A method and system for analyzing early warning information

Abstract

The application provides a pre-warning information analysis method and system, and relates to the technical field of data processing. The active acquisition ability of the intelligent agent checking module for multi-source data and the synchronous simulation ability of the twin simulation module are combined in an evidence fusion manner, thereby constructing a false alarm automatic identification mechanism, solving the problem that additional evidence cannot be actively acquired to check the authenticity of the pre-warning in the prior art. Meanwhile, through cross-validation support of the comparison and verification module and real alarm confidence calculation of the fusion recognition module, quantitative evaluation and hierarchical disposal of the pre-warning information are realized, effectively reducing the misjudgment rate and avoiding disposal resource waste.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and more specifically, to a method and system for analyzing early warning information. Background Technology

[0002] In industrial monitoring, security surveillance, and other scenarios, various monitoring systems are widely deployed to monitor the operational status of physical objects in real time. When monitored parameters exceed preset thresholds, the system automatically generates and reports early warning information. However, due to various factors such as sensor drift, environmental interference, and improper trigger rule settings, a large number of false alarms (false alarms) are reported by the monitoring system. False alarms not only consume a lot of processing resources but may also cause operators to experience a "crying wolf" effect, thus ignoring real alarms and causing serious safety hazards. Existing technologies face multiple challenges in processing early warning information: First, many systems adopt a uniform processing strategy for all early warning information, either treating them all as real alarms and initiating the response process, or relying entirely on manual verification, lacking an automatic mechanism to distinguish between real and false alarms. This leads to frequent false alarms triggering unnecessary actions, resulting in resource waste and inefficiency. Secondly, some early warning solutions based on digital twin technology attempt to use real-time production data to drive simulation models to predict the potential impact of alarms on the system. However, these solutions rely solely on existing real-time data within the system. When the data itself is unreliable (e.g., sensor drift generates erroneous data), they cannot proactively acquire additional verification evidence to validate the authenticity of the early warning information. This results in simulation conclusions being based on unreliable inputs, making it difficult to guarantee their accuracy. Thirdly, existing solutions often rely on a single data source or a single criterion for judgment, lacking a cross-validation mechanism based on multiple independent evidence. Anomalies from a single data source are easily misjudged as real alarms, leading to a persistently high false alarm rate. Fourthly, the judgment results of existing technologies typically lack quantitative confidence metrics and cannot provide a tiered handling mechanism. When confidence is insufficient, a conclusion cannot be rejected or a downgraded treatment cannot be implemented. Furthermore, there is a lack of effective backtracking and self-adjustment mechanisms for misjudged cases, preventing the system's judgment rules from being continuously optimized over time, leading to a gradual increase in the false alarm rate after long-term use. Therefore, there is an urgent need for an early warning information analysis method and system that can proactively acquire verification data, combine digital twin simulation with multi-source evidence cross-verification, output quantitative confidence levels, and automatically identify real and false alarms.

[0003] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0004] The purpose of this application is to provide a method and system for analyzing early warning information, which has the advantages of automatically identifying the categories of real and false alarms in early warning information, significantly reducing the false alarm rate, and improving the accuracy of early warning.

[0005] This application provides a method for analyzing early warning information, and the technical solution is as follows: Executed by a computer, including: Obtain early warning information reported by the monitoring system. The early warning information shall include at least the early warning type, early warning location, and early warning time. The intelligent agent parses the early warning information, generates a data verification task corresponding to the early warning information, and executes the data verification task to obtain further verification data. The generation of the data verification task includes: determining the target data source and the corresponding collection parameters from multiple candidate data sources according to the early warning type, and the further verification data is collected by the target data source according to the collection parameters. The early warning information and further verification data are mapped to a pre-built digital twin model, which drives the synchronous simulation of the digital twin model to obtain the twin simulation state variables corresponding to the early warning location; the digital twin model is a virtual mapping model corresponding to the monitored physical object; The simulation deviation is obtained by comparing the early warning trigger parameters corresponding to the early warning information with the state variables of the twin simulation, and the cross-validation support is obtained by cross-validating further verification data from multiple sources. The simulation bias and cross-validation support are fused to obtain the real alarm confidence score; based on the comparison between the real alarm confidence score and the preset confidence threshold, the category of the warning information is identified as a real alarm or a false alarm. Output the recognition results. Furthermore, this application also proposes that the early warning information includes the parameter values ​​of the early warning level and the early warning triggering parameter; the early warning information originates from at least one of the sensing and monitoring equipment, video surveillance equipment, or manual reporting terminal deployed on the monitored physical object. Furthermore, this application proposes determining the target data source and corresponding acquisition parameters from multiple candidate data sources based on the warning type, including: determining the target data type matching the warning type according to a preset mapping rule between warning types and data types; determining the candidate data sources that can provide the target data type as the target data source, and generating corresponding acquisition parameters based on the warning location, wherein the acquisition parameters include at least one of acquisition viewpoint, acquisition frequency, and acquisition duration; wherein the agent generates the data verification task based on a trained reinforcement learning decision model, the reinforcement learning decision model is trained with a preset reward function, and the reward value of the reward function is positively correlated with the contribution of the generated data verification task to the identification of real-time warnings and negatively correlated with the data acquisition cost of the data verification task. Furthermore, this application proposes that candidate data sources include at least two of the following: video surveillance equipment, sensor monitoring equipment, inspection robots, drones, and historical operation databases; the data verification task includes: the intelligent agent sending a collection instruction to the collection device corresponding to the target data source, the collection instruction being used to control the collection device to collect data according to the collection parameters; when the target data source is an inspection robot or a drone, the intelligent agent also plans the movement path of the inspection robot or drone based on the warning location. Furthermore, this application proposes that the construction method of the digital twin model includes: establishing a geometric model and a physical mechanism model of the monitored physical object, and mapping the real-time operation data of the monitored physical object to the geometric model and the physical mechanism model for parameter correction, so that the twin simulation state variables of the digital twin model are synchronized with the actual state of the monitored physical object; the physical mechanism model includes multiple mechanism sub-models corresponding to different warning types; driving the synchronous simulation of the digital twin model includes: starting the target mechanism sub-model corresponding to the warning type of the warning information, using the warning time as a benchmark, and inputting further verification data as boundary conditions into the target mechanism sub-model for deduction, until the state change of two adjacent simulation steps is lower than the preset change threshold, and stopping the deduction, and determining the stable state variable at the time of stopping the deduction as the twin simulation state variable corresponding to the warning position. Furthermore, this application also proposes that the deviation comparison includes: calculating the deviation value between the warning triggering parameter and the twin simulation state quantity, and comparing the deviation value with a preset deviation threshold. The smaller the deviation value, the more likely the warning information is a real alarm. The cross-validation includes: counting the number of data sources that support the establishment of the warning information in the further verification data from multiple sources, and calculating the matching degree between the verification data that support the establishment of the warning information and the preset real alarm criterion features. The cross-validation support degree is determined based on the number of data sources and the matching degree. Furthermore, this application also proposes that the fusion process includes incorporating historical case matching: a historical early warning case database is pre-constructed, storing feature vectors of historical early warning information and corresponding real or false alarm judgment results; the feature vectors of the early warning information are compared with the feature vectors in the historical early warning case database to calculate similarity, wherein historical early warning cases within a set recent time period are assigned a higher similarity weight than other historical early warning cases, and the historical case matching degree is obtained by statistically analyzing the judgment results of similar cases whose similarity exceeds a preset similarity threshold; historical early warning cases with similar feature vectors but different judgment results are marked as disputed cases and transferred to [the relevant authority / organization]. Manual calibration; the fusion process involves: weighting the normalized simulation bias according to the first preset weighting coefficient, weighting the cross-validation support according to the second preset weighting coefficient, and weighting the historical case matching degree according to the third preset weighting coefficient, and then summing the results to obtain the real alarm confidence score; the preset confidence thresholds include the real alarm threshold and the false alarm threshold, with the real alarm threshold being higher than the false alarm threshold; when the real alarm confidence score is not lower than the real alarm threshold, the warning information is determined to be a real alarm; when the real alarm confidence score is not higher than the false alarm threshold, the warning information is determined to be a false alarm; when the real alarm confidence score is between the real alarm threshold and the false alarm threshold, the warning information is marked as a warning pending review and transferred to manual review. Furthermore, this application proposes that when the warning information is determined to be a real warning, the objects to be handled are divided into at least two levels according to the warning level. Differentiated graded handling instructions and push frequencies are matched to the objects to be handled at different levels and pushed to the corresponding handling terminals. The graded handling instructions include instructions for linkage with emergency response plans. During the push process, the delivery rate of each push channel is monitored. When the delivery rate of a certain push channel is lower than the preset delivery rate threshold, the system automatically switches to the backup push channel for resending. When the warning information is determined to be a false alarm, the warning information is not pushed to the handling terminal. Instead, the warning information is recorded as a false alarm sample, and the cause of the false alarm sample is traced back. The cause of the false alarm includes at least one of sensor monitoring device drift and false triggering of warning triggering rules. Furthermore, this application proposes to use the feature vectors of the identification results and corresponding early warning information as new cases to write back to the historical early warning case library; to statistically analyze the false alarm samples within a preset statistical period according to the causes of false alarms, and to perform adaptive adjustment when the proportion of the number of false alarm samples corresponding to the same early warning type to the total number of early warnings of that type exceeds a preset proportion threshold; the adaptive adjustment includes: raising the early warning trigger threshold corresponding to the early warning type, correcting the model parameters of the digital twin model, and adjusting at least one of the data types corresponding to the early warning type in the mapping rules.

[0006] Furthermore, this application also proposes an early warning acquisition module for acquiring early warning information reported by the monitoring system, wherein the early warning information includes at least the early warning type, early warning location, and early warning time; The intelligent agent verification module is used to parse the early warning information by the intelligent agent, generate data verification tasks corresponding to the early warning information, and execute the data verification tasks to obtain further verification data. Among them, generating data verification tasks includes: determining the target data source and corresponding collection parameters from multiple candidate data sources according to the early warning type, and the further verification data is collected by the target data source according to the collection parameters. The twin simulation module is used to map early warning information and further verification data to a pre-built digital twin model, drive the digital twin model to simulate synchronously, and obtain the twin simulation state variables corresponding to the early warning location; the digital twin model is a virtual mapping model corresponding to the monitored physical object; The comparison and verification module is used to compare the early warning trigger parameters corresponding to the early warning information with the state variables of the twin simulation to obtain the simulation deviation, and to perform cross-validation on further verification data from multiple sources to obtain the cross-validation support. The fusion identification module is used to fuse simulation bias and cross-validation support to obtain the real alarm confidence score; based on the comparison result between the real alarm confidence score and the preset confidence threshold, the alarm information is identified as a real alarm or a false alarm. The results output module is used to output the recognition results. As can be seen from the above, the early warning information analysis method and system provided in this application can automatically identify the real and false alarm categories of early warning information by actively generating data verification tasks by an intelligent agent, driving the digital twin model to perform synchronous simulation, and combining the deviation comparison between early warning trigger parameters and simulation state quantities with cross-validation of multi-source verification data. This significantly reduces the false alarm rate and improves the accuracy of early warning. It has the advantages of automatically identifying the real and false alarm categories of early warning information, significantly reducing the false alarm rate, and improving the accuracy of early warning. Attached Figure Description

[0007] Figure 1 This is a flowchart illustrating the first embodiment of the early warning information analysis method of this application; Figure 2 This is a structural block diagram of the first embodiment of the early warning information analysis system of this application. Detailed Implementation

[0008] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0009] Traditional early warning processing methods in industrial monitoring and security surveillance scenarios generate a large number of false alarms. They lack an automatic mechanism to distinguish between real and false alarms, leading to wasted resources and the overlooking of genuine incidents. Existing digital twin-based early warning solutions rely solely on existing system data, failing to proactively acquire additional evidence to verify the authenticity of warnings. Furthermore, their judgments depend on a single data source or criterion, resulting in a high false alarm rate. In addition, existing solutions lack quantitative confidence metrics, tiered handling mechanisms, and mechanisms for reviewing and self-adjusting misjudgments, hindering continuous optimization of system judgment rules.

[0010] To address this, embodiments of this application provide a method for analyzing early warning information, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the early warning information analysis method of this application.

[0011] In this embodiment, the early warning information analysis method includes the following steps: Step S10: Obtain the early warning information reported by the monitoring system, which includes at least the early warning type, early warning location, and early warning time; Step S20: The agent parses the warning information, generates a data verification task corresponding to the warning information, and executes the data verification task to obtain further verification data; wherein, the generation of the data verification task includes: determining the target data source and the corresponding collection parameters from multiple candidate data sources according to the warning type, and the further verification data is collected by the target data source according to the collection parameters; Step S30: Map the early warning information and the further verification data to a pre-built digital twin model, drive the digital twin model to perform synchronous simulation, and obtain the twin simulation state variables corresponding to the early warning location; the digital twin model is a virtual mapping model corresponding to the monitored physical object; Step S40: Compare the early warning trigger parameters corresponding to the early warning information with the twin simulation state variables to obtain the simulation deviation, and perform cross-validation on the further verification data from multiple sources to obtain the cross-validation support. Step S50: Fuse the simulation bias and the cross-validation support to obtain the real alarm confidence score; based on the comparison result between the real alarm confidence score and the preset confidence threshold, identify whether the warning information is a real alarm or a false alarm; Step S60: Output the recognition result.

[0012] For ease of understanding, the following explains some key terms in this embodiment: Early warning information refers to a set of data reported by a monitoring system that indicates potential abnormalities or dangerous situations. It usually includes basic elements such as warning type, warning location, and warning time, and is used to preliminarily describe the nature, location, and time of the event.

[0013] An intelligent agent refers to a software entity or hardware system that can perceive the environment, make decisions, and perform actions. In this embodiment, it is responsible for parsing the early warning information, generating and performing data verification tasks to proactively obtain additional evidence.

[0014] Data verification tasks refer to a series of data collection or query instructions generated to verify the authenticity of early warning information. They aim to obtain supplementary data related to the early warning information from specific data sources.

[0015] Further verification data refers to supplementary data obtained through data verification tasks to help determine the authenticity of early warning information. This data can come from various external or internal data sources.

[0016] A digital twin model is a virtual mapping model established in virtual space with the monitored physical object. This model can reflect the operating status, behavior and performance of the physical object in real time, and achieve synchronous simulation with the physical object through data-driven methods.

[0017] The state quantity of a digital twin simulation refers to the quantitative data that reflects the state of a physical object at a specific warning location, which is derived or calculated by the digital twin model during synchronous simulation.

[0018] Simulation deviation refers to the degree of difference quantified after comparing the warning trigger parameters indicated in the warning information with the twin simulation state quantities derived from the digital twin model. This deviation value reflects the consistency between the warning information and the model prediction.

[0019] Cross-validation support refers to a quantitative indicator that assesses the degree to which data from multiple sources mutually validate each other to evaluate the authenticity of early warning information. It reflects the consistency and reliability of multi-source evidence.

[0020] Real-time alert confidence level refers to an indicator that quantitatively assesses the probability that an alert is a real emergency after comprehensively considering various factors such as simulation bias and cross-validation support. This indicator is used to guide subsequent identification and handling decisions.

[0021] A real alarm is an alarm that has been confirmed to have actually occurred after analysis and verification; a false alarm is an alarm that has been confirmed to be a false alarm or an alarm that has not actually occurred after analysis and verification.

[0022] This embodiment provides a method for analyzing early warning information, the specific process of which may include the following steps: First, the system acquires early warning information reported by the monitoring system. This early warning information is data generated and sent by the monitoring system when it detects an anomaly, and it includes at least the warning type, warning location, and warning time. For example, the monitoring system can be a simple sensor network that generates an early warning message when a sensor detects a physical quantity exceeding a threshold. This early warning information may only contain a basic event description, such as "abnormal temperature, location A, time T".

[0023] Next, the agent parses the warning information and generates a data verification task corresponding to it. This task is then executed to obtain further verification data. Specifically, generating the data verification task involves determining the target data source and its corresponding acquisition parameters from multiple candidate data sources based on the warning type. This further verification data is collected by the target data source according to the acquisition parameters. For example, when receiving a warning of "abnormal temperature," the agent can determine "nearby cameras" as the target data source based on preset rules and set "take a photo" as the acquisition parameter. The agent then sends a command to the camera to obtain a photo of the scene as further verification data. Alternatively, the agent can determine "historical operation logs" as the target data source based on the warning type and set "query temperature records for the past hour" as the acquisition parameter to obtain historical data for verification.

[0024] Subsequently, the early warning information and the further verification data are mapped to a pre-built digital twin model, and the digital twin model is driven to simulate synchronously to obtain the twin simulation state variables corresponding to the early warning location. This digital twin model is a virtual mapping model corresponding to the monitored physical object. For example, for an industrial piece of equipment, a digital twin model can be built to simulate the equipment's operating state. When early warning information and verification data are received, these data are input into the digital twin model, and the model performs simulations based on these inputs, such as simulating the temperature change trend of the equipment under the current abnormal conditions, thereby obtaining the steady-state variables of the early warning location in the simulation environment.

[0025] Furthermore, the warning trigger parameter corresponding to the warning information is compared with the deviation of the twin simulation state quantity to obtain the simulation deviation. Simultaneously, cross-validation is performed on this further verification data from multiple sources to obtain the cross-validation support. For example, the warning information may contain a trigger parameter such as "temperature exceeds 80 degrees Celsius." The twin simulation state quantity obtained from the digital twin model simulation may show that the temperature at that location is 75 degrees Celsius. In this case, the difference between the two can be directly calculated as the simulation deviation. For cross-validation, if the agent obtains further verification data from "on-site photos" and "nearby sensors," it can simply check whether these data all indicate an anomaly and give the support based on the degree of consistency.

[0026] Finally, the simulation bias and the cross-validation support are fused to obtain the real alarm confidence score. Based on the comparison between this real alarm confidence score and a preset confidence threshold, the category of the warning information is identified as either a real alarm or a false alarm. For example, a simple weighted average of the simulation bias and the cross-validation support can be used to obtain a comprehensive real alarm confidence score. If this score is higher than a preset single confidence threshold, it is determined to be a real alarm; otherwise, it is determined to be a false alarm.

[0027] Finally, the recognition result is output. This result can be a simple text prompt, such as "real alarm" or "false alarm," or an indicator light status, to inform the user or subsequent system warning information of the final classification.

[0028] This embodiment actively acquires further verification data, combines it with a digital twin model for synchronous simulation to obtain twin simulation state variables, and compares the deviation between the warning trigger parameters and the twin simulation state variables. Simultaneously, it performs cross-validation on multi-source verification data, fusing simulation deviations and cross-validation support to obtain a quantified real-alarm confidence score, and automatically identifies the category of the warning information as a real or false alarm. Therefore, this embodiment effectively solves the problems of high false alarm rates, wasted resources, and lack of automatic identification and multi-source verification mechanisms in traditional warning processing, improving the accuracy and reliability of warning judgment.

[0029] While the aforementioned methods for analyzing early warning information can parse the information and perform simulation comparisons using digital twin models, if the initially acquired early warning information only includes basic elements such as warning type, location, and time, the subsequent data verification tasks, digital twin simulation analysis, and real-time warning confidence calculations may lack sufficient refinement. For example, without an understanding of the severity of the warning or specific triggering conditions, the system struggles to differentiate between different warnings, thus affecting the accuracy and efficiency of the analysis.

[0030] In this regard, this embodiment further proposes that the warning information also includes the parameter values ​​of the warning level and the warning triggering parameter, and the warning information comes from at least one of the following: sensor monitoring equipment, video surveillance equipment or manual reporting terminal deployed on the monitored physical object.

[0031] Specifically, the warning level is used to characterize the severity or urgency of the warning event, and can be represented by a hierarchical system (e.g., levels 1 to 5 from low to high) or descriptive labels (e.g., general, important, urgent, and extremely urgent). This level information can guide the system to adopt differentiated response strategies for warnings of different severity levels in subsequent processing. For example, a high-level warning may trigger faster and more comprehensive data verification and a higher-level handling process.

[0032] The parameter value of the warning triggering parameter refers to the real-time value of the specific monitoring parameter that causes the warning to be triggered. For example, if the warning type is "temperature exceeding limit", the parameter value of the warning triggering parameter may be "current temperature 85℃, exceeding limit threshold 80℃". These specific parameter values ​​provide quantitative evidence of the warning event, which plays an important supporting role in subsequent digital twin simulation comparison and real-time warning confidence calculation, and can be directly used for accurate deviation comparison with the state variables of the digital twin simulation.

[0033] The sources of early warning information are diverse, including at least one of sensor monitoring devices, video surveillance equipment, or manual reporting terminals. Sensor monitoring devices typically refer to various sensors deployed on monitored physical objects, such as temperature sensors, pressure sensors, vibration sensors, and gas detectors, which can collect operational data of the physical objects in real time. Video surveillance equipment provides visual information through images or video streams, which can be used for manual or intelligent analysis to detect anomalies. Manual reporting terminals refer to operators or on-site inspectors manually submitting early warning information based on on-site observations using dedicated equipment or applications. Clarifying the source of early warning information helps the system assess the reliability of the information and provides a basis for selecting appropriate target data sources for subsequent data verification tasks.

[0034] Through the above technical solutions, this method can significantly improve the refinement and accuracy of early warning information analysis. The early warning level included in the early warning information allows the system to prioritize and allocate resources according to the severity of events, ensuring that critical early warnings are handled promptly and efficiently. The parameter values ​​of the early warning trigger parameters provide a quantitative basis for the early warning event, enabling more precise quantitative analysis when comparing deviations with the simulation state variables of the digital twin model, thereby improving the accuracy of simulation deviation calculations. Furthermore, clearly defining the sources of early warning information as multiple channels, such as sensor monitoring equipment, video surveillance equipment, or manual reporting terminals, not only helps assess the reliability of the initial early warning but also provides contextual information for the agent to select the most suitable target data source when generating data verification tasks. For example, for early warnings from sensors, cross-validation with other relevant sensor data or video data may be prioritized. This multi-dimensional, high-precision input of early warning information makes subsequent data verification, digital twin simulation, deviation comparison, and cross-validation more accurate and efficient, ultimately improving the calculation accuracy of real alarm confidence and effectively reducing the false alarm rate, thereby enhancing the reliability and practicality of the entire early warning information analysis method.

[0035] In some of the embodiments described above, when an early warning message is received, the intelligent agent needs to parse the message and generate a data verification task to obtain further verification data. However, when determining the target data source and corresponding collection parameters from multiple candidate data sources based on the warning type, relying solely on simple preset rules may result in low efficiency in generating the data verification task, or the collected data may fail to maximize support for real-time alarm identification. Furthermore, it may incur unnecessary collection costs, thereby affecting the accuracy of the early warning analysis and the efficiency of resource utilization.

[0036] To address this, this embodiment further proposes that when determining the target data source and corresponding acquisition parameters from multiple candidate data sources based on the warning type, the specific steps include: determining the target data type matching the warning type according to a preset mapping rule between warning types and data types; identifying candidate data sources that can provide the target data type as the target data source, and generating corresponding acquisition parameters based on the warning location, wherein the acquisition parameters include at least one of acquisition viewpoint, acquisition frequency, and acquisition duration; wherein the agent generates the data verification task based on a trained reinforcement learning decision model, the reinforcement learning decision model being trained with a preset reward function, the reward value of the reward function being positively correlated with the contribution of the generated data verification task to real-time alarm identification and negatively correlated with the data acquisition cost of the data verification task.

[0037] Specifically, upon receiving an early warning message, the system first queries pre-established mapping rules based on the warning type, such as "high temperature warning," "smoke warning," or "abnormal equipment vibration warning." These mapping rules define the data types required for verification for different warning types. For example, a high temperature warning might require "temperature data" and "infrared image data," while a smoke warning might require "smoke concentration data" and "visible light video data." This mapping allows for the rapid and targeted determination of the target data type needed to verify the warning. Subsequently, the system identifies all candidate data sources that can provide these target data types, such as various sensors deployed on monitored physical objects, video surveillance equipment, and inspection robots, and identifies the devices or systems that can provide the required data types as the target data sources. This approach ensures that the initial direction of data verification is accurate and relevant. After determining the target data sources, in order to obtain high-quality and effective data for further verification, it is necessary to dynamically generate refined data acquisition parameters based on the warning location indicated in the warning message. For example, if an alert indicates an anomaly in a certain area, the viewing angle of video surveillance equipment can be adjusted to focus on that area; for sensors, the acquisition frequency can be increased to capture more subtle changes; and the acquisition duration can be set according to the urgency or duration of the alert. These acquisition parameters are designed to make the data acquisition process more targeted, avoid collecting irrelevant data, and improve the efficiency and accuracy of data verification.

[0038] Furthermore, when generating data verification tasks, the agent does not simply follow fixed rules, but utilizes a trained reinforcement learning decision model. This model, through interactive learning with the environment, can autonomously make decisions based on information such as the current warning type, warning location, and available candidate data sources to generate the optimal data verification task. This reinforcement learning-based decision-making mechanism enables the system to adapt to complex and ever-changing environments and continuously optimize its decision-making strategies from historical experience, thereby improving the intelligence level of data verification task generation. Training the reinforcement learning decision model is key to its intelligence. This model is trained using a carefully designed reward function. The core idea of ​​the reward function is to balance "effect" and "cost." Specifically, when the data verification task generated by the agent significantly improves the accuracy or confidence of subsequent real-time alarm identification, the reward value increases accordingly, prompting the model to learn how to select the data source and collection parameters that best reveal the true situation. Conversely, if the generated data verification task leads to excessively high data collection costs (e.g., using expensive equipment, long collection times, transmitting large amounts of data), the reward value decreases. Through this positive and negative incentive mechanism, the reinforcement learning model is guided to learn a strategy that optimizes resource utilization and reduces data collection overhead while ensuring the effectiveness of real-time police identification, thereby achieving efficient and accurate data verification.

[0039] Through the above technical solution, this embodiment can generate data verification tasks more intelligently and efficiently after receiving early warning information. First, by using preset mapping rules between early warning types and data types, the system can quickly identify the data types most relevant to the early warning type and determine the target data sources that can provide this data, ensuring the accuracy of the verification direction. Second, by dynamically generating refined collection parameters such as collection perspective, collection frequency, and collection duration based on the early warning location, data collection becomes more targeted, avoiding the acquisition of invalid or redundant data. More importantly, the agent executes the generation of data verification tasks based on a reinforcement learning decision model. This model is trained using a reward function, where the reward value is positively correlated with the contribution to real-time alarm identification and negatively correlated with data collection cost. This means that during the learning process, the model will not only tend to select data sources and collection parameters that can effectively identify real-time alarms but will also consider the cost-effectiveness of data collection. Therefore, this embodiment can effectively solve the problems of low efficiency, high cost, and insufficient accuracy in data verification task generation in traditional methods, significantly improving the intelligence level and resource utilization efficiency of early warning information analysis, ensuring that the most critical verification data is obtained with limited resources, thereby improving the accuracy and timeliness of real-time alarm identification.

[0040] In some of the embodiments described above, an intelligent agent is proposed to dynamically determine the target data source and corresponding collection parameters based on a reinforcement learning decision model to generate a data verification task. However, in actual early warning information analysis scenarios, there are many types of data sources available for data verification, and the collection methods and operational requirements of different data sources vary significantly. If the heterogeneity of these data sources and how to efficiently and accurately control their data collection are not fully considered, especially for devices that need to be moved to a specific location to collect data, the execution efficiency of the data verification task may be low, or even the key further verification data may not be obtained, thus affecting the accuracy of the early warning judgment.

[0041] To address this, this embodiment further proposes that the candidate data sources include at least two of the following: video surveillance equipment, sensor monitoring equipment, inspection robots, drones, and historical operation databases. Specifically, video surveillance equipment refers to camera systems deployed around the monitored physical object, capable of providing real-time or recorded video streams, offering intuitive visual information; examples include fixed high-definition cameras, PTZ cameras, and thermal imaging cameras. Sensor monitoring equipment refers to sensors used to monitor various physical or chemical quantities of a physical object in real time, such as temperature sensors, humidity sensors, pressure sensors, gas detectors, and vibration sensors, providing quantified environmental data or equipment operating status data. Inspection robots refer to robotic platforms with autonomous mobility and data acquisition capabilities, capable of carrying multiple sensors and traveling to warning locations along preset or dynamically planned paths for close-range, multi-angle data acquisition; examples include wheeled robots and tracked robots. Drones refer to aircraft with aerial flight and data acquisition capabilities, capable of quickly reaching inaccessible areas and collecting large-scale or high-altitude data from an aerial perspective; examples include multi-rotor drones and fixed-wing drones. A historical operational database refers to a database system that stores long-term operational data, event records, maintenance logs, and other information about monitored physical objects. It can be used to provide background information, trend analysis, or as a comparison benchmark. The combined use of these candidate data sources allows the agent to flexibly select the most suitable data acquisition tools when facing different types of alerts, thereby obtaining comprehensive and targeted verification data.

[0042] The execution of the data verification task includes: the agent issuing a collection command to the collection device corresponding to the target data source. This command controls the collection device to collect data according to the specified parameters. This means that after determining the target data source and collection parameters, the agent needs the ability to communicate and control the underlying collection devices of these data sources. For example, if the target data source is a video surveillance device, the collection command might include specifying the camera ID, adjusting its pan / tilt angle, setting the recording duration and frame rate, etc.; if the target data source is a sensor monitoring device, the command might include specifying the sensor ID, setting the sampling frequency, and activating a specific measurement mode, etc. This command issuance mechanism ensures the automation and accuracy of data collection, avoiding delays and errors caused by manual intervention. When the target data source is an inspection robot or drone, the agent also plans the movement path of the inspection robot or drone based on the warning location. Since inspection robots and drones are mobile collection devices, the effectiveness of their data collection highly depends on their movement trajectory. The agent needs to generate an optimal or suboptimal movement path based on the geographical coordinates of the warning location, environmental obstacle information, and the expected collection target. For example, for inspection robots, path planning may need to consider factors such as obstacle avoidance, shortest path, and coverage area; for drones, it may need to consider factors such as flight altitude, flight path, no-fly zones, and battery life. By having an intelligent agent plan the movement path, it can be ensured that inspection robots or drones can reach the warning location efficiently and safely, and collect key further verification data from the best viewpoint or position.

[0043] Through the above technical solution, this embodiment, based on the data verification task generated by the intelligent agent using a reinforcement learning decision model, further clarifies the diverse candidate data source types and their specific execution mechanisms. By including various heterogeneous data sources such as video surveillance equipment, sensor monitoring equipment, inspection robots, drones, and historical operation databases in the candidate range, the intelligent agent can flexibly and comprehensively select the most suitable data acquisition tool according to the actual needs of the warning type and location, greatly expanding the breadth and depth of data verification. The intelligent agent can directly issue precise acquisition commands to the selected acquisition devices, realizing the automation and fine-grained control of the data acquisition process, ensuring the timeliness and accuracy of the verification data. In particular, for mobile data sources such as inspection robots and drones, the intelligent agent can intelligently plan their movement paths according to the warning location, enabling these devices to efficiently and accurately reach the target area and acquire key data, effectively overcoming the limitations of traditional fixed sensors in terms of coverage and flexibility. This not only improved the efficiency of data verification tasks and the targeting of data collection, but also significantly enhanced the system's ability to acquire high-quality, multi-dimensional data for further verification, thus providing a more solid data foundation for subsequent real-time alarm confidence fusion identification, and ultimately improving the accuracy and reliability of early warning information analysis.

[0044] In some of the embodiments described above, a method is proposed to map early warning information and further verification data to a digital twin model and drive its synchronous simulation to obtain the state variables of the twin simulation. However, in practical applications, the construction and simulation process of the digital twin model often faces the challenge of ensuring its accuracy, real-time performance, and adaptability to different early warning scenarios. Especially under complex physical objects and variable early warning types, if the model cannot accurately reflect the real state of the physical object or cannot efficiently perform targeted simulations, it will seriously affect the reliability and efficiency of early warning analysis.

[0045] To address this, this embodiment further proposes an optimized method for constructing and driving the digital twin model. The method for constructing the digital twin model includes: establishing a geometric model and a physical mechanism model of the monitored physical object; mapping the real-time operating data of the monitored physical object to the geometric model and the physical mechanism model for parameter correction, so that the twin simulation state variables of the digital twin model are synchronized with the actual state of the monitored physical object; the physical mechanism model includes multiple mechanism sub-models corresponding to different warning types; driving the synchronous simulation of the digital twin model includes: starting the target mechanism sub-model corresponding to the warning type of the warning information; using the warning time as a benchmark and the further verification data as boundary conditions, inputting the target mechanism sub-model for deduction; stopping the deduction when the state change of two adjacent simulation steps is lower than a preset change threshold; and determining the stable state variable at the point of stopping the deduction as the twin simulation state variable corresponding to the warning location.

[0046] Specifically, in constructing a digital twin model, the first step is to build a precise geometric model of the monitored physical object. This can be achieved through 3D scanning or importing data from computer-aided design (CAD) to provide its spatial structural information. Simultaneously, based on physical laws and engineering principles, a physical mechanism model reflecting the operating mechanism of the physical object is established. For example, for an industrial device, this might include models based on thermodynamics, fluid mechanics, or structural mechanics. To ensure the accuracy and real-time performance of the digital twin model, the system continuously acquires real-time operational data of the monitored physical object and maps this data to the established geometric and physical mechanism models. By comparing the actual data with the model output, key parameters in the physical mechanism model can be dynamically corrected and optimized. For example, the Kalman filter algorithm can be used to identify and calibrate model parameters in real time, ensuring that the state variables of the digital twin model are highly synchronized with the actual state of the monitored physical object.

[0047] To improve simulation efficiency and relevance, the physical mechanism model is designed as a modular structure, containing multiple sub-models corresponding to different warning types. For example, when the warning type is "temperature anomaly," the system will call a sub-model specifically for heat conduction and heat dissipation analysis; when the warning type is "pressure exceeding limits," it will call a sub-model for fluid dynamics or structural stress analysis. This design allows the system to flexibly select and activate the most relevant simulation model based on the specific warning type received, avoiding redundant calculations of the entire complex physical mechanism model.

[0048] When driving the synchronous simulation of the digital twin model, the system precisely activates the corresponding target mechanism sub-model based on the type of warning information. The starting time of the simulation is set to the warning time indicated in the warning information, ensuring that the simulation begins from the moment the warning occurs. Simultaneously, further verification data acquired by the agent, such as environmental parameters, equipment load, or relevant sensor readings at the time of the warning, are input into the target mechanism sub-model as boundary conditions. These boundary conditions provide the simulation with initial and external environmental parameters that are closer to reality, ensuring the accuracy of the simulation results. The target mechanism sub-model performs iterative calculations under the given boundary conditions, simulating the dynamic response of the physical object after the warning occurs. The simulation process continues until the change in key state variables calculated by two adjacent simulation steps falls below a preset threshold. This threshold is typically set based on the characteristics of the physical object and the sensitivity of the warning, indicating that the system has reached a relatively stable state or the trend of change has leveled off. Once the conditions for stopping the simulation are met, the simulation state variables at this point, such as temperature, pressure, or stress at the warning location, are determined as the twin simulation state variables corresponding to the warning location.

[0049] Through the above technical solutions, this embodiment optimizes the construction and simulation driving of digital twin models, solving the challenges of traditional digital twin models in dealing with diverse early warnings and ensuring simulation accuracy and efficiency. First, by establishing geometric and physical mechanism models of the monitored physical object and using real-time operational data for parameter correction, the state variables of the digital twin model are ensured to be highly synchronized with the actual state of the monitored physical object, significantly improving the model's realism and prediction accuracy. Second, the physical mechanism model is subdivided into multiple mechanism sub-models corresponding to different early warning types, enabling the system to accurately select and activate the most relevant sub-model for simulation based on the specific early warning type, avoiding unnecessary full-model calculations and thus greatly improving simulation efficiency and resource utilization. Furthermore, using the early warning time as a benchmark, further verification data is input as boundary conditions into the target mechanism sub-model for deduction, and a stopping deduction mechanism based on state change is set, ensuring the convergence of the simulation process and the stability of the results. The resulting twin simulation state variables are more accurate and reliable, providing a solid data foundation for subsequent real-world early warning identification. This sophisticated and adaptive digital twin simulation mechanism enables early warning analysis methods to more effectively identify real alarms, reduce the false alarm rate, and improve the intelligence level and decision support capabilities of the entire early warning system.

[0050] In some of the embodiments described above, a method is proposed to obtain the simulation deviation by comparing the deviation between the warning trigger parameters corresponding to the warning information and the state variables of the twin simulation, and to obtain the cross-validation support by cross-validating further verification data from multiple sources, and then fusing the two to identify the warning information category. However, in practical applications, how to accurately and reliably quantify the simulation deviation and cross-validation support to ensure the accuracy and robustness of the final real-world warning confidence is a key challenge affecting the performance of the warning analysis system. If the deviation comparison and cross-validation mechanisms are not refined enough, it may lead to misjudgment or missed judgment, reducing the reliability of the warning system.

[0051] To address this, this embodiment further proposes that the deviation comparison includes: calculating the deviation value between the warning triggering parameter and the twin simulation state quantity, and comparing the deviation value with a preset deviation threshold. The smaller the deviation value, the more likely the warning information is a real warning. The cross-validation includes: counting the number of data sources supporting the establishment of the warning information from multiple sources in the further verification data, and calculating the matching degree between the verification data supporting the establishment of the warning information and the preset real warning criterion features. The cross-validation support degree is determined based on the number of data sources and the matching degree.

[0052] Specifically, in terms of deviation comparison, the warning trigger parameter refers to the specific parameter value that leads to the generation of the warning information, such as temperature, pressure, vibration frequency, etc., detected by the sensor. It is usually a physical quantity of the monitored physical object at a specific moment. The twin simulation state quantity is the virtual physical quantity obtained by the digital twin model for the warning location after synchronous simulation, representing the expected state of the monitored physical object under current conditions. The calculation of the deviation value aims to quantify the degree of difference between these two parameters. For example, when the parameter is a single value, its absolute difference or relative difference can be calculated; when the parameter is a multi-dimensional vector, Euclidean distance, Mahalanobis distance, or cosine similarity can be calculated. Through this quantification, the consistency between the warning information and the predicted state of the digital twin model can be intuitively reflected. Subsequently, the calculated deviation value is compared with a preset deviation threshold. This preset deviation threshold is a pre-set threshold used to determine whether the deviation value is within an acceptable range, and it can be set based on historical data, expert experience, or system performance requirements. When the deviation value is below the threshold, it indicates that the warning information is highly consistent with the predicted state of the digital twin model, and the probability that the warning is a real warning is relatively high. Conversely, if the deviation value is above the threshold, it may mean that there is a significant difference between the warning information and the model prediction, and the probability that the warning is a false alarm increases. This design clarifies the negative correlation between the deviation value and the probability of a real alarm; that is, a smaller deviation value means that the warning triggering parameters are highly consistent with the simulation results of the digital twin model, enhancing the confidence that the warning information is a real event.

[0053] In terms of cross-validation, further verification data is collected from multiple candidate data sources (such as video surveillance equipment, sensor monitoring equipment, inspection robots, drones, and at least two of the historical operation databases) to verify the early warning information from multiple dimensions. First, the number of data sources supporting the establishment of the early warning information is counted. This refers to identifying and counting data sources that can independently or jointly confirm the authenticity of the early warning event. For example, if the early warning type is "equipment overheating," temperature data from infrared thermal imagers, readings from internal temperature sensors of the equipment, and videos of smoke emanating from the equipment captured by inspection robots can all be considered data sources supporting the establishment of the early warning information. By counting the number of sources, the reliability of the early warning information can be assessed broadly. Second, the matching degree between the verification data supporting the establishment of the early warning information and the preset real-time alarm criteria features is calculated. Real-time alarm criteria features are predefined feature patterns or conditions for a specific early warning type that can clearly indicate the occurrence of a real event. For example, for an "equipment overheating" early warning, real-time alarm criteria features may include a continuous temperature rise exceeding a specific threshold, accompanied by abnormal vibration patterns, or the appearance of flames in the video. The matching degree calculation aims to quantify the degree of conformity between the verification data and these preset real-time alarm criteria features. This can be done through pattern recognition algorithms (such as feature vector matching, time series similarity analysis), rule engine comparison, or machine learning models. Finally, the cross-validation support is determined based on the number of data sources and the matching degree. Cross-validation support is an indicator that comprehensively evaluates the authenticity of early warning information. It combines the breadth (quantity) and depth (matching degree with real-time alarm criteria features) of the data sources supporting the establishment of the early warning information. The determination method may include, but is not limited to: normalizing the number of data sources and then weighting and summing them with the matching degree; or setting a decision matrix, where the cross-validation support is considered high only when the number of data sources reaches a certain threshold and the matching degree is higher than a certain threshold.

[0054] Through the above technical solution, this embodiment provides a more refined and reliable real-time alarm confidence calculation mechanism for early warning information analysis. Specifically, by clearly calculating the deviation value between the early warning triggering parameters and the state quantity of the digital twin simulation, and comparing it with a preset deviation threshold, the degree of consistency between the early warning information and the predicted state of the digital twin model can be quantified, thus providing an objective basis for judging the authenticity of the early warning based on model simulation. Simultaneously, by statistically analyzing the number of verification data sources supporting the establishment of the early warning information and calculating the matching degree between the verification data and the preset real-time alarm criterion features, this embodiment can comprehensively and deeply verify the authenticity of the early warning information from the perspective of multi-source heterogeneous data. This mechanism, which combines digital twin model simulation verification and multi-source data cross-validation, ensures that the final real-time alarm confidence not only considers the rationality of the physical mechanism but also fully utilizes the corroboration of multi-dimensional real-time data, significantly improving the accuracy and robustness of early warning information identification, effectively reducing the false alarm rate and missed alarm rate, thereby enhancing the decision-making reliability of the entire early warning system.

[0055] To address this, this embodiment proposes a method for analyzing early warning information. This method acquires early warning information reported by a monitoring system, which is then parsed by an intelligent agent to generate and execute data verification tasks to obtain further verification data. Subsequently, the early warning information and further verification data are mapped to a pre-constructed digital twin model, driving synchronous simulation of the digital twin model to obtain the twin simulation state quantity corresponding to the early warning location. Next, the early warning trigger parameters corresponding to the early warning information are compared with the twin simulation state quantity to obtain the simulation deviation, and cross-validation is performed on further verification data from multiple sources to obtain cross-validation support. Finally, the simulation deviation and cross-validation support are fused to obtain the real alarm confidence score. Based on the comparison result between the real alarm confidence score and a preset confidence threshold, the category of the early warning information is identified as a real alarm or a false alarm, and the identification result is output. However, relying solely on real-time data and simulation results to identify real and false alarms may not fully utilize historical experience and case knowledge. This results in a need to improve the accuracy and confidence of the identification when faced with complex or ambiguous warning scenarios, especially when there is a lack of sufficient real-time evidence or uncertainty in real-time data, making it difficult to make more accurate judgments.

[0056] To address the aforementioned issues, this embodiment further proposes incorporating historical case matching scores into the fusion process. Specifically, a historical early warning case library is pre-built, storing feature vectors of historical early warning information and corresponding real or false alarm determination results. When new early warning information arrives, the feature vector of this early warning information is compared with the feature vectors in the historical early warning case library for similarity calculation. During the calculation, historical early warning cases within a set recent time period are assigned a higher similarity weight than other historical early warning cases to reflect the higher reference value of recent cases. Based on the determination results of similar cases with similarity scores exceeding a preset similarity threshold, the historical case matching score is statistically obtained. Furthermore, for historical early warning cases with similar feature vectors but different determination results, the system marks them as disputed cases and transfers them for manual calibration to ensure the accuracy and reliability of the case library.

[0057] Historical case matching refers to assessing the likelihood of a current warning being a false alarm by comparing its similarity to historically occurring and determined warning cases. Its role is to introduce historical experience and knowledge, providing additional, past-event-based evidence to support the calculation of false alarm confidence, thereby improving the accuracy and robustness of identification. A pre-built historical warning case library is a structured database used to store relevant information about past warning events. Each case typically contains a feature vector, which is an abstract and quantified representation of the warning information (such as warning type, location, time, trigger parameter values, further verification data, etc.). Each case also records whether the warning event was ultimately determined to be a false alarm or a real alarm. The feature vector of historical warning information is a multi-dimensional numerical representation used to capture the key attributes of the warning information. For example, it may include the warning type code, the geographic coordinates or region ID of the warning location, the normalized value of the warning trigger parameter, and the statistical characteristics of the further verification data (such as mean, peak value, rate of change), etc. These features can be extracted and encoded using machine learning algorithms or expert experience. Similarity calculation involves first extracting the feature vector of a new warning message. Then, a similarity measurement algorithm (such as cosine similarity, the reciprocal of Euclidean distance, or the Gaussian kernel function) is used to calculate the similarity between the feature vector of the new warning message and the feature vector of each case in the historical warning case database. Historical warning cases from recent periods are assigned a higher similarity weight than other historical warning cases. This means that when calculating similarity, the similarity calculation result for historical cases that occurred within a recent period (e.g., the past week, month, or quarter) is multiplied by a weight coefficient greater than 1. This is based on the assumption that "recent events are more valuable for reference," because the system operating environment, monitoring equipment status, and external interference factors may change over time, and recent cases better reflect the current system's true behavior patterns. The matching degree of historical cases is statistically obtained based on the judgment results of similar cases whose similarity exceeds a preset similarity threshold. That is, a similarity threshold is set; only when the similarity between a historical case and the current warning message exceeds this threshold is the historical case considered a "similar case." Then, the proportion or number of these similar cases that are judged as actual warnings is statistically analyzed, and this is used as the historical case matching degree. For example, if 8 out of 10 similar cases are real alerts, the matching degree is 0.8. Historical alert cases with similar feature vectors but different judgment results are marked as disputed cases and transferred to manual calibration. If it is found that the feature vectors of some historical cases are highly similar to the current alert information, but their final judgment results (real alert / false alert) are inconsistent, this may indicate that the feature boundaries of such cases are blurred, or that there are errors in historical judgments. At this time, the system will mark these cases as "disputed cases" and transfer them to human experts for review and calibration to ensure the accuracy and reliability of the case database.

[0058] The fusion process specifically involves: weighting the normalized simulation bias according to a first preset weighting coefficient, weighting the cross-validation support according to a second preset weighting coefficient, and weighting the historical case matching degree according to a third preset weighting coefficient, then summing the results to obtain the real-warning confidence score. This step is the core decision fusion process, which quantitatively integrates evidence from different dimensions (digital twin simulation, multi-source data cross-validation, and historical experience) to generate a comprehensive real-warning confidence score. Through weighted summation, the reliability and importance of different evidence sources can be flexibly configured, allowing the final confidence score to more comprehensively and accurately reflect the probability that the warning information is a real-warning event. The normalized simulation bias is usually a non-negative value, representing the difference between the warning triggering parameters and the twin simulation state variables. To fuse with other indicators, it needs to be normalized to the range of 0 to 1, for example, through linear mapping, the Sigmoid function, or Min-Max standardization. After normalization, the bias value is usually converted into "simulation support," meaning the smaller the bias, the higher the support. Weighted summation involves setting three preset weight coefficients (first, second, and third preset weight coefficients). These coefficients are typically determined based on expert experience, historical data analysis, or machine learning optimization (such as learning optimal weights through model training). They reflect the relative importance of simulation bias, cross-validation support, and historical case matching in the final real-crime confidence calculation. The normalized simulation bias (or its converted support), cross-validation support, and historical case matching are multiplied by their respective weight coefficients, and then the weighted results are summed to obtain the final real-crime confidence score.

[0059] The preset confidence thresholds include a real alarm threshold and a false alarm threshold, with the real alarm threshold being higher than the false alarm threshold. Setting two different confidence thresholds aims to provide a more refined warning information classification mechanism, rather than simply a binary judgment. This allows the system to mark warning information as "warnings requiring manual intervention" when the confidence level is unclear, thereby improving the accuracy and security of decision-making and avoiding false alarms or missed alarms. The real alarm threshold and the false alarm threshold are two preset values, typically between 0 and 1. The real alarm threshold is set to a higher value (e.g., 0.8), indicating that a real alarm is only confirmed as a real alarm when the confidence level is very high. The false alarm threshold is set to a lower value (e.g., 0.3), indicating that a false alarm is only confirmed as a false alarm when the confidence level is very low. An "uncertainty zone" is formed between these two thresholds to identify warnings requiring manual intervention.

[0060] When the confidence level of the real alarm is not lower than the real alarm threshold, the warning information is determined to be a real alarm. This is a clear rule for real alarm determination, ensuring that the system only confirms a warning information as a real alarm when it has a high degree of confidence in it. The system compares the calculated real alarm confidence level with the preset real alarm threshold. If the real alarm confidence level is greater than or equal to the real alarm threshold, the system outputs the recognition result that the warning information is a "real alarm".

[0061] When the confidence level of the real alarm is not higher than the false alarm threshold, the warning information is determined to be a false alarm. This is a clear rule for false alarm determination, ensuring that the system only confirms a warning information as a false alarm when it has a high degree of confidence that it is a false alarm. The system compares the calculated confidence level of the real alarm with the preset false alarm threshold. If the confidence level of the real alarm is less than or equal to the false alarm threshold, the system outputs the identification result that the warning information is a "false alarm".

[0062] When the actual alarm confidence level falls between the actual alarm threshold and the false alarm threshold, the warning information is marked as a warning awaiting review and transferred to human review. This mechanism is crucial for handling uncertain warnings. It prevents the system from making incorrect automatic judgments when the confidence level is neither high nor low. Instead, it entrusts these ambiguous warnings requiring further judgment to human experts for final confirmation, thereby significantly reducing the risk of misjudgment and improving the overall reliability of the warning system. If the actual alarm confidence level does not meet either the actual alarm threshold or the false alarm threshold (i.e., actual alarm threshold > actual alarm confidence level > false alarm threshold), the system will mark the warning information as a "warning awaiting review." Subsequently, the system will generate a review task, sending the warning information and its related raw data, simulation results, cross-validation results, historical case matching degree, and other detailed information to human reviewers or expert systems for manual analysis and final judgment.

[0063] By introducing historical case matching, this embodiment can fully utilize past early warning experience and knowledge to provide multi-dimensional evidence support for the calculation of real-time alarm confidence. When there is uncertainty in real-time data or simulation results, the reference value of historical cases can effectively compensate for the limitations of a single data source, making the calculation of real-time alarm confidence more comprehensive and robust. In addition, by setting real-time alarm thresholds and false alarm thresholds and introducing a "review-pending early warning" mechanism, this embodiment can perform more refined classification processing of early warning information, avoiding false alarms or missed alarms that may be caused by forced automatic judgment in areas of ambiguous confidence. It accurately identifies early warning information that requires manual intervention and transfers it to manual review, thereby significantly improving the accuracy, reliability, and security of early warning identification, reducing the burden of manual review, and optimizing the overall early warning response process.

[0064] In some of the embodiments described above, although the type of early warning information (real or false alarm) can be accurately identified based on the confidence level of the real alarm, simply identifying the nature of the early warning cannot fully meet the needs of effective management and response to early warning events in practical applications. How to efficiently and accurately handle events identified as real alarms, ensuring timely information delivery and triggering appropriate response mechanisms, is a problem that urgently needs to be solved. At the same time, for events identified as false alarms, there is a lack of clear follow-up mechanisms on how to effectively utilize this false alarm information for system optimization and to prevent future recurrence.

[0065] To address this, this embodiment further proposes that when the warning information is determined to be a real warning, the objects to be handled are divided into at least two levels according to the warning level. Differentiated graded handling instructions and push frequencies are matched to objects at different levels and pushed to the corresponding handling terminals. The graded handling instructions include instructions for linkage with emergency response plans. During the push process, the delivery rate of each push channel is monitored. When the delivery rate of a certain push channel is lower than a preset delivery rate threshold, the system automatically switches to a backup push channel for resending. When the warning information is determined to be a false alarm, the warning information is not pushed to the handling terminal. Instead, the warning information is recorded as a false alarm sample, and the cause of the false alarm sample is traced back. The cause of the false alarm includes at least one of sensor monitoring device drift and false triggering of warning triggering rules.

[0066] Specifically, when the system identifies a warning as a real alarm based on the comparison between the real alarm confidence level and the preset confidence threshold, the corresponding response process needs to be initiated immediately. First, based on the warning level contained in the warning information, the responding entities are stratified. For example, they can be divided into at least two levels: Level 1 responders (such as on-site supervisors and core technical experts) and Level 2 responders (such as relevant department heads and maintenance teams). This stratified approach aims to ensure that warnings of different urgency levels receive corresponding levels of attention and resource allocation.

[0067] Subsequently, the system matches differentiated tiered response instructions and push frequencies to different levels of responders. For example, for Level 1 responders, the instruction might be "Immediately activate the highest-level emergency plan, proceed to the scene to verify and direct the response," and this instruction would be pushed frequently (e.g., every 5 minutes) through multiple channels. For Level 2 responders, the instruction might be "Closely monitor the situation and prepare for support," and this instruction would be pushed less frequently (e.g., every 15 minutes). Among these tiered response instructions, the instructions linking to emergency plans are particularly important. This means the system can automatically trigger pre-set emergency plans based on the type and level of the warning, thereby ensuring that all parties can coordinate and respond quickly and effectively in emergencies. These instructions and information are pushed through corresponding response terminals, which can be mobile applications, SMS platforms, email systems, dispatch center displays, etc., to ensure that information is delivered accurately and promptly to the target recipients.

[0068] To further improve the reliability of early warning information delivery, this embodiment also proposes monitoring the delivery rate of each delivery channel during the delivery process. The delivery rate can be statistically analyzed in real time through recipient confirmation, system log feedback, etc. When the system detects that the delivery rate of a certain delivery channel is lower than a preset delivery rate threshold, for example, if the SMS sending failure rate exceeds 10%, the system will automatically switch to a backup delivery channel for resending. The backup channel can be telephone voice notification, other instant messaging tools, etc., to ensure that even if the main channel fails, critical information can still be delivered through other means, avoiding delays in handling due to information transmission interruption.

[0069] On the other hand, when the system determines that a warning is a false alarm, to avoid unnecessary interference and resource waste, the system will not push the warning information to the handling terminal. Instead, the system will record the false alarm information as a false alarm sample and initiate a backtracking mechanism to analyze its cause. False alarm causes may include various situations, such as sensor drift, where the sensor's measurement accuracy or calibration status deviates after long-term operation, leading to inaccurate output data and thus falsely triggering the warning; or false triggering of the warning triggering rules, where the preset warning judgment logic or threshold setting is inappropriate and may be triggered even under normal circumstances. In-depth analysis of these false alarm causes can provide a basis for subsequent system optimization and model adjustment.

[0070] Through the above technical solution, this embodiment, after identifying the category of early warning information, can take differentiated and intelligent follow-up measures for real and false alarms. For early warning information identified as real, the system can stratify the objects to be handled according to the warning level and match differentiated handling instructions and push frequencies. In particular, it can link with emergency plans, thereby ensuring that information can be efficiently and accurately transmitted to relevant personnel and systems in emergency situations and trigger appropriate response mechanisms, greatly improving the efficiency and accuracy of emergency response and effectively reducing potential risks and losses. At the same time, by monitoring the delivery rate of push channels and automatically switching to backup channels, the reliable delivery of key information is further guaranteed. For early warning information identified as false alarms, the system will no longer push invalid information, avoiding waste of handling resources and the "boy who cried wolf" effect. Instead, it will record it as a false alarm sample and deeply trace the cause of the false alarm, such as sensor monitoring equipment drift or false triggering of early warning trigger rules. This mechanism provides valuable data and insights for subsequent system optimization and model iteration, which helps to continuously improve the accuracy and robustness of the early warning system, reduce the occurrence of false alarms, and thus improve the practicality and reliability of the entire early warning analysis method.

[0071] In some of the embodiments described above, the system can identify real or false alarm information based on the confidence level of the real alarm, and transfer warning information with a confidence level between the real alarm threshold and the false alarm threshold to manual review. However, if the system continuously generates false alarms during operation, or if its identification accuracy fails to improve effectively over time and with the accumulation of new data, it may lead to a decline in system performance, increase the cost of manual intervention, and affect the overall efficiency and reliability of warning analysis.

[0072] To address this, this embodiment further proposes a method for analyzing early warning information, which includes: using the identification results and the corresponding feature vectors of the early warning information as new cases to write back to the historical early warning case library; statistically analyzing false alarm samples within a preset statistical period according to the causes of false alarms; and performing adaptive adjustment when the proportion of the number of false alarm samples corresponding to the same early warning type to the total number of early warnings of that type exceeds a preset proportion threshold; the adaptive adjustment includes: increasing the early warning trigger threshold corresponding to the early warning type, correcting the model parameters of the digital twin model, and adjusting at least one of the data types corresponding to the early warning type in the mapping rules.

[0073] Specifically, after the early warning information is analyzed and the identification result is obtained (real warning, false alarm, or result after manual verification), the system will store and write back the identification result and the corresponding feature vector (such as warning type, warning location, warning time, further verification data, simulation deviation, cross-validation support, etc.) as a new case in the historical early warning case database. This aims to continuously expand the historical early warning case database, providing richer and more comprehensive historical data support for the calculation of the real warning confidence of subsequent early warning information, thereby improving the accuracy of similarity calculation.

[0074] Meanwhile, to address potential false alarm issues during system operation, this embodiment also incorporates a statistical and adaptive adjustment mechanism for false alarm samples. The system collects and analyzes all samples identified as false alarms within a preset statistical period (e.g., daily, weekly, or monthly). These false alarm samples are categorized and statistically analyzed based on their causes (e.g., sensor drift, false triggering of warning rules, etc., as described in the above implementation). When the proportion of false alarm samples for a specific warning type within the statistical period exceeds a preset threshold, the system determines that the warning type has a systemic false alarm problem and automatically triggers the adaptive adjustment process.

[0075] The adaptive adjustment process may include one or more of the following measures: First, the warning trigger threshold corresponding to the warning type can be increased. For example, if a certain temperature warning frequently generates false alarms, the temperature value required to trigger the warning can be increased from a lower threshold to a higher threshold to reduce false alarms caused by environmental fluctuations or minor anomalies. Second, the model parameters of the digital twin model can be corrected. For example, if the digital twin model does not accurately simulate the behavior of the physical object under specific operating conditions, resulting in excessive simulation deviation and false alarms, the physical mechanism model parameters inside the digital twin model can be calibrated and optimized based on actual operating data and historical false alarm samples to more accurately reflect the real state of the physical object. Third, the data type corresponding to the warning type in the mapping rules can be adjusted. For example, if it is found that a certain warning type is prone to false alarms when relying on only a single data source during verification, the mapping rules between the warning type and the data type can be modified to add or replace more discriminative verification data types to obtain more comprehensive further verification data.

[0076] Through the above technical solution, this embodiment constructs a closed-loop system of continuous learning and adaptive optimization. The identification results are written back to the historical early warning case database, enabling the system to continuously accumulate experience and enrich the database, thereby improving the accuracy of matching subsequent early warning information with historical cases and enhancing the reliability of real-warning confidence calculation. Simultaneously, by statistically analyzing false alarm samples and triggering adaptive adjustments, the system can proactively identify and correct systemic problems leading to false alarms. For example, by raising the early warning trigger threshold, correcting the parameters of the digital twin model, or adjusting data collection rules, the false alarm rate is effectively reduced, minimizing unnecessary manual review and resource waste. This dynamic adjustment mechanism ensures that the early warning analysis system can continuously optimize its performance as the operating environment changes and data accumulates, significantly improving the accuracy of early warning identification and the robustness of the system.

[0077] Various monitoring systems are widely deployed in industrial monitoring, security surveillance, and other scenarios. When monitoring parameters exceed preset thresholds, the monitoring system reports early warning information. However, due to sensor drift, environmental interference, improper trigger rule settings, and other reasons, a large number of false alarms exist in the early warning information. Existing technologies lack an automatic mechanism to distinguish between real and false alarms, leading to wasted resources and the "crying wolf" effect. At the same time, existing early warning solutions based on digital twins rely solely on existing data within the system, failing to proactively acquire additional evidence to verify the authenticity of the early warnings. Furthermore, the judgment process depends on a single data source or criterion, resulting in a high false alarm rate. The system also lacks quantitative confidence measurement and a tiered handling mechanism, and the system's judgment rules cannot be continuously optimized as it operates.

[0078] The core innovation of this embodiment lies in combining the active acquisition capability of the intelligent agent verification module to multi-source data with the synchronous simulation capability of the twin simulation module through evidence fusion, thereby constructing an automatic false alarm identification mechanism. This solves the problem in existing technologies that cannot actively acquire additional evidence to verify the authenticity of warnings. At the same time, by comparing the cross-validation support of the verification module and calculating the real alarm confidence of the fusion identification module, quantitative evaluation and hierarchical handling of warning information are achieved, effectively reducing the false judgment rate and avoiding waste of handling resources.

[0079] Reference Figure 2 , Figure 2 This is a structural block diagram of the first embodiment of the early warning information analysis system of this application.

[0080] like Figure 2 As shown, the early warning information analysis system proposed in this application implements the above-described early warning information analysis method.

[0081] include: The early warning acquisition module 10 is used to acquire early warning information reported by the monitoring system. The early warning information includes at least the early warning type, early warning location, and early warning time. The intelligent agent verification module 20 is used to parse the warning information by the intelligent agent, generate a data verification task corresponding to the warning information, and execute the data verification task to obtain further verification data; wherein, the generated data verification task includes: determining the target data source and the corresponding collection parameters from multiple candidate data sources according to the warning type, and the further verification data is collected by the target data source according to the collection parameters; The twin simulation module 30 is used to map the early warning information and the further verification data to a pre-built digital twin model, drive the digital twin model to simulate synchronously, and obtain the twin simulation state quantity corresponding to the early warning location; the digital twin model is a virtual mapping model corresponding to the monitored physical object; The comparison and verification module 40 is used to compare the early warning trigger parameters corresponding to the early warning information with the twin simulation state quantity to obtain the simulation deviation, and to perform cross-validation on the further verification data from multiple sources to obtain the cross-validation support. The fusion identification module 50 is used to fuse the simulation bias and the cross-validation support to obtain the real alarm confidence score; based on the comparison result between the real alarm confidence score and the preset confidence threshold, the category of the warning information is identified as a real alarm or a false alarm. The result output module 60 is used to output the recognition results.

[0082] In this embodiment, the early warning acquisition module first acquires the early warning information reported by the monitoring system, which includes basic elements such as early warning type, location, and time. The intelligent agent verification module then parses the early warning information, determines the target data source and acquisition parameters from candidate data sources based on the early warning type, and performs a data verification task to obtain further verification data. The twin simulation module inputs the early warning information and further verification data into a digital twin model, driving the model to perform synchronous simulation and deduce the twin simulation state variables of the early warning location. The comparison and verification module calculates the deviation between the early warning trigger parameters and the twin simulation state variables, and performs cross-validation on the multi-source further verification data to assess their consistency. The fusion identification module integrates the simulation deviation and cross-validation support to calculate the real-time early warning confidence level, and compares it with a preset confidence threshold to identify the early warning category. The result output module finally outputs the identification result, realizing the automatic identification of early warning information.

[0083] Through the above technical solution, this embodiment realizes the active acquisition of evidence, multi-source cross-validation and quantitative confidence assessment of early warning information, effectively solves the problems of high false alarm rate and waste of processing resources in traditional early warning processing, significantly improves the accuracy and reliability of the early warning system, and provides efficient technical support for industrial monitoring and security monitoring scenarios.

[0084] It should be understood that the above are merely illustrative examples and do not constitute any limitation on the technical solution of this application. In specific applications, those skilled in the art can make settings as needed, and this application does not impose any restrictions on this.

[0085] It should be noted that the workflow described above is merely illustrative and does not limit the scope of protection of this application. In practical applications, those skilled in the art can select some or all of it to achieve the purpose of this embodiment according to actual needs, and no restrictions are imposed here.

[0086] In addition, for technical details not described in detail in this embodiment, please refer to the method of early warning information analysis provided in any embodiment of this application, which will not be repeated here.

[0087] Furthermore, it should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0088] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0089] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory (ROM) / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of this application. The above are only preferred embodiments of this application and do not limit the patent scope of this application. All equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A method for analyzing early warning information, characterized in that, Executed by a computer, including: Obtain early warning information reported by the monitoring system, wherein the early warning information includes at least the early warning type, early warning location, and early warning time; The intelligent agent parses the warning information, generates a data verification task corresponding to the warning information, and executes the data verification task to obtain further verification data; wherein, generating the data verification task includes: determining a target data source and corresponding collection parameters from multiple candidate data sources according to the warning type, and the further verification data is collected by the target data source according to the collection parameters; The early warning information and the further verification data are mapped to a pre-constructed digital twin model, and the digital twin model is driven to simulate synchronously to obtain the twin simulation state quantity corresponding to the early warning location; the digital twin model is a virtual mapping model corresponding to the monitored physical object; The simulation deviation is obtained by comparing the early warning trigger parameters corresponding to the early warning information with the twin simulation state quantities, and the cross-validation support is obtained by cross-validating the further verification data from multiple sources. The simulation bias and the cross-validation support are fused to obtain the real alarm confidence score; based on the comparison result between the real alarm confidence score and the preset confidence threshold, the category of the warning information is identified as a real alarm or a false alarm. Output the recognition results.

2. The early warning information analysis method according to claim 1, characterized in that, The early warning information also includes the parameter values ​​of the early warning level and the early warning triggering parameters; the early warning information comes from at least one of the following: sensor monitoring equipment, video surveillance equipment, or manual reporting terminal deployed on the monitored physical object.

3. The early warning information analysis method according to claim 1, characterized in that, The step of determining the target data source and corresponding acquisition parameters from multiple candidate data sources according to the warning type includes: determining the target data type matching the warning type according to a preset mapping rule between warning types and data types; determining the candidate data source that can provide the target data type as the target data source, and generating corresponding acquisition parameters according to the warning location, wherein the acquisition parameters include at least one of acquisition viewpoint, acquisition frequency, and acquisition duration; wherein the agent executes the generation of the data verification task based on a trained reinforcement learning decision model, the reinforcement learning decision model being trained with a preset reward function, the reward value of the reward function being positively correlated with the contribution of the generated data verification task to real-time alarm identification and negatively correlated with the data acquisition cost of the data verification task.

4. The early warning information analysis method according to claim 3, characterized in that, The candidate data sources include at least two of the following: video surveillance equipment, sensor monitoring equipment, inspection robots, drones, and historical operation databases; the execution of the data verification task includes: the intelligent agent sending a collection instruction to the collection device corresponding to the target data source, the collection instruction being used to control the collection device to collect data according to the collection parameters; when the target data source is an inspection robot or a drone, the intelligent agent also plans the movement path of the inspection robot or drone based on the warning location.

5. The early warning information analysis method according to claim 1, characterized in that, The construction method of the digital twin model includes: establishing a geometric model and a physical mechanism model of the monitored physical object, and mapping the real-time operation data of the monitored physical object to the geometric model and the physical mechanism model for parameter correction, so that the twin simulation state quantity of the digital twin model is synchronized with the actual state of the monitored physical object; the physical mechanism model includes multiple mechanism sub-models corresponding to different warning types; driving the synchronous simulation of the digital twin model includes: starting the target mechanism sub-model corresponding to the warning type of the warning information, using the warning time as a reference and the further verification data as boundary conditions input into the target mechanism sub-model for deduction, until the state change of two adjacent simulation steps is lower than the preset change threshold, and stopping the deduction, and determining the stable state quantity at the time of stopping the deduction as the twin simulation state quantity corresponding to the warning position.

6. The early warning information analysis method according to claim 1, characterized in that, The deviation comparison includes: calculating the deviation value between the warning triggering parameter and the twin simulation state quantity, and comparing the deviation value with a preset deviation threshold. The smaller the deviation value, the more likely the warning information is a real warning. The cross-validation includes: counting the number of data sources that support the establishment of the warning information in the further verification data from multiple sources, calculating the matching degree between the verification data that supports the establishment of the warning information and the preset real warning criterion features, and determining the cross-validation support degree based on the number of data sources and the matching degree.

7. The early warning information analysis method according to claim 1, characterized in that, The fusion also includes incorporating historical case matching: a historical early warning case library is pre-built, which stores feature vectors of historical early warning information and corresponding real or false alarm judgment results; the feature vectors of the early warning information are compared with the feature vectors in the historical early warning case library to calculate similarity, wherein historical early warning cases within a set recent time period are assigned a higher similarity weight than other historical early warning cases, and the historical case matching degree is obtained by statistically analyzing the judgment results of similar cases whose similarity exceeds a preset similarity threshold; Historical early warning cases with similar feature vectors but different judgment results are marked as disputed cases and transferred to manual calibration; the fusion specifically involves: weighting the normalized simulation bias according to a first preset weight coefficient, weighting the cross-validation support according to a second preset weight coefficient, and weighting the historical case matching degree according to a third preset weight coefficient, and then summing the results to obtain the real alarm confidence; the preset confidence threshold includes a real alarm threshold and a false alarm threshold, wherein the real alarm threshold is higher than the false alarm threshold; When the confidence level of the real alarm is not lower than the real alarm threshold, the warning information is determined to be a real alarm; when the confidence level of the real alarm is not higher than the false alarm threshold, the warning information is determined to be a false alarm; when the confidence level of the real alarm is between the real alarm threshold and the false alarm threshold, the warning information is marked as a warning pending review and transferred to manual review.

8. The early warning information analysis method according to claim 7, characterized in that, Also includes: When the warning information is determined to be a real warning, the objects to be handled are divided into at least two levels according to the warning level. Differentiated graded handling instructions and push frequencies are matched to objects at different levels and pushed to the corresponding handling terminals. The graded handling instructions include instructions for linkage with emergency response plans. During the push process, the delivery rate of each push channel is monitored. When the delivery rate of a certain push channel is lower than the preset delivery rate threshold, it is automatically switched to the backup push channel for resending. When the warning information is determined to be a false alarm, the warning information is not pushed to the handling terminal. Instead, the warning information is recorded as a false alarm sample, and the cause of the false alarm sample is traced back. The cause of the false alarm includes at least one of sensor monitoring device drift and false triggering of warning triggering rules.

9. The early warning information analysis method according to claim 7, characterized in that, Also includes: The identification results and the corresponding feature vectors of the warning information are used as new cases and written back into the historical warning case library. False alarm samples within a preset statistical period are statistically analyzed according to their causes. When the proportion of the number of false alarm samples corresponding to the same warning type to the total number of warnings of that warning type exceeds a preset proportion threshold, adaptive adjustment is performed. The adaptive adjustment includes: raising the warning trigger threshold corresponding to the warning type, correcting the model parameters of the digital twin model, and adjusting at least one of the data types corresponding to the warning type in the mapping rules.

10. An early warning information analysis system, characterized in that, include: The early warning acquisition module is used to acquire early warning information reported by the monitoring system. The early warning information includes at least the early warning type, early warning location, and early warning time. The intelligent agent verification module is used to parse the warning information by an intelligent agent, generate a data verification task corresponding to the warning information, and execute the data verification task to obtain further verification data; wherein, generating the data verification task includes: determining a target data source and corresponding collection parameters from multiple candidate data sources according to the warning type, and the further verification data is collected by the target data source according to the collection parameters; The twin simulation module is used to map the early warning information and the further verification data to a pre-constructed digital twin model, drive the digital twin model to simulate synchronously, and obtain the twin simulation state quantity corresponding to the early warning location; the digital twin model is a virtual mapping model corresponding to the monitored physical object; The comparison and verification module is used to compare the early warning trigger parameters corresponding to the early warning information with the twin simulation state quantity to obtain the simulation deviation, and to perform cross-validation on the further verification data from multiple sources to obtain the cross-validation support. The fusion identification module is used to fuse the simulation bias and the cross-validation support to obtain the real alarm confidence score; and to identify the category of the warning information as a real alarm or a false alarm based on the comparison result between the real alarm confidence score and a preset confidence threshold. The results output module is used to output the recognition results.

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