A smart power utilization safety monitoring method based on an internet of things

Abstract

The application belongs to the technical field of electric safety monitoring, and specifically discloses a smart electric safety monitoring method based on the Internet of Things, which comprises the following steps: marking abnormalities by monitoring and analyzing the current, voltage and power of electric equipment and main lines, generating a candidate weak line set by a depth-first traversal algorithm in combination with an electrical topology graph, analyzing current fluctuation rate to determine weak lines, calculating the influence degree of temperature, discharge current and ambient temperature, and finally comprehensively evaluating the fire hazard warning level and giving a warning; by means of the depth-first traversal algorithm and in combination with a preset electrical topology graph, the upstream and downstream lines are traversed step by step, and then the associated lines of each level are located, so that the limitation of single-point investigation is avoided, and at the same time, the weak lines are screened based on the analysis result of current fluctuation, and the influence degree of temperature, discharge current and ambient temperature is associated, the warning level is divided by multi-level threshold comparison, and the comprehensiveness of electric 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 of each peak point to obtain the normalized current fluctuation amplitude of each peak point.

[0050] It should be added that the processing process of the normalization processing is: obtaining the maximum value and the minimum value from the current fluctuation amplitude of the peak point, and the difference between the two is obtained to obtain the 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 reference current fluctuation amplitude difference is taken as the normalized current fluctuation amplitude of each peak point.

[0052] W4-3, the normalized current fluctuation amplitude of each peak point is calculated by mean value to obtain the current fluctuation amplitude of each candidate branch line.

[0053] W5, the peak value of each candidate branch line is counted, the ratio of the preset peak value is calculated, and the minimum value of the ratio and 1 is obtained to obtain the fluctuation frequency coefficient of each candidate branch line.

[0054] It should be added that the preset peak value is a reference value for measuring whether the current fluctuation frequency of the candidate branch line is normal, that is, the reasonable upper limit of the number of current fluctuation peaks in a preset time period, and the preset peak value is obtained based on statistical analysis of historical operation data. The historical current data of the line in the fault-free state is obtained from the historical current data, the number of current peaks in each preset time period is identified and counted, and the mean value is calculated to obtain the preset peak value.

[0055] It should be added that when calculating the fluctuation frequency coefficient, if the ratio of the peak value of the candidate branch line to the preset peak value is greater than 1, it means that the number of current peaks of the line exceeds the preset normal range. At this time, the minimum value of the ratio and 1 is taken, which can limit the fluctuation frequency coefficient within 1, so that the value will not increase indefinitely, so that the calculation result is more reasonable and comparable.

[0056] The embodiment of the application realizes multi-dimensional quantitative analysis of the frequency, amplitude and overrun degree of current fluctuation by constructing the power consumption current change curve, calculating the overrun rate by reference line, obtaining the fluctuation amplitude by analyzing the peak value and valley value deviation, and generating the fluctuation frequency coefficient by counting the peak value, and improves the reliability of current fluctuation analysis.

[0057] W6, the current overrun rate, current fluctuation amplitude and fluctuation frequency coefficient are weighted and fused to calculate the current fluctuation rate of each candidate branch line.

[0058] It should be added that the current fluctuation rate is calculated as follows: the current fluctuation rate is obtained by multiplying the current over-limit rate, current fluctuation amplitude, and fluctuation frequency coefficient by their respective preset influence weights and then summing them. The preset influence weights are the influence weights of the current over-limit rate, current fluctuation amplitude, and fluctuation frequency coefficient, respectively, and are used to quantify the contribution of different characteristics to the weakness of the line.

[0059] The current fluctuation rate is quantified by using a weighted summation method. On the one hand, the weights reflect the actual differences in the threat to line safety posed by current over-limit rate, fluctuation amplitude, and fluctuation frequency. On the other hand, the integration of multi-dimensional features avoids the one-sidedness of single-indicator evaluation and more accurately reflects the true weak state of the line.

[0060] The preset influence weights can be set based on industry experience or obtained through a limited number of test data. For example, first collect line fault data, statistically analyze the historical correlation between the over-limit rate, fluctuation amplitude, fluctuation frequency and the line weakness, use regression analysis or logistic regression analysis to determine the contribution of each feature, and finally normalize the data to convert the contribution into preset influence weights with a sum of 1.

[0061] It's important to add that the current over-limit rate reflects the degree and frequency of the line current exceeding the safety threshold. A higher over-limit rate indicates that the line is in a prolonged or frequent state of overload or other abnormal conditions, posing a significant safety hazard. Current fluctuation amplitude reflects the severity of current fluctuations. Large fluctuation amplitudes may indicate sudden load changes, poor contact, or other problems in the line, adversely affecting the insulation and equipment lifespan. The fluctuation frequency coefficient measures the frequency of current fluctuations. Frequent current fluctuations subject line components to repeated electrical and thermal stresses, accelerating component aging and increasing the probability of failure. By comprehensively considering these three parameters, we can more accurately identify lines with genuine weak points, while avoiding the limitations of single-parameter assessments and improving the accuracy of subsequent anomaly warnings.

[0062] This invention analyzes the current over-limit rate, fluctuation amplitude, and fluctuation frequency coefficient to obtain the current fluctuation rate. Then, it combines the current fluctuation rate with a preset threshold to screen weak lines, thus transforming the early warning mechanism from a post-event response to a pre-event prediction. This effectively shortens the time difference between the development of hidden dangers and the early warning, and realizes real-time dynamic monitoring of power safety.

[0063] S5. By analyzing temperature changes and discharge current changes, the influence of temperature and discharge current on each weak circuit is obtained.

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

[0065] Furthermore, the analysis of the temperature change rate of each weak line includes: Y1-1, using the time difference within the monitoring time group as the interval duration.

[0066] Y1-2. The temperature difference between the next monitoring time point and the previous monitoring time point within each monitoring time group is calculated to obtain the temperature difference of each monitoring time group, and the ratio of the difference to the interval length is taken as the temperature change rate of each monitoring time group.

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

[0068] Y2. Select the maximum value from the temperatures as the maximum temperature of each weak circuit.

[0069] Y3. Normalize the temperature change rate and maximum temperature of each weak line, and then perform a weighted fusion calculation on the normalized temperature change rate and maximum temperature to obtain the temperature influence degree of each weak line.

[0070] It should be added that the analysis method for the influence of discharge current is the same as that for the influence of temperature, and will not be repeated here.

[0071] It should be added that the normalization of the temperature change rate is as follows: the maximum and minimum values ​​of the temperature change rate of each weak line are obtained respectively, and the difference between the two is used to obtain the reference temperature difference. Then, the temperature change rate of each weak line is subtracted from the minimum temperature change rate, and the ratio of the difference to the reference temperature difference is used as the normalized temperature change rate of each weak line.

[0072] The normalization of the maximum temperature is as follows: According to the technical specifications of the line, determine its maximum allowable temperature, divide the maximum temperature of each weak line by the maximum allowable temperature, and select the minimum value between the ratio and 1 as the normalized maximum temperature of each weak line.

[0073] It should be added that the temperature influence is calculated as follows: the normalized temperature change rate and the maximum temperature are multiplied by their preset influence weights, and the two products are added together to obtain the temperature influence. The preset influence weights are the influence weights of the normalized temperature change rate and the maximum temperature, respectively, used to quantify the contribution of different features to the temperature influence.

[0074] The quantitative calculation of temperature impact is performed by using a weighted summation method. On the one hand, the weights reflect the actual difference between the rate of temperature change and the maximum temperature on the impact of temperature. On the other hand, the multi-dimensional features are integrated to avoid the one-sidedness of a single indicator assessment and to more accurately reflect the degree of temperature impact.

[0075] The preset influence weights can be set based on industry experience or obtained through a limited number of test data. For example, historical temperature change data can be collected first, and the correlation data between temperature change rate, maximum temperature and insulation aging degree in historical data can be statistically analyzed. Regression analysis or logistic regression analysis can be used to determine the contribution of each feature. Finally, after normalization, the contribution is converted into preset influence weights and their sum is 1.

[0076] It should be added that temperature is a direct physical manifestation of electrical faults. Circuit abnormalities lead to increased power loss, which in turn is converted into heat energy, causing the temperature to rise. The comprehensive assessment of temperature change rate and maximum temperature, through both trend and state analysis, enables early identification of electrical faults.

[0077] S6. Monitor the ambient temperature of each weak line, match the temperature range corresponding to the ambient temperature influence degree, and obtain the ambient temperature influence degree of each weak line.

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

[0079] The preset temperature ranges corresponding to each environmental temperature influence level are obtained based on historical operation data analysis. The specific process is as follows: collect historical environmental temperature data of the area where the line is located from historical operation data, and combine the overall operation status of the line to conduct correlation analysis between environmental temperature and line risk. Use statistical methods to locate the temperature threshold of risk mutation, thereby dividing different influence level ranges, and then deriving the temperature ranges corresponding to each environmental temperature influence level.

[0080] S7. Based on the aforementioned temperature influence, discharge current influence, and ambient temperature influence, assess the fire hazard warning level of each weak circuit and issue corresponding warnings.

[0081] For example, the assessment of the fire hazard warning level of each weak line includes comparing the temperature influence, discharge current influence, and ambient temperature influence of each weak line with their preset thresholds.

[0082] Condition 1 is that the influence of temperature is greater than its preset temperature influence threshold; condition 2 is that the influence of discharge current is greater than its preset discharge current influence threshold; and condition 3 is that the influence of ambient temperature is greater than its preset ambient temperature influence threshold.

[0083] When none of the conditions are met, the fire hazard warning level is determined to be no warning required.

[0084] When any one of the conditions is met, the fire hazard warning level is determined to be Level III.

[0085] When both conditions are met, the fire hazard warning level is determined to be Level II.

[0086] When all conditions are met, the fire hazard warning level is determined to be Level 1, and then the fire hazard warning level of each vulnerable line is obtained.

[0087] This invention improves the comprehensiveness of electricity safety monitoring by screening weak lines based on current fluctuation analysis results, and by correlating the influence of temperature, discharge current, and ambient temperature, and classifying early warning levels through multi-level threshold comparison.

[0088] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

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

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

[0091] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0092] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0093] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart power utilization safety monitoring method based on the Internet of Things, characterized in that: The method comprises: S1, monitoring the current, voltage and power of the electrical equipment and the main line, and marking the electrical equipment and the main line with parameters exceeding the preset threshold as abnormal; S2, traversing the upstream line of the abnormal equipment by breadth-first search, and combining the downstream line of the abnormal line by depth-first search to generate a candidate weak line set; S3, monitoring the electrical 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; S4, constructing a change curve based on the electrical 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 exceeding the preset value as a weak line; S5, obtaining the temperature influence degree and discharge current influence degree of each weak line through temperature change analysis and discharge current change analysis; 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; Collecting historical ambient temperature data of the line area from historical operation data, and combining the overall operation state of the line to correlate the ambient temperature and the line risk, positioning the temperature threshold of risk mutation by statistical method, thereby dividing different influence degree intervals, and deducing the temperature interval corresponding to each ambient temperature influence degree; matching the ambient temperature of each weak line with the preset temperature interval corresponding to each ambient temperature influence degree, and obtaining the ambient temperature influence degree of each weak line; S7, based on the temperature influence degree, discharge current influence degree and ambient temperature influence degree, evaluating the fire hazard early warning level of each weak line, and performing corresponding early warning. 2.The smart power utilization safety monitoring method based on the Internet of Things according to claim 1, characterized in that: The method comprises: Comparing the current, voltage and power of each electrical equipment with the preset threshold value thereof respectively; If any parameter of the current, voltage and power of the electrical equipment or the main line exceeds the preset threshold value, it is marked as abnormal, and then each abnormal device and each abnormal line is obtained. 3.The smart power utilization safety monitoring method based on the Internet of Things according to claim 1, characterized in that: The method comprises: G1, determining the total upstream associated line set of the abnormal device by using the breadth-first search algorithm; G2, using the depth-first search algorithm, taking the abnormal line as the root node, and positioning the branch line directly connected thereto by the preset electrical topology relationship diagram; G3, traversing each branch line directly connected to the abnormal line; G4, taking each branch line as the starting point to access the lower layer line layer by layer until there is no new line to access, generating the downstream associated line set containing all levels, merging the downstream associated line set of each abnormal line and removing the duplicate, and obtaining the total downstream associated line set of the abnormal line; G5, merging and removing the duplicate of the total upstream associated line set and the total downstream associated line set to obtain the candidate weak line set.

4. The smart power utilization safety monitoring method based on the Internet of Things according to claim 3, characterized in that: The method comprises: Taking the abnormal device as the root node, positioning the branch line directly connected thereto by the preset electrical topology relationship diagram; Accessing each level line in turn and recording as the corresponding upstream line until there is no new upstream line to access, generating the upstream associated line set containing all levels of upstream lines; Merge the upstream associated line set of each abnormal device, and remove the duplicates to obtain the total upstream associated line set of the abnormal device. 5.The smart power utilization safety monitoring method based on the Internet of Things according to claim 1, characterized in that: The analysis of the current fluctuation rate comprises: W1, taking each branch line in the candidate weak line set as a candidate branch line; W2, constructing a power consumption current change curve of each candidate branch line with the monitoring time point as the horizontal coordinate and the power consumption current as the vertical coordinate; W3, constructing a reference line in the power consumption current change curve based on a preset power consumption current threshold, and determining a current out-of-limit rate of each candidate branch line; W4, performing deviation analysis on the power consumption current of each peak point and the next valley point adjacent thereto extracted from the power consumption current change curve, to obtain a current fluctuation amplitude of each candidate branch line; W5, counting the number of peaks of each candidate branch line, calculating the ratio of the number of peaks to a preset peak number, and taking the minimum value of the ratio and 1 to obtain a fluctuation frequency coefficient of each candidate branch line; W6, performing weighted fusion calculation on the current out-of-limit rate, the current fluctuation amplitude, and the fluctuation frequency coefficient to obtain a current fluctuation rate of each candidate branch line. 6.The smart power utilization safety monitoring method based on the Internet of Things according to claim 5, characterized in that: The determination of the current out-of-limit rate of each candidate branch line comprises: 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; taking the ratio of the length of the curve segment above the reference line to the length of the power consumption current change curve as the current out-of-limit rate of each candidate branch line. 7.The smart power utilization safety monitoring method 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 comprises: performing subtraction on 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; performing normalization processing on the current fluctuation amplitude of each peak point to obtain the normalized current fluctuation amplitude of each peak point; performing mean value calculation on the normalized current fluctuation amplitude of each peak point to obtain the current fluctuation amplitude of each candidate branch line. 8.The smart power utilization safety monitoring method based on the Internet of Things according to claim 1, characterized in that: The analysis of the temperature influence degree of each weak line comprises: Y1, combining each monitoring time point with the next monitoring time point adjacent thereto to obtain each monitoring time group, and performing deviation analysis on the temperature in each monitoring time group to obtain a temperature change rate of each weak line; Y2, screening the maximum value from the temperature as the maximum temperature of each weak line; 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 maximum temperature to obtain the temperature influence degree of each weak line. 9.The smart power utilization safety monitoring method based on the Internet of Things according to claim 8, characterized in that: The analysis of the temperature change rate of each weak line comprises: taking the time difference in the monitoring time group as the interval length; performing subtraction on the temperature of the next monitoring time point and the previous 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; screening the maximum value from the temperature change rate of each monitoring time group as the temperature change rate of each weak line. 10.The smart power utilization safety monitoring method based on the Internet of Things according to claim 1, characterized in that: The assessment of the fire hazard early warning level of each weak line comprises: comparing the temperature influence degree, the discharge current influence degree, and the environmental temperature influence degree of each weak line with the preset threshold values thereof respectively; 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; When none of the conditions is established, it is determined that the fire hazard early warning level is not required; When one of the conditions is established, it is determined that the fire hazard early warning level is a third-level early warning; When two of the conditions are established, it is determined that the fire hazard early warning level is a second-level early warning; 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.

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