A resource security interaction method and system of a smart education cloud platform

Abstract

The present application relates to the technical field of information security, and more particularly to a resource security interaction method and system of a smart education cloud platform. The method comprises the following steps: obtaining an education resource access request and calculating a resource leakage risk value using the education resource access request; evaluating resource interaction credibility based on the resource leakage risk value; executing a resource interaction task according to the resource interaction credibility and recording a resource interaction track; identifying a firmware signature state according to the resource interaction track; detecting a firmware tampering risk value according to the firmware signature state; identifying a corresponding security chip monitoring unit based on the firmware tampering risk value; calculating a temperature rise abnormal value according to the security chip monitoring unit; and evaluating a chip failure probability according to the temperature rise abnormal value. The present application realizes dynamic security evaluation and adaptive security control of the education resource interaction process based on information security technology, thereby reducing the resource leakage risk and improving the security and reliability of resource interaction.

Description

Technical Field

[0001] This invention relates to the field of information security technology, and in particular to a resource security interaction method and system for a smart education cloud platform. Background Technology

[0002] With the widespread application of smart education cloud platforms, a large amount of course resources, teaching data, and examination materials are stored, shared, and interacted through the cloud, making resource security issues increasingly prominent. Existing technologies typically ensure resource interaction security through identity authentication, access control, and data encryption, but most only focus on user behavior and network-level security protection, making it difficult to detect underlying security vulnerabilities such as firmware tampering, abnormal interface configuration, or unstable operation of security chips in a timely manner. When terminal firmware is maliciously modified or the crystal oscillation stability of the security chip decreases due to abnormal temperature rise, it may lead to problems such as failure of resource access control and increased risk of data leakage. Existing solutions lack a mechanism to correlate resource interaction security with the terminal hardware operating status and cannot dynamically adjust resource security strategies based on the trusted state of the hardware, thus limiting the security protection and risk response capabilities during resource interaction. Summary of the Invention

[0003] Therefore, the present invention needs to provide a resource security interaction method and system for a smart education cloud platform to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for secure resource interaction in a smart education cloud platform includes the following steps: Step S1: Obtain educational resource access requests and use them to calculate resource leakage risk values; assess the trustworthiness of resource interactions based on the resource leakage risk values. Step S2: Execute resource interaction tasks based on resource interaction trustworthiness and record resource interaction trajectory; identify firmware signature status based on resource interaction trajectory; detect firmware tampering risk value based on firmware signature status; Step S3: Identify the corresponding security chip monitoring unit based on the firmware tampering risk value; calculate the temperature rise anomaly value based on the security chip monitoring unit; assess the chip failure probability based on the temperature rise anomaly value; Step S4: Correct the resource leakage risk value based on the chip failure risk probability to obtain the corrected resource leakage risk value; use the corrected resource leakage risk value to generate a resource security interaction strategy.

[0005] Preferably, this specification also provides a resource security interaction system for a smart education cloud platform, used to execute the aforementioned resource security interaction method for the smart education cloud platform. This resource security interaction system for the smart education cloud platform includes: The risk identification module is used to acquire access requests for educational resources and calculate resource leakage risk values ​​using these requests; and to assess the trustworthiness of resource interactions based on these risk values. The trajectory recognition module is used to execute resource interaction tasks based on the trustworthiness of resource interactions and record resource interaction trajectories; identify firmware signature status based on resource interaction trajectories; and detect firmware tampering risk values ​​based on firmware signature status. The temperature rise calculation module is used to locate the security chip monitoring unit corresponding to the tampering behavior based on the firmware tampering risk value; calculate the temperature rise anomaly value based on the security chip monitoring unit; and assess the chip failure probability based on the temperature rise anomaly value. The strategy generation module is used to correct the resource leakage risk value based on the chip failure risk probability, record the corrected resource leakage risk value, and generate a resource security interaction strategy using the corrected resource leakage risk value.

[0006] The beneficial effects of this invention are as follows: 1. It realizes the transformation from traditional resource security management based on access permissions and identity authentication to collaborative protection of "resource risk, firmware trust, and hardware status", avoiding the problem that existing methods only focus on user identity and access behavior while ignoring the underlying security status of the terminal; by introducing firmware signature verification, firmware tampering risk analysis and security chip operation status monitoring, the resource interaction process takes into account both logical security and hardware security, improving the accuracy of resource access control and the reliability of resource protection.

[0007] 2. It realizes full-link correlation analysis of resource access, firmware control area, hardware operating status and security chip failure risk, avoiding the problem that traditional methods are difficult to find the coupling relationship between firmware tampering and hardware anomalies; by constructing a risk transmission mechanism of "resource interaction trajectory, firmware signature status, hardware control anomaly, and chip failure probability", it effectively identifies the impact of potential security threats on the resource interaction process, and improves the ability to identify resource leakage risks and assess the terminal's trusted status.

[0008] 3. It achieves dynamic linkage between the physical operating status of the security chip and the resource security strategy, avoiding the problem of fixed configuration and lack of adaptive adjustment capability of existing resource security protection strategies; by analyzing the abnormal temperature rise of the security chip, crystal structure deformation and oscillation stability, the chip failure probability is assessed, and the chip failure probability is used to dynamically correct the risk of resource leakage, thereby generating a resource security interaction strategy that matches the current security status, improving the dynamic protection capability, risk response capability and overall security level of the smart education cloud platform in the resource interaction process. Attached Figure Description

[0009] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating the steps of a resource security interaction method for a smart education cloud platform according to the present invention. Figure 2 This is a schematic diagram of a resource security interaction pipeline according to one embodiment; Figure 3 This is a schematic diagram of the architecture of a resource security interaction system of a smart education cloud platform according to one embodiment; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0010] The technical method of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of this invention.

[0011] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0012] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0013] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides a method for secure resource interaction on a smart education cloud platform, the method comprising the following steps: Step S1: Obtain educational resource access requests and use them to calculate resource leakage risk values; assess the trustworthiness of resource interactions based on the resource leakage risk values. In one embodiment, the smart education cloud platform receives an educational resource access request initiated by a terminal device. The request includes user identity information, resource type information, access time information, terminal device identification information, and access behavior information. The access request is standardized, and risk factors such as resource access frequency, resource download scale, degree of abnormality during access time, and terminal trust status are extracted. Then, a resource leakage risk assessment model is constructed based on these risk factors to calculate the resource leakage risk value. Finally, the resource leakage risk value is correlated with a preset trustworthiness mapping rule to obtain the resource interaction trustworthiness.

[0014] In another embodiment, suppose a user initiates 18 consecutive access requests for test question resources within 5 minutes, with a total requested resource capacity of 2.8GB. The access time occurs outside of teaching hours (02:15 AM), and the terminal device is the first access device. The calculated risk factors are: access frequency 0.72, resource size 0.81, time anomaly 0.67, and terminal trustworthiness 0.58. After weighted calculation, the resource leakage risk value is 0.74. According to the preset trustworthiness mapping model, the risk value falls within the range of 0.70 to 0.80, corresponding to a resource interaction trustworthiness of 0.42. Therefore, it is determined that this access request needs to enter the enhanced security review process.

[0015] Step S2: Execute resource interaction tasks based on resource interaction trustworthiness and record resource interaction trajectory; identify firmware signature status based on resource interaction trajectory; detect firmware tampering risk value based on firmware signature status; In one embodiment, a corresponding resource interaction mode is determined based on the trustworthiness of the resource interaction, and resource download, resource browsing, or resource sharing tasks are executed. During the resource interaction process, the resource interaction trajectory is recorded in real time, including terminal identification information, resource access records, interface call records, and firmware call records. The firmware hash digest and digital signature information of the target terminal are read and compared with standard firmware information stored on the cloud platform to identify the firmware signature status. Then, based on the firmware signature status, the corresponding firmware control area is determined, and startup configuration data, clock configuration data, and interface control data are obtained. Startup deviation values, clock deviation values, and interface configuration deviation values ​​are calculated. A firmware tampering risk value is calculated based on the degree of hardware control anomaly.

[0016] In another embodiment, it is assumed that 1250 resource interaction trajectory data entries are recorded during the resource interaction process, including 340 firmware call records and 510 interface access records. The target terminal firmware hash value is read as H1, and the standard firmware hash value is H0, with a matching deviation of 18%; the digital signature verification failure rate is 12%, and the system determines it to be a signature abnormal state. After analyzing the startup configuration parameters, clock configuration parameters, and interface control parameters, the startup deviation value is 0.26, the clock deviation value is 0.31, and the interface configuration deviation value is 0.42; after fusion calculation, the hardware control abnormal value is obtained as 0.37, and based on this, the firmware tampering risk value is determined to be 0.63.

[0017] Step S3: Identify the corresponding security chip monitoring unit based on the firmware tampering risk value; calculate the temperature rise anomaly value based on the security chip monitoring unit; assess the chip failure probability based on the temperature rise anomaly value; In one embodiment, a security chip monitoring unit associated with the target firmware control area is determined based on the firmware tampering risk value, and internal temperature monitoring data of the security chip is acquired. Temperature rise anomalies are calculated based on the difference between historical reference temperatures and real-time temperatures. The thermal expansion state of the crystal oscillator package structure is analyzed based on the temperature rise anomalies, and the thermal expansion of the crystal structure is calculated. The crystal vibrating plate deformation, crystal structure offset, and vibration center offset are determined based on the thermal expansion, and the internal stress distribution of the crystal is analyzed. The oscillation stability of the crystal is determined based on the oscillation energy attenuation, and the chip failure probability is assessed.

[0018] In another embodiment, assuming a firmware tampering risk value of 0.63, the corresponding location is the security chip monitoring unit A3. The monitored base temperature of the security chip is 42°C, the real-time temperature is 68°C, and the calculated temperature rise anomaly value is 26°C. Based on the temperature rise anomaly value, the expansion amount of the package housing is calculated as follows: The expansion amount of the crystal oscillator is The thermal expansion of the crystal oscillator structure is The deformation of the crystal vibration plate was obtained as follows: The offset value of the crystal oscillator structure is The offset of the vibration center is The analysis revealed a stress concentration factor of 1.37, an oscillation energy attenuation factor of 0.29, a crystal oscillation stability that decreased to 0.71, and an estimated chip failure probability of 0.58.

[0019] Step S4: Correct the resource leakage risk value based on the chip failure risk probability to obtain the corrected resource leakage risk value; use the corrected resource leakage risk value to generate a resource security interaction strategy.

[0020] In one embodiment, assuming a resource leakage risk value of 0.74 and an assessed chip failure probability of 0.58, the system corrects the resource leakage risk value based on the chip failure probability, resulting in a corrected resource leakage risk value of 0.86. According to preset risk level rules, 0.86 corresponds to a high-risk level range; therefore, the system adjusts the resource interaction control mode to an isolated interaction mode. Simultaneously, it closes two external resource sharing interfaces, restricts three high-privilege download channels, and enables a full access log recording mechanism. This generates a resource security interaction strategy that includes resource access restriction rules, interface call restriction rules, and terminal isolation rules, achieving dynamic security protection for the educational resource interaction process.

[0021] Preferably, step S2, which involves identifying the firmware signature status based on the resource interaction trajectory, includes: The resource interaction trajectory is parsed to extract the target terminal identification information; the firmware hash digest and firmware digital signature are read based on the target terminal identification information. In one embodiment, access logs, interface call logs, and device authentication logs generated during resource interaction are parsed to extract target terminal identification information from the resource interaction trajectory. This target terminal identification information includes the device serial number, motherboard identifier, security chip identifier, and network interface identifier. Based on the target terminal identification information, the firmware storage area in the terminal device is accessed, and the firmware hash digest and firmware digital signature corresponding to the currently running firmware are read. The firmware hash digest is obtained by calculating the firmware image using the SHA-256 algorithm, and the firmware digital signature is generated by the device manufacturer using a private key to sign the firmware digest.

[0022] In another embodiment, it is assumed that the resource interaction trajectory records a total of 850 resource access logs, 420 interface call logs, and 160 device authentication logs. The target terminal identification information is obtained through parsing, including the device serial number DEV-20260108-001, the motherboard identification code MB-A37F-9D21, and the security chip identification code TPM-56C8-1035. Based on the above identification information, the target firmware image file (32MB in size) is read, and a 256-bit firmware hash digest H1 is generated using the SHA-256 algorithm. Simultaneously, a 512-bit RSA digital signature S1 is read. The obtained hash digest and digital signature will be used as input data for firmware trusted state analysis.

[0023] Obtain the standard firmware hash digest and standard digital signature; calculate the hash deviation value based on the standard firmware hash digest and the firmware hash digest; calculate the signature deviation value based on the standard digital signature and the firmware digital signature; In one embodiment, assume the standard firmware hash digest stored in the cloud platform database is H0, and the firmware hash digest uploaded by the target terminal is H1. A bit-by-bit comparison reveals 18 bits of difference out of 256 bits, resulting in a hash deviation value of 7.03%. Simultaneously, the standard digital signature S0 and the target digital signature S1 are read. RSA verification reveals 11 inconsistent verification fields, corresponding to a signature deviation value of 11.00% out of 100 verification fields. Therefore, the hash deviation value is 0.0703, and the signature deviation value is 0.1100.

[0024] The firmware signature status is determined based on the hash deviation value and the signature deviation value. The firmware signature status includes normal signature status, abnormal signature status, and invalid signature status.

[0025] In one embodiment, it is assumed that the system presets a normal signature threshold of 0.90, an abnormal signature threshold range of 0.60 to 0.90, and a signature invalidation threshold of 0.60. For terminal A, its hash deviation value is 0.012 and its signature deviation value is 0.018, and the overall credibility score obtained after fusion calculation is 0.96, therefore it is determined to be in a normal signature state; for terminal B, its hash deviation value is 0.083 and its signature deviation value is 0.117, and the overall credibility score obtained after fusion calculation is 0.74, therefore it is determined to be in a abnormal signature state; for terminal C, its hash deviation value is 0.276 and its signature deviation value is 0.352, and the overall credibility score obtained after fusion calculation is 0.41, therefore it is determined to be in a signature invalidation state.

[0026] Preferably, step S2, which detects the firmware tampering risk value based on the firmware signature status, includes: Determine the firmware control region based on the firmware signature status; obtain the boot configuration data, clock configuration data, and interface control data corresponding to the firmware control region; calculate the boot deviation value based on the boot configuration data. In one embodiment, the firmware signature verification result is parsed based on the firmware signature status, and the corresponding firmware functional integrity level is determined. The firmware functional integrity level is mapped to a preset set of firmware control regions to identify the target firmware control region. The firmware control regions include a boot control region, a clock control region, and an interface control region. Then, boot configuration data in the boot control region, clock configuration data in the clock control region, and interface control data in the interface control region are read respectively. The boot configuration data includes secure boot enable status, boot sequence configuration parameters, and boot verification parameters. The current boot configuration data is compared with a standard boot configuration template to analyze configuration differences in the boot process, and a boot deviation value is calculated based on the degree of difference.

[0027] In another embodiment, assuming the firmware signature status of the target terminal is in an abnormal signature state, the firmware functional integrity level is determined to be Level II after parsing. The boot control area A1, clock control area A2, and interface control area A3 are identified according to a preset mapping relationship. Boot configuration data is read, where the secure boot enable status changes from "on" to "off," the boot order changes from "BootROM, secure bootloader, operating system" to "BootROM, operating system," and three boot verification parameters show anomalies. Comparing the current configuration with the standard configuration yields a boot chain integrity deviation of 0.18, a boot order deviation of 0.23, and a boot verification deviation of 0.31, respectively. A combined calculation yields a boot deviation value of 0.24.

[0028] Calculate the clock deviation value based on the clock configuration data; calculate the interface configuration deviation value based on the interface control data; calculate the hardware control anomaly value based on the startup deviation value, clock deviation value, and interface configuration deviation value; determine the firmware tampering risk value based on the hardware control anomaly value.

[0029] In one embodiment, assuming the clock configuration data shows the target crystal oscillator standard frequency is 25MHz, and the actual measured oscillation frequency is 24.32MHz, the frequency offset is 0.68MHz. Analysis shows the crystal oscillation trend coefficient is 0.27, and the calculated clock deviation value is 0.29. Simultaneously, interface control data indicates the USB controller is operating in high-speed transmission mode, with a signal attenuation of 0.16 and a handshake response offset time of 2.8ms. The calculated interface transmission instability coefficient is 0.34, thus determining the interface configuration deviation value to be 0.33. Combining this with the startup deviation value of 0.24, a weighted fusion calculation is performed on the three deviation values, with the startup deviation value having a weight of 0.30, the clock deviation value having a weight of 0.35, and the interface configuration deviation value having a weight of 0.35, resulting in a hardware control anomaly value of 0.29. According to the preset risk mapping model, when the hardware control anomaly value is in the range of 0.25 to 0.40, it corresponds to a medium-to-high risk level, so the firmware tampering risk value is determined to be 0.62; if the hardware control anomaly value exceeds 0.50, it corresponds to a high risk level, and the firmware tampering risk value will increase to above 0.80.

[0030] Preferably, determining the firmware control region based on the firmware signature status includes: The firmware signature verification result is parsed based on the firmware signature status; the firmware functional integrity level is determined based on the firmware signature verification result; the firmware functional integrity level is mapped to a preset set of firmware control areas to determine the mapping relationship; wherein, the set of firmware control areas includes a boot control area, a clock control area, and an interface control area. In one embodiment, assuming an educational terminal device is being tested, the signature verification results include hash consistency of 98.7%, certificate validity of 100%, and signature integrity of 96.5%. A comprehensive evaluation determines the firmware functional integrity level to be compromised. Anomaly distribution is statistically analyzed, with the startup module accounting for 15%, the clock module for 48%, and the interface module for 37%. According to preset mapping rules, the compromise level is mapped to the startup control area, clock control area, and interface control area, respectively, with the clock control area designated as a key monitoring area.

[0031] Calculate the region weight vector based on the mapping relationship; calculate the region matching score based on the region weight vector; identify the target firmware control region based on the region matching score.

[0032] In one embodiment, the weights of the startup control region, clock control region, and interface control region are assumed to be 0.25, 0.45, and 0.30, respectively. Based on the abnormal distribution, the matching scores for the startup control region are calculated to be 0.41, the clock control region 0.86, and the interface control region 0.63. The matching scores of each region are sorted, resulting in clock control region > interface control region > startup control region. Since the clock control region has the highest matching score, it is identified as the target firmware control region. At this point, key hardware modules such as the crystal oscillator driver circuit, clock buffer, and clock divider unit can be located for subsequent clock deviation analysis and firmware tampering risk assessment.

[0033] Preferably, calculating the clock deviation value based on the clock configuration data includes: Determine the clock drive circuit region based on the clock configuration data; identify the crystal oscillation state within the clock drive circuit region; calculate the crystal oscillation frequency based on the crystal oscillation state. In one embodiment, based on the firmware clock configuration register read results, the target clock source type (e.g., external crystal oscillator HSE, internal RC oscillator HSI) and clock division coefficient are parsed, and the clock drive circuit area is located accordingly, such as the crystal oscillator drive unit, PLL frequency multiplication unit, and clock distribution tree node. Within the clock drive circuit area, periodic waveform parameters of the crystal oscillation signal are acquired, including rising edge interval, period jitter, and phase noise level, to identify the crystal oscillation state, specifically including stable oscillation state, slight jitter state, and abnormal oscillation state. The crystal oscillation frequency is calculated based on the oscillation period statistics, for example, by converting the average period count per unit time to obtain the actual output frequency, and a preliminary consistency check is performed with the nominal frequency.

[0034] In another embodiment, assuming an educational terminal device is equipped with an external crystal oscillator with a nominal frequency of 24MHz, and 1000 consecutive oscillation cycles are sampled through a clock drive circuit, with an average period of 41.85ns, the actual oscillation frequency of the crystal oscillator is calculated to be approximately 23.91MHz; simultaneously, the observed period jitter range is... Phase noise approximately Therefore, the crystal oscillator is determined to be in a state of slight jitter, with a frequency deviation of approximately .

[0035] Predict the crystal oscillation trend based on the crystal oscillation frequency; calculate the clock oscillation offset based on the crystal oscillation trend; calculate the clock deviation value based on the clock oscillation offset.

[0036] In one embodiment, the time series of the crystal oscillation frequency is input into a trend prediction model to model and analyze the frequency drift. For example, a sliding window linear regression or Kalman filter method is used to predict the drift trend of the crystal oscillation frequency with temperature and load changes, and the slope of the frequency change is obtained. Based on this trend, the clock oscillation offset is calculated to characterize the cumulative deviation of the actual crystal oscillation frequency from the nominal frequency in the time dimension. Then, the clock deviation value is calculated in combination with the frequency division link amplification effect, thereby reflecting the system clock stability.

[0037] In another embodiment, assuming the crystal oscillator frequency is sampled every 5 minutes for one hour, a total of 12 sets of data are obtained. The frequency gradually decreases from 23.92MHz to 23.84MHz, and the fitted frequency drift slope is approximately... It is predicted that the clock frequency will continue to decrease by approximately 0.195MHz over the next 30 minutes, based on which the cumulative clock oscillation offset is calculated to be approximately 0.32%; combined with the PLL multiplication factor... The calculated system clock deviation is approximately 1.28%, which exceeds the preset threshold of 1.0%, indicating a potential instability risk in the clock system.

[0038] Preferably, step S3, which assesses the chip failure probability based on abnormal temperature rise values, includes: Calculate the thermal expansion of the crystal oscillator structure based on the abnormal temperature rise value; determine the deformation of the vibrating plate based on the thermal expansion of the crystal oscillator structure; calculate the offset value of the crystal oscillator structure based on the deformation of the vibrating plate. In one embodiment, abnormal temperature rise values ​​of the crystal oscillator during operation are obtained. And based on the material's coefficient of thermal expansion (e.g., quartz materials) Construct a thermal expansion calculation model Thus, the linear thermal expansion of the crystal oscillator structure is obtained; where The equivalent length parameter of the vibrating plate is used. The thermal expansion is mapped to the structural mechanical model of the vibrating plate, and the deformation of the vibrating plate is calculated through finite element method with equivalent simplification. This establishes a temperature-structural deformation mapping relationship; based on the geometric constraints of the vibrating plate and the support boundary conditions, the deformation is converted into the overall offset value of the crystal oscillator structure. .

[0039] In another embodiment, it is assumed that the initial equivalent length of the crystal oscillator is... abnormal temperature rise The temperatures were [5℃, 8℃, 12℃, 15℃, 20℃] for multiple sampling periods; the coefficient of thermal expansion was taken. The thermal expansion was calculated. The wavelengths are approximately [6.9 nm, 11.0 nm, 16.5 nm, 20.6 nm, 27.5 nm], respectively. Based on the structural compliance coefficient... The deformation of the vibrating plate is estimated as [5.5nm, 8.8nm, 13.2nm, 16.4nm, 22.0nm]. Then, based on the amplification factor of the supporting structure... The offset value of the crystal oscillator structure is obtained. Approximately [8.8nm, 14.1nm, 21.1nm, 26.2nm, 35.2nm], where... The time offset growth rate increases significantly, exhibiting structural thermosensitive enhancement characteristics.

[0040] Of particular importance is the calculation of the thermal expansion of the crystal oscillator structure based on the temperature rise anomaly, which includes: Obtain the crystal oscillator package housing parameters, crystal oscillator parameters, and coefficient of thermal expansion; determine the crystal oscillator package housing temperature and crystal oscillator temperature based on abnormal temperature rise values; calculate the package housing expansion amount based on the crystal oscillator package housing temperature, package housing parameters, and coefficient of thermal expansion. In one embodiment, the basic parameters of the crystal oscillator structure are obtained, including the type of packaging housing material, geometric dimensions (such as length, thickness, volume, etc.), and the equivalent dimensions of the crystal oscillator, and the coefficient of thermal expansion of the material is obtained. (e.g., metal packaging housing) Quartz oscillator Based on real-time collected temperature rise anomalies. The overall temperature rise is mapped to the package housing temperature using a thermal field distribution model. With crystal oscillator temperature This considers the encapsulation thermal conduction delay and the difference in internal thermal gradient. It is based on the linear thermal expansion formula. The expansion of the enclosure in each direction is calculated to obtain the spatial expansion vector of the enclosure structure.

[0041] In another embodiment, assume the initial size of the package housing is... equivalent length of crystal oscillator abnormal temperature rise The temperatures are [5℃, 10℃, 15℃, 20℃, 25℃]. The package shell temperature is obtained through a heat distribution model. The crystal oscillator temperatures are [36℃, 41℃, 46℃, 51℃, 56℃]. The temperatures are [34℃, 38℃, 42℃, 47℃, 52℃]. The coefficient of thermal expansion of the packaging shell is taken as... The expansion amount of the packaging shell was calculated. Approximately It shows a linear growth trend with temperature rise, but the growth rate is slightly enhanced in the high temperature range (due to the additional stress effect caused by structural constraints).

[0042] The expansion of the crystal oscillator is calculated based on the crystal oscillator temperature, crystal oscillator parameters, and coefficient of thermal expansion; the thermal expansion of the crystal oscillator structure is calculated based on the expansion of the package housing and the expansion of the crystal oscillator.

[0043] In one embodiment, based on the crystal oscillator temperature and the geometric parameters of the vibrator (such as length, thickness, and vibration direction), combined with the thermal expansion coefficient of quartz material. Calculate the expansion of the crystal oscillator in the principal vibration direction. The expansion amount of the packaging shell With the expansion of the crystal oscillator A coupling analysis was performed, considering the encapsulation constraint effect and the interface stress transfer coefficient. (For example ), calculate the overall thermal expansion of the crystal oscillator structure. .

[0044] In another embodiment, assuming the initial length of the crystal diaphragm is... Crystal oscillator temperature The range is [34℃, 38℃, 42℃, 47℃, 52℃], using the coefficient of thermal expansion of quartz. The expansion amount of the crystal oscillator was calculated. Approximately Simultaneously, considering the expansion amount of the packaging shell. Take the structural coupling coefficient The thermal expansion of the crystal oscillator structure was obtained. The calculation result is approximately In the high-temperature zone ( When the thermal expansion of the structure is significantly amplified by superposition, it indicates that the encapsulation shell has a dominant influence on the thermal deformation of the diaphragm.

[0045] Of particular importance is the determination of the deformation of the oscillator plate based on the thermal expansion of the crystal structure, including: The thermal stress distribution value is determined based on the thermal expansion of the crystal structure; the degree of crystal bending deformation is evaluated based on the thermal stress distribution value; and the curvature change is calculated based on the degree of crystal bending deformation. In one embodiment, a thermal stress model is established based on the difference in the coefficients of thermal expansion between the crystal oscillator packaging material (such as Kovar alloy) and the quartz crystal, and the thermal expansion of the crystal oscillator structure is calculated. Mapped to thermal stress distribution along the package boundary Specifically, the packaging shell is divided into sections using finite element discretization. For each element of the grid, calculate the local thermal stress at each grid node. ,in It is the equivalent elastic modulus (approximately 70 GPa). The equivalent thermal expansion coefficient (approximately) ), This is due to localized temperature rise differences (range 0.5–8K). Based on this, a stress gradient field is used... The tendency of crystal bending deformation is determined, and a curvature approximation model is adopted. Calculate the change in curvature, where The crystal flexural displacement function is used to obtain the overall bending response characteristics of the crystal under packaging constraints.

[0046] In another embodiment, it is assumed that the abnormal temperature rise of the crystal oscillator during operation is [value missing]. There is a temperature difference of 3–6K between the edge and center of the package. The calculated maximum thermal stress of the package shell is approximately Average thermal stress is approximately The degree of crystal bending deformation is obtained based on stress distribution fitting. Approximately 0.012–0.035 (dimensionless normalization coefficient), with the most significant deformation occurring in the edge regions. Corresponding curvature change. Distribution range is The maximum local curvature appears in the four corners of the package, indicating that this area is a potential sensitive area for subsequent vibration instability.

[0047] Identify the point of maximum deformation displacement based on the curvature change; calculate the deflection change at the point of maximum deformation displacement; determine the crystal vibrating plate deformation based on the deflection change.

[0048] In one embodiment, in obtaining the curvature field Then, the point of maximum curvature is determined using a global extremum search algorithm. That is, satisfying Based on Kirchhoff's thin-plate theory, the local curvature at this point is converted into a change in deflection. The maximum offset of the vibrating plate at that point is calculated by combining the boundary constraints (four-sided fixed support or simply supported model). Through integral form Estimate the overall crystal oscillator plate deformation to reflect the degree of structural offset of the crystal oscillator under thermo-mechanical coupling conditions.

[0049] In another embodiment, it is assumed that the calculated point of maximum curvature on the crystal surface is located in the region slightly to the right of the package center, and the curvature at this point is... Corresponding local deflection change Approximately 6.8 μm; average deflection in the edge region is approximately The average deformation of the entire vibrating plate is approximately Comparison under different temperature rise conditions: When Time-varying variables ,when Rising to ,when Time available The crystal vibrator exhibits a significant nonlinear amplification effect, indicating that the crystal vibrator exhibits enhanced structural sensitivity under high temperature gradients.

[0050] The vibration center offset is calculated based on the crystal structure offset value; the stress distribution state is identified based on the vibration center offset; the oscillation energy attenuation coefficient is calculated based on the stress distribution state; the oscillation stability of the crystal is determined based on the oscillation energy attenuation coefficient; and the chip failure probability is assessed based on the oscillation stability of the crystal.

[0051] In one embodiment, based on the crystal structure offset value By combining the vibration mode function to reconstruct the position of the vibration center, the offset of the vibration center is calculated. By analyzing the components of the vibration center offset in the X, Y, and Z directions, a stress distribution inversion model is established, mapping the offset gradient to the stress field to identify the internal stress distribution state of the crystal oscillator (uniform stress, concentrated stress, or asymmetric stress). Based on this, an energy loss model is introduced to calculate the oscillation energy attenuation coefficient according to the degree of stress concentration. Based on this, the stability index of the crystal oscillator was evaluated. (like Value stability or frequency drift stability). Based on the mapping relationship between stability indicators and historical failure data, the chip failure probability is output. .

[0052] In another embodiment, assuming the crystal structure offset value for The offset of the vibration center is calculated. Approximately The vibration center offset gradient is divided into three stress states: when When the stress is uniform, At this time, it is a state of moderate stress concentration. The initial stress state corresponds to the subsequent two groups of samples gradually entering the asymmetric stress distribution range. The oscillation energy attenuation coefficient is calculated based on the stress state. They are respectively The stability of the crystal oscillator exhibits a non-linear increasing trend with increasing offset. They are respectively Based on the stability-failure probability mapping function (e.g., exponential fitting relationship), the chip failure probability is estimated. Approximately When the temperature rise corresponds to the latter two sets of data, the system enters the high-risk failure warning zone.

[0053] Preferably, identifying the stress distribution state based on the vibration center offset includes: Calculate the mass center offset distance based on the vibration center offset; calculate the force value of the crystal vibrator based on the mass center offset distance; calculate the tensile stress value based on the force value of the crystal vibrator. In one embodiment, the offset coordinates of the crystal oscillator's vibration center relative to the designed center position are first obtained, and the offset distance of the mass center is calculated based on the offset coordinates. The force value of the crystal oscillator is obtained by correlating the offset distance of the center of mass with the mass parameters of the crystal oscillator, the parameters of the supporting structure, and the vibration acceleration parameters. Subsequently, based on the cross-sectional area of ​​the crystal vibrator... And the direction of force, using a tensile stress calculation model. Calculate the tensile stress value. When the vibration center shifts towards the edge region of the crystal vibrator, the corresponding area experiences tensile stress, resulting in a tensile stress distribution. Quantitative analysis of the tensile stress value reflects the stress changes in the local structure of the crystal vibrator, providing fundamental data for subsequent stress concentration analysis.

[0054] In another embodiment, assuming the crystal oscillator has a length of 8.0 mm, a width of 4.0 mm, and a thickness of 0.12 mm, and the vibration center offset coordinates are (0.18 mm, 0.12 mm), the mass center offset distance is calculated. The diameter is approximately 0.216 mm. Further setting the mass of the crystal oscillator to 0.015 g and the vibration acceleration to 220 m / s², the calculated force on the crystal oscillator is then obtained. The tensile stress is approximately 3.3 mN. If the effective cross-sectional area of ​​the stressed region is 0.48 mm², the corresponding tensile stress value is approximately 6.88 MPa. Analysis revealed that the tensile stress is mainly concentrated in the cantilever connection area of ​​the vibrating plate, with the maximum tensile stress point located approximately 1.2 mm from the fixed end. This area may become the main location for subsequent fatigue propagation in the structure.

[0055] The compressive stress value is calculated based on the force value of the crystal vibrator; the stress concentration factor is determined based on the tensile stress value and the compressive stress value; and the stress distribution state is identified based on the stress concentration factor.

[0056] In one embodiment, the compressive stress value is calculated based on the force applied to the crystal vibrator and the effective bearing area of ​​the compressed region. Since the crystal oscillator exhibits both tensile and compressive regions during oscillation, tensile stress values ​​are obtained separately. With compressive stress value The stress difference between the two stresses and the local peak stress are calculated. Then, the ratio of the maximum local stress to the average stress is used as the stress concentration factor. ,Right now The stress distribution state is identified based on the magnitude of the stress concentration factor. When the stress concentration factor is within a preset safe range, it is determined to be a uniform stress distribution state; when the stress concentration factor exceeds a preset warning threshold, it is determined to be a localized stress concentration state; when the stress concentration factor further exceeds the failure threshold, it is determined to be a high-risk stress concentration state.

[0057] In another embodiment, it is assumed that the compressive stress value is calculated based on the force value of the crystal vibrator. The tensile stress value is 5.94 MPa. The stress was 6.88 MPa. 120 stress sampling points were extracted from the surface of the vibrating plate using finite element nodal analysis, where the average stress was... The maximum local stress is 4.25 MPa. The stress concentration factor is calculated to be 11.48 MPa. Further settings: Corresponding to a uniform stress distribution state, Corresponding to localized stress concentration state, This corresponds to a high-risk stress concentration state. Due to the current... The value was 2.70, thus identifying it as a high-risk stress concentration state. At this point, approximately 15% of the area near the fixed end of the vibrating plate bore more than 60% of the total stress load, and the maximum stress point was in the same direction as the vibration center offset. This indicates that thermal deformation and structural offset have caused stress to accumulate significantly in local areas, posing a risk of decreased oscillation stability and structural fatigue failure.

[0058] Preferably, step S4, which generates a resource security interaction strategy using the corrected resource leakage risk value, includes: Record the risk level range of the corrected resource leakage risk value; determine the resource interaction control mode based on the risk level range; generate interface-level access control parameters based on the resource interaction control mode; In one embodiment, assuming the risk value range for correcting resource leakage is 0~1, the risk level interval is divided into: low risk [0, 0.3), medium risk [0.3, 0.7], and high risk [0.7, 1]. When the risk value for a certain access scenario is 0.22, the system determines it as low risk, sets the interface access frequency to 100 times / s, the permission level to L3, and the data anonymization level to level 1. When the risk value is 0.55, it is determined as medium risk, the access frequency is reduced to 40 times / s, the permission level to L2, and the data anonymization level is increased to level 2. When the risk value is 0.86, it is determined as high risk, the access frequency is limited to 5 times / s, the permission level is L1, only read-only access is allowed, and a level 3 strong anonymization strategy is enabled, thereby forming a differentiated interface-level access control parameter set.

[0059] Configure resource access channel constraints based on interface-level access control parameters and output secure interaction control instructions; generate resource security interaction policies based on the secure interaction control instructions.

[0060] In one embodiment, the resource access channel is constrained and configured according to interface-level access control parameters, including bandwidth limitation, access whitelist filtering, encryption strength configuration, and request replay protection mechanism. Based on the configuration results, a security interaction control command is generated to drive the system to perform access control and risk isolation operations. The control command is transformed into a resource security interaction policy to realize the generation of dynamic access control and security protection policies under different risk level scenarios.

[0061] In another embodiment, it is assumed that the system has three types of access channels: API channel, file access channel, and database query channel. In a low-risk state, the API channel bandwidth is 100Mbps, allowing 1000 concurrent requests, with AES-128 encryption and a whitelist covering all registered terminals. In a medium-risk state, the API channel bandwidth is reduced to 30Mbps, with concurrency limited to 300 requests, allowing only authenticated terminals to access. In a high-risk state, the bandwidth is compressed to 5Mbps, with concurrency limited to 20 requests, while AES-256 encryption and a two-way authentication mechanism are enabled. The corresponding generated security interaction control commands are "Open Policy Command," "Restriction Policy Command," and "Isolation Policy Command," forming a three-tiered resource security interaction policy system: low-risk open access policy, medium-risk controlled access policy, and high-risk isolated access policy.

[0062] Preferably, the resource interaction control mode determined according to the risk level range includes: The risk level range is matched with the preset risk level range to determine the corresponding risk level category; the risk level category is mapped to the preset resource interaction control mode set to filter candidate control modes; the resource interaction control mode set includes full-function interaction mode, restricted interaction mode and isolated interaction mode. In one embodiment, it is assumed that the risk level intervals are divided into [0, 0.3), [0.3, 0.7), and [0.7, 1.0], corresponding to low, medium, and high risk categories, respectively. When the resource leakage risk value is 0.18, it matches the low risk category, and the candidate control mode is the full-function interactive mode. When the risk value is 0.52, it matches the medium risk category, and the candidate control mode is the restricted interactive mode. When the risk value is 0.86, it matches the high risk category, and the candidate control mode is the isolated interactive mode. In another time window, the system identified the following candidate control modes: full-function mode accounts for 35%, restricted mode accounts for 40%, and isolated mode accounts for 25%.

[0063] The candidate control modes are prioritized to determine the resource interaction control mode.

[0064] In one embodiment, assuming the candidate control modes are scored as follows at a certain moment: full-function interaction mode scores 0.62, restricted interaction mode scores 0.85, and isolated interaction mode scores 0.78, the ranking is restricted interaction mode > isolated interaction mode > full-function interaction mode. In another set of test samples, the full-function mode scores 0.91 (low-risk scenario), the restricted mode scores 0.73, and the isolated mode scores 0.55, so the priority becomes full-function mode > restricted mode > isolated mode. After the system counts 10 consecutive interaction cycles, the proportion of selecting restricted interaction mode is 50%, full-function mode is 30%, and isolated mode is 20%, thus determining the resource interaction control mode.

[0065] Of particular importance is the generation of resource security interaction policies based on security interaction control instructions, including: The target control field information is determined based on the security interaction control instructions; the target control field information is parsed into resource interaction constraint types; and the resource interaction constraint types are mapped to resource control units. In one embodiment, assume that the target control field information obtained from the security interaction control command parsing is: Access Subject ID = U1024, Resource Type = Sensitive Data Interface A, Operation Type = Read, Risk Flag = 0.82; where the risk flag exceeds a preset threshold of 0.75. This field information is mapped to three types of resource interaction constraints: access frequency constraint (maximum request frequency ≤ 50 times / min), permission level constraint (requirement level ≥ L3), and data range constraint (only allowing the proportion of returned anonymized fields ≥ 70%). According to the mapping relationship, the access frequency constraint is assigned to the traffic control unit, the permission level constraint is assigned to the identity authentication control unit, and the data range constraint is assigned to the data anonymization control unit, forming three parallel control paths.

[0066] Generate interface-level control parameters according to the resource control unit; construct a resource access control rule set based on the generated interface-level control parameters, and use the resource access control rule set as a resource security interaction strategy.

[0067] In an embodiment, a set of interface-level control parameters is generated for the control constraint information output by each resource control unit , which includes request rate threshold parameters, access token verification parameters, and data return filtering parameters; through parameter normalization processing, control parameters of different dimensions are uniformly mapped to the [0,1] interval to form a standardized interface control vector. A resource access control rule set is constructed based on the interface control vector, and the rule set is expressed in the form of "condition-action", for example: IF the access frequency exceeds the limit THEN perform current limiting, IF the permission level is insufficient THEN deny access, IF the data sensitivity is too high THEN perform desensitization processing, thereby forming a resource security interaction strategy model.

[0068] In another embodiment, it is assumed that the interface-level control parameters are specifically: request rate threshold times / min, permission verification threshold , data desensitization ratio ; after inputting the above parameters into the rule generation module, a resource access control rule set containing 5 core rules is generated, for example: when the request rate > 45 times / min, the delayed response mechanism is triggered (delay time 200ms); when the permission level < L3.5, access is denied and error code 403 is returned; when the data sensitivity score > 0.7, field-level desensitization is performed (desensitization ratio ≥ 75%); when the number of consecutive abnormal requests ≥ 3 times, temporary freezing is triggered (freezing duration 10min); when the cross-interface access frequency difference > 30%, enhanced audit log recording is triggered. The rule set is solidified as a resource security interaction strategy and used for subsequent interface access control execution.

[0069] It should be noted that, please refer to Figure 2 , first, in the resource request and risk assessment stage, the educational resource access request is obtained, the resource leakage risk value is calculated, and the interaction credibility is assessed; entering the firmware security detection stage, the resource interaction task is executed and the trajectory is recorded, the firmware signature status is identified, and the firmware tampering risk value is detected; in the third stage, the chip state monitoring locates the security chip monitoring unit based on the aforementioned risk value, calculates the temperature rise abnormal value, and assesses the chip failure probability; finally, in the risk correction and strategy generation stage, the resource leakage risk value is corrected, the resource security interaction strategy is generated, and the security interaction control instruction is output.

[0070] It should be noted that, please refer to Figure 3The risk identification module is responsible for acquiring access requests, calculating resource leakage risk values, and assessing interaction credibility; the trajectory identification module executes resource interaction tasks, records trajectories, and completes firmware signature recognition and tamper detection; the temperature rise calculation module locates the security chip monitoring unit, calculates abnormal temperature rise values, and assesses the chip failure probability; the policy generation module corrects risk values ​​and generates resource security interaction policies. The middle layer is the data storage and management layer, including request and access log libraries, firmware and signature databases, chip monitoring databases, and policy and rule bases, providing data support for each module. The bottom layer is the secure communication and control layer, covering encrypted channels, access control, and command issuance functions to ensure the secure operation of the entire system.

[0071] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0072] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. 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 the invention. Therefore, the present invention 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 of the invention herein.

Claims

1. A method for secure resource interaction in a smart education cloud platform, characterized in that, Includes the following steps: Step S1: Obtain educational resource access requests and use the educational resource access requests to calculate the resource leakage risk value; Assess the credibility of resource interactions based on resource leakage risk values; Step S2: Execute the resource interaction task based on the resource interaction credibility and record the resource interaction trajectory; Identify firmware signature status based on resource interaction trajectory; Detect firmware tampering risk value based on firmware signature status; Step S3: Identify the corresponding security chip monitoring unit based on the firmware tampering risk value; calculate the temperature rise anomaly value based on the security chip monitoring unit; assess the chip failure probability based on the temperature rise anomaly value; Step S4: Adjust the resource leakage risk value according to the chip failure risk probability to obtain the corrected resource leakage risk value; Generate resource security interaction strategies by correcting resource leakage risk values.

2. The resource security interaction method of the smart education cloud platform according to claim 1, characterized in that, Step S2, which identifies the firmware signature status based on the resource interaction trajectory, includes: The resource interaction trajectory is parsed to extract the target terminal identification information; the firmware hash digest and firmware digital signature are read based on the target terminal identification information. Obtain the standard firmware hash digest and standard digital signature; calculate the hash deviation value based on the standard firmware hash digest and the firmware hash digest; calculate the signature deviation value based on the standard digital signature and the firmware digital signature; The firmware signature status is determined based on the hash deviation value and the signature deviation value. The firmware signature status includes normal signature status, abnormal signature status, and invalid signature status.

3. The resource security interaction method of the smart education cloud platform according to claim 1, characterized in that, Step S2, which detects firmware tampering risk values ​​based on firmware signature status, includes: Determine the firmware control region based on the firmware signature status; obtain the boot configuration data, clock configuration data, and interface control data corresponding to the firmware control region; calculate the boot deviation value based on the boot configuration data. Calculate the clock deviation value based on the clock configuration data; calculate the interface configuration deviation value based on the interface control data; calculate the hardware control anomaly value based on the startup deviation value, clock deviation value, and interface configuration deviation value; determine the firmware tampering risk value based on the hardware control anomaly value.

4. The resource security interaction method of the smart education cloud platform according to claim 3, characterized in that, The firmware control region is determined based on the firmware signature status, including: The firmware signature verification result is parsed based on the firmware signature status; the firmware functional integrity level is determined based on the firmware signature verification result; the firmware functional integrity level is mapped to a preset set of firmware control areas to determine the mapping relationship; wherein, the set of firmware control areas includes a boot control area, a clock control area, and an interface control area. Calculate the region weight vector based on the mapping relationship; calculate the region matching score based on the region weight vector; identify the target firmware control region based on the region matching score.

5. The resource security interaction method of the smart education cloud platform according to claim 3, characterized in that, The clock deviation value is calculated based on the clock configuration data, including: Determine the clock drive circuit region based on the clock configuration data; identify the crystal oscillation state within the clock drive circuit region; calculate the crystal oscillation frequency based on the crystal oscillation state. Predict the crystal oscillation trend based on the crystal oscillation frequency; calculate the clock oscillation offset based on the crystal oscillation trend; calculate the clock deviation value based on the clock oscillation offset.

6. The resource security interaction method of the smart education cloud platform according to claim 1, characterized in that, Step S3, which assesses the chip failure probability based on abnormal temperature rise values, includes: Calculate the thermal expansion of the crystal oscillator structure based on the abnormal temperature rise value; determine the deformation of the vibrating plate based on the thermal expansion of the crystal oscillator structure; calculate the offset value of the crystal oscillator structure based on the deformation of the vibrating plate. The vibration center offset is calculated based on the crystal structure offset value; the stress distribution state is identified based on the vibration center offset; the oscillation energy attenuation coefficient is calculated based on the stress distribution state; the oscillation stability of the crystal is determined based on the oscillation energy attenuation coefficient; and the chip failure probability is assessed based on the oscillation stability of the crystal.

7. The resource security interaction method of the smart education cloud platform according to claim 6, characterized in that, Identifying the stress distribution based on the vibration center offset includes: Calculate the mass center offset distance based on the vibration center offset; calculate the force value of the crystal vibrator based on the mass center offset distance; calculate the tensile stress value based on the force value of the crystal vibrator. The compressive stress value is calculated based on the force value of the crystal vibrator; the stress concentration factor is determined based on the tensile stress value and the compressive stress value; and the stress distribution state is identified based on the stress concentration factor.

8. The resource security interaction method of the smart education cloud platform according to claim 1, characterized in that, Step S4, which generates a resource security interaction strategy using the corrected resource leakage risk value, includes: Record the risk level range of the corrected resource leakage risk value; determine the resource interaction control mode based on the risk level range; generate interface-level access control parameters based on the resource interaction control mode; Configure resource access channel constraints based on interface-level access control parameters and output secure interaction control instructions; generate resource security interaction policies based on the secure interaction control instructions.

9. The resource security interaction method of the smart education cloud platform according to claim 8, characterized in that, Based on the risk level range, the resource interaction control mode is determined as follows: The risk level range is matched with the preset risk level range to determine the corresponding risk level category; the risk level category is mapped to the preset resource interaction control mode set to filter candidate control modes; the resource interaction control mode set includes full-function interaction mode, restricted interaction mode and isolated interaction mode. The candidate control modes are prioritized to determine the resource interaction control mode.

10. A resource security interaction system for a smart education cloud platform, characterized in that, For executing the resource security interaction method of the smart education cloud platform as described in claim 1, the resource security interaction system of the smart education cloud platform includes: The risk identification module is used to acquire access requests for educational resources and calculate resource leakage risk values ​​using these requests; and to assess the trustworthiness of resource interactions based on these risk values. The trajectory recognition module is used to execute resource interaction tasks based on the trustworthiness of resource interactions and record resource interaction trajectories; identify firmware signature status based on resource interaction trajectories; and detect firmware tampering risk values ​​based on firmware signature status. The temperature rise calculation module is used to locate the security chip monitoring unit corresponding to the tampering behavior based on the firmware tampering risk value; calculate the temperature rise anomaly value based on the security chip monitoring unit; and assess the chip failure probability based on the temperature rise anomaly value. The strategy generation module is used to correct the resource leakage risk value based on the chip failure risk probability, record the corrected resource leakage risk value, and generate a resource security interaction strategy using the corrected resource leakage risk value.

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