Safety education training management method and system

Abstract

The invention relates to the technical field of safety education training management, and discloses a safety education training management method and system. The method comprises the steps of generating a core data seed containing initial parameter definition and parameter evolution rules, establishing a corresponding question bank, and performing capability detection and comprehensive evaluation. A plurality of parameter evolution interaction paths are constructed with the seeds as starting points, and observation sentinel points and behavior signal monitors are arranged on the paths and used for capturing state switching, behavior triggering and other original signals in the training process. When a signal is responded, dynamic mapping of the signal to a specific whistle point on a specific path is established, and the signal is analyzed into an adjusting or resetting instruction for a specific parameter according to the dynamic mapping. According to the method, automatic evolution of management parameters according to preset rules is realized, discrete events in training can be accurately converted into control instructions for driving self-optimization of the system, and closed-loop and self-adaptive regulation and control of a safety management strategy are completed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of safety education and training management, in particular to a safety education and training management method and system. BACKGROUND

[0002] The current safety education and training management system generally adopts a management mode based on fixed rules and static parameters. The system relies on pre-set and mutually independent parameter configurations for the management of dimensions such as question bank establishment, daily ability detection, and personal comprehensive assessment. These parameters remain unchanged during system operation, and the management logic is fixed in the program flow. When new data or state changes occur during the training process, the system can only perform the pre-set corresponding operations and cannot trigger the adjustment of the management parameters themselves.

[0003] This static architecture results in the inability of the management strategy to be optimized according to the actual effect of the training activities. The system's response to events such as question bank updates, detection behaviors, or assessment cycle replacements is limited to recording results or generating reports. The data value behind the events fails to be converted into instructions to drive the iteration of the core parameters of the management system. The management closed loop has a fault, and the system's response is lagging and passive, lacking the ability to adaptively adjust based on continuous feedback. Therefore, a technical solution is needed that can automatically evolve the core parameters of the management according to the built-in rules and accurately and automatically map the original events in the training process into parameter adjustment instructions. SUMMARY

[0004] The purpose of the present application is to provide a safety education and training management method and system to solve the problems raised in the background.

[0005] To achieve the above purpose, the present application provides a safety education and training management method, which comprises: generating core data seeds for three management dimensions of safety education and training question bank establishment, daily safety ability detection, and personal safety education ability comprehensive assessment; the core data seeds encapsulate the initial parameter definitions and parameter evolution rules of the corresponding management dimensions; starting from the core data seeds, around the initial parameter definitions of each management dimension, multiple parameter evolution interaction paths are constructed; each of the parameter evolution interaction paths is used to describe how the parameters change and pass according to the parameter evolution rules in the time sequence; multiple observation sentinels are set on the parameter evolution interaction paths, and each observation sentinel is configured with a behavior signal listener; the behavior signal listener is used to capture the original behavior signals triggered by the question bank establishment state switching, detection behavior occurrence, or assessment cycle replacement in the safety education and training process; In response to the native behavior signal captured by the behavior signal listener, a dynamic mapping relationship of an observation checkpoint on a specific parameter evolution interaction path from the native behavior signal is established; According to the dynamic mapping relationship, the native behavior signal is parsed into an operation instruction for adjusting or resetting a specific parameter on a specific path.

[0006] Preferably, the core data seed for establishing the three management dimensions of safety education training question bank, daily safety ability detection, and personal safety education ability comprehensive evaluation is generated, including: Define the question parameters, knowledge point association parameters, and question frequency parameters for the safety education training question bank establishment dimension, and encapsulate the generation logic of the question parameters, the binding rules of the knowledge point association parameters, and the scheduling strategy of the question frequency parameters as the first group of parameter evolution rules; Define the detection content parameters, trigger period parameters, and feedback threshold parameters for the daily safety ability detection dimension, and encapsulate the assembly logic of the detection content parameters, the calibration strategy of the trigger period parameters, and the adjustment mechanism of the feedback threshold parameters as the second group of parameter evolution rules; Define the ability dimension parameters, weight configuration parameters, and historical trajectory parameters for the personal safety education ability comprehensive evaluation dimension, and encapsulate the decomposition logic of the ability dimension parameters, the distribution strategy of the weight configuration parameters, and the recording and calling mechanism of the historical trajectory parameters as the third group of parameter evolution rules; Encapsulate the question parameters, knowledge point association parameters, and question frequency parameters together with the first group of parameter evolution rules to form the first core data seed; encapsulate the detection content parameters, trigger period parameters, and feedback threshold parameters together with the second group of parameter evolution rules to form the second core data seed; and encapsulate the ability dimension parameters, weight configuration parameters, and historical trajectory parameters together with the third group of parameter evolution rules to form the third core data seed.

[0007] Preferably, the core data seed is taken as a starting point, and a plurality of parameter evolution interaction paths are constructed around the initial parameter definitions of each management dimension, including: Expand the first core data seed, take the scheduling strategy of the question frequency parameters as a guide, and construct a first type of parameter evolution interaction path connecting the question parameters and the knowledge point association parameters; the first type of parameter evolution interaction path describes how the questions are evolved and generated according to the knowledge point association rules under a specific question frequency; Expand the second core data seed, take the calibration strategy of the trigger period parameters as a guide, and construct a second type of parameter evolution interaction path connecting the detection content parameters and the feedback threshold parameters; the second type of parameter evolution interaction path describes how the detection content adjusts in response to changes in the feedback threshold under a specific detection period; Unfolding the third core data seed, guided by the allocation strategy of the weight configuration parameter, a third type of parameter evolution interaction path connecting the capability dimension parameter and the historical trajectory parameter is constructed; the third type of parameter evolution interaction path describes how the personal capability dimension is dynamically evaluated in combination with the historical trajectory under a specific weight allocation.

[0008] Preferably, a plurality of observation sentinels are set on the parameter evolution interaction path, and a behavior signal listener is configured for each observation sentinel, including: On the first type of parameter evolution interaction path, a first observation sentinel and a second observation sentinel are set before and after the application of the knowledge point association rule, respectively; a first behavior signal listener for listening to the knowledge point structure change behavior of the question bank is configured for the first observation sentinel, and a second behavior signal listener for listening to the new question generation and storage behavior is configured for the second observation sentinel; On the second type of parameter evolution interaction path, a third observation sentinel and a fourth observation sentinel are set before and after the feedback threshold comparison, respectively; a third behavior signal listener for listening to the new daily detection task issuing behavior is configured for the third observation sentinel, and a fourth behavior signal listener for listening to the detection result feedback behavior is configured for the fourth observation sentinel; On the third type of parameter evolution interaction path, a fifth observation sentinel and a sixth observation sentinel are set before the historical trajectory parameter is called and after the evaluation result is generated, respectively; a fifth behavior signal listener for listening to the new round of personal capability evaluation start behavior is configured for the fifth observation sentinel, and a sixth behavior signal listener for listening to the evaluation report generation behavior is configured for the sixth observation sentinel.

[0009] Preferably, in response to the native behavior signal captured by the behavior signal listener, a dynamic mapping relationship from the native behavior signal to the observation sentinel on the specific parameter evolution interaction path is established, including: When the first behavior signal listener captures the knowledge point structure change behavior of the question bank, a direct mapping of the first behavior signal to the first observation sentinel is established, and an operation instruction for resetting the rule of the knowledge point association parameter is generated; When the third behavior signal listener and the fifth behavior signal listener simultaneously capture the detection task issuing behavior and the capability evaluation start behavior, a joint mapping of the combination of the two behavior signals to the third observation sentinel and the fifth observation sentinel is established, and an operation instruction for cooperatively adjusting the trigger period parameter and the weight configuration parameter is generated; The captured isolated behavior signal is matched with the historical mapping record, if the matching is successful, the existing mapping relationship is activated; if the matching fails, a new mapping relationship is created, and the new mapping relationship is stored in the historical mapping record.

[0010] Preferably, the first core data seed is expanded to guide the construction of the first type of parameter evolution interaction path connecting the question parameters and the knowledge point association parameters according to the scheduling strategy of the question generation frequency parameter, including: The scheduling strategy of the question generation frequency parameter encapsulated by the first core data seed is analyzed to determine the time interval and triggering condition of question generation; According to the time interval and triggering condition, the key evolution time points of the question parameters are marked on the time sequence; At each key evolution time point, the association strength between the question parameters and the knowledge point association parameters is calculated according to the binding rules of the knowledge point association parameters; Based on the association strength, a one-way dependent link from the question parameters to the knowledge point association parameters is constructed, and all one-way dependent links at the key evolution time points are connected in time sequence to form the first type of parameter evolution interaction path.

[0011] Preferably, the second core data seed is expanded to guide the construction of the second type of parameter evolution interaction path connecting the detection content parameters and the feedback threshold parameters according to the calibration strategy of the trigger period parameter, including: The calibration strategy of the trigger period parameter encapsulated by the second core data seed is analyzed to obtain the period length and dynamic adjustment rule of the detection task initiation; According to the period length and dynamic adjustment rule, a series of continuous detection period windows are drawn on the time axis; In each detection period window, based on the adjustment mechanism of the feedback threshold parameter, the adaptive adjustment sequence of the detection content parameter with the feedback threshold is derived; The adaptive adjustment sequence in each detection period window is connected at the beginning and end to form the second type of parameter evolution interaction path.

[0012] Preferably, the third core data seed is expanded to guide the construction of the third type of parameter evolution interaction path connecting the ability dimension parameters and the historical trajectory parameters according to the distribution strategy of the weight configuration parameter, including: The distribution strategy of the weight configuration parameter encapsulated by the third core data seed is analyzed to determine the initial weight of each ability dimension and the conditions for weight redistribution; According to the initial weight and the conditions for weight redistribution, the possible change trajectory of the weight configuration parameter in multiple rounds of evaluation periods is simulated; In each round of evaluation period, the dependence relationship of the ability dimension parameter evaluation results on the historical trajectory data is established according to the record and calling mechanism of the historical trajectory parameter; The simulated weight change trajectory and the dependence relationship in each round of evaluation period are integrated to generate the third type of parameter evolution interaction path.

[0013] Preferably, the first observation sentry and the second observation sentry are respectively set before and after the application of the knowledge point association rule on the first type of parameter evolution interaction path, comprising: Identifying the rule application node in which the knowledge point association rule in the first type of parameter evolution interaction path is triggered to execute; Setting the first observation sentry one time unit before the rule application node, for monitoring the state of the original knowledge point association rule about to be applied; Setting the second observation sentry one time unit after the rule application node, for monitoring the newly generated knowledge point association structure after the rule application; Configuring a first behavior signal listener for the first observation sentry, which is set to continuously monitor the knowledge point structure change behavior, and record the change timestamp and change content summary when the change behavior is monitored; Configuring a second behavior signal listener for the second observation sentry, which is set to continuously monitor the new question generation and storage behavior, and capture the identifier of the new question and its associated knowledge point set when the storage behavior is monitored.

[0014] Preferably, the present application further comprises a safety education and training management system, which comprises a memory, a processor and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the safety education and training management method as described above when executing the computer program.

[0015] Compared with the prior art, the present application has the following advantages: By generating a core data seed encapsulating initial parameter definitions and parameter evolution rules, an initial kernel with self-updating logic is provided for management dimensions such as item bank establishment, ability detection and comprehensive evaluation. The parameter evolution rules are encapsulated together with the initial definitions, so that key parameters such as evaluation weights, detection thresholds and item attributes are no longer static values, but have built-in logic for changing according to conditions. This enables the management system to have an endogenous dynamic adjustment capability after initialization, and the parameters can respond to system internal state changes according to preset rules, realizing a fundamental change of management logic from static configuration to dynamic evolution, and the system has the basis for autonomously optimizing its management strategy over time and feedback.

[0016] By constructing parameter evolution interaction path and setting observation sentinels on it, the configuration behavior signal listener captures native events such as state switching, behavior occurrence, period replacement, and establishes dynamic mapping relationship from these native signals to specific sentinels on specific path. Discrete and surface events in training process are analyzed into adjustment instructions for specific parameter nodes on specific evolution path through dynamic mapping. This means that the system can automatically identify the real management intention of the event and accurately act on the core parameters that affect the future process, rather than just complete one-time recording or reporting. Event response is no longer a simple and fixed trigger action, but becomes the source driving the whole management system parameters to evolve along the established rule path in a fine and automated iteration, realizing the closed-loop intelligent linkage between business events and system core parameter adaptive adjustment. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 The working principle diagram of the safety education training management method described in the application; Figure 2 The flowchart for generating core data seeds; Figure 3 The parameter optimization effect trend chart driven by dynamic mapping; Figure 4 The flowchart for constructing the first type of parameter evolution interaction path; Figure 5 The ability evaluation score time sequence comparison chart under the third type of parameter evolution interaction path. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the application will be clearly and completely described below with reference to the drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, rather than all the embodiments of the application. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the application.

[0019] Please refer to Figure 1The application provides a safety education training management method, which comprises the following steps: generating core data seeds for three management dimensions of safety education training question bank establishment, daily safety ability detection and personal safety education ability comprehensive evaluation. The initial parameter definition and parameter evolution rule of the corresponding management dimension are encapsulated in the core data seeds. Starting from the core data seeds, a plurality of parameter evolution interaction paths are constructed around the initial parameter definition of each management dimension. Each parameter evolution interaction path is used for describing how the related parameters change and transfer according to the parameter evolution rule in the time sequence. On the constructed parameter evolution interaction paths, a plurality of observation sentinels are set, and a behavior signal listener is configured for each observation sentinel. The function of the behavior signal listener is to capture the original behavior signal triggered by the question bank establishment state switching, detection behavior occurrence or evaluation period replacement in the safety education training process. The system establishes a dynamic mapping relationship from the original behavior signal to a certain observation sentinel on a certain parameter evolution interaction path in response to the original behavior signal captured by the behavior signal listener. According to the dynamic mapping relationship, the captured original behavior signal is analyzed into specific operation instructions for adjusting or resetting specific parameters on a specific path.

[0020] Embodiment 1: refer to Figure 2 The core data seeds for the three management dimensions of safety education training question bank establishment, daily safety ability detection and personal safety education ability comprehensive evaluation are generated, and the specific implementation is as follows. The question parameters, knowledge point association parameters and question setting frequency parameters are defined for the safety education training question bank establishment dimension, and the generation logic of the question parameters, the binding rule of the knowledge point association parameters and the scheduling strategy of the question setting frequency parameters are encapsulated as the first group of parameter evolution rules. The detection content parameters, trigger period parameters and feedback threshold parameters are defined for the daily safety ability detection dimension, and the assembly logic of the detection content parameters, the calibration strategy of the trigger period parameters and the adjustment mechanism of the feedback threshold parameters are encapsulated as the second group of parameter evolution rules. The ability dimension parameters, weight configuration parameters and history trajectory parameters are defined for the personal safety education ability comprehensive evaluation dimension, and the decomposition logic of the ability dimension parameters, the distribution strategy of the weight configuration parameters and the recording and calling mechanism of the history trajectory parameters are encapsulated as the third group of parameter evolution rules. The defined question parameters, knowledge point association parameters and question setting frequency parameters and the encapsulated first group of parameter evolution rules are encapsulated together to form the first core data seed. The defined detection content parameters, trigger period parameters and feedback threshold parameters and the encapsulated second group of parameter evolution rules are encapsulated together to form the second core data seed. The defined ability dimension parameters, weight configuration parameters and history trajectory parameters and the encapsulated third group of parameter evolution rules are encapsulated together to form the third core data seed.

[0021] In specific implementation, when generating the core data seed for the safety education training question bank establishment dimension, the question bank establishment dimension defines the question parameters, knowledge point association parameters and question setting frequency parameters, the question parameters include question identifier, question text and difficulty coefficient, the knowledge point association parameters include knowledge point identifier and association weight, and the question setting frequency parameters include time interval and trigger condition, the generation logic of the question parameters is encapsulated as a random selection algorithm based on difficulty coefficient and knowledge point coverage, the binding rule of the knowledge point association parameters is encapsulated as a function of calculating the connection strength of the question and the knowledge point according to the association weight, and the scheduling strategy of the question setting frequency parameters is encapsulated as a mechanism of dynamically adjusting the time interval based on historical question setting records and user performance, in some embodiments, the scheduling strategy of the question setting frequency parameters adopts an exponential decay model, and the formula is:

[0022] wherein: represents the question setting frequency, represents the initial frequency constant, represents the decay coefficient, represents the user historical correct rate, it can be understood that the first group of parameter evolution rules is composed of the generation logic, the binding rule and the scheduling strategy, and the first core data seed is formed by encapsulating the question parameters, the knowledge point association parameters, the question setting frequency parameters and the first group of parameter evolution rules together.

[0023] In specific implementation, when generating the core data seed for the daily safety ability detection dimension, the daily safety ability detection dimension defines the detection content parameters, the trigger period parameters and the feedback threshold parameters, the detection content parameters include the detection item list and the scoring standard, the trigger period parameters include the fixed period length and the event trigger condition, and the feedback threshold parameters include the passing score line and the warning threshold, the assembly logic of the detection content parameters is encapsulated as a process of selecting content according to the detection item priority and resource availability, the calibration strategy of the trigger period parameters is encapsulated as a rule of adjusting the period length according to the detection result deviation, and the adjustment mechanism of the feedback threshold parameters is encapsulated as a method of dynamically updating the threshold based on the overall performance distribution, optionally, the assembly logic of the detection content parameters adopts weighted random sampling, the calibration strategy of the trigger period parameters adopts linear adjustment, and the adjustment mechanism of the feedback threshold parameters adopts percentile calculation, data comparison shows that when the feedback threshold parameter is set as dynamic percentile, the core data seed adapts to the ability distribution of different user groups, while the fixed threshold seed may cause evaluation deviation, the second group of parameter evolution rules is composed of the assembly logic, the calibration strategy and the adjustment mechanism, and the second core data seed is formed by encapsulating the detection content parameters, the trigger period parameters, the feedback threshold parameters and the second group of parameter evolution rules together.

[0024] In a specific implementation, when generating the core data seed for the personal safety education capability comprehensive assessment dimension, the ability dimension parameters, weight configuration parameters, and historical trajectory parameters are defined for the personal safety education capability comprehensive assessment dimension, the ability dimension parameters include knowledge mastery, skill proficiency, and awareness level, the weight configuration parameters include initial weights of each dimension and redistribution conditions, and the historical trajectory parameters include previous assessment scores and behavior records. The decomposition logic of the ability dimension parameters is encapsulated as a conversion matrix based on the mapping of assessment indicators to dimensions, the allocation strategy of the weight configuration parameters is encapsulated as an algorithm for adjusting weights according to historical performance and assessment targets, and the recording and calling mechanism of the historical trajectory parameters is encapsulated as a storage and retrieval interface of a time series database. It can be understood that the third group of parameter evolution rules is composed of decomposition logic, allocation strategy, and recording and calling mechanism. The ability dimension parameters, weight configuration parameters, historical trajectory parameters, and the third group of parameter evolution rules are encapsulated together to form the third core data seed. In some embodiments, the allocation strategy of the weight configuration parameters uses the entropy weight method to calculate the weights. Optionally, the recording and calling mechanism of the historical trajectory parameters uses a sliding window model to manage data, and the size of the sliding window is configured according to the assessment frequency.

[0025] In some embodiments, the allocation strategy of the weight configuration parameters uses the entropy weight method to calculate the weights. Optionally, the recording and calling mechanism of the historical trajectory parameters uses a sliding window model to manage data, and the size of the sliding window is configured according to the assessment frequency.

[0026] In a specific implementation, the first core data seed is expanded to construct a first type of parameter evolution interaction path, based on the scheduling strategy of the question generation frequency parameter encapsulated by the first core data seed as a guide, the scheduling strategy of the question generation frequency parameter is analyzed to determine the time interval and trigger condition of question generation, the time interval can be defined as a fixed number of days or a dynamic value based on user activity, the trigger condition can include a user login event or a last round of detection completion event, according to the determined time interval and trigger condition, the key evolution time points of the question parameter are marked on the time sequence, the key evolution time points constitute the nodes of the parameter evolution interaction path, at each key evolution time point, the association strength between the question parameter and the knowledge point association parameter is calculated according to the binding rule of the knowledge point association parameter encapsulated by the first core data seed, the association strength determines the closeness of the connection between the question and the knowledge point, in some embodiments, the calculation formula of the association strength is represented as:

[0027] wherein: represents the association strength, represents the scaling factor, represents the knowledge point association weight, represents the time decay factor, represents the time difference from the last related knowledge point, based on the calculated association strength, a one-way dependent link from the question parameter to the knowledge point association parameter is constructed, the one-way dependent link represents the dependency relationship of question generation on a specific knowledge point, all one-way dependent links at the key evolution time points are connected in time sequence to form a first type of parameter evolution interaction path describing how the question evolves and generates according to the knowledge point association rule, it can be understood that the first type of parameter evolution interaction path completely depicts the dynamic generation process of the question parameter under a specific question generation frequency.

[0028] In specific implementation, the second core data seed is expanded to construct a second type of parameter evolution interaction path, and a calibration strategy of a trigger period parameter encapsulated by the second core data seed is taken as a guide. The calibration strategy of the trigger period parameter is analyzed to obtain a period length and a dynamic adjustment rule of a detection task initiation. The period length can be set as every week or every month, and the dynamic adjustment rule can be extended or shortened according to a last period detection completion rate. According to the obtained period length and dynamic adjustment rule, a series of continuous detection period windows are demarcated on a time axis. Each detection period window represents a complete detection task execution interval. In each detection period window, an adaptive adjustment sequence of a detection content parameter changing with a feedback threshold is derived based on an adjustment mechanism of a feedback threshold parameter encapsulated by the second core data seed. The adaptive adjustment sequence describes specific changes of the detection content to match the current feedback threshold. Optionally, the adaptive adjustment sequence is embodied as linear adjustment of a detection question difficulty or proportional increase or decrease of a question number. The adaptive adjustment sequences derived in each detection period window are connected in a head-to-tail manner, so that an output of a previous period serves as an input reference of a next period. A second type of parameter evolution interaction path describing how the detection content adjusts to adapt to the feedback threshold changes is constructed. Data comparison shows that the second type of parameter evolution interaction path constructed based on the trigger period parameter calibration strategy can realize automatic optimization of a detection rhythm, while a fixed period path lacks such adaptability.

[0029] In specific implementation, the third core data seed is expanded to construct a third type of parameter evolution interaction path. A weight configuration parameter distribution strategy encapsulated by the third core data seed is taken as a guide. The weight configuration parameter distribution strategy is analyzed to determine initial weights of each ability dimension and conditions for weight redistribution. The initial weights can be set as 40% for knowledge mastery, 30% for skill proficiency, and 30% for consciousness level. The conditions for weight redistribution can be based on stability of historical evaluation results or changes in external training priorities. According to the determined initial weights and conditions for weight redistribution, a possible change trajectory of the weight configuration parameter in multiple evaluation periods is simulated. The change trajectory reflects iterative updates of the weight over time and conditions. In each simulated evaluation period, a dependence relationship of an ability dimension parameter evaluation result on historical trajectory data is established according to a record and calling mechanism of historical trajectory parameters encapsulated by the third core data seed. The dependence relationship indicates that the current evaluation needs to query and refer to past evaluation records. In some embodiments, the dependence relationship is quantified by calculating a deviation of a current evaluation score from a historical average score. The simulated weight change trajectory and the dependence relationship established in each evaluation period are integrated. The integration process ensures that the weight distribution is associated with historical performance. A third type of parameter evolution interaction path describing how individual ability dimensions are dynamically evaluated in combination with historical trajectories is generated.

[0030] Embodiment 3: Set multiple observation sentinels on the parameter evolution interaction path, and configure a behavior signal listener for each observation sentinel. The specific implementation is as follows. On the first type of parameter evolution interaction path, a first observation sentinel and a second observation sentinel are set before and after the application of the knowledge point association rule. The first observation sentinel is configured with a first behavior signal listener for monitoring the knowledge point structure change behavior of the question bank, and the second observation sentinel is configured with a second behavior signal listener for monitoring the new question generation and storage behavior. Identify the rule application node where the knowledge point association rule in the first type of parameter evolution interaction path is triggered for execution, and set the first observation sentinel one time unit before the rule application node to monitor the state of the original knowledge point association rule to be applied. Set the second observation sentinel one time unit after the rule application node to monitor the newly generated knowledge point association structure after the rule application. The first behavior signal listener configured for the first observation sentinel is set to continuously monitor the knowledge point structure change behavior of the question bank, and record the change timestamp and change content summary when the change behavior is monitored. The second behavior signal listener configured for the second observation sentinel is set to continuously monitor the new question generation and storage behavior, and capture the identifier of the new question and its associated knowledge point set when the storage behavior is monitored. On the second type of parameter evolution interaction path, a third observation sentinel and a fourth observation sentinel are set before and after the feedback threshold comparison. The third observation sentinel is configured with a third behavior signal listener for monitoring the new daily detection task issuing behavior, and the fourth observation sentinel is configured with a fourth behavior signal listener for monitoring the detection result feedback behavior. On the third type of parameter evolution interaction path, a fifth observation sentinel and a sixth observation sentinel are set before the historical trajectory parameter call and after the evaluation result generation. The fifth observation sentinel is configured with a fifth behavior signal listener for monitoring the new round of personal ability evaluation start behavior, and the sixth observation sentinel is configured with a sixth behavior signal listener for monitoring the evaluation report generation behavior.

[0031] In a specific implementation, an observation sentry is set on the first type of parameter evolution interaction path and a behavior signal listener is configured, a rule application node in which a knowledge point association rule is triggered to execute is identified in the first type of parameter evolution interaction path, the rule application node is an execution time point of the knowledge point association parameter binding rule acting on the question parameter, a first observation sentry is set one time unit before the rule application node, the time unit is the minimum time granularity of system processing, the first observation sentry is used to monitor the state of the original knowledge point association rule about to be applied, a second observation sentry is set one time unit after the rule application node, the second observation sentry is used to monitor the newly generated knowledge point association structure after the rule application, a first behavior signal listener is configured for the first observation sentry, the first behavior signal listener is set to continuously monitor the knowledge point structure change behavior, and records the change timestamp and change content summary when the change behavior is monitored, the change content summary includes the list of knowledge point identifiers that are modified, added or deleted, a second behavior signal listener is configured for the second observation sentry, the second behavior signal listener is set to continuously monitor the new question generation and storage behavior, and captures the identifier of the new question and its associated knowledge point set when the storage behavior is monitored. It can be understood that the first observation sentry and the second observation sentry cover the complete state transition before and after the application of the knowledge point association rule.

[0032] In a specific implementation, an observation sentry is set on the second type of parameter evolution interaction path and a behavior signal listener is configured, a third observation sentry and a fourth observation sentry are set before and after the feedback threshold comparison on the second type of parameter evolution interaction path, the feedback threshold comparison refers to the operation of comparing the detection result with the feedback threshold parameter, a third behavior signal listener for monitoring the new daily detection task issuing behavior is configured for the third observation sentry, a fourth behavior signal listener for monitoring the detection result feedback behavior is configured for the fourth observation sentry, in some embodiments, the behavior signal monitored by the third behavior signal listener includes the detection task identifier, the target user list and the planned execution time, the behavior signal monitored by the fourth behavior signal listener includes the user identifier, the detection score and the completion state, data comparison shows that the observation sentries deployed before and after the feedback threshold comparison can independently capture the task triggering and result feedback events, and a single sentry may mix the behavior signals of the two different stages.

[0033] In specific implementations, the observation sentinels are set and the behavior signal listeners are configured on the third type of parameter evolution interaction path. On the third type of parameter evolution interaction path, a fifth observation sentinel and a sixth observation sentinel are set before the historical trajectory parameter invocation and after the evaluation result generation, respectively. The historical trajectory parameter invocation refers to the process of retrieving user historical evaluation data from storage, and the evaluation result generation refers to the time when all calculations are completed and the comprehensive evaluation report is output. The fifth observation sentinel is configured with a fifth behavior signal listener for listening to the starting behavior of a new round of personal ability evaluation, and the sixth observation sentinel is configured with a sixth behavior signal listener for listening to the evaluation report generation behavior. In some embodiments, the fifth behavior signal listener records the identifier of the evaluated user and the evaluation template version when it listens to the evaluation starting behavior. Optionally, when the fifth behavior signal listener and the third behavior signal listener capture signals at the same time, a cooperative processing logic can be triggered, and the trigger condition can be expressed by the formula:

[0034] Wherein: represents a joint trigger event, represents an evaluation starting behavior signal, represents a detection task issuing behavior signal, represents a logical AND operation.

[0035] In specific implementations, configuring a behavior signal listener involves the specific implementation of the listening logic. The first behavior signal listener continuously listens to the knowledge point structure change behavior of the question bank, and the listening logic includes monitoring the write operation log of the knowledge graph database or the call request of a specific application program interface. The second behavior signal listener continuously listens to the new question generation and storage behavior, and the listening logic includes monitoring the insertion transaction of the question database or the message event published by the question management service. The third behavior signal listener listens to the new daily detection task issuing behavior, and the listening logic includes parsing new entries of the task scheduling queue or intercepting network requests of the task distribution service. The fourth behavior signal listener listens to the detection result feedback behavior, and the listening logic includes subscribing to the message topic of the result collection service or polling the state change field of the result database. It can be understood that the implementation of different listeners is configured differently according to the location and form of the monitored behavior. The fifth behavior signal listener listens to the starting behavior of a new round of personal ability evaluation, and the listening logic usually includes intercepting the initialization call of the evaluation engine or parsing the update of the evaluation schedule. The sixth behavior signal listener listens to the evaluation report generation behavior, and the listening logic usually includes monitoring the completion event of the report generation service or checking the file creation operation of the report storage directory. Optionally, all behavior signal listeners generate a standardized signal object containing the behavior type, timestamp, initiator, and key load data after capturing the original behavior signal.

[0036] Embodiment 4: In response to the native behavior signals captured by the behavior signal listener, a dynamic mapping relationship is established from the native behavior signals to the observation checkpoints on the specific parameter evolution interaction path, the specific implementation is as follows. When the first behavior signal listener captures the knowledge point structure change behavior of the question bank, a direct mapping from the first behavior signal to the first observation checkpoint is established, and an operation instruction for resetting the rules of the knowledge point associated parameters is generated. When the third behavior signal listener and the fifth behavior signal listener capture the detection task issuing behavior and the ability evaluation starting behavior at the same time, a joint mapping of the combination of the two behavior signals to the third observation checkpoint and the fifth observation checkpoint is established, and an operation instruction for adjusting the trigger period parameter and the weight configuration parameter is generated. The captured isolated behavior signal is matched with the historical mapping record, if the matching is successful, the existing mapping relationship is activated. If the matching fails, a new mapping relationship is created, and the new mapping relationship is stored in the historical mapping record.

[0037] In specific implementation, in response to the native behavior signals captured by the behavior signal listener and establishing a dynamic mapping relationship, when the first behavior signal listener captures the knowledge point structure change behavior of the question bank, the system immediately establishes a direct mapping relationship from the native behavior signal to the first observation checkpoint, the direct mapping relationship means that there is a one-to-one fixed association between the signal source and the target observation checkpoint, and an operation instruction for resetting the rules of the knowledge point associated parameters is generated according to the direct mapping relationship, the operation instruction contains the new knowledge point structure data and the rule version number according to which the reset is performed. In some embodiments, the establishment of the direct mapping relationship is completed by querying the preconfigured signal-checkpoint mapping table, the signal-checkpoint mapping table stores the encoding of different types of behavior signals and their default corresponding observation checkpoints, refer to Table 1, which shows the mapping of behavior signals and observation checkpoints.

[0038] Table 1: Mapping table of behavior signals and observation checkpoints

[0039] In specific implementation, the situation of multiple behavior signal listeners capturing signals at the same time is handled, when the third behavior signal listener and the fifth behavior signal listener capture the detection task issuing behavior and the new round of personal ability evaluation starting behavior at the same time, the system establishes a joint mapping of the combination of the two behavior signals to the third observation checkpoint and the fifth observation checkpoint, the joint mapping means that a composite event needs to be associated with multiple observation checkpoints for collaborative response, based on the established joint mapping, an operation instruction for adjusting the trigger period parameter and the weight configuration parameter is generated, the collaborative adjustment operation instruction ensures that the detection period and the evaluation weight are updated synchronously in time, it can be understood that the establishment of the joint mapping depends on the judgment of the time window overlap of the behavior signals, and the judgment logic is defined by the formula , where: a Boolean value representing whether a joint mapping is established, a representative indicating function, a representative evaluation start behavior signal occurrence time, a representative detection task assignment behavior signal occurrence time, a representative preset time window threshold.

[0040] In specific implementation, the captured isolated behavior signal is processed, and the system matches the captured isolated behavior signal with a historical mapping record, which is a database storing past successfully established signal-whistle point mapping relationships. The matching process first extracts the feature vector of the isolated behavior signal, including signal type, signal source module, and load data fingerprint, and then performs similarity calculation on the feature vector and the past signal features stored in the historical mapping record. If the similarity calculation result exceeds the preset matching threshold, it is determined that the matching is successful, and the existing mapping relationship in the historical mapping record is activated, which includes loading the corresponding observation whistle point configuration and parameter operation instruction template. If the similarity calculation result does not exceed the matching threshold, it is determined that the matching fails, and a new mapping relationship is created, which includes analyzing the potential target of the behavior signal, defining the mapping rule, and generating a new parameter operation instruction. It can be understood that the new mapping relationship is immediately stored in the historical mapping record for subsequent matching after being created. In some embodiments, the similarity calculation uses the cosine similarity algorithm, and optionally, the historical mapping record uses the LRU cache mechanism to manage the storage space.

[0041] In specific implementation, the establishment of dynamic mapping relationship drives the analysis and execution of parameter operation instructions. According to the dynamic mapping relationship, the original behavior signal is analyzed into an operation instruction for adjusting or resetting a specific parameter on a specific path. The operation instruction contains operation type, target parameter identifier, parameter new value or adjustment rule. The operation instruction is dispatched to the corresponding parameter management service for execution. The parameter management service is responsible for modifying the target parameter on the specified parameter evolution interaction path. Data comparison shows that the system based on dynamic mapping relationship can accurately convert multi-source heterogeneous behavior signals such as question bank knowledge point structure change and detection task assignment into adjustment actions for specific parameters such as knowledge point associated parameters and trigger cycle parameters. Static mapping rules cannot generate effective instructions when facing new or composite behavior signals. Optionally, the execution of the operation instruction has atomicity and transactionality to ensure the consistency of the parameter state. It can be understood that the entire process from signal capture to instruction execution constitutes a closed-loop feedback control loop.

[0042] Referring to Figure 3In the project parameter adjustment phase, the parameter optimization effect driven by dynamic mapping is reflected through the time sequence changes of the three indicators of knowledge point association parameter accuracy, trigger period parameter stability, and weight configuration parameter rationality. Specifically, before adjustment, the three parameter effect indicators are at a low level (knowledge point association parameter accuracy is 0.65, trigger period parameter stability is 0.58, and weight configuration parameter rationality is 0.62); with the advancement of parameter adjustment period, each indicator shows a continuous upward trend: after 1 week of adjustment, the knowledge point association parameter accuracy is improved to 0.72, the trigger period parameter stability is increased to 0.65, and the weight configuration parameter rationality reaches 0.70; after 2 weeks of adjustment, the knowledge point association parameter accuracy breaks through 0.85, the trigger period parameter stability and the weight configuration parameter rationality reach 0.78 and 0.79 respectively; after 4 weeks of adjustment, the knowledge point association parameter accuracy is close to 0.91, the trigger period parameter stability and the weight configuration parameter rationality reach 0.86 and 0.87 respectively. The trend reflects the driving effect of dynamic mapping relationship on parameter evolution: the original behavior signal is converted into precise parameter adjustment instructions through mapping with the observed sentinels, making the effects of core parameters such as knowledge point association, trigger period, and weight configuration continuously optimized with the adjustment period, verifying the effectiveness of the dynamic mapping mechanism in parameter iteration.

[0043] Embodiment 5: refer to Figure 4 , expand the first core data seed, guided by the scheduling strategy of the question frequency parameter, to build the first type of parameter evolution interaction path connecting the question parameter and the knowledge point association parameter, and the specific implementation includes: analyzing the scheduling strategy of the question frequency parameter encapsulated by the first core data seed to determine the time interval and trigger condition of question generation. According to the determined time interval and trigger condition, mark the key evolution time points of the question parameter on the time sequence. At each key evolution time point, calculate the association strength between the question parameter and the knowledge point association parameter according to the binding rules of the knowledge point association parameter. Based on the calculated association strength, build a one-way dependent link from the question parameter to the knowledge point association parameter, and connect all one-way dependent links at key evolution time points in time sequence to form the first type of parameter evolution interaction path.

[0044] The second core data seed is expanded, and guided by the calibration strategy of the trigger cycle parameter, a second type of parameter evolution interaction path is constructed connecting the detection content parameter and the feedback threshold parameter. The specific implementation includes: parsing the calibration strategy of the trigger cycle parameter encapsulated in the second core data seed to obtain the cycle length and dynamic adjustment rules of the detection task. Based on the obtained cycle length and dynamic adjustment rules, a series of continuous detection cycle windows are defined on the time axis. Within each detection cycle window, based on the adjustment mechanism of the feedback threshold parameter, an adaptive adjustment sequence of the detection content parameter as the feedback threshold changes is derived. The adaptive adjustment sequences within each detection cycle window are then concatenated to construct the second type of parameter evolution interaction path.

[0045] This paper expands upon the third core data seed, using the weight configuration parameter allocation strategy as a guide to construct a third type of parameter evolution interaction path connecting capability dimension parameters and historical trajectory parameters. The specific implementation includes: parsing the weight configuration parameter allocation strategy encapsulated in the third core data seed, clarifying the initial weights of each capability dimension and the conditions for weight redistribution; simulating the possible change trajectories of weight configuration parameters across multiple evaluation cycles based on the clarified initial weights and weight redistribution conditions; establishing the dependency relationship between the capability dimension parameter evaluation results and historical trajectory data within each evaluation cycle based on the historical trajectory parameter recording and retrieval mechanism; and integrating the simulated weight change trajectory with the dependencies established in each evaluation cycle to generate the third type of parameter evolution interaction path.

[0046] In practical implementation, parsing the first core data seed to construct the first type of parameter evolution interaction path involves the execution of specific steps, including parsing the scheduling strategy of the question frequency parameters encapsulated in the first core data seed, determining the time interval and triggering conditions for question generation, where the time interval can be a fixed value in days, such as 7 days, and the triggering condition can be set as "the user completed the last test and the score is below the threshold". Based on the determined time interval and triggering conditions, key evolution time points of the question parameters are marked on the time series. These key evolution time points represent the specific moments when new questions are allowed or must be generated. At each key evolution time point, the association strength between the question parameters and the knowledge point association parameters is calculated according to the binding rules of the knowledge point association parameters encapsulated in the first core data seed. The association strength calculation depends on the algorithm defined in the binding rules. In some embodiments, the binding rules may stipulate that the association strength is inversely proportional to the historical frequency of knowledge point calls to promote balanced coverage of knowledge points. This relationship can be expressed as:

[0047] in: This represents the currently calculated correlation strength. Represents the base strength of the rule. This represents the number of times knowledge point i has been invoked within a recent window. Representing the total number of related knowledge points, a one-way dependency link from the question parameters to the knowledge point association parameters is constructed based on the calculated association strength. The one-way dependency link clearly identifies which one or more core knowledge points the question generated at a specific time point will be bound to. All one-way dependency links at key evolution time points are connected in sequence to form the first type of parameter evolution interaction path. This path clearly shows the evolution process of the question generation event and its knowledge dependencies on the timeline.

[0048] In practical implementation, parsing the second core data seed to construct the second type of parameter evolution interaction path involves the execution of specific steps. This includes parsing the calibration strategy for the trigger cycle parameters encapsulated in the second core data seed, obtaining the cycle length and dynamic adjustment rules for initiating the detection task. The initial cycle length might be 30 days, and the dynamic adjustment rule might be expressed as "if the average completion rate of the last detection is higher than X%, then shorten the cycle by Y%; if it is lower than Z%, then extend the cycle by W%." Based on the obtained cycle length and dynamic adjustment rules, a series of continuous detection cycle windows are defined on the timeline. The starting point of the first detection cycle window is the system initialization time, and the end point of each subsequent detection cycle window is the starting point of the next detection cycle window. Within each detection cycle window, based on the adjustment mechanism of the feedback threshold parameters encapsulated in the second core data seed, the detection content parameters are derived according to the feedback threshold. The adaptive adjustment sequence for value changes, and the feedback threshold parameter adjustment mechanism, may stipulate that when the feedback threshold rises, the difficulty benchmark of the questions in the detection content parameters should be increased synchronously. The adaptive adjustment sequence specifically describes the value or proportion of the difficulty benchmark increase. It can be understood that the adaptive adjustment sequence is a bridge connecting the feedback threshold change and the response of the detection content parameters. The adaptive adjustment sequences derived in each detection cycle window are connected end to end. The connection means that the final detection content and feedback result of the previous cycle will be used as input to affect the initial adaptive adjustment sequence of the next cycle window, thus constructing a second type of parameter evolution interaction path. Data comparison shows that the detection cycle window length defined by the dynamic adjustment rule is variable. The second type of parameter evolution interaction path constructed in this way exhibits a non-equidistant node distribution, while the node interval of the path constructed according to the fixed cycle is uniform.

[0049] In specific implementations, parsing the third core data seed to construct the third type of parameter evolution interaction path involves the execution of specific steps, parsing the allocation strategy of the weight configuration parameter encapsulated by the third core data seed, and determining the initial weight of each capability dimension and the condition for weight redistribution. The initial weight can be set to knowledge mastery 0.5, skill proficiency 0.3, and awareness level 0.2. The condition for weight redistribution can be "when the score of a certain dimension increases by more than a set value in three consecutive evaluations, increase its weight proportion". According to the determined initial weight and weight redistribution condition, simulate the possible change trajectory of the weight configuration parameter in multiple evaluation cycles. The simulation process calculates the updated value of the weight after each evaluation according to the redistribution condition, thereby generating a sequence of weight values changing over time. In each simulated evaluation cycle, establish the dependence of the capability dimension parameter evaluation result on the historical trajectory data according to the record and calling mechanism of the historical trajectory parameter encapsulated by the third core data seed. The dependence can be quantified as the current capability dimension score being the weighted average of the recent historical scores, and the weight of the recent historical scores is determined by the record and calling mechanism. In some embodiments, the record and calling mechanism can specify the use of the historical trajectory data of the last five evaluations, and calculate the weighted value with a decay coefficient of 0.5. Integrate the simulated weight change trajectory and the dependence established in each evaluation cycle. The integration operation substitutes the weight value of each round into the corresponding evaluation calculation model, thereby generating a complete path containing weight change and evaluation dependence, i.e. the third type of parameter evolution interaction path. Optionally, the integration process can be regarded as an iterative computation graph. It can be understood that the finally generated third type of parameter evolution interaction path comprehensively reflects the dynamic coupling relationship between weight allocation and historical performance.

[0050] Referring to Figure 5In the analysis of the capability evaluation score of the third type of parameter evolution interaction path, the data presents a dynamic coupling relationship based on the weight configuration parameter and the historical trajectory parameter. Specifically, taking the evaluation period as the time sequence axis, the evolution process of the scores of the three capability dimensions of "knowledge mastery", "skill proficiency" and "awareness level" and the "comprehensive score" under the comprehensive evaluation dimension of personal safety education capability is displayed: the knowledge mastery score (blue column) fluctuates after a significant peak in period 2, reflecting the evolution of the knowledge dimension parameter driven by the topic frequency and knowledge point association rule; the skill proficiency score (green column) presents a phased change with the evaluation period, reflecting the dependence of the historical trajectory parameter on the skill dimension evaluation; the fluctuation of the awareness level score (orange column) corresponds to the effect of dimension weight adjustment after the trigger of the weight redistribution condition; the comprehensive score (red line) as the weighted result of each dimension score, its trend intuitively presents the integrated effect of the weight configuration parameter change trajectory and the capability dimension dependence relationship. At the parameter configuration level, the initial weight setting (knowledge mastery 0.5, skill proficiency 0.3, awareness level 0.2) and the weight adjustment condition of "continuous three times evaluation score increase exceeding the set value" are the core rules driving the time sequence change of each dimension score and the comprehensive score.

[0051] It should be noted that in this document, the terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between such entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0052] Although embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made thereto without departing from the principles and spirit of the present application, the scope of which is defined by the following claims and their equivalents.

Claims

1. A method for managing safety education and training, characterized in that, Includes the following steps: Generate core data seeds for three management dimensions: the establishment of a safety education and training question bank, daily safety capability testing, and comprehensive assessment of individual safety education capabilities. The core data seed encapsulates the initial parameter definitions and parameter evolution rules for the corresponding management dimension; Starting with the core data seed, multiple parameter evolution and interaction paths are constructed around the initial parameter definitions for each management dimension; Each parameter evolution interaction path is used to describe how parameters change and propagate over time according to parameter evolution rules; Multiple observation outposts are set up along the parameter evolution interaction path, and a behavior signal listener is configured for each observation outpost; The behavior signal listener is used to capture native behavior signals triggered by the switching of the question bank establishment state, the occurrence of detection behavior, or the change of evaluation cycle during the safety education and training process. In response to the native behavioral signals captured by the behavioral signal listener, a dynamic mapping relationship is established from the native behavioral signals to the observation sentinels on the specific parameter evolution interaction path; Based on the dynamic mapping relationship, the native behavior signal is parsed into an operation instruction to adjust or reset specific parameters on a specific path.

2. The safety education and training management method according to claim 1, characterized in that, The core data seeds generated for the three management dimensions of establishing a safety education and training question bank, daily safety competence testing, and comprehensive assessment of individual safety education competence include: To establish a dimension-defined question bank for safety education and training, we define question parameters, knowledge point association parameters, and question frequency parameters. We also encapsulate the generation logic of question parameters, the binding rules of knowledge point association parameters, and the scheduling strategy of question frequency parameters into the first set of parameter evolution rules. Define detection content parameters, trigger cycle parameters, and feedback threshold parameters for the daily security capability detection dimension, and encapsulate the assembly logic of detection content parameters, the calibration strategy of trigger cycle parameters, and the adjustment mechanism of feedback threshold parameters into a second set of parameter evolution rules; Define capability dimension parameters, weight configuration parameters, and historical trajectory parameters for the comprehensive assessment of personal safety education capabilities. Encapsulate the decomposition logic of capability dimension parameters, the allocation strategy of weight configuration parameters, and the recording and calling mechanism of historical trajectory parameters into a third set of parameter evolution rules. The question parameters, knowledge point association parameters, question frequency parameters, and the first set of parameter evolution rules are jointly encapsulated to form the first core data seed; the detection content parameters, trigger cycle parameters, feedback threshold parameters, and the second set of parameter evolution rules are jointly encapsulated to form the second core data seed; and the ability dimension parameters, weight configuration parameters, historical trajectory parameters, and the third set of parameter evolution rules are jointly encapsulated to form the third core data seed.

3. The safety education and training management method according to claim 2, characterized in that, Starting with the core data seed, multiple parameter evolution and interaction paths are constructed around the initial parameter definitions of each management dimension, including: Expanding the first core data seed, guided by the scheduling strategy of the question frequency parameter, a first type of parameter evolution interaction path is constructed to connect the question parameters and the knowledge point association parameters; the first type of parameter evolution interaction path describes how questions evolve and are generated according to the knowledge point association rules under a specific question frequency; Expanding the second core data seed, guided by the calibration strategy of triggering the periodic parameters, a second type of parameter evolution interaction path is constructed to connect the detection content parameters and the feedback threshold parameters; the second type of parameter evolution interaction path describes how the detection content adapts to the changes in the feedback threshold under a specific detection period; Expanding the third core data seed, guided by the weight configuration parameter allocation strategy, a third type of parameter evolution interaction path is constructed to connect capability dimension parameters and historical trajectory parameters; the third type of parameter evolution interaction path describes how an individual capability dimension is dynamically evaluated in conjunction with historical trajectory under a specific weight allocation.

4. The safety education and training management method according to claim 3, characterized in that, The step of setting multiple observation sentinels along the parameter evolution interaction path and configuring a behavior signal listener for each observation sentinel includes: On the first type of parameter evolution interaction path, a first observation sentinel and a second observation sentinel are set before and after the application of knowledge point association rules, respectively; a first behavior signal listener is configured for the first observation sentinel to listen to the behavior of changing the structure of knowledge points in the question bank, and a second behavior signal listener is configured for the second observation sentinel to listen to the behavior of generating and entering new questions into the question bank. On the second type of parameter evolution interaction path, a third observation sentinel and a fourth observation sentinel are set before and after the feedback threshold comparison, respectively; a third behavior signal listener is configured for the third observation sentinel to listen to the behavior under the new daily detection task, and a fourth behavior signal listener is configured for the fourth observation sentinel to listen to the behavior of the detection result feedback. On the third type of parameter evolution interaction path, a fifth observation sentinel and a sixth observation sentinel are set before the historical trajectory parameter is called and after the evaluation result is generated, respectively. A fifth behavior signal listener is configured for the fifth observation sentinel to listen to the start behavior of a new round of personal ability evaluation, and a sixth behavior signal listener is configured for the sixth observation sentinel to listen to the evaluation report generation behavior.

5. A safety education and training management method according to claim 4, characterized in that, The process of establishing a dynamic mapping relationship from the original behavioral signals captured by the behavioral signal listener to the observation sentinel points on the specific parameter evolution interaction path, in response to the original behavioral signals, includes: When the first behavior signal listener detects a change in the structure of the knowledge points in the question bank, it establishes a direct mapping from the first behavior signal to the first observation sentinel and generates an operation instruction to reset the rules of the knowledge point association parameters. When the third behavior signal listener and the fifth behavior signal listener simultaneously capture the detection task initiation behavior and the capability assessment initiation behavior, a joint mapping of the combination of these two behavior signals to the third observation sentinel and the fifth observation sentinel is established, and an operation instruction for coordinated adjustment of the triggering period parameter and the weight configuration parameter is generated. The captured isolated behavioral signals are matched with historical mapping records. If the match is successful, the existing mapping relationship is activated; if the match fails, a new mapping relationship is created and stored in the historical mapping record.

6. The safety education and training management method according to claim 3, characterized in that, The process of expanding the first core data seed, guided by the scheduling strategy of the question frequency parameter, constructs a first type of parameter evolution interaction path connecting question parameters and knowledge point association parameters, including: The scheduling strategy of the question frequency parameter encapsulated in the first core data seed is analyzed to determine the time interval and triggering conditions for question generation; Based on the time interval and triggering conditions, mark the key evolution time points of the problem parameters on the time series; At each critical evolutionary point, the correlation strength between the question parameters and the knowledge point correlation parameters is calculated based on the binding rules of the knowledge point association parameters. Based on the aforementioned correlation strength, a one-way dependency link is constructed from the question parameters to the knowledge point correlation parameters, and the one-way dependency links at all key evolution time points are sequentially connected to form the first type of parameter evolution interaction path.

7. A safety education and training management method according to claim 3, characterized in that, The process of unfolding the second core data seed, guided by a calibration strategy for triggering periodic parameters, constructs a second type of parameter evolution interaction path connecting the detection content parameters and the feedback threshold parameters, including: The calibration strategy of the trigger cycle parameter encapsulated in the second core data seed is analyzed to obtain the cycle length and dynamic adjustment rules of the detection task. Based on the cycle length and dynamic adjustment rules, a series of continuous detection cycle windows are defined on the time axis; Within each detection cycle window, based on the adjustment mechanism of the feedback threshold parameter, the adaptive adjustment sequence of the detection content parameters as the feedback threshold changes is derived; By connecting the adaptive adjustment sequences within each detection period window end to end, a second type of parameter evolution interaction path is constructed.

8. A safety education and training management method according to claim 3, characterized in that, The expansion of the third core data seed, guided by the weight configuration parameter allocation strategy, constructs a third type of parameter evolution interaction path for connectivity dimension parameters and historical trajectory parameters, including: The allocation strategy of the weight configuration parameters of the third core data seed encapsulation is analyzed to clarify the initial weights of each capability dimension and the conditions for weight redistribution. Based on the initial weights and the conditions for weight redistribution, the possible trajectory of weight configuration parameters during multiple evaluation cycles is simulated. Within each evaluation cycle, based on the recording and recall mechanism of historical trajectory parameters, the dependency relationship between the evaluation results of capability dimension parameters and historical trajectory data is established. The simulated weight change trajectory is integrated with the dependencies within each evaluation cycle to generate the third type of parameter evolution interaction path.

9. A safety education and training management method according to claim 4, characterized in that, The step of setting a first observation sentinel and a second observation sentinel before and after the application of the knowledge point association rule on the first type of parameter evolution interaction path includes: Identify the rule application nodes in the first type of parameter evolution interaction path where the knowledge point association rules are triggered and executed; The first observation sentinel is set one time unit before the rule application node to monitor the status of the original knowledge point association rule that is about to be applied; The second observation sentinel is set one time unit after the rule application node to monitor the newly generated knowledge point association structure after the rule application. Configure a first behavior signal listener for the first observation post. The first behavior signal listener is set to continuously monitor the behavior of changing the structure of knowledge points in the question bank, and record the change timestamp and a summary of the change content when the change behavior is detected. Configure a second behavior signal listener for the second observation sentinel. The second behavior signal listener is set to continuously listen for the behavior of generating and adding new questions to the database, and capture the identifier of the new question and its associated set of knowledge points when the behavior of adding questions to the database is detected.

10. A safety education and training management system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the safety education and training management method according to any one of claims 1 to 9.

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