A data processing method of an intelligent security comprehensive management and control platform

Abstract

The application relates to the technical field of intelligent security and protection, and discloses a data processing method of an intelligent security and protection comprehensive management and control platform, which comprises the following steps: generating an original security and protection data set, performing multi-modal data preprocessing on the original security and protection data set, extracting key features from the preprocessed security and protection data based on a deep learning model, constructing a multi-modal data fusion mechanism, formulating standardized preprocessing rules and implementing space-time alignment on heterogeneous data during real-time security and protection monitoring, establishing a cooperative analysis basis for video streams, audio signals and sensor data, simultaneously extracting a multi-dimensional feature vector from the fused data by using the deep learning model, effectively reducing the interference of environmental noise and equipment differences on feature extraction, guaranteeing the reliability of security threat feature recognition, reducing the false alarm rate and the risk of missing reports, and dynamically adjusting the response level when the event situation changes, thereby improving the timeliness and accuracy of emergency decision-making.

Description

Technical Field

[0001] This invention relates to the field of intelligent security technology, specifically to a data processing method for an intelligent security integrated management and control platform. Background Technology

[0002] Intelligent security refers to the information technology of services, image transmission and storage technology, and should have functions such as burglar alarm system, video surveillance alarm system, access control alarm system, security personnel patrol alarm system, GPS vehicle alarm management system and 110 alarm network transmission system.

[0003] The intelligent security integrated management and control platform is the core hub of the urban security management system. It integrates multi-source sensing devices and data analysis capabilities, aiming to improve the real-time performance and accuracy of security response. The core function of the platform is to integrate and process heterogeneous data from video surveillance, environmental sensing, and biometrics, and to achieve proactive identification and early warning of abnormal behavior through artificial intelligence algorithms, providing intelligent support for security decision-making.

[0004] Currently, due to the independent storage of multi-source data in security scenarios and the significant differences in format, the spatiotemporal alignment accuracy between multimodal data is insufficient when performing real-time threat analysis. When environmental noise interference and data timestamp inaccuracy occur, it will lead to deviations in behavioral feature extraction, and the reliability of threat identification cannot be guaranteed.

[0005] Therefore, a data processing method for an intelligent security integrated management and control platform is proposed to solve the above problems. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a data processing method for an intelligent security integrated management and control platform, which solves the problems mentioned in the background section.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a data processing method for an intelligent security integrated management and control platform, the method comprising the following steps: S1. Collect multi-source security data through a distributed sensor network to generate an original security dataset. The multi-source security data includes video stream data, audio signal data, environmental sensor data, and biometric identification data. S2. Perform multimodal data preprocessing on the original security dataset, including data denoising, timestamp alignment and format standardization, to generate preprocessed security data; S3. Based on a deep learning model, extract key features from the preprocessed security data to generate a security feature vector set. The key features include motion trajectory features, abnormal voiceprint features, and abnormal environmental parameters. S4. Analyze the security feature vector set using a real-time event detection algorithm to identify potential security threat events and generate security event identification data; S5. Combining historical security database and rule engine, perform risk assessment and classification on the security event identification data, and output risk assessment level data, wherein the risk assessment classification includes low risk, medium risk and high risk levels; S6. Based on the risk assessment level data, dynamically generate a response instruction set and trigger security response actions through the execution interface; S7. Collect response effect data based on the feedback mechanism, update the historical security database, and iteratively optimize the deep learning model and the real-time event detection algorithm.

[0008] Preferably, step S1 includes the following steps: S11. Deploy video acquisition units at key locations in the monitored area to capture high-definition video stream data in real time and add spatial location tags; S12. Configure an audio sensor array to collect ambient audio signal data and perform background noise suppression processing on the audio signal data; S13. Integrate IoT environmental sensors to periodically acquire temperature, humidity, light intensity, and motion detection data, and generate time-series sensor datasets; S14. Collect facial, fingerprint, and iris biometric identification data through biometric devices and store them in encrypted form; S15. The video stream data, audio signal data, time series sensor dataset, and biometric identification data are fused to construct the original security dataset, and a unified timestamp index is added.

[0009] Preferably, step S2 includes the following steps: S21. Apply inter-frame difference method and Gaussian filtering to the video stream data for dynamic noise reduction to reduce interference from lighting changes and lens shake. S22. Wavelet transform technology is used to separate human voice from environmental noise in the audio signal data, and spectrum normalization is performed. S23. The time series sensor dataset is imputed for missing values ​​and outliers are removed. The Z-score standardization method is used to unify the data scale. S24. Perform feature encryption and decryption verification on the biometric identification data to ensure data privacy; S25. Based on the timestamp index, the preprocessed video stream data, audio signal data, time series sensor dataset and biometric identification data are aligned and integrated to generate the preprocessed security data.

[0010] Preferably, step S3 includes the following steps: S31. Construct a convolutional neural network model to process the video stream data and extract motion trajectory features and object recognition features; S32. Analyze the audio signal data using a recurrent neural network model to extract abnormal voiceprint features and voice command features; S33. The time series sensor dataset is processed using a time series analysis algorithm to extract environmental anomaly parameters and pattern change characteristics; S34. The motion trajectory features, object recognition features, abnormal voiceprint features, voice command features, environmental anomaly parameters, and pattern change features are combined into a multi-dimensional feature vector through the feature fusion layer; S35. Reduce the dimensionality and normalize the multidimensional feature vectors to generate the security feature vector set.

[0011] Preferably, step S4 includes the following steps: S41. Design a real-time event detection algorithm based on the YOLO architecture and load a pre-trained threat identification model; S42. Input the security feature vector set into the threat identification model and perform real-time parallel calculation; S43. Identify event types, including intrusion detection, fire warning, violent behavior, and equipment failure, and calculate confidence scores; S44. When the confidence score exceeds a preset threshold, it is marked as a valid security threat event, and the security event identification data is generated, wherein the confidence score is calculated using the following normalization formula: In the formula: This represents the normalized confidence score, with a value range of [0,1]. Indicates the video feature matching degree. This represents the upper limit threshold for video feature matching, with a value of 1.0. Indicates the intensity of audio abnormalities. This represents the upper limit threshold for audio anomaly intensity, with a value of 1.0. Indicates the deviation value of environmental parameters. This represents the upper limit threshold for environmental parameter deviation, with a value of 3 times the baseline standard deviation. These are weighting coefficients. ,default , It can be dynamically adjusted based on historical false alarm rates; when When determined to be a valid security threat event; S45. Initiate a manual review mechanism for low-confidence events, such as initiating a manual review mechanism for events with a confidence level below 0.75, and feed the review results back to the threat identification model.

[0012] Preferably, step S5 includes the following steps: S51. Query the historical security database to obtain historical response records and consequence data of similar events; S52. Apply the rule engine to match predefined risk assessment rule sets, including event location, time sensitivity, and potential impact factors; S53. Calculate the risk index, which is derived by weighting the severity of the event, the probability of occurrence, and the rate of spread, and integrates the multidimensional parameters using the following normalization formula: In the formula: This represents the normalized risk index, with a value range of [0,1]. This represents the normalized value of the event severity. This represents the normalized probability value. This represents the normalized value of the diffusion rate. , , These are the weighting coefficients. ,default , , ; The risk level threshold is set as follows: Low risk: Medium risk: High risk: ; S54. Output the risk assessment level data and add mitigation suggestions.

[0013] Preferably, step S6 includes the following steps: S61. Generate early warning instructions for low-risk levels and send them to user terminals via message push interface; S62. Generate linkage control commands for medium-risk levels to trigger access control locking, lighting adjustment, and alarm activation; S63. Generate emergency response commands for high-risk levels, and link fire protection systems, security robots and emergency broadcasts; S64. Dynamically adjust the response priority and optimize the response instruction set based on real-time environmental data; S65. Transmit the response instruction set to the execution device through the API interface and monitor the execution status.

[0014] Preferably, step S7 includes the following steps: S71. Collect execution logs and performance metrics of response actions, including response time, event resolution rate, and false alarm rate; S72. Associate the execution logs and performance metrics with the security event identification data to generate a feedback dataset; S73. Update the historical security database using an incremental learning algorithm and optimize the risk assessment rule set; S74. Retrain the deep learning model and the real-time event detection algorithm online and adjust the model parameters; S75. Regularly generate performance reports, evaluate optimization effects, and adjust data processing workflows.

[0015] Preferably, the method further includes the following steps: S8 integrates edge computing nodes to perform preliminary feature extraction and event filtering at the data acquisition end, reducing the load on the central platform; S9. Use blockchain technology to store the hash values ​​of key security data to ensure data immutability and audit traceability; S10. Deploy an adaptive learning mechanism to dynamically adjust the data collection frequency and model sensitivity according to environmental changes.

[0016] Preferably, the method further includes the following steps: S101. Establish a multi-tenant data isolation layer to isolate and encrypt security data for different users and areas. S102. Implement a cross-platform data sharing interface to exchange event data and response protocols with third-party security systems; S103. Employ reinforcement learning to optimize response strategies and simulate the best response path under different scenarios; S104. Embedded energy efficiency management module to reduce sensor power consumption during low-threat periods; S105. Generate a visual security dashboard to display data processing results and system status in real time.

[0017] Compared with existing technologies, the present invention provides a data processing method for an intelligent security integrated management and control platform, which has the following beneficial effects: 1. In this invention, by constructing a multimodal data fusion mechanism, standardized preprocessing rules are formulated and heterogeneous data is spatiotemporally aligned during real-time security monitoring. This establishes a foundation for collaborative analysis of video streams, audio signals, and sensor data. At the same time, a deep learning model is used to extract multidimensional feature vectors from the fused data, which can effectively reduce the interference of environmental noise and equipment differences on feature extraction, ensure the reliability of security threat feature identification, and reduce the false alarm rate and the risk of missed alarms.

[0018] 2. In this invention, by establishing a dynamic risk assessment engine, when analyzing security incidents, the system integrates real-time feature detection results with historical response databases, automatically matches event types and threat level classification rules, enabling the system to accurately determine the triggering conditions for low-risk warnings, medium-risk linkage control, and high-risk emergency responses. Furthermore, the system dynamically adjusts the response level as the event situation changes, ensuring real-time matching between security response actions and the actual threat level, thereby improving the timeliness and accuracy of emergency decision-making.

[0019] 3. In this invention, by deploying a closed-loop optimization strategy, the feedback mechanism collects action execution effect data and iteratively updates the analysis model during the security response execution process, enabling the system to continuously optimize the event detection threshold and risk assessment parameters of the feature extraction algorithm. This effectively solves the problem of insufficient scene adaptability caused by the rigidity of traditional rule bases, and further enhances the multi-dimensional collaborative defense capabilities in complex security scenarios. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Please see Figure 1 The data processing method of the intelligent security integrated management and control platform includes the following steps: S1. Collect multi-source security data through a distributed sensor network to generate a raw security dataset. The multi-source security data includes video stream data, audio signal data, environmental sensor data, and biometric identification data. S2. Perform multimodal data preprocessing on the original security dataset, including data denoising, timestamp alignment, and format standardization, to generate preprocessed security data; S3. Based on a deep learning model, key features are extracted from preprocessed security data to generate a security feature vector set. Key features include motion trajectory features, abnormal voiceprint features, and abnormal environmental parameters. S4. Analyze the security feature vector set using a real-time event detection algorithm to identify potential security threat events and generate security event identification data; S5. Combining historical security databases and rule engines, risk assessment and classification are performed on security event identification data, and risk assessment level data is output. The risk assessment classification includes low risk, medium risk and high risk levels. S6. Based on the risk assessment level data, dynamically generate a set of response instructions and trigger security response actions through the execution interface; S7. Collect response effect data based on the feedback mechanism, update the historical security database, and iteratively optimize the deep learning model and real-time event detection algorithm; S1 includes the following steps: S11. Deploy video acquisition units at key locations in the monitored area to capture high-definition video stream data in real time and add spatial location tags; S12. Configure an audio sensor array to collect ambient audio signal data and perform background noise suppression processing on the audio signal data. S13. Integrate IoT environmental sensors to periodically acquire temperature, humidity, light intensity, and motion detection data, and generate time-series sensor datasets; S14. Collect facial, fingerprint, and iris biometric identification data through biometric devices and store them in encrypted form; S15. Integrate video stream data, audio signal data, time-series sensor datasets, and biometric recognition data to construct the original security dataset, and add a unified timestamp index, which is verified by the following formula: in, The data integrity coefficient. For the first The number of missing fields in the class data The total number of data types, The default number of required fields is set for each type of data. Required fields for video stream data include: device ID, timestamp, spatial location tag, image data, etc. when The data re-sampling mechanism is triggered in time; S2 includes the following steps: S21. Apply inter-frame difference method and Gaussian filtering to the video stream data for dynamic noise reduction to reduce interference from lighting changes and lens shake. S22. Wavelet transform is used to separate human voice from environmental noise in the audio signal data, and spectrum normalization is performed. The noise suppression effect is calculated by the following formula: in, The noise suppression ratio is a normalized value [0,1]. For raw audio energy, This is residual noise energy; when Enable enhanced filtering at this time; S23. Impute missing values ​​and remove outliers from the time series sensor dataset, and use the Z-score standardization method to unify the data scale. S24. Perform feature encryption and decryption verification on biometric identification data to ensure data privacy; S25. Based on the timestamp index, the preprocessed video stream data, audio signal data, time series sensor dataset and biometric identification data are aligned and integrated to generate preprocessed security data; S3 includes the following steps: S31. Construct a convolutional neural network model to process video stream data and extract motion trajectory features and object recognition features; S32. Apply a recurrent neural network model to analyze audio signal data and extract abnormal voiceprint features and voice command features; S33. Use time series analysis algorithms to process time series sensor datasets and extract environmental anomaly parameters and pattern change characteristics; S34. The feature fusion layer combines motion trajectory features, object recognition features, abnormal voiceprint features, voice command features, environmental anomaly parameters, and pattern change features into a multi-dimensional feature vector. S35. Reduce the dimensionality and normalize the multidimensional feature vectors to generate a security feature vector set; S4 includes the following steps: S41. Design a real-time event detection algorithm based on the YOLO architecture and load a pre-trained threat identification model; S42. Input the security feature vector set into the threat identification model and perform real-time parallel computation; S43. Identify event types, including intrusion detection, fire warning, violent behavior, and equipment failure, and calculate confidence scores; S44. When the confidence score exceeds a preset threshold, it is marked as a valid security threat event, and security event identification data is generated. The confidence score is calculated using the following normalization formula:

[0023] In the formula: This represents the normalized confidence score, with a value range of [0,1]. Indicates the video feature matching degree. This represents the upper limit threshold for video feature matching, with a value of 1.0. Indicates the intensity of audio abnormalities. This represents the upper limit threshold for audio anomaly intensity, with a value of 1.0. Indicates the deviation value of environmental parameters. This represents the upper limit threshold for environmental parameter deviation, with a value of 3 times the baseline standard deviation. These are weighting coefficients. ,default , It can be dynamically adjusted based on historical false alarm rates; when When determined to be a valid security threat event; S45. Initiate a manual review mechanism for low-confidence events and feed the review results back to the threat identification model; S5 includes the following steps: S51. Query the historical security database to obtain historical response records and consequence data for similar events; S52. Apply the rule engine to match predefined risk assessment rule sets, including event location, time sensitivity, and potential impact factors; S53. Calculate the risk index, which is derived by weighting the severity of the event, the probability of occurrence, and the rate of spread. The following normalization formula is used to integrate the multidimensional parameters: In the formula: This represents the normalized risk index, with a value range of [0,1]. This represents the normalized value of the event severity. This represents the normalized probability value. This represents the normalized value of the diffusion rate. , , These are the weighting coefficients. ,default , , ; Preferably, the severity of the event Sn can be quantified according to the preset asset value level and personal safety level, and mapped to the [0,1] range. For example, the highest threat level is mapped to 1, and no threat is mapped to 0. The risk level threshold is set as follows: Low risk: Medium risk: High risk: ; S54. Output risk assessment level data and attach mitigation recommendations; S6 includes the following steps: S61. Generate early warning instructions for low-risk levels and send them to user terminals via message push interface; S62. Generate linkage control commands for medium-risk levels to trigger access control locking, lighting adjustment, and alarm activation; S63. Generate emergency response commands for high-risk levels, and link fire protection systems, security robots and emergency broadcasts; S64. Dynamically adjust response priority, optimize the response instruction set based on real-time environmental data, and update the priority weights using a formula: in, To provide real-time response weights, The current risk index, The duration of the event, in seconds. This is the risk factor, with a default value of 0.7. The maximum permissible duration; This formula guarantees Follow Increase and increase, and through Functions restricted to the range [0,1] will be executed first. The response command is valid, but the threshold needs to be adjusted according to the test. The threshold can be dynamically adjusted based on historical response data, with an adjustment range of [0.75, 0.95]. S65. Transmit the response instruction set to the execution device through the API interface and monitor the execution status; S7 includes the following steps: S71. Collect execution logs and performance metrics of response actions, including response time, event resolution rate, and false alarm rate; S72. Associate the execution logs and performance metrics with security event identification data to generate a feedback dataset; S73. Update the historical security database using incremental learning algorithms and optimize the risk assessment rule set; S74. Retrain the deep learning model and real-time event detection algorithm online and adjust the model parameters; S75. Regularly generate performance reports, evaluate optimization effects, and adjust data processing workflows; It also includes the following steps: S8 integrates edge computing nodes to perform preliminary feature extraction and event filtering at the data acquisition end, reducing the load on the central platform; S9. Utilize blockchain technology to store the hash values ​​of critical security data, ensuring data immutability and audit traceability. The data tampering detection formula is as follows: in, To tamper with the alarm index, For the first stored on the chain hash value, The corresponding hash value is calculated in real time. For the difference bit statistics function, To verify the total amount of data; when Data tracing is triggered in a timely manner; S10. Deploy an adaptive learning mechanism to dynamically adjust the data collection frequency and model sensitivity according to environmental changes; It also includes the following steps: S101. Establish a multi-tenant data isolation layer to isolate and encrypt security data for different users and areas. S102. Implement a cross-platform data sharing interface to exchange event data and response protocols with third-party security systems; S103. Employ reinforcement learning to optimize the response strategy, simulate the optimal response path under different scenarios, and design the policy reward function as follows: in, The sigmoid function compresses the output to (0,1). The overall return value of the strategy, This represents the actual response time. To allow the maximum response time, This represents the resource consumption rate, calculated as the weighted average of the current CPU utilization and memory utilization. To improve the success rate of action execution, These are the weighting coefficients. The default is 0.5 / 0.2 / 0.3; The training objective is to maximize ; S104. Embedded energy efficiency management module to reduce sensor power consumption during low-threat periods; S105. Generate a visual security dashboard to display data processing results and system status in real time.

[0024] The data processing method of an intelligent security integrated management and control platform operates as follows: First operational phase: Multi-source heterogeneous data fusion and processing: A distributed sensor network is initiated to synchronously collect video surveillance streams, audio sensor signals, environmental parameter time-series data, and biometric features to generate a raw security dataset. Data cleaning is performed through a multimodal preprocessing engine, inter-frame differential decluttering filtering is applied to the video stream, wavelet domain voice separation is performed on the audio signal, Z-score normalization is performed on the sensor data, and timestamp indexing is used to align all data sources. The standardized data is then input into a hybrid model composed of a 3D convolutional neural network and a recurrent neural network to extract motion trajectory features, abnormal voiceprint fingerprints, and environmental mutation parameters. A feature fusion layer generates a security feature vector set with unified dimensions, establishing a data foundation for real-time event detection.

[0025] Second operational phase: Dynamic risk assessment decision chain: The security feature vector set is input into a pre-trained YOLO event detection model to identify threat types such as intrusion, fire, violent behavior, and equipment failure. The model outputs an event confidence score. When the confidence score exceeds a threshold, the rule engine is activated to query similar event records in the historical security database. Combining factors such as the sensitivity of the incident location, the urgency of time, and the potential spread rate, a normalized risk index is calculated. Based on a preset threshold, the risk level is dynamically classified, triggering a response decision tree. Low-risk events push early warning messages to user terminals, medium-risk events trigger access control and alarm devices, and high-risk events activate fire protection systems and emergency robots, achieving real-time matching of response strategies and threat levels.

[0026] Third operational phase: Closed-loop self-optimization mechanism: When executing response actions, metrics such as response time, false alarm rate, and event resolution rate are collected simultaneously to generate a feedback dataset. An incremental learning algorithm is used to associate the feedback data with the historical database to optimize the parameters of the risk assessment model and the event detection threshold. The reinforcement learning module simulates response paths in multiple scenarios, updates the weights of the response strategy reward function, improves resource allocation efficiency, and uses blockchain to store the hash value of key decisions. When abnormal data tampering is detected, a traceability audit is automatically initiated to ensure the verifiability of security decisions.

[0027] Fourth operational phase: Edge collaboration and resource scheduling: A lightweight feature extraction model is deployed at the data acquisition end to pre-filter the raw data, upload high-confidence feature vectors to the central platform, dynamically adjust the sensor sampling frequency based on the energy efficiency management function, reduce device power consumption during low-threat periods, build a multi-tenant data sandbox, realize permission isolation of different security areas, and exchange event response protocols with third-party systems through standardized API interfaces to form a cross-platform collaborative defense network.

[0028] Fifth operational phase: Continuous environmental adaptation The system periodically analyzes its operation logs to identify variables such as changes in ambient light, new device types, and new attack patterns. It adjusts the sensitivity parameters of the feature extraction model through an adaptive learning mechanism to enhance robustness in complex scenarios. When an increase in the frequency of regional threats is detected, the system automatically increases the risk assessment weight coefficient for that region, creating a local reinforcement effect for threat defense and enabling the system to continuously evolve.

[0029] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus 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 apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0030] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A data processing method for an intelligent security integrated management and control platform, characterized in that: The method includes the following steps: S1. Collect multi-source security data through a distributed sensor network to generate an original security dataset. The multi-source security data includes video stream data, audio signal data, environmental sensor data, and biometric identification data. S2. Perform multimodal data preprocessing on the original security dataset, including data denoising, timestamp alignment and format standardization, to generate preprocessed security data; S3. Based on a deep learning model, extract key features from the preprocessed security data to generate a security feature vector set. The key features include motion trajectory features, abnormal voiceprint features, and abnormal environmental parameters. S4. Analyze the security feature vector set using a real-time event detection algorithm to identify potential security threat events and generate security event identification data; S5. Combining historical security database and rule engine, perform risk assessment and classification on the security event identification data, and output risk assessment level data, wherein the risk assessment classification includes low risk, medium risk and high risk levels; S6. Based on the risk assessment level data, dynamically generate a response instruction set and trigger security response actions through the execution interface; S7. Collect response effect data based on the feedback mechanism, update the historical security database, and iteratively optimize the deep learning model and the real-time event detection algorithm.

2. The data processing method of an intelligent security integrated management and control platform according to claim 1, characterized in that: S1 includes the following steps: S11. Deploy video acquisition units at key locations in the monitored area to capture high-definition video stream data in real time and add spatial location tags; S12. Configure an audio sensor array to collect ambient audio signal data and perform background noise suppression processing on the audio signal data; S13. Integrate IoT environmental sensors to periodically acquire temperature, humidity, light intensity, and motion detection data, and generate time-series sensor datasets; S14. Collect facial, fingerprint, and iris biometric identification data through biometric devices and store them in encrypted form; S15. The video stream data, audio signal data, time series sensor dataset, and biometric identification data are fused to construct the original security dataset, and a unified timestamp index is added.

3. The data processing method of an intelligent security integrated management and control platform according to claim 1, characterized in that: S2 includes the following steps: S21. Apply inter-frame difference method and Gaussian filtering to the video stream data for dynamic noise reduction to reduce interference from lighting changes and lens shake. S22. Wavelet transform technology is used to separate human voice from environmental noise in the audio signal data, and spectrum normalization is performed. S23. The time series sensor dataset is imputed for missing values ​​and outliers are removed. The Z-score standardization method is used to unify the data scale. S24. Perform feature encryption and decryption verification on the biometric identification data to ensure data privacy; S25. Based on the timestamp index, the preprocessed video stream data, audio signal data, time series sensor dataset and biometric identification data are aligned and integrated to generate the preprocessed security data.

4. The data processing method of an intelligent security integrated management and control platform according to claim 1, characterized in that: S3 includes the following steps: S31. Construct a convolutional neural network model to process the video stream data and extract motion trajectory features and object recognition features; S32. Analyze the audio signal data using a recurrent neural network model to extract abnormal voiceprint features and voice command features; S33. The time series sensor dataset is processed using a time series analysis algorithm to extract environmental anomaly parameters and pattern change characteristics; S34. The motion trajectory features, object recognition features, abnormal voiceprint features, voice command features, environmental anomaly parameters, and pattern change features are combined into a multi-dimensional feature vector through the feature fusion layer; S35. Reduce the dimensionality and normalize the multidimensional feature vectors to generate the security feature vector set.

5. The data processing method of an intelligent security integrated management and control platform according to claim 1, characterized in that: S4 includes the following steps: S41. Design a real-time event detection algorithm based on the YOLO architecture and load a pre-trained threat identification model; S42. Input the security feature vector set into the threat identification model and perform real-time parallel calculation; S43. Identify event types, including intrusion detection, fire warning, violent behavior, and equipment failure, and calculate confidence scores; S44. When the confidence score exceeds a preset threshold, it is marked as a valid security threat event, and the security event identification data is generated, wherein the confidence score is calculated using the following normalization formula: In the formula: This represents the normalized confidence score, with a value range of [0,1]. Indicates the video feature matching degree. This represents the upper limit threshold for video feature matching, with a value of 1.

0. Indicates the abnormal intensity of the audio. This represents the upper limit threshold for audio anomaly intensity, with a value of 1.

0. Indicates the deviation value of environmental parameters. This represents the upper limit threshold for environmental parameter deviation, with a value of 3 times the baseline standard deviation. These are weighting coefficients. ,default , It can be dynamically adjusted based on historical false alarm rates; when When determined to be a valid security threat event; S45. Initiate a manual review mechanism for low-confidence events and feed the review results back to the threat identification model.

6. The data processing method of an intelligent security integrated management and control platform according to claim 1, characterized in that: S5 includes the following steps: S51. Query the historical security database to obtain historical response records and consequence data of similar events; S52. Apply the rule engine to match predefined risk assessment rule sets, including event location, time sensitivity, and potential impact factors; S53. Calculate the risk index, which is derived by weighting the severity of the event, the probability of occurrence, and the rate of spread, and integrates the multidimensional parameters using the following normalization formula: In the formula: This represents the normalized risk index, with a value range of [0,1]. This represents the normalized value of the event severity. This represents the normalized probability value. This represents the normalized value of the diffusion rate. , , These are the weighting coefficients. ,default , , ; The risk level threshold is set as follows: Low risk: Medium risk: High risk: ; S54. Output the risk assessment level data and add mitigation suggestions.

7. The data processing method of an intelligent security integrated management and control platform according to claim 1, characterized in that: S6 includes the following steps: S61. Generate early warning instructions for low-risk levels and send them to user terminals via message push interface; S62. Generate linkage control commands for medium-risk levels to trigger access control locking, lighting adjustment, and alarm activation; S63. Generate emergency response commands for high-risk levels, and link fire protection systems, security robots and emergency broadcasts; S64. Dynamically adjust the response priority and optimize the response instruction set based on real-time environmental data; S65. Transmit the response instruction set to the execution device through the API interface and monitor the execution status.

8. The data processing method of an intelligent security integrated management and control platform according to claim 1, characterized in that: S7 includes the following steps: S71. Collect execution logs and performance metrics of response actions, including response time, event resolution rate, and false alarm rate; S72. Associate the execution logs and performance metrics with the security event identification data to generate a feedback dataset; S73. Update the historical security database using an incremental learning algorithm and optimize the risk assessment rule set; S74. Retrain the deep learning model and the real-time event detection algorithm online and adjust the model parameters; S75. Regularly generate performance reports, evaluate optimization effects, and adjust data processing workflows.

9. The data processing method of an intelligent security integrated management and control platform according to claim 1, characterized in that: It also includes the following steps: S8 integrates edge computing nodes to perform preliminary feature extraction and event filtering at the data acquisition end, reducing the load on the central platform; S9. Use blockchain technology to store the hash values ​​of key security data to ensure data immutability and audit traceability; S10. Deploy an adaptive learning mechanism to dynamically adjust the data collection frequency and model sensitivity according to environmental changes.

10. The data processing method of an intelligent security integrated management and control platform according to claim 1, characterized in that: It also includes the following steps: S101. Establish a multi-tenant data isolation layer to isolate and encrypt security data for different users and areas. S102. Implement a cross-platform data sharing interface to exchange event data and response protocols with third-party security systems; S103. Employ reinforcement learning to optimize response strategies and simulate the best response path under different scenarios; S104. Embedded energy efficiency management module to reduce sensor power consumption during low-threat periods; S105. Generate a visual security dashboard to display data processing results and system status in real time.

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