Indoor electrical potential safety hazard intelligent detection method and system

Abstract

The invention relates to the technical field of electrical safety monitoring, in particular to an intelligent detection method and system for indoor electrical potential safety hazards, and the method comprises the following steps: constructing a synchronous data set based on electrical equipment and environment data, extracting a power current sequence, analyzing the change rate, screening abrupt change signals, and combining with environment recognition risk fragments; and extracting a fluctuation interval to judge critical offset, positioning an electrical sequence offset distance, and counting risk frequency to mark a high-incidence area to generate an alarm node list. According to the method, high-precision synchronization of power consumption behaviors and environmental conditions is realized by collecting the electric participation environment data of the electric connection points and unifying time labels, the anomaly identification accuracy is improved, the power and the current sequence change rate are extracted, the line offset is dynamically pre-warned, and the hidden anomaly in the stable stage is revealed through track mapping; a high-incidence area is positioned through frequency statistics and space mapping, closed-loop monitoring from multi-dimensional data fusion to risk area marking is achieved, and the intelligence and foresight of electrical hidden danger recognition are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical safety monitoring, and particularly to an intelligent detection method and system for indoor electrical safety hazards. Background Art

[0002] The technical field of electrical safety monitoring includes collecting, analyzing, and warning the real-time status of electrical equipment and lines inside buildings, aiming to reduce the incidence of safety accidents such as fires and electric shocks caused by electrical faults. The core content of this technical field mainly includes electrical parameter collection, current and voltage anomaly identification, cable overload and short-circuit identification, residual current and ground fault judgment, etc., and realizes continuous monitoring of indoor electricity consumption through sensors, measurement circuits, and control centers.

[0003] Among them, the intelligent detection method for indoor electrical safety hazards refers to identifying typical hazards such as wire aging, poor contact, abnormal load, sudden change in leakage current, and abnormal ground resistance by means of information collection, data comparison, and rule reasoning for the electrical fault risks existing in enclosed spaces such as homes, offices, and commercial buildings. This detection method collects multi-point data at regular intervals by deploying sensors such as current, voltage, temperature, and residual current, combines preset hazard discrimination thresholds and classification logics, compares and analyzes the collected real-time data, and classifies the data patterns that meet the hazard characteristics through an inference and judgment step.

[0004] The prior art mainly focuses on single-point electrical parameter collection and mainly relies on the over-limit judgment of indicators such as voltage, current, and residual current at fixed thresholds. This mode has limitations that cannot be adapted to in various usage scenarios. For example, the impact of environmental temperature and humidity changes on line performance cannot be effectively included in the evaluation scope, resulting in the inability to warn of hazards caused by poor contact and decreased cable insulation performance under high-temperature and high-humidity conditions. The static comparison logic ignores the fluctuating characteristics of electricity consumption behavior over time, making behaviors such as sudden power surges or frequent switching within a short period be judged as normal ranges, thus missing the early warning window. The lack of traceability analysis of historical operation trajectories makes it difficult to identify continuous abnormal development trends from behavior patterns, unable to accurately locate high-risk areas, and ultimately forming only responsive processing after a fault occurs, which is difficult to meet the active governance requirements for potential hazards in complex indoor electrical environments. For example, in an office area, due to the start of periodic equipment such as air conditioners and electric heaters causing a short-term high-load impact but not causing a voltage drop, it is difficult to trigger the existing system alarm, and over time, heat hazards are likely to accumulate and cause faults. Summary of the Invention

[0005] In order to solve the technical problems existing in the prior art, an embodiment of the present invention provides an intelligent detection method and system for indoor electrical safety hazards. The technical solution is as follows: On the one hand, an intelligent detection method for indoor electrical safety hazards is provided, and the method includes: S1: Based on the data of electrical equipment connection points in the residence, including current, voltage, and power information, organize and classify the environmental data of the room temperature and humidity monitoring equipment in the same period, label each value with a time tag, and generate an indoor load and environment synchronization data set; S2: Based on the indoor load and environment synchronization data set, extract the time series of power and current of the power branch terminal group, analyze the change amplitude in a unit time period, compare the change rate in the upper and lower periods, screen the signal sequences with the change rate exceeding the limit, and combine the environmental data to analyze the abnormal correlation to obtain the fire risk load mutation segment; S3: Call the fire risk load mutation segment, extract the current and voltage values in each segment, identify the fluctuation range of the ratio in a continuous time period, and when the fluctuation interval exceeds the line operation reference boundary, mark it as a critical fluctuation, and summarize and output the voltage and current offset abnormal section; S4: According to the voltage and current offset abnormal section, identify the power, temperature, and humidity time series arrangement in the corresponding time period, select the high-frequency fluctuation points for behavior position mapping, judge the difference trajectory during the stable operation period, and mark the interval with the offset amplitude greater than the normal section to obtain the indoor electrical sequence offset distance.

[0006] As a further solution of the present invention, the indoor load and environment synchronization data set includes electrical node numerical tags, environmental parameter timestamps, and multi-source data alignment fields. The fire risk load mutation segment includes abnormal power change values, periodic change difference amounts, and environmental coupling indication factors. The voltage and current offset abnormal section includes upper and lower fluctuation limit values, ratio abnormal determination points, and continuous abnormal identification identifiers. The indoor electrical sequence offset distance includes stable trajectory offset amounts, high-frequency change position points, and operating state comparison features.

[0007] As a further solution of the present invention, the steps of the indoor load and environment synchronization data set are specifically as follows: S101: Based on the data of electrical equipment connection points in the residence, extract the current value, voltage value, and power value of the socket node, call the environmental parameters of the indoor temperature and humidity monitoring equipment, match and sort the two types of data according to the time tag, and generate a corresponding set of electrical and environmental parameters; S102: According to the corresponding set of electrical and environmental parameters, perform interval discrimination on the current value and power value under the time tag, analyze the combined fluctuation amplitude between power and humidity, and screen the record segments that continuously exceed the current upper limit threshold and the combined fluctuation amplitude is abnormal to obtain the abnormal load fluctuation interval value; S103: Based on the abnormal load fluctuation interval value, extract the interval voltage offset and the room temperature change rate. Combine the synchronous amplitude difference and the duration to judge the abnormalities of electrical load overload and poor contact, and generate an indoor load and environment synchronous dataset.

[0008] As a further solution of the present invention, the steps of the fire risk load mutation segment are specifically as follows: S201: Based on the indoor load and environment synchronous dataset, extract the power branch terminal group current and power sequence, identify the periodic change rate, screen the power and current change rate records exceeding the fluctuation threshold, and generate a load mutation fluctuation interval; S202: According to the load mutation fluctuation interval, call the room temperature and humidity corresponding to the corresponding time period, extract the temperature and humidity combination corresponding to the power, and judge whether the difference in the period before the mutation exceeds the electrical safety threshold, mark the abnormal linkage area, and obtain the fire risk load mutation segment.

[0009] As a further solution of the present invention, the steps of the voltage and current offset abnormal section are specifically as follows: S301: Call the continuous current value and voltage value data in the fire risk load mutation segment, pair them according to the same time period, calculate the ratio of each group, and generate a voltage and current ratio fluctuation sequence; S302: Based on the voltage and current ratio fluctuation sequence, extract the upper and lower boundaries of the ratio interval in the continuous time period, and compare them with the line operation reference value to identify the section exceeding the reference value, determine the start and end time of the abnormal section and the ratio fluctuation range, and mark the fluctuation period meeting the conditions as the critical section to obtain the ratio critical fluctuation interval range; S303: According to the ratio critical fluctuation interval range, extract the current value and voltage value corresponding to the corresponding time period in the mutation segment, analyze the offset amplitude, summarize the offset combinations in the critical section, and output the voltage and current offset abnormal section.

[0010] As a further solution of the present invention, the steps of the indoor electrical sequence offset distance are specifically as follows: S401: According to the voltage and current offset abnormal section, extract the power value, temperature and humidity sequence within the time period, analyze the instantaneous offset rate of power change, extract the mutation points and record their synchronous positions in the multi-source sequence, and obtain the indoor high-frequency abnormal behavior nodes; S402: Based on the indoor high-frequency abnormal behavior nodes, compare the difference between the change trajectories of the corresponding temperature values and humidity values and the baseline trajectory of the steady-state section, identify the range of behavior points where the continuous trajectory offset exceeds the thermal and humidity stability threshold, locate the offset direction and duration characteristics, and establish an indoor thermal and humidity offset interference section; S403: According to the indoor thermal and humidity offset interference section, extract the power offset trend and temperature and humidity disturbance quantity in the time period, calculate the total offset in the composite interference period, identify the section with low overlap rate and offset continuously exceeding the reference duration, and obtain the indoor electrical sequence offset distance.

[0011] As a further solution of the present invention, the method further includes step S5: S5: Based on the indoor electrical sequence offset distance, count the trigger frequency of risk events in the continuous section, locate the high-incidence area according to the frequency cumulative trend, assign corresponding grade labels to each area, and output the list of indoor electrical hidden danger warning nodes; The list of indoor electrical hidden danger warning nodes includes risk level partitions, event frequency thresholds, and warning node identification numbers.

[0012] As a further solution of the present invention, the steps of the list of indoor electrical hidden danger warning nodes are specifically as follows: S501: Based on the indoor electrical sequence offset distance, extract the offset event records in the continuous time period, count the number of trigger times of the offset events in each section, calculate the cumulative growth trend value of the events in the section, identify the continuous position interval with continuously increasing trigger frequency, and obtain the electrical risk high-incidence section group; S502: According to the electrical risk high-incidence section group, combine the event trigger frequency, duration, and offset amplitude in the section, screen the sections with trigger frequency greater than the risk assessment grading threshold, label the corresponding time positions according to the trigger level, and output the list of indoor electrical hidden danger warning nodes.

[0013] On the other hand, an electric vehicle status monitoring system is provided. The electric vehicle status monitoring system is used to execute the above-mentioned electric vehicle status monitoring method. The system includes: The data synchronization module is based on the electrical equipment connection point data in the residence, including socket node current values, voltage values, and power values. Sort the time series according to the period, extract the room temperature and humidity values in each period, analyze the mapping relationship between the time label and the parameters, and form an electrical environment association data set; The hidden danger identification module is based on the electrical environment association data set, extracts the power and current sequences of each power branch, compares the change amplitude and increase and decrease ratio of adjacent periods, screens the abnormal fluctuation frequency bands, and identifies the room temperature and humidity values in the corresponding periods, and generates an abnormal load mutation feature group; The fluctuation discrimination module is based on the abnormal load mutation feature group, extracts the ratio of the current value to the voltage value of each group of data, analyzes the ratio fluctuation range in the continuous time, determines whether it exceeds the operation boundary threshold, locates the over-limit paragraph, and obtains the critical electrical parameter abnormal section; Based on the critical electrical parameter abnormal section, the trajectory analysis module matches the power sequence and temperature-humidity sequence of each time period, extracts high-frequency points to identify the behavioral time trajectory, and compares it with the stable operation behavioral trajectory for offset comparison to identify the trajectory variation characteristics and establish an electrical behavior offset path group; Based on the electrical behavior offset path group, the node warning module counts the number of abnormal fluctuation triggers within the path, analyzes the regional mapping relationship, classifies the high-frequency points, locates the trigger concentration area, and outputs a list of warning nodes for indoor electrical hazards.

[0014] The beneficial effects brought by the technical solution provided by the embodiment of the present invention at least include: By sorting the current, voltage, power values and environmental information collected at the internal electrical connection points of the residence with unified time tags, the high-precision synchronization of electricity consumption behavior and environmental conditions is realized, so that abnormal events can be accurately located under multi-factor cross-verification. Combining the time-series analysis of the power and current of the power distribution terminal group, a detection mechanism for short-time sudden load behavior is constructed. Through the identification of the change rate exceeding the limit and the judgment of the environmental correlation, the abnormal power fluctuations induced by factors such as humidity and temperature rise are effectively identified. Further, the fluctuation range of the voltage-current ratio is extracted to establish an operating boundary reference system, so that the small offset of the circuit state can be dynamically warned. Mapping the behavioral trajectory of high-frequency fluctuation points breaks through the previous static parameter judgment method and can identify hidden abnormal trends during the stable electricity consumption stage. Based on the method of risk frequency statistics and spatial mapping, the hierarchical positioning of the risk area is realized, and the time-space chain of risk evolution is established, so as to output a list of warning nodes with time series characteristics and spatial accuracy, forming a full-process closed loop from data fusion, feature extraction, dynamic judgment to spatial mapping, realizing multi-dimensional monitoring and warning of sudden, hidden and environment-induced electrical hazards, and enhancing the intelligent identification ability and prediction judgment efficiency compared with the static method relying only on threshold discrimination. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the working process of the present invention; Figure 2 It is a detailed flowchart of S1 of the present invention; Figure 3 It is a detailed flowchart of S2 of the present invention; Figure 4 It is a detailed flowchart of S3 of the present invention; Figure 5 It is a detailed flowchart of S4 of the present invention; Figure 6 It is a detailed flowchart of S5 of the present invention; Figure 7 It is a system flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0016] The technical solutions in the present invention will be described below with reference to the accompanying drawings.

[0017] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or more advantageous than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0018] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0019] In the embodiments of the present invention, sometimes subscripts such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0020] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0021] Please refer to Figure 1 , an embodiment of the present invention provides an intelligent detection method for indoor electrical safety hazards. The processing flow of this method may include the following steps: S1: Based on the electrical equipment connection point data in the residence, including the current, voltage, and power information of socket nodes, organize and classify the environmental data of the room temperature and humidity monitoring equipment in the same period, label each value with a time tag, and generate an indoor load and environment synchronization dataset; S2: Based on the indoor load and environment synchronization dataset, extract the time series of the power and current of the power branch terminal group, analyze the change amplitude per unit time period, compare the change rates of the upper and lower cycles, screen the signal sequences with over-limit change rates, and combine the environmental data to analyze the abnormal correlation to obtain the fire risk load mutation segments; S3: Call the fire risk load mutation segments, extract the current and voltage values in each segment, identify the fluctuation range of the ratio within a continuous time period, and when the fluctuation interval exceeds the line operation reference boundary, mark it as a critical fluctuation, summarize and output the voltage and current offset abnormal sections; S4: Based on the voltage and current offset abnormal section, identify the power, temperature and humidity time series arrangement within the corresponding period, select the high-frequency fluctuation points for behavior position mapping, judge the difference trajectory during the stable operation period, and mark the section where the offset amplitude is greater than the normal section to obtain the indoor electrical sequence offset distance; S5: Based on the indoor electrical sequence offset distance, count the trigger frequency of risk events within the continuous section, locate the high-incidence area according to the frequency cumulative trend, assign corresponding grade labels to each area, and output the list of indoor electrical hidden danger warning nodes; The indoor load and environment synchronization dataset includes electrical node numerical labels, environmental parameter timestamps, and multi-source data alignment fields. The fire risk load mutation segment includes abnormal power change values, periodic change difference amounts, and environmental coupling indicator factors. The voltage and current offset abnormal section includes fluctuation upper and lower limit values, ratio abnormal determination points, and continuous abnormal identification identifiers. The indoor electrical sequence offset distance includes stable trajectory offset amounts, high-frequency change position points, and operation state comparison features. The list of indoor electrical hidden danger warning nodes includes risk level partitions, event frequency thresholds, and warning node identification numbers.

[0022] Specifically, as Figure 2 shown, the steps of the indoor load and environment synchronization dataset are specifically as follows: S101: Based on the data of electrical equipment connection points in the residence, extract the current value, voltage value and power value of the socket node, call the environmental parameters of the indoor temperature and humidity monitoring equipment, match and sort the two types of data according to the time label, and generate the corresponding set of electrical and environmental parameters; In residential electrical equipment, current, voltage and power values are common monitoring parameters. As the key connection point of electrical equipment, the socket node provides real-time current, voltage and power and other parameters of electrical equipment. First, obtain the current value, voltage value and power value of the socket from the electrical connection point, which can be collected in real time through an electric meter or a sensor. Based on the environmental parameters obtained by the indoor temperature and humidity monitoring equipment, extract the indoor temperature and humidity data. The environmental parameters are collected by special monitoring equipment, including temperature and humidity sensor data. The data is matched through time labels, that is, the time of electrical data and environmental data is synchronized to ensure that each pair of electrical parameters and environmental parameters can correspond to the same time period. In this way, a complete dataset can be obtained, which includes timestamps, electrical data (current, voltage, power) and environmental data (temperature, humidity) at the corresponding time. For example, at a certain moment, the current value of the socket node is 5 amperes, the voltage is 220 volts, the power is 1100 watts, and the indoor temperature is 23°C and the humidity is 60%. The data will be synchronously recorded and organized into a corresponding set of electrical and environmental parameters. Through this set of data, subsequent current and power analysis, temperature and humidity fluctuations, and abnormal detection of electrical load conditions will be more accurate.

[0023] S102: Based on the corresponding set of electrical and environmental parameters, perform interval discrimination on the current value and power value under the time tag, analyze the combined fluctuation amplitude between power and humidity, screen out the record segments that continuously exceed the current upper limit threshold and have abnormal combined fluctuation amplitude, and obtain the abnormal load fluctuation interval value; Extract the current value and power value according to the time tag, and then perform interval discrimination. The process involves dividing the numerical values of the current value and power value into intervals. Set a current upper limit threshold (for example, 10 amperes) and discriminate the current value. If the current value exceeds this threshold, it is considered abnormal. The discrimination process for the power value is similar. Set a power upper limit threshold (such as 1500 watts). When the power value exceeds this threshold, it will also trigger abnormal discrimination. Analyze the combined fluctuation amplitude between the power value and humidity, which is achieved by calculating the change amplitude of humidity and power value within a certain time interval. For example, if the humidity changes from 60% to 75% and the power value fluctuates from 1000 watts to 1300 watts, the fluctuation amplitude can be calculated and compared with the set fluctuation threshold. If it exceeds the predetermined range, it is considered an abnormal fluctuation. By this method, it is possible to screen out the record segments in which the current and power values exceed the upper limit threshold and the fluctuation amplitude between power and humidity is abnormal within a continuous time period, and finally obtain the abnormal load fluctuation interval value. For example, if the current value continuously exceeds 10 amperes and the combined fluctuation amplitude of humidity and power value reaches the preset abnormal range (such as the humidity change exceeds 15% and the power fluctuation amplitude exceeds 300 watts), then this segment of the record will be identified as an abnormal load fluctuation interval.

[0024] S103: Based on the abnormal load fluctuation interval value, extract the interval voltage offset and the room temperature change rate, combine the synchronous amplitude difference and the duration, judge the abnormalities of electrical load overload and poor contact, and generate the indoor load and environment synchronization data set; Based on the abnormal load fluctuation interval value, the extraction of voltage offset and room temperature change rate is carried out next. Voltage offset refers to the offset of the voltage value relative to the normal voltage range within a specific time interval, which can be calculated by measuring the difference between the current voltage and the standard voltage (such as 220 volts). If the voltage value fluctuates significantly and exceeds the set threshold range (such as the voltage fluctuation exceeds ±10 volts), it indicates that there is abnormal voltage fluctuation. The room temperature change rate refers to the speed of room temperature change within a certain time period, which can be calculated by the ratio of the difference in room temperature change between two points in time to the time interval. For example, if the room temperature rises from 22°C to 28°C within one hour, the change rate is 6°C / hour. If the change rate exceeds the set threshold (such as exceeding 3°C / hour), it can be considered that the room temperature fluctuates too much within this interval. Combining the synchronous amplitude differences, that is, the change amplitude differences of current, voltage, and temperature and humidity, by comparing the synchronous change situations of different parameters, the overload and poor contact problems of electrical loads can be further judged. Overload is manifested as the current value continuously exceeding the upper limit and the power value being abnormally high, while poor contact is accompanied by the asynchronous phenomenon of voltage fluctuation and current fluctuation. Combining the data to generate an indoor load and environment synchronous data set provides data support for the subsequent analysis of the relationship between load and environment.

[0025] Specifically, as Figure 3 shown, the steps of the fire risk load mutation segment are specifically as follows: S201: Based on the indoor load and environment synchronous data set, extract the current and power sequences of the power distribution terminal group, identify the periodic change rate, screen the power and current change rate records that exceed the fluctuation threshold, and generate a load mutation fluctuation interval; From the indoor load and environment synchronization dataset, extract the current and power sequences of the power branch terminal group. The current and power data of the power branch terminal group can reflect the electricity consumption of each load in the residence. By sorting the current values and power values in chronological order and matching them with the corresponding environmental parameters, a continuous data sequence can be formed to identify the periodic change rate, which involves calculating the change rate of each current and power data point with respect to the data at the previous time point. For example, if the current changes from 5 amperes to 6 amperes at a certain time point, the change rate is 20%. This process requires calculating the difference for each data in the current and power sequences and dividing it by the value at the previous time point. After calculating the change rate for each current value and power value, the change rate needs to be screened according to a preset fluctuation threshold. Assume the current change rate threshold is 10% and the power change rate threshold is 15%. If the current change rate exceeds 10% or the power change rate exceeds 15%, the data point is considered an abnormal fluctuation. Screen out those records that exceed the fluctuation threshold. Assume that during a certain period, the current change rate reaches 12% and the power change rate is 18%. Then the current and power changes during this period both exceed the set thresholds and are determined as the load mutation fluctuation interval. For example, from 8 pm to 8:15 pm, the current suddenly increases from 5 amperes to 6 amperes and the power value increases from 1000 watts to 1200 watts, resulting in a load mutation fluctuation interval.

[0026] S202: According to the load mutation fluctuation interval, call the room temperature and humidity for the corresponding time period, extract the temperature-humidity combination corresponding to the power, and determine whether the difference in the period before the mutation exceeds the electrical safety threshold, mark the abnormal linkage area, and obtain the fire risk load mutation segment; Call the corresponding room temperature and humidity data during this time period. By querying the indoor temperature and humidity data recorded by the environmental monitoring equipment, find the environmental data that coincides with the time of the load mutation fluctuation interval. For example, assume that the load mutation fluctuation interval is from 8:00 pm to 8:15, and the room temperature during this time period is 28°C and the humidity is 55%. Then, extract the temperature-humidity combination corresponding to the power, that is, during this time period, when the power value is 1200 watts, the room temperature is 28°C and the humidity is 55%. The combined data can be analyzed as the relationship between power mutation and environmental change. Determine whether the difference in the period before the mutation exceeds the electrical safety threshold. The electrical safety threshold is set according to the design standards and actual operating environment of the electrical equipment. For example, the set safety threshold is that the temperature change exceeds 5°C or the humidity change exceeds 10%. Assume that 15 minutes before the load mutation, the room temperature rises from 23°C to 28°C, a change of 5°C, which meets the requirements of the safety threshold. However, if the temperature change exceeds the set safety threshold (e.g., exceeds 5°C), then this will be marked as an abnormal linkage area at this time, which means that there is an abnormal linkage between the load mutation and the environmental change, resulting in a fire risk. For example, if after the load mutation, the room temperature continues to rise and the humidity suddenly fluctuates greatly, this will affect the stability of the electrical equipment, leading to short circuits or overheating, thus generating a fire risk. In this case, the load mutation segment will be marked as a fire risk load mutation segment.

[0027] Specifically, as Figure 4 shown, the steps for the abnormal section of voltage and current offset are as follows: S301: Call the continuous current value and voltage value data in the fire risk load mutation segment, pair them according to the same time period, calculate the ratio of each group, and generate a voltage-current ratio fluctuation sequence; For the continuous current value and voltage value data in the fire risk load mutation segment, extract them according to the time period. Each current and voltage data point is recorded based on time synchronization, so each pair of current value and voltage value can be paired according to the time tag. For example, during the same time period, the current is 5 amperes and the voltage is 220 volts. Pair these two values and calculate their ratio. The ratio of voltage to current is obtained through a simple division operation, such as 220V divided by 5A, resulting in an impedance value of 44 ohms. Repeat this calculation process for each paired current and voltage data to obtain the voltage-current ratio for each time period. By calculating the current and voltage ratios for all time points, a complete voltage-current ratio fluctuation sequence can be formed. Assume that during a certain time period, the current is 6 amperes and the voltage is 230 volts, then the ratio is 230V / 6A = 38.33 ohms. This ratio sequence reflects the changes in voltage and current over time and provides basic data for subsequent fluctuation analysis. By calculating and comparing each group of ratios, the fluctuation situation between voltage and current can be obtained.

[0028] S302: Based on the voltage-current ratio fluctuation sequence, extract the upper and lower boundaries of the ratio interval for continuous time periods, compare them with the line operation reference values, identify the sections that exceed the reference values, determine the start and end times of the abnormal sections and the ratio fluctuation range, mark the fluctuation periods that meet the conditions as critical sections, and obtain the critical ratio fluctuation interval range; The extraction of the upper and lower boundaries of the ratio for continuous time periods is carried out by setting a maximum and minimum value range in the time series. For example, in a certain time period, the fluctuation range of the voltage-current ratio is from 40 ohms to 50 ohms, then the upper and lower boundaries of this ratio interval are 40 ohms and 50 ohms. It is necessary to compare the extracted ratio interval with the line operation reference value, and the line operation reference value can be a threshold range set based on equipment standards or historical operation data. For example, the reference value is 45 ohms ± 5 ohms, that is, the voltage-current ratio fluctuation is allowed to be between 40 ohms and 50 ohms. If the ratio interval exceeds the range (such as below 40 ohms or above 50 ohms), then the ratio fluctuation in this time period is considered abnormal. By comparing the reference value with the ratio fluctuation range, the sections that exceed the reference value can be identified, and the start and end times of the abnormal sections and the ratio fluctuation range can be determined. For example, if the ratio drops to 35 ohms or rises to 55 ohms in a certain period of time, then mark this time period as an abnormal fluctuation section and determine its start and end times. The fluctuation periods that meet the conditions are marked as critical sections, which means that the voltage-current ratio fluctuation in this period of time exceeds the set safety threshold and has a greater risk. Obtain the critical ratio fluctuation interval range to provide data support for subsequent abnormal analysis.

[0029] S303: According to the critical ratio fluctuation interval range, extract the current value and voltage value corresponding to the corresponding time period in the mutation segment, analyze the offset amplitude, summarize the offset combinations of the critical sections, and output the voltage-current offset abnormal sections; Extract the current value and voltage value corresponding to the corresponding period in the mutation segment. During the time period of each critical fluctuation range of the ratio, it is necessary to find the current and voltage data that coincide with this time period. Assuming that within the critical fluctuation range, the current value is 7 amperes and the voltage value is 240 volts, then the current and voltage data of this time period can be extracted. It is necessary to analyze the offset amplitude of this time period. The offset amplitude refers to the difference in the current and voltage values between the current time period and the previous time period. For example, the current in the previous time period was 6 amperes and the voltage was 230 volts, while the current in the current time period is 7 amperes and the voltage is 240 volts, then the offset amplitudes are 1 ampere and 10 volts respectively. The analysis of the offset amplitude helps to identify abnormal fluctuations in the electrical load. Summarize all offset combinations in the critical section, that is, summarize and statistically analyze the offset amplitudes of the current and voltage that meet the conditions of the critical fluctuation section, and analyze its change law. If similar offset combinations appear in multiple critical sections, such as a large current offset and a small voltage offset, it indicates that there is an unstable situation in the equipment load. Through this analysis of the offset amplitude, it is used for further load monitoring and fault diagnosis. For example, in a mutation segment, the current value suddenly increases from 6 amperes to 8 amperes, while the voltage value increases from 230 volts to 240 volts. This mutation fluctuation can be marked as an abnormal section of voltage-current offset.

[0030] Specifically, as Figure 5 shown, the steps of the indoor electrical sequence offset distance are specifically as follows: S401: According to the abnormal section of voltage-current offset, extract the power value, temperature and humidity sequence within the time period, analyze the instantaneous offset rate of power change, extract the mutation points and record their synchronous positions in the multi-source sequence, and obtain the indoor high-frequency abnormal behavior nodes; Data is provided by the corresponding monitoring devices and can be extracted through time synchronization. The change in power value is mainly related to the fluctuations of current and voltage. Therefore, when the voltage and current deviate abnormally, the power value also shows significant changes, and the instantaneous deviation rate of power change is calculated. The instantaneous deviation rate refers to the rate of change of power between adjacent time periods and can be obtained by performing a difference operation on the power value and dividing it by the time interval. For example, if the power is 1000 watts at a certain moment and 1100 watts at the next moment, the instantaneous deviation rate is (1100 - 1000) / time interval (unit: second). This calculation helps to identify the sharp changes in power and then determine the mutation points. The mutation point refers to the moment when the power value or current and voltage values change significantly within a very short time. For example, if the power value rises sharply from 1000 watts to 1500 watts and the change rate far exceeds the predetermined threshold (such as 20%), it is a mutation point. By marking the mutation points and recording their synchronous positions in the multi-source sequence, the high-frequency abnormal behavior nodes can be accurately identified. For example, within a certain period of time, the mutation point of the power value appears at 9 pm, and the current and voltage also fluctuate significantly synchronously. This moment is considered a high-frequency abnormal behavior node and is recorded for further analysis.

[0031] S402: Based on the indoor high-frequency abnormal behavior nodes, compare the change trajectories of the corresponding temperature and humidity values with the baseline trajectory of the steady-state section, identify the range of behavior points where the continuous trajectory deviation exceeds the thermal-humidity stability threshold, locate the characteristics of the deviation direction and duration, and establish the indoor thermal-humidity deviation interference section; Compare the change trajectories of the temperature and humidity values within this period with the baseline trajectory of the steady-state section. The baseline trajectory refers to the stable change pattern of temperature and humidity under normal operating conditions, which is defined by long-term historical data or the design parameters of the equipment. By calculating the changes in temperature and humidity before and after the high-frequency abnormal behavior nodes and comparing their differences with the baseline trajectory, if the temperature change exceeds the set threshold (such as exceeding 3°C) and the humidity change exceeds the set threshold (such as exceeding 10%), it is considered that the temperature and humidity changes in this period significantly deviate from the normal fluctuation range. The range of behavior points where the continuous trajectory deviation exceeds the thermal-humidity stability threshold refers to those time periods during which the temperature and humidity changes reach or exceed the set threshold. For example, if the temperature increases from 23°C to 28°C within one hour and the humidity changes from 60% to 75%, the changes during this period are regarded as deviation behavior points. Locate the characteristics of the deviation direction and duration. For example, if the temperature gradually increases and lasts for more than a certain duration (such as 1 hour), this deviation behavior can be considered a thermal interference, and the indoor thermal-humidity deviation interference section can be established. The characteristics of this section are the continuous changes in temperature and humidity and exceeding the set stable range.

[0032] S403: According to the indoor thermal and humidity offset interference section, extract the time-period power offset trend and the temperature and humidity disturbance quantity, calculate the total offset of the composite interference period, identify the section with a low overlap rate and an offset continuously exceeding the reference duration, and obtain the indoor electrical sequence offset distance; Extract the time-period power offset trend from the power monitoring data. By performing time series analysis on the power consumption data, obtain the power fluctuation information for each time period. Suppose the power consumption in a certain time period fluctuates from 0.5 kW to 0.8 kW. It is necessary to calculate that the power offset of this time period is 0.3 kW. Superimpose and analyze the power data with the temperature and humidity disturbance data to obtain the power change trend that may be related to the thermal and humidity disturbance. The process involves correlating the temperature fluctuation with the power consumption change to determine whether the electrical equipment has an offset due to the thermal and humidity disturbance. Calculate the total offset of the composite interference period. The specific method is to weight and sum the power offsets within each interference section. Suppose the power offsets within a certain period of time are 0.3 kW, 0.5 kW, and 0.2 kW. Then the total offset of the composite interference period is 0.3 + 0.5 + 0.2 = 1.0 kW. Identify the section with a low overlap rate and an offset continuously exceeding the reference duration. The execution of this operation depends on the set overlap rate and reference duration. Suppose the set overlap rate is 10% and the reference duration is 5 minutes. When the overlapping area of two consecutive interference periods is less than 10% and the offset duration exceeds 5 minutes, this section can be determined as the target section. By screening the section with a low overlap and an offset continuously exceeding the reference duration, the indoor electrical sequence offset distance can be effectively identified.

[0033] Specifically, as Figure 6 shown, the steps of the indoor electrical hazard warning node list are specifically as follows: S501: Based on the indoor electrical sequence offset distance, extract the offset event records within a continuous time period, count the number of times the offset event is triggered in each section, and calculate the cumulative growth trend value of the event within the section. Identify the continuous position interval where the trigger frequency continuously increases, and obtain the high-risk electrical hazard section group; Extract the offset event records within consecutive time periods. Each offset event record contains the offset changes of data such as current, voltage, and power of the electrical equipment within a specific time period. By sorting the recorded times continuously, multiple sections can be divided. For example, assuming the analysis of electrical data within 24 hours, it can be divided into one section per hour, and the offset events of the electrical equipment within each section need to be counted. The number of trigger times needs to be counted, which refers to the number of times the current or voltage value fluctuates significantly within a certain section. For example, if the current value has 5 changes with a fluctuation amplitude greater than 10% within a certain section, then the number of trigger times in this section is 5. Calculate the cumulative growth trend value of the event within the section, which can be achieved by accumulating the number of trigger times in each section and calculating its change trend. For example, if the number of trigger times in three consecutive sections is 5, 7, and 9 respectively, then the cumulative growth trend value is (7 - 5) + (9 - 7) = 4, indicating that the trigger frequency shows an increasing trend. Through the analysis process, the continuous position intervals with continuously increasing trigger frequencies can be identified. For example, assuming that the number of trigger times keeps increasing within certain sections, then these sections form a group of high-risk sections for electrical risks, which can be used as areas that need to be monitored key points. Extract the high-risk sections and mark them as a group of high-risk sections for electrical risks for further risk assessment and monitoring in the future.

[0034] S502: According to the group of high-risk sections for electrical risks, combined with the event trigger frequency, duration, and offset amplitude within the section, screen the sections with a trigger frequency greater than the risk assessment grading threshold, label the corresponding time positions according to the trigger level, and output the list of indoor electrical hazard warning nodes; Analyze risks by combining the event trigger frequency, duration, and offset amplitude in each section. For example, a section with a trigger frequency greater than the predetermined risk assessment classification threshold means that within a certain section, the number of event triggers exceeds the set safety threshold. For example, if the trigger frequency exceeds 10 times per hour, then this section is regarded as a high-risk section. The duration and offset amplitude of each event also need to be considered. The duration refers to the time length from the start to the end of the triggered event, and the offset amplitude refers to the change amplitude of electrical parameters (such as current, voltage, etc.). For example, if the current of a certain triggered event increases from 10 amperes to 20 amperes, this change amplitude is 10 amperes, and the duration is the time length during which this current change persists, assumed to be 5 minutes. By combining these factors, sections with a trigger frequency greater than the risk assessment classification threshold can be further screened out. These sections represent unstable areas of electrical loads, leading to equipment failures or safety hazards. The screened sections are labeled according to the trigger level. For example, sections with a trigger frequency above 10 times per hour can be labeled as "high risk", and those with a trigger frequency of 5 times per hour can be labeled as "medium risk". In this way, the specific time positions of each high-risk section are marked, and finally a list of indoor electrical hazard warning nodes is generated. This list can be used for further investigation and maintenance work to ensure that the equipment operates in a safe state.

[0035] As Figure 7 shown, an intelligent detection system for indoor electrical safety hazards, the system includes: The data synchronization module sorts the time series according to the period based on the data of electrical equipment connection points in the residence, including socket node current values, voltage values, and power values, extracts the room temperature and humidity values within each period, analyzes the mapping relationship between the time tags and parameters, and forms an electrical environment correlation data set; The hazard identification module extracts the power and current sequences of each power branch based on the electrical environment correlation data set, compares the change amplitude and increase / decrease ratio of adjacent periods, screens out the abnormal fluctuation frequency bands, and identifies the room temperature and humidity values corresponding to the corresponding periods to generate an abnormal load mutation feature group; The fluctuation discrimination module extracts the ratio of the current value to the voltage value of each group of data based on the abnormal load mutation feature group, analyzes the ratio fluctuation range within a continuous time, determines whether it exceeds the operation boundary threshold, locates the over-limit section, and obtains the critical electrical parameter abnormal section; The trajectory analysis module matches the power sequence, temperature and humidity sequence of each period based on the critical electrical parameter abnormal section, extracts the high-frequency points to identify the behavior time trajectory, and compares it with the stable operation behavior trajectory for offset comparison to identify the trajectory variation characteristics and establish an electrical behavior offset path group; The node warning module, based on the electrical behavior offset path group, counts the number of abnormal fluctuation triggers within the path, analyzes the regional mapping relationship, performs level division on the high-frequency points, locates the trigger concentration area, and outputs a list of warning nodes for indoor electrical hazards.

[0036] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. An intelligent detection method for indoor electrical safety hazards, characterized in that, It includes the following steps: S1: Based on the data of the connection points of electrical equipment in the residence, including current, voltage, and power information, organize and classify the environmental data of the room temperature and humidity monitoring equipment in the same period, label each value with a time tag, and generate an indoor load and environment synchronization dataset; S2: Based on the indoor load and environment synchronization dataset, extract the time series of the power and current of the power branch terminal group, analyze the change amplitude in a unit time period, compare the change rates in the upper and lower cycles, screen the signal sequences with the change rate exceeding the limit, and combine the environmental data to analyze the abnormal correlation to obtain the fire risk load mutation segment; S3: Call the fire risk load mutation segment, extract the current and voltage values in each segment, identify the fluctuation range of the ratio in a continuous time period, and when the fluctuation interval exceeds the reference boundary of the line operation, mark it as a critical fluctuation, summarize and output the voltage and current offset abnormal section; S4: According to the voltage and current offset abnormal section, identify the power, temperature, and humidity time series arrangement in the corresponding time period, select the high-frequency fluctuation points for behavior position mapping, judge the difference trajectory during the stable operation period, and mark the interval with the offset amplitude greater than the normal section to obtain the indoor electrical sequence offset distance.

2. The intelligent detection method for indoor electrical safety hazards according to claim 1, wherein, The indoor load and environment synchronization dataset includes electrical node numerical tags, environmental parameter timestamps, and multi-source data alignment fields. The fire risk load mutation segment includes abnormal power change values, periodic change difference amounts, and environmental coupling indicator factors. The voltage and current offset abnormal section includes upper and lower fluctuation limit values, ratio abnormal determination points, and continuous abnormal identification identifiers. The indoor electrical sequence offset distance includes stable trajectory offset amounts, high-frequency change position points, and operation state comparison features.

3. The intelligent detection method for indoor electrical safety hazards according to claim 1, wherein The steps of the indoor load and environment synchronization dataset are specifically as follows: S101: Based on the data of the connection points of electrical equipment in the residence, extract the current value, voltage value, and power value of the socket node, call the environmental parameters of the indoor temperature and humidity monitoring equipment, match and sort the two types of data according to the time tag, and generate a corresponding set of electrical and environmental parameters; S102: According to the corresponding set of electrical and environmental parameters, perform interval discrimination on the current value and power value under the time tag, and analyze the combined fluctuation amplitude between the power and humidity, screen the record segments that continuously exceed the current upper limit threshold and the combined fluctuation amplitude is abnormal, and obtain the abnormal load fluctuation interval value; S103: Based on the abnormal load fluctuation interval value, extract the interval voltage offset and room temperature change rate, combine the synchronization amplitude difference and duration, judge the abnormalities of electrical load overload and poor contact, and generate an indoor load and environment synchronization dataset.

4. The intelligent detection method for indoor electrical safety hazards according to claim 3, wherein The steps of the fire risk load mutation segment are specifically as follows: S201: Based on the indoor load and environment synchronization dataset, extract the current and power sequences of the power branch terminal group, identify the change rate between cycles, screen the records of the power and current change rates that exceed the fluctuation threshold, and generate a load mutation fluctuation interval; S202: According to the load mutation fluctuation range, call the room temperature and humidity in the corresponding time period, extract the temperature-humidity combination corresponding to the power, and determine whether the difference in the time period before the mutation exceeds the electrical safety threshold, mark the abnormal linkage area, and obtain the fire risk load mutation segment.

5. The intelligent detection method for indoor electrical safety hazards according to claim 4, characterized in that The steps of the voltage and current offset abnormal section are specifically as follows: S301: Call the continuous current value and voltage value data in the fire risk load mutation segment, pair them according to the same time period, calculate the ratio of each group, and generate a voltage-current ratio fluctuation sequence; S302: Based on the voltage-current ratio fluctuation sequence, extract the upper and lower boundaries of the ratio interval in the continuous time period, compare them with the line operation reference value, identify the section exceeding the reference value, determine the start and end time of the abnormal section and the ratio fluctuation range, and mark the fluctuation time period that meets the conditions as the critical section to obtain the ratio critical fluctuation interval range; S303: According to the ratio critical fluctuation interval range, extract the current value and voltage value in the corresponding time period of the mutation segment, analyze the offset amplitude, summarize the offset combinations of the critical sections, and output the voltage and current offset abnormal section.

6. The intelligent detection method for indoor electrical safety hazards according to claim 5, wherein The steps of the indoor electrical sequence offset distance are specifically as follows: S401: According to the voltage and current offset abnormal section, extract the power value, temperature, and humidity sequence in the time period, analyze the instantaneous offset rate of the power change, extract the mutation points and record their synchronous positions in the multi-source sequence, and obtain the indoor high-frequency abnormal behavior nodes; S402: Based on the indoor high-frequency abnormal behavior nodes, compare the change trajectories of the corresponding temperature and humidity values with the baseline trajectory of the steady-state section, identify the range of behavior points where the continuous trajectory offset exceeds the thermal and humidity stability threshold, locate the offset direction and duration characteristics, and establish an indoor thermal and humidity offset interference section; S403: According to the indoor thermal and humidity offset interference section, extract the power offset trend and thermal and humidity disturbance amount in the time period, calculate the total offset amount of the composite interference time period, identify the section with a low overlap rate and an offset duration exceeding the reference duration, and obtain the indoor electrical sequence offset distance.

7. The intelligent detection method for indoor electrical safety hazards according to claim 1, wherein The method further includes step S5: S5: Based on the indoor electrical sequence offset distance, count the trigger frequency of risk events in the continuous section, locate the high-incidence area according to the frequency cumulative trend, assign corresponding grade labels to each area, and output the list of indoor electrical hazard warning nodes; The list of indoor electrical hazard warning nodes includes risk level partitions, event frequency thresholds, and warning node identification numbers.

8. The intelligent detection method for indoor electrical safety hazards according to claim 7, characterized in that The steps of the list of indoor electrical hazard warning nodes are specifically as follows: S501: Based on the indoor electrical sequence offset distance, extract the offset event records in the continuous time period, count the number of trigger times of the offset events in each section, and calculate the cumulative growth trend value of the events in the section, identify the continuous position interval where the trigger frequency continuously increases, and obtain the electrical risk high-incidence section group; S502: According to the electrical risk high-incidence section group, combine the event trigger frequency, duration, and offset amplitude in the section, screen the sections with a trigger frequency greater than the risk assessment classification threshold, mark the corresponding time positions according to the trigger level, and output the list of indoor electrical hazard warning nodes.

9. An intelligent detection system for indoor electrical safety hazards, characterized in that, The system is used to implement the intelligent detection method for indoor electrical safety hazards described in any one of claims 1-8. The system includes: The data synchronization module sorts the time series according to the period based on the data of the connection points of electrical equipment in the residence, including the socket node current value, voltage value, and power value, extracts the room temperature and humidity values in each period, analyzes the mapping relationship between the time tag and the parameters, and forms an electrical environment correlation dataset; The hazard identification module extracts the power and current sequences of each power branch based on the electrical environment correlation dataset, compares the change amplitude and increase / decrease ratio of adjacent periods, filters out the abnormal fluctuation frequency bands, and identifies the room temperature and humidity values in the corresponding time periods to generate an abnormal load mutation feature group; The fluctuation discrimination module extracts the ratio of the current value to the voltage value of each group of data based on the abnormal load mutation feature group, analyzes the ratio fluctuation range in continuous time, determines whether it exceeds the operation boundary threshold, locates the over-limit section, and obtains the critical electrical parameter abnormal section; The trajectory analysis module matches the power sequence, temperature and humidity sequence of each time period based on the critical electrical parameter abnormal section, extracts the high-frequency points to identify the behavior time trajectory, and compares it with the stable operation behavior trajectory for offset comparison to identify the trajectory variation characteristics and establish an electrical behavior offset path group; The node warning module counts the number of times of abnormal fluctuation triggering in the path based on the electrical behavior offset path group, analyzes the regional mapping relationship, performs level division on the high-frequency points of the frequency, locates the trigger concentration area, and outputs a list of warning nodes for indoor electrical hazards.

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