Hot-line work scene thermal shooting risk early warning method, equipment and medium

Abstract

The invention relates to the technical field of intelligent sensor temperature measurement, in particular to a thermal shooting risk early warning method and device in a hot-line work scene and a medium, and the method comprises the steps: obtaining a thermal shooting risk early warning result based on the average distribution condition and dispersion degree of all temperature data of each intelligent sensor in each time period and the distribution condition of deviation data; determining the temperature deviation of each intelligent sensor; determining the temperature change similarity based on the correlation of all temperature correction data between any two intelligent sensors; determining a temperature deviation value based on the average distribution condition of all the temperature change similarities in each detection area; and based on the abnormal score condition of the electromagnetic interference vector, determining an abnormal total score of each intelligent sensor so as to determine a risk assessment coefficient, and carrying out early warning on the thermal emission risk in the live working scene. The invention aims to eliminate the influence of electromagnetic interference on the accuracy of thermal emission risk early warning and improve the accuracy of thermal emission risk early warning in a live working scene.

Description

Technical Field

[0001] The present application relates to the field of intelligent sensor temperature measurement technology, and specifically to a method, device and medium for early warning of heat radiation risk in live working scenarios. Background Art

[0002] Live working refers to a method of maintenance and testing on high-voltage electrical equipment in operation, which can avoid maintenance power outages and ensure normal power supply. High-voltage electrical equipment and transmission lines are installed at high locations, and the live working shielding suits worn by power workers have poor air permeability, which increases the risk of heat stroke for power workers who are working on live lines due to high temperatures. Due to the special live working scenes of power workers, it is impossible to take cooling measures as soon as mild symptoms appear, and the evacuation of power workers is difficult and takes a long time, further increasing the risk of heat stroke. Therefore, it is an important measure to protect the life safety of power workers to issue heat stroke risk warnings to ensure that power workers evacuate in time in live working scenes.

[0003] At present, the power industry uses smart sensors to detect the temperature of various places inside the live-line shielding suits of power workers in real time to warn of the risk of thermal radiation in live-line working scenarios. In high-voltage live-line working scenarios, the strong electromagnetic environment generated by high-voltage electrical equipment and transmission lines around power workers causes significant electromagnetic interference to smart sensors, affecting the internal working circuits of smart sensors, causing the error of the temperature data measured by them to increase, which in turn leads to a decrease in the accuracy of the thermal radiation risk warning for power workers in live-line working scenarios. Summary of the invention

[0004] In a first aspect, an embodiment of the present application provides a method for early warning of thermal radiation risk in a live working scenario, the method comprising the following steps:

[0005] In the live working shielding suit of the power workers, the temperature data of each smart sensor in different detection areas at the current moment and all the collection moments before the preset time are obtained, and the preset time is divided into multiple time periods;

[0006] Based on the average distribution and discreteness of all temperature data of each smart sensor in each time period, all deviation data of each smart sensor in each time period are determined; based on the difference between each deviation data of each smart sensor in each time period and the average distribution of all temperature data, as well as the distribution of all deviation data of each smart sensor within a preset time period, the temperature deviation of each smart sensor in any detection area in each time period is determined;

[0007] For each smart sensor, all temperature data except deviation data within a preset time period are recorded as temperature correction data. Based on the correlation of all temperature correction data between any two smart sensors, the temperature change similarity between any two smart sensors in the same period is determined;

[0008] Determine the temperature offset value of each smart sensor in any detection area within the same time period based on the average distribution of all the temperature change similarities in each detection area;

[0009] The temperature deviation and temperature offset value of each smart sensor in each time period are combined into an electromagnetic interference vector, and based on the abnormal score of the electromagnetic interference vector, the total abnormal score of each smart sensor is determined;

[0010] In each detection area, based on the difference between the total abnormal score of each smart sensor and the extreme distribution of the total abnormal scores of all smart sensors, as well as the average distribution of temperature data of all smart sensors at the current moment and the change trend of all temperature correction data of each smart sensor in each time period, the risk assessment coefficient of each detection area at the current moment is determined, and an early warning of the thermal radiation risk in the live working scenario at the current moment is issued.

[0011] Preferably, the method for determining all deviation data of each intelligent sensor in each time period is:

[0012] The mean and standard deviation of all temperature data of each smart sensor in each time period are calculated respectively, and the temperature data that differs from the mean by more than three times the standard deviation is used as the deviation data of each smart sensor in each time period.

[0013] Preferably, the method for determining the temperature deviation of each intelligent sensor in any detection area within each time period is:

[0014] In any detection area, the sum of all deviation data of all smart sensors within a preset time period before the current moment is calculated, and recorded as the first deviation sum value of any detection area;

[0015] Calculate the cumulative sum of the differences between all deviation data of each smart sensor in each time period and the mean value of all temperature data, and record it as the second deviation sum value of each smart sensor in each time period;

[0016] The temperature deviation of each intelligent sensor in any detection area in each time period is the result of the fusion of the first deviation sum value and the second deviation sum value.

[0017] Preferably, the method for determining the temperature change similarity between any two smart sensors in the same period is:

[0018] In each detection area, a cross-correlation sequence of all temperature correction data between any two intelligent sensors in the same period is obtained, and all elements in the cross-correlation sequence are numbered;

[0019] Calculate the difference between the maximum value in the cross-correlation sequence between any two smart sensors in the same period and the corresponding sequence number of the element value located in the middle of the cross-correlation sequence, and record it as the correlation difference between any two smart sensors in the same period;

[0020] Calculate the cumulative sum of the ratios of all element values ​​in the cross-correlation sequence between any two smart sensors in the same period to the element value located in the middle of the cross-correlation sequence, and record it as the correlation sum value between any two smart sensors in the same period;

[0021] The temperature change similarity between any two smart sensors in the same period is the ratio of the correlation sum value to the correlation difference between any two smart sensors in the same period.

[0022] Preferably, the expression of the temperature offset value of each smart sensor in any detection area within the same period is: In the formula, represents the temperature offset value of smart sensor m in the rth detection area within the time period s; represents the mean value of the temperature variation similarity of all smart sensors in the rth detection area within the time period s; represents the mean value of the temperature change similarity of all smart sensors in the rth detection area and the ith detection area within the time period s; represents the mean value of the temperature variation similarity between the smart sensor m in the rth detection area and the smart sensor j in the ith detection area within the time period s; R represents the number of all detection areas; M i Represents the number of all smart sensors in the detection area i; ε represents a preset constant greater than 0.

[0023] Preferably, the method for determining the total abnormal score of each smart sensor is:

[0024] The electromagnetic interference vectors of all intelligent sensors in all detection areas at all time periods are used as inputs of the isolation forest algorithm, wherein the number of isolated trees is set to a preset first value, the number of randomly selected samples is set to a preset second value, and the abnormality score of each electromagnetic interference vector under each isolated tree is output;

[0025] The total abnormal score F of the smart sensor m in the rth detection area r,m The expression is: In the formula, It represents the time interval between the end time of the period of the wth electromagnetic interference vector belonging to the smart sensor m in the detection area r in the randomly selected sample and the current time; W represents the mean of the abnormal scores of the w-th electromagnetic interference vector of the smart sensor m in the detection area r in the randomly selected sample under all isolated trees; r,m It represents the number of all electromagnetic interference vectors selected as random samples of the smart sensor m in the detection area r in all time periods; ln() represents a logarithmic function with a natural constant as the base; δ represents a constant preset to be greater than 0.

[0026] Preferably, the method for determining the risk assessment coefficient of each detection area at the current moment is:

[0027] Obtain the backward difference sequence of all temperature correction data of each smart sensor in each time period, and calculate the cumulative sum of all elements in the backward difference sequence, which is recorded as the temperature difference sum value of each smart sensor in each time period;

[0028] Calculate the average of all temperature differences and values ​​of all smart sensors in any detection area within a preset time period, and record it as the average temperature difference of any detection area at the current moment;

[0029] The risk assessment coefficient E of the detection area r at the current moment r The expression is: In the formula, represents the maximum value of the total abnormal scores of all smart sensors in the detection area r at the current moment; F r,m It represents the total abnormal score of the mth smart sensor in the detection area r at the current moment; Represents the average value of the temperature data of all smart sensors in the detection area r at the current moment; It represents the mean temperature difference of the detection area r at the current moment; exp() represents an exponential function with a natural constant as the base.

[0030] Preferably, the process of early warning of the risk of thermal emission in the live working scenario at the current moment is:

[0031] According to the method for obtaining the risk assessment coefficient of each detection area at the current moment, the risk assessment coefficients of all detection areas at multiple moments during the normal working state of the power staff are calculated before the current moment, and the maximum value of the risk assessment coefficients of all detection areas at multiple moments is used as the risk assessment threshold;

[0032] If the risk assessment coefficient of any detection area at the current moment is greater than the risk assessment threshold, there is a thermal radiation risk in the live working scenario at the current moment; otherwise, there is no thermal radiation risk in the live working scenario at the current moment.

[0033] In a second aspect, an embodiment of the present application provides a thermal radiation risk warning device in a live working scenario, the device comprising:

[0034] The intelligent sensor data acquisition module is used to obtain the temperature data of each intelligent sensor in different detection areas at the current time and all acquisition times within the preset time before it in the live working shielding suit of the power workers, and divide the preset time into multiple time periods;

[0035] The smart sensor data analysis module is used to determine all deviation data of each smart sensor in each time period based on the average distribution and discreteness of all temperature data of each smart sensor in each time period; based on the difference between each deviation data of each smart sensor in each time period and the average distribution of all temperature data, and the distribution of all deviation data of each smart sensor within a preset time period, determine the temperature deviation of each smart sensor in any detection area in each time period;

[0036] For each smart sensor, all temperature data except deviation data within a preset time period are recorded as temperature correction data. Based on the correlation of all temperature correction data between any two smart sensors, the temperature change similarity between any two smart sensors in the same period is determined;

[0037] Determine the temperature offset value of each smart sensor in any detection area within the same time period based on the average distribution of all the temperature change similarities in each detection area;

[0038] The temperature deviation and temperature offset value of each smart sensor in each time period are combined into an electromagnetic interference vector, and based on the abnormal score of the electromagnetic interference vector, the total abnormal score of each smart sensor is determined;

[0039] The intelligent sensor early warning module is used to determine the risk assessment coefficient of each detection area at the current moment in each detection area based on the difference between the total abnormal score of each intelligent sensor and the extreme distribution of the total abnormal scores of all intelligent sensors, the average distribution of temperature data of all intelligent sensors at the current moment, and the change trend of all temperature correction data of each intelligent sensor in each time period, and to issue an early warning for the risk of thermal radiation in the live working scenario at the current moment.

[0040] In the third aspect, an embodiment of the present application also provides a thermal radiation risk warning medium in a live working scenario, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the steps of a thermal radiation risk warning method in a live working scenario as described in any one of the above items are implemented.

[0041] It can be seen from the above embodiments that the thermal emission risk warning method in a live working scenario provided by the embodiments of the present application has at least the following beneficial effects:

[0042] The present application constructs a temperature deviation by analyzing the distribution and discreteness of temperature data in each time period. By calculating the temperature deviation, the deviation data caused by electromagnetic interference can be identified and eliminated, thereby improving the data quality. The deviation data in the temperature data is further eliminated to obtain the temperature correction data. By analyzing the correlation between the temperature correction data of different sensors, the temperature change similarity is constructed, which reflects the similarity of the temperature data change trends between the smart sensors, helps to screen out more accurate temperature data, and improves the accuracy of the heat radiation risk warning. Furthermore, by analyzing the average distribution of the temperature change similarity in each detection area, a temperature offset value is constructed, which helps to reduce the impact of electromagnetic interference on the temperature data. The influence of the temperature data can be more accurately evaluated, thereby improving the accuracy of the thermal radiation risk warning; further, by forming an electromagnetic interference vector based on the temperature deviation and temperature offset value, and based on the abnormal score of the electromagnetic interference vector, an abnormal total score is constructed, which reflects the degree of electromagnetic interference of the intelligent sensor, helps to identify abnormal data points, improve data quality, more accurately evaluate the thermal radiation risk, and improve the accuracy of the warning. Finally, the total abnormal score, the distribution of temperature data at the current moment, and the changing trend of temperature data at all collection moments before the current moment are combined to construct a risk assessment coefficient, thereby improving the accuracy of the thermal radiation risk warning in the live working scenario. This application analyzes the degree of influence of electromagnetic interference on the accuracy of the thermal radiation risk warning, eliminates the problem of inaccurate thermal radiation risk warning caused by electromagnetic interference, and improves the accuracy of the thermal radiation risk warning in the live working scenario. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 A flowchart of a method for early warning of thermal radiation risk in a live working scenario provided by an embodiment of the present application;

[0045] Figure 2 A schematic diagram of an intelligent sensor installation area provided in one embodiment of the present application;

[0046] Figure 3 A schematic diagram of a process for acquiring a mutual correlation sequence provided by an embodiment of the present application;

[0047] Figure 4 A block diagram of a thermal radiation risk warning device in a live working scenario provided in one embodiment of the present application. DETAILED DESCRIPTION

[0048] In order to further explain the technical means and effects adopted by this application to achieve the predetermined invention purpose, the following is a detailed description of the method, device and medium for early warning of thermal radiation risk in live working scenarios proposed in this application, in combination with the accompanying drawings and preferred embodiments, as well as its specific implementation method, structure, features and effects. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0049] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0050] The following is a detailed description of a specific scheme of a thermal radiation risk warning method, equipment and medium in a live working scenario provided by the present application in conjunction with the accompanying drawings.

[0051] See also Figure 1 , which shows a flowchart of a method for early warning of thermal emission risk in a live working scenario provided by an embodiment of the present application, the method comprising the following steps:

[0052] Step S1: In the live working shielding suit of the power worker, the temperature data of each smart sensor in different detection areas at the current moment and all collection moments before the preset time are obtained, and the preset time is divided into multiple time periods.

[0053] Taking into account the high temperature environment in live working scenarios, intelligent sensors are arranged on the live working shielding clothing to provide real-time warning of the heat radiation risk of live working workers to ensure their life safety.

[0054] In order to detect the temperature change at different positions on the live working shielding suit worn by the live working personnel, a total of R detection areas are divided on the live working shielding suit. In this embodiment, R is set to 7, and the smart sensor is DS18B20 smart sensor. Figure 2 As shown, Figure 1 The left image is the front view of the live working shielding suit, and the right image is the rear view of the live working shielding suit. The 7 shaded parts represent different detection areas. Figure 1Serial number 1 represents the right arm area, serial number 2 represents the left arm area, serial number 3 represents the abdomen area, serial number 4 represents the right leg area, serial number 5 represents the left leg area, serial number 6 represents the head area, and serial number 7 represents the back area. M smart sensors are evenly installed in each detection area of ​​the live working shielding suit. In this embodiment, the value of M is 8.

[0055] In the live working shielding suits of power workers, the temperature data of each smart sensor in different detection areas at all collection moments at the current moment and the preset time before it are obtained, and the preset time is evenly divided into K time periods, each of which contains temperature data at S collection moments, where the data sampling frequency is set to f.

[0056] It should be noted that the value of the preset time length, the value of the sampling frequency f, and the values ​​of K and S are all manually set. In this embodiment, the value of the preset time length is 600s, the value of the sampling frequency f is 1Hz, the value of K is 20, and the value of S is 30. The implementer can also set them according to the specific situation. This embodiment does not impose any special restrictions.

[0057] Step S2: Based on the average distribution and discreteness of all temperature data of each smart sensor in each time period, all deviation data of each smart sensor in each time period are determined; based on the difference between each deviation data of each smart sensor in each detection area in each time period and the average distribution of all temperature data, as well as the distribution of all deviation data of each smart sensor within a preset time period, the temperature deviation of each smart sensor in any detection area in each time period is determined.

[0058] When inspecting high-voltage electrical equipment and transmission lines, the distances between each smart sensor and the electrical equipment in different detection areas will vary due to the body rotation of the power workers. The closer the smart sensor is to the high-voltage electrical equipment, the greater the impact of electromagnetic interference and electrostatic discharge. The internal circuit of the smart sensor has a higher instantaneous electrical interference, which increases the deviation of the detected temperature data. The greater the electrical interference to the smart sensor, the more deviated temperature data it reads and the greater the degree of deviation.

[0059] Since the time interval for reading temperature data is short and the change of temperature data in the same detection area is continuous, the temperature measurement of different smart sensors in the same detection area in the same period can be approximately regarded as multiple continuous temperature data measurements of the same location by the same model of smart sensor. Therefore, all temperature data of different smart sensors in the same detection area in the same period approximately obey Gaussian distribution.

[0060] Therefore, according to the characteristic that the temperature data of different intelligent sensors in the same detection area in the same period are approximately Gaussian distribution under normal circumstances, the temperature data with large deviations are screened out to improve the accuracy of the thermal radiation risk warning, specifically:

[0061] The mean and standard deviation of all temperature data of each smart sensor in each time period are calculated respectively, and the temperature data that differs from the mean by more than three times the standard deviation is used as the deviation data of each smart sensor in each time period, indicating that the temperature data is seriously affected by electromagnetic interference and has a large deviation.

[0062] It should be noted that the reason why three times the standard deviation is chosen as the screening criterion is because the method used here is the Laida criterion, which complies with the 3σ principle. In this embodiment, when the temperature data differs from the calculated standard deviation by more than three times the standard deviation, the temperature data is regarded as deviation data. The Laida criterion is a well-known technique in statistical methods, and the specific principles and processes of the Laida criterion will not be elaborated on.

[0063] Furthermore, in order to analyze the interference of electromagnetic interference on temperature data, thereby reducing the accuracy and timeliness of electromagnetic interference on thermal radiation risk warning, the temperature deviation of each smart sensor in any detection area in each time period is determined by analyzing the difference between each deviation data of each smart sensor in each time period and the average distribution of all temperature data, as well as the distribution of all deviation data of each smart sensor within a preset time period. Specifically:

[0064] In any detection area, the sum of all deviation data of all smart sensors within a preset time period before the current moment is calculated, and recorded as the first deviation sum value of any detection area;

[0065] Further, the cumulative sum of the differences between all deviation data of each smart sensor in each time period and the mean value of all temperature data is calculated, and recorded as the second deviation sum value of each smart sensor in each time period;

[0066] It should be noted that there are many methods for measuring the difference between data. In this embodiment, the square of the difference between all deviation data of each intelligent sensor in each time period and the mean of all temperature data is calculated as a measure of the difference between the deviation data and the mean of temperature data. The implementer may also reasonably adopt other methods for measuring the difference between data, such as taking the absolute value or ratio of the difference, based on the specific circumstances. This embodiment does not impose any special restrictions on the selection of the method for measuring the difference between data.

[0067] Further, based on the first deviation sum value and the second deviation sum value, the temperature deviation of each smart sensor in any detection area in each time period is determined, and the temperature deviation of each smart sensor in any detection area in each time period is the result of the fusion of the first deviation sum value and the second deviation sum value.

[0068] It should be understood that fusion refers to combining two or more indicators by adding or multiplying them together in order to obtain a comprehensive indicator, thereby more comprehensively and accurately evaluating a phenomenon or problem. This fusion method is not limited to simple arithmetic operations, but can also include more complex statistical models and analysis methods. The implementer can choose according to the specific situation, and this embodiment does not impose any special restrictions.

[0069] Preferably, in this embodiment, the temperature deviation of each smart sensor in any detection area within each time period is the product of the first deviation sum value and the second deviation sum value; in actual application, as other implementation methods, the temperature deviation of each smart sensor in any detection area within each time period is an exponential function value with a natural constant as the base and the addition result of the first deviation sum value and the second deviation sum value as the independent variable.

[0070] Furthermore, according to the temperature deviation of each smart sensor in any detection area in each time period, it can be understood that, on the one hand, the more the number of deviation data of all the smart sensors in the detection area in each time period, it reflects that the smart sensors in the detection area are subjected to relatively serious electromagnetic interference as a whole for a long time, the greater the degree of temperature data deviation, and the greater the obtained temperature deviation; on the other hand, the greater the difference between the deviation data and the mean of the temperature data in each time period, the greater the degree of temperature data deviation caused by the electromagnetic interference to the smart sensor, and the greater the obtained temperature deviation; conversely, if the number of deviation data of all the smart sensors in the detection area in each time period is smaller, and the difference between the deviation data and the mean of the temperature value is smaller, then the obtained temperature deviation is smaller, indicating that the degree of temperature data deviation caused by the electromagnetic interference to the smart sensor is smaller.

[0071] Step S3: For each smart sensor, all temperature data except deviation data within a preset time period are recorded as temperature correction data, and based on the correlation of all temperature correction data between any two smart sensors, the temperature change similarity between any two smart sensors in the same time period is determined.

[0072] Due to the differences between different smart sensors and their different anti-electromagnetic interference capabilities, the temperature data drift degree caused by electromagnetic interference affecting the internal electronic components of different smart sensors varies. In addition, the rotation of power workers during operation causes different detection areas to be in the light area at different times, which in turn causes the temperature data of different smart sensors to lag.

[0073] Considering that most smart sensors maintain drift errors within the normal range, the more temperature data obtained, the higher the corresponding data redundancy. In order to expand the comparison range of temperature data and filter out more accurate temperature data based on the similarity of temperature data change trends, the correlation of all temperature correction data between any two smart sensors is analyzed to determine the temperature change similarity between any two smart sensors in the same period, specifically:

[0074] First, considering that the deviation data is quite different from the actual temperature data, in order to eliminate the interference of the deviation data, all deviation data within the preset time length are eliminated; further, the linear interpolation method is used to replace the removed deviation data, and all temperature data in each time period after processing are recorded as temperature correction data.

[0075] Among them, linear interpolation is a well-known technology in interpolation methods, and its specific principle process of estimating the value of unknown data points between known data points will not be repeated here.

[0076] Further, in each detection area, a cross-correlation sequence of all temperature correction data between any two smart sensors in the same period is obtained, and all elements in the cross-correlation sequence are numbered;

[0077] Calculate the difference between the maximum value in the cross-correlation sequence between any two smart sensors in the same period and the corresponding sequence number of the element value located in the middle of the cross-correlation sequence, and record it as the correlation difference between any two smart sensors in the same period;

[0078] Calculate the cumulative sum of the ratios of all element values ​​in the cross-correlation sequence between any two smart sensors in the same period to the element value located in the middle of the cross-correlation sequence, and record it as the correlation sum value between any two smart sensors in the same period;

[0079] Furthermore, the ratio of the correlation sum value to the correlation difference between any two smart sensors in the same period is taken as the temperature change similarity between any two smart sensors in the same period.

[0080] It should be noted that the process of obtaining the cross-correlation sequence of two known data sequences is a well-known technology. In order to facilitate understanding, a simple diagram process is provided in this embodiment. The schematic diagram of the cross-correlation sequence obtaining process is shown in FIG. Figure 3 As shown, Figure 3 In the equation, sequence A = {1, 2, 3}, sequence B = {4, 5, 6}, and the final cross-correlation sequence of sequence A and sequence B is {12, 23, 32, 17, 6}.

[0081] Furthermore, it should be understood that according to the process of solving the mutual correlation sequence, the number of all elements in the mutual correlation sequence is an odd number, and there is no even number. Therefore, there is only one element located in the middle of the mutual correlation sequence, and there is no such thing as no element.

[0082] Further, according to the temperature variation similarity between any two smart sensors in the same period, it can be understood that the mutual correlation sequence between any two smart sensors in the same period reflects the correlation degree of the temperature data variation between any two smart sensors. On the one hand, the smaller the difference between the corresponding serial number of the maximum value in the mutual correlation sequence and the element value located in the middle of the mutual correlation sequence, the smaller the time interval between the maximum value in the mutual correlation sequence and the element value located in the middle of the mutual correlation sequence, which indicates that the hysteresis of the temperature fluctuation data between the corresponding smart sensors is smaller and the temperature variation similarity is greater. On the other hand, the larger the ratio of each element in the mutual correlation sequence to the element located in the middle of the mutual correlation sequence, the more gentle the temperature data variation between the corresponding smart sensors is, the closer the variation trend of the temperature data of the corresponding two smart sensors is, and the greater the temperature variation similarity is; on the contrary, the smaller the ratio of each element in the mutual correlation sequence to the element located in the middle of the sequence, the more violent the temperature data fluctuation between the two smart sensors is, and the greater the difference in the variation trend of the temperature data is, the smaller the temperature variation similarity between the two smart sensors is.

[0083] Step S4: based on the average distribution of all the temperature change similarities in each detection area, determine the temperature offset value of each smart sensor in any detection area within the same time period.

[0084] In order to eliminate the monitoring and judgment of thermal radiation risk caused by electromagnetic interference, and thus protect the personal safety of power workers, the temperature offset value is determined by analyzing the average distribution of all the temperature change similarities in each detection area to improve the accuracy and reliability of temperature data, thereby improving the accuracy of thermal radiation risk warning, specifically:

[0085] Temperature offset value of smart sensor m in detection area r during time period s The expression is: In the formula, represents the mean value of the temperature variation similarity of all smart sensors in the rth detection area within the time period s; represents the mean value of the temperature change similarity of all smart sensors in the rth detection area and the ith detection area within the time period s; represents the mean value of the temperature variation similarity between the smart sensor m in the rth detection area and the smart sensor j in the ith detection area within the time period s; R represents the number of all detection areas; M iRepresents the number of all smart sensors in the detection area i; ε represents a preset constant greater than 0, which is used to prevent the denominator from being 0, wherein the value of ε is artificially set. In this embodiment, the value of ε is 0.01. Under the premise of ensuring that the denominator is not 0 and does not excessively affect the calculation result, the implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0086] According to the temperature offset value of each smart sensor in any detection area in the same period, it can be understood that the larger the mean value of the temperature change similarity of all smart sensors in the detection area in the same period, the greater the influence of the location of the power workers. The mean value of the temperature change similarity of all smart sensors in different detection areas in the same period will decrease. The greater the decrease, the greater the difference in the degree of temperature change in the detection area, which means that the effect of this area on screening more accurate temperature data is smaller. The larger the value is, the smaller the calculated temperature offset value is. In addition, the smaller the average value of the temperature change similarity between the smart sensor in the detection area and the smart sensor in another detection area during the same period is, the closer the change trend of the temperature data read by the two smart sensors is. When the change trend is closer to the change trend of the temperature data read by a larger number of smart sensors, it reflects that the average error caused by electromagnetic interference during the time period of temperature data reading of the smart sensor is smaller, and the calculated temperature offset value is smaller.

[0087] On the contrary, if The smaller it is, the smaller the difference between the temperature data of smart sensors in different detection areas is, and the larger the mean value of the temperature change similarity between the smart sensor in the detection area and the smart sensor in another detection area within the same period is, indicating that the difference in the change trend of the temperature data read by the two smart sensors is greater, reflecting that the average error of the smart sensor caused by electromagnetic interference during the time period of temperature data reading is greater, and the calculated temperature offset value is greater.

[0088] Step S5: The temperature deviation and temperature offset value of each smart sensor in each time period are combined into an electromagnetic interference vector, and based on the abnormal score of the electromagnetic interference vector, the total abnormal score of each smart sensor is determined.

[0089] In order to further identify abnormal data points in temperature data and improve data quality, thereby improving the accuracy of thermal radiation risk warning in live working scenarios, the electromagnetic interference vector is formed by analyzing the temperature deviation and temperature offset value of each smart sensor in each period, and the total abnormal score of each smart sensor is determined based on the abnormal score of the electromagnetic interference vector to filter out abnormal data points and improve the accuracy and reliability of early warning. Specifically:

[0090] The temperature deviation and temperature offset value of each smart sensor in each time period constitute the electromagnetic interference vector of each smart sensor in each time period, which represents the instantaneous error caused by strong electromagnetic interference of the smart sensor in a short period of time and the drift error that persists for a long time.

[0091] The electromagnetic interference vectors of all intelligent sensors in all detection areas at all time periods are used as inputs of the isolation forest algorithm, wherein the number of isolated trees is set to a preset first value, the number of randomly selected samples is set to a preset second value, and the abnormality score of each electromagnetic interference vector under each isolated tree is output;

[0092] It should be noted that the values ​​of the preset first numerical value and the preset second numerical value are both manually set. In this embodiment, the value of the preset first numerical value is 256, and the value of the preset second numerical value is 100. The implementer can also set them according to the specific situation. This embodiment does not impose any special restrictions.

[0093] Among them, the isolation forest algorithm is a well-known technology in the field of anomaly detection algorithms, and its specific principle process will not be repeated here.

[0094] Furthermore, based on the abnormal score of the electromagnetic interference vector, the total abnormal score of each smart sensor is determined, specifically:

[0095] The total abnormal score F of the smart sensor m in the rth detection area r,m The expression is: In the formula, It represents the time interval between the end time of the period of the wth electromagnetic interference vector belonging to the smart sensor m in the detection area r in the randomly selected sample and the current time; W represents the mean of the abnormal scores of the w-th electromagnetic interference vector of the smart sensor m in the detection area r in the randomly selected sample under all isolated trees; r,m It represents the number of all electromagnetic interference vectors selected as random samples of the smart sensor m in the detection area r in all time periods; ln() represents a logarithmic function with a natural constant as the base; δ represents a constant preset greater than 0, which is used to prevent the calculation result from being 0. In this embodiment, the value of δ is 2. Under the premise of ensuring that the calculation result is not 0 and not excessively affecting the calculation result, the implementer can also set it according to the specific situation. This embodiment does not impose any special restrictions.

[0096] According to the total abnormality score of each smart sensor, it can be understood that the abnormality score of the electromagnetic interference vector output by the isolation forest algorithm in each isolated tree reflects the abnormality of the corresponding smart sensor in the sub-sample. The higher the abnormality score, the greater the degree of influence of the electromagnetic interference of the high-voltage transmission line on the smart sensor in the corresponding period, and the greater the average of the abnormality scores of the electromagnetic interference vectors of the smart sensors in the detection area in the randomly selected samples under all isolated trees; at the same time, the shorter the time interval between the end time of the electromagnetic interference vector and the current time, the closer the electromagnetic interference vector is to the current time, and the greater the impact on the thermal radiation risk warning;

[0097] On the contrary, the lower the anomaly score, the less the smart sensor is affected by the electromagnetic interference of the high-voltage transmission line in the corresponding period, and the smaller the average anomaly score of the electromagnetic interference vector of the smart sensor in the detection area in the randomly selected sample under all isolated trees; at the same time, the longer the time interval between the end time of the period where the electromagnetic interference vector is located and the current time, the farther the period where the electromagnetic interference vector is located is from the current time, and the smaller the impact on the thermal radiation risk warning.

[0098] Step S6: In each detection area, based on the difference between the total abnormal score of each smart sensor and the extreme distribution of the total abnormal scores of all smart sensors, as well as the average distribution of temperature data of all smart sensors at the current moment and the difference in temperature data between different smart sensors, the risk assessment coefficient of each detection area at the current moment is determined, and an early warning is issued for the thermal radiation risk in the live working scenario at the current moment.

[0099] Considering the temperature differences in different parts of the human body, when conducting heat stroke risk warning, different detection areas correspond to different parts of the power workers, and the temperature thresholds when heat stroke risks occur are different. Therefore, the backward difference sequence of all temperature correction data of each smart sensor in each time period is obtained, and the cumulative sum of all elements in the backward difference sequence is calculated, which is recorded as the temperature difference sum value of each smart sensor in each time period;

[0100] Calculate the cumulative sum of the temperature difference values ​​of all time periods of each smart sensor in any detection area within the preset time length, and record it as the cumulative sum of the temperatures of each smart sensor in any detection area at the current moment, and record the average of the cumulative sum of the temperatures of all smart sensors in any detection area at the current moment as the average temperature difference of any detection area at the current moment.

[0101] The process of acquiring the backward difference sequence is a well-known technique, and the specific acquisition steps are not described in detail.

[0102] Furthermore, based on the difference between the total abnormal score of each smart sensor and the extreme distribution of the total abnormal score of all smart sensors, as well as the average distribution of the temperature data of all smart sensors at the current moment and the difference in temperature data between different smart sensors, the risk assessment coefficient of each detection area at the current moment is determined, specifically:

[0103] The risk assessment coefficient E of the detection area r at the current moment r The expression is: In the formula, represents the maximum value of the total abnormal scores of all smart sensors in the detection area r at the current moment; F r,m It represents the total abnormal score of the mth smart sensor in the detection area r at the current moment; Represents the average value of the temperature data of all smart sensors in the detection area r at the current moment; It represents the mean temperature difference of the detection area r at the current moment; exp() represents an exponential function with a natural constant as the base.

[0104] According to the risk assessment coefficient of each detection area at the current moment, it can be understood that the total abnormal score of the smart sensor reflects the degree of influence of the electromagnetic interference of the high-voltage power transmission equipment. The larger the ratio of the maximum value of the total abnormal score of all smart sensors to the abnormal score value of each smart sensor, the lower the accuracy of the temperature data, and the greater the weight when calculating the risk assessment coefficient; in addition, the larger the average value of the temperature data of all smart sensors in the detection area at the current moment, the larger the temperature difference mean of the detection area at the current moment, and the larger the calculated risk assessment coefficient;

[0105] On the contrary, the smaller the ratio of the maximum total anomaly score of all smart sensors to the anomaly score of each smart sensor, the higher the accuracy of the temperature data, and the smaller the weight when calculating the risk assessment coefficient; in addition, the smaller the average value of the temperature data of all smart sensors in the detection area at the current moment, the smaller the temperature difference mean of the detection area at the current moment, and the smaller the calculated risk assessment coefficient.

[0106] According to the method for obtaining the risk assessment coefficient of each detection area at the current moment, the risk assessment coefficients of all detection areas at multiple moments during the normal working state of the power staff are calculated before the current moment, and the maximum value of the risk assessment coefficients of all detection areas at multiple moments is used as the risk assessment threshold;

[0107] If the risk assessment coefficient of any detection area at the current moment is greater than the risk assessment threshold, there is a thermal radiation risk in the live working scenario at the current moment, and the digital signal processor sends a warning signal to the buzzer to remind the power workers to evacuate in time; conversely, if the risk assessment coefficient risk assessment threshold of all detection areas at the current moment is less than or equal to the risk assessment threshold, there is no thermal radiation risk in the live working scenario at the current moment.

[0108] Based on the same inventive concept as the above method, the embodiment of the present application also provides a thermal radiation risk warning device in a live working scenario, including:

[0109] The intelligent sensor data acquisition module is used to obtain the temperature data of each intelligent sensor in different detection areas at the current time and all acquisition times within the preset time before it in the live working shielding suit of the power workers, and divide the preset time into multiple time periods;

[0110] The smart sensor data analysis module is used to determine all deviation data of each smart sensor in each time period based on the average distribution and discreteness of all temperature data of each smart sensor in each time period; based on the difference between each deviation data of each smart sensor in each time period and the average distribution of all temperature data, and the distribution of all deviation data of each smart sensor within a preset time period, determine the temperature deviation of each smart sensor in any detection area in each time period;

[0111] For each smart sensor, all temperature data except deviation data within a preset time period are recorded as temperature correction data. Based on the correlation of all temperature correction data between any two smart sensors, the temperature change similarity between any two smart sensors in the same period is determined;

[0112] Determine the temperature offset value of each smart sensor in any detection area within the same time period based on the average distribution of all the temperature change similarities in each detection area;

[0113] The temperature deviation and temperature offset value of each smart sensor in each time period are combined into an electromagnetic interference vector, and based on the abnormal score of the electromagnetic interference vector, the total abnormal score of each smart sensor is determined;

[0114] The intelligent sensor early warning module is used to determine the risk assessment coefficient of each detection area at the current moment in each detection area based on the difference between the total abnormal score of each intelligent sensor and the extreme distribution of the total abnormal scores of all intelligent sensors, the average distribution of temperature data of all intelligent sensors at the current moment, and the change trend of all temperature correction data of each intelligent sensor in each time period, and to issue an early warning for the risk of thermal radiation in the live working scenario at the current moment.

[0115] The embodiment of the present application provides a block diagram of a thermal radiation risk warning device in a live working scenario, such as Figure 4 shown.

[0116] Based on the same inventive concept as the above method, an embodiment of the present application also provides a thermal radiation risk warning medium in a live working scenario, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-mentioned thermal radiation risk warning methods in a live working scenario.

[0117] It should be noted that the above sequence of the embodiments of the present application is for description only and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0118] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0119] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for early warning of thermal radiation risk in live working scenarios, characterized in that: The method comprises the following steps: S1. In the live working shielding suit of the power workers, the temperature data of each smart sensor in different detection areas at the current moment and all the collection moments before the preset time are obtained, and the preset time is divided into multiple time periods; S2. Based on the average distribution and discreteness of all temperature data of each smart sensor in each time period, all deviation data of each smart sensor in each time period are determined; based on the difference between each deviation data of each smart sensor in each detection area in each time period and the average distribution of all temperature data, as well as the distribution of all deviation data of each smart sensor within a preset time period, the temperature deviation of each smart sensor in any detection area in each time period is determined; S3. For each smart sensor, all temperature data except deviation data within a preset time period are recorded as temperature correction data. Based on the correlation of all temperature correction data between any two smart sensors, the temperature change similarity between any two smart sensors in the same period is determined; S4. Determine the temperature offset value of each smart sensor in any detection area within the same time period based on the average distribution of all the temperature change similarities in each detection area; S5. The temperature deviation and temperature offset value of each smart sensor in each time period are combined into an electromagnetic interference vector, and based on the abnormal score of the electromagnetic interference vector, the total abnormal score of each smart sensor is determined; S6. In each detection area, based on the difference between the total abnormal score of each smart sensor and the extreme distribution of the total abnormal scores of all smart sensors, as well as the average distribution of temperature data of all smart sensors at the current moment and the change trend of all temperature correction data of each smart sensor in each time period, the risk assessment coefficient of each detection area at the current moment is determined, and an early warning of the thermal radiation risk in the live working scenario at the current moment is issued.

2. A method for early warning of thermal radiation risk in live working scenarios according to claim 1, characterized in that: The method for determining all deviation data of each intelligent sensor in each time period is as follows: The mean and standard deviation of all temperature data of each smart sensor in each time period are calculated respectively, and the temperature data that differs from the mean by more than three times the standard deviation is used as the deviation data of each smart sensor in each time period.

3. A method for early warning of thermal radiation risk in live working scenarios according to claim 1, characterized in that: The method for determining the temperature deviation of each intelligent sensor in any detection area within each time period is as follows: In any detection area, the sum of all deviation data of all smart sensors within a preset time period before the current moment is calculated, and recorded as the first deviation sum value of any detection area; Calculate the cumulative sum of the differences between all deviation data of each smart sensor in each time period and the mean value of all temperature data, and record it as the second deviation sum value of each smart sensor in each time period; The temperature deviation of each intelligent sensor in any detection area in each time period is the result of the fusion of the first deviation sum value and the second deviation sum value.

4. A method for early warning of thermal radiation risk in live working scenarios according to claim 1, characterized in that: The method for determining the temperature change similarity between any two intelligent sensors in the same period is: In each detection area, a cross-correlation sequence of all temperature correction data between any two intelligent sensors in the same period is obtained, and all elements in the cross-correlation sequence are numbered; Calculate the difference between the maximum value in the cross-correlation sequence between any two smart sensors in the same period and the corresponding sequence number of the element value located in the middle of the cross-correlation sequence, and record it as the correlation difference between any two smart sensors in the same period; Calculate the cumulative sum of the ratios of all element values ​​in the cross-correlation sequence between any two smart sensors in the same period to the element value located in the middle of the cross-correlation sequence, and record it as the correlation sum value between any two smart sensors in the same period; The temperature change similarity between any two smart sensors in the same period is the ratio of the correlation sum value to the correlation difference between any two smart sensors in the same period.

5. The method for early warning of thermal radiation risk in live working scenarios according to claim 1, characterized in that: The expression of the temperature offset value of each smart sensor in any detection area within the same period is: In the formula, represents the temperature offset value of smart sensor m in the rth detection area within the time period s; represents the mean value of the temperature variation similarity of all smart sensors in the rth detection area within the time period s; represents the mean value of the temperature change similarity of all smart sensors in the rth detection area and the ith detection area within the time period s; represents the mean value of the temperature variation similarity between the smart sensor m in the rth detection area and the smart sensor j in the ith detection area within the time period s; R represents the number of all detection areas; M i represents the number of all smart sensors in detection area i; ε represents a constant that is preset to be greater than 0.

6. A method for early warning of thermal radiation risk in live working scenarios according to claim 1, characterized in that: The method for determining the total abnormal score of each intelligent sensor is as follows: The electromagnetic interference vectors of all intelligent sensors in all detection areas at all time periods are used as inputs of the isolation forest algorithm, wherein the number of isolated trees is set to a preset first value, the number of randomly selected samples is set to a preset second value, and the abnormality score of each electromagnetic interference vector under each isolated tree is output; The total abnormal score F of the smart sensor m in the rth detection area r,m The expression is: In the formula, It represents the time interval between the end time of the period of the wth electromagnetic interference vector belonging to the smart sensor m in the detection area r in the randomly selected sample and the current time; represents the mean of the abnormal scores of the w-th electromagnetic interference vector of the smart sensor m in the detection area r in the randomly selected sample under all isolated trees; W r,m It represents the number of all electromagnetic interference vectors selected as random samples of the smart sensor m in the detection area r in all time periods; ln() represents a logarithmic function with a natural constant as the base; δ represents a constant preset to be greater than 0.

7. The method for early warning of thermal radiation risk in live working scenarios according to claim 1, characterized in that: The method for determining the risk assessment coefficient of each detection area at the current moment is: Obtain the backward difference sequence of all temperature correction data of each smart sensor in each time period, and calculate the cumulative sum of all elements in the backward difference sequence, which is recorded as the temperature difference sum value of each smart sensor in each time period; Calculate the average of all temperature differences and values ​​of all smart sensors in any detection area within a preset time period, and record it as the average temperature difference of any detection area at the current moment; The risk assessment coefficient E of the detection area r at the current moment r The expression is: In the formula, represents the maximum value of the total abnormal scores of all smart sensors in the detection area r at the current moment; F r,m It represents the total abnormal score of the mth smart sensor in the detection area r at the current moment; Represents the average value of the temperature data of all smart sensors in the detection area r at the current moment; It represents the mean temperature difference of the detection area r at the current moment; exp() represents an exponential function with a natural constant as the base.

8. The method for early warning of thermal radiation risk in live working scenarios according to claim 1, characterized in that: The process of early warning of the risk of thermal emission in the live working scenario at the current moment is as follows: According to the method for obtaining the risk assessment coefficient of each detection area at the current moment, the risk assessment coefficients of all detection areas at multiple moments during the normal working state of the power staff are calculated before the current moment, and the maximum value of the risk assessment coefficients of all detection areas at multiple moments is used as the risk assessment threshold; If the risk assessment coefficient of any detection area at the current moment is greater than the risk assessment threshold, there is a risk of thermal radiation in the live working scenario at the current moment; On the contrary, there is no risk of thermal radiation in the live working scenario at the current moment.

9. A thermal radiation risk warning device in a live working scenario, implementing a thermal radiation risk warning method in a live working scenario as claimed in claim 1, characterized in that: The device comprises: The intelligent sensor data acquisition module is used to obtain the temperature data of each intelligent sensor in different detection areas at the current time and all acquisition times within the preset time before it in the live working shielding suit of the power workers, and divide the preset time into multiple time periods; The smart sensor data analysis module is used to determine all deviation data of each smart sensor in each time period based on the average distribution and discreteness of all temperature data of each smart sensor in each time period; based on the difference between each deviation data of each smart sensor in each time period and the average distribution of all temperature data, and the distribution of all deviation data of each smart sensor within a preset time period, determine the temperature deviation of each smart sensor in any detection area in each time period; For each smart sensor, all temperature data except deviation data within a preset time period are recorded as temperature correction data. Based on the correlation of all temperature correction data between any two smart sensors, the temperature change similarity between any two smart sensors in the same period is determined; Determine the temperature offset value of each smart sensor in any detection area within the same time period based on the average distribution of all the temperature change similarities in each detection area; The temperature deviation and temperature offset value of each smart sensor in each time period are combined into an electromagnetic interference vector, and based on the abnormal score of the electromagnetic interference vector, the total abnormal score of each smart sensor is determined; The intelligent sensor early warning module is used to determine the risk assessment coefficient of each detection area at the current moment in each detection area based on the difference between the total abnormal score of each intelligent sensor and the extreme distribution of the total abnormal scores of all intelligent sensors, the average distribution of temperature data of all intelligent sensors at the current moment, and the change trend of all temperature correction data of each intelligent sensor in each time period, and to issue an early warning for the risk of thermal radiation in the live working scenario at the current moment.

10. A thermal radiation risk warning medium in a live working scenario, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of a method for early warning of thermal radiation risk in a live working scenario as described in any one of claims 1 to 8 are implemented.

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