A low-voltage intelligent electric-controlled titanium wire lock and laboratory cabinet intelligent transformation system

Abstract

The present invention relates to the technical field of intelligent transformation of laboratory cabinets, and discloses a low-voltage intelligent electric-controlled titanium wire lock and an intelligent transformation system for laboratory cabinets. The system comprises: constructing an associated data space containing an environmental perception layer, a mechanical state layer, and a security assessment layer through multi-sensor fusion, integrating multi-dimensional parameters such as temperature, humidity, and vibration intensity, respectively obtaining feature sets using normal and abnormal feature extraction strategies, extracting common rules from different working condition strategies to construct a control strategy library to adapt to new scenarios, monitoring anomalies in real time, and triggering emergency strategies such as parameter adjustment and operation interruption. The low-voltage intelligent electric-controlled titanium wire lock uses titanium alloy wire to form a multi-stage linkage lock tongue mechanism including a main lock tongue and a secondary lock tongue. The electric control drive unit executes control instructions, and the state feedback unit uploads lock tongue position and motor torque data in real time. The present invention realizes intelligent monitoring, decision-making, and safety protection of laboratory cabinets, improving management efficiency and safety.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transformation of laboratory cabinets, and in particular to a low-voltage intelligent electric-controlled titanium wire lock and an intelligent transformation system for laboratory cabinets. Background Art

[0002] In modern scientific research and experimental environments, laboratory cabinets are important carriers for storing various experimental equipment, reagents and samples. Their safety, reliability and intelligent management level are crucial. Traditional laboratory cabinets generally have the following significant problems:

[0003] When it comes to cabinet status monitoring, traditional cabinets lack effective multi-dimensional monitoring methods. Most are only capable of simple monitoring of a single parameter, such as temperature and humidity, or a rough assessment of lock status. They are unable to construct a comprehensive data space linking the cabinet environment and lock status through multi-sensor fusion. This makes it impossible to accurately and comprehensively understand the actual operating status of the cabinet, making it difficult to provide early warnings and effectively prevent potential safety hazards. For example, when the cabinet is subject to abnormal vibration or electromagnetic interference, traditional monitoring methods often fail to detect it in time, potentially leading to problems such as lock failure and reagent deterioration.

[0004] In terms of feature extraction and analysis, traditional systems lack scientific and reasonable feature extraction strategies. For the data generated during cabinet operation, it is impossible to effectively extract normal and abnormal features, making it difficult to accurately distinguish between normal operating conditions and abnormal conditions. When extracting normal features, multi-level sampling and hierarchical processing methods are not used, making it impossible to efficiently screen and process samples, resulting in the extracted features being inaccurate and inadequately representative. When extracting abnormal features, there is a lack of dynamic early warning models, making it impossible to promptly detect key abnormal features such as abnormal vibration frequencies and temperature gradients. This delays the response to abnormal events and increases the risk of accidents.

[0005] In terms of strategy optimization and decision-making, traditional cabinet management systems lack a strategy optimization and decision-making mechanism. Unable to extract common rules from feature extraction strategies for different working states and environmental conditions to build a control strategy library, they are also unable to generate adaptive feature extraction strategies based on new working scenarios. This results in poor system adaptability and flexibility, making it difficult to meet the diverse needs of different laboratories and working environments. For example, when the laboratory's ambient temperature and humidity fluctuate significantly or the frequency of cabinet use changes, traditional systems cannot automatically adjust monitoring and control strategies, affecting the normal operation and safety of the cabinet.

[0006] Traditional cabinets have limited security incident handling capabilities. Real-time monitoring of multi-dimensional monitoring parameters is not comprehensive and timely. When abnormal events are detected, there is a lack of comprehensive pre-defined emergency response strategies, making it impossible to quickly and effectively handle abnormal situations, reducing system security and reliability. Furthermore, for frequently occurring abnormal events, the emergency threshold cannot be adjusted according to actual conditions, resulting in the possibility of false alarms or missed alarms.

[0007] Traditional locks have numerous shortcomings in terms of design and application. The lock body structure is not advanced enough, and the materials used have limited strength and durability, making it difficult to meet the high-security and frequent use requirements of laboratories. The electronic drive unit has low driving accuracy and response speed, making it difficult to accurately execute control commands. The state feedback unit cannot accurately collect lock tongue position signals and motor torque data in real time, affecting the overall performance of the intelligent cabinet transformation system. Summary of the Invention

[0008] The object of the present invention is to provide a low-voltage intelligent electric-controlled titanium wire lock and an intelligent transformation system for laboratory cabinets to solve the problems raised in the above background technology.

[0009] To achieve the above objectives, the present invention provides the following technical solutions: a low-voltage intelligent electric-controlled titanium wire lock and laboratory cabinet intelligent transformation system, the system comprising:

[0010] The cabinet status monitoring module is used to construct a correlation data space between the cabinet environment and the lock status through multi-sensor fusion, and integrate multi-dimensional monitoring parameters, such as temperature and humidity, vibration intensity, opening and closing angle, and electromagnetic interference. The correlation data space includes an environmental perception layer, a mechanical status layer, and a safety assessment layer.

[0011] Running a feature extraction module to execute a normal feature extraction strategy and an abnormal feature extraction strategy, respectively. The normal feature extraction strategy uses a multi-level sampling method to extract a third feature set; the abnormal feature extraction strategy extracts a fourth feature set by building a dynamic warning model. The third feature set includes: unlocking time, closing force, and position offset; the fourth feature set includes: abnormal vibration frequency, temperature gradient, and electromagnetic mutation point.

[0012] The strategy optimization decision module is used to extract common rules from the normal feature extraction strategies and abnormal feature extraction strategies trained under different working states and environmental conditions, build a control strategy library, and generate feature extraction strategies adapted to new working scenarios based on the control strategy library;

[0013] The security event processing module is used to monitor multi-dimensional monitoring parameters in real time based on the associated data space, and trigger the preset emergency strategy when an abnormal event is detected.

[0014] Preferably, a correlation data space between the cabinet environment and the lock status is constructed through multi-sensor fusion, and multi-dimensional monitoring parameters are integrated. The specific steps include:

[0015] Use a synchronous acquisition device to obtain time series data of the cabinet environment and lock status, and perform data synchronization calibration;

[0016] By analyzing the correlation between parameters, environmental perception modeling, mechanical state simulation and safety assessment analysis are performed on the associated data space;

[0017] Obtain real-time multi-dimensional monitoring parameters of the actual working environment, and input the multi-dimensional monitoring parameters into the associated data space. Through calculation and analysis in the data space, anomalies are fed back to the user in real time.

[0018] Preferably, the specific steps of the normal feature extraction strategy include:

[0019] Using a fixed period method to divide the working data, the data segment includes M samples, each sample is represented by a third feature vector composed of a third feature set;

[0020] Construct a multi-level sampling framework, wherein the multi-level sampling framework includes: a low-frequency monitoring layer, a medium-frequency response layer, and a high-frequency control layer;

[0021] The samples were processed using a graded treatment method;

[0022] The specific steps of the hierarchical treatment method include:

[0023] S1, calculates the monitoring characteristics, response characteristics, and control characteristics of the sample based on the multi-stage sampling framework;

[0024] S2, divides the samples into three groups according to feature consistency: high consistency samples, medium consistency samples and low consistency samples;

[0025] S3, retains highly consistent samples, performs smoothing and reorganization operations on medium consistent samples, and performs probability elimination operations on low consistent samples;

[0026] S4, based on the monitoring characteristics, response characteristics and control characteristics of the samples, hierarchical clustering is used to screen and select the benchmark samples, among which the samples with the best matching monitoring characteristics, response characteristics and control characteristics are used as the benchmark samples;

[0027] S5: When the processing quantity reaches the preset maximum processing quantity, the current benchmark sample is output, and the sample that best meets the current working scenario is selected according to actual needs, otherwise return to S2.

[0028] Preferably, the normal feature extraction strategy and the abnormal feature extraction strategy are converted into a normal strategy vector and an abnormal strategy vector, respectively, and cascaded to obtain a control strategy vector; for the control strategy vectors of different working states and different environmental conditions, the repetition frequency of each strategy in the control strategy vector is calculated in turn. When the repetition frequency is greater than a third preset threshold, it is determined that the strategy has high universality between different states and conditions, and it is regarded as a common rule. The extracted common rules are combined to obtain a control strategy library, which provides a universal feature extraction framework for new working scenarios;

[0029] The control strategy library provides a general feature extraction framework for new working scenarios. The specific application methods include: inputting the basic parameters of the new working scenario into the control strategy library, matching the closest historical working status and environmental conditions, extracting the corresponding common rules, and combining the specific parameters of the new scenario to generate an adaptive feature extraction strategy. The specific parameters include: cabinet material, installation location and frequency of use.

[0030] Preferably, the security event processing module monitors the multi-dimensional monitoring parameters in real time according to the associated data space. When the multi-dimensional monitoring parameters exceed the preset safety threshold, it determines that the transformation system is operating abnormally and triggers the preset emergency strategy; sets a statistical period and records the number of times the same event is triggered within the period. If the same event occurs multiple times within the statistical period and the transformation system is still operating stably, the emergency threshold of the event is adjusted.

[0031] Preferably, the specific steps of the abnormal feature extraction strategy include:

[0032] According to the operating status of the transformation system, an event matrix including the fourth feature set is constructed, where the expression of the event matrix is ​​a multidimensional array including timestamp, temperature and humidity values, vibration intensity, opening and closing angle, and electromagnetic interference degree;

[0033] A rapid response model is used to construct an early warning network, which takes the event matrix as input and outputs an early warning indicator of the fourth feature set. The early warning indicator represents the abnormal change rate of the feature.

[0034] Constructing an event space, where the event space represents the value range of the warning indicator of the fourth feature set;

[0035] Construct the evaluation function of the dynamic early warning model;

[0036] Update the early warning network using a gradient optimization method.

[0037] Preferably, a synchronous acquisition device is used to obtain time series data of the cabinet environment and the lock status, and data synchronization calibration is performed. The specific steps include:

[0038] Configure independent time-series recording units for cabinet environment and lock status respectively;

[0039] All recording units are started simultaneously through hardware synchronization pulses to ensure consistent initial timing;

[0040] The collected time series data is tested for time difference, and the time offset is corrected by linear regression method to achieve data synchronization between parameters.

[0041] Preferably, the preset emergency strategies include: parameter adjustment strategy, operation interruption strategy and equipment protection strategy, wherein the parameter adjustment strategy is used to automatically adjust the lock sensitivity and sampling frequency, the operation interruption strategy is used to suspend the current operation process while the event continues, and the equipment protection strategy is used to activate the power-off protection mechanism in the event of a serious event.

[0042] Preferably, the present invention further includes a low-voltage intelligent electric-controlled titanium wire lock, which is applied to the above-mentioned laboratory cabinet intelligent transformation system, and the system includes:

[0043] The lock body structure unit is used to form a multi-stage linkage lock tongue mechanism through titanium alloy wire, and the multi-stage linkage lock tongue mechanism includes: a main lock tongue, an auxiliary lock tongue and an emergency unlocking tongue;

[0044] An electronically controlled drive unit, configured to drive the bolt according to control instructions issued by the modification system, wherein the control instructions include: a lock instruction, an unlock instruction, and a forced reset instruction;

[0045] The state feedback unit is used to collect the lock tongue position signal and motor torque data in real time, and upload the collected data to the laboratory cabinet intelligent transformation system.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] In terms of cabinet status monitoring, the system uses multi-sensor fusion to construct a correlated data space between the cabinet environment and lock status. This integrates multi-dimensional monitoring parameters such as temperature and humidity, vibration intensity, opening and closing angles, and electromagnetic interference, and is divided into an environmental perception layer, a mechanical status layer, and a security assessment layer. This multi-dimensional, multi-layered monitoring approach can comprehensively and accurately reflect the actual operating status of the cabinet, providing rich and reliable data support for subsequent feature extraction, strategy optimization, and security incident processing. For example, by monitoring these parameters in real time, the system can promptly detect subtle changes in the cabinet environment and abnormal lock status, providing early warning of potential safety hazards.

[0048] The operational feature extraction module utilizes both normal and abnormal feature extraction strategies. The normal feature extraction strategy, through multi-stage sampling and hierarchical processing, efficiently and accurately extracts a third feature set, including unlocking duration, closing force, and position offset, ensuring accurate understanding of the cabinet's normal operating status. The abnormal feature extraction strategy, through the construction of a dynamic early warning model, promptly captures a fourth feature set, including abnormal vibration frequency, temperature gradient, and electromagnetic mutation points, enabling rapid identification of abnormal events. This differentiated feature extraction approach enhances the system's ability to analyze and determine the cabinet's operating status, significantly reducing the probability of false positives and missed positives.

[0049] The strategy optimization decision module extracts common rules from feature extraction strategies under different working states and environmental conditions, builds a control strategy library, and can generate adaptive feature extraction strategies based on new working scenarios. This makes the system highly adaptable and flexible, and can automatically adjust monitoring and control strategies based on factors such as different laboratory environments, cabinet materials, installation locations, and frequency of use, ensuring that the system can operate efficiently under various complex working conditions. For example, when the laboratory's usage scenario changes, the system can quickly match the appropriate strategy from the control strategy library and generate a new feature extraction strategy without manual intervention, thereby improving management efficiency.

[0050] The security event processing module monitors multi-dimensional monitoring parameters in real time based on the associated data space. When an abnormal event is detected, it triggers pre-set emergency strategies such as parameter adjustment, operation interruption, and equipment protection, effectively responding to various abnormal situations and ensuring the safety of the cabinet and its contents. Furthermore, the system dynamically adjusts emergency thresholds based on the number of times the same event is triggered within a statistical period, enhancing the system's intelligence and reliability. For example, for abnormal events that occur frequently but do not affect the stable operation of the system, the system can automatically adjust the threshold to avoid frequent alarms, thus improving the system's practicality.

[0051] The low-voltage intelligent electronically controlled titanium wire lock utilizes titanium alloy wire to form a multi-stage locking mechanism, comprising a primary locking tongue, a secondary locking tongue, and an emergency release tongue. This structural design not only improves the lock's strength and durability, but also enhances its security and reliability. The electronically controlled drive unit precisely actuates the locking tongue according to control commands issued by the retrofit system. The state feedback unit collects and uploads real-time locking tongue position signals and motor torque data, enabling real-time interaction and precise control between the lock and the intelligent retrofit system, further enhancing the performance and intelligence of the entire system. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a working principle diagram of the low-voltage intelligent electric-controlled titanium wire lock and laboratory cabinet intelligent transformation system of the present invention;

[0053] Figure 2 Flowchart constructed for multi-sensor fusion and correlated data space;

[0054] Figure 3 Design diagrams for smoothing and reorganizing operations;

[0055] Figure 4 This is the design diagram of the security incident processing module. DETAILED DESCRIPTION

[0056] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0057] See also Figure 1-Figure 4 The present invention relates to a low-voltage intelligent electric-controlled titanium wire lock and a laboratory cabinet intelligent transformation system, which includes: a cabinet status monitoring module, an operation feature extraction module, a strategy optimization decision module and a security event processing module. The specific implementation method is as follows:

[0058] The cabinet status monitoring module uses multi-sensor fusion to construct a correlated data space between the cabinet environment and lock status, integrating multi-dimensional monitoring parameters such as temperature and humidity, vibration intensity, opening and closing angles, and electromagnetic interference. This correlated data space comprises an environmental perception layer, a mechanical status layer, and a security assessment layer. The operational feature extraction module implements both normal and abnormal feature extraction strategies. The normal feature extraction strategy uses a multi-level sampling method to extract a third feature set consisting of unlocking duration, closing force, and position offset. The abnormal feature extraction strategy uses a dynamic early warning model to extract a fourth feature set consisting of abnormal vibration frequency, temperature gradient, and electromagnetic mutation points. The strategy optimization decision module extracts common rules from the normal and abnormal feature extraction strategies trained under different operating states and environmental conditions, constructs a control strategy library, and generates feature extraction strategies adapted to new operating scenarios based on this control strategy library. The security event processing module monitors the multi-dimensional monitoring parameters in real time based on the correlated data space and triggers pre-set emergency response strategies when abnormal events are detected.

[0059] Example 1:

[0060] In the cabinet status monitoring module, when constructing the associated data space of the cabinet environment and lock status and integrating multi-dimensional monitoring parameters through multi-sensor fusion, relevant operations need to be performed according to specific procedures.

[0061] A synchronous data acquisition device is used to acquire time series data on the cabinet environment and lock status, and data synchronization and calibration are performed. Independent time series recording units are configured for the cabinet environment and lock status, respectively. These recording units, each equipped with precise time-stamping capabilities, record changes in cabinet environment and lock status parameters in real time. All recording units are simultaneously activated by a hardware synchronization pulse. This hardware synchronization pulse is a highly consistent and accurate pulse signal generated by specialized hardware. It ensures that all recording units begin operating at the same time, thus ensuring initial time consistency. After collecting the time series data, time difference detection (TDD) is performed on the collected data due to potential time offsets caused by factors such as different sensor sampling frequencies and transmission delays. TDD analyzes the time differences between different data sequences and then corrects for these offsets using linear regression. Based on the detected time difference patterns, the linear regression method develops a mathematical model to predict and compensate for these offsets, thereby achieving data synchronization between parameters and ensuring temporal comparability and consistency across data sources.

[0062] After data synchronization and calibration are complete, the associated data space needs to be subjected to environmental perception modeling, mechanical state simulation, and safety assessment analysis by analyzing the correlations between parameters. During the environmental perception modeling process, it is necessary to thoroughly study the interplay between environmental parameters such as temperature, humidity, and electromagnetic interference, as well as their overall impact on the cabinet environment. For example, changes in temperature and humidity can affect the propagation and intensity of electromagnetic interference, which in turn can impact the measurement accuracy of temperature and humidity sensors. By establishing mathematical models and logical relationships, these environmental parameters are organically integrated into the environmental perception layer to achieve a comprehensive perception and description of the cabinet environment. Mechanical state simulation primarily focuses on mechanical parameters such as vibration intensity and opening and closing angles. The vibration characteristics of the lock at different opening and closing angles, as well as the impact of vibration intensity on the lock's mechanical structure, need to be analyzed. By constructing a mechanical model, the mechanical behavior of the lock under various operating conditions is simulated, accurately reflecting the actual operating state of the lock in the mechanical state layer. The safety assessment analysis comprehensively considers various parameters from both the environmental perception layer and the mechanical state layer to establish a set of safety assessment indicators and standards. For example, when the temperature and humidity exceed a certain range, the vibration intensity is too high, or the electromagnetic interference exceeds the threshold, it may pose a threat to the safe operation of the cabinet and locks. The security assessment analysis will evaluate and predict the security status of the system based on the changes in these parameters to form a security assessment layer.

[0063] Real-time, multi-dimensional monitoring parameters of the actual working environment are acquired and entered into a linked data space. These parameters are collected in real time by various sensors deployed on the cabinet and locks, including temperature and humidity sensors, vibration sensors, angle sensors, and electromagnetic interference sensors. The collected real-time data undergoes signal conditioning, analog-to-digital conversion, and other processing steps to convert it into digital signals. The data is then calibrated according to previously established data synchronization rules to ensure temporal consistency. The calibrated real-time, multi-dimensional monitoring parameters are then entered into the established linked data space for processing and analysis in the environmental perception layer, mechanical status layer, and security assessment layer. The environmental perception layer updates the cabinet environment description based on real-time temperature, humidity, and electromagnetic interference data. The mechanical status layer updates the lock's mechanical status model based on real-time vibration intensity and opening and closing angle data. The security assessment layer integrates this real-time data to reassess the system's security status, providing accurate data support for subsequent operational feature extraction and security incident handling.

[0064] Throughout the entire process, every step is closely linked, and data synchronization and calibration are fundamental. Only by ensuring the accuracy and consistency of time-series data can the reliability of subsequent environmental perception modeling, mechanical state simulation, and safety assessment analysis be guaranteed. The construction of a correlated data space provides an effective framework for integrating multidimensional monitoring parameters, enabling different types of parameters to be rationally processed and applied at different levels, thereby achieving comprehensive and accurate monitoring of the cabinet environment and lock status. The input of real-time multidimensional monitoring parameters enables the correlated data space to reflect changes in the actual working environment in real time, ensuring that the system can promptly detect anomalies and take appropriate measures.

[0065] Example 2:

[0066] The execution of the normal feature extraction strategy in the operational feature extraction module must follow a rigorous process. From data division to sample processing to benchmark sample selection, each link has clear operating specifications and technical points. The fixed-cycle method is used to divide the working data. The fixed cycle here needs to be set according to the actual operating frequency of the system and the data collection requirements. The continuous working data is divided into several data segments, and each data segment contains M samples. Each sample is composed of a third feature set to form a third feature vector. The third feature set covers key parameters such as unlocking time, closing force, and position offset. These parameters are collected in real time by sensors and form sample data after preliminary preprocessing to ensure that each sample can accurately reflect the operating status of the lock within a specific time period.

[0067] A multi-level sampling framework is constructed, which includes a low-frequency monitoring layer, a medium-frequency response layer, and a high-frequency control layer. The low-frequency monitoring layer is mainly used for long-term monitoring of the macroscopic state of the system operation. It has a low sampling frequency and is suitable for capturing trend characteristics such as long-term temperature changes and slow position offsets. The medium-frequency response layer has a moderate sampling frequency and is used to respond to regular changes in the system operation process, such as force fluctuations and medium-frequency vibrations during normal opening and closing. The high-frequency control layer has a higher sampling frequency and is used to monitor the rapid movement and key control links of the lock in real time, such as instantaneous closing force peaks and position changes during high-speed unlocking. The three-level sampling framework achieves multi-dimensional and full-time coverage of the lock's operating status through sampling settings of different frequencies, providing comprehensive data support for subsequent feature extraction.

[0068] After constructing a multi-level sampling framework, a hierarchical processing method is used to process the samples. The first step is to calculate the monitoring characteristics, response characteristics, and control characteristics of the samples based on the multi-level sampling framework. The monitoring characteristics correspond to the data of the low-frequency monitoring layer. By statistically analyzing the data collected over a long period of time, characteristics such as the average unlocking time and temperature change trends are obtained; the response characteristics are based on the data of the medium-frequency response layer, and analyze the response of the samples to various operations under normal operating conditions, such as the fluctuation range of the closing force and the normal amplitude of the position offset; the control characteristics come from the high-frequency control layer, focusing on the instantaneous characteristics of the samples in key control links, such as the maximum closing force and the position change rate at the moment of unlocking.

[0069] In the second step, the samples are divided into three groups based on feature consistency: high-consistency samples, medium-consistency samples, and low-consistency samples. Feature consistency is determined by the degree of deviation between the characteristic parameters in the sample and the standard characteristic parameters during normal system operation. The characteristic parameters of the highly consistent samples are highly consistent with the standard parameters, indicating that they reflect the operation of the system in a stable and normal state; the characteristic parameters of the medium-consistent samples deviate from the standard parameters to a certain extent, but are still within a reasonable range, which may be due to slight environmental changes or normal equipment wear; the characteristic parameters of the low-consistent samples deviate greatly from the standard parameters, which may reflect abnormal conditions in system operation or interference during data collection.

[0070] The third step is to adopt different processing methods for different types of samples. Highly consistent samples are directly retained because they accurately reflect the normal operating status of the system and can be used as baseline data for subsequent analysis. Smoothing and reorganization operations are performed on moderately consistent samples. The smoothing operation uses curve fitting methods to generate new samples. Specifically, the parameter curves of two adjacent samples are connected through Bezier interpolation. Bezier interpolation can generate a smooth curve based on known sample points, thereby constructing a continuous working sample data segment between the two samples and eliminating data discontinuities caused by sampling intervals. The reorganization operation uses state parameter reconstruction methods to update the samples, mapping the feature vectors of the smoothed samples to the feature subspace. The core components are extracted through principal component analysis. Principal component analysis can extract the main components that best represent the sample characteristics from high-dimensional feature vectors, generating a compressed feature vector. This reduces the data dimension while retaining the main features of the sample, improving processing efficiency. Probabilistic elimination is performed on low-consistent samples. Based on a preset probability threshold, samples whose feature parameters deviate significantly from the standard are eliminated to reduce the impact of abnormal data on subsequent analysis.

[0071] In the fourth step, hierarchical clustering is used to screen samples based on their monitoring, response, and control characteristics, and to select benchmark samples. Hierarchical clustering is a method that groups samples hierarchically based on their similarity. Each sample is first treated as a separate cluster, and clusters are then gradually merged based on similarity until pre-defined criteria are met. When selecting benchmark samples, a pre-set standard feature vector is a set of standard feature parameters derived through statistical analysis (such as mean and variance) of historical data collected during normal system operation. These include baseline values ​​for unlocking time, closing force, and position offset. Similarity is calculated using the Euclidean distance algorithm; the smaller the Euclidean distance, the higher the similarity between the sample and the pre-set standard feature vector. The samples with the best matching monitoring, response, and control characteristics are selected as benchmark samples. For monitoring characteristics, the sample that most closely matches the standard monitoring characteristics is selected; for response and control characteristics, the sample that best matches their respective standard characteristics is similarly selected. These benchmark samples fully represent the characteristics of the system under normal operation.

[0072] The fifth step is to determine whether the number of samples processed has reached the preset maximum number of samples. If so, the current benchmark sample is output and the sample that best matches the current work scenario is selected based on actual needs. For example, benchmark samples that match different laboratory environments, different cabinet materials, or different usage frequencies are selected to ensure the adaptability of the feature extraction strategy. If not, the process returns to step 2 and continues to divide and process the remaining samples for feature consistency until the maximum number of samples is reached or all samples are processed.

[0073] The entire normal feature extraction strategy achieves effective processing and feature extraction of lock operation status data through fixed-period data division, the construction of a multi-level sampling framework, the implementation of a hierarchical processing method, and the selection of benchmark samples. It can accurately capture various characteristics of the system under normal operation, and provide reliable benchmark data for subsequent abnormal feature identification and strategy optimization, ensuring that the system has a solid data foundation for stability and reliability evaluation under normal operation.

[0074] Example 3:

[0075] The strategy optimization decision module undertakes the core function of strategy extraction and optimization in the entire laboratory cabinet intelligent transformation system. Its implementation method needs to be explained in detail from multiple aspects such as strategy vector conversion, common rule extraction, control strategy library construction and new scenario adaptation. This module converts the normal feature extraction strategy and the abnormal feature extraction strategy into a normal strategy vector and an abnormal strategy vector respectively. This conversion process converts the text-described strategy into a numerical vector form based on the specific operation steps, parameter settings and logical rules contained in the strategy. For example, the multi-level sampling framework parameters and the weights of the hierarchical processing steps in the normal feature extraction strategy are all encoded as various dimensions in the vector, and the abnormal strategy vector corresponds to the structural parameters of the dynamic early warning model, the coefficients of the evaluation function, etc. In this way, the strategy has a computable and comparable mathematical expression.

[0076] The normal strategy vector and the abnormal strategy vector are concatenated to obtain a control strategy vector. The concatenation operation sequentially connects the two vectors into a higher-dimensional vector, allowing the control strategy vector to simultaneously contain all the information from both the normal and abnormal feature extraction strategies. For control strategy vectors under different operating states (such as daily operation, frequent use, and long-term static conditions) and different environmental conditions (such as high temperature and humidity, strong electromagnetic interference, and frequent vibration), the system needs to sequentially calculate the repetition frequency of each strategy in the vector. The repetition frequency here refers to the ratio of the number of times the same strategy appears in the control strategy vectors for different operating states or environmental conditions to the total number of vectors. When the repetition frequency of a particular strategy is greater than a third preset threshold, it indicates that the strategy is adopted in multiple states and conditions. Therefore, it is determined to have high universality across different states and conditions and is considered a common rule.

[0077] When extracting common rules, the system performs statistical analysis on a large number of control strategy vectors from different scenarios. For example, among 100 sets of control strategy vectors with different ambient temperatures, humidity, and cabinet usage frequencies, if the strategy of "performing smoothing operations on moderately consistent samples" appears in 80 sets of vectors and the repetition frequency exceeds the preset third threshold (such as 70%), it is identified as a common rule. By combining these extracted common rules, a control strategy library can be obtained. The control strategy library is essentially a collection of multiple universal strategies. It provides a general feature extraction framework for new work scenarios. This framework integrates successful strategy experiences in different scenarios, avoiding the tedious process of redesigning strategies for each new scenario.

[0078] The specific application of the control strategy library in a new working scenario requires the following steps: First, the basic parameters of the new working scenario are entered into the control strategy library. Basic parameters include but are not limited to the initial range of ambient temperature and humidity, the expected vibration intensity level, the regular opening and closing frequency of the cabinet, etc. These parameters are initially collected by sensors or set according to scenario planning. Next, the control strategy library will match the case closest to the new scenario in the historical working conditions and environmental conditions stored internally. The matching process is based on a similarity algorithm, comparing the differences between the new scenario parameters and the historical scenario parameters to find several historical scenarios with the smallest differences. For example, if the ambient temperature of the new scenario is 25℃±2℃, the humidity is 50%±5%, and the vibration intensity is low, the control strategy library will retrieve the scene data with the same temperature and humidity range and low vibration intensity in the history.

[0079] After extracting the corresponding common rules, it is necessary to generate an adaptive feature extraction strategy based on the specific parameters of the new scenario. These include cabinet material (such as steel, wood, or composite materials), installation location (such as the center of the lab, a corner, or near large equipment), and frequency of use (such as less than 10 times per day, 10-50 times, or more than 50 times). These specific parameters will affect the specific implementation of the strategy. For example, when the cabinet is made of steel, the propagation characteristics of electromagnetic interference differ from those of a wooden cabinet. Therefore, in the abnormal feature extraction strategy, the detection threshold of electromagnetic mutation points needs to be adjusted based on the material. When the installation location is close to large equipment, the environmental baseline parameters for ambient vibration and electromagnetic interference exceed the normal range, and the multi-level sampling frame frequency in the normal feature extraction strategy may need to be reset.

[0080] When combined with specific parameters, the system will make adaptive adjustments to the common rules. For example, if the cabinet material in the historical scenario is wood, the sampling frequency of the vibration characteristics in the corresponding common rules is 10Hz, and the cabinet material in the new scenario is steel. Since the vibration transmission of the steel cabinet is faster, the sampling frequency may need to be increased to 20Hz. This adjustment is based on the analysis of the mechanism of the influence of materials on vibration propagation, rather than verification through experimental data, to ensure the rationality and logic of the adjustment. After generating the adaptation strategy, the system will conduct a preliminary verification of the strategy to check whether the strategy parameters are within a reasonable range and whether the logic is coherent, to ensure that the new strategy can be effectively applied to the feature extraction of the new work scenario.

[0081] The strategy optimization decision module uses a data-driven approach to extract common rules from historical experience, build a universal control strategy library, and adapt the strategies based on the specific parameters of new scenarios, achieving dynamic optimization of feature extraction strategies and scenario-specific adaptability. This approach not only improves the system's adaptability to diverse operating environments but also reduces the workload of strategy design by reusing proven strategies, thereby enhancing the operational efficiency and reliability of the entire intelligent transformation system.

[0082] Example 4:

[0083] The security event processing module plays a key role in real-time monitoring and anomaly response in the intelligent laboratory cabinet renovation system. Its implementation needs to be tailored to specific application scenarios, with detailed explanations covering monitoring mechanisms, anomaly determination, emergency strategy triggering, and dynamic threshold adjustment. Taking the cabinet renovation scenario of a university's chemistry laboratory as an example, the module first integrates real-time data from temperature and humidity sensors, vibration sensors, angle sensors, and electromagnetic interference sensors through a correlated data space to build a multi-dimensional monitoring system. When volatile chemical reagents are stored inside the cabinet, abnormal fluctuations in temperature and humidity can cause reagent deterioration or safety risks. The module continuously collects temperature and humidity parameters and cross-checks them with the opening and closing angle data of the mechanical status layer and the risk thresholds of the safety assessment layer.

[0084] During real-time monitoring, the module sets preset safety thresholds for multiple monitoring parameters. For example, the temperature safety threshold is set at 15-30°C, the humidity threshold is 40%-60%, the vibration intensity threshold is 50dB, and the electromagnetic interference threshold is 30μT. If the temperature and humidity sensors detect that the cabinet temperature rises to 32°C and the humidity drops to 35% for more than 5 minutes, the parameters will exceed the preset safety thresholds. The module will immediately determine that the modified system is operating abnormally and trigger the preset emergency response strategy. This abnormality determination is not based solely on a single parameter exceeding the limit, but rather incorporates multi-level analysis of the associated data space. For example, whether the sudden temperature rise is accompanied by an abnormal change in the cabinet opening angle (to eliminate temperature fluctuations caused by manual opening) or whether the electromagnetic interference level increases simultaneously (to determine whether abnormal heating is caused by equipment failure).

[0085] The preset emergency strategies include parameter adjustment, operation interruption, and equipment protection. In the case of excessive temperature and humidity mentioned above, the parameter adjustment strategy is triggered first: the system automatically adjusts the lock sensitivity, temporarily increasing the closing force threshold required for unlocking from 20N to 25N to reduce environmental fluctuations caused by frequent openings; at the same time, the sampling frequency is increased from the conventional 1 time / minute to 1 time / 10 seconds to more intensively monitor temperature and humidity changes. If the temperature continues to rise to 35°C after 10 minutes of the parameter adjustment strategy being executed, the operation interruption strategy is triggered, suspending the current electrical control operation process of all cabinets, prohibiting unauthorized personnel from opening the cabinet, and preventing the external environment from further affecting the status inside the cabinet.

[0086] If the temperature continues to rise to 40°C and the vibration sensor detects abnormal vibrations inside the cabinet (such as the collision of reagent bottles caused by the expansion of high temperatures), the equipment protection strategy is triggered, and the power-off protection mechanism is activated, shutting off the power to the cabinet's electrical control system to prevent circuit failures or more serious safety accidents caused by high temperatures. When the power-off protection mechanism is activated, the system will simultaneously send an alert message to the laboratory manager's terminal, containing detailed data such as the abnormal parameters, the triggering strategy, and the power-off time, so that managers can receive timely notifications and data.

[0087] To prevent false triggering due to minor environmental fluctuations or brief sensor errors, the module also sets a statistical period (such as 24 hours) to record the number of times the same event is triggered. For example, within 24 hours, a laboratory experienced a brief malfunction in the air conditioning system, causing the temperature inside the cabinet to exceed 30°C three times. However, each time the temperature exceeded the limit, it lasted less than two minutes. The system quickly recovered to normal thanks to the parameter adjustment strategy, and the cabinet's operating status remained stable. In this case, the module would determine that the temperature exceedance event was an occasional minor anomaly, adjust the emergency threshold, and report it to the administrator, temporarily raising the temperature warning threshold from 30°C to 32°C to reduce false alarms in similar situations.

[0088] The threshold adjustment process is as follows: When the same event triggers a preset number of times (e.g., three times) within a statistical period, and the system maintains stable operation after the trigger (no operational interruptions or device protection policy activation), the module analyzes the parameter fluctuation range and duration of the event and calculates a new threshold based on historical normal operation data in the associated data space. For example, in the temperature example above, the three temperature violations were 31°C, 30.5°C, and 31.2°C, each lasting 1-2 minutes. The historical maximum temperature during normal operation was 29.8°C. Therefore, the new threshold was set to 32°C, maintaining a safety margin while preventing frequent false triggers.

[0089] In another scenario, if the cabinet is impacted by an external force and the vibration intensity instantly reaches 80dB, exceeding the safety threshold of 50dB, the module immediately triggers an operation interruption strategy, suspending unlocking operations. It also records the time of the impact, the peak vibration intensity, and the duration of the impact, and reports this to the administrator. If the impact occurs only once within 24 hours and the system exhibits no other anomalies, the original vibration threshold is maintained. If the same location experiences three impacts within 24 hours, causing the vibration threshold to be frequently triggered, the module, based on historical data from the installation location (e.g., proximity to a corridor makes it prone to collisions), adjusts the vibration warning threshold to 60dB, and notifies the administrator of an alert to take appropriate action.

[0090] The security event processing module implements dynamic protection of laboratory cabinet operation through this multi-level monitoring and response mechanism. From real-time data collection to anomaly linkage analysis, and then to the triggering of hierarchical emergency response strategies and adaptive threshold adjustment, each link closely relies on the multidimensional data support of the associated data space to ensure that interference with normal operations is minimized while ensuring safety.

[0091] Example 5:

[0092] In the intelligent laboratory cabinet retrofit system, the implementation of an abnormal feature extraction strategy requires constructing an event matrix based on the specific operating status and enabling dynamic monitoring through an early warning network. For example, when biological samples requiring a constant temperature and humidity are stored in a cabinet in a research institution's biological laboratory, the system needs to capture abnormal features that could affect sample safety in real time. In this case, the abnormal feature extraction strategy constructs an event matrix containing a fourth feature set based on the operational status of the retrofit system. This matrix records parameters such as timestamps, temperature and humidity values, vibration intensity, opening and closing angles, and electromagnetic interference levels in a multidimensional array format. For example, at a specific moment, the event matrix records the timestamp of 2:30:25 PM, temperature of 26.5°C, humidity of 52%, vibration intensity of 45 dB, opening and closing angle of 0°, and electromagnetic interference level of 28 μT. These data constitute the basic unit reflecting the current status.

[0093] The low-voltage, intelligent, electronically controlled titanium wire lock used in this laboratory cabinet features a centrally located main bolt in the lock body, with secondary bolts symmetrically located on either side. These secondary bolts are connected to the gear slots on either side of the main bolt via titanium alloy wire. The emergency release bolt, located at the bottom of the lock body, is spring-coupled to the rear end of the main bolt. The electronic drive unit utilizes a low-voltage (12V) stepper motor, featuring high-precision control (1.8° step angle) and low power consumption, making it suitable for laboratory cabinet security requirements. The stepper motor's output shaft is connected to a driving gear, which meshes with a driven gear, which in turn meshes with the gear slot of the main bolt. The transmission ratio is 1:3, ensuring stable bolt movement. When the system issues an "unlock command," the stepper motor rotates forward, driving the driving gear. This drives the primary bolt backward, while the secondary bolt retracts synchronously, while the emergency release bolt remains stationary. If the main bolt becomes stuck, the emergency release bolt can be mechanically unlocked by manually depressing the spring. The state feedback unit will collect the lock tongue position signal and the torque data of the stepper motor in real time, and upload the collected data to the laboratory cabinet intelligent transformation system.

[0094] A rapid response model is used to construct an early warning network. This network takes the event matrix as input and outputs a warning indicator for the fourth feature set, namely the abnormal rate of change of the feature. Using the biological laboratory as an example, if a malfunction in the air conditioning system within a cabinet causes a temperature rise, the early warning network analyzes the temperature changes over the time series. Assuming that the temperature rises continuously from 26.5°C to 28.8°C over the five minutes from 2:30 PM to 2:35 PM, the early warning network calculates the temperature gradient as (28.8 - 26.5) / 5 = 0.46°C / min. This rate of change is compared with historical normal data. If the temperature gradient is typically less than 0.1°C / min during normal operation, the current temperature gradient is considered abnormal and the corresponding warning indicator is output, indicating the risk of an abnormal temperature rise.

[0095] When constructing the event space, it's important to clearly define the range of warning indicators for the fourth feature set. For example, during normal operation in a biological laboratory, cabinet vibration intensity typically ranges from 30-50dB, with the frequency typically being low (e.g., below 10Hz). Therefore, the warning indicator range for abnormal vibration frequency in the event space can be set to frequencies greater than 15Hz and intensities exceeding 60dB. If heavy equipment is activated near the cabinet, causing the vibration sensor to detect a vibration frequency of 20Hz and an intensity of 65dB, this data falls within the warning range of the event space. The system immediately identifies this as an abnormal vibration feature, triggering the subsequent evaluation process.

[0096] The dynamic early warning model's evaluation function comprehensively determines the severity of abnormal features. For example, the evaluation function calculates a comprehensive warning value based on the weights and deviations of each feature: temperature gradient, electromagnetic mutation point, and abnormal vibration frequency. In a biological laboratory scenario, temperature has the greatest impact on sample safety, with a weight of 0.5. Electromagnetic interference and vibration have weights of 0.3 and 0.2, respectively. If, at a certain moment, the temperature gradient reaches 0.5°C / min (exceeding the normal threshold of 0.1°C / min), the electromagnetic interference suddenly rises to 45μT (normal threshold of 30μT), and the vibration frequency reaches 18Hz (normal threshold of 15Hz), the evaluation function calculates a comprehensive warning value based on the deviation ratios of each feature (e.g., temperature deviation of 400%, electromagnetic deviation of 50%, and vibration deviation of 20%) and their weights, thereby determining the urgency of the abnormal event.

[0097] When updating the early warning network using a gradient optimization method, the system continuously adjusts network parameters based on historical anomaly data to improve early warning accuracy. For example, during the initial operation of the biological laboratory, a refrigerator startup caused a brief increase in electromagnetic interference (EMI) levels to 35 μT, which the system mistakenly identified as an EMI mutation point. Using the gradient optimization method, the electromagnetic fluctuation data generated by normal equipment startup was incorporated into training, adjusting the EMI warning threshold and rate of change calculation method. This enabled the early warning network to accurately distinguish between normal fluctuations and true EMI mutation points in similar situations. In practice, gradient optimization calculates the direction and magnitude of network parameter adjustments based on the errors in each warning (such as false positives or missed negatives), gradually reducing the misjudgment rate.

[0098] Taking a chemical laboratory as an example, when a cabinet stores flammable and explosive reagents, the anomaly feature extraction strategy focuses on temperature gradients and electromagnetic mutation points. If, at a certain moment, the electromagnetic interference level suddenly increases from 25 μT to 50 μT, and the temperature gradient simultaneously reaches 0.6°C / min, the event matrix will record this sudden change, and the early warning network will output abnormal warning indicators for the electromagnetic mutation point and temperature gradient. In the event space, the criteria for determining an electromagnetic mutation point are an instantaneous increase exceeding 50% and an absolute value exceeding 40 μT. In this case, the event meets the warning conditions, and the evaluation function calculates a high-risk warning value, triggering the emergency response strategy of the security event processing module.

[0099] During long-term operation, if a chemical laboratory experiences frequent temperature and humidity fluctuations due to aging ventilation equipment, the system will continuously adjust the temperature gradient warning threshold through gradient optimization. For example, if the initial threshold is 0.1°C / min and the temperature gradient reaches 0.15°C / min multiple times within a month due to normal ventilation equipment operation, triggering a warning without any safety incidents, gradient optimization will gradually adjust the threshold to 0.2°C / min, avoiding frequent false warnings while maintaining a sufficient safety margin.

[0100] The abnormal feature extraction strategy forms an adaptive anomaly identification system through the dynamic construction of an event matrix, real-time analysis of the early warning network, precise definition of the event space, comprehensive calculation of evaluation functions, and continuous iteration of gradient optimization. From temperature and humidity monitoring in biological laboratories to electromagnetic safety protection in chemical laboratories, this strategy can accurately capture key features such as abnormal vibration frequency, temperature gradient, and electromagnetic mutation points according to the needs of different scenarios. This provides strong support for early warning and timely handling of safety incidents, ensuring the safe and stable operation of laboratory cabinets in complex environments.

[0101] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0102] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A laboratory cabinet intelligent transformation system, characterized in that: include: The cabinet status monitoring module is used to construct a correlation data space between the cabinet environment and the lock status through multi-sensor fusion, and integrate multi-dimensional monitoring parameters, such as temperature and humidity, vibration intensity, opening and closing angle, and electromagnetic interference. The correlation data space includes an environmental perception layer, a mechanical status layer, and a safety assessment layer. Running a feature extraction module to execute a normal feature extraction strategy and an abnormal feature extraction strategy, respectively. The normal feature extraction strategy uses a multi-level sampling method to extract a third feature set; the abnormal feature extraction strategy extracts a fourth feature set by building a dynamic warning model. The third feature set includes: unlocking time, closing force, and position offset; the fourth feature set includes: abnormal vibration frequency, temperature gradient, and electromagnetic mutation point. The strategy optimization decision module is used to extract common rules from normal feature extraction strategies and abnormal feature extraction strategies trained under different working states and environmental conditions, build a control strategy library, and generate feature extraction and alarm prompt strategies adapted to new working scenarios based on the control strategy library; The security event processing module is used to monitor multi-dimensional monitoring parameters in real time based on the associated data space, and trigger the preset emergency strategy when an abnormal event is detected.

2. The intelligent transformation system for laboratory cabinets according to claim 1 is characterized in that: Through multi-sensor fusion, a data space is constructed that correlates the cabinet environment and lock status. Multi-dimensional monitoring parameters are then integrated to provide feedback to the user. The specific steps include: Use a synchronous acquisition device to obtain time series data of the cabinet environment and lock status, and perform data synchronization calibration; By analyzing the correlation between parameters, environmental perception modeling, mechanical state simulation and safety assessment analysis are performed on the associated data space; Obtain real-time multi-dimensional monitoring parameters of the actual working environment, and input the multi-dimensional monitoring parameters into the associated data space. Through calculation and analysis in the data space, anomalies are fed back to the user in real time.

3. The intelligent transformation system for laboratory cabinets according to claim 1 is characterized in that: The specific steps of the normal feature extraction strategy include: Using a fixed period method to divide the working data, the data segment includes M samples, each sample is represented by a third feature vector composed of a third feature set; Construct a multi-level sampling framework, wherein the multi-level sampling framework includes: a low-frequency monitoring layer, a medium-frequency response layer, and a high-frequency control layer; The samples were processed using a graded treatment method; The specific steps of the hierarchical treatment method include: S1, calculates the monitoring characteristics, response characteristics, and control characteristics of the sample based on the multi-stage sampling framework; S2, divides the samples into three groups according to feature consistency: high consistency samples, medium consistency samples and low consistency samples; S3, retains highly consistent samples, performs smoothing and reorganization operations on medium consistent samples, and performs probability elimination operations on low consistent samples; S4, based on the monitoring characteristics, response characteristics, and control characteristics of the samples, hierarchical clustering is used to screen and select benchmark samples, wherein the samples with the highest degree of consistency with the preset benchmark characteristics are selected as the benchmark samples for the monitoring characteristics, response characteristics, and control characteristics, respectively; S5: When the processing quantity reaches the preset maximum processing quantity, the current benchmark sample is output, and the sample that best meets the current working scenario is selected according to actual needs, otherwise return to S2.

4. The intelligent transformation system for laboratory cabinets according to claim 1, characterized in that: The normal feature extraction strategy and the abnormal feature extraction strategy are respectively converted into a normal strategy vector and an abnormal strategy vector, and cascaded to obtain a control strategy vector; for the control strategy vectors of different working states and different environmental conditions, the repetition frequency of each strategy in the control strategy vector is calculated in turn. When the repetition frequency is greater than a third preset threshold, the strategy is determined to have high universality across different states and conditions and is regarded as a common rule. The extracted common rules are combined to obtain a control strategy library, which provides a universal feature extraction framework for new working scenarios; The control strategy library provides a general feature extraction framework for new working scenarios. The specific application methods include: inputting the basic parameters of the new working scenario into the control strategy library, matching the closest historical working status and environmental conditions, extracting the corresponding common rules, and combining the specific parameters of the new scenario to generate an adaptive feature extraction strategy. The specific parameters include: cabinet material, installation location and frequency of use.

5. The intelligent transformation system for laboratory cabinets according to claim 1 is characterized in that: The security event processing module monitors multi-dimensional monitoring parameters in real time based on the associated data space. When the multi-dimensional monitoring parameters exceed the preset safety threshold, it determines that the transformation system is operating abnormally and triggers the preset emergency strategy; Set a statistical period and record the number of times the same event is triggered within the period. If the same event occurs multiple times within the statistical period and the transformation system still runs stably, adjust the emergency threshold of the event.

6. The intelligent transformation system for laboratory cabinets according to claim 1, characterized in that: The specific steps of the abnormal feature extraction strategy include: According to the operating status of the transformation system, an event matrix including the fourth feature set is constructed, where the expression of the event matrix is ​​a multidimensional array including timestamp, temperature and humidity values, vibration intensity, opening and closing angle, and electromagnetic interference degree; A rapid response model is used to construct an early warning network, which takes the event matrix as input and outputs an early warning indicator of the fourth feature set. The early warning indicator represents the abnormal change rate of the feature. Constructing an event space, where the event space represents the value range of the warning indicator of the fourth feature set; Construct the evaluation function of the dynamic early warning model; Update the early warning network using a gradient optimization method.

7. The intelligent transformation system for laboratory cabinets according to claim 2, characterized in that: Use a synchronous acquisition device to obtain time series data of the cabinet environment and lock status, and perform data synchronization calibration. The specific steps include: Configure independent time-series recording units for cabinet environment and lock status respectively; All recording units are started simultaneously through hardware synchronization pulses to ensure consistent initial timing; The collected time series data is tested for time difference, and the time offset is corrected by linear regression method to achieve data synchronization between parameters.

8. The laboratory cabinet intelligent transformation system according to claim 5, characterized in that: The preset emergency strategies include: parameter adjustment strategy, operation interruption strategy and equipment protection strategy. Among them, the parameter adjustment strategy is used to automatically adjust the lock sensitivity and sampling frequency, the operation interruption strategy is used to suspend the current operation process while the event continues, and the equipment protection strategy is used to activate the power-off protection mechanism in serious events.

9. A low-voltage intelligent electric-controlled titanium wire lock, applied to a laboratory cabinet intelligent transformation system according to any one of claims 1 to 8, characterized in that: include: The lock body structure unit is used to form a multi-stage linkage lock tongue mechanism using titanium alloy wire. The multi-stage linkage lock tongue mechanism includes: a main lock tongue, an auxiliary lock tongue and an emergency unlocking tongue, wherein the main lock tongue is located at the center of the lock body, the auxiliary lock tongues are symmetrically distributed on both sides of the main lock tongue, and the emergency unlocking tongue is arranged at the bottom of the lock body and is linked to the main lock tongue; The electronically controlled drive unit includes a stepper motor and a transmission gear set, which is used to drive the main lock tongue, auxiliary lock tongue and emergency unlocking tongue through the transmission gear set according to the control instructions issued by the modified system, including locking instructions, unlocking instructions and forced reset instructions; The state feedback unit is used to collect the lock tongue position signal and motor torque data in real time, and upload the collected data to the laboratory cabinet intelligent transformation system.

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