A safety tool compliance inspection SOP dynamic synchronization and intelligent early warning method

Abstract

The application discloses a kind of safety tool compliance inspection SOP dynamic synchronization and intelligent early warning method, it is related to safety tool intelligent early warning field, comprising: through multi-dimensional sensor, action, load and environmental data in the inspection process are collected, form low-dimensional feature matrix by standardization, sliding window division, feature extraction and PCA dimensionality reduction, and establish historical compliance sample.Based on continuous time window, construct time series input, use long short-term memory network to extract current operation behavior state, calculate behavior deviation degree and risk score;Further match with SOP compliance item library, judge operation compliance, mild deviation or obvious deviation, and generate intelligent early warning and disposal suggestion in combination with continuous deviation trend.Artificial review is carried out, and normal fluctuation data is updated to sample library and SOP item library, to realize rule dynamic optimization.

Description

Technical Field

[0001] This invention relates to the field of intelligent early warning technology for safety tools and equipment, and in particular to a method for dynamic synchronization and intelligent early warning of SOPs for compliance inspection of safety tools and equipment. Background Technology

[0002] Safety tools are crucial for ensuring personnel safety and stable equipment operation during power maintenance, operation, and safety protection work. Their inspection process typically requires strict adherence to standard operating procedures (SOPs). Currently, safety tool inspections largely rely on manual operation, recording, and experience-based judgment. Inspectors must complete steps such as visual inspection, load testing, insulation performance testing, and environmental condition verification according to SOP requirements. However, in actual inspections, data such as the standardization of actions, changes in applied load, duration, and ambient temperature and humidity are difficult to collect and analyze continuously and in real time. When steps are omitted, operations deviate, loading becomes unstable, or environmental conditions do not meet requirements, these issues are often only discovered through post-inspection review, leading to delayed risk identification. Meanwhile, existing SOP management methods are mostly based on fixed text or static rule bases, lacking dynamic correlation with real-time on-site data and historical compliance samples. This makes it difficult to adapt to differences in various types of tools, inspection environments, and fluctuations in normal operations, easily leading to misjudgments or omissions. Furthermore, traditional early warning methods typically judge based solely on whether a single test result exceeds limits, lacking comprehensive analysis of continuous operational behavior, deviation trends, and risk accumulation. Therefore, there is an urgent need for a safety tool compliance inspection method that can integrate multi-dimensional sensor data, behavior recognition, SOP matching, intelligent early warning, and manual review feedback. Summary of the Invention

[0003] In view of the aforementioned existing problems, the present invention is proposed.

[0004] Therefore, this invention provides a method for dynamic synchronization and intelligent early warning of SOPs for compliance inspection of safety tools and equipment, which solves the problems of difficulty in real-time perception of SOP execution status, difficulty in timely identification of operational deviations, reliance on human experience for early warning judgment, lack of dynamic closed-loop for abnormal handling, and inability of SOP rules to be adaptively updated according to normal fluctuation data on site during the existing safety tool and equipment inspection process.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution: This invention provides a method for dynamic synchronization and intelligent early warning of SOPs for compliance inspection of safety tools and equipment, comprising: Collect multi-dimensional sensor data of safety tools and equipment during the inspection process, standardize the multi-dimensional sensor data, divide it into sliding windows and extract statistical features, and form a low-dimensional feature matrix by PCA dimensionality reduction. At the same time, establish compliance samples based on historical compliance inspection processes. Based on the low-dimensional feature matrix, a time-series input segment is composed of continuous time windows. The current operation behavior status is extracted using a long short-term memory network. Then, the difference is compared with the historical compliant operation status center to calculate the behavior deviation, abnormal risk score and smoothing risk score, and complete the risk level determination of the current operation behavior. The current time window is matched with the standard low-dimensional feature range, standard behavior state range, risk tolerance range, and environmental applicability conditions in the SOP compliance item library. The comprehensive matching degree is calculated and the target SOP item is determined, thereby judging whether the current operation is compliant, slightly deviates, or significantly deviates. A comprehensive early warning score is constructed by combining behavioral risks, the degree of deviation from SOP matching, and the trend of continuous deviation, generating suggestive or mandatory intelligent early warnings and corresponding handling suggestions; after manual review, data confirmed to be normal fluctuations are synchronously updated to the historical compliance sample library and SOP compliance item library; As a preferred embodiment of the method for dynamic synchronization and intelligent early warning of SOPs for compliance inspection of safety tools and equipment described in this invention, the following steps are taken: Collecting multi-dimensional sensor data of the safety tools and equipment during the inspection process, standardizing the multi-dimensional sensor data, dividing it into sliding windows, and extracting statistical features refers to collecting multi-dimensional sensor data of the inspected safety tools and equipment to obtain a complete data profile of the tools and equipment during actual operation, and forming a time-series data vector. ; All sensor data are standardized to obtain standardized sensor data. ; Based on standardized sensor data Extract each time series vector Mean characteristics Standard deviation characteristics Peak characteristics ; For all Repeated vector operations on multiple sensors are used to obtain features for different time periods of the entire time series. .

[0006] As a preferred embodiment of the method for dynamic synchronization and intelligent early warning of SOPs for compliance inspection of safety tools and equipment described in this invention, wherein: the step of forming a low-dimensional feature matrix through PCA dimensionality reduction refers to the pairing of the feature matrix... Calculate the covariance matrix ; For covariance matrix Perform eigenvalue decomposition to obtain eigenvalues. and its corresponding eigenvectors ; Sort by eigenvalue from largest to smallest, and select the top... One principal component makes the cumulative variance contribution rate reach 85-90%; Before The eigenvectors are combined column-wise to obtain the weight matrix. ; Based on the weight matrix , the original feature matrix Mapping to a low-dimensional space yields a low-dimensional feature matrix. .

[0007] As a preferred embodiment of the method for dynamic synchronization and intelligent early warning of SOPs for compliance inspection of safety tools and equipment described in this invention, the step of establishing compliance samples based on historical compliance inspection processes refers to applying the same data processing method as real-time acquired data to sensor data in the historical compliance inspection sample library, sequentially performing standardization, time window division, feature extraction, and PCA dimensionality reduction to obtain low-dimensional feature samples of historical compliance. .

[0008] As a preferred embodiment of the method for dynamic synchronization and intelligent early warning of SOPs for compliance inspection of safety tools and equipment described in this invention, the method involves: forming time-series input segments based on low-dimensional feature matrices arranged in continuous time windows, and utilizing a long short-term memory network to extract the current operational behavior state index. The low-dimensional feature vectors of each time window are combined to form a time-series input segment. ; Input timing segment Inputting the data into a pre-trained long short-term memory network model yields the current operational behavior state vector. ; Historical compliance inspection data The first in The historical compliance low-dimensional feature matrix corresponding to each compliance operation sample ,continuous The time windows are combined to form historical compliant time sequence input segments. Inputting the same long short-term memory network model yields the corresponding historical compliant operation state vector. And calculate the average of all historical compliance operation state vectors to obtain the compliance operation behavior state center. .

[0009] As a preferred embodiment of the method for dynamic synchronization and intelligent early warning of SOPs for compliance inspection of safety tools and equipment described in this invention, the following steps are taken: comparing the differences with historical compliance operation status centers, calculating behavioral deviation, abnormal risk score, and smoothing risk score, and completing the risk level determination of the current operation behavior refers to determining the risk level based on the current operation behavior status vector. Compliance Operation Behavior Status Center The difference is used to calculate the behavioral deviation in the current time window. ; behavioral deviation Convert to normalized anomaly risk score ; The abnormal risk scores from multiple consecutive time windows are smoothed to obtain a dynamic behavioral risk score. ; Based on dynamic behavioral risk scoring The current operation behavior is compared with a preset threshold to determine its level. ; The calculated data are compiled into a set, and the current time window operation status is output. .

[0010] As a preferred embodiment of the method for dynamic synchronization and intelligent early warning of SOPs for compliance inspection of safety tools and equipment described in this invention, the following steps are taken: matching the current time window with the standard low-dimensional feature range, standard behavioral state range, risk allowable range, and environmental applicable conditions in the SOP compliance item library, and calculating the comprehensive matching degree refers to establishing a SOP compliance item library for safety tools and equipment, and representing each SOP item in the SOP compliance item library as a standard compliance vector. ; Current time window operation status With each SOP compliance entry in the SOP compliance entry library Perform matching and calculate the low-dimensional feature matching degree. Operation behavior state matching degree Risk score matching degree Environmental operating condition matching degree ; Based on low-dimensional feature matching Operation behavior state matching degree Risk score matching degree Environmental operating condition matching degree Calculate the current operation behavior and the first Matching degree of each SOP compliance item .

[0011] As a preferred embodiment of the method for dynamic synchronization and intelligent early warning of SOPs for compliance inspection of safety tools and equipment described in this invention, the step of determining whether the current operation is compliant, slightly deviated, or significantly deviated refers to selecting the SOP item corresponding to the maximum matching degree as the target SOP item for the current time window. ; The maximum matching degree is compared with the preset SOP compliance matching threshold to obtain the SOP compliance inspection result for the current time window. including when When, it is deemed compliant; when When, it is judged as a SOP suggestive deviation; when When this occurs, it is determined to be an abnormal deviation from the SOP; when or At that time, the deviation information of the corresponding time window is extracted to generate the SOP deviation description vector. ; SOP compliance verification results based on continuous time windows To determine whether there is a persistent deviation from the standard operating procedure (SOP) in the current operation process.

[0012] As a preferred embodiment of the method for dynamic synchronization and intelligent early warning of SOPs for compliance inspection of safety tools and equipment described in this invention, the method of constructing a comprehensive early warning score by combining behavioral risk, degree of deviation from SOP matching, and continuous deviation trend, and generating suggestive or mandatory intelligent early warnings and corresponding handling suggestions, refers to constructing a comprehensive early warning score based on the current operational behavioral risk, degree of deviation from SOP matching, and continuous deviation trend. ; Based on comprehensive early warning score SOP compliance inspection results and risk level marking Generate intelligent early warning levels ; Based on deviation description vector Intelligent early warning level Maximum SOP matching degree and warning trigger time Generate warning content ; According to the warning level Generate dynamic handling suggestions .

[0013] As a preferred embodiment of the method for dynamic synchronization and intelligent early warning of SOPs for compliance inspection of safety tools and equipment described in this invention, the step of synchronously updating data confirmed to be of normal fluctuation to the historical compliance sample library and the SOP compliance item library refers to the set of early warning information. Recommendations for dynamic handling and deviation from the description vector Stored in the early warning record database to form an early warning record. ; Once the warning record is manually reviewed or confirmed by subsequent inspection results, the historical compliance inspection sample library and SOP compliance item library are dynamically and synchronously updated, and the standard low-dimensional feature center and standard operating behavior status center of the SOP item are updated. Based on the updated set of historical compliance samples, for the first The standard low-dimensional feature range and standard operating behavior status range of each SOP compliance item are updated synchronously. The updated results will be synchronized to the SOP compliance item library, and a dynamic synchronization record of SOPs will be generated. Output intelligent early warning results and SOP dynamic synchronization results. .

[0014] The beneficial effects of this invention are as follows: This invention collects action, load, and environmental data during the inspection of safety tools and equipment using multi-dimensional sensors, and combines this with standardization processing, sliding window partitioning, feature extraction, and PCA dimensionality reduction to form a low-dimensional feature matrix that reflects the local operational status. Furthermore, it utilizes a long short-term memory network to extract continuous operational behavior states and compares the deviation with historical compliant operational states to achieve dynamic identification of abnormal operational risks. Simultaneously, it matches real-time operational states with SOP compliance items to accurately determine whether the current operation is compliant, slightly deviates, or significantly deviates. Based on this, it generates intelligent early warnings and handling suggestions by comprehensively considering behavioral risks, the degree of SOP matching deviation, and continuous deviation trends. Normal fluctuation data confirmed by manual review is synchronously updated to the historical compliance sample library and the SOP compliance item library, thereby improving the real-time performance, accuracy, and adaptive update capability of SOP execution supervision. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a flowchart of a method for dynamic synchronization and intelligent early warning of SOPs for compliance inspection of safety tools and equipment.

[0017] Figure 2 A flowchart for collecting multidimensional sensor data, processing the data, and performing PCA dimensionality reduction. Detailed Implementation

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0019] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0020] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0021] Reference Figures 1-2 This is one embodiment of the present invention, which provides a method for dynamic synchronization and intelligent early warning of SOPs for compliance inspection of safety tools and equipment, including the following steps: S1. Collect multi-dimensional sensor data of safety tools and equipment during the inspection process, standardize the multi-dimensional sensor data, divide it into sliding windows and extract statistical features, and form a low-dimensional feature matrix by PCA dimensionality reduction. At the same time, establish compliance samples based on historical compliance inspection processes. First, multi-dimensional sensor data (including accelerometers, load sensors, environmental sensors, humidity sensors, etc.) of the safety equipment under inspection are collected to obtain a complete data profile of the equipment during actual operation and form a time-series data vector. :

[0022] in, Acceleration of the action; For tool load; The ambient temperature; For ambient humidity; The vibration frequency or other environmental parameters are determined based on the type of tool. All sensor data are standardized to obtain standardized sensor data. :

[0023] in, The data is standardized. The historical average values ​​for each sensor. The historical standard deviation; Based on standardized sensor data Extract each time series vector Mean characteristics Standard deviation characteristics Peak characteristics :

[0024]

[0025]

[0026] in, For the first The total number of standardized data sampling points collected by each sensor; The three features extracted by each sensor are combined into a vector row by row:

[0027] For all Repeated vector operations on multiple sensors are used to obtain features for different time periods of the entire time series. :

[0028] in, The number of time windows and the number of rows in the feature matrix are used for calculation. ， The window length is represented by 100 sampling points, which ensures that each window reflects the local operation characteristics; the sliding step size S can be... It can capture more detailed changes; Indicates the first The sensor at the first Three features extracted from each window; Based on the characteristics of different time periods of the entire time series PCA dimensionality reduction is performed to obtain the local operational features Y of the time window after removing redundant information; firstly, the feature matrix is... Calculate the covariance matrix :

[0029] in, Representation of the characteristic matrix Transpose of; It is A symmetric matrix, each element Indicates the first The first feature and the second The linear correlation between features, when hour, It is the variance of that feature, when hour, It is the first The and the first The covariance of each feature; For covariance matrix Perform eigenvalue decomposition to obtain eigenvalues. and its corresponding eigenvectors : in, Let be the eigenvalues ​​corresponding to the i-th principal component, and each eigenvalue represents the variance of the principal component. ， in, They represent the first The weight coefficients corresponding to each original statistical feature in each principal component direction, i.e., the principal component directions; Sort by eigenvalue from largest to smallest, and select the top... Each principal component contributes to the cumulative variance. Reaching 85-90%, it can retain most of the information while reducing redundancy and improving modeling efficiency; Before The eigenvectors are combined column-wise to obtain the weight matrix. :

[0030] Based on the weight matrix , the original feature matrix Mapping to a low-dimensional space yields a low-dimensional feature matrix. :

[0031] Furthermore, while collecting data on the safety tools under inspection in real time, a historical compliance inspection sample library is established. The data in the historical compliance inspection sample library comes from past work processes that were inspected in accordance with the corresponding safety tool SOP specifications and did not trigger any abnormal records. The sensor data in the historical compliance inspection sample library is processed using the same data processing method as the real-time data, including standardization, time window division, feature extraction, and PCA dimensionality reduction, to obtain historical compliance low-dimensional feature samples.

[0032] S2. Based on the low-dimensional feature matrix, a time-series input segment is formed by continuous time windows. The current operation behavior state is extracted using a long short-term memory network. Then, the difference is compared with the historical compliant operation status center to calculate the behavior deviation, abnormal risk score and smoothing risk score, thereby completing the risk level determination of the current operation behavior. Based on low-dimensional feature matrix The time series modeling of the operational behavior of the inspected safety tools and equipment under different time windows is performed, and the abnormal risk score of the current operational behavior relative to the compliant operational behavior is calculated. low-dimensional feature matrix Each row represents a local operational characteristic of the safety tool within a time window, denoted as ;in, Indicates the first The time window in the first Projection values ​​along the directions of each principal component ; Continuous The low-dimensional feature vectors of each time window are combined to form a time-series input segment. :

[0033] in, Indicates the first The operation behavior segment with a time window as the end point ; This represents the number of continuous time windows involved in the modeling, with a preferred value of [value to be filled in]. It can reflect short-term abnormal shocks without compromising the real-time nature of early warnings due to excessively long time spans; The first window is used for data accumulation, the second... Each window begins to form a complete time sequence segment and enters the risk identification, SOP matching and early warning output process; Input timing segment Inputting the data into a pre-trained long short-term memory network model yields the current operational behavior state vector. :

[0034] in, This represents the state vector of the operation behavior in the previous time window. Initially, since there is no previous valid time sequence input segment, the initial state vector can be set to zero. Furthermore, historical compliance inspection data The first in The historical compliance low-dimensional feature matrix corresponding to each compliance operation sample ,continuous The time windows are combined to form historical compliant time sequence input segments. , Input the same Long Short-Term Memory network model to obtain the corresponding historical compliant operation state vector. Similarly, This represents the historical compliance operation state vector from the previous moment. Initially, since there is no previous valid time-series input segment, the initial state vector can be set to zero. The average of all historical compliance operation state vectors is then calculated to obtain the compliance operation behavior state center. :

[0035] in, Indicates the first A vector of historical compliant operation status; This indicates the number of historical compliance operation samples; Based on the current operation behavior state vector Compliance Operation Behavior Status Center The difference is used to calculate the behavioral deviation in the current time window. :

[0036] in, This represents the Euclidean distance between the current operational behavior status and the compliant operational behavior status. The Euclidean norm representing the state center of compliant operational behavior; To prevent the stability coefficient from being zero; Further behavioral deviation Convert to normalized anomaly risk score :

[0037] in, The risk amplification factor is preferably set to a value of [value to be filled in]. ,when When the value is too small, the score difference between minor and obvious abnormalities is not significant, which can easily reduce the sensitivity of the warning; when When the value is too large, even slight fluctuations may be amplified as high risk, which can easily lead to false alarms. The abnormal risk scores from multiple consecutive time windows are smoothed to obtain a dynamic behavioral risk score. :

[0038] in, Indicates the first Smoothed risk score for each time window; This represents the smoothed risk score for the previous time window; The weighting coefficient for the current window risk score is preferably set to a value of [value to be filled in]. Intelligent early warning needs to prioritize responding to current operational risks while still retaining the impact of risks from the previous time window on current judgments, in order to improve the continuity and stability of early warning results. Based on dynamic behavioral risk scoring The current operation behavior is compared with a preset threshold to determine its level. :

[0039] in, Indicates the first Risk level markings for each time window; For a slight deviation from the risk threshold, the preferred value is [value]. It can identify operational segments that exceed the normal fluctuation range as mild deviation risks while maintaining normal operational tolerance. The preferred value for the abnormal warning threshold is [value to be filled in]. This enables the system to promptly trigger clear anomaly warnings after an abnormal trend has formed; The calculated data are compiled into a set, and the current time window operation status is output. :

[0040] in, Indicates the first Real-time operation inspection status corresponding to each time window; Indicates the first Low-dimensional feature vectors for each time window; Indicates the first A state vector of operational behavior within a time window; Indicates the degree of behavioral deviation; Indicates a smoothed risk score; Indicates risk level marking; S3. Match the current time window with the standard low-dimensional feature range, standard behavior state range, risk allowable range and environmental applicable conditions in the SOP compliance item library, calculate the comprehensive matching degree and determine the target SOP item, and then determine whether the current operation is compliant, slightly deviates or significantly deviates. First, establish a SOP compliance item library for safety tools and equipment, including standard operating procedures, permissible operating ranges, key action sequences, load variation constraints, applicable environmental conditions, and prohibited items during the inspection process for different types of safety tools and equipment; represent each SOP item in the SOP compliance item library as a standard compliance vector:

[0041] in, Indicates the first One SOP compliance item; This indicates the standard low-dimensional feature range corresponding to this SOP item, and can take values ​​of: ， in This represents the mean vector of historical compliance low-dimensional feature samples under this SOP item. This represents the corresponding standard deviation vector; This indicates the range of standard operating behavior states corresponding to this SOP item. For example, it can take the following values: ， in, This represents the mean vector of historical compliant operational behavior status vectors under this SOP item. This represents the corresponding standard deviation vector; This indicates the allowed risk scoring range for this SOP item; for example, it can take the following values: This indicates that the actions taken under this SOP item should be within the scope of compliance risks; This indicates the applicable environmental conditions or operating constraints corresponding to this SOP item. For example, it can take the following values: ,in, Indicates the current ambient temperature. Indicates the current ambient humidity. , and Set according to the corresponding safety tool and equipment inspection specifications; Current time window operation status With each SOP compliance entry in the SOP compliance entry library Perform matching and calculate the low-dimensional feature matching degree. Operation behavior state matching degree Risk score matching degree Environmental operating condition matching degree :

[0042]

[0043]

[0044] in, Indicates the first The standard low-dimensional feature center corresponding to each SOP compliance item. , for The k-th historical compliance low-dimensional feature sample in the middle, This represents the total number of historical compliant low-dimensional feature samples. This is a stability coefficient used to prevent the denominator from being zero; Indicates the first The standard operating procedure status center corresponding to each SOP compliance item. , This is the k-th historical compliant operation status vector under the same SOP item; This indicates the distance within the allowed risk score range. The nearest boundary value, let the first one be... The allowed risk score range for each SOP compliance item is: , ; Indicates the first Environmental sensor data (including ambient temperature and ambient humidity) corresponding to each time window; Furthermore, based on low-dimensional feature matching degree Operation behavior state matching degree Risk score matching degree Environmental operating condition matching degree Calculate the current operation behavior and the first Matching degree of each SOP compliance item :

[0045] in, Indicates the first The operational behavior of the first time window and the first The degree of matching of each SOP compliance item; These are the weight coefficients for each matching item, and they satisfy: Preferably, , , , Operation behavior state vector It can reflect the temporal behavior characteristics of the current operation process, and therefore is given high weight, resulting in a low-dimensional feature vector. It can reflect changes in local sensor features, serving as an important basis for behavior matching and smoothing risk scoring. It directly reflects the degree of abnormal risk and is used to enhance the sensitivity of SOP deviation judgment. Environmental conditions are mainly used to determine whether the current operation is within the applicable conditions allowed by the SOP, and serve as an auxiliary constraint. The SOP entry with the highest matching degree is selected as the target SOP entry for the current time window. :

[0046] in, Indicates the first The most matching SOP compliance item within the given time window; The maximum matching degree is compared with the preset SOP compliance matching threshold to obtain the SOP compliance inspection result for the current time window. :

[0047] in, Indicates the first SOP compliance inspection results for each time window; This indicates the maximum match between the current time window and the SOP entry library; For compliance matching threshold, a value of 0.80 is preferred, which can ensure the reliability of SOP compliance judgment while avoiding misjudgment as deviation due to slight fluctuations; For severe deviations from the matching threshold, a value of 0.60 is preferred. This value can promptly identify abnormal deviations when the current operational behavior is significantly insufficient to match the SOP standard items, and trigger subsequent intelligent warnings. When multiple judgment conditions are met simultaneously within the same time window, they are prioritized according to risk level from high to low. When the current operation matches the SOP item, it is considered compliant; when When this occurs, it indicates a slight deviation from the SOP item, and is judged as a SOP-suggestive deviation; when When this occurs, it indicates that the current operation deviates significantly from the SOP item, and is judged as an abnormal deviation from the SOP; when or At that time, the deviation information of the corresponding time window is extracted to generate the SOP deviation description vector. It is used to record the time window number where the deviation occurred, the target SOP item, the low-dimensional feature vector, the operational behavior state vector, the behavior deviation degree, the smoothing risk score, the risk level label, and the SOP compliance inspection results; SOP compliance verification results based on continuous time windows Determine if there is a persistent deviation from the standard operating procedure (SOP) in the current operation process:

[0048] in, Indicates recent The number of windows in a time window where SOP deviation occurs; Indicates the first SOP compliance verification results for each time window. The summation index has a range of values. ; This is an indicator function; it takes the value 1 if the condition is met, and 0 otherwise. This indicates the number of consecutive inspection windows, with a preferred value of 5 to reduce false judgments caused by occasional sensor fluctuations or short-term errors; when When it is determined that the current operation process of safety tools and equipment shows a continuous deviation trend from the SOP; when there are consecutive windows... When the inspection results are obtained, it is directly determined that there is a significant deviation from the standard operating procedure (SOP) in the current operation process; S4. Combine behavioral risks, deviations from SOP matching, and continuous deviation trends to construct a comprehensive early warning score, generate suggestive or mandatory intelligent early warnings and corresponding handling suggestions; after manual review, data confirmed to be normal fluctuations will be synchronously updated to the historical compliance sample library and SOP compliance item library to achieve dynamic synchronization of SOP rules; First, in order to simultaneously characterize the current operational risk, the degree of deviation from SOP matching, and the trend of continuous deviation, a comprehensive early warning score is constructed. :

[0049] in, Indicates the first A comprehensive early warning score for each time window; Indicates the percentage of persistent deviation; These represent the weighting coefficients for behavioral risk, the degree of deviation from SOP matching, and the trend of persistent deviation, respectively, and satisfy the following conditions: Preferably, , , Smooth risk scoring SOP matching deviations can directly reflect the degree of abnormal risk in current operational behavior. It can reflect the degree of difference between the current operational behavior and the standard SOP items. Both are core warning criteria for the current window, and therefore are given high weight; the proportion of persistent deviation It is used to determine whether deviations occur repeatedly within a continuous time window, and mainly plays an auxiliary and enhancing role, so it is given a relatively low weight. Furthermore, based on the comprehensive early warning score SOP compliance inspection results and risk level marking Generate intelligent early warning levels :

[0050] in, Indicates the first Intelligent early warning levels for each time window; when When the current safety equipment inspection operation is in compliance with regulations, no warning will be triggered; when When this occurs, it indicates a slight risk of deviation from the current operation, triggering a warning; when When this occurs, it indicates that the current operation has obvious deviations from the SOP or a high risk of behavior, triggering a mandatory abnormality warning; The preferred threshold for alerting operators is 0.45, which can generate alerts to remind operators to adjust their operations in a timely manner. The mandatory abnormal warning threshold is preferably set at 0.70. If the comprehensive warning score is higher than 0.70, it may affect the reliability of the safety tool inspection results and the safety of the operation if intervention is not timely. Therefore, it is necessary to trigger a mandatory abnormal warning. When multiple judgment conditions are met at the same time window, the judgment is prioritized according to the warning level from high to low. Based on deviation description vector Intelligent early warning level Maximum SOP matching degree and warning trigger time Generate warning content :

[0051] when When the current operation deviates slightly from the target SOP item, a warning message is generated, indicating that the current operation deviates slightly from the target SOP item, and the corresponding target SOP item, deviation time window, smoothing risk score, and suggested adjustments to the operation are output; when When the current operation deviates significantly from the standard operating procedure (SOP) or has significant behavioral risks, a mandatory abnormal warning will be generated, indicating that the current operation has obvious deviations from the SOP or has obvious behavioral risks. The abnormal time window, target SOP item, deviation description vector, maximum matching degree, risk level mark, and suggestions to suspend or review the current inspection operation will be output. Then according to the warning level Generate dynamic handling suggestions :

[0052] in, Indicates the first Dynamic handling suggestions corresponding to each time window; This indicates that the current inspection process will continue. This indicates a prompt for operators to check whether their current operating posture is consistent with the target SOP item, and to check whether environmental conditions such as temperature and humidity meet the requirements of the current SOP item; This indicates that the current inspection operation is suspended, and the operator is prompted to review the safety equipment, inspection environment, and operating procedures, and record any abnormal information. Furthermore, the early warning information will be aggregated. Recommendations for dynamic handling and deviation from the description vector Stored in the early warning record database to form an early warning record. :

[0053] in, Indicates the first The warning records corresponding to each time window are used for subsequent tracing, review and dynamic synchronization of SOPs; Once the warning record is manually reviewed or confirmed by subsequent inspection results, the historical compliance inspection sample library and SOP compliance item library are dynamically updated. If the operation corresponding to the current warning record is confirmed to be a normal operational fluctuation and does not affect the inspection results of safety tools and equipment, then the corresponding low-dimensional feature vector is... Operation behavior state vector and environmental sensor data Add the corresponding target SOP entry In the historical compliance sample set, update the standard low-dimensional feature center and standard operating behavior state center of the SOP entry:

[0054] in, This represents the updated standard low-dimensional feature center; This represents the updated standard operating procedure status center; At the same time, the first Number of historical compliance samples under each SOP compliance item Updated to :

[0055] Furthermore, based on the updated set of historical compliance samples, the first... The standard low-dimensional feature range and standard operating behavior status range of each SOP compliance item are updated synchronously:

[0056] in, This represents the updated standard low-dimensional feature range; This indicates the range of updated standard operating procedure (SOP) behavior states. and represent the updated mean and standard deviation of the historical compliance low-dimensional feature samples, respectively; and These represent the mean and standard deviation of the updated historical compliant operation behavior state vector, respectively. The updated results will be synchronized to the SOP compliance item library, and a dynamic synchronization record of SOPs will be generated. :

[0057] in, Indicates the first Dynamic synchronization record of each SOP compliance item; This indicates the SOP compliance entry that has been updated; This represents the updated standard low-dimensional feature range; This indicates the range of updated standard operating procedure (SOP) behavior states. This indicates the updated number of historical compliant samples; This indicates the time when the SOP entry is synchronized and updated.

[0058] Finally, the intelligent early warning results and SOP dynamic synchronization results are output. :

[0059] In summary, this invention collects action, load, and environmental data during the inspection of safety tools using multi-dimensional sensors. It then combines this data with standardization, sliding window partitioning, feature extraction, and PCA dimensionality reduction to form a low-dimensional feature matrix reflecting local operational states. Furthermore, it utilizes a Long Short-Term Memory (LSTM) network to extract continuous operational behavior states and compares these deviations with historical compliant operational states, enabling dynamic identification of abnormal operational risks. Simultaneously, it matches real-time operational states with SOP compliance items to accurately determine whether the current operation is compliant, slightly deviates, or significantly deviates. Based on this, it generates intelligent early warnings and handling suggestions by comprehensively considering behavioral risks, the degree of SOP matching deviation, and continuous deviation trends. Normal fluctuation data confirmed by manual review is simultaneously updated to the historical compliance sample library and the SOP compliance item library, thereby improving the real-time performance, accuracy, and adaptive update capability of SOP execution supervision.

[0060] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for dynamic synchronization and intelligent early warning of SOPs for compliance inspection of safety tools and equipment, characterized in that, include: Collect multi-dimensional sensor data of safety tools and equipment during the inspection process, standardize the multi-dimensional sensor data, divide it into sliding windows and extract statistical features, and form a low-dimensional feature matrix by PCA dimensionality reduction. At the same time, establish compliance samples based on historical compliance inspection processes. Based on the low-dimensional feature matrix, a time-series input segment is composed of continuous time windows. The current operation behavior status is extracted using a long short-term memory network. Then, the difference is compared with the historical compliant operation status center to calculate the behavior deviation, abnormal risk score and smoothing risk score, and complete the risk level determination of the current operation behavior. The current time window is matched with the standard low-dimensional feature range, standard behavior state range, risk tolerance range, and environmental applicability conditions in the SOP compliance item library. The comprehensive matching degree is calculated and the target SOP item is determined, thereby judging whether the current operation is compliant, slightly deviates, or significantly deviates. A comprehensive early warning score is constructed by combining behavioral risks, the degree of deviation from SOP matching, and the trend of continuous deviation, and prompts or mandatory intelligent early warnings and corresponding handling suggestions are generated. After manual review, data confirmed to be within the normal range of fluctuations will be synchronously updated to the historical compliance sample library and the SOP compliance item library.

2. The method for dynamic synchronization and intelligent early warning of SOP for compliance inspection of safety tools and equipment as described in claim 1, characterized in that: The process involves collecting multi-dimensional sensor data from safety tools during the inspection process, standardizing the multi-dimensional sensor data, dividing it into sliding windows, and extracting statistical features. This process collects multi-dimensional sensor data from the inspected safety tools to obtain a complete data profile of the tools during actual operation and forms a time-series data vector. ; All sensor data are standardized to obtain standardized sensor data. ; Based on standardized sensor data Extract each time series vector Mean characteristics Standard deviation characteristics Peak characteristics ; For all Repeated vector operations on multiple sensors are used to obtain features for different time periods of the entire time series. .

3. The method for dynamic synchronization and intelligent early warning of SOP for compliance inspection of safety tools and equipment as described in claim 2, characterized in that: The process of forming a low-dimensional feature matrix through PCA dimensionality reduction refers to the feature matrix... Calculate the covariance matrix ; For covariance matrix Perform eigenvalue decomposition to obtain eigenvalues. and its corresponding eigenvectors ; Sort by eigenvalue from largest to smallest, and select the top... One principal component; Before The eigenvectors are combined column-wise to obtain the weight matrix. ; Based on the weight matrix , the original feature matrix Mapping to a low-dimensional space yields a low-dimensional feature matrix. .

4. The method for dynamic synchronization and intelligent early warning of SOP for compliance inspection of safety tools and equipment as described in claim 3, characterized in that: The establishment of compliance samples based on historical compliance verification processes refers to applying the same data processing methods as real-time data acquisition to sensor data in the historical compliance verification sample library, sequentially performing standardization, time window segmentation, feature extraction, and PCA dimensionality reduction to obtain low-dimensional feature samples of historical compliance. .

5. The method for dynamic synchronization and intelligent early warning of SOP for compliance inspection of safety tools and equipment as described in claim 4, characterized in that: The process involves assembling temporal input segments based on low-dimensional feature matrices and consecutive time windows, then utilizing a long short-term memory network to extract the current operational state. The low-dimensional feature vectors of each time window are combined to form a time-series input segment. ; Input timing segment Inputting the data into a pre-trained long short-term memory network model yields the current operational behavior state vector. ; Historical compliance inspection data The first in The historical compliance low-dimensional feature matrix corresponding to each compliance operation sample ,continuous The time windows are combined to form historical compliant time sequence input segments. Inputting the same long short-term memory network model yields the corresponding historical compliant operation state vector. And calculate the average of all historical compliance operation state vectors to obtain the compliance operation behavior state center. .

6. The method for dynamic synchronization and intelligent early warning of SOP for compliance inspection of safety tools and equipment as described in claim 5, characterized in that: The process of comparing the current operational behavior with historical compliance operation status centers, calculating behavioral deviation, abnormal risk score, and smoothing risk score, and determining the risk level of the current operational behavior is based on the current operational behavior status vector. Compliance Operation Behavior Status Center The difference is used to calculate the behavioral deviation in the current time window. ; behavioral deviation Convert to normalized anomaly risk score ; The abnormal risk scores from multiple consecutive time windows are smoothed to obtain a dynamic behavioral risk score. ; Based on dynamic behavioral risk scoring The current operation behavior is compared with a preset threshold to determine its level. ; The calculated data are compiled into a set, and the current time window operation status is output. .

7. The method for dynamic synchronization and intelligent early warning of SOP for compliance inspection of safety tools and equipment as described in claim 6, characterized in that: The process involves matching the current time window with the standard low-dimensional feature range, standard behavioral state range, risk tolerance range, and environmental applicability conditions in the SOP compliance item library, and calculating the comprehensive matching degree. This refers to establishing a safety tool SOP compliance item library, where each SOP item is represented as a standard compliance vector. ; Current time window operation status With each SOP compliance entry in the SOP compliance entry library Perform matching and calculate the low-dimensional feature matching degree. Operation behavior state matching degree Risk score matching degree Environmental operating condition matching degree ; Based on low-dimensional feature matching Operation behavior state matching degree Risk score matching degree Environmental operating condition matching degree Calculate the current operation behavior and the first Matching degree of each SOP compliance item .

8. The method for dynamic synchronization and intelligent early warning of SOP for compliance inspection of safety tools and equipment as described in claim 7, characterized in that: The determination of whether the current operation is compliant, slightly deviates, or significantly deviates refers to selecting the SOP item corresponding to the maximum matching degree as the target SOP item for the current time window. ; The maximum matching degree is compared with the preset SOP compliance matching threshold to obtain the SOP compliance inspection result for the current time window. including when When, it is deemed compliant; when When, it is judged as a SOP suggestive deviation; when When this occurs, it is determined to be an abnormal deviation from the SOP; when or At that time, the deviation information of the corresponding time window is extracted to generate the SOP deviation description vector. ; SOP compliance verification results based on continuous time windows To determine whether there is a persistent deviation from the standard operating procedure (SOP) in the current operation process.

9. The method for dynamic synchronization and intelligent early warning of SOP for compliance inspection of safety tools and equipment as described in claim 8, characterized in that: The comprehensive early warning score, which combines behavioral risk, the degree of deviation from SOP matching, and the trend of continuous deviation, generates suggestive or mandatory intelligent early warnings and corresponding handling suggestions. This comprehensive early warning score is constructed based on the current operational behavioral risk, the degree of deviation from SOP matching, and the trend of continuous deviation. ; Based on comprehensive early warning score SOP compliance inspection results and risk level marking Generate intelligent early warning levels ; Based on deviation description vector Intelligent early warning level Maximum SOP matching degree and warning trigger time Generate warning content ; According to the warning level Generate dynamic handling suggestions .

10. The method for dynamic synchronization and intelligent early warning of SOP for compliance inspection of safety tools and equipment as described in claim 9, characterized in that: The step of synchronously updating data confirmed to be within normal fluctuations to the historical compliance sample database and SOP compliance item database refers to the collection of early warning information. Recommendations for dynamic handling and deviation from the description vector Stored in the early warning record database to form an early warning record. ; Once the warning record is manually reviewed or confirmed by subsequent inspection results, the historical compliance inspection sample library and SOP compliance item library are dynamically and synchronously updated, and the standard low-dimensional feature center and standard operating behavior status center of the SOP item are updated. Based on the updated set of historical compliance samples, for the first The standard low-dimensional feature range and standard operating behavior status range of each SOP compliance item are updated synchronously. The updated results will be synchronized to the SOP compliance item library, and a dynamic synchronization record of SOPs will be generated. Output intelligent early warning results and SOP dynamic synchronization results. .

Images