Intelligent electricity utilization safety monitoring method based on Internet of Things

Abstract

The invention belongs to the technical field of power utilization safety monitoring, and particularly discloses and provides an intelligent power utilization safety monitoring method based on the Internet of Things, which comprises the following steps: marking abnormity by monitoring and analyzing current, voltage and power of power utilization equipment and a main line, generating a candidate weak line set through a depth-first traversal algorithm in combination with an electrical topological relation graph, the current fluctuation ratio is analyzed to determine a weak line, the influence degree of the temperature, the discharge current and the environment temperature is calculated, and finally the fire hazard early warning level is comprehensively evaluated and early warning is carried out. According to the method, upstream and downstream lines are traversed step by step through a depth-first traversal algorithm and in combination with a preset electrical topological relation graph, then associated lines of each layer are positioned, limitation of single-point troubleshooting is avoided, weak lines are screened based on a current fluctuation analysis result, and influence degrees of temperature, discharge current and environment temperature are associated, so that the reliability of the current fluctuation analysis result is improved. And early warning levels are divided through multi-level threshold comparison, so that the comprehensiveness of power utilization safety monitoring is improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of electric safety monitoring, and relates to a smart electric safety monitoring method based on Internet of Things. BACKGROUND

[0002] Smart electricity refers to a power consumption mode that real-time monitors, analyzes data and intelligently manages electrical parameters of power consumption equipment and lines by means of Internet of Things, big data, artificial intelligence and sensors. The core is to realize the measurability, monitorability, controllability and early warning of electric safety through digital means. By monitoring parameters such as current fluctuation rate and temperature change rate in real time, hidden dangers such as overload and discharge can be identified in advance, and the probability of fire can be reduced. Therefore, the safety monitoring of smart electricity is needed.

[0003] A power safety management service system is disclosed in Chinese patent CN118411155B, which includes a device safety monitoring module that collects data from power consumption terminals, obtains power consumption information and log data of the power consumption terminals, a central control analysis module that establishes a preliminary analysis model to perform preliminary analysis on the device safety monitoring module and obtains preliminary analysis results, a management server module that establishes a server architecture, establishes a senior analysis model, performs senior analysis on the device safety monitoring module and obtains senior analysis results, performs level evaluation on the central control analysis module, performs level evaluation on the device safety monitoring module, and performs safety management on the device safety monitoring module or the power consumption terminal according to the level of the device safety monitoring module, to realize rapid response to complex security threats.

[0004] The above prior art has the following deficiencies: 1. The current data is only collected by the device safety monitoring module, and a preliminary analysis model is established by the central control analysis module, but the line correlation is not analyzed based on the electrical topology structure, so the abnormal line cannot be accurately located.

[0005] 2. The current only performs preliminary and senior analysis by obtaining power consumption information and log data, without specifying the specific analysis method of current fluctuation, lacking quantitative evaluation of current overrun, fluctuation amplitude and fluctuation frequency, and thus the early warning mechanism lags behind the actual hidden danger development, making it difficult to realize real-time dynamic monitoring of power consumption safety. SUMMARY

[0006] In view of this, in order to solve the problems raised in the background art, a smart electric safety monitoring method based on Internet of Things is proposed.

[0007] The purpose of the application can be achieved by the following technical scheme: the application provides a smart electric safety monitoring method based on Internet of Things, which comprises the following steps: S1, monitoring the current, voltage and power of power consumption equipment and main lines, and marking the power consumption equipment and main lines with parameters exceeding the preset threshold as abnormal.

[0008] S2, generating a candidate weak line set by breadth-first search to traverse upstream lines of the abnormal device and combining depth-first traversal to traverse downstream lines of the abnormal line.

[0009] S3, monitoring the power consumption current, temperature and discharge current of each branch line in the candidate weak line set at each monitoring time point within a preset time period.

[0010] S4, constructing a change curve based on the power consumption current, analyzing the current fluctuation rate by weighted calculation of the current overrun rate, fluctuation amplitude and frequency coefficient, and marking the branch line exceeding the preset value as a weak line.

[0011] S5, obtaining the temperature influence degree and discharge current influence degree of each weak line through temperature change analysis and discharge current change analysis.

[0012] S6, monitoring the ambient temperature of each weak line, matching the temperature interval corresponding to the ambient temperature influence degree, and obtaining the ambient temperature influence degree of each weak line.

[0013] S7, based on the temperature influence degree, discharge current influence degree and ambient temperature influence degree, evaluating the fire hazard warning level of each weak line and performing corresponding warning.

[0014] Compared with the prior art, the beneficial effects of the present application are as follows: (1) the present application uses depth-first traversal algorithm and breadth-first search algorithm, combined with a preset electrical topology relationship diagram, to traverse upstream and downstream lines level by level with the abnormal device or abnormal line as the root node, thereby locating the associated lines at each level, avoiding the limitations of single-point troubleshooting, realizing full-line tracking from device abnormality to line hidden danger, and improving the accuracy of abnormal positioning.

[0015] (2) the present application realizes multi-dimensional quantitative analysis of the frequency, amplitude and overrun degree of current fluctuation by constructing a power consumption current change curve, calculating the overrun rate by reference line, analyzing the peak-valley deviation to obtain the fluctuation amplitude, and generating the fluctuation frequency coefficient, thereby improving the reliability of current fluctuation analysis.

[0016] (3) the present application obtains the current fluctuation rate by analyzing the current overrun rate, fluctuation amplitude and fluctuation frequency coefficient, and then combines the preset threshold to screen weak lines, so that the warning mechanism changes from post-response to pre-prediction, effectively shortening the time difference between hidden danger development and warning, and realizing real-time dynamic monitoring of power safety.

[0017] (4) the present application screens weak lines based on the current fluctuation analysis result, simultaneously associates the temperature influence degree, discharge current influence degree and ambient temperature influence degree, divides the warning level by multi-level threshold comparison, and improves the comprehensiveness of power safety monitoring. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0019] Figure 1 The schematic diagram for connecting each step of the method of the present application is shown.

[0020] Figure 2 The schematic diagram for connecting the step of generating the candidate weak line set of the present application is shown.

[0021] Figure 3 The schematic diagram for connecting the current fluctuation rate analysis step of the present application is shown. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort belong to the scope of protection of the present application.

[0023] Please refer to Figure 1 As shown in the drawings, the present application provides a smart power utilization safety monitoring method based on Internet of Things, which comprises the following steps: S1, monitoring the current, voltage and power of the power utilization equipment and the main line, and marking the power utilization equipment and the main line with parameters exceeding the preset threshold as abnormal.

[0024] Exemplarily, the step of marking the power utilization equipment and the main line with parameters exceeding the preset threshold as abnormal comprises the following steps: comparing the current, voltage and power of each power utilization equipment with the preset threshold of each power utilization equipment, respectively.

[0025] If any parameter of the current, voltage and power of the power utilization equipment or the main line exceeds the preset threshold, the power utilization equipment or the main line is marked as abnormal, and then each abnormal equipment and each abnormal line are obtained.

[0026] It should be noted that the current, voltage and power are the key parameters of the running state of the power utilization equipment, and the exceeding of the threshold value represents the abnormality. The three parameters are the basic language of the running state of the power utilization equipment and the line, and any abnormal running will be directly or indirectly embodied through them, so the current, voltage and power are selected for abnormality determination. The preset threshold is a safety critical value set based on the electrical equipment safety standard and the line design specification. For example, the preset current threshold is usually set as 1.2 times of the rated current, for example, the preset current threshold is 120A when the rated current of a line is 100A.

[0027] S2, generating a candidate weak line set by breadth-first search to traverse the upstream lines of the abnormal device, and combining depth-first traversal to traverse the downstream lines of the abnormal line.

[0028] Please refer to Figure 2 As shown, the generating of the candidate weak line set includes: G1, determining the total upstream associated line set of the abnormal device by using a breadth-first search algorithm.

[0029] Further, the determining of the total upstream associated line set of the abnormal device includes: G1-1, taking the abnormal device as a root node, and locating the branch lines directly connected thereto by using a preset electrical topology relationship diagram.

[0030] G1-2, sequentially accessing each level line upwards and recording as a corresponding upstream line, until no new upstream line is accessible, and generating an upstream associated line set containing all levels of upstream lines.

[0031] G1-3, merging the upstream associated line sets of each abnormal device, and performing deduplication to obtain the total upstream associated line set of the abnormal device.

[0032] G2, taking the abnormal line as a root node, and locating the branch lines directly connected thereto by using a preset electrical topology relationship diagram.

[0033] G3, traversing each branch line directly connected to the abnormal line.

[0034] G4, taking each branch line as a starting point to access the lower layer line layer by layer, until no new line is accessible, generating a downstream associated line set containing all levels, merging the downstream associated line sets of each abnormal line, and performing deduplication to obtain the total downstream associated line set of the abnormal line.

[0035] G5, merging and deduplicating the total upstream associated line set and the total downstream associated line set to obtain the candidate weak line set.

[0036] The embodiment of the application traverses the upstream and downstream lines by using the depth-first traversal algorithm and the breadth-first search algorithm, and combining the preset electrical topology relationship diagram, and taking the abnormal device or the abnormal line as a root node to traverse the upstream and downstream lines level by level, and then locates the associated lines of each level, thereby avoiding the limitation of single-point troubleshooting, realizing the full-line tracking from device abnormality to line hidden danger, and improving the accuracy of abnormal positioning.

[0037] S3, monitoring the power current, temperature and discharge current of each branch line in the candidate weak line set at each monitoring time point in a preset time period.

[0038] It should be noted that the power current is monitored by a current sensor, the temperature is monitored by a temperature sensor, and the discharge current is monitored by a high-frequency current sensor.

[0039] S4, constructing a change curve based on the power consumption current, analyzing the current fluctuation rate by weighted calculation of the current out-of-limit rate, fluctuation amplitude and frequency coefficient, and marking the branch line whose value exceeds the preset value as a weak line.

[0040] It should be noted that the preset value refers to the reference threshold for judging whether the current fluctuation is abnormal, and the acquisition method is: obtaining the current data of the line for 30 days from the historical data, calculating the average current fluctuation rate, and taking it as the preset current fluctuation rate.

[0041] Please refer to Figure 3 As shown in the figure, the analysis of the current fluctuation rate includes: W1, taking each branch line of the candidate weak line set as a candidate branch line.

[0042] W2, constructing the power consumption current change curve of each candidate branch line with the monitoring time point as the abscissa and the power consumption current as the ordinate.

[0043] W3, constructing a reference line in the power consumption current change curve based on the preset power consumption current threshold, and further determining the current out-of-limit rate of each candidate branch line.

[0044] It should be noted that the preset power consumption current threshold is a pre-set current safety threshold for ensuring the safe operation of the line, which is used to measure whether the actual monitored current exceeds the safety range. The preset power consumption current threshold is set based on the rated parameters of the line, that is, the threshold is set according to the rated current, overload capacity and other parameters of the line, to ensure that the operating current of the line does not exceed its safe carrying range.

[0045] Further, the determination of the current out-of-limit rate of each candidate branch line includes: W3-1, extracting the length of the curve segment above the reference line and the length of the power consumption current change curve from the power consumption current change curve.

[0046] W3-2, taking the ratio of the length of the curve segment above the reference line and the length of the power consumption current change curve as the current out-of-limit rate of each candidate branch line.

[0047] W4, extracting the power consumption current of each peak point and the next valley point adjacent thereto from the power consumption current change curve for deviation analysis to obtain the current fluctuation amplitude of each candidate branch line.

[0048] Further, the analysis of the current fluctuation amplitude of each candidate branch line includes: W4-1, taking the difference between the power consumption current of each peak point and the next valley point adjacent thereto to obtain the current fluctuation amplitude of each peak point.

[0049] W4-2. Normalize the current fluctuation amplitude at each peak point to obtain the normalized current fluctuation amplitude at each peak point.

[0050] It should be added that the normalization process is as follows: obtaining the maximum value and the minimum value from the current fluctuation amplitude at the peak point, and performing a difference between the two to obtain a reference current fluctuation amplitude difference.

[0051] The current fluctuation amplitude of each peak point is subtracted from the minimum current fluctuation amplitude to obtain the current fluctuation amplitude difference of each peak point, and the ratio of the current fluctuation amplitude difference to the reference current fluctuation amplitude difference is used as the normalized current fluctuation amplitude of each peak point.

[0052] W4-3. Calculate the average of the normalized current fluctuation amplitudes of each peak point to obtain the current fluctuation amplitude of each candidate branch line.

[0053] W5. Count the peak times of each candidate branch line, calculate the ratio of the peak times to the preset peak times, and take the minimum value of the ratio and 1 to obtain the fluctuation frequency coefficient of each candidate branch line.

[0054] It should be noted that the preset peak count is a benchmark used to measure the normality of the current fluctuation frequency in the candidate branch line. It represents the upper limit of the reasonable number of current fluctuation peaks within a preset time period. The preset peak count is derived from statistical analysis of historical operating data. The historical current data of the line in a fault-free state is obtained from this data. The current peak count within each preset time period is identified and counted, and the average is calculated to obtain the preset peak count.

[0055] It's important to note that when calculating the fluctuation frequency coefficient, if the ratio of the candidate branch line's peak frequency to the preset peak frequency is greater than 1, it indicates that the current peak frequency of that line exceeds the preset normal range. In this case, taking the minimum value of this ratio and 1 limits the fluctuation frequency coefficient to within 1, ensuring that its value does not increase indefinitely, thereby making the calculation results more reasonable and comparable.

[0056] The embodiment of the present invention constructs an electricity consumption current change curve, calculates the over-limit rate in combination with a reference line, analyzes the peak-to-valley deviation to obtain the fluctuation amplitude, and counts the number of peaks to generate the fluctuation frequency coefficient, thereby realizing a multi-dimensional quantitative analysis of the frequency, amplitude, and over-limit degree of current fluctuations, thereby improving the reliability of current fluctuation analysis.

[0057] W6. Perform weighted fusion calculation on the current over-limit rate, current fluctuation amplitude and fluctuation frequency coefficient to obtain the current fluctuation rate of each candidate branch line.

[0058] It should be added that the current fluctuation rate is calculated in the following manner: the current fluctuation rate is obtained by multiplying the current overrun rate, the current fluctuation amplitude and the fluctuation frequency coefficient by their respective preset influence weights and then adding them together. Among them, the preset influence weights are the influence weights of the current overrun rate, the current fluctuation amplitude and the fluctuation frequency coefficient, and are used to quantify the contribution of different features to the line weakness.

[0059] The quantitative calculation of the current fluctuation rate is performed by weighted summation, on the one hand, the weights reflect the actual differences in the safety threat of the line caused by the current overrun rate, the fluctuation amplitude and the fluctuation frequency, and on the other hand, the multi-dimensional features are fused to avoid the one-sidedness of single index evaluation, and the real weakness state of the line is more accurately reflected.

[0060] Among them, the preset influence weights can be set according to industry experience or obtained through limited trial data, for example, first collect line fault data, statistically analyze the historical correlation between the overrun rate, the fluctuation amplitude, the fluctuation frequency and the line weakness, use regression analysis or logistic regression analysis to determine the contribution of each feature, and finally normalize the contribution to convert it into the preset influence weight and make the sum equal to 1.

[0061] It should be added that the current overrun rate reflects the degree and frequency of the line current exceeding the safety threshold, and the higher the overrun rate, the longer or more frequently the line is in an abnormal state such as overload, which poses a greater safety hazard. The current fluctuation amplitude reflects the severity of the current fluctuation, and a larger fluctuation amplitude may indicate that there are problems such as load mutation and poor contact in the line, which will adversely affect the insulation and equipment life of the line. The fluctuation frequency coefficient measures the frequency of current fluctuation, and frequent current fluctuation will subject the line elements to repeated electrical and thermal stress, accelerating the aging of the elements and increasing the probability of failure. By considering these three parameters comprehensively, the line with real weak links can be accurately identified, and the limitations of single parameter evaluation are avoided, improving the accuracy of subsequent abnormal early warning.

[0062] The embodiment of the present application obtains the current fluctuation rate by analyzing the current overrun rate, the fluctuation amplitude and the fluctuation frequency coefficient, and then screens the weak lines in combination with the preset threshold, so that the early warning mechanism is changed from post-response to pre-prediction, effectively shortening the time difference between hazard development and early warning, and realizing real-time dynamic monitoring of power safety.

[0063] S5, the temperature influence degree and the discharge current influence degree of each weak line are obtained through temperature change analysis and discharge current change analysis.

[0064] Exemplarily, the analysis of the temperature influence degree of each weak line includes: Y1, combining each monitoring time point with its adjacent next monitoring time point two by two to obtain each monitoring time group, and performing deviation analysis on the temperature in each monitoring time group to obtain the temperature change rate of each weak line.

[0065] Further, the analysis of the temperature change rate of each weak line includes: Y1-1, taking the time difference in the monitoring time group as the interval length.

[0066] Y1-2, performing subtraction on the temperatures at the previous monitoring time and the next monitoring time in each monitoring time group to obtain the temperature difference of each monitoring time group, and taking the ratio of the temperature difference to the interval length as the temperature change rate of each monitoring time group.

[0067] Y1-3, selecting the maximum value from the temperature change rates of each monitoring time group as the temperature change rate of each weak line.

[0068] Y2, screening the maximum value from the temperatures as the maximum temperature of each weak line.

[0069] Y3, performing normalization processing on the temperature change rate and the maximum temperature of each weak line respectively, and performing weighted fusion calculation on the normalized temperature change rate and the normalized maximum temperature to obtain the temperature influence degree of each weak line.

[0070] It should be noted that the analysis method of the discharge current influence degree is consistent with the analysis method of the temperature influence degree, which will not be repeated here.

[0071] It should be noted that the normalization processing of the temperature change rate is: obtaining the maximum value and the minimum value from the temperature change rates of each weak line respectively, performing subtraction on the two values to obtain a reference temperature difference, and then performing subtraction on the temperature change rate of each weak line and the minimum value of the temperature change rate, taking the ratio of the difference value to the reference temperature difference as the normalized temperature change rate of each weak line.

[0072] The normalization processing of the maximum temperature is: determining the highest allowable temperature according to the technical specification of the line, dividing the maximum temperature of each weak line by the highest allowable temperature, and taking the minimum value of the ratio and 1 as the normalized maximum temperature of each weak line.

[0073] It should be noted that the calculation method of the temperature influence degree is: multiplying the normalized temperature change rate and the normalized maximum temperature by their preset influence weights respectively, and adding the two products to obtain the temperature influence degree. The preset influence weights are the influence weights of the normalized temperature change rate and the normalized maximum temperature, respectively, which are used to quantify the contribution of different features to the temperature influence degree.

[0074] The temperature influence degree is quantitatively calculated by a calculation mode of weighted summation. On the one hand, the weight is used to reflect the actual difference between the temperature change rate and the maximum temperature on the temperature influence. On the other hand, the multi-dimensional characteristics are fused to avoid one-sidedness of single index evaluation and more accurately reflect the temperature influence degree.

[0075] The preset influence weight can be set according to the experience in the industry, or can be obtained through a limited number of test data. For example, historical temperature change data is first collected, the correlation data of the temperature change rate, the maximum temperature and the insulation aging degree in the historical data is counted, the contribution degree of each feature is determined by using regression analysis or logistic regression analysis, and finally the contribution degree is converted into the preset influence weight after normalization processing, and the sum of the contribution degrees is 1.

[0076] It should be added that the temperature is a direct physical representation of electrical failure, and line abnormalities can cause an increase in power loss, which in turn is converted into heat energy to increase the temperature. The comprehensive evaluation of the temperature change rate and the maximum temperature realizes early identification of electrical failure through double analysis of trends and states.

[0077] S6, monitor the environmental temperature of each weak line, match the temperature interval corresponding to the environmental temperature influence degree, and obtain the environmental temperature influence degree of each weak line.

[0078] It should be added that the matching of the temperature interval corresponding to the environmental temperature influence degree is matching the environmental temperature of each weak line with the preset temperature interval corresponding to each environmental temperature influence degree to obtain the environmental temperature influence degree of each weak line.

[0079] The preset temperature interval corresponding to each environmental temperature influence degree is obtained based on historical operation data analysis. The specific process is as follows: historical environmental temperature data of the area where the line is located is collected from historical operation data, and the environmental temperature is analyzed in combination with the overall operation state of the line to correlate the environmental temperature with the line risk. The temperature threshold value of risk mutation is located by statistical method, different influence degree intervals are divided, and the temperature interval corresponding to each environmental temperature influence degree is derived.

[0080] S7, based on the temperature influence degree, the discharge current influence degree and the environmental temperature influence degree, the fire hazard early warning level of each weak line is evaluated, and corresponding early warning is performed.

[0081] Exemplarily, the evaluation of the fire hazard early warning level of each weak line includes comparing the temperature influence degree, the discharge current influence degree and the environmental temperature influence degree of each weak line with the preset threshold value thereof, respectively.

[0082] The temperature influence degree greater than a preset temperature influence degree threshold is taken as condition 1, the discharge current influence degree greater than a preset discharge current influence degree threshold is taken as condition 2, and the environment temperature influence degree greater than a preset environment temperature influence degree threshold is taken as condition 3.

[0083] When none of the conditions is established, it is determined that the fire hazard early warning level is not required.

[0084] When one of the conditions is established, it is determined that the fire hazard early warning level is a third-level early warning.

[0085] When two of the conditions are established, it is determined that the fire hazard early warning level is a second-level early warning.

[0086] When all of the conditions are established, it is determined that the fire hazard early warning level is a first-level early warning, and then the fire hazard early warning level of each weak line is obtained.

[0087] The embodiment of the present application selects weak lines based on current fluctuation analysis results, simultaneously associates temperature influence degree, discharge current influence degree and environment temperature influence degree, divides early warning levels through multi-level threshold comparison, and improves the comprehensiveness of power utilization safety monitoring.

[0088] The above formulas are all dimensionless values, and the formulas are obtained by software simulation of a large amount of collected data to obtain a formula of the nearest real situation, and the preset parameters in the formula are set by a person skilled in the art according to actual conditions.

[0089] The above embodiments can be realized by software, hardware, firmware or any combination thereof, in whole or in part. When realized by software, the above embodiments can be realized in the form of a computer program product in whole or in part.

[0090] Those skilled in the art can realize that the modules and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0091] In addition, each functional module in each embodiment of the present application can be integrated in one processing module, or each module can exist physically alone, or two or more modules can be integrated in one module.

[0092] The above merely provides the specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of the changes or replacements within the technical range disclosed by the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0093] Finally, the above merely provides the preferred embodiments of the present application, but is not used to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A smart electricity safety monitoring method based on the Internet of Things, characterized by: The method includes: S1. Monitor the current, voltage, and power of electrical devices and main lines, and mark electrical devices and main lines whose parameters exceed preset thresholds as abnormal; S2. Generate a set of candidate weak links by traversing the upstream lines of the abnormal device through breadth-first search and traversing the downstream lines of the abnormal line through depth-first search; S3. Monitoring the power consumption, temperature, and discharge current of each branch line in the candidate weak line set at each monitoring time point within a preset time period; S4. Constructing a change curve based on the power consumption current, analyzing the current fluctuation rate through weighted calculation of the current over-limit rate, fluctuation amplitude, and frequency coefficient, and marking the branch line exceeding the preset value as a weak line; S5. Analyzing temperature changes and discharge current changes to obtain the temperature impact and discharge current impact of each weak line; S6. Monitor the ambient temperature of each weak line, match the temperature range corresponding to the ambient temperature impact, and obtain the ambient temperature impact of each weak line; S7. Based on the temperature influence, discharge current influence, and ambient temperature influence, the fire hazard warning level of each weak line is assessed, and corresponding warnings are issued.

2. The method for intelligent electricity safety monitoring based on the Internet of Things according to claim 1, characterized in that: The step of marking the electrical equipment and the main line whose parameters exceed the preset threshold as abnormal includes: Compare the current, voltage and power of each electrical device with its preset thresholds; If any parameter of the current, voltage, or power of an electrical device or main line exceeds a preset threshold, it is marked as abnormal, and the abnormal devices and abnormal lines are obtained.

3. The method for intelligent electricity safety monitoring based on the Internet of Things according to claim 1, characterized in that: Generating a candidate weak link set includes: G1. Use breadth-first search algorithm to determine the total upstream associated line set of the abnormal device; G2. Use a depth-first search algorithm, taking the abnormal line as the root node, and locate its directly connected branch lines through a preset electrical topology diagram; G3. Traverse all branch lines directly connected to the abnormal line; G4. Starting from each branch line, access the lower-level lines layer by layer until no new lines are accessible. Generate a downstream associated line set containing all levels. Merge the downstream associated line sets of each abnormal line and remove duplicates to obtain the total downstream associated line set of the abnormal line. G5. Combine the total upstream associated line set and the total downstream associated line set to remove duplicates and obtain a candidate weak line set.

4. The method for intelligent electricity safety monitoring based on the Internet of Things according to claim 3 is characterized in that: The total upstream associated line set of the abnormal device is determined to include: Taking the abnormal device as the root node, locate the branch line directly connected to it through the preset electrical topology diagram; Visit each level of lines in turn and record them as corresponding upstream lines until there are no new upstream lines to visit, then generate an upstream associated line set containing all levels of upstream lines; The upstream associated line sets of each abnormal device are merged and deduplicated to obtain the total upstream associated line set of the abnormal device.

5. The method for intelligent electricity safety monitoring based on the Internet of Things according to claim 1, characterized in that: The analyzing current fluctuation rate includes: W1. Collect the branch lines of the candidate weak lines as candidate branch lines; W2. Construct a current change curve for each candidate branch line, using the monitoring time point as the horizontal axis and the current as the vertical axis; W3. Based on a preset power current threshold, construct a reference line in the power current change curve to further determine the current over-limit rate of each candidate branch line; W4. Extract the current at each peak point and the next adjacent valley point from the current variation curve, perform deviation analysis, and obtain the current fluctuation amplitude of each candidate branch line; W5. Count the peak times of each candidate branch line, calculate the ratio of the peak times to the preset peak times, and take the minimum value of the ratio and 1 to obtain the fluctuation frequency coefficient of each candidate branch line; W6. Perform weighted fusion calculation on the current over-limit rate, current fluctuation amplitude and fluctuation frequency coefficient to obtain the current fluctuation rate of each candidate branch line.

6. The method for intelligent electricity safety monitoring based on the Internet of Things according to claim 5, characterized in that: Determining the current over-limit rate of each candidate branch line includes: Extracting the length of the curve segment located above the reference line and the length of the electric current variation curve from the electric current variation curve; The ratio of the length of the curve segment above the reference line to the length of the power consumption current change curve is used as the current over-limit rate of each candidate branch line.

7. The method for intelligent electricity safety monitoring based on the Internet of Things according to claim 5, characterized in that: The analysis of the current fluctuation amplitude of each candidate branch line includes: The current fluctuation amplitude of each peak point is obtained by subtracting the current of each peak point from the next adjacent valley point; Normalizing the current fluctuation amplitude at each peak point to obtain the normalized current fluctuation amplitude at each peak point; The current fluctuation amplitude after normalization of each peak point is averaged to obtain the current fluctuation amplitude of each candidate branch line.

8. The method for intelligent electricity safety monitoring based on the Internet of Things according to claim 1, characterized in that: The analysis of the temperature influence of each weak line includes: Y1. Combine each monitoring time point with its adjacent next monitoring time point to obtain each monitoring time group, and perform deviation analysis on the temperature within each monitoring time group to obtain the temperature change rate of each weak line; Y2. Screen out the maximum value from the temperatures as the maximum temperature of each weak line; Y3. Normalize the temperature change rate and maximum temperature of each weak line respectively, and perform weighted fusion calculation on the normalized temperature change rate and maximum temperature to obtain the temperature influence degree of each weak line.

9. The method for intelligent electricity safety monitoring based on the Internet of Things according to claim 8, characterized in that: The analysis of the temperature change rate of each weak line includes: The time difference within the monitoring time group is used as the interval duration; The temperature difference of each monitoring time group is obtained by subtracting the temperature of the next monitoring time point from the temperature of the previous monitoring time point, and the ratio of the temperature difference to the interval time is used as the temperature change rate of each monitoring time group; The maximum value is selected from the temperature change rate of each monitoring time group as the temperature change rate of each weak line.

10. The method for intelligent electricity safety monitoring based on the Internet of Things according to claim 1, characterized in that: The fire hazard warning level of each weak line is assessed as follows: Compare the temperature influence, discharge current influence and ambient temperature influence of each weak line with their preset thresholds respectively; The temperature influence degree is greater than its preset temperature influence degree threshold as condition 1, the discharge current influence degree is greater than its preset discharge current influence degree threshold as condition 2, and the ambient temperature influence degree is greater than its preset ambient temperature influence degree threshold as condition 3; When none of the conditions are met, the fire hazard warning level is determined to be no warning required; When one of the conditions is met, the fire hazard warning level is determined to be level three warning; When two conditions are met, the fire hazard warning level is determined to be level 2 warning; When all conditions are met, the fire hazard warning level is determined to be level one, and then the fire hazard warning level of each weak line is obtained.

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