Anti-cheating weighing data encryption transmission method and system of electronic scale

Abstract

The application provides an anti-cheating weighing data encryption transmission method and system of an electronic scale, and relates to the technical field of intelligent electronic scales, wherein the method part comprises the following steps: S1: a physical layer feature and identity association verification stage, calculating an initial trust degree index of a device; S2: a behavior baseline self-learning and abnormal deviation analysis stage, calculating a behavior mode deviation degree index; S3: a comprehensive security risk assessment stage, calculating a dynamic security risk level index; S4: an encryption strength level adaptive configuration stage, calculating an encryption strength level value for dynamically adjusting a key refresh period and an encryption algorithm strength level. The application constructs a closed-loop dynamic protection system with two-way adjustment of encryption strength according to security situation through the progressive processing of the four stages of physical layer feature and identity association verification, behavior baseline self-learning and abnormal deviation analysis, comprehensive security risk assessment and encryption strength level adaptive configuration.

Description

Technical Field

[0001] This invention relates to the field of intelligent electronic scale technology, specifically to a method and system for encrypted transmission of anti-cheating weighing data in electronic scales. Background Technology

[0003] In the prior art, patent CN119880115A discloses a method and device for preventing measurement fraud on intelligent electronic scales. This technology includes: reading measurement fraud prevention parameters; constructing a measurement fraud prevention system based on these parameters; the measurement fraud prevention system integrating a dynamic encrypted communication protocol, a multi-dimensional biometric authentication protocol, an adaptive anomaly detection protocol, a blockchain evidence storage architecture, a multi-dimensional environmental perception protocol, and a dynamic measurement calibration protocol; the adaptive anomaly detection protocol integrates circular buffer data to capture anomaly probabilities; the blockchain evidence storage architecture uses block hashing to achieve private chain distributed storage of operation records; and the multi-dimensional environmental perception protocol obtains a fraud risk index through fraud perception parameters. This invention achieves multi-dimensional verification of electronic scales, comprehensive judgment of fraud risk during the middle stage, and measurement calibration of electronic scales in the later stage.

[0004] However, the aforementioned existing technologies rely on logical layer methods such as digital keys or biometrics for authentication, lacking an unclonable trust foundation based on the physical uniqueness of hardware. Key leakage or feature forgery will cause the authentication system to fail. The encryption strategy uses fixed algorithms and preset cycles, which cannot be dynamically adjusted according to the security situation. This results in wasted resources at low risk and insufficient protection at high risk. Anomaly detection is based on fixed rules or preset thresholds, lacking the ability to self-learn baseline modeling and continuous deviation accumulation of multi-dimensional behavioral characteristics such as operation frequency, data traffic, and instruction sequences. It is difficult to capture slow and gradual micro-change cheating behavior. In risk assessment, historical records are only used for post-event auditing and are not incorporated into real-time judgment in a dynamic weighted manner. Physical anti-tampering and communication encryption are disconnected. After a physical security incident, the encryption strength of the communication layer cannot be upgraded in real time, and there is a lack of linkage defense mechanism.

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

[0006] The purpose of this invention is to provide a method and system for encrypted transmission of weighing data to prevent cheating in electronic scales, thereby solving the problems mentioned in the background art. This invention overcomes the shortcomings of existing technologies, such as easily compromised authentication, difficulty in detecting subtle cheating, static encryption strategies, and the separation of physical and communication security, through physical layer anti-tampering linkage authentication, cumulative analysis of behavioral baseline deviations, real-time feedback assessment of historical risks, and bidirectional adjustment of encryption strength according to risk. It achieves proactive defense by dynamically adapting encryption strength to the security situation.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] The method for encrypting and transmitting weighing data to prevent cheating on electronic scales includes the following steps:

[0009] S1: Physical layer feature and identity association verification stage, calculate the initial trust index of the device to determine the basic trust level when the device is connected; the initial trust index of the device is obtained by weighting the operator's identity features, the physical layer non-cloning features of the device and the security attributes of the operating environment; the physical layer non-cloning features are calculated based on the fluctuation of electrical parameters caused by inherent hardware process deviations, packaging structure features and anti-tamper sensing status, which is different from the logical layer key authentication.

[0010] S2: Behavioral baseline self-learning and abnormal deviation analysis stage. The behavioral pattern deviation index is calculated to monitor the statistical deviation between the operating behavior and the self-learning baseline during equipment operation. The system dynamically statistically models the operating behavior under normal operating conditions and continuously updates the baseline. The pattern deviation index is calculated based on the standard deviation distance weighted by the current behavioral parameters relative to the historical baseline mean. It covers four-dimensional features: operation frequency, data flow, time distribution, and instruction sequence. The system achieves gradual capture of subtle cheating behaviors by continuously accumulating the deviation.

[0011] S3: In the comprehensive safety risk assessment phase, a dynamic safety risk level index is calculated to determine the overall risk level of the equipment's current operating environment. The dynamic safety risk level index is obtained by fusing the complementary value of the initial trust level index, the behavioral pattern deviation index, and the weighted historical risk factors. The historical risk factors are weighted using exponential decay, so that the impact of recent risk events on the current assessment has a higher weight than that of long-term events. The dynamic safety risk level index is updated in real time with the equipment's operating status and is not a fixed value calculated once.

[0012] S4: Adaptive configuration phase of encryption strength level. The encryption strength level value is calculated to dynamically adjust the key refresh cycle and encryption algorithm strength level. The encryption strength level value is determined by the ratio of the current dynamic security risk level indicator to the preset risk threshold, supplemented by the constraints of available computing resources on the device. When the security risk level increases, the encryption strength level value increases, driving the key refresh cycle to shorten and the encryption algorithm strength to improve. When the security risk level decreases, the level value decreases to achieve resource saving and encryption efficiency optimization.

[0013] Furthermore, the initial trust level index of the device is calculated according to the following formula:

[0014]

[0015] in: This refers to the initial trust level index of the device.

[0016] Operator identity characteristic factor;

[0017] This is a physical layer non-clonable characteristic factor;

[0018] For runtime environment security attribute factors;

[0019] These are the weighting coefficients for the operator identity characteristic factor, the physical layer non-cloning characteristic factor, and the operating environment security attribute factor, respectively. .

[0020] Furthermore, the operator identity feature factor is obtained by collecting three biometric features—face, fingerprint, and voiceprint—and calculating the ratio of the number of matching biometric features to the total number of biometric features collected. This ratio is then combined with the liveness detection pass rate, and the average value is taken to confirm the operator's legal identity and that they are a real, living person. The physical layer non-cloning feature factor is obtained by reading the matching degree between the current physical layer electrical response deviation value caused by inherent hardware process deviations and the standard electrical response deviation value registered at the factory. This is combined with the packaging structure coding consistency test results and the integrity status of the tamper-proof switch, and is used to verify that the device's physical identity is unique and its structure is intact. The operating environment security attribute factor is obtained by determining whether the current geographical location falls within the authorized area and whether the current time is within the preset safe period, and by evaluating network signal strength, encryption protocol version, and abnormal sniffing situations, and taking the average value. This is used to ensure that the device completes authentication only in a safe and reliable time, space, and network environment.

[0021] Furthermore, the behavioral pattern deviation index is calculated using the following formula:

[0022]

[0023] in: Deviation in operation frequency;

[0024] Data traffic deviation;

[0025] This represents the deviation from the time distribution.

[0026] This refers to the deviation of the instruction sequence.

[0027] These are the weighting coefficients for operation frequency deviation, data flow deviation, time distribution deviation, and instruction sequence deviation, respectively. .

[0028] Furthermore, the method for obtaining the operation frequency deviation is as follows: count the number of operations within the current time window, calculate the standard deviation distance of the operation relative to the historical mean and standard deviation under normal operation, and obtain it after normalization, which reflects the abnormal change in the frequency of operation.

[0029] The data traffic deviation is obtained by: statistically analyzing the average data packet size within the current time window, calculating the standard deviation distance relative to the historical mean and standard deviation under normal operating conditions, and normalizing the result to reflect abnormal fluctuations in data transmission volume; the time distribution deviation is obtained by: statistically analyzing the timestamps of various operations, calculating the standard deviation distance of the current occurrence time of each operation relative to the historical average time of that type of operation under normal operating conditions, taking the average of all operation types and normalizing the result to reflect abnormal shifts in the operation time sequence distribution; the instruction sequence deviation is obtained by: performing pattern matching between the current operation instruction sequence and the historical behavior pattern library established by the system through normal operation, using the proportion of failed matches as a measure to reflect abnormal changes in the overall behavior pattern of the operation sequence.

[0030] Furthermore, the dynamic security risk level index is calculated using the following formula:

[0031]

[0032] in: As a dynamic safety risk level indicator;

[0033] Historical risk factors;

[0034] These are complementary values ​​to the initial trust level index;

[0035] These are the complementary values ​​of the initial trust level index, the deviation index of behavioral patterns, and the weighting coefficients of historical risk factors, respectively. .

[0036] Furthermore, the historical risk factor is obtained as follows: the system records the dynamic safety risk level value calculated each time according to a preset assessment cycle, forming a historical risk level sequence arranged chronologically from the earliest record to the most recent record; the system presets an exponential decay coefficient, with the current assessment time as the benchmark, and the decay weight corresponding to each historical record is an exponential function value with the natural constant as the base and the decay coefficient multiplied by the negative number of the interval between the historical record and the current record as the exponent, the larger the interval, the smaller the weight; each historical risk level value is multiplied by its corresponding decay weight, summed, and then divided by the sum of the decay weights, and the resulting ratio is used as the historical risk factor output; the historical risk factor is updated on a rolling basis with the completion of each assessment cycle, and the decay coefficient is preset to a fixed value according to the equipment operating characteristics, so that the impact weight of recent risk events on the current assessment is higher than that of long-term events, and the contribution of historical information naturally decreases as the operating time progresses; when the dynamic safety risk level index is calculated for the first time, the system has no historical risk records, and the historical risk factor cannot be obtained by the conventional decay weighting method, so it is directly assigned a preset initial value; after the first calculation is completed, the obtained risk level value is stored in the historical sequence as the basis for calculating the historical risk factor in subsequent assessment cycles.

[0037] Furthermore, the encryption strength level value is calculated using the following formula:

[0038]

[0039] in: This represents the encryption strength level value.

[0040] Preset risk benchmark threshold;

[0041] The device's currently available computing resources;

[0042] The computational resources required to perform the highest level of encryption;

[0043] For safety risk ratio; This refers to the ratio of computing resources.

[0044] Furthermore, the system determines the key refresh cycle and encryption algorithm combination in different ranges based on the different values ​​of the encryption strength level. When the encryption strength level is in the range of [0, 0.3), the device refreshes the key at a preset baseline cycle and enables a lightweight symmetric encryption algorithm. When it is in the range of [0.3, 0.6), the key refresh cycle is slightly shortened, and standard symmetric encryption with additional integrity verification is used. When it is in the range of [0.6, 0.8), the key refresh cycle is significantly shortened, and a hybrid scheme of symmetric and asymmetric encryption is switched to. When it is in the range of [0.8, 1.0], the key refresh cycle is shortened to the minimum value, and multiple encryption combined with a dynamic key mechanism is enabled. The higher the encryption strength level, the shorter the key refresh cycle and the stronger the encryption algorithm, forming a hierarchical mapping relationship between risk level and encryption strength.

[0045] An anti-cheating weighing data encryption transmission system for electronic scales, wherein the encryption transmission system is used to execute the above-mentioned encryption transmission method, and the encryption transmission system includes:

[0046] Physical feature authentication module: Physical layer feature and identity association verification stage, calculates the initial trust index of the device to determine the basic trust level when the device is connected; the initial trust index of the device is obtained by weighting the operator's identity features, the physical layer non-cloning features of the device and the security attributes of the operating environment; the physical layer non-cloning features are calculated based on the fluctuation of electrical parameters caused by inherent hardware process deviations, packaging structure features and anti-tamper sensing status, which is different from the logical layer key authentication.

[0047] Behavioral baseline monitoring module: During the self-learning and abnormal deviation analysis phase of the behavioral baseline, the behavioral pattern deviation index is calculated to monitor the statistical deviation between the operating behavior and the self-learning baseline during equipment operation. The system dynamically statistically models the operating behavior under normal operating conditions and continuously updates the baseline. The pattern deviation index is calculated based on the standard deviation distance weighted by the current behavioral parameters relative to the historical baseline mean, covering four-dimensional features: operation frequency, data flow, time distribution, and instruction sequence. By continuously accumulating the deviation, the system achieves the gradual capture of subtle cheating behaviors.

[0048] Comprehensive Risk Assessment Module: In the comprehensive safety risk assessment phase, dynamic safety risk level indicators are calculated to determine the overall risk level of the equipment's current operating environment. The dynamic safety risk level indicators are obtained by fusing complementary values ​​of the initial trust level indicators, behavioral pattern deviation indicators, and weighted historical risk factors. Historical risk factors are weighted using exponential decay, making the impact of recent risk events on the current assessment higher than that of long-term events. The dynamic safety risk level indicators are updated in real time according to the equipment's operating status and are not fixed values ​​after a single calculation.

[0049] The encryption strategy adaptive configuration module calculates the encryption strength level value during the encryption strength level adaptive configuration phase. This value is used to dynamically adjust the key refresh cycle and encryption algorithm strength level. The encryption strength level value is determined by the ratio of the current dynamic security risk level indicator to the preset risk threshold, supplemented by the constraints of available computing resources on the device. When the security risk level increases, the encryption strength level value increases, driving a shorter key refresh cycle and an improved encryption algorithm strength. When the security risk level decreases, the level value decreases, achieving resource conservation and encryption efficiency optimization.

[0050] Compared with existing technologies, the beneficial effects of this invention are: through authentication with physical layer non-clonable features and anti-tamper linkage, self-learning and deviation accumulation analysis of multi-dimensional behavioral baselines, evaluation with real-time feedback of historical risks, and four-stage closed-loop control with bidirectional adaptive adjustment of encryption strength according to risk, it overcomes the defects of existing technologies such as easy-to-break logic layer authentication, inability of fixed rules to capture micro-change cheating, static solidification of encryption strategies, and separation of physical and communication security, and achieves active defense with encryption strength dynamically adapted to the security situation. Attached Figure Description

[0051] Figure 1 This is a schematic diagram of the anti-cheating weighing data encryption transmission method of the electronic scale of the present invention;

[0052] Figure 2 This is a schematic diagram of the anti-cheating weighing data encryption transmission system of the electronic scale of the present invention. Detailed Implementation

[0053] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0054] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0055] Example:

[0056] Please see Figures 1-2The present invention provides the following technical solutions:

[0057] The method for encrypting and transmitting weighing data to prevent cheating on electronic scales includes the following steps:

[0058] S1: Physical Layer Feature and Identity Association Verification Phase. This phase calculates the initial trust level index of the device to determine the basic trust level when the device connects. This phase is executed when the device starts up or establishes a communication connection. It adapts to typical deployment scenarios of IoT encryption and Industrial Internet encryption. Through multi-dimensional joint evaluation of operator identity legitimacy, device physical layer hardware uniqueness, and operating environment security compliance, it provides an initial trust benchmark for subsequent behavior monitoring and risk assessment. The index value is limited to between 0 and 1. A higher index value indicates a better basic trust state when the device connects. When the value is below a preset authentication threshold, the device connection request is rejected or transferred to an enhanced authentication process; only after reaching the threshold can the device enter the normal operation monitoring phase. This index provides basic trust constraints for subsequent behavior deviation analysis, comprehensive risk judgment, and encryption strategy configuration.

[0059] The initial trust level index of the device is calculated according to the following formula:

[0060]

[0061] in: This refers to the initial trust level index of the device.

[0062] Operator identity characteristic factor;

[0063] This is a physical layer non-clonable characteristic factor;

[0064] For runtime environment security attribute factors;

[0065] These are the weighting coefficients for the operator identity characteristic factor, the physical layer non-cloning characteristic factor, and the operating environment security attribute factor, respectively. α represents the contribution of the operator's biometric identification results to the initial trust assessment of the device. A larger weight indicates that the device access decision relies more heavily on the legitimacy and liveness verification of the operator's identity, suitable for scenarios with extremely strict personnel access control. 𝛽 represents the contribution of the device's physical hardware identity to the initial trust assessment. A larger weight indicates that the device access decision relies more heavily on the matching degree and structural integrity of the device's physical layer's non-clonable features, suitable for high-security scenarios that prevent the entire device from being replaced or its hardware dismantled. 𝛾 represents the contribution of the operating environment compliance to the initial trust assessment. A larger weight indicates that the device access decision relies more heavily on the security of geographical location, time window, and network environment, suitable for scenarios with strict restrictions on device deployment locations and usage periods.

[0066] The operator identity feature factor is calculated as follows: The system simultaneously collects three biometric features of the operator—face, fingerprint, and voiceprint—and compares them separately with the registration template. The proportion of successfully matched features out of the total number of features collected is calculated to obtain the biometric matching rate. Simultaneously, the system uses multi-channel liveness detection methods, including infrared thermometry, motion response detection, and random voice repetition, and calculates the proportion of successful liveness detection out of the total number of detected features to obtain the liveness detection pass rate. The arithmetic mean of the biometric matching rate and the liveness detection pass rate is the operator identity feature factor value, which is limited to between 0 and 1. This factor comprehensively reflects the legitimacy of the operator's identity and the authenticity of their liveness. When a single biometric feature is compromised or a single liveness detection is bypassed, the factor value drops significantly, thus effectively preventing identity forgery attacks.

[0067] The physical layer unclonable characteristic factor is calculated as follows: The system utilizes the inherent physical unclonable function source within the device to read the current electrical response deviation value caused by semiconductor manufacturing process variations, including ring oscillator frequency deviation and delay path delay deviation. This value is then compared with the standard electrical response deviation value written into the secure storage area during factory registration to calculate the matching degree. The measured value from the onboard temperature sensor is used for temperature compensation correction of the matching degree. Simultaneously, the system reads the current output value of the structural encoder embedded in the device's package structure and compares it with the standard package structure code registered at the factory. The system detects the current on / off state of the tamper switch circuit. If the casing is intact and the tamper switch is closed, the integrity coefficient is set to 1; if the tamper switch is triggered or the casing integrity is compromised, the integrity coefficient is set to 0. The arithmetic mean of the temperature-compensated electrical response matching degree, package structure code consistency, and tamper integrity coefficient is taken as the value of the physical layer unclonable characteristic factor, which is limited to a range between 0 and 1. When the anti-tamper integrity coefficient of this factor is 0, the overall value of the factor drops sharply, and the device will fail the initial authentication, thus effectively preventing the continued use of the device after physical tampering.

[0068] The calculation method for the operating environment security attribute factor is as follows: The system obtains the current geographical coordinates through the positioning module and matches them item by item against the system's preset list of authorized geographical locations, calculating the proportion of matched locations to the total number of authorized locations. Simultaneously, the system reads the real-time clock to obtain the current time and determines whether it falls within a preset safe time window. The proportion of time spent within the safe time window to the total window duration is used as the time period compliance. The system collects current wireless signal strength, negotiated encryption protocol version information, and anomaly detection results through the network protocol stack to comprehensively evaluate the network environment security score. The arithmetic mean of the geographical location matching rate, time period compliance, and network environment security score is the value of the operating environment security attribute factor, which is limited to a range of 0 to 1. This factor ensures that devices can only complete authentication access within authorized physical locations, permitted time windows, and secure and reliable network environments.

[0069] S2: Behavioral Baseline Self-Learning and Anomaly Deviation Analysis Phase. This phase calculates a behavioral pattern deviation index to monitor the statistical deviation between operational behavior and the self-learned baseline during device operation. This phase continues after the device passes initial authentication and enters normal operation. The system first dynamically statistically models multi-dimensional operational behavior parameters under normal operation, establishing a historical baseline model covering four dimensions: operation frequency, data flow, time distribution, and instruction sequence. The baseline parameters are continuously updated as the device continues to operate, strengthening data security protection capabilities. During real-time monitoring, the system collects current operational behavior data and calculates the standard deviation of each dimension parameter relative to the historical baseline mean. After weighted fusion, the behavioral pattern deviation index is obtained. This index, through continuous accumulation of deviation rather than single threshold triggering, effectively identifies and quantitatively represents slowly evolving, subtle cheating behaviors, with a value range limited to 0 to 1. When the index value increases, it indicates a statistically significant difference between the current operational behavior and the normal operation mode, providing quantitative input for subsequent comprehensive security risk assessment of the degree of behavioral anomaly. This indicator is continuously updated as the equipment continues to operate, adapting to the long-term evolution of normal equipment use and avoiding false alarms caused by changes in the equipment's operating mode.

[0070] The behavioral pattern deviation index is calculated using the following formula:

[0071]

[0072] in: Deviation in operation frequency;

[0073] Data traffic deviation;

[0074] This represents the deviation from the time distribution.

[0075] This refers to the deviation of the instruction sequence.

[0076] These are the weighting coefficients for operation frequency deviation, data flow deviation, time distribution deviation, and instruction sequence deviation, respectively. . This represents the contribution of the frequency of operations to the assessment of behavioral pattern deviation. A higher weight indicates that the determination of abnormal device behavior depends more on the change in the number of operations per unit time, making it suitable for scenarios sensitive to operational rhythm. This weight represents the contribution of data transmission volume deviation to the behavioral pattern deviation assessment. A larger weight indicates that the determination of abnormal device behavior depends more on changes in data packet size, making it suitable for scenarios that prevent data injection or theft. This represents the contribution of deviations in the timing of various operations to the assessment of behavioral pattern deviation. A higher weight indicates that the determination of abnormal device behavior relies more heavily on the compliance of the operation sequence, making it suitable for scenarios with strict requirements on operation time periods. This weight represents the contribution of deviation from the operational instruction sequence pattern to the assessment of behavioral pattern deviation. A higher weight indicates that the determination of abnormal device behavior relies more on the overall operational pattern's matching degree, making it suitable for scenarios that prevent abnormal operator intent.

[0077] The operation frequency deviation is obtained as follows: the total number of times the device performs weighing operations within the current time window is counted, the difference between the current frequency value and the historical average of the operation frequency of the device under normal operation is taken and the absolute value is divided by the historical standard deviation under normal operation to obtain the deviation multiple in units of standard deviation. The deviation multiple is scaled by a preset normalization coefficient and truncated with an upper limit of 1. The resulting value is the operation frequency deviation, which is used to characterize the abnormal change in the frequency of operation compared to the normal baseline.

[0078] The data traffic deviation is obtained by: statistically analyzing the average data packet length of each transmission during the communication interaction between the device and the external environment within the current time window; subtracting the current average packet length from the historical average data packet length of the device under normal operating conditions and taking the absolute value; then dividing the absolute value by the historical standard deviation under normal operating conditions to obtain the deviation multiple in units of standard deviation; scaling the deviation multiple by a preset normalization coefficient and truncating it with an upper limit of 1; the resulting value is the data traffic deviation, used to characterize the degree of abnormal fluctuation in data transmission volume compared to the normal baseline.

[0079] The time distribution deviation is obtained as follows: the timestamps of various operations of the equipment are counted. These operations include weighing, calibration, parameter modification, and data transmission. For each type of operation, the difference between its current occurrence time and the historical average occurrence time under normal operating conditions is taken, and the absolute value is divided by the historical time standard deviation of the operation under normal operating conditions to obtain the time deviation multiple of the operation. The arithmetic mean of the deviation multiples of each operation type is taken, scaled by a preset normalization coefficient, and truncated with an upper limit of 1. The resulting value is the time distribution deviation, which is used to characterize the degree of abnormal deviation of the time distribution of various operations from the normal time sequence pattern.

[0080] The method for obtaining the instruction sequence deviation is as follows: the operation instructions currently being executed by the device are arranged into an instruction sequence according to their execution order. The current instruction sequence is compared with a preset sliding window one by one. The number of subsequences that match the operation subsequences in the system's pre-stored behavior pattern library is counted. The matching rate is calculated as the ratio of the number of successfully matched subsequences to the total number of patterns in the pattern library. The difference between one and the matching rate is used as the instruction sequence deviation, which is used to characterize the degree of abnormal change in the overall behavior pattern of the operation sequence compared with normal operating habits.

[0081] S3: The comprehensive security risk assessment phase calculates a dynamic security risk level index to determine the overall risk level of the device's current operating environment. This is the core calculation step of the entire risk assessment system and provides real-time risk data support for hazard prediction and situational awareness forecasting. This phase is executed after the behavioral deviation analysis is completed. It integrates three inputs: the initial trust complementarity value when the device connects, the deviation of the current behavioral pattern, and the weighted historical risk factors. The weighted summation yields the comprehensive risk level value. This index updates in real time with the device's operating status and is not a fixed value calculated once. Its value range is limited to 0 to 1 and mapped to different risk intervals according to preset thresholds. This provides a risk level basis for the adaptive configuration of subsequent encryption strategies, realizing a complete assessment closed loop from initial authentication and behavioral monitoring to comprehensive judgment.

[0082] The dynamic security risk level index is calculated using the following formula:

[0083]

[0084] in: As a dynamic safety risk level indicator;

[0085] Historical risk factors;

[0086] These are complementary values ​​to the initial trust level index;

[0087] These are the complementary values ​​of the initial trust level index, the deviation index of behavioral patterns, and the weighting coefficients of historical risk factors, respectively. . Used to adjust the impact of the initial trust level index of the equipment on the overall risk; Used to adjust the degree of impact of behavioral pattern deviation indicators on overall risk; Used to adjust historical risk record indicators The extent of its impact on overall risk.

[0088] The historical risk factor is obtained as follows: The system records the dynamic safety risk level value calculated each time according to a preset assessment cycle, forming a historical risk level sequence arranged chronologically from the earliest record to the most recent record. The system presets an exponential decay coefficient. Based on the current assessment time, the decay weight corresponding to each historical record is an exponential function value with the natural constant as the base and the decay coefficient multiplied by the negative of the interval between the historical record and the current record as the exponent. The larger the interval, the smaller the weight. Each historical risk level value is multiplied by its corresponding decay weight, summed, and then divided by the sum of all decay weights. The resulting ratio is used as the historical risk factor output. The historical risk factor is updated on a rolling basis with the completion of each assessment cycle. That is, the dynamic safety risk level value calculated in each new cycle is added to the end of the historical sequence, while the earliest record is removed, maintaining a constant sequence length. The decay coefficient is preset to a fixed value based on the equipment operating characteristics, so that the impact weight of recent risk events on the current assessment is higher than that of distant events, and the contribution of historical information naturally decreases as the operating time progresses.

[0089] When calculating the dynamic safety risk level index for the first time, the system has no historical risk records, and historical risk factors cannot be obtained using the conventional attenuation weighting method. Therefore, a preset initial value is directly assigned. This initial value is set according to the equipment's factory calibration status and defaults to 0, indicating that no risk prediction is made for unknown historical data in the absence of prior information. After the initial calculation, the obtained dynamic safety risk level value is stored in the historical sequence as the basis for calculating historical risk factors in the next assessment cycle. Subsequently, the system continuously updates historical risk factors using the conventional attenuation weighting method. As the assessment cycle accumulates, historical risk factors gradually move away from the initial state and are driven entirely by the historical risk level records during the actual operation of the equipment, forming a rolling update mechanism that iterates from scratch. The continuous accumulation of the historical sequence gradually frees the calculation of historical risk factors from dependence on the initial preset value, and its value tends to reflect the true risk trend of the equipment's long-term operation.

[0090] S4: Adaptive Configuration Phase for Encryption Strength Level. This phase calculates the encryption strength level value to dynamically adjust the key refresh cycle and encryption algorithm strength level. Performed after the comprehensive security risk assessment, this phase uses the ratio of the current dynamic security risk level indicator to a preset risk threshold as the core driving factor, supplemented by the ratio of the device's currently available computing resources to the computing resources required for the highest encryption strength as constraint factors. The encryption strength level value is calculated and ranges from 0 to 1. This value serves as the basis for encryption strategy configuration. A higher value indicates stronger encryption protection, driving a corresponding shortening of the key refresh cycle and a switch to a higher-strength encryption algorithm combination. Conversely, a lower value extends the key refresh cycle and moderately downgrades the encryption algorithm. This allows for on-demand allocation of device computing resources while ensuring security, forming an adaptive mechanism that adjusts encryption strength bidirectionally with the security situation, providing a configuration basis for subsequent continuous monitoring and dynamic adjustments.

[0091] The encryption strength level value is calculated using the following formula:

[0092]

[0093] in: This represents the encryption strength level value.

[0094] Preset risk benchmark threshold;

[0095] The device's currently available computing resources;

[0096] The computational resources required to perform the highest level of encryption;

[0097] The safety risk ratio reflects the multiple relationship between the current safety risk level and the benchmark risk threshold. When R... When the ratio is 1, the encryption strength level is the normal value; when At this point, the ratio is 2, and after truncation to the upper limit, the encryption strength level is 1, which is the highest level. This ratio ensures that the higher the risk, the greater the encryption strength, achieving a positive correlation between risk assessment results and encryption protection strength; The computing resource ratio reflects the sufficiency of currently available computing resources relative to the highest encryption strength requirement. When available resources are lower than the requirement, this ratio is less than 1, imposing a downgrade constraint on encryption strength to prevent excessive resource consumption for encryption computation from causing device response timeouts or malfunctions, thus balancing the security and operational efficiency of the industrial internet cryptographic system.

[0098] The system determines the key refresh cycle and encryption algorithm combination in different ranges based on the encryption strength level, forming a hierarchical mapping relationship between risk level and encryption strength. The higher the encryption strength level, the shorter the key refresh cycle and the stronger the encryption algorithm, allowing the encryption strategy to dynamically adapt to different security situations. Specifically, when the encryption strength level is in the range [0, 0.3), the device refreshes the key at a preset baseline cycle and uses a lightweight symmetric encryption algorithm to minimize computational resource consumption while meeting basic data security requirements, adapting to the encryption deployment requirements of IoT in low-risk scenarios; when the encryption strength level is in the range [0.3, 0.6), the key refresh cycle is moderately shortened, standard symmetric encryption is used with additional integrity checks, improving data anti-tampering capabilities and strengthening the effectiveness of information protection while ensuring transmission efficiency; when the encryption strength level is in the range [0.6, 0.8), the key refresh cycle is significantly shortened, switching to a lightweight symmetric encryption algorithm. This hybrid encryption and asymmetric encryption scheme leverages the key distribution advantages of asymmetric encryption combined with the high efficiency of symmetric encryption to meet the secure communication needs of medium- to high-risk scenarios. It further enhances the dynamic adaptability of key management and provides multi-layered protection for data. When the encryption strength level is in the range of [0.8, 1.0], the key refresh cycle is shortened to the minimum value. Multiple encryptions are enabled in conjunction with a dynamic key mechanism, and a session key is generated independently for each communication. Even if the key for a single communication is cracked, it will not affect the data security of other communication periods. Thus, it provides the strongest protection at the highest risk level and fully adapts to the extreme security needs of high-risk industrial internet cryptographic scenarios.

[0099] The encryption strength level value is calculated once in each evaluation cycle. After each calculation, the corresponding key refresh and algorithm switching operations are performed according to the range to which the current level value belongs, ensuring that the encryption strategy continuously and dynamically adapts to changes in device operating status and security situation. The continuous two-way adjustment mechanism of the encryption strength level value ensures that the encryption strategy always maintains the best match with the current risk level, avoiding the defects of fixed encryption schemes causing resource waste at low risk and insufficient protection at high risk. It realizes the closed-loop linkage of risk assessment, situation prediction results and encryption protection capabilities, and comprehensively protects information security and data security in IoT and industrial internet scenarios. This embodiment also provides an anti-cheating weighing data encryption transmission system for electronic scales. The encryption transmission system is used to execute the above-mentioned encryption transmission method. The encryption transmission system includes:

[0100] Physical feature authentication module: Physical layer feature and identity association verification stage, calculates the initial trust index of the device to determine the basic trust level when the device is connected; the initial trust index of the device is obtained by weighting the operator's identity features, the physical layer non-cloning features of the device and the security attributes of the operating environment; the physical layer non-cloning features are calculated based on the fluctuation of electrical parameters caused by inherent hardware process deviations, packaging structure features and anti-tamper sensing status, which is different from the logical layer key authentication.

[0101] Behavioral baseline monitoring module: During the self-learning and abnormal deviation analysis phase of the behavioral baseline, the behavioral pattern deviation index is calculated to monitor the statistical deviation between the operating behavior and the self-learning baseline during equipment operation. The system dynamically statistically models the operating behavior under normal operating conditions and continuously updates the baseline. The pattern deviation index is calculated based on the standard deviation distance weighted by the current behavioral parameters relative to the historical baseline mean, covering four-dimensional features: operation frequency, data flow, time distribution, and instruction sequence. By continuously accumulating the deviation, the system achieves the gradual capture of subtle cheating behaviors.

[0102] Comprehensive Risk Assessment Module: In the comprehensive safety risk assessment phase, dynamic safety risk level indicators are calculated to determine the overall risk level of the equipment's current operating environment. The dynamic safety risk level indicators are obtained by fusing complementary values ​​of the initial trust level indicators, behavioral pattern deviation indicators, and weighted historical risk factors. Historical risk factors are weighted using exponential decay, making the impact of recent risk events on the current assessment higher than that of long-term events. The dynamic safety risk level indicators are updated in real time according to the equipment's operating status and are not fixed values ​​after a single calculation.

[0103] The encryption strategy adaptive configuration module calculates the encryption strength level value during the encryption strength level adaptive configuration phase. This value is used to dynamically adjust the key refresh cycle and encryption algorithm strength level. The encryption strength level value is determined by the ratio of the current dynamic security risk level indicator to the preset risk threshold, supplemented by the constraints of available computing resources on the device. When the security risk level increases, the encryption strength level value increases, driving a shorter key refresh cycle and an improved encryption algorithm strength. When the security risk level decreases, the level value decreases, achieving resource conservation and encryption efficiency optimization.

[0104] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0105] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0106] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0107] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for encrypting and transmitting anti-cheating weighing data in electronic scales, characterized in that, Includes the following steps: S1: Physical layer feature and identity association verification stage, calculate the initial trust index of the device to determine the basic trust level when the device is connected; the initial trust index of the device is obtained by weighting the operator's identity features, the physical layer non-cloning features of the device and the security attributes of the operating environment; the physical layer non-cloning features are calculated based on the fluctuation of electrical parameters caused by inherent hardware process deviations, packaging structure features and anti-tamper sensing status, which is different from the logical layer key authentication. S2: Behavioral baseline self-learning and abnormal deviation analysis stage. The behavioral pattern deviation index is calculated to monitor the statistical deviation between the operating behavior and the self-learning baseline during equipment operation. The system dynamically statistically models the operating behavior under normal operating conditions and continuously updates the baseline. The pattern deviation index is calculated based on the standard deviation distance weighted by the current behavioral parameters relative to the historical baseline mean. It covers four-dimensional features: operation frequency, data flow, time distribution, and instruction sequence. The system achieves gradual capture of subtle cheating behaviors by continuously accumulating the deviation. S3: In the comprehensive safety risk assessment phase, a dynamic safety risk level index is calculated to determine the overall risk level of the equipment's current operating environment. The dynamic safety risk level index is obtained by fusing the complementary value of the initial trust level index, the behavioral pattern deviation index, and the weighted historical risk factors. The historical risk factors are weighted using exponential decay, so that the impact of recent risk events on the current assessment has a higher weight than that of long-term events. The dynamic safety risk level index is updated in real time with the equipment's operating status and is not a fixed value calculated once. S4: Adaptive configuration phase of encryption strength level. The encryption strength level value is calculated to dynamically adjust the key refresh cycle and encryption algorithm strength level. The encryption strength level value is determined by the ratio of the current dynamic security risk level indicator to the preset risk threshold, supplemented by the constraints of available computing resources on the device. When the security risk level increases, the encryption strength level value increases, driving the key refresh cycle to shorten and the encryption algorithm strength to improve. When the security risk level decreases, the level value decreases to achieve resource saving and encryption efficiency optimization.

2. The method for encrypting and transmitting anti-cheating weighing data of an electronic scale according to claim 1, characterized in that: The initial trust level index of the device is calculated according to the following formula: in: This refers to the initial trust level index of the device. Operator identity characteristic factor; This is a physical layer non-clonable characteristic factor; For runtime environment security attribute factors; These are the weighting coefficients for the operator identity characteristic factor, the physical layer non-cloning characteristic factor, and the operating environment security attribute factor, respectively. .

3. The method for encrypted transmission of anti-cheating weighing data of an electronic scale according to claim 2, characterized in that: The operator identity feature factor is obtained by collecting three biometric features: face, fingerprint, and voiceprint. The ratio of the number of matching biometric features to the total number of biometric features collected is calculated and combined with the liveness detection pass rate. The average value is used to confirm that the operator's identity is legitimate and that the operator is a real living person. The physical layer non-cloning feature factor is obtained by reading the matching degree between the current physical layer electrical response deviation value caused by the inherent process deviation of the hardware and the standard electrical response deviation value registered at the factory. It is combined with the packaging structure coding consistency test results and the integrity status of the tamper switch. It is used to verify that the device's physical identity is unique and its structure is intact. The operating environment security attribute factor is obtained by determining whether the current geographical location falls within the authorized area and whether the current time is within the preset security period, and by evaluating the average value of network signal strength, encryption protocol version and abnormal sniffing. It is used to ensure that the device completes authentication only in a safe and reliable time, space and network environment.

4. The method for encrypted transmission of anti-cheating weighing data of an electronic scale according to claim 1, characterized in that: The behavioral pattern deviation index is calculated using the following formula: in: Deviation in operation frequency; Data traffic deviation; The deviation of the time distribution; This refers to the deviation of the instruction sequence. These are the weighting coefficients for operation frequency deviation, data flow deviation, time distribution deviation, and instruction sequence deviation, respectively. .

5. The method for encrypted transmission of anti-cheating weighing data of an electronic scale according to claim 4, characterized in that: The method for obtaining the operation frequency deviation is as follows: count the number of operations within the current time window, calculate the standard deviation distance of the operation relative to the historical mean and standard deviation under normal operating conditions, and obtain the result after normalization, which reflects the abnormal changes in the frequency of operations. The data traffic deviation is obtained by: statistically analyzing the average data packet size within the current time window, calculating the standard deviation distance relative to the historical mean and standard deviation under normal operating conditions, and normalizing the result to reflect abnormal fluctuations in data transmission volume; the time distribution deviation is obtained by: statistically analyzing the timestamps of various operations, calculating the standard deviation distance of the current occurrence time of each operation relative to the historical average time of that type of operation under normal operating conditions, taking the average of all operation types and normalizing the result to reflect abnormal shifts in the operation time sequence distribution; the instruction sequence deviation is obtained by: performing pattern matching between the current operation instruction sequence and the historical behavior pattern library established by the system through normal operation, using the proportion of failed matches as a measure to reflect abnormal changes in the overall behavior pattern of the operation sequence.

6. The method for encrypting and transmitting anti-cheating weighing data of an electronic scale according to claim 1, characterized in that: The dynamic security risk level index is calculated using the following formula: in: As a dynamic safety risk level indicator; Historical risk factors; These are complementary values ​​to the initial trust level index; These are the complementary values ​​of the initial trust level index, the behavioral pattern deviation index, and the weighting coefficients of historical risk factors, respectively. .

7. The method for encrypted transmission of anti-cheating weighing data of an electronic scale according to claim 6, characterized in that: The historical risk factors are obtained as follows: the system records the dynamic safety risk level value calculated each time according to a preset assessment cycle, forming a historical risk level sequence arranged in chronological order, from the earliest record to the most recent record; The system presets an exponential decay coefficient. Based on the current assessment time, the decay weight corresponding to each historical record is an exponential function value with the natural constant as the base and the decay coefficient multiplied by the negative of the interval between the historical record and the current record as the exponent. The larger the interval, the smaller the weight. The historical risk level value is multiplied by its corresponding decay weight, summed, and then divided by the sum of all decay weights. The resulting ratio is used as the historical risk factor output. The historical risk factor is updated on a rolling basis with the completion of each assessment cycle, and the decay coefficient is preset to a fixed value based on the equipment operating characteristics. This ensures that the impact of recent risk events on the current assessment is higher than that of long-term events, and the contribution of historical information naturally decreases as the operating time progresses. When calculating the dynamic safety risk level index for the first time, the system has no historical risk records and the historical risk factors cannot be obtained by the conventional attenuation weighting method, so a preset initial value is directly assigned. After the first calculation is completed, the obtained risk level value is stored in the historical sequence as the basis for calculating the historical risk factors in subsequent evaluation cycles.

8. The method for encrypting and transmitting anti-cheating weighing data of an electronic scale according to claim 1, characterized in that: The encryption strength level value is calculated using the following formula: in: This represents the encryption strength level value. Preset risk benchmark threshold; The device's currently available computing resources; The computational resources required to perform the highest level of encryption; For safety risk ratio; This refers to the ratio of computing resources.

9. The method for encrypted transmission of anti-cheating weighing data of an electronic scale according to claim 8, characterized in that: The system determines the key refresh cycle and encryption algorithm combination in different ranges based on the encryption strength level value. When the encryption strength level value is in the range of [0, 0.3), the device refreshes the key at a preset baseline cycle and enables a lightweight symmetric encryption algorithm. When it is in the range of [0.3, 0.6), the key refresh cycle is slightly shortened, and standard symmetric encryption with additional integrity verification is used. When it is in the range of [0.6, 0.8), the key refresh cycle is significantly shortened, and a hybrid scheme of symmetric and asymmetric encryption is switched to. When it is in the range of [0.8, 1.0], the key refresh cycle is shortened to the minimum value, and multiple encryption combined with a dynamic key mechanism is enabled. The higher the encryption strength level value, the shorter the key refresh cycle and the stronger the encryption algorithm, forming a hierarchical mapping relationship between risk level and encryption strength.

10. An anti-cheating weighing data encryption transmission system for electronic scales, characterized in that: The encrypted transmission system is used to execute the encrypted transmission method as described in any one of claims 1-9, and the encrypted transmission system comprises: Physical feature authentication module: Physical layer feature and identity association verification stage, calculates the initial trust index of the device to determine the basic trust level when the device is connected; the initial trust index of the device is obtained by weighting the operator's identity features, the physical layer non-cloning features of the device and the security attributes of the operating environment; the physical layer non-cloning features are calculated based on the fluctuation of electrical parameters caused by inherent hardware process deviations, packaging structure features and anti-tamper sensing status, which is different from the logical layer key authentication. Behavioral baseline monitoring module: During the self-learning and abnormal deviation analysis phase of the behavioral baseline, the behavioral pattern deviation index is calculated to monitor the statistical deviation between the operating behavior and the self-learning baseline during equipment operation. The system dynamically statistically models the operating behavior under normal operating conditions and continuously updates the baseline. The pattern deviation index is calculated based on the standard deviation distance weighted by the current behavioral parameters relative to the historical baseline mean, covering four-dimensional features: operation frequency, data flow, time distribution, and instruction sequence. By continuously accumulating the deviation, the system achieves the gradual capture of subtle cheating behaviors. Comprehensive Risk Assessment Module: In the comprehensive safety risk assessment phase, dynamic safety risk level indicators are calculated to determine the overall risk level of the equipment's current operating environment. The dynamic safety risk level indicators are obtained by fusing complementary values ​​of the initial trust level indicators, behavioral pattern deviation indicators, and weighted historical risk factors. Historical risk factors are weighted using exponential decay, making the impact of recent risk events on the current assessment higher than that of long-term events. The dynamic safety risk level indicators are updated in real time according to the equipment's operating status and are not fixed values ​​after a single calculation. The encryption strategy adaptive configuration module calculates the encryption strength level value during the encryption strength level adaptive configuration phase. This value is used to dynamically adjust the key refresh cycle and encryption algorithm strength level. The encryption strength level value is determined by the ratio of the current dynamic security risk level indicator to the preset risk threshold, supplemented by the constraints of available computing resources on the device. When the security risk level increases, the encryption strength level value increases, driving a shorter key refresh cycle and an improved encryption algorithm strength. When the security risk level decreases, the level value decreases, achieving resource conservation and encryption efficiency optimization.

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