Icing monitoring system and method based on artificial intelligence

Abstract

The invention relates to the technical field of icing monitoring, in particular to an icing monitoring system and method based on artificial intelligence, and the system comprises a monitoring positioning module, an information acquisition module, a data collection module, a data analysis module, a fusion simulation unit, a monitoring terminal and a background management module. According to the method, related data of a region where the power transmission line is located is collected, line capacitance values under various conditions are collected, a data model is formed for analysis and simulation, and environment temperature and humidity, snowfall and final icing thickness which may occur under various conditions are simulated; accurate monitoring digital information of the ice thickness and the ice coating of the power transmission line is formed by acquiring the change of the ice coating thickness of the area where the power transmission line is located over the years and the rule of the environment temperature and humidity and the wind speed, the problems that in the prior art, on-line monitoring of the ice coating is low in accuracy and poor in reliability are effectively solved, and the digital intelligent monitoring level of power grid ice disaster defense is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of ice coverage monitoring, and in particular to an ice coverage monitoring system and method based on artificial intelligence. Background Art

[0002] As the construction of power grids has entered a new stage of development of power systems characterized by UHV and smart grids, the ever-expanding scale of power grids, the increasing capacity of power grids, the increasingly complex power grid structure, and the gradually increasing geographical span have increased the degree and intensity of the impact of disastrous meteorological conditions and weather phenomena on power grid production.

[0003] At present, the greatest impact on the safe and stable operation of the power grid is the ice and snow accumulation on the transmission lines and the ice shedding and jumping, which will cause various electrical and mechanical failures. In order to monitor the ice on the transmission lines in real time and know its thickness, most of them are carried out through online video and image monitoring or manual ice measurement.

[0004] For example, the existing public technical scheme, announcement number CN118224986A discloses an online monitoring system and method for equivalent ice thickness of transmission lines; it includes a three-dimensional solid model analysis module for the transmission line monitoring object, a transmission line ice thickness monitoring path planning module, and a transmission line ice thickness monitoring and analysis module; through the shooting lens to collect the ice thickness characteristic image parameters of different monitoring points of the efficient dynamic transmission line, combined with the intelligent recognition algorithm of artificial intelligence and the ice thickness numerical parameters corresponding to the characteristic image of the standard ice thickness of the transmission line, the image feature matching is carried out, and the ice thickness value of the surface of the transmission line at each measuring point is scientifically analyzed.

[0005] When the above technical solution is actually implemented, the online video image monitoring is affected by visibility and lens icing, resulting in its clarity failing to meet the requirements, making it difficult to achieve efficient, reliable and accurate monitoring of the ice thickness of the transmission lines. Manual ice measurement is difficult and intensive, and in rainy and snowy weather, it is difficult for personnel to reach the site for monitoring and measurement. Summary of the invention

[0006] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an ice monitoring system and method based on artificial intelligence.

[0007] In order to achieve the above-mentioned object, the present invention adopts the following technical scheme: an artificial intelligence-based ice monitoring system, comprising a monitoring and positioning module, wherein the output end of the monitoring and positioning module is connected to an information acquisition module via an API port, and the output end of the information acquisition module is connected to a data acquisition module via an API port;

[0008] The monitoring and positioning module includes a GPS satellite navigator;

[0009] The monitoring and positioning module is used to obtain the location, precise location and distribution area data of the power transmission line to be monitored through a GPS satellite navigator;

[0010] The information acquisition module is used to acquire transmission line monitoring data;

[0011] The data acquisition module is used to respond to the monitoring data of the information acquisition module;

[0012] The output end of the data acquisition module is connected to the data analysis module through the API port, and the output end of the data analysis module is connected to the fusion simulation unit through the API port;

[0013] Wherein, the data analysis module is used to analyze and predict the change trend of line capacitance value under different ice thickness;

[0014] The fusion simulation unit is used to perform fault judgment on the normal operation of the transmission line according to the capacitance value prediction result;

[0015] The output end of the monitoring and positioning module is also connected to a monitoring terminal through an API port, the output end of the monitoring terminal is connected to a field acquisition module through an API port, and the output end of the monitoring terminal is also connected to a background management module through an API port, and the output end of the monitoring terminal is connected to an alarm unit through an API port;

[0016] Wherein, the monitoring terminal is used to record the communication base stations matching the area where the power transmission line is located, and at the same time receive the data collected by the field collection module;

[0017] The field acquisition module is used for field data of the power transmission line to be monitored;

[0018] The alarm unit is used to initiate a power transmission line early warning status to the corresponding communication base station in the monitoring area;

[0019] The background management module is used to manage and update the background data.

[0020] Preferably, the background management module includes a background data update module, and the output end of the background data update module is connected to a relevant database through an API port;

[0021] Wherein, the background data updating module is used to update the data stored in the relevant database;

[0022] The related database is used to provide data support for data acquisition by the information acquisition module.

[0023] Preferably, the relevant database includes a static database and a dynamic database;

[0024] The static database is used to store altitude differences, topography, water distribution, number of passes, power transmission power, power consumption and regional wind speed data of different monitoring areas;

[0025] The dynamic database is used to store the ambient temperature and humidity of different monitoring areas in previous winters, and the ice thickness data of the transmission lines in previous years.

[0026] Preferably, the data analysis module includes a model building unit, and the output end of the model building unit is connected to a tolerance threshold preset unit via an API port;

[0027] Wherein, the model building unit is used to generate an ice thickness estimation data model for the area where the transmission line is located;

[0028] The tolerance threshold preset unit obtains the winter ambient temperature, humidity and wind speed change data under the ice thickness of previous years according to the monitored area through a dynamic database, selects the year with the same ambient temperature, humidity and wind speed, and presets the ice thickness tolerance threshold based on this.

[0029] Preferably, the output end of the tolerance threshold preset unit is connected to a data comparison unit via an API port, and the output end of the data comparison unit is connected to a result output unit via an API port;

[0030] Wherein, the data comparison unit is used to compare whether the ice thickness of the transmission line in the current year is within a preset ice thickness tolerance threshold;

[0031] The result output unit is used to upload the data to the model building unit according to the comparison result of the data comparison unit, and update the model building unit.

[0032] Preferably, the fusion simulation unit comprises a 3D fusion model unit, an output end of the 3D fusion model unit is connected to a variation range preset unit via an API port, and an output end of the variation range preset unit is connected to a simulation data storage unit via an API port;

[0033] The 3D fusion model unit is based on the preset ice thickness tolerance threshold, the preset capacitance risk threshold, and the regional altitude difference, topography, water distribution, number of passes, transmission power and power consumption in the transmission line static database as basic data to build a 3D fusion model of the digital twin of the transmission line risk.

[0034] The variation range preset unit presets a variation range of the basic data matched by the 3D fusion model obtained by the 3D fusion model unit;

[0035] The simulation data storage unit is used to store basic data in the 3D fusion model obtained by the 3D fusion model unit.

[0036] Preferably, the output end of the analog data storage unit is connected to the analog data calculation unit through an API port, and the output end of the analog data calculation unit is connected to the curve drawing unit through an API port;

[0037] Among them, the simulation data calculation unit simulates the environmental temperature, humidity and wind speed data of the area where the transmission line is located under different change values ​​by changing the basic data in the 3D fusion model, simulates and calculates the ice thickness of the transmission line under different environmental temperature, humidity and wind speeds and the line capacitance value matching the ice thickness, and determines it as the capacitance value prediction result, and performs fault judgment on the normal operation of the transmission line based on the capacitance value prediction result.

[0038] Preferably, the output end of the curve drawing unit is connected to a function model building unit via an API port;

[0039] The curve drawing unit calculates the corresponding differences in the changes of the ice thickness and the line capacitance in different years, the ambient temperature and humidity, and the wind speed in the disaster year through the simulation data calculation unit, divides the corresponding differences in the ice thickness and the line capacitance into at least three step intervals, and draws the ROC curve according to the corresponding changes in the intermediate values;

[0040] The function model building unit generates a Sigmoid function model according to the step-type ice thickness difference data obtained by the curve drawing unit.

[0041] Preferably, the Sigmoid function model obtained in the function model building unit is also used to preset a lower limit line capacitance value and an upper limit line capacitance value, and simulate the change of ice thickness from the lower limit line capacitance value to the upper limit line capacitance value.

[0042] The present invention also provides an artificial intelligence-based ice coverage monitoring method, comprising the following steps:

[0043] Obtaining location attributes and environmental attributes of the area where the power transmission line is located, as well as the distribution area and monthly power consumption of the power transmission line in the area;

[0044] During the icing season in the area, the line capacitance values ​​of the transmission line under different ice thicknesses when the transmission line is iced and the line capacitance values ​​when not iced are collected to determine the change trend of the line capacitance values ​​under different ice thicknesses;

[0045] Generate an ice thickness estimation data model for the area where the transmission line is located based on the line capacitance values ​​under different ice thicknesses;

[0046] Presetting a capacitance risk threshold based on a tolerance threshold, and generating a 3D fusion model based on the location attributes, transmission power, and power consumption;

[0047] Based on the preset variation range of the 3D fusion model, the ambient temperature, humidity and wind speed under different variation values ​​are simulated. When the capacitance value of the line does not exceed the capacitance risk threshold, the system determines that it will not cause failure to the normal operation of the transmission line. Otherwise, the system determines that it will cause failure to the normal operation of the transmission line.

[0048] The present invention has the following beneficial effects:

[0049] The artificial intelligence-based ice monitoring system and method designed by the present invention collects the snowfall, environmental temperature and humidity, wind speed and ice thickness in each year in the area where the transmission line is located, and collects the line capacitance value in each case to form a data model for analysis and simulation. The system simulates the possible environmental temperature and humidity and snowfall in each case, as well as the final ice thickness, by acquiring the altitude difference, topography, water distribution and number of passes in the area where the transmission line is located, as well as the local wind speed. At the same time, by acquiring the changes in ice thickness in the area where the transmission line is located over the years and the laws of environmental temperature and humidity and wind speed, digital information for accurate monitoring of ice thickness and ice coverage of the transmission line is formed, which effectively solves the problems of low accuracy and poor reliability of online ice monitoring under the existing technology, and improves the digital intelligent monitoring level of ice disaster defense for the power grid.

[0050] The artificial intelligence-based ice monitoring system and method designed by the present invention has carefully designed the data collection and analysis simulation process, and comprehensively considered the multi-dimensional environmental factors of the area where the power transmission line is located. By accurately collecting key data such as snowfall, ambient temperature and humidity, wind speed, and ice thickness in each year and combining the line capacitance value under different scenarios, a detailed data model is constructed. Combined with the complex geographical features such as altitude difference, topography, water distribution, and number of passes in the area where the power transmission line is located, as well as detailed monitoring of local wind speed, various possible ambient temperature and humidity and snowfall conditions are simulated, and the final ice thickness is predicted. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the overall modular structure of the present invention;

[0052] Figure 2 It is a schematic diagram of the modular structure of the monitoring terminal of the present invention;

[0053] Figure 3 It is a schematic diagram of the modular structure of the data analysis module of the present invention;

[0054] Figure 4 It is a schematic diagram of the modular structure of the fusion simulation unit of the present invention;

[0055] Figure 5 It is a schematic diagram of the modular structure of the relevant database of the present invention;

[0056] Figure 6 It is a schematic diagram of the process of the present invention;

[0057] Figure 7 It is a correlation change curve diagram of the intermediate values ​​of the lower ice coating thickness difference, the medium ice coating thickness difference and the higher ice coating thickness difference of the present invention.

[0058] In the figure: 1. Monitoring and positioning module; 11. GPS satellite navigator; 2. Information acquisition module; 3. Data acquisition module; 4. Data analysis module; 41. Model building unit; 42. Tolerance threshold preset unit; 43. Data comparison unit; 44. Result output unit; 5. Fusion simulation unit; 51. 3D fusion model unit; 52. Variation range preset unit; 53. Simulation data storage unit; 54. Simulation data calculation unit; 55. Curve drawing unit; 56. Function model building unit; 6. Monitoring terminal; 7. On-site acquisition module; 8. Alarm unit; 9. Background management module; 91. Background data update module; 92. Related database; 921. Static database; 922. Dynamic database. DETAILED DESCRIPTION

[0059] Reference Figure 1-7 , the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] The present invention provides a technical solution:

[0061] See also Figure 1 and Figure 2 , an ice coverage monitoring system based on artificial intelligence, comprising a monitoring and positioning module 1, an output end of the monitoring and positioning module 1 is connected to an information acquisition module 2 through an API port, and an output end of the information acquisition module 2 is connected to a data acquisition module 3 through an API port;

[0062] The monitoring and positioning module 1 includes a GPS satellite navigator 11;

[0063] The monitoring and positioning module 1 is used to obtain the location, precise location and distribution area data of the power transmission line to be monitored through the GPS satellite navigator 11;

[0064] The information acquisition module 2 is used to obtain the transmission line monitoring data;

[0065] Data acquisition module 3, used for responding to monitoring data of information acquisition module 2;

[0066] The output end of the data acquisition module 3 is connected to the data analysis module 4 through an API port, and the output end of the data analysis module 4 is connected to the fusion simulation unit 5 through an API port;

[0067] The data analysis module 4 is used to analyze and predict the change trend of the line capacitance value under different ice thicknesses;

[0068] The fusion simulation unit 5 is used to make a fault judgment on the normal operation of the transmission line according to the capacitance value prediction result;

[0069] The output end of the monitoring and positioning module 1 is also connected to the monitoring terminal 6 through the API port, the output end of the monitoring terminal 6 is connected to the field acquisition module 7 through the API port, and the output end of the monitoring terminal 6 is also connected to the background management module 9 through the API port, and the output end of the monitoring terminal 6 is connected to the alarm unit 8 through the API port;

[0070] Among them, the monitoring terminal 6 is used to record the communication base stations that match the area where the power transmission line is located, and at the same time receive the data collected by the field collection module 7;

[0071] The field acquisition module 7 is used for the field data of the power transmission line to be monitored;

[0072] The alarm unit 8 is used to initiate a power transmission line early warning status to the corresponding communication base station in the monitoring area;

[0073] The background management module 9 is used to manage and update the background data.

[0074] By adopting the above technical solution, the monitoring and positioning module 1 includes a GPS satellite navigator 11, which is used to obtain the area and precise location of the power transmission line to be monitored, and to obtain the distribution area of ​​the power transmission line in the area;

[0075] The information acquisition module 2 is connected to the monitoring and positioning module 1, and is used to obtain the altitude difference, topography, water distribution, number of passes, and regional wind speed data of the area where the power transmission line is located, and is also used to obtain the ambient temperature and humidity, snowfall times, and ice thickness data of the power transmission line in the area over the past winters;

[0076] The data acquisition module 3 is used to respond to the relevant data of the transmission line obtained by the information acquisition module 2, and at the same time collect the line capacitance values ​​of the transmission line when it is covered with ice and when it is not covered with ice in the winter in the area where the transmission line is located. At the same time, it is used to collect the line capacitance values ​​of the transmission line under different ice thicknesses, and then sort them according to the ice thickness when the line capacitance value increases when the transmission line is covered with ice.

[0077] Specifically, Figure 3As shown, the data analysis module 4 includes a model building unit 41, and the output end of the model building unit 41 is connected to a tolerance threshold preset unit 42 through an API port;

[0078] The model building unit 41 is used to generate an ice thickness estimation data model for the area where the transmission line is located;

[0079] The tolerance threshold preset unit 42 obtains the winter ambient temperature, humidity and wind speed change data under the ice thickness of previous years according to the monitored area through the dynamic database 922, selects the year with the same ambient temperature, humidity and wind speed, and presets the ice thickness tolerance threshold based on this.

[0080] The output end of the tolerance threshold preset unit 42 is connected to the data comparison unit 43 through the API port, and the output end of the data comparison unit 43 is connected to the result output unit 44 through the API port;

[0081] The data comparison unit 43 is used to compare whether the ice thickness of the transmission line in the current year is within a preset ice thickness tolerance threshold;

[0082] The result output unit 44 is used to upload the data to the model building unit 41 according to the comparison result of the data comparison unit 43 , and update the model building unit 41 .

[0083] By adopting the above technical solution, the data analysis module 4 is interconnected with the data acquisition module 3, and is used to analyze and predict the changing trend of the line capacitance value under different ice coating thicknesses, and generate an ice coating thickness estimation data model for the area where the transmission line is located according to the line capacitance value under different ice coating thicknesses; specifically, the change data of the ambient temperature, humidity and wind speed under the ice coating thickness in different years are obtained, and the years with the same ambient temperature, humidity and wind speed are selected, and based on this, the ice coating thickness tolerance threshold is preset to determine whether the ice coating thickness of the transmission line in the above years is within the ice coating thickness tolerance threshold. If the difference in ice coating thickness is within the preset tolerance threshold, the system determines that the obtained change data of ambient temperature, humidity and wind speed is non-changing data. On the contrary, if the difference in ice coating thickness is not within the preset tolerance threshold, the system determines it as changing data, and uploads the non-changing data and / or changing data to the data model respectively, and updates the data model at the same time.

[0084] Specifically, Figure 5 As shown, the background management module 9 includes a background data update module 91, and the output end of the background data update module 91 is connected to a related database 92 through an API port;

[0085] Among them, the background data updating module 91 is used to update the data stored in the relevant database 92;

[0086] The relevant database 92 is used to provide data support for the data acquisition of the information acquisition module 2 .

[0087] The relevant database 92 includes a static database 921 and a dynamic database 922;

[0088] The static database 921 is used to store altitude differences, topography, water distribution, number of passes, power transmission power, power consumption and regional wind speed data of different monitoring areas;

[0089] The dynamic database 922 is used to store the ambient temperature and humidity of different monitoring areas in previous winters, as well as the ice thickness data of the transmission lines over the years.

[0090] By adopting the above technical solution, the monitoring terminal 6 is used to record the communication base station and / or the communication base station operator that matches the area where the transmission line is located, and receive the ice thickness data of the transmission line collected on site, and update the background data at the same time. When the ice thickness and / or the line capacitance value of the transmission line in the monitoring area reaches the ice thickness and / or the line capacitance value in the marked year, an information acquisition request for the early warning status of the transmission line is initiated to the corresponding communication base station and / or the communication base station operator in the monitoring area.

[0091] Specifically, Figure 4 and Figure 7 As shown, the fusion simulation unit 5 includes a 3D fusion model unit 51, the output end of the 3D fusion model unit 51 is connected to a variation range preset unit 52 through an API port, and the output end of the variation range preset unit 52 is connected to a simulation data storage unit 53 through an API port;

[0092] The 3D fusion model unit 51 is based on the preset ice thickness tolerance threshold, the preset capacitance risk threshold, and combines the regional altitude difference, topography, water distribution, number of passes, transmission power and power consumption in the transmission line static database 921 as basic data to build a 3D fusion model of the digital twin of the transmission line risk;

[0093] The variation range preset unit 52 presets the variation range of the basic data matched by the 3D fusion model obtained by the 3D fusion model unit 51;

[0094] The simulation data storage unit 53 is used to store the basic data in the 3D fusion model obtained by the 3D fusion model unit 51 .

[0095] The output end of the analog data storage unit 53 is connected to the analog data calculation unit 54 through the API port, and the output end of the analog data calculation unit 54 is connected to the curve drawing unit 55 through the API port;

[0096] Among them, the simulation data calculation unit 54 simulates the environmental temperature, humidity and wind speed data of the area where the transmission line is located under different change values ​​by changing the basic data in the 3D fusion model, simulates and calculates the ice thickness of the transmission line under different environmental temperature, humidity and wind speeds and the line capacitance value matching the ice thickness, and determines it as the capacitance value prediction result, and performs fault judgment on the normal operation of the transmission line based on the capacitance value prediction result.

[0097] The output end of the curve drawing unit 55 is connected to the function model building unit 56 through the API port;

[0098] The curve drawing unit 55 calculates the corresponding differences in the changes of the ice thickness and the line capacitance in different years and the ambient temperature and humidity and the wind speed in the disaster year through the simulation data calculation unit 54, divides the corresponding differences in the ice thickness and the line capacitance into at least three step intervals, and draws the ROC curve according to the corresponding changes in the intermediate values;

[0099] The function model building unit 56 generates a Sigmoid function model according to the step-type ice thickness difference data obtained by the curve drawing unit 55 .

[0100] The Sigmoid function model obtained in the function model building unit 56 is also used to preset the lower limit line capacitance value and the upper limit line capacitance value, and simulate the change of the lower limit line capacitance value to the ice thickness of the upper limit line.

[0101] By adopting the above technical solution, the fusion simulation unit 5 is connected to the data analysis module 4, the capacitance risk threshold is preset based on the tolerance threshold, and the altitude difference, topography, water distribution, number of passes, transmission power and power consumption data of the area where the transmission line is located are used as the reference basis for capacitance risk to generate a 3D fusion model of the digital twin, wherein the 3D fusion model of the digital twin combines the 3D model with real-time data to create a virtual copy of the physical entity, and the 3D fusion model can be used for various purposes of the geographical environment of the area where the transmission line is located, including but not limited to simulating, analyzing and optimizing the area where the transmission line is located. The performance of physical objects; based on the preset range of changes in the 3D fusion model, the range of changes includes changes in the altitude difference, topography, water distribution, number of passes, transmission power and power consumption in the area. By changing the reference basis of the above-mentioned capacitance risk, the ambient temperature, humidity and wind speed under different change values ​​are simulated, and the ice thickness of the transmission line under different ambient temperature, humidity and wind speed and the line capacitance value under the ice thickness are simulated respectively. When the line capacitance value does not exceed the capacitance risk threshold, the system determines that it will not cause a fault to the normal operation of the transmission line. Otherwise, the system determines that it will cause a fault to the normal operation of the transmission line.

[0102] Further, the fusion simulation unit 5, based on the change data of the difference in ice thickness that is not within the preset tolerance threshold, collects years in which the ambient temperature, humidity and wind speed are different within a ten-year period, and obtains the ice thickness in each year, and at the same time calculates the change in the ice thickness and the change trend of the ambient temperature, humidity and wind speed, and selects the year with dangerous meteorological conditions of ambient temperature, humidity and wind speed according to the year and defines it as the disaster year, and at the same time obtains the ice thickness and line capacitance value of this year, and based on other years with different ambient temperature, humidity and wind speed in the year, respectively calculates the difference between the ambient temperature, humidity and wind speed in the year and the ambient temperature, humidity and wind speed in the disaster year, and at the same time obtains the difference in ice thickness in the year, and obtains the characteristic data therein, and when rain and snow weather occurs in the future period, calculates the probability of the ambient temperature, humidity and wind speed changing to the ambient temperature, humidity and wind speed in the disaster year under this weather condition, and calculates it by the following formula:

[0103]

[0104] in, is the predicted value, i.e. the probability that the ambient temperature, humidity and wind speed under the current weather conditions will change to the ambient temperature, humidity or wind speed in the disaster year;

[0105] In the formula, ε i represents the average ice thickness of the transmission line in years with different ambient temperature, humidity and wind speed, i = 1 represents a complete winter monitoring period, N represents the number of ice occurrences during the monitoring period, (f i ) represents an indicator function, which indicates the difference in ice thickness in years with different ambient temperature, humidity and wind speed;

[0106] At the same time, when calculating the ice thickness and line capacitance values ​​in different years, based on the difference in ice thickness, the corresponding difference in line capacitance in years with different ambient temperature, humidity and wind speed is preset, and the corresponding difference is divided into at least three step-type ice thickness differences, and the ROC curve is drawn according to the corresponding changes in the intermediate values; in addition, it should be noted that the three step-type ice thickness differences are a lower ice thickness difference, a medium ice thickness difference and a higher ice thickness difference, and the intermediate values ​​are selected from the lower ice thickness difference, the medium ice thickness difference and the higher ice thickness difference, respectively.

[0107] It should be emphasized that a Sigmoid function model is generated based on the step-type ice thickness difference, and the lower limit line capacitance value and the upper limit line capacitance value are preset in the Sigmoid function model. At the same time, the change of ice thickness is simulated in response to the lower limit line capacitance value and the upper limit line capacitance value, and the corresponding ambient temperature and humidity values ​​and wind speed values ​​are calculated, which are calculated according to the following formula:

[0108]

[0109] Where Z is the actual ice thickness simulated in a single time;

[0110] Where θ is the ice density assumed based on the wind speed value, represents the constant ambient temperature and humidity value in the selected year, θ i represents the ice density obtained for the i-th time, η represents the total number of ice density items obtained, and b represents the line capacitance value of the ice density obtained in the case of half thickness and full thickness selected from the actual ice thickness at a certain time;

[0111] Then, the 3D fusion model is used to analyze and predict the change trend of the line capacitance value under different ice thicknesses. According to the line capacitance values ​​under different ice thicknesses, a water thickness estimation data model for the area where the transmission line is located is generated, and the calculation response of the lower limit line capacitance value and the upper limit line capacitance value in the data analysis module 4 is completed to simulate the change of ice thickness respectively;

[0112] It can be seen that the 3D fusion model forms accurate digital information for monitoring ice thickness and ice cover on transmission lines by obtaining the changes in ice cover thickness over the years in the areas where transmission lines are located and the laws of ambient temperature, humidity and wind speed. This effectively solves the problems of low accuracy and poor reliability of online ice cover monitoring under existing technologies, and improves the level of digital intelligent monitoring for ice disaster prevention in power grids.

[0113] On the above basis, specifically based on the ambient temperature, humidity and wind speed in the disaster year, similar data of ambient temperature, humidity and wind speed in the area where the transmission line is located within a time unit are obtained, and similar data of ice thickness and line capacitance value within a time unit are obtained, and the year is associated and marked. At the same time, a safety threshold is preset based on the data of the marked year. When the ice thickness and / or line capacitance value in the future time period does not exceed the preset safety threshold, the system determines that the transmission line is in a safe state. Otherwise, the transmission line is determined to be in a risky state.

[0114] Among them, the 3D fusion model obtains the change of ice thickness of the transmission line in winter in each individual year within the time unit, and obtains the capacitance value of the transmission line under various changing conditions, analyzes the change law of the capacitance value of the transmission line, and collects the change law at every increase of 0.5 cm in thickness when the ice starts to cover. At the same time, the total thickness increase of the transmission line each time it is iced in each individual year is obtained. When the transmission line is iced in the future period, if the increase in ice thickness is lower than the maximum increase in ice thickness in the collected year, the system determines that the transmission line is in normal operation. Otherwise, the system determines that the transmission line is in risky operation.

[0115] On the basis of the above, the 3D fusion model is based on the disaster year and the relevant data of the meteorological department and / or emergency management department in the area where the transmission line is located. The risk operation status of the transmission line is analyzed according to the corresponding difference between the ice thickness and the line capacitance value. In the future period, when the ice thickness and the line capacitance value exceed this threshold, the system determines that the transmission line is in a risky operation state. Otherwise, the system determines that it is in a normal operation state.

[0116] This embodiment further obtains the environmental temperature, humidity and wind speed in the area where the transmission line is located when the disaster year exceeds the disaster year from the meteorological department and / or the emergency management department, as well as the snowfall, ice thickness of the transmission line and line capacitance value in this case. At the same time, thresholds are preset for the disaster year and the ice thickness and line capacitance value beyond this disaster year respectively. In the future period, when the ice thickness and the line capacitance value exceed this threshold, the system determines that the transmission line is in a risky operating state; otherwise, the system determines that it is in a normal operating state.

[0117] Based on the above, the present invention collects the snowfall, ambient temperature and humidity, wind speed and ice thickness in each year in the area where the transmission line is located, and collects the line capacitance value in each case to form a data model for analysis and simulation, thereby solving the problems of low accuracy and poor reliability of online ice monitoring under the existing technology, and improving the level of digital intelligent monitoring of ice disaster prevention in the power grid.

[0118] The present invention provides a technical solution:

[0119] See also Figure 6 , an artificial intelligence-based ice monitoring method, comprising the following steps:

[0120] Obtain the location attributes and environmental attributes of the area where the transmission line is located, as well as the distribution area and monthly power consumption of the transmission line in the area. The location attributes include the altitude difference, topography, water distribution and number of passes in the area where the transmission line is located. The environmental attributes include wind speed data, ambient temperature and humidity in winter over the years, snowfall times and ice thickness data of the transmission line over the years.

[0121] During the icing season in the area, the line capacitance values ​​of the transmission lines under different ice thicknesses when the transmission lines are covered with ice and the line capacitance values ​​when not covered with ice are collected, and the changing trend of the line capacitance values ​​under different ice thicknesses is determined, that is, after collecting the transmission lines, the transmission lines are sorted according to the ice thickness when the line capacitance values ​​increase when the transmission lines are covered with ice;

[0122] Generate an ice thickness estimation data model for the area where the transmission line is located according to the line capacitance value under different ice thicknesses; specifically obtain the change data of ambient temperature, humidity and wind speed under ice thicknesses in different years, select the years with the same ambient temperature, humidity and wind speed, and based on this, preset an ice thickness tolerance threshold, and judge whether the ice thickness of the transmission line in the above years is within the ice thickness tolerance threshold; if the difference in ice thickness is within the preset tolerance threshold, the system determines that the obtained change data of ambient temperature, humidity and wind speed are non-changing data; otherwise, if the difference in ice thickness is not within the preset tolerance threshold, the system determines that it is changing data, and uploads the non-changing data and / or changing data to the data model respectively, and updates the data model at the same time;

[0123] The capacitance risk threshold is preset based on the tolerance threshold, and then combined with the location attributes in the transmission line-related data, such as regional altitude difference, topography, water distribution, number of passes, and transmission power and power consumption as basic data, to build a 3D fusion model of the digital twin of the transmission line risk;

[0124] Based on the preset variation range of the basic data matched by the 3D fusion model, the ambient temperature, humidity and wind speed data of the area where the transmission line is located under different variation values ​​are simulated by changing the basic data in the above 3D fusion model, and the ice thickness of the transmission line under different ambient temperature, humidity and wind speed and the line capacitance value matched with the ice thickness are simulated respectively, and determined as the capacitance value prediction result, and the normal operation of the transmission line fault judgment is performed according to the capacitance value prediction result;

[0125] In the 3D fusion model, based on the change data, the years with different ambient temperature, humidity and wind speed in the past ten years are collected, and the ice thickness in each year is obtained respectively. At the same time, the change in ice thickness and the change trend of ambient temperature, humidity and wind speed are analyzed, and the probability of ambient temperature, humidity and wind speed changing to the ambient temperature, humidity or wind speed in the disaster year under the current weather conditions is analyzed;

[0126] The communication base station and / or the communication base station operator matching the area where the transmission line is located is recorded through the monitoring terminal, and the ice thickness data of the transmission line collected on site is received, and the background data is updated at the same time; when the ice thickness and / or the line capacitance value of the transmission line in the monitoring area reaches the ice thickness and / or the line capacitance value in the marked year, an information acquisition request for the early warning status of the transmission line is initiated to the corresponding communication base station and / or the communication base station operator in the monitoring area.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. An artificial intelligence-based ice monitoring system, characterized in that: It comprises a monitoring and positioning module (1), wherein the output end of the monitoring and positioning module (1) is connected to an information acquisition module (2) via an API port, and the output end of the information acquisition module (2) is connected to a data acquisition module (3) via an API port; The monitoring and positioning module (1) comprises a GPS satellite navigator (11); The monitoring and positioning module (1) is used to obtain the location, precise location and distribution area data of the power transmission line to be monitored through a GPS satellite navigator (11); The information acquisition module (2) is used to acquire transmission line monitoring data; The data acquisition module (3) is used to respond to the monitoring data of the information acquisition module (2); The output end of the data acquisition module (3) is connected to a data analysis module (4) via an API port, and the output end of the data analysis module (4) is connected to a fusion simulation unit (5) via an API port; The data analysis module (4) is used to analyze and predict the variation trend of the line capacitance value under different ice thicknesses; The fusion simulation unit (5) is used to perform fault judgment on the normal operation of the power transmission line according to the capacitance value prediction result; The output end of the monitoring and positioning module (1) is also connected to a monitoring terminal (6) via an API port, the output end of the monitoring terminal (6) is connected to a field acquisition module (7) via an API port, and the output end of the monitoring terminal (6) is also connected to a background management module (9) via an API port, and the output end of the monitoring terminal (6) is connected to an alarm unit (8) via an API port; The monitoring terminal (6) is used to record the communication base station matching the area where the power transmission line is located, and at the same time receive the data collected by the field collection module (7); The field acquisition module (7) is used for collecting field data of the power transmission line to be monitored; The alarm unit (8) is used to initiate a power transmission line early warning status to a corresponding communication base station in the monitoring area; The background management module (9) is used to manage and update data in the background.

2. The artificial intelligence-based ice monitoring system according to claim 1, characterized in that: The background management module (9) comprises a background data updating module (91), and the output end of the background data updating module (91) is connected to a relevant database (92) via an API port; Wherein, the background data updating module (91) is used to update the data stored in the relevant database (92); The related database (92) is used to provide data support for data acquisition by the information acquisition module (2).

3. The artificial intelligence-based ice monitoring system according to claim 2, characterized in that: The related database (92) includes a static database (921) and a dynamic database (922); The static database (921) is used to store altitude differences, topography, water distribution, number of passes, power transmission power, power consumption and regional wind speed data of different monitoring areas; The dynamic database (922) is used to store the environmental temperature and humidity of different monitoring areas in previous winters, as well as the ice thickness data of the power transmission lines in previous years.

4. The artificial intelligence-based ice monitoring system according to claim 3, characterized in that: The data analysis module (4) comprises a model building unit (41), and an output end of the model building unit (41) is connected to a tolerance threshold preset unit (42) via an API port; The model building unit (41) is used to generate an ice thickness estimation data model for the area where the power transmission line is located; The tolerance threshold preset unit (42) obtains the environmental temperature, humidity and wind speed change data of winter under the ice thickness in previous years according to the monitored area through the dynamic database (922), selects the year with the same environmental temperature, humidity and wind speed, and presets the ice thickness tolerance threshold based on the year.

5. The artificial intelligence-based ice monitoring system according to claim 4, characterized in that: The output end of the tolerance threshold preset unit (42) is connected to a data comparison unit (43) via an API port, and the output end of the data comparison unit (43) is connected to a result output unit (44) via an API port; The data comparison unit (43) is used to compare whether the ice thickness of the power transmission line in the current year is within a preset ice thickness tolerance threshold; The result output unit (44) is used to upload data to the model building unit (41) according to the comparison result of the data comparison unit (43), and to update the model building unit (41).

6. The artificial intelligence-based ice monitoring system according to claim 5, characterized in that: The fusion simulation unit (5) comprises a 3D fusion model unit (51), the output end of the 3D fusion model unit (51) is connected to a variation range preset unit (52) via an API port, and the output end of the variation range preset unit (52) is connected to a simulation data storage unit (53) via an API port; The 3D fusion model unit (51) is based on a preset ice thickness tolerance threshold, a preset capacitance risk threshold, and combines the regional altitude difference, topography, water body distribution, number of passes, transmission power and power consumption in the transmission line static database (921) as basic data to build a 3D fusion model of the digital twin of the transmission line risk; The variation range preset unit (52) presets the variation range of the basic data matched by the 3D fusion model obtained by the 3D fusion model unit (51); The simulation data storage unit (53) is used to store basic data in the 3D fusion model obtained by the 3D fusion model unit (51).

7. The artificial intelligence-based ice monitoring system according to claim 6, characterized in that: The output end of the analog data storage unit (53) is connected to the analog data calculation unit (54) through an API port, and the output end of the analog data calculation unit (54) is connected to the curve drawing unit (55) through an API port; The simulation data calculation unit (54) simulates the environmental temperature, humidity and wind speed data of the area where the power transmission line is located under different change values ​​by changing the basic data in the 3D fusion model, simulates and calculates the ice thickness of the power transmission line under different environmental temperature, humidity and wind speed and the line capacitance value matching the ice thickness, determines them as the capacitance value prediction result, and performs fault judgment on the normal operation of the power transmission line based on the capacitance value prediction result.

8. The artificial intelligence-based ice monitoring system according to claim 7, characterized in that: The output end of the curve drawing unit (55) is connected to a function model building unit (56) via an API port; The curve drawing unit (55) calculates the corresponding differences in ice thickness and line capacitance values ​​in different years and in changes in ambient temperature and humidity or wind speed in disaster years through the simulation data calculation unit (54), divides the corresponding differences in ice thickness and line capacitance values ​​into at least three step intervals, and draws the ROC curve according to the corresponding changes in the intermediate values; The function model building unit (56) generates a Sigmoid function model based on the step-wise ice thickness difference data obtained by the curve drawing unit (55).

9. The artificial intelligence-based ice monitoring system according to claim 8, characterized in that: The Sigmoid function model obtained in the function model building unit (56) is also used to preset a lower limit line capacitance value and an upper limit line capacitance value, and simulate the change of the lower limit line capacitance value to the ice thickness of the upper limit line.

10. An artificial intelligence-based ice monitoring method, characterized in that: include: Obtaining location attributes and environmental attributes of the area where the power transmission line is located, as well as the distribution area and monthly power consumption of the power transmission line in the area; During the icing season in the area, the line capacitance values ​​of the transmission line under different ice thicknesses when the transmission line is iced and the line capacitance values ​​when not iced are collected to determine the change trend of the line capacitance values ​​under different ice thicknesses; Generate an ice thickness estimation data model for the area where the transmission line is located based on the line capacitance values ​​under different ice thicknesses; The capacitance risk threshold is preset based on the tolerance threshold and the position attribute is 、 Generate a 3D fusion model based on transmission power and power consumption; Based on the preset variation range of the 3D fusion model, the ambient temperature, humidity and wind speed under different variation values ​​are simulated. When the capacitance value of the line does not exceed the capacitance risk threshold, the system determines that it will not cause failure to the normal operation of the transmission line. Otherwise, the system determines that it will cause failure to the normal operation of the transmission line.

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