotation dimension.
[0063] By comparing the corresponding entries of the company's evidence records in the service standard evidence package with the structured data of the service standard in the contract, it is determined whether there are any discrepancies between the actual service content and the content stipulated in the contract. If there are discrepancies, a reminder is issued to the company, thereby verifying the integrity of the data and improving the transparency and traceability of property services.
[0064] Furthermore, when responding to user queries or performing background audits, the system not only obtains a single evidence record directly associated with a specific task ID or blockchain transaction ID, but also actively retrieves all on-chain evidence records associated with the same service subject, service type, and key related dimensions, such as a specific spatial grid, within the preset time window of the contract service period corresponding to the task (in this embodiment, daily), and accurately counts their total number.
[0065] The service frequency statistics and comparison logic is multi-dimensional and strongly correlated, rather than simply counting the total number of uploads. Instead, it can identify the prescribed service frequency applicable to the current service task based on the generated digital contract terms (e.g., "Grid G-101 can record a maximum of 2 cleanings per day"). When, within a single preset time window, for the same billable or verifiable smallest service unit (such as a specific grid), the number of successfully uploaded service evidence packages exceeds the maximum frequency threshold stipulated in the contract, an anomaly detection is triggered.
[0066] Building upon this foundation, this embodiment further implements refined cost accounting based on trusted evidence records. The system aggregates and analyzes all evidence records for each spatial grid within the billing cycle, according to the billing rules stipulated in the contract. For service evidence confirmed after deduplication and validity verification, multiple reports confirming belonging to the same continuous operation will be merged. The system will then automatically calculate the effective service volume of the grid, such as cleaning area and service duration, and generate a detailed cost breakdown using the contract unit price.
[0067] For records that trigger abnormal frequency, if the review confirms that it is a reasonable overclocking service, the additional fee standard stipulated in the contract will be charged; if it is determined to be an invalid duplicate or tampering attempt, the relevant record will not be charged, and an audit report containing the blockchain transaction ID (TxID) as evidence will be generated.
[0068] Subsequently, the identified issues were fed back to the server and the user, further ensuring the authenticity and integrity of the data. This achieved a closed-loop business model from behavioral evidence storage to fair billing, ensuring the reliability of property service trace data processing and improving the transparency and traceability of property services.
[0069] In addition, refer to Figure 5 In one embodiment, step S22 is added before step S2: S22. Images or videos collected during the service process shall be used as evidence records.
[0070] Specifically, in this embodiment, for each training image frame or video segment, not only is the type of service behavior occurring within it labeled, such as cleaning or inspection, and the corresponding property service contract text fragment, but it is also further associated with a unique grid code corresponding to the specific physical location where the behavior occurs. For example, a picture of a cleaner wiping an elevator lobby will have the following labeling information: behavior "cleaning", associated contract clause "clean the public elevator lobby once a day", and location "grid G-101 (East elevator lobby of Building 1)".
[0071] Based on the above grid coding annotation method, a set of triplet training samples of "service behavior image - contract terms text - spatial grid location" is established, for example {action: "cleaning", contract compliance: "complies with standard 3.2", occurrence grid: [G-101, G-102], confidence: 0.96}. This is the fundamental premise for realizing refined recognition from "whether service is provided" to "where service is provided".
[0072] In addition, refer to Figure 6 In one embodiment, step S23 is added before step S2: S23. GPS location, timestamp, and device ID during the data collection process shall be recorded as evidence.
[0073] Specifically, by introducing time and space data generated during service delivery, the judgment logic not only relies on whether the behavior itself occurred and its confidence level, but also on the comparison of spatial grid statistics with the contract. Furthermore, when verifying the service's evidence records and the service standards in the contract, it can automatically determine that the service not only needs to meet the requirements in a single action, but also in terms of actual service content such as covering the specified area and reaching the specified frequency. This enables a deeper and more objective automated audit of the performance of property service contracts.
[0074] The system will accumulate the identification results of similar services that occur in the same grid or the same contract responsibility area within a specific task period, and compare them with the service standards specified in the contract for that area, such as "grid G-101 daily cleaning frequency ≥ 1".
[0075] The conclusion of "whether it is standardized or completed" is based on the degree of matching between the quantitative data after spatiotemporal aggregation and the contract terms. For example, "Grid G-101 area, today's cleaning task is completed (detected 1 time, meets the requirements)".
[0076] In addition, refer to Figure 7 Furthermore, in one embodiment, step S40 is refined into the following sub-steps: S400, playback service standard evidence package containing videos.
[0077] Specifically, within the property management system's interface, technicians or auditors can trigger functions such as "evidence playback" to access the original video data stored within the system that is associated with the service standard evidence package. The system will accurately locate and retrieve the video file for the corresponding time period based on the timestamp information recorded within the evidence package. Playback can be performed at original speed, multiple speeds (such as 0.5x, 2x, 4x, etc.), or frame-by-frame, allowing for detailed viewing of the specific scenes during the service process and direct verification of the authenticity and standardization of the service behavior.
[0078] For example, when auditing the "cleaning task for grid G-101 area today", the playback video can clearly show the time when the cleaning staff entered the grid, the cleaning tools used, the specific scope of the cleaning work, such as the ground, tabletops, public facility surfaces, etc., as well as the state of the area after cleaning, providing direct visual evidence for judging whether the service meets the cleaning quality standards in the contract.
[0079] In addition, when playing back videos within the service standard evidence package, AI-generated annotations are overlaid on the videos. Specifically, the video playback interface also displays other key information within the service standard evidence package, such as service start time, end time, service personnel identification, and associated grid number annotations. This facilitates auditors in verifying the video content against the stored evidence data, ensuring the consistency and completeness of the information.
[0080] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.
[0081] This application also provides a property service trace data processing system based on AI and blockchain evidence storage, which corresponds one-to-one with the property service trace data processing method based on AI and blockchain evidence storage in the embodiments.
[0082] refer to Figure 8 A property service record data processing system based on AI and blockchain notarization includes: a contract generation service standard module 1, a service standard evidence package encryption module 2, a service standard evidence package notarization module 3, and a notarization record query module 4. Detailed descriptions of each functional module are as follows: Contract generation service standard module 1: Used to input property service contracts into several pre-trained deep learning models and output the structured contract service content recognition results as service standards; Service Standard Evidence Package Encryption Module 2: This module is used to package the company's evidence records and service standards into a service standard evidence package, and generate a unique digital fingerprint through encryption. The evidence records include service records recorded according to the service standards. Service Standard Evidence Package Storage Module 3: This module is used to record the unique digital fingerprint and attributes of the service standard evidence package into the blockchain and use the returned blockchain transaction ID as the storage certificate. Evidence storage record query module 4: Used to respond to query requests and retrieve the corresponding evidence storage record from the blockchain network based on the task ID or blockchain transaction ID in the evidence package attributes.
[0083] The system comprises several modules: Module 1 (Contract Generation Service Standards) uses an AI model to accurately extract key contract clauses and output structured service standards, improving the efficiency and accuracy of contract parsing; Module 2 (Service Standard Evidence Package Encryption) packages and encrypts service records and standards, generating a unique digital fingerprint to ensure evidence integrity and tamper-proofness; Module 3 (Service Standard Evidence Package Preservation) records the digital fingerprint and attributes into the blockchain, generating an immutable preservation certificate and enabling public traceability of the preservation process; and Module 4 (Preservation Record Query) supports querying by task ID or blockchain transaction ID, quickly locating and displaying preservation records through identity verification and blockchain interaction. The combination of these modules achieves standardized, digitalized, and trustworthy management of the entire property service process. From intelligent parsing of contract clauses to encrypted preservation of service traces and secure querying of preservation information, a closed-loop system of "contract-service-preservation-traceability" is formed. This system not only protects homeowners' right to know and supervise the service process but also provides technical support for compliant operation of property service companies and meets the transparent management needs of third-party regulatory agencies.
[0084] Specific limitations regarding the AI- and blockchain-based property service trace data processing system can be found in the context of the limitations on the AI- and blockchain-based property service trace data processing method, and will not be repeated here. Each module in the aforementioned AI- and blockchain-based property service trace data processing system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in an electronic device, or stored in the memory of the electronic device as software, so that the processor can call and execute the corresponding operations of each module. In one embodiment, an electronic device is provided, which is a user terminal. (Reference) Figure 9 The electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores detection data tables. The network interface communicates with external terminals via a network connection. When executed by the processor, the computer program implements a property service trace data processing method based on AI and blockchain evidence storage.
[0085] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: S1. Input the property service contract into several pre-trained deep learning models, and output the structured contract service content recognition results as the service standard.
[0086] S2. Package the company's evidence records and service standards into a service standard evidence package, and generate a unique digital fingerprint through encryption. The evidence records include service records recorded according to the service standards.
[0087] S3. Record the unique digital fingerprint and attributes of the service standard evidence package into the blockchain, and use the returned blockchain transaction ID as a certificate of evidence storage.
[0088] S4. Respond to the query request and retrieve the corresponding evidence record from the blockchain network based on the task ID or blockchain transaction ID in the evidence package attributes.
[0089] In one embodiment, the sub-steps of step S2 refinement include: S20. Package the evidence records and service standards into a JSON format service standard evidence package.
[0090] S21. Calculate and obtain the SHA-256 hash value of the service standard evidence package as a unique digital fingerprint.
[0091] In one embodiment, the sub-steps of step S3 refinement include: S30. Record the hash value, timestamp, and task ID of the service standard evidence package into the blockchain.
[0092] S31. Use the returned blockchain transaction ID as proof of authenticity.
[0093] In one embodiment, the additional steps following step S4 include: S40. Compare the service content evidence package with the service standard evidence package to verify the integrity of the data.
[0094] In one embodiment, the additional step before step S2 includes: S22. Images or videos collected during the service process shall be used as evidence records.
[0095] In one embodiment, the additional step before step S2 includes: S23. GPS location, timestamp, and device ID during the data collection process shall be recorded as evidence.
[0096] In one embodiment, the sub-steps of step S40 are further refined as follows: S400, playback service standard evidence package containing videos.
[0097] Furthermore, embodiments of the present invention provide a computer program product, including a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, wherein when the program instructions are executed by a computer, the computer executes the property fee pre-assessment and post-payment supervision method based on dynamic performance determination of any of the above embodiments.
[0098] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
[0099] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
[0100] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.
Claims
1. A method for processing property service trace data based on AI and blockchain evidence storage, characterized in that, include: Input the property service contract into several pre-trained deep learning models, and output the structured contract service content recognition results as the service standard; The enterprise's evidence records are packaged with the service standards into a service standard evidence package, and a unique digital fingerprint is generated through encryption. The evidence records include service records recorded according to the service standards. The unique digital fingerprint and attributes of the service standard evidence package are entered into the blockchain, and the returned blockchain transaction ID is used as the evidence storage certificate. In response to a query request, the corresponding evidence record is retrieved from the blockchain network based on the task ID or blockchain transaction ID in the evidence package attributes.
2. The method according to claim 1, characterized in that, The step of packaging the enterprise's evidence records and the service standards into a service standard evidence package, and generating a unique digital fingerprint through encryption, wherein the evidence records include service records recorded according to the service standards, includes: The evidence records and service standards are packaged into a JSON format service standard evidence package; The SHA-256 hash value is calculated for the service standard evidence package and used as a unique digital fingerprint.
3. The method according to claim 2, characterized in that, The step of recording the unique digital fingerprint and attributes of the service standard evidence package into the blockchain, and using the returned blockchain transaction ID as proof of evidence, includes: The hash value, timestamp, and task ID of the service standard evidence package are entered into the blockchain; The returned blockchain transaction ID will be used as proof of authenticity.
4. The method according to claim 1, characterized in that, After the step of retrieving the corresponding evidence record from the blockchain network based on the task ID or blockchain transaction ID in the evidence package attributes in response to the query request, the method further includes: The integrity of the data is verified by comparing the evidence records and service standards in the service standard evidence package.
5. The method according to claim 1, characterized in that, Before the step of packaging the enterprise's evidence records and the service standards into a service standard evidence package, and generating a unique digital fingerprint through encryption, wherein the evidence records include service records recorded according to the service standards, the method further includes: Images or videos collected during the service process are used as evidence.
6. The method according to claim 1, characterized in that, Before the step of packaging the enterprise's evidence records and the service standards into a service standard evidence package, and generating a unique digital fingerprint through encryption, wherein the evidence records include service records recorded according to the service standards, the method further includes: GPS location, timestamp, and device ID are recorded as evidence during the data collection process.
7. The method according to claim 5, characterized in that, The step of verifying data integrity by comparing the evidence records and service standards in the service standard evidence package includes: Play back the video from the service standard evidence package.
8. A property service trace data processing system based on AI and blockchain evidence storage, characterized in that, include: Contract generation service standard module (1): Used to input property service contracts into several pre-trained deep learning models and output the structured contract service content recognition results as service standards; Service Standard Evidence Package Encryption Module (2): Used to package the enterprise's evidence records and the service standards into a service standard evidence package, and generate a unique digital fingerprint through encryption. The evidence records include service records recorded according to the service standards. Service Standard Evidence Package Storage Module (3): Used to record the unique digital fingerprint and evidence package attributes of the service standard evidence package into the blockchain, and use the returned blockchain transaction ID as the storage certificate; Evidence storage record query module (4): Used to respond to query requests and obtain the corresponding evidence storage record from the blockchain network based on the task ID or blockchain transaction ID in the evidence package attribute.
9. An electronic device, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as any one of the AI and blockchain-based property service trace data processing methods as described in claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer program is stored and can be loaded by a processor and execute any one of the property service trace data processing methods based on AI and blockchain evidence storage as described in claims 1 to 7.
11. A computer program product, characterized in that, The computer program product includes a computer program stored on a non-transitory computer-readable storage medium, the computer program including program instructions, wherein when the program instructions are executed by a computer, the computer performs the property service trace data processing method based on AI and blockchain evidence storage as described in any one of claims 1 to 7.