A security monitoring and early warning method and system for an archival storage environment

Abstract

The application provides a security monitoring and early warning method and system for an archive storage environment, extracts an environment steady-state baseline of each sampling period from environment parameter information of the archive storage environment, determines a periodic fluctuation feature of the archive storage environment according to all the environment steady-state baselines, and further obtains an environment degradation index of the archive storage space; when the environment degradation index is greater than an environment warning value of the archive storage space, a detection frame is sent to each sensor node at an arithmetic sequence time interval through a network coordinator, a response time delay of each sensor node is recorded until the standard deviation of all the response time delays converges within a stable threshold of the archive storage environment, a node response feature vector of the archive storage environment is constructed according to all the response time delays; the response time delay is corrected according to the node response feature vector, and a security early warning level of the archive storage environment is generated. Based on the above scheme, dynamic verification of the credibility of sensor data in the archive storage environment can be realized.

Description

Technical Field

[0001] This application relates to the field of archive storage security technology, and more specifically, to a security monitoring and early warning method and system for archive storage environments. Background Technology

[0002] Archival storage refers to the long-term preservation of various archival media, such as paper archives, film archives, and electronic archives, in dedicated warehouses that meet specific temperature, humidity, air quality, and security standards. The warehouses must maintain constant environmental parameters to prevent archives from becoming damp, moldy, infested by insects, or corroded by harmful gases, ensuring the enduring protection of the physical integrity and readability of the archives.

[0003] Existing methods for monitoring archival storage environments typically treat environmental parameter acquisition and equipment status inspection as two separate technical steps, lacking an effective feedback mechanism between them. In actual deployments, wireless sensor nodes, operating in the constant temperature and humidity or low-ventilation microenvironment of storage rooms, are highly susceptible to corrosion from slowly penetrating moisture or chemical gases, leading to drift in the measurement reference of the sensing unit. Simultaneously, dust accumulation and aging on the surface of temperature and humidity sensors, as well as intermittent disconnections caused by channel contention or signal attenuation in the wireless communication module, all contribute to varying degrees of distortion in the temperature, humidity, and air quality data reported by the sensor nodes. However, existing monitoring methods directly use this collected environmental parameter information as true values ​​for quantitative assessment and early warning decisions regarding environmental degradation, lacking a verification process to ensure the reliability of the data source. When sensor nodes output abnormal values ​​that deviate from reality due to moisture, corrosion, or communication failure, existing methods cannot distinguish whether the abnormality is caused by the deterioration of the actual environment or by the failure of the sensor itself. This can easily trigger false alarms based on distorted data, or cause alarm signals to be missed due to node failure when the actual environment has deteriorated. This seriously reduces the accuracy and reliability of security warnings for archive storage environments. Summary of the Invention

[0004] This application provides a security monitoring and early warning method and system for archival storage environments, which can realize dynamic verification of the reliability of sensor data in archival storage environments, thereby improving the accuracy of security early warning for archival storage environments.

[0005] Firstly, this application provides a security monitoring and early warning method for an archive storage environment, including: Collect temperature, humidity and air quality data from each sensor node within a specified time period to obtain environmental parameter information of the archive storage environment; The environmental steady-state baselines for each sampling period are extracted from the environmental parameter information. Based on all environmental steady-state baselines, the periodic fluctuation characteristics of the archive storage environment are determined. The transient information of each sensor node at the current moment is compared with the periodic fluctuation characteristics to obtain the environmental degradation index of the archive storage space. When the environmental degradation index is greater than the environmental warning value of the archive storage space, the network coordinator sends detection frames to each sensor node at equal time intervals, records the response delay of each sensor node, until the standard deviation of all response delays converges to within the stable threshold of the archive storage environment, and constructs the node response feature vector of the archive storage environment based on all response delays. The environmental degradation index is corrected by the node response feature vector, thereby generating a security warning level for the archive storage environment.

[0006] In some embodiments, extracting the environmental steady-state baseline for each sampling period from the environmental parameter information specifically includes: The environmental parameter information is divided into multiple time-period data blocks according to a preset sampling period; Extract the initial steady-state values ​​of each sampling period from the temperature, humidity and air quality data within each time period data block; All initial steady-state values ​​are arranged in chronological order, and outlier periods are identified, removed, and smoothed sequentially to obtain the environmental steady-state baseline for each sampling period.

[0007] In some embodiments, determining the periodic fluctuation characteristics of the archive storage environment based on all environmental steady-state baselines specifically includes: The values ​​of the same environmental factor in different sampling periods are obtained from all environmental steady-state baselines to obtain the time series of each environmental factor. Identify the recurring maxima and minima in each time series to obtain the fluctuation amplitude of each environmental factor; The time span between adjacent maxima and adjacent minima in each time series is statistically analyzed to obtain the fluctuation cycle of each environmental factor. By combining the fluctuation amplitude and fluctuation period of each environmental factor, the periodic fluctuation characteristics of the archive storage environment are obtained.

[0008] In some embodiments, comparing the transient information of each sensor node at the current moment with the periodic fluctuation characteristics to obtain the environmental degradation index of the archive storage space specifically includes: Acquire the transient values ​​of temperature, humidity, and air quality collected by each sensor node at the current moment; Calculate the percentage deviation of the transient value from the fluctuation amplitude of the corresponding environmental factor in the periodic fluctuation characteristics; The deviation percentages of all environmental factors within the same sensor node are fused to obtain the single-point degradation degree of each sensor node. By spatially interpolating and synthesizing the individual degradation levels of all sensor nodes, the environmental degradation index of the archive storage space is obtained.

[0009] In some embodiments, recording the response delay of each sensor node specifically includes: For each sensor node, obtain the timestamp of the probe frame sent by the network coordinator to the sensor node to obtain the sending time point; After receiving the detection frame, the sensor node immediately returns a response frame carrying the reception timestamp, and the network coordinator records the arrival time of the response frame. The round-trip response delay of each sensor node is obtained by taking half of the difference between the arrival time and the transmission time.

[0010] In some embodiments, constructing the node response feature vector of the archive storage environment based on all response latencies specifically includes: For each sensor node, calculate the mathematical expectation of all response delays of the sensor node within a specified time period to obtain the average response delay of the sensor node. Calculate the standard deviation of all response delays of the sensor node within a specified time period to obtain the response jitter amplitude of the sensor node; Extract the number of anomalies in the response delay of the sensor node that exceed the preset delay tolerance limit within a specified time period to obtain the communication instability frequency of the sensor node; The node response characteristics of the sensor nodes are determined by the average response delay, the response jitter amplitude, and the communication instability frequency, thereby obtaining the node response characteristics of each sensor node. The node response feature vector of the archive storage environment is determined based on all node response characteristics.

[0011] In some embodiments, the process of correcting the environmental degradation index using the node response feature vector to generate a security warning level for the archive storage environment specifically includes: The node response features of each sensor node in the node response feature vector are input into a preset regression model, and the confidence decay coefficient of each sensor node is output. The confidence attenuation coefficient of each sensor node is used to reduce the weight of the single-point degradation degree when participating in the synthesis of the environmental degradation index. Then, the environmental degradation index is recalculated with the reduced single-point degradation degree to obtain the environmental degradation index after response correction. The corrected environmental degradation index is input into the grading mapper, which outputs the security warning level of the archive storage environment.

[0012] Secondly, this application provides a security monitoring and early warning system for an archive storage environment, comprising: The data acquisition module is used to collect temperature, humidity and air quality data from each sensor node within a specified time period to obtain environmental parameter information of the archive storage environment. The processing module is used to extract the environmental steady-state baseline of each sampling period from the environmental parameter information, determine the periodic fluctuation characteristics of the archive storage environment based on all environmental steady-state baselines, and compare the transient information of each sensor node at the current moment with the periodic fluctuation characteristics to obtain the environmental degradation index of the archive storage space. The processing module is also used to send detection frames to each sensor node at equal time intervals through the network coordinator when the environmental degradation index is greater than the environmental warning value of the archive storage space, record the response delay of each sensor node, until the standard deviation of all response delays converges to within the stable threshold of the archive storage environment, and construct the node response feature vector of the archive storage environment based on all response delays. The execution module is used to respond to and correct the environmental degradation index through the node response feature vector, thereby generating a security warning level for the archive storage environment.

[0013] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device performs the above-described security monitoring and early warning method for an archive storage environment.

[0014] Fourthly, this application provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the aforementioned security monitoring and early warning method for an archive storage environment.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: This application provides a method and system for security monitoring and early warning of archival storage environments. The method involves collecting temperature, humidity, and air quality data from various sensor nodes within a specified time period to obtain environmental parameter information for the archival storage environment. From this environmental parameter information, a steady-state baseline for each sampling period is extracted. Based on all steady-state baselines, the periodic fluctuation characteristics of the archival storage environment are determined. The transient information of each sensor node at the current moment is compared with the periodic fluctuation characteristics to obtain an environmental degradation index for the archival storage space. When the environmental degradation index exceeds the environmental warning value for the archival storage space, a network coordinator sends detection frames to each sensor node at arithmetic time intervals, recording the response delay of each sensor node until the standard deviation of all response delays converges to within the stable threshold of the archival storage environment. A node response feature vector for the archival storage environment is constructed based on all response delays. The environmental degradation index is then corrected using the node response feature vector to generate a security early warning level for the archival storage environment.

[0016] Therefore, in this application, the environmental degradation index is corrected by the node response feature vector to generate a security warning level for the archive storage environment. First, determining the environmental degradation index yields a comprehensive quantitative evaluation result of the environmental state deviating from its periodic fluctuation characteristics within the archive storage space, thus providing a trigger basis and benchmark for dynamic verification of sensor data reliability. The environmental degradation index, through spatial synthesis of the deviation between the transient information of each sensor node and the historical steady-state baseline, can reflect the overall abnormality of the warehouse environment at a macroscopic level. When the index exceeds the warning value, the environmental parameters have shown a significant deviation beyond the normal fluctuation range. Initiating communication detection can accurately pinpoint the coupling moment between environmental anomalies and sensor failures, avoiding unnecessary communication inspections when the environment is normal. At the same time, the spatial distribution characteristics of the environmental degradation index can indicate the area where sensor nodes are more likely to output distorted data due to moisture or corrosion, defining the scope of concern for communication status verification. Then, the node... The response feature vector provides a set of quantifiable features characterizing the communication performance of each sensor node from three dimensions: average response delay, response jitter amplitude, and communication instability frequency. This enables dynamic verification of sensor data reliability. The node response feature vector statistically models the response delay recorded during arithmetic interval detection, transforming the communication quality degradation caused by moisture, aging, or channel attenuation of wireless sensor nodes into quantifiable multidimensional feature parameters. When a node exhibits persistently high response delay, increased jitter amplitude, or increased instability frequency, this vector accurately characterizes the degree of communication degradation of that node, providing a numerical basis for calculating the confidence attenuation coefficient. This proportionally reduces the distortion component contributed by untrusted nodes in the environmental degradation index, ensuring effective verification of the reliability of the environmental data used for early warning decisions at the communication level. In summary, based on the above scheme, dynamic verification of sensor data reliability in archival storage environments can be achieved, thereby improving the accuracy of security early warnings for archival storage environments. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is an exemplary flowchart of a security monitoring and early warning method for an archive storage environment, according to some embodiments of this application; Figure 2 This is a flowchart illustrating the process of determining the environmental degradation index according to some embodiments of this application; Figure 3This is a schematic diagram of the structure of a security monitoring and early warning system for an archive storage environment, according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a computer device that implements a security monitoring and early warning method for an archive storage environment, according to some embodiments of this application. Detailed Implementation

[0019] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] refer to Figure 1 The figure is an exemplary flowchart of a security monitoring and early warning method for an archive storage environment according to some embodiments of this application. The security monitoring and early warning method for an archive storage environment mainly includes the following steps: In step 101, temperature, humidity and air quality data of each sensor node are collected within a specified time period to obtain environmental parameter information of the archive storage environment.

[0021] It should be noted that, in this application, the environmental parameter information is a set of raw data reflecting the overall temperature, humidity, and air quality of the archive storage environment within a specified time period. In specific implementation, sensor nodes are deployed on the exterior walls, windows, air conditioning vents, and return air vents of the archive storage room. At the same time, sensor nodes are deployed at the center of each shelf of the archive mobile shelving and inside the storage equipment. Based on the net height of the storage room, sensor nodes are arranged in a uniform grid pattern on the horizontal plane according to the upper, middle, and lower height layers. The temperature, humidity, and air quality values ​​output by these sensor nodes are continuously collected through a data gateway at a fixed time step. After binding the collected values ​​with the device identifier and sampling timestamp of the corresponding sensor node, a multi-dimensional time-series data set can be obtained. This multi-dimensional time-series data set is used as the environmental parameter information.

[0022] In step 102, the steady-state baseline of each sampling period is extracted from the environmental parameter information. The periodic fluctuation characteristics of the archive storage environment are determined based on all the steady-state baselines. The transient information of each sensor node at the current moment is compared with the periodic fluctuation characteristics to obtain the environmental degradation index of the archive storage space.

[0023] In some embodiments, extracting the environmental steady-state baseline for each sampling period from the environmental parameter information can be achieved using the following steps: The environmental parameter information is divided into multiple time-period data blocks according to a preset sampling period; Extract the initial steady-state values ​​of each sampling period from the temperature, humidity and air quality data within each time period data block; All initial steady-state values ​​are arranged in chronological order, and outlier periods are identified, removed, and smoothed sequentially to obtain the environmental steady-state baseline for each sampling period.

[0024] It should be noted that, in this application, the time period data block is a subset of all environmental factor values ​​within a single sampling period; the initial steady-state value is a benchmark value characterizing the central trend of temperature, humidity and air quality data within a single time period data block; and the environmental steady-state baseline is a sequence of benchmark values ​​reflecting the stable level of temperature, humidity and air quality in the archive storage environment within a single sampling period.

[0025] In practice, the sampling period is first set to 30 minutes. Starting from the earliest timestamp in the environmental parameter information, each 30-minute period is divided into a time interval. Temperature, humidity, and air quality values ​​falling within the same time interval are grouped into a data subset, which is then used as the time interval data block. Next, the median of temperature, humidity, and air quality values ​​within each time interval data block is calculated. The three medians are combined into a ternary value set, which is used as the initial steady-state value for the corresponding sampling period of the time interval data block. Finally, all initial steady-state values ​​are arranged into a time series according to the chronological order of the sampling periods. The Euclidean distance between the ternary value set of each initial steady-state value in the time series and the overall mean of the series is calculated. Initial steady-state values ​​whose Euclidean distance exceeds three times the standard deviation of the overall Euclidean distance of the series are marked as outliers and removed. The remaining initial steady-state value series after removal is smoothed using a moving average method with a moving window width of 5 periods. The smoothed value series is used as the environmental steady-state baseline for the corresponding sampling period. The environmental steady-state baseline for each sampling period can be obtained through the above method.

[0026] In some embodiments, determining the periodic fluctuation characteristics of the archive storage environment based on all environmental steady-state baselines can be achieved using the following steps: The values ​​of the same environmental factor in different sampling periods are obtained from all environmental steady-state baselines to obtain the time series of each environmental factor. Identify the recurring maxima and minima in each time series to obtain the fluctuation amplitude of each environmental factor; The time span between adjacent maxima and adjacent minima in each time series is statistically analyzed to obtain the fluctuation cycle of each environmental factor. By combining the fluctuation amplitude and fluctuation period of each environmental factor, the periodic fluctuation characteristics of the archive storage environment are obtained.

[0027] It should be noted that, in this application, the time series of environmental factors is a one-dimensional numerical sequence describing the evolution of the steady-state value of a single environmental factor over time in all sampling periods; the fluctuation amplitude is a numerical value that measures the strength of the fluctuation of a single environmental factor around the central level in its time series; the fluctuation period is a numerical value that measures the time span during which a single environmental factor completes one full fluctuation in its time series; and the periodic fluctuation characteristics are a set of parameters that describe the regular fluctuation characteristics of all environmental factors in the archive storage environment. In practice, firstly, from the environmental steady-state baseline ternary numerical set of all sampling periods, the steady-state values ​​of temperature, humidity, and air quality are extracted separately and arranged in chronological order of the sampling periods to obtain three independent one-dimensional numerical sequences. Each sequence is used as the time series of the corresponding environmental factor. Secondly, peak detection is performed on the time series of each environmental factor. High points in the sequence where the value is greater than the two adjacent values ​​are recorded as maxima, and low points where the value is less than the two adjacent values ​​are recorded as minima. The arithmetic mean of all maxima and the arithmetic mean of all minima are calculated. The difference between the values ​​is taken as the fluctuation amplitude of the environmental factor. Then, for each environmental factor's time series, the time interval between two adjacent maxima and the time interval between two adjacent minima are calculated. The arithmetic mean of all the intervals between maxima and minima is taken as the fluctuation period of the environmental factor. Finally, the fluctuation amplitude and fluctuation period of the temperature factor, the fluctuation amplitude and fluctuation period of the humidity factor, and the fluctuation amplitude and fluctuation period of the air quality factor are arranged in sequence to obtain the combined data containing 6 parameters. This combined data is taken as the periodic fluctuation characteristics of the archive storage environment.

[0028] In some embodiments, the transient information of each sensor node at the current moment is compared with the periodic fluctuation characteristics to obtain the environmental degradation index of the archive storage space, with reference to... Figure 2 The diagram is a flowchart illustrating the process of determining the environmental degradation index in some embodiments of this application. In this embodiment, the environmental degradation index can be determined using the following steps: In step 1021, the transient values ​​of temperature, humidity and air quality collected by each sensor node at the current moment are obtained; In step 1022, the percentage deviation of the transient value from the fluctuation amplitude of the corresponding environmental factor in the periodic fluctuation characteristic is calculated; In step 1023, the deviation percentages of all environmental factors within the same sensor node are fused to obtain the single-point degradation degree of each sensor node. In step 1024, the single-point degradation degree of all sensor nodes is spatially interpolated and synthesized to obtain the environmental degradation index of the archive storage space.

[0029] It should be noted that, in this application, transient values ​​are real-time data reflecting the measured temperature, humidity and air quality of a single sensor node at the current sampling moment; deviation percentage is a ratio of the transient value of a single environmental factor to the degree of deviation of its fluctuation amplitude; single-point degradation degree is a comprehensive indicator for evaluating the degree of environmental degradation at the location of a single sensor node; and environmental degradation index is a comprehensive evaluation value that quantifies the overall deviation of the environmental state within the entire archive storage space.

[0030] In specific implementation, firstly, for each sensor node deployed in the archive storage room, the latest temperature, humidity, and air quality measurements uploaded through the data gateway at the current sampling time are read. These three measurements are combined into a ternary value set, which is used as the transient value of that sensor node. Secondly, the fluctuation amplitudes of the temperature factor, humidity factor, and air quality factor are obtained from the six parameter combinations of the periodic fluctuation characteristics. The ratio of the transient temperature measurement value to the fluctuation amplitude of the temperature factor is used as the temperature deviation percentage; the ratio of the transient humidity measurement value to the fluctuation amplitude of the humidity factor is used as the humidity deviation percentage; and the ratio of the transient air quality measurement value to the fluctuation amplitude of the air quality factor is used as the air quality deviation percentage. The percentage deviation is calculated first; then, the percentage deviations in temperature, humidity, and air quality corresponding to each sensor node are weighted and summed. The weighting method is preset to analyze the correlation between various environmental factors and archive damage in historical data, calculate the contribution rate of each factor using principal component analysis, normalize the contribution rate and use it as the weight, and use the weighted sum as the single-point degradation degree of the sensor node. Finally, using the single-point degradation degree of all sensor nodes as known point values, the inverse distance weighted interpolation method is used to estimate the numerical values ​​of the locations in the archive storage space not directly covered by the sensor nodes, generating a three-dimensional degradation distribution covering the entire archive storage space. The volume integral of all spatial location values ​​of the three-dimensional degradation distribution is divided by the total volume of the archive storage space, and the arithmetic mean of the result is used as the environmental degradation index.

[0031] In step 103, when the environmental degradation index is greater than the environmental warning value of the archive storage space, the network coordinator sends detection frames to each sensor node at equal time intervals, records the response delay of each sensor node, until the standard deviation of all response delays converges to within the stable threshold of the archive storage environment, and constructs the node response feature vector of the archive storage environment based on all response delays.

[0032] It should be noted that, in this application, the increase in the degradation index of the archive storage environment is often accompanied by abnormal operation of environmental control equipment or changes in the sealing structure of the warehouse. This physical disturbance will cause changes in the multipath effect and signal attenuation of the wireless communication channel. By sending detection frames to the sensor node with arithmetic progression and recording the response delay, the network response characteristics under different communication loads and channel competition conditions can be captured. The fluctuation of the response delay directly reflects the change law of communication stability of the wireless sensor node after being affected by environmental degradation, providing a quantitative basis for correcting the communication quality of the environmental degradation index through the node response feature vector.

[0033] In practice, the environmental warning value is pre-set to the 95th percentile of the historical environmental degradation index. When the environmental degradation index exceeds this warning value, the network coordinator initiates an active detection process, sequentially sending detection frames carrying transmission timestamps to all sensor nodes in the archive. The initial transmission interval for the first round of detection is 50 milliseconds, and the transmission interval is increased by an arithmetic step of 50 milliseconds after each subsequent round of detection. After receiving the detection frame, each sensor node immediately sends back a response frame carrying both the reception and transmission timestamps. The network coordinator records the arrival time of the response frame and uses half of the difference between the arrival time and the transmission timestamp as the round-trip response delay for that detection. This process is repeated for multiple rounds of arithmetic interval detection until the standard deviation of the response delay of all sensor nodes in the same round converges to within 0.5 milliseconds. The round-trip response delay value recorded in the last round is then used as the response delay of each sensor node.

[0034] Preferably, in some embodiments, recording the response delay of each sensor node can be achieved using the following steps: For each sensor node, obtain the timestamp of the probe frame sent by the network coordinator to the sensor node to obtain the sending time point; After receiving the detection frame, the sensor node immediately returns a response frame carrying the reception timestamp, and the network coordinator records the arrival time of the response frame. The round-trip response delay of each sensor node is obtained by taking half of the difference between the arrival time and the transmission time.

[0035] It should be noted that, in this application, the sending time point is the precise moment when the network coordinator sends a probe frame to the sensor node; the response latency is a value that measures the time taken for a single round-trip communication between the network coordinator and a single sensor node.

[0036] In practice, firstly, for each sensor node, when the network coordinator prepares to send a probe frame to the target sensor node, it reads its own real-time clock and embeds this clock reading as a transmission timestamp into the data field of the probe frame, recording the time of the transmission timestamp as the transmission time point. Then, after receiving the probe frame, the target sensor node immediately extracts the transmission timestamp from the probe frame, reads its own real-time clock as the reception timestamp, generates a response frame containing both the transmission and reception timestamps, and sends it back via the wireless channel. When the network coordinator receives this response frame, it reads its own real-time clock and records the clock reading as the arrival time. Finally, it calculates the time difference between the arrival time and the transmission time point during a single probe, divides the time difference by 2, and uses the quotient as the round-trip response delay for that probe. By performing three consecutive probes on the same sensor node and arranging the values ​​of the three round-trip response delays sequentially, a sequence containing three values ​​is obtained. This sequence is used as the response delay of the sensor node. The response delay of each sensor node can be obtained in the above manner.

[0037] In some embodiments, constructing the node response feature vector of the archive storage environment based on all response latencies can be achieved using the following steps: For each sensor node, calculate the mathematical expectation of all response delays of the sensor node within a specified time period to obtain the average response delay of the sensor node. Calculate the standard deviation of all response delays of the sensor node within a specified time period to obtain the response jitter amplitude of the sensor node; Extract the number of anomalies in the response delay of the sensor node that exceed the preset delay tolerance limit within a specified time period to obtain the communication instability frequency of the sensor node; The node response characteristics of the sensor nodes are determined by the average response delay, the response jitter amplitude, and the communication instability frequency, thereby obtaining the node response characteristics of each sensor node. The node response feature vector of the archive storage environment is determined based on all node response characteristics.

[0038] It should be noted that in this application, the average response delay is a value that measures the central trend of the communication response speed of a single sensor node within a specified time period; the response jitter amplitude is a value that measures the degree of fluctuation in the communication time consumption of a single sensor node within a specified time period; the communication instability frequency is a count value that measures the frequency of communication anomalies of a single sensor node within a specified time period; the node response feature is a three-dimensional data set that characterizes the communication state of a single sensor node from three dimensions: delay level, fluctuation degree, and anomaly frequency; the node response feature vector is a multi-dimensional data structure that integrates the communication state features of all sensor nodes in the archive storage environment; in the specific implementation, the default value of the specified time period is set to all round-trip response delays recorded in the most recent 1 hour. This time period length matches the calculation cycle of a single environmental degradation index, and can cover all detection records generated by multiple rounds of arithmetic interval detection, ensuring that the number of response delay samples participating in the statistical calculation is sufficient and the timeliness meets the requirements of real-time early warning.

[0039] In practice, firstly, all round-trip response delay values ​​recorded by the sensor node within a specified time period are summed. The sum is then divided by the number of round-trip response delays, and the quotient is used as the average response delay of the sensor node. Secondly, using all round-trip response delay values ​​and the average response delay of the sensor node within the specified time period, the square of the difference between each round-trip response delay and the average response delay is calculated. All squares are summed and divided by the number of round-trip response delays. The square root of this quotient is then taken as the response jitter amplitude of the sensor node. Finally, with a preset latency tolerance limit of 10 milliseconds, the response jitter amplitude of each round-trip response delay of the sensor node within the specified time period is compared one by one. The relationship between round-trip response delay and 10 milliseconds is analyzed. The number of times the round-trip response delay value is greater than 10 milliseconds is counted, and this count value is used as the communication instability frequency of the sensor node. Then, the average response delay, response jitter amplitude, and communication instability frequency of each sensor node are arranged in sequence and concatenated into a three-dimensional numerical group. This three-dimensional numerical group is used as the node response feature of the sensor node. Finally, the node response features corresponding to all sensor nodes in the archive are arranged in the order of the sensor node device identifier and combined into a multi-dimensional data structure containing the three-dimensional numerical groups of all sensor nodes. This multi-dimensional data structure is used as the node response feature vector of the archive storage environment.

[0040] In step 104, the environmental degradation index is corrected by the node response feature vector to generate a security warning level for the archive storage environment.

[0041] In some embodiments, generating a security warning level for the archive storage environment by correcting the environmental degradation index using the node response feature vector can be achieved through the following steps: The node response features of each sensor node in the node response feature vector are input into a preset regression model, and the confidence decay coefficient of each sensor node is output. The confidence attenuation coefficient of each sensor node is used to reduce the weight of the single-point degradation degree when participating in the synthesis of the environmental degradation index. Then, the environmental degradation index is recalculated with the reduced single-point degradation degree to obtain the environmental degradation index after response correction. The corrected environmental degradation index is input into the grading mapper, which outputs the security warning level of the archive storage environment.

[0042] It should be noted that, in this application, the confidence decay coefficient is a weighted correction factor that quantifies the degree of decline in the reliability of environmental data of a single sensor node due to the degradation of its communication status; the response-corrected environmental degradation index is an evaluation value that reflects the overall deviation of the environment within the archive storage space; and the security warning level is a graded indicator that indicates the severity of the current comprehensive risk in the archive storage environment.

[0043] In practice, firstly, a training sample set with communication fault labels and historical environmental data deviation labels is used to train a multiple linear regression model with average response delay, response jitter amplitude, and communication instability frequency as input variables and data confidence attenuation ratio as output variable. The three-dimensional numerical set of node response characteristics of each sensor node is input into this regression model to calculate a value between 0 and 1, which is used as the confidence attenuation coefficient for that sensor node. Then, the single-point degradation degree of each sensor node is multiplied by the corresponding confidence attenuation coefficient, and the product is used as the reduced single-point degradation degree. The reduced single-point degradation degree replaces the original single-point degradation degree, and a new three-dimensional degradation distribution covering the entire archive storage space is generated using inverse distance weighted interpolation. The numerical values ​​of all spatial locations of this three-dimensional degradation distribution are then calculated. After volume integration, the result is divided by the total volume of the archive storage space, and the resulting arithmetic mean is used as the response-corrected environmental degradation index. Finally, three warning thresholds are pre-set. These thresholds are obtained by statistically analyzing the distribution of all response-corrected environmental degradation index values ​​over a historical period, taking the 60th percentile as the first threshold, the 80th percentile as the second threshold, and the 95th percentile as the third threshold. The response-corrected environmental degradation index is compared sequentially with the three thresholds. If the index is less than the first threshold, a normal level is output; if the index is greater than or equal to the first threshold and less than the second threshold, a concern level is output; if the index is greater than or equal to the second threshold and less than the third threshold, a warning level is output; and if the index is greater than or equal to the third threshold, a danger level is output. The output level is used as the safety warning level for the archive storage environment.

[0044] In another aspect, in some embodiments, this application provides a security monitoring and early warning system for an archive storage environment, with reference to... Figure 3 The figure is a schematic diagram of the structure of a security monitoring and early warning system for an archive storage environment according to some embodiments of this application. The security monitoring and early warning system for an archive storage environment includes: a data acquisition module 201, a processing module 202, and an execution module 203, which are described below: The acquisition module 201 in this application is mainly used to acquire temperature, humidity and air quality data of each sensor node within a specified time period to obtain environmental parameter information of the archive storage environment. Processing module 202 in this application is used to extract the environmental steady-state baseline of each sampling period from the environmental parameter information, determine the periodic fluctuation characteristics of the archive storage environment based on all environmental steady-state baselines, compare the transient information of each sensor node at the current moment with the periodic fluctuation characteristics, and obtain the environmental degradation index of the archive storage space. It should be noted that the processing module 202 is also used to send detection frames to each sensor node at equal time intervals through the network coordinator when the environmental degradation index is greater than the environmental warning value of the archive storage space, record the response delay of each sensor node, until the standard deviation of all response delays converges to within the stable threshold of the archive storage environment, and construct the node response feature vector of the archive storage environment based on all response delays. The execution module 203 in this application is mainly used to respond to and correct the environmental degradation index through the node response feature vector, thereby generating a security warning level for the archive storage environment.

[0045] The foregoing has detailed examples of a security monitoring and early warning method and system for an archive storage environment provided in the embodiments of this application. It is understood that the corresponding apparatus, in order to achieve the above functions, includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0046] In some embodiments, this application also provides a computer device, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device performs the above-described security monitoring and early warning method for an archive storage environment.

[0047] In some embodiments, reference Figure 4 The dashed lines in the figure indicate that the unit or module is optional. This figure is a structural schematic diagram of a computer device implementing a security monitoring and early warning method for an archive storage environment according to an embodiment of this application. The security monitoring and early warning method for an archive storage environment described in the above embodiments can... Figure 4 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device may be a terminal device, a server or a chip.

[0048] Processor 301 can be a general-purpose processor or a special-purpose processor. For example, processor 301 can be a central processing unit (CPU), which can be used to control computer devices, execute software programs, and process data from software programs. The computer device may also include a communication unit 305 for inputting (receiving) and outputting (transmitting) signals.

[0049] For example, the computer device may be a chip, and the communication unit 305 may be the input and / or output circuit of the chip, or the communication unit 305 may be the communication interface of the chip, which may be a component of a terminal device, network device or other device.

[0050] For example, the computer device may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0051] The computer device may include one or more memories 302 storing a program 304. The program 304 can be executed by a processor 301 to generate instructions 303, causing the processor 301 to execute the method described in the above method embodiments according to the instructions 303. Optionally, the memory 302 may also store data (such as a target audit model). Optionally, the processor 301 may also read data stored in the memory 302, which may be stored at the same storage address as the program 304, or it may be stored at a different storage address than the program 304.

[0052] The processor 301 and memory 302 can be configured separately or integrated together, for example, integrated on the system on chip (SOC) of the terminal device.

[0053] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software instructions in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, such as discrete gate, transistor logic devices, or discrete hardware components.

[0054] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0055] For example, in some embodiments, this application also provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the above-described security monitoring and early warning method for an archive storage environment.

[0056] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0057] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A security monitoring and early warning method for an archive storage environment, characterized in that, The steps include the following: Collect temperature, humidity and air quality data from each sensor node within a specified time period to obtain environmental parameter information of the archive storage environment; The environmental steady-state baselines for each sampling period are extracted from the environmental parameter information. Based on all environmental steady-state baselines, the periodic fluctuation characteristics of the archive storage environment are determined. The transient information of each sensor node at the current moment is compared with the periodic fluctuation characteristics to obtain the environmental degradation index of the archive storage space. When the environmental degradation index is greater than the environmental warning value of the archive storage space, the network coordinator sends detection frames to each sensor node at equal time intervals, records the response delay of each sensor node, until the standard deviation of all response delays converges to within the stable threshold of the archive storage environment, and constructs the node response feature vector of the archive storage environment based on all response delays. The environmental degradation index is corrected by the node response feature vector, thereby generating a security warning level for the archive storage environment.

2. The method as described in claim 1, characterized in that, Extracting the environmental steady-state baseline for each sampling period from the environmental parameter information specifically includes: The environmental parameter information is divided into multiple time-period data blocks according to a preset sampling period; Extract the initial steady-state values ​​of each sampling period from the temperature, humidity and air quality data within each time period data block; All initial steady-state values ​​are arranged in chronological order, and outlier periods are identified, removed, and smoothed sequentially to obtain the environmental steady-state baseline for each sampling period.

3. The method as described in claim 1, characterized in that, The periodic fluctuation characteristics of the archival storage environment, determined based on all environmental steady-state baselines, specifically include: The values ​​of the same environmental factor in all environmental steady-state baselines at different sampling periods are obtained to obtain the time series of each environmental factor. Identify the recurring maxima and minima in each time series to obtain the fluctuation amplitude of each environmental factor; By statistically analyzing the time span between adjacent maxima and minima in each time series, the fluctuation cycle of each environmental factor can be obtained. By combining the fluctuation amplitude and fluctuation period of each environmental factor, the periodic fluctuation characteristics of the archive storage environment are obtained.

4. The method as described in claim 1, characterized in that, By comparing the transient information of each sensor node at the current moment with the periodic fluctuation characteristics, the environmental degradation index of the archive storage space is obtained, specifically including: Acquire the transient values ​​of temperature, humidity, and air quality collected by each sensor node at the current moment; Calculate the percentage deviation of the transient value from the fluctuation amplitude of the corresponding environmental factor in the periodic fluctuation characteristics; The deviation percentages of all environmental factors within the same sensor node are fused to obtain the single-point degradation degree of each sensor node. By spatially interpolating and synthesizing the individual degradation levels of all sensor nodes, the environmental degradation index of the archive storage space is obtained.

5. The method as described in claim 1, characterized in that, The specific details of recording the response delay of each sensor node include: For each sensor node, obtain the timestamp of the probe frame sent by the network coordinator to the sensor node to obtain the sending time point; After receiving the detection frame, the sensor node immediately returns a response frame carrying the reception timestamp, and the network coordinator records the arrival time of the response frame. The round-trip response delay of each sensor node is obtained by taking half of the difference between the arrival time and the transmission time.

6. The method as described in claim 1, characterized in that, The node response feature vector of the archive storage environment, constructed based on all response latency, specifically includes: For each sensor node, calculate the mathematical expectation of all response delays of the sensor node within a specified time period to obtain the average response delay of the sensor node. Calculate the standard deviation of all response delays of the sensor node within a specified time period to obtain the response jitter amplitude of the sensor node; Extract the number of anomalies in the response delay of the sensor node that exceed the preset delay tolerance limit within a specified time period to obtain the communication instability frequency of the sensor node; The node response characteristics of the sensor nodes are determined by the average response delay, the response jitter amplitude, and the communication instability frequency, thereby obtaining the node response characteristics of each sensor node. The node response feature vector of the archive storage environment is determined based on all node response characteristics.

7. The method as described in claim 1, characterized in that, The environmental degradation index is corrected by using the node response feature vector, thereby generating a security warning level for the archive storage environment. Specifically, this includes: The node response features of each sensor node in the node response feature vector are input into a preset regression model, and the confidence decay coefficient of each sensor node is output. The confidence attenuation coefficient of each sensor node is used to reduce the weight of the single-point degradation degree when participating in the synthesis of the environmental degradation index. Then, the environmental degradation index is recalculated with the reduced single-point degradation degree to obtain the environmental degradation index after response correction. The corrected environmental degradation index is input into the grading mapper, which outputs the security warning level of the archive storage environment.

8. A security monitoring and early warning system for an archive storage environment, characterized in that, include: The data acquisition module is used to collect temperature, humidity and air quality data from each sensor node within a specified time period to obtain environmental parameter information of the archive storage environment. The processing module is used to extract the environmental steady-state baseline of each sampling period from the environmental parameter information, determine the periodic fluctuation characteristics of the archive storage environment based on all environmental steady-state baselines, and compare the transient information of each sensor node at the current moment with the periodic fluctuation characteristics to obtain the environmental degradation index of the archive storage space. The processing module is also used to send detection frames to each sensor node at equal time intervals through the network coordinator when the environmental degradation index is greater than the environmental warning value of the archive storage space, record the response delay of each sensor node, until the standard deviation of all response delays converges to within the stable threshold of the archive storage environment, and construct the node response feature vector of the archive storage environment based on all response delays. The execution module is used to respond to and correct the environmental degradation index through the node response feature vector, thereby generating a security warning level for the archive storage environment.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to call and run the computer programs from the memory, so that the computer device performs the security monitoring and early warning method for the archive storage environment as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions or code that, when executed on a computer, cause the computer to implement the security monitoring and early warning method for an archive storage environment as described in any one of claims 1 to 7.

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