Method and system for data erasure of second-hand mobile phone

Abstract

The application discloses a second-hand mobile phone data clearing method and system, relates to the technical field of data processing, and comprises the following steps: collecting space mapping information, cache writing rhythm information and index rewriting record information of a file system before and after remounting, and constructing continuous time sequence data; calculating the difference between the idle mark distribution and the actual writing distribution based on the time sequence data, extracting the overlapping position according to the difference result, and generating a fragment trigger list. The application constructs time sequence data by collecting multi-source information before and after the file system is mounted, realizes time sequence correlation analysis of the writing behavior of the logical layer and the physical layer, accurately identifies the old data residues and delimits the high-risk area; by constructing an interferable time interval distribution diagram and implementing reverse writing adjustment, the old data splicing path is broken from the time dimension, and the second-hand mobile phone data is completely and irrecoverably cleared.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, specifically to a method and system for erasing data from used mobile phones. Background Technology

[0002] Second-hand mobile phone data erasure refers to the technical process of thoroughly and irreversibly erasing various types of user data left on the internal storage media of a mobile phone before it is transferred, recycled, repaired, or resold. This process is supported by big data processing, which involves the unified collection, classification, modeling, and matching of erasure strategies for multi-source and multi-type data generated during device operation. This covers not only explicit data such as contacts, SMS messages, photos, audio and video, application caches, and system logs, but also implicit data in encrypted areas, system remnant areas, virtual storage areas, and application private directories. Based on this, through multiple overlay, encryption destruction, logical reconstruction, and verification detection techniques, massive storage units are processed in an orderly manner and their integrity is verified, ensuring that the original data cannot be reconstructed by any recovery algorithm. This prevents personal privacy information, account credentials, payment records, and sensitive files from being illegally extracted or misused during the circulation of the device, ensuring information security and compliance in the reuse of second-hand mobile phones.

[0003] The existing technology has the following shortcomings:

[0004] In existing technologies, file systems typically perform a remount operation after data erasure to restore the device's normal read / write state. However, during this process, the system automatically generates a checksum table for space verification and resource management, marking residual old data fragments in the erased segment as available space. Since these fragments are not physically overwritten, they are only logically identified as free areas. When the system subsequently writes new data, old data fragments can easily become interleaved with new data fragments. As the file system's automatic verification and space reclamation mechanisms continue to operate, these mislabeled old data fragments may be reassembled into identifiable historical file fragments, causing the data erasure results to fail and even leading to the accidental recovery of private information, seriously affecting the security and reliability of data erasure on used mobile phones.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for erasing data on second-hand mobile phones, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: 1. A method for clearing data from a used mobile phone, characterized by comprising the following steps:

[0008] Collect space mapping information, cache write rhythm information, and index rewrite record information of the file system before and after remounting, and construct continuous time series data containing time sequence identifiers to provide a time dimension basis for subsequent difference analysis;

[0009] The difference between the idle marker distribution and the actual write distribution is calculated based on time series data. The overlapping positions are extracted based on the difference results and a fragment trigger list is generated to identify the locations of potential old data remnants.

[0010] Based on the fragment trigger list, the data recovery stage and mapping jump point are tracked to identify mislabeled areas and generate high-risk splicing window information, providing a spatial basis for the subsequent intervention area delineation.

[0011] By tracing back the historical write rhythm around the high-risk splicing window and combining time series data analysis to identify the backflow path of old data fragments, an intervention-friendly time interval distribution map is constructed to determine the time window for reverse write adjustment.

[0012] Based on the controllable time interval distribution map, a reverse write adjustment operation is performed. Random data overwriting is carried out before the time window, the write rhythm is dispersed by the interval write method during the time window, and the write order is adjusted by the delayed disk write method after the time window. This blocks the re-splicing process of old data fragments from the time dimension, thereby achieving the irreversible erasure of second-hand mobile phone data.

[0013] Preferably, the steps for constructing time series data are as follows:

[0014] Before the remount operation begins, the space mapping structure in the file system running state is scanned and recorded. Logical block allocation relationship, data area and free area distribution information, inode pointing relationship and cache queue write arrangement status are continuously read through the file system interface, and initial time series data is generated in time order.

[0015] When the remount operation begins, monitor the mapping changes of the file system during the initialization, loading, updating and resource recovery phases, record logical block state transition events, cache write rhythm changes and inode re-pointing paths, and append the corresponding change information to the time series data;

[0016] At the end of the remounting process, the collected spatial mapping information, cache write rhythm information and index rewrite record information are integrated and sorted in time, and the data of the previous and subsequent stages are uniformly incorporated into the time series data structure.

[0017] Once the time series data integration is complete, all information is embedded with time stamps and logically archived to form a continuous time series data set.

[0018] Preferably, during the construction of time series data, the spatial mapping information, cache write rhythm information, and index rewrite record information maintain consistent time tags during collection. All records in the time series data are arranged continuously in chronological order, and a temporal correlation is established between changes in logical block state and changes in cache write rhythm to ensure that the time series data maintains continuity and consistency throughout the entire mounting period.

[0019] Preferably, the steps for calculating the difference between the free tag distribution and the actual write distribution based on time series data are as follows:

[0020] In the completed time-series data, the space mapping information, cache write rhythm information and index rewrite record information collected before and after the file system is remounted are uniformly aligned and divided into stages to generate a free marker distribution sequence and an actual write distribution sequence.

[0021] After obtaining the idle tag distribution sequence and the actual write distribution sequence, the logical block state at each time node is correlated and compared to form a difference mapping table between the idle tags and the actual write behavior;

[0022] In the difference mapping table, the overlapping positions where idle markers and write operations intersect are extracted according to time order to identify potential old data residue areas and establish a set of overlapping positions.

[0023] Once the set of overlapping locations is determined, the overlapping areas are integrated and summarized to generate a fragment trigger list that includes the start and end positions of the time series, the trajectory of the idle marker state change, and the duration of the write activity.

[0024] Preferably, the steps for tracking data recycling phases and mapping jump points based on the fragment trigger list are as follows:

[0025] When the fragmentation trigger list is obtained, the overlapping positions and their time series identifiers recorded in the list are tracked, and the recorded content is mapped to the space mapping information of the file system to determine the state changes in the data reclamation phase.

[0026] During the data recovery phase, the physical address correspondence of logical blocks in the time series is compared to identify the mapped jump points and record the jump events, thereby forming a candidate region for mislabeling.

[0027] When identifying candidate mislabeled regions, the jump concentration area and the idle marker transition area are cross-compared to generate a set of mislabeled regions containing time interval attributes and spatial location attributes.

[0028] When the mislabeled region set is formed, the mislabeled regions that are temporally adjacent and spatially continuous are aggregated to generate high-risk splicing window information with time extension attributes.

[0029] Preferably, when generating high-risk splicing window information, the overlapping positions in the fragment trigger list are mapped and matched with the mislabeled area set, the mislabeled areas that are temporally adjacent and spatially continuous are aggregated, and the time range, logical block distribution range and historical write behavior start and end relationship of each splicing window are recorded to form a spatial range basis for subsequent reverse write adjustment operations.

[0030] Preferably, the steps for tracing back the historical write rhythm around the high-risk splicing window and combining it with time series data analysis of the old data fragment backflow path are as follows:

[0031] When high-risk splicing window information is obtained, the time series data is backtracked according to the time range and logical block distribution range to extract write records and cache activity information in order to restore the write rhythm.

[0032] When completing the historical write rhythm backtracking, the time series data is grouped into stages, and logical blocks with continuous write relationships are grouped into the same stage to identify the backflow path of old data fragments;

[0033] When identifying the backflow path of old data fragments, the time interval is extracted based on the time series identifier and the writing order is analyzed to form a time interval dataset that reflects the relationship between writing rhythm and spatial changes.

[0034] When obtaining the time-distance dataset, the data is integrated and summarized to construct an intervention-friendly time-distance distribution map to determine the time window for reverse write adjustments.

[0035] Preferably, when constructing an interferable time-distance distribution map, the time range in the high-risk splicing window is aligned with the time-distance dataset, and the correspondence between time intervals and logical block addresses is established based on the time series identifier, so that each time period is associated with a specific spatial location and historical write events, thereby forming a continuous time distribution curve to determine the concentrated period of activity of old data fragments.

[0036] Preferably, the reverse write adjustment operation steps based on the interferable time interval distribution map are as follows:

[0037] When generating the interferable time-distance distribution map, a random data overwrite operation is performed on the time segment before the start point of the time window, and logical blocks are selected and overwritten according to the spatial mapping relationship recorded in the time series.

[0038] When completing random data overwrite operations, interval write operations are performed during the time window phase marked by the time interval distribution map that can be intervened, so as to disperse the file system write rhythm and break the continuous write pattern.

[0039] When completing the interval write operation, a delayed disk write operation is performed on the time segment after the time window, so that the logical write order is no longer consistent with the physical write order and forms a discrete distribution.

[0040] During the delayed disk write operation, the random overwrite, interval write, and delayed disk write operations are distributed and organized in time to form a continuous reverse write chain to prevent the splicing of old data fragments.

[0041] The used mobile phone data erasure system includes a data acquisition module, a difference analysis module, a risk positioning module, a time-distance modeling module, and a rhythm intervention module.

[0042] The data acquisition module collects space mapping information, cache write rhythm information, and index rewrite record information of the file system before and after remounting, and constructs continuous time series data containing time sequence identifiers to provide a time dimension basis for subsequent difference analysis.

[0043] The difference analysis module calculates the difference between the free marker distribution and the actual write distribution based on time series data, extracts overlapping positions based on the difference results and generates a fragment trigger list to identify potential old data remnant fragment locations;

[0044] The risk positioning module tracks the data recovery stage and mapping jump points based on the fragment trigger list, identifies mislabeled areas and generates high-risk splicing window information, providing a spatial basis for the subsequent intervention area delineation.

[0045] The time-distance modeling module traces back the historical write rhythm around the high-risk splicing window, combines time series data analysis to analyze the backflow path of old data fragments, and constructs an intervention-friendly time-distance distribution map to determine the time window for reverse write adjustment.

[0046] The rhythm intervention module performs reverse write adjustment operations based on the controllable time interval distribution map. Before the time window, it performs random data overwriting, disperses the write rhythm by using an interval write method within the time window, and adjusts the write order by delaying disk write after the time window. This blocks the re-splicing process of old data fragments from the time dimension, achieving irreversible erasure of second-hand mobile phone data.

[0047] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0048] This invention constructs continuous time-series data with temporal sequence identifiers by collecting space mapping information, cache write rhythm information, and index rewrite record information before and after file system remounting. Based on this, it performs difference comparison and mislabeled region identification, enabling temporal correlation analysis of write activities at the logical and physical layers. In this way, potential residual old data fragments can be dynamically identified during file system operation, and high-risk splicing windows can be precisely defined. This elevates the data erasure process from static erasure to time-controlled dynamic erasure, achieving more targeted residual data discovery and risk isolation, effectively improving the thoroughness and security of data erasure.

[0049] This invention constructs an interveneable time-distance distribution map around a high-risk splicing window and performs reverse write adjustment operations. It employs a multi-stage intervention strategy, including random overwriting, interval writing, and delayed disk writes, at different time window stages to orderly disrupt the file system's write order and caching rhythm. By breaking the continuity and spatial hierarchy of old data fragments in the time dimension, it prevents any old data fragments from being reassembled into identifiable files in subsequent operations, fundamentally eliminating the risk of historical data regeneration. This achieves irreversible erasure of second-hand mobile phone data, ensuring the stability and reliability of the data security processing. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0051] Figure 1 This is a flowchart of the method for clearing data on a used mobile phone according to the present invention.

[0052] Figure 2 This is a schematic diagram of the modules of the used mobile phone data erasure system of the present invention. Detailed Implementation

[0053] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0054] This invention provides, for example Figure 1 The method for clearing data on a used mobile phone, as shown, includes the following steps:

[0055] Collect space mapping information, cache write rhythm information, and index rewrite record information of the file system before and after remounting, and construct continuous time series data containing time sequence identifiers to provide a time dimension basis for subsequent difference analysis;

[0056] When performing the steps of collecting space mapping information, cache write rhythm information, and index rewrite record information of the file system before and after remounting, continuous time series data containing time sequence identifiers are constructed to form a time dimension basis that can be used for subsequent difference analysis. The specific implementation steps are as follows:

[0057] Before the remount operation begins, a comprehensive scan and recording of the space mapping structure in the current running state of the file system is performed. During this process, the logical block allocation relationship of the storage medium, the distribution information of the data area and free area, the pointing relationship of inodes, and the write arrangement status in the cache queue are continuously read through the file system interface. All collected information is archived in chronological order. The core of this stage is to capture the true state of the file system after the cleanup operation is completed but before remounting, ensuring that each logical block number, cache write order, and index mapping path are completely preserved, thus forming a static initial reference plane in time. To ensure the continuity of information collection, space mapping information and cache write rhythm information are recorded synchronously, and index rewrite records are appended to the corresponding logical block data items with the same timestamp, enabling different types of information to be correlated in the time dimension.

[0058] At the start of the remount operation, the system continuously monitors the file system's mapping changes during the initialization, loading, updating, and resource recovery phases, dynamically capturing the newly generated space mapping table, the updated cache scheduling strategy, and index rewriting behavior. During this process, the system continuously records the change trajectory between the old and new mappings in chronological order, marking each logical block state transition event, cache write timing changes, and inode rewriting paths in the time series. By storing this real-time change information in correspondence with the initial reference information from the previous stage, a continuous recording line spanning the entire mounting process can be formed, ensuring that the state changes of each logical block, the write interval of the cache queue, and the index table rewriting order are all displayed on a unified time dimension. The key to this stage is maintaining strict continuity of time markers, ensuring that space mapping changes and cache write timing changes are captured synchronously on the same time axis, thereby guaranteeing direct comparison of differences between two time points during subsequent analysis.

[0059] After the remounting process is completed, the collected space mapping information, cache write rhythm information, and index rewrite record information are integrated and sequentially edited along a time dimension. At this point, based on the collected data from different time points in the preceding and following stages, the state evolution process of each logical block is rearranged, creating a continuous data trajectory from the initial state before clearing to the final state after remounting. To ensure the integrity of the time series data, the system maintains consistent timestamps across all information sources during the integration process, uniformly aligns the overlapping time portions between space mapping records and cache write records, and concatenates the time points in the index rewrite records corresponding to space mapping changes, constructing a continuous time chain spanning the entire mounting cycle. During this process, the space mapping content, cache write status, and index rewrite behavior of each time point are also labeled with their corresponding operation stage information, ensuring that the time series not only reflects the chronological order but also demonstrates the logical structural changes of the file system at different stages.

[0060] After time-series integration, all collected information undergoes unified time-series identifier embedding and logical archiving. During this stage, based on the timeline formed throughout the mounting process, each spatial mapping record, cache write rhythm record, and index rewrite record is assigned an independent time identifier number, allowing the state changes of the same logical block at different points in time to be directly traced along the time-series sequence. In this way, the time-series data not only includes spatial-level logical allocation information but also integrates dynamic rhythm information of the cache write process and historical records of index path changes, enabling each set of time identifiers to establish a correspondence between multi-dimensional information. After time identifier embedding is completed, all data is continuously arranged to form a complete time-series set. This set covers the entire process of spatial mapping differences, cache write rhythm changes, and index rewrite behavior before and after file system remounting, providing continuous, accurate, and time-extended reference data for subsequent difference analysis steps.

[0061] The difference between the idle marker distribution and the actual write distribution is calculated based on time series data. The overlapping positions are extracted based on the difference results and a fragment trigger list is generated to identify the locations of potential old data remnants.

[0062] The difference between the free data marker distribution and the actual write distribution is calculated based on time series data. Overlapping locations are extracted based on the difference results to generate a fragmentation trigger list, which is used to identify potential locations of old data remnants. The specific implementation steps are as follows:

[0063] In the constructed time-series data, the space mapping information, cache write rhythm information, and index rewrite record information collected before and after file system remounting are uniformly aligned and divided into stages. This stage focuses on time sequence identification, analyzing the state changes of logical blocks at each time node to establish a mapping relationship between logical blocks marked as free and those performing write operations on the time axis. In specific implementation, for the space mapping state at each moment, the allocation identifier field and free area marker field of the logical block are extracted, and the concentrated area and duration range of write activity at that moment are determined in conjunction with the cache write rhythm information. In this way, a continuous free marker distribution sequence and actual write distribution sequence can be obtained on the time axis, providing a basic reference for subsequent difference calculation. The core of this stage is to ensure that the free marker distribution and the actual write distribution are unfolded on the same time base, so that they can achieve complete time correspondence in subsequent processing, thereby ensuring that the difference relationship reflects the real write and release activities within the same time interval.

[0064] After obtaining the corresponding free marker distribution sequence and actual write distribution sequence in the time series, the logical block states at each time node are correlated and compared to reveal the differences between the logical marker states and the actual write behavior. This stage maintains the temporal order of each logical block in the two distribution sequences and categorizes logical blocks marked as free but with write records in the actual write distribution as differences. To maintain temporal continuity, all logical blocks with discovered state differences retain their corresponding timestamp information and are recorded along with state changes at adjacent time nodes. This method allows for the acquisition of a set of regions where state inconsistencies occur between the file system's logical layer and physical write layer. Simultaneously, cached write rhythm information is included in the difference comparison, ensuring that overlaps between free markers and write activities occurring during periods of high write frequency are fully recorded. This reveals the misalignment between the system's rewrite behavior shortly after data clearing and free marker management. This stage forms a time-dimensional difference mapping table between free markers and actual write behavior, providing accurate time and location references for subsequent overlap location extraction.

[0065] After constructing the difference mapping table, overlapping logical block regions in the difference data are analyzed based on time sequence. The specific locations where free markers and write operations intersect are extracted to identify potential old data remnants. This stage uses spatial mapping information as the main thread, sorting overlapping logical blocks found in the time series according to their continuous distribution in the storage medium to identify continuous or intermittently distributed overlapping segments. To ensure the accuracy of overlap extraction, index rewrite record information is combined during the extraction process to trace the historical index path of each overlapping logical block to determine whether it corresponds to a residual pointing relationship in the original data file. In this way, ordinary overlapping regions where only logical layer marker conflicts occur can be distinguished from potential remnant regions with historical index pointing, making the final set of overlapping locations more targeted and valuable for identification. Throughout this process, the time and spatial dimensions of the data are kept synchronously correlated, ensuring that each overlapping location not only corresponds to a specific logical block address but also has a corresponding time series identifier, allowing for the tracing of its occurrence time and duration.

[0066] After determining the set of overlapping locations, these overlapping regions are integrated and summarized to generate a fragment trigger list for identifying potential old data remnants. This stage uses the set of overlapping locations as input, grouping each overlapping logical block according to its chronological order in the time series. Physically adjacent overlapping logical blocks belonging to the same time period are merged into a single fragment unit to reflect potential old data remnants during a specific mounting phase. Simultaneously, for each fragment unit, its corresponding time series start and end positions, idle marker status change trajectory, and write activity duration are recorded. This ensures the fragment trigger list includes not only spatial location information but also temporal extension information and the context of write behavior. In this way, the fragment trigger list serves as an intermediary bridge connecting the differences between the idle marker distribution and the actual write distribution, providing direct input data for subsequent location of mislabeled areas and identification of high-risk splicing windows.

[0067] Based on the fragment trigger list, the data recovery stage and mapping jump point are tracked to identify mislabeled areas and generate high-risk splicing window information, providing a spatial basis for the subsequent intervention area delineation.

[0068] Based on the fragment trigger list, the data retrieval stage and mapping jump points are tracked to identify mislabeled areas and generate high-risk splicing window information, so as to provide a spatial basis for subsequent intervention area delineation. The specific implementation steps are as follows:

[0069] After obtaining the fragmentation trigger list, the overlapping positions recorded in the list and their corresponding time-series identifiers are traced item by item to clarify the lifecycle trajectory of each potential residual fragment in the file system. This stage uses time-series data as the main thread, expanding each record in the list chronologically and mapping it to the spatial mapping information of the file system to trace its state changes during data reclamation. Specifically, for each fragmentation trigger, the time point when it was first marked as idle and the time point when it was last recorded as actually written are extracted in the time series, and the mapping relationship between the two is analyzed to identify the transfer path of the logical block during the reclamation phase. To ensure the integrity of the tracing process, the temporal continuity between adjacent logical blocks in the list is analyzed. When multiple logical blocks have overlapping idle markings and writes within a similar time interval, these logical blocks are treated as continuous fragments in the same data reclamation phase and jointly traced. In this way, the state evolution process of each logical block in the file system during the post-cleanup reclamation process can be reconstructed in the time dimension, expanding the static overlap information in the fragmentation trigger list into a data trajectory sequence with dynamic evolutionary attributes, providing a prerequisite for the identification of subsequent mapping jump points.

[0070] After completing the data reclamation phase tracking, the aforementioned trajectory sequence is analyzed spatially to determine the location and changing patterns of mapping jump points. This phase focuses on spatial mapping information, identifying the location of file system jumps in the space allocation table by comparing the physical address correspondences of logical blocks at different time points. When a logical block experiences an address change or index mapping path interruption in the time series, that moment is recorded as a mapping jump event. To ensure the continuity of the analysis, these mapping jump events are matched with the reclamation trajectory obtained in the previous phase to determine whether the jump occurred at a critical node in the data reclamation phase. If the jump event overlaps with the idle marker state transition, it indicates that the logical block may not have been effectively overwritten during the cleanup process and has been incorrectly reassigned to the new data writing area. At this time, the spatial segment where the logical block is located is tentatively designated as a candidate area for mislabeling. By continuously analyzing jump behavior at multiple time points, the spatial distribution characteristics of mislabeling phenomena can be determined, including the division between concentrated jump areas and sparse jump areas. This stage of processing allows the potential mislabeling behavior of the file system during mounting and recycling to be clearly revealed from the spatial mapping relationship, laying the foundation for further identification of mislabeled areas.

[0071] After identifying potential mislabeled candidate regions, these regions are merged and filtered to form a valid set of mislabeled regions. This stage cross-compares the jump concentration areas and free mark transition areas identified in the previous stage. When frequent jump events and free mark anomalies exist simultaneously in the same physical segment, the segment is confirmed as a mislabeled region. To further improve the accuracy of mislabeling, the corresponding time markers in the fragmentation trigger list are aligned with the mislabeled regions, so that each mislabeled region has not only spatial location attributes but also time interval attributes. In this way, the mislabeled regions are defined in both time and space dimensions, clearly representing their start and end times and the range of their physical addresses. During this process, index rewrite record information is also included in the analysis to determine whether there are cases where old index paths have not been completely covered in the mislabeled regions, thereby identifying mixed segments that simultaneously retain historical index pointers and newly written paths. These types of regions are highly prone to causing incorrect splicing of old data fragments during subsequent file system operation and are therefore considered key monitoring areas. Through this continuous tracking and cross-analysis, a set of mislabeled regions is eventually formed, providing accurate data input for the generation of high-risk splicing windows.

[0072] After determining the set of mislabeled regions, spatial continuity and temporal correlation analysis are performed on these regions to generate high-risk splicing window information. This stage uses the mislabeled regions as the core unit, aggregating multiple temporally adjacent and spatially continuous mislabeled regions to form spatial windows with temporal extension attributes. For each aggregated window, its time range, logical block distribution range, and the start and end relationships of historical write operations are recorded, ensuring that the window not only reflects the physical segments where data reorganization may occur but also provides a temporal reference for subsequent intervention. During the generation process, the overlapping positions recorded in the fragment trigger list are mapped and matched again with the set of mislabeled regions, so that each high-risk splicing window corresponds to one or more fragment triggers, thus logically forming a complete closed loop from difference identification to risk definition. After generation, this high-risk splicing window information is arranged in chronological order and stored corresponding to the spatial mapping table of the file system, forming a spatial range basis that can be called upon in subsequent steps to guide the implementation of reverse write adjustment operations within specific regions.

[0073] By tracing back the historical write rhythm around the high-risk splicing window and combining time series data analysis to identify the backflow path of old data fragments, an intervention-friendly time interval distribution map is constructed to determine the time window for reverse write adjustment.

[0074] By tracing back historical write rhythms around high-risk splicing windows and combining time series data analysis to identify the backflow paths of old data fragments, an intervention-friendly time-distance distribution map is constructed to determine the time window for reverse write adjustments. The specific implementation steps are as follows:

[0075] After obtaining information about high-risk splicing windows, historical backtracking analysis is performed on the file system's time-series data based on the time range and logical block distribution range contained within the window. This stage uses the starting point of the high-risk splicing window as the starting point for tracing, locating the set of logical blocks covered by the window one by one in the time-series data, and extracting their write records and cache activity information in the early stages. In this way, the write rhythm of each logical block at different time points can be reconstructed along the time dimension, including write frequency, duration, and adjacent write intervals, thereby restoring the write behavior characteristics of this region in past operating cycles. During this process, the fragment trigger list is synchronously associated with the corresponding time markers in the mislabeled region set to ensure that the backtracking time range is consistent with the formation cycle of the high-risk splicing window, enabling the backtracking analysis to accurately reflect the historical write trajectory of old data fragments after being mislabeled. Through continuous time-reverse extraction, the write distribution changes of this region in each stage before and after clearing are gradually restored, thereby revealing the dynamic backflow trend of old data during the file system's operation.

[0076] After backtracking the historical write rhythm within the high-risk splicing window, the write rhythm changes reflected in the time series data are grouped into stages to identify the reflow path of old data fragments. This stage arranges each write action recorded in the time series in chronological order and, combined with the allocation of logical blocks in the spatial mapping information, groups logical blocks with continuous write relationships into the same write stage. This method clarifies the write continuity between different stages, thereby tracing the flow trajectory of old data fragments across multiple time periods. In practice, when a logical block is found to be written in an early stage, marked as idle in a middle stage, and then re-emerges with write activity in a later stage, this logical block and its adjacent blocks can be identified as nodes in the reflow path of old data fragments. To ensure the continuity of path identification, adjacent nodes in the time series are connected based on time intervals, so that the old data reflow path not only reflects the spatial relationship between logical blocks but also has a coherent temporal span. Through this tracing method, a reflow path chain spanning multiple time periods can be formed, completely recording the entire process of old data fragments from mislabeling to rewriting.

[0077] After identifying the backflow path of the old data fragments, the time intervals and write order within the path are statistically analyzed to construct a preliminary time-distance relationship model. This stage uses time series identifiers as a basis to extract the time intervals between each write operation on the path and analyzes the distribution patterns of these time intervals at different stages. To ensure the continuity of the time-distance data, all time nodes in the backflow path are arranged according to the write order, and the time intervals are associated with their corresponding logical block addresses, ensuring that each time interval corresponds to a specific spatial location. Through this process, a time-distance dataset reflecting the relationship between the old data write rhythm and spatial changes is obtained. Simultaneously, the time range of the high-risk splicing window is aligned with this time-distance data, ensuring that the window's time boundaries match the start and end times of the old data backflow path. This ensures that the subsequently generated interventionible time-distance distribution map accurately covers the core periods of old data fragment activity, avoiding time misalignment or omissions. Through this stage of processing, the relationship between write operations and time intervals is fully revealed, providing a quantitative basis for subsequently determining the time window for reverse write adjustments.

[0078] After obtaining the time-distance dataset, this data is integrated and summarized to construct an interventionizable time-distance distribution map. This stage arranges all time intervals corresponding to high-risk splicing windows in chronological order and, combined with the changing trends of historical write rhythms, comprehensively describes the write density, duration, and interval distribution within different time periods. In this way, a continuous time-distance distribution curve can be formed on the time axis to reflect the concentrated and sparse periods of old data reflow activity. During the construction process, the time markers in the fragment trigger list and mislabeled area set are again mapped onto this time-distance distribution map, so that each time period in the map corresponds to a specific spatial location and historical write event. Through this dual correspondence, it can be clearly identified which time intervals have frequent old data fragment activity and which intervals have relatively dispersed write rhythms, thus forming an interventionizable window range in the time dimension. The final interventionizable time-distance distribution map not only reflects the reflow pattern of old data fragments but also provides a precise time reference for subsequent reverse write adjustments, enabling intervention operations to be implemented within the time period most likely to trigger data reorganization, thereby interrupting the recombination process of old data fragments.

[0079] Based on the interventionable time interval distribution map, reverse write adjustment operation is performed. Random data overwriting is performed before the time window, the writing rhythm is dispersed by interval writing during the time window, and the writing order is adjusted by delayed disk writing after the time window. This blocks the re-splicing process of old data fragments from the time dimension, thereby achieving irreversible erasure of second-hand mobile phone data.

[0080] Based on the intervened time-distance distribution map, a reverse write adjustment operation is performed. This involves randomly overwriting data before the time window, dispersing the write rhythm with interval writing within the time window, and adjusting the write order by delaying disk writes after the time window. This blocks the reassembly process of old data fragments from a time perspective, achieving irreversible erasure of second-hand mobile phone data. The specific implementation steps are as follows:

[0081] After the interventionable time interval distribution map is generated, a random data overwrite operation is performed on the time segment before the marked start point of the time window. In this stage, based on the time markers in the distribution map, the continuous time period before the time window is defined as the pre-intervention interval. Based on the distribution of logical blocks within this interval, an overwrite operation is performed on each logical block. This process selects logical blocks within the pre-intervention interval one by one through the spatial mapping relationship recorded in the time series, and randomizes their storage content, ensuring that no residual information related to old data fragments is retained in the physical storage units of these logical blocks. To ensure coordination between the overwrite operation and time control, the correspondence with the time series markers is maintained during execution, ensuring that each write operation falls within the predetermined time range. In this way, the potential spatial continuation path of old data fragments can be interrupted before the time window opens, forming a time barrier completely isolated from subsequent window activities. This creates a complete break between the old data's index pointing at the logical layer and the block allocation at the physical layer, laying a safe foundation for subsequent rhythm-distribution operations.

[0082] After completing the random data overwrite operation before the time window, the system enters the time window phase indicated in the maneuverable time interval distribution map. During this period, interval write operations are performed to disperse the file system's write rhythm. This phase is constrained by the start and end times of the time window, and by controlling the time interval and order of write operations, the write rhythm is kept irregularly distributed on the time axis. Specifically, within the time window, each write operation determines its execution time based on the cached write rhythm information recorded in the time series data, and maintains a time interval difference with adjacent write operations. In this way, the regularity of continuous writes can be broken in the time dimension, avoiding the formation of a fixed write pattern when the system performs file log updates or spatial remapping in the background. While performing interval writes, logical blocks corresponding to high-risk splicing windows are preferentially included in the write target, so that the area where old data fragments are located is interrupted and overwritten. Since there is a temporal and spatial correspondence between the time window and the high-risk splicing window, the write activities in this phase can directly affect the potentially reorganized risk area, so that the old data fragments are simultaneously disturbed in terms of temporal rhythm and spatial distribution, thereby preventing the formation of any continuous fragments.

[0083] After the interval write operations within the time window are completed, delayed disk write operations are performed on subsequent time segments of the time window to further adjust the write order and disrupt the continuity of data writes to disk. This stage uses the end time of the time window as a dividing point, and subsequent write actions are sequentially delayed, so that they are logically recorded but physically written later. In this way, a time difference can be created in the file system cache layer, making the physical write order of new data inconsistent with the logical commit order. When write tasks in the cache queue are gradually written to disk according to the delay mechanism, their distribution in physical space becomes discrete, thus breaking the continuity between write actions generated in the time window and subsequent write actions in the physical storage layer. This combined effect of time delay and spatial rearrangement prevents the file system from forming a continuously traceable write sequence at any point in time, completely blocking the possibility of splicing old data fragments and new data fragments in the time dimension. Simultaneously, during the delayed disk write period, the write delay interval is dynamically adjusted based on the cache write rhythm information recorded in the time series data, making the physical write process non-linear in time distribution, further enhancing the unpredictability of the write order.

[0084] After the delayed disk write operation is completed, the time distribution of the entire reverse write adjustment process is synchronized to form a complete time intervention closed loop. This stage merges the random overwrite operations before the time window, the interval write operations within the time window, and the delayed disk write operations after the time window in chronological order, forming a continuous reverse write chain on the time axis. This chain-like association ensures that there are no uninterrupted write paths between the pre-intervention stage before the start of the time window and the disk write delay stage after the end of the time window, thus completely disrupting the continuity of old data fragments in the time dimension. To ensure the temporal consistency of the reverse write adjustment operation, the time nodes in the intervened time interval distribution diagram are mapped one-to-one with the actual execution records, ensuring that the write activity in each time period is fully mapped on the time axis. In this way, a complete reverse correspondence can be formed between the time domain and the spatial domain, synchronously disrupting the time traces and spatial mappings of old data fragments in the file system. The execution result of this stage makes the file system's write behavior segmented, discrete, and interleaved in time, so that no old data fragments can be reassembled based on the original time logic, thus achieving complete and unrecoverable erasure.

[0085] This invention constructs continuous time-series data with temporal sequence identifiers by collecting space mapping information, cache write rhythm information, and index rewrite record information before and after file system remounting. Based on this, it performs difference comparison and mislabeled region identification, enabling temporal correlation analysis of write activities at the logical and physical layers. In this way, potential residual old data fragments can be dynamically identified during file system operation, and high-risk splicing windows can be precisely defined. This elevates the data erasure process from static erasure to time-controlled dynamic erasure, achieving more targeted residual data discovery and risk isolation, effectively improving the thoroughness and security of data erasure.

[0086] This invention constructs an interveneable time-distance distribution map around a high-risk splicing window and performs reverse write adjustment operations. It employs a multi-stage intervention strategy, including random overwriting, interval writing, and delayed disk writes, at different time window stages to orderly disrupt the file system's write order and caching rhythm. By breaking the continuity and spatial hierarchy of old data fragments in the time dimension, it prevents any old data fragments from being reassembled into identifiable files in subsequent operations, fundamentally eliminating the risk of historical data regeneration. This achieves irreversible erasure of second-hand mobile phone data, ensuring the stability and reliability of the data security processing.

[0087] This invention provides, for example Figure 2 The secondhand mobile phone data erasure system shown includes a data acquisition module, a difference analysis module, a risk positioning module, a time-distance modeling module, and a rhythm intervention module.

[0088] The data acquisition module collects space mapping information, cache write rhythm information, and index rewrite record information of the file system before and after remounting, and constructs continuous time series data containing time sequence identifiers to provide a time dimension basis for subsequent difference analysis.

[0089] The difference analysis module calculates the difference between the free marker distribution and the actual write distribution based on time series data, extracts overlapping positions based on the difference results and generates a fragment trigger list to identify potential old data remnant fragment locations;

[0090] The risk positioning module tracks the data recovery stage and mapping jump points based on the fragment trigger list, identifies mislabeled areas and generates high-risk splicing window information, providing a spatial basis for the subsequent intervention area delineation.

[0091] The time-distance modeling module traces back the historical write rhythm around the high-risk splicing window, combines time series data analysis to analyze the backflow path of old data fragments, and constructs an intervention-friendly time-distance distribution map to determine the time window for reverse write adjustment.

[0092] The rhythm intervention module performs reverse write adjustment operations based on the controllable time interval distribution map. Before the time window, it performs random data overwriting, disperses the write rhythm by using an interval write method within the time window, and adjusts the write order by delaying disk write after the time window. This blocks the re-splicing process of old data fragments from the time dimension, achieving irreversible erasure of second-hand mobile phone data.

[0093] The method for clearing data on a used mobile phone provided in this embodiment of the invention is implemented through the aforementioned used mobile phone data clearing system. For details of the specific methods and processes of the used mobile phone data clearing system, please refer to the embodiments of the above-mentioned method for clearing data on a used mobile phone, which will not be repeated here.

[0094] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for erasing data on a used mobile phone, characterized in that, Includes the following steps: Collect space mapping information, cache write rhythm information, and index rewrite record information of the file system before and after remounting to construct continuous time series data; The difference between the free marker distribution and the actual write distribution is calculated based on time series data. The overlapping positions are extracted based on the difference results and a fragmentation trigger list is generated. Based on the fragment trigger list, track the data recycling stage and mapping jump point, identify mislabeled areas, and generate high-risk splicing window information; By tracing back the historical writing rhythm around high-risk splicing windows and combining time series data analysis to identify the backflow path of old data fragments, an intervention-friendly time interval distribution map is constructed. Based on the controllable time interval distribution map, a reverse write adjustment operation is performed. Random data overwriting is carried out before the time window, the write rhythm is dispersed by the interval write method during the time window, and the write order is adjusted by the delayed disk write method after the time window. This blocks the re-splicing process of old data fragments from the time dimension, thereby achieving the irreversible erasure of second-hand mobile phone data.

2. The method for clearing data on a used mobile phone according to claim 1, characterized in that, The steps for constructing time series data are as follows: Before the remount operation begins, the space mapping structure in the file system running state is scanned and recorded. Logical block allocation relationship, data area and free area distribution information, inode pointing relationship and cache queue write arrangement status are continuously read through the file system interface, and initial time series data is generated in time order. When the remount operation begins, monitor the mapping changes of the file system during the initialization, loading, updating and resource recovery phases, record logical block state transition events, cache write rhythm changes and inode re-pointing paths, and append the corresponding change information to the time series data; At the end of the remounting process, the collected spatial mapping information, cache write rhythm information and index rewrite record information are integrated and sorted in time, and the data of the previous and subsequent stages are uniformly incorporated into the time series data structure. Once the time series data integration is complete, all information is embedded with time stamps and logically archived to form a continuous time series data set.

3. The method for erasing data on a used mobile phone according to claim 2, characterized in that, During the construction of time series data, spatial mapping information, cache write rhythm information and index rewrite record information are kept consistent with time tags during collection. All records in the time series data are arranged continuously in chronological order, and a time correlation is established between changes in logical block state and changes in cache write rhythm to ensure that the time series data maintains continuity and consistency throughout the entire mounting cycle.

4. The method for erasing data on a used mobile phone according to claim 2, characterized in that, The steps for calculating the difference between the free marker distribution and the actual write distribution based on time series data are as follows: In the completed time-series data, the space mapping information, cache write rhythm information and index rewrite record information collected before and after the file system is remounted are uniformly aligned and divided into stages to generate a free marker distribution sequence and an actual write distribution sequence. After obtaining the idle tag distribution sequence and the actual write distribution sequence, the logical block state at each time node is correlated and compared to form a difference mapping table between the idle tags and the actual write behavior; In the difference mapping table, the overlapping positions where idle markers and write operations intersect are extracted according to time order to identify potential old data residue areas and establish a set of overlapping positions. Once the set of overlapping locations is determined, the overlapping areas are integrated and summarized to generate a fragment trigger list that includes the start and end positions of the time series, the trajectory of the idle marker state change, and the duration of the write activity.

5. The method for erasing data on a used mobile phone according to claim 4, characterized in that, The steps for tracking data recycling phases and mapping jump points based on the fragment trigger list are as follows: When the fragmentation trigger list is obtained, the overlapping positions and their time series identifiers recorded in the list are tracked, and the recorded content is mapped to the space mapping information of the file system to determine the state changes in the data reclamation phase. During the data recovery phase, the physical address correspondence of logical blocks in the time series is compared to identify the mapped jump points and record the jump events, thereby forming a candidate region for mislabeling. When identifying candidate mislabeled regions, the jump concentration area and the idle marker transition area are cross-compared to generate a set of mislabeled regions containing time interval attributes and spatial location attributes. When the mislabeled region set is formed, the mislabeled regions that are temporally adjacent and spatially continuous are aggregated to generate high-risk splicing window information with time extension attributes.

6. The method for erasing data on a used mobile phone according to claim 5, characterized in that, When generating high-risk splicing window information, the overlapping positions in the fragment trigger list are mapped and matched with the mislabeled area set. Mislabeled areas that are temporally adjacent and spatially continuous are aggregated, and the time range, logical block distribution range, and historical write behavior start and end relationship of each splicing window are recorded to form a spatial range basis for subsequent reverse write adjustment operations.

7. The method for erasing data on a used mobile phone according to claim 5, characterized in that, The steps for tracing back the historical write rhythm around high-risk splicing windows and analyzing the backflow path of old data fragments using time series data are as follows: When high-risk splicing window information is obtained, the time series data is backtracked according to the time range and logical block distribution range to extract write records and cache activity information in order to restore the write rhythm. When completing the historical write rhythm backtracking, the time series data is grouped into stages, and logical blocks with continuous write relationships are grouped into the same stage to identify the backflow path of old data fragments; When identifying the backflow path of old data fragments, the time interval is extracted based on the time series identifier and the writing order is analyzed to form a time interval dataset that reflects the relationship between writing rhythm and spatial changes. When obtaining the time-distance dataset, the data is integrated and summarized to construct an intervention-friendly time-distance distribution map to determine the time window for reverse write adjustments.

8. The method for erasing data on a used mobile phone according to claim 7, characterized in that, When constructing an intervention-friendly time-distance distribution map, the time range in the high-risk splicing window is aligned with the time-distance dataset, and the correspondence between time intervals and logical block addresses is established based on the time series identifier, so that each time period is associated with a specific spatial location and historical write events, thereby forming a continuous time distribution curve to determine the concentrated period of activity of old data fragments.

9. The method for erasing data on a used mobile phone according to claim 7, characterized in that, The steps for reverse write adjustment based on the intervened time interval distribution map are as follows: When generating the interferable time-distance distribution map, a random data overwrite operation is performed on the time segment before the start point of the time window, and logical blocks are selected and overwritten according to the spatial mapping relationship recorded in the time series. When completing random data overwrite operations, interval write operations are performed during the time window phase marked by the time interval distribution map that can be intervened, so as to disperse the file system write rhythm and break the continuous write pattern. When completing the interval write operation, a delayed disk write operation is performed on the time segment after the time window, so that the logical write order is no longer consistent with the physical write order and forms a discrete distribution. During the delayed disk write operation, the random overwrite, interval write, and delayed disk write operations are distributed and organized in time to form a continuous reverse write chain to prevent the splicing of old data fragments.

10. A used mobile phone data erasure system, used to implement the used mobile phone data erasure method according to any one of claims 1-9, characterized in that, It includes a data acquisition module, a differential analysis module, a risk identification module, a time-distance modeling module, and a rhythm intervention module: The data acquisition module collects space mapping information, cache write rhythm information, and index rewrite record information of the file system before and after remounting, and constructs continuous time series data. The difference analysis module calculates the difference between the free marker distribution and the actual write distribution based on time series data, extracts overlapping positions based on the difference results, and generates a fragmentation trigger list. The risk positioning module tracks the data recovery stage and mapping jump points based on the fragment trigger list, identifies mislabeled areas, and generates high-risk splicing window information. The time-distance modeling module traces back the historical writing rhythm around the high-risk splicing window, combines time series data analysis to analyze the backflow path of old data fragments, and constructs an intervention-friendly time-distance distribution map; The rhythm intervention module performs reverse write adjustment operations based on the controllable time interval distribution map. Before the time window, it performs random data overwriting, disperses the write rhythm by using an interval write method within the time window, and adjusts the write order by delaying disk write after the time window. This blocks the re-splicing process of old data fragments from the time dimension, achieving irreversible erasure of second-hand mobile phone data.

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