Remote intelligent monitoring system and method for safety of town houses

Abstract

The invention relates to the technical field of remote monitoring, in particular to an urban house safety remote intelligent monitoring system and method, and the system comprises a data collection module, a data processing module, an event-driven analysis module, an early warning notification module, a safety control module and a maintenance scheduling module. According to the invention, by comprehensively collecting temperature, humidity, pressure and photoelectric data, the environment can be monitored more comprehensively, real-time data stream comparison can not only accurately identify tiny changes in the environment, but also rapidly respond through abnormal environment indexes, and when the environment data deviates from a normal range, the system can give an alarm in time, so that the environment is protected. The early warning efficiency and accuracy are improved, the measures of emergency response are automatically adjusted according to risk assessment of different levels, the system can take the most appropriate emergency measures according to the actual situation, for example, a water spraying system is automatically started in the case of a fire, the effectiveness and accuracy of house safety monitoring are effectively improved, and the life and property safety of residents is ensured.

Description

Technical Field

[0001] The present invention relates to the field of remote monitoring technology, and in particular to a remote intelligent monitoring system and method for urban housing safety. Background Art

[0002] The field of remote monitoring technology covers various technologies that enable real-time monitoring and management of specific environments or facilities through the Internet, allowing users to monitor and control physical objects or processes from a remote location using computers or mobile devices via the Internet. Remote monitoring systems include cameras, sensors, alarm systems, and data transmission and processing equipment, and are widely used in home security, industrial operation and maintenance, traffic management, environmental monitoring and other fields. These systems can provide real-time video streaming, environmental data, operating status and other information, and can perform data analysis, early warning of potential problems or automatically adjust system configurations to adapt to changing environmental conditions.

[0003] Among them, the remote intelligent monitoring system for urban housing safety refers to a system that uses remote monitoring technology to ensure the safety of residential areas or individual houses. This type of system collects data on the safety status of houses (such as intrusion detection, smoke alarms, sudden floods, etc.) in real time through smart sensors, cameras and monitoring equipment installed in the houses, and transmits the data to the remote monitoring center or the user's mobile device. Users can check the status of the house at any time, and the system can also automatically send alarms to users or contact emergency services when an abnormality is detected. The main purpose of this system is to enhance residential safety, prevent accidents or crimes, and protect people's lives and property.

[0004] Although the existing remote monitoring technology is widely used in many fields, it still has shortcomings in processing efficiency and early warning accuracy. The current technology focuses on monitoring and collecting information, but lacks sufficient depth and detail in data processing and immediate response, resulting in insufficient timely or accurate responses when emergencies occur. For example, after collecting data, general remote monitoring systems require manual intervention to judge and implement subsequent measures. This manual processing process may delay emergency response time. Ordinary systems cannot conduct in-depth analysis of the collected data and cannot effectively predict and prevent potential safety risks. This technical limitation cannot make the most effective response when facing emergencies such as fires and floods, affecting residents' safety and property protection. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a remote intelligent monitoring system and method for urban housing safety.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: a remote intelligent monitoring system for urban housing safety comprises: The data acquisition module uses temperature, humidity, pressure and photoelectric sensors to collect data from town houses, and generates raw environmental data based on the voltage and current changes fed back by the sensors; The data processing module uses real-time data stream comparison based on the original environmental data to compare the real-time monitoring data with the normal environmental data, identify deviations and anomalies in the data, and generate abnormal environmental indicators; The event-driven analysis module determines the risk level by comparing the abnormal environment indicators with the preset thresholds, performs an urgency assessment on the data exceeding the thresholds, marks them, and generates an event urgency score; The warning notification module selects a communication channel according to the urgency score of the event, sends the warning information to the residents' mobile phones and the local emergency response center, and generates a sent warning signal; The safety control module activates the associated emergency prevention measures according to the sent warning signal, starts the automatic sprinkler according to the fire alarm, starts the corresponding level of prevention measures for the signals with differentiated emergency levels, and obtains the emergency response startup status; The maintenance scheduling module checks the functions and working status of the safety equipment in the house in real time according to the emergency response startup status, evaluates the processing efficiency through the performance data fed back by the equipment, and obtains the safety detection operation log.

[0007] As a further solution of the present invention, the original environmental data includes temperature readings, humidity levels, atmospheric pressure values ​​and light intensity indicators, the abnormal environmental indicators include temperature abnormal values, humidity abnormal values, pressure abnormal values ​​and light abnormal values, the event urgency score includes low, medium and high risk levels, the sent warning signals include fire warning signals, flood warning signals and structural damage warning signals, the emergency response activation status includes fire emergency status, flood emergency status and earthquake emergency status, and the safety detection operation log includes equipment operation status records, maintenance measures and performance evaluation results.

[0008] As a further solution of the present invention, the data acquisition module includes: The signal acquisition submodule collects raw signals from town houses from temperature, humidity, pressure and photoelectric sensors, optimizes signal strength through signal amplifiers, removes noise, analyzes environmental variables, and generates raw signal data; The signal conversion submodule uses the original signal data to convert the analog signal into a digital signal, uses data smoothing technology to avoid sampling errors, extracts key features, optimizes data reliability, and obtains environmental parameter analysis results; The environmental monitoring submodule analyzes the changing trend of the real-time environment according to the environmental parameter analysis results, identifies and marks abnormal fluctuations in the environmental parameters, verifies the integrity of the monitoring data, and generates original environmental data.

[0009] As a further solution of the present invention, the data processing module includes: The data comparison submodule compares the original environmental data with the preset normal environmental data according to the real-time data stream, and obtains the data deviation identification result by numerical difference analysis and identification of data deviation; The abnormal fluctuation analysis submodule detects and analyzes abnormal fluctuations in the data according to the data deviation identification result, sets critical conditions for determining abnormalities through thresholds, identifies abnormal points in the data, and generates abnormal identification results; The abnormal summary submodule uses the abnormal identification results, integrates abnormal data points, classifies and summarizes the abnormal situations, and obtains abnormal environment indicators.

[0010] As a further solution of the present invention, the event-driven analysis module includes: The numerical mapping submodule uses the abnormal environment indicators to determine the risk level of each indicator through comparative analysis with the set thresholds, performs numerical mapping and classification for each level, and generates a risk level judgment record; The emergency assessment submodule judges the records according to the risk level, uses the random forest algorithm to classify the abnormal environmental indicators that exceed the threshold, evaluates the potential environmental risks and the scope of impact, and obtains a priority ranking table; The event marking submodule uses the priority ranking table to mark the identified risk events, including event type, occurrence time and impact description, automatically records the urgency of the event, and obtains the event urgency score.

[0011] As a further solution of the present invention, the formula of the random forest algorithm is as follows: in, represents information gain, Represents the dataset of the current node, is the attribute used for splitting, It is a dataset The number of samples in and is the subset after splitting and The number of samples in It is a dataset The entropy of and Is a subset and The entropy of .

[0012] As a further solution of the present invention, the early warning notification module includes: The demand reference submodule selects SMS and emergency network broadcast based on the event urgency score, and obtains the selected communication channel by referring to the urgency of the event and the coverage requirements of the audience; The information formatting submodule uses the selected communication channel to format the warning information content, including the event type, urgency and action guide, sends the warning information in batches to residents' mobile phones and local emergency response centers, checks the transmission speed of the information, and generates a warning information sending record; The signal recording submodule sends records according to the warning information, records the sending of each warning notification, including the sending time, recipient and sending status, verifies the integrity of the records through data synchronization, and obtains the sent warning signals.

[0013] As a further solution of the present invention, the safety control module includes: The emergency measures design submodule uses the sent warning signal to analyze the urgency and alarm type in the signal, including fire alarm, activate the corresponding emergency preventive measures, perform automatic water spraying, determine and set preventive measures that match the current risk level, and obtain preventive measures activation records; The regional safety inspection submodule activates the corresponding level of preventive measures according to the preventive measures activation record, including differentiated levels of safety lock, alarm activation and emergency evacuation guidance, executes multi-level response measures to inspect regional safety, and obtains the implementation status of the measures; The implementation status recording submodule uses the execution status of the measures to record the implementation status of the initiated emergency prevention measures, including the type of measures, execution time and execution effect, and obtains the emergency response startup status.

[0014] As a further solution of the present invention, the maintenance scheduling module includes: The safety equipment inspection submodule performs functional inspection and status monitoring of safety equipment in the house based on the emergency response activation status, including the working performance of fire alarms and automatic sprinkler equipment, checks the normal operation of the equipment, and generates equipment status inspection records; The performance data analysis submodule uses the equipment status inspection record to analyze the performance data fed back by the equipment, calculates the processing efficiency and failure rate of the equipment, and obtains the performance evaluation result; The performance indicator recording submodule uses the performance evaluation results to record equipment performance indicators and evaluate the probability of equipment failure in differentiated time periods, and obtains a safety detection operation log by collating and storing data and verifying the consistency and traceability of the information.

[0015] A method for remote intelligent monitoring of urban housing safety is provided, wherein the method is based on the above-mentioned remote intelligent monitoring system for urban housing safety and comprises the following steps: S1: Collect the environmental data of urban houses using temperature, humidity, pressure and photoelectric sensors, and obtain the original environmental data results according to the voltage and current changes of the sensors; S2: Based on the original environmental data results, perform real-time data stream comparison, compare the monitoring data with the standard environmental data, identify and calculate data deviations, and generate abnormal environmental indicators; S3: Using the abnormal environment indicators, using preset thresholds for comparison, determining whether the data exceeds a safe range, calculating a risk level for the data exceeding the threshold, and obtaining a risk level analysis result; S4: According to the risk level analysis result, a communication channel is selected to send an emergency warning to residents' mobile phones and a local emergency response center, and the sending status of the warning information is recorded to generate a sent warning signal; S5: Activate the associated emergency prevention measures through the sent warning signal, detect the start-up status of the emergency response, check the function and working status of the safety equipment in the house in real time, and obtain the safety detection operation log.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by comprehensively collecting temperature, humidity, pressure and photoelectric data, the system can monitor the environment more comprehensively and achieve more detailed data analysis. Real-time data stream comparison can not only accurately identify small changes in the environment, but also respond quickly through abnormal environmental indicators. When environmental data deviates from the normal range, the system can issue an alarm in time. This instant data analysis and evaluation enables the system to respond quickly when potential risks are discovered, improving the efficiency and accuracy of early warning. Automatically adjusting emergency response measures according to different levels of risk assessment allows the system to take the most appropriate emergency measures according to actual conditions, such as automatically starting a sprinkler system in the event of a fire. This dynamic safety control not only reduces delays in human operations, but also greatly improves the ability to respond to emergencies, effectively improves the effectiveness and accuracy of housing safety monitoring, and ensures the safety of residents' lives and property. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a system flow chart of the present invention; Figure 2 It is a schematic diagram of the system framework of the present invention; Figure 3 It is a flow chart of the data acquisition module of the present invention; Figure 4 It is a flow chart of the data processing module of the present invention; Figure 5 This is a flow chart of the event-driven analysis module of the present invention; Figure 6 This is a flow chart of the early warning notification module of the present invention; Figure 7 It is a flow chart of the safety control module of the present invention; Figure 8 This is a flow chart of the maintenance scheduling module of the present invention; Fig. 9 It is a schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0020] See also Figure 1 to Figure 2 The present invention provides a technical solution, a remote intelligent monitoring system for urban housing safety, comprising: The data acquisition module uses temperature, humidity, pressure and photoelectric sensors to collect data from urban houses, and converts the voltage and current changes fed back by the sensors into corresponding temperature, humidity, pressure and light intensity data to generate raw environmental data; The data processing module uses real-time data stream comparison based on the original environmental data to compare the real-time monitoring data with the normal environmental data, identify deviations and anomalies in the data, and generate abnormal environmental indicators; The event-driven analysis module determines the risk level by comparing abnormal environment indicators with preset thresholds, conducts urgency assessment on data exceeding the threshold, marks it, and generates an event urgency score; The warning notification module selects communication channels, including SMS and emergency network broadcast, based on the incident urgency score, sends warning information to residents’ mobile phones and local emergency response centers, and generates a sent warning signal; The safety control module activates the associated emergency prevention measures according to the sent warning signal, starts the automatic sprinkler according to the fire alarm, starts the corresponding level of prevention measures for the signals with different emergency levels, records the start-up status, and obtains the emergency response start-up status; The maintenance scheduling module checks the functions and working status of safety equipment in the house in real time according to the emergency response activation status, evaluates the processing efficiency through the performance data fed back by the equipment, and conducts long-term trend analysis of structural safety to obtain a safety detection operation log.

[0021] The original environmental data includes temperature readings, humidity levels, atmospheric pressure values ​​and light intensity indicators. Abnormal environmental indicators include temperature abnormal values, humidity abnormal values, pressure abnormal values ​​and light abnormal values. The event urgency score includes low, medium and high risk levels. The warning signals sent include fire warning signals, flood warning signals and structural damage warning signals. The emergency response activation status includes fire emergency status, flood emergency status and earthquake emergency status. The safety detection operation log includes equipment operation status records, maintenance measures and performance evaluation results.

[0022] See also Figure 2 , 3 , the data acquisition module includes: The signal acquisition submodule collects the original signals of town houses from temperature, humidity, pressure and photoelectric sensors, optimizes the signal strength through the signal amplifier, removes noise, analyzes environmental variables, and generates the execution flow of the original signal data as follows; During signal processing, the original signals from temperature, humidity, pressure and photoelectric sensors are collected. These signals represent physical quantities under different environmental conditions. For example, the temperature sensor collects data on changes in ambient temperature, the humidity sensor monitors the humidity level in the air, the pressure sensor measures changes in air pressure in the environment, and the photoelectric sensor detects changes in light intensity. After the initial collection of these data, the signal strength is optimized through the signal amplifier to ensure the stability and reliability of the signal during transmission. The main function of the signal amplifier is to amplify the weak signal output by the sensor to ensure that the signal will not lose important information due to insufficient intensity in subsequent processing. The noise removal process involves filtering out random interference that is not a valid signal. This is achieved by using a filter. The filter only allows signals in a specific frequency range to pass according to preset standards, blocking or weakening signals of that frequency. The processed signal is relatively pure, which is convenient for subsequent data analysis and processing to generate original signal data.

[0023] The signal conversion submodule uses the original signal data to convert the analog signal into a digital signal, uses data smoothing technology to avoid sampling errors, extracts key features, optimizes data reliability, and obtains the environmental parameter analysis results. The execution process is as follows; The raw signal data is converted through the analog to digital conversion process according to the formula , calculate the digital signal value. In the formula, represents the amplification factor, Represents the analog signal strength, represents the calibration deviation, Represents the baseline calibration value. Detailed explanation of the formula and the derivation process of the formula calculation: Considering is the average amplification factor obtained from the experimental data, set to 2.0, is the analog signal strength output by the sensor, for example 0.5V, is a fixed deviation caused by device manufacturing error, set to 0.05V, For the value used to calibrate the device baseline, take 1 volt. Substituting these values ​​into the formula, we get , this digital signal value is the converted signal strength that can be used for further data processing.

[0024] The environmental monitoring submodule analyzes the real-time environmental change trend based on the environmental parameter analysis results, identifies and marks abnormal fluctuations in environmental parameters, verifies the integrity of the monitoring data, and generates the execution process of the original environmental data as follows; Real-time analysis of environmental parameter analysis results is performed to identify abnormal fluctuations in environmental parameters. This requires statistical analysis of long-term data of environmental variables such as temperature, humidity, pressure, and luminosity. Thresholds are set to determine what changes count as abnormalities. The abnormal criteria are set based on the highest and lowest values ​​in the data. By comparing real-time data with these thresholds, data points that are out of the normal range are marked to verify the integrity of the monitoring data. This involves checking whether the data points are continuous and whether there is any data loss or damage. Ensuring data integrity is the basis for evaluating environmental conditions. Without complete data, the actual environmental conditions cannot be accurately reflected. This includes not only data collection and anomaly detection, but also the need to organize and store the collected data for subsequent trend analysis and environmental assessment to generate raw environmental data.

[0025] See also Figure 2 , 4 , the data processing module includes: The data comparison submodule compares the original environment data with the preset normal environment data according to the real-time data stream, and obtains the data deviation identification result through numerical difference analysis and identification of data deviation. The execution process is as follows; In the process of realizing environmental monitoring, according to the real-time data flow, the continuity and real-time nature of the data are ensured, high-frequency data collection and processing are supported, and various sensor devices can be seamlessly connected to collect physical parameters such as temperature, humidity, pressure, etc. in the environment in real time. These raw environmental data collected in real time are compared with the preset normal environmental data. The normal environmental data is the average value obtained based on the analysis of historical data of the same period. This comparison can timely discover small changes or irregular fluctuations in environmental parameters. Through numerical difference analysis, it can identify which data points deviate from the normal level. The basis for identifying data deviation is whether the difference between the two exceeds the preset threshold. If the difference exceeds the threshold, it is marked as data deviation. The integrity and accuracy of the data in the environmental monitoring system are guaranteed to ensure that the monitoring system can accurately reflect the environmental conditions, provide a basis for subsequent environmental assessment and decision-making, and obtain data deviation identification results.

[0026] The abnormal fluctuation analysis submodule detects and analyzes abnormal fluctuations in the data based on the data deviation identification results, sets critical conditions for determining abnormalities through thresholds, identifies abnormal points in the data, and generates the execution flow of abnormal identification results as follows; Analyze the abnormal fluctuations in the data and determine the abnormality by setting the threshold according to the formula , calculate the critical condition of abnormality. In the formula, represents the sensitivity coefficient, represents the average value of the data, represents the standard deviation, Represents the multiple relationship with the standard deviation. Detailed explanation of the formula and the process of formula calculation: Consider To adjust the sensitivity coefficient, set the value to 1.5, which represents medium sensitivity. is the average value of a set of data, for example 20°C, is the standard deviation, set to 3°C, representing the fluctuation range of the data distribution, is the threshold multiple, set to 2, indicating that the range of two standard deviations above and below the mean is considered. Substituting these values ​​into the formula, we get , this value is the threshold for determining anomalies. If the data exceeds or falls below this threshold, it is identified as an anomaly.

[0027] The abnormal summary submodule uses the abnormal identification results, integrates the abnormal data points, classifies and summarizes the abnormal situations, and obtains the execution process of the abnormal environment indicators as follows; Using the anomaly identification results, the abnormal data points are integrated and classified. During the integration process, all data points marked as abnormal are collected. These data points are determined by previous analysis to be inconsistent with the normal range of environmental parameter changes. These abnormal points are classified according to the abnormality type. Common classifications include temperature abnormalities, humidity abnormalities, pressure abnormalities, etc. Each category is classified according to specific environmental indicators and influencing factors. The summarized data will show the frequency and distribution of each abnormality type. By classifying and summarizing the abnormal situations, we can more clearly understand the specific problem points in environmental changes, provide key decision-making information for the monitoring system, and help relevant personnel quickly respond and adjust environmental management strategies to ensure environmental safety and sustainability, and obtain abnormal environmental indicators.

[0028] See also Figure 2 , 5 , the event-driven analysis module includes: The numerical mapping submodule uses abnormal environment indicators to determine the risk level of each indicator through comparative analysis of the set thresholds, performs numerical mapping and classification for each level, and generates the execution process of risk level judgment records as follows; By comparing the thresholds, the risk level is mapped according to the formula , calculate the risk level of each environmental indicator. In the formula, Represents the risk level, Representative The weight of the indicator, Representative The value of an indicator. Detailed explanation of the formula and the process of formula calculation: Setting Based on the weights obtained from historical data analysis, three indicators are set with weights of 0.3, 0.5, and 0.2, and the corresponding indicator values ​​are 50, 60, and 70. Substituting these values ​​into the formula, we get , this value is the risk level determined by numerical mapping, based on which further risk classification and management are performed.

[0029] The urgency assessment submodule judges the records according to the risk level, uses the random forest algorithm to classify the abnormal environmental indicators that exceed the threshold, evaluates the potential environmental risks and the scope of impact, and obtains the execution process of the priority ranking table as follows; The formula for the random forest algorithm is as follows: in, represents information gain, Represents the dataset of the current node, is the attribute used for splitting, It is a dataset The number of samples in and is the subset after splitting and The number of samples in It is a dataset The entropy of and Is a subset and The entropy of .

[0030] Detailed explanation of the formula and the process of formula calculation and derivation: This information gain formula is designed to evaluate the Next, the data set The splitting effect of Representation dataset The entropy of a dataset measures the volatility of the dataset. The specific calculation of entropy involves the proportion of each category in the dataset.

[0031] Set data set The CCP has two categories, Category 1 and Category 2, with the frequencies of occurrence being and .but Entropy Calculated by the following formula: Set the pass attribute Split, dataset Divided into two subsets and . and are the number of samples in these two subsets, and The original dataset The total number of samples. and The subsets are and The entropy of .

[0032] Now set up the dataset There are 100 samples, through the attributes After the split, the subset There are 40 samples, subset There are 60 samples. The proportion of category 1 is 0.75, and the proportion of category 2 is 0.25; The proportion of category 1 is 0.5 and the proportion of category 2 is 0.5.

[0033] entropy and The calculation is as follows:

[0034] Substitute the numerical calculation:

[0035] Consider the total number of samples:

[0036] Substituting these values ​​into the information gain formula:

[0037] set up The value of is 1 (completely uncertain situation), then:

[0038] This result shows that the property The use of data sets reduces The volatility of It is an effective splitting attribute that helps to distinguish the categories in the dataset, which improves the classification accuracy for the environmental risk assessment module.

[0039] The event marking submodule uses a priority ranking table to mark the identified risk events, including event type, occurrence time and impact description, and automatically records the urgency of the event. The execution process of obtaining the event urgency score is as follows; Mark risk events through the priority ranking table and automatically record the urgency of the event according to the formula , calculate the event urgency score. In the formula, Represents the urgency score, Represents the time when the event occurred. Represents the impact level of the event. Detailed explanation of the formula and the process of formula calculation: Set the time when the event occurs Closer to the current time, For the impact level, rated from 1 to 5, set (Higher impact). Based on these parameters, a function is used to calculate the urgency of the event. Can be set to 3 if The time is less than 1 hour away from the current time and , the urgency score is high. This approach ensures that the urgency of the incident can be quickly and effectively assessed based on its urgency and potential impact, optimizing response strategies and resource allocation.

[0040] See also Figure 2 , 6 , the warning notification module includes: The demand reference submodule selects SMS and emergency network broadcast based on the event urgency score, and refers to the urgency of the event and the coverage requirements of the audience, and the execution process of the selected communication channel is as follows; Select the communication channel based on the urgency score, according to the formula , determine the most suitable communication channel. In the formula, represents the selected communication channel, Represents the urgency of the event. Represents the audience's coverage needs. Formula explanation and formula calculation process: Setting urgency The rating is from 1 to 10, covering the needs For audience coverage percentage, set (higher urgency), (covering a larger population). Based on these parameters, the function of selecting the communication channel Can be set as: If and , then choose network broadcast plus SMS notification. This method ensures that every audience can be notified quickly and widely under high urgency and large coverage requirements, ensuring the efficiency and scope of information transmission.

[0041] The information formatting submodule uses the selected communication channel to format the warning information content, including the event type, urgency and action guide, sends the warning information in batches to residents' mobile phones and local emergency response centers, checks the transmission speed of the information, and generates the execution process of the warning information sending record as follows; Responsible for properly formatting the warning information content for easy sending. The formatted content includes event type, urgency and action guide. This information is clearly identified and organized to ensure accuracy and comprehensibility during the communication process. Once the formatting is completed, the information is sent to the target audience through the selected communication channels, such as SMS and webcasting, including residents' mobile phones and local emergency response centers. The speed of information transmission is also monitored to ensure that all warning information can be sent and received in the shortest time. This is very critical for subsequent evaluation and review, providing a detailed evidence base for evaluating the efficiency and coverage of the warning system and generating warning information sending records.

[0042] The signal recording submodule sends records based on the warning information, records the sending of each warning notification, including the sending time, recipient and sending status, checks the integrity of the records through data synchronization, and obtains the execution process of the sent warning signal as follows; According to the early warning information sending record, the sending situation of each early warning notification is recorded in detail, including the sending time, recipient and sending status. These data are crucial for the subsequent analysis and improvement of the early warning transmission system. By combining with data synchronization technology, it not only ensures the integrity of the record, but also can update the sending status in real time to improve the real-time and accuracy of the data. By comparing the sending records and the actual receiving situation, the existing transmission delays or failures can be found, and the system settings or communication strategies can be adjusted in time to ensure that in future early warning events, the information can be accurately conveyed to all relevant personnel and institutions, improve the overall response efficiency and effectiveness of the early warning system, and obtain the sent early warning signals.

[0043] See also Figure 2 , 7 , the safety control module includes: The emergency measures design submodule uses the sent warning signals to analyze the urgency and alarm types in the signals, including fire alarms, activate the corresponding emergency preventive measures, perform automatic water spraying, determine and set preventive measures that match the current risk level, and obtain the execution process of the preventive measures activation record as follows; By analyzing the early warning signals and activating emergency preventive measures, according to the formula , determine and set matching preventive measures. Represents preventive measures, Represents the urgency of the signal. Represents the alarm type. Formula explanation and formula calculation process: Setting the urgency level Rating from 1 to 5, alert type For fire, if , then choose the automatic sprinkler system. This decision ensures that efficient preventive measures are quickly activated at high emergency levels, improving the timeliness and safety of the response.

[0044] The regional safety inspection submodule starts the corresponding level of preventive measures according to the preventive measures activation record, including differentiated levels of safety lock, alarm activation and emergency evacuation guidance, and executes multi-level response measures to check the regional safety. The execution process of obtaining the implementation status of the measures is as follows; According to the preventive measures activation records, the corresponding level of preventive measures are initiated. These measures are implemented differently according to the different levels of emergency, including safety lock, alarm activation and emergency evacuation instructions. The selection and implementation of these measures are based on previous analysis and records. Through specific preventive measures such as the safety lock system to lock important areas, the alarm system to provide real-time danger warnings, and emergency evacuation instructions to help personnel evacuate dangerous areas quickly and safely. In the process of implementing these multi-level response measures, the safety status of the area is continuously checked to ensure the correct implementation of all measures, and the response strategy is adjusted in real time to cope with changes and obtain the implementation status of the measures.

[0045] The implementation status record submodule uses the execution of measures to record the implementation status of the initiated emergency prevention measures, including the type of measures, execution time and execution effect. The execution process for obtaining the emergency response startup status is as follows; Use the measures implementation status to record in detail the implementation status of each emergency prevention measure initiated, including the type, execution time and implementation effect of each measure. By collecting this data, a comprehensive view can be provided on the implementation effect of the emergency response to ensure that each preventive measure can achieve the expected response effect. The types of measures are classified in detail, such as automatic sprinkler, emergency evacuation and safety lock, etc. The execution time of each measure is also accurately recorded to analyze the response speed and timeliness. The records of execution effects help management departments evaluate the actual impact and efficiency of response measures, provide data support to optimize future emergency response plans, ensure more effective protection of personnel and property safety in similar emergency situations, and obtain the emergency response initiation status.

[0046] See also Figure 2 , 8 , the maintenance scheduling module includes: The safety equipment inspection submodule performs functional inspection and status monitoring of the safety equipment in the house based on the emergency response startup status, including the working performance of the fire alarm and automatic sprinkler equipment, checks the normal operation of the equipment, and generates the execution process of the equipment status inspection record as follows; Carry out detailed functional inspection and status monitoring for the activated emergency response status. During the process, pay attention to the working performance of key safety equipment in the house, including fire alarms and automatic sprinkler equipment. Through actual operation tests and performance evaluations, ensure that these equipment can work normally in emergency situations. During the inspection, monitor key performance indicators such as equipment response time, failure frequency and operation accuracy, check the normal operating status of each device, and immediately record and mark any performance anomalies or failures found for further maintenance or replacement, provide accurate data support for subsequent maintenance work, and generate equipment status inspection records.

[0047] The performance data analysis submodule uses the equipment status inspection record to analyze the performance data fed back by the equipment, calculates the processing efficiency and failure rate of the equipment, and obtains the performance evaluation results. The execution process is as follows; Through the equipment status inspection record, analyze the equipment performance data according to the formula , the processing efficiency of the computing device. Represents processing efficiency, Represents the total working time of the equipment. Represents the number of failures. Detailed explanation of the formula and calculation process of the formula: Assume that the equipment works for a total of 1200 hours within a certain period of time and fails 3 times. Substitute these values ​​into the formula and calculate that, for every failure, the equipment works normally for an average of 400 hours. This provides a quantitative efficiency indicator for performance evaluation and helps management departments understand the reliability and maintenance needs of the equipment.

[0048] The performance indicator recording submodule uses the performance evaluation results to record the equipment performance indicators and evaluate the equipment failure probability in differentiated time periods. By collating and storing data, the consistency and traceability of the information are verified, and the execution process of the safety detection operation log is obtained as follows; Using the performance evaluation results, the performance indicators of each device are recorded in detail, including the operating efficiency, failure rate and key performance parameters of the equipment. By organizing and storing these data, not only the consistency and traceability of the information are ensured, but also the access efficiency and use value of the data are improved. During the recording process, special attention is paid to evaluating the probability of equipment failure in different time periods. This information is crucial for predicting equipment maintenance time windows and preventive maintenance measures. It provides data-based support for equipment management and maintenance decisions, helps relevant personnel optimize resource allocation and maintenance strategies, extends the service life of equipment and reduces emergencies caused by failures, and obtains safety detection operation logs.

[0049] See also Fig. 9 A remote intelligent monitoring method for urban housing safety is provided. The remote intelligent monitoring method for urban housing safety is performed based on the above-mentioned remote intelligent monitoring system for urban housing safety, and comprises the following steps: S1: Collect the environmental data of urban houses using temperature, humidity, pressure and photoelectric sensors, and obtain the original environmental data results according to the voltage and current changes of the sensors; S2: Based on the original environmental data results, real-time data stream comparison is performed to compare the monitoring data with the standard environmental data, identify and calculate data deviations, and generate abnormal environmental indicators; S3: By comparing the abnormal environment indicators with the preset thresholds, determine whether the data exceeds the safety range, calculate the risk level for the data exceeding the threshold, and obtain the risk level analysis results; S4: According to the risk level analysis results, select the communication channel, send the emergency warning to the residents' mobile phones and the local emergency response center, record the status of the warning information sending, and generate the sent warning signal; S5: Through the sent early warning signal, activate the associated emergency prevention measures, detect the startup status of the emergency response, check the function and working status of the safety equipment in the house in real time, and obtain the safety detection operation log.

[0050] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A remote intelligent monitoring system for urban housing safety, characterized in that: The system comprises: The data acquisition module uses temperature, humidity, pressure and photoelectric sensors to collect data from town houses, and generates raw environmental data based on the voltage and current changes fed back by the sensors; The data processing module uses real-time data stream comparison based on the original environmental data to compare the real-time monitoring data with the normal environmental data, identify deviations and anomalies in the data, and generate abnormal environmental indicators; The event-driven analysis module determines the risk level by comparing the abnormal environment indicators with the preset thresholds, performs an urgency assessment on the data exceeding the thresholds, marks them, and generates an event urgency score; The warning notification module selects a communication channel according to the urgency score of the event, sends the warning information to the residents' mobile phones and the local emergency response center, and generates a sent warning signal; The safety control module activates the associated emergency prevention measures according to the sent warning signal, starts the automatic sprinkler according to the fire alarm, starts the corresponding level of prevention measures for the signals with differentiated emergency levels, and obtains the emergency response startup status; The maintenance scheduling module checks the functions and working status of the safety equipment in the house in real time according to the emergency response startup status, evaluates the processing efficiency through the performance data fed back by the equipment, and obtains the safety detection operation log.

2. The remote intelligent monitoring system for urban housing safety according to claim 1 is characterized in that: The original environmental data includes temperature readings, humidity levels, atmospheric pressure values ​​and light intensity indicators, the abnormal environmental indicators include temperature abnormal values, humidity abnormal values, pressure abnormal values ​​and light abnormal values, the event urgency score includes low, medium and high risk levels, the sent warning signals include fire warning signals, flood warning signals and structural damage warning signals, the emergency response activation status includes fire emergency status, flood emergency status and earthquake emergency status, and the safety detection operation log includes equipment operation status records, maintenance measures and performance evaluation results.

3. The remote intelligent monitoring system for urban housing safety according to claim 1 is characterized in that: The data acquisition module comprises: The signal acquisition submodule collects raw signals from town houses from temperature, humidity, pressure and photoelectric sensors, optimizes signal strength through signal amplifiers, removes noise, analyzes environmental variables, and generates raw signal data; The signal conversion submodule uses the original signal data to convert the analog signal into a digital signal, uses data smoothing technology to avoid sampling errors, extracts key features, optimizes data reliability, and obtains environmental parameter analysis results; The environmental monitoring submodule analyzes the changing trend of the real-time environment according to the environmental parameter analysis results, identifies and marks abnormal fluctuations in the environmental parameters, verifies the integrity of the monitoring data, and generates original environmental data.

4. The remote intelligent monitoring system for urban housing safety according to claim 1 is characterized in that: The data processing module comprises: The data comparison submodule compares the original environmental data with the preset normal environmental data according to the real-time data stream, and obtains a data deviation identification result by numerical difference analysis and identification of data deviation; The abnormal fluctuation analysis submodule detects and analyzes abnormal fluctuations in the data according to the data deviation identification result, sets critical conditions for determining abnormalities through thresholds, identifies abnormal points in the data, and generates abnormal identification results; The abnormal summary submodule uses the abnormal identification results, integrates abnormal data points, classifies and summarizes the abnormal situations, and obtains abnormal environment indicators.

5. The remote intelligent monitoring system for urban housing safety according to claim 1 is characterized in that: The event-driven analysis module includes: The numerical mapping submodule uses the abnormal environment indicators to determine the risk level of each indicator through comparative analysis with the set thresholds, performs numerical mapping and classification for each level, and generates a risk level judgment record; The emergency assessment submodule judges the records according to the risk level, uses the random forest algorithm to classify the abnormal environmental indicators that exceed the threshold, evaluates the potential environmental risks and the scope of impact, and obtains a priority ranking table; The event marking submodule uses the priority ranking table to mark the identified risk events, including event type, occurrence time and impact description, automatically records the urgency of the event, and obtains the event urgency score.

6. The remote intelligent monitoring system for urban housing safety according to claim 5 is characterized in that: The formula of the random forest algorithm is as follows: in, represents information gain, Represents the dataset of the current node, is the attribute used for splitting, It is a dataset The number of samples in and is the subset after splitting and The number of samples in It is a dataset The entropy of and Is a subset and The entropy of .

7. The remote intelligent monitoring system for urban housing safety according to claim 1 is characterized in that: The early warning notification module includes: The demand reference submodule selects SMS and emergency network broadcast based on the event urgency score, and obtains the selected communication channel by referring to the urgency of the event and the coverage requirements of the audience; The information formatting submodule uses the selected communication channel to format the warning information content, including the event type, urgency and action guide, sends the warning information in batches to residents' mobile phones and local emergency response centers, checks the transmission speed of the information, and generates a warning information sending record; The signal recording submodule sends records according to the warning information, records the sending of each warning notification, including the sending time, recipient and sending status, verifies the integrity of the records through data synchronization, and obtains the sent warning signals.

8. The remote intelligent monitoring system for urban housing safety according to claim 1 is characterized in that: The safety control module comprises: The emergency measures design submodule uses the sent warning signal to analyze the urgency and alarm type in the signal, including fire alarm, activate the corresponding emergency preventive measures, perform automatic water spraying, determine and set preventive measures that match the current risk level, and obtain preventive measures activation records; The regional safety inspection submodule activates the corresponding level of preventive measures according to the preventive measures activation record, including differentiated levels of safety lock, alarm activation and emergency evacuation guidance, executes multi-level response measures to inspect regional safety, and obtains the implementation status of the measures; The implementation status recording submodule uses the execution status of the measures to record the implementation status of the initiated emergency prevention measures, including the type of measures, execution time and execution effect, and obtains the emergency response startup status.

9. The remote intelligent monitoring system for urban housing safety according to claim 1 is characterized in that: The maintenance scheduling module includes: The safety equipment inspection submodule performs functional inspection and status monitoring of safety equipment in the house based on the emergency response activation status, including the working performance of fire alarms and automatic sprinkler equipment, checks the normal operation of the equipment, and generates equipment status inspection records; The performance data analysis submodule uses the equipment status inspection record to analyze the performance data fed back by the equipment, calculates the processing efficiency and failure rate of the equipment, and obtains the performance evaluation result; The performance indicator recording submodule uses the performance evaluation results to record equipment performance indicators and evaluate the probability of equipment failure in differentiated time periods, and obtains a safety detection operation log by collating and storing data and verifying the consistency and traceability of the information.

10. A remote intelligent monitoring method for urban housing safety, characterized in that: According to any one of claims 1 to 9, the remote intelligent monitoring system for urban housing safety is implemented, comprising the following steps: Use temperature, humidity, pressure and photoelectric sensors to collect urban housing environmental data, and obtain raw environmental data results based on the voltage and current changes of the sensors; Based on the raw environmental data results, real-time data stream comparison is performed to compare the monitoring data with the standard environmental data, identify and calculate data deviations, and generate abnormal environmental indicators; By comparing the abnormal environment indicators with preset thresholds, it is determined whether the data exceeds a safe range, the risk level is calculated for the data exceeding the threshold, and the risk level analysis result is obtained; According to the risk level analysis results, select a communication channel, send an emergency warning to residents' mobile phones and the local emergency response center, record the status of the warning information sending, and generate a sent warning signal; Through the sent early warning signal, the associated emergency prevention measures are activated, the start-up status of the emergency response is detected, the function and working status of the safety equipment in the house are checked in real time, and the safety detection operation log is obtained.

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