Method for realizing data storage of law enforcement officers based on offline data storage technology

Abstract

The invention discloses a method for realizing data storage of law enforcement officers based on an off-line data storage technology, which is applied to a mobile operation terminal and comprises the following steps: S1, acquiring and submitting field data; s2, carrying out data localization processing based on a network state; s3, intelligent synchronization and combination of off-line data; and S4, scene data display. According to the method, the problems of data acquisition stagnation, poor safety, difficulty in synchronization and the like caused by network interruption in a complex operation environment are solved, so that the uninterrupted operation flow, safe and complete data and accurate and efficient background synchronization are ensured, and the operation flexibility and the intelligent level in a complex scene are improved.

Description

Technical Field

[0001] This invention relates to the fields of electronic business and information technology, and specifically to a method for storing law enforcement personnel's data based on offline data storage technology. Background Technology

[0002] In mobile operational scenarios within the electronic business sector, such as maritime safety inspections of fishing vessels, border patrols, and environmental inspections in remote areas, work environments with no or weak network signals are frequently encountered. Personnel must input basic information, safety hazards, and compliance data of the inspected entities in real-time on-site, and ultimately synchronize the data to a cloud-based monitoring platform to achieve management objectives such as operational verification, violation tracing, and hazard tracking. Existing technical solutions typically rely on stable online networks for data transmission; once the network is down, data entry is forced to stop, severely impacting the continuity of the workflow.

[0003] Some solutions support offline operation, such as the one disclosed in public access number CN115586929A, which describes an app's offline operation method. Its core logic is: when the mobile app starts, it checks the network status; if there is a network connection, it runs online and uploads data; otherwise, it simply maintains the app's startup state without performing data interaction. This solution only solves the basic problem of preventing the application from crashing when offline, but it has the following significant drawbacks: (1) Functional limitations: It cannot support core operations such as data entry, editing, and deletion in an offline environment, causing work to stop; (2) Lack of security: There is no effective encryption and access control mechanism for locally stored offline data, which poses a risk of data leakage and tampering; (3) Insufficient synchronization capability: After the network is restored, it can only upload local temporary data, lacking incremental synchronization and intelligent conflict handling mechanism, which can easily lead to data duplication, loss or contradiction; (4) Low efficiency: The lack of fast offline data retrieval and visualization verification functions affects on-site work efficiency; (5) Poor adaptability: It has not been optimized for hardware limitations and data collection diversity in different work scenarios, resulting in weak versatility.

[0004] Therefore, there is an urgent need for a technical solution that can achieve secure, continuous, and efficient management of the entire lifecycle of operational data (collection, storage, management, and synchronization) in unstable network environments. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a method for saving law enforcement personnel's data based on offline storage data technology, so as to solve the problems of data collection stagnation, poor security and synchronization difficulties caused by network interruption in complex operating environments, thereby ensuring uninterrupted operation process, data security and integrity, accurate and efficient background synchronization, and improving the flexibility and intelligence level of operation in complex scenarios.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows.

[0007] A method for saving law enforcement personnel's data based on offline storage technology, applied to mobile work terminals, includes the following steps: S1. On-site data collection and submission: After arriving at the site, the operators input the operation data information through the mobile operation terminal; after the input is completed, the current network status is checked; S2. Data localization processing based on network status: If it is determined that there is no network connection, the job data information is submitted to the local database of the mobile job terminal for encrypted storage, forming data to be synchronized; if it is determined that there is a network connection, the job data information is directly submitted to the remote server online. S3. Intelligent synchronization and merging of offline data: During the subsequent operation of the mobile terminal, the network status is continuously monitored; when the network status is restored from the state of no network, the data to be synchronized stored in the local database is merged and submitted to the remote server through the dual-trigger synchronization mechanism, and the incremental data extraction and conflict handling mechanism is used. S4. Contextualized data display: Dynamically display a data list based on the current network status of the mobile terminal.

[0008] Preferably, network status is monitored through a dual mechanism of timed detection and event triggering, and network quality is identified through bandwidth testing; the step S1 is preceded by the following step: S0. Data preloading: When the mobile work terminal is in a network environment, a collaborative filtering algorithm is used to filter high-frequency data based on the work scenario characteristics of the workers, and the synchronization amount is dynamically adjusted according to the network quality to preload the data into the local database of the mobile work terminal for use in offline environments. The local database adopts a layered storage architecture, including: a core storage layer based on an embedded database, a backup storage layer that supports external encrypted storage devices, and a cache acceleration layer based on the device memory of mobile operating terminals that uses a cache eviction policy for management.

[0009] Preferably, in step S0, the strategy of dynamically adjusting the synchronization amount according to network quality is as follows: synchronize all data in a strong network environment, and synchronize only the preset core data in a weak network environment.

[0010] Preferably, in step S1, the operation data information is collected by at least one of the following methods: scanning code, voice input, image capture, and manual input. The collected operation data information is then standardized and converted into a unified structured format before being stored. The voice input acquisition uses an integrated offline speech recognition SDK to convert speech into text in real time and matches it with a preset encoding to generate structured data. After the images are captured, they are compressed and a unique identifier is generated and associated with GPS coordinates for storage. The standardization process includes field mapping and encoding normalization.

[0011] Preferably, in step S2, the data stored in the local database is subjected to offline data security storage and full lifecycle management; The offline data security storage includes: three-layer encryption protection of data, namely memory encryption, database encryption and sensitive field encryption; and multi-dimensional access control through device binding, secondary identity verification and hierarchical permission. The full lifecycle management specifically refers to supporting full lifecycle operations such as editing, deleting, and retrieving offline data, and recording operation logs.

[0012] Preferably, the memory encryption configuration is as follows: when data is processed in memory, it is encrypted using a randomly generated temporary key; The database encryption configuration is as follows: the SQLCipher tool is used to encrypt the SQLite database file with AES-256. The encryption key is generated by combining the International Mobile Equipment Identity (IMEI) of the mobile terminal with the hash value of the user password, and key verification is required when accessing the database. The sensitive field encryption configuration is as follows: the specified sensitive information is encrypted separately using a key independent of the database encryption key.

[0013] Preferably, the dual-trigger synchronization mechanism in step S3 includes automatic triggering and manual triggering; the incremental data extraction and conflict handling mechanism specifically includes: Incremental data extraction: The local data to be synchronized is triple-checked using data identifier, timestamp, and version number, and only newly added or modified data is extracted; Conflict handling: During the upload process, if a conflict is found between the data to be uploaded and existing data on the remote server, the data conflict will be handled by a combination of rule priority and manual intervention. The rules include: for core field conflicts, the data with the latest timestamp will be used to overwrite the old data; for non-core field conflicts, automatic merging will be performed.

[0014] Preferably, when uploading incremental data in step S3, a fragmented verification transmission mechanism is adopted. After the data is fragmented, a verification code is generated for each fragment. After the remote server verifies the data, it is stored and supports resuming the transmission after interruption.

[0015] Preferably, step S3 further includes redundant data cleanup: marking the data that has been successfully synchronized in the local database as soft-deleted and retaining it for a preset time before soft-deleting it, and periodically cleaning up expired soft-deleted data, unaccessed cached data and expired backup data, and recording the cleanup log in the data_clean_log table.

[0016] Preferably, the dynamic display of the data list in step S4 specifically involves: When there is a network connection: Display real-time data from the remote server and synchronously show the consistency status between the local database and the remote server data; When there is no network: Display the "queue to be synchronized" data in the local database, marking the entry time, priority, and storage status, and supporting offline retrieval and verification.

[0017] Due to the adoption of the above technical solutions, the technical progress achieved by this invention is as follows.

[0018] This invention achieves full-scenario operation coverage: breaking through the limitation of existing technology that "only starts without network", it realizes full functions of data collection, editing, storage and retrieval in the absence of network, and the operation process is not interrupted, especially suitable for complex scenarios such as the open sea and mountainous areas.

[0019] This invention offers a high level of data security: three-layer encryption and multi-dimensional access control, which solves the risk of offline data leakage in existing technologies and complies with e-government data security standards.

[0020] This invention boasts high synchronization accuracy: incremental synchronization combined with intelligent conflict handling avoids data duplication and contradictions, significantly improving data consistency compared to existing technologies that simply "upload".

[0021] This invention improves work efficiency: personalized preloading reduces data query time, multi-mode collection simplifies operation, offline retrieval responds quickly, and the on-site work efficiency of operators is greatly improved.

[0022] This invention has good versatility and scalability: its layered architecture is compatible with different mobile operating terminals (phones / tablets) and can be extended to multiple fields such as border defense and environmental protection, without the need for repeated development. Attached Figure Description

[0023] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0024] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0025] A method for storing law enforcement personnel's data based on offline storage technology is proposed and applied to mobile operation terminals. It constructs an offline operation data management system through a technical path of "intelligent network adaptation → layered storage architecture → full-process data management → seamless cloud collaboration," combined with... Figure 1 As shown, it includes the following steps: S0. Data preloading: When the mobile work terminal is in a network environment, a collaborative filtering algorithm is used to filter high-frequency data based on the work scenario characteristics of the workers, and the synchronization amount is dynamically adjusted according to the network quality to preload the data into the local database of the mobile work terminal for use in offline environments.

[0026] During system startup and operation, network status is monitored through a dual mechanism of timed detection (the period can be customized, with a default of 5 minutes) and event triggering (network switching, APP wake-up). This not only determines whether there is a network, but also identifies network quality (weak network / strong network) through bandwidth testing (such as transmitting a 10KB test packet within 10 seconds), providing a basis for subsequent data processing.

[0027] Specifically, this step involves: when a network is available, based on the operator's historical operating area (e.g., "Bohai Bay fishing area"), commonly used inspection items (e.g., "fire-fighting equipment inspection"), and working hours (e.g., "fishing ban period"), high-frequency data (e.g., basic information of fishing vessels in the target area, the latest fishing ban policy, and historical violation records) is filtered through a collaborative filtering algorithm and prioritized for synchronization to the local database; at the same time, the synchronization volume is dynamically adjusted according to network quality: in a strong network environment, all data is synchronized, while in a weak network environment, only core data (e.g., fishing vessel ID, key operating standards) is synchronized to reduce traffic consumption.

[0028] The local database adopts a tiered storage architecture, specifically a "core storage layer + backup storage layer + cache acceleration layer" architecture, as follows: Core storage layer: Based on an embedded database (SQLite database), five core tables are designed according to "business type + priority" (such as offline_law_data (main table of job data) and violation_type (table of violation types)) to achieve structured storage.

[0029] Backup storage layer: Supports external encrypted storage devices (USB flash drive / dedicated TF card) for real-time mirror backup of core table data, and supports breakpoint resumption during the backup process.

[0030] Cache acceleration layer: A cache eviction strategy is adopted for management. Specifically, a cache area is divided in the device memory (RAM) of the mobile terminal, and a LRU strategy is used to store high-frequency data (such as fishing boat information in the current area) to ensure millisecond-level query response.

[0031] S1. On-site data collection and submission: After arriving at the site, the operators enter the operation data information through the mobile operation terminal; after the data entry is completed, the current network status is determined.

[0032] Operational data is collected through at least one of the following methods: QR code scanning, voice input, image capture, and manual entry, achieving a four-dimensional data collection entry point to adapt to diverse needs at the work site. The collected operational data is then standardized and stored in a unified structured format. Details are as follows: Scan to collect data: Scan the QR code / electronic tag of the fishing vessel, automatically parse the fishing vessel ID, port of registry and other information, and directly write it into the main table to avoid manual errors; Voice input acquisition: Integrate an offline voice SDK (such as Baidu Offline Voice). When the operator verbally states "Fishing boat XX, life raft expired", the data is converted into text in real time and matched with a preset code (such as "life raft expired" corresponding to 005) to generate structured data. Image capture and acquisition: After taking photos of potential hazards, the images are automatically compressed (resolution 1280×720, size ≤500KB), a unique identifier (device ID_timestamp_random number) is generated, and GPS coordinates are associated with the storage. The image files are stored separately in a dedicated folder. Manual data entry: Provides structured forms with format validation for required fields (such as fishing vessel number) (e.g., "XX port + 6 digits") and real-time error alerts; Standardization processing: All raw data is transformed into a unified format through "field mapping + code normalization". For example, "violation type" only stores the code (01=fishing during the closed season). When querying, the Chinese name is matched in reverse to reduce the storage volume.

[0033] S2. Data localization processing based on network status: If it is determined that there is no network connection, the job data information is submitted to the local database of the mobile job terminal for encrypted storage, forming data to be synchronized; if it is determined that there is a network connection, the job data information is submitted directly to the remote server online.

[0034] In this step, offline data security storage and full lifecycle management are performed on the data stored in the local database.

[0035] Offline data security storage includes: three layers of encryption protection—memory encryption, database encryption, and sensitive field encryption—and multi-dimensional access control through device binding, secondary authentication, and tiered access permissions. Details are as follows: Three layers of encryption protection: The memory encryption configuration is as follows: when data is processed in memory, it is encrypted using a randomly generated temporary key to prevent memory dump attacks; The database encryption configuration is as follows: the SQLCipher tool is used to encrypt the SQLite database file (.db) using AES-256. The encryption key is generated by combining the International Mobile Equipment Identity (IMEI) of the mobile terminal with the hash value of the user password, and key verification is required when accessing the database. The sensitive field encryption configuration is as follows: For specified sensitive information (such as the fishing boat owner's ID number, contact number, etc.), a separate key independent of the database encryption key is used for encryption, so as to separate it from the database encryption key.

[0036] Multi-dimensional access control: Device binding: The APP binds to the device IMEI upon first installation. Reinstallation requires administrator authorization to unbind. Two-factor authentication: Password / fingerprint / facial recognition verification is required when editing / deleting data; Access control hierarchy: Regular operators can only access their own data, team leaders can view the data of their team, and administrators manage all data. Access control configurations are stored in the user_permission table.

[0037] Full lifecycle management specifically refers to supporting all lifecycle operations on offline data, including editing, deleting, and retrieving, and recording operation logs. Details are as follows: Editing: Automatically logs changes made to data (data_edit_log table), including the person who made the change, the time, and the original / new data, for easy traceability; Deletion: Regular soft deletion (set delete_flag=1, supports recovery from recycle bin), permanent deletion requires two password verifications, and backup data is cleaned up simultaneously; Search: Supports "multi-dimensional filtering (date / fishing vessel number) + fuzzy search", indexes are built for commonly used fields, and the search response time is ≤1 second.

[0038] S3. Intelligent synchronization and merging of offline data: During the subsequent operation of the mobile terminal, the network status is continuously monitored; when the network status is restored from the no-network state, the data to be synchronized stored in the local database is merged and submitted to the remote server through the dual-trigger synchronization mechanism, and the incremental data extraction and conflict handling mechanism is used.

[0039] In this step, the dual-trigger synchronization mechanism includes automatic triggering and manual triggering, as detailed below: Automatic triggering: Synchronization starts in real time when the network is available, and timed synchronization is supported (e.g., every 30 minutes). Manual trigger: The APP's "Sync Management" interface displays the number of devices to be synced and the estimated traffic, allowing users to manually trigger the sync.

[0040] In this step, the incremental data extraction and conflict resolution mechanism specifically includes: Incremental data extraction: The local data to be synchronized is triple-checked using "data identifier + timestamp + version number" to extract only the newly added or modified data, avoiding full synchronization; Conflict Handling: During the upload process, if a conflict is found between the data to be uploaded and existing data on the remote server, a combination of rule-based priority and manual intervention will be used to handle the data conflict, as detailed below: Rule priority: For conflicts in core fields (such as risk level), retain the data with the latest timestamp, that is, use the data with the latest timestamp to overwrite the old data. For conflicts in non-core fields (notes), automatically merge them. Manual intervention: When core fields conflict (such as inconsistent violation types between the two parties), the data is marked and pushed to a comparison interface for manual confirmation.

[0041] In this step, a fragmented verification transmission mechanism is used when uploading incremental data. The data is fragmented and a verification code is generated for each fragment (for example, the data is split into 100KB fragments and an MD5 checksum is generated for each fragment). The remote server stores the data after verification and supports resuming interrupted transmission.

[0042] This step also includes redundant data cleanup: marking successfully synchronized data in the local database as soft-delete data and retaining it for a preset time before soft-deleting it; regularly cleaning up expired soft-deleted data, inaccessible cached data, and expired backup data; and recording the cleanup log in the data_clean_log table, as detailed below: Soft deletion flag: After successful synchronization, the data sync_status is set to 1 and retained for 7 days for viewing. After the expiration period, it is soft deleted. Regular cleanup: Clean up expired soft-deleted data, cached data that has not been accessed for 72 hours, and redundant backup data for 30 days every day at 2:00 AM (when the device is idle). The cleanup log is recorded in the data_clean_log table.

[0043] S4. Contextualized data display: Dynamically display a data list based on the current network status of the mobile terminal.

[0044] The data list is displayed dynamically, specifically as follows: When there is a network connection: By default, real-time data from the remote server is displayed, and the consistency status between the local database and the remote server data is displayed synchronously.

[0045] When there is no network: Display the "queue to be synchronized" data in the local database, marking the entry time, priority, and storage status, and supporting offline retrieval and verification.

[0046] When using this invention, the operator starts the APP → the system uses a dual mechanism to detect the network status (timed + event-triggered) → if there is a network connection, high-frequency data is preloaded to the local database in a personalized manner → data is collected on-site in multiple modes → the network status is determined (with network connection → cloud submission / without network connection → local encrypted storage) → incremental synchronization is automatically triggered when the network recovers → intelligent conflict handling → batch upload to the cloud → local redundant data cleanup → data list is displayed according to network status (online cloud / offline local).

[0047] The present invention achieves the following innovations: (1) Intelligent network adaptation and scenario-based preloading mechanism: The system employs a dual mechanism of "timed + event-triggered" to monitor network quality, and combines this with collaborative filtering algorithms to preload high-frequency data and dynamically adjust synchronization strategies based on operational scenarios.

[0048] (2) Multi-mode offline data acquisition and standardized processing scheme: It integrates four dimensions of data collection: QR code scanning, voice, image, and manual input. Data standardization is achieved through code normalization to adapt to diverse operational needs.

[0049] (3) A security system with three layers of encryption and multi-dimensional permissions: By employing three layers of encryption—memory, database, and sensitive fields—along with device binding, secondary verification, and hierarchical access control, a comprehensive security protection system has been built.

[0050] (4) Incremental synchronization and intelligent conflict handling mechanism: Triple verification enables incremental data extraction, "rule priority + manual intervention" hierarchical conflict handling, and fragmented verification ensures synchronization integrity.

[0051] (5) Offline data lifecycle management and cross-device backup: It supports full operations of editing, deleting, and searching, and combines dual backup (built-in + external) and near-field sharing to achieve data security and collaborative work.

Claims

1. A method for saving law enforcement personnel's data based on offline storage technology, applied to mobile operating terminals, characterized in that: Includes the following steps: S1. On-site data collection and submission: After arriving at the site, the operators enter the operation data information through the mobile operation terminal; After data entry is complete, check the current network status; S2. Data localization processing based on network status: If it is determined that there is no network connection, the job data information is submitted to the local database of the mobile job terminal for encrypted storage, forming data to be synchronized; if it is determined that there is a network connection, the job data information is directly submitted to the remote server online. S3. Intelligent synchronization and merging of offline data: During the subsequent operation of the mobile terminal, the network status is continuously monitored; when the network status is restored from the no-network state, the data to be synchronized stored in the local database is merged and submitted to the remote server through the dual-trigger synchronization mechanism, and the incremental data extraction and conflict handling mechanism is used. S4. Contextualized data display: Dynamically display a data list based on the current network status of the mobile terminal.

2. The method for storing law enforcement personnel's data based on offline storage technology according to claim 1, characterized in that: Network status is monitored through a dual mechanism of timed detection and event triggering, and network quality is identified through bandwidth testing; the step S1 is preceded by the following step: S0. Data preloading: When the mobile work terminal is in a network environment, a collaborative filtering algorithm is used to filter high-frequency data based on the work scenario characteristics of the workers, and the synchronization amount is dynamically adjusted according to the network quality to preload the data into the local database of the mobile work terminal for use in offline environments. The local database adopts a layered storage architecture, including: a core storage layer based on an embedded database, a backup storage layer that supports external encrypted storage devices, and a cache acceleration layer based on the device memory of mobile operating terminals that uses a cache eviction policy for management.

3. The method for storing law enforcement personnel data based on offline storage technology according to claim 2, characterized in that: In step S0, the strategy of dynamically adjusting the synchronization amount according to network quality is as follows: synchronize all data in a strong network environment, and synchronize only the preset core data in a weak network environment.

4. The method for storing law enforcement personnel data based on offline storage technology according to claim 1, characterized in that: In step S1, work data information is collected by at least one of the following methods: scanning code, voice input, image capture, and manual input. The collected work data information is then standardized and converted into a unified structured format before being stored. The voice input acquisition uses an integrated offline speech recognition SDK to convert speech into text in real time and matches it with a preset encoding to generate structured data. After the images are captured, they are compressed and a unique identifier is generated and associated with GPS coordinates for storage. The standardization process includes field mapping and encoding normalization.

5. A method for storing law enforcement personnel's data based on offline storage technology according to claim 1, characterized in that: In step S2, offline data security storage and full lifecycle management are performed on the data stored in the local database. The offline data security storage includes three layers of encryption protection: memory encryption, database encryption, and sensitive field encryption. Multi-dimensional access control is implemented through device binding, secondary identity verification, and hierarchical permission settings. The full lifecycle management specifically refers to supporting full lifecycle operations such as editing, deleting, and retrieving offline data, and recording operation logs.

6. A method for storing law enforcement personnel data based on offline storage technology according to claim 5, characterized in that: The memory encryption configuration is as follows: when data is processed in memory, it is encrypted using a randomly generated temporary key; The database encryption configuration is as follows: the SQLCipher tool is used to encrypt the SQLite database file with AES-256. The encryption key is generated by combining the International Mobile Equipment Identity (IMEI) of the mobile terminal with the hash value of the user password, and key verification is required when accessing the database. The sensitive field encryption configuration is as follows: the specified sensitive information is encrypted separately using a key independent of the database encryption key.

7. A method for storing law enforcement personnel's data based on offline storage technology according to claim 1, characterized in that: The dual-trigger synchronization mechanism in step S3 includes automatic triggering and manual triggering; the incremental data extraction and conflict handling mechanism specifically includes: Incremental data extraction: The local data to be synchronized is triple-checked using data identifier, timestamp, and version number, and only newly added or modified data is extracted; Conflict handling: During the upload process, if a conflict is found between the data to be uploaded and existing data on the remote server, the data conflict will be handled by a combination of rule priority and manual intervention. The rules include: for core field conflicts, the data with the latest timestamp will be used to overwrite the old data; for non-core field conflicts, automatic merging will be performed.

8. A method for storing law enforcement personnel data based on offline storage technology according to claim 7, characterized in that: In step S3, incremental data is uploaded using a fragmented verification transmission mechanism. The data is fragmented, and a verification code is generated for each fragment. The remote server verifies the data and stores it. The mechanism also supports resuming interrupted transmissions.

9. A method for storing law enforcement personnel's data based on offline storage technology according to claim 1, characterized in that: Step S3 further includes redundant data cleanup: marking the data that has been successfully synchronized in the local database as soft-deleted and retaining it for a preset time before soft-deleting it, and regularly cleaning up expired soft-deleted data, unaccessed cached data and expired backup data, and recording the cleanup log in the data_clean_log table.

10. A method for storing law enforcement personnel's data based on offline storage technology according to claim 1, characterized in that: The dynamic display of the data list in step S4 is specifically as follows: When there is a network connection: Display real-time data from the remote server and synchronously show the consistency status between the local database and the remote server data; When there is no network: Display the "queue to be synchronized" data in the local database, marking the entry time, priority, and storage status, and supporting offline retrieval and verification.

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