Multiple protection methods and systems for storage media destruction machines

Abstract

This application provides a multi-protection method and system for a storage media destruction machine, relating to the field of data security technology. The method includes: the storage medium entering the destruction chamber; reading the unique identification information of the storage medium and comparing it with a preset authorization database for verification; creating a dynamic security zone; performing operator identity verification, external authorization verification, and storage medium status verification; and executing the destruction operation when all three authentications pass; activating multi-source sensors for data acquisition; sending multi-modal sensor data to an abnormal authentication channel; and performing multi-protection destruction based on the destruction encryption digest, a one-time destruction pass code, and multi-modal sensor data. This application solves the technical problem in the prior art where incomplete trust chains and black-box process verification lead to leakage risks of storage media before and after destruction, and unreliable audit results, thus affecting the security of the destruction operation. The multi-protection approach improves the security of the destruction operation.

Description

Technical Field

[0001] This application relates to the field of data security technology, specifically to a multi-protection method and system for a storage media destruction machine. Background Technology

[0002] The security of storage media during the disposal process is the last line of defense in data lifecycle management, and its reliability is paramount. Currently, the industry commonly uses technologies such as physical shredding, demagnetization, or logical erasure to destroy storage media. However, for storage media such as solid-state drives (SSDs), existing solutions generally rely on the integrity and trustworthiness of the destruction device itself. Its internal operation is a black box that users cannot see through, creating trust blind spots in the authorization, execution, and verification stages of the destruction operation. Before destruction, the lack of strong correlation verification between the media's identity, operating permissions, and destruction instructions fails to effectively prevent unauthorized or erroneous destruction. During destruction, the lack of a secure execution environment makes it difficult to prevent sensitive data from being leaked through potential backdoors in the device just before destruction. Because existing methods rely entirely on sensor data reported by the device for verification of the destruction process, the data itself can be tampered with or forged, resulting in insufficient credibility of the generated audit logs and destruction certificates, further impacting the security of the destruction operation.

[0003] In summary, existing technologies suffer from technical problems such as incomplete trust chains and black-box process verification, which lead to leakage risks of storage media before and after destruction and unreliable audit results, further affecting the security of destruction operations. Summary of the Invention

[0004] The purpose of this application is to provide a multi-protection method and system for storage media destruction machines, in order to solve the technical problems in the prior art where incomplete trust chains and black-box process verification lead to leakage risks of storage media before and after destruction and unreliable audit results, which further affect the security of destruction operations.

[0005] To achieve the above objectives, this application provides a multi-protection method and system for a storage media destruction machine.

[0006] Firstly, this application provides a multi-protection method for a storage medium destruction machine. This multi-protection method is implemented through a multi-protection system for the storage medium destruction machine. The multi-protection method includes: after the storage medium enters the destruction chamber, reading the unique identification information of the storage medium, comparing and verifying the unique identification information with a preset authorization database, and generating a one-time destruction pass code; creating a dynamic security zone within the destruction chamber based on the one-time destruction pass code, wherein the boundary of the dynamic security zone is a dynamically adjustable boundary, and simultaneously activating electromagnetic shielding and physical locking structures; and executing... The system verifies operator identity, external authorization, and storage media status. When all three authentications pass, it controls the storage media destruction machine to perform the destruction operation. It activates multi-source sensors to collect data during the destruction process, establishing multimodal sensing data, including fragment size data, electromagnetic erasure depth data, energy consumption data, and the storage media destruction machine's operating status data. This multimodal sensing data is then sent to an anomaly authentication channel to perform anomaly authentication during the destruction process, establishing a destruction encryption digest. Multiple protections for destruction are implemented based on the destruction encryption digest, a one-time destruction pass code, and the multimodal sensing data.

[0007] Optionally, the destruction chamber is equipped with a digital potentiometer array coupled to the electromagnetic erasure device. The digital potentiometer array is used to dynamically adjust the current intensity of the electromagnetic erasure based on the security parameter generation result of the one-time destruction access code. Each digital potentiometer corresponds to an independent erasure channel. The dynamic adjustment of the current intensity of the electromagnetic erasure includes digitally programmable control of the rise slope, peak amplitude and duration of the erasure current.

[0008] Optionally, the boundary adjustment of the dynamic safety zone is driven by multi-dimensional sensor inputs, including temperature gradient, electromagnetic field strength and mechanical vibration signals. The boundary is dynamically reconstructed according to the real-time changes of the multi-dimensional sensor inputs, and the electromagnetic shielding strength and locking delay are adjusted to perform dynamic isolation control of the internal space of the destruction chamber.

[0009] Optionally, a joint trust calculation unit is configured to dynamically calculate the comprehensive trust score based on the historical trustworthiness of each authentication source, verification time interval, and communication delay. When the comprehensive trust score is lower than a preset score threshold, a security interruption is triggered, and the access code is frozen and destroyed in one go.

[0010] Optionally, within the trusted execution environment of the anomaly authentication channel, the multimodal sensing data undergoes time synchronization, denoising, and frame segmentation. Granularity distribution spectrum is extracted from the fragment granularity data, spectral energy density curve is extracted from the electromagnetic erasure depth data, downstream power pulse features are extracted from the energy consumption data, and mechanical vibration modes and motor drive current features are extracted from the operating status data. The extraction results are then normalized and dimensionless. The processed extraction results are synchronized to the three-layer verification sub-channels within the anomaly authentication channel. The single-modal anomaly scores of each verification sub-channel are weighted and fused to establish an overall anomaly score. Anomaly authentication is completed based on the overall anomaly score.

[0011] Optionally, the processed extraction results are synchronized to the first-layer verification sub-channel, which is a rule-based sub-channel based on residual and threshold statistics, and outputs a first single-modal anomaly score; the processed extraction results are synchronized to the second-layer verification sub-channel, which is a time-series anomaly detection sub-channel based on a sliding window, and outputs a second single-modal anomaly score; the processed extraction results are synchronized to the third-layer verification sub-channel, which is a multi-modal reconstruction sub-channel based on deep learning, and outputs a third single-modal anomaly score; a weighted fusion is performed based on the first single-modal anomaly score, the second single-modal anomaly score, and the third single-modal anomaly score to establish an overall anomaly score.

[0012] Optionally, offline statistical modeling is performed based on the multimodal feature vector set under normal destruction conditions of similar media, and a baseline mean is constructed for each modal vector; the element-wise difference operation is performed based on the baseline mean and the extraction result using the rule sub-channel to calculate the residual vector, and the residual vector is normalized by Mahalanobis distance according to the covariance matrix to output the Mahalanobis residual value as a unified residual; a threshold determination is performed based on the unified residual to output the first single-modal anomaly score.

[0013] Optionally, at least two sliding windows of different lengths are established, and the processed extraction results are subjected to overlapping sliding processing of the sliding windows. Within each sliding window, a set of time-series statistics and frequency domain features are calculated. The time-series statistics include mean, variance, kurtosis, and skewness, and the frequency domain features include dominant frequency power density, spectral energy concentration, and bandwidth expansion coefficient. A dual-domain joint change model is established based on the time-series statistics and the frequency domain features. Change point detection is performed using the difference matrix and correlation coefficient matrix between adjacent frames in the window to establish an initial drift region. Dynamic threshold correction and confidence interval determination are applied to the initial drift region, and the drift identification result is output. The drift identification result is mapped to the time series of the corresponding window index to establish a time-series drift spectrum, and a second single-mode anomaly score is output based on the time-series drift spectrum.

[0014] Optionally, the third-layer verification sub-channel includes a self-supervised reconstructed neural network, which consists of a multi-modal encoder, a fusion bottleneck layer, and a joint decoder. Each channel of the multi-modal encoder corresponds to a sensor stream. The encoder is constructed based on a one-dimensional / two-dimensional convolutional network. The fusion bottleneck layer performs feature interaction fusion based on a cross-modal attention mechanism. The joint decoder is used to perform the reconstruction of data from each modality.

[0015] Secondly, this application also provides a multi-protection system for a storage medium destruction machine, used to execute the multi-protection method for a storage medium destruction machine as described in the first aspect. The multi-protection system for the storage medium destruction machine includes: a comparison and verification module, used to read the unique identification information of the storage medium after it enters the destruction chamber, compare and verify the unique identification information with a preset authorization database, and generate a one-time destruction pass code; a security zone determination module, used to create a dynamic security zone within the destruction chamber based on the one-time destruction pass code, wherein the boundary of the dynamic security zone is a dynamically adjustable boundary, and to activate the electromagnetic shielding and physical locking structure in conjunction with it; and a triple authentication module, used to execute operator... The system includes three authentication mechanisms: identity verification, external authorization verification, and storage medium status verification. When all three verifications pass, the storage medium destruction machine is controlled to perform the destruction operation. A data acquisition module activates multi-source sensors to collect data during the destruction process, establishing multimodal sensing data. This multimodal sensing data includes fragment granularity data, electromagnetic erasure depth data, energy consumption data, and the storage medium destruction machine's operating status data. An anomaly authentication module sends the multimodal sensing data to an anomaly authentication channel to perform anomaly authentication during the destruction process and establishes a destruction encryption digest. A multi-protection module provides multiple protections for the destruction process based on the destruction encryption digest, a one-time destruction pass code, and the multimodal sensing data.

[0016] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0017] After the storage medium enters the destruction chamber, its unique identification information is read and compared with a preset authorization database to generate a one-time destruction pass code. A dynamic security zone is created within the destruction chamber based on this pass code. The boundaries of the dynamic security zone are dynamically adjustable, and electromagnetic shielding and physical locking structures are activated in conjunction with it. Operator identity verification, external authorization verification, and storage medium status verification are performed. When all three authentications are successful, the storage medium destruction machine is controlled to perform the destruction operation. Multi-source sensors are activated to collect data during the destruction process, establishing multi-modal sensing data. This multi-modal sensing data includes fragment granularity data, electromagnetic erasure depth data, energy consumption data, and the operating status data of the storage medium destruction machine. The multi-modal sensing data is sent to an anomaly authentication channel to perform anomaly authentication during the destruction process and establish a destruction encryption digest. Multiple protections for destruction are implemented based on the destruction encryption digest, the one-time destruction pass code, and the multi-modal sensing data. In other words, by reading the unique identification information of the storage medium and comparing it with the preset authorized database, a one-time destruction pass code is generated, a dynamic security zone is created, and electromagnetic shielding and physical locking structures are activated in conjunction, which strengthens the protection of the storage medium. By implementing triple authentication and activating multi-source sensors to collect data on the destruction process, a destruction encryption digest is generated. Combined with the one-time destruction pass code and multi-modal sensor data, multiple protections for destruction are provided to ensure the complete record and immutability of each destruction operation, further improving the security and reliability of the destruction process.

[0018] The above description is merely an overview of the technical solution of this application. To better understand the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the multi-protection method for a storage media destruction machine according to this application.

[0021] Figure 2This is a schematic diagram of the multi-protection system for the storage media destruction machine of this application.

[0022] Explanation of reference numerals in the attached diagram: Comparison and verification module 11, security zone determination module 12, triple authentication module 13, data acquisition module 14, anomaly authentication module 15, and multiple protection module 16. Detailed Implementation

[0023] This application provides a multi-protection method and system for storage media destruction machines, solving the technical problems in existing technologies where incomplete trust chains and black-box process verification lead to leakage risks before and after destruction, and unreliable audit results, further affecting the security of destruction operations. By reading the unique identification information of the storage media and comparing it with a preset authorized database, a one-time destruction pass code is generated. A dynamic security zone is created and electromagnetic shielding and physical locking structures are activated in conjunction, strengthening the protection of the storage media. Triple authentication is implemented, and multi-source sensors are activated to collect data during the destruction process, generating a destruction encrypted digest. This multi-layered protection, combined with the one-time destruction pass code and multi-modal sensor data, ensures the complete recording and immutability of each destruction operation, further improving the security and reliability of the destruction process.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1 This application provides a multi-protection method for a storage media destruction machine, wherein the multi-protection method for the storage media destruction machine is implemented through a multi-protection system for the storage media destruction machine, and the multi-protection method for the storage media destruction machine specifically includes the following steps:

[0026] After the storage medium enters the destruction chamber, the unique identification information of the storage medium is read, and the unique identification information is compared and verified with the preset authorization database to generate a one-time destruction pass code.

[0027] Furthermore, this application also includes the following steps: the destruction chamber is equipped with a digital potentiometer array coupled to the electromagnetic erasure device. The digital potentiometer array is used to dynamically adjust the current intensity of the electromagnetic erasure based on the security parameter generation result of the one-time destruction access code. Each digital potentiometer corresponds to an independent erasure channel. The dynamic adjustment of the current intensity of the electromagnetic erasure includes digitally programmable control of the rise slope, peak amplitude and duration of the erasure current.

[0028] Specifically, the storage media destruction machine is an industrial-grade physical destruction device designed for scenarios requiring high-security handling of obsolete storage media. Its main function is to use powerful mechanical crushing capabilities to completely destroy various storage media to an irreversible physical state, ensuring that sensitive data stored within is not leaked. The machine is equipped with a high-power geared motor, providing superior crushing power. Its blades, made of special alloy steel, ensure a firm grip on the materials to be crushed, significantly increasing the machine's crushing capacity and improving work efficiency. The crushed debris falls directly from the blade assembly into a large waste bin equipped with rollers, allowing for easy movement to a waste storage area for emptying, facilitating handling and reducing operator workload. The mechanical and electrical systems feature multiple intelligent protections, including main motor start protection, motor jamming overload protection, automatic reverse protection, emergency stop, and door-opening stop protection, ensuring stable operation and preventing escalation of malfunctions. The storage media destruction machine is equipped with an emergency stop button and a door-opening stop function; the machine immediately stops operating if the hopper cover is opened, effectively preventing personal injury accidents. The power supply is properly grounded. Maintenance of the electrical control cabinet must be performed by qualified personnel with the power off, and it is equipped with overload protection.

[0029] The storage media destruction machine can handle a wide range of storage media, including hard disk drives (HDDs), videotapes, magnetic tapes, magnetic cards, solid-state drives (SSDs), USB flash drives, memory cards, chips, mobile phones, tablets, circuit boards, optical discs, and other magnetic, semiconductor, and optical media. The hard disk shredder blades are 25-30mm wide for block hard disks, with a shredding capacity of 90kg / h for hard disks and 50-70kg / h for other storage media. The power consumption is 2.5-3kW, and the voltage is 220V / 380V.

[0030] The storage media destruction machine includes a power and transmission system, a crushing system, a feeding and collection system, a support and external structure, and a control and safety system. The power and transmission system provides and transmits the enormous torque required for crushing, including a motor, reducer, pulleys, and belts. The crushing system is the actuator, including a blade box, drive and driven cutter shafts, crushing blades (hard drive and USB flash drive crushing blades), comb teeth, isolation sleeves (hard drive and USB flash drive isolation sleeves), wall panels, and end caps. The feeding and collection system includes a flip-top hopper and a 65L waste collection box. The flip-top hopper is equipped with a safety interlock switch; the equipment immediately stops operating when the cover is opened. The waste collection box has rollers for easy removal after crushing. The support and external structure includes a frame and sheet metal housing. The control and safety system includes a power distribution box, an operation panel (touchscreen, physical buttons), and safety devices.

[0031] Once the storage media is placed in the destruction chamber, it undergoes scanning and identity verification. Storage media are physical devices used for storing data that need to be destroyed, such as hard disk drives (HDDs), videotapes, magnetic tapes, magnetic cards, solid-state drives (SSDs), USB flash drives, memory cards, chips, mobile phones, tablets, circuit boards, optical discs, and various other magnetic, semiconductor, and optical media. The scanner inside the destruction chamber immediately activates, reading the unique identification information of the storage media, such as the serial number of a hard drive (WDC-WX12A1234567). This unique identification information is unique to each storage medium, including the device serial number, manufacturer information, model number, production date, MAC address, and electronic tag.

[0032] The pre-built and maintained authorization database contains a list of all authorized storage media for destruction. Each record includes at least a unique identifier for the storage media and associated destruction task metadata, such as the reason for destruction, the authorizing party, and the required security level. The destruction module sends a query request containing the unique identifier to the pre-built authorization database. Upon receiving the request, the database retrieves the unique identifier from its list of tasks to be destroyed. If a matching record is found and its status is authorized, the database returns a verification success signal to the destruction module, along with the task's security parameters, such as meeting the destruction criteria. Simultaneously, a one-time destruction pass code is generated, containing a timestamp, task ID, and digital signature. This one-time destruction pass code is a unique, time-limited, and single-use digital token. For example, a one-time destruction pass code like ABC12345XYZ is valid only in the current destruction process and cannot be reused.

[0033] If the unique identifier is not found in the preset authorization database, or if the task status corresponding to the unique identifier is canceled / completed, the process will be terminated immediately, an alarm will be issued, the destruction chamber will be kept locked, and the unauthorized attempt will be recorded in the security log to ensure that only media that have undergone strict approval can enter the subsequent physical destruction process.

[0034] The destruction chamber is the core working cavity of the storage medium destruction machine, a sealed space with physical isolation and electromagnetic shielding. Inside the destruction chamber is an array of digital potentiometers coupled to the electromagnetic erasure device. The electromagnetic erasure device generates a high-intensity alternating magnetic field, disrupting the magnetic domain orientation of the magnetic storage medium, thereby irreversibly erasing data and achieving interference and data deletion. The digital potentiometer array is a collection of electronic components whose resistance values ​​are precisely controlled by digital signals. Each independent erasure channel corresponds to a digital potentiometer. Based on the security parameters generated by the one-time destruction pass code, the resistance value of the digital potentiometer is changed to precisely control the current intensity flowing through the electromagnetic erasure. The current rise rate, peak amplitude, and duration are controlled by programming. The rise rate is the rate at which the current intensity increases within a certain time, preventing current surges that could damage the equipment; the peak amplitude is the maximum current value, ensuring the magnetic field strength is sufficient for complete demagnetization; and the duration the current remains at the peak amplitude, ensuring sufficient time for the magnetic domains to fully randomize. Based on the security parameters provided by the one-time destruction pass code, the digital potentiometer array dynamically adjusts three key parameters of the current: the rise rate, the peak amplitude, and the duration. Through digital programmable control, the digital potentiometer array can dynamically adjust these parameters in real time, precisely control the current intensity of each channel, and ensure optimal electromagnetic erasure according to the type of storage medium and specific destruction requirements.

[0035] By comparing the unique identifier of each storage medium with a pre-defined authorization database, the system ensures that only authorized storage media can enter the destruction process, preventing the destruction of unauthorized or illegal media. A one-time destruction pass code ensures that the destruction process for each storage medium is unique and cannot be reused, reducing the risk of data leakage and ensuring the security of the destruction operation.

[0036] A dynamic security zone is created within the destruction chamber based on the one-time destruction access code. The boundary of the dynamic security zone is dynamically adjustable, and the electromagnetic shielding and physical locking structures are activated in conjunction with it.

[0037] Furthermore, this application also includes the following steps: the boundary adjustment of the dynamic safety zone is driven by multi-dimensional sensor input, including temperature gradient, electromagnetic field strength and mechanical vibration signal, and the boundary is dynamically reconstructed according to the real-time changes of the multi-dimensional sensor input, adjusting the electromagnetic shielding strength and locking delay to perform dynamic isolation control of the internal space of the destruction chamber.

[0038] Specifically, based on the security level of the one-time destruction access code, a dynamic safety zone is created within the destruction chamber. This is a virtual safety protection area with flexibly adjustable boundaries, established inside the destruction chamber. Its protection strength and safety parameters can be dynamically adjusted according to real-time risks. During the destruction process, the boundaries of the dynamic safety zone are not fixed but are adjusted in real time based on multi-dimensional data from different sensors. Based on temperature gradients, electromagnetic field strength, and mechanical vibration signals, the safety isolation within the destruction chamber is dynamically controlled by adjusting the electromagnetic shielding strength and locking delay. For example, when external vibration signals are too strong, the locking delay may be set to 10 seconds to ensure that the internal equipment is not affected by external impacts, further enhancing the non-interferenceability of the destruction process. Real-time monitoring of multi-dimensional data drives the boundary adjustment of the dynamic safety zone. If external electromagnetic interference increases, the range of the electromagnetic shielding zone is automatically expanded according to the electromagnetic field strength, ensuring effective isolation between the internal area of ​​the destruction chamber and the outside.

[0039] Multidimensional sensor inputs include temperature gradients, electromagnetic field strength, and mechanical vibration signals. Based on real-time changes in these inputs, the boundaries are dynamically reconstructed, and parameters such as the safety zone's range and shielding strength are recalculated and set. For example, if a rapid temperature rise of 40°C in the upper right corner of the destruction chamber within 3 seconds is detected, indicating a short circuit and fire, boundary reconstruction is immediately performed: this high-temperature area is marked as a high-risk core zone, the electromagnetic shielding strength within a 20cm radius around it is increased from the basic 60dB to 90dB, and the delay for all physical lockouts is extended from the standard 30 seconds to 300 seconds. Even if someone presses the emergency door opening button at this time, the door will not unlock, thus achieving heat source isolation. Similarly, if an abnormal 1.2GHz high-intensity pulse signal is detected inside the chamber, possibly originating from an implanted wireless transmitter, the electromagnetic shielding strength of the entire chamber is immediately increased to a maximum of 120dB, and a safety incident alarm is generated.

[0040] The system integrates electromagnetic shielding and physical interlocking structures. The electromagnetic shielding, made of conductive or magnetic materials, forms a sealed enclosure to prevent electromagnetic signals from leaking out of the destruction chamber, thus preventing remote data theft before destruction. The physical interlocking structure consists of mechanical locking devices such as the destruction chamber door and internal isolation barriers, ensuring unauthorized access during critical operations. By creating a dynamic safety zone driven by multi-dimensional sensors, security protection is upgraded from a static, one-size-fits-all approach to a dynamic model that responds precisely to real-time risk situations, significantly enhancing the intelligence level of the security boundary.

[0041] The system performs operator identity verification, external authorization verification, and storage medium status verification. When all three verifications are successful, it controls the storage medium destruction machine to perform the destruction operation.

[0042] Furthermore, this application also includes the following steps: configuring a joint trust calculation unit, using the joint trust calculation unit to dynamically calculate a comprehensive trust score based on the historical trustworthiness of each authentication source, verification time interval, and communication delay; when the comprehensive trust score is lower than a preset score threshold, a security interruption is triggered, and the access code is frozen and destroyed in one go.

[0043] Specifically, the destruction operation requires triple authentication: operator identity verification, external authorization verification, and storage media status verification. Operator identity verification refers to the process of verifying the operator's identity before executing the storage media destruction operation. This is done through login credentials, fingerprint recognition, facial recognition, or other identity verification technologies to ensure that only authorized personnel can perform the destruction operation. For example, the operator enters a password or verifies their identity through a biometric device. If identity verification fails, the destruction operation cannot proceed. External authorization verification refers to confirming the external authorization for the storage media destruction operation, such as interfacing with the company's access control system or a dedicated authentication center to verify whether the destruction operation for this storage media is authorized. Storage media status verification verifies the state of the storage media itself, ensuring that it is physically in place, its model matches the task, and that the media is in an appropriate state before the destruction operation, such as being fault-free and meeting the destruction conditions.

[0044] When the operator selects to perform destruction on the console, three confirmation requests are initiated in parallel: operator identity verification, external authorization verification, and storage media status verification. Only when all three authentications pass, ensuring that each step of the destruction operation meets security and legality requirements, can the storage media destruction machine be controlled to execute the destruction operation. If any verification process fails, the destruction operation will not continue.

[0045] Simultaneously, the joint trust calculation unit is activated, receiving real-time metadata from the three authentication sources. It acquires the historical trustworthiness, verification time interval, and communication latency of each source, dynamically calculating the comprehensive trust score. Historical trustworthiness is a reliability weight assigned to an authentication source based on its success and failure records over a past period. The verification time interval is the time interval of the authentication process. Communication latency is the time from sending a request to receiving a response in authentication processes requiring external communication. For example, suppose anomalies in verification time are detected to detect potential attacks. A simulated attack scenario is described: an operator uses a highly realistic fingerprint for authentication, while latency is injected into the network link to simulate a man-in-the-middle attack. The total time for operator fingerprint verification was recorded as 4.8 seconds, while the normal baseline is 1.2 ± 0.3 seconds. Based on this unusually long verification interval, the joint confidence unit reduced the weight of operator identity verification from the basic 0.95 to 0.55. An external authorization request was issued, but due to a simulated network attack, the communication delay reached 18 seconds, while the normal delay is <3 seconds. Therefore, the weight of external authorization verification was reduced from 0.98 to 0.2. The storage medium status was verified as normal, taking 0.9 seconds with a weight of 1.0. Assuming the weights of operator identity verification, external authorization verification, and storage medium status verification are 0.3, 0.5, and 0.2, respectively, the joint confidence calculation unit performed the calculation to obtain a comprehensive confidence score of 0.55*0.3 + 0.2*0.5 + 1*0.2 = 0.465, or 46.5 points.

[0046] When the overall confidence score falls below a preset threshold, a security interruption is triggered, and the access code is frozen and destroyed in one go. The preset threshold is a predefined confidence score value; the destruction operation is only allowed if the overall confidence score is above the preset threshold. If it is below the preset threshold, the current authentication result is deemed untrustworthy, thus triggering a security interruption. For example, assuming the preset threshold is 0.85 (85 points), the aforementioned overall confidence score of 0.465 is far below the preset threshold, immediately triggering a security interruption, freezing the access code, and issuing an alert. The security team subsequently confirmed the abnormal authentication timeout and network latency in the logs, successfully preventing this potential security breach. They indicated that the main reason for the low confidence score was the timeout of authentication and external authorization responses. Through triple authentication and overall confidence calculation, each step of the verification process is ensured to be fully validated, preventing illegal or unauthorized destruction operations.

[0047] Activate multi-source sensors to collect data during the execution process of the storage medium destruction machine, and establish multimodal sensing data. The multimodal sensing data includes fragment particle size data, electromagnetic erasure depth data, energy consumption data, and operating status data of the storage medium destruction machine.

[0048] Specifically, this involves activating multi-source sensors to enable comprehensive process monitoring. Multi-source sensors are multiple sensors of different types that simultaneously collect various environmental or operational data during the storage media destruction process, yielding multimodal sensing data, including fragment particle size data, electromagnetic erasure depth data, energy consumption data, and the storage media destruction machine's operational status data. Fragment particle size data is the size distribution of media fragments after physical shredding, typically obtained through visual sensors or laser scanning; electromagnetic erasure depth data is the remaining magnetic field strength after demagnetization of the magnetic media, reflecting the thoroughness of data logical erasure; energy consumption data is the change in parameters such as motor drive power and current over time during the destruction process, reflecting the load status of the storage media destruction machine; operational status data includes vibration spectrum, bearing temperature, and rotational speed, reflecting the mechanical state of the destruction machine itself.

[0049] For example, a high-frequency industrial camera located at the discharge port, with a sampling rate of 2000 frames / second, combined with image processing algorithms, analyzes the projected size of the fragments in real time and generates a fragment particle size distribution curve. 1.5 seconds after the start of the destruction process, it is found that 96% of the fragments pass through a 2mm×2mm sieve, and the particle size distribution meets the secondary standard. A Hall effect sensor array closely attached to the demagnetizing assembly monitors magnetic field changes at a sampling rate of 100kHz. By calculating the ratio of the remaining magnetic field strength to the initial strength, it outputs the percentage of electromagnetic erasure depth. Within 0.1 seconds after the demagnetizing pulse is applied, the magnetic field strength on the disk surface changes from the initial... The power output decreased from 120kA / m to 0.45kA / m, achieving an erasure depth of 99.63%. An integrated power quality analyzer accurately captured energy consumption data at a 10kHz sampling rate, including transient power and cumulative energy consumption. The spindle motor's peak power at the moment of pulverization was 3.8kW, and the total energy consumption over the entire 15-second destruction cycle was 48kJ. Vibration sensors and infrared temperature sensors mounted on the spindle and bearing housing collected mechanical vibration spectra and temperature rise data at sampling rates of 50kHz and 10Hz, respectively. The spindle exhibited characteristic vibration at 1850Hz, with amplitude within a safe range of <5m / s². 2 The temperature sensor showed that the highest bearing temperature was 67℃, which is below the alarm threshold of 85℃. All sensor data were synchronized at the microsecond level via the PTP precision clock protocol. Analysis revealed that the peak energy consumption coincided perfectly with the most intense crushing stage in time, while the appearance of the vibration characteristic peak precisely corresponded to the moment when a particularly robust disk assembly was crushed, demonstrating a strong causal correlation between the multimodal data.

[0050] The destruction process, which was originally a black box, is transformed into a series of measurable and analyzable objective physical parameters, providing precise data support for the evaluation of destruction effectiveness. The synchronicity of multimodal data in the time dimension and the complementarity in the physical dimension constitute a complete evidence package describing a destruction event, which greatly enhances the credibility and non-repudiation of audit results.

[0051] The multimodal sensing data is sent to the anomaly authentication channel to perform anomaly authentication during the destruction process and to establish a destruction encryption digest.

[0052] Furthermore, this application also includes the following steps: performing time synchronization, denoising, and frame segmentation processing on the multimodal sensing data within the trusted execution environment of the anomaly authentication channel; extracting the granularity distribution spectrum from the fragment granularity data; extracting the spectral energy density curve from the electromagnetic erasure depth data; extracting the follow-mode power pulse features from the energy consumption data; and extracting the mechanical vibration mode and motor drive current features from the operating status data; and normalizing and dimensionless processing the extraction results; synchronizing the processed extraction results to the three-layer verification sub-channels within the anomaly authentication channel; weighting and fusing the single-modal anomaly scores of each verification sub-channel to establish an overall anomaly score; and completing anomaly authentication based on the overall anomaly score.

[0053] Specifically, multimodal sensing data is sent to an anomaly authentication channel for anomaly authentication. Data from different types of sensors may have temporal discrepancies during acquisition; to ensure data consistency, these data must first be time-synchronized. Electromagnetic erasure depth data, energy consumption data, fragment granularity, and other data can then be accurately mapped to the same timeline. During data acquisition, raw data often contains noise or outliers; denoising processing must be performed to remove irrelevant interference signals and ensure data reliability. After denoising, the data is processed in frames, with each frame becoming the basic unit of analysis, facilitating subsequent feature extraction and modeling.

[0054] The particle size distribution spectrum, a continuous curve describing the size distribution of fragments, is extracted from the fragment particle size data. The horizontal axis represents fragment size, and the vertical axis represents the percentage of fragments of the corresponding size. This shows the size distribution of the fragments after destruction, ensuring that the destruction meets the predetermined particle size standard, i.e., the fragments after hard drive shredding should be within the range of 25-30mm. If the particle size is too large, it indicates that the shredding process is not fully completed, and the efficiency of the shredding equipment needs to be improved. The spectral energy density curve is extracted from the electromagnetic erasure depth data. A fast Fourier transform is performed on the electromagnetic erasure depth data to extract the spectral energy density curve in the 0-10kHz frequency band. This analyzes the effect of electromagnetic erasure and confirms whether the standard for complete data erasure has been met. Generally, an electromagnetic field strength of 40V / m or higher is considered sufficient to erase data in the storage medium. The transient characteristics of power pulses are extracted from the energy consumption data. These characteristics are the transient properties of power changing over time, including parameters such as rise time, peak value, and fall time. Mechanical vibration modes and motor drive current features were extracted from the operational status data. The mechanical vibration modes are the vibration characteristics during the destruction operation, while the motor drive current features are extracted from the motor drive current data, including the total harmonic distortion (THD) and each harmonic component. All extracted feature parameters were normalized and converted into dimensionless values ​​within the range of [0,1].

[0055] For example, assume the following settings: visual sampling rate for fragment particle size is 2000 frames / second, electromagnetic sensor sampling rate is 100kHz, power sensor sampling rate is 10kHz, and vibration sensor sampling rate is 50kHz. The particle size distribution spectrum shows D10=1.2mm, D50=2.1mm, and D90=3.8mm, with the distribution curve exhibiting a normal distribution characteristic. During demagnetization, a significant peak appears at the 50Hz power frequency and its harmonics in the spectral energy density curve, with the fundamental energy density reaching -25dB / Hz. The power pulse characteristic detects three main power pulses with peak values ​​of 3.2kW, 3.5kW, and 3.1kW, and rise times of 45ms, 52ms, and 48ms, respectively. The mechanical vibration mode indicates that a main vibration component with an amplitude of 4.8m / s was detected at 1850Hz. 2 The amplitude of the secondary component at 3200 Hz is 2.1 m / s. 2The motor drive current characteristics include a total harmonic distortion (THD) of 7.2%, a third harmonic content of 3.8%, and a fifth harmonic content of 2.1%. After normalization and dimensionless processing, the particle size parameters D10 are 0.12, D50 is 0.21, and D90 is 0.38. The fundamental energy density of the spectral energy density curve is 0.5. The peak power of the following power pulse characteristics are 0.64, 0.70, and 0.62, and the rise time is 0.42, 0.52, and 0.48. The amplitude of the main vibration component of the mechanical vibration mode is 0.48, and the amplitude of the secondary component is 0.21. The THD of the motor drive current characteristics is 0.072, the third harmonic content is 0.038, and the fifth harmonic content is 0.021.

[0056] The processed extraction results are synchronously distributed to three layers of verification sub-channels. Each sub-channel independently calculates anomaly scores based on different algorithm principles, and the overall anomaly score is finally obtained through weighted fusion. The overall anomaly score reflects the degree of anomaly during the destruction process and is used to ultimately determine whether the destruction operation meets safety and quality standards. Anomaly authentication is completed based on the overall anomaly score. If the score is higher than a preset threshold, it indicates that an anomaly has occurred during the destruction process, and an alarm is triggered or the destruction operation is stopped. If the score is acceptable, the destruction process continues.

[0057] Furthermore, this application also includes the following steps: synchronizing the processed extraction results to a first-layer verification sub-channel, wherein the first-layer verification sub-channel is a rule-based sub-channel based on residual and threshold statistics, and outputting a first single-modal anomaly score; synchronizing the processed extraction results to a second-layer verification sub-channel, wherein the second-layer verification sub-channel is a time-series anomaly detection sub-channel based on a sliding window, and outputting a second single-modal anomaly score; synchronizing the processed extraction results to a third-layer verification sub-channel, wherein the third-layer verification sub-channel is a multi-modal reconstruction sub-channel based on deep learning, and outputting a third single-modal anomaly score; and performing weighted fusion based on the first single-modal anomaly score, the second single-modal anomaly score, and the third single-modal anomaly score to establish an overall anomaly score.

[0058] Furthermore, this application also includes the following steps: performing offline statistical modeling based on the multimodal feature vector set under normal destruction conditions of similar media, and constructing a baseline mean for each modal vector; using the rule sub-channel to perform element-wise difference calculation based on the baseline mean and extraction results to calculate the residual vector, performing Mahalanobis distance normalization on the residual vector according to the covariance matrix, and outputting the Mahalanobis residual value as a unified residual; and performing threshold determination based on the unified residual to output the first single-modal anomaly score.

[0059] Furthermore, this application also includes the following steps: establishing sliding windows of at least two window lengths, and performing overlapping sliding processing on the processed extraction results; calculating a set of time-series statistics and frequency domain features within each sliding window, wherein the time-series statistics include mean, variance, kurtosis, and skewness, and the frequency domain features include main frequency power density, spectral energy concentration, and bandwidth expansion coefficient; establishing a dual-domain joint change model based on the time-series statistics and the frequency domain features, performing change point detection through the difference matrix and correlation coefficient matrix of adjacent frames between windows, and establishing an initial drift region; performing dynamic threshold correction and confidence interval determination on the initial drift region, and outputting drift identification results; mapping the drift identification results to the time series of the corresponding window index to establish a time-series drift spectrum, and outputting a second single-mode anomaly score based on the time-series drift spectrum.

[0060] Furthermore, this application also includes the following steps: the third-layer verification sub-channel includes a self-supervised reconstructed neural network, which consists of a multi-modal encoder, a fusion bottleneck layer and a joint decoder. Each channel of the multi-modal encoder corresponds to a sensor stream. The encoder is constructed based on a one-dimensional / two-dimensional convolutional network. The fusion bottleneck layer performs feature interaction fusion based on a cross-modal attention mechanism. The joint decoder is used to perform the reconstruction of each modality data.

[0061] Specifically, the processed extraction results are synchronized to the first-layer verification sub-channel. This first-layer verification sub-channel is the first of three layers and employs a rule-based approach. It calculates the residual between real-time data and historical baselines and performs statistical threshold judgment to output anomaly scores. In the offline phase, a large amount of multimodal feature vector data of similar media under normal destruction conditions is collected. For example, features such as granularity, power, and vibration are extracted from 100 normal destructions, and the baseline mean vector and covariance matrix of each feature are calculated. For instance, the mean D50 is 0.20, the mean peak power is 0.65, and the mean vibration amplitude is 0.45.

[0062] A multimodal feature vector set is a vector composed of multiple features extracted from multi-source sensor data, representing the overall state of the destruction process. Offline statistical modeling uses historical normal destruction data for statistical analysis to establish a baseline model, without involving real-time data. In offline modeling, the average vector of each feature under normal conditions is calculated and used as the baseline.

[0063] Using rule-based sub-channels, element-wise interpolation is performed based on the baseline mean and the extracted results. This calculates the difference between the baseline mean and the extracted result for each modality vector, resulting in a residual vector. Each modality vector generates a residual value, representing the deviation between the current operation and the normal state. The residual vectors are then processed using the covariance matrix and normalized by Mahalanobis distance to standardize the data and eliminate scale differences between modalities. Although the data magnitudes of different modalities may differ, the normalized data can be directly compared. The normalized Mahalanobis residual values ​​are then aggregated into a single, unified residual, representing the overall degree of deviation of all modality data from the normal state.

[0064] Threshold determination is based on the uniform residual. If the uniform residual is below the threshold, the anomaly score is low; if it exceeds the threshold, the score is high. The score is usually normalized to the [0,1] interval, where 0 represents normal and 1 represents severe anomaly. The first unimodal anomaly score is the anomaly score calculated in the first-layer validation sub-channel, used to indicate whether the input data conforms to the normal destruction process. A high score means that the data has a significant anomaly. For example, normal destruction data of 500 hard drives of the same model are collected, and multimodal features are extracted, including normalized granularity D50, peak power, and vibration amplitude; the baseline mean vector μ=[0.20,0.65,0.45]μ=[0.20,0.65,0.45] is calculated; the covariance matrix [0.001,0.0005,0.0002], [0.0005,0.002,0.0003], [0.0002,0.0003,0.0015] is calculated; based on the 99% confidence interval of historical data, the Mahalanobis distance threshold is 1.0. In a real-time destruction process, the extracted feature vector is x=[0.21,0.70,0.48]; the residual vector is calculated as x−μ=[0.01,0.05,0.03]. First, the inverse of the covariance matrix is ​​calculated, and then the Mahalanobis residual value is calculated to be 0.85, i.e., the uniform residual value = 0.85 < threshold 1.0, therefore the anomaly score is low. According to the linear mapping, the first single-mode anomaly score is 0.85. If the feature vector is [0.30,0.80,0.60], the residual vector is [0.10,0.15,0.15], the Mahalanobis distance is calculated to be 2.5, which exceeds the threshold 1.0, and the anomaly score = 1.0, indicating possible tool wear or media anomaly.

[0065] The second-layer validation subchannel is the second layer of the three-layer validation subchannel, focusing on detecting anomalous behavior in time-series data. At least two sliding windows of different lengths are established on the time-series data, such as a 200ms short window to capture rapid transients and a 1000ms long window to detect slow drifts, with an overlap rate of 50%. The short and long windows can capture local changes and overall trends in the data, respectively. The sliding windows overlap and slide on the data, each slide covering a different time period to ensure that features from different time intervals are fully extracted. The sliding window is a fixed-length time interval that slides on the time-series data; the overlapping sliding processing is an analysis method where adjacent sliding windows overlap in time, improving the temporal resolution of the detection.

[0066] Within each sliding window, time-domain and frequency-domain features are computed in parallel. In the time domain, the mean, variance, kurtosis, and skewness are calculated. In the frequency domain, the dominant frequency power density, spectral energy concentration, and bandwidth expansion coefficient are calculated using FFT transform. The mean is the average value of the data within the window; the variance is a measure of the data dispersion; the kurtosis is a statistical measure of the sharpness of the data distribution; and the skewness is a measure of the asymmetry of the data distribution. The dominant frequency power density is the energy intensity of the main frequency components; the spectral energy concentration is the degree of energy concentration at a few frequencies; and the bandwidth expansion coefficient is a measure of the frequency distribution range of the signal.

[0067] Based on time-series statistics and frequency domain characteristics, a dual-domain joint change model is established. This model comprehensively analyzes changes in both the time and frequency domains to capture anomalies that might not be identified in one domain alone. By combining features from both the time and frequency domains, the dual-domain joint change model constructs a comprehensive model to represent data changes, helping to capture patterns of change in data across two dimensions (time and frequency), thereby more accurately identifying anomalies.

[0068] Based on the dual-domain joint change model, change points are detected by calculating the difference matrix and correlation coefficient matrix between adjacent frames in the window. When the Frobenius norm of the difference matrix exceeds twice the baseline and the correlation coefficient changes by more than 0.3, it is marked as an initial drift region. Change point detection refers to identifying time points in the time series where statistical characteristics change significantly. A drift region is defined as a data deviation that occurs continuously over a period of time, usually exhibiting a continuous trend change. Dynamic threshold correction is applied to the initial drift region, adjusting the threshold based on the historical behavior of the current window to prevent the static threshold from being too rigid and unable to adapt to changes in different data. The confidence interval determination method is used to evaluate the confidence of the drift region. If the confidence interval is small, the confidence of the anomaly is considered high. For example, by combining the real-time process confidence to dynamically correct the initial drift region, only drift regions that last for more than 3 windows and have a confidence greater than 80% are retained, and the drift identification result is output. The drift identification result is mapped back to the time series of the corresponding window index to generate a time-series drift spectrum. The second single-modal anomaly score is calculated based on the density, duration, and intensity of the drift region in the spectrum. The second single-mode anomaly score is an anomaly score calculated based on the data processed by the second-layer verification sub-channel, and is used to represent the degree of anomaly in the data in terms of time-series and frequency-domain characteristics.

[0069] For example, a 5-minute hard drive destruction process was analyzed, comprising 30 consecutive destruction operations. The sliding window consisted of a short window of 200ms and a long window of 1000ms, with a 50% overlap. Monitoring characteristics included vibration frequency amplitude, power consumption, and shredding efficiency. During the normal phase (0-3 minutes), the vibration frequency amplitude stabilized at 4.2-4.5 m / s. 2 Power consumption remained between 3.2 and 3.4 kW; spectral energy concentration remained between 0.75 and 0.82; the difference matrix norm was between 0.05 and 0.12; and the correlation coefficient varied between 0.1 and 0.2. An anomaly appeared at 3 minutes and 20 seconds, with the amplitude of the dominant vibration frequency slowly increasing to 4.8 m / s². 2 Power consumption decreased to 2.9kW; spectral energy concentration decreased to 0.68; the difference matrix norm surged to 0.35, and the correlation coefficient changed to 0.45; an initial drift region was detected with a confidence level of 72%. An anomaly was confirmed between 3 minutes 40 seconds and 4 minutes 50 seconds, with the vibration amplitude continuously increasing to 5.5 m / s². 2 The power further decreased to 2.6kW; the spectral characteristics showed the emergence of new harmonic components; the drift region lasted for 8 long windows, with the confidence level increasing to 92%; the time-series drift spectrum showed obvious anomalous patterns. Based on a drift density of 0.35, a duration of 70 seconds, and an intensity index of 0.78, the second single-mode anomaly score was calculated to be 0.82.

[0070] The third verification sub-channel is the last layer of the three-layer verification sub-channel. It is the deep learning analysis layer in the anomaly authentication channel, employing a self-supervised learning approach to detect anomalies through reconstruction errors. A reconstructive neural network is pre-trained on a large amount of normally destroyed data, using an architecture of a multi-modal encoder, a fusion bottleneck layer, and a joint decoder. The multi-modal encoder is part of the reconstructive neural network, containing multiple independent encoding paths, each specifically processing a type of sensor data stream. The fusion bottleneck layer is the key layer in the reconstructive neural network, responsible for deep fusion and interaction of the encoded features from different modalities. The joint decoder is the output part of the reconstructive neural network, reconstructing the original input data of all modalities from the fused features. For example, the reconstructive neural network is configured as follows: the granular encoder includes 5 layers of 1D CNN with a kernel size of 5; the vibration encoder includes 4 layers of 2D CNN with a kernel size of 3×3; the power encoder includes 4 layers of 1D CNN with a kernel size of 7; the fusion bottleneck is 256-dimensional with an 8-head attention mechanism; the training cycle is 100 epochs, and the final reconstruction error is <0.05.

[0071] In a multi-channel modal encoder, each channel is designed for a specific sensor data stream: granular data uses a one-dimensional convolutional network to process the time series, vibration spectrum uses a two-dimensional convolutional network to process the time-frequency graph, and power data uses a one-dimensional convolutional network to process the waveform. Features extracted by each modal encoder enter the fusion bottleneck layer, employing a cross-modal attention mechanism to automatically calculate the correlation weights between features from different modes, achieving intelligent feature fusion. For example, when an abnormal power is detected, the attention mechanism strengthens the weight of vibration features to confirm whether it is a mechanical fault. The joint decoder attempts to accurately reconstruct the original input data of each mode from the fused bottleneck features. During the inference phase, the real-time data reconstruction error is calculated: Reconstruction Error = |Original Data - Reconstructed Data| 2 A larger error indicates a higher degree of anomaly. A dynamic threshold is set based on the statistical distribution of the reconstruction error, mapping the reconstruction error to anomaly scores in the [0,1] interval, where 0 represents perfect reconstruction (normal) and 1 represents severe reconstruction failure (abnormal). For example, with input features including granularity D50=0.21, vibration amplitude=0.48, and peak power=0.68, the reconstruction neural network outputs 0.20, 0.47, and 0.67, resulting in a reconstruction error of 0.003. Therefore, the third single-mode anomaly score is 0.15, within the normal range. With input features including granularity D50=0.35, vibration amplitude=0.62, and peak power=0.45, the network reconstruction outputs 0.22, 0.46, and 0.66. The network tends to reconstruct a normal mode, resulting in a reconstruction error of 0.186. Therefore, the third single-mode anomaly score is min(1,(0.186-0.005) / 0.002 / 100)=0.905.

[0072] The overall anomaly score is obtained by weighted fusion of the first, second, and third single-modal anomaly scores. Weights are assigned to the first, second, and third single-modal anomaly scores based on their importance in the overall detection, such as 0.2, 0.3, and 0.5 respectively. Therefore, the overall anomaly score = (first single-modal anomaly score * 0.2) + (second single-modal anomaly score * 0.3) + (third single-modal anomaly score * 0.5). For example, if the first single-modal anomaly score is 0.85, the second single-modal anomaly score is 0.82, and the third single-modal anomaly score is 0.905, the overall anomaly score is 0.8625, used to ultimately determine whether there is an anomaly in the storage medium destruction process. The overall anomaly score is significantly higher than the normal range threshold of 0.7, therefore, the destruction process is deemed abnormal, and appropriate intervention measures are required.

[0073] By employing a hierarchical structure, data is analyzed from different dimensions, with each layer focusing on different types of anomalies to comprehensively capture potential anomaly patterns. Anomaly scores from different levels are weighted and fused, and detection sensitivity is dynamically adjusted based on the importance of different verification channels to improve anomaly detection accuracy. The overall anomaly score provides a comprehensive assessment, helping to promptly identify anomalies during the data destruction process and ensuring the security and integrity of data destruction.

[0074] The system employs multiple protections for destruction, including the destruction encryption digest, one-time destruction passcode, and multimodal sensor data.

[0075] Specifically, the destruction cryptographic digest is a digital fingerprint generated based on multimodal sensor data and anomaly authentication results. Calculated using a cryptographic hash algorithm, it possesses the characteristics of being tamper-proof and unforgeable. The destruction cryptographic digest plays a crucial role in the data destruction process, ensuring traceability and tamper-proofness. By combining the destruction cryptographic digest, one-time destruction passcode, and multimodal sensor data, even if an attacker gains access to some destroyed data, any attempt to reproduce or tamper with the destruction process will be detected because each destruction process's passcode and cryptographic digest are unique. It is impossible to reconstruct the same cryptographic digest and destruction passcode, and all operations and data can be traced. Multiple protection mechanisms ensure that every step of the entire destruction process is monitored and verified. If an anomaly is detected at any stage, an alarm is immediately triggered and the operation is interrupted.

[0076] Employing a cryptographic hash algorithm, any modification to the destruction record is immediately detected, successfully defending against all attempts to tamper with audit logs in actual testing. Through digital signatures and timestamps, the destruction record possesses legally recognized evidentiary value and has been accepted as valid evidence in multiple compliance audits. By cryptographically binding destruction instructions, process data, and execution results, a complete traceability chain is established, allowing auditors to complete comprehensive verification of a single destruction in the shortest possible time, improving efficiency by 80%.

[0077] In summary, the multi-protection method for a storage medium destruction machine provided in this application has the following technical effects: After the storage medium enters the destruction chamber, its unique identification information is read, and compared with a preset authorization database to generate a one-time destruction pass code; a dynamic security zone is created within the destruction chamber based on the one-time destruction pass code, the boundary of which is dynamically adjustable, and electromagnetic shielding and physical locking structures are activated in conjunction; operator identity verification, external authorization verification, and storage medium status verification are performed, and when all three authentications are successful, the storage medium destruction machine is controlled to perform the destruction operation; multi-source sensors are activated to collect data during the storage medium destruction process, establishing multi-modal sensing data, including fragment granularity data, electromagnetic erasure depth data, energy consumption data, and the operating status data of the storage medium destruction machine; the multi-modal sensing data is sent to an abnormal authentication channel to perform abnormal authentication of the destruction process and establish a destruction encryption digest; and multiple protections for destruction are implemented based on the destruction encryption digest, the one-time destruction pass code, and the multi-modal sensing data. In other words, by reading the unique identification information of the storage medium and comparing it with the preset authorized database, a one-time destruction pass code is generated, a dynamic security zone is created, and electromagnetic shielding and physical locking structures are activated in conjunction, which strengthens the protection of the storage medium. By implementing triple authentication and activating multi-source sensors to collect data on the destruction process, a destruction encryption digest is generated. Combined with the one-time destruction pass code and multi-modal sensor data, multiple protections for destruction are provided to ensure the complete record and immutability of each destruction operation, further improving the security and reliability of the destruction process.

[0078] Example 2: Based on the same inventive concept as the multi-protection method for a storage media destruction machine in Example 1, this application also provides a multi-protection system for a storage media destruction machine. Please refer to the appendix. Figure 2 The multi-protection system for the storage medium destruction machine includes:

[0079] The comparison and verification module 11 is used to read the unique identification information of the storage medium after it enters the destruction chamber, compare and verify the unique identification information with a preset authorization database, and generate a one-time destruction pass code. The security zone determination module 12 is used to create a dynamic security zone in the destruction chamber based on the one-time destruction pass code. The boundary of the dynamic security zone is a dynamically adjustable boundary, and the electromagnetic shielding and physical locking structure are activated in conjunction with it. The triple authentication module 13 is used to perform operator identity verification, external authorization verification, and storage medium status verification. When all three authentications are successful, the storage medium destruction machine is controlled to execute the operation. The system performs a destruction operation; a data acquisition module 14 is used to activate multi-source sensors to acquire data during the execution process of the storage medium destruction machine, and establish multimodal sensing data, which includes fragment granularity data, electromagnetic erasure depth data, energy consumption data, and the operating status data of the storage medium destruction machine; an anomaly authentication module 15 is used to send the multimodal sensing data to the anomaly authentication channel to perform anomaly authentication during the destruction process and establish a destruction encryption digest; a multi-protection module 16 is used to perform multiple protections for destruction based on the destruction encryption digest, the one-time destruction pass code, and the multimodal sensing data.

[0080] Furthermore, the comparison and verification module 11 in the multi-protection system for the storage medium destruction machine is also used for: the destruction chamber is equipped with a digital potentiometer array coupled to the electromagnetic erasure device, the digital potentiometer array is used to dynamically adjust the current intensity of the electromagnetic erasure based on the security parameter generation result of the one-time destruction pass code, wherein each digital potentiometer corresponds to an independent erasure channel, and dynamically adjusting the current intensity of the electromagnetic erasure includes digitally programmable control of the rise slope, peak amplitude and holding time of the erasure current.

[0081] Furthermore, the security zone determination module 12 in the multi-protection system for the storage medium destruction machine is also used for: the boundary adjustment of the dynamic security zone is driven by multi-dimensional sensor input, including temperature gradient, electromagnetic field strength and mechanical vibration signal, and performs dynamic boundary reconstruction according to the real-time changes of multi-dimensional sensor input, adjusting the electromagnetic shielding strength and locking delay to perform dynamic isolation control of the internal space of the destruction chamber.

[0082] Furthermore, the triple authentication module 13 in the multi-protection system for the storage medium destruction machine is also used to: configure a joint confidence calculation unit, and use the joint confidence calculation unit to dynamically calculate a comprehensive confidence score based on the historical credibility of each authentication source, verification time interval, and communication delay; when the comprehensive confidence score is lower than a preset score threshold, a security interruption is triggered, and the one-time destruction access code is frozen.

[0083] Furthermore, the anomaly authentication module 15 in the multi-protection system for the storage medium destruction machine is also used to: perform time-series synchronization, noise reduction, and frame segmentation processing on the multimodal sensing data within the trusted execution environment of the anomaly authentication channel; extract the granularity distribution spectrum from the fragment granularity data; extract the spectral energy density curve from the electromagnetic erasure depth data; extract the downstream power pulse feature from the energy consumption data; and extract the mechanical vibration mode and motor drive current feature from the operating status data; normalize and dimensionless process the extraction results; synchronize the processed extraction results to the three-layer verification sub-channel within the anomaly authentication channel; weight and fuse the single-modal anomaly scores of each verification sub-channel to establish an overall anomaly score; and complete the anomaly authentication based on the overall anomaly score.

[0084] Furthermore, the anomaly authentication module 15 in the multi-protection system for the storage medium destruction machine is also used to: synchronize the processed extraction results to the first-layer verification sub-channel, which is a rule-based sub-channel based on residual and threshold statistics, and output a first single-modal anomaly score; synchronize the processed extraction results to the second-layer verification sub-channel, which is a time-series anomaly detection sub-channel based on a sliding window, and output a second single-modal anomaly score; synchronize the processed extraction results to the third-layer verification sub-channel, which is a multi-modal reconstruction sub-channel based on deep learning, and output a third single-modal anomaly score; and perform weighted fusion based on the first single-modal anomaly score, the second single-modal anomaly score, and the third single-modal anomaly score to establish an overall anomaly score.

[0085] Furthermore, the anomaly authentication module 15 in the multi-protection system for the storage medium destruction machine is also used for: performing offline statistical modeling based on the multimodal feature vector set under normal destruction conditions of similar media, constructing a baseline mean for each modal vector; using the rule sub-channel to perform element-wise difference calculation based on the baseline mean and extraction results to calculate the residual vector, performing Mahalanobis distance normalization on the residual vector according to the covariance matrix, and outputting the Mahalanobis residual value as a unified residual; and performing threshold determination based on the unified residual to output the first single-modal anomaly score.

[0086] Furthermore, the anomaly authentication module 15 in the multi-protection system for the storage medium destruction machine is also used to: establish sliding windows of at least two window lengths, and perform overlapping sliding processing on the processed extraction results; calculate a set of time-series statistics and frequency domain features within each sliding window, wherein the time-series statistics include mean, variance, kurtosis, and skewness, and the frequency domain features include main frequency power density, spectral energy concentration, and bandwidth expansion coefficient; establish a dual-domain joint change model based on the time-series statistics and the frequency domain features, perform change point detection through the difference matrix and correlation coefficient matrix of adjacent frames between windows, and establish an initial drift region; perform dynamic threshold correction and confidence interval determination on the initial drift region, and output the drift identification result; perform time-series mapping of the drift identification result to the corresponding window index, establish a time-series drift spectrum, and output a second single-mode anomaly score based on the time-series drift spectrum.

[0087] Furthermore, the anomaly authentication module 15 in the multi-protection system for the storage medium destruction machine is also used for: the third-layer verification sub-channel includes a self-supervised reconstructed neural network, the reconstructed neural network is composed of a multi-modal encoder, a fusion bottleneck layer and a joint decoder, each channel of the multi-modal encoder corresponds to a sensor stream, the encoder is constructed based on a one-dimensional / two-dimensional convolutional network, the fusion bottleneck layer performs feature interaction fusion based on a cross-modal attention mechanism, and the joint decoder is used to perform reconstruction of each modality data.

[0088] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Figure 1 The multi-protection method and specific examples for the storage media destroyer in Embodiment 1 are also applicable to the multi-protection system for the storage media destroyer in this embodiment. Through the foregoing detailed description of the multi-protection method for the storage media destroyer, those skilled in the art can clearly understand the multi-protection system for the storage media destroyer in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0089] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0090] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for multiple protection of a storage medium destruction machine, characterized in that, The application relates to a storage medium destruction system and method. After the storage medium enters the destruction cabin, unique identification information of the storage medium is read, the unique identification information is compared and verified with a preset authorization database, and a one-time destruction password is generated; A dynamic security zone is created in the destruction cabin according to the one-time destruction password, the boundary of the dynamic security zone is a dynamically adjusted boundary, and the electromagnetic shielding and the physical locking structure are started in linkage; An operator identity confirmation, an external authorization confirmation and a storage medium state confirmation are performed, and when the three confirmations are passed, the storage medium destruction machine is controlled to perform a destruction operation; A multi-source sensor is activated to collect data of the execution process of the storage medium destruction machine, multi-modal sensing data is established, and the multi-modal sensing data includes residual fragment granularity data, electromagnetic erasing depth data, energy consumption data and running state data of the storage medium destruction machine; The multi-modal sensing data is sent to an abnormality authentication channel, an abnormality authentication of the destruction process is performed, and a destruction encryption digest is established; The destruction encryption digest, the one-time destruction password and the multi-modal sensing data are used for multiple protection of the destruction; The multi-modal sensing data is sent to an abnormality authentication channel, an abnormality authentication of the destruction process is performed, including: In a trusted execution environment in the abnormality authentication channel, the multi-modal sensing data is subjected to time sequence synchronization, denoising and frame processing, a granularity distribution spectrum is extracted from the residual fragment granularity data, a frequency spectrum energy density curve is extracted from the electromagnetic erasing depth data, a potential power pulse feature is extracted from the energy consumption data, mechanical vibration modes and motor driving current features are extracted from the running state data, and the extracted results are subjected to normalization and dimensionless processing; The processed extracted results are synchronously sent to three-layer verification sub-channels in the abnormality authentication channel, single-modal abnormality scores of each layer of the verification sub-channels are weighted and fused, and an overall abnormality score is established; The overall abnormality score is used for completing the abnormality authentication.

2. The multiple protection method for a storage medium destruction machine according to claim 1, wherein The processed extracted results are synchronously sent to three-layer verification sub-channels in the abnormality authentication channel, including: The processed extracted results are synchronously sent to a first layer of verification sub-channels, the first layer of verification sub-channels is a rule sub-channel based on residual error and threshold value statistics, and a first single-modal abnormality score is outputted; The processed extracted results are synchronously sent to a second layer of verification sub-channels, the second layer of verification sub-channels is a time sequence abnormality detection sub-channel based on a sliding window, and a second single-modal abnormality score is outputted; The processed extracted results are synchronously sent to a third layer of verification sub-channels, the third layer of verification sub-channels is a multi-modal reconstruction sub-channel based on deep learning, and a third single-modal abnormality score is outputted; The first single-modal abnormality score, the second single-modal abnormality score and the third single-modal abnormality score are weighted and fused to establish an overall abnormality score.

3. The multiple protection method for a storage medium destruction machine according to claim 2, wherein The processed extracted results are synchronously sent to a first layer of verification sub-channels, including: Baseline means are constructed for each modal vector according to an offline statistical modeling of multi-modal feature vector sets under normal destruction states of the same type of medium; The rule sub-channel is used to perform element-by-element difference operation based on baseline mean value and extraction result, calculate residual error vector, perform Mahalanobis distance normalization processing on the residual error vector according to the covariance matrix, and output the Mahalanobis residual value as a unified residual amount; According to the unified residual amount, threshold judgment is performed, and a first single-modal anomaly score is output.

4. The multiple protection method for a storage medium destruction machine according to claim 2, wherein The processed extraction result is synchronized to the second layer verification sub-channel, including: At least two window lengths are established to form sliding windows, and the processed extraction result is subjected to overlapping sliding processing of the sliding windows; A set of time series statistics and frequency domain features are calculated in each sliding window, the time series statistics including mean, variance, kurtosis and skewness, and the frequency domain features including main frequency power density, spectral energy concentration and bandwidth expansion coefficient; A dual-domain joint change model is established according to the time series statistics and the frequency domain features, a change point detection is performed through a difference matrix and a correlation coefficient matrix of adjacent frames between windows, and an initial drift region is established; The initial drift region is subjected to dynamic threshold correction and confidence interval judgment, and a drift recognition result is output; The drift recognition result is subjected to time series mapping corresponding to the window index, a time series drift spectrum is established, and a second single-modal anomaly score is output according to the time series drift spectrum.

5. The multiple protection method for a storage medium destruction machine according to claim 2, wherein The third layer verification sub-channel includes a self-supervised reconstruction neural network, the reconstruction neural network is composed of a multi-path modal encoder, a fusion bottleneck layer and a joint decoder, each path of the multi-path modal encoder corresponds to a sensor stream, the encoder is constructed based on a one-dimensional / two-dimensional convolutional network, the fusion bottleneck layer performs feature interaction and fusion based on a cross-modal attention mechanism, and the joint decoder is used to perform reconstruction of each modal data.

6. The multiple protection method for a storage medium destruction machine according to Claim 1, wherein The destruction cabin is provided with a digital potentiometer array coupled with the electromagnetic erasing device, the digital potentiometer array is used to dynamically adjust the current intensity of electromagnetic erasing based on the security parameter generation result of the one-time destruction access code, wherein each digital potentiometer corresponds to an independent erasing channel, and the dynamic adjustment of the current intensity of electromagnetic erasing includes digital programmable control of the rising slope, peak amplitude and maintenance time of the erasing current.

7. The multiple protection method for a storage medium destruction machine according to Claim 1, wherein The boundary adjustment of the dynamic safety zone is driven by multi-dimensional sensing input including temperature gradient, electromagnetic field intensity and mechanical vibration signal, and the boundary dynamic reconstruction is performed according to the real-time change of the multi-dimensional sensing input to adjust the electromagnetic shielding intensity and the lockout delay to perform dynamic isolation control of the internal space of the destruction cabin.

8. The multiple protection method for a storage medium destruction machine according to Claim 1, wherein The operator identity confirmation, external authorization confirmation and storage medium state confirmation are performed, including: A joint confidence calculation unit is configured, and the joint confidence calculation unit is used to dynamically calculate a comprehensive confidence score based on the historical credibility, verification time interval and communication delay of each authentication source; When the comprehensive confidence score is lower than a preset score threshold, a safety interruption is triggered, and the one-time destruction access code is frozen.

9. A multiple protection system for a storage media destruction machine, characterized in that, The steps for implementing the multiple protection method for the storage medium destruction machine according to any one of claims 1 to 8, the multiple protection system for the storage medium destruction machine includes: The comparison verification module is configured to read the unique identification information of the storage medium after the storage medium enters the destruction cabin, to compare and verify the unique identification information with a preset authorization database, and to generate a one-time destruction password; The security zone determination module is configured to create a dynamic security zone in the destruction cabin according to the one-time destruction password, and to dynamically adjust the boundary of the dynamic security zone and start the electromagnetic shielding and the physical locking structure in linkage; The triple authentication module is configured to perform operator identity confirmation, external authorization confirmation and storage medium state confirmation, and to control the storage medium destruction machine to perform a destruction operation when the triple authentication is passed; The data acquisition module is configured to activate a multi-source sensor to collect data of a storage medium destruction machine execution process, and to establish multi-modal sensing data, wherein the multi-modal sensing data includes residual fragment granularity data, electromagnetic erasing depth data, energy consumption data and running state data of the storage medium destruction machine; The anomaly authentication module is configured to send the multi-modal sensing data to an anomaly authentication channel, to perform anomaly authentication of a destruction process, and to establish a destruction encryption digest; The multiple protection module is configured to perform multiple protection of the destruction according to the destruction encryption digest, the one-time destruction password and the multi-modal sensing data.

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